From 90d2a014abaaa001998da447cc6d06388c7f137c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Louis-F=C3=A9lix=20Nothias?= Date: Thu, 23 Apr 2026 14:35:11 +0200 Subject: [PATCH 01/21] Update litellm version to 1.83.0 in environment.yml and requirements.txt --- environment.yml | 2 +- requirements.txt | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/environment.yml b/environment.yml index c1c9393..6b95d39 100644 --- a/environment.yml +++ b/environment.yml @@ -25,7 +25,7 @@ dependencies: - langchain_core==0.1.30 - langchain_openai==0.0.8 - langgraph==0.0.26 - - litellm==1.61.6 + - litellm==1.83.0 - langsmith==0.1.22 - matplotlib==3.8.3 - networkx==3.2.1 diff --git a/requirements.txt b/requirements.txt index 9590676..3516bd8 100644 --- a/requirements.txt +++ b/requirements.txt @@ -10,7 +10,7 @@ langchain_community==0.0.27 langchain_core==0.1.30 langchain_openai==0.0.8 langgraph==0.0.26 -litellm==1.61.6 +litellm==1.83.0 langsmith==0.1.22 matplotlib==3.8.3 networkx==3.2.1 From 1a46419c70bd7b4da43b4ac964caaaf40c07cf75 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Louis-F=C3=A9lix=20Nothias?= Date: Thu, 23 Apr 2026 16:59:11 +0200 Subject: [PATCH 02/21] Update dependencies in environment.yml and requirements.txt for langchain, openai, pydantic, and numpy --- environment.yml | 18 +++++++++--------- requirements.txt | 18 +++++++++--------- 2 files changed, 18 insertions(+), 18 deletions(-) diff --git a/environment.yml b/environment.yml index 6b95d39..497403d 100644 --- a/environment.yml +++ b/environment.yml @@ -19,21 +19,21 @@ dependencies: - ipykernel==6.29.3 - jupyter-kernel-gateway==3.0.1 - kaleido==0.2.1 - - langchain==0.1.11 + - langchain==0.3.27 - langchainhub==0.1.15 - - langchain_community==0.0.27 - - langchain_core==0.1.30 - - langchain_openai==0.0.8 - - langgraph==0.0.26 + - langchain_community==0.3.31 + - langchain_core==0.3.84 + - langchain_openai==0.3.35 + - langgraph==0.3.34 - litellm==1.83.0 - - langsmith==0.1.22 + - langsmith==0.3.45 - matplotlib==3.8.3 - networkx==3.2.1 - - openai==1.61.0 + - openai==2.32.0 - pandas==2.2.1 - plotly==5.20.0 - psycopg2-binary - - pydantic==2.7.0 + - pydantic==2.13.3 - python-dotenv==1.0.1 - rdflib==7.0.0 - Requests==2.31.0 @@ -42,7 +42,7 @@ dependencies: - SQLAlchemy==2.0.22 - tiktoken>=0.7.0 - tqdm==4.66.1 - - numpy==1.26.0 + - numpy==1.26.4 - httpx==0.27.2 - scikit-learn==1.5.1 - spectrum_utils diff --git a/requirements.txt b/requirements.txt index 3516bd8..fd76f39 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,21 +4,21 @@ faiss-cpu==1.8.0 kaleido==0.2.1 ipykernel==6.29.3 jupyter-kernel-gateway==3.0.1 -langchain==0.1.11 +langchain==0.3.27 langchainhub==0.1.15 -langchain_community==0.0.27 -langchain_core==0.1.30 -langchain_openai==0.0.8 -langgraph==0.0.26 +langchain_community==0.3.31 +langchain_core==0.3.84 +langchain_openai==0.3.35 +langgraph==0.3.34 litellm==1.83.0 -langsmith==0.1.22 +langsmith==0.3.45 matplotlib==3.8.3 networkx==3.2.1 -openai==1.61.0 +openai==2.32.0 pandas==2.2.1 plotly==5.20.0 psycopg2-binary -pydantic==2.7.0 +pydantic==2.13.3 python-dotenv==1.0.1 rdflib==7.0.0 Requests==2.31.0 @@ -27,6 +27,6 @@ streamlit==1.44.0 SPARQLWrapper==2.0.0 tiktoken>=0.7.0 tqdm==4.66.1 -numpy==1.26.0 +numpy==1.26.4 httpx==0.27.2 scikit-learn==1.5.1 From 10e9a555da1248a6a846f8d7290c85065683bcd9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Louis-F=C3=A9lix=20Nothias?= Date: Thu, 23 Apr 2026 17:20:51 +0200 Subject: [PATCH 03/21] Update python-dotenv, requests, streamlit, and tqdm versions in environment.yml and requirements.txt --- environment.yml | 6 +++--- requirements.txt | 8 ++++---- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/environment.yml b/environment.yml index 497403d..be14ab7 100644 --- a/environment.yml +++ b/environment.yml @@ -34,14 +34,14 @@ dependencies: - plotly==5.20.0 - psycopg2-binary - pydantic==2.13.3 - - python-dotenv==1.0.1 + - python-dotenv==1.2.2 - rdflib==7.0.0 - - Requests==2.31.0 + - requests==2.33.1 - seaborn==0.13.2 - SPARQLWrapper==2.0.0 - SQLAlchemy==2.0.22 - tiktoken>=0.7.0 - - tqdm==4.66.1 + - tqdm==4.66.6 - numpy==1.26.4 - httpx==0.27.2 - scikit-learn==1.5.1 diff --git a/requirements.txt b/requirements.txt index fd76f39..bccbbf8 100644 --- a/requirements.txt +++ b/requirements.txt @@ -19,14 +19,14 @@ pandas==2.2.1 plotly==5.20.0 psycopg2-binary pydantic==2.13.3 -python-dotenv==1.0.1 +python-dotenv==1.2.2 rdflib==7.0.0 -Requests==2.31.0 +requests==2.33.1 seaborn==0.13.2 -streamlit==1.44.0 +streamlit==1.54.0 SPARQLWrapper==2.0.0 tiktoken>=0.7.0 -tqdm==4.66.1 +tqdm==4.66.6 numpy==1.26.4 httpx==0.27.2 scikit-learn==1.5.1 From f9bf608f111c76fbe67df5b22cf941abd4270198 Mon Sep 17 00:00:00 2001 From: madina1203 Date: Thu, 23 Apr 2026 18:51:03 +0200 Subject: [PATCH 04/21] Add big_benchmark.csv for automated evaluation --- app/data/big_benchmark.csv | 51 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 51 insertions(+) create mode 100644 app/data/big_benchmark.csv diff --git a/app/data/big_benchmark.csv b/app/data/big_benchmark.csv new file mode 100644 index 0000000..640b72c --- /dev/null +++ b/app/data/big_benchmark.csv @@ -0,0 +1,51 @@ +messages,__end__,dataset_split +"[{""type"": ""human"", ""content"": ""Which plant has extracts containing compounds that demonstrated inhibition rates above 50% against Trypanosoma cruzi and are above 800 Da in mass? Provide the lab extracts"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which plant has extracts containing compounds that demonstrated inhibition rates above 50% against Trypanosoma cruzi and are above 800 Da in mass? Provide the lab extracts"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which plant has extracts containing compounds that demonstrated inhibition rates above 50% against Trypanosoma cruzi and are above 800 Da in mass? Provide the lab extracts."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid. It is related to the ENPKG nodes/entities (Raw Material, Lab Extract, BioAssayResult, Chemical Entity). The question asks for information about plant extracts containing compounds that have demonstrated a certain level of inhibition against a specific target and have a certain mass, which can be retrieved from the ENPKG."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Trypanosoma cruzi, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL368, ChEMBLTarget\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The query has been processed successfully. Here is the information:\n\n- **Question**: Which plant has extracts containing compounds that demonstrated inhibition rates above 50% against Trypanosoma cruzi and are above 800 Da in mass? Provide the lab extracts.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?plant ?labExtract\n WHERE {\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_parent_mass ?mass .\n FILTER(?mass > 800)\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\n ?bioAssayResult ns2:inhibition_percentage ?inhibition .\n FILTER(?inhibition > 50)\n ?bioAssayResult ns2:target_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?rawMaterial ns1:submitted_taxon ?plant .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/cbb95402a0b148239141ff5dfd609728/tmpixjks0gp.csv`\n\nPlease refer to the file for detailed results on the plants and their corresponding lab extracts."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The analysis of the file indicates that there are no plant extracts meeting the specified criteria of having compounds with inhibition rates above 50% against Trypanosoma cruzi and a mass above 800 Da. If you have any other questions or need further analysis, please let me know. \n\nNo visualization is available, but the data can be downloaded for further inspection. \n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""Filter the pos ionization mode features of the Melochia umbellata taxon annotated as [M+H]+ by SIRIUS to keep the ones for which a feature in neg ionization mode is detected with the same retention time (+/- 3 seconds) and a mass corresponding to the [M-H]- SIRIUS adduct (+/- 5ppm). Provide the features and retention time."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Filter the pos ionization mode features of the Melochia umbellata taxon annotated as [M+H]+ by SIRIUS to keep the ones for which a feature in neg ionization mode is detected with the same retention time (+/- 3 seconds) and a mass corresponding to the [M-H]- SIRIUS adduct (+/- 5ppm). Provide the features and retention time."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Melochia umbellata, http://www.wikidata.org/entity/Q6813281, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n### Question:\nFilter the pos ionization mode features of the Melochia umbellata taxon annotated as [M+H]+ by SIRIUS to keep the ones for which a feature in neg ionization mode is detected with the same retention time (+/- 3 seconds) and a mass corresponding to the [M-H]- SIRIUS adduct (+/- 5ppm).\n\n### Generated SPARQL Query:\n```sparql\nPREFIX ns1: \nPREFIX ns2: \nPREFIX wdt: \n\nSELECT ?posFeature ?negFeature\nWHERE {\n ?rawMaterial ns1:has_wd_id ;\n ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?posAnalysis, ?negAnalysis .\n ?posAnalysis a ns1:LCMSAnalysisPos ;\n ns1:has_lcms_feature_list ?posFeatureList .\n ?negAnalysis a ns1:LCMSAnalysisNeg ;\n ns1:has_lcms_feature_list ?negFeatureList .\n ?posFeatureList ns1:has_lcms_feature ?posFeature .\n ?negFeatureList ns1:has_lcms_feature ?negFeature .\n ?posFeature ns1:has_sirius_annotation ?posAnnotation ;\n ns1:has_retention_time ?posRT ;\n ns1:has_parent_mass ?posMass .\n ?negFeature ns1:has_sirius_annotation ?negAnnotation ;\n ns1:has_retention_time ?negRT ;\n ns1:has_parent_mass ?negMass .\n ?posAnnotation ns1:has_sirius_adduct \""[M+H]+\"" .\n ?negAnnotation ns1:has_sirius_adduct \""[M-H]-\"" .\n FILTER (ABS(?posRT - ?negRT) <= 3)\n FILTER (ABS(?posMass - ?negMass) / ?posMass <= 0.000005)\n}\n```\n\n### File Path:\nThe results of the query have been saved to the following file path: `/tmp/kgbot/tmprvccrkkb.csv`\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""How unique InChIKey2D are among the structural annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""How unique InChIKey2D are among the structural annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: How unique InChIKey2D are among the structural annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid. It is related to the ENPKG nodes/entities (InChIKey2D, Annotation). The question is asking for unique InChIKey2D among the structural annotations, which is a valid query for the knowledge graph."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The number of unique InChIKey2D among the structural annotations is 106,647.\n\nHere is the information related to the SPARQL query:\n\n- **Question**: How unique InChIKey2D are among the structural annotations?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT (COUNT(DISTINCT ?InChIkey2D) AS ?uniqueInChIkey2DCount)\n WHERE {\n ?annotation ns1:has_InChIkey2D ?InChIkey2D .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/73fb3b1ea55e4c8d93e92080edfdd449/tmphyuvm3dr.csv`\n\nThis result indicates the count of unique InChIKey2D identifiers found among the structural annotations in the knowledge graph."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""What are the retention times and molecular masses of compounds identified in the negative ionization mode LCMS analysis from Lovoa trichilioides extracts, and what are the CANOPUS chemical class annotations for those features?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the retention times and molecular masses of compounds identified in the negative ionization mode LCMS analysis from Lovoa trichilioides extracts, and what are the CANOPUS chemical class annotations for those features?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the retention times and molecular masses of compounds identified in the negative ionization mode LCMS analysis from Lovoa trichilioides extracts, and what are the CANOPUS chemical class annotations for those features?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Lovoa trichilioides, http://www.wikidata.org/entity/Q8008828, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: What are the retention times and molecular masses of compounds identified in the negative ionization mode LCMS analysis from Lovoa trichilioides extracts, and what are the CANOPUS chemical class annotations for those features?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n PREFIX wd: \n\n SELECT ?retentionTime ?molecularMass ?canopusClass\n WHERE {\n ?rawMaterial ns1:has_wd_id wd:Q8008828 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysisNeg .\n ?lcmsAnalysisNeg a ns1:LCMSAnalysisNeg .\n ?lcmsAnalysisNeg ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_retention_time ?retentionTime .\n ?feature ns1:has_parent_mass ?molecularMass .\n ?feature ns1:has_canopus_annotation ?canopusAnnotation .\n ?canopusAnnotation ns1:has_canopus_npc_class ?canopusClass .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/1ee4538b31f946faaa8a05370b67774f/tmpwx92t25x.csv`\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The retention times, molecular masses, and CANOPUS chemical class annotations for the compounds identified in the negative ionization mode LCMS analysis from Lovoa trichilioides extracts have been processed. "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""What are the retention times and adduct types of LCMS features from Desmodium heterophyllum annotated by SIRIUS with a cosmic score greater than 0.4??"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the retention times and adduct types of LCMS features from Desmodium heterophyllum annotated by SIRIUS with a cosmic score greater than 0.4??"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the retention times and adduct types of LCMS features from Desmodium heterophyllum annotated by SIRIUS with a cosmic score greater than 0.4?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Desmodium heterophyllum, http://www.wikidata.org/entity/Q10770714, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here is the information:\n\n- **Question**: What are the retention times and adduct types of LCMS features from Desmodium heterophyllum annotated by SIRIUS with a cosmic score greater than 0.4?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?retentionTime ?adduct\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_retention_time ?retentionTime .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?siriusAnnotation ns1:has_cosmic_score ?cosmicScore .\n ?siriusAnnotation ns1:has_adduct ?adduct .\n FILTER(?cosmicScore > 0.4)\n }\n ```\n- **File Path**: The results are stored in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/25ad11d8258b4723b0a4653d1b0de5e7/tmpvmkstwx8.csv`.\n\nPlease let me know if you need further assistance or analysis of the results!"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""Can you provide the SMILES notation and the Wikidata IDs for the compound with the highest precursor ion mass in the MS2 spectra from extracts of Lovoa trichilioides by looking at SIRIUS annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Can you provide the SMILES notation and the Wikidata IDs for the compound with the highest precursor ion mass in the MS2 spectra from extracts of Lovoa trichilioides by looking at SIRIUS annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Can you provide the SMILES notation and the Wikidata IDs for the compound with the highest precursor ion mass in the MS2 spectra from extracts of Lovoa trichilioides by looking at SIRIUS annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Lovoa trichilioides, http://www.wikidata.org/entity/Q8008828, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The compound with the highest precursor ion mass in the MS2 spectra from extracts of Lovoa trichilioides, as annotated by SIRIUS, has the following details:\n\n- **SMILES Notation**: `CC(C)(C)C1=C(C=CC(=C1)C(C)(CC(=O)OCCOC(=O)CC(C)(C2=CC(=C(C=C2)O)C(C)(C)C)C3=CC(=C(C=C3)O)C(C)(C)C)C4=CC(=C(C=C4)O)C(C)(C)C)O`\n- **Wikidata ID**: [Q27254797](http://www.wikidata.org/entity/Q27254797)\n\nHere is the SPARQL query used to obtain this information:\n\n```sparql\nPREFIX ns1: \nPREFIX ns2: \n\nSELECT ?smiles ?wd_id\nWHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?annotation .\n ?annotation ns1:has_InChIkey2D ?InChIkey2D .\n ?InChIkey2D ns1:is_InChIkey2D_of ?chemicalEntity .\n ?chemicalEntity ns1:has_smiles ?smiles ;\n ns1:has_wd_id ?wd_id .\n ?feature ns1:has_parent_mass ?mass .\n}\nORDER BY DESC(?mass)\nLIMIT 1\n```\n\nThe data was retrieved from the file located at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/c05df7beae71402fa925205f656dba83/tmpj8dw4tu8.csv`."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""For features from the Melochia umbellata taxon in pos ionization mode with SIRIUS annotations, get the ones for which a feature in neg ionization mode with the same retention time (+/- 0.05 minutes) has the same SIRIUS annotation by comparing the InCHIKey 2D. Return the features, retention times, and InChIKey2D"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""For features from the Melochia umbellata taxon in pos ionization mode with SIRIUS annotations, get the ones for which a feature in neg ionization mode with the same retention time (+/- 0.05 minutes) has the same SIRIUS annotation by comparing the InCHIKey 2D. Return the features, retention times, and InChIKey2D"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: For features from the Melochia umbellata taxon in pos ionization mode with SIRIUS annotations, get the ones for which a feature in neg ionization mode with the same retention time (+/- 0.05 minutes) has the same SIRIUS annotation by comparing the InCHIKey 2D. Return the features, retention times, and InChIKey2D"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Melochia umbellata, http://www.wikidata.org/entity/Q6813281, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n### Question:\nFor features from the Melochia umbellata taxon in pos ionization mode with SIRIUS annotations, get the ones for which a feature in neg ionization mode with the same retention time (+/- 0.05 minutes) has the same SIRIUS annotation by comparing the InCHIKey 2D. Return the features, retention times, and InChIKey2D.\n\n### Generated SPARQL Query:\n```sparql\nPREFIX ns1: \nPREFIX ns2: \nPREFIX wd: \n\nSELECT ?featurePos ?retentionTime ?InChIkey2D\nWHERE {\n ?rawMaterial ns1:has_wd_id wd:Q6813281 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsPos .\n ?lcmsPos a ns1:LCMSAnalysisPos .\n ?lcmsPos ns1:has_lcms_feature_list ?featureListPos .\n ?featureListPos ns1:has_lcms_feature ?featurePos .\n ?featurePos ns1:has_retention_time ?retentionTime .\n ?featurePos ns1:has_sirius_annotation ?siriusAnnotationPos .\n ?siriusAnnotationPos ns1:has_InChIkey2D ?InChIkey2D .\n\n ?labExtract ns1:has_LCMS ?lcmsNeg .\n ?lcmsNeg a ns1:LCMSAnalysisNeg .\n ?lcmsNeg ns1:has_lcms_feature_list ?featureListNeg .\n ?featureListNeg ns1:has_lcms_feature ?featureNeg .\n ?featureNeg ns1:has_retention_time ?retentionTimeNeg .\n ?featureNeg ns1:has_sirius_annotation ?siriusAnnotationNeg .\n ?siriusAnnotationNeg ns1:has_InChIkey2D ?InChIkey2D .\n\n FILTER (ABS(?retentionTime - ?retentionTimeNeg) <= 0.05)\n}\n```\n\n### File Path:\nThe results of the query have been saved to the following file path:\n`/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/6966291902c9490ab2b949339c4c2797/tmp_cptewcz.csv`\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""What are the LCMS features that have feature areas greater than 1,000,000 in positive ionization mode? Sort by decreasing feature area value."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the LCMS features that have feature areas greater than 1,000,000 in positive ionization mode? Sort by decreasing feature area value."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the LCMS features that have feature areas greater than 1,000,000 in positive ionization mode? Sort by decreasing feature area value."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed. Here are the details:\n\n- **Question**: What are the LCMS features that have feature areas greater than 1,000,000 in positive ionization mode? Sort by decreasing feature area value.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT ?feature\n WHERE {\n ?analysis a ns1:LCMSAnalysisPos .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_feature_area ?area .\n FILTER(?area > 1000000)\n } ORDER BY DESC(?area)\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/61f189ee29f1402eae7ec344e7ac1ef7/tmpv676p86i.csv\n\nPlease review the file for the detailed results."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The LCMS features with feature areas greater than 1,000,000 in positive ionization mode have been filtered and saved. You can download the detailed results from the following file:\n\n- **Filtered Features File**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/61f189ee29f1402eae7ec344e7ac1ef7/filtered_features.csv\n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""List the lab extracts that have inhibition percentages above 50% bioassay results at 2µg/mL against Trypanosoma brucei rhodesiense ."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""List the lab extracts that have inhibition percentages above 50% bioassay results at 2µg/mL against Trypanosoma brucei rhodesiense ."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: List the lab extracts that have inhibition percentages above 50% bioassay results at 2µg/mL against Trypanosoma brucei rhodesiense."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Trypanosoma brucei rhodesiense, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL612348, ChEMBLTarget\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: List the lab extracts that have inhibition percentages above 50% bioassay results at 2µg/mL against Trypanosoma brucei rhodesiense.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?labExtract\n WHERE {\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\n ?bioAssayResult ns2:target_id .\n {\n ?bioAssayResult a ns2:Tbrucei2ugml .\n }\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/fc41480ada32471b948feb8e1d6babcf/tmp4p9xu66f.csv`\n\nI will now analyze the SPARQL output to provide the final answer.\nThe lab extracts that have bioassay results at 2µg/mL against Trypanosoma brucei rhodesiense are listed in the file located at the following path:\n\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/fc41480ada32471b948feb8e1d6babcf/tmp4p9xu66f.csv`\n\nHere is the dictionary containing the question, generated SPARQL query, and file path:\n\n```json\n{\n \""question\"": \""List the lab extracts that have inhibition percentages above 50% bioassay results at 2µg/mL against Trypanosoma brucei rhodesiense\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\nPREFIX ns2: \\n\\nSELECT ?labExtract\\nWHERE {\\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\\n ?bioAssayResult ns2:target_id .\\n {\\n ?bioAssayResult a ns2:Tbrucei2ugml .\\n } ?bioAssayResult ns2:inhibition_percentage ?inhibition .\n FILTER(?inhibition > 50)\\n}\"",\n \""file_path\"": \""/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/fc41480ada32471b948feb8e1d6babcf/tmp4p9xu66f.csv\""\n}\n```\n\nPlease review the file for the detailed list of lab extracts."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""Retrieve the most frequent Spec2Vec peaks and losses characterizing the MS2 spectra from the lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the most frequent Spec2Vec peaks and losses characterizing the MS2 spectra from the lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the most frequent Spec2Vec peaks and losses characterizing the MS2 spectra from the lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: Retrieve the most frequent Spec2Vec peaks and losses characterizing the MS2 spectra from the lab extracts of Tabernaemontana coffeoides.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n PREFIX wd: \n\n SELECT ?spec2vecPeak ?spec2vecLoss (COUNT(?spec2vecPeak) AS ?peakFrequency) (COUNT(?spec2vecLoss) AS ?lossFrequency)\n WHERE {\n ?rawMaterial ns1:has_wd_id wd:Q15376858 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_spec2vec_doc ?spec2vecDoc .\n ?spec2vecDoc ns1:has_spec2vec_peak ?spec2vecPeak .\n ?spec2vecDoc ns1:has_spec2vec_loss ?spec2vecLoss .\n } GROUP BY ?spec2vecPeak ?spec2vecLoss \n ORDER BY DESC(?peakFrequency) DESC(?lossFrequency)\n LIMIT 1\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/2e7991542808441db6e152ef5e034607/tmpdwgxk7xv.csv`\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The most frequent Spec2Vec peaks and losses characterizing the MS2 spectra from the lab extracts of Tabernaemontana coffeoides have been successfully retrieved "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""What are the LC-MS features that have GNPS library analogs annotation in Tabernaemontana coffeoides? Provide the features."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the LC-MS features that have GNPS library analogs annotation in Tabernaemontana coffeoides? Provide the features."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the LC-MS features that have GNPS library analogs annotation in Tabernaemontana coffeoides? Provide the features."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The LC-MS features that have GNPS library analogs annotation in Tabernaemontana coffeoides can be found in the provided file. Here is the information related to your query:\n\n- **Question**: What are the LC-MS features that have GNPS library analogs annotation in Tabernaemontana coffeoides? Provide the features.\n- **Generated SPARQL Query**: \n ```sparql\n PREFIX ns1: \n\n SELECT ?feature\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:fast_search_gnpslibrary_analog ?analog .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/c00a7dfa6ebf4a978b7a894760588603/tmp94vi0nsp.csv`\n\nPlease refer to the file for the detailed list of features."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""Retrieve the Spec2Vec documents associated with MS2 spectra from the lab extracts of Tabernaemontana coffeoides"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the Spec2Vec documents associated with MS2 spectra from the lab extracts of Tabernaemontana coffeoides"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the Spec2Vec documents associated with MS2 spectra from the lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: Retrieve the Spec2Vec documents associated with MS2 spectra from the lab extracts of Tabernaemontana coffeoides.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?spec2vecDoc\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_spec2vec_doc ?spec2vecDoc .\n }\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/aa6e48ec3a194a14a7a9f918774c080a/tmpxvtl3hcs.csv\n\nI will now analyze the SPARQL output to provide the final answer.\nThe final answer based on the SPARQL output is as follows:\n\nThe Spec2Vec documents (Spec2VecDoc) associated with MS2 spectra (MS2Spectrum) from the lab extracts (LabExtract) of Tabernaemontana coffeoides have been retrieved. The details are stored in the file located at:\n\n**File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/aa6e48ec3a194a14a7a9f918774c080a/tmpxvtl3hcs.csv\n\nHere is the dictionary containing the question, generated SPARQL query, and file path:\n\n```json\n{\n \""question\"": \""Retrieve the Spec2Vec documents (Spec2VecDoc) associated with MS2 spectra (MS2Spectrum) from the lab extracts (LabExtract) of Tabernaemontana coffeoides.\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\nPREFIX ns2: \\n\\nSELECT ?spec2vecDoc\\nWHERE {\\n ?rawMaterial ns1:has_wd_id .\\n ?rawMaterial ns1:has_lab_process ?labExtract .\\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\\n ?featureList ns1:has_lcms_feature ?feature .\\n ?feature ns1:has_spec2vec_doc ?spec2vecDoc .\\n}\"",\n \""file_path\"": \""/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/aa6e48ec3a194a14a7a9f918774c080a/tmpxvtl3hcs.csv\""\n}\n```\n\nPlease review the file for the detailed Spec2Vec documents."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""What are the taxons, lab process and label (if one exists) for each sample? Sort by sample and then lab process"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the taxons, lab process and label (if one exists) for each sample? Sort by sample and then lab process"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the taxons, lab process and label (if one exists) for each sample? Sort by sample and then lab process"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed. Here are the details:\n\n- **Question**: What are the taxons, lab process and label (if one exists) for each sample? Sort by sample and then lab process.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n SELECT ?sample ?taxon ?labProcess ?label\n WHERE {\n ?sample ns1:has_wd_id ?taxon .\n ?sample ns1:has_lab_process ?labProcess .\n OPTIONAL { ?sample rdfs:label ?label . }\n }\n ORDER BY ?sample ?labProcess\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/5642f76045424334a801e191d6ed4563/tmp9g_oyjum.csv\n\nPlease review the file at the provided path for the detailed results."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The taxons, lab process, and labels (if any) for each sample have been sorted by sample and then by lab process. Here is a snippet of the sorted data:\n\n| Sample | Taxon | Lab Process | Label |\n|------------------------------------------|------------------------------------------|--------------------------------------------|-------|\n| http://www.wikidata.org/entity/Q11086771 | http://www.wikidata.org/entity/Q11086771 | https://enpkg.commons-lab.org/kg/KP001_Positive | NaN |\n| http://www.wikidata.org/entity/Q11272377 | http://www.wikidata.org/entity/Q11272377 | https://enpkg.commons-lab.org/kg/KP002_Positive | NaN |\n| http://www.wikidata.org/entity/Q11086771 | http://www.wikidata.org/entity/Q11086771 | https://enpkg.commons-lab.org/kg/KP003_Positive | NaN |\n| http://www.wikidata.org/entity/Q11272377 | http://www.wikidata.org/entity/Q11272377 | https://enpkg.commons-lab.org/kg/KP004_Positive | NaN |\n| http://www.wikidata.org/entity/Q5978345 | http://www.wikidata.org/entity/Q5978345 | https://enpkg.commons-lab.org/kg/KP005_Positive | NaN |\n\nThe full path of the file generated is:\n- /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/5642f76045424334a801e191d6ed4563/tmp9g_oyjum.csv\n\nYou can download the file for the complete details."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""Which extracts have features (pos ionization mode) annotated as the class, aspidosperma-type alkaloids, by CANOPUS with a probability score above 0.5, ordered by the decreasing count of features as aspidosperma-type alkaloids? Group by extract."", ""example"": false, ""additional_kwargs"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which extracts have features (pos ionization mode) annotated as the class, aspidosperma-type alkaloids, by CANOPUS with a probability score above 0.5, ordered by the decreasing count of features as aspidosperma-type alkaloids? Group by extract."", ""example"": false, ""additional_kwargs"": {}}, {""name"": ""Entry_agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: \""Which extracts have features (pos ionization mode) annotated as the class, aspidosperma-type alkaloids, by CANOPUS with a probability score above 0.5, ordered by the decreasing count of features as aspidosperma-type alkaloids? Group by extract .\"""", ""example"": false, ""additional_kwargs"": {}}, {""name"": ""Validator_agent"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""The entity \""aspidosperma-type alkaloids\"" was resolved to the following IRIs:\n\naspidosperma-type alkaloids, https://enpkg.commons-lab.org/kg/npc_Aspidosperma_type, NPCClass;\nNPCPathway, https://enpkg.commons-lab.org/kg/npc_Alkaloids, NPCPathway;\nNPCSuperClass, https://enpkg.commons-lab.org/kg/npc_Tyrosine_alkaloids, NPCSuperClass."", ""example"": false, ""additional_kwargs"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The extracts with features (pos ionization mode) annotated as the class, aspidosperma-type alkaloids, by CANOPUS with a probability score above 0.5, ordered by the decreasing count of features as aspidosperma-type alkaloids, are as follows:\n\n1. VGF152_B02: 74 features\n2. VGF157_D02: 11 features\n3. VGF147_B11: 10 features\n4. VGF153_C03: 7 features\n5. VGF157_E02: 2 features\n6. VGF154_D02: 2 features\n7. VGF147_A10: 2 features\n8. VGF140_F02: 2 features\n9. KP146_Positive: 1 feature\n10. KP317_Positive: 1 feature\n11. VGF159_B11: 1 feature\n12. VGF155_D07: 1 feature\n13. VGF154_H02: 1 feature\n14. VGF153_E07: 1 feature\n15. VGF153_G10: 1 feature\n16. VGF153_D03: 1 feature\n17. VGF153_F04: 1 feature\n18. VGF153_G09: 1 feature\n19. VGF153_F11: 1 feature\n20. VGF153_H04: 1 feature\n21. VGF153_B06: 1 feature\n22. VGF152_E06: 1 feature\n23. VGF151_F09: 1 feature\n24. VGF151_D06: 1 feature\n25. VGF151_E03: 1 feature\n26. VGF150_B11: 1 feature\n27. VGF150_B06: 1 feature\n28. VGF150_F09: 1 feature\n29. VGF150_H05: 1 feature\n30. VGF150_F06: 1 feature\n31. VGF147_D11: 1 feature\n32. VGF147_E10: 1 feature\n33. VGF143_E07: 1 feature\n34. VGF140_E02: 1 feature\n35. VGF139_D09: 1 feature\n36. SC_AP_Wi_DCM: 1 feature\n\nThe SPARQL query used to retrieve this information is:\n```sparql\nPREFIX ns1: \n\nSELECT ?labExtract (COUNT(?feature) AS ?featureCount)\nWHERE {\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis a ns1:LCMSAnalysisPos .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_class .\n ?annotation ns1:has_canopus_npc_class_prob ?probability .\n FILTER(?probability > 0.5)\n}\nGROUP BY ?labExtract\nORDER BY DESC(?featureCount)\n```\n\nThe results are saved in the file located at: `/tmp/kgbot/f36215b1a3114803ad38a18a9462eb29/tmp8u8o5vcv.csv`\n\nNext, I will generate a bar chart based on this data.\nHere is the dictionary containing the question, generated SPARQL query, and the file path to the results:\n\n```json\n{\n \""question\"": \""Which extracts have features (pos ionization mode) annotated as the class, aspidosperma-type alkaloids, by CANOPUS with a probability score above 0.5, ordered by the decreasing count of features as aspidosperma-type alkaloids? Group by extract and provide a bar chart.\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\n\\nSELECT ?labExtract (COUNT(?feature) AS ?featureCount)\\nWHERE {\\n ?labExtract ns1:has_LCMS ?analysis .\\n ?analysis a ns1:LCMSAnalysisPos .\\n ?analysis ns1:has_lcms_feature_list ?feature_list .\\n ?feature_list ns1:has_lcms_feature ?feature .\\n ?feature ns1:has_canopus_annotation ?annotation .\\n ?annotation ns1:has_canopus_npc_class .\\n ?annotation ns1:has_canopus_npc_class_prob ?probability .\\n FILTER(?probability > 0.5)\\n}\\nGROUP BY ?labExtract\\nORDER BY DESC(?featureCount)\"",\n \""file_path\"": \""/tmp/kgbot/f36215b1a3114803ad38a18a9462eb29/tmp8u8o5vcv.csv\""\n}\n```\n\nPlease use this information to generate the bar chart."", ""example"": false, ""additional_kwargs"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The extracts with features (pos ionization mode) annotated as the class, aspidosperma-type alkaloids, by CANOPUS with a probability score above 0.5, ordered by the decreasing count of features as aspidosperma-type alkaloids, are as follows:\n\n1. VGF152_B02: 74 features\n2. VGF157_D02: 11 features\n3. VGF147_B11: 10 features\n4. VGF153_C03: 7 features\n5. VGF157_E02: 2 features\n6. VGF154_D02: 2 features\n7. VGF147_A10: 2 features\n8. VGF140_F02: 2 features\n9. KP146_Positive: 1 feature\n10. KP317_Positive: 1 feature\n11. VGF159_B11: 1 feature\n12. VGF155_D07: 1 feature\n13. VGF154_H02: 1 feature\n14. VGF153_E07: 1 feature\n15. VGF153_G10: 1 feature\n16. VGF153_D03: 1 feature\n17. VGF153_F04: 1 feature\n18. VGF153_G09: 1 feature\n19. VGF153_F11: 1 feature\n20. VGF153_H04: 1 feature\n21. VGF153_B06: 1 feature\n22. VGF152_E06: 1 feature\n23. VGF151_F09: 1 feature\n24. VGF151_D06: 1 feature\n25. VGF151_E03: 1 feature\n26. VGF150_B11: 1 feature\n27. VGF150_B06: 1 feature\n28. VGF150_F09: 1 feature\n29. VGF150_H05: 1 feature\n30. VGF150_F06: 1 feature\n31. VGF147_D11: 1 feature\n32. VGF147_E10: 1 feature\n33. VGF143_E07: 1 feature\n34. VGF140_E02: 1 feature\n35. VGF139_D09: 1 feature\n36. SC_AP_Wi_DCM: 1 feature\n\nT"", ""example"": false, ""additional_kwargs"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""Which ISDB annotations have consistency scores above 0.7 and taxonomic scores above 0.5 for LCMS features from Tabernaemontana coffeoides? Provide annotations."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which ISDB annotations have consistency scores above 0.7 and taxonomic scores above 0.5 for LCMS features from Tabernaemontana coffeoides? Provide annotations."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which ISDB annotations have consistency scores above 0.7 and taxonomic scores above 0.5 for LCMS features from Tabernaemontana coffeoides? Provide annotations."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully executed to find the ISDB annotations with consistency scores above 0.7 and taxonomic scores above 0.5 for LCMS features from Tabernaemontana coffeoides. Here is the information:\n\n- **Question**: Which ISDB annotations have consistency scores above 0.7 and taxonomic scores above 0.5 for LCMS features from Tabernaemontana coffeoides? Provide annotations.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?Annotation\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_isdb_annotation ?Annotation .\n ?Annotation ns1:has_consistency_score ?consistencyScore .\n ?Annotation ns1:has_taxo_score ?taxoScore .\n FILTER(?consistencyScore > 0.7 && ?taxoScore > 0.5)\n }\n ```\n- **File Path**: The results are stored in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/002a9f76c921447fbfe4490492614cba/tmpgkgukng8.csv`.\n\nPlease refer to the file for the detailed annotations."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""List the LC-MS features that have a chemical class annotation by CANOPUS and have a retention time between 5-7 minutes."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""List the LC-MS features that have a chemical class annotation by CANOPUS and have a retention time between 5-7 minutes. "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: List the LC-MS features that have a chemical class annotation by CANOPUS and have a retention time between 5-7 minutes."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed. Here are the details:\n\n- **Question:** List the LC-MS features that have a chemical class annotation by CANOPUS and have a retention time between 5-7 minutes.\n- **Generated SPARQL Query:**\n ```sparql\n PREFIX ns1: \n\n SELECT ?feature\n WHERE {\n ?feature ns1:has_canopus_annotation ?annotation .\n ?feature ns1:has_retention_time ?retention_time .\n ?annotation ns1:has_canopus_npc_class ?chemicalClass .\n FILTER(?retentionTime >= 5 && ?retentionTime <= 7) }\n ```\n- **File Path:** /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/aadd02d3086647dd9408cbc9098e5862/tmpx_kt8dmi.csv\n\nYou can now review the results in the provided file path."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""What are the mass spectrometry features detected only in the plant Rumex nepalensis?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the mass spectrometry features detected only in the plant Rumex nepalensis?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the mass spectrometry features detected only in the plant Rumex nepalensis?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Rumex nepalensis, http://www.wikidata.org/entity/Q11041204, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: What are the mass spectrometry features detected only in the plant Rumex nepalensis?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n PREFIX wd: \n\n SELECT ?feature\n WHERE {\n ?rawMaterial ns1:has_wd_id wd:Q11041204 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n }\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/966c850620f7440182889fe9b80a1edb/tmp4z32di1l.csv\n\nI will now analyze the SPARQL output to provide the final answer.\nThe mass spectrometry features detected for the plant Rumex nepalensis can be found in the file located at the following path:\n\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/966c850620f7440182889fe9b80a1edb/tmp4z32di1l.csv\n\nHere is the dictionary containing the question, generated SPARQL query, and file path:\n\n```json\n{\n \""question\"": \""What are the mass spectrometry features detected for the plant Rumex nepalensis?\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\nPREFIX ns2: \\nPREFIX wd: \\n\\nSELECT ?feature\\nWHERE {\\n ?rawMaterial ns1:has_wd_id wd:Q11041204 .\\n ?rawMaterial ns1:has_lab_process ?labExtract .\\n ?labExtract ns1:has_LCMS ?analysis .\\n ?analysis ns1:has_lcms_feature_list ?featureList .\\n ?featureList ns1:has_lcms_feature ?feature .\\n}\"",\n \""file_path\"": \""/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/966c850620f7440182889fe9b80a1edb/tmp4z32di1l.csv\""\n}\n```\n\nPlease review the file for the detailed mass spectrometry features."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The mass spectrometry features detected for the plant Rumex nepalensis are as follows:\n\n1. [LCMS Feature 1](https://enpkg.commons-lab.org/kg/lcms_feature_1)\n2. [LCMS Feature 2](https://enpkg.commons-lab.org/kg/lcms_feature_2)\n3. [LCMS Feature 3](https://enpkg.commons-lab.org/kg/lcms_feature_3)\n4. [LCMS Feature 4](https://enpkg.commons-lab.org/kg/lcms_feature_4)\n5. [LCMS Feature 5](https://enpkg.commons-lab.org/kg/lcms_feature_5)\n\nNo visualization was requested for this analysis. The data can be downloaded from the provided file path."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""How many distinct Wikidata identifiers (WD IDs) have the same metabolite annotation (considering only the plannar structure)?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""How many distinct Wikidata identifiers (WD IDs) have the same metabolite annotation (considering only the plannar structure)?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: How many distinct Wikidata identifiers (WD IDs) have the same metabolite annotation (considering only the planar structure)?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid. It is related to the ENPKG nodes/entities (Wikidata IDs, InChIkey2D) and asks for a count of distinct Wikidata IDs that have the same metabolite annotation, considering only the planar structure. This information can be retrieved from the ENPKG."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The query has been executed successfully. There are 138,146 distinct Wikidata identifiers (WD IDs) that share the same metabolite annotation based on their planar structure (InChIkey2D).\n\nHere is the information related to the query:\n\n- **Question**: How many distinct Wikidata identifiers (WD IDs) have the same metabolite annotation (considering only the planar structure)?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT (COUNT(DISTINCT ?wd_id) AS ?distinct_wd_ids)\n WHERE {\n ?annotation ns1:has_InChIkey2D ?InChIkey2D .\n ?InChIkey2D ns1:is_InChIkey2D_of ?InChIkey .\n ?InChIkey ns1:has_wd_id ?wd_id .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/49c4b98b94034c86ace8c37139fa6a29/tmpnnlg58pc.csv`\n\nThis result indicates the number of unique Wikidata IDs that are associated with the same planar structure annotation in the ENPKG database."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""What is the distribution of chemical superclasses in the LCMS features annotated by CANOPUS identified from Desmodium heterophyllum extracts?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What is the distribution of chemical superclasses in the LCMS features annotated by CANOPUS identified from Desmodium heterophyllum extracts?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What is the distribution of chemical superclasses in the LCMS features annotated by CANOPUS identified from Desmodium heterophyllum extracts?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Desmodium heterophyllum, http://www.wikidata.org/entity/Q10770714, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The distribution of chemical superclasses in the LCMS features annotated by CANOPUS identified from Desmodium heterophyllum extracts is as follows:\n\n- Flavonoids: 600\n- Octadecanoids: 105\n- Triterpenoids: 50\n- Isoflavonoids: 147\n- Sesquiterpenoids: 27\n- Lignans: 19\n- Small peptides: 19\n- Monoterpenoids: 16\n- Coumarins: 180\n- Tryptophan alkaloids: 6\n- Stilbenoids: 20\n- Nucleosides: 8\n- Glycerophospholipids: 2\n- Polycyclic aromatic polyketides: 11\n- Aromatic polyketides: 13\n- Xanthones: 9\n- Phenylpropanoids (C6-C3): 11\n- Naphthalenes: 5\n- Anthranilic acid alkaloids: 3\n- Diarylheptanoids: 2\n- Diterpenoids: 18\n- Steroids: 51\n- Ornithine alkaloids: 2\n- Aminosugars and aminoglycosides: 5\n- Meroterpenoids: 27\n- Diazotetronic acids and derivatives: 3\n- Glycerolipids: 62\n- Fatty Acids and Conjugates: 44\n- Sphingolipids: 3\n- Oligopeptides: 17\n- Phenolic acids (C6-C1): 15\n- Pseudoalkaloids: 3\n- β-lactams: 1\n- Fatty amides: 17\n- Tyrosine alkaloids: 1\n- Phloroglucinols: 2\n- Unknown: 198\n- Chromanes: 7\n- Linear polyketides: 9\n- Nicotinic acid alkaloids: 2\n- Spingolipids: 24\n- Fatty esters: 3\n- Pseudoalkaloids (transamidation): 1\n- Amino acid glycosides: 6\n- Histidine alkaloids: 1\n- Eicosanoids: 15\n- Fatty acyl glycosides: 2\n- Fatty acyls: 6\n- Polyethers: 2\n- Macrolides: 1\n- Polyols: 2\n- Cyclic polyketides: 3\n- Apocarotenoids: 3\n\nHere is the dictionary containing the question, generated SPARQL query, and file path to the query result:\n\n```json\n{\n \""question\"": \""What is the distribution of chemical superclasses in the LCMS features annotated by CANOPUS identified from Desmodium heterophyllum extracts?\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\nPREFIX ns2: \\nPREFIX wd: \\n\\nSELECT ?superclass (COUNT(?superclass) AS ?count)\\nWHERE {\\n ?rawMaterial ns1:has_wd_id wd:Q10770714 .\\n ?rawMaterial ns1:has_lab_process ?labExtract .\\n ?labExtract ns1:has_LCMS ?analysis .\\n ?analysis ns1:has_lcms_feature_list ?feature_list .\\n ?feature_list ns1:has_lcms_feature ?feature .\\n ?feature ns1:has_canopus_annotation ?annotation .\\n ?annotation ns1:has_canopus_npc_superclass ?superclass .\\n}\\nGROUP BY ?superclass\"",\n \""file_path\"": \""/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/228ff50f729b45f29855d7d12d4c18c9/tmp23kaqct7.csv\""\n}\n```"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""Retrieve the lab extracts that have more than 2,000 features detected. "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the lab extracts that have more than 2,000 features detected. "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the lab extracts that have more than 2,000 features detected."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid. It is related to the ENPKG nodes/entities (Lab Extract, LCMS Feature). The question asks for lab extracts that have a certain number of features detected, which is information that can be retrieved from the ENPKG."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The query to retrieve lab extracts with more than 2,000 features detected has been executed. Here is the information:\n\n- **Question**: Retrieve the lab extracts that have more than 2,000 features detected.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT ?labExtract\n WHERE {\n ?labExtract ns1:has_LCMS ?analysis.\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n {\n SELECT ?labExtract (COUNT(?feature) AS ?featureCount)\n WHERE {\n ?labExtract ns1:has_LCMS ?analysis. \n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n }\n GROUP BY ?labExtract\n HAVING (?featureCount > 2000)\n }\n }\n ```\n- **File Path**: The results are saved in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/1db966f1a9d44c269baae69dc24b9dbe/tmpgxqshpaa.csv`.\n\nPlease refer to the file for the detailed results."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The file containing the results of the SPARQL query has been processed. nIf you have any other specific criteria or information you would like to extract from the data, please let me know. Otherwise, you can download the data for further analysis.\n\nThe full path of the file generated is: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/1db966f1a9d44c269baae69dc24b9dbe/tmpgxqshpaa.csv`.\n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""What are the LCMS features with the highest area annotated as alkaloids by CANOPUS in Tabernaemontana coffeoides extracts?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the LCMS features with the highest area annotated as alkaloids by CANOPUS in Tabernaemontana coffeoides extracts?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the LCMS features with the highest area annotated as alkaloids by CANOPUS in Tabernaemontana coffeoides extracts?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""alkaloids, NPCClass: https://enpkg.commons-lab.org/kg/npc_Steroidal_alkaloids, NPCClass;\nTabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The LCMS feature with the highest area annotated as alkaloids by CANOPUS in Tabernaemontana coffeoides extracts is identified by the feature IRI [https://enpkg.commons-lab.org/kg/lcms_feature_mzspec:MSV000087728:VGF152_A02_features_ms2_pos.mgf:scan:245](https://enpkg.commons-lab.org/kg/lcms_feature_mzspec:MSV000087728:VGF152_A02_features_ms2_pos.mgf:scan:245) with an area of 18,114,637.02276565.\n\nHere is the dictionary containing the question, generated SPARQL query, and file path:\n\n- **Question**: What are the LCMS features with the highest area annotated as alkaloids by CANOPUS in Tabernaemontana coffeoides extracts?\n- **Generated SPARQL Query**: \n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?feature ?featureArea\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_feature_area ?featureArea .\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_class .\n }\n ORDER BY DESC(?featureArea)\n LIMIT 1\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/84a24128512b453598395d8ed7f429dc/tmp8qjwsoaz.csv`"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""Count the number of LCMS features in negative ionization mode"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Count the number of LCMS features in negative ionization mode"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Count the number of LCMS features in negative ionization mode"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The number of LCMS features in negative ionization mode is **364,967**.\n\nHere is the detailed information:\n- **Question**: Count the number of LCMS features in negative ionization mode.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT (COUNT(?feature) AS ?featureCount)\n WHERE {\n ?analysis a ns1:LCMSAnalysisNeg .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n }\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/5a7c1625b95143fcb9f58943371653e9/tmp_dhsfq67.csv"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""Retrieve the retention times and parent masses of LCMS features annotated as Terpenoids by CANOPUS in Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the retention times and parent masses of LCMS features annotated as Terpenoids by CANOPUS in Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the retention times and parent masses of LCMS features annotated as Terpenoids by CANOPUS in Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Terpenoids, https://enpkg.commons-lab.org/kg/npc_Terpenoids, NPCPathway;\nTabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The retention times and parent masses of LCMS features annotated as Terpenoids by CANOPUS in Tabernaemontana coffeoides have been retrieved. Here is the information:\n\n- **SPARQL Query**: \n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?retentionTime ?parentMass\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_pathway .\n ?feature ns1:has_retention_time ?retentionTime .\n ?feature ns1:has_parent_mass ?parentMass .\n }\n ```\n\n- **File Path**: The results are saved in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/610f86c362554edfa3e22989fd52acdf/tmpgt4cvneq.csv`.\n\nPlease check the file for detailed results."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The retention times and parent masses of LCMS features annotated as Terpenoids by CANOPUS in Tabernaemontana coffeoides have been visualized. You can download the visualization file from the following path:\n\nVisualization filepath: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/610f86c362554edfa3e22989fd52acdf/tmpgt4cvneq.json`\n\nPlease use this file to view the detailed results. \n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""Retrieve the top-10 lab extracts with the most unique features detected (minimum feature area intensity of 500,000)."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the top-10 lab extracts with the most unique features detected (minimum feature area intensity of 500,000)."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the top-10 lab extracts with the most unique features detected (minimum feature area intensity of 500,000)."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The top-10 lab extracts with the most unique features detected, with a minimum feature area intensity of 500,000, are as follows:\n\n1. **Lab Extract:** [SC_AP_Wi_DCM](https://enpkg.commons-lab.org/kg/SC_AP_Wi_DCM) - **Unique Features:** 3437\n2. **Lab Extract:** [VGF140_F11](https://enpkg.commons-lab.org/kg/VGF140_F11) - **Unique Features:** 2035\n3. **Lab Extract:** [VGF153_D04](https://enpkg.commons-lab.org/kg/VGF153_D04) - **Unique Features:** 1952\n4. **Lab Extract:** [VGF154_G05](https://enpkg.commons-lab.org/kg/VGF154_G05) - **Unique Features:** 1813\n5. **Lab Extract:** [VGF153_F06](https://enpkg.commons-lab.org/kg/VGF153_F06) - **Unique Features:** 1786\n6. **Lab Extract:** [VGF154_C03](https://enpkg.commons-lab.org/kg/VGF154_C03) - **Unique Features:** 1640\n7. **Lab Extract:** [VGF153_G10](https://enpkg.commons-lab.org/kg/VGF153_G10) - **Unique Features:** 1617\n8. **Lab Extract:** [VGF154_A09](https://enpkg.commons-lab.org/kg/VGF154_A09) - **Unique Features:** 1615\n9. **Lab Extract:** [VGF154_B09](https://enpkg.commons-lab.org/kg/VGF154_B09) - **Unique Features:** 1601\n10. **Lab Extract:** [VGF142_D11](https://enpkg.commons-lab.org/kg/VGF142_D11) - **Unique Features:** 1543\n\nHere is the SPARQL query used to retrieve this information:\n\n```sparql\nPREFIX ns1: \nSELECT ?labExtract (COUNT(DISTINCT ?feature) AS ?uniqueFeatures)\nWHERE {\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_feature_area ?featureArea .\n FILTER(?featureArea >= 500000)\n}\nGROUP BY ?labExtract\nORDER BY DESC(?uniqueFeatures)\nLIMIT 10\n```\n\nThe data is stored in the file located at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/e85c6df097e3431884ddbfcdc34f620d/tmp1u9qq1p9.csv`."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""List the top 3 lab extracts with the highest number of unique compounds annotated by CANOPUS with a probability greater than 0.9."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""List the top 3 lab extracts with the highest number of unique compounds annotated by CANOPUS with a probability greater than 0.9."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: List the top 3 lab extracts with the highest number of unique compounds annotated by CANOPUS with a probability greater than 0.9."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The top 3 lab extracts with the highest number of unique compounds annotated by CANOPUS with a probability greater than 0.9 are:\n\n1. Lab Extract: [SC_AP_Wi_DCM](https://enpkg.commons-lab.org/kg/SC_AP_Wi_DCM) with 69 unique compounds.\n2. Lab Extract: [VGF153_F06](https://enpkg.commons-lab.org/kg/VGF153_F06) with 58 unique compounds.\n3. Lab Extract: [VGF156_B04](https://enpkg.commons-lab.org/kg/VGF156_B04) with 49 unique compounds.\n\nHere is the SPARQL query used to obtain this information:\n\n```sparql\nPREFIX ns1: \n\nSELECT ?labExtract (COUNT(DISTINCT ?compound) AS ?uniqueCompounds)\nWHERE {\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_class ?compound .\n ?annotation ns1:has_canopus_npc_class_prob ?probability .\n FILTER(?probability > 0.9)\n}\nGROUP BY ?labExtract\nORDER BY DESC(?uniqueCompounds)\nLIMIT 3\n```\n\nThe results are saved in the file located at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/5cf09a28bf1049dbb23b09915c0fc8e1/tmp35c6yy5y.csv`."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""What are the Wikidata IDs of chemical entities that have ChEMBL activity against Trypanosoma cruzi target?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the Wikidata IDs of chemical entities that have ChEMBL activity against Trypanosoma cruzi target?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the Wikidata IDs of chemical entities that have ChEMBL activity against Trypanosoma cruzi target?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Trypanosoma cruzi, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL368, ChEMBLTarget\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""Here is the information you requested:\n\n- **Question**: What are the Wikidata IDs of chemical entities that have ChEMBL activity against Trypanosoma cruzi target?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?wd_id\n WHERE {\n ?chemicalEntity ns2:has_chembl_id ?chemblChemical .\n ?chemblChemical ns2:has_chembl_activity ?assayResults .\n ?assayResults ns2:target_id .\n ?chemicalEntity ns1:has_wd_id ?wd_id .\n }\n ```\n- **File Path**: The results are saved in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/18465df0915e441aa7285e242e5b5927/tmpsavbcot0.csv`.\n\nPlease let me know if you need further assistance!"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The file has been processed, and here are some of the Wikidata IDs of chemical entities that have ChEMBL activity against the Trypanosoma cruzi target:\n\n1. [Q82238903](http://www.wikidata.org/entity/Q82238903)\n2. [Q27095836](http://www.wikidata.org/entity/Q27095836)\n\nThese IDs were extracted from the provided file. If you need further analysis or a specific visualization, please let me know. \n\nNo visualization file was generated, but the data can be downloaded from the provided file path."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""What are the chemical structure ISDB annotations for Lovoa trichilioides ?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the chemical structure ISDB annotations for Lovoa trichilioides ?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the chemical structure ISDB annotations for Lovoa trichilioides?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""Lovoa trichilioides, http://www.wikidata.org/entity/Q8008828, Wikidata"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed. Here are the details:\n\n- **Question**: What are the chemical structure ISDB annotations for Lovoa trichilioides?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?Annotation\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_isdb_annotation ?Annotation .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/597fde39a0474507b67d3ce263df2a5e/tmp9ukxy4rj.csv`\n\nI will now analyze the SPARQL output to provide the final answer.\nThe chemical structure ISDB annotations for Lovoa trichilioides have been retrieved. Here is the final answer:\n\n- **Question**: What are the chemical structure ISDB annotations for Lovoa trichilioides?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?Annotation\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_isdb_annotation ?Annotation .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/597fde39a0474507b67d3ce263df2a5e/tmp9ukxy4rj.csv`\n\nThe file contains the ISDB annotations for Lovoa trichilioides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""Retrieve the Wikidata IDs of chemical entities from Desmodium heterophyllum that are annotated as rotenoids by CANOPUS and have ChEMBL assay results showing activity against Trypanosoma cruzi. Provide also the USIs to the corresponding MS/MS spectra"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the Wikidata IDs of chemical entities from Desmodium heterophyllum that are annotated as rotenoids by CANOPUS and have ChEMBL assay results showing activity against Trypanosoma cruzi. Provide also the USIs to the corresponding MS/MS spectra"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the Wikidata IDs of chemical entities from Desmodium heterophyllum that are annotated as rotenoids by CANOPUS and have ChEMBL assay results showing activity against Trypanosoma cruzi. Provide also the USIs to the corresponding MS/MS spectra."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Desmodium heterophyllum, http://www.wikidata.org/entity/Q10770714, Wikidata;\nrotenoids, https://enpkg.commons-lab.org/kg/npc_Rotenoids, NPCClass;\nTrypanosoma cruzi, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL368, ChEMBLTarget\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been executed successfully. Here is the information retrieved:\n\n### Question\nRetrieve the Wikidata IDs of chemical entities from Desmodium heterophyllum that are annotated as rotenoids by CANOPUS and have ChEMBL assay results showing activity against Trypanosoma cruzi. Provide also the USIs to the corresponding MS/MS spectra.\n\n### SPARQL Query\n```sparql\nPREFIX ns1: \nPREFIX ns2: \n\nSELECT ?wd_id ?usi\nWHERE {\n ?rawMaterial ns1:has_wd_id ;\n ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_canopus_annotation ?annotation ;\n ns1:has_usi ?usi .\n ?annotation ns1:has_canopus_npc_class .\n ?labExtract ns2:has_bioassay_results ?assayResult .\n ?assayResult ns2:target_id .\n ?chemicalEntity ns1:has_wd_id ?wd_id .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?siriusAnnotation ns1:has_InChIkey2D ?InChIkey2D .\n ?InChIkey2D ns1:is_InChIkey2D_of ?chemicalEntity .\n }\n```\n\n### File Path\nThe results have been saved to a temporary file, which can be accessed at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/be66080199fb4930ae3c599b8c11507d/tmpn40oy4b_.csv`\n\nPlease let me know if you need further assistance or analysis on this data!"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The analysis of the file has been completed. The data contains Wikidata IDs of chemical entities from Desmodium heterophyllum that are annotated as rotenoids by CANOPUS and have ChEMBL assay results showing activity against Trypanosoma cruzi. It also includes the corresponding USIs to the MS/MS spectra.\n\nHere are the first few entries:\n- **Wikidata ID**: Q104399962\n- **USI**: mzspec:MSV000087728:VGF155_D05_features_ms2_ne...\n\nNo visualization was requested or generated for this analysis. The data can be downloaded for further examination.\n\nIf you need any additional insights or visualizations, please let me know!"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""Which molecules (represented by their InChIkey) have reported ChEMBL activity with activity type IC50 and activity values below 500 nM against Leishmania donovani target?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which molecules (represented by their InChIkey) have reported ChEMBL activity with activity type IC50 and activity values below 500 nM against Leishmania donovani target?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which molecules (represented by their InChIkey) have reported ChEMBL activity with activity type IC50 and activity values below 500 nM against Leishmania donovani target?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""Leishmania donovani, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL367, ChEMBLTarget"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed successfully. Here are the details:\n\n- **Question**: Which molecules (represented by their InChIkey) have reported ChEMBL activity with activity type IC50 and activity values below 500 nM against Leishmania donovani target?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?InChIkey\n WHERE {\n ?chemicalEntity ns2:has_chembl_id ?chemblChemical .\n ?chemblChemical ns2:has_chembl_activity ?assayResults .\n ?assayResults ns2:target_id ;\n ns2:activity_type \""IC50\"" ;\n ns2:activity_value ?activityValue .\n FILTER(?activityValue < 500)\n ?chemicalEntity ns1:has_wd_id ?wd_id .\n ?InChIkey ns1:has_wd_id ?wd_id .\n }\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/e7807b50a3824198a3233798d22d9a4b/tmpp66dmbc6.csv\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The molecules that meet the criteria have been identified with the following InChIkey: [APKFDSVGJQXUKY-INPOYWNPSA-N](APKFDSVGJQXUKY-INPOYWNPSA-N).\n\n"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""Which compounds from Desmodium heterophyllum extracts have annotations from both ISDB and SIRIUS, and what are their molecular masses?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which compounds from Desmodium heterophyllum extracts have annotations from both ISDB and SIRIUS, and what are their molecular masses?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which compounds from Desmodium heterophyllum extracts have annotations from both ISDB and SIRIUS, and what are their molecular masses?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""Desmodium heterophyllum, http://www.wikidata.org/entity/Q10770714, Wikidata"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The compounds from Desmodium heterophyllum extracts that have annotations from both ISDB and SIRIUS, along with their molecular masses, are as follows:\n\n1. Compound: WITLAWYGGVAFLU, Mass: 379.1186828613281\n2. Compound: PKNGEQDEJRDPJZ, Mass: 407.2225646972656\n3. Compound: FHKQMPVRIQJSEJ, Mass: 313.1062774658203\n4. Compound: BWDGXQDVXMUNDT, Mass: 293.1019897460937\n5. Compound: QFTWSMLVOZYJPP, Mass: 337.1435089111328\n6. Compound: MIUNHNXJASJSIX, Mass: 425.1591796875\n7. Compound: ZSCRTFONTNMQBL, Mass: 247.16928100585935\n8. Compound: VZPAURMDJZOGHU, Mass: 445.208984375\n9. Compound: HEORSHPDPZGZCN, Mass: 349.143310546875\n10. Compound: GIVRDLOWBBXCNM, Mass: 351.1228332519531\n11. Compound: VZPAURMDJZOGHU, Mass: 445.2112426757813\n12. Compound: QQWYBWNEILZOKI, Mass: 321.1480407714844\n13. Compound: DTOSIQBPPRVQHS, Mass: 279.2320251464844\n14. Compound: DTOSIQBPPRVQHS, Mass: 279.2322692871094\n15. Compound: KSVKECXWDNCRTM, Mass: 403.20166015625\n16. Compound: CEAWIMABVSITMV, Mass: 383.1479187011719\n17. Compound: CVULDJMCSSACEO, Mass: 507.2290344238281\n18. Compound: KEJNXXMRDHDOCO, Mass: 279.10035705566406\n19. Compound: QQWYBWNEILZOKI, Mass: 321.14996337890625\n20. Compound: QQWYBWNEILZOKI, Mass: 321.1477355957031\n21. Compound: AOMUHOFOVNGZAN, Mass: 288.2526550292969\n22. Compound: YHZDBBUEVZEOIY, Mass: 271.0964050292969\n23. Compound: WRINUBCCCQLTPU, Mass: 365.13720703125\n24. Compound: GAGKUHAKUBMORD, Mass: 339.12274169921875\n25. Compound: CDZUQIFWCXAIFB, Mass: 443.13368225097656\n26. Compound: ZSCRTFONTNMQBL, Mass: 247.1693420410156\n27. Compound: HOMYIYLRRDTKAA, Mass: 714.5514831542969\n28. Compound: AOMUHOFOVNGZAN, Mass: 288.2525939941406\n29. Compound: OIMXTYUHMBQQJM, Mass: 393.3157348632813\n30. Compound: MEJLVLSNRYLDSG, Mass: 743.2880859375\n31. Compound: HEQLSVWMTJXPGS, Mass: 413.1593017578125\n32. Compound: WXWGKIBMPDYPTH, Mass: 265.08551025390625\n33. Compound: ICFXJOAKQGDRCT, Mass: 429.3733520507813\n34. Compound: QQWYBWNEILZOKI, Mass: 321.1485290527344\n35. Compound: XTLSKKJNOIMMBK, Mass: 295.0967102050781\n36. Compound: XTLSKKJNOIMMBK, Mass: 295.0966186523437\n37. Compound: XTLSKKJNOIMMBK, Mass: 295.0966491699219\n38. Compound: DTOSIQBPPRVQHS, Mass: 279.232177734375\n39. Compound: UKMCCFHULJHJNS, Mass: 427.3577575683594\n40. Compound: UKMCCFHULJHJNS, Mass: 427.3577423095703\n41. Compound: XTLSKKJNOIMMBK, Mass: 295.0967712402344\n42. Compound: XTLSKKJNOIMMBK, Mass: 295.0967102050781\n43. Compound: WECGLUPZRHILCT, Mass: 355.2845458984375\n44. Compound: QNPIEBGHSXVAOA, Mass: 702.5513305664062\n45. Compound: BWDGXQDVXMUNDT, Mass: 293.1017761230469\n46. Compound: XEVQXKKKAVVSMW, Mass: 197.1173095703125\n47. Compound: LUZSWWYKKLTDHU, Mass: 295.2268981933594\n48. Compound: ICFXJOAKQGDRCT, Mass: 429.3731689453125\n49. Compound: MIUNHNXJASJSIX, Mass: 425.158935546875\n50. Compound: AOMUHOFOVNGZAN, Mass: 288.2529754638672\n51. Compound: OZNMJHSEUFVTTG, Mass: 409.3470458984375\n52. Compound: GQSRIBUSVVSLSQ, Mass: 463.37884521484375\n53. Compound: HOMYIYLRRDTKAA, Mass: 714.550537109375\n54. Compound: HXYVTAGFYLMHSO, Mass: 300.2903747558594\n55. Compound: ORDAZKGHSNRHTD, Mass: 395.14947509765625\n56. Compound: AOMUHOFOVNGZAN, Mass: 288.2536163330078\n\nThe SPARQL query used to retrieve this data is:\n```sparql\nPREFIX ns1: \nPREFIX ns2: \nPREFIX wd: \n\nSELECT ?compound ?mass\nWHERE {\n ?rawMaterial ns1:has_wd_id wd:Q10770714 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_isdb_annotation ?isdbAnnotation .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?feature ns1:has_parent_mass ?mass .\n ?isdbAnnotation ns1:has_InChIkey2D ?compound .\n ?siriusAnnotation ns1:has_InChIkey2D ?compound .\n}\n```\n\nThe file path to the SPARQL output is: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/c6e1f9c0990546a88ca140657c7354d1/tmpez5ixi4n.csv`\n\nI will now provide this information to the supervisor."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""What are the most frequent adducts of LCMS features annotated by SIRIUS detected in positive ionization mode from Tabernaemontana coffeoides?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the most frequent adducts of LCMS features annotated by SIRIUS detected in positive ionization mode from Tabernaemontana coffeoides?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the most frequent adducts of LCMS features annotated by SIRIUS detected in positive ionization mode from Tabernaemontana coffeoides?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The most frequent adducts of LCMS features annotated by SIRIUS detected in positive ionization mode from Tabernaemontana coffeoides are:\n\n1. [M+H]+ with a frequency of 1079\n2. [M+NH4]+ with a frequency of 432\n3. [M+Na]+ with a frequency of 263\n4. [M+K]+ with a frequency of 65\n\nHere is the dictionary containing the question, generated SPARQL query, and file path:\n\n- **Question**: What are the most frequent adducts of LCMS features annotated by SIRIUS detected in positive ionization mode from Tabernaemontana coffeoides?\n- **Generated SPARQL Query**: \n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?adduct (COUNT(?adduct) AS ?frequency)\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis a ns1:LCMSAnalysisPos .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?annotation .\n ?annotation ns1:has_adduct ?adduct .\n }\n GROUP BY ?adduct\n ORDER BY DESC(?frequency)\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/d8669ea7b6ab4a75a171344bc71c5b61/tmpnm0aoqfg.csv`"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""What are the most frequent SIRIUS chemical structure annotations (planar structure) observed from the extracts belonging to Hibiscus syriacus?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the most frequent SIRIUS chemical structure annotations (planar structure) observed from the extracts belonging to Hibiscus syriacus?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the most frequent SIRIUS chemical structure annotations (planar structure) observed from the extracts belonging to Hibiscus syriacus?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Hibiscus syriacus, http://www.wikidata.org/entity/Q157756, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The most frequent SIRIUS chemical structure annotations (planar structure) observed from the extracts belonging to Hibiscus syriacus are as follows:\n\n1. InChIkey2D: [KEMQGTRYUADPNZ](https://enpkg.commons-lab.org/kg/KEMQGTRYUADPNZ) - Frequency: 2\n2. InChIkey2D: [DTOSIQBPPRVQHS](https://enpkg.commons-lab.org/kg/DTOSIQBPPRVQHS) - Frequency: 2\n3. InChIkey2D: [OUMMUORTBYSLFO](https://enpkg.commons-lab.org/kg/OUMMUORTBYSLFO) - Frequency: 2\n\nThese are the most frequently observed annotations, each appearing twice in the dataset. Here is the SPARQL query that was generated:\n\n```sparql\n PREFIX ns1: \\nPREFIX ns2: \\n\\nSELECT ?InChIkey2D (COUNT(?InChIkey2D) AS ?frequency)\\nWHERE {\\n ?rawMaterial ns1:has_wd_id .\\n ?rawMaterial ns1:has_lab_process ?labExtract .\\n ?labExtract ns1:has_LCMS ?analysis .\\n ?analysis ns1:has_lcms_feature_list ?feature_list .\\n ?feature_list ns1:has_lcms_feature ?feature .\\n ?feature ns1:has_sirius_annotation ?annotation .\\n ?annotation ns1:has_InChIkey2D ?InChIkey2D .\\n}\\nGROUP BY ?InChIkey2D\\nORDER BY DESC(?frequency)'"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""How many features (pos ionization and neg ionization modes) have the same SIRIUS/CSI:FingerID and ISDB annotation by comparing the InCHIKey2D of the annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""How many features (pos ionization and neg ionization modes) have the same SIRIUS/CSI:FingerID and ISDB annotation by comparing the InCHIKey2D of the annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question:How many features (pos ionization and neg ionization modes) have the same SIRIUS/CSI:FingerID and ISDB annotation by comparing the InCHIKey2D of the annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The analysis has been completed. Here are the details:\n\n- **Question**: How many features (pos ionization and neg ionization modes) have the same SIRIUS/CSI:FingerID and ISDB annotation by comparing the InCHIKey2D of the annotations?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT (COUNT(?feature) AS ?featureCount)\n WHERE {\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?feature ns1:has_isdb_annotation ?isdbAnnotation .\n ?siriusAnnotation ns1:has_InChIkey2D ?InChIkey2D .\n ?isdbAnnotation ns1:has_InChIkey2D ?InChIkey2D .\n }\n ```\n- **Result**: 33,255 features have the same SIRIUS/CSI:FingerID and ISDB annotation by comparing the InCHIKey2D of the annotations.\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/ec07f70ba3ec4a18abbb424a48145459/tmppclv2pge.csv`\n\nThis information should provide the necessary details for further analysis or reporting."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""Which lab extracts from Melochia umbellata yield compounds that, when analyzed in positive ionization mode LCMS, have a retention time of less than 6 minutes and demonstrate an inhibition percentage greater than 70% in bioassay results? Provide lab extracts, retention times and inhibition percentage."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which lab extracts from Melochia umbellata yield compounds that, when analyzed in positive ionization mode LCMS, have a retention time of less than 6 minutes and demonstrate an inhibition percentage greater than 70% in bioassay results? Provide lab extracts, retention times and inhibition percentage."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which lab extracts from Melochia umbellata yield compounds that, when analyzed in positive ionization mode LCMS, have a retention time of less than 6 minutes and demonstrate an inhibition percentage greater than 70% in bioassay results? Provide lab extracts, retention times and inhibition percentage."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""Melochia umbellata, http://www.wikidata.org/entity/Q6813281, Wikidata"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The lab extracts from Melochia umbellata that yield compounds analyzed in positive ionization mode LCMS with a retention time of less than 6 minutes and demonstrate an inhibition percentage greater than 70% in bioassay results are as follows:\n\n- **Lab Extract**: [VGF156_A06](https://enpkg.commons-lab.org/kg/VGF156_A06)\n - **Retention Time**: 4.198916666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 4.54375 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.5790666666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7661 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.6830500000000002 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.089 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.6979 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.5034166666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.26885 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.1985333333333337 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.11295 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7891 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.2292333333333334 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.089 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.5863333333333334 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7204833333333334 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7815333333333332 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7054833333333332 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.4656833333333334 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.2918833333333335 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.3785166666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.2292333333333334 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.33065 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.5962 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.4250166666666668 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.511016666666667 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.6655166666666663 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.8533833333333332 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.8533833333333332 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.5034166666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.5034166666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.3630666666666669 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7512166666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 0.6451166666666667 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7661 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.9627166666666669 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.2918833333333335 minutes\n - **Inhibition Percentage**: 89.9%\n\nThe SPARQL query used to retrieve this information is as follows:\n\n```sparql\nPREFIX ns1: \nPREFIX ns2: \nPREFIX wd: \n\nSELECT ?labExtract ?retentionTime ?inhibitionPercentage\nWHERE {\n ?rawMaterial ns1:has_wd_id wd:Q6813281 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis a ns1:LCMSAnalysisPos .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_retention_time ?retentionTime .\n FILTER(?retentionTime < 6)\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\n ?bioAssayResult ns2:inhibition_percentage ?inhibitionPercentage .\n FILTER(?inhibitionPercentage > 70)\n}\n```\n\nThe results are saved in the file located at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/461195db838a4810afb06014c87c0e6b/tmp_7t2umr1.csv`"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""How many features annotated as 'Tetraketide meroterpenoids' by CANOPUS are found for each submitted taxon and extract in database?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""How many features annotated as 'Tetraketide meroterpenoids' by CANOPUS are found for each submitted taxon and extract in database?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: How many features annotated as 'Tetraketide meroterpenoids' by CANOPUS are found for each submitted taxon and extract in database?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tetraketide meroterpenoids, https://enpkg.commons-lab.org/kg/npc_Tetraketide_meroterpenoids, NPCClass\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed. Here are the details:\n\n- **Question**: How many features annotated as 'Tetraketide meroterpenoids' by CANOPUS are found for each submitted taxon and extract in database?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT ?submittedTaxon ?extract (COUNT(?feature) AS ?featureCount)\n WHERE {\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_class .\n ?featureList ns1:has_lcms_feature ?feature .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?extract ns1:has_LCMS ?analysis .\n ?rawMaterial ns1:has_lab_process ?extract .\n ?rawMaterial ns1:submitted_taxon ?submittedTaxon .\n }\n GROUP BY ?submittedTaxon ?extract\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/093ee098fbba4dca93f0404833ba35bb/tmpjqdqbx0d.csv\n\nI will now analyze the SPARQL output to generate the final answer.\nThe SPARQL query has been executed successfully. Here is the final answer based on the SPARQL output:\n\nThe number of features annotated as 'Tetraketide meroterpenoids' by CANOPUS for each submitted taxon and extract in the database is available in the generated CSV file.\n\n- **Question**: How many features annotated as 'Tetraketide meroterpenoids' by CANOPUS are found for each submitted taxon and extract in database?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT ?submittedTaxon ?extract (COUNT(?feature) AS ?featureCount)\n WHERE {\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_class .\n ?featureList ns1:has_lcms_feature ?feature .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?extract ns1:has_LCMS ?analysis .\n ?rawMaterial ns1:has_lab_process ?extract .\n ?rawMaterial ns1:submitted_taxon ?submittedTaxon .\n }\n GROUP BY ?submittedTaxon ?extract\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/093ee098fbba4dca93f0404833ba35bb/tmpjqdqbx0d.csv\n\nYou can find the detailed results in the provided CSV file."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""Provide the wikidata ids of the chemical entities annotated by SIRIUS for Tabernaemontana coffeoides seeds extract taxon obtained in positive mode which are also reported in the Tabernaemontana genus in Wikidata."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Provide the wikidata ids of the chemical entities annotated by SIRIUS for Tabernaemontana coffeoides seeds extract taxon obtained in positive mode which are also reported in the Tabernaemontana genus in Wikidata."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Provide the wikidata ids of the chemical entities annotated by SIRIUS for Tabernaemontana coffeoides seeds extract taxon obtained in positive mode which are also reported in the Tabernaemontana genus in Wikidata."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": [""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata"", ""Tabernaemontana genus, http://www.wikidata.org/entity/Q310915, Wikidata""], ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The final merged output file has been generated. Here are the details:\n\n- **Question**: Provide the wikidata ids of the chemical entities annotated by SIRIUS for Tabernaemontana coffeoides seeds extract taxon obtained in positive mode which are also reported in the Tabernaemontana genus in Wikidata.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?wd_id\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis a ns1:LCMSAnalysisPos .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?annotation .\n ?annotation ns1:has_InChIkey2D ?inchiKey2D .\n ?inchiKey2D ns1:is_InChIkey2D_of ?chemicalEntity .\n ?chemicalEntity ns1:has_wd_id ?wd_id .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/3b8bb2a213ab4b1faf41570486facaae/tmp4g0h_2d5.csv`\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The wikidata IDs of the chemical entities annotated by SIRIUS for Tabernaemontana coffeoides seeds extract taxon obtained in positive mode which are also reported in the Tabernaemontana genus in Wikidata have been processed. \n\n"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""What are the retention times, parent masses, and associated Spec2Vec peaks for features in the LC-MS analysis of the plant Rumex nepalensis in positive ion mode?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the retention times, parent masses, and associated Spec2Vec peaks for features in the LC-MS analysis of the plant Rumex nepalensis in positive ion mode?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question:What are the retention times, parent masses, and associated Spec2Vec peaks for features in the LC-MS analysis of the plant Rumex nepalensis in positive ion mode?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Rumex nepalensis, http://www.wikidata.org/entity/Q11041204, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: What are the retention times, parent masses, and associated Spec2Vec peaks for features in the LC-MS analysis of the plant Rumex nepalensis in positive ion mode?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?retention_time ?parent_mass ?spec2vec_peak\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis a ns1:LCMSAnalysisPos .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_retention_time ?retention_time .\n ?feature ns1:has_parent_mass ?parent_mass .\n ?feature ns1:has_spec2vec_doc ?spec2vecDoc .\n ?spec2vecDoc ns1:has_spec2vec_peak ?spec2vec_peak .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/7e32d9e48e274df9a04b4a01d1635276/tmpxmt6gmzo.csv`\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The LC-MS analysis of the plant Rumex nepalensis in positive ion mode provided the following details:\n\n- **Retention Time**: 5.938233\n- **Parent Mass**: 584.475128\n- **Spec2Vec Peaks**:\n - [Peak 130.14](https://enpkg.commons-lab.org/kg/peak_130.14)\n - [Peak 131.07](https://enpkg.commons-lab.org/kg/peak_131.07)\n - [Peak 221.14](https://enpkg.commons-lab.org/kg/peak_221.14)\n - [Peak 233.11](https://enpkg.commons-lab.org/kg/peak_233.11)\n - [Peak 265.17](https://enpkg.commons-lab.org/kg/peak_265.17)\n\nNo visualization was requested for this analysis. The data can be downloaded from the provided file path."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""What is the highest inhibition percentage recorded for compounds observed in Rauvolfia vomitoria extract?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What is the highest inhibition percentage recorded for compounds observed in Rauvolfia vomitoria extract?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What is the highest inhibition percentage recorded for compounds observed in Rauvolfia vomitoria extract?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Rauvolfia vomitoria, http://www.wikidata.org/entity/Q5225969, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The highest inhibition percentage recorded for compounds observed in Rauvolfia vomitoria extract is 17.4%.\n\nHere is the detailed information:\n- **Question**: What is the highest inhibition percentage recorded for compounds from Rauvolfia vomitoria extract?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n PREFIX wd: \n\n SELECT ?inhibition_percentage\n WHERE {\n ?rawMaterial ns1:has_wd_id wd:Q5225969 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\n ?bioAssayResult ns2:inhibition_percentage ?inhibition_percentage .\n }\n ORDER BY DESC(?inhibition_percentage)\n LIMIT 1\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/2b0f38f9dd7b4e818282a1c620449ab2/tmpjoi_glcf.csv"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""List the bioassay results at 10µg/mL against T.cruzi for lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""List the bioassay results at 10µg/mL against T.cruzi for lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: List the bioassay results at 10µg/mL against T.cruzi for lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The bioassay results at 10µg/mL against T.cruzi for lab extracts of Tabernaemontana coffeoides are as follows:\n\n1. Lab Extract: [VGF152_B02](https://enpkg.commons-lab.org/kg/VGF152_B02)\n - BioAssay Result: [VGF152_B02_Tcruzi_10ugml](https://enpkg.commons-lab.org/module/VGF152_B02_Tcruzi_10ugml)\n\n2. Lab Extract: [VGF152_A02](https://enpkg.commons-lab.org/kg/VGF152_A02)\n - BioAssay Result: [VGF152_A02_Tcruzi_10ugml](https://enpkg.commons-lab.org/module/VGF152_A02_Tcruzi_10ugml)\n\n3. Lab Extract: [VGF151_G11](https://enpkg.commons-lab.org/kg/VGF151_G11)\n - BioAssay Result: [VGF151_G11_Tcruzi_10ugml](https://enpkg.commons-lab.org/module/VGF151_G11_Tcruzi_10ugml)\n\n4. Lab Extract: [VGF151_H11](https://enpkg.commons-lab.org/kg/VGF151_H11)\n - BioAssay Result: [VGF151_H11_Tcruzi_10ugml](https://enpkg.commons-lab.org/module/VGF151_H11_Tcruzi_10ugml)\n\nHere is the dictionary containing the question, generated SPARQL query, and file path to the query result:\n\n```json\n{\n \""question\"": \""List the bioassay results (BioAssayResults) at 10µg/mL against T.cruzi (Tcruzi10ugml) for lab extracts (LabExtract) of Tabernaemontana coffeoides.\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\nPREFIX ns2: \\nPREFIX wd: \\n\\nSELECT ?labExtract ?bioAssayResult\\nWHERE {\\n ?rawMaterial ns1:has_wd_id wd:Q15376858 .\\n ?rawMaterial ns1:has_lab_process ?labExtract .\\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\\n ?bioAssayResult a ns2:Tcruzi10ugml .\\n}\"",\n \""file_path\"": \""/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/341300dcf0174e67999e30cc1e34324b/tmpvt5pfz18.csv\""\n}\n```"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""What are the SIRIUS structural annotations associated with the MS2 spectra from the lab extracts of Tabernaemontana coffeoides ?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the SIRIUS structural annotations associated with the MS2 spectra from the lab extracts of Tabernaemontana coffeoides ?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the SIRIUS structural annotations associated with the MS2 spectra from the lab extracts of Tabernaemontana coffeoides?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: What are the SIRIUS structural annotations associated with the MS2 spectra from the lab extracts of Tabernaemontana coffeoides?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?siriusAnnotation\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?siriusAnnotation a ns1:SiriusStructureAnnotation .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/d851d6aa70ca4256a4862a6e4811b956/tmpr1p_smpd.csv`\n\nI will now analyze the SPARQL output to provide the final answer.\nThe SPARQL query has been executed successfully, and the results are stored in the specified file. Here is the final answer based on the SPARQL output:\n\n- **Question**: What are the SIRIUS structural annotations (SiriusStructureAnnotation) associated with the MS2 spectra (MS2Spectrum) from the lab extracts (LabExtract) of Tabernaemontana coffeoides?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?siriusAnnotation\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?siriusAnnotation a ns1:SiriusStructureAnnotation .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/d851d6aa70ca4256a4862a6e4811b956/tmpr1p_smpd.csv`\n\nThe file contains the SIRIUS structural annotations associated with the MS2 spectra from the lab extracts of Tabernaemontana coffeoides. You can access the detailed results in the provided file path."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""For the LC-MS feature observed in the extracts of Tabernaemontana coffeoides, retrieve those that are annotated with a NPCPathways with CANOPUS."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""For the LC-MS feature observed in the extracts of Tabernaemontana coffeoides, retrieve those that are annotated with a NPCPathways with CANOPUS."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: For the LC-MS feature observed in the extracts of Tabernaemontana coffeoides, retrieve those that are annotated with a NPCPathways with CANOPUS."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been executed to retrieve the LC-MS features observed in the extracts of Tabernaemontana coffeoides that are annotated with NPCPathways using CANOPUS. Here is the information:\n\n- **SPARQL Query**: \n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?feature\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_pathway ?npcPathway .\n }\n ```\n\n- **File Path**: The results are saved in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/0a0e72082aef4788a45ec734c4f9b49c/tmpqlgm7k3g.csv`.\n\nPlease check the file for the detailed results of the query."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The LC-MS features observed in the extracts of Tabernaemontana coffeoides that are annotated with NPCPathways using CANOPUS have been successfully retrieved. "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""List the tissue types used to produce the lab extracts of Tabernaemontana coffeoides. Provide the tissue types and lab extracts. "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""List the tissue types used to produce the lab extracts of Tabernaemontana coffeoides. Provide the tissue types and lab extracts. "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: List the tissue types used to produce the lab extracts of Tabernaemontana coffeoides. Provide the tissue types and lab extracts."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The tissue type used to produce the lab extracts of Tabernaemontana coffeoides is \""green tissue.\"" The lab extracts associated with this tissue type are:\n\n1. Lab Extract: VGF152_B02\n2. Lab Extract: VGF152_A02\n3. Lab Extract: VGF151_G11\n4. Lab Extract: VGF151_H11\n\nHere is the SPARQL query used to retrieve this information:\n\n```sparql\nPREFIX ns1: \nPREFIX ns2: \n\nSELECT ?tissue ?labExtract\nWHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?rawMaterial ns2:has_tissue ?tissue .\n}\n```\n\nThe data was retrieved from the file located at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/79cf06dcfb4c4635bf92d845c42ea938/tmpo0nqoke3.csv`."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""Retrieve the Wikidata IDs of chemical entities classified as Terpenoids in the lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the Wikidata IDs of chemical entities classified as Terpenoids in the lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the Wikidata IDs of chemical entities classified as Terpenoids in the lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Terpenoids, https://enpkg.commons-lab.org/kg/npc_Terpenoids, NPCPathway;\nTabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""Here is the information retrieved from the SPARQL query:\n\n- **Question**: Retrieve the Wikidata IDs of chemical entities classified as Terpenoids in the lab extracts of Tabernaemontana coffeoides.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT ?wd_id\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?annotation .\n ?annotation ns1:has_InChIkey2D ?InChIkey2D .\n ?InChIkey2D ns1:is_InChIkey2D_of ?chemicalEntity .\n ?chemicalEntity ns1:has_npc_pathway .\n ?chemicalEntity ns1:has_wd_id ?wd_id .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/40c708550e684b0685c41a14cae47bd2/tmpcgattsm4.csv`\n\nThis information will be provided to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The Wikidata IDs of the chemical entities classified as Terpenoids in the lab extracts of Tabernaemontana coffeoides are:\n\n1. [Q105262918](http://www.wikidata.org/entity/Q105262918)\n2. [Q104398790](http://www.wikidata.org/entity/Q104398790)\n3. [Q105262918](http://www.wikidata.org/entity/Q105262918)\n4. [Q104398790](http://www.wikidata.org/entity/Q104398790)\n5. [Q104953850](http://www.wikidata.org/entity/Q104953850)\n\nNo visualization is available, but the data can be downloaded if needed. If you need further analysis or a specific visualization, please let me know. \n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""Retrieve the mass differences and cosine similarity scores for spectral pairs involving MS2 spectra from Desmodium heterophyllum."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the mass differences and cosine similarity scores for spectral pairs involving MS2 spectra from Desmodium heterophyllum."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the mass differences and cosine similarity scores for spectral pairs involving MS2 spectra from Desmodium heterophyllum."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid. It is related to the ENPKG nodes/entities (Raw Material, LCMS Analysis, SpectralPair). The plant name \""Desmodium heterophyllum\"" is present in the database. The question asks for mass differences and cosine similarity scores, which are properties of the SpectralPair entity in the ENPKG."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Desmodium heterophyllum, http://www.wikidata.org/entity/Q10770714, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The mass differences and cosine similarity scores for spectral pairs involving MS2 spectra from Desmodium heterophyllum have been retrieved. Here is the information:\n\n- **SPARQL Query**: \n ```sparql\n PREFIX ns1: \n\n SELECT ?mass_difference ?cosine\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?spectralPair ns1:has_member ?feature .\n ?spectralPair ns1:has_mass_difference ?mass_difference .\n ?spectralPair ns1:has_cosine ?cosine .\n }\n ```\n\n- **File Path**: The results are stored in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/03572d09329f4855ab27963f8832666f/tmpsuxk8web.csv`.\n\nPlease refer to the file for detailed results."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The mass differences and cosine similarity scores for spectral pairs involving MS2 spectra from Desmodium heterophyllum have been successfully interpreted. Here are some sample entries from the data:\n\n- Mass Difference: 218.2978, Cosine Similarity: 0.999612\n- Mass Difference: 46.0057, Cosine Similarity: 0.999888\n- Mass Difference: 140.0989, Cosine Similarity: 0.985793\n- Mass Difference: 160.1252, Cosine Similarity: 0.993816\n- Mass Difference: 162.1407, Cosine Similarity: 0.984198\n\nNo visualization is available, but the data can be downloaded from the provided file path. If you need a visualization, please let me know, and I can generate a plotly graph for you.\n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""Which ChEMBL documents cited in ChEMBL assay results involve compounds (identified by InChIKeys) that are also observed in SIRIUS structural annotations within lab extracts, and which lab extracts contain the highest number of these shared compounds?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which ChEMBL documents cited in ChEMBL assay results involve compounds (identified by InChIKeys) that are also observed in SIRIUS structural annotations within lab extracts, and which lab extracts contain the highest number of these shared compounds?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which ChEMBL documents cited in ChEMBL assay results involve compounds (identified by InChIKeys) that are also observed in SIRIUS structural annotations within lab extracts, and which lab extracts contain the highest number of these shared compounds?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here is the information:\n\n- **Question**: Which ChEMBL documents cited in ChEMBL assay results involve compounds (identified by InChIKeys) that are also observed in SIRIUS structural annotations within lab extracts, and which lab extracts contain the highest number of these shared compounds?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?chemblDocument ?labExtract (COUNT(?InChIkey2D) AS ?sharedCompoundCount)\n WHERE {\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?siriusAnnotation ns1:has_InChIkey2D ?InChIkey2D .\n\n ?InChIkey2D ns1:is_InChIkey2D_of ?chemicalEntity .\n ?chemicalEntity ns2:has_chembl_id ?chemblChemical .\n\n ?chemblChemical ns2:has_chembl_activity ?assayResult .\n ?assayResult ns2:stated_in_document ?chemblDocument .\n }\n GROUP BY ?chemblDocument ?labExtract\n ORDER BY DESC(?sharedCompoundCount)\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/ff49c14268314ce9948ca2f02f8fc1d0/tmpy5x90ltz.csv`\n\nPlease refer to the file for detailed results."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The ChEMBL document and lab extract with the highest number of shared compounds are as follows:\n\n- **ChEMBL Document**: [https://www.ebi.ac.uk/chembl/document_report_c...](https://www.ebi.ac.uk/chembl/document_report_c...)\n- **Lab Extract**: [https://enpkg.commons-lab.org/kg/VGF142_G04](https://enpkg.commons-lab.org/kg/VGF142_G04)\n- **Shared Compound Count**: 125\n\nThis information provides the details of the ChEMBL document and lab extract with the highest number of shared compounds. If you need further analysis or visualization, please let me know.\n\nThe full path of the files generated are: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/ff49c14268314ce9948ca2f02f8fc1d0/tmpy5x90ltz.csv`\n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""What are the ChEMBL targets linked to ChEMBL assay results for chemicals sharing InChIKeys with SIRIUS structural annotations ?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the ChEMBL targets linked to ChEMBL assay results for chemicals sharing InChIKeys with SIRIUS structural annotations ?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the ChEMBL targets linked to ChEMBL assay results for chemicals sharing InChIKeys with SIRIUS structural annotations ?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed. Here are the details:\n\n- **Question**: What are the ChEMBL targets linked to ChEMBL assay results for chemicals sharing InChIKeys with SIRIUS structural annotations?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?chembl_target\n WHERE {\n ?sirius_annotation ns1:has_InChIkey2D ?InChIkey2D .\n ?InChIkey2D ns1:is_InChIkey2D_of ?InChIkey .\n ?InChIkey ns2:has_chembl_id ?chembl_chemical .\n ?chembl_chemical ns2:has_chembl_activity ?chembl_assay_results .\n ?chembl_assay_results ns2:target_id ?chembl_target .\n }\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/f016f234d53147b3a0454bd652bc8f6b/tmppen79nmc.csv\n\nI will now analyze the SPARQL output to generate the final answer.\nThe final answer based on the SPARQL output is as follows:\n\n- **ChEMBL Targets (ChEMBLTarget)**: The ChEMBL targets linked to ChEMBL assay results for chemicals sharing InChIKeys with SIRIUS structural annotations are listed in the file located at the following path:\n - **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/f016f234d53147b3a0454bd652bc8f6b/tmppen79nmc.csv\n\nHere is the dictionary containing the question, generated SPARQL query, and file path:\n\n```json\n{\n \""question\"": \""What are the ChEMBL targets (ChEMBLTarget) linked to ChEMBL assay results (ChEMBLAssayResults) for chemicals (ChEMBLChemical) sharing InChIKeys (InChIkey) with SIRIUS structural annotations (SiriusStructureAnnotation)?\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\nPREFIX ns2: \\n\\nSELECT ?chembl_target\\nWHERE {\\n ?sirius_annotation ns1:has_InChIkey2D ?InChIkey2D .\\n ?InChIkey2D ns1:is_InChIkey2D_of ?InChIkey .\\n ?InChIkey ns2:has_chembl_id ?chembl_chemical .\\n ?chembl_chemical ns2:has_chembl_activity ?chembl_assay_results .\\n ?chembl_assay_results ns2:target_id ?chembl_target .\\n}\"",\n \""file_path\"": \""/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/f016f234d53147b3a0454bd652bc8f6b/tmppen79nmc.csv\""\n}\n```\n\nPlease review the file for the detailed list of ChEMBL targets."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""Retrieve the top 5 tissues used to produce lab extracts that contain compounds with inhibition activity greater than 70% against Leishmania donovani target at 10 µg/mL."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the top 5 tissues used to produce lab extracts that contain compounds with inhibition activity greater than 70% against Leishmania donovani target at 10 µg/mL."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the top 5 tissues used to produce lab extracts that contain compounds with inhibition activity greater than 70% against Leishmania donovani target at 10 µg/mL."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Leishmania donovani, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL367, ChEMBLTarget\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The top 5 tissues used to produce lab extracts containing compounds with inhibition activity greater than 70% against the Leishmania donovani target at 10 µg/mL are:\n\n1. Green Tissue (24 extracts)\n2. Root Tissue (17 extracts)\n3. Multiple Tissues (9 extracts)\n4. Woody Tissue (7 extracts)\n\nThe SPARQL query used to retrieve this information is as follows:\n\n```sparql\nPREFIX ns1: \nPREFIX ns2: \n\nSELECT ?tissue (COUNT(?labExtract) AS ?count)\nWHERE {\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\n ?bioAssayResult ns2:inhibition_percentage ?inhibitionPercentage ;\n ns2:target_id .\n FILTER(?inhibitionPercentage > 70)\n ?rawMaterial ns1:has_lab_process ?labExtract ;\n ns2:has_tissue ?tissue .\n}\nGROUP BY ?tissue\nORDER BY DESC(?count)\nLIMIT 5\n```\n\nThe data is stored in the file located at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/9aa4b47f0e764929989be2184095b3b0/tmp6u2rkep7.csv`."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""What are the CANOPUS annotations for pairs of features from Desmodium heterophyllum extracts that share a cosine similarity score greater than 0.8? Provide the features and annotations for each feature."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the CANOPUS annotations for pairs of features from Desmodium heterophyllum extracts that share a cosine similarity score greater than 0.8? Provide the features and annotations for each feature. "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the CANOPUS annotations for pairs of features from Desmodium heterophyllum extracts that share a cosine similarity score greater than 0.8? Provide the features and annotations for each feature."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Desmodium heterophyllum, http://www.wikidata.org/entity/Q10770714, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: What are the CANOPUS annotations for pairs of features from Desmodium heterophyllum extracts that share a cosine similarity score greater than 0.8? Provide the features and annotations for each feature .\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?feature1 ?feature2 ?annotation1 ?annotation2 \n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature1, ?feature2 .\n ?pair ns1:has_member ?feature1, ?feature2 .\n ?pair ns1:has_cosine ?cosine .\n FILTER(?cosine > 0.8)\n ?feature1 ns1:has_canopus_annotation ?annotation1 .\n ?feature2 ns1:has_canopus_annotation ?annotation2 .\n }\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/e88e690ae0ad4816a764af78599a625a/tmp2igkyrj2.csv\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The analysis has been completed, and there are 14,484 pairs of features from Desmodium heterophyllum extracts that share a cosine similarity score greater than 0.8. The features, annotations, and cosine scores have been extracted.\n\nIf you need more specific details or a visualization of this data, please let me know. The data can be downloaded for further analysis. \n\nThe full path of the generated file is:\n- /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/e88e690ae0ad4816a764af78599a625a/tmp2igkyrj2.csv\n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""What are SIRIUS structural annotations for LCMS features with cosmic scores above 0.3 associated with Tabernaemontana coffeoides? Report their ZODIAC scores."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are SIRIUS structural annotations for LCMS features with cosmic scores above 0.3 associated with Tabernaemontana coffeoides? Report their ZODIAC scores."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the cosmic scores and zodiac scores associated with SIRIUS structural annotations for LCMS features from Tabernaemontana coffeoides?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The zodiac scores associated with SIRIUS structural annotations for LCMS features from Tabernaemontana coffeoides have been retrieved. Here is the information:\n\n- **SPARQL Query**: \n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?cosmic_score ?zodiac_score\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?siriusAnnotation ns1:has_cosmic_score ?cosmic_score .\n FILTER (?cosmic_score > 0.3) \n ?siriusAnnotation ns1:has_zodiac_score ?zodiac_score .\n }\n ```\n\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/5137b0a478de42c5b07851d5252b3e4c/tmpn4ce1k2v.csv`\n\nPlease refer to the file for detailed scores."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" +"[{""type"": ""human"", ""content"": ""Which lab extracts have bioassay results with inhibition percentages above 50% against Leishmania donovani target at 10 µg/mL?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which lab extracts have bioassay results with inhibition percentages above 50% against Leishmania donovani target at 10 µg/mL?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which lab extracts have bioassay results with inhibition percentages above 50% against Leishmania donovani target at 10 µg/mL?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Leishmania donovani, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL367, ChEMBLTarget\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The lab extracts that have bioassay results with inhibition percentages above 50% against the Leishmania donovani target are identified by the following identifiers:\n\n- VGF159_F04\n- VGF159_B08\n- VGF159_B09\n- VGF159_E03\n- VGF159_H03\n- VGF158_C03\n- VGF158_B03\n- VGF158_G06\n- VGF158_A10\n- VGF158_H02\n- VGF158_A03\n- VGF158_D03\n- VGF158_D08\n- VGF158_C04\n- VGF158_H09\n- VGF158_G09\n- VGF157_C04\n- VGF157_D03\n- VGF157_H05\n- VGF157_C03\n- VGF157_C05\n- VGF157_D10\n- VGF157_E04\n- VGF156_E04\n- VGF156_D09\n- VGF156_A05\n- VGF155_B07\n- VGF155_A08\n- VGF155_E10\n- VGF155_D09\n- VGF155_C04\n- VGF155_B05\n- VGF155_C02\n- VGF155_A10\n- VGF155_F10\n- VGF155_D08\n- VGF154_A10\n- VGF154_A02\n- VGF154_A04\n- VGF152_G03\n- VGF152_H03\n- VGF152_D08\n- VGF152_H09\n- VGF152_A10\n- VGF151_A06\n- VGF151_A03\n- VGF151_H08\n- VGF151_H11\n- VGF147_H05\n- VGF147_F04\n- VGF147_G04\n- VGF146_F02\n- VGF145_G10\n- VGF144_E05\n- VGF143_G02\n- VGF142_G04\n- VGF142_B07\n- VGF142_G05\n- VGF142_B10\n- VGF141_C06\n- VGF141_B11\n- VGF140_E05\n- VGF140_A09\n- VGF139_A09\n- VGF139_D08\n- VGF138_B06\n- VGF138_E06\n- VGF138_E03\n- VGF138_E05\n- VGF138_G03\n\nThese identifiers correspond to specific lab extracts that have shown significant inhibition against the Leishmania donovani target.nHere is the SPARQL query that was generated:\n\n```sparql\nPREFIX ns1: \nPREFIX ns2: \n\nSELECT ?labExtract\nWHERE {\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\n ?bioAssayResult ns2:inhibition_percentage ?inhibitionPercentage ;\n ns2:target_id .\n ?bioAssayResult a ns2:Ldono10ugml.\n FILTER(?inhibitionPercentage > 50)\n}\n```\n\nIf you have any further questions or need additional information, feel free to ask!"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","[""base""]" From 69ec4a5b48cb884593ef552802ec85aaf131116e Mon Sep 17 00:00:00 2001 From: madina1203 Date: Thu, 23 Apr 2026 21:40:03 +0200 Subject: [PATCH 05/21] Fix deprecated pydantic imports and update requirements --- app/core/agents/enpkg/tool_chemicals.py | 4 +- app/core/agents/enpkg/tool_smiles.py | 4 +- app/core/agents/enpkg/tool_target.py | 4 +- app/core/agents/enpkg/tool_taxon.py | 10 +- app/core/agents/entry/tool_filesparser.py | 8 +- .../agents/interpreter/tool_interpreter.py | 4 +- app/core/agents/interpreter/tool_spectrum.py | 4 +- app/core/agents/sparql/tool_merge_result.py | 4 +- app/core/agents/sparql/tool_sparql.py | 10 +- app/core/agents/sparql/tool_wikidata_query.py | 10 +- app/core/agents/validator/tool_validator.py | 4 +- app/core/memory/custom_sqlite_file.py | 6 +- app/core/memory/database_manager.py | 79 ++++++----- app/core/tests/evaluation.py | 133 ++++++++++-------- app/core/workflow/langraph_workflow.py | 2 +- environment.yml | 26 ++-- requirements.txt | 28 ++-- 17 files changed, 180 insertions(+), 160 deletions(-) diff --git a/app/core/agents/enpkg/tool_chemicals.py b/app/core/agents/enpkg/tool_chemicals.py index 0736ff8..8f126bf 100644 --- a/app/core/agents/enpkg/tool_chemicals.py +++ b/app/core/agents/enpkg/tool_chemicals.py @@ -16,7 +16,7 @@ from langchain_openai import OpenAIEmbeddings -from langchain.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain.tools import BaseTool from typing import Optional @@ -47,7 +47,7 @@ class ChemicalResolver(BaseTool): Dict[str, str]: a dictionary that contains the output chemical name and corresponding NPC Class URI. """ - args_schema = ChemicalInput + args_schema: type[BaseModel] = ChemicalInput csv_data: List[Document] = None retriever: Any = None openai_key: str = None diff --git a/app/core/agents/enpkg/tool_smiles.py b/app/core/agents/enpkg/tool_smiles.py index 32d1a4b..0c904ed 100644 --- a/app/core/agents/enpkg/tool_smiles.py +++ b/app/core/agents/enpkg/tool_smiles.py @@ -1,7 +1,7 @@ import requests from langchain.tools import BaseTool -from langchain.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from typing import Optional @@ -36,7 +36,7 @@ class SMILESResolver(BaseTool): smiles_string = "CCC12CCCN3C1C4(CC3)C(CC2)NC5=CC=CC=C45" inchikey = _run(smiles_string) """ - args_schema = SMILESInput + args_schema: type[BaseModel] = SMILESInput openai_key: str = None def __init__(self, openai_key: str = None): diff --git a/app/core/agents/enpkg/tool_target.py b/app/core/agents/enpkg/tool_target.py index 7bf6bc7..07fd11d 100644 --- a/app/core/agents/enpkg/tool_target.py +++ b/app/core/agents/enpkg/tool_target.py @@ -5,7 +5,7 @@ -from langchain.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from typing import Optional @@ -38,7 +38,7 @@ class TargetResolver(BaseTool): str: A string containing the ChEMBLTarget notation. """ - args_schema = TargetInput + args_schema: type[BaseModel] = TargetInput openai_key: str = None def __init__(self, openai_key: str = None): diff --git a/app/core/agents/enpkg/tool_taxon.py b/app/core/agents/enpkg/tool_taxon.py index 04f8aa6..b049406 100644 --- a/app/core/agents/enpkg/tool_taxon.py +++ b/app/core/agents/enpkg/tool_taxon.py @@ -1,9 +1,9 @@ -from typing import Optional +from typing import ClassVar, Optional from SPARQLWrapper import JSON, SPARQLWrapper -from langchain.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from typing import Optional @@ -34,10 +34,10 @@ class TaxonResolver(BaseTool): Returns: str: A string that contains the Wikidata IRI if found, otherwise `None`. """ - args_schema = TaxonInput + args_schema: type[BaseModel] = TaxonInput - ENDPOINT_URL = "https://query.wikidata.org/sparql" - PREFIXES = """ + ENDPOINT_URL: ClassVar[str] = "https://query.wikidata.org/sparql" + PREFIXES: ClassVar[str] = """ PREFIX prov: PREFIX pr: PREFIX wdt: diff --git a/app/core/agents/entry/tool_filesparser.py b/app/core/agents/entry/tool_filesparser.py index a68e3ed..6f0a103 100644 --- a/app/core/agents/entry/tool_filesparser.py +++ b/app/core/agents/entry/tool_filesparser.py @@ -2,18 +2,22 @@ import pandas as pd from datetime import datetime from langchain.tools import BaseTool -from langchain.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from pathlib import Path from app.core.session import setup_logger, create_user_session logger = setup_logger(__name__) +class FileAnalyzerInput(BaseModel): + """Input schema for FileAnalyzer - no input fields required.""" + pass + class FileAnalyzer(BaseTool): name: str = "FILE_ANALYZER" description: str = """ Analyzes files in a specified directory and provides a summary of their content. """ - args_schema = BaseModel # Using BaseModel directly since no specific input fields are necessary + args_schema: type[BaseModel] = FileAnalyzerInput folder_path: Path = None openai_key: str = None session_id: str = None diff --git a/app/core/agents/interpreter/tool_interpreter.py b/app/core/agents/interpreter/tool_interpreter.py index d51a2e0..a29d5d7 100644 --- a/app/core/agents/interpreter/tool_interpreter.py +++ b/app/core/agents/interpreter/tool_interpreter.py @@ -2,7 +2,7 @@ from codeinterpreterapi import CodeInterpreterSession, File, settings -from langchain.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain.tools import BaseTool from typing import Optional @@ -37,7 +37,7 @@ class Interpreter(BaseTool): Returns: None: Outputs the response after interpreting files. """ - args_schema = InterpreterInput + args_schema: type[BaseModel] = InterpreterInput openai_key: str = None session_id: str = None diff --git a/app/core/agents/interpreter/tool_spectrum.py b/app/core/agents/interpreter/tool_spectrum.py index 5289749..65ae373 100644 --- a/app/core/agents/interpreter/tool_spectrum.py +++ b/app/core/agents/interpreter/tool_spectrum.py @@ -1,7 +1,7 @@ from __future__ import annotations from pathlib import Path from typing import Any, Dict, Optional, Union -from langchain.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain.tools import BaseTool from langchain.callbacks.manager import CallbackManagerForToolRun from app.core.memory.database_manager import tools_database @@ -29,7 +29,7 @@ class SpectrumPlotter(BaseTool): Returns: An url with spectrum plot. """ - args_schema = SpectrumPlotInput + args_schema: type[BaseModel] = SpectrumPlotInput session_id: str = None openai_key: str = None def __init__(self, openai_key: str, session_id: str): diff --git a/app/core/agents/sparql/tool_merge_result.py b/app/core/agents/sparql/tool_merge_result.py index 0336891..4264102 100644 --- a/app/core/agents/sparql/tool_merge_result.py +++ b/app/core/agents/sparql/tool_merge_result.py @@ -1,4 +1,4 @@ -from langchain.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain.tools import BaseTool from typing import Optional @@ -40,7 +40,7 @@ class OutputMerger(BaseTool): output_file_path (str): The path to the temporary CSV file where the output with common Wikidata IDs will be saved. """ - args_schema = MergerInput + args_schema: type[BaseModel] = MergerInput session_id: str = None def __init__(self, session_id: str): diff --git a/app/core/agents/sparql/tool_sparql.py b/app/core/agents/sparql/tool_sparql.py index a761a5c..efa51f0 100644 --- a/app/core/agents/sparql/tool_sparql.py +++ b/app/core/agents/sparql/tool_sparql.py @@ -10,7 +10,7 @@ from langchain.chains.llm import LLMChain from langchain_core.prompts.prompt import PromptTemplate -from langchain.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain.tools import BaseTool from langchain_community.vectorstores import FAISS from langchain_openai import OpenAIEmbeddings @@ -207,8 +207,8 @@ class SparqlInput(BaseModel): ##Question-answering against an RDF or OWL graph by generating SPARQL statements. class GraphSparqlQAChain(BaseTool): - name = "SPARQL_QUERY_RUNNER" - description = """ + name: str = "SPARQL_QUERY_RUNNER" + description: str = """ The agent resolve the user's question by querying the knowledge graph database. The two inputs should be a string containing the user's question and a string containing the resolved entities in the question. @@ -226,10 +226,10 @@ class GraphSparqlQAChain(BaseTool): """ verbose: bool = True - args_schema = SparqlInput + args_schema: type[BaseModel] = SparqlInput sparql_generation_select_chain: LLMChain = None sparql_improvement_chain: LLMChain = None - requires_params = True + requires_params: bool = True graph: RdfGraph = None session_id: str = None diff --git a/app/core/agents/sparql/tool_wikidata_query.py b/app/core/agents/sparql/tool_wikidata_query.py index 8fac383..64a6906 100644 --- a/app/core/agents/sparql/tool_wikidata_query.py +++ b/app/core/agents/sparql/tool_wikidata_query.py @@ -3,13 +3,13 @@ import csv import tempfile from pathlib import Path -from typing import List, Optional +from typing import ClassVar, List, Optional from langchain_core.tools import tool from SPARQLWrapper import JSON, SPARQLWrapper from app.core.session import setup_logger, create_user_session -from langchain.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain.tools import BaseTool from langchain.callbacks.manager import ( CallbackManagerForToolRun, @@ -33,10 +33,10 @@ class WikidataStructureSearch(BaseTool): Returns: str: A string that contains the path to the file with Wikidata IRIs if found, otherwise `None`. """ - args_schema = WikidataInput + args_schema: type[BaseModel] = WikidataInput - ENDPOINT_URL = "https://query.wikidata.org/sparql" - PREFIXES = """ + ENDPOINT_URL: ClassVar[str] = "https://query.wikidata.org/sparql" + PREFIXES: ClassVar[str] = """ PREFIX wd: PREFIX wdt: """ diff --git a/app/core/agents/validator/tool_validator.py b/app/core/agents/validator/tool_validator.py index 4d57c5f..a4bc652 100644 --- a/app/core/agents/validator/tool_validator.py +++ b/app/core/agents/validator/tool_validator.py @@ -5,7 +5,7 @@ from langchain.callbacks.manager import ( CallbackManagerForToolRun, ) -from langchain.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain.tools import BaseTool from typing import Optional @@ -32,7 +32,7 @@ class PlantDatabaseChecker(BaseTool): """ - args_schema = PlantInput + args_schema: type[BaseModel] = PlantInput def __init__(self): super().__init__() diff --git a/app/core/memory/custom_sqlite_file.py b/app/core/memory/custom_sqlite_file.py index cbb93de..5709287 100644 --- a/app/core/memory/custom_sqlite_file.py +++ b/app/core/memory/custom_sqlite_file.py @@ -6,9 +6,9 @@ import sqlite3 import threading from contextlib import contextmanager -from typing import Optional +from typing import ClassVar, Optional + -from langchain_core.pydantic_v1 import Field from langchain_core.runnables import RunnableConfig from langchain_core.runnables.utils import ConfigurableFieldSpec from langgraph.checkpoint.base import BaseCheckpointSaver, Checkpoint @@ -20,7 +20,7 @@ class SqliteCheckpointerSaver(BaseCheckpointSaver): # Specify the path to the database file - database_path = "langgraph_checkpoint.db" + database_path: ClassVar[str] = "langgraph_checkpoint.db" def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) # Ensure proper Pydantic initialization diff --git a/app/core/memory/database_manager.py b/app/core/memory/database_manager.py index 6a9a67c..b3bcf05 100644 --- a/app/core/memory/database_manager.py +++ b/app/core/memory/database_manager.py @@ -1,39 +1,40 @@ -import os -from app.core.memory.tools_database import SqliteToolsDatabaseManager -from app.core.memory.custom_sqlite_file import SqliteCheckpointerSaver -import importlib - -def tools_database(): - """ - Get the database manager based on the environment variables - - :return: The database manager - """ - if os.getenv('DATABASE_URL') and os.getenv("TOOLS_DATABASE_MANAGER_CLASS"): - class_path = os.getenv('TOOLS_DATABASE_MANAGER_CLASS') - if class_path: - # Dynamically import the module and class based on the environment variable - module_name, class_name = class_path.rsplit('.', 1) - module = importlib.import_module(module_name) - manager_class = getattr(module, class_name) - return manager_class() - else: - return SqliteToolsDatabaseManager() - -def memory_database(): - """ - Get the database manager based on the environment variables - - :return: The database manager - """ - - if os.getenv('DATABASE_URL') and os.getenv("MEMORY_DATABASE_MANAGER_CLASS"): - class_path = os.getenv('MEMORY_DATABASE_MANAGER_CLASS') - if class_path: - # Dynamically import the module and class based on the environment variable - module_name, class_name = class_path.rsplit('.', 1) - module = importlib.import_module(module_name) - manager_class = getattr(module, class_name) - return manager_class() - else: - return SqliteCheckpointerSaver() \ No newline at end of file +import os +from app.core.memory.tools_database import SqliteToolsDatabaseManager +import importlib + +def tools_database(): + """ + Get the database manager based on the environment variables + + :return: The database manager + """ + if os.getenv('DATABASE_URL') and os.getenv("TOOLS_DATABASE_MANAGER_CLASS"): + class_path = os.getenv('TOOLS_DATABASE_MANAGER_CLASS') + if class_path: + # Dynamically import the module and class based on the environment variable + module_name, class_name = class_path.rsplit('.', 1) + module = importlib.import_module(module_name) + manager_class = getattr(module, class_name) + return manager_class() + else: + return SqliteToolsDatabaseManager() + +def memory_database(): + """ + Get the database manager based on the environment variables + + :return: The database manager + """ + + if os.getenv('DATABASE_URL') and os.getenv("MEMORY_DATABASE_MANAGER_CLASS"): + class_path = os.getenv('MEMORY_DATABASE_MANAGER_CLASS') + if class_path: + # Dynamically import the module and class based on the environment variable + module_name, class_name = class_path.rsplit('.', 1) + module = importlib.import_module(module_name) + manager_class = getattr(module, class_name) + return manager_class() + else: + # Use langgraph's built-in MemorySaver which implements the current checkpoint API + from langgraph.checkpoint.memory import MemorySaver + return MemorySaver() \ No newline at end of file diff --git a/app/core/tests/evaluation.py b/app/core/tests/evaluation.py index b182a9b..2e06f56 100644 --- a/app/core/tests/evaluation.py +++ b/app/core/tests/evaluation.py @@ -1,60 +1,87 @@ -from dotenv import load_dotenv import os -from langsmith import Client -from langchain_core.messages import BaseMessage, HumanMessage -# from langgraph.graph import StateGraph +import pandas as pd +import json -from app.core.workflow.langraph_workflow import create_workflow, process_workflow +from uuid import uuid4 +from dotenv import load_dotenv -from app.core.main import llm_creation +from langsmith import Client +from langchain_core.messages import HumanMessage from langsmith.evaluation import EvaluationResult, run_evaluator -from langchain.evaluation import EvaluatorType -from langsmith.schemas import Example, Run +from langchain.evaluation import EvaluatorType, load_evaluator from langchain.smith import run_on_dataset, RunEvalConfig -from langchain.evaluation import load_evaluator -from uuid import uuid4 + +from app.core.workflow.langraph_workflow import create_workflow +from app.core.main import llm_creation from app.core.utils import setup_logger -# Load environment variables +# 1. Load environment variables load_dotenv() - logger = setup_logger(__name__) -# Set environment variables for LangSmith -os.environ["LANGCHAIN_TRACING_V2"] = "true" -os.environ["LANGCHAIN_PROJECT"] = ( - os.environ.get("LANGCHAIN_PROJECT") - or os.environ.get("LANGSMITH_PROJECT") - or "MetaboT evaluation" -) -os.environ["LANGCHAIN_ENDPOINT"] = ( - os.environ.get("LANGCHAIN_ENDPOINT") - or os.environ.get("LANGSMITH_ENDPOINT") - or os.environ.get("LANGSMITH_BASE_URL") - or "https://api.smith.langchain.com" -) - -# Initialize Langsmith client +# 2. Check for required API keys safely api_key = os.getenv("LANGCHAIN_API_KEY") or os.environ.get("LANGSMITH_API_KEY") +openai_key = os.getenv("OPENAI_API_KEY") + if not api_key: - raise ValueError("The environment variable LANGCHAIN_API_KEY is not defined") + raise ValueError("Missing LANGCHAIN_API_KEY. Please copy .env.template to .env and add your key.") +if not openai_key: + raise ValueError("Missing OPENAI_API_KEY. Please copy .env.template to .env and add your key.") +# Set environment variables for LangSmith +os.environ["LANGCHAIN_TRACING_V2"] = "true" +os.environ["LANGCHAIN_PROJECT"] = os.environ.get("LANGCHAIN_PROJECT", "MetaboT evaluation") +os.environ["LANGCHAIN_ENDPOINT"] = os.environ.get("LANGCHAIN_ENDPOINT", "https://api.smith.langchain.com") + +current_dir = os.path.dirname(os.path.abspath(__file__)) +core_dir = os.path.dirname(current_dir) +app_dir = os.path.dirname(core_dir) +local_data_path = os.path.join(app_dir, "data", "big_benchmark.csv") client = Client(api_key=api_key) -# Dataset configuration -dataset_name = "smaller_benchmark_temporal" +dataset_name = "test_metabot" +# Check if the dataset exists in the current user's workspace +try: + client.read_dataset(dataset_name=dataset_name) + logger.info(f"Dataset '{dataset_name}' found in LangSmith workspace.") +except Exception: + logger.info(f"Dataset '{dataset_name}' not found. Attempting to create it from local file...") + + + if not os.path.exists(local_data_path): + raise FileNotFoundError( + f"Could not find '{local_data_path}'. Ensure the local dataset file is shared alongside this script.") + + + # Load local data and create the dataset in the user's LangSmith account + df = pd.read_csv(local_data_path) + dataset = client.create_dataset(dataset_name=dataset_name, description="MetaboT Benchmark") + + inputs = [] + outputs = [] + + for _, row in df.iterrows(): + try: + # The CSV stores the JSON arrays as strings, so we need to parse them back into Python objects + parsed_messages = json.loads(row["messages"]) + parsed_end_state = json.loads(row["__end__"]) + inputs.append({"messages": parsed_messages}) + outputs.append({"__end__": parsed_end_state}) + + except json.JSONDecodeError as e: + logger.warning(f"Skipping a row due to JSON parsing error: {e}") + continue + + client.create_examples(inputs=inputs, outputs=outputs, dataset_id=dataset.id) + logger.info(f"Successfully created dataset '{dataset_name}' in LangSmith.") # Custom criteria for SPARQL query evaluation custom_criteria = { "structural similarity of SPARQL queries": "How similar is the structure of the generated SPARQL query to the reference SPARQL query? Does the generated query correctly match subjects to their corresponding objects as in the reference query" } -# Load evaluator with custom criteria -eval_chain_new = load_evaluator( - EvaluatorType.LABELED_CRITERIA, - criteria=custom_criteria, -) +eval_chain_new = load_evaluator(EvaluatorType.LABELED_CRITERIA, criteria=custom_criteria) # Define the evaluation configuration evaluation_config = RunEvalConfig( @@ -75,47 +102,35 @@ custom_evaluators=[eval_chain_new], ) -endpoint_url = os.environ.get("KG_ENDPOINT_URL") or "https://enpkg.commons-lab.org/graphdb/repositories/ENPKG" +endpoint_url = os.environ.get("KG_ENDPOINT_URL", "https://enpkg.commons-lab.org/graphdb/repositories/ENPKG") models = llm_creation() + # Create workflow in evaluation mode app = create_workflow( models=models, endpoint_url=endpoint_url, evaluation=True, - api_key=os.getenv("OPENAI_API_KEY") + api_key=openai_key ) + def evaluate_result(_input, thread_id: int = 1): - """ - Evaluate the result based on input. - - Args: - _input (dict): Input containing messages to process - - Returns: - dict: The evaluation result - """ - - # Prepare the message + """Evaluate the result based on input.""" + # Note: Adjust the 'question' key below if your dataset uses a different input key + input_text = _input.get("question") or _input["messages"][0]["content"] + message = { - "messages": [ - HumanMessage(content=_input["messages"][0]["content"]) - ] + "messages": [HumanMessage(content=input_text)] } - - response = app.invoke(message, - {"configurable": {"thread_id": thread_id}},) - + response = app.invoke(message, {"configurable": {"thread_id": thread_id}}) return {"output": response} -# Generate a unique identifier for the project unique_id = uuid4().hex[0:8] - -# Run evaluation on the dataset +# Run evaluation chain_results = run_on_dataset( dataset_name=dataset_name, llm_or_chain_factory=evaluate_result, @@ -128,4 +143,4 @@ def evaluate_result(_input, thread_id: int = 1): }, ) - +logger.info(f"Evaluation complete. View results in LangSmith under project: Testing the app-{unique_id}") \ No newline at end of file diff --git a/app/core/workflow/langraph_workflow.py b/app/core/workflow/langraph_workflow.py index 7d5ee16..bb93662 100644 --- a/app/core/workflow/langraph_workflow.py +++ b/app/core/workflow/langraph_workflow.py @@ -250,5 +250,5 @@ def process_workflow(app: StateGraph, question: str, thread_id: int = 1) -> NoRe logger.info(s) logger.info("----") except Exception as e: - logger.error(f"An error occurred: {e}") + logger.error(f"An error occurred: {e}", exc_info=True) diff --git a/environment.yml b/environment.yml index c1c9393..be14ab7 100644 --- a/environment.yml +++ b/environment.yml @@ -19,30 +19,30 @@ dependencies: - ipykernel==6.29.3 - jupyter-kernel-gateway==3.0.1 - kaleido==0.2.1 - - langchain==0.1.11 + - langchain==0.3.27 - langchainhub==0.1.15 - - langchain_community==0.0.27 - - langchain_core==0.1.30 - - langchain_openai==0.0.8 - - langgraph==0.0.26 - - litellm==1.61.6 - - langsmith==0.1.22 + - langchain_community==0.3.31 + - langchain_core==0.3.84 + - langchain_openai==0.3.35 + - langgraph==0.3.34 + - litellm==1.83.0 + - langsmith==0.3.45 - matplotlib==3.8.3 - networkx==3.2.1 - - openai==1.61.0 + - openai==2.32.0 - pandas==2.2.1 - plotly==5.20.0 - psycopg2-binary - - pydantic==2.7.0 - - python-dotenv==1.0.1 + - pydantic==2.13.3 + - python-dotenv==1.2.2 - rdflib==7.0.0 - - Requests==2.31.0 + - requests==2.33.1 - seaborn==0.13.2 - SPARQLWrapper==2.0.0 - SQLAlchemy==2.0.22 - tiktoken>=0.7.0 - - tqdm==4.66.1 - - numpy==1.26.0 + - tqdm==4.66.6 + - numpy==1.26.4 - httpx==0.27.2 - scikit-learn==1.5.1 - spectrum_utils diff --git a/requirements.txt b/requirements.txt index 9590676..bccbbf8 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,29 +4,29 @@ faiss-cpu==1.8.0 kaleido==0.2.1 ipykernel==6.29.3 jupyter-kernel-gateway==3.0.1 -langchain==0.1.11 +langchain==0.3.27 langchainhub==0.1.15 -langchain_community==0.0.27 -langchain_core==0.1.30 -langchain_openai==0.0.8 -langgraph==0.0.26 -litellm==1.61.6 -langsmith==0.1.22 +langchain_community==0.3.31 +langchain_core==0.3.84 +langchain_openai==0.3.35 +langgraph==0.3.34 +litellm==1.83.0 +langsmith==0.3.45 matplotlib==3.8.3 networkx==3.2.1 -openai==1.61.0 +openai==2.32.0 pandas==2.2.1 plotly==5.20.0 psycopg2-binary -pydantic==2.7.0 -python-dotenv==1.0.1 +pydantic==2.13.3 +python-dotenv==1.2.2 rdflib==7.0.0 -Requests==2.31.0 +requests==2.33.1 seaborn==0.13.2 -streamlit==1.44.0 +streamlit==1.54.0 SPARQLWrapper==2.0.0 tiktoken>=0.7.0 -tqdm==4.66.1 -numpy==1.26.0 +tqdm==4.66.6 +numpy==1.26.4 httpx==0.27.2 scikit-learn==1.5.1 From 2dd513533bb75167be389ec1aa2f760428bb70cc Mon Sep 17 00:00:00 2001 From: madina1203 Date: Thu, 23 Apr 2026 21:42:22 +0200 Subject: [PATCH 06/21] Update the order of questions to be consistent with automated evaluation --- app/data/evaluation_dataset.csv | 935 ++++++++++++++++---------------- 1 file changed, 470 insertions(+), 465 deletions(-) diff --git a/app/data/evaluation_dataset.csv b/app/data/evaluation_dataset.csv index c3b14dd..c35614e 100644 --- a/app/data/evaluation_dataset.csv +++ b/app/data/evaluation_dataset.csv @@ -1,80 +1,146 @@ -#question,Questions,Traces,SPARQL Query,Complexity -1,"Which extracts have features (pos ionization mode) annotated as the class, aspidosperma-type alkaloids, by CANOPUS with a probability score above 0.5, ordered by the decreasing count of features as aspidosperma-type alkaloids? Group by extract.","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which extracts have features (pos ionization mode) annotated as the class, aspidosperma-type alkaloids, by CANOPUS with a probability score above 0.5, ordered by the decreasing count of features as aspidosperma-type alkaloids? Group by extract."", ""example"": false, ""additional_kwargs"": {}}, {""name"": ""Entry_agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: \""Which extracts have features (pos ionization mode) annotated as the class, aspidosperma-type alkaloids, by CANOPUS with a probability score above 0.5, ordered by the decreasing count of features as aspidosperma-type alkaloids? Group by extract .\"""", ""example"": false, ""additional_kwargs"": {}}, {""name"": ""Validator_agent"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""The entity \""aspidosperma-type alkaloids\"" was resolved to the following IRIs:\n\naspidosperma-type alkaloids, https://enpkg.commons-lab.org/kg/npc_Aspidosperma_type, NPCClass;\nNPCPathway, https://enpkg.commons-lab.org/kg/npc_Alkaloids, NPCPathway;\nNPCSuperClass, https://enpkg.commons-lab.org/kg/npc_Tyrosine_alkaloids, NPCSuperClass."", ""example"": false, ""additional_kwargs"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The extracts with features (pos ionization mode) annotated as the class, aspidosperma-type alkaloids, by CANOPUS with a probability score above 0.5, ordered by the decreasing count of features as aspidosperma-type alkaloids, are as follows:\n\n1. VGF152_B02: 74 features\n2. VGF157_D02: 11 features\n3. VGF147_B11: 10 features\n4. VGF153_C03: 7 features\n5. VGF157_E02: 2 features\n6. VGF154_D02: 2 features\n7. VGF147_A10: 2 features\n8. VGF140_F02: 2 features\n9. KP146_Positive: 1 feature\n10. KP317_Positive: 1 feature\n11. VGF159_B11: 1 feature\n12. VGF155_D07: 1 feature\n13. VGF154_H02: 1 feature\n14. VGF153_E07: 1 feature\n15. VGF153_G10: 1 feature\n16. VGF153_D03: 1 feature\n17. VGF153_F04: 1 feature\n18. VGF153_G09: 1 feature\n19. VGF153_F11: 1 feature\n20. VGF153_H04: 1 feature\n21. VGF153_B06: 1 feature\n22. VGF152_E06: 1 feature\n23. VGF151_F09: 1 feature\n24. VGF151_D06: 1 feature\n25. VGF151_E03: 1 feature\n26. VGF150_B11: 1 feature\n27. VGF150_B06: 1 feature\n28. VGF150_F09: 1 feature\n29. VGF150_H05: 1 feature\n30. VGF150_F06: 1 feature\n31. VGF147_D11: 1 feature\n32. VGF147_E10: 1 feature\n33. VGF143_E07: 1 feature\n34. VGF140_E02: 1 feature\n35. VGF139_D09: 1 feature\n36. SC_AP_Wi_DCM: 1 feature\n\nThe SPARQL query used to retrieve this information is:\n```sparql\nPREFIX ns1: \n\nSELECT ?labExtract (COUNT(?feature) AS ?featureCount)\nWHERE {\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis a ns1:LCMSAnalysisPos .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_class .\n ?annotation ns1:has_canopus_npc_class_prob ?probability .\n FILTER(?probability > 0.5)\n}\nGROUP BY ?labExtract\nORDER BY DESC(?featureCount)\n```\n\nThe results are saved in the file located at: `/tmp/kgbot/f36215b1a3114803ad38a18a9462eb29/tmp8u8o5vcv.csv`\n\nNext, I will generate a bar chart based on this data.\nHere is the dictionary containing the question, generated SPARQL query, and the file path to the results:\n\n```json\n{\n \""question\"": \""Which extracts have features (pos ionization mode) annotated as the class, aspidosperma-type alkaloids, by CANOPUS with a probability score above 0.5, ordered by the decreasing count of features as aspidosperma-type alkaloids? Group by extract and provide a bar chart.\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\n\\nSELECT ?labExtract (COUNT(?feature) AS ?featureCount)\\nWHERE {\\n ?labExtract ns1:has_LCMS ?analysis .\\n ?analysis a ns1:LCMSAnalysisPos .\\n ?analysis ns1:has_lcms_feature_list ?feature_list .\\n ?feature_list ns1:has_lcms_feature ?feature .\\n ?feature ns1:has_canopus_annotation ?annotation .\\n ?annotation ns1:has_canopus_npc_class .\\n ?annotation ns1:has_canopus_npc_class_prob ?probability .\\n FILTER(?probability > 0.5)\\n}\\nGROUP BY ?labExtract\\nORDER BY DESC(?featureCount)\"",\n \""file_path\"": \""/tmp/kgbot/f36215b1a3114803ad38a18a9462eb29/tmp8u8o5vcv.csv\""\n}\n```\n\nPlease use this information to generate the bar chart."", ""example"": false, ""additional_kwargs"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The extracts with features (pos ionization mode) annotated as the class, aspidosperma-type alkaloids, by CANOPUS with a probability score above 0.5, ordered by the decreasing count of features as aspidosperma-type alkaloids, are as follows:\n\n1. VGF152_B02: 74 features\n2. VGF157_D02: 11 features\n3. VGF147_B11: 10 features\n4. VGF153_C03: 7 features\n5. VGF157_E02: 2 features\n6. VGF154_D02: 2 features\n7. VGF147_A10: 2 features\n8. VGF140_F02: 2 features\n9. KP146_Positive: 1 feature\n10. KP317_Positive: 1 feature\n11. VGF159_B11: 1 feature\n12. VGF155_D07: 1 feature\n13. VGF154_H02: 1 feature\n14. VGF153_E07: 1 feature\n15. VGF153_G10: 1 feature\n16. VGF153_D03: 1 feature\n17. VGF153_F04: 1 feature\n18. VGF153_G09: 1 feature\n19. VGF153_F11: 1 feature\n20. VGF153_H04: 1 feature\n21. VGF153_B06: 1 feature\n22. VGF152_E06: 1 feature\n23. VGF151_F09: 1 feature\n24. VGF151_D06: 1 feature\n25. VGF151_E03: 1 feature\n26. VGF150_B11: 1 feature\n27. VGF150_B06: 1 feature\n28. VGF150_F09: 1 feature\n29. VGF150_H05: 1 feature\n30. VGF150_F06: 1 feature\n31. VGF147_D11: 1 feature\n32. VGF147_E10: 1 feature\n33. VGF143_E07: 1 feature\n34. VGF140_E02: 1 feature\n35. VGF139_D09: 1 feature\n36. SC_AP_Wi_DCM: 1 feature\n\nT"", ""example"": false, ""additional_kwargs"": {}}]}","PREFIX ns1: +#,Questions,Traces,SPARQL Query,Complexity +1," ""Which plant has extracts containing compounds that demonstrated inhibition rates above 50% against Trypanosoma cruzi and are above 800 Da in mass? Provide the lab extracts.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which plant has extracts containing compounds that demonstrated inhibition rates above 50% against Trypanosoma cruzi and are above 800 Da in mass? Provide the lab extracts"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which plant has extracts containing compounds that demonstrated inhibition rates above 50% against Trypanosoma cruzi and are above 800 Da in mass? Provide the lab extracts."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid. It is related to the ENPKG nodes/entities (Raw Material, Lab Extract, BioAssayResult, Chemical Entity). The question asks for information about plant extracts containing compounds that have demonstrated a certain level of inhibition against a specific target and have a certain mass, which can be retrieved from the ENPKG."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Trypanosoma cruzi, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL368, ChEMBLTarget\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The query has been processed successfully. Here is the information:\n\n- **Question**: Which plant has extracts containing compounds that demonstrated inhibition rates above 50% against Trypanosoma cruzi and are above 800 Da in mass? Provide the lab extracts.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?plant ?labExtract\n WHERE {\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_parent_mass ?mass .\n FILTER(?mass > 800)\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\n ?bioAssayResult ns2:inhibition_percentage ?inhibition .\n FILTER(?inhibition > 50)\n ?bioAssayResult ns2:target_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?rawMaterial ns1:submitted_taxon ?plant .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/cbb95402a0b148239141ff5dfd609728/tmpixjks0gp.csv`\n\nPlease refer to the file for detailed results on the plants and their corresponding lab extracts."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The analysis of the file indicates that there are no plant extracts meeting the specified criteria of having compounds with inhibition rates above 50% against Trypanosoma cruzi and a mass above 800 Da. If you have any other questions or need further analysis, please let me know. \n\nNo visualization is available, but the data can be downloaded for further inspection. \n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + PREFIX ns2: -SELECT ?labExtract (COUNT(?feature) AS ?featureCount) -WHERE { - ?labExtract ns1:has_LCMS ?analysis . - ?analysis a ns1:LCMSAnalysisPos . - ?analysis ns1:has_lcms_feature_list ?feature_list . - ?feature_list ns1:has_lcms_feature ?feature . - ?feature ns1:has_canopus_annotation ?annotation . - ?annotation ns1:has_canopus_npc_class . - ?annotation ns1:has_canopus_npc_class_prob ?probability . - FILTER(?probability > 0.5) -} -GROUP BY ?labExtract -ORDER BY DESC(?featureCount)",High -2,"Which lab extracts from Melochia umbellata yield compounds that, when analyzed in positive ionization mode LCMS, have a retention time of less than 6 minutes and demonstrate an inhibition percentage greater than 70% in bioassay results? Provide lab extracts, retention times and inhibition percentage.","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which lab extracts from Melochia umbellata yield compounds that, when analyzed in positive ionization mode LCMS, have a retention time of less than 6 minutes and demonstrate an inhibition percentage greater than 70% in bioassay results? Provide lab extracts, retention times and inhibition percentage."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which lab extracts from Melochia umbellata yield compounds that, when analyzed in positive ionization mode LCMS, have a retention time of less than 6 minutes and demonstrate an inhibition percentage greater than 70% in bioassay results? Provide lab extracts, retention times and inhibition percentage."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""Melochia umbellata, http://www.wikidata.org/entity/Q6813281, Wikidata"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The lab extracts from Melochia umbellata that yield compounds analyzed in positive ionization mode LCMS with a retention time of less than 6 minutes and demonstrate an inhibition percentage greater than 70% in bioassay results are as follows:\n\n- **Lab Extract**: [VGF156_A06](https://enpkg.commons-lab.org/kg/VGF156_A06)\n - **Retention Time**: 4.198916666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 4.54375 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.5790666666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7661 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.6830500000000002 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.089 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.6979 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.5034166666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.26885 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.1985333333333337 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.11295 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7891 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.2292333333333334 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.089 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.5863333333333334 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7204833333333334 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7815333333333332 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7054833333333332 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.4656833333333334 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.2918833333333335 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.3785166666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.2292333333333334 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.33065 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.5962 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.4250166666666668 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.511016666666667 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.6655166666666663 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.8533833333333332 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.8533833333333332 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.5034166666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.5034166666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.3630666666666669 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7512166666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 0.6451166666666667 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7661 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.9627166666666669 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.2918833333333335 minutes\n - **Inhibition Percentage**: 89.9%\n\nThe SPARQL query used to retrieve this information is as follows:\n\n```sparql\nPREFIX ns1: \nPREFIX ns2: \nPREFIX wd: \n\nSELECT ?labExtract ?retentionTime ?inhibitionPercentage\nWHERE {\n ?rawMaterial ns1:has_wd_id wd:Q6813281 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis a ns1:LCMSAnalysisPos .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_retention_time ?retentionTime .\n FILTER(?retentionTime < 6)\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\n ?bioAssayResult ns2:inhibition_percentage ?inhibitionPercentage .\n FILTER(?inhibitionPercentage > 70)\n}\n```\n\nThe results are saved in the file located at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/461195db838a4810afb06014c87c0e6b/tmp_7t2umr1.csv`"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + SELECT ?plant ?labExtract + WHERE { + ?labExtract ns1:has_LCMS ?analysis . + ?analysis ns1:has_lcms_feature_list ?feature_list . + ?feature_list ns1:has_lcms_feature ?feature . + ?feature ns1:has_parent_mass ?mass . + FILTER(?mass > 800) + ?labExtract ns2:has_bioassay_results ?bioAssayResult . + ?bioAssayResult ns2:inhibition_percentage ?inhibition . + FILTER(?inhibition > 50) + ?bioAssayResult ns2:target_id . + ?rawMaterial ns1:has_lab_process ?labExtract . + ?rawMaterial ns1:submitted_taxon ?plant . + }",High +2,"""Filter the pos ionization mode features of the Melochia umbellata taxon annotated as [M+H]+ by SIRIUS to keep the ones for which a feature in neg ionization mode is detected with the same retention time (+/- 3 seconds) and a mass corresponding to the [M-H]- SIRIUS adduct (+/- 5ppm). Provide the features and retention time.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Filter the pos ionization mode features of the Melochia umbellata taxon annotated as [M+H]+ by SIRIUS to keep the ones for which a feature in neg ionization mode is detected with the same retention time (+/- 3 seconds) and a mass corresponding to the [M-H]- SIRIUS adduct (+/- 5ppm). Provide the features and retention time."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Melochia umbellata, http://www.wikidata.org/entity/Q6813281, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n### Question:\nFilter the pos ionization mode features of the Melochia umbellata taxon annotated as [M+H]+ by SIRIUS to keep the ones for which a feature in neg ionization mode is detected with the same retention time (+/- 3 seconds) and a mass corresponding to the [M-H]- SIRIUS adduct (+/- 5ppm).\n\n### Generated SPARQL Query:\n```sparql\nPREFIX ns1: \nPREFIX ns2: \nPREFIX wdt: \n\nSELECT ?posFeature ?negFeature\nWHERE {\n ?rawMaterial ns1:has_wd_id ;\n ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?posAnalysis, ?negAnalysis .\n ?posAnalysis a ns1:LCMSAnalysisPos ;\n ns1:has_lcms_feature_list ?posFeatureList .\n ?negAnalysis a ns1:LCMSAnalysisNeg ;\n ns1:has_lcms_feature_list ?negFeatureList .\n ?posFeatureList ns1:has_lcms_feature ?posFeature .\n ?negFeatureList ns1:has_lcms_feature ?negFeature .\n ?posFeature ns1:has_sirius_annotation ?posAnnotation ;\n ns1:has_retention_time ?posRT ;\n ns1:has_parent_mass ?posMass .\n ?negFeature ns1:has_sirius_annotation ?negAnnotation ;\n ns1:has_retention_time ?negRT ;\n ns1:has_parent_mass ?negMass .\n ?posAnnotation ns1:has_sirius_adduct \""[M+H]+\"" .\n ?negAnnotation ns1:has_sirius_adduct \""[M-H]-\"" .\n FILTER (ABS(?posRT - ?negRT) <= 3)\n FILTER (ABS(?posMass - ?negMass) / ?posMass <= 0.000005)\n}\n```\n\n### File Path:\nThe results of the query have been saved to the following file path: `/tmp/kgbot/tmprvccrkkb.csv`\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: PREFIX ns2: -PREFIX wd: +PREFIX wdt: -SELECT ?labExtract ?retentionTime ?inhibitionPercentage +SELECT ?posFeature ?negFeature WHERE { - ?rawMaterial ns1:has_wd_id wd:Q6813281 . - ?rawMaterial ns1:has_lab_process ?labExtract . - ?labExtract ns1:has_LCMS ?analysis . - ?analysis a ns1:LCMSAnalysisPos . - ?analysis ns1:has_lcms_feature_list ?featureList . - ?featureList ns1:has_lcms_feature ?feature . - ?feature ns1:has_retention_time ?retentionTime . - FILTER(?retentionTime < 6) - ?labExtract ns2:has_bioassay_results ?bioAssayResult . - ?bioAssayResult ns2:inhibition_percentage ?inhibitionPercentage . - FILTER(?inhibitionPercentage > 70) -}",Medium -3,"Which compounds from Desmodium heterophyllum extracts have annotations from both ISDB and SIRIUS, and what are their molecular masses?","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which compounds from Desmodium heterophyllum extracts have annotations from both ISDB and SIRIUS, and what are their molecular masses?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which compounds from Desmodium heterophyllum extracts have annotations from both ISDB and SIRIUS, and what are their molecular masses?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""Desmodium heterophyllum, http://www.wikidata.org/entity/Q10770714, Wikidata"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The compounds from Desmodium heterophyllum extracts that have annotations from both ISDB and SIRIUS, along with their molecular masses, are as follows:\n\n1. Compound: WITLAWYGGVAFLU, Mass: 379.1186828613281\n2. Compound: PKNGEQDEJRDPJZ, Mass: 407.2225646972656\n3. Compound: FHKQMPVRIQJSEJ, Mass: 313.1062774658203\n4. Compound: BWDGXQDVXMUNDT, Mass: 293.1019897460937\n5. Compound: QFTWSMLVOZYJPP, Mass: 337.1435089111328\n6. Compound: MIUNHNXJASJSIX, Mass: 425.1591796875\n7. Compound: ZSCRTFONTNMQBL, Mass: 247.16928100585935\n8. Compound: VZPAURMDJZOGHU, Mass: 445.208984375\n9. Compound: HEORSHPDPZGZCN, Mass: 349.143310546875\n10. Compound: GIVRDLOWBBXCNM, Mass: 351.1228332519531\n11. Compound: VZPAURMDJZOGHU, Mass: 445.2112426757813\n12. Compound: QQWYBWNEILZOKI, Mass: 321.1480407714844\n13. Compound: DTOSIQBPPRVQHS, Mass: 279.2320251464844\n14. Compound: DTOSIQBPPRVQHS, Mass: 279.2322692871094\n15. Compound: KSVKECXWDNCRTM, Mass: 403.20166015625\n16. Compound: CEAWIMABVSITMV, Mass: 383.1479187011719\n17. Compound: CVULDJMCSSACEO, Mass: 507.2290344238281\n18. Compound: KEJNXXMRDHDOCO, Mass: 279.10035705566406\n19. Compound: QQWYBWNEILZOKI, Mass: 321.14996337890625\n20. Compound: QQWYBWNEILZOKI, Mass: 321.1477355957031\n21. Compound: AOMUHOFOVNGZAN, Mass: 288.2526550292969\n22. Compound: YHZDBBUEVZEOIY, Mass: 271.0964050292969\n23. Compound: WRINUBCCCQLTPU, Mass: 365.13720703125\n24. Compound: GAGKUHAKUBMORD, Mass: 339.12274169921875\n25. Compound: CDZUQIFWCXAIFB, Mass: 443.13368225097656\n26. Compound: ZSCRTFONTNMQBL, Mass: 247.1693420410156\n27. Compound: HOMYIYLRRDTKAA, Mass: 714.5514831542969\n28. Compound: AOMUHOFOVNGZAN, Mass: 288.2525939941406\n29. Compound: OIMXTYUHMBQQJM, Mass: 393.3157348632813\n30. Compound: MEJLVLSNRYLDSG, Mass: 743.2880859375\n31. Compound: HEQLSVWMTJXPGS, Mass: 413.1593017578125\n32. Compound: WXWGKIBMPDYPTH, Mass: 265.08551025390625\n33. Compound: ICFXJOAKQGDRCT, Mass: 429.3733520507813\n34. Compound: QQWYBWNEILZOKI, Mass: 321.1485290527344\n35. Compound: XTLSKKJNOIMMBK, Mass: 295.0967102050781\n36. Compound: XTLSKKJNOIMMBK, Mass: 295.0966186523437\n37. Compound: XTLSKKJNOIMMBK, Mass: 295.0966491699219\n38. Compound: DTOSIQBPPRVQHS, Mass: 279.232177734375\n39. Compound: UKMCCFHULJHJNS, Mass: 427.3577575683594\n40. Compound: UKMCCFHULJHJNS, Mass: 427.3577423095703\n41. Compound: XTLSKKJNOIMMBK, Mass: 295.0967712402344\n42. Compound: XTLSKKJNOIMMBK, Mass: 295.0967102050781\n43. Compound: WECGLUPZRHILCT, Mass: 355.2845458984375\n44. Compound: QNPIEBGHSXVAOA, Mass: 702.5513305664062\n45. Compound: BWDGXQDVXMUNDT, Mass: 293.1017761230469\n46. Compound: XEVQXKKKAVVSMW, Mass: 197.1173095703125\n47. Compound: LUZSWWYKKLTDHU, Mass: 295.2268981933594\n48. Compound: ICFXJOAKQGDRCT, Mass: 429.3731689453125\n49. Compound: MIUNHNXJASJSIX, Mass: 425.158935546875\n50. Compound: AOMUHOFOVNGZAN, Mass: 288.2529754638672\n51. Compound: OZNMJHSEUFVTTG, Mass: 409.3470458984375\n52. Compound: GQSRIBUSVVSLSQ, Mass: 463.37884521484375\n53. Compound: HOMYIYLRRDTKAA, Mass: 714.550537109375\n54. Compound: HXYVTAGFYLMHSO, Mass: 300.2903747558594\n55. Compound: ORDAZKGHSNRHTD, Mass: 395.14947509765625\n56. Compound: AOMUHOFOVNGZAN, Mass: 288.2536163330078\n\nThe SPARQL query used to retrieve this data is:\n```sparql\nPREFIX ns1: \nPREFIX ns2: \nPREFIX wd: \n\nSELECT ?compound ?mass\nWHERE {\n ?rawMaterial ns1:has_wd_id wd:Q10770714 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_isdb_annotation ?isdbAnnotation .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?feature ns1:has_parent_mass ?mass .\n ?isdbAnnotation ns1:has_InChIkey2D ?compound .\n ?siriusAnnotation ns1:has_InChIkey2D ?compound .\n}\n```\n\nThe file path to the SPARQL output is: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/c6e1f9c0990546a88ca140657c7354d1/tmpez5ixi4n.csv`\n\nI will now provide this information to the supervisor."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + ?rawMaterial ns1:has_wd_id ; + ns1:has_lab_process ?labExtract . + ?labExtract ns1:has_LCMS ?posAnalysis, ?negAnalysis . + ?posAnalysis a ns1:LCMSAnalysisPos ; + ns1:has_lcms_feature_list ?posFeatureList . + ?negAnalysis a ns1:LCMSAnalysisNeg ; + ns1:has_lcms_feature_list ?negFeatureList . + ?posFeatureList ns1:has_lcms_feature ?posFeature . + ?negFeatureList ns1:has_lcms_feature ?negFeature . + ?posFeature ns1:has_sirius_annotation ?posAnnotation ; + ns1:has_retention_time ?posRT ; + ns1:has_parent_mass ?posMass . + ?negFeature ns1:has_sirius_annotation ?negAnnotation ; + ns1:has_retention_time ?negRT ; + ns1:has_parent_mass ?negMass . + ?posAnnotation ns1:has_sirius_adduct ""[M+H]+"" . + ?negAnnotation ns1:has_sirius_adduct ""[M-H]-"" . + FILTER (ABS(?posRT - ?negRT) <= 3) + FILTER (ABS(?posMass - ?negMass) / ?posMass <= 0.000005) +}",High +3," ""How unique InChIKey2D are among the structural annotations?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""How unique InChIKey2D are among the structural annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: How unique InChIKey2D are among the structural annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid. It is related to the ENPKG nodes/entities (InChIKey2D, Annotation). The question is asking for unique InChIKey2D among the structural annotations, which is a valid query for the knowledge graph."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The number of unique InChIKey2D among the structural annotations is 106,647.\n\nHere is the information related to the SPARQL query:\n\n- **Question**: How unique InChIKey2D are among the structural annotations?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT (COUNT(DISTINCT ?InChIkey2D) AS ?uniqueInChIkey2DCount)\n WHERE {\n ?annotation ns1:has_InChIkey2D ?InChIkey2D .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/73fb3b1ea55e4c8d93e92080edfdd449/tmphyuvm3dr.csv`\n\nThis result indicates the count of unique InChIKey2D identifiers found among the structural annotations in the knowledge graph."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + + SELECT (COUNT(DISTINCT ?InChIkey2D) AS ?uniqueInChIkey2DCount) + WHERE { + ?annotation ns1:has_InChIkey2D ?InChIkey2D . + }",Low +4," ""What are the retention times and molecular masses of compounds identified in the negative ionization mode LCMS analysis from Lovoa trichilioides extracts, and what are the CANOPUS chemical class annotations for those features?"" +","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the retention times and molecular masses of compounds identified in the negative ionization mode LCMS analysis from Lovoa trichilioides extracts, and what are the CANOPUS chemical class annotations for those features?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the retention times and molecular masses of compounds identified in the negative ionization mode LCMS analysis from Lovoa trichilioides extracts, and what are the CANOPUS chemical class annotations for those features?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Lovoa trichilioides, http://www.wikidata.org/entity/Q8008828, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: What are the retention times and molecular masses of compounds identified in the negative ionization mode LCMS analysis from Lovoa trichilioides extracts, and what are the CANOPUS chemical class annotations for those features?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n PREFIX wd: \n\n SELECT ?retentionTime ?molecularMass ?canopusClass\n WHERE {\n ?rawMaterial ns1:has_wd_id wd:Q8008828 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysisNeg .\n ?lcmsAnalysisNeg a ns1:LCMSAnalysisNeg .\n ?lcmsAnalysisNeg ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_retention_time ?retentionTime .\n ?feature ns1:has_parent_mass ?molecularMass .\n ?feature ns1:has_canopus_annotation ?canopusAnnotation .\n ?canopusAnnotation ns1:has_canopus_npc_class ?canopusClass .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/1ee4538b31f946faaa8a05370b67774f/tmpwx92t25x.csv`\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The retention times, molecular masses, and CANOPUS chemical class annotations for the compounds identified in the negative ionization mode LCMS analysis from Lovoa trichilioides extracts have been processed. "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + PREFIX ns2: + PREFIX wd: + + SELECT ?retentionTime ?molecularMass ?canopusClass + WHERE { + ?rawMaterial ns1:has_wd_id wd:Q8008828 . + ?rawMaterial ns1:has_lab_process ?labExtract . + ?labExtract ns1:has_LCMS ?lcmsAnalysisNeg . + ?lcmsAnalysisNeg a ns1:LCMSAnalysisNeg . + ?lcmsAnalysisNeg ns1:has_lcms_feature_list ?featureList . + ?featureList ns1:has_lcms_feature ?feature . + ?feature ns1:has_retention_time ?retentionTime . + ?feature ns1:has_parent_mass ?molecularMass . + ?feature ns1:has_canopus_annotation ?canopusAnnotation . + ?canopusAnnotation ns1:has_canopus_npc_class ?canopusClass . + }",High +5," ""What are the retention times and adduct types of LCMS features from Desmodium heterophyllum annotated by SIRIUS with a cosmic score greater than 0.4??"" +","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the retention times and adduct types of LCMS features from Desmodium heterophyllum annotated by SIRIUS with a cosmic score greater than 0.4??"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the retention times and adduct types of LCMS features from Desmodium heterophyllum annotated by SIRIUS with a cosmic score greater than 0.4?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Desmodium heterophyllum, http://www.wikidata.org/entity/Q10770714, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here is the information:\n\n- **Question**: What are the retention times and adduct types of LCMS features from Desmodium heterophyllum annotated by SIRIUS with a cosmic score greater than 0.4?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?retentionTime ?adduct\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_retention_time ?retentionTime .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?siriusAnnotation ns1:has_cosmic_score ?cosmicScore .\n ?siriusAnnotation ns1:has_adduct ?adduct .\n FILTER(?cosmicScore > 0.4)\n }\n ```\n- **File Path**: The results are stored in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/25ad11d8258b4723b0a4653d1b0de5e7/tmpvmkstwx8.csv`.\n\nPlease let me know if you need further assistance or analysis of the results!"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + PREFIX ns2: + + SELECT ?retentionTime ?adduct + WHERE { + ?rawMaterial ns1:has_wd_id . + ?rawMaterial ns1:has_lab_process ?labExtract . + ?labExtract ns1:has_LCMS ?analysis . + ?analysis ns1:has_lcms_feature_list ?featureList . + ?featureList ns1:has_lcms_feature ?feature . + ?feature ns1:has_retention_time ?retentionTime . + ?feature ns1:has_sirius_annotation ?siriusAnnotation . + ?siriusAnnotation ns1:has_cosmic_score ?cosmicScore . + ?siriusAnnotation ns1:has_adduct ?adduct . + FILTER(?cosmicScore > 0.4) + }",High +6," ""What are the LCMS features that have feature areas greater than 1,000,000 in positive ionization mode ? Sort by decreasing feature area value.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the LCMS features that have feature areas greater than 1,000,000 in positive ionization mode? Sort by decreasing feature area value."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the LCMS features that have feature areas greater than 1,000,000 in positive ionization mode? Sort by decreasing feature area value."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed. Here are the details:\n\n- **Question**: What are the LCMS features that have feature areas greater than 1,000,000 in positive ionization mode? Sort by decreasing feature area value.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT ?feature\n WHERE {\n ?analysis a ns1:LCMSAnalysisPos .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_feature_area ?area .\n FILTER(?area > 1000000)\n } ORDER BY DESC(?area)\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/61f189ee29f1402eae7ec344e7ac1ef7/tmpv676p86i.csv\n\nPlease review the file for the detailed results."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The LCMS features with feature areas greater than 1,000,000 in positive ionization mode have been filtered and saved. You can download the detailed results from the following file:\n\n- **Filtered Features File**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/61f189ee29f1402eae7ec344e7ac1ef7/filtered_features.csv\n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + + SELECT ?feature + WHERE { + ?analysis a ns1:LCMSAnalysisPos . + ?analysis ns1:has_lcms_feature_list ?feature_list . + ?feature_list ns1:has_lcms_feature ?feature . + ?feature ns1:has_feature_area ?area . + FILTER(?area > 1000000) + } ORDER BY DESC(?area)",Low +7," ""Can you provide the SMILES notation and the Wikidata IDs for the compound with the highest precusor ion mass in the MS2 spectra from extracts of Lovoa trichilioides by looking at SIRIUS annotations?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Can you provide the SMILES notation and the Wikidata IDs for the compound with the highest precursor ion mass in the MS2 spectra from extracts of Lovoa trichilioides by looking at SIRIUS annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Can you provide the SMILES notation and the Wikidata IDs for the compound with the highest precursor ion mass in the MS2 spectra from extracts of Lovoa trichilioides by looking at SIRIUS annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Lovoa trichilioides, http://www.wikidata.org/entity/Q8008828, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The compound with the highest precursor ion mass in the MS2 spectra from extracts of Lovoa trichilioides, as annotated by SIRIUS, has the following details:\n\n- **SMILES Notation**: `CC(C)(C)C1=C(C=CC(=C1)C(C)(CC(=O)OCCOC(=O)CC(C)(C2=CC(=C(C=C2)O)C(C)(C)C)C3=CC(=C(C=C3)O)C(C)(C)C)C4=CC(=C(C=C4)O)C(C)(C)C)O`\n- **Wikidata ID**: [Q27254797](http://www.wikidata.org/entity/Q27254797)\n\nHere is the SPARQL query used to obtain this information:\n\n```sparql\nPREFIX ns1: \nPREFIX ns2: \n\nSELECT ?smiles ?wd_id\nWHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?annotation .\n ?annotation ns1:has_InChIkey2D ?InChIkey2D .\n ?InChIkey2D ns1:is_InChIkey2D_of ?chemicalEntity .\n ?chemicalEntity ns1:has_smiles ?smiles ;\n ns1:has_wd_id ?wd_id .\n ?feature ns1:has_parent_mass ?mass .\n}\nORDER BY DESC(?mass)\nLIMIT 1\n```\n\nThe data was retrieved from the file located at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/c05df7beae71402fa925205f656dba83/tmpj8dw4tu8.csv`."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: PREFIX ns2: -PREFIX wd: -SELECT ?compound ?mass +SELECT ?smiles ?wd_id WHERE { - ?rawMaterial ns1:has_wd_id wd:Q10770714 . + ?rawMaterial ns1:has_wd_id . ?rawMaterial ns1:has_lab_process ?labExtract . ?labExtract ns1:has_LCMS ?analysis . ?analysis ns1:has_lcms_feature_list ?feature_list . ?feature_list ns1:has_lcms_feature ?feature . - ?feature ns1:has_isdb_annotation ?isdbAnnotation . - ?feature ns1:has_sirius_annotation ?siriusAnnotation . + ?feature ns1:has_sirius_annotation ?annotation . + ?annotation ns1:has_InChIkey2D ?InChIkey2D . + ?InChIkey2D ns1:is_InChIkey2D_of ?chemicalEntity . + ?chemicalEntity ns1:has_smiles ?smiles ; + ns1:has_wd_id ?wd_id . ?feature ns1:has_parent_mass ?mass . - ?isdbAnnotation ns1:has_InChIkey2D ?compound . - ?siriusAnnotation ns1:has_InChIkey2D ?compound . -}",High -4,How many features annotated as 'Tetraketide meroterpenoids' by CANOPUS are found for each submitted taxon and extract in database?,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""How many features annotated as 'Tetraketide meroterpenoids' by CANOPUS are found for each submitted taxon and extract in database?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: How many features annotated as 'Tetraketide meroterpenoids' by CANOPUS are found for each submitted taxon and extract in database?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tetraketide meroterpenoids, https://enpkg.commons-lab.org/kg/npc_Tetraketide_meroterpenoids, NPCClass\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed. Here are the details:\n\n- **Question**: How many features annotated as 'Tetraketide meroterpenoids' by CANOPUS are found for each submitted taxon and extract in database?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT ?submittedTaxon ?extract (COUNT(?feature) AS ?featureCount)\n WHERE {\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_class .\n ?featureList ns1:has_lcms_feature ?feature .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?extract ns1:has_LCMS ?analysis .\n ?rawMaterial ns1:has_lab_process ?extract .\n ?rawMaterial ns1:submitted_taxon ?submittedTaxon .\n }\n GROUP BY ?submittedTaxon ?extract\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/093ee098fbba4dca93f0404833ba35bb/tmpjqdqbx0d.csv\n\nI will now analyze the SPARQL output to generate the final answer.\nThe SPARQL query has been executed successfully. Here is the final answer based on the SPARQL output:\n\nThe number of features annotated as 'Tetraketide meroterpenoids' by CANOPUS for each submitted taxon and extract in the database is available in the generated CSV file.\n\n- **Question**: How many features annotated as 'Tetraketide meroterpenoids' by CANOPUS are found for each submitted taxon and extract in database?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT ?submittedTaxon ?extract (COUNT(?feature) AS ?featureCount)\n WHERE {\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_class .\n ?featureList ns1:has_lcms_feature ?feature .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?extract ns1:has_LCMS ?analysis .\n ?rawMaterial ns1:has_lab_process ?extract .\n ?rawMaterial ns1:submitted_taxon ?submittedTaxon .\n }\n GROUP BY ?submittedTaxon ?extract\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/093ee098fbba4dca93f0404833ba35bb/tmpjqdqbx0d.csv\n\nYou can find the detailed results in the provided CSV file."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +} +ORDER BY DESC(?mass) +LIMIT 1",High +8," ""Retrieve the most frequent Spec2Vec peaks and losses characterizing the MS2 spectra from the lab extracts of Tabernaemontana coffeoides.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the most frequent Spec2Vec peaks and losses characterizing the MS2 spectra from the lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the most frequent Spec2Vec peaks and losses characterizing the MS2 spectra from the lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: Retrieve the most frequent Spec2Vec peaks and losses characterizing the MS2 spectra from the lab extracts of Tabernaemontana coffeoides.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n PREFIX wd: \n\n SELECT ?spec2vecPeak ?spec2vecLoss (COUNT(?spec2vecPeak) AS ?peakFrequency) (COUNT(?spec2vecLoss) AS ?lossFrequency)\n WHERE {\n ?rawMaterial ns1:has_wd_id wd:Q15376858 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_spec2vec_doc ?spec2vecDoc .\n ?spec2vecDoc ns1:has_spec2vec_peak ?spec2vecPeak .\n ?spec2vecDoc ns1:has_spec2vec_loss ?spec2vecLoss .\n } GROUP BY ?spec2vecPeak ?spec2vecLoss \n ORDER BY DESC(?peakFrequency) DESC(?lossFrequency)\n LIMIT 1\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/2e7991542808441db6e152ef5e034607/tmpdwgxk7xv.csv`\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The most frequent Spec2Vec peaks and losses characterizing the MS2 spectra from the lab extracts of Tabernaemontana coffeoides have been successfully retrieved "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + PREFIX ns2: + PREFIX wd: - SELECT ?submittedTaxon ?extract (COUNT(?feature) AS ?featureCount) + SELECT ?spec2vecPeak ?spec2vecLoss (COUNT(?spec2vecPeak) AS ?peakFrequency) (COUNT(?spec2vecLoss) AS ?lossFrequency) WHERE { - ?feature ns1:has_canopus_annotation ?annotation . - ?annotation ns1:has_canopus_npc_class . - ?featureList ns1:has_lcms_feature ?feature . + ?rawMaterial ns1:has_wd_id wd:Q15376858 . + ?rawMaterial ns1:has_lab_process ?labExtract . + ?labExtract ns1:has_LCMS ?analysis . ?analysis ns1:has_lcms_feature_list ?featureList . - ?extract ns1:has_LCMS ?analysis . - ?rawMaterial ns1:has_lab_process ?extract . - ?rawMaterial ns1:submitted_taxon ?submittedTaxon . - } - GROUP BY ?submittedTaxon ?extract",Medium -5,How many features (pos ionization and neg ionization modes) have the same SIRIUS/CSI:FingerID and ISDB annotation by comparing the InCHIKey2D of the annotations?,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""How many features (pos ionization and neg ionization modes) have the same SIRIUS/CSI:FingerID and ISDB annotation by comparing the InCHIKey2D of the annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question:How many features (pos ionization and neg ionization modes) have the same SIRIUS/CSI:FingerID and ISDB annotation by comparing the InCHIKey2D of the annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The analysis has been completed. Here are the details:\n\n- **Question**: How many features (pos ionization and neg ionization modes) have the same SIRIUS/CSI:FingerID and ISDB annotation by comparing the InCHIKey2D of the annotations?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT (COUNT(?feature) AS ?featureCount)\n WHERE {\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?feature ns1:has_isdb_annotation ?isdbAnnotation .\n ?siriusAnnotation ns1:has_InChIkey2D ?InChIkey2D .\n ?isdbAnnotation ns1:has_InChIkey2D ?InChIkey2D .\n }\n ```\n- **Result**: 33,255 features have the same SIRIUS/CSI:FingerID and ISDB annotation by comparing the InCHIKey2D of the annotations.\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/ec07f70ba3ec4a18abbb424a48145459/tmppclv2pge.csv`\n\nThis information should provide the necessary details for further analysis or reporting."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + ?featureList ns1:has_lcms_feature ?feature . + ?feature ns1:has_spec2vec_doc ?spec2vecDoc . + ?spec2vecDoc ns1:has_spec2vec_peak ?spec2vecPeak . + ?spec2vecDoc ns1:has_spec2vec_loss ?spec2vecLoss . + } GROUP BY ?spec2vecPeak ?spec2vecLoss + ORDER BY DESC(?peakFrequency) DESC(?lossFrequency) + LIMIT 1",Medium +9," ""What are the LC-MS features that have GNPS library analogs annotation in Tabernaemontana coffeoides? Provide the features.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the LC-MS features that have GNPS library analogs annotation in Tabernaemontana coffeoides? Provide the features."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the LC-MS features that have GNPS library analogs annotation in Tabernaemontana coffeoides? Provide the features."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The LC-MS features that have GNPS library analogs annotation in Tabernaemontana coffeoides can be found in the provided file. Here is the information related to your query:\n\n- **Question**: What are the LC-MS features that have GNPS library analogs annotation in Tabernaemontana coffeoides? Provide the features.\n- **Generated SPARQL Query**: \n ```sparql\n PREFIX ns1: \n\n SELECT ?feature\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:fast_search_gnpslibrary_analog ?analog .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/c00a7dfa6ebf4a978b7a894760588603/tmp94vi0nsp.csv`\n\nPlease refer to the file for the detailed list of features."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: - SELECT (COUNT(?feature) AS ?featureCount) + SELECT ?feature WHERE { + ?rawMaterial ns1:has_wd_id . + ?rawMaterial ns1:has_lab_process ?labExtract . ?labExtract ns1:has_LCMS ?analysis . - ?analysis ns1:has_lcms_feature_list ?feature_list . - ?feature_list ns1:has_lcms_feature ?feature . - ?feature ns1:has_sirius_annotation ?siriusAnnotation . - ?feature ns1:has_isdb_annotation ?isdbAnnotation . - ?siriusAnnotation ns1:has_InChIkey2D ?InChIkey2D . - ?isdbAnnotation ns1:has_InChIkey2D ?InChIkey2D . - }",Medium -6,"For features from the Melochia umbellata taxon in pos ionization mode with SIRIUS annotations, get the ones for which a feature in neg ionization mode with the same retention time (+/- 0.05 minutes) has the same SIRIUS annotation by comparing the InCHIKey 2D. Return the features, retention times, and InChIKey2D","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""For features from the Melochia umbellata taxon in pos ionization mode with SIRIUS annotations, get the ones for which a feature in neg ionization mode with the same retention time (+/- 0.05 minutes) has the same SIRIUS annotation by comparing the InCHIKey 2D. Return the features, retention times, and InChIKey2D"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: For features from the Melochia umbellata taxon in pos ionization mode with SIRIUS annotations, get the ones for which a feature in neg ionization mode with the same retention time (+/- 0.05 minutes) has the same SIRIUS annotation by comparing the InCHIKey 2D. Return the features, retention times, and InChIKey2D"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Melochia umbellata, http://www.wikidata.org/entity/Q6813281, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n### Question:\nFor features from the Melochia umbellata taxon in pos ionization mode with SIRIUS annotations, get the ones for which a feature in neg ionization mode with the same retention time (+/- 0.05 minutes) has the same SIRIUS annotation by comparing the InCHIKey 2D. Return the features, retention times, and InChIKey2D.\n\n### Generated SPARQL Query:\n```sparql\nPREFIX ns1: \nPREFIX ns2: \nPREFIX wd: \n\nSELECT ?featurePos ?retentionTime ?InChIkey2D\nWHERE {\n ?rawMaterial ns1:has_wd_id wd:Q6813281 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsPos .\n ?lcmsPos a ns1:LCMSAnalysisPos .\n ?lcmsPos ns1:has_lcms_feature_list ?featureListPos .\n ?featureListPos ns1:has_lcms_feature ?featurePos .\n ?featurePos ns1:has_retention_time ?retentionTime .\n ?featurePos ns1:has_sirius_annotation ?siriusAnnotationPos .\n ?siriusAnnotationPos ns1:has_InChIkey2D ?InChIkey2D .\n\n ?labExtract ns1:has_LCMS ?lcmsNeg .\n ?lcmsNeg a ns1:LCMSAnalysisNeg .\n ?lcmsNeg ns1:has_lcms_feature_list ?featureListNeg .\n ?featureListNeg ns1:has_lcms_feature ?featureNeg .\n ?featureNeg ns1:has_retention_time ?retentionTimeNeg .\n ?featureNeg ns1:has_sirius_annotation ?siriusAnnotationNeg .\n ?siriusAnnotationNeg ns1:has_InChIkey2D ?InChIkey2D .\n\n FILTER (ABS(?retentionTime - ?retentionTimeNeg) <= 0.05)\n}\n```\n\n### File Path:\nThe results of the query have been saved to the following file path:\n`/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/6966291902c9490ab2b949339c4c2797/tmp_cptewcz.csv`\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + ?analysis ns1:has_lcms_feature_list ?featureList . + ?featureList ns1:has_lcms_feature ?feature . + ?feature ns1:fast_search_gnpslibrary_analog ?analog . + }",High +10,"""For features from the Melochia umbellata taxon in pos ionization mode with SIRIUS annotations, get the ones for which a feature in neg ionization mode with the same retention time (+/- 0.05 minutes) has the same SIRIUS annotation by comparing the InCHIKey 2D. Return the features, retention times, and InChIKey2D""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""For features from the Melochia umbellata taxon in pos ionization mode with SIRIUS annotations, get the ones for which a feature in neg ionization mode with the same retention time (+/- 0.05 minutes) has the same SIRIUS annotation by comparing the InCHIKey 2D. Return the features, retention times, and InChIKey2D"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: For features from the Melochia umbellata taxon in pos ionization mode with SIRIUS annotations, get the ones for which a feature in neg ionization mode with the same retention time (+/- 0.05 minutes) has the same SIRIUS annotation by comparing the InCHIKey 2D. Return the features, retention times, and InChIKey2D"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Melochia umbellata, http://www.wikidata.org/entity/Q6813281, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n### Question:\nFor features from the Melochia umbellata taxon in pos ionization mode with SIRIUS annotations, get the ones for which a feature in neg ionization mode with the same retention time (+/- 0.05 minutes) has the same SIRIUS annotation by comparing the InCHIKey 2D. Return the features, retention times, and InChIKey2D.\n\n### Generated SPARQL Query:\n```sparql\nPREFIX ns1: \nPREFIX ns2: \nPREFIX wd: \n\nSELECT ?featurePos ?retentionTime ?InChIkey2D\nWHERE {\n ?rawMaterial ns1:has_wd_id wd:Q6813281 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsPos .\n ?lcmsPos a ns1:LCMSAnalysisPos .\n ?lcmsPos ns1:has_lcms_feature_list ?featureListPos .\n ?featureListPos ns1:has_lcms_feature ?featurePos .\n ?featurePos ns1:has_retention_time ?retentionTime .\n ?featurePos ns1:has_sirius_annotation ?siriusAnnotationPos .\n ?siriusAnnotationPos ns1:has_InChIkey2D ?InChIkey2D .\n\n ?labExtract ns1:has_LCMS ?lcmsNeg .\n ?lcmsNeg a ns1:LCMSAnalysisNeg .\n ?lcmsNeg ns1:has_lcms_feature_list ?featureListNeg .\n ?featureListNeg ns1:has_lcms_feature ?featureNeg .\n ?featureNeg ns1:has_retention_time ?retentionTimeNeg .\n ?featureNeg ns1:has_sirius_annotation ?siriusAnnotationNeg .\n ?siriusAnnotationNeg ns1:has_InChIkey2D ?InChIkey2D .\n\n FILTER (ABS(?retentionTime - ?retentionTimeNeg) <= 0.05)\n}\n```\n\n### File Path:\nThe results of the query have been saved to the following file path:\n`/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/6966291902c9490ab2b949339c4c2797/tmp_cptewcz.csv`\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: PREFIX ns2: PREFIX wd: @@ -100,219 +166,96 @@ WHERE { FILTER (ABS(?retentionTime - ?retentionTimeNeg) <= 0.05) }",High -7,"What are the taxons, lab process and label (if one exists) for each sample? Sort by sample and then lab process","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the taxons, lab process and label (if one exists) for each sample? Sort by sample and then lab process"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the taxons, lab process and label (if one exists) for each sample? Sort by sample and then lab process"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed. Here are the details:\n\n- **Question**: What are the taxons, lab process and label (if one exists) for each sample? Sort by sample and then lab process.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n SELECT ?sample ?taxon ?labProcess ?label\n WHERE {\n ?sample ns1:has_wd_id ?taxon .\n ?sample ns1:has_lab_process ?labProcess .\n OPTIONAL { ?sample rdfs:label ?label . }\n }\n ORDER BY ?sample ?labProcess\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/5642f76045424334a801e191d6ed4563/tmp9g_oyjum.csv\n\nPlease review the file at the provided path for the detailed results."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The taxons, lab process, and labels (if any) for each sample have been sorted by sample and then by lab process. Here is a snippet of the sorted data:\n\n| Sample | Taxon | Lab Process | Label |\n|------------------------------------------|------------------------------------------|--------------------------------------------|-------|\n| http://www.wikidata.org/entity/Q11086771 | http://www.wikidata.org/entity/Q11086771 | https://enpkg.commons-lab.org/kg/KP001_Positive | NaN |\n| http://www.wikidata.org/entity/Q11272377 | http://www.wikidata.org/entity/Q11272377 | https://enpkg.commons-lab.org/kg/KP002_Positive | NaN |\n| http://www.wikidata.org/entity/Q11086771 | http://www.wikidata.org/entity/Q11086771 | https://enpkg.commons-lab.org/kg/KP003_Positive | NaN |\n| http://www.wikidata.org/entity/Q11272377 | http://www.wikidata.org/entity/Q11272377 | https://enpkg.commons-lab.org/kg/KP004_Positive | NaN |\n| http://www.wikidata.org/entity/Q5978345 | http://www.wikidata.org/entity/Q5978345 | https://enpkg.commons-lab.org/kg/KP005_Positive | NaN |\n\nThe full path of the file generated is:\n- /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/5642f76045424334a801e191d6ed4563/tmp9g_oyjum.csv\n\nYou can download the file for the complete details."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: - SELECT ?sample ?taxon ?labProcess ?label - WHERE { - ?sample ns1:has_wd_id ?taxon . - ?sample ns1:has_lab_process ?labProcess . - OPTIONAL { ?sample rdfs:label ?label . } - } - ORDER BY ?sample ?labProcess",Low -8,"What are the retention times, parent masses, and associated Spec2Vec peaks for features in the LC-MS analysis of the plant Rumex nepalensis in positive ion mode?","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the retention times, parent masses, and associated Spec2Vec peaks for features in the LC-MS analysis of the plant Rumex nepalensis in positive ion mode?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question:What are the retention times, parent masses, and associated Spec2Vec peaks for features in the LC-MS analysis of the plant Rumex nepalensis in positive ion mode?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Rumex nepalensis, http://www.wikidata.org/entity/Q11041204, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: What are the retention times, parent masses, and associated Spec2Vec peaks for features in the LC-MS analysis of the plant Rumex nepalensis in positive ion mode?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?retention_time ?parent_mass ?spec2vec_peak\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis a ns1:LCMSAnalysisPos .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_retention_time ?retention_time .\n ?feature ns1:has_parent_mass ?parent_mass .\n ?feature ns1:has_spec2vec_doc ?spec2vecDoc .\n ?spec2vecDoc ns1:has_spec2vec_peak ?spec2vec_peak .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/7e32d9e48e274df9a04b4a01d1635276/tmpxmt6gmzo.csv`\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The LC-MS analysis of the plant Rumex nepalensis in positive ion mode provided the following details:\n\n- **Retention Time**: 5.938233\n- **Parent Mass**: 584.475128\n- **Spec2Vec Peaks**:\n - [Peak 130.14](https://enpkg.commons-lab.org/kg/peak_130.14)\n - [Peak 131.07](https://enpkg.commons-lab.org/kg/peak_131.07)\n - [Peak 221.14](https://enpkg.commons-lab.org/kg/peak_221.14)\n - [Peak 233.11](https://enpkg.commons-lab.org/kg/peak_233.11)\n - [Peak 265.17](https://enpkg.commons-lab.org/kg/peak_265.17)\n\nNo visualization was requested for this analysis. The data can be downloaded from the provided file path."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +11,"""List the lab extracts that have inhibition percentages above 50% at 2µg/mL against Trypanosoma brucei rhodesiense .""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""List the lab extracts that have inhibition percentages above 50% bioassay results at 2碌g/mL against Trypanosoma brucei rhodesiense ."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: List the lab extracts that have inhibition percentages above 50% bioassay results at 2碌g/mL against Trypanosoma brucei rhodesiense."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Trypanosoma brucei rhodesiense, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL612348, ChEMBLTarget\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: List the lab extracts that have inhibition percentages above 50% bioassay results at 2碌g/mL against Trypanosoma brucei rhodesiense.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?labExtract\n WHERE {\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\n ?bioAssayResult ns2:target_id .\n {\n ?bioAssayResult a ns2:Tbrucei2ugml .\n }\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/fc41480ada32471b948feb8e1d6babcf/tmp4p9xu66f.csv`\n\nI will now analyze the SPARQL output to provide the final answer.\nThe lab extracts that have bioassay results at 2碌g/mL against Trypanosoma brucei rhodesiense are listed in the file located at the following path:\n\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/fc41480ada32471b948feb8e1d6babcf/tmp4p9xu66f.csv`\n\nHere is the dictionary containing the question, generated SPARQL query, and file path:\n\n```json\n{\n \""question\"": \""List the lab extracts that have inhibition percentages above 50% bioassay results at 2碌g/mL against Trypanosoma brucei rhodesiense\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\nPREFIX ns2: \\n\\nSELECT ?labExtract\\nWHERE {\\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\\n ?bioAssayResult ns2:target_id .\\n {\\n ?bioAssayResult a ns2:Tbrucei2ugml .\\n } ?bioAssayResult ns2:inhibition_percentage ?inhibition .\n FILTER(?inhibition > 50)\\n}\"",\n \""file_path\"": \""/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/fc41480ada32471b948feb8e1d6babcf/tmp4p9xu66f.csv\""\n}\n```\n\nPlease review the file for the detailed list of lab extracts."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: PREFIX ns2: - SELECT ?retention_time ?parent_mass ?spec2vec_peak + SELECT ?labExtract WHERE { - ?rawMaterial ns1:has_wd_id . - ?rawMaterial ns1:has_lab_process ?labExtract . - ?labExtract ns1:has_LCMS ?lcmsAnalysis . - ?lcmsAnalysis a ns1:LCMSAnalysisPos . - ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList . - ?featureList ns1:has_lcms_feature ?feature . - ?feature ns1:has_retention_time ?retention_time . - ?feature ns1:has_parent_mass ?parent_mass . - ?feature ns1:has_spec2vec_doc ?spec2vecDoc . - ?spec2vecDoc ns1:has_spec2vec_peak ?spec2vec_peak . + ?labExtract ns2:has_bioassay_results ?bioAssayResult . + ?bioAssayResult ns2:target_id . + { + ?bioAssayResult a ns2:Tbrucei2ugml . + } }",Medium -9,Count the number of LCMS features in negative ionization mode,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Count the number of LCMS features in negative ionization mode"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Count the number of LCMS features in negative ionization mode"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The number of LCMS features in negative ionization mode is **364,967**.\n\nHere is the detailed information:\n- **Question**: Count the number of LCMS features in negative ionization mode.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT (COUNT(?feature) AS ?featureCount)\n WHERE {\n ?analysis a ns1:LCMSAnalysisNeg .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n }\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/5a7c1625b95143fcb9f58943371653e9/tmp_dhsfq67.csv"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: - - SELECT (COUNT(?feature) AS ?featureCount) - WHERE { - ?analysis a ns1:LCMSAnalysisNeg . - ?analysis ns1:has_lcms_feature_list ?featureList . - ?featureList ns1:has_lcms_feature ?feature . - }",Low -10,Which molecules (represented by their InChIkey) have reported ChEMBL activity with activity type IC50 and activity values below 500 nM against Leishmania donovani target?,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which molecules (represented by their InChIkey) have reported ChEMBL activity with activity type IC50 and activity values below 500 nM against Leishmania donovani target?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which molecules (represented by their InChIkey) have reported ChEMBL activity with activity type IC50 and activity values below 500 nM against Leishmania donovani target?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""Leishmania donovani, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL367, ChEMBLTarget"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed successfully. Here are the details:\n\n- **Question**: Which molecules (represented by their InChIkey) have reported ChEMBL activity with activity type IC50 and activity values below 500 nM against Leishmania donovani target?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?InChIkey\n WHERE {\n ?chemicalEntity ns2:has_chembl_id ?chemblChemical .\n ?chemblChemical ns2:has_chembl_activity ?assayResults .\n ?assayResults ns2:target_id ;\n ns2:activity_type \""IC50\"" ;\n ns2:activity_value ?activityValue .\n FILTER(?activityValue < 500)\n ?chemicalEntity ns1:has_wd_id ?wd_id .\n ?InChIkey ns1:has_wd_id ?wd_id .\n }\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/e7807b50a3824198a3233798d22d9a4b/tmpp66dmbc6.csv\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The molecules that meet the criteria have been identified with the following InChIkey: [APKFDSVGJQXUKY-INPOYWNPSA-N](APKFDSVGJQXUKY-INPOYWNPSA-N).\n\n"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +12," ""Retrieve the Spec2Vec documents associated with MS2 spectra from the lab extracts of Tabernaemontana coffeoides""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the Spec2Vec documents associated with MS2 spectra from the lab extracts of Tabernaemontana coffeoides"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the Spec2Vec documents associated with MS2 spectra from the lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: Retrieve the Spec2Vec documents associated with MS2 spectra from the lab extracts of Tabernaemontana coffeoides.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?spec2vecDoc\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_spec2vec_doc ?spec2vecDoc .\n }\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/aa6e48ec3a194a14a7a9f918774c080a/tmpxvtl3hcs.csv\n\nI will now analyze the SPARQL output to provide the final answer.\nThe final answer based on the SPARQL output is as follows:\n\nThe Spec2Vec documents (Spec2VecDoc) associated with MS2 spectra (MS2Spectrum) from the lab extracts (LabExtract) of Tabernaemontana coffeoides have been retrieved. The details are stored in the file located at:\n\n**File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/aa6e48ec3a194a14a7a9f918774c080a/tmpxvtl3hcs.csv\n\nHere is the dictionary containing the question, generated SPARQL query, and file path:\n\n```json\n{\n \""question\"": \""Retrieve the Spec2Vec documents (Spec2VecDoc) associated with MS2 spectra (MS2Spectrum) from the lab extracts (LabExtract) of Tabernaemontana coffeoides.\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\nPREFIX ns2: \\n\\nSELECT ?spec2vecDoc\\nWHERE {\\n ?rawMaterial ns1:has_wd_id .\\n ?rawMaterial ns1:has_lab_process ?labExtract .\\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\\n ?featureList ns1:has_lcms_feature ?feature .\\n ?feature ns1:has_spec2vec_doc ?spec2vecDoc .\\n}\"",\n \""file_path\"": \""/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/aa6e48ec3a194a14a7a9f918774c080a/tmpxvtl3hcs.csv\""\n}\n```\n\nPlease review the file for the detailed Spec2Vec documents."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: PREFIX ns2: - SELECT ?InChIkey - WHERE { - ?chemicalEntity ns2:has_chembl_id ?chemblChemical . - ?chemblChemical ns2:has_chembl_activity ?assayResults . - ?assayResults ns2:target_id ; - ns2:activity_type ""IC50"" ; - ns2:activity_value ?activityValue . - FILTER(?activityValue < 500) - ?chemicalEntity ns1:has_wd_id ?wd_id . - ?InChIkey ns1:has_wd_id ?wd_id . - }",High -11,What are the mass spectrometry features detected only in the plant Rumex nepalensis?,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the mass spectrometry features detected only in the plant Rumex nepalensis?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the mass spectrometry features detected only in the plant Rumex nepalensis?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Rumex nepalensis, http://www.wikidata.org/entity/Q11041204, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: What are the mass spectrometry features detected only in the plant Rumex nepalensis?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n PREFIX wd: \n\n SELECT ?feature\n WHERE {\n ?rawMaterial ns1:has_wd_id wd:Q11041204 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n }\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/966c850620f7440182889fe9b80a1edb/tmp4z32di1l.csv\n\nI will now analyze the SPARQL output to provide the final answer.\nThe mass spectrometry features detected for the plant Rumex nepalensis can be found in the file located at the following path:\n\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/966c850620f7440182889fe9b80a1edb/tmp4z32di1l.csv\n\nHere is the dictionary containing the question, generated SPARQL query, and file path:\n\n```json\n{\n \""question\"": \""What are the mass spectrometry features detected for the plant Rumex nepalensis?\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\nPREFIX ns2: \\nPREFIX wd: \\n\\nSELECT ?feature\\nWHERE {\\n ?rawMaterial ns1:has_wd_id wd:Q11041204 .\\n ?rawMaterial ns1:has_lab_process ?labExtract .\\n ?labExtract ns1:has_LCMS ?analysis .\\n ?analysis ns1:has_lcms_feature_list ?featureList .\\n ?featureList ns1:has_lcms_feature ?feature .\\n}\"",\n \""file_path\"": \""/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/966c850620f7440182889fe9b80a1edb/tmp4z32di1l.csv\""\n}\n```\n\nPlease review the file for the detailed mass spectrometry features."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The mass spectrometry features detected for the plant Rumex nepalensis are as follows:\n\n1. [LCMS Feature 1](https://enpkg.commons-lab.org/kg/lcms_feature_1)\n2. [LCMS Feature 2](https://enpkg.commons-lab.org/kg/lcms_feature_2)\n3. [LCMS Feature 3](https://enpkg.commons-lab.org/kg/lcms_feature_3)\n4. [LCMS Feature 4](https://enpkg.commons-lab.org/kg/lcms_feature_4)\n5. [LCMS Feature 5](https://enpkg.commons-lab.org/kg/lcms_feature_5)\n\nNo visualization was requested for this analysis. The data can be downloaded from the provided file path."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: - PREFIX ns2: - PREFIX wd: - - SELECT ?feature + SELECT ?spec2vecDoc WHERE { - ?rawMaterial ns1:has_wd_id wd:Q11041204 . + ?rawMaterial ns1:has_wd_id . ?rawMaterial ns1:has_lab_process ?labExtract . - ?labExtract ns1:has_LCMS ?analysis . - ?analysis ns1:has_lcms_feature_list ?featureList . + ?labExtract ns1:has_LCMS ?lcmsAnalysis . + ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList . ?featureList ns1:has_lcms_feature ?feature . - }",Low -12,What are the chemical structure ISDB annotations for Lovoa trichilioides ?,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the chemical structure ISDB annotations for Lovoa trichilioides ?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the chemical structure ISDB annotations for Lovoa trichilioides?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""Lovoa trichilioides, http://www.wikidata.org/entity/Q8008828, Wikidata"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed. Here are the details:\n\n- **Question**: What are the chemical structure ISDB annotations for Lovoa trichilioides?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?Annotation\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_isdb_annotation ?Annotation .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/597fde39a0474507b67d3ce263df2a5e/tmp9ukxy4rj.csv`\n\nI will now analyze the SPARQL output to provide the final answer.\nThe chemical structure ISDB annotations for Lovoa trichilioides have been retrieved. Here is the final answer:\n\n- **Question**: What are the chemical structure ISDB annotations for Lovoa trichilioides?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?Annotation\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_isdb_annotation ?Annotation .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/597fde39a0474507b67d3ce263df2a5e/tmp9ukxy4rj.csv`\n\nThe file contains the ISDB annotations for Lovoa trichilioides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: - PREFIX ns2: - - SELECT ?Annotation - WHERE { - ?rawMaterial ns1:has_wd_id . - ?rawMaterial ns1:has_lab_process ?labExtract . - ?labExtract ns1:has_LCMS ?analysis . - ?analysis ns1:has_lcms_feature_list ?feature_list . - ?feature_list ns1:has_lcms_feature ?feature . - ?feature ns1:has_isdb_annotation ?Annotation . + ?feature ns1:has_spec2vec_doc ?spec2vecDoc . }",Medium -13,What is the highest inhibition percentage recorded for compounds observed in Rauvolfia vomitoria extract?,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What is the highest inhibition percentage recorded for compounds observed in Rauvolfia vomitoria extract?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What is the highest inhibition percentage recorded for compounds observed in Rauvolfia vomitoria extract?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Rauvolfia vomitoria, http://www.wikidata.org/entity/Q5225969, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The highest inhibition percentage recorded for compounds observed in Rauvolfia vomitoria extract is 17.4%.\n\nHere is the detailed information:\n- **Question**: What is the highest inhibition percentage recorded for compounds from Rauvolfia vomitoria extract?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n PREFIX wd: \n\n SELECT ?inhibition_percentage\n WHERE {\n ?rawMaterial ns1:has_wd_id wd:Q5225969 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\n ?bioAssayResult ns2:inhibition_percentage ?inhibition_percentage .\n }\n ORDER BY DESC(?inhibition_percentage)\n LIMIT 1\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/2b0f38f9dd7b4e818282a1c620449ab2/tmpjoi_glcf.csv"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: - PREFIX ns2: - PREFIX wd: - - SELECT ?inhibition_percentage - WHERE { - ?rawMaterial ns1:has_wd_id wd:Q5225969 . - ?rawMaterial ns1:has_lab_process ?labExtract . - ?labExtract ns2:has_bioassay_results ?bioAssayResult . - ?bioAssayResult ns2:inhibition_percentage ?inhibition_percentage . - } - ORDER BY DESC(?inhibition_percentage) - LIMIT 1",Low -14,"What are the retention times and molecular masses of compounds identified in the negative ionization mode LCMS analysis from Lovoa trichilioides extracts, and what are the CANOPUS chemical class annotations for those features?","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the retention times and molecular masses of compounds identified in the negative ionization mode LCMS analysis from Lovoa trichilioides extracts, and what are the CANOPUS chemical class annotations for those features?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the retention times and molecular masses of compounds identified in the negative ionization mode LCMS analysis from Lovoa trichilioides extracts, and what are the CANOPUS chemical class annotations for those features?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Lovoa trichilioides, http://www.wikidata.org/entity/Q8008828, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: What are the retention times and molecular masses of compounds identified in the negative ionization mode LCMS analysis from Lovoa trichilioides extracts, and what are the CANOPUS chemical class annotations for those features?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n PREFIX wd: \n\n SELECT ?retentionTime ?molecularMass ?canopusClass\n WHERE {\n ?rawMaterial ns1:has_wd_id wd:Q8008828 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysisNeg .\n ?lcmsAnalysisNeg a ns1:LCMSAnalysisNeg .\n ?lcmsAnalysisNeg ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_retention_time ?retentionTime .\n ?feature ns1:has_parent_mass ?molecularMass .\n ?feature ns1:has_canopus_annotation ?canopusAnnotation .\n ?canopusAnnotation ns1:has_canopus_npc_class ?canopusClass .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/1ee4538b31f946faaa8a05370b67774f/tmpwx92t25x.csv`\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The retention times, molecular masses, and CANOPUS chemical class annotations for the compounds identified in the negative ionization mode LCMS analysis from Lovoa trichilioides extracts have been processed. "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: - PREFIX ns2: - PREFIX wd: - - SELECT ?retentionTime ?molecularMass ?canopusClass - WHERE { - ?rawMaterial ns1:has_wd_id wd:Q8008828 . - ?rawMaterial ns1:has_lab_process ?labExtract . - ?labExtract ns1:has_LCMS ?lcmsAnalysisNeg . - ?lcmsAnalysisNeg a ns1:LCMSAnalysisNeg . - ?lcmsAnalysisNeg ns1:has_lcms_feature_list ?featureList . - ?featureList ns1:has_lcms_feature ?feature . - ?feature ns1:has_retention_time ?retentionTime . - ?feature ns1:has_parent_mass ?molecularMass . - ?feature ns1:has_canopus_annotation ?canopusAnnotation . - ?canopusAnnotation ns1:has_canopus_npc_class ?canopusClass . - }",High -15,What is the distribution of chemical superclasses in the LCMS features annotated by CANOPUS identified from Desmodium heterophyllum extracts?,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What is the distribution of chemical superclasses in the LCMS features annotated by CANOPUS identified from Desmodium heterophyllum extracts?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What is the distribution of chemical superclasses in the LCMS features annotated by CANOPUS identified from Desmodium heterophyllum extracts?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Desmodium heterophyllum, http://www.wikidata.org/entity/Q10770714, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The distribution of chemical superclasses in the LCMS features annotated by CANOPUS identified from Desmodium heterophyllum extracts is as follows:\n\n- Flavonoids: 600\n- Octadecanoids: 105\n- Triterpenoids: 50\n- Isoflavonoids: 147\n- Sesquiterpenoids: 27\n- Lignans: 19\n- Small peptides: 19\n- Monoterpenoids: 16\n- Coumarins: 180\n- Tryptophan alkaloids: 6\n- Stilbenoids: 20\n- Nucleosides: 8\n- Glycerophospholipids: 2\n- Polycyclic aromatic polyketides: 11\n- Aromatic polyketides: 13\n- Xanthones: 9\n- Phenylpropanoids (C6-C3): 11\n- Naphthalenes: 5\n- Anthranilic acid alkaloids: 3\n- Diarylheptanoids: 2\n- Diterpenoids: 18\n- Steroids: 51\n- Ornithine alkaloids: 2\n- Aminosugars and aminoglycosides: 5\n- Meroterpenoids: 27\n- Diazotetronic acids and derivatives: 3\n- Glycerolipids: 62\n- Fatty Acids and Conjugates: 44\n- Sphingolipids: 3\n- Oligopeptides: 17\n- Phenolic acids (C6-C1): 15\n- Pseudoalkaloids: 3\n- 尾-lactams: 1\n- Fatty amides: 17\n- Tyrosine alkaloids: 1\n- Phloroglucinols: 2\n- Unknown: 198\n- Chromanes: 7\n- Linear polyketides: 9\n- Nicotinic acid alkaloids: 2\n- Spingolipids: 24\n- Fatty esters: 3\n- Pseudoalkaloids (transamidation): 1\n- Amino acid glycosides: 6\n- Histidine alkaloids: 1\n- Eicosanoids: 15\n- Fatty acyl glycosides: 2\n- Fatty acyls: 6\n- Polyethers: 2\n- Macrolides: 1\n- Polyols: 2\n- Cyclic polyketides: 3\n- Apocarotenoids: 3\n\nHere is the dictionary containing the question, generated SPARQL query, and file path to the query result:\n\n```json\n{\n \""question\"": \""What is the distribution of chemical superclasses in the LCMS features annotated by CANOPUS identified from Desmodium heterophyllum extracts?\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\nPREFIX ns2: \\nPREFIX wd: \\n\\nSELECT ?superclass (COUNT(?superclass) AS ?count)\\nWHERE {\\n ?rawMaterial ns1:has_wd_id wd:Q10770714 .\\n ?rawMaterial ns1:has_lab_process ?labExtract .\\n ?labExtract ns1:has_LCMS ?analysis .\\n ?analysis ns1:has_lcms_feature_list ?feature_list .\\n ?feature_list ns1:has_lcms_feature ?feature .\\n ?feature ns1:has_canopus_annotation ?annotation .\\n ?annotation ns1:has_canopus_npc_superclass ?superclass .\\n}\\nGROUP BY ?superclass\"",\n \""file_path\"": \""/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/228ff50f729b45f29855d7d12d4c18c9/tmp23kaqct7.csv\""\n}\n```"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: -PREFIX ns2: -PREFIX wd: +13," ""Which extracts have features (pos ionization mode) annotated as the class, aspidosperma-type alkaloids, by CANOPUS with a probability score above 0.5, ordered by the decreasing count of features as aspidosperma-type alkaloids? Group by extract.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which extracts have features (pos ionization mode) annotated as the class, aspidosperma-type alkaloids, by CANOPUS with a probability score above 0.5, ordered by the decreasing count of features as aspidosperma-type alkaloids? Group by extract."", ""example"": false, ""additional_kwargs"": {}}, {""name"": ""Entry_agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: \""Which extracts have features (pos ionization mode) annotated as the class, aspidosperma-type alkaloids, by CANOPUS with a probability score above 0.5, ordered by the decreasing count of features as aspidosperma-type alkaloids? Group by extract .\"""", ""example"": false, ""additional_kwargs"": {}}, {""name"": ""Validator_agent"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""The entity \""aspidosperma-type alkaloids\"" was resolved to the following IRIs:\n\naspidosperma-type alkaloids, https://enpkg.commons-lab.org/kg/npc_Aspidosperma_type, NPCClass;\nNPCPathway, https://enpkg.commons-lab.org/kg/npc_Alkaloids, NPCPathway;\nNPCSuperClass, https://enpkg.commons-lab.org/kg/npc_Tyrosine_alkaloids, NPCSuperClass."", ""example"": false, ""additional_kwargs"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The extracts with features (pos ionization mode) annotated as the class, aspidosperma-type alkaloids, by CANOPUS with a probability score above 0.5, ordered by the decreasing count of features as aspidosperma-type alkaloids, are as follows:\n\n1. VGF152_B02: 74 features\n2. VGF157_D02: 11 features\n3. VGF147_B11: 10 features\n4. VGF153_C03: 7 features\n5. VGF157_E02: 2 features\n6. VGF154_D02: 2 features\n7. VGF147_A10: 2 features\n8. VGF140_F02: 2 features\n9. KP146_Positive: 1 feature\n10. KP317_Positive: 1 feature\n11. VGF159_B11: 1 feature\n12. VGF155_D07: 1 feature\n13. VGF154_H02: 1 feature\n14. VGF153_E07: 1 feature\n15. VGF153_G10: 1 feature\n16. VGF153_D03: 1 feature\n17. VGF153_F04: 1 feature\n18. VGF153_G09: 1 feature\n19. VGF153_F11: 1 feature\n20. VGF153_H04: 1 feature\n21. VGF153_B06: 1 feature\n22. VGF152_E06: 1 feature\n23. VGF151_F09: 1 feature\n24. VGF151_D06: 1 feature\n25. VGF151_E03: 1 feature\n26. VGF150_B11: 1 feature\n27. VGF150_B06: 1 feature\n28. VGF150_F09: 1 feature\n29. VGF150_H05: 1 feature\n30. VGF150_F06: 1 feature\n31. VGF147_D11: 1 feature\n32. VGF147_E10: 1 feature\n33. VGF143_E07: 1 feature\n34. VGF140_E02: 1 feature\n35. VGF139_D09: 1 feature\n36. SC_AP_Wi_DCM: 1 feature\n\nThe SPARQL query used to retrieve this information is:\n```sparql\nPREFIX ns1: \n\nSELECT ?labExtract (COUNT(?feature) AS ?featureCount)\nWHERE {\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis a ns1:LCMSAnalysisPos .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_class .\n ?annotation ns1:has_canopus_npc_class_prob ?probability .\n FILTER(?probability > 0.5)\n}\nGROUP BY ?labExtract\nORDER BY DESC(?featureCount)\n```\n\nThe results are saved in the file located at: `/tmp/kgbot/f36215b1a3114803ad38a18a9462eb29/tmp8u8o5vcv.csv`\n\nNext, I will generate a bar chart based on this data.\nHere is the dictionary containing the question, generated SPARQL query, and the file path to the results:\n\n```json\n{\n \""question\"": \""Which extracts have features (pos ionization mode) annotated as the class, aspidosperma-type alkaloids, by CANOPUS with a probability score above 0.5, ordered by the decreasing count of features as aspidosperma-type alkaloids? Group by extract and provide a bar chart.\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\n\\nSELECT ?labExtract (COUNT(?feature) AS ?featureCount)\\nWHERE {\\n ?labExtract ns1:has_LCMS ?analysis .\\n ?analysis a ns1:LCMSAnalysisPos .\\n ?analysis ns1:has_lcms_feature_list ?feature_list .\\n ?feature_list ns1:has_lcms_feature ?feature .\\n ?feature ns1:has_canopus_annotation ?annotation .\\n ?annotation ns1:has_canopus_npc_class .\\n ?annotation ns1:has_canopus_npc_class_prob ?probability .\\n FILTER(?probability > 0.5)\\n}\\nGROUP BY ?labExtract\\nORDER BY DESC(?featureCount)\"",\n \""file_path\"": \""/tmp/kgbot/f36215b1a3114803ad38a18a9462eb29/tmp8u8o5vcv.csv\""\n}\n```\n\nPlease use this information to generate the bar chart."", ""example"": false, ""additional_kwargs"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The extracts with features (pos ionization mode) annotated as the class, aspidosperma-type alkaloids, by CANOPUS with a probability score above 0.5, ordered by the decreasing count of features as aspidosperma-type alkaloids, are as follows:\n\n1. VGF152_B02: 74 features\n2. VGF157_D02: 11 features\n3. VGF147_B11: 10 features\n4. VGF153_C03: 7 features\n5. VGF157_E02: 2 features\n6. VGF154_D02: 2 features\n7. VGF147_A10: 2 features\n8. VGF140_F02: 2 features\n9. KP146_Positive: 1 feature\n10. KP317_Positive: 1 feature\n11. VGF159_B11: 1 feature\n12. VGF155_D07: 1 feature\n13. VGF154_H02: 1 feature\n14. VGF153_E07: 1 feature\n15. VGF153_G10: 1 feature\n16. VGF153_D03: 1 feature\n17. VGF153_F04: 1 feature\n18. VGF153_G09: 1 feature\n19. VGF153_F11: 1 feature\n20. VGF153_H04: 1 feature\n21. VGF153_B06: 1 feature\n22. VGF152_E06: 1 feature\n23. VGF151_F09: 1 feature\n24. VGF151_D06: 1 feature\n25. VGF151_E03: 1 feature\n26. VGF150_B11: 1 feature\n27. VGF150_B06: 1 feature\n28. VGF150_F09: 1 feature\n29. VGF150_H05: 1 feature\n30. VGF150_F06: 1 feature\n31. VGF147_D11: 1 feature\n32. VGF147_E10: 1 feature\n33. VGF143_E07: 1 feature\n34. VGF140_E02: 1 feature\n35. VGF139_D09: 1 feature\n36. SC_AP_Wi_DCM: 1 feature\n\nT"", ""example"": false, ""additional_kwargs"": {}}]}","PREFIX ns1: -SELECT ?superclass (COUNT(?superclass) AS ?count) +SELECT ?labExtract (COUNT(?feature) AS ?featureCount) WHERE { - ?rawMaterial ns1:has_wd_id wd:Q10770714 . - ?rawMaterial ns1:has_lab_process ?labExtract . ?labExtract ns1:has_LCMS ?analysis . + ?analysis a ns1:LCMSAnalysisPos . ?analysis ns1:has_lcms_feature_list ?feature_list . ?feature_list ns1:has_lcms_feature ?feature . ?feature ns1:has_canopus_annotation ?annotation . - ?annotation ns1:has_canopus_npc_superclass ?superclass . + ?annotation ns1:has_canopus_npc_class . + ?annotation ns1:has_canopus_npc_class_prob ?probability . + FILTER(?probability > 0.5) } -GROUP BY ?superclass",Medium -16,What are the retention times and adduct types of LCMS features from Desmodium heterophyllum annotated by SIRIUS with a cosmic score greater than 0.4??,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the retention times and adduct types of LCMS features from Desmodium heterophyllum annotated by SIRIUS with a cosmic score greater than 0.4??"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the retention times and adduct types of LCMS features from Desmodium heterophyllum annotated by SIRIUS with a cosmic score greater than 0.4?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Desmodium heterophyllum, http://www.wikidata.org/entity/Q10770714, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here is the information:\n\n- **Question**: What are the retention times and adduct types of LCMS features from Desmodium heterophyllum annotated by SIRIUS with a cosmic score greater than 0.4?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?retentionTime ?adduct\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_retention_time ?retentionTime .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?siriusAnnotation ns1:has_cosmic_score ?cosmicScore .\n ?siriusAnnotation ns1:has_adduct ?adduct .\n FILTER(?cosmicScore > 0.4)\n }\n ```\n- **File Path**: The results are stored in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/25ad11d8258b4723b0a4653d1b0de5e7/tmpvmkstwx8.csv`.\n\nPlease let me know if you need further assistance or analysis of the results!"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +GROUP BY ?labExtract +ORDER BY DESC(?featureCount)",High +14," ""Which ISDB annotations have consistency scores above 0.7 and taxonomic scores above 0.5 for LCMS features from Tabernaemontana coffeoides? Provide annotations.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which ISDB annotations have consistency scores above 0.7 and taxonomic scores above 0.5 for LCMS features from Tabernaemontana coffeoides? Provide annotations."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which ISDB annotations have consistency scores above 0.7 and taxonomic scores above 0.5 for LCMS features from Tabernaemontana coffeoides? Provide annotations."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully executed to find the ISDB annotations with consistency scores above 0.7 and taxonomic scores above 0.5 for LCMS features from Tabernaemontana coffeoides. Here is the information:\n\n- **Question**: Which ISDB annotations have consistency scores above 0.7 and taxonomic scores above 0.5 for LCMS features from Tabernaemontana coffeoides? Provide annotations.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?Annotation\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_isdb_annotation ?Annotation .\n ?Annotation ns1:has_consistency_score ?consistencyScore .\n ?Annotation ns1:has_taxo_score ?taxoScore .\n FILTER(?consistencyScore > 0.7 && ?taxoScore > 0.5)\n }\n ```\n- **File Path**: The results are stored in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/002a9f76c921447fbfe4490492614cba/tmpgkgukng8.csv`.\n\nPlease refer to the file for the detailed annotations."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: PREFIX ns2: - SELECT ?retentionTime ?adduct + SELECT ?Annotation WHERE { - ?rawMaterial ns1:has_wd_id . + ?rawMaterial ns1:has_wd_id . ?rawMaterial ns1:has_lab_process ?labExtract . ?labExtract ns1:has_LCMS ?analysis . - ?analysis ns1:has_lcms_feature_list ?featureList . - ?featureList ns1:has_lcms_feature ?feature . - ?feature ns1:has_retention_time ?retentionTime . - ?feature ns1:has_sirius_annotation ?siriusAnnotation . - ?siriusAnnotation ns1:has_cosmic_score ?cosmicScore . - ?siriusAnnotation ns1:has_adduct ?adduct . - FILTER(?cosmicScore > 0.4) - }",High -17,Retrieve the top 5 tissues used to produce lab extracts that contain compounds with inhibition activity greater than 70% against Leishmania donovani target at 10 碌g/mL.,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the top 5 tissues used to produce lab extracts that contain compounds with inhibition activity greater than 70% against Leishmania donovani target at 10 碌g/mL."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the top 5 tissues used to produce lab extracts that contain compounds with inhibition activity greater than 70% against Leishmania donovani target at 10 碌g/mL."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Leishmania donovani, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL367, ChEMBLTarget\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The top 5 tissues used to produce lab extracts containing compounds with inhibition activity greater than 70% against the Leishmania donovani target at 10 碌g/mL are:\n\n1. Green Tissue (24 extracts)\n2. Root Tissue (17 extracts)\n3. Multiple Tissues (9 extracts)\n4. Woody Tissue (7 extracts)\n\nThe SPARQL query used to retrieve this information is as follows:\n\n```sparql\nPREFIX ns1: \nPREFIX ns2: \n\nSELECT ?tissue (COUNT(?labExtract) AS ?count)\nWHERE {\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\n ?bioAssayResult ns2:inhibition_percentage ?inhibitionPercentage ;\n ns2:target_id .\n FILTER(?inhibitionPercentage > 70)\n ?rawMaterial ns1:has_lab_process ?labExtract ;\n ns2:has_tissue ?tissue .\n}\nGROUP BY ?tissue\nORDER BY DESC(?count)\nLIMIT 5\n```\n\nThe data is stored in the file located at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/9aa4b47f0e764929989be2184095b3b0/tmp6u2rkep7.csv`."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: -PREFIX ns2: - -SELECT ?tissue (COUNT(?labExtract) AS ?count) -WHERE { - ?labExtract ns2:has_bioassay_results ?bioAssayResult . - ?bioAssayResult ns2:inhibition_percentage ?inhibitionPercentage ; - ns2:target_id . - FILTER(?inhibitionPercentage > 70) - ?rawMaterial ns1:has_lab_process ?labExtract ; - ns2:has_tissue ?tissue . -} -GROUP BY ?tissue -ORDER BY DESC(?count) -LIMIT 5",High -18,"Retrieve the top-10 lab extracts with the most unique features detected (minimum feature area intensity of 500,000).","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the top-10 lab extracts with the most unique features detected (minimum feature area intensity of 500,000)."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the top-10 lab extracts with the most unique features detected (minimum feature area intensity of 500,000)."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The top-10 lab extracts with the most unique features detected, with a minimum feature area intensity of 500,000, are as follows:\n\n1. **Lab Extract:** [SC_AP_Wi_DCM](https://enpkg.commons-lab.org/kg/SC_AP_Wi_DCM) - **Unique Features:** 3437\n2. **Lab Extract:** [VGF140_F11](https://enpkg.commons-lab.org/kg/VGF140_F11) - **Unique Features:** 2035\n3. **Lab Extract:** [VGF153_D04](https://enpkg.commons-lab.org/kg/VGF153_D04) - **Unique Features:** 1952\n4. **Lab Extract:** [VGF154_G05](https://enpkg.commons-lab.org/kg/VGF154_G05) - **Unique Features:** 1813\n5. **Lab Extract:** [VGF153_F06](https://enpkg.commons-lab.org/kg/VGF153_F06) - **Unique Features:** 1786\n6. **Lab Extract:** [VGF154_C03](https://enpkg.commons-lab.org/kg/VGF154_C03) - **Unique Features:** 1640\n7. **Lab Extract:** [VGF153_G10](https://enpkg.commons-lab.org/kg/VGF153_G10) - **Unique Features:** 1617\n8. **Lab Extract:** [VGF154_A09](https://enpkg.commons-lab.org/kg/VGF154_A09) - **Unique Features:** 1615\n9. **Lab Extract:** [VGF154_B09](https://enpkg.commons-lab.org/kg/VGF154_B09) - **Unique Features:** 1601\n10. **Lab Extract:** [VGF142_D11](https://enpkg.commons-lab.org/kg/VGF142_D11) - **Unique Features:** 1543\n\nHere is the SPARQL query used to retrieve this information:\n\n```sparql\nPREFIX ns1: \nSELECT ?labExtract (COUNT(DISTINCT ?feature) AS ?uniqueFeatures)\nWHERE {\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_feature_area ?featureArea .\n FILTER(?featureArea >= 500000)\n}\nGROUP BY ?labExtract\nORDER BY DESC(?uniqueFeatures)\nLIMIT 10\n```\n\nThe data is stored in the file located at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/e85c6df097e3431884ddbfcdc34f620d/tmp1u9qq1p9.csv`."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: -SELECT ?labExtract (COUNT(DISTINCT ?feature) AS ?uniqueFeatures) -WHERE { - ?labExtract ns1:has_LCMS ?analysis . - ?analysis ns1:has_lcms_feature_list ?feature_list . - ?feature_list ns1:has_lcms_feature ?feature . - ?feature ns1:has_feature_area ?featureArea . - FILTER(?featureArea >= 500000) -} -GROUP BY ?labExtract -ORDER BY DESC(?uniqueFeatures) -LIMIT 10",Low -19,"For the LC-MS feature observed in the extracts of Tabernaemontana coffeoides, retrieve those that are annotated with a NPCPathways with CANOPUS.","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""For the LC-MS feature observed in the extracts of Tabernaemontana coffeoides, retrieve those that are annotated with a NPCPathways with CANOPUS."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: For the LC-MS feature observed in the extracts of Tabernaemontana coffeoides, retrieve those that are annotated with a NPCPathways with CANOPUS."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been executed to retrieve the LC-MS features observed in the extracts of Tabernaemontana coffeoides that are annotated with NPCPathways using CANOPUS. Here is the information:\n\n- **SPARQL Query**: \n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?feature\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_pathway ?npcPathway .\n }\n ```\n\n- **File Path**: The results are saved in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/0a0e72082aef4788a45ec734c4f9b49c/tmpqlgm7k3g.csv`.\n\nPlease check the file for the detailed results of the query."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The LC-MS features observed in the extracts of Tabernaemontana coffeoides that are annotated with NPCPathways using CANOPUS have been successfully retrieved. "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + ?analysis ns1:has_lcms_feature_list ?feature_list . + ?feature_list ns1:has_lcms_feature ?feature . + ?feature ns1:has_isdb_annotation ?Annotation . + ?Annotation ns1:has_consistency_score ?consistencyScore . + ?Annotation ns1:has_taxo_score ?taxoScore . + FILTER(?consistencyScore > 0.7 && ?taxoScore > 0.5) + }",Medium +15,"""What are the taxons, lab process and label (if one exists) for each sample? Sort by sample and then lab process""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the taxons, lab process and label (if one exists) for each sample? Sort by sample and then lab process"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the taxons, lab process and label (if one exists) for each sample? Sort by sample and then lab process"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed. Here are the details:\n\n- **Question**: What are the taxons, lab process and label (if one exists) for each sample? Sort by sample and then lab process.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n SELECT ?sample ?taxon ?labProcess ?label\n WHERE {\n ?sample ns1:has_wd_id ?taxon .\n ?sample ns1:has_lab_process ?labProcess .\n OPTIONAL { ?sample rdfs:label ?label . }\n }\n ORDER BY ?sample ?labProcess\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/5642f76045424334a801e191d6ed4563/tmp9g_oyjum.csv\n\nPlease review the file at the provided path for the detailed results."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The taxons, lab process, and labels (if any) for each sample have been sorted by sample and then by lab process. Here is a snippet of the sorted data:\n\n| Sample | Taxon | Lab Process | Label |\n|------------------------------------------|------------------------------------------|--------------------------------------------|-------|\n| http://www.wikidata.org/entity/Q11086771 | http://www.wikidata.org/entity/Q11086771 | https://enpkg.commons-lab.org/kg/KP001_Positive | NaN |\n| http://www.wikidata.org/entity/Q11272377 | http://www.wikidata.org/entity/Q11272377 | https://enpkg.commons-lab.org/kg/KP002_Positive | NaN |\n| http://www.wikidata.org/entity/Q11086771 | http://www.wikidata.org/entity/Q11086771 | https://enpkg.commons-lab.org/kg/KP003_Positive | NaN |\n| http://www.wikidata.org/entity/Q11272377 | http://www.wikidata.org/entity/Q11272377 | https://enpkg.commons-lab.org/kg/KP004_Positive | NaN |\n| http://www.wikidata.org/entity/Q5978345 | http://www.wikidata.org/entity/Q5978345 | https://enpkg.commons-lab.org/kg/KP005_Positive | NaN |\n\nThe full path of the file generated is:\n- /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/5642f76045424334a801e191d6ed4563/tmp9g_oyjum.csv\n\nYou can download the file for the complete details."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + SELECT ?sample ?taxon ?labProcess ?label + WHERE { + ?sample ns1:has_wd_id ?taxon . + ?sample ns1:has_lab_process ?labProcess . + OPTIONAL { ?sample rdfs:label ?label . } + } + ORDER BY ?sample ?labProcess",Low +16,"""What are the mass spectrometry features detected only in the plant Rumex nepalensis?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the mass spectrometry features detected only in the plant Rumex nepalensis?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the mass spectrometry features detected only in the plant Rumex nepalensis?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Rumex nepalensis, http://www.wikidata.org/entity/Q11041204, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: What are the mass spectrometry features detected only in the plant Rumex nepalensis?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n PREFIX wd: \n\n SELECT ?feature\n WHERE {\n ?rawMaterial ns1:has_wd_id wd:Q11041204 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n }\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/966c850620f7440182889fe9b80a1edb/tmp4z32di1l.csv\n\nI will now analyze the SPARQL output to provide the final answer.\nThe mass spectrometry features detected for the plant Rumex nepalensis can be found in the file located at the following path:\n\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/966c850620f7440182889fe9b80a1edb/tmp4z32di1l.csv\n\nHere is the dictionary containing the question, generated SPARQL query, and file path:\n\n```json\n{\n \""question\"": \""What are the mass spectrometry features detected for the plant Rumex nepalensis?\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\nPREFIX ns2: \\nPREFIX wd: \\n\\nSELECT ?feature\\nWHERE {\\n ?rawMaterial ns1:has_wd_id wd:Q11041204 .\\n ?rawMaterial ns1:has_lab_process ?labExtract .\\n ?labExtract ns1:has_LCMS ?analysis .\\n ?analysis ns1:has_lcms_feature_list ?featureList .\\n ?featureList ns1:has_lcms_feature ?feature .\\n}\"",\n \""file_path\"": \""/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/966c850620f7440182889fe9b80a1edb/tmp4z32di1l.csv\""\n}\n```\n\nPlease review the file for the detailed mass spectrometry features."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The mass spectrometry features detected for the plant Rumex nepalensis are as follows:\n\n1. [LCMS Feature 1](https://enpkg.commons-lab.org/kg/lcms_feature_1)\n2. [LCMS Feature 2](https://enpkg.commons-lab.org/kg/lcms_feature_2)\n3. [LCMS Feature 3](https://enpkg.commons-lab.org/kg/lcms_feature_3)\n4. [LCMS Feature 4](https://enpkg.commons-lab.org/kg/lcms_feature_4)\n5. [LCMS Feature 5](https://enpkg.commons-lab.org/kg/lcms_feature_5)\n\nNo visualization was requested for this analysis. The data can be downloaded from the provided file path."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: PREFIX ns2: + PREFIX wd: SELECT ?feature WHERE { - ?rawMaterial ns1:has_wd_id . + ?rawMaterial ns1:has_wd_id wd:Q11041204 . ?rawMaterial ns1:has_lab_process ?labExtract . ?labExtract ns1:has_LCMS ?analysis . - ?analysis ns1:has_lcms_feature_list ?feature_list . - ?feature_list ns1:has_lcms_feature ?feature . - ?feature ns1:has_canopus_annotation ?annotation . - ?annotation ns1:has_canopus_npc_pathway ?npcPathway . - }",Medium -20,Retrieve the Wikidata IDs of chemical entities from Desmodium heterophyllum that are annotated as rotenoids by CANOPUS and have ChEMBL assay results showing activity against Trypanosoma cruzi. Provide also the USIs to the corresponding MS/MS spectra,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the Wikidata IDs of chemical entities from Desmodium heterophyllum that are annotated as rotenoids by CANOPUS and have ChEMBL assay results showing activity against Trypanosoma cruzi. Provide also the USIs to the corresponding MS/MS spectra"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the Wikidata IDs of chemical entities from Desmodium heterophyllum that are annotated as rotenoids by CANOPUS and have ChEMBL assay results showing activity against Trypanosoma cruzi. Provide also the USIs to the corresponding MS/MS spectra."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Desmodium heterophyllum, http://www.wikidata.org/entity/Q10770714, Wikidata;\nrotenoids, https://enpkg.commons-lab.org/kg/npc_Rotenoids, NPCClass;\nTrypanosoma cruzi, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL368, ChEMBLTarget\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been executed successfully. Here is the information retrieved:\n\n### Question\nRetrieve the Wikidata IDs of chemical entities from Desmodium heterophyllum that are annotated as rotenoids by CANOPUS and have ChEMBL assay results showing activity against Trypanosoma cruzi. Provide also the USIs to the corresponding MS/MS spectra.\n\n### SPARQL Query\n```sparql\nPREFIX ns1: \nPREFIX ns2: \n\nSELECT ?wd_id ?usi\nWHERE {\n ?rawMaterial ns1:has_wd_id ;\n ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_canopus_annotation ?annotation ;\n ns1:has_usi ?usi .\n ?annotation ns1:has_canopus_npc_class .\n ?labExtract ns2:has_bioassay_results ?assayResult .\n ?assayResult ns2:target_id .\n ?chemicalEntity ns1:has_wd_id ?wd_id .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?siriusAnnotation ns1:has_InChIkey2D ?InChIkey2D .\n ?InChIkey2D ns1:is_InChIkey2D_of ?chemicalEntity .\n }\n```\n\n### File Path\nThe results have been saved to a temporary file, which can be accessed at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/be66080199fb4930ae3c599b8c11507d/tmpn40oy4b_.csv`\n\nPlease let me know if you need further assistance or analysis on this data!"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The analysis of the file has been completed. The data contains Wikidata IDs of chemical entities from Desmodium heterophyllum that are annotated as rotenoids by CANOPUS and have ChEMBL assay results showing activity against Trypanosoma cruzi. It also includes the corresponding USIs to the MS/MS spectra.\n\nHere are the first few entries:\n- **Wikidata ID**: Q104399962\n- **USI**: mzspec:MSV000087728:VGF155_D05_features_ms2_ne...\n\nNo visualization was requested or generated for this analysis. The data can be downloaded for further examination.\n\nIf you need any additional insights or visualizations, please let me know!"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: -PREFIX ns2: - -SELECT ?wd_id ?usi -WHERE { - ?rawMaterial ns1:has_wd_id ; - ns1:has_lab_process ?labExtract . - ?labExtract ns1:has_LCMS ?analysis . - ?analysis ns1:has_lcms_feature_list ?feature_list . - ?feature_list ns1:has_lcms_feature ?feature . - ?feature ns1:has_canopus_annotation ?annotation ; - ns1:has_usi ?usi . - ?annotation ns1:has_canopus_npc_class . - ?labExtract ns2:has_bioassay_results ?assayResult . - ?assayResult ns2:target_id . - ?chemicalEntity ns1:has_wd_id ?wd_id . - ?feature ns1:has_sirius_annotation ?siriusAnnotation . - ?siriusAnnotation ns1:has_InChIkey2D ?InChIkey2D . - ?InChIkey2D ns1:is_InChIkey2D_of ?chemicalEntity . - }",High -21,List the lab extracts that have inhibition percentages above 50% bioassay results at 2碌g/mL against Trypanosoma brucei rhodesiense .,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""List the lab extracts that have inhibition percentages above 50% bioassay results at 2碌g/mL against Trypanosoma brucei rhodesiense ."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: List the lab extracts that have inhibition percentages above 50% bioassay results at 2碌g/mL against Trypanosoma brucei rhodesiense."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Trypanosoma brucei rhodesiense, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL612348, ChEMBLTarget\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: List the lab extracts that have inhibition percentages above 50% bioassay results at 2碌g/mL against Trypanosoma brucei rhodesiense.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?labExtract\n WHERE {\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\n ?bioAssayResult ns2:target_id .\n {\n ?bioAssayResult a ns2:Tbrucei2ugml .\n }\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/fc41480ada32471b948feb8e1d6babcf/tmp4p9xu66f.csv`\n\nI will now analyze the SPARQL output to provide the final answer.\nThe lab extracts that have bioassay results at 2碌g/mL against Trypanosoma brucei rhodesiense are listed in the file located at the following path:\n\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/fc41480ada32471b948feb8e1d6babcf/tmp4p9xu66f.csv`\n\nHere is the dictionary containing the question, generated SPARQL query, and file path:\n\n```json\n{\n \""question\"": \""List the lab extracts that have inhibition percentages above 50% bioassay results at 2碌g/mL against Trypanosoma brucei rhodesiense\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\nPREFIX ns2: \\n\\nSELECT ?labExtract\\nWHERE {\\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\\n ?bioAssayResult ns2:target_id .\\n {\\n ?bioAssayResult a ns2:Tbrucei2ugml .\\n } ?bioAssayResult ns2:inhibition_percentage ?inhibition .\n FILTER(?inhibition > 50)\\n}\"",\n \""file_path\"": \""/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/fc41480ada32471b948feb8e1d6babcf/tmp4p9xu66f.csv\""\n}\n```\n\nPlease review the file for the detailed list of lab extracts."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: - PREFIX ns2: + ?analysis ns1:has_lcms_feature_list ?featureList . + ?featureList ns1:has_lcms_feature ?feature . + }",Low +17," ""How many distinct Wikidata identifiers (WD IDs) have the same metabolite annotation (considering only the plannar structure)?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""How many distinct Wikidata identifiers (WD IDs) have the same metabolite annotation (considering only the plannar structure)?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: How many distinct Wikidata identifiers (WD IDs) have the same metabolite annotation (considering only the planar structure)?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid. It is related to the ENPKG nodes/entities (Wikidata IDs, InChIkey2D) and asks for a count of distinct Wikidata IDs that have the same metabolite annotation, considering only the planar structure. This information can be retrieved from the ENPKG."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The query has been executed successfully. There are 138,146 distinct Wikidata identifiers (WD IDs) that share the same metabolite annotation based on their planar structure (InChIkey2D).\n\nHere is the information related to the query:\n\n- **Question**: How many distinct Wikidata identifiers (WD IDs) have the same metabolite annotation (considering only the planar structure)?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT (COUNT(DISTINCT ?wd_id) AS ?distinct_wd_ids)\n WHERE {\n ?annotation ns1:has_InChIkey2D ?InChIkey2D .\n ?InChIkey2D ns1:is_InChIkey2D_of ?InChIkey .\n ?InChIkey ns1:has_wd_id ?wd_id .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/49c4b98b94034c86ace8c37139fa6a29/tmpnnlg58pc.csv`\n\nThis result indicates the number of unique Wikidata IDs that are associated with the same planar structure annotation in the ENPKG database."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: - SELECT ?labExtract + SELECT (COUNT(DISTINCT ?wd_id) AS ?distinct_wd_ids) WHERE { - ?labExtract ns2:has_bioassay_results ?bioAssayResult . - ?bioAssayResult ns2:target_id . - { - ?bioAssayResult a ns2:Tbrucei2ugml . - } + ?annotation ns1:has_InChIkey2D ?InChIkey2D . + ?InChIkey2D ns1:is_InChIkey2D_of ?InChIkey . + ?InChIkey ns1:has_wd_id ?wd_id . }",Medium -22,"What are the LCMS features that have feature areas greater than 1,000,000 in positive ionization mode? Sort by decreasing feature area value.","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the LCMS features that have feature areas greater than 1,000,000 in positive ionization mode? Sort by decreasing feature area value."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the LCMS features that have feature areas greater than 1,000,000 in positive ionization mode? Sort by decreasing feature area value."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed. Here are the details:\n\n- **Question**: What are the LCMS features that have feature areas greater than 1,000,000 in positive ionization mode? Sort by decreasing feature area value.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT ?feature\n WHERE {\n ?analysis a ns1:LCMSAnalysisPos .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_feature_area ?area .\n FILTER(?area > 1000000)\n } ORDER BY DESC(?area)\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/61f189ee29f1402eae7ec344e7ac1ef7/tmpv676p86i.csv\n\nPlease review the file for the detailed results."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The LCMS features with feature areas greater than 1,000,000 in positive ionization mode have been filtered and saved. You can download the detailed results from the following file:\n\n- **Filtered Features File**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/61f189ee29f1402eae7ec344e7ac1ef7/filtered_features.csv\n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +18," ""List the LC-MS features that have a chemical class annotation by CANOPUS and have a retention time between 5-7 minutes."" ""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""List the LC-MS features that have a chemical class annotation by CANOPUS and have a retention time between 5-7 minutes. "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: List the LC-MS features that have a chemical class annotation by CANOPUS and have a retention time between 5-7 minutes."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed. Here are the details:\n\n- **Question:** List the LC-MS features that have a chemical class annotation by CANOPUS and have a retention time between 5-7 minutes.\n- **Generated SPARQL Query:**\n ```sparql\n PREFIX ns1: \n\n SELECT ?feature\n WHERE {\n ?feature ns1:has_canopus_annotation ?annotation .\n ?feature ns1:has_retention_time ?retention_time .\n ?annotation ns1:has_canopus_npc_class ?chemicalClass .\n FILTER(?retentionTime >= 5 && ?retentionTime <= 7) }\n ```\n- **File Path:** /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/aadd02d3086647dd9408cbc9098e5862/tmpx_kt8dmi.csv\n\nYou can now review the results in the provided file path."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: SELECT ?feature WHERE { - ?analysis a ns1:LCMSAnalysisPos . - ?analysis ns1:has_lcms_feature_list ?feature_list . - ?feature_list ns1:has_lcms_feature ?feature . - ?feature ns1:has_feature_area ?area . - FILTER(?area > 1000000) - } ORDER BY DESC(?area)",Low -23,"Retrieve the lab extracts that have more than 2,000 features detected. ","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the lab extracts that have more than 2,000 features detected. "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the lab extracts that have more than 2,000 features detected."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid. It is related to the ENPKG nodes/entities (Lab Extract, LCMS Feature). The question asks for lab extracts that have a certain number of features detected, which is information that can be retrieved from the ENPKG."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The query to retrieve lab extracts with more than 2,000 features detected has been executed. Here is the information:\n\n- **Question**: Retrieve the lab extracts that have more than 2,000 features detected.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT ?labExtract\n WHERE {\n ?labExtract ns1:has_LCMS ?analysis.\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n {\n SELECT ?labExtract (COUNT(?feature) AS ?featureCount)\n WHERE {\n ?labExtract ns1:has_LCMS ?analysis. \n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n }\n GROUP BY ?labExtract\n HAVING (?featureCount > 2000)\n }\n }\n ```\n- **File Path**: The results are saved in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/1db966f1a9d44c269baae69dc24b9dbe/tmpgxqshpaa.csv`.\n\nPlease refer to the file for the detailed results."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The file containing the results of the SPARQL query has been processed. nIf you have any other specific criteria or information you would like to extract from the data, please let me know. Otherwise, you can download the data for further analysis.\n\nThe full path of the file generated is: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/1db966f1a9d44c269baae69dc24b9dbe/tmpgxqshpaa.csv`.\n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + ?feature ns1:has_canopus_annotation ?annotation . + ?feature ns1:has_retention_time ?retention_time . + ?annotation ns1:has_canopus_npc_class ?chemicalClass . + FILTER(?retentionTime >= 5 && ?retentionTime <= 7) }",Medium +19," ""Retrieve the lab extracts that have more than 2,000 features detected.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the lab extracts that have more than 2,000 features detected. "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the lab extracts that have more than 2,000 features detected."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid. It is related to the ENPKG nodes/entities (Lab Extract, LCMS Feature). The question asks for lab extracts that have a certain number of features detected, which is information that can be retrieved from the ENPKG."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The query to retrieve lab extracts with more than 2,000 features detected has been executed. Here is the information:\n\n- **Question**: Retrieve the lab extracts that have more than 2,000 features detected.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT ?labExtract\n WHERE {\n ?labExtract ns1:has_LCMS ?analysis.\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n {\n SELECT ?labExtract (COUNT(?feature) AS ?featureCount)\n WHERE {\n ?labExtract ns1:has_LCMS ?analysis. \n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n }\n GROUP BY ?labExtract\n HAVING (?featureCount > 2000)\n }\n }\n ```\n- **File Path**: The results are saved in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/1db966f1a9d44c269baae69dc24b9dbe/tmpgxqshpaa.csv`.\n\nPlease refer to the file for the detailed results."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The file containing the results of the SPARQL query has been processed. nIf you have any other specific criteria or information you would like to extract from the data, please let me know. Otherwise, you can download the data for further analysis.\n\nThe full path of the file generated is: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/1db966f1a9d44c269baae69dc24b9dbe/tmpgxqshpaa.csv`.\n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: SELECT ?labExtract WHERE { @@ -330,49 +273,49 @@ WHERE { HAVING (?featureCount > 2000) } }",Low -24,Which plant has extracts containing compounds that demonstrated inhibition rates above 50% against Trypanosoma cruzi and are above 800 Da in mass? Provide the lab extracts,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which plant has extracts containing compounds that demonstrated inhibition rates above 50% against Trypanosoma cruzi and are above 800 Da in mass? Provide the lab extracts"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which plant has extracts containing compounds that demonstrated inhibition rates above 50% against Trypanosoma cruzi and are above 800 Da in mass? Provide the lab extracts."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid. It is related to the ENPKG nodes/entities (Raw Material, Lab Extract, BioAssayResult, Chemical Entity). The question asks for information about plant extracts containing compounds that have demonstrated a certain level of inhibition against a specific target and have a certain mass, which can be retrieved from the ENPKG."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Trypanosoma cruzi, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL368, ChEMBLTarget\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The query has been processed successfully. Here is the information:\n\n- **Question**: Which plant has extracts containing compounds that demonstrated inhibition rates above 50% against Trypanosoma cruzi and are above 800 Da in mass? Provide the lab extracts.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?plant ?labExtract\n WHERE {\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_parent_mass ?mass .\n FILTER(?mass > 800)\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\n ?bioAssayResult ns2:inhibition_percentage ?inhibition .\n FILTER(?inhibition > 50)\n ?bioAssayResult ns2:target_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?rawMaterial ns1:submitted_taxon ?plant .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/cbb95402a0b148239141ff5dfd609728/tmpixjks0gp.csv`\n\nPlease refer to the file for detailed results on the plants and their corresponding lab extracts."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The analysis of the file indicates that there are no plant extracts meeting the specified criteria of having compounds with inhibition rates above 50% against Trypanosoma cruzi and a mass above 800 Da. If you have any other questions or need further analysis, please let me know. \n\nNo visualization is available, but the data can be downloaded for further inspection. \n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: - PREFIX ns2: - - SELECT ?plant ?labExtract - WHERE { - ?labExtract ns1:has_LCMS ?analysis . - ?analysis ns1:has_lcms_feature_list ?feature_list . - ?feature_list ns1:has_lcms_feature ?feature . - ?feature ns1:has_parent_mass ?mass . - FILTER(?mass > 800) - ?labExtract ns2:has_bioassay_results ?bioAssayResult . - ?bioAssayResult ns2:inhibition_percentage ?inhibition . - FILTER(?inhibition > 50) - ?bioAssayResult ns2:target_id . - ?rawMaterial ns1:has_lab_process ?labExtract . - ?rawMaterial ns1:submitted_taxon ?plant . - }",High -25,How unique InChIKey2D are among the structural annotations?,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""How unique InChIKey2D are among the structural annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: How unique InChIKey2D are among the structural annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid. It is related to the ENPKG nodes/entities (InChIKey2D, Annotation). The question is asking for unique InChIKey2D among the structural annotations, which is a valid query for the knowledge graph."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The number of unique InChIKey2D among the structural annotations is 106,647.\n\nHere is the information related to the SPARQL query:\n\n- **Question**: How unique InChIKey2D are among the structural annotations?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT (COUNT(DISTINCT ?InChIkey2D) AS ?uniqueInChIkey2DCount)\n WHERE {\n ?annotation ns1:has_InChIkey2D ?InChIkey2D .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/73fb3b1ea55e4c8d93e92080edfdd449/tmphyuvm3dr.csv`\n\nThis result indicates the count of unique InChIKey2D identifiers found among the structural annotations in the knowledge graph."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: - - SELECT (COUNT(DISTINCT ?InChIkey2D) AS ?uniqueInChIkey2DCount) - WHERE { - ?annotation ns1:has_InChIkey2D ?InChIkey2D . - }",Low -26,Can you provide the SMILES notation and the Wikidata IDs for the compound with the highest precursor ion mass in the MS2 spectra from extracts of Lovoa trichilioides by looking at SIRIUS annotations?,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Can you provide the SMILES notation and the Wikidata IDs for the compound with the highest precursor ion mass in the MS2 spectra from extracts of Lovoa trichilioides by looking at SIRIUS annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Can you provide the SMILES notation and the Wikidata IDs for the compound with the highest precursor ion mass in the MS2 spectra from extracts of Lovoa trichilioides by looking at SIRIUS annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Lovoa trichilioides, http://www.wikidata.org/entity/Q8008828, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The compound with the highest precursor ion mass in the MS2 spectra from extracts of Lovoa trichilioides, as annotated by SIRIUS, has the following details:\n\n- **SMILES Notation**: `CC(C)(C)C1=C(C=CC(=C1)C(C)(CC(=O)OCCOC(=O)CC(C)(C2=CC(=C(C=C2)O)C(C)(C)C)C3=CC(=C(C=C3)O)C(C)(C)C)C4=CC(=C(C=C4)O)C(C)(C)C)O`\n- **Wikidata ID**: [Q27254797](http://www.wikidata.org/entity/Q27254797)\n\nHere is the SPARQL query used to obtain this information:\n\n```sparql\nPREFIX ns1: \nPREFIX ns2: \n\nSELECT ?smiles ?wd_id\nWHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?annotation .\n ?annotation ns1:has_InChIkey2D ?InChIkey2D .\n ?InChIkey2D ns1:is_InChIkey2D_of ?chemicalEntity .\n ?chemicalEntity ns1:has_smiles ?smiles ;\n ns1:has_wd_id ?wd_id .\n ?feature ns1:has_parent_mass ?mass .\n}\nORDER BY DESC(?mass)\nLIMIT 1\n```\n\nThe data was retrieved from the file located at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/c05df7beae71402fa925205f656dba83/tmpj8dw4tu8.csv`."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +20,"""What is the distribution of chemical superclasses in the LCMS features annotated by CANOPUS identified from Desmodium heterophyllum extracts?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What is the distribution of chemical superclasses in the LCMS features annotated by CANOPUS identified from Desmodium heterophyllum extracts?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What is the distribution of chemical superclasses in the LCMS features annotated by CANOPUS identified from Desmodium heterophyllum extracts?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Desmodium heterophyllum, http://www.wikidata.org/entity/Q10770714, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The distribution of chemical superclasses in the LCMS features annotated by CANOPUS identified from Desmodium heterophyllum extracts is as follows:\n\n- Flavonoids: 600\n- Octadecanoids: 105\n- Triterpenoids: 50\n- Isoflavonoids: 147\n- Sesquiterpenoids: 27\n- Lignans: 19\n- Small peptides: 19\n- Monoterpenoids: 16\n- Coumarins: 180\n- Tryptophan alkaloids: 6\n- Stilbenoids: 20\n- Nucleosides: 8\n- Glycerophospholipids: 2\n- Polycyclic aromatic polyketides: 11\n- Aromatic polyketides: 13\n- Xanthones: 9\n- Phenylpropanoids (C6-C3): 11\n- Naphthalenes: 5\n- Anthranilic acid alkaloids: 3\n- Diarylheptanoids: 2\n- Diterpenoids: 18\n- Steroids: 51\n- Ornithine alkaloids: 2\n- Aminosugars and aminoglycosides: 5\n- Meroterpenoids: 27\n- Diazotetronic acids and derivatives: 3\n- Glycerolipids: 62\n- Fatty Acids and Conjugates: 44\n- Sphingolipids: 3\n- Oligopeptides: 17\n- Phenolic acids (C6-C1): 15\n- Pseudoalkaloids: 3\n- 尾-lactams: 1\n- Fatty amides: 17\n- Tyrosine alkaloids: 1\n- Phloroglucinols: 2\n- Unknown: 198\n- Chromanes: 7\n- Linear polyketides: 9\n- Nicotinic acid alkaloids: 2\n- Spingolipids: 24\n- Fatty esters: 3\n- Pseudoalkaloids (transamidation): 1\n- Amino acid glycosides: 6\n- Histidine alkaloids: 1\n- Eicosanoids: 15\n- Fatty acyl glycosides: 2\n- Fatty acyls: 6\n- Polyethers: 2\n- Macrolides: 1\n- Polyols: 2\n- Cyclic polyketides: 3\n- Apocarotenoids: 3\n\nHere is the dictionary containing the question, generated SPARQL query, and file path to the query result:\n\n```json\n{\n \""question\"": \""What is the distribution of chemical superclasses in the LCMS features annotated by CANOPUS identified from Desmodium heterophyllum extracts?\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\nPREFIX ns2: \\nPREFIX wd: \\n\\nSELECT ?superclass (COUNT(?superclass) AS ?count)\\nWHERE {\\n ?rawMaterial ns1:has_wd_id wd:Q10770714 .\\n ?rawMaterial ns1:has_lab_process ?labExtract .\\n ?labExtract ns1:has_LCMS ?analysis .\\n ?analysis ns1:has_lcms_feature_list ?feature_list .\\n ?feature_list ns1:has_lcms_feature ?feature .\\n ?feature ns1:has_canopus_annotation ?annotation .\\n ?annotation ns1:has_canopus_npc_superclass ?superclass .\\n}\\nGROUP BY ?superclass\"",\n \""file_path\"": \""/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/228ff50f729b45f29855d7d12d4c18c9/tmp23kaqct7.csv\""\n}\n```"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: PREFIX ns2: +PREFIX wd: -SELECT ?smiles ?wd_id +SELECT ?superclass (COUNT(?superclass) AS ?count) WHERE { - ?rawMaterial ns1:has_wd_id . + ?rawMaterial ns1:has_wd_id wd:Q10770714 . ?rawMaterial ns1:has_lab_process ?labExtract . ?labExtract ns1:has_LCMS ?analysis . ?analysis ns1:has_lcms_feature_list ?feature_list . ?feature_list ns1:has_lcms_feature ?feature . - ?feature ns1:has_sirius_annotation ?annotation . - ?annotation ns1:has_InChIkey2D ?InChIkey2D . - ?InChIkey2D ns1:is_InChIkey2D_of ?chemicalEntity . - ?chemicalEntity ns1:has_smiles ?smiles ; - ns1:has_wd_id ?wd_id . - ?feature ns1:has_parent_mass ?mass . + ?feature ns1:has_canopus_annotation ?annotation . + ?annotation ns1:has_canopus_npc_superclass ?superclass . } -ORDER BY DESC(?mass) -LIMIT 1",High -27,What are the LCMS features with the highest area annotated as alkaloids by CANOPUS in Tabernaemontana coffeoides extracts?,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the LCMS features with the highest area annotated as alkaloids by CANOPUS in Tabernaemontana coffeoides extracts?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the LCMS features with the highest area annotated as alkaloids by CANOPUS in Tabernaemontana coffeoides extracts?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""alkaloids, NPCClass: https://enpkg.commons-lab.org/kg/npc_Steroidal_alkaloids, NPCClass;\nTabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The LCMS feature with the highest area annotated as alkaloids by CANOPUS in Tabernaemontana coffeoides extracts is identified by the feature IRI [https://enpkg.commons-lab.org/kg/lcms_feature_mzspec:MSV000087728:VGF152_A02_features_ms2_pos.mgf:scan:245](https://enpkg.commons-lab.org/kg/lcms_feature_mzspec:MSV000087728:VGF152_A02_features_ms2_pos.mgf:scan:245) with an area of 18,114,637.02276565.\n\nHere is the dictionary containing the question, generated SPARQL query, and file path:\n\n- **Question**: What are the LCMS features with the highest area annotated as alkaloids by CANOPUS in Tabernaemontana coffeoides extracts?\n- **Generated SPARQL Query**: \n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?feature ?featureArea\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_feature_area ?featureArea .\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_class .\n }\n ORDER BY DESC(?featureArea)\n LIMIT 1\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/84a24128512b453598395d8ed7f429dc/tmp8qjwsoaz.csv`"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +GROUP BY ?superclass",Medium +21," ""Retrieve the top-10 lab extracts with the most unique features detected (minimum feature area intensity of 500,000.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the top-10 lab extracts with the most unique features detected (minimum feature area intensity of 500,000)."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the top-10 lab extracts with the most unique features detected (minimum feature area intensity of 500,000)."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The top-10 lab extracts with the most unique features detected, with a minimum feature area intensity of 500,000, are as follows:\n\n1. **Lab Extract:** [SC_AP_Wi_DCM](https://enpkg.commons-lab.org/kg/SC_AP_Wi_DCM) - **Unique Features:** 3437\n2. **Lab Extract:** [VGF140_F11](https://enpkg.commons-lab.org/kg/VGF140_F11) - **Unique Features:** 2035\n3. **Lab Extract:** [VGF153_D04](https://enpkg.commons-lab.org/kg/VGF153_D04) - **Unique Features:** 1952\n4. **Lab Extract:** [VGF154_G05](https://enpkg.commons-lab.org/kg/VGF154_G05) - **Unique Features:** 1813\n5. **Lab Extract:** [VGF153_F06](https://enpkg.commons-lab.org/kg/VGF153_F06) - **Unique Features:** 1786\n6. **Lab Extract:** [VGF154_C03](https://enpkg.commons-lab.org/kg/VGF154_C03) - **Unique Features:** 1640\n7. **Lab Extract:** [VGF153_G10](https://enpkg.commons-lab.org/kg/VGF153_G10) - **Unique Features:** 1617\n8. **Lab Extract:** [VGF154_A09](https://enpkg.commons-lab.org/kg/VGF154_A09) - **Unique Features:** 1615\n9. **Lab Extract:** [VGF154_B09](https://enpkg.commons-lab.org/kg/VGF154_B09) - **Unique Features:** 1601\n10. **Lab Extract:** [VGF142_D11](https://enpkg.commons-lab.org/kg/VGF142_D11) - **Unique Features:** 1543\n\nHere is the SPARQL query used to retrieve this information:\n\n```sparql\nPREFIX ns1: \nSELECT ?labExtract (COUNT(DISTINCT ?feature) AS ?uniqueFeatures)\nWHERE {\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_feature_area ?featureArea .\n FILTER(?featureArea >= 500000)\n}\nGROUP BY ?labExtract\nORDER BY DESC(?uniqueFeatures)\nLIMIT 10\n```\n\nThe data is stored in the file located at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/e85c6df097e3431884ddbfcdc34f620d/tmp1u9qq1p9.csv`."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +SELECT ?labExtract (COUNT(DISTINCT ?feature) AS ?uniqueFeatures) +WHERE { + ?labExtract ns1:has_LCMS ?analysis . + ?analysis ns1:has_lcms_feature_list ?feature_list . + ?feature_list ns1:has_lcms_feature ?feature . + ?feature ns1:has_feature_area ?featureArea . + FILTER(?featureArea >= 500000) +} +GROUP BY ?labExtract +ORDER BY DESC(?uniqueFeatures) +LIMIT 10",Low +22,"""List the top 3 lab extracts with the highest number of unique compounds annotated by CANOPUS with a probability greater than 0.9.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""List the top 3 lab extracts with the highest number of unique compounds annotated by CANOPUS with a probability greater than 0.9."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: List the top 3 lab extracts with the highest number of unique compounds annotated by CANOPUS with a probability greater than 0.9."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The top 3 lab extracts with the highest number of unique compounds annotated by CANOPUS with a probability greater than 0.9 are:\n\n1. Lab Extract: [SC_AP_Wi_DCM](https://enpkg.commons-lab.org/kg/SC_AP_Wi_DCM) with 69 unique compounds.\n2. Lab Extract: [VGF153_F06](https://enpkg.commons-lab.org/kg/VGF153_F06) with 58 unique compounds.\n3. Lab Extract: [VGF156_B04](https://enpkg.commons-lab.org/kg/VGF156_B04) with 49 unique compounds.\n\nHere is the SPARQL query used to obtain this information:\n\n```sparql\nPREFIX ns1: \n\nSELECT ?labExtract (COUNT(DISTINCT ?compound) AS ?uniqueCompounds)\nWHERE {\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_class ?compound .\n ?annotation ns1:has_canopus_npc_class_prob ?probability .\n FILTER(?probability > 0.9)\n}\nGROUP BY ?labExtract\nORDER BY DESC(?uniqueCompounds)\nLIMIT 3\n```\n\nThe results are saved in the file located at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/5cf09a28bf1049dbb23b09915c0fc8e1/tmp35c6yy5y.csv`."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + +SELECT ?labExtract (COUNT(DISTINCT ?compound) AS ?uniqueCompounds) +WHERE { + ?labExtract ns1:has_LCMS ?analysis . + ?analysis ns1:has_lcms_feature_list ?featureList . + ?featureList ns1:has_lcms_feature ?feature . + ?feature ns1:has_canopus_annotation ?annotation . + ?annotation ns1:has_canopus_npc_class ?compound . + ?annotation ns1:has_canopus_npc_class_prob ?probability . + FILTER(?probability > 0.9) +} +GROUP BY ?labExtract +ORDER BY DESC(?uniqueCompounds) +LIMIT 3",High +23," ""What are the LCMS features with the highest area annotated as alkaloids by CANOPUS in Tabernaemontana coffeoides extracts?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the LCMS features with the highest area annotated as alkaloids by CANOPUS in Tabernaemontana coffeoides extracts?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the LCMS features with the highest area annotated as alkaloids by CANOPUS in Tabernaemontana coffeoides extracts?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""alkaloids, NPCClass: https://enpkg.commons-lab.org/kg/npc_Steroidal_alkaloids, NPCClass;\nTabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The LCMS feature with the highest area annotated as alkaloids by CANOPUS in Tabernaemontana coffeoides extracts is identified by the feature IRI [https://enpkg.commons-lab.org/kg/lcms_feature_mzspec:MSV000087728:VGF152_A02_features_ms2_pos.mgf:scan:245](https://enpkg.commons-lab.org/kg/lcms_feature_mzspec:MSV000087728:VGF152_A02_features_ms2_pos.mgf:scan:245) with an area of 18,114,637.02276565.\n\nHere is the dictionary containing the question, generated SPARQL query, and file path:\n\n- **Question**: What are the LCMS features with the highest area annotated as alkaloids by CANOPUS in Tabernaemontana coffeoides extracts?\n- **Generated SPARQL Query**: \n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?feature ?featureArea\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_feature_area ?featureArea .\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_class .\n }\n ORDER BY DESC(?featureArea)\n LIMIT 1\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/84a24128512b453598395d8ed7f429dc/tmp8qjwsoaz.csv`"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: PREFIX ns2: SELECT ?feature ?featureArea @@ -388,101 +331,47 @@ LIMIT 1",High } ORDER BY DESC(?featureArea) LIMIT 1",Low -28,List the LC-MS features that have a chemical class annotation by CANOPUS and have a retention time between 5-7 minutes.,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""List the LC-MS features that have a chemical class annotation by CANOPUS and have a retention time between 5-7 minutes. "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: List the LC-MS features that have a chemical class annotation by CANOPUS and have a retention time between 5-7 minutes."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed. Here are the details:\n\n- **Question:** List the LC-MS features that have a chemical class annotation by CANOPUS and have a retention time between 5-7 minutes.\n- **Generated SPARQL Query:**\n ```sparql\n PREFIX ns1: \n\n SELECT ?feature\n WHERE {\n ?feature ns1:has_canopus_annotation ?annotation .\n ?feature ns1:has_retention_time ?retention_time .\n ?annotation ns1:has_canopus_npc_class ?chemicalClass .\n FILTER(?retentionTime >= 5 && ?retentionTime <= 7) }\n ```\n- **File Path:** /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/aadd02d3086647dd9408cbc9098e5862/tmpx_kt8dmi.csv\n\nYou can now review the results in the provided file path."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: - - SELECT ?feature - WHERE { - ?feature ns1:has_canopus_annotation ?annotation . - ?feature ns1:has_retention_time ?retention_time . - ?annotation ns1:has_canopus_npc_class ?chemicalClass . - FILTER(?retentionTime >= 5 && ?retentionTime <= 7) }",Medium -29,What are the most frequent SIRIUS chemical structure annotations (planar structure) observed from the extracts belonging to Hibiscus syriacus?,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the most frequent SIRIUS chemical structure annotations (planar structure) observed from the extracts belonging to Hibiscus syriacus?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the most frequent SIRIUS chemical structure annotations (planar structure) observed from the extracts belonging to Hibiscus syriacus?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Hibiscus syriacus, http://www.wikidata.org/entity/Q157756, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The most frequent SIRIUS chemical structure annotations (planar structure) observed from the extracts belonging to Hibiscus syriacus are as follows:\n\n1. InChIkey2D: [KEMQGTRYUADPNZ](https://enpkg.commons-lab.org/kg/KEMQGTRYUADPNZ) - Frequency: 2\n2. InChIkey2D: [DTOSIQBPPRVQHS](https://enpkg.commons-lab.org/kg/DTOSIQBPPRVQHS) - Frequency: 2\n3. InChIkey2D: [OUMMUORTBYSLFO](https://enpkg.commons-lab.org/kg/OUMMUORTBYSLFO) - Frequency: 2\n\nThese are the most frequently observed annotations, each appearing twice in the dataset. Here is the SPARQL query that was generated:\n\n```sparql\n PREFIX ns1: \\nPREFIX ns2: \\n\\nSELECT ?InChIkey2D (COUNT(?InChIkey2D) AS ?frequency)\\nWHERE {\\n ?rawMaterial ns1:has_wd_id .\\n ?rawMaterial ns1:has_lab_process ?labExtract .\\n ?labExtract ns1:has_LCMS ?analysis .\\n ?analysis ns1:has_lcms_feature_list ?feature_list .\\n ?feature_list ns1:has_lcms_feature ?feature .\\n ?feature ns1:has_sirius_annotation ?annotation .\\n ?annotation ns1:has_InChIkey2D ?InChIkey2D .\\n}\\nGROUP BY ?InChIkey2D\\nORDER BY DESC(?frequency)'"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: -PREFIX ns2: - -SELECT ?InChIkey2D (COUNT(?InChIkey2D) AS ?frequency) -WHERE { - ?rawMaterial ns1:has_wd_id . - ?rawMaterial ns1:has_lab_process ?labExtract . - ?labExtract ns1:has_LCMS ?analysis . - ?analysis ns1:has_lcms_feature_list ?feature_list . - ?feature_list ns1:has_lcms_feature ?feature . - ?feature ns1:has_sirius_annotation ?annotation . - ?annotation ns1:has_InChIkey2D ?InChIkey2D . -} -GROUP BY ?InChIkey2D -ORDER BY DESC(?frequency)",Medium -30,What are the LC-MS features that have GNPS library analogs annotation in Tabernaemontana coffeoides? Provide the features.,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the LC-MS features that have GNPS library analogs annotation in Tabernaemontana coffeoides? Provide the features."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the LC-MS features that have GNPS library analogs annotation in Tabernaemontana coffeoides? Provide the features."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The LC-MS features that have GNPS library analogs annotation in Tabernaemontana coffeoides can be found in the provided file. Here is the information related to your query:\n\n- **Question**: What are the LC-MS features that have GNPS library analogs annotation in Tabernaemontana coffeoides? Provide the features.\n- **Generated SPARQL Query**: \n ```sparql\n PREFIX ns1: \n\n SELECT ?feature\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:fast_search_gnpslibrary_analog ?analog .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/c00a7dfa6ebf4a978b7a894760588603/tmp94vi0nsp.csv`\n\nPlease refer to the file for the detailed list of features."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +24," ""Count the number of LCMS features in negative ionization mode""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Count the number of LCMS features in negative ionization mode"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Count the number of LCMS features in negative ionization mode"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The number of LCMS features in negative ionization mode is **364,967**.\n\nHere is the detailed information:\n- **Question**: Count the number of LCMS features in negative ionization mode.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT (COUNT(?feature) AS ?featureCount)\n WHERE {\n ?analysis a ns1:LCMSAnalysisNeg .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n }\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/5a7c1625b95143fcb9f58943371653e9/tmp_dhsfq67.csv"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: - SELECT ?feature + SELECT (COUNT(?feature) AS ?featureCount) WHERE { - ?rawMaterial ns1:has_wd_id . - ?rawMaterial ns1:has_lab_process ?labExtract . - ?labExtract ns1:has_LCMS ?analysis . + ?analysis a ns1:LCMSAnalysisNeg . ?analysis ns1:has_lcms_feature_list ?featureList . ?featureList ns1:has_lcms_feature ?feature . - ?feature ns1:fast_search_gnpslibrary_analog ?analog . - }",High -31,How many distinct Wikidata identifiers (WD IDs) have the same metabolite annotation (considering only the plannar structure)?,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""How many distinct Wikidata identifiers (WD IDs) have the same metabolite annotation (considering only the plannar structure)?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: How many distinct Wikidata identifiers (WD IDs) have the same metabolite annotation (considering only the planar structure)?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid. It is related to the ENPKG nodes/entities (Wikidata IDs, InChIkey2D) and asks for a count of distinct Wikidata IDs that have the same metabolite annotation, considering only the planar structure. This information can be retrieved from the ENPKG."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The query has been executed successfully. There are 138,146 distinct Wikidata identifiers (WD IDs) that share the same metabolite annotation based on their planar structure (InChIkey2D).\n\nHere is the information related to the query:\n\n- **Question**: How many distinct Wikidata identifiers (WD IDs) have the same metabolite annotation (considering only the planar structure)?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT (COUNT(DISTINCT ?wd_id) AS ?distinct_wd_ids)\n WHERE {\n ?annotation ns1:has_InChIkey2D ?InChIkey2D .\n ?InChIkey2D ns1:is_InChIkey2D_of ?InChIkey .\n ?InChIkey ns1:has_wd_id ?wd_id .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/49c4b98b94034c86ace8c37139fa6a29/tmpnnlg58pc.csv`\n\nThis result indicates the number of unique Wikidata IDs that are associated with the same planar structure annotation in the ENPKG database."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: - - SELECT (COUNT(DISTINCT ?wd_id) AS ?distinct_wd_ids) - WHERE { - ?annotation ns1:has_InChIkey2D ?InChIkey2D . - ?InChIkey2D ns1:is_InChIkey2D_of ?InChIkey . - ?InChIkey ns1:has_wd_id ?wd_id . - }",Medium -32,Retrieve the mass differences and cosine similarity scores for spectral pairs involving MS2 spectra from Desmodium heterophyllum.,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the mass differences and cosine similarity scores for spectral pairs involving MS2 spectra from Desmodium heterophyllum."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the mass differences and cosine similarity scores for spectral pairs involving MS2 spectra from Desmodium heterophyllum."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid. It is related to the ENPKG nodes/entities (Raw Material, LCMS Analysis, SpectralPair). The plant name \""Desmodium heterophyllum\"" is present in the database. The question asks for mass differences and cosine similarity scores, which are properties of the SpectralPair entity in the ENPKG."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Desmodium heterophyllum, http://www.wikidata.org/entity/Q10770714, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The mass differences and cosine similarity scores for spectral pairs involving MS2 spectra from Desmodium heterophyllum have been retrieved. Here is the information:\n\n- **SPARQL Query**: \n ```sparql\n PREFIX ns1: \n\n SELECT ?mass_difference ?cosine\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?spectralPair ns1:has_member ?feature .\n ?spectralPair ns1:has_mass_difference ?mass_difference .\n ?spectralPair ns1:has_cosine ?cosine .\n }\n ```\n\n- **File Path**: The results are stored in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/03572d09329f4855ab27963f8832666f/tmpsuxk8web.csv`.\n\nPlease refer to the file for detailed results."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The mass differences and cosine similarity scores for spectral pairs involving MS2 spectra from Desmodium heterophyllum have been successfully interpreted. Here are some sample entries from the data:\n\n- Mass Difference: 218.2978, Cosine Similarity: 0.999612\n- Mass Difference: 46.0057, Cosine Similarity: 0.999888\n- Mass Difference: 140.0989, Cosine Similarity: 0.985793\n- Mass Difference: 160.1252, Cosine Similarity: 0.993816\n- Mass Difference: 162.1407, Cosine Similarity: 0.984198\n\nNo visualization is available, but the data can be downloaded from the provided file path. If you need a visualization, please let me know, and I can generate a plotly graph for you.\n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: - - SELECT ?mass_difference ?cosine - WHERE { - ?rawMaterial ns1:has_wd_id . - ?rawMaterial ns1:has_lab_process ?labExtract . - ?labExtract ns1:has_LCMS ?lcmsAnalysis . - ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList . - ?featureList ns1:has_lcms_feature ?feature . - ?spectralPair ns1:has_member ?feature . - ?spectralPair ns1:has_mass_difference ?mass_difference . - ?spectralPair ns1:has_cosine ?cosine . - }",High -33,What are SIRIUS structural annotations for LCMS features with cosmic scores above 0.3 associated with Tabernaemontana coffeoides? Report their ZODIAC scores.,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are SIRIUS structural annotations for LCMS features with cosmic scores above 0.3 associated with Tabernaemontana coffeoides? Report their ZODIAC scores."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the cosmic scores and zodiac scores associated with SIRIUS structural annotations for LCMS features from Tabernaemontana coffeoides?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The zodiac scores associated with SIRIUS structural annotations for LCMS features from Tabernaemontana coffeoides have been retrieved. Here is the information:\n\n- **SPARQL Query**: \n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?cosmic_score ?zodiac_score\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?siriusAnnotation ns1:has_cosmic_score ?cosmic_score .\n FILTER (?cosmic_score > 0.3) \n ?siriusAnnotation ns1:has_zodiac_score ?zodiac_score .\n }\n ```\n\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/5137b0a478de42c5b07851d5252b3e4c/tmpn4ce1k2v.csv`\n\nPlease refer to the file for detailed scores."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: - PREFIX ns2: - - SELECT ?cosmic_score ?zodiac_score - WHERE { - ?rawMaterial ns1:has_wd_id . - ?rawMaterial ns1:has_lab_process ?labExtract . - ?labExtract ns1:has_LCMS ?analysis . - ?analysis ns1:has_lcms_feature_list ?feature_list . - ?feature_list ns1:has_lcms_feature ?feature . - ?feature ns1:has_sirius_annotation ?siriusAnnotation . - ?siriusAnnotation ns1:has_cosmic_score ?cosmic_score . - FILTER (?cosmic_score > 0.3) - ?siriusAnnotation ns1:has_zodiac_score ?zodiac_score . - }",High -34,Which ISDB annotations have consistency scores above 0.7 and taxonomic scores above 0.5 for LCMS features from Tabernaemontana coffeoides? Provide annotations.,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which ISDB annotations have consistency scores above 0.7 and taxonomic scores above 0.5 for LCMS features from Tabernaemontana coffeoides? Provide annotations."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which ISDB annotations have consistency scores above 0.7 and taxonomic scores above 0.5 for LCMS features from Tabernaemontana coffeoides? Provide annotations."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully executed to find the ISDB annotations with consistency scores above 0.7 and taxonomic scores above 0.5 for LCMS features from Tabernaemontana coffeoides. Here is the information:\n\n- **Question**: Which ISDB annotations have consistency scores above 0.7 and taxonomic scores above 0.5 for LCMS features from Tabernaemontana coffeoides? Provide annotations.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?Annotation\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_isdb_annotation ?Annotation .\n ?Annotation ns1:has_consistency_score ?consistencyScore .\n ?Annotation ns1:has_taxo_score ?taxoScore .\n FILTER(?consistencyScore > 0.7 && ?taxoScore > 0.5)\n }\n ```\n- **File Path**: The results are stored in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/002a9f76c921447fbfe4490492614cba/tmpgkgukng8.csv`.\n\nPlease refer to the file for the detailed annotations."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + }",Low +25,"""What are the chemical structure ISDB annotations for Lovoa trichilioides ?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the chemical structure ISDB annotations for Lovoa trichilioides ?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the chemical structure ISDB annotations for Lovoa trichilioides?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""Lovoa trichilioides, http://www.wikidata.org/entity/Q8008828, Wikidata"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed. Here are the details:\n\n- **Question**: What are the chemical structure ISDB annotations for Lovoa trichilioides?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?Annotation\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_isdb_annotation ?Annotation .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/597fde39a0474507b67d3ce263df2a5e/tmp9ukxy4rj.csv`\n\nI will now analyze the SPARQL output to provide the final answer.\nThe chemical structure ISDB annotations for Lovoa trichilioides have been retrieved. Here is the final answer:\n\n- **Question**: What are the chemical structure ISDB annotations for Lovoa trichilioides?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?Annotation\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_isdb_annotation ?Annotation .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/597fde39a0474507b67d3ce263df2a5e/tmp9ukxy4rj.csv`\n\nThe file contains the ISDB annotations for Lovoa trichilioides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: PREFIX ns2: SELECT ?Annotation WHERE { - ?rawMaterial ns1:has_wd_id . + ?rawMaterial ns1:has_wd_id . ?rawMaterial ns1:has_lab_process ?labExtract . ?labExtract ns1:has_LCMS ?analysis . ?analysis ns1:has_lcms_feature_list ?feature_list . ?feature_list ns1:has_lcms_feature ?feature . ?feature ns1:has_isdb_annotation ?Annotation . - ?Annotation ns1:has_consistency_score ?consistencyScore . - ?Annotation ns1:has_taxo_score ?taxoScore . - FILTER(?consistencyScore > 0.7 && ?taxoScore > 0.5) }",Medium -35,List the tissue types used to produce the lab extracts of Tabernaemontana coffeoides. Provide the tissue types and lab extracts.,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""List the tissue types used to produce the lab extracts of Tabernaemontana coffeoides. Provide the tissue types and lab extracts. "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: List the tissue types used to produce the lab extracts of Tabernaemontana coffeoides. Provide the tissue types and lab extracts."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The tissue type used to produce the lab extracts of Tabernaemontana coffeoides is \""green tissue.\"" The lab extracts associated with this tissue type are:\n\n1. Lab Extract: VGF152_B02\n2. Lab Extract: VGF152_A02\n3. Lab Extract: VGF151_G11\n4. Lab Extract: VGF151_H11\n\nHere is the SPARQL query used to retrieve this information:\n\n```sparql\nPREFIX ns1: \nPREFIX ns2: \n\nSELECT ?tissue ?labExtract\nWHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?rawMaterial ns2:has_tissue ?tissue .\n}\n```\n\nThe data was retrieved from the file located at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/79cf06dcfb4c4635bf92d845c42ea938/tmpo0nqoke3.csv`."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +26," ""Retrieve the Wikidata IDs of chemical entities from Desmodium heterophyllum that are annotated as rotenoids by CANOPUS and have ChEMBL assay results showing activity against Trypanosoma cruzi. Provide also the USIs to the corresponding MS/MS spectra.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the Wikidata IDs of chemical entities from Desmodium heterophyllum that are annotated as rotenoids by CANOPUS and have ChEMBL assay results showing activity against Trypanosoma cruzi. Provide also the USIs to the corresponding MS/MS spectra"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the Wikidata IDs of chemical entities from Desmodium heterophyllum that are annotated as rotenoids by CANOPUS and have ChEMBL assay results showing activity against Trypanosoma cruzi. Provide also the USIs to the corresponding MS/MS spectra."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Desmodium heterophyllum, http://www.wikidata.org/entity/Q10770714, Wikidata;\nrotenoids, https://enpkg.commons-lab.org/kg/npc_Rotenoids, NPCClass;\nTrypanosoma cruzi, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL368, ChEMBLTarget\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been executed successfully. Here is the information retrieved:\n\n### Question\nRetrieve the Wikidata IDs of chemical entities from Desmodium heterophyllum that are annotated as rotenoids by CANOPUS and have ChEMBL assay results showing activity against Trypanosoma cruzi. Provide also the USIs to the corresponding MS/MS spectra.\n\n### SPARQL Query\n```sparql\nPREFIX ns1: \nPREFIX ns2: \n\nSELECT ?wd_id ?usi\nWHERE {\n ?rawMaterial ns1:has_wd_id ;\n ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_canopus_annotation ?annotation ;\n ns1:has_usi ?usi .\n ?annotation ns1:has_canopus_npc_class .\n ?labExtract ns2:has_bioassay_results ?assayResult .\n ?assayResult ns2:target_id .\n ?chemicalEntity ns1:has_wd_id ?wd_id .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?siriusAnnotation ns1:has_InChIkey2D ?InChIkey2D .\n ?InChIkey2D ns1:is_InChIkey2D_of ?chemicalEntity .\n }\n```\n\n### File Path\nThe results have been saved to a temporary file, which can be accessed at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/be66080199fb4930ae3c599b8c11507d/tmpn40oy4b_.csv`\n\nPlease let me know if you need further assistance or analysis on this data!"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The analysis of the file has been completed. The data contains Wikidata IDs of chemical entities from Desmodium heterophyllum that are annotated as rotenoids by CANOPUS and have ChEMBL assay results showing activity against Trypanosoma cruzi. It also includes the corresponding USIs to the MS/MS spectra.\n\nHere are the first few entries:\n- **Wikidata ID**: Q104399962\n- **USI**: mzspec:MSV000087728:VGF155_D05_features_ms2_ne...\n\nNo visualization was requested or generated for this analysis. The data can be downloaded for further examination.\n\nIf you need any additional insights or visualizations, please let me know!"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: PREFIX ns2: -SELECT ?tissue ?labExtract +SELECT ?wd_id ?usi WHERE { - ?rawMaterial ns1:has_wd_id . - ?rawMaterial ns1:has_lab_process ?labExtract . - ?rawMaterial ns2:has_tissue ?tissue . -}",Low -36,Retrieve the retention times and parent masses of LCMS features annotated as Terpenoids by CANOPUS in Tabernaemontana coffeoides.,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the retention times and parent masses of LCMS features annotated as Terpenoids by CANOPUS in Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the retention times and parent masses of LCMS features annotated as Terpenoids by CANOPUS in Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Terpenoids, https://enpkg.commons-lab.org/kg/npc_Terpenoids, NPCPathway;\nTabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The retention times and parent masses of LCMS features annotated as Terpenoids by CANOPUS in Tabernaemontana coffeoides have been retrieved. Here is the information:\n\n- **SPARQL Query**: \n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?retentionTime ?parentMass\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_pathway .\n ?feature ns1:has_retention_time ?retentionTime .\n ?feature ns1:has_parent_mass ?parentMass .\n }\n ```\n\n- **File Path**: The results are saved in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/610f86c362554edfa3e22989fd52acdf/tmpgt4cvneq.csv`.\n\nPlease check the file for detailed results."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The retention times and parent masses of LCMS features annotated as Terpenoids by CANOPUS in Tabernaemontana coffeoides have been visualized. You can download the visualization file from the following path:\n\nVisualization filepath: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/610f86c362554edfa3e22989fd52acdf/tmpgt4cvneq.json`\n\nPlease use this file to view the detailed results. \n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + ?rawMaterial ns1:has_wd_id ; + ns1:has_lab_process ?labExtract . + ?labExtract ns1:has_LCMS ?analysis . + ?analysis ns1:has_lcms_feature_list ?feature_list . + ?feature_list ns1:has_lcms_feature ?feature . + ?feature ns1:has_canopus_annotation ?annotation ; + ns1:has_usi ?usi . + ?annotation ns1:has_canopus_npc_class . + ?labExtract ns2:has_bioassay_results ?assayResult . + ?assayResult ns2:target_id . + ?chemicalEntity ns1:has_wd_id ?wd_id . + ?feature ns1:has_sirius_annotation ?siriusAnnotation . + ?siriusAnnotation ns1:has_InChIkey2D ?InChIkey2D . + ?InChIkey2D ns1:is_InChIkey2D_of ?chemicalEntity . + }",High +27," ""Retrieve the retention times and parent masses of LCMS features annotated as Terpenoids by CANOPUS in Tabernaemontana coffeoides.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the retention times and parent masses of LCMS features annotated as Terpenoids by CANOPUS in Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the retention times and parent masses of LCMS features annotated as Terpenoids by CANOPUS in Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Terpenoids, https://enpkg.commons-lab.org/kg/npc_Terpenoids, NPCPathway;\nTabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The retention times and parent masses of LCMS features annotated as Terpenoids by CANOPUS in Tabernaemontana coffeoides have been retrieved. Here is the information:\n\n- **SPARQL Query**: \n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?retentionTime ?parentMass\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_pathway .\n ?feature ns1:has_retention_time ?retentionTime .\n ?feature ns1:has_parent_mass ?parentMass .\n }\n ```\n\n- **File Path**: The results are saved in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/610f86c362554edfa3e22989fd52acdf/tmpgt4cvneq.csv`.\n\nPlease check the file for detailed results."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The retention times and parent masses of LCMS features annotated as Terpenoids by CANOPUS in Tabernaemontana coffeoides have been visualized. You can download the visualization file from the following path:\n\nVisualization filepath: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/610f86c362554edfa3e22989fd52acdf/tmpgt4cvneq.json`\n\nPlease use this file to view the detailed results. \n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: PREFIX ns2: SELECT ?retentionTime ?parentMass @@ -497,58 +386,122 @@ WHERE { ?feature ns1:has_retention_time ?retentionTime . ?feature ns1:has_parent_mass ?parentMass . }",Medium -37,Retrieve the most frequent Spec2Vec peaks and losses characterizing the MS2 spectra from the lab extracts of Tabernaemontana coffeoides.,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the most frequent Spec2Vec peaks and losses characterizing the MS2 spectra from the lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the most frequent Spec2Vec peaks and losses characterizing the MS2 spectra from the lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: Retrieve the most frequent Spec2Vec peaks and losses characterizing the MS2 spectra from the lab extracts of Tabernaemontana coffeoides.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n PREFIX wd: \n\n SELECT ?spec2vecPeak ?spec2vecLoss (COUNT(?spec2vecPeak) AS ?peakFrequency) (COUNT(?spec2vecLoss) AS ?lossFrequency)\n WHERE {\n ?rawMaterial ns1:has_wd_id wd:Q15376858 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_spec2vec_doc ?spec2vecDoc .\n ?spec2vecDoc ns1:has_spec2vec_peak ?spec2vecPeak .\n ?spec2vecDoc ns1:has_spec2vec_loss ?spec2vecLoss .\n } GROUP BY ?spec2vecPeak ?spec2vecLoss \n ORDER BY DESC(?peakFrequency) DESC(?lossFrequency)\n LIMIT 1\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/2e7991542808441db6e152ef5e034607/tmpdwgxk7xv.csv`\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The most frequent Spec2Vec peaks and losses characterizing the MS2 spectra from the lab extracts of Tabernaemontana coffeoides have been successfully retrieved "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +28,"""Which compounds from Desmodium heterophyllum extracts have annotations from both ISDB and SIRIUS, and what are their molecular masses?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which compounds from Desmodium heterophyllum extracts have annotations from both ISDB and SIRIUS, and what are their molecular masses?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which compounds from Desmodium heterophyllum extracts have annotations from both ISDB and SIRIUS, and what are their molecular masses?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""Desmodium heterophyllum, http://www.wikidata.org/entity/Q10770714, Wikidata"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The compounds from Desmodium heterophyllum extracts that have annotations from both ISDB and SIRIUS, along with their molecular masses, are as follows:\n\n1. Compound: WITLAWYGGVAFLU, Mass: 379.1186828613281\n2. Compound: PKNGEQDEJRDPJZ, Mass: 407.2225646972656\n3. Compound: FHKQMPVRIQJSEJ, Mass: 313.1062774658203\n4. Compound: BWDGXQDVXMUNDT, Mass: 293.1019897460937\n5. Compound: QFTWSMLVOZYJPP, Mass: 337.1435089111328\n6. Compound: MIUNHNXJASJSIX, Mass: 425.1591796875\n7. Compound: ZSCRTFONTNMQBL, Mass: 247.16928100585935\n8. Compound: VZPAURMDJZOGHU, Mass: 445.208984375\n9. Compound: HEORSHPDPZGZCN, Mass: 349.143310546875\n10. Compound: GIVRDLOWBBXCNM, Mass: 351.1228332519531\n11. Compound: VZPAURMDJZOGHU, Mass: 445.2112426757813\n12. Compound: QQWYBWNEILZOKI, Mass: 321.1480407714844\n13. Compound: DTOSIQBPPRVQHS, Mass: 279.2320251464844\n14. Compound: DTOSIQBPPRVQHS, Mass: 279.2322692871094\n15. Compound: KSVKECXWDNCRTM, Mass: 403.20166015625\n16. Compound: CEAWIMABVSITMV, Mass: 383.1479187011719\n17. Compound: CVULDJMCSSACEO, Mass: 507.2290344238281\n18. Compound: KEJNXXMRDHDOCO, Mass: 279.10035705566406\n19. Compound: QQWYBWNEILZOKI, Mass: 321.14996337890625\n20. Compound: QQWYBWNEILZOKI, Mass: 321.1477355957031\n21. Compound: AOMUHOFOVNGZAN, Mass: 288.2526550292969\n22. Compound: YHZDBBUEVZEOIY, Mass: 271.0964050292969\n23. Compound: WRINUBCCCQLTPU, Mass: 365.13720703125\n24. Compound: GAGKUHAKUBMORD, Mass: 339.12274169921875\n25. Compound: CDZUQIFWCXAIFB, Mass: 443.13368225097656\n26. Compound: ZSCRTFONTNMQBL, Mass: 247.1693420410156\n27. Compound: HOMYIYLRRDTKAA, Mass: 714.5514831542969\n28. Compound: AOMUHOFOVNGZAN, Mass: 288.2525939941406\n29. Compound: OIMXTYUHMBQQJM, Mass: 393.3157348632813\n30. Compound: MEJLVLSNRYLDSG, Mass: 743.2880859375\n31. Compound: HEQLSVWMTJXPGS, Mass: 413.1593017578125\n32. Compound: WXWGKIBMPDYPTH, Mass: 265.08551025390625\n33. Compound: ICFXJOAKQGDRCT, Mass: 429.3733520507813\n34. Compound: QQWYBWNEILZOKI, Mass: 321.1485290527344\n35. Compound: XTLSKKJNOIMMBK, Mass: 295.0967102050781\n36. Compound: XTLSKKJNOIMMBK, Mass: 295.0966186523437\n37. Compound: XTLSKKJNOIMMBK, Mass: 295.0966491699219\n38. Compound: DTOSIQBPPRVQHS, Mass: 279.232177734375\n39. Compound: UKMCCFHULJHJNS, Mass: 427.3577575683594\n40. Compound: UKMCCFHULJHJNS, Mass: 427.3577423095703\n41. Compound: XTLSKKJNOIMMBK, Mass: 295.0967712402344\n42. Compound: XTLSKKJNOIMMBK, Mass: 295.0967102050781\n43. Compound: WECGLUPZRHILCT, Mass: 355.2845458984375\n44. Compound: QNPIEBGHSXVAOA, Mass: 702.5513305664062\n45. Compound: BWDGXQDVXMUNDT, Mass: 293.1017761230469\n46. Compound: XEVQXKKKAVVSMW, Mass: 197.1173095703125\n47. Compound: LUZSWWYKKLTDHU, Mass: 295.2268981933594\n48. Compound: ICFXJOAKQGDRCT, Mass: 429.3731689453125\n49. Compound: MIUNHNXJASJSIX, Mass: 425.158935546875\n50. Compound: AOMUHOFOVNGZAN, Mass: 288.2529754638672\n51. Compound: OZNMJHSEUFVTTG, Mass: 409.3470458984375\n52. Compound: GQSRIBUSVVSLSQ, Mass: 463.37884521484375\n53. Compound: HOMYIYLRRDTKAA, Mass: 714.550537109375\n54. Compound: HXYVTAGFYLMHSO, Mass: 300.2903747558594\n55. Compound: ORDAZKGHSNRHTD, Mass: 395.14947509765625\n56. Compound: AOMUHOFOVNGZAN, Mass: 288.2536163330078\n\nThe SPARQL query used to retrieve this data is:\n```sparql\nPREFIX ns1: \nPREFIX ns2: \nPREFIX wd: \n\nSELECT ?compound ?mass\nWHERE {\n ?rawMaterial ns1:has_wd_id wd:Q10770714 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_isdb_annotation ?isdbAnnotation .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?feature ns1:has_parent_mass ?mass .\n ?isdbAnnotation ns1:has_InChIkey2D ?compound .\n ?siriusAnnotation ns1:has_InChIkey2D ?compound .\n}\n```\n\nThe file path to the SPARQL output is: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/c6e1f9c0990546a88ca140657c7354d1/tmpez5ixi4n.csv`\n\nI will now provide this information to the supervisor."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +PREFIX ns2: +PREFIX wd: + +SELECT ?compound ?mass +WHERE { + ?rawMaterial ns1:has_wd_id wd:Q10770714 . + ?rawMaterial ns1:has_lab_process ?labExtract . + ?labExtract ns1:has_LCMS ?analysis . + ?analysis ns1:has_lcms_feature_list ?feature_list . + ?feature_list ns1:has_lcms_feature ?feature . + ?feature ns1:has_isdb_annotation ?isdbAnnotation . + ?feature ns1:has_sirius_annotation ?siriusAnnotation . + ?feature ns1:has_parent_mass ?mass . + ?isdbAnnotation ns1:has_InChIkey2D ?compound . + ?siriusAnnotation ns1:has_InChIkey2D ?compound . +}",High +29," ""What are the most frequent adducts of LCMS features annotated by SIRIUS detected in positive ionization mode from Tabernaemontana coffeoides?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the most frequent adducts of LCMS features annotated by SIRIUS detected in positive ionization mode from Tabernaemontana coffeoides?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the most frequent adducts of LCMS features annotated by SIRIUS detected in positive ionization mode from Tabernaemontana coffeoides?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The most frequent adducts of LCMS features annotated by SIRIUS detected in positive ionization mode from Tabernaemontana coffeoides are:\n\n1. [M+H]+ with a frequency of 1079\n2. [M+NH4]+ with a frequency of 432\n3. [M+Na]+ with a frequency of 263\n4. [M+K]+ with a frequency of 65\n\nHere is the dictionary containing the question, generated SPARQL query, and file path:\n\n- **Question**: What are the most frequent adducts of LCMS features annotated by SIRIUS detected in positive ionization mode from Tabernaemontana coffeoides?\n- **Generated SPARQL Query**: \n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?adduct (COUNT(?adduct) AS ?frequency)\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis a ns1:LCMSAnalysisPos .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?annotation .\n ?annotation ns1:has_adduct ?adduct .\n }\n GROUP BY ?adduct\n ORDER BY DESC(?frequency)\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/d8669ea7b6ab4a75a171344bc71c5b61/tmpnm0aoqfg.csv`"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: PREFIX ns2: - PREFIX wd: - SELECT ?spec2vecPeak ?spec2vecLoss (COUNT(?spec2vecPeak) AS ?peakFrequency) (COUNT(?spec2vecLoss) AS ?lossFrequency) + SELECT ?adduct (COUNT(?adduct) AS ?frequency) WHERE { - ?rawMaterial ns1:has_wd_id wd:Q15376858 . + ?rawMaterial ns1:has_wd_id . ?rawMaterial ns1:has_lab_process ?labExtract . - ?labExtract ns1:has_LCMS ?analysis . - ?analysis ns1:has_lcms_feature_list ?featureList . + ?labExtract ns1:has_LCMS ?lcmsAnalysis . + ?lcmsAnalysis a ns1:LCMSAnalysisPos . + ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList . ?featureList ns1:has_lcms_feature ?feature . - ?feature ns1:has_spec2vec_doc ?spec2vecDoc . - ?spec2vecDoc ns1:has_spec2vec_peak ?spec2vecPeak . - ?spec2vecDoc ns1:has_spec2vec_loss ?spec2vecLoss . - } GROUP BY ?spec2vecPeak ?spec2vecLoss - ORDER BY DESC(?peakFrequency) DESC(?lossFrequency) - LIMIT 1",Medium -38,What are the ChEMBL targets linked to ChEMBL assay results for chemicals sharing InChIKeys with SIRIUS structural annotations ?,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the ChEMBL targets linked to ChEMBL assay results for chemicals sharing InChIKeys with SIRIUS structural annotations ?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the ChEMBL targets linked to ChEMBL assay results for chemicals sharing InChIKeys with SIRIUS structural annotations ?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed. Here are the details:\n\n- **Question**: What are the ChEMBL targets linked to ChEMBL assay results for chemicals sharing InChIKeys with SIRIUS structural annotations?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?chembl_target\n WHERE {\n ?sirius_annotation ns1:has_InChIkey2D ?InChIkey2D .\n ?InChIkey2D ns1:is_InChIkey2D_of ?InChIkey .\n ?InChIkey ns2:has_chembl_id ?chembl_chemical .\n ?chembl_chemical ns2:has_chembl_activity ?chembl_assay_results .\n ?chembl_assay_results ns2:target_id ?chembl_target .\n }\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/f016f234d53147b3a0454bd652bc8f6b/tmppen79nmc.csv\n\nI will now analyze the SPARQL output to generate the final answer.\nThe final answer based on the SPARQL output is as follows:\n\n- **ChEMBL Targets (ChEMBLTarget)**: The ChEMBL targets linked to ChEMBL assay results for chemicals sharing InChIKeys with SIRIUS structural annotations are listed in the file located at the following path:\n - **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/f016f234d53147b3a0454bd652bc8f6b/tmppen79nmc.csv\n\nHere is the dictionary containing the question, generated SPARQL query, and file path:\n\n```json\n{\n \""question\"": \""What are the ChEMBL targets (ChEMBLTarget) linked to ChEMBL assay results (ChEMBLAssayResults) for chemicals (ChEMBLChemical) sharing InChIKeys (InChIkey) with SIRIUS structural annotations (SiriusStructureAnnotation)?\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\nPREFIX ns2: \\n\\nSELECT ?chembl_target\\nWHERE {\\n ?sirius_annotation ns1:has_InChIkey2D ?InChIkey2D .\\n ?InChIkey2D ns1:is_InChIkey2D_of ?InChIkey .\\n ?InChIkey ns2:has_chembl_id ?chembl_chemical .\\n ?chembl_chemical ns2:has_chembl_activity ?chembl_assay_results .\\n ?chembl_assay_results ns2:target_id ?chembl_target .\\n}\"",\n \""file_path\"": \""/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/f016f234d53147b3a0454bd652bc8f6b/tmppen79nmc.csv\""\n}\n```\n\nPlease review the file for the detailed list of ChEMBL targets."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + ?feature ns1:has_sirius_annotation ?annotation . + ?annotation ns1:has_adduct ?adduct . + } + GROUP BY ?adduct + ORDER BY DESC(?frequency)",Medium +30," ""What are the Wikidata IDs of chemical entities that have ChEMBL activity against Trypanosoma cruzi target?"" + +","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the Wikidata IDs of chemical entities that have ChEMBL activity against Trypanosoma cruzi target?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the Wikidata IDs of chemical entities that have ChEMBL activity against Trypanosoma cruzi target?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Trypanosoma cruzi, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL368, ChEMBLTarget\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""Here is the information you requested:\n\n- **Question**: What are the Wikidata IDs of chemical entities that have ChEMBL activity against Trypanosoma cruzi target?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?wd_id\n WHERE {\n ?chemicalEntity ns2:has_chembl_id ?chemblChemical .\n ?chemblChemical ns2:has_chembl_activity ?assayResults .\n ?assayResults ns2:target_id .\n ?chemicalEntity ns1:has_wd_id ?wd_id .\n }\n ```\n- **File Path**: The results are saved in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/18465df0915e441aa7285e242e5b5927/tmpsavbcot0.csv`.\n\nPlease let me know if you need further assistance!"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The file has been processed, and here are some of the Wikidata IDs of chemical entities that have ChEMBL activity against the Trypanosoma cruzi target:\n\n1. [Q82238903](http://www.wikidata.org/entity/Q82238903)\n2. [Q27095836](http://www.wikidata.org/entity/Q27095836)\n\nThese IDs were extracted from the provided file. If you need further analysis or a specific visualization, please let me know. \n\nNo visualization file was generated, but the data can be downloaded from the provided file path."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: PREFIX ns2: - SELECT ?chembl_target + SELECT ?wd_id + WHERE { + ?chemicalEntity ns2:has_chembl_id ?chemblChemical . + ?chemblChemical ns2:has_chembl_activity ?assayResults . + ?assayResults ns2:target_id . + ?chemicalEntity ns1:has_wd_id ?wd_id . + }",Medium +31," ""How many features (pos ionization and neg ionization modes) have the same SIRIUS/CSI:FingerID and ISDB annotation by comparing the InCHIKey2D of the annotations?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""How many features (pos ionization and neg ionization modes) have the same SIRIUS/CSI:FingerID and ISDB annotation by comparing the InCHIKey2D of the annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question:How many features (pos ionization and neg ionization modes) have the same SIRIUS/CSI:FingerID and ISDB annotation by comparing the InCHIKey2D of the annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The analysis has been completed. Here are the details:\n\n- **Question**: How many features (pos ionization and neg ionization modes) have the same SIRIUS/CSI:FingerID and ISDB annotation by comparing the InCHIKey2D of the annotations?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT (COUNT(?feature) AS ?featureCount)\n WHERE {\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?feature ns1:has_isdb_annotation ?isdbAnnotation .\n ?siriusAnnotation ns1:has_InChIkey2D ?InChIkey2D .\n ?isdbAnnotation ns1:has_InChIkey2D ?InChIkey2D .\n }\n ```\n- **Result**: 33,255 features have the same SIRIUS/CSI:FingerID and ISDB annotation by comparing the InCHIKey2D of the annotations.\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/ec07f70ba3ec4a18abbb424a48145459/tmppclv2pge.csv`\n\nThis information should provide the necessary details for further analysis or reporting."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + + SELECT (COUNT(?feature) AS ?featureCount) + WHERE { + ?labExtract ns1:has_LCMS ?analysis . + ?analysis ns1:has_lcms_feature_list ?feature_list . + ?feature_list ns1:has_lcms_feature ?feature . + ?feature ns1:has_sirius_annotation ?siriusAnnotation . + ?feature ns1:has_isdb_annotation ?isdbAnnotation . + ?siriusAnnotation ns1:has_InChIkey2D ?InChIkey2D . + ?isdbAnnotation ns1:has_InChIkey2D ?InChIkey2D . + }",Medium +32,"""How many features annotated as 'Tetraketide meroterpenoids' by CANOPUS are found for each submitted taxon and extract in database?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""How many features annotated as 'Tetraketide meroterpenoids' by CANOPUS are found for each submitted taxon and extract in database?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: How many features annotated as 'Tetraketide meroterpenoids' by CANOPUS are found for each submitted taxon and extract in database?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tetraketide meroterpenoids, https://enpkg.commons-lab.org/kg/npc_Tetraketide_meroterpenoids, NPCClass\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed. Here are the details:\n\n- **Question**: How many features annotated as 'Tetraketide meroterpenoids' by CANOPUS are found for each submitted taxon and extract in database?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT ?submittedTaxon ?extract (COUNT(?feature) AS ?featureCount)\n WHERE {\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_class .\n ?featureList ns1:has_lcms_feature ?feature .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?extract ns1:has_LCMS ?analysis .\n ?rawMaterial ns1:has_lab_process ?extract .\n ?rawMaterial ns1:submitted_taxon ?submittedTaxon .\n }\n GROUP BY ?submittedTaxon ?extract\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/093ee098fbba4dca93f0404833ba35bb/tmpjqdqbx0d.csv\n\nI will now analyze the SPARQL output to generate the final answer.\nThe SPARQL query has been executed successfully. Here is the final answer based on the SPARQL output:\n\nThe number of features annotated as 'Tetraketide meroterpenoids' by CANOPUS for each submitted taxon and extract in the database is available in the generated CSV file.\n\n- **Question**: How many features annotated as 'Tetraketide meroterpenoids' by CANOPUS are found for each submitted taxon and extract in database?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT ?submittedTaxon ?extract (COUNT(?feature) AS ?featureCount)\n WHERE {\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_class .\n ?featureList ns1:has_lcms_feature ?feature .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?extract ns1:has_LCMS ?analysis .\n ?rawMaterial ns1:has_lab_process ?extract .\n ?rawMaterial ns1:submitted_taxon ?submittedTaxon .\n }\n GROUP BY ?submittedTaxon ?extract\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/093ee098fbba4dca93f0404833ba35bb/tmpjqdqbx0d.csv\n\nYou can find the detailed results in the provided CSV file."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + + SELECT ?submittedTaxon ?extract (COUNT(?feature) AS ?featureCount) WHERE { - ?sirius_annotation ns1:has_InChIkey2D ?InChIkey2D . - ?InChIkey2D ns1:is_InChIkey2D_of ?InChIkey . - ?InChIkey ns2:has_chembl_id ?chembl_chemical . - ?chembl_chemical ns2:has_chembl_activity ?chembl_assay_results . - ?chembl_assay_results ns2:target_id ?chembl_target . - }",High -39,Retrieve the Spec2Vec documents associated with MS2 spectra from the lab extracts of Tabernaemontana coffeoides,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the Spec2Vec documents associated with MS2 spectra from the lab extracts of Tabernaemontana coffeoides"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the Spec2Vec documents associated with MS2 spectra from the lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: Retrieve the Spec2Vec documents associated with MS2 spectra from the lab extracts of Tabernaemontana coffeoides.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?spec2vecDoc\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_spec2vec_doc ?spec2vecDoc .\n }\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/aa6e48ec3a194a14a7a9f918774c080a/tmpxvtl3hcs.csv\n\nI will now analyze the SPARQL output to provide the final answer.\nThe final answer based on the SPARQL output is as follows:\n\nThe Spec2Vec documents (Spec2VecDoc) associated with MS2 spectra (MS2Spectrum) from the lab extracts (LabExtract) of Tabernaemontana coffeoides have been retrieved. The details are stored in the file located at:\n\n**File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/aa6e48ec3a194a14a7a9f918774c080a/tmpxvtl3hcs.csv\n\nHere is the dictionary containing the question, generated SPARQL query, and file path:\n\n```json\n{\n \""question\"": \""Retrieve the Spec2Vec documents (Spec2VecDoc) associated with MS2 spectra (MS2Spectrum) from the lab extracts (LabExtract) of Tabernaemontana coffeoides.\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\nPREFIX ns2: \\n\\nSELECT ?spec2vecDoc\\nWHERE {\\n ?rawMaterial ns1:has_wd_id .\\n ?rawMaterial ns1:has_lab_process ?labExtract .\\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\\n ?featureList ns1:has_lcms_feature ?feature .\\n ?feature ns1:has_spec2vec_doc ?spec2vecDoc .\\n}\"",\n \""file_path\"": \""/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/aa6e48ec3a194a14a7a9f918774c080a/tmpxvtl3hcs.csv\""\n}\n```\n\nPlease review the file for the detailed Spec2Vec documents."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + ?feature ns1:has_canopus_annotation ?annotation . + ?annotation ns1:has_canopus_npc_class . + ?featureList ns1:has_lcms_feature ?feature . + ?analysis ns1:has_lcms_feature_list ?featureList . + ?extract ns1:has_LCMS ?analysis . + ?rawMaterial ns1:has_lab_process ?extract . + ?rawMaterial ns1:submitted_taxon ?submittedTaxon . + } + GROUP BY ?submittedTaxon ?extract",Medium +33," ""What are the retention times, parent masses, and associated Spec2Vec peaks for features in the LC-MS analysis of the plant Rumex nepalensis in positive ion mode?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the retention times, parent masses, and associated Spec2Vec peaks for features in the LC-MS analysis of the plant Rumex nepalensis in positive ion mode?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question:What are the retention times, parent masses, and associated Spec2Vec peaks for features in the LC-MS analysis of the plant Rumex nepalensis in positive ion mode?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Rumex nepalensis, http://www.wikidata.org/entity/Q11041204, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: What are the retention times, parent masses, and associated Spec2Vec peaks for features in the LC-MS analysis of the plant Rumex nepalensis in positive ion mode?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?retention_time ?parent_mass ?spec2vec_peak\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis a ns1:LCMSAnalysisPos .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_retention_time ?retention_time .\n ?feature ns1:has_parent_mass ?parent_mass .\n ?feature ns1:has_spec2vec_doc ?spec2vecDoc .\n ?spec2vecDoc ns1:has_spec2vec_peak ?spec2vec_peak .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/7e32d9e48e274df9a04b4a01d1635276/tmpxmt6gmzo.csv`\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The LC-MS analysis of the plant Rumex nepalensis in positive ion mode provided the following details:\n\n- **Retention Time**: 5.938233\n- **Parent Mass**: 584.475128\n- **Spec2Vec Peaks**:\n - [Peak 130.14](https://enpkg.commons-lab.org/kg/peak_130.14)\n - [Peak 131.07](https://enpkg.commons-lab.org/kg/peak_131.07)\n - [Peak 221.14](https://enpkg.commons-lab.org/kg/peak_221.14)\n - [Peak 233.11](https://enpkg.commons-lab.org/kg/peak_233.11)\n - [Peak 265.17](https://enpkg.commons-lab.org/kg/peak_265.17)\n\nNo visualization was requested for this analysis. The data can be downloaded from the provided file path."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: PREFIX ns2: - SELECT ?spec2vecDoc + SELECT ?retention_time ?parent_mass ?spec2vec_peak WHERE { - ?rawMaterial ns1:has_wd_id . + ?rawMaterial ns1:has_wd_id . ?rawMaterial ns1:has_lab_process ?labExtract . ?labExtract ns1:has_LCMS ?lcmsAnalysis . + ?lcmsAnalysis a ns1:LCMSAnalysisPos . ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList . ?featureList ns1:has_lcms_feature ?feature . + ?feature ns1:has_retention_time ?retention_time . + ?feature ns1:has_parent_mass ?parent_mass . ?feature ns1:has_spec2vec_doc ?spec2vecDoc . + ?spec2vecDoc ns1:has_spec2vec_peak ?spec2vec_peak . }",Medium -40,List the bioassay results at 10碌g/mL against T.cruzi for lab extracts of Tabernaemontana coffeoides.,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""List the bioassay results at 10碌g/mL against T.cruzi for lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: List the bioassay results at 10碌g/mL against T.cruzi for lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The bioassay results at 10碌g/mL against T.cruzi for lab extracts of Tabernaemontana coffeoides are as follows:\n\n1. Lab Extract: [VGF152_B02](https://enpkg.commons-lab.org/kg/VGF152_B02)\n - BioAssay Result: [VGF152_B02_Tcruzi_10ugml](https://enpkg.commons-lab.org/module/VGF152_B02_Tcruzi_10ugml)\n\n2. Lab Extract: [VGF152_A02](https://enpkg.commons-lab.org/kg/VGF152_A02)\n - BioAssay Result: [VGF152_A02_Tcruzi_10ugml](https://enpkg.commons-lab.org/module/VGF152_A02_Tcruzi_10ugml)\n\n3. Lab Extract: [VGF151_G11](https://enpkg.commons-lab.org/kg/VGF151_G11)\n - BioAssay Result: [VGF151_G11_Tcruzi_10ugml](https://enpkg.commons-lab.org/module/VGF151_G11_Tcruzi_10ugml)\n\n4. Lab Extract: [VGF151_H11](https://enpkg.commons-lab.org/kg/VGF151_H11)\n - BioAssay Result: [VGF151_H11_Tcruzi_10ugml](https://enpkg.commons-lab.org/module/VGF151_H11_Tcruzi_10ugml)\n\nHere is the dictionary containing the question, generated SPARQL query, and file path to the query result:\n\n```json\n{\n \""question\"": \""List the bioassay results (BioAssayResults) at 10碌g/mL against T.cruzi (Tcruzi10ugml) for lab extracts (LabExtract) of Tabernaemontana coffeoides.\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\nPREFIX ns2: \\nPREFIX wd: \\n\\nSELECT ?labExtract ?bioAssayResult\\nWHERE {\\n ?rawMaterial ns1:has_wd_id wd:Q15376858 .\\n ?rawMaterial ns1:has_lab_process ?labExtract .\\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\\n ?bioAssayResult a ns2:Tcruzi10ugml .\\n}\"",\n \""file_path\"": \""/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/341300dcf0174e67999e30cc1e34324b/tmpvt5pfz18.csv\""\n}\n```"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +34, “What are the most frequent SIRIUS chemical structure annotations (planar structure) observed from the extracts belonging to Hibiscus syriacus?”,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the most frequent SIRIUS chemical structure annotations (planar structure) observed from the extracts belonging to Hibiscus syriacus?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the most frequent SIRIUS chemical structure annotations (planar structure) observed from the extracts belonging to Hibiscus syriacus?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Hibiscus syriacus, http://www.wikidata.org/entity/Q157756, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The most frequent SIRIUS chemical structure annotations (planar structure) observed from the extracts belonging to Hibiscus syriacus are as follows:\n\n1. InChIkey2D: [KEMQGTRYUADPNZ](https://enpkg.commons-lab.org/kg/KEMQGTRYUADPNZ) - Frequency: 2\n2. InChIkey2D: [DTOSIQBPPRVQHS](https://enpkg.commons-lab.org/kg/DTOSIQBPPRVQHS) - Frequency: 2\n3. InChIkey2D: [OUMMUORTBYSLFO](https://enpkg.commons-lab.org/kg/OUMMUORTBYSLFO) - Frequency: 2\n\nThese are the most frequently observed annotations, each appearing twice in the dataset. Here is the SPARQL query that was generated:\n\n```sparql\n PREFIX ns1: \\nPREFIX ns2: \\n\\nSELECT ?InChIkey2D (COUNT(?InChIkey2D) AS ?frequency)\\nWHERE {\\n ?rawMaterial ns1:has_wd_id .\\n ?rawMaterial ns1:has_lab_process ?labExtract .\\n ?labExtract ns1:has_LCMS ?analysis .\\n ?analysis ns1:has_lcms_feature_list ?feature_list .\\n ?feature_list ns1:has_lcms_feature ?feature .\\n ?feature ns1:has_sirius_annotation ?annotation .\\n ?annotation ns1:has_InChIkey2D ?InChIkey2D .\\n}\\nGROUP BY ?InChIkey2D\\nORDER BY DESC(?frequency)'"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: PREFIX ns2: -PREFIX wd: -SELECT ?labExtract ?bioAssayResult +SELECT ?InChIkey2D (COUNT(?InChIkey2D) AS ?frequency) WHERE { - ?rawMaterial ns1:has_wd_id wd:Q15376858 . + ?rawMaterial ns1:has_wd_id . ?rawMaterial ns1:has_lab_process ?labExtract . - ?labExtract ns2:has_bioassay_results ?bioAssayResult . - ?bioAssayResult a ns2:Tcruzi10ugml . -}",Medium -41,What are the SIRIUS structural annotations associated with the MS2 spectra from the lab extracts of Tabernaemontana coffeoides ?,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the SIRIUS structural annotations associated with the MS2 spectra from the lab extracts of Tabernaemontana coffeoides ?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the SIRIUS structural annotations associated with the MS2 spectra from the lab extracts of Tabernaemontana coffeoides?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: What are the SIRIUS structural annotations associated with the MS2 spectra from the lab extracts of Tabernaemontana coffeoides?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?siriusAnnotation\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?siriusAnnotation a ns1:SiriusStructureAnnotation .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/d851d6aa70ca4256a4862a6e4811b956/tmpr1p_smpd.csv`\n\nI will now analyze the SPARQL output to provide the final answer.\nThe SPARQL query has been executed successfully, and the results are stored in the specified file. Here is the final answer based on the SPARQL output:\n\n- **Question**: What are the SIRIUS structural annotations (SiriusStructureAnnotation) associated with the MS2 spectra (MS2Spectrum) from the lab extracts (LabExtract) of Tabernaemontana coffeoides?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?siriusAnnotation\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?siriusAnnotation a ns1:SiriusStructureAnnotation .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/d851d6aa70ca4256a4862a6e4811b956/tmpr1p_smpd.csv`\n\nThe file contains the SIRIUS structural annotations associated with the MS2 spectra from the lab extracts of Tabernaemontana coffeoides. You can access the detailed results in the provided file path."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + ?labExtract ns1:has_LCMS ?analysis . + ?analysis ns1:has_lcms_feature_list ?feature_list . + ?feature_list ns1:has_lcms_feature ?feature . + ?feature ns1:has_sirius_annotation ?annotation . + ?annotation ns1:has_InChIkey2D ?InChIkey2D . +} +GROUP BY ?InChIkey2D +ORDER BY DESC(?frequency)",Medium +35,"""Which molecules (represented by their InChIkey) have reported ChEMBL activity with activity type IC50 and activity values below 500 nM against Leishmania donovani target?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which molecules (represented by their InChIkey) have reported ChEMBL activity with activity type IC50 and activity values below 500 nM against Leishmania donovani target?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which molecules (represented by their InChIkey) have reported ChEMBL activity with activity type IC50 and activity values below 500 nM against Leishmania donovani target?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""Leishmania donovani, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL367, ChEMBLTarget"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed successfully. Here are the details:\n\n- **Question**: Which molecules (represented by their InChIkey) have reported ChEMBL activity with activity type IC50 and activity values below 500 nM against Leishmania donovani target?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?InChIkey\n WHERE {\n ?chemicalEntity ns2:has_chembl_id ?chemblChemical .\n ?chemblChemical ns2:has_chembl_activity ?assayResults .\n ?assayResults ns2:target_id ;\n ns2:activity_type \""IC50\"" ;\n ns2:activity_value ?activityValue .\n FILTER(?activityValue < 500)\n ?chemicalEntity ns1:has_wd_id ?wd_id .\n ?InChIkey ns1:has_wd_id ?wd_id .\n }\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/e7807b50a3824198a3233798d22d9a4b/tmpp66dmbc6.csv\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The molecules that meet the criteria have been identified with the following InChIkey: [APKFDSVGJQXUKY-INPOYWNPSA-N](APKFDSVGJQXUKY-INPOYWNPSA-N).\n\n"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + PREFIX ns2: + + SELECT ?InChIkey + WHERE { + ?chemicalEntity ns2:has_chembl_id ?chemblChemical . + ?chemblChemical ns2:has_chembl_activity ?assayResults . + ?assayResults ns2:target_id ; + ns2:activity_type ""IC50"" ; + ns2:activity_value ?activityValue . + FILTER(?activityValue < 500) + ?chemicalEntity ns1:has_wd_id ?wd_id . + ?InChIkey ns1:has_wd_id ?wd_id . + }",High +36," ""What are the SIRIUS structural annotations associated with the MS2 spectra from the lab extracts of Tabernaemontana coffeoides ?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the SIRIUS structural annotations associated with the MS2 spectra from the lab extracts of Tabernaemontana coffeoides ?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the SIRIUS structural annotations associated with the MS2 spectra from the lab extracts of Tabernaemontana coffeoides?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: What are the SIRIUS structural annotations associated with the MS2 spectra from the lab extracts of Tabernaemontana coffeoides?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?siriusAnnotation\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?siriusAnnotation a ns1:SiriusStructureAnnotation .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/d851d6aa70ca4256a4862a6e4811b956/tmpr1p_smpd.csv`\n\nI will now analyze the SPARQL output to provide the final answer.\nThe SPARQL query has been executed successfully, and the results are stored in the specified file. Here is the final answer based on the SPARQL output:\n\n- **Question**: What are the SIRIUS structural annotations (SiriusStructureAnnotation) associated with the MS2 spectra (MS2Spectrum) from the lab extracts (LabExtract) of Tabernaemontana coffeoides?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?siriusAnnotation\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?siriusAnnotation a ns1:SiriusStructureAnnotation .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/d851d6aa70ca4256a4862a6e4811b956/tmpr1p_smpd.csv`\n\nThe file contains the SIRIUS structural annotations associated with the MS2 spectra from the lab extracts of Tabernaemontana coffeoides. You can access the detailed results in the provided file path."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: PREFIX ns2: SELECT ?siriusAnnotation @@ -561,33 +514,25 @@ WHERE { ?feature ns1:has_sirius_annotation ?siriusAnnotation . ?siriusAnnotation a ns1:SiriusStructureAnnotation . }",Low -42,Filter the pos ionization mode features of the Melochia umbellata taxon annotated as [M+H]+ by SIRIUS to keep the ones for which a feature in neg ionization mode is detected with the same retention time (+/- 3 seconds) and a mass corresponding to the [M-H]- SIRIUS adduct (+/- 5ppm). Provide the features and retention time.,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Filter the pos ionization mode features of the Melochia umbellata taxon annotated as [M+H]+ by SIRIUS to keep the ones for which a feature in neg ionization mode is detected with the same retention time (+/- 3 seconds) and a mass corresponding to the [M-H]- SIRIUS adduct (+/- 5ppm). Provide the features and retention time."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Melochia umbellata, http://www.wikidata.org/entity/Q6813281, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n### Question:\nFilter the pos ionization mode features of the Melochia umbellata taxon annotated as [M+H]+ by SIRIUS to keep the ones for which a feature in neg ionization mode is detected with the same retention time (+/- 3 seconds) and a mass corresponding to the [M-H]- SIRIUS adduct (+/- 5ppm).\n\n### Generated SPARQL Query:\n```sparql\nPREFIX ns1: \nPREFIX ns2: \nPREFIX wdt: \n\nSELECT ?posFeature ?negFeature\nWHERE {\n ?rawMaterial ns1:has_wd_id ;\n ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?posAnalysis, ?negAnalysis .\n ?posAnalysis a ns1:LCMSAnalysisPos ;\n ns1:has_lcms_feature_list ?posFeatureList .\n ?negAnalysis a ns1:LCMSAnalysisNeg ;\n ns1:has_lcms_feature_list ?negFeatureList .\n ?posFeatureList ns1:has_lcms_feature ?posFeature .\n ?negFeatureList ns1:has_lcms_feature ?negFeature .\n ?posFeature ns1:has_sirius_annotation ?posAnnotation ;\n ns1:has_retention_time ?posRT ;\n ns1:has_parent_mass ?posMass .\n ?negFeature ns1:has_sirius_annotation ?negAnnotation ;\n ns1:has_retention_time ?negRT ;\n ns1:has_parent_mass ?negMass .\n ?posAnnotation ns1:has_sirius_adduct \""[M+H]+\"" .\n ?negAnnotation ns1:has_sirius_adduct \""[M-H]-\"" .\n FILTER (ABS(?posRT - ?negRT) <= 3)\n FILTER (ABS(?posMass - ?negMass) / ?posMass <= 0.000005)\n}\n```\n\n### File Path:\nThe results of the query have been saved to the following file path: `/tmp/kgbot/tmprvccrkkb.csv`\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +37," ""Which lab extracts from Melochia umbellata yield compounds that, when analyzed in positive ionization mode LCMS, have a retention time of less than 6 minutes and demonstrate an inhibition percentage greater than 70% in bioassay results? Provide lab extracts, retention times and inhibition percentage.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which lab extracts from Melochia umbellata yield compounds that, when analyzed in positive ionization mode LCMS, have a retention time of less than 6 minutes and demonstrate an inhibition percentage greater than 70% in bioassay results? Provide lab extracts, retention times and inhibition percentage."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which lab extracts from Melochia umbellata yield compounds that, when analyzed in positive ionization mode LCMS, have a retention time of less than 6 minutes and demonstrate an inhibition percentage greater than 70% in bioassay results? Provide lab extracts, retention times and inhibition percentage."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""Melochia umbellata, http://www.wikidata.org/entity/Q6813281, Wikidata"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The lab extracts from Melochia umbellata that yield compounds analyzed in positive ionization mode LCMS with a retention time of less than 6 minutes and demonstrate an inhibition percentage greater than 70% in bioassay results are as follows:\n\n- **Lab Extract**: [VGF156_A06](https://enpkg.commons-lab.org/kg/VGF156_A06)\n - **Retention Time**: 4.198916666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 4.54375 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.5790666666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7661 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.6830500000000002 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.089 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.6979 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.5034166666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.26885 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.1985333333333337 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.11295 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7891 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.2292333333333334 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.089 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.5863333333333334 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7204833333333334 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7815333333333332 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7054833333333332 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.4656833333333334 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.2918833333333335 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.3785166666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.2292333333333334 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.33065 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.5962 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.4250166666666668 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.511016666666667 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.6655166666666663 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.8533833333333332 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.8533833333333332 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.5034166666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.5034166666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.3630666666666669 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7512166666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 0.6451166666666667 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7661 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.9627166666666669 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.2918833333333335 minutes\n - **Inhibition Percentage**: 89.9%\n\nThe SPARQL query used to retrieve this information is as follows:\n\n```sparql\nPREFIX ns1: \nPREFIX ns2: \nPREFIX wd: \n\nSELECT ?labExtract ?retentionTime ?inhibitionPercentage\nWHERE {\n ?rawMaterial ns1:has_wd_id wd:Q6813281 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis a ns1:LCMSAnalysisPos .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_retention_time ?retentionTime .\n FILTER(?retentionTime < 6)\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\n ?bioAssayResult ns2:inhibition_percentage ?inhibitionPercentage .\n FILTER(?inhibitionPercentage > 70)\n}\n```\n\nThe results are saved in the file located at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/461195db838a4810afb06014c87c0e6b/tmp_7t2umr1.csv`"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: PREFIX ns2: -PREFIX wdt: +PREFIX wd: -SELECT ?posFeature ?negFeature +SELECT ?labExtract ?retentionTime ?inhibitionPercentage WHERE { - ?rawMaterial ns1:has_wd_id ; - ns1:has_lab_process ?labExtract . - ?labExtract ns1:has_LCMS ?posAnalysis, ?negAnalysis . - ?posAnalysis a ns1:LCMSAnalysisPos ; - ns1:has_lcms_feature_list ?posFeatureList . - ?negAnalysis a ns1:LCMSAnalysisNeg ; - ns1:has_lcms_feature_list ?negFeatureList . - ?posFeatureList ns1:has_lcms_feature ?posFeature . - ?negFeatureList ns1:has_lcms_feature ?negFeature . - ?posFeature ns1:has_sirius_annotation ?posAnnotation ; - ns1:has_retention_time ?posRT ; - ns1:has_parent_mass ?posMass . - ?negFeature ns1:has_sirius_annotation ?negAnnotation ; - ns1:has_retention_time ?negRT ; - ns1:has_parent_mass ?negMass . - ?posAnnotation ns1:has_sirius_adduct ""[M+H]+"" . - ?negAnnotation ns1:has_sirius_adduct ""[M-H]-"" . - FILTER (ABS(?posRT - ?negRT) <= 3) - FILTER (ABS(?posMass - ?negMass) / ?posMass <= 0.000005) -}",High -43,Provide the wikidata ids of the chemical entities annotated by SIRIUS for Tabernaemontana coffeoides seeds extract taxon obtained in positive mode which are also reported in the Tabernaemontana genus in Wikidata.,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Provide the wikidata ids of the chemical entities annotated by SIRIUS for Tabernaemontana coffeoides seeds extract taxon obtained in positive mode which are also reported in the Tabernaemontana genus in Wikidata."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Provide the wikidata ids of the chemical entities annotated by SIRIUS for Tabernaemontana coffeoides seeds extract taxon obtained in positive mode which are also reported in the Tabernaemontana genus in Wikidata."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": [""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata"", ""Tabernaemontana genus, http://www.wikidata.org/entity/Q310915, Wikidata""], ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The final merged output file has been generated. Here are the details:\n\n- **Question**: Provide the wikidata ids of the chemical entities annotated by SIRIUS for Tabernaemontana coffeoides seeds extract taxon obtained in positive mode which are also reported in the Tabernaemontana genus in Wikidata.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?wd_id\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis a ns1:LCMSAnalysisPos .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?annotation .\n ?annotation ns1:has_InChIkey2D ?inchiKey2D .\n ?inchiKey2D ns1:is_InChIkey2D_of ?chemicalEntity .\n ?chemicalEntity ns1:has_wd_id ?wd_id .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/3b8bb2a213ab4b1faf41570486facaae/tmp4g0h_2d5.csv`\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The wikidata IDs of the chemical entities annotated by SIRIUS for Tabernaemontana coffeoides seeds extract taxon obtained in positive mode which are also reported in the Tabernaemontana genus in Wikidata have been processed. \n\n"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + ?rawMaterial ns1:has_wd_id wd:Q6813281 . + ?rawMaterial ns1:has_lab_process ?labExtract . + ?labExtract ns1:has_LCMS ?analysis . + ?analysis a ns1:LCMSAnalysisPos . + ?analysis ns1:has_lcms_feature_list ?featureList . + ?featureList ns1:has_lcms_feature ?feature . + ?feature ns1:has_retention_time ?retentionTime . + FILTER(?retentionTime < 6) + ?labExtract ns2:has_bioassay_results ?bioAssayResult . + ?bioAssayResult ns2:inhibition_percentage ?inhibitionPercentage . + FILTER(?inhibitionPercentage > 70) +}",Medium +38," ""Provide the wikidata ids of the chemical entities annotated by SIRIUS for Tabernaemontana coffeoides seeds extract taxon obtained in positive mode which are also reported in the Tabernaemontana genus in Wikidata.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Provide the wikidata ids of the chemical entities annotated by SIRIUS for Tabernaemontana coffeoides seeds extract taxon obtained in positive mode which are also reported in the Tabernaemontana genus in Wikidata."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Provide the wikidata ids of the chemical entities annotated by SIRIUS for Tabernaemontana coffeoides seeds extract taxon obtained in positive mode which are also reported in the Tabernaemontana genus in Wikidata."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": [""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata"", ""Tabernaemontana genus, http://www.wikidata.org/entity/Q310915, Wikidata""], ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The final merged output file has been generated. Here are the details:\n\n- **Question**: Provide the wikidata ids of the chemical entities annotated by SIRIUS for Tabernaemontana coffeoides seeds extract taxon obtained in positive mode which are also reported in the Tabernaemontana genus in Wikidata.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?wd_id\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis a ns1:LCMSAnalysisPos .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?annotation .\n ?annotation ns1:has_InChIkey2D ?inchiKey2D .\n ?inchiKey2D ns1:is_InChIkey2D_of ?chemicalEntity .\n ?chemicalEntity ns1:has_wd_id ?wd_id .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/3b8bb2a213ab4b1faf41570486facaae/tmp4g0h_2d5.csv`\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The wikidata IDs of the chemical entities annotated by SIRIUS for Tabernaemontana coffeoides seeds extract taxon obtained in positive mode which are also reported in the Tabernaemontana genus in Wikidata have been processed. \n\n"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: PREFIX ns2: SELECT ?wd_id @@ -603,62 +548,81 @@ WHERE { ?inchiKey2D ns1:is_InChIkey2D_of ?chemicalEntity . ?chemicalEntity ns1:has_wd_id ?wd_id . }",High -44,"Which ChEMBL documents cited in ChEMBL assay results involve compounds (identified by InChIKeys) that are also observed in SIRIUS structural annotations within lab extracts, and which lab extracts contain the highest number of these shared compounds?","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which ChEMBL documents cited in ChEMBL assay results involve compounds (identified by InChIKeys) that are also observed in SIRIUS structural annotations within lab extracts, and which lab extracts contain the highest number of these shared compounds?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which ChEMBL documents cited in ChEMBL assay results involve compounds (identified by InChIKeys) that are also observed in SIRIUS structural annotations within lab extracts, and which lab extracts contain the highest number of these shared compounds?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here is the information:\n\n- **Question**: Which ChEMBL documents cited in ChEMBL assay results involve compounds (identified by InChIKeys) that are also observed in SIRIUS structural annotations within lab extracts, and which lab extracts contain the highest number of these shared compounds?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?chemblDocument ?labExtract (COUNT(?InChIkey2D) AS ?sharedCompoundCount)\n WHERE {\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?siriusAnnotation ns1:has_InChIkey2D ?InChIkey2D .\n\n ?InChIkey2D ns1:is_InChIkey2D_of ?chemicalEntity .\n ?chemicalEntity ns2:has_chembl_id ?chemblChemical .\n\n ?chemblChemical ns2:has_chembl_activity ?assayResult .\n ?assayResult ns2:stated_in_document ?chemblDocument .\n }\n GROUP BY ?chemblDocument ?labExtract\n ORDER BY DESC(?sharedCompoundCount)\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/ff49c14268314ce9948ca2f02f8fc1d0/tmpy5x90ltz.csv`\n\nPlease refer to the file for detailed results."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The ChEMBL document and lab extract with the highest number of shared compounds are as follows:\n\n- **ChEMBL Document**: [https://www.ebi.ac.uk/chembl/document_report_c...](https://www.ebi.ac.uk/chembl/document_report_c...)\n- **Lab Extract**: [https://enpkg.commons-lab.org/kg/VGF142_G04](https://enpkg.commons-lab.org/kg/VGF142_G04)\n- **Shared Compound Count**: 125\n\nThis information provides the details of the ChEMBL document and lab extract with the highest number of shared compounds. If you need further analysis or visualization, please let me know.\n\nThe full path of the files generated are: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/ff49c14268314ce9948ca2f02f8fc1d0/tmpy5x90ltz.csv`\n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +39," ""What is the highest inhibition percentage recorded for compounds observed in Rauvolfia vomitoria extract?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What is the highest inhibition percentage recorded for compounds observed in Rauvolfia vomitoria extract?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What is the highest inhibition percentage recorded for compounds observed in Rauvolfia vomitoria extract?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Rauvolfia vomitoria, http://www.wikidata.org/entity/Q5225969, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The highest inhibition percentage recorded for compounds observed in Rauvolfia vomitoria extract is 17.4%.\n\nHere is the detailed information:\n- **Question**: What is the highest inhibition percentage recorded for compounds from Rauvolfia vomitoria extract?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n PREFIX wd: \n\n SELECT ?inhibition_percentage\n WHERE {\n ?rawMaterial ns1:has_wd_id wd:Q5225969 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\n ?bioAssayResult ns2:inhibition_percentage ?inhibition_percentage .\n }\n ORDER BY DESC(?inhibition_percentage)\n LIMIT 1\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/2b0f38f9dd7b4e818282a1c620449ab2/tmpjoi_glcf.csv"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: PREFIX ns2: + PREFIX wd: - SELECT ?chemblDocument ?labExtract (COUNT(?InChIkey2D) AS ?sharedCompoundCount) + SELECT ?inhibition_percentage WHERE { - ?labExtract ns1:has_LCMS ?analysis . - ?analysis ns1:has_lcms_feature_list ?featureList . - ?featureList ns1:has_lcms_feature ?feature . - ?feature ns1:has_sirius_annotation ?siriusAnnotation . - ?siriusAnnotation ns1:has_InChIkey2D ?InChIkey2D . - - ?InChIkey2D ns1:is_InChIkey2D_of ?chemicalEntity . - ?chemicalEntity ns2:has_chembl_id ?chemblChemical . - - ?chemblChemical ns2:has_chembl_activity ?assayResult . - ?assayResult ns2:stated_in_document ?chemblDocument . + ?rawMaterial ns1:has_wd_id wd:Q5225969 . + ?rawMaterial ns1:has_lab_process ?labExtract . + ?labExtract ns2:has_bioassay_results ?bioAssayResult . + ?bioAssayResult ns2:inhibition_percentage ?inhibition_percentage . } - GROUP BY ?chemblDocument ?labExtract - ORDER BY DESC(?sharedCompoundCount)",High -45,What are the Wikidata IDs of chemical entities that have ChEMBL activity against Trypanosoma cruzi target?,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the Wikidata IDs of chemical entities that have ChEMBL activity against Trypanosoma cruzi target?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the Wikidata IDs of chemical entities that have ChEMBL activity against Trypanosoma cruzi target?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Trypanosoma cruzi, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL368, ChEMBLTarget\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""Here is the information you requested:\n\n- **Question**: What are the Wikidata IDs of chemical entities that have ChEMBL activity against Trypanosoma cruzi target?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?wd_id\n WHERE {\n ?chemicalEntity ns2:has_chembl_id ?chemblChemical .\n ?chemblChemical ns2:has_chembl_activity ?assayResults .\n ?assayResults ns2:target_id .\n ?chemicalEntity ns1:has_wd_id ?wd_id .\n }\n ```\n- **File Path**: The results are saved in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/18465df0915e441aa7285e242e5b5927/tmpsavbcot0.csv`.\n\nPlease let me know if you need further assistance!"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The file has been processed, and here are some of the Wikidata IDs of chemical entities that have ChEMBL activity against the Trypanosoma cruzi target:\n\n1. [Q82238903](http://www.wikidata.org/entity/Q82238903)\n2. [Q27095836](http://www.wikidata.org/entity/Q27095836)\n\nThese IDs were extracted from the provided file. If you need further analysis or a specific visualization, please let me know. \n\nNo visualization file was generated, but the data can be downloaded from the provided file path."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + ORDER BY DESC(?inhibition_percentage) + LIMIT 1",Low +40," ""List the bioassay results at 10µg/mL against T.cruzi for lab extracts of Tabernaemontana coffeoides.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""List the bioassay results at 10碌g/mL against T.cruzi for lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: List the bioassay results at 10碌g/mL against T.cruzi for lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The bioassay results at 10碌g/mL against T.cruzi for lab extracts of Tabernaemontana coffeoides are as follows:\n\n1. Lab Extract: [VGF152_B02](https://enpkg.commons-lab.org/kg/VGF152_B02)\n - BioAssay Result: [VGF152_B02_Tcruzi_10ugml](https://enpkg.commons-lab.org/module/VGF152_B02_Tcruzi_10ugml)\n\n2. Lab Extract: [VGF152_A02](https://enpkg.commons-lab.org/kg/VGF152_A02)\n - BioAssay Result: [VGF152_A02_Tcruzi_10ugml](https://enpkg.commons-lab.org/module/VGF152_A02_Tcruzi_10ugml)\n\n3. Lab Extract: [VGF151_G11](https://enpkg.commons-lab.org/kg/VGF151_G11)\n - BioAssay Result: [VGF151_G11_Tcruzi_10ugml](https://enpkg.commons-lab.org/module/VGF151_G11_Tcruzi_10ugml)\n\n4. Lab Extract: [VGF151_H11](https://enpkg.commons-lab.org/kg/VGF151_H11)\n - BioAssay Result: [VGF151_H11_Tcruzi_10ugml](https://enpkg.commons-lab.org/module/VGF151_H11_Tcruzi_10ugml)\n\nHere is the dictionary containing the question, generated SPARQL query, and file path to the query result:\n\n```json\n{\n \""question\"": \""List the bioassay results (BioAssayResults) at 10碌g/mL against T.cruzi (Tcruzi10ugml) for lab extracts (LabExtract) of Tabernaemontana coffeoides.\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\nPREFIX ns2: \\nPREFIX wd: \\n\\nSELECT ?labExtract ?bioAssayResult\\nWHERE {\\n ?rawMaterial ns1:has_wd_id wd:Q15376858 .\\n ?rawMaterial ns1:has_lab_process ?labExtract .\\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\\n ?bioAssayResult a ns2:Tcruzi10ugml .\\n}\"",\n \""file_path\"": \""/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/341300dcf0174e67999e30cc1e34324b/tmpvt5pfz18.csv\""\n}\n```"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +PREFIX ns2: +PREFIX wd: + +SELECT ?labExtract ?bioAssayResult +WHERE { + ?rawMaterial ns1:has_wd_id wd:Q15376858 . + ?rawMaterial ns1:has_lab_process ?labExtract . + ?labExtract ns2:has_bioassay_results ?bioAssayResult . + ?bioAssayResult a ns2:Tcruzi10ugml . +}",Medium +41,"""For the LC-MS feature observed in the extracts of Tabernaemontana coffeoides, retrieve those that are annotated with a NPCPathways with CANOPUS.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""For the LC-MS feature observed in the extracts of Tabernaemontana coffeoides, retrieve those that are annotated with a NPCPathways with CANOPUS."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: For the LC-MS feature observed in the extracts of Tabernaemontana coffeoides, retrieve those that are annotated with a NPCPathways with CANOPUS."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been executed to retrieve the LC-MS features observed in the extracts of Tabernaemontana coffeoides that are annotated with NPCPathways using CANOPUS. Here is the information:\n\n- **SPARQL Query**: \n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?feature\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_pathway ?npcPathway .\n }\n ```\n\n- **File Path**: The results are saved in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/0a0e72082aef4788a45ec734c4f9b49c/tmpqlgm7k3g.csv`.\n\nPlease check the file for the detailed results of the query."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The LC-MS features observed in the extracts of Tabernaemontana coffeoides that are annotated with NPCPathways using CANOPUS have been successfully retrieved. "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: PREFIX ns2: - SELECT ?wd_id + SELECT ?feature WHERE { - ?chemicalEntity ns2:has_chembl_id ?chemblChemical . - ?chemblChemical ns2:has_chembl_activity ?assayResults . - ?assayResults ns2:target_id . - ?chemicalEntity ns1:has_wd_id ?wd_id . + ?rawMaterial ns1:has_wd_id . + ?rawMaterial ns1:has_lab_process ?labExtract . + ?labExtract ns1:has_LCMS ?analysis . + ?analysis ns1:has_lcms_feature_list ?feature_list . + ?feature_list ns1:has_lcms_feature ?feature . + ?feature ns1:has_canopus_annotation ?annotation . + ?annotation ns1:has_canopus_npc_pathway ?npcPathway . }",Medium -46,Which lab extracts have bioassay results with inhibition percentages above 50% against Leishmania donovani target at 10 碌g/mL?,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which lab extracts have bioassay results with inhibition percentages above 50% against Leishmania donovani target at 10 碌g/mL?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which lab extracts have bioassay results with inhibition percentages above 50% against Leishmania donovani target at 10 碌g/mL?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Leishmania donovani, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL367, ChEMBLTarget\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The lab extracts that have bioassay results with inhibition percentages above 50% against the Leishmania donovani target are identified by the following identifiers:\n\n- VGF159_F04\n- VGF159_B08\n- VGF159_B09\n- VGF159_E03\n- VGF159_H03\n- VGF158_C03\n- VGF158_B03\n- VGF158_G06\n- VGF158_A10\n- VGF158_H02\n- VGF158_A03\n- VGF158_D03\n- VGF158_D08\n- VGF158_C04\n- VGF158_H09\n- VGF158_G09\n- VGF157_C04\n- VGF157_D03\n- VGF157_H05\n- VGF157_C03\n- VGF157_C05\n- VGF157_D10\n- VGF157_E04\n- VGF156_E04\n- VGF156_D09\n- VGF156_A05\n- VGF155_B07\n- VGF155_A08\n- VGF155_E10\n- VGF155_D09\n- VGF155_C04\n- VGF155_B05\n- VGF155_C02\n- VGF155_A10\n- VGF155_F10\n- VGF155_D08\n- VGF154_A10\n- VGF154_A02\n- VGF154_A04\n- VGF152_G03\n- VGF152_H03\n- VGF152_D08\n- VGF152_H09\n- VGF152_A10\n- VGF151_A06\n- VGF151_A03\n- VGF151_H08\n- VGF151_H11\n- VGF147_H05\n- VGF147_F04\n- VGF147_G04\n- VGF146_F02\n- VGF145_G10\n- VGF144_E05\n- VGF143_G02\n- VGF142_G04\n- VGF142_B07\n- VGF142_G05\n- VGF142_B10\n- VGF141_C06\n- VGF141_B11\n- VGF140_E05\n- VGF140_A09\n- VGF139_A09\n- VGF139_D08\n- VGF138_B06\n- VGF138_E06\n- VGF138_E03\n- VGF138_E05\n- VGF138_G03\n\nThese identifiers correspond to specific lab extracts that have shown significant inhibition against the Leishmania donovani target.nHere is the SPARQL query that was generated:\n\n```sparql\nPREFIX ns1: \nPREFIX ns2: \n\nSELECT ?labExtract\nWHERE {\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\n ?bioAssayResult ns2:inhibition_percentage ?inhibitionPercentage ;\n ns2:target_id .\n ?bioAssayResult a ns2:Ldono10ugml.\n FILTER(?inhibitionPercentage > 50)\n}\n```\n\nIf you have any further questions or need additional information, feel free to ask!"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +42," ""List the tissue types used to produce the lab extracts of Tabernaemontana coffeoides. Provide the tissue types and lab extracts. ""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""List the tissue types used to produce the lab extracts of Tabernaemontana coffeoides. Provide the tissue types and lab extracts. "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: List the tissue types used to produce the lab extracts of Tabernaemontana coffeoides. Provide the tissue types and lab extracts."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The tissue type used to produce the lab extracts of Tabernaemontana coffeoides is \""green tissue.\"" The lab extracts associated with this tissue type are:\n\n1. Lab Extract: VGF152_B02\n2. Lab Extract: VGF152_A02\n3. Lab Extract: VGF151_G11\n4. Lab Extract: VGF151_H11\n\nHere is the SPARQL query used to retrieve this information:\n\n```sparql\nPREFIX ns1: \nPREFIX ns2: \n\nSELECT ?tissue ?labExtract\nWHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?rawMaterial ns2:has_tissue ?tissue .\n}\n```\n\nThe data was retrieved from the file located at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/79cf06dcfb4c4635bf92d845c42ea938/tmpo0nqoke3.csv`."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: PREFIX ns2: -SELECT ?labExtract +SELECT ?tissue ?labExtract +WHERE { + ?rawMaterial ns1:has_wd_id . + ?rawMaterial ns1:has_lab_process ?labExtract . + ?rawMaterial ns2:has_tissue ?tissue . +}",Low +43,"""Retrieve the mass differences and cosine similarity scores for spectral pairs involving MS2 spectra from Desmodium heterophyllum.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the mass differences and cosine similarity scores for spectral pairs involving MS2 spectra from Desmodium heterophyllum."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the mass differences and cosine similarity scores for spectral pairs involving MS2 spectra from Desmodium heterophyllum."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid. It is related to the ENPKG nodes/entities (Raw Material, LCMS Analysis, SpectralPair). The plant name \""Desmodium heterophyllum\"" is present in the database. The question asks for mass differences and cosine similarity scores, which are properties of the SpectralPair entity in the ENPKG."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Desmodium heterophyllum, http://www.wikidata.org/entity/Q10770714, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The mass differences and cosine similarity scores for spectral pairs involving MS2 spectra from Desmodium heterophyllum have been retrieved. Here is the information:\n\n- **SPARQL Query**: \n ```sparql\n PREFIX ns1: \n\n SELECT ?mass_difference ?cosine\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?spectralPair ns1:has_member ?feature .\n ?spectralPair ns1:has_mass_difference ?mass_difference .\n ?spectralPair ns1:has_cosine ?cosine .\n }\n ```\n\n- **File Path**: The results are stored in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/03572d09329f4855ab27963f8832666f/tmpsuxk8web.csv`.\n\nPlease refer to the file for detailed results."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The mass differences and cosine similarity scores for spectral pairs involving MS2 spectra from Desmodium heterophyllum have been successfully interpreted. Here are some sample entries from the data:\n\n- Mass Difference: 218.2978, Cosine Similarity: 0.999612\n- Mass Difference: 46.0057, Cosine Similarity: 0.999888\n- Mass Difference: 140.0989, Cosine Similarity: 0.985793\n- Mass Difference: 160.1252, Cosine Similarity: 0.993816\n- Mass Difference: 162.1407, Cosine Similarity: 0.984198\n\nNo visualization is available, but the data can be downloaded from the provided file path. If you need a visualization, please let me know, and I can generate a plotly graph for you.\n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + + SELECT ?mass_difference ?cosine + WHERE { + ?rawMaterial ns1:has_wd_id . + ?rawMaterial ns1:has_lab_process ?labExtract . + ?labExtract ns1:has_LCMS ?lcmsAnalysis . + ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList . + ?featureList ns1:has_lcms_feature ?feature . + ?spectralPair ns1:has_member ?feature . + ?spectralPair ns1:has_mass_difference ?mass_difference . + ?spectralPair ns1:has_cosine ?cosine . + }",High +44," ""Retrieve the top 5 tissues used to produce lab extracts that contain compounds with inhibition activity greater than 70% against Leishmania donovani target at 10 µg/mL""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the top 5 tissues used to produce lab extracts that contain compounds with inhibition activity greater than 70% against Leishmania donovani target at 10 碌g/mL."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the top 5 tissues used to produce lab extracts that contain compounds with inhibition activity greater than 70% against Leishmania donovani target at 10 碌g/mL."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Leishmania donovani, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL367, ChEMBLTarget\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The top 5 tissues used to produce lab extracts containing compounds with inhibition activity greater than 70% against the Leishmania donovani target at 10 碌g/mL are:\n\n1. Green Tissue (24 extracts)\n2. Root Tissue (17 extracts)\n3. Multiple Tissues (9 extracts)\n4. Woody Tissue (7 extracts)\n\nThe SPARQL query used to retrieve this information is as follows:\n\n```sparql\nPREFIX ns1: \nPREFIX ns2: \n\nSELECT ?tissue (COUNT(?labExtract) AS ?count)\nWHERE {\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\n ?bioAssayResult ns2:inhibition_percentage ?inhibitionPercentage ;\n ns2:target_id .\n FILTER(?inhibitionPercentage > 70)\n ?rawMaterial ns1:has_lab_process ?labExtract ;\n ns2:has_tissue ?tissue .\n}\nGROUP BY ?tissue\nORDER BY DESC(?count)\nLIMIT 5\n```\n\nThe data is stored in the file located at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/9aa4b47f0e764929989be2184095b3b0/tmp6u2rkep7.csv`."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +PREFIX ns2: + +SELECT ?tissue (COUNT(?labExtract) AS ?count) WHERE { ?labExtract ns2:has_bioassay_results ?bioAssayResult . ?bioAssayResult ns2:inhibition_percentage ?inhibitionPercentage ; ns2:target_id . - ?bioAssayResult a ns2:Ldono10ugml. - FILTER(?inhibitionPercentage > 50) -}",Medium -47,List the top 3 lab extracts with the highest number of unique compounds annotated by CANOPUS with a probability greater than 0.9.,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""List the top 3 lab extracts with the highest number of unique compounds annotated by CANOPUS with a probability greater than 0.9."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: List the top 3 lab extracts with the highest number of unique compounds annotated by CANOPUS with a probability greater than 0.9."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The top 3 lab extracts with the highest number of unique compounds annotated by CANOPUS with a probability greater than 0.9 are:\n\n1. Lab Extract: [SC_AP_Wi_DCM](https://enpkg.commons-lab.org/kg/SC_AP_Wi_DCM) with 69 unique compounds.\n2. Lab Extract: [VGF153_F06](https://enpkg.commons-lab.org/kg/VGF153_F06) with 58 unique compounds.\n3. Lab Extract: [VGF156_B04](https://enpkg.commons-lab.org/kg/VGF156_B04) with 49 unique compounds.\n\nHere is the SPARQL query used to obtain this information:\n\n```sparql\nPREFIX ns1: \n\nSELECT ?labExtract (COUNT(DISTINCT ?compound) AS ?uniqueCompounds)\nWHERE {\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_class ?compound .\n ?annotation ns1:has_canopus_npc_class_prob ?probability .\n FILTER(?probability > 0.9)\n}\nGROUP BY ?labExtract\nORDER BY DESC(?uniqueCompounds)\nLIMIT 3\n```\n\nThe results are saved in the file located at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/5cf09a28bf1049dbb23b09915c0fc8e1/tmp35c6yy5y.csv`."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: - -SELECT ?labExtract (COUNT(DISTINCT ?compound) AS ?uniqueCompounds) -WHERE { - ?labExtract ns1:has_LCMS ?analysis . - ?analysis ns1:has_lcms_feature_list ?featureList . - ?featureList ns1:has_lcms_feature ?feature . - ?feature ns1:has_canopus_annotation ?annotation . - ?annotation ns1:has_canopus_npc_class ?compound . - ?annotation ns1:has_canopus_npc_class_prob ?probability . - FILTER(?probability > 0.9) + FILTER(?inhibitionPercentage > 70) + ?rawMaterial ns1:has_lab_process ?labExtract ; + ns2:has_tissue ?tissue . } -GROUP BY ?labExtract -ORDER BY DESC(?uniqueCompounds) -LIMIT 3",High -48,Retrieve the Wikidata IDs of chemical entities classified as Terpenoids in the lab extracts of Tabernaemontana coffeoides.,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the Wikidata IDs of chemical entities classified as Terpenoids in the lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the Wikidata IDs of chemical entities classified as Terpenoids in the lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Terpenoids, https://enpkg.commons-lab.org/kg/npc_Terpenoids, NPCPathway;\nTabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""Here is the information retrieved from the SPARQL query:\n\n- **Question**: Retrieve the Wikidata IDs of chemical entities classified as Terpenoids in the lab extracts of Tabernaemontana coffeoides.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT ?wd_id\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?annotation .\n ?annotation ns1:has_InChIkey2D ?InChIkey2D .\n ?InChIkey2D ns1:is_InChIkey2D_of ?chemicalEntity .\n ?chemicalEntity ns1:has_npc_pathway .\n ?chemicalEntity ns1:has_wd_id ?wd_id .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/40c708550e684b0685c41a14cae47bd2/tmpcgattsm4.csv`\n\nThis information will be provided to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The Wikidata IDs of the chemical entities classified as Terpenoids in the lab extracts of Tabernaemontana coffeoides are:\n\n1. [Q105262918](http://www.wikidata.org/entity/Q105262918)\n2. [Q104398790](http://www.wikidata.org/entity/Q104398790)\n3. [Q105262918](http://www.wikidata.org/entity/Q105262918)\n4. [Q104398790](http://www.wikidata.org/entity/Q104398790)\n5. [Q104953850](http://www.wikidata.org/entity/Q104953850)\n\nNo visualization is available, but the data can be downloaded if needed. If you need further analysis or a specific visualization, please let me know. \n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +GROUP BY ?tissue +ORDER BY DESC(?count) +LIMIT 5",High +45," ""Retrieve the Wikidata IDs of chemical entities classified as Terpenoids in the lab extracts of Tabernaemontana coffeoides.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the Wikidata IDs of chemical entities classified as Terpenoids in the lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the Wikidata IDs of chemical entities classified as Terpenoids in the lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Terpenoids, https://enpkg.commons-lab.org/kg/npc_Terpenoids, NPCPathway;\nTabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""Here is the information retrieved from the SPARQL query:\n\n- **Question**: Retrieve the Wikidata IDs of chemical entities classified as Terpenoids in the lab extracts of Tabernaemontana coffeoides.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT ?wd_id\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?annotation .\n ?annotation ns1:has_InChIkey2D ?InChIkey2D .\n ?InChIkey2D ns1:is_InChIkey2D_of ?chemicalEntity .\n ?chemicalEntity ns1:has_npc_pathway .\n ?chemicalEntity ns1:has_wd_id ?wd_id .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/40c708550e684b0685c41a14cae47bd2/tmpcgattsm4.csv`\n\nThis information will be provided to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The Wikidata IDs of the chemical entities classified as Terpenoids in the lab extracts of Tabernaemontana coffeoides are:\n\n1. [Q105262918](http://www.wikidata.org/entity/Q105262918)\n2. [Q104398790](http://www.wikidata.org/entity/Q104398790)\n3. [Q105262918](http://www.wikidata.org/entity/Q105262918)\n4. [Q104398790](http://www.wikidata.org/entity/Q104398790)\n5. [Q104953850](http://www.wikidata.org/entity/Q104953850)\n\nNo visualization is available, but the data can be downloaded if needed. If you need further analysis or a specific visualization, please let me know. \n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: SELECT ?wd_id WHERE { @@ -673,23 +637,7 @@ LIMIT 3",High ?chemicalEntity ns1:has_npc_pathway . ?chemicalEntity ns1:has_wd_id ?wd_id . }",High -49,What are the most frequent adducts of LCMS features annotated by SIRIUS detected in positive ionization mode from Tabernaemontana coffeoides?,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the most frequent adducts of LCMS features annotated by SIRIUS detected in positive ionization mode from Tabernaemontana coffeoides?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the most frequent adducts of LCMS features annotated by SIRIUS detected in positive ionization mode from Tabernaemontana coffeoides?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The most frequent adducts of LCMS features annotated by SIRIUS detected in positive ionization mode from Tabernaemontana coffeoides are:\n\n1. [M+H]+ with a frequency of 1079\n2. [M+NH4]+ with a frequency of 432\n3. [M+Na]+ with a frequency of 263\n4. [M+K]+ with a frequency of 65\n\nHere is the dictionary containing the question, generated SPARQL query, and file path:\n\n- **Question**: What are the most frequent adducts of LCMS features annotated by SIRIUS detected in positive ionization mode from Tabernaemontana coffeoides?\n- **Generated SPARQL Query**: \n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?adduct (COUNT(?adduct) AS ?frequency)\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis a ns1:LCMSAnalysisPos .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?annotation .\n ?annotation ns1:has_adduct ?adduct .\n }\n GROUP BY ?adduct\n ORDER BY DESC(?frequency)\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/d8669ea7b6ab4a75a171344bc71c5b61/tmpnm0aoqfg.csv`"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: - PREFIX ns2: - - SELECT ?adduct (COUNT(?adduct) AS ?frequency) - WHERE { - ?rawMaterial ns1:has_wd_id . - ?rawMaterial ns1:has_lab_process ?labExtract . - ?labExtract ns1:has_LCMS ?lcmsAnalysis . - ?lcmsAnalysis a ns1:LCMSAnalysisPos . - ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList . - ?featureList ns1:has_lcms_feature ?feature . - ?feature ns1:has_sirius_annotation ?annotation . - ?annotation ns1:has_adduct ?adduct . - } - GROUP BY ?adduct - ORDER BY DESC(?frequency)",Medium -50,What are the CANOPUS annotations for pairs of features from Desmodium heterophyllum extracts that share a cosine similarity score greater than 0.8? Provide the features and annotations for each feature.,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the CANOPUS annotations for pairs of features from Desmodium heterophyllum extracts that share a cosine similarity score greater than 0.8? Provide the features and annotations for each feature. "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the CANOPUS annotations for pairs of features from Desmodium heterophyllum extracts that share a cosine similarity score greater than 0.8? Provide the features and annotations for each feature."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Desmodium heterophyllum, http://www.wikidata.org/entity/Q10770714, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: What are the CANOPUS annotations for pairs of features from Desmodium heterophyllum extracts that share a cosine similarity score greater than 0.8? Provide the features and annotations for each feature .\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?feature1 ?feature2 ?annotation1 ?annotation2 \n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature1, ?feature2 .\n ?pair ns1:has_member ?feature1, ?feature2 .\n ?pair ns1:has_cosine ?cosine .\n FILTER(?cosine > 0.8)\n ?feature1 ns1:has_canopus_annotation ?annotation1 .\n ?feature2 ns1:has_canopus_annotation ?annotation2 .\n }\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/e88e690ae0ad4816a764af78599a625a/tmp2igkyrj2.csv\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The analysis has been completed, and there are 14,484 pairs of features from Desmodium heterophyllum extracts that share a cosine similarity score greater than 0.8. The features, annotations, and cosine scores have been extracted.\n\nIf you need more specific details or a visualization of this data, please let me know. The data can be downloaded for further analysis. \n\nThe full path of the generated file is:\n- /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/e88e690ae0ad4816a764af78599a625a/tmp2igkyrj2.csv\n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +46,"""What are the CANOPUS annotations for pairs of features from Desmodium heterophyllum extracts that share a cosine similarity score greater than 0.8? Provide the features, and annotations""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the CANOPUS annotations for pairs of features from Desmodium heterophyllum extracts that share a cosine similarity score greater than 0.8? Provide the features and annotations for each feature. "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the CANOPUS annotations for pairs of features from Desmodium heterophyllum extracts that share a cosine similarity score greater than 0.8? Provide the features and annotations for each feature."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Desmodium heterophyllum, http://www.wikidata.org/entity/Q10770714, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: What are the CANOPUS annotations for pairs of features from Desmodium heterophyllum extracts that share a cosine similarity score greater than 0.8? Provide the features and annotations for each feature .\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?feature1 ?feature2 ?annotation1 ?annotation2 \n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature1, ?feature2 .\n ?pair ns1:has_member ?feature1, ?feature2 .\n ?pair ns1:has_cosine ?cosine .\n FILTER(?cosine > 0.8)\n ?feature1 ns1:has_canopus_annotation ?annotation1 .\n ?feature2 ns1:has_canopus_annotation ?annotation2 .\n }\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/e88e690ae0ad4816a764af78599a625a/tmp2igkyrj2.csv\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The analysis has been completed, and there are 14,484 pairs of features from Desmodium heterophyllum extracts that share a cosine similarity score greater than 0.8. The features, annotations, and cosine scores have been extracted.\n\nIf you need more specific details or a visualization of this data, please let me know. The data can be downloaded for further analysis. \n\nThe full path of the generated file is:\n- /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/e88e690ae0ad4816a764af78599a625a/tmp2igkyrj2.csv\n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: PREFIX ns2: SELECT ?feature1 ?feature2 ?annotation1 ?annotation2 @@ -704,4 +652,61 @@ LIMIT 3",High FILTER(?cosine > 0.8) ?feature1 ns1:has_canopus_annotation ?annotation1 . ?feature2 ns1:has_canopus_annotation ?annotation2 . - }",High \ No newline at end of file + }",High +47,"""Which ChEMBL documents cited in ChEMBL assay results involve compounds (identified by InChIKeys) that are also observed in SIRIUS structural annotations within lab extracts, and which lab extracts contain the highest number of these shared compounds?","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which ChEMBL documents cited in ChEMBL assay results involve compounds (identified by InChIKeys) that are also observed in SIRIUS structural annotations within lab extracts, and which lab extracts contain the highest number of these shared compounds?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which ChEMBL documents cited in ChEMBL assay results involve compounds (identified by InChIKeys) that are also observed in SIRIUS structural annotations within lab extracts, and which lab extracts contain the highest number of these shared compounds?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here is the information:\n\n- **Question**: Which ChEMBL documents cited in ChEMBL assay results involve compounds (identified by InChIKeys) that are also observed in SIRIUS structural annotations within lab extracts, and which lab extracts contain the highest number of these shared compounds?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?chemblDocument ?labExtract (COUNT(?InChIkey2D) AS ?sharedCompoundCount)\n WHERE {\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?siriusAnnotation ns1:has_InChIkey2D ?InChIkey2D .\n\n ?InChIkey2D ns1:is_InChIkey2D_of ?chemicalEntity .\n ?chemicalEntity ns2:has_chembl_id ?chemblChemical .\n\n ?chemblChemical ns2:has_chembl_activity ?assayResult .\n ?assayResult ns2:stated_in_document ?chemblDocument .\n }\n GROUP BY ?chemblDocument ?labExtract\n ORDER BY DESC(?sharedCompoundCount)\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/ff49c14268314ce9948ca2f02f8fc1d0/tmpy5x90ltz.csv`\n\nPlease refer to the file for detailed results."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The ChEMBL document and lab extract with the highest number of shared compounds are as follows:\n\n- **ChEMBL Document**: [https://www.ebi.ac.uk/chembl/document_report_c...](https://www.ebi.ac.uk/chembl/document_report_c...)\n- **Lab Extract**: [https://enpkg.commons-lab.org/kg/VGF142_G04](https://enpkg.commons-lab.org/kg/VGF142_G04)\n- **Shared Compound Count**: 125\n\nThis information provides the details of the ChEMBL document and lab extract with the highest number of shared compounds. If you need further analysis or visualization, please let me know.\n\nThe full path of the files generated are: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/ff49c14268314ce9948ca2f02f8fc1d0/tmpy5x90ltz.csv`\n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + PREFIX ns2: + + SELECT ?chemblDocument ?labExtract (COUNT(?InChIkey2D) AS ?sharedCompoundCount) + WHERE { + ?labExtract ns1:has_LCMS ?analysis . + ?analysis ns1:has_lcms_feature_list ?featureList . + ?featureList ns1:has_lcms_feature ?feature . + ?feature ns1:has_sirius_annotation ?siriusAnnotation . + ?siriusAnnotation ns1:has_InChIkey2D ?InChIkey2D . + + ?InChIkey2D ns1:is_InChIkey2D_of ?chemicalEntity . + ?chemicalEntity ns2:has_chembl_id ?chemblChemical . + + ?chemblChemical ns2:has_chembl_activity ?assayResult . + ?assayResult ns2:stated_in_document ?chemblDocument . + } + GROUP BY ?chemblDocument ?labExtract + ORDER BY DESC(?sharedCompoundCount)",High +48," “What are the ChEMBL targets +linked to ChEMBL assay results for chemicals sharing InChIKeys with SIRIUS structural annotations ?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the ChEMBL targets linked to ChEMBL assay results for chemicals sharing InChIKeys with SIRIUS structural annotations ?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the ChEMBL targets linked to ChEMBL assay results for chemicals sharing InChIKeys with SIRIUS structural annotations ?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed. Here are the details:\n\n- **Question**: What are the ChEMBL targets linked to ChEMBL assay results for chemicals sharing InChIKeys with SIRIUS structural annotations?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?chembl_target\n WHERE {\n ?sirius_annotation ns1:has_InChIkey2D ?InChIkey2D .\n ?InChIkey2D ns1:is_InChIkey2D_of ?InChIkey .\n ?InChIkey ns2:has_chembl_id ?chembl_chemical .\n ?chembl_chemical ns2:has_chembl_activity ?chembl_assay_results .\n ?chembl_assay_results ns2:target_id ?chembl_target .\n }\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/f016f234d53147b3a0454bd652bc8f6b/tmppen79nmc.csv\n\nI will now analyze the SPARQL output to generate the final answer.\nThe final answer based on the SPARQL output is as follows:\n\n- **ChEMBL Targets (ChEMBLTarget)**: The ChEMBL targets linked to ChEMBL assay results for chemicals sharing InChIKeys with SIRIUS structural annotations are listed in the file located at the following path:\n - **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/f016f234d53147b3a0454bd652bc8f6b/tmppen79nmc.csv\n\nHere is the dictionary containing the question, generated SPARQL query, and file path:\n\n```json\n{\n \""question\"": \""What are the ChEMBL targets (ChEMBLTarget) linked to ChEMBL assay results (ChEMBLAssayResults) for chemicals (ChEMBLChemical) sharing InChIKeys (InChIkey) with SIRIUS structural annotations (SiriusStructureAnnotation)?\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\nPREFIX ns2: \\n\\nSELECT ?chembl_target\\nWHERE {\\n ?sirius_annotation ns1:has_InChIkey2D ?InChIkey2D .\\n ?InChIkey2D ns1:is_InChIkey2D_of ?InChIkey .\\n ?InChIkey ns2:has_chembl_id ?chembl_chemical .\\n ?chembl_chemical ns2:has_chembl_activity ?chembl_assay_results .\\n ?chembl_assay_results ns2:target_id ?chembl_target .\\n}\"",\n \""file_path\"": \""/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/f016f234d53147b3a0454bd652bc8f6b/tmppen79nmc.csv\""\n}\n```\n\nPlease review the file for the detailed list of ChEMBL targets."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + PREFIX ns2: + + SELECT ?chembl_target + WHERE { + ?sirius_annotation ns1:has_InChIkey2D ?InChIkey2D . + ?InChIkey2D ns1:is_InChIkey2D_of ?InChIkey . + ?InChIkey ns2:has_chembl_id ?chembl_chemical . + ?chembl_chemical ns2:has_chembl_activity ?chembl_assay_results . + ?chembl_assay_results ns2:target_id ?chembl_target . + }",High +49,"""What are SIRIUS structural annotations for LCMS features with cosmic scores above 0.3 associated with Tabernaemontana coffeoides? Report their ZODIAC scores. ""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are SIRIUS structural annotations for LCMS features with cosmic scores above 0.3 associated with Tabernaemontana coffeoides? Report their ZODIAC scores."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the cosmic scores and zodiac scores associated with SIRIUS structural annotations for LCMS features from Tabernaemontana coffeoides?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The zodiac scores associated with SIRIUS structural annotations for LCMS features from Tabernaemontana coffeoides have been retrieved. Here is the information:\n\n- **SPARQL Query**: \n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?cosmic_score ?zodiac_score\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?siriusAnnotation ns1:has_cosmic_score ?cosmic_score .\n FILTER (?cosmic_score > 0.3) \n ?siriusAnnotation ns1:has_zodiac_score ?zodiac_score .\n }\n ```\n\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/5137b0a478de42c5b07851d5252b3e4c/tmpn4ce1k2v.csv`\n\nPlease refer to the file for detailed scores."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: + PREFIX ns2: + + SELECT ?cosmic_score ?zodiac_score + WHERE { + ?rawMaterial ns1:has_wd_id . + ?rawMaterial ns1:has_lab_process ?labExtract . + ?labExtract ns1:has_LCMS ?analysis . + ?analysis ns1:has_lcms_feature_list ?feature_list . + ?feature_list ns1:has_lcms_feature ?feature . + ?feature ns1:has_sirius_annotation ?siriusAnnotation . + ?siriusAnnotation ns1:has_cosmic_score ?cosmic_score . + FILTER (?cosmic_score > 0.3) + ?siriusAnnotation ns1:has_zodiac_score ?zodiac_score . + }",High +50,""" Which lab extracts have bioassay results with inhibition percentages above 50% against Leishmania donovani target at 10 µg/mL ??""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which lab extracts have bioassay results with inhibition percentages above 50% against Leishmania donovani target at 10 碌g/mL?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which lab extracts have bioassay results with inhibition percentages above 50% against Leishmania donovani target at 10 碌g/mL?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Leishmania donovani, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL367, ChEMBLTarget\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The lab extracts that have bioassay results with inhibition percentages above 50% against the Leishmania donovani target are identified by the following identifiers:\n\n- VGF159_F04\n- VGF159_B08\n- VGF159_B09\n- VGF159_E03\n- VGF159_H03\n- VGF158_C03\n- VGF158_B03\n- VGF158_G06\n- VGF158_A10\n- VGF158_H02\n- VGF158_A03\n- VGF158_D03\n- VGF158_D08\n- VGF158_C04\n- VGF158_H09\n- VGF158_G09\n- VGF157_C04\n- VGF157_D03\n- VGF157_H05\n- VGF157_C03\n- VGF157_C05\n- VGF157_D10\n- VGF157_E04\n- VGF156_E04\n- VGF156_D09\n- VGF156_A05\n- VGF155_B07\n- VGF155_A08\n- VGF155_E10\n- VGF155_D09\n- VGF155_C04\n- VGF155_B05\n- VGF155_C02\n- VGF155_A10\n- VGF155_F10\n- VGF155_D08\n- VGF154_A10\n- VGF154_A02\n- VGF154_A04\n- VGF152_G03\n- VGF152_H03\n- VGF152_D08\n- VGF152_H09\n- VGF152_A10\n- VGF151_A06\n- VGF151_A03\n- VGF151_H08\n- VGF151_H11\n- VGF147_H05\n- VGF147_F04\n- VGF147_G04\n- VGF146_F02\n- VGF145_G10\n- VGF144_E05\n- VGF143_G02\n- VGF142_G04\n- VGF142_B07\n- VGF142_G05\n- VGF142_B10\n- VGF141_C06\n- VGF141_B11\n- VGF140_E05\n- VGF140_A09\n- VGF139_A09\n- VGF139_D08\n- VGF138_B06\n- VGF138_E06\n- VGF138_E03\n- VGF138_E05\n- VGF138_G03\n\nThese identifiers correspond to specific lab extracts that have shown significant inhibition against the Leishmania donovani target.nHere is the SPARQL query that was generated:\n\n```sparql\nPREFIX ns1: \nPREFIX ns2: \n\nSELECT ?labExtract\nWHERE {\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\n ?bioAssayResult ns2:inhibition_percentage ?inhibitionPercentage ;\n ns2:target_id .\n ?bioAssayResult a ns2:Ldono10ugml.\n FILTER(?inhibitionPercentage > 50)\n}\n```\n\nIf you have any further questions or need additional information, feel free to ask!"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +PREFIX ns2: + +SELECT ?labExtract +WHERE { + ?labExtract ns2:has_bioassay_results ?bioAssayResult . + ?bioAssayResult ns2:inhibition_percentage ?inhibitionPercentage ; + ns2:target_id . + ?bioAssayResult a ns2:Ldono10ugml. + FILTER(?inhibitionPercentage > 50) +}",Medium From 3aa945820fe124dc4a45891410371de3314cbefa Mon Sep 17 00:00:00 2001 From: madina1203 Date: Fri, 24 Apr 2026 10:27:30 +0200 Subject: [PATCH 07/21] Update installation_test --- app/tests/installation_test.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/app/tests/installation_test.py b/app/tests/installation_test.py index d81cc33..3e2b439 100644 --- a/app/tests/installation_test.py +++ b/app/tests/installation_test.py @@ -1,12 +1,14 @@ -# installation_test.py - import sys +from pathlib import Path +from app.core.main import main + +REPO_ROOT = Path(__file__).resolve().parents[2] +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) -# Automatically provide a question argument for a quick installation test. if "-q" not in sys.argv and "--question" not in sys.argv: sys.argv.extend(["-q", "1"]) -from app.core.main import main -if __name__ == '__main__': - main() \ No newline at end of file +if __name__ == "__main__": + main() From 538479643c619dbca8e6ad7dc0d5d13bcd0785b2 Mon Sep 17 00:00:00 2001 From: madina1203 Date: Fri, 24 Apr 2026 11:49:45 +0200 Subject: [PATCH 08/21] Clean the evaluation dateset (remove redundant column) --- app/data/evaluation_dataset.csv | 102 ++++++++++++++++---------------- 1 file changed, 51 insertions(+), 51 deletions(-) diff --git a/app/data/evaluation_dataset.csv b/app/data/evaluation_dataset.csv index c35614e..ef5b369 100644 --- a/app/data/evaluation_dataset.csv +++ b/app/data/evaluation_dataset.csv @@ -1,5 +1,5 @@ -#,Questions,Traces,SPARQL Query,Complexity -1," ""Which plant has extracts containing compounds that demonstrated inhibition rates above 50% against Trypanosoma cruzi and are above 800 Da in mass? Provide the lab extracts.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which plant has extracts containing compounds that demonstrated inhibition rates above 50% against Trypanosoma cruzi and are above 800 Da in mass? Provide the lab extracts"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which plant has extracts containing compounds that demonstrated inhibition rates above 50% against Trypanosoma cruzi and are above 800 Da in mass? Provide the lab extracts."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid. It is related to the ENPKG nodes/entities (Raw Material, Lab Extract, BioAssayResult, Chemical Entity). The question asks for information about plant extracts containing compounds that have demonstrated a certain level of inhibition against a specific target and have a certain mass, which can be retrieved from the ENPKG."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Trypanosoma cruzi, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL368, ChEMBLTarget\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The query has been processed successfully. Here is the information:\n\n- **Question**: Which plant has extracts containing compounds that demonstrated inhibition rates above 50% against Trypanosoma cruzi and are above 800 Da in mass? Provide the lab extracts.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?plant ?labExtract\n WHERE {\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_parent_mass ?mass .\n FILTER(?mass > 800)\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\n ?bioAssayResult ns2:inhibition_percentage ?inhibition .\n FILTER(?inhibition > 50)\n ?bioAssayResult ns2:target_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?rawMaterial ns1:submitted_taxon ?plant .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/cbb95402a0b148239141ff5dfd609728/tmpixjks0gp.csv`\n\nPlease refer to the file for detailed results on the plants and their corresponding lab extracts."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The analysis of the file indicates that there are no plant extracts meeting the specified criteria of having compounds with inhibition rates above 50% against Trypanosoma cruzi and a mass above 800 Da. If you have any other questions or need further analysis, please let me know. \n\nNo visualization is available, but the data can be downloaded for further inspection. \n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +#,Questions,Reference SPARQL Query,Complexity +1," ""Which plant has extracts containing compounds that demonstrated inhibition rates above 50% against Trypanosoma cruzi and are above 800 Da in mass? Provide the lab extracts.""","PREFIX ns1: PREFIX ns2: SELECT ?plant ?labExtract @@ -16,7 +16,7 @@ ?rawMaterial ns1:has_lab_process ?labExtract . ?rawMaterial ns1:submitted_taxon ?plant . }",High -2,"""Filter the pos ionization mode features of the Melochia umbellata taxon annotated as [M+H]+ by SIRIUS to keep the ones for which a feature in neg ionization mode is detected with the same retention time (+/- 3 seconds) and a mass corresponding to the [M-H]- SIRIUS adduct (+/- 5ppm). Provide the features and retention time.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Filter the pos ionization mode features of the Melochia umbellata taxon annotated as [M+H]+ by SIRIUS to keep the ones for which a feature in neg ionization mode is detected with the same retention time (+/- 3 seconds) and a mass corresponding to the [M-H]- SIRIUS adduct (+/- 5ppm). Provide the features and retention time."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Melochia umbellata, http://www.wikidata.org/entity/Q6813281, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n### Question:\nFilter the pos ionization mode features of the Melochia umbellata taxon annotated as [M+H]+ by SIRIUS to keep the ones for which a feature in neg ionization mode is detected with the same retention time (+/- 3 seconds) and a mass corresponding to the [M-H]- SIRIUS adduct (+/- 5ppm).\n\n### Generated SPARQL Query:\n```sparql\nPREFIX ns1: \nPREFIX ns2: \nPREFIX wdt: \n\nSELECT ?posFeature ?negFeature\nWHERE {\n ?rawMaterial ns1:has_wd_id ;\n ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?posAnalysis, ?negAnalysis .\n ?posAnalysis a ns1:LCMSAnalysisPos ;\n ns1:has_lcms_feature_list ?posFeatureList .\n ?negAnalysis a ns1:LCMSAnalysisNeg ;\n ns1:has_lcms_feature_list ?negFeatureList .\n ?posFeatureList ns1:has_lcms_feature ?posFeature .\n ?negFeatureList ns1:has_lcms_feature ?negFeature .\n ?posFeature ns1:has_sirius_annotation ?posAnnotation ;\n ns1:has_retention_time ?posRT ;\n ns1:has_parent_mass ?posMass .\n ?negFeature ns1:has_sirius_annotation ?negAnnotation ;\n ns1:has_retention_time ?negRT ;\n ns1:has_parent_mass ?negMass .\n ?posAnnotation ns1:has_sirius_adduct \""[M+H]+\"" .\n ?negAnnotation ns1:has_sirius_adduct \""[M-H]-\"" .\n FILTER (ABS(?posRT - ?negRT) <= 3)\n FILTER (ABS(?posMass - ?negMass) / ?posMass <= 0.000005)\n}\n```\n\n### File Path:\nThe results of the query have been saved to the following file path: `/tmp/kgbot/tmprvccrkkb.csv`\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +2,"""Filter the pos ionization mode features of the Melochia umbellata taxon annotated as [M+H]+ by SIRIUS to keep the ones for which a feature in neg ionization mode is detected with the same retention time (+/- 3 seconds) and a mass corresponding to the [M-H]- SIRIUS adduct (+/- 5ppm). Provide the features and retention time.""","PREFIX ns1: PREFIX ns2: PREFIX wdt: @@ -42,14 +42,14 @@ WHERE { FILTER (ABS(?posRT - ?negRT) <= 3) FILTER (ABS(?posMass - ?negMass) / ?posMass <= 0.000005) }",High -3," ""How unique InChIKey2D are among the structural annotations?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""How unique InChIKey2D are among the structural annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: How unique InChIKey2D are among the structural annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid. It is related to the ENPKG nodes/entities (InChIKey2D, Annotation). The question is asking for unique InChIKey2D among the structural annotations, which is a valid query for the knowledge graph."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The number of unique InChIKey2D among the structural annotations is 106,647.\n\nHere is the information related to the SPARQL query:\n\n- **Question**: How unique InChIKey2D are among the structural annotations?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT (COUNT(DISTINCT ?InChIkey2D) AS ?uniqueInChIkey2DCount)\n WHERE {\n ?annotation ns1:has_InChIkey2D ?InChIkey2D .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/73fb3b1ea55e4c8d93e92080edfdd449/tmphyuvm3dr.csv`\n\nThis result indicates the count of unique InChIKey2D identifiers found among the structural annotations in the knowledge graph."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +3," ""How unique InChIKey2D are among the structural annotations?""","PREFIX ns1: SELECT (COUNT(DISTINCT ?InChIkey2D) AS ?uniqueInChIkey2DCount) WHERE { ?annotation ns1:has_InChIkey2D ?InChIkey2D . }",Low 4," ""What are the retention times and molecular masses of compounds identified in the negative ionization mode LCMS analysis from Lovoa trichilioides extracts, and what are the CANOPUS chemical class annotations for those features?"" -","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the retention times and molecular masses of compounds identified in the negative ionization mode LCMS analysis from Lovoa trichilioides extracts, and what are the CANOPUS chemical class annotations for those features?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the retention times and molecular masses of compounds identified in the negative ionization mode LCMS analysis from Lovoa trichilioides extracts, and what are the CANOPUS chemical class annotations for those features?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Lovoa trichilioides, http://www.wikidata.org/entity/Q8008828, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: What are the retention times and molecular masses of compounds identified in the negative ionization mode LCMS analysis from Lovoa trichilioides extracts, and what are the CANOPUS chemical class annotations for those features?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n PREFIX wd: \n\n SELECT ?retentionTime ?molecularMass ?canopusClass\n WHERE {\n ?rawMaterial ns1:has_wd_id wd:Q8008828 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysisNeg .\n ?lcmsAnalysisNeg a ns1:LCMSAnalysisNeg .\n ?lcmsAnalysisNeg ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_retention_time ?retentionTime .\n ?feature ns1:has_parent_mass ?molecularMass .\n ?feature ns1:has_canopus_annotation ?canopusAnnotation .\n ?canopusAnnotation ns1:has_canopus_npc_class ?canopusClass .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/1ee4538b31f946faaa8a05370b67774f/tmpwx92t25x.csv`\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The retention times, molecular masses, and CANOPUS chemical class annotations for the compounds identified in the negative ionization mode LCMS analysis from Lovoa trichilioides extracts have been processed. "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +","PREFIX ns1: PREFIX ns2: PREFIX wd: @@ -67,7 +67,7 @@ WHERE { ?canopusAnnotation ns1:has_canopus_npc_class ?canopusClass . }",High 5," ""What are the retention times and adduct types of LCMS features from Desmodium heterophyllum annotated by SIRIUS with a cosmic score greater than 0.4??"" -","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the retention times and adduct types of LCMS features from Desmodium heterophyllum annotated by SIRIUS with a cosmic score greater than 0.4??"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the retention times and adduct types of LCMS features from Desmodium heterophyllum annotated by SIRIUS with a cosmic score greater than 0.4?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Desmodium heterophyllum, http://www.wikidata.org/entity/Q10770714, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here is the information:\n\n- **Question**: What are the retention times and adduct types of LCMS features from Desmodium heterophyllum annotated by SIRIUS with a cosmic score greater than 0.4?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?retentionTime ?adduct\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_retention_time ?retentionTime .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?siriusAnnotation ns1:has_cosmic_score ?cosmicScore .\n ?siriusAnnotation ns1:has_adduct ?adduct .\n FILTER(?cosmicScore > 0.4)\n }\n ```\n- **File Path**: The results are stored in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/25ad11d8258b4723b0a4653d1b0de5e7/tmpvmkstwx8.csv`.\n\nPlease let me know if you need further assistance or analysis of the results!"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +","PREFIX ns1: PREFIX ns2: SELECT ?retentionTime ?adduct @@ -83,7 +83,7 @@ WHERE { ?siriusAnnotation ns1:has_adduct ?adduct . FILTER(?cosmicScore > 0.4) }",High -6," ""What are the LCMS features that have feature areas greater than 1,000,000 in positive ionization mode ? Sort by decreasing feature area value.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the LCMS features that have feature areas greater than 1,000,000 in positive ionization mode? Sort by decreasing feature area value."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the LCMS features that have feature areas greater than 1,000,000 in positive ionization mode? Sort by decreasing feature area value."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed. Here are the details:\n\n- **Question**: What are the LCMS features that have feature areas greater than 1,000,000 in positive ionization mode? Sort by decreasing feature area value.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT ?feature\n WHERE {\n ?analysis a ns1:LCMSAnalysisPos .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_feature_area ?area .\n FILTER(?area > 1000000)\n } ORDER BY DESC(?area)\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/61f189ee29f1402eae7ec344e7ac1ef7/tmpv676p86i.csv\n\nPlease review the file for the detailed results."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The LCMS features with feature areas greater than 1,000,000 in positive ionization mode have been filtered and saved. You can download the detailed results from the following file:\n\n- **Filtered Features File**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/61f189ee29f1402eae7ec344e7ac1ef7/filtered_features.csv\n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +6," ""What are the LCMS features that have feature areas greater than 1,000,000 in positive ionization mode ? Sort by decreasing feature area value.""","PREFIX ns1: SELECT ?feature WHERE { @@ -93,7 +93,7 @@ WHERE { ?feature ns1:has_feature_area ?area . FILTER(?area > 1000000) } ORDER BY DESC(?area)",Low -7," ""Can you provide the SMILES notation and the Wikidata IDs for the compound with the highest precusor ion mass in the MS2 spectra from extracts of Lovoa trichilioides by looking at SIRIUS annotations?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Can you provide the SMILES notation and the Wikidata IDs for the compound with the highest precursor ion mass in the MS2 spectra from extracts of Lovoa trichilioides by looking at SIRIUS annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Can you provide the SMILES notation and the Wikidata IDs for the compound with the highest precursor ion mass in the MS2 spectra from extracts of Lovoa trichilioides by looking at SIRIUS annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Lovoa trichilioides, http://www.wikidata.org/entity/Q8008828, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The compound with the highest precursor ion mass in the MS2 spectra from extracts of Lovoa trichilioides, as annotated by SIRIUS, has the following details:\n\n- **SMILES Notation**: `CC(C)(C)C1=C(C=CC(=C1)C(C)(CC(=O)OCCOC(=O)CC(C)(C2=CC(=C(C=C2)O)C(C)(C)C)C3=CC(=C(C=C3)O)C(C)(C)C)C4=CC(=C(C=C4)O)C(C)(C)C)O`\n- **Wikidata ID**: [Q27254797](http://www.wikidata.org/entity/Q27254797)\n\nHere is the SPARQL query used to obtain this information:\n\n```sparql\nPREFIX ns1: \nPREFIX ns2: \n\nSELECT ?smiles ?wd_id\nWHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?annotation .\n ?annotation ns1:has_InChIkey2D ?InChIkey2D .\n ?InChIkey2D ns1:is_InChIkey2D_of ?chemicalEntity .\n ?chemicalEntity ns1:has_smiles ?smiles ;\n ns1:has_wd_id ?wd_id .\n ?feature ns1:has_parent_mass ?mass .\n}\nORDER BY DESC(?mass)\nLIMIT 1\n```\n\nThe data was retrieved from the file located at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/c05df7beae71402fa925205f656dba83/tmpj8dw4tu8.csv`."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +7," ""Can you provide the SMILES notation and the Wikidata IDs for the compound with the highest precusor ion mass in the MS2 spectra from extracts of Lovoa trichilioides by looking at SIRIUS annotations?""","PREFIX ns1: PREFIX ns2: SELECT ?smiles ?wd_id @@ -112,7 +112,7 @@ WHERE { } ORDER BY DESC(?mass) LIMIT 1",High -8," ""Retrieve the most frequent Spec2Vec peaks and losses characterizing the MS2 spectra from the lab extracts of Tabernaemontana coffeoides.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the most frequent Spec2Vec peaks and losses characterizing the MS2 spectra from the lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the most frequent Spec2Vec peaks and losses characterizing the MS2 spectra from the lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: Retrieve the most frequent Spec2Vec peaks and losses characterizing the MS2 spectra from the lab extracts of Tabernaemontana coffeoides.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n PREFIX wd: \n\n SELECT ?spec2vecPeak ?spec2vecLoss (COUNT(?spec2vecPeak) AS ?peakFrequency) (COUNT(?spec2vecLoss) AS ?lossFrequency)\n WHERE {\n ?rawMaterial ns1:has_wd_id wd:Q15376858 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_spec2vec_doc ?spec2vecDoc .\n ?spec2vecDoc ns1:has_spec2vec_peak ?spec2vecPeak .\n ?spec2vecDoc ns1:has_spec2vec_loss ?spec2vecLoss .\n } GROUP BY ?spec2vecPeak ?spec2vecLoss \n ORDER BY DESC(?peakFrequency) DESC(?lossFrequency)\n LIMIT 1\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/2e7991542808441db6e152ef5e034607/tmpdwgxk7xv.csv`\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The most frequent Spec2Vec peaks and losses characterizing the MS2 spectra from the lab extracts of Tabernaemontana coffeoides have been successfully retrieved "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +8," ""Retrieve the most frequent Spec2Vec peaks and losses characterizing the MS2 spectra from the lab extracts of Tabernaemontana coffeoides.""","PREFIX ns1: PREFIX ns2: PREFIX wd: @@ -129,7 +129,7 @@ LIMIT 1",High } GROUP BY ?spec2vecPeak ?spec2vecLoss ORDER BY DESC(?peakFrequency) DESC(?lossFrequency) LIMIT 1",Medium -9," ""What are the LC-MS features that have GNPS library analogs annotation in Tabernaemontana coffeoides? Provide the features.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the LC-MS features that have GNPS library analogs annotation in Tabernaemontana coffeoides? Provide the features."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the LC-MS features that have GNPS library analogs annotation in Tabernaemontana coffeoides? Provide the features."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The LC-MS features that have GNPS library analogs annotation in Tabernaemontana coffeoides can be found in the provided file. Here is the information related to your query:\n\n- **Question**: What are the LC-MS features that have GNPS library analogs annotation in Tabernaemontana coffeoides? Provide the features.\n- **Generated SPARQL Query**: \n ```sparql\n PREFIX ns1: \n\n SELECT ?feature\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:fast_search_gnpslibrary_analog ?analog .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/c00a7dfa6ebf4a978b7a894760588603/tmp94vi0nsp.csv`\n\nPlease refer to the file for the detailed list of features."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +9," ""What are the LC-MS features that have GNPS library analogs annotation in Tabernaemontana coffeoides? Provide the features.""","PREFIX ns1: SELECT ?feature WHERE { @@ -140,7 +140,7 @@ LIMIT 1",High ?featureList ns1:has_lcms_feature ?feature . ?feature ns1:fast_search_gnpslibrary_analog ?analog . }",High -10,"""For features from the Melochia umbellata taxon in pos ionization mode with SIRIUS annotations, get the ones for which a feature in neg ionization mode with the same retention time (+/- 0.05 minutes) has the same SIRIUS annotation by comparing the InCHIKey 2D. Return the features, retention times, and InChIKey2D""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""For features from the Melochia umbellata taxon in pos ionization mode with SIRIUS annotations, get the ones for which a feature in neg ionization mode with the same retention time (+/- 0.05 minutes) has the same SIRIUS annotation by comparing the InCHIKey 2D. Return the features, retention times, and InChIKey2D"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: For features from the Melochia umbellata taxon in pos ionization mode with SIRIUS annotations, get the ones for which a feature in neg ionization mode with the same retention time (+/- 0.05 minutes) has the same SIRIUS annotation by comparing the InCHIKey 2D. Return the features, retention times, and InChIKey2D"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Melochia umbellata, http://www.wikidata.org/entity/Q6813281, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n### Question:\nFor features from the Melochia umbellata taxon in pos ionization mode with SIRIUS annotations, get the ones for which a feature in neg ionization mode with the same retention time (+/- 0.05 minutes) has the same SIRIUS annotation by comparing the InCHIKey 2D. Return the features, retention times, and InChIKey2D.\n\n### Generated SPARQL Query:\n```sparql\nPREFIX ns1: \nPREFIX ns2: \nPREFIX wd: \n\nSELECT ?featurePos ?retentionTime ?InChIkey2D\nWHERE {\n ?rawMaterial ns1:has_wd_id wd:Q6813281 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsPos .\n ?lcmsPos a ns1:LCMSAnalysisPos .\n ?lcmsPos ns1:has_lcms_feature_list ?featureListPos .\n ?featureListPos ns1:has_lcms_feature ?featurePos .\n ?featurePos ns1:has_retention_time ?retentionTime .\n ?featurePos ns1:has_sirius_annotation ?siriusAnnotationPos .\n ?siriusAnnotationPos ns1:has_InChIkey2D ?InChIkey2D .\n\n ?labExtract ns1:has_LCMS ?lcmsNeg .\n ?lcmsNeg a ns1:LCMSAnalysisNeg .\n ?lcmsNeg ns1:has_lcms_feature_list ?featureListNeg .\n ?featureListNeg ns1:has_lcms_feature ?featureNeg .\n ?featureNeg ns1:has_retention_time ?retentionTimeNeg .\n ?featureNeg ns1:has_sirius_annotation ?siriusAnnotationNeg .\n ?siriusAnnotationNeg ns1:has_InChIkey2D ?InChIkey2D .\n\n FILTER (ABS(?retentionTime - ?retentionTimeNeg) <= 0.05)\n}\n```\n\n### File Path:\nThe results of the query have been saved to the following file path:\n`/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/6966291902c9490ab2b949339c4c2797/tmp_cptewcz.csv`\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +10,"""For features from the Melochia umbellata taxon in pos ionization mode with SIRIUS annotations, get the ones for which a feature in neg ionization mode with the same retention time (+/- 0.05 minutes) has the same SIRIUS annotation by comparing the InCHIKey 2D. Return the features, retention times, and InChIKey2D""","PREFIX ns1: PREFIX ns2: PREFIX wd: @@ -166,7 +166,7 @@ WHERE { FILTER (ABS(?retentionTime - ?retentionTimeNeg) <= 0.05) }",High -11,"""List the lab extracts that have inhibition percentages above 50% at 2µg/mL against Trypanosoma brucei rhodesiense .""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""List the lab extracts that have inhibition percentages above 50% bioassay results at 2碌g/mL against Trypanosoma brucei rhodesiense ."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: List the lab extracts that have inhibition percentages above 50% bioassay results at 2碌g/mL against Trypanosoma brucei rhodesiense."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Trypanosoma brucei rhodesiense, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL612348, ChEMBLTarget\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: List the lab extracts that have inhibition percentages above 50% bioassay results at 2碌g/mL against Trypanosoma brucei rhodesiense.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?labExtract\n WHERE {\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\n ?bioAssayResult ns2:target_id .\n {\n ?bioAssayResult a ns2:Tbrucei2ugml .\n }\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/fc41480ada32471b948feb8e1d6babcf/tmp4p9xu66f.csv`\n\nI will now analyze the SPARQL output to provide the final answer.\nThe lab extracts that have bioassay results at 2碌g/mL against Trypanosoma brucei rhodesiense are listed in the file located at the following path:\n\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/fc41480ada32471b948feb8e1d6babcf/tmp4p9xu66f.csv`\n\nHere is the dictionary containing the question, generated SPARQL query, and file path:\n\n```json\n{\n \""question\"": \""List the lab extracts that have inhibition percentages above 50% bioassay results at 2碌g/mL against Trypanosoma brucei rhodesiense\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\nPREFIX ns2: \\n\\nSELECT ?labExtract\\nWHERE {\\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\\n ?bioAssayResult ns2:target_id .\\n {\\n ?bioAssayResult a ns2:Tbrucei2ugml .\\n } ?bioAssayResult ns2:inhibition_percentage ?inhibition .\n FILTER(?inhibition > 50)\\n}\"",\n \""file_path\"": \""/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/fc41480ada32471b948feb8e1d6babcf/tmp4p9xu66f.csv\""\n}\n```\n\nPlease review the file for the detailed list of lab extracts."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +11,"""List the lab extracts that have inhibition percentages above 50% at 2µg/mL against Trypanosoma brucei rhodesiense .""","PREFIX ns1: PREFIX ns2: SELECT ?labExtract @@ -177,7 +177,7 @@ WHERE { ?bioAssayResult a ns2:Tbrucei2ugml . } }",Medium -12," ""Retrieve the Spec2Vec documents associated with MS2 spectra from the lab extracts of Tabernaemontana coffeoides""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the Spec2Vec documents associated with MS2 spectra from the lab extracts of Tabernaemontana coffeoides"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the Spec2Vec documents associated with MS2 spectra from the lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: Retrieve the Spec2Vec documents associated with MS2 spectra from the lab extracts of Tabernaemontana coffeoides.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?spec2vecDoc\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_spec2vec_doc ?spec2vecDoc .\n }\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/aa6e48ec3a194a14a7a9f918774c080a/tmpxvtl3hcs.csv\n\nI will now analyze the SPARQL output to provide the final answer.\nThe final answer based on the SPARQL output is as follows:\n\nThe Spec2Vec documents (Spec2VecDoc) associated with MS2 spectra (MS2Spectrum) from the lab extracts (LabExtract) of Tabernaemontana coffeoides have been retrieved. The details are stored in the file located at:\n\n**File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/aa6e48ec3a194a14a7a9f918774c080a/tmpxvtl3hcs.csv\n\nHere is the dictionary containing the question, generated SPARQL query, and file path:\n\n```json\n{\n \""question\"": \""Retrieve the Spec2Vec documents (Spec2VecDoc) associated with MS2 spectra (MS2Spectrum) from the lab extracts (LabExtract) of Tabernaemontana coffeoides.\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\nPREFIX ns2: \\n\\nSELECT ?spec2vecDoc\\nWHERE {\\n ?rawMaterial ns1:has_wd_id .\\n ?rawMaterial ns1:has_lab_process ?labExtract .\\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\\n ?featureList ns1:has_lcms_feature ?feature .\\n ?feature ns1:has_spec2vec_doc ?spec2vecDoc .\\n}\"",\n \""file_path\"": \""/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/aa6e48ec3a194a14a7a9f918774c080a/tmpxvtl3hcs.csv\""\n}\n```\n\nPlease review the file for the detailed Spec2Vec documents."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +12," ""Retrieve the Spec2Vec documents associated with MS2 spectra from the lab extracts of Tabernaemontana coffeoides""","PREFIX ns1: PREFIX ns2: SELECT ?spec2vecDoc @@ -189,7 +189,7 @@ WHERE { ?featureList ns1:has_lcms_feature ?feature . ?feature ns1:has_spec2vec_doc ?spec2vecDoc . }",Medium -13," ""Which extracts have features (pos ionization mode) annotated as the class, aspidosperma-type alkaloids, by CANOPUS with a probability score above 0.5, ordered by the decreasing count of features as aspidosperma-type alkaloids? Group by extract.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which extracts have features (pos ionization mode) annotated as the class, aspidosperma-type alkaloids, by CANOPUS with a probability score above 0.5, ordered by the decreasing count of features as aspidosperma-type alkaloids? Group by extract."", ""example"": false, ""additional_kwargs"": {}}, {""name"": ""Entry_agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: \""Which extracts have features (pos ionization mode) annotated as the class, aspidosperma-type alkaloids, by CANOPUS with a probability score above 0.5, ordered by the decreasing count of features as aspidosperma-type alkaloids? Group by extract .\"""", ""example"": false, ""additional_kwargs"": {}}, {""name"": ""Validator_agent"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""The entity \""aspidosperma-type alkaloids\"" was resolved to the following IRIs:\n\naspidosperma-type alkaloids, https://enpkg.commons-lab.org/kg/npc_Aspidosperma_type, NPCClass;\nNPCPathway, https://enpkg.commons-lab.org/kg/npc_Alkaloids, NPCPathway;\nNPCSuperClass, https://enpkg.commons-lab.org/kg/npc_Tyrosine_alkaloids, NPCSuperClass."", ""example"": false, ""additional_kwargs"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The extracts with features (pos ionization mode) annotated as the class, aspidosperma-type alkaloids, by CANOPUS with a probability score above 0.5, ordered by the decreasing count of features as aspidosperma-type alkaloids, are as follows:\n\n1. VGF152_B02: 74 features\n2. VGF157_D02: 11 features\n3. VGF147_B11: 10 features\n4. VGF153_C03: 7 features\n5. VGF157_E02: 2 features\n6. VGF154_D02: 2 features\n7. VGF147_A10: 2 features\n8. VGF140_F02: 2 features\n9. KP146_Positive: 1 feature\n10. KP317_Positive: 1 feature\n11. VGF159_B11: 1 feature\n12. VGF155_D07: 1 feature\n13. VGF154_H02: 1 feature\n14. VGF153_E07: 1 feature\n15. VGF153_G10: 1 feature\n16. VGF153_D03: 1 feature\n17. VGF153_F04: 1 feature\n18. VGF153_G09: 1 feature\n19. VGF153_F11: 1 feature\n20. VGF153_H04: 1 feature\n21. VGF153_B06: 1 feature\n22. VGF152_E06: 1 feature\n23. VGF151_F09: 1 feature\n24. VGF151_D06: 1 feature\n25. VGF151_E03: 1 feature\n26. VGF150_B11: 1 feature\n27. VGF150_B06: 1 feature\n28. VGF150_F09: 1 feature\n29. VGF150_H05: 1 feature\n30. VGF150_F06: 1 feature\n31. VGF147_D11: 1 feature\n32. VGF147_E10: 1 feature\n33. VGF143_E07: 1 feature\n34. VGF140_E02: 1 feature\n35. VGF139_D09: 1 feature\n36. SC_AP_Wi_DCM: 1 feature\n\nThe SPARQL query used to retrieve this information is:\n```sparql\nPREFIX ns1: \n\nSELECT ?labExtract (COUNT(?feature) AS ?featureCount)\nWHERE {\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis a ns1:LCMSAnalysisPos .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_class .\n ?annotation ns1:has_canopus_npc_class_prob ?probability .\n FILTER(?probability > 0.5)\n}\nGROUP BY ?labExtract\nORDER BY DESC(?featureCount)\n```\n\nThe results are saved in the file located at: `/tmp/kgbot/f36215b1a3114803ad38a18a9462eb29/tmp8u8o5vcv.csv`\n\nNext, I will generate a bar chart based on this data.\nHere is the dictionary containing the question, generated SPARQL query, and the file path to the results:\n\n```json\n{\n \""question\"": \""Which extracts have features (pos ionization mode) annotated as the class, aspidosperma-type alkaloids, by CANOPUS with a probability score above 0.5, ordered by the decreasing count of features as aspidosperma-type alkaloids? Group by extract and provide a bar chart.\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\n\\nSELECT ?labExtract (COUNT(?feature) AS ?featureCount)\\nWHERE {\\n ?labExtract ns1:has_LCMS ?analysis .\\n ?analysis a ns1:LCMSAnalysisPos .\\n ?analysis ns1:has_lcms_feature_list ?feature_list .\\n ?feature_list ns1:has_lcms_feature ?feature .\\n ?feature ns1:has_canopus_annotation ?annotation .\\n ?annotation ns1:has_canopus_npc_class .\\n ?annotation ns1:has_canopus_npc_class_prob ?probability .\\n FILTER(?probability > 0.5)\\n}\\nGROUP BY ?labExtract\\nORDER BY DESC(?featureCount)\"",\n \""file_path\"": \""/tmp/kgbot/f36215b1a3114803ad38a18a9462eb29/tmp8u8o5vcv.csv\""\n}\n```\n\nPlease use this information to generate the bar chart."", ""example"": false, ""additional_kwargs"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The extracts with features (pos ionization mode) annotated as the class, aspidosperma-type alkaloids, by CANOPUS with a probability score above 0.5, ordered by the decreasing count of features as aspidosperma-type alkaloids, are as follows:\n\n1. VGF152_B02: 74 features\n2. VGF157_D02: 11 features\n3. VGF147_B11: 10 features\n4. VGF153_C03: 7 features\n5. VGF157_E02: 2 features\n6. VGF154_D02: 2 features\n7. VGF147_A10: 2 features\n8. VGF140_F02: 2 features\n9. KP146_Positive: 1 feature\n10. KP317_Positive: 1 feature\n11. VGF159_B11: 1 feature\n12. VGF155_D07: 1 feature\n13. VGF154_H02: 1 feature\n14. VGF153_E07: 1 feature\n15. VGF153_G10: 1 feature\n16. VGF153_D03: 1 feature\n17. VGF153_F04: 1 feature\n18. VGF153_G09: 1 feature\n19. VGF153_F11: 1 feature\n20. VGF153_H04: 1 feature\n21. VGF153_B06: 1 feature\n22. VGF152_E06: 1 feature\n23. VGF151_F09: 1 feature\n24. VGF151_D06: 1 feature\n25. VGF151_E03: 1 feature\n26. VGF150_B11: 1 feature\n27. VGF150_B06: 1 feature\n28. VGF150_F09: 1 feature\n29. VGF150_H05: 1 feature\n30. VGF150_F06: 1 feature\n31. VGF147_D11: 1 feature\n32. VGF147_E10: 1 feature\n33. VGF143_E07: 1 feature\n34. VGF140_E02: 1 feature\n35. VGF139_D09: 1 feature\n36. SC_AP_Wi_DCM: 1 feature\n\nT"", ""example"": false, ""additional_kwargs"": {}}]}","PREFIX ns1: +13," ""Which extracts have features (pos ionization mode) annotated as the class, aspidosperma-type alkaloids, by CANOPUS with a probability score above 0.5, ordered by the decreasing count of features as aspidosperma-type alkaloids? Group by extract.""","PREFIX ns1: SELECT ?labExtract (COUNT(?feature) AS ?featureCount) WHERE { @@ -204,7 +204,7 @@ WHERE { } GROUP BY ?labExtract ORDER BY DESC(?featureCount)",High -14," ""Which ISDB annotations have consistency scores above 0.7 and taxonomic scores above 0.5 for LCMS features from Tabernaemontana coffeoides? Provide annotations.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which ISDB annotations have consistency scores above 0.7 and taxonomic scores above 0.5 for LCMS features from Tabernaemontana coffeoides? Provide annotations."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which ISDB annotations have consistency scores above 0.7 and taxonomic scores above 0.5 for LCMS features from Tabernaemontana coffeoides? Provide annotations."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully executed to find the ISDB annotations with consistency scores above 0.7 and taxonomic scores above 0.5 for LCMS features from Tabernaemontana coffeoides. Here is the information:\n\n- **Question**: Which ISDB annotations have consistency scores above 0.7 and taxonomic scores above 0.5 for LCMS features from Tabernaemontana coffeoides? Provide annotations.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?Annotation\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_isdb_annotation ?Annotation .\n ?Annotation ns1:has_consistency_score ?consistencyScore .\n ?Annotation ns1:has_taxo_score ?taxoScore .\n FILTER(?consistencyScore > 0.7 && ?taxoScore > 0.5)\n }\n ```\n- **File Path**: The results are stored in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/002a9f76c921447fbfe4490492614cba/tmpgkgukng8.csv`.\n\nPlease refer to the file for the detailed annotations."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +14," ""Which ISDB annotations have consistency scores above 0.7 and taxonomic scores above 0.5 for LCMS features from Tabernaemontana coffeoides? Provide annotations.""","PREFIX ns1: PREFIX ns2: SELECT ?Annotation @@ -219,7 +219,7 @@ ORDER BY DESC(?featureCount)",High ?Annotation ns1:has_taxo_score ?taxoScore . FILTER(?consistencyScore > 0.7 && ?taxoScore > 0.5) }",Medium -15,"""What are the taxons, lab process and label (if one exists) for each sample? Sort by sample and then lab process""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the taxons, lab process and label (if one exists) for each sample? Sort by sample and then lab process"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the taxons, lab process and label (if one exists) for each sample? Sort by sample and then lab process"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed. Here are the details:\n\n- **Question**: What are the taxons, lab process and label (if one exists) for each sample? Sort by sample and then lab process.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n SELECT ?sample ?taxon ?labProcess ?label\n WHERE {\n ?sample ns1:has_wd_id ?taxon .\n ?sample ns1:has_lab_process ?labProcess .\n OPTIONAL { ?sample rdfs:label ?label . }\n }\n ORDER BY ?sample ?labProcess\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/5642f76045424334a801e191d6ed4563/tmp9g_oyjum.csv\n\nPlease review the file at the provided path for the detailed results."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The taxons, lab process, and labels (if any) for each sample have been sorted by sample and then by lab process. Here is a snippet of the sorted data:\n\n| Sample | Taxon | Lab Process | Label |\n|------------------------------------------|------------------------------------------|--------------------------------------------|-------|\n| http://www.wikidata.org/entity/Q11086771 | http://www.wikidata.org/entity/Q11086771 | https://enpkg.commons-lab.org/kg/KP001_Positive | NaN |\n| http://www.wikidata.org/entity/Q11272377 | http://www.wikidata.org/entity/Q11272377 | https://enpkg.commons-lab.org/kg/KP002_Positive | NaN |\n| http://www.wikidata.org/entity/Q11086771 | http://www.wikidata.org/entity/Q11086771 | https://enpkg.commons-lab.org/kg/KP003_Positive | NaN |\n| http://www.wikidata.org/entity/Q11272377 | http://www.wikidata.org/entity/Q11272377 | https://enpkg.commons-lab.org/kg/KP004_Positive | NaN |\n| http://www.wikidata.org/entity/Q5978345 | http://www.wikidata.org/entity/Q5978345 | https://enpkg.commons-lab.org/kg/KP005_Positive | NaN |\n\nThe full path of the file generated is:\n- /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/5642f76045424334a801e191d6ed4563/tmp9g_oyjum.csv\n\nYou can download the file for the complete details."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +15,"""What are the taxons, lab process and label (if one exists) for each sample? Sort by sample and then lab process""","PREFIX ns1: SELECT ?sample ?taxon ?labProcess ?label WHERE { ?sample ns1:has_wd_id ?taxon . @@ -227,7 +227,7 @@ ORDER BY DESC(?featureCount)",High OPTIONAL { ?sample rdfs:label ?label . } } ORDER BY ?sample ?labProcess",Low -16,"""What are the mass spectrometry features detected only in the plant Rumex nepalensis?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the mass spectrometry features detected only in the plant Rumex nepalensis?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the mass spectrometry features detected only in the plant Rumex nepalensis?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Rumex nepalensis, http://www.wikidata.org/entity/Q11041204, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: What are the mass spectrometry features detected only in the plant Rumex nepalensis?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n PREFIX wd: \n\n SELECT ?feature\n WHERE {\n ?rawMaterial ns1:has_wd_id wd:Q11041204 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n }\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/966c850620f7440182889fe9b80a1edb/tmp4z32di1l.csv\n\nI will now analyze the SPARQL output to provide the final answer.\nThe mass spectrometry features detected for the plant Rumex nepalensis can be found in the file located at the following path:\n\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/966c850620f7440182889fe9b80a1edb/tmp4z32di1l.csv\n\nHere is the dictionary containing the question, generated SPARQL query, and file path:\n\n```json\n{\n \""question\"": \""What are the mass spectrometry features detected for the plant Rumex nepalensis?\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\nPREFIX ns2: \\nPREFIX wd: \\n\\nSELECT ?feature\\nWHERE {\\n ?rawMaterial ns1:has_wd_id wd:Q11041204 .\\n ?rawMaterial ns1:has_lab_process ?labExtract .\\n ?labExtract ns1:has_LCMS ?analysis .\\n ?analysis ns1:has_lcms_feature_list ?featureList .\\n ?featureList ns1:has_lcms_feature ?feature .\\n}\"",\n \""file_path\"": \""/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/966c850620f7440182889fe9b80a1edb/tmp4z32di1l.csv\""\n}\n```\n\nPlease review the file for the detailed mass spectrometry features."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The mass spectrometry features detected for the plant Rumex nepalensis are as follows:\n\n1. [LCMS Feature 1](https://enpkg.commons-lab.org/kg/lcms_feature_1)\n2. [LCMS Feature 2](https://enpkg.commons-lab.org/kg/lcms_feature_2)\n3. [LCMS Feature 3](https://enpkg.commons-lab.org/kg/lcms_feature_3)\n4. [LCMS Feature 4](https://enpkg.commons-lab.org/kg/lcms_feature_4)\n5. [LCMS Feature 5](https://enpkg.commons-lab.org/kg/lcms_feature_5)\n\nNo visualization was requested for this analysis. The data can be downloaded from the provided file path."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +16,"""What are the mass spectrometry features detected only in the plant Rumex nepalensis?""","PREFIX ns1: PREFIX ns2: PREFIX wd: @@ -239,7 +239,7 @@ ORDER BY DESC(?featureCount)",High ?analysis ns1:has_lcms_feature_list ?featureList . ?featureList ns1:has_lcms_feature ?feature . }",Low -17," ""How many distinct Wikidata identifiers (WD IDs) have the same metabolite annotation (considering only the plannar structure)?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""How many distinct Wikidata identifiers (WD IDs) have the same metabolite annotation (considering only the plannar structure)?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: How many distinct Wikidata identifiers (WD IDs) have the same metabolite annotation (considering only the planar structure)?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid. It is related to the ENPKG nodes/entities (Wikidata IDs, InChIkey2D) and asks for a count of distinct Wikidata IDs that have the same metabolite annotation, considering only the planar structure. This information can be retrieved from the ENPKG."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The query has been executed successfully. There are 138,146 distinct Wikidata identifiers (WD IDs) that share the same metabolite annotation based on their planar structure (InChIkey2D).\n\nHere is the information related to the query:\n\n- **Question**: How many distinct Wikidata identifiers (WD IDs) have the same metabolite annotation (considering only the planar structure)?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT (COUNT(DISTINCT ?wd_id) AS ?distinct_wd_ids)\n WHERE {\n ?annotation ns1:has_InChIkey2D ?InChIkey2D .\n ?InChIkey2D ns1:is_InChIkey2D_of ?InChIkey .\n ?InChIkey ns1:has_wd_id ?wd_id .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/49c4b98b94034c86ace8c37139fa6a29/tmpnnlg58pc.csv`\n\nThis result indicates the number of unique Wikidata IDs that are associated with the same planar structure annotation in the ENPKG database."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +17," ""How many distinct Wikidata identifiers (WD IDs) have the same metabolite annotation (considering only the plannar structure)?""","PREFIX ns1: SELECT (COUNT(DISTINCT ?wd_id) AS ?distinct_wd_ids) WHERE { @@ -247,7 +247,7 @@ ORDER BY DESC(?featureCount)",High ?InChIkey2D ns1:is_InChIkey2D_of ?InChIkey . ?InChIkey ns1:has_wd_id ?wd_id . }",Medium -18," ""List the LC-MS features that have a chemical class annotation by CANOPUS and have a retention time between 5-7 minutes."" ""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""List the LC-MS features that have a chemical class annotation by CANOPUS and have a retention time between 5-7 minutes. "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: List the LC-MS features that have a chemical class annotation by CANOPUS and have a retention time between 5-7 minutes."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed. Here are the details:\n\n- **Question:** List the LC-MS features that have a chemical class annotation by CANOPUS and have a retention time between 5-7 minutes.\n- **Generated SPARQL Query:**\n ```sparql\n PREFIX ns1: \n\n SELECT ?feature\n WHERE {\n ?feature ns1:has_canopus_annotation ?annotation .\n ?feature ns1:has_retention_time ?retention_time .\n ?annotation ns1:has_canopus_npc_class ?chemicalClass .\n FILTER(?retentionTime >= 5 && ?retentionTime <= 7) }\n ```\n- **File Path:** /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/aadd02d3086647dd9408cbc9098e5862/tmpx_kt8dmi.csv\n\nYou can now review the results in the provided file path."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +18," ""List the LC-MS features that have a chemical class annotation by CANOPUS and have a retention time between 5-7 minutes."" ""","PREFIX ns1: SELECT ?feature WHERE { @@ -255,7 +255,7 @@ ORDER BY DESC(?featureCount)",High ?feature ns1:has_retention_time ?retention_time . ?annotation ns1:has_canopus_npc_class ?chemicalClass . FILTER(?retentionTime >= 5 && ?retentionTime <= 7) }",Medium -19," ""Retrieve the lab extracts that have more than 2,000 features detected.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the lab extracts that have more than 2,000 features detected. "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the lab extracts that have more than 2,000 features detected."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid. It is related to the ENPKG nodes/entities (Lab Extract, LCMS Feature). The question asks for lab extracts that have a certain number of features detected, which is information that can be retrieved from the ENPKG."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The query to retrieve lab extracts with more than 2,000 features detected has been executed. Here is the information:\n\n- **Question**: Retrieve the lab extracts that have more than 2,000 features detected.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT ?labExtract\n WHERE {\n ?labExtract ns1:has_LCMS ?analysis.\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n {\n SELECT ?labExtract (COUNT(?feature) AS ?featureCount)\n WHERE {\n ?labExtract ns1:has_LCMS ?analysis. \n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n }\n GROUP BY ?labExtract\n HAVING (?featureCount > 2000)\n }\n }\n ```\n- **File Path**: The results are saved in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/1db966f1a9d44c269baae69dc24b9dbe/tmpgxqshpaa.csv`.\n\nPlease refer to the file for the detailed results."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The file containing the results of the SPARQL query has been processed. nIf you have any other specific criteria or information you would like to extract from the data, please let me know. Otherwise, you can download the data for further analysis.\n\nThe full path of the file generated is: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/1db966f1a9d44c269baae69dc24b9dbe/tmpgxqshpaa.csv`.\n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +19," ""Retrieve the lab extracts that have more than 2,000 features detected.""","PREFIX ns1: SELECT ?labExtract WHERE { @@ -273,7 +273,7 @@ ORDER BY DESC(?featureCount)",High HAVING (?featureCount > 2000) } }",Low -20,"""What is the distribution of chemical superclasses in the LCMS features annotated by CANOPUS identified from Desmodium heterophyllum extracts?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What is the distribution of chemical superclasses in the LCMS features annotated by CANOPUS identified from Desmodium heterophyllum extracts?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What is the distribution of chemical superclasses in the LCMS features annotated by CANOPUS identified from Desmodium heterophyllum extracts?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Desmodium heterophyllum, http://www.wikidata.org/entity/Q10770714, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The distribution of chemical superclasses in the LCMS features annotated by CANOPUS identified from Desmodium heterophyllum extracts is as follows:\n\n- Flavonoids: 600\n- Octadecanoids: 105\n- Triterpenoids: 50\n- Isoflavonoids: 147\n- Sesquiterpenoids: 27\n- Lignans: 19\n- Small peptides: 19\n- Monoterpenoids: 16\n- Coumarins: 180\n- Tryptophan alkaloids: 6\n- Stilbenoids: 20\n- Nucleosides: 8\n- Glycerophospholipids: 2\n- Polycyclic aromatic polyketides: 11\n- Aromatic polyketides: 13\n- Xanthones: 9\n- Phenylpropanoids (C6-C3): 11\n- Naphthalenes: 5\n- Anthranilic acid alkaloids: 3\n- Diarylheptanoids: 2\n- Diterpenoids: 18\n- Steroids: 51\n- Ornithine alkaloids: 2\n- Aminosugars and aminoglycosides: 5\n- Meroterpenoids: 27\n- Diazotetronic acids and derivatives: 3\n- Glycerolipids: 62\n- Fatty Acids and Conjugates: 44\n- Sphingolipids: 3\n- Oligopeptides: 17\n- Phenolic acids (C6-C1): 15\n- Pseudoalkaloids: 3\n- 尾-lactams: 1\n- Fatty amides: 17\n- Tyrosine alkaloids: 1\n- Phloroglucinols: 2\n- Unknown: 198\n- Chromanes: 7\n- Linear polyketides: 9\n- Nicotinic acid alkaloids: 2\n- Spingolipids: 24\n- Fatty esters: 3\n- Pseudoalkaloids (transamidation): 1\n- Amino acid glycosides: 6\n- Histidine alkaloids: 1\n- Eicosanoids: 15\n- Fatty acyl glycosides: 2\n- Fatty acyls: 6\n- Polyethers: 2\n- Macrolides: 1\n- Polyols: 2\n- Cyclic polyketides: 3\n- Apocarotenoids: 3\n\nHere is the dictionary containing the question, generated SPARQL query, and file path to the query result:\n\n```json\n{\n \""question\"": \""What is the distribution of chemical superclasses in the LCMS features annotated by CANOPUS identified from Desmodium heterophyllum extracts?\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\nPREFIX ns2: \\nPREFIX wd: \\n\\nSELECT ?superclass (COUNT(?superclass) AS ?count)\\nWHERE {\\n ?rawMaterial ns1:has_wd_id wd:Q10770714 .\\n ?rawMaterial ns1:has_lab_process ?labExtract .\\n ?labExtract ns1:has_LCMS ?analysis .\\n ?analysis ns1:has_lcms_feature_list ?feature_list .\\n ?feature_list ns1:has_lcms_feature ?feature .\\n ?feature ns1:has_canopus_annotation ?annotation .\\n ?annotation ns1:has_canopus_npc_superclass ?superclass .\\n}\\nGROUP BY ?superclass\"",\n \""file_path\"": \""/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/228ff50f729b45f29855d7d12d4c18c9/tmp23kaqct7.csv\""\n}\n```"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +20,"""What is the distribution of chemical superclasses in the LCMS features annotated by CANOPUS identified from Desmodium heterophyllum extracts?""","PREFIX ns1: PREFIX ns2: PREFIX wd: @@ -288,7 +288,7 @@ WHERE { ?annotation ns1:has_canopus_npc_superclass ?superclass . } GROUP BY ?superclass",Medium -21," ""Retrieve the top-10 lab extracts with the most unique features detected (minimum feature area intensity of 500,000.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the top-10 lab extracts with the most unique features detected (minimum feature area intensity of 500,000)."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the top-10 lab extracts with the most unique features detected (minimum feature area intensity of 500,000)."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The top-10 lab extracts with the most unique features detected, with a minimum feature area intensity of 500,000, are as follows:\n\n1. **Lab Extract:** [SC_AP_Wi_DCM](https://enpkg.commons-lab.org/kg/SC_AP_Wi_DCM) - **Unique Features:** 3437\n2. **Lab Extract:** [VGF140_F11](https://enpkg.commons-lab.org/kg/VGF140_F11) - **Unique Features:** 2035\n3. **Lab Extract:** [VGF153_D04](https://enpkg.commons-lab.org/kg/VGF153_D04) - **Unique Features:** 1952\n4. **Lab Extract:** [VGF154_G05](https://enpkg.commons-lab.org/kg/VGF154_G05) - **Unique Features:** 1813\n5. **Lab Extract:** [VGF153_F06](https://enpkg.commons-lab.org/kg/VGF153_F06) - **Unique Features:** 1786\n6. **Lab Extract:** [VGF154_C03](https://enpkg.commons-lab.org/kg/VGF154_C03) - **Unique Features:** 1640\n7. **Lab Extract:** [VGF153_G10](https://enpkg.commons-lab.org/kg/VGF153_G10) - **Unique Features:** 1617\n8. **Lab Extract:** [VGF154_A09](https://enpkg.commons-lab.org/kg/VGF154_A09) - **Unique Features:** 1615\n9. **Lab Extract:** [VGF154_B09](https://enpkg.commons-lab.org/kg/VGF154_B09) - **Unique Features:** 1601\n10. **Lab Extract:** [VGF142_D11](https://enpkg.commons-lab.org/kg/VGF142_D11) - **Unique Features:** 1543\n\nHere is the SPARQL query used to retrieve this information:\n\n```sparql\nPREFIX ns1: \nSELECT ?labExtract (COUNT(DISTINCT ?feature) AS ?uniqueFeatures)\nWHERE {\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_feature_area ?featureArea .\n FILTER(?featureArea >= 500000)\n}\nGROUP BY ?labExtract\nORDER BY DESC(?uniqueFeatures)\nLIMIT 10\n```\n\nThe data is stored in the file located at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/e85c6df097e3431884ddbfcdc34f620d/tmp1u9qq1p9.csv`."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +21," ""Retrieve the top-10 lab extracts with the most unique features detected (minimum feature area intensity of 500,000.""","PREFIX ns1: SELECT ?labExtract (COUNT(DISTINCT ?feature) AS ?uniqueFeatures) WHERE { ?labExtract ns1:has_LCMS ?analysis . @@ -300,7 +300,7 @@ WHERE { GROUP BY ?labExtract ORDER BY DESC(?uniqueFeatures) LIMIT 10",Low -22,"""List the top 3 lab extracts with the highest number of unique compounds annotated by CANOPUS with a probability greater than 0.9.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""List the top 3 lab extracts with the highest number of unique compounds annotated by CANOPUS with a probability greater than 0.9."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: List the top 3 lab extracts with the highest number of unique compounds annotated by CANOPUS with a probability greater than 0.9."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The top 3 lab extracts with the highest number of unique compounds annotated by CANOPUS with a probability greater than 0.9 are:\n\n1. Lab Extract: [SC_AP_Wi_DCM](https://enpkg.commons-lab.org/kg/SC_AP_Wi_DCM) with 69 unique compounds.\n2. Lab Extract: [VGF153_F06](https://enpkg.commons-lab.org/kg/VGF153_F06) with 58 unique compounds.\n3. Lab Extract: [VGF156_B04](https://enpkg.commons-lab.org/kg/VGF156_B04) with 49 unique compounds.\n\nHere is the SPARQL query used to obtain this information:\n\n```sparql\nPREFIX ns1: \n\nSELECT ?labExtract (COUNT(DISTINCT ?compound) AS ?uniqueCompounds)\nWHERE {\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_class ?compound .\n ?annotation ns1:has_canopus_npc_class_prob ?probability .\n FILTER(?probability > 0.9)\n}\nGROUP BY ?labExtract\nORDER BY DESC(?uniqueCompounds)\nLIMIT 3\n```\n\nThe results are saved in the file located at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/5cf09a28bf1049dbb23b09915c0fc8e1/tmp35c6yy5y.csv`."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +22,"""List the top 3 lab extracts with the highest number of unique compounds annotated by CANOPUS with a probability greater than 0.9.""","PREFIX ns1: SELECT ?labExtract (COUNT(DISTINCT ?compound) AS ?uniqueCompounds) WHERE { @@ -315,7 +315,7 @@ WHERE { GROUP BY ?labExtract ORDER BY DESC(?uniqueCompounds) LIMIT 3",High -23," ""What are the LCMS features with the highest area annotated as alkaloids by CANOPUS in Tabernaemontana coffeoides extracts?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the LCMS features with the highest area annotated as alkaloids by CANOPUS in Tabernaemontana coffeoides extracts?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the LCMS features with the highest area annotated as alkaloids by CANOPUS in Tabernaemontana coffeoides extracts?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""alkaloids, NPCClass: https://enpkg.commons-lab.org/kg/npc_Steroidal_alkaloids, NPCClass;\nTabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The LCMS feature with the highest area annotated as alkaloids by CANOPUS in Tabernaemontana coffeoides extracts is identified by the feature IRI [https://enpkg.commons-lab.org/kg/lcms_feature_mzspec:MSV000087728:VGF152_A02_features_ms2_pos.mgf:scan:245](https://enpkg.commons-lab.org/kg/lcms_feature_mzspec:MSV000087728:VGF152_A02_features_ms2_pos.mgf:scan:245) with an area of 18,114,637.02276565.\n\nHere is the dictionary containing the question, generated SPARQL query, and file path:\n\n- **Question**: What are the LCMS features with the highest area annotated as alkaloids by CANOPUS in Tabernaemontana coffeoides extracts?\n- **Generated SPARQL Query**: \n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?feature ?featureArea\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_feature_area ?featureArea .\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_class .\n }\n ORDER BY DESC(?featureArea)\n LIMIT 1\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/84a24128512b453598395d8ed7f429dc/tmp8qjwsoaz.csv`"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +23," ""What are the LCMS features with the highest area annotated as alkaloids by CANOPUS in Tabernaemontana coffeoides extracts?""","PREFIX ns1: PREFIX ns2: SELECT ?feature ?featureArea @@ -331,7 +331,7 @@ LIMIT 3",High } ORDER BY DESC(?featureArea) LIMIT 1",Low -24," ""Count the number of LCMS features in negative ionization mode""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Count the number of LCMS features in negative ionization mode"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Count the number of LCMS features in negative ionization mode"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The number of LCMS features in negative ionization mode is **364,967**.\n\nHere is the detailed information:\n- **Question**: Count the number of LCMS features in negative ionization mode.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT (COUNT(?feature) AS ?featureCount)\n WHERE {\n ?analysis a ns1:LCMSAnalysisNeg .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n }\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/5a7c1625b95143fcb9f58943371653e9/tmp_dhsfq67.csv"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +24," ""Count the number of LCMS features in negative ionization mode""","PREFIX ns1: SELECT (COUNT(?feature) AS ?featureCount) WHERE { @@ -339,7 +339,7 @@ LIMIT 3",High ?analysis ns1:has_lcms_feature_list ?featureList . ?featureList ns1:has_lcms_feature ?feature . }",Low -25,"""What are the chemical structure ISDB annotations for Lovoa trichilioides ?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the chemical structure ISDB annotations for Lovoa trichilioides ?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the chemical structure ISDB annotations for Lovoa trichilioides?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""Lovoa trichilioides, http://www.wikidata.org/entity/Q8008828, Wikidata"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed. Here are the details:\n\n- **Question**: What are the chemical structure ISDB annotations for Lovoa trichilioides?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?Annotation\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_isdb_annotation ?Annotation .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/597fde39a0474507b67d3ce263df2a5e/tmp9ukxy4rj.csv`\n\nI will now analyze the SPARQL output to provide the final answer.\nThe chemical structure ISDB annotations for Lovoa trichilioides have been retrieved. Here is the final answer:\n\n- **Question**: What are the chemical structure ISDB annotations for Lovoa trichilioides?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?Annotation\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_isdb_annotation ?Annotation .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/597fde39a0474507b67d3ce263df2a5e/tmp9ukxy4rj.csv`\n\nThe file contains the ISDB annotations for Lovoa trichilioides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +25,"""What are the chemical structure ISDB annotations for Lovoa trichilioides ?""","PREFIX ns1: PREFIX ns2: SELECT ?Annotation @@ -351,7 +351,7 @@ LIMIT 3",High ?feature_list ns1:has_lcms_feature ?feature . ?feature ns1:has_isdb_annotation ?Annotation . }",Medium -26," ""Retrieve the Wikidata IDs of chemical entities from Desmodium heterophyllum that are annotated as rotenoids by CANOPUS and have ChEMBL assay results showing activity against Trypanosoma cruzi. Provide also the USIs to the corresponding MS/MS spectra.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the Wikidata IDs of chemical entities from Desmodium heterophyllum that are annotated as rotenoids by CANOPUS and have ChEMBL assay results showing activity against Trypanosoma cruzi. Provide also the USIs to the corresponding MS/MS spectra"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the Wikidata IDs of chemical entities from Desmodium heterophyllum that are annotated as rotenoids by CANOPUS and have ChEMBL assay results showing activity against Trypanosoma cruzi. Provide also the USIs to the corresponding MS/MS spectra."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Desmodium heterophyllum, http://www.wikidata.org/entity/Q10770714, Wikidata;\nrotenoids, https://enpkg.commons-lab.org/kg/npc_Rotenoids, NPCClass;\nTrypanosoma cruzi, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL368, ChEMBLTarget\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been executed successfully. Here is the information retrieved:\n\n### Question\nRetrieve the Wikidata IDs of chemical entities from Desmodium heterophyllum that are annotated as rotenoids by CANOPUS and have ChEMBL assay results showing activity against Trypanosoma cruzi. Provide also the USIs to the corresponding MS/MS spectra.\n\n### SPARQL Query\n```sparql\nPREFIX ns1: \nPREFIX ns2: \n\nSELECT ?wd_id ?usi\nWHERE {\n ?rawMaterial ns1:has_wd_id ;\n ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_canopus_annotation ?annotation ;\n ns1:has_usi ?usi .\n ?annotation ns1:has_canopus_npc_class .\n ?labExtract ns2:has_bioassay_results ?assayResult .\n ?assayResult ns2:target_id .\n ?chemicalEntity ns1:has_wd_id ?wd_id .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?siriusAnnotation ns1:has_InChIkey2D ?InChIkey2D .\n ?InChIkey2D ns1:is_InChIkey2D_of ?chemicalEntity .\n }\n```\n\n### File Path\nThe results have been saved to a temporary file, which can be accessed at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/be66080199fb4930ae3c599b8c11507d/tmpn40oy4b_.csv`\n\nPlease let me know if you need further assistance or analysis on this data!"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The analysis of the file has been completed. The data contains Wikidata IDs of chemical entities from Desmodium heterophyllum that are annotated as rotenoids by CANOPUS and have ChEMBL assay results showing activity against Trypanosoma cruzi. It also includes the corresponding USIs to the MS/MS spectra.\n\nHere are the first few entries:\n- **Wikidata ID**: Q104399962\n- **USI**: mzspec:MSV000087728:VGF155_D05_features_ms2_ne...\n\nNo visualization was requested or generated for this analysis. The data can be downloaded for further examination.\n\nIf you need any additional insights or visualizations, please let me know!"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +26," ""Retrieve the Wikidata IDs of chemical entities from Desmodium heterophyllum that are annotated as rotenoids by CANOPUS and have ChEMBL assay results showing activity against Trypanosoma cruzi. Provide also the USIs to the corresponding MS/MS spectra.""","PREFIX ns1: PREFIX ns2: SELECT ?wd_id ?usi @@ -371,7 +371,7 @@ WHERE { ?siriusAnnotation ns1:has_InChIkey2D ?InChIkey2D . ?InChIkey2D ns1:is_InChIkey2D_of ?chemicalEntity . }",High -27," ""Retrieve the retention times and parent masses of LCMS features annotated as Terpenoids by CANOPUS in Tabernaemontana coffeoides.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the retention times and parent masses of LCMS features annotated as Terpenoids by CANOPUS in Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the retention times and parent masses of LCMS features annotated as Terpenoids by CANOPUS in Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Terpenoids, https://enpkg.commons-lab.org/kg/npc_Terpenoids, NPCPathway;\nTabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The retention times and parent masses of LCMS features annotated as Terpenoids by CANOPUS in Tabernaemontana coffeoides have been retrieved. Here is the information:\n\n- **SPARQL Query**: \n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?retentionTime ?parentMass\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_pathway .\n ?feature ns1:has_retention_time ?retentionTime .\n ?feature ns1:has_parent_mass ?parentMass .\n }\n ```\n\n- **File Path**: The results are saved in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/610f86c362554edfa3e22989fd52acdf/tmpgt4cvneq.csv`.\n\nPlease check the file for detailed results."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The retention times and parent masses of LCMS features annotated as Terpenoids by CANOPUS in Tabernaemontana coffeoides have been visualized. You can download the visualization file from the following path:\n\nVisualization filepath: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/610f86c362554edfa3e22989fd52acdf/tmpgt4cvneq.json`\n\nPlease use this file to view the detailed results. \n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +27," ""Retrieve the retention times and parent masses of LCMS features annotated as Terpenoids by CANOPUS in Tabernaemontana coffeoides.""","PREFIX ns1: PREFIX ns2: SELECT ?retentionTime ?parentMass @@ -386,7 +386,7 @@ WHERE { ?feature ns1:has_retention_time ?retentionTime . ?feature ns1:has_parent_mass ?parentMass . }",Medium -28,"""Which compounds from Desmodium heterophyllum extracts have annotations from both ISDB and SIRIUS, and what are their molecular masses?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which compounds from Desmodium heterophyllum extracts have annotations from both ISDB and SIRIUS, and what are their molecular masses?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which compounds from Desmodium heterophyllum extracts have annotations from both ISDB and SIRIUS, and what are their molecular masses?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""Desmodium heterophyllum, http://www.wikidata.org/entity/Q10770714, Wikidata"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The compounds from Desmodium heterophyllum extracts that have annotations from both ISDB and SIRIUS, along with their molecular masses, are as follows:\n\n1. Compound: WITLAWYGGVAFLU, Mass: 379.1186828613281\n2. Compound: PKNGEQDEJRDPJZ, Mass: 407.2225646972656\n3. Compound: FHKQMPVRIQJSEJ, Mass: 313.1062774658203\n4. Compound: BWDGXQDVXMUNDT, Mass: 293.1019897460937\n5. Compound: QFTWSMLVOZYJPP, Mass: 337.1435089111328\n6. Compound: MIUNHNXJASJSIX, Mass: 425.1591796875\n7. Compound: ZSCRTFONTNMQBL, Mass: 247.16928100585935\n8. Compound: VZPAURMDJZOGHU, Mass: 445.208984375\n9. Compound: HEORSHPDPZGZCN, Mass: 349.143310546875\n10. Compound: GIVRDLOWBBXCNM, Mass: 351.1228332519531\n11. Compound: VZPAURMDJZOGHU, Mass: 445.2112426757813\n12. Compound: QQWYBWNEILZOKI, Mass: 321.1480407714844\n13. Compound: DTOSIQBPPRVQHS, Mass: 279.2320251464844\n14. Compound: DTOSIQBPPRVQHS, Mass: 279.2322692871094\n15. Compound: KSVKECXWDNCRTM, Mass: 403.20166015625\n16. Compound: CEAWIMABVSITMV, Mass: 383.1479187011719\n17. Compound: CVULDJMCSSACEO, Mass: 507.2290344238281\n18. Compound: KEJNXXMRDHDOCO, Mass: 279.10035705566406\n19. Compound: QQWYBWNEILZOKI, Mass: 321.14996337890625\n20. Compound: QQWYBWNEILZOKI, Mass: 321.1477355957031\n21. Compound: AOMUHOFOVNGZAN, Mass: 288.2526550292969\n22. Compound: YHZDBBUEVZEOIY, Mass: 271.0964050292969\n23. Compound: WRINUBCCCQLTPU, Mass: 365.13720703125\n24. Compound: GAGKUHAKUBMORD, Mass: 339.12274169921875\n25. Compound: CDZUQIFWCXAIFB, Mass: 443.13368225097656\n26. Compound: ZSCRTFONTNMQBL, Mass: 247.1693420410156\n27. Compound: HOMYIYLRRDTKAA, Mass: 714.5514831542969\n28. Compound: AOMUHOFOVNGZAN, Mass: 288.2525939941406\n29. Compound: OIMXTYUHMBQQJM, Mass: 393.3157348632813\n30. Compound: MEJLVLSNRYLDSG, Mass: 743.2880859375\n31. Compound: HEQLSVWMTJXPGS, Mass: 413.1593017578125\n32. Compound: WXWGKIBMPDYPTH, Mass: 265.08551025390625\n33. Compound: ICFXJOAKQGDRCT, Mass: 429.3733520507813\n34. Compound: QQWYBWNEILZOKI, Mass: 321.1485290527344\n35. Compound: XTLSKKJNOIMMBK, Mass: 295.0967102050781\n36. Compound: XTLSKKJNOIMMBK, Mass: 295.0966186523437\n37. Compound: XTLSKKJNOIMMBK, Mass: 295.0966491699219\n38. Compound: DTOSIQBPPRVQHS, Mass: 279.232177734375\n39. Compound: UKMCCFHULJHJNS, Mass: 427.3577575683594\n40. Compound: UKMCCFHULJHJNS, Mass: 427.3577423095703\n41. Compound: XTLSKKJNOIMMBK, Mass: 295.0967712402344\n42. Compound: XTLSKKJNOIMMBK, Mass: 295.0967102050781\n43. Compound: WECGLUPZRHILCT, Mass: 355.2845458984375\n44. Compound: QNPIEBGHSXVAOA, Mass: 702.5513305664062\n45. Compound: BWDGXQDVXMUNDT, Mass: 293.1017761230469\n46. Compound: XEVQXKKKAVVSMW, Mass: 197.1173095703125\n47. Compound: LUZSWWYKKLTDHU, Mass: 295.2268981933594\n48. Compound: ICFXJOAKQGDRCT, Mass: 429.3731689453125\n49. Compound: MIUNHNXJASJSIX, Mass: 425.158935546875\n50. Compound: AOMUHOFOVNGZAN, Mass: 288.2529754638672\n51. Compound: OZNMJHSEUFVTTG, Mass: 409.3470458984375\n52. Compound: GQSRIBUSVVSLSQ, Mass: 463.37884521484375\n53. Compound: HOMYIYLRRDTKAA, Mass: 714.550537109375\n54. Compound: HXYVTAGFYLMHSO, Mass: 300.2903747558594\n55. Compound: ORDAZKGHSNRHTD, Mass: 395.14947509765625\n56. Compound: AOMUHOFOVNGZAN, Mass: 288.2536163330078\n\nThe SPARQL query used to retrieve this data is:\n```sparql\nPREFIX ns1: \nPREFIX ns2: \nPREFIX wd: \n\nSELECT ?compound ?mass\nWHERE {\n ?rawMaterial ns1:has_wd_id wd:Q10770714 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_isdb_annotation ?isdbAnnotation .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?feature ns1:has_parent_mass ?mass .\n ?isdbAnnotation ns1:has_InChIkey2D ?compound .\n ?siriusAnnotation ns1:has_InChIkey2D ?compound .\n}\n```\n\nThe file path to the SPARQL output is: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/c6e1f9c0990546a88ca140657c7354d1/tmpez5ixi4n.csv`\n\nI will now provide this information to the supervisor."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +28,"""Which compounds from Desmodium heterophyllum extracts have annotations from both ISDB and SIRIUS, and what are their molecular masses?""","PREFIX ns1: PREFIX ns2: PREFIX wd: @@ -403,7 +403,7 @@ WHERE { ?isdbAnnotation ns1:has_InChIkey2D ?compound . ?siriusAnnotation ns1:has_InChIkey2D ?compound . }",High -29," ""What are the most frequent adducts of LCMS features annotated by SIRIUS detected in positive ionization mode from Tabernaemontana coffeoides?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the most frequent adducts of LCMS features annotated by SIRIUS detected in positive ionization mode from Tabernaemontana coffeoides?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the most frequent adducts of LCMS features annotated by SIRIUS detected in positive ionization mode from Tabernaemontana coffeoides?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The most frequent adducts of LCMS features annotated by SIRIUS detected in positive ionization mode from Tabernaemontana coffeoides are:\n\n1. [M+H]+ with a frequency of 1079\n2. [M+NH4]+ with a frequency of 432\n3. [M+Na]+ with a frequency of 263\n4. [M+K]+ with a frequency of 65\n\nHere is the dictionary containing the question, generated SPARQL query, and file path:\n\n- **Question**: What are the most frequent adducts of LCMS features annotated by SIRIUS detected in positive ionization mode from Tabernaemontana coffeoides?\n- **Generated SPARQL Query**: \n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?adduct (COUNT(?adduct) AS ?frequency)\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis a ns1:LCMSAnalysisPos .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?annotation .\n ?annotation ns1:has_adduct ?adduct .\n }\n GROUP BY ?adduct\n ORDER BY DESC(?frequency)\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/d8669ea7b6ab4a75a171344bc71c5b61/tmpnm0aoqfg.csv`"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +29," ""What are the most frequent adducts of LCMS features annotated by SIRIUS detected in positive ionization mode from Tabernaemontana coffeoides?""","PREFIX ns1: PREFIX ns2: SELECT ?adduct (COUNT(?adduct) AS ?frequency) @@ -421,7 +421,7 @@ WHERE { ORDER BY DESC(?frequency)",Medium 30," ""What are the Wikidata IDs of chemical entities that have ChEMBL activity against Trypanosoma cruzi target?"" -","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the Wikidata IDs of chemical entities that have ChEMBL activity against Trypanosoma cruzi target?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the Wikidata IDs of chemical entities that have ChEMBL activity against Trypanosoma cruzi target?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Trypanosoma cruzi, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL368, ChEMBLTarget\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""Here is the information you requested:\n\n- **Question**: What are the Wikidata IDs of chemical entities that have ChEMBL activity against Trypanosoma cruzi target?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?wd_id\n WHERE {\n ?chemicalEntity ns2:has_chembl_id ?chemblChemical .\n ?chemblChemical ns2:has_chembl_activity ?assayResults .\n ?assayResults ns2:target_id .\n ?chemicalEntity ns1:has_wd_id ?wd_id .\n }\n ```\n- **File Path**: The results are saved in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/18465df0915e441aa7285e242e5b5927/tmpsavbcot0.csv`.\n\nPlease let me know if you need further assistance!"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The file has been processed, and here are some of the Wikidata IDs of chemical entities that have ChEMBL activity against the Trypanosoma cruzi target:\n\n1. [Q82238903](http://www.wikidata.org/entity/Q82238903)\n2. [Q27095836](http://www.wikidata.org/entity/Q27095836)\n\nThese IDs were extracted from the provided file. If you need further analysis or a specific visualization, please let me know. \n\nNo visualization file was generated, but the data can be downloaded from the provided file path."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +","PREFIX ns1: PREFIX ns2: SELECT ?wd_id @@ -431,7 +431,7 @@ WHERE { ?assayResults ns2:target_id . ?chemicalEntity ns1:has_wd_id ?wd_id . }",Medium -31," ""How many features (pos ionization and neg ionization modes) have the same SIRIUS/CSI:FingerID and ISDB annotation by comparing the InCHIKey2D of the annotations?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""How many features (pos ionization and neg ionization modes) have the same SIRIUS/CSI:FingerID and ISDB annotation by comparing the InCHIKey2D of the annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question:How many features (pos ionization and neg ionization modes) have the same SIRIUS/CSI:FingerID and ISDB annotation by comparing the InCHIKey2D of the annotations?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The analysis has been completed. Here are the details:\n\n- **Question**: How many features (pos ionization and neg ionization modes) have the same SIRIUS/CSI:FingerID and ISDB annotation by comparing the InCHIKey2D of the annotations?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT (COUNT(?feature) AS ?featureCount)\n WHERE {\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?feature ns1:has_isdb_annotation ?isdbAnnotation .\n ?siriusAnnotation ns1:has_InChIkey2D ?InChIkey2D .\n ?isdbAnnotation ns1:has_InChIkey2D ?InChIkey2D .\n }\n ```\n- **Result**: 33,255 features have the same SIRIUS/CSI:FingerID and ISDB annotation by comparing the InCHIKey2D of the annotations.\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/ec07f70ba3ec4a18abbb424a48145459/tmppclv2pge.csv`\n\nThis information should provide the necessary details for further analysis or reporting."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +31," ""How many features (pos ionization and neg ionization modes) have the same SIRIUS/CSI:FingerID and ISDB annotation by comparing the InCHIKey2D of the annotations?""","PREFIX ns1: SELECT (COUNT(?feature) AS ?featureCount) WHERE { @@ -443,7 +443,7 @@ WHERE { ?siriusAnnotation ns1:has_InChIkey2D ?InChIkey2D . ?isdbAnnotation ns1:has_InChIkey2D ?InChIkey2D . }",Medium -32,"""How many features annotated as 'Tetraketide meroterpenoids' by CANOPUS are found for each submitted taxon and extract in database?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""How many features annotated as 'Tetraketide meroterpenoids' by CANOPUS are found for each submitted taxon and extract in database?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: How many features annotated as 'Tetraketide meroterpenoids' by CANOPUS are found for each submitted taxon and extract in database?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tetraketide meroterpenoids, https://enpkg.commons-lab.org/kg/npc_Tetraketide_meroterpenoids, NPCClass\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed. Here are the details:\n\n- **Question**: How many features annotated as 'Tetraketide meroterpenoids' by CANOPUS are found for each submitted taxon and extract in database?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT ?submittedTaxon ?extract (COUNT(?feature) AS ?featureCount)\n WHERE {\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_class .\n ?featureList ns1:has_lcms_feature ?feature .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?extract ns1:has_LCMS ?analysis .\n ?rawMaterial ns1:has_lab_process ?extract .\n ?rawMaterial ns1:submitted_taxon ?submittedTaxon .\n }\n GROUP BY ?submittedTaxon ?extract\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/093ee098fbba4dca93f0404833ba35bb/tmpjqdqbx0d.csv\n\nI will now analyze the SPARQL output to generate the final answer.\nThe SPARQL query has been executed successfully. Here is the final answer based on the SPARQL output:\n\nThe number of features annotated as 'Tetraketide meroterpenoids' by CANOPUS for each submitted taxon and extract in the database is available in the generated CSV file.\n\n- **Question**: How many features annotated as 'Tetraketide meroterpenoids' by CANOPUS are found for each submitted taxon and extract in database?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT ?submittedTaxon ?extract (COUNT(?feature) AS ?featureCount)\n WHERE {\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_class .\n ?featureList ns1:has_lcms_feature ?feature .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?extract ns1:has_LCMS ?analysis .\n ?rawMaterial ns1:has_lab_process ?extract .\n ?rawMaterial ns1:submitted_taxon ?submittedTaxon .\n }\n GROUP BY ?submittedTaxon ?extract\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/093ee098fbba4dca93f0404833ba35bb/tmpjqdqbx0d.csv\n\nYou can find the detailed results in the provided CSV file."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +32,"""How many features annotated as 'Tetraketide meroterpenoids' by CANOPUS are found for each submitted taxon and extract in database?""","PREFIX ns1: SELECT ?submittedTaxon ?extract (COUNT(?feature) AS ?featureCount) WHERE { @@ -456,7 +456,7 @@ WHERE { ?rawMaterial ns1:submitted_taxon ?submittedTaxon . } GROUP BY ?submittedTaxon ?extract",Medium -33," ""What are the retention times, parent masses, and associated Spec2Vec peaks for features in the LC-MS analysis of the plant Rumex nepalensis in positive ion mode?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the retention times, parent masses, and associated Spec2Vec peaks for features in the LC-MS analysis of the plant Rumex nepalensis in positive ion mode?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question:What are the retention times, parent masses, and associated Spec2Vec peaks for features in the LC-MS analysis of the plant Rumex nepalensis in positive ion mode?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Rumex nepalensis, http://www.wikidata.org/entity/Q11041204, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: What are the retention times, parent masses, and associated Spec2Vec peaks for features in the LC-MS analysis of the plant Rumex nepalensis in positive ion mode?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?retention_time ?parent_mass ?spec2vec_peak\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis a ns1:LCMSAnalysisPos .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_retention_time ?retention_time .\n ?feature ns1:has_parent_mass ?parent_mass .\n ?feature ns1:has_spec2vec_doc ?spec2vecDoc .\n ?spec2vecDoc ns1:has_spec2vec_peak ?spec2vec_peak .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/7e32d9e48e274df9a04b4a01d1635276/tmpxmt6gmzo.csv`\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The LC-MS analysis of the plant Rumex nepalensis in positive ion mode provided the following details:\n\n- **Retention Time**: 5.938233\n- **Parent Mass**: 584.475128\n- **Spec2Vec Peaks**:\n - [Peak 130.14](https://enpkg.commons-lab.org/kg/peak_130.14)\n - [Peak 131.07](https://enpkg.commons-lab.org/kg/peak_131.07)\n - [Peak 221.14](https://enpkg.commons-lab.org/kg/peak_221.14)\n - [Peak 233.11](https://enpkg.commons-lab.org/kg/peak_233.11)\n - [Peak 265.17](https://enpkg.commons-lab.org/kg/peak_265.17)\n\nNo visualization was requested for this analysis. The data can be downloaded from the provided file path."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +33," ""What are the retention times, parent masses, and associated Spec2Vec peaks for features in the LC-MS analysis of the plant Rumex nepalensis in positive ion mode?""","PREFIX ns1: PREFIX ns2: SELECT ?retention_time ?parent_mass ?spec2vec_peak @@ -472,7 +472,7 @@ WHERE { ?feature ns1:has_spec2vec_doc ?spec2vecDoc . ?spec2vecDoc ns1:has_spec2vec_peak ?spec2vec_peak . }",Medium -34, “What are the most frequent SIRIUS chemical structure annotations (planar structure) observed from the extracts belonging to Hibiscus syriacus?”,"{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the most frequent SIRIUS chemical structure annotations (planar structure) observed from the extracts belonging to Hibiscus syriacus?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the most frequent SIRIUS chemical structure annotations (planar structure) observed from the extracts belonging to Hibiscus syriacus?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Hibiscus syriacus, http://www.wikidata.org/entity/Q157756, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The most frequent SIRIUS chemical structure annotations (planar structure) observed from the extracts belonging to Hibiscus syriacus are as follows:\n\n1. InChIkey2D: [KEMQGTRYUADPNZ](https://enpkg.commons-lab.org/kg/KEMQGTRYUADPNZ) - Frequency: 2\n2. InChIkey2D: [DTOSIQBPPRVQHS](https://enpkg.commons-lab.org/kg/DTOSIQBPPRVQHS) - Frequency: 2\n3. InChIkey2D: [OUMMUORTBYSLFO](https://enpkg.commons-lab.org/kg/OUMMUORTBYSLFO) - Frequency: 2\n\nThese are the most frequently observed annotations, each appearing twice in the dataset. Here is the SPARQL query that was generated:\n\n```sparql\n PREFIX ns1: \\nPREFIX ns2: \\n\\nSELECT ?InChIkey2D (COUNT(?InChIkey2D) AS ?frequency)\\nWHERE {\\n ?rawMaterial ns1:has_wd_id .\\n ?rawMaterial ns1:has_lab_process ?labExtract .\\n ?labExtract ns1:has_LCMS ?analysis .\\n ?analysis ns1:has_lcms_feature_list ?feature_list .\\n ?feature_list ns1:has_lcms_feature ?feature .\\n ?feature ns1:has_sirius_annotation ?annotation .\\n ?annotation ns1:has_InChIkey2D ?InChIkey2D .\\n}\\nGROUP BY ?InChIkey2D\\nORDER BY DESC(?frequency)'"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +34, “What are the most frequent SIRIUS chemical structure annotations (planar structure) observed from the extracts belonging to Hibiscus syriacus?”,"PREFIX ns1: PREFIX ns2: SELECT ?InChIkey2D (COUNT(?InChIkey2D) AS ?frequency) @@ -487,7 +487,7 @@ WHERE { } GROUP BY ?InChIkey2D ORDER BY DESC(?frequency)",Medium -35,"""Which molecules (represented by their InChIkey) have reported ChEMBL activity with activity type IC50 and activity values below 500 nM against Leishmania donovani target?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which molecules (represented by their InChIkey) have reported ChEMBL activity with activity type IC50 and activity values below 500 nM against Leishmania donovani target?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which molecules (represented by their InChIkey) have reported ChEMBL activity with activity type IC50 and activity values below 500 nM against Leishmania donovani target?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""Leishmania donovani, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL367, ChEMBLTarget"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed successfully. Here are the details:\n\n- **Question**: Which molecules (represented by their InChIkey) have reported ChEMBL activity with activity type IC50 and activity values below 500 nM against Leishmania donovani target?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?InChIkey\n WHERE {\n ?chemicalEntity ns2:has_chembl_id ?chemblChemical .\n ?chemblChemical ns2:has_chembl_activity ?assayResults .\n ?assayResults ns2:target_id ;\n ns2:activity_type \""IC50\"" ;\n ns2:activity_value ?activityValue .\n FILTER(?activityValue < 500)\n ?chemicalEntity ns1:has_wd_id ?wd_id .\n ?InChIkey ns1:has_wd_id ?wd_id .\n }\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/e7807b50a3824198a3233798d22d9a4b/tmpp66dmbc6.csv\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The molecules that meet the criteria have been identified with the following InChIkey: [APKFDSVGJQXUKY-INPOYWNPSA-N](APKFDSVGJQXUKY-INPOYWNPSA-N).\n\n"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +35,"""Which molecules (represented by their InChIkey) have reported ChEMBL activity with activity type IC50 and activity values below 500 nM against Leishmania donovani target?""","PREFIX ns1: PREFIX ns2: SELECT ?InChIkey @@ -501,7 +501,7 @@ ORDER BY DESC(?frequency)",Medium ?chemicalEntity ns1:has_wd_id ?wd_id . ?InChIkey ns1:has_wd_id ?wd_id . }",High -36," ""What are the SIRIUS structural annotations associated with the MS2 spectra from the lab extracts of Tabernaemontana coffeoides ?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the SIRIUS structural annotations associated with the MS2 spectra from the lab extracts of Tabernaemontana coffeoides ?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the SIRIUS structural annotations associated with the MS2 spectra from the lab extracts of Tabernaemontana coffeoides?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: What are the SIRIUS structural annotations associated with the MS2 spectra from the lab extracts of Tabernaemontana coffeoides?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?siriusAnnotation\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?siriusAnnotation a ns1:SiriusStructureAnnotation .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/d851d6aa70ca4256a4862a6e4811b956/tmpr1p_smpd.csv`\n\nI will now analyze the SPARQL output to provide the final answer.\nThe SPARQL query has been executed successfully, and the results are stored in the specified file. Here is the final answer based on the SPARQL output:\n\n- **Question**: What are the SIRIUS structural annotations (SiriusStructureAnnotation) associated with the MS2 spectra (MS2Spectrum) from the lab extracts (LabExtract) of Tabernaemontana coffeoides?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?siriusAnnotation\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?siriusAnnotation a ns1:SiriusStructureAnnotation .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/d851d6aa70ca4256a4862a6e4811b956/tmpr1p_smpd.csv`\n\nThe file contains the SIRIUS structural annotations associated with the MS2 spectra from the lab extracts of Tabernaemontana coffeoides. You can access the detailed results in the provided file path."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +36," ""What are the SIRIUS structural annotations associated with the MS2 spectra from the lab extracts of Tabernaemontana coffeoides ?""","PREFIX ns1: PREFIX ns2: SELECT ?siriusAnnotation @@ -514,7 +514,7 @@ ORDER BY DESC(?frequency)",Medium ?feature ns1:has_sirius_annotation ?siriusAnnotation . ?siriusAnnotation a ns1:SiriusStructureAnnotation . }",Low -37," ""Which lab extracts from Melochia umbellata yield compounds that, when analyzed in positive ionization mode LCMS, have a retention time of less than 6 minutes and demonstrate an inhibition percentage greater than 70% in bioassay results? Provide lab extracts, retention times and inhibition percentage.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which lab extracts from Melochia umbellata yield compounds that, when analyzed in positive ionization mode LCMS, have a retention time of less than 6 minutes and demonstrate an inhibition percentage greater than 70% in bioassay results? Provide lab extracts, retention times and inhibition percentage."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which lab extracts from Melochia umbellata yield compounds that, when analyzed in positive ionization mode LCMS, have a retention time of less than 6 minutes and demonstrate an inhibition percentage greater than 70% in bioassay results? Provide lab extracts, retention times and inhibition percentage."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""Melochia umbellata, http://www.wikidata.org/entity/Q6813281, Wikidata"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The lab extracts from Melochia umbellata that yield compounds analyzed in positive ionization mode LCMS with a retention time of less than 6 minutes and demonstrate an inhibition percentage greater than 70% in bioassay results are as follows:\n\n- **Lab Extract**: [VGF156_A06](https://enpkg.commons-lab.org/kg/VGF156_A06)\n - **Retention Time**: 4.198916666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 4.54375 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.5790666666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7661 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.6830500000000002 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.089 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.6979 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.5034166666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.26885 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.1985333333333337 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.11295 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7891 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.2292333333333334 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.089 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.5863333333333334 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7204833333333334 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7815333333333332 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7054833333333332 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.4656833333333334 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.2918833333333335 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.3785166666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.2292333333333334 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.33065 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.5962 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.4250166666666668 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.511016666666667 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.6655166666666663 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.8533833333333332 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.8533833333333332 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.5034166666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.5034166666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.3630666666666669 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7512166666666666 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 0.6451166666666667 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.7661 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.9627166666666669 minutes\n - **Inhibition Percentage**: 89.9%\n \n - **Retention Time**: 1.2918833333333335 minutes\n - **Inhibition Percentage**: 89.9%\n\nThe SPARQL query used to retrieve this information is as follows:\n\n```sparql\nPREFIX ns1: \nPREFIX ns2: \nPREFIX wd: \n\nSELECT ?labExtract ?retentionTime ?inhibitionPercentage\nWHERE {\n ?rawMaterial ns1:has_wd_id wd:Q6813281 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis a ns1:LCMSAnalysisPos .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_retention_time ?retentionTime .\n FILTER(?retentionTime < 6)\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\n ?bioAssayResult ns2:inhibition_percentage ?inhibitionPercentage .\n FILTER(?inhibitionPercentage > 70)\n}\n```\n\nThe results are saved in the file located at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/461195db838a4810afb06014c87c0e6b/tmp_7t2umr1.csv`"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +37," ""Which lab extracts from Melochia umbellata yield compounds that, when analyzed in positive ionization mode LCMS, have a retention time of less than 6 minutes and demonstrate an inhibition percentage greater than 70% in bioassay results? Provide lab extracts, retention times and inhibition percentage.""","PREFIX ns1: PREFIX ns2: PREFIX wd: @@ -532,7 +532,7 @@ WHERE { ?bioAssayResult ns2:inhibition_percentage ?inhibitionPercentage . FILTER(?inhibitionPercentage > 70) }",Medium -38," ""Provide the wikidata ids of the chemical entities annotated by SIRIUS for Tabernaemontana coffeoides seeds extract taxon obtained in positive mode which are also reported in the Tabernaemontana genus in Wikidata.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Provide the wikidata ids of the chemical entities annotated by SIRIUS for Tabernaemontana coffeoides seeds extract taxon obtained in positive mode which are also reported in the Tabernaemontana genus in Wikidata."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Provide the wikidata ids of the chemical entities annotated by SIRIUS for Tabernaemontana coffeoides seeds extract taxon obtained in positive mode which are also reported in the Tabernaemontana genus in Wikidata."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": [""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata"", ""Tabernaemontana genus, http://www.wikidata.org/entity/Q310915, Wikidata""], ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The final merged output file has been generated. Here are the details:\n\n- **Question**: Provide the wikidata ids of the chemical entities annotated by SIRIUS for Tabernaemontana coffeoides seeds extract taxon obtained in positive mode which are also reported in the Tabernaemontana genus in Wikidata.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?wd_id\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis a ns1:LCMSAnalysisPos .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?annotation .\n ?annotation ns1:has_InChIkey2D ?inchiKey2D .\n ?inchiKey2D ns1:is_InChIkey2D_of ?chemicalEntity .\n ?chemicalEntity ns1:has_wd_id ?wd_id .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/3b8bb2a213ab4b1faf41570486facaae/tmp4g0h_2d5.csv`\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The wikidata IDs of the chemical entities annotated by SIRIUS for Tabernaemontana coffeoides seeds extract taxon obtained in positive mode which are also reported in the Tabernaemontana genus in Wikidata have been processed. \n\n"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +38," ""Provide the wikidata ids of the chemical entities annotated by SIRIUS for Tabernaemontana coffeoides seeds extract taxon obtained in positive mode which are also reported in the Tabernaemontana genus in Wikidata.""","PREFIX ns1: PREFIX ns2: SELECT ?wd_id @@ -548,7 +548,7 @@ WHERE { ?inchiKey2D ns1:is_InChIkey2D_of ?chemicalEntity . ?chemicalEntity ns1:has_wd_id ?wd_id . }",High -39," ""What is the highest inhibition percentage recorded for compounds observed in Rauvolfia vomitoria extract?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What is the highest inhibition percentage recorded for compounds observed in Rauvolfia vomitoria extract?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What is the highest inhibition percentage recorded for compounds observed in Rauvolfia vomitoria extract?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Rauvolfia vomitoria, http://www.wikidata.org/entity/Q5225969, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The highest inhibition percentage recorded for compounds observed in Rauvolfia vomitoria extract is 17.4%.\n\nHere is the detailed information:\n- **Question**: What is the highest inhibition percentage recorded for compounds from Rauvolfia vomitoria extract?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n PREFIX wd: \n\n SELECT ?inhibition_percentage\n WHERE {\n ?rawMaterial ns1:has_wd_id wd:Q5225969 .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\n ?bioAssayResult ns2:inhibition_percentage ?inhibition_percentage .\n }\n ORDER BY DESC(?inhibition_percentage)\n LIMIT 1\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/2b0f38f9dd7b4e818282a1c620449ab2/tmpjoi_glcf.csv"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +39," ""What is the highest inhibition percentage recorded for compounds observed in Rauvolfia vomitoria extract?""","PREFIX ns1: PREFIX ns2: PREFIX wd: @@ -561,7 +561,7 @@ WHERE { } ORDER BY DESC(?inhibition_percentage) LIMIT 1",Low -40," ""List the bioassay results at 10µg/mL against T.cruzi for lab extracts of Tabernaemontana coffeoides.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""List the bioassay results at 10碌g/mL against T.cruzi for lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: List the bioassay results at 10碌g/mL against T.cruzi for lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The bioassay results at 10碌g/mL against T.cruzi for lab extracts of Tabernaemontana coffeoides are as follows:\n\n1. Lab Extract: [VGF152_B02](https://enpkg.commons-lab.org/kg/VGF152_B02)\n - BioAssay Result: [VGF152_B02_Tcruzi_10ugml](https://enpkg.commons-lab.org/module/VGF152_B02_Tcruzi_10ugml)\n\n2. Lab Extract: [VGF152_A02](https://enpkg.commons-lab.org/kg/VGF152_A02)\n - BioAssay Result: [VGF152_A02_Tcruzi_10ugml](https://enpkg.commons-lab.org/module/VGF152_A02_Tcruzi_10ugml)\n\n3. Lab Extract: [VGF151_G11](https://enpkg.commons-lab.org/kg/VGF151_G11)\n - BioAssay Result: [VGF151_G11_Tcruzi_10ugml](https://enpkg.commons-lab.org/module/VGF151_G11_Tcruzi_10ugml)\n\n4. Lab Extract: [VGF151_H11](https://enpkg.commons-lab.org/kg/VGF151_H11)\n - BioAssay Result: [VGF151_H11_Tcruzi_10ugml](https://enpkg.commons-lab.org/module/VGF151_H11_Tcruzi_10ugml)\n\nHere is the dictionary containing the question, generated SPARQL query, and file path to the query result:\n\n```json\n{\n \""question\"": \""List the bioassay results (BioAssayResults) at 10碌g/mL against T.cruzi (Tcruzi10ugml) for lab extracts (LabExtract) of Tabernaemontana coffeoides.\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\nPREFIX ns2: \\nPREFIX wd: \\n\\nSELECT ?labExtract ?bioAssayResult\\nWHERE {\\n ?rawMaterial ns1:has_wd_id wd:Q15376858 .\\n ?rawMaterial ns1:has_lab_process ?labExtract .\\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\\n ?bioAssayResult a ns2:Tcruzi10ugml .\\n}\"",\n \""file_path\"": \""/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/341300dcf0174e67999e30cc1e34324b/tmpvt5pfz18.csv\""\n}\n```"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +40," ""List the bioassay results at 10µg/mL against T.cruzi for lab extracts of Tabernaemontana coffeoides.""","PREFIX ns1: PREFIX ns2: PREFIX wd: @@ -572,7 +572,7 @@ WHERE { ?labExtract ns2:has_bioassay_results ?bioAssayResult . ?bioAssayResult a ns2:Tcruzi10ugml . }",Medium -41,"""For the LC-MS feature observed in the extracts of Tabernaemontana coffeoides, retrieve those that are annotated with a NPCPathways with CANOPUS.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""For the LC-MS feature observed in the extracts of Tabernaemontana coffeoides, retrieve those that are annotated with a NPCPathways with CANOPUS."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: For the LC-MS feature observed in the extracts of Tabernaemontana coffeoides, retrieve those that are annotated with a NPCPathways with CANOPUS."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been executed to retrieve the LC-MS features observed in the extracts of Tabernaemontana coffeoides that are annotated with NPCPathways using CANOPUS. Here is the information:\n\n- **SPARQL Query**: \n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?feature\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_canopus_annotation ?annotation .\n ?annotation ns1:has_canopus_npc_pathway ?npcPathway .\n }\n ```\n\n- **File Path**: The results are saved in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/0a0e72082aef4788a45ec734c4f9b49c/tmpqlgm7k3g.csv`.\n\nPlease check the file for the detailed results of the query."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The LC-MS features observed in the extracts of Tabernaemontana coffeoides that are annotated with NPCPathways using CANOPUS have been successfully retrieved. "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +41,"""For the LC-MS feature observed in the extracts of Tabernaemontana coffeoides, retrieve those that are annotated with a NPCPathways with CANOPUS.""","PREFIX ns1: PREFIX ns2: SELECT ?feature @@ -585,7 +585,7 @@ WHERE { ?feature ns1:has_canopus_annotation ?annotation . ?annotation ns1:has_canopus_npc_pathway ?npcPathway . }",Medium -42," ""List the tissue types used to produce the lab extracts of Tabernaemontana coffeoides. Provide the tissue types and lab extracts. ""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""List the tissue types used to produce the lab extracts of Tabernaemontana coffeoides. Provide the tissue types and lab extracts. "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: List the tissue types used to produce the lab extracts of Tabernaemontana coffeoides. Provide the tissue types and lab extracts."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The tissue type used to produce the lab extracts of Tabernaemontana coffeoides is \""green tissue.\"" The lab extracts associated with this tissue type are:\n\n1. Lab Extract: VGF152_B02\n2. Lab Extract: VGF152_A02\n3. Lab Extract: VGF151_G11\n4. Lab Extract: VGF151_H11\n\nHere is the SPARQL query used to retrieve this information:\n\n```sparql\nPREFIX ns1: \nPREFIX ns2: \n\nSELECT ?tissue ?labExtract\nWHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?rawMaterial ns2:has_tissue ?tissue .\n}\n```\n\nThe data was retrieved from the file located at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/79cf06dcfb4c4635bf92d845c42ea938/tmpo0nqoke3.csv`."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +42," ""List the tissue types used to produce the lab extracts of Tabernaemontana coffeoides. Provide the tissue types and lab extracts. ""","PREFIX ns1: PREFIX ns2: SELECT ?tissue ?labExtract @@ -594,7 +594,7 @@ WHERE { ?rawMaterial ns1:has_lab_process ?labExtract . ?rawMaterial ns2:has_tissue ?tissue . }",Low -43,"""Retrieve the mass differences and cosine similarity scores for spectral pairs involving MS2 spectra from Desmodium heterophyllum.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the mass differences and cosine similarity scores for spectral pairs involving MS2 spectra from Desmodium heterophyllum."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the mass differences and cosine similarity scores for spectral pairs involving MS2 spectra from Desmodium heterophyllum."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid. It is related to the ENPKG nodes/entities (Raw Material, LCMS Analysis, SpectralPair). The plant name \""Desmodium heterophyllum\"" is present in the database. The question asks for mass differences and cosine similarity scores, which are properties of the SpectralPair entity in the ENPKG."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Desmodium heterophyllum, http://www.wikidata.org/entity/Q10770714, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The mass differences and cosine similarity scores for spectral pairs involving MS2 spectra from Desmodium heterophyllum have been retrieved. Here is the information:\n\n- **SPARQL Query**: \n ```sparql\n PREFIX ns1: \n\n SELECT ?mass_difference ?cosine\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?lcmsAnalysis .\n ?lcmsAnalysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?spectralPair ns1:has_member ?feature .\n ?spectralPair ns1:has_mass_difference ?mass_difference .\n ?spectralPair ns1:has_cosine ?cosine .\n }\n ```\n\n- **File Path**: The results are stored in the file located at `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/03572d09329f4855ab27963f8832666f/tmpsuxk8web.csv`.\n\nPlease refer to the file for detailed results."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The mass differences and cosine similarity scores for spectral pairs involving MS2 spectra from Desmodium heterophyllum have been successfully interpreted. Here are some sample entries from the data:\n\n- Mass Difference: 218.2978, Cosine Similarity: 0.999612\n- Mass Difference: 46.0057, Cosine Similarity: 0.999888\n- Mass Difference: 140.0989, Cosine Similarity: 0.985793\n- Mass Difference: 160.1252, Cosine Similarity: 0.993816\n- Mass Difference: 162.1407, Cosine Similarity: 0.984198\n\nNo visualization is available, but the data can be downloaded from the provided file path. If you need a visualization, please let me know, and I can generate a plotly graph for you.\n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +43,"""Retrieve the mass differences and cosine similarity scores for spectral pairs involving MS2 spectra from Desmodium heterophyllum.""","PREFIX ns1: SELECT ?mass_difference ?cosine WHERE { @@ -607,7 +607,7 @@ WHERE { ?spectralPair ns1:has_mass_difference ?mass_difference . ?spectralPair ns1:has_cosine ?cosine . }",High -44," ""Retrieve the top 5 tissues used to produce lab extracts that contain compounds with inhibition activity greater than 70% against Leishmania donovani target at 10 µg/mL""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the top 5 tissues used to produce lab extracts that contain compounds with inhibition activity greater than 70% against Leishmania donovani target at 10 碌g/mL."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the top 5 tissues used to produce lab extracts that contain compounds with inhibition activity greater than 70% against Leishmania donovani target at 10 碌g/mL."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Leishmania donovani, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL367, ChEMBLTarget\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The top 5 tissues used to produce lab extracts containing compounds with inhibition activity greater than 70% against the Leishmania donovani target at 10 碌g/mL are:\n\n1. Green Tissue (24 extracts)\n2. Root Tissue (17 extracts)\n3. Multiple Tissues (9 extracts)\n4. Woody Tissue (7 extracts)\n\nThe SPARQL query used to retrieve this information is as follows:\n\n```sparql\nPREFIX ns1: \nPREFIX ns2: \n\nSELECT ?tissue (COUNT(?labExtract) AS ?count)\nWHERE {\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\n ?bioAssayResult ns2:inhibition_percentage ?inhibitionPercentage ;\n ns2:target_id .\n FILTER(?inhibitionPercentage > 70)\n ?rawMaterial ns1:has_lab_process ?labExtract ;\n ns2:has_tissue ?tissue .\n}\nGROUP BY ?tissue\nORDER BY DESC(?count)\nLIMIT 5\n```\n\nThe data is stored in the file located at: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/9aa4b47f0e764929989be2184095b3b0/tmp6u2rkep7.csv`."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +44," ""Retrieve the top 5 tissues used to produce lab extracts that contain compounds with inhibition activity greater than 70% against Leishmania donovani target at 10 µg/mL""","PREFIX ns1: PREFIX ns2: SELECT ?tissue (COUNT(?labExtract) AS ?count) @@ -622,7 +622,7 @@ WHERE { GROUP BY ?tissue ORDER BY DESC(?count) LIMIT 5",High -45," ""Retrieve the Wikidata IDs of chemical entities classified as Terpenoids in the lab extracts of Tabernaemontana coffeoides.""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Retrieve the Wikidata IDs of chemical entities classified as Terpenoids in the lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Retrieve the Wikidata IDs of chemical entities classified as Terpenoids in the lab extracts of Tabernaemontana coffeoides."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Terpenoids, https://enpkg.commons-lab.org/kg/npc_Terpenoids, NPCPathway;\nTabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""Here is the information retrieved from the SPARQL query:\n\n- **Question**: Retrieve the Wikidata IDs of chemical entities classified as Terpenoids in the lab extracts of Tabernaemontana coffeoides.\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n\n SELECT ?wd_id\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?annotation .\n ?annotation ns1:has_InChIkey2D ?InChIkey2D .\n ?InChIkey2D ns1:is_InChIkey2D_of ?chemicalEntity .\n ?chemicalEntity ns1:has_npc_pathway .\n ?chemicalEntity ns1:has_wd_id ?wd_id .\n }\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/40c708550e684b0685c41a14cae47bd2/tmpcgattsm4.csv`\n\nThis information will be provided to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The Wikidata IDs of the chemical entities classified as Terpenoids in the lab extracts of Tabernaemontana coffeoides are:\n\n1. [Q105262918](http://www.wikidata.org/entity/Q105262918)\n2. [Q104398790](http://www.wikidata.org/entity/Q104398790)\n3. [Q105262918](http://www.wikidata.org/entity/Q105262918)\n4. [Q104398790](http://www.wikidata.org/entity/Q104398790)\n5. [Q104953850](http://www.wikidata.org/entity/Q104953850)\n\nNo visualization is available, but the data can be downloaded if needed. If you need further analysis or a specific visualization, please let me know. \n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +45," ""Retrieve the Wikidata IDs of chemical entities classified as Terpenoids in the lab extracts of Tabernaemontana coffeoides.""","PREFIX ns1: SELECT ?wd_id WHERE { @@ -637,7 +637,7 @@ LIMIT 5",High ?chemicalEntity ns1:has_npc_pathway . ?chemicalEntity ns1:has_wd_id ?wd_id . }",High -46,"""What are the CANOPUS annotations for pairs of features from Desmodium heterophyllum extracts that share a cosine similarity score greater than 0.8? Provide the features, and annotations""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the CANOPUS annotations for pairs of features from Desmodium heterophyllum extracts that share a cosine similarity score greater than 0.8? Provide the features and annotations for each feature. "", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the CANOPUS annotations for pairs of features from Desmodium heterophyllum extracts that share a cosine similarity score greater than 0.8? Provide the features and annotations for each feature."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Desmodium heterophyllum, http://www.wikidata.org/entity/Q10770714, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here are the details:\n\n- **Question**: What are the CANOPUS annotations for pairs of features from Desmodium heterophyllum extracts that share a cosine similarity score greater than 0.8? Provide the features and annotations for each feature .\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?feature1 ?feature2 ?annotation1 ?annotation2 \n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature1, ?feature2 .\n ?pair ns1:has_member ?feature1, ?feature2 .\n ?pair ns1:has_cosine ?cosine .\n FILTER(?cosine > 0.8)\n ?feature1 ns1:has_canopus_annotation ?annotation1 .\n ?feature2 ns1:has_canopus_annotation ?annotation2 .\n }\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/e88e690ae0ad4816a764af78599a625a/tmp2igkyrj2.csv\n\nI will now provide this information to the supervisor for further analysis."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The analysis has been completed, and there are 14,484 pairs of features from Desmodium heterophyllum extracts that share a cosine similarity score greater than 0.8. The features, annotations, and cosine scores have been extracted.\n\nIf you need more specific details or a visualization of this data, please let me know. The data can be downloaded for further analysis. \n\nThe full path of the generated file is:\n- /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/e88e690ae0ad4816a764af78599a625a/tmp2igkyrj2.csv\n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +46,"""What are the CANOPUS annotations for pairs of features from Desmodium heterophyllum extracts that share a cosine similarity score greater than 0.8? Provide the features, and annotations""","PREFIX ns1: PREFIX ns2: SELECT ?feature1 ?feature2 ?annotation1 ?annotation2 @@ -653,7 +653,7 @@ LIMIT 5",High ?feature1 ns1:has_canopus_annotation ?annotation1 . ?feature2 ns1:has_canopus_annotation ?annotation2 . }",High -47,"""Which ChEMBL documents cited in ChEMBL assay results involve compounds (identified by InChIKeys) that are also observed in SIRIUS structural annotations within lab extracts, and which lab extracts contain the highest number of these shared compounds?","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which ChEMBL documents cited in ChEMBL assay results involve compounds (identified by InChIKeys) that are also observed in SIRIUS structural annotations within lab extracts, and which lab extracts contain the highest number of these shared compounds?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which ChEMBL documents cited in ChEMBL assay results involve compounds (identified by InChIKeys) that are also observed in SIRIUS structural annotations within lab extracts, and which lab extracts contain the highest number of these shared compounds?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been successfully generated and executed. Here is the information:\n\n- **Question**: Which ChEMBL documents cited in ChEMBL assay results involve compounds (identified by InChIKeys) that are also observed in SIRIUS structural annotations within lab extracts, and which lab extracts contain the highest number of these shared compounds?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?chemblDocument ?labExtract (COUNT(?InChIkey2D) AS ?sharedCompoundCount)\n WHERE {\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?featureList .\n ?featureList ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?siriusAnnotation ns1:has_InChIkey2D ?InChIkey2D .\n\n ?InChIkey2D ns1:is_InChIkey2D_of ?chemicalEntity .\n ?chemicalEntity ns2:has_chembl_id ?chemblChemical .\n\n ?chemblChemical ns2:has_chembl_activity ?assayResult .\n ?assayResult ns2:stated_in_document ?chemblDocument .\n }\n GROUP BY ?chemblDocument ?labExtract\n ORDER BY DESC(?sharedCompoundCount)\n ```\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/ff49c14268314ce9948ca2f02f8fc1d0/tmpy5x90ltz.csv`\n\nPlease refer to the file for detailed results."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Interpreter_agent"", ""type"": ""human"", ""content"": ""The ChEMBL document and lab extract with the highest number of shared compounds are as follows:\n\n- **ChEMBL Document**: [https://www.ebi.ac.uk/chembl/document_report_c...](https://www.ebi.ac.uk/chembl/document_report_c...)\n- **Lab Extract**: [https://enpkg.commons-lab.org/kg/VGF142_G04](https://enpkg.commons-lab.org/kg/VGF142_G04)\n- **Shared Compound Count**: 125\n\nThis information provides the details of the ChEMBL document and lab extract with the highest number of shared compounds. If you need further analysis or visualization, please let me know.\n\nThe full path of the files generated are: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/ff49c14268314ce9948ca2f02f8fc1d0/tmpy5x90ltz.csv`\n\nFINISHED"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +47,"""Which ChEMBL documents cited in ChEMBL assay results involve compounds (identified by InChIKeys) that are also observed in SIRIUS structural annotations within lab extracts, and which lab extracts contain the highest number of these shared compounds?","PREFIX ns1: PREFIX ns2: SELECT ?chemblDocument ?labExtract (COUNT(?InChIkey2D) AS ?sharedCompoundCount) @@ -673,7 +673,7 @@ LIMIT 5",High GROUP BY ?chemblDocument ?labExtract ORDER BY DESC(?sharedCompoundCount)",High 48," “What are the ChEMBL targets -linked to ChEMBL assay results for chemicals sharing InChIKeys with SIRIUS structural annotations ?""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are the ChEMBL targets linked to ChEMBL assay results for chemicals sharing InChIKeys with SIRIUS structural annotations ?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the ChEMBL targets linked to ChEMBL assay results for chemicals sharing InChIKeys with SIRIUS structural annotations ?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The SPARQL query has been generated and executed. Here are the details:\n\n- **Question**: What are the ChEMBL targets linked to ChEMBL assay results for chemicals sharing InChIKeys with SIRIUS structural annotations?\n- **Generated SPARQL Query**:\n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?chembl_target\n WHERE {\n ?sirius_annotation ns1:has_InChIkey2D ?InChIkey2D .\n ?InChIkey2D ns1:is_InChIkey2D_of ?InChIkey .\n ?InChIkey ns2:has_chembl_id ?chembl_chemical .\n ?chembl_chemical ns2:has_chembl_activity ?chembl_assay_results .\n ?chembl_assay_results ns2:target_id ?chembl_target .\n }\n ```\n- **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/f016f234d53147b3a0454bd652bc8f6b/tmppen79nmc.csv\n\nI will now analyze the SPARQL output to generate the final answer.\nThe final answer based on the SPARQL output is as follows:\n\n- **ChEMBL Targets (ChEMBLTarget)**: The ChEMBL targets linked to ChEMBL assay results for chemicals sharing InChIKeys with SIRIUS structural annotations are listed in the file located at the following path:\n - **File Path**: /var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/f016f234d53147b3a0454bd652bc8f6b/tmppen79nmc.csv\n\nHere is the dictionary containing the question, generated SPARQL query, and file path:\n\n```json\n{\n \""question\"": \""What are the ChEMBL targets (ChEMBLTarget) linked to ChEMBL assay results (ChEMBLAssayResults) for chemicals (ChEMBLChemical) sharing InChIKeys (InChIkey) with SIRIUS structural annotations (SiriusStructureAnnotation)?\"",\n \""generated_sparql_query\"": \""PREFIX ns1: \\nPREFIX ns2: \\n\\nSELECT ?chembl_target\\nWHERE {\\n ?sirius_annotation ns1:has_InChIkey2D ?InChIkey2D .\\n ?InChIkey2D ns1:is_InChIkey2D_of ?InChIkey .\\n ?InChIkey ns2:has_chembl_id ?chembl_chemical .\\n ?chembl_chemical ns2:has_chembl_activity ?chembl_assay_results .\\n ?chembl_assay_results ns2:target_id ?chembl_target .\\n}\"",\n \""file_path\"": \""/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/f016f234d53147b3a0454bd652bc8f6b/tmppen79nmc.csv\""\n}\n```\n\nPlease review the file for the detailed list of ChEMBL targets."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +linked to ChEMBL assay results for chemicals sharing InChIKeys with SIRIUS structural annotations ?""","PREFIX ns1: PREFIX ns2: SELECT ?chembl_target @@ -684,7 +684,7 @@ linked to ChEMBL assay results for chemicals sharing InChIKeys with SIRIUS struc ?chembl_chemical ns2:has_chembl_activity ?chembl_assay_results . ?chembl_assay_results ns2:target_id ?chembl_target . }",High -49,"""What are SIRIUS structural annotations for LCMS features with cosmic scores above 0.3 associated with Tabernaemontana coffeoides? Report their ZODIAC scores. ""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""What are SIRIUS structural annotations for LCMS features with cosmic scores above 0.3 associated with Tabernaemontana coffeoides? Report their ZODIAC scores."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: What are the cosmic scores and zodiac scores associated with SIRIUS structural annotations for LCMS features from Tabernaemontana coffeoides?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Tabernaemontana coffeoides, http://www.wikidata.org/entity/Q15376858, Wikidata\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The zodiac scores associated with SIRIUS structural annotations for LCMS features from Tabernaemontana coffeoides have been retrieved. Here is the information:\n\n- **SPARQL Query**: \n ```sparql\n PREFIX ns1: \n PREFIX ns2: \n\n SELECT ?cosmic_score ?zodiac_score\n WHERE {\n ?rawMaterial ns1:has_wd_id .\n ?rawMaterial ns1:has_lab_process ?labExtract .\n ?labExtract ns1:has_LCMS ?analysis .\n ?analysis ns1:has_lcms_feature_list ?feature_list .\n ?feature_list ns1:has_lcms_feature ?feature .\n ?feature ns1:has_sirius_annotation ?siriusAnnotation .\n ?siriusAnnotation ns1:has_cosmic_score ?cosmic_score .\n FILTER (?cosmic_score > 0.3) \n ?siriusAnnotation ns1:has_zodiac_score ?zodiac_score .\n }\n ```\n\n- **File Path**: `/var/folders/20/4kgcw5656h12ss_nj18mndwm0000gn/T/kgbot/5137b0a478de42c5b07851d5252b3e4c/tmpn4ce1k2v.csv`\n\nPlease refer to the file for detailed scores."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +49,"""What are SIRIUS structural annotations for LCMS features with cosmic scores above 0.3 associated with Tabernaemontana coffeoides? Report their ZODIAC scores. ""","PREFIX ns1: PREFIX ns2: SELECT ?cosmic_score ?zodiac_score @@ -699,7 +699,7 @@ linked to ChEMBL assay results for chemicals sharing InChIKeys with SIRIUS struc FILTER (?cosmic_score > 0.3) ?siriusAnnotation ns1:has_zodiac_score ?zodiac_score . }",High -50,""" Which lab extracts have bioassay results with inhibition percentages above 50% against Leishmania donovani target at 10 µg/mL ??""","{""next"": ""FINISH"", ""messages"": [{""type"": ""human"", ""content"": ""Which lab extracts have bioassay results with inhibition percentages above 50% against Leishmania donovani target at 10 碌g/mL?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Entry_Agent"", ""type"": ""human"", ""content"": ""Starting the processing of the question: Which lab extracts have bioassay results with inhibition percentages above 50% against Leishmania donovani target at 10 碌g/mL?"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Validator"", ""type"": ""human"", ""content"": ""The question is valid."", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""ENPKG_agent"", ""type"": ""human"", ""content"": ""\""Leishmania donovani, https://www.ebi.ac.uk/chembl/target_report_card/CHEMBL367, ChEMBLTarget\"""", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}, {""name"": ""Sparql_query_runner"", ""type"": ""human"", ""content"": ""The lab extracts that have bioassay results with inhibition percentages above 50% against the Leishmania donovani target are identified by the following identifiers:\n\n- VGF159_F04\n- VGF159_B08\n- VGF159_B09\n- VGF159_E03\n- VGF159_H03\n- VGF158_C03\n- VGF158_B03\n- VGF158_G06\n- VGF158_A10\n- VGF158_H02\n- VGF158_A03\n- VGF158_D03\n- VGF158_D08\n- VGF158_C04\n- VGF158_H09\n- VGF158_G09\n- VGF157_C04\n- VGF157_D03\n- VGF157_H05\n- VGF157_C03\n- VGF157_C05\n- VGF157_D10\n- VGF157_E04\n- VGF156_E04\n- VGF156_D09\n- VGF156_A05\n- VGF155_B07\n- VGF155_A08\n- VGF155_E10\n- VGF155_D09\n- VGF155_C04\n- VGF155_B05\n- VGF155_C02\n- VGF155_A10\n- VGF155_F10\n- VGF155_D08\n- VGF154_A10\n- VGF154_A02\n- VGF154_A04\n- VGF152_G03\n- VGF152_H03\n- VGF152_D08\n- VGF152_H09\n- VGF152_A10\n- VGF151_A06\n- VGF151_A03\n- VGF151_H08\n- VGF151_H11\n- VGF147_H05\n- VGF147_F04\n- VGF147_G04\n- VGF146_F02\n- VGF145_G10\n- VGF144_E05\n- VGF143_G02\n- VGF142_G04\n- VGF142_B07\n- VGF142_G05\n- VGF142_B10\n- VGF141_C06\n- VGF141_B11\n- VGF140_E05\n- VGF140_A09\n- VGF139_A09\n- VGF139_D08\n- VGF138_B06\n- VGF138_E06\n- VGF138_E03\n- VGF138_E05\n- VGF138_G03\n\nThese identifiers correspond to specific lab extracts that have shown significant inhibition against the Leishmania donovani target.nHere is the SPARQL query that was generated:\n\n```sparql\nPREFIX ns1: \nPREFIX ns2: \n\nSELECT ?labExtract\nWHERE {\n ?labExtract ns2:has_bioassay_results ?bioAssayResult .\n ?bioAssayResult ns2:inhibition_percentage ?inhibitionPercentage ;\n ns2:target_id .\n ?bioAssayResult a ns2:Ldono10ugml.\n FILTER(?inhibitionPercentage > 50)\n}\n```\n\nIf you have any further questions or need additional information, feel free to ask!"", ""example"": false, ""additional_kwargs"": {}, ""response_metadata"": {}}]}","PREFIX ns1: +50,""" Which lab extracts have bioassay results with inhibition percentages above 50% against Leishmania donovani target at 10 µg/mL ??""","PREFIX ns1: PREFIX ns2: SELECT ?labExtract From 8adb7a3fc5ad044f8eff9de9cb1f6255c6f0e7e6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Louis-F=C3=A9lix=20Nothias?= Date: Fri, 24 Apr 2026 15:23:03 +0200 Subject: [PATCH 09/21] Replace codeinterpreterapi with PythonREPL and update models to gpt-5.4 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The interpreter tool previously relied on codeinterpreterapi (remote sandbox) which is no longer compatible with the current dependency stack. This replaces it with langchain_experimental PythonREPL (local execution) while preserving the same agent contract. Key changes: - tool_interpreter.py: rewrite using PythonREPL — injects file previews (columns, dtypes, 3-row sample) into the LLM prompt so generated code uses exact column names; truncates REPL stdout at 2000 chars to prevent context overflow; wires shared llm_instance from the agent instead of instantiating a separate model - agent.py: passes llm_instance to tool constructor via import_tools introspection - params.ini: bump default OpenAI models from gpt-4o / gpt-4o-mini to gpt-5.4 / gpt-5.4-mini - requirements.txt / environment.yml: pin langchain_experimental==0.3.4 and remove codeinterpreterapi / codeboxapi dependencies - Remove custom_sqlite_file.py (unused after langgraph checkpoint refactor) Security note: PythonREPL executes LLM-generated code in-process without sandboxing. A warning comment has been added; restrict to trusted deployment environments. --- app/config/params.ini | 8 +- app/core/agents/enpkg/tool_taxon.py | 2 - app/core/agents/interpreter/agent.py | 1 + .../agents/interpreter/tool_interpreter.py | 234 ++++++++++-------- app/core/memory/custom_sqlite_file.py | 97 -------- environment.yml | 3 +- requirements.txt | 3 +- 7 files changed, 139 insertions(+), 209 deletions(-) delete mode 100644 app/core/memory/custom_sqlite_file.py diff --git a/app/config/params.ini b/app/config/params.ini index c2b9a26..8812aae 100644 --- a/app/config/params.ini +++ b/app/config/params.ini @@ -1,16 +1,16 @@ [llm_preview] -id = gpt-4o +id = gpt-5.4 temperature = 0.3 max_retries = 3 [llm_o] -id = gpt-4o +id = gpt-5.4 temperature = 0 max_retries = 3 [llm_mini] -id = gpt-4o-mini +id = gpt-5.4-mini temperature = 0 max_retries = 3 @@ -48,7 +48,7 @@ max_retries = 3 base_url = https://llama-3-1-70b-instruct.endpoints.kepler.ai.cloud.ovh.net/api/openai_compat/v1 [llm_litellm_openai] -id = gpt-4o +id = gpt-5.4 temperature = 0 diff --git a/app/core/agents/enpkg/tool_taxon.py b/app/core/agents/enpkg/tool_taxon.py index b049406..5b0e971 100644 --- a/app/core/agents/enpkg/tool_taxon.py +++ b/app/core/agents/enpkg/tool_taxon.py @@ -5,8 +5,6 @@ from pydantic import BaseModel, Field -from typing import Optional - from langchain.callbacks.manager import ( CallbackManagerForToolRun, ) diff --git a/app/core/agents/interpreter/agent.py b/app/core/agents/interpreter/agent.py index 3c755b6..5a3d406 100644 --- a/app/core/agents/interpreter/agent.py +++ b/app/core/agents/interpreter/agent.py @@ -18,6 +18,7 @@ def create_agent(llms, graph, openai_key, session_id) -> AgentExecutor: tool_parameters = { "openai_key": openai_key, "session_id": session_id, + "llm_instance": llms.get(MODEL_CHOICE), } tools = import_tools(directory, module_prefix, **tool_parameters) diff --git a/app/core/agents/interpreter/tool_interpreter.py b/app/core/agents/interpreter/tool_interpreter.py index a29d5d7..049c6d1 100644 --- a/app/core/agents/interpreter/tool_interpreter.py +++ b/app/core/agents/interpreter/tool_interpreter.py @@ -1,29 +1,35 @@ from __future__ import annotations +# SECURITY WARNING: This tool uses langchain_experimental PythonREPL, which executes +# LLM-generated Python code directly in the server process with no sandboxing. +# Only deploy in trusted environments where the server host is not exposed to +# untrusted users. Do not use in public-facing deployments without additional +# isolation (e.g. Docker, restricted OS user, network policy). -from codeinterpreterapi import CodeInterpreterSession, File, settings -from pydantic import BaseModel, Field -from langchain.tools import BaseTool - -from typing import Optional - -from langchain.callbacks.manager import ( - CallbackManagerForToolRun, -) import json -from app.core.session import setup_logger, create_user_session - import os import re -import tempfile from pathlib import Path +from typing import Any, Optional + +from langchain.callbacks.manager import CallbackManagerForToolRun +from langchain.tools import BaseTool +from langchain_core.messages import HumanMessage, SystemMessage +from langchain_experimental.utilities import PythonREPL +from langchain_openai import ChatOpenAI +from pydantic import BaseModel, Field + from app.core.memory.database_manager import tools_database +from app.core.session import create_user_session, setup_logger logger = setup_logger(__name__) class InterpreterInput(BaseModel): - input: str = Field(description="Input from Interpreter Agent containing the user's question, necessary file paths and other information.") + input: str = Field( + description="Input from Interpreter Agent containing the user's question, necessary file paths and other information." + ) + class Interpreter(BaseTool): @@ -38,111 +44,135 @@ class Interpreter(BaseTool): None: Outputs the response after interpreting files. """ args_schema: type[BaseModel] = InterpreterInput - openai_key: str = None - session_id: str = None + openai_key: Optional[str] = None + session_id: Optional[str] = None + llm_instance: Optional[Any] = None - def __init__(self, openai_key: str, session_id: str): + def __init__(self, openai_key: Optional[str] = None, session_id: Optional[str] = None, llm_instance: Optional[Any] = None): super().__init__() - self.openai_key = openai_key + self.openai_key = openai_key or os.getenv("OPENAI_API_KEY") self.session_id = session_id + self.llm_instance = llm_instance def _run( self, input: str, run_manager: Optional[CallbackManagerForToolRun] = None, - ) -> None: - + ) -> str: logger.info(f"Input: {input}") file_paths = self.extract_file_paths(input) logger.info(f"File paths: {file_paths}") session_dir = create_user_session(self.session_id, user_session_dir=True) - - settings.OPENAI_API_KEY = self.openai_key - settings.MODEL = "gpt-3.5-turbo" - - with CodeInterpreterSession() as session: - - db_manager = tools_database() - - user_request = ( - "You are an interpreter helping to analyze different questions, files and outputs generated from a series of LLMs." - f"The details of the current request: {input}" - "Please interpret the current request to generate a meaningful answer." - "Here's some instructions that you have to follow for acheiving the task:" - "1. For any file provided, analyse if and provide clear and brief information about it unless something else is asked." - "2. Only if a specific visualization (e.g., bar chart, diagram) is requested in the question, use the provided information to generate a .json file containing the JSON code for a Plotly graph. This file should be named identically to the analyzed file." - "3. After you finish your tasks, your answer should contain both the interpretation asked and the full visualization file name if visualization was requested." - ) - - files = [] - - for file in file_paths: - files.append(File.from_path(file)) - logger.info(f"File added to interpreter Agent: {file}") - - if not files: - - logger.info("No files provided from the Interpreter Agent. Manually scrapping the database") - output_merged = db_manager.get("tool_merge_result") # Get all file paths from the database associated with the tool_interpreter - if output_merged is not None: - output_merged_json = json.loads(output_merged) - merged_filepaths = output_merged_json['output']['paths'] - - for merged_filepath in merged_filepaths: - logger.info(f"File added to interpreter Agent from the Output Merged tool: {merged_filepath}") - files.append(File.from_path(merged_filepath)) - - if not files: - - sparql_output = db_manager.get("tool_sparql") # Get all file paths from the database associated with the tool_interpreter - if sparql_output is not None: - sparql_output_json = json.loads(sparql_output) - sparql_filepaths = sparql_output_json['output']['paths'] - - for sparql_filepath in sparql_filepaths: - logger.info(f"File added to interpreter Agent from the SPARQL tool: {sparql_filepath}") - files.append(File.from_path(sparql_filepath)) - - # generate the response - logger.info(f"Files submitted: {files}") - response = session.generate_response(user_request, files=files) - logger.info(f"Interpreter Agent Response: {response}") - - filepaths = [] - - # Handling and saving output files - if response.files: - for file in response.files: - logger.info(f"File: {file.name}") - - generated_file_path = session_dir / file.name - with open(generated_file_path, 'wb') as f: - f.write(file.content) - filepaths.append(str(generated_file_path)) - - logger.info(f"File saved: {file.name}") - else: - logger.info("No files generated by Interpreter Tool.") - - output_data = { - "output": { - "paths": filepaths - } - } - - + db_manager = tools_database() + + if not file_paths: + output_merged = db_manager.get("tool_merge_result") + if output_merged is not None: + merged_json = json.loads(output_merged) + file_paths = merged_json.get("output", {}).get("paths", []) + logger.info(f"File paths from merge result: {file_paths}") + + if not file_paths: + sparql_output = db_manager.get("tool_sparql") + if sparql_output is not None: + sparql_json = json.loads(sparql_output) + file_paths = sparql_json.get("output", {}).get("paths", []) + logger.info(f"File paths from SPARQL tool: {file_paths}") + + llm = self.llm_instance or ChatOpenAI( + api_key=self.openai_key, + model="gpt-5.4", + temperature=0, + ) + + system_prompt = ( + "You are a Python data analysis assistant.\n" + "Write a self-contained Python script that reads the provided file(s), " + "fulfils the user's request, and prints a concise text summary to stdout.\n\n" + "Rules:\n" + "- pandas, numpy, json, pathlib, and plotly are available.\n" + "- Use the exact column names shown in the file previews — do not guess them.\n" + "- Print a SHORT summary (counts, stats, top-N rows) — never print an entire dataframe.\n" + f"- Save any output files (visualizations, processed data) to: {session_dir}\n" + "- For visualizations use Plotly and write JSON with: " + f'fig.write_json(str(Path("{session_dir}") / ".json"))\n' + "- Do NOT call plt.show(), fig.show(), or any interactive display.\n" + "- Do NOT install packages inside the script.\n" + "- Return ONLY the Python code inside a single ```python ... ``` block." + ) + + file_previews = [] + for fp in file_paths: try: - db_manager.put(data=json.dumps(output_data), tool_name="tool_interpreter") - except Exception as e: - logger.error(f"Error saving to database: {e}") - - return f"{response}.\n\n The full path of the files generated are:\n" + "\n".join(filepaths) + import pandas as pd + sep = "\t" if str(fp).endswith((".tsv", ".txt")) else "," + preview = pd.read_csv(fp, sep=sep, nrows=3) + # Truncate long cell values so wide/URL-heavy columns don't blow context + preview = preview.map( + lambda v: str(v)[:80] + "…" if isinstance(v, str) and len(str(v)) > 80 else v + ) + file_previews.append( + f"File: {fp}\n" + f"Columns: {preview.columns.tolist()}\n" + f"Dtypes: {preview.dtypes.to_dict()}\n" + f"Preview (3 rows):\n{preview.to_string(index=False)}" + ) + except Exception: + file_previews.append(f"File: {fp} (could not preview)") + + preview_block = "\n\n".join(file_previews) if file_previews else "(no files)" + user_message = f"File previews:\n{preview_block}\n\nRequest: {input}" + + logger.info("Requesting analysis code from LLM") + response = llm.invoke( + [SystemMessage(content=system_prompt), HumanMessage(content=user_message)] + ) + + code_blocks = re.findall(r"```python\s*(.*?)\s*```", response.content, re.DOTALL) + if not code_blocks: + code_blocks = re.findall(r"```\s*(.*?)\s*```", response.content, re.DOTALL) + + if not code_blocks: + logger.error("LLM did not return a Python code block") + return "Interpreter could not generate analysis code for this request." + + code = code_blocks[0] + logger.info(f"Generated code:\n{code}") + + # Snapshot existing files so we can detect what was newly created + existing_files = set(session_dir.iterdir()) if session_dir.exists() else set() + + repl = PythonREPL() + execution_output = repl.run(code) + # Truncate to keep the agent context manageable (~2000 chars ≈ ~500 tokens) + _MAX_OUTPUT = 2000 + if len(execution_output) > _MAX_OUTPUT: + execution_output = execution_output[:_MAX_OUTPUT] + f"\n… [output truncated at {_MAX_OUTPUT} chars]" + logger.info(f"Execution output: {execution_output}") + + # Collect files written by the executed code + new_files = ( + set(session_dir.iterdir()) - existing_files if session_dir.exists() else set() + ) + filepaths = [str(f) for f in new_files] + logger.info(f"Files generated: {filepaths}") + + output_data = {"output": {"paths": filepaths}} + try: + db_manager.put(data=json.dumps(output_data), tool_name="tool_interpreter") + except Exception as e: + logger.error(f"Error saving to database: {e}") + + suffix = ( + "\n\nThe full path of the files generated are:\n" + "\n".join(filepaths) + if filepaths + else "" + ) + return f"{execution_output}{suffix}" def extract_file_paths(self, text: str): - # Regex to find file paths or filenames with extensions, possibly surrounded by quotes regex = r"['\"]?([a-zA-Z0-9_/\\-]+(?:\.csv|\.tsv|\.mgf|\.txt|\.xlsx|\.xls))['\"]?" matches = re.finditer(regex, text) - file_paths = [match.group(1).replace("'", "").replace('"', '') for match in matches] - return file_paths \ No newline at end of file + return [match.group(1).replace("'", "").replace('"', "") for match in matches] diff --git a/app/core/memory/custom_sqlite_file.py b/app/core/memory/custom_sqlite_file.py deleted file mode 100644 index 5709287..0000000 --- a/app/core/memory/custom_sqlite_file.py +++ /dev/null @@ -1,97 +0,0 @@ -# This is a custom sqlite checkpoint for langgraph based on the orininal one https://github.com/langchain-ai/langgraph/blob/main/langgraph/checkpoint/sqlite.py commit @f2ad930 https://github.com/langchain-ai/langgraph/commit/f2ad930cd4cf383ccaefd18d497aa2e0f459e5bd -# This custom sqlite allows multithreading access, necessary for running the langgraph with memory in Streamlit. - -import os -import pickle -import sqlite3 -import threading -from contextlib import contextmanager -from typing import ClassVar, Optional - - -from langchain_core.runnables import RunnableConfig -from langchain_core.runnables.utils import ConfigurableFieldSpec -from langgraph.checkpoint.base import BaseCheckpointSaver, Checkpoint - -from app.core.session import setup_logger - -logger = setup_logger(__name__) - - -class SqliteCheckpointerSaver(BaseCheckpointSaver): - # Specify the path to the database file - database_path: ClassVar[str] = "langgraph_checkpoint.db" - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) # Ensure proper Pydantic initialization - self.__dict__["_local"] = threading.local() - self._initialize_database() - - def _initialize_database(self): - # Check if the database file exists and delete it if so - if os.path.exists(self.database_path): - os.remove(self.database_path) - # Establish a new database connection to the file - self._get_connection(force_new=True) - - def _get_connection(self, force_new=False): - if not hasattr(self._local, "connection") or force_new: - self._local.connection = sqlite3.connect( - self.database_path, check_same_thread=False - ) - else: - pass - return self._local.connection - - def setup(self): - if hasattr(self._local, "is_setup") and self._local.is_setup: - return - - conn = self._get_connection() - conn.executescript( - """ - CREATE TABLE IF NOT EXISTS checkpoints ( - thread_id TEXT PRIMARY KEY, - checkpoint BLOB - ); - """ - ) - self._local.is_setup = True - - @contextmanager - def cursor(self, transaction: bool = True): - self.setup() - conn = self._get_connection() - cur = conn.cursor() - try: - yield cur - finally: - if transaction: - conn.commit() - cur.close() - - def get(self, config: RunnableConfig) -> Optional[Checkpoint]: - with self.cursor(transaction=False) as cur: - cur.execute( - "SELECT checkpoint FROM checkpoints WHERE thread_id = ?", - (config["configurable"]["thread_id"],), - ) - row = cur.fetchone() - return pickle.loads(row[0]) if row else None - - def put(self, config: RunnableConfig, checkpoint: Checkpoint) -> None: - with self.cursor() as cur: - cur.execute( - "INSERT OR REPLACE INTO checkpoints (thread_id, checkpoint) VALUES (?, ?)", - (config["configurable"]["thread_id"], pickle.dumps(checkpoint)), - ) - - # For debugging - def print_database_contents(self): - with self.cursor(transaction=False) as cur: - cur.execute("SELECT thread_id, checkpoint FROM checkpoints") - rows = cur.fetchall() - for row in rows: - print( - f"Thread ID: {row[0]}, Checkpoint (Raw): {row[1][:100]}..." - ) # Print a portion of the checkpoint to avoid flooding the output diff --git a/environment.yml b/environment.yml index be14ab7..387c89e 100644 --- a/environment.yml +++ b/environment.yml @@ -12,14 +12,13 @@ dependencies: - xz==5.4.6 - pip: - app==0.0.1 - - "codeboxapi>=0.0.19,<0.2.0" - chromadb==0.4.24 - - codeinterpreterapi==0.0.14 - faiss-cpu==1.8.0 - ipykernel==6.29.3 - jupyter-kernel-gateway==3.0.1 - kaleido==0.2.1 - langchain==0.3.27 + - langchain_experimental==0.3.4 - langchainhub==0.1.15 - langchain_community==0.3.31 - langchain_core==0.3.84 diff --git a/requirements.txt b/requirements.txt index bccbbf8..1299a35 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,10 +1,9 @@ -codeboxapi>=0.0.19,<0.2.0 -codeinterpreterapi==0.0.14 faiss-cpu==1.8.0 kaleido==0.2.1 ipykernel==6.29.3 jupyter-kernel-gateway==3.0.1 langchain==0.3.27 +langchain_experimental==0.3.4 langchainhub==0.1.15 langchain_community==0.3.31 langchain_core==0.3.84 From 722787230874787d2cafd4537a41914a3082269d Mon Sep 17 00:00:00 2001 From: madina1203 Date: Fri, 24 Apr 2026 16:13:02 +0200 Subject: [PATCH 10/21] Fix node name mismatch --- streamlit_webapp/streamlit_utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/streamlit_webapp/streamlit_utils.py b/streamlit_webapp/streamlit_utils.py index c48cf40..de6460b 100644 --- a/streamlit_webapp/streamlit_utils.py +++ b/streamlit_webapp/streamlit_utils.py @@ -305,10 +305,10 @@ def new_process_langgraph_output(k, v, session_id): contents.append({"type": "text", "content": f"Agent Supervisor is calling {agent_name}. \n\n"}) elif 'next' in v and v['next'] == "FINISH": contents.append({"type": "text", "content": "Chain finished"}) - elif k == "Entry_agent": + elif k == "Entry_Agent": content = v['messages'][0].content contents.append({"type": "text", "content": f"Entry agent: {content}. \n\n"}) - elif k == "Validator_agent": + elif k == "Validator": content = v['messages'][0].content contents.append({"type": "text", "content": f"Validator agent: {content}. \n\n"}) elif k == "ENPKG_agent": From b41b61193d9c4de5888c4ff9f854351cb8b46a3c Mon Sep 17 00:00:00 2001 From: madina1203 Date: Fri, 24 Apr 2026 17:42:46 +0200 Subject: [PATCH 11/21] add CLI file input support for FILE_ANALYZER tool --- app/core/main.py | 117 +++++++++++++++++++++++++++++++++++++---------- 1 file changed, 93 insertions(+), 24 deletions(-) diff --git a/app/core/main.py b/app/core/main.py index 33a163d..691928c 100644 --- a/app/core/main.py +++ b/app/core/main.py @@ -1,13 +1,17 @@ import os +import shutil import argparse -from typing import Optional +import configparser +from typing import Optional, List +from pathlib import Path + from dotenv import load_dotenv from langsmith import Client -from pathlib import Path +from langchain_community.chat_models import ChatOpenAI, ChatLiteLLM + from app.core.workflow.langraph_workflow import create_workflow, process_workflow +from app.core.session import create_user_session, initialize_session_context from app.core.utils import IntRange, setup_logger -import configparser -from langchain_community.chat_models import ChatOpenAI, ChatLiteLLM from app.core.questions import standard_questions @@ -198,52 +202,117 @@ def langsmith_setup() -> Optional[Client]: return None +def _prepare_session_files(session_id: str, file_paths: List[str]) -> Path: + """ + Copy user-supplied local files into the session's input directory so that + the FILE_ANALYZER tool can discover them at runtime. + + Args: + session_id: Active session identifier. + file_paths: List of local file paths provided via the CLI. + + Returns: + Path to the session input directory. + + Raises: + FileNotFoundError: If any of the supplied paths do not exist. + """ + input_dir = create_user_session(session_id, input_dir=True) + + for raw_path in file_paths: + src = Path(raw_path).resolve() + if not src.exists(): + raise FileNotFoundError(f"File not found: {src}") + dest = input_dir / src.name + shutil.copy2(str(src), str(dest)) + logger.info(f"Copied '{src}' -> '{dest}'") + + return input_dir + + def main(): - """Main function to run the workflow.""" - # Define command line arguments - - parser = argparse.ArgumentParser(description="Process a workflow with a predefined question number.") - parser.add_argument('-q', '--question', type=int, choices=IntRange(1, len(standard_questions)), - help=f"Choose a standard question number from 1 to {len(standard_questions)}.") - parser.add_argument('-c', '--custom', type=str, - help="Provide a custom question.") - parser.add_argument('-e', '--evaluation', action='store_true', - help="Enable evaluation mode") - parser.add_argument('--api-key', type=str, - help="OpenAI API key (optional, defaults to environment variable)") - parser.add_argument('--endpoint', type=str, - help="Knowledge graph endpoint URL (optional)") + """ + CLI entry-point for running the MetaboT workflow. + + Usage examples: + python -m app.core.main -q 1 + python -m app.core.main -c "Describe my dataset" -f data.csv + python -m app.core.main -c "Compare files" -f file1.csv file2.tsv + """ + parser = argparse.ArgumentParser( + description="Process a workflow with a predefined question number." + ) + parser.add_argument( + '-q', '--question', type=int, + choices=IntRange(1, len(standard_questions)), + help=f"Choose a standard question number from 1 to {len(standard_questions)}.", + ) + parser.add_argument( + '-c', '--custom', type=str, + help="Provide a custom question.", + ) + parser.add_argument( + '-f', '--file', type=str, nargs='+', + help="One or more local file paths to make available for the FILE_ANALYZER tool.", + ) + parser.add_argument( + '-e', '--evaluation', action='store_true', + help="Enable evaluation mode.", + ) + parser.add_argument( + '--api-key', type=str, + help="OpenAI API key (optional, defaults to environment variable).", + ) + parser.add_argument( + '--endpoint', type=str, + help="Knowledge graph endpoint URL (optional).", + ) args = parser.parse_args() + # Resolve the question if args.question: question = standard_questions[args.question - 1] elif args.custom: question = args.custom else: - print("You must provide either a standard question number or a custom question.") + print("You must provide either a standard question number (-q) or a custom question (-c).") return # Initialize LangSmith if available - langsmith_client = langsmith_setup() + langsmith_setup() - # Get endpoint URL from arguments or environment + # Resolve endpoint URL endpoint_url = ( args.endpoint or os.environ.get("KG_ENDPOINT_URL") or "https://enpkg.commons-lab.org/graphdb/repositories/ENPKG" ) + + # Initialize language models models = llm_creation() + # Create a user session (mirrors the Streamlit session lifecycle) + session_id = create_user_session() + initialize_session_context(session_id) + + # Stage user-provided files into the session's input directory + if args.file: + try: + _prepare_session_files(session_id, args.file) + except FileNotFoundError as exc: + logger.error(str(exc)) + print(f"Error: {exc}") + return + try: - # Create and process workflow workflow = create_workflow( models=models, + session_id=session_id, endpoint_url=endpoint_url, evaluation=False, - api_key=args.api_key + api_key=args.api_key, ) - process_workflow(workflow, question) except Exception as e: From 6e65f99faa8439e1700fc489ee1d792f4fa74765 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Louis-F=C3=A9lix=20Nothias?= Date: Fri, 24 Apr 2026 18:34:23 +0200 Subject: [PATCH 12/21] feat: Implement Interpreter Agent with sandbox execution and trusted mode - Added Interpreter Agent functionality to execute user-generated code in a controlled environment. - Introduced security measures to restrict imports and filesystem access within the interpreter. - Configured trusted mode to enable or disable Interpreter Agent features based on environment variables. - Updated load_config to conditionally include Interpreter Agent based on trusted mode. - Enhanced Streamlit app to reflect trusted mode status and manage file uploads accordingly. - Created sandbox_runner for executing code with resource limits and timeout handling. - Added security module for managing trusted mode and session path validation. - Implemented tests for security features and configuration loading behavior. - Documented changes and security implications in the agentic engineering log. --- app/config/langgraph.json | 7 +- app/core/agents/agents_factory.py | 3 +- app/core/agents/interpreter/sandbox_runner.py | 287 ++++++++++++++ .../agents/interpreter/tool_interpreter.py | 370 ++++++++++++++---- app/core/agents/supervisor/agent.py | 3 +- app/core/agents/supervisor/prompt.py | 13 +- app/core/security.py | 61 +++ app/core/tests/test_security.py | 43 ++ app/core/utils.py | 39 +- streamlit_webapp/streamlit_app.py | 95 +++-- 10 files changed, 779 insertions(+), 142 deletions(-) create mode 100644 app/core/agents/interpreter/sandbox_runner.py create mode 100644 app/core/security.py create mode 100644 app/core/tests/test_security.py diff --git a/app/config/langgraph.json b/app/config/langgraph.json index 741203d..baf5ff3 100644 --- a/app/config/langgraph.json +++ b/app/config/langgraph.json @@ -3,14 +3,12 @@ {"name": "Entry_Agent", "path": "app.core.agents.entry.agent", "llm_choice": "llm_o"}, {"name": "ENPKG_agent", "path": "app.core.agents.enpkg.agent", "llm_choice": "llm_o"}, {"name": "Sparql_query_runner", "path": "app.core.agents.sparql.agent", "llm_choice": "llm_o"}, - {"name": "Interpreter_agent", "path": "app.core.agents.interpreter.agent", "llm_choice": "llm_o"}, {"name": "supervisor", "path": "app.core.agents.supervisor.agent", "llm_choice": "llm_o"}, {"name": "Validator", "path": "app.core.agents.validator.agent", "llm_choice": "llm_o"} ], "edges": [ {"source": "ENPKG_agent", "target": "supervisor"}, - {"source": "Sparql_query_runner", "target": "supervisor"}, - {"source": "Interpreter_agent", "target": "supervisor"} + {"source": "Sparql_query_runner", "target": "supervisor"} ], "conditional_edges": [ { @@ -19,7 +17,6 @@ "targets": [ {"condition_value": "ENPKG_agent", "target": "ENPKG_agent"}, {"condition_value": "Sparql_query_runner", "target": "Sparql_query_runner"}, - {"condition_value": "Interpreter_agent", "target": "Interpreter_agent"}, {"condition_value": "FINISH", "target": "__end__"} ] }, @@ -41,6 +38,6 @@ "entry_point": "Entry_Agent", "supervisor": { "name": "supervisor", - "members": ["ENPKG_agent", "Sparql_query_runner", "Interpreter_agent"] + "members": ["ENPKG_agent", "Sparql_query_runner"] } } diff --git a/app/core/agents/agents_factory.py b/app/core/agents/agents_factory.py index bf93e38..9700ed4 100644 --- a/app/core/agents/agents_factory.py +++ b/app/core/agents/agents_factory.py @@ -10,8 +10,6 @@ logger = setup_logger(__name__) -config = load_config() - def create_all_agents(llms, graph, openai_key=None, session_id=None): """ @@ -27,6 +25,7 @@ def create_all_agents(llms, graph, openai_key=None, session_id=None): dict: A dictionary mapping agent names to their created executor instances. """ + config = load_config() agents = config["agents"] executors = {} diff --git a/app/core/agents/interpreter/sandbox_runner.py b/app/core/agents/interpreter/sandbox_runner.py new file mode 100644 index 0000000..e59112b --- /dev/null +++ b/app/core/agents/interpreter/sandbox_runner.py @@ -0,0 +1,287 @@ +from __future__ import annotations + +import argparse +import ast +import builtins +import contextlib +import io +import json +import os +import signal +import sys +import tempfile +import traceback +from pathlib import Path + + +ALLOWED_IMPORT_ROOTS = { + "collections", + "csv", + "itertools", + "json", + "math", + "numpy", + "pandas", + "pathlib", + "plotly", + "re", + "statistics", +} + +REAL_IMPORT = builtins.__import__ +REAL_OPEN = builtins.open +REAL_IO_OPEN = io.open +REAL_OS_OPEN = os.open + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--payload", required=True) + parser.add_argument("--result", required=True) + return parser.parse_args() + + +def write_result(result_path: Path, payload: dict) -> None: + with REAL_OPEN(result_path, "w", encoding="utf-8") as handle: + json.dump(payload, handle) + + +def validate_code(code: str) -> None: + tree = ast.parse(code, mode="exec") + blocked_names = {"__import__", "eval", "exec", "compile", "globals", "locals", "vars", "breakpoint"} + + for node in ast.walk(tree): + if isinstance(node, ast.Import): + for alias in node.names: + if alias.name.split(".", 1)[0] not in ALLOWED_IMPORT_ROOTS: + raise ValueError(f"Import of '{alias.name}' is not allowed.") + elif isinstance(node, ast.ImportFrom): + module = node.module or "" + if module.split(".", 1)[0] not in ALLOWED_IMPORT_ROOTS: + raise ValueError(f"Import from '{module}' is not allowed.") + elif isinstance(node, ast.Name) and node.id in blocked_names: + raise ValueError(f"Use of '{node.id}' is not allowed.") + + +def apply_resource_limits(config: dict) -> None: + try: + import resource + except ImportError: + return + + cpu_seconds = int(config["cpu_seconds"]) + memory_bytes = int(config["memory_bytes"]) + file_size_bytes = int(config["file_size_bytes"]) + + limits = [ + ("RLIMIT_CPU", (cpu_seconds, cpu_seconds)), + ("RLIMIT_AS", (memory_bytes, memory_bytes)), + ("RLIMIT_DATA", (memory_bytes, memory_bytes)), + ("RLIMIT_FSIZE", (file_size_bytes, file_size_bytes)), + ("RLIMIT_NOFILE", (64, 64)), + ("RLIMIT_NPROC", (1, 1)), + ] + + for limit_name, limit_value in limits: + if not hasattr(resource, limit_name): + continue + try: + resource.setrlimit(getattr(resource, limit_name), limit_value) + except (OSError, ValueError): + continue + + +def configure_timeout(timeout_seconds: int) -> None: + def handle_timeout(signum, frame): + raise TimeoutError(f"Interpreter execution timed out after {timeout_seconds} seconds.") + + signal.signal(signal.SIGALRM, handle_timeout) + signal.alarm(timeout_seconds) + + +def disable_network() -> None: + import socket + + def blocked(*args, **kwargs): + raise PermissionError("Outbound network access is disabled for the interpreter.") + + socket.socket = blocked + socket.create_connection = blocked + socket.fromfd = blocked + socket.socketpair = blocked + socket.getaddrinfo = blocked + socket.gethostbyname = blocked + + +def patch_filesystem_access(session_dir: Path, allowed_input_paths: list[str]): + allowed_reads = {Path(path).resolve(strict=False) for path in allowed_input_paths} + created_paths: set[Path] = set() + + tempfile.tempdir = str(session_dir) + + def resolve_candidate(path_value) -> Path: + if isinstance(path_value, bytes): + path_value = os.fsdecode(path_value) + if isinstance(path_value, int): + raise PermissionError("File descriptor access is not allowed in the interpreter sandbox.") + + candidate = Path(path_value) + if not candidate.is_absolute(): + candidate = session_dir / candidate + + return candidate.resolve(strict=False) + + def ensure_allowed(path_value, mode: str) -> Path: + resolved = resolve_candidate(path_value) + write_mode = any(flag in mode for flag in ("w", "a", "x", "+")) + + if write_mode: + if not is_within_directory(resolved, session_dir): + raise PermissionError("Writes are restricted to the active session directory.") + resolved.parent.mkdir(parents=True, exist_ok=True) + created_paths.add(resolved) + return resolved + + if resolved in allowed_reads or resolved in created_paths: + return resolved + + raise PermissionError("Reads are restricted to validated session input files.") + + def safe_open(file, mode="r", *args, **kwargs): + resolved = ensure_allowed(file, mode) + return REAL_OPEN(resolved, mode, *args, **kwargs) + + def safe_io_open(file, mode="r", *args, **kwargs): + resolved = ensure_allowed(file, mode) + return REAL_IO_OPEN(resolved, mode, *args, **kwargs) + + def safe_os_open(file, flags, mode=0o777, *args, **kwargs): + write_flags = os.O_WRONLY | os.O_RDWR | os.O_APPEND | os.O_CREAT | os.O_TRUNC + open_mode = "r+" if flags & write_flags else "r" + resolved = ensure_allowed(file, open_mode) + return REAL_OS_OPEN(os.fspath(resolved), flags, mode, *args, **kwargs) + + builtins.open = safe_open + io.open = safe_io_open + os.open = safe_os_open + Path.open = lambda self, *args, **kwargs: safe_open(self, *args, **kwargs) + + return safe_open + + +def is_within_directory(path: Path, directory: Path) -> bool: + resolved_path = path.resolve(strict=False) + resolved_directory = directory.resolve(strict=False) + return resolved_path == resolved_directory or resolved_directory in resolved_path.parents + + +def guarded_import(name, globals=None, locals=None, fromlist=(), level=0): + caller = (globals or {}).get("__name__", "") + root = name.split(".", 1)[0] + if caller == "__sandbox__" and root not in ALLOWED_IMPORT_ROOTS: + raise ImportError(f"Import of '{root}' is not allowed in the interpreter sandbox.") + return REAL_IMPORT(name, globals, locals, fromlist, level) + + +def build_safe_builtins(safe_open): + return { + "__import__": guarded_import, + "abs": abs, + "all": all, + "any": any, + "bool": bool, + "bytes": bytes, + "dict": dict, + "enumerate": enumerate, + "Exception": Exception, + "FileNotFoundError": FileNotFoundError, + "filter": filter, + "float": float, + "int": int, + "isinstance": isinstance, + "KeyError": KeyError, + "len": len, + "list": list, + "map": map, + "max": max, + "min": min, + "next": next, + "open": safe_open, + "PermissionError": PermissionError, + "print": print, + "range": range, + "round": round, + "RuntimeError": RuntimeError, + "set": set, + "sorted": sorted, + "str": str, + "sum": sum, + "TimeoutError": TimeoutError, + "tuple": tuple, + "ValueError": ValueError, + "zip": zip, + } + + +def execute_user_code(config: dict) -> dict: + session_dir = Path(config["session_dir"]).resolve(strict=False) + session_dir.mkdir(parents=True, exist_ok=True) + os.chdir(session_dir) + + if hasattr(os, "geteuid") and os.geteuid() == 0: + raise PermissionError( + "The interpreter runner must not execute as root. " + "Run MetaboT under a restricted OS user before enabling trusted mode." + ) + + validate_code(config["code"]) + apply_resource_limits(config) + configure_timeout(int(config["timeout_seconds"])) + disable_network() + safe_open = patch_filesystem_access(session_dir, config["allowed_input_paths"]) + + stdout_buffer = io.StringIO() + stderr_buffer = io.StringIO() + safe_builtins = build_safe_builtins(safe_open) + sandbox_globals = {"__builtins__": safe_builtins, "__name__": "__sandbox__"} + + try: + with contextlib.redirect_stdout(stdout_buffer), contextlib.redirect_stderr(stderr_buffer): + exec(compile(config["code"], "", "exec"), sandbox_globals, sandbox_globals) + return { + "success": True, + "stdout": stdout_buffer.getvalue(), + "stderr": stderr_buffer.getvalue(), + "error": "", + } + finally: + signal.alarm(0) + + +def main() -> int: + args = parse_args() + payload_path = Path(args.payload) + result_path = Path(args.result) + + try: + with REAL_OPEN(payload_path, "r", encoding="utf-8") as handle: + config = json.load(handle) + result = execute_user_code(config) + write_result(result_path, result) + return 0 if result.get("success") else 1 + except Exception as exc: + write_result( + result_path, + { + "success": False, + "stdout": "", + "stderr": "", + "error": str(exc), + "traceback": traceback.format_exc(), + }, + ) + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/app/core/agents/interpreter/tool_interpreter.py b/app/core/agents/interpreter/tool_interpreter.py index 049c6d1..f595210 100644 --- a/app/core/agents/interpreter/tool_interpreter.py +++ b/app/core/agents/interpreter/tool_interpreter.py @@ -1,29 +1,38 @@ from __future__ import annotations -# SECURITY WARNING: This tool uses langchain_experimental PythonREPL, which executes -# LLM-generated Python code directly in the server process with no sandboxing. -# Only deploy in trusted environments where the server host is not exposed to -# untrusted users. Do not use in public-facing deployments without additional -# isolation (e.g. Docker, restricted OS user, network policy). - import json import os import re +import signal +import subprocess +import sys +import tempfile from pathlib import Path from typing import Any, Optional +import pandas as pd from langchain.callbacks.manager import CallbackManagerForToolRun from langchain.tools import BaseTool from langchain_core.messages import HumanMessage, SystemMessage -from langchain_experimental.utilities import PythonREPL from langchain_openai import ChatOpenAI from pydantic import BaseModel, Field from app.core.memory.database_manager import tools_database +from app.core.security import ( + get_interpreter_cpu_seconds, + get_interpreter_max_file_bytes, + get_interpreter_memory_bytes, + get_interpreter_timeout_seconds, + is_trusted_mode_enabled, + resolve_session_path, +) from app.core.session import create_user_session, setup_logger logger = setup_logger(__name__) +_ALLOWED_INPUT_SUFFIXES = {".csv", ".tsv", ".mgf", ".txt", ".xlsx", ".xls", ".json"} +_MAX_OUTPUT_CHARS = 2000 + class InterpreterInput(BaseModel): input: str = Field( @@ -48,7 +57,12 @@ class Interpreter(BaseTool): session_id: Optional[str] = None llm_instance: Optional[Any] = None - def __init__(self, openai_key: Optional[str] = None, session_id: Optional[str] = None, llm_instance: Optional[Any] = None): + def __init__( + self, + openai_key: Optional[str] = None, + session_id: Optional[str] = None, + llm_instance: Optional[Any] = None, + ): super().__init__() self.openai_key = openai_key or os.getenv("OPENAI_API_KEY") self.session_id = session_id @@ -61,25 +75,29 @@ def _run( ) -> str: logger.info(f"Input: {input}") - file_paths = self.extract_file_paths(input) - logger.info(f"File paths: {file_paths}") + if not is_trusted_mode_enabled(): + logger.warning("Rejected interpreter execution because trusted mode is disabled.") + return ( + "Interpreter execution is disabled in this deployment. " + "Set METABOT_TRUSTED_MODE=true only in a trusted environment with host-level isolation." + ) session_dir = create_user_session(self.session_id, user_session_dir=True) + session_dir.mkdir(parents=True, exist_ok=True) db_manager = tools_database() - if not file_paths: - output_merged = db_manager.get("tool_merge_result") - if output_merged is not None: - merged_json = json.loads(output_merged) - file_paths = merged_json.get("output", {}).get("paths", []) - logger.info(f"File paths from merge result: {file_paths}") + file_paths = self.collect_allowed_file_paths( + input_text=input, + session_dir=session_dir, + db_manager=db_manager, + ) + logger.info(f"Validated file paths: {file_paths}") if not file_paths: - sparql_output = db_manager.get("tool_sparql") - if sparql_output is not None: - sparql_json = json.loads(sparql_output) - file_paths = sparql_json.get("output", {}).get("paths", []) - logger.info(f"File paths from SPARQL tool: {file_paths}") + return ( + "Interpreter could not find any readable session files. " + "Only files created or uploaded in the current session are allowed." + ) llm = self.llm_instance or ChatOpenAI( api_key=self.openai_key, @@ -87,42 +105,8 @@ def _run( temperature=0, ) - system_prompt = ( - "You are a Python data analysis assistant.\n" - "Write a self-contained Python script that reads the provided file(s), " - "fulfils the user's request, and prints a concise text summary to stdout.\n\n" - "Rules:\n" - "- pandas, numpy, json, pathlib, and plotly are available.\n" - "- Use the exact column names shown in the file previews — do not guess them.\n" - "- Print a SHORT summary (counts, stats, top-N rows) — never print an entire dataframe.\n" - f"- Save any output files (visualizations, processed data) to: {session_dir}\n" - "- For visualizations use Plotly and write JSON with: " - f'fig.write_json(str(Path("{session_dir}") / ".json"))\n' - "- Do NOT call plt.show(), fig.show(), or any interactive display.\n" - "- Do NOT install packages inside the script.\n" - "- Return ONLY the Python code inside a single ```python ... ``` block." - ) - - file_previews = [] - for fp in file_paths: - try: - import pandas as pd - sep = "\t" if str(fp).endswith((".tsv", ".txt")) else "," - preview = pd.read_csv(fp, sep=sep, nrows=3) - # Truncate long cell values so wide/URL-heavy columns don't blow context - preview = preview.map( - lambda v: str(v)[:80] + "…" if isinstance(v, str) and len(str(v)) > 80 else v - ) - file_previews.append( - f"File: {fp}\n" - f"Columns: {preview.columns.tolist()}\n" - f"Dtypes: {preview.dtypes.to_dict()}\n" - f"Preview (3 rows):\n{preview.to_string(index=False)}" - ) - except Exception: - file_previews.append(f"File: {fp} (could not preview)") - - preview_block = "\n\n".join(file_previews) if file_previews else "(no files)" + system_prompt = self.build_system_prompt(session_dir=session_dir, file_paths=file_paths) + preview_block = self.build_preview_block(file_paths) user_message = f"File previews:\n{preview_block}\n\nRequest: {input}" logger.info("Requesting analysis code from LLM") @@ -130,49 +114,265 @@ def _run( [SystemMessage(content=system_prompt), HumanMessage(content=user_message)] ) - code_blocks = re.findall(r"```python\s*(.*?)\s*```", response.content, re.DOTALL) - if not code_blocks: - code_blocks = re.findall(r"```\s*(.*?)\s*```", response.content, re.DOTALL) - - if not code_blocks: + code = self.extract_python_code(response.content) + if code is None: logger.error("LLM did not return a Python code block") return "Interpreter could not generate analysis code for this request." - code = code_blocks[0] logger.info(f"Generated code:\n{code}") - # Snapshot existing files so we can detect what was newly created - existing_files = set(session_dir.iterdir()) if session_dir.exists() else set() + existing_files = self.list_session_files(session_dir) + execution_result = self.execute_in_subprocess( + code=code, + session_dir=session_dir, + allowed_input_paths=file_paths, + ) + execution_output = execution_result.get("stdout", "").strip() + error_output = execution_result.get("error", "").strip() + + if error_output: + logger.warning(f"Interpreter subprocess reported an error: {error_output}") - repl = PythonREPL() - execution_output = repl.run(code) - # Truncate to keep the agent context manageable (~2000 chars ≈ ~500 tokens) - _MAX_OUTPUT = 2000 - if len(execution_output) > _MAX_OUTPUT: - execution_output = execution_output[:_MAX_OUTPUT] + f"\n… [output truncated at {_MAX_OUTPUT} chars]" - logger.info(f"Execution output: {execution_output}") + combined_output = execution_output or error_output or "Interpreter execution completed." + combined_output = self.truncate_output(combined_output) + logger.info(f"Execution output: {combined_output}") - # Collect files written by the executed code - new_files = ( - set(session_dir.iterdir()) - existing_files if session_dir.exists() else set() + new_files = sorted( + str(path) + for path in (self.list_session_files(session_dir) - existing_files) ) - filepaths = [str(f) for f in new_files] - logger.info(f"Files generated: {filepaths}") + logger.info(f"Files generated: {new_files}") - output_data = {"output": {"paths": filepaths}} + output_data = {"output": {"paths": new_files}} try: db_manager.put(data=json.dumps(output_data), tool_name="tool_interpreter") except Exception as e: logger.error(f"Error saving to database: {e}") suffix = ( - "\n\nThe full path of the files generated are:\n" + "\n".join(filepaths) - if filepaths + "\n\nThe full path of the files generated are:\n" + "\n".join(new_files) + if new_files else "" ) - return f"{execution_output}{suffix}" + return f"{combined_output}{suffix}" + + def collect_allowed_file_paths(self, input_text: str, session_dir: Path, db_manager) -> list[Path]: + candidate_paths = self.extract_file_paths(input_text) + + if not candidate_paths: + candidate_paths = self.extract_paths_from_db(db_manager, "tool_merge_result") + + if not candidate_paths: + candidate_paths = self.extract_paths_from_db(db_manager, "tool_sparql") + + validated_paths: list[Path] = [] + rejected_paths: list[str] = [] + + for raw_path in candidate_paths: + try: + validated_path = resolve_session_path(raw_path, session_dir=session_dir, must_exist=True) + if not validated_path.is_file(): + raise ValueError(f"Path '{raw_path}' is not a file.") + if validated_path.suffix.lower() not in _ALLOWED_INPUT_SUFFIXES: + raise ValueError( + f"Unsupported file type '{validated_path.suffix}'. " + f"Allowed types are: {sorted(_ALLOWED_INPUT_SUFFIXES)}." + ) + if validated_path not in validated_paths: + validated_paths.append(validated_path) + except ValueError as exc: + rejected_paths.append(str(exc)) + + for rejected_path in rejected_paths: + logger.warning(f"Rejected interpreter input path: {rejected_path}") + + return validated_paths + + def extract_paths_from_db(self, db_manager, tool_name: str) -> list[str]: + payload = db_manager.get(tool_name) + if payload is None: + return [] - def extract_file_paths(self, text: str): - regex = r"['\"]?([a-zA-Z0-9_/\\-]+(?:\.csv|\.tsv|\.mgf|\.txt|\.xlsx|\.xls))['\"]?" - matches = re.finditer(regex, text) - return [match.group(1).replace("'", "").replace('"', "") for match in matches] + try: + payload_json = json.loads(payload) + except json.JSONDecodeError as exc: + logger.warning(f"Could not decode tool payload for {tool_name}: {exc}") + return [] + + return payload_json.get("output", {}).get("paths", []) + + def build_system_prompt(self, session_dir: Path, file_paths: list[Path]) -> str: + allowed_paths = "\n".join(f"- {path}" for path in file_paths) + return ( + "You are a Python data analysis assistant.\n" + "Write a self-contained Python script that reads the provided file(s), " + "fulfils the user's request, and prints a concise text summary to stdout.\n\n" + "Rules:\n" + "- pandas, numpy, json, pathlib, plotly, math, statistics, csv, itertools, collections, and re are available.\n" + "- Read only the explicitly provided session files listed below.\n" + "- Save any output files only inside the session directory shown below.\n" + "- Do not attempt network access, subprocess execution, package installation, or filesystem access outside the session directory.\n" + "- Use the exact column names shown in the file previews; do not guess them.\n" + "- Print a SHORT summary (counts, stats, top-N rows); never print an entire dataframe.\n" + f"- Session directory for outputs: {session_dir}\n" + f"- Allowed input files:\n{allowed_paths}\n" + "- For visualizations use Plotly and write JSON with: " + f'fig.write_json(str(Path("{session_dir}") / ".json"))\n' + "- Do NOT call plt.show(), fig.show(), or any interactive display.\n" + "- Return ONLY the Python code inside a single ```python ... ``` block." + ) + + def build_preview_block(self, file_paths: list[Path]) -> str: + file_previews = [] + for file_path in file_paths: + try: + file_previews.append(self.preview_file(file_path)) + except Exception as exc: + logger.warning(f"Could not preview {file_path}: {exc}") + file_previews.append(f"File: {file_path} (could not preview)") + return "\n\n".join(file_previews) if file_previews else "(no files)" + + def preview_file(self, file_path: Path) -> str: + suffix = file_path.suffix.lower() + if suffix in {".csv", ".tsv", ".txt"}: + return self.preview_delimited_file(file_path) + if suffix in {".xls", ".xlsx"}: + preview = pd.read_excel(file_path, nrows=3) + return self.format_dataframe_preview(file_path, preview) + return self.preview_text_file(file_path) + + def preview_delimited_file(self, file_path: Path) -> str: + sep = "\t" if file_path.suffix.lower() in {".tsv", ".txt"} else "," + preview = pd.read_csv(file_path, sep=sep, nrows=3) + return self.format_dataframe_preview(file_path, preview) + + def format_dataframe_preview(self, file_path: Path, preview: pd.DataFrame) -> str: + if hasattr(preview, "map"): + preview = preview.map(self.truncate_cell) + else: + preview = preview.applymap(self.truncate_cell) + return ( + f"File: {file_path}\n" + f"Columns: {preview.columns.tolist()}\n" + f"Dtypes: {preview.dtypes.astype(str).to_dict()}\n" + f"Preview (3 rows):\n{preview.to_string(index=False)}" + ) + + def preview_text_file(self, file_path: Path) -> str: + preview_lines = [] + with file_path.open("r", encoding="utf-8", errors="replace") as handle: + for index, line in enumerate(handle): + if index >= 10: + break + preview_lines.append(line.rstrip("\n")[:200]) + + preview_body = "\n".join(preview_lines) if preview_lines else "(empty file)" + return f"File: {file_path}\nPreview (10 lines max):\n{preview_body}" + + def execute_in_subprocess( + self, + code: str, + session_dir: Path, + allowed_input_paths: list[Path], + ) -> dict[str, Any]: + runner_path = Path(__file__).with_name("sandbox_runner.py") + timeout_seconds = get_interpreter_timeout_seconds() + + payload = { + "code": code, + "session_dir": str(session_dir), + "allowed_input_paths": [str(path) for path in allowed_input_paths], + "timeout_seconds": timeout_seconds, + "cpu_seconds": get_interpreter_cpu_seconds(), + "memory_bytes": get_interpreter_memory_bytes(), + "file_size_bytes": get_interpreter_max_file_bytes(), + } + + with tempfile.NamedTemporaryFile("w", suffix=".json", delete=False) as payload_file: + json.dump(payload, payload_file) + payload_path = Path(payload_file.name) + + with tempfile.NamedTemporaryFile("w", suffix=".json", delete=False) as result_file: + result_path = Path(result_file.name) + + process = None + try: + process = subprocess.Popen( + [sys.executable, "-I", str(runner_path), "--payload", str(payload_path), "--result", str(result_path)], + cwd=session_dir, + env={ + "HOME": str(session_dir), + "PYTHONIOENCODING": "utf-8", + "TMPDIR": str(session_dir), + }, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + start_new_session=True, + ) + + stdout, stderr = process.communicate(timeout=timeout_seconds + 2) + except subprocess.TimeoutExpired: + if process is not None: + try: + os.killpg(process.pid, signal.SIGKILL) + except ProcessLookupError: + process.kill() + process.wait() + result_path.unlink(missing_ok=True) + return { + "success": False, + "stdout": "", + "error": f"Interpreter execution timed out after {timeout_seconds} seconds.", + } + finally: + payload_path.unlink(missing_ok=True) + + result: dict[str, Any] | None = None + if result_path.exists(): + try: + result = json.loads(result_path.read_text(encoding="utf-8")) + except json.JSONDecodeError as exc: + logger.warning(f"Could not decode sandbox result: {exc}") + finally: + result_path.unlink(missing_ok=True) + + if result is None: + error_message = stderr.strip() or stdout.strip() or "Interpreter execution failed." + return {"success": False, "stdout": "", "error": error_message} + + if process.returncode != 0 and not result.get("error"): + result["error"] = stderr.strip() or "Interpreter subprocess failed." + + return result + + def extract_python_code(self, text: str) -> str | None: + code_blocks = re.findall(r"```python\s*(.*?)\s*```", text, re.DOTALL) + if not code_blocks: + code_blocks = re.findall(r"```\s*(.*?)\s*```", text, re.DOTALL) + return code_blocks[0] if code_blocks else None + + def extract_file_paths(self, text: str) -> list[str]: + regex = r"['\"]?([^'\"\s]+(?:\.csv|\.tsv|\.mgf|\.txt|\.xlsx|\.xls|\.json))['\"]?" + return [ + match.group(1).replace("'", "").replace('"', "") + for match in re.finditer(regex, text) + ] + + def list_session_files(self, session_dir: Path) -> set[Path]: + return { + path.resolve(strict=False) + for path in session_dir.rglob("*") + if path.is_file() + } + + def truncate_output(self, output: str) -> str: + if len(output) <= _MAX_OUTPUT_CHARS: + return output + return output[:_MAX_OUTPUT_CHARS] + f"\n... [output truncated at {_MAX_OUTPUT_CHARS} chars]" + + @staticmethod + def truncate_cell(value: Any) -> Any: + if isinstance(value, str) and len(value) > 80: + return value[:80] + "..." + return value diff --git a/app/core/agents/supervisor/agent.py b/app/core/agents/supervisor/agent.py index 0dfa69a..e875924 100644 --- a/app/core/agents/supervisor/agent.py +++ b/app/core/agents/supervisor/agent.py @@ -10,13 +10,12 @@ logger = setup_logger(__name__) -config = load_config() - def create_agent(llms, graph, llm_instance=None) -> AgentExecutor: """Configure and return a supervisor agent with decision-making logic for task routing.""" llm = llm_instance if llm_instance is not None else llms[MODEL_CHOICE] + config = load_config() members = config["supervisor"]["members"] options = ["FINISH"] + members diff --git a/app/core/agents/supervisor/prompt.py b/app/core/agents/supervisor/prompt.py index 47915e6..600d528 100644 --- a/app/core/agents/supervisor/prompt.py +++ b/app/core/agents/supervisor/prompt.py @@ -13,21 +13,24 @@ If the Sparql_query_runner does not provide the path to the file, it means that there is no answer to the question. In that case, mark the process as FINISH. -If the Sparql_query_runner provides a SPARQL query and the path to the file containing the SPARQL output without directly providing the answer (implying that the answer is too long to be directly included), then delegate this information to the Interpreter_agent for further analysis and interpretation. Provide the Interpreter_agent with the question, SPARQL query, and the path to the file provided by the Sparql_query_runner. Await the Interpreter_agent's response for the final answer. +Only delegate work to Interpreter_agent when Interpreter_agent appears in the available team members list. +If Interpreter_agent is not available, do not attempt file interpretation or chart generation. Use the SPARQL answer directly when possible, otherwise explain that interpreter features are disabled in this deployment. + +If the Sparql_query_runner provides a SPARQL query and the path to the file containing the SPARQL output without directly providing the answer (implying that the answer is too long to be directly included), then delegate this information to the Interpreter_agent for further analysis and interpretation only when Interpreter_agent is available. Provide the Interpreter_agent with the question, SPARQL query, and the path to the file provided by the Sparql_query_runner. Await the Interpreter_agent's response for the final answer. Once the Interpreter_agent has completed its task mark the process as FINISH. Do not call the Interpreter_agent again. -If the Sparql_query_runner agent provides a SPARQL query, the path to the file containing the SPARQL output and final answer to the question, and there is no immediate need for further interpretation, normally mark the process as FINISH. However, if there is a need to visualize the results (regardless of the length of the SPARQL output), also call the Interpreter_agent to generate the necessary plot, chart, or graph based on the SPARQL output. The need for visualization should be assessed based on the user's request or if the nature of the data implies that visualization would enhance understanding. Once the Interpreter_agent has completed its task mark the process as FINISH. Do not call the Interpreter_agent again. +If the Sparql_query_runner agent provides a SPARQL query, the path to the file containing the SPARQL output and final answer to the question, and there is no immediate need for further interpretation, normally mark the process as FINISH. However, if there is a need to visualize the results (regardless of the length of the SPARQL output), call the Interpreter_agent only when it is available to generate the necessary plot, chart, or graph based on the SPARQL output. The need for visualization should be assessed based on the user's request or if the nature of the data implies that visualization would enhance understanding. If Interpreter_agent is unavailable, explain that visualization features are disabled in this deployment and finish with the available SPARQL answer. -If the question provided by the user does mention a figure, chart or visualization of the mass spectra you should call the Interpreter_agent to generate the necessary plot, chart, graph or visualization of the mass spectra based on the SPARQL output. Once the Interpreter_agent has completed its task mark the process as FINISH. Do not call the Interpreter_agent again. +If the question provided by the user does mention a figure, chart or visualization of the mass spectra you should call the Interpreter_agent to generate the necessary plot, chart, graph or visualization of the mass spectra based on the SPARQL output only when Interpreter_agent is available. If it is unavailable, explain that visualization features are disabled in this deployment and finish with the available non-visual answer. -For example, the user provides the following question: For features from Melochia umbellata in PI mode with SIRIUS annotations, get the ones for which a feature in NI mode with the same retention time has the same SIRIUS annotation. Since the question mentions Melochia umbellata you should firstly delegate it to the ENPKG_agent which would provide wikidata IRI with TAXON_RESOLVER tool, then, you should delegate the question together with the output generated by ENPKG_agent to the Sparql_query_runner agent. Afterwards, if the Sparql_query_runner agent provided the answer to the question, SPARQL query and path to the file containing the SPARQL output and there is no need to visualize the output you should mark the process as FINISH. If the Sparql_query_runner agent provided only SPARQL query and path to the file you should call Interpreter_agent which would interpret the results provided by Sparql_query_runner to generate the final response to the question. +For example, the user provides the following question: For features from Melochia umbellata in PI mode with SIRIUS annotations, get the ones for which a feature in NI mode with the same retention time has the same SIRIUS annotation. Since the question mentions Melochia umbellata you should firstly delegate it to the ENPKG_agent which would provide wikidata IRI with TAXON_RESOLVER tool, then, you should delegate the question together with the output generated by ENPKG_agent to the Sparql_query_runner agent. Afterwards, if the Sparql_query_runner agent provided the answer to the question, SPARQL query and path to the file containing the SPARQL output and there is no need to visualize the output you should mark the process as FINISH. If the Sparql_query_runner agent provided only SPARQL query and path to the file you should call Interpreter_agent only when it is available; otherwise explain that interpreter features are disabled in this deployment and finish with the SPARQL answer. Avoid calling the same agent if this agent has already been called previously and provided the answer. For example, if you have called ENPKG_agent and it provided the output do not call this agent again. Always tell the user the SPARQL query that has been returned by the Sparql_query_runner. -If interpretation of a file is needed, delegate the task directly to interpreter agent unless it needs to resolve any chemical entity or retrieve extra information from sparql_query_runner. In this case, delegate the task to the aproppriate agent. +If interpretation of a file is needed, delegate the task directly to interpreter agent only when it is available, unless it needs to resolve any chemical entity or retrieve extra information from sparql_query_runner. In this case, delegate the task to the aproppriate agent. If the agent does not provide the expected output mark the process as FINISH. diff --git a/app/core/security.py b/app/core/security.py new file mode 100644 index 0000000..561010f --- /dev/null +++ b/app/core/security.py @@ -0,0 +1,61 @@ +from __future__ import annotations + +import os +from pathlib import Path + + +_TRUE_VALUES = {"1", "true", "yes", "on", "y", "t"} + + +def env_flag(name: str, default: bool = False) -> bool: + value = os.getenv(name) + if value is None: + return default + return value.strip().lower() in _TRUE_VALUES + + +def is_trusted_mode_enabled() -> bool: + """Trusted mode opt-in for features that execute generated code.""" + return env_flag("METABOT_TRUSTED_MODE", default=False) + + +def get_interpreter_timeout_seconds() -> int: + return max(1, int(os.getenv("METABOT_INTERPRETER_TIMEOUT_SECONDS", "15"))) + + +def get_interpreter_cpu_seconds() -> int: + timeout_seconds = get_interpreter_timeout_seconds() + return max(1, int(os.getenv("METABOT_INTERPRETER_CPU_SECONDS", str(timeout_seconds)))) + + +def get_interpreter_memory_bytes() -> int: + memory_mb = max(64, int(os.getenv("METABOT_INTERPRETER_MEMORY_MB", "512"))) + return memory_mb * 1024 * 1024 + + +def get_interpreter_max_file_bytes() -> int: + file_mb = max(8, int(os.getenv("METABOT_INTERPRETER_MAX_FILE_MB", "64"))) + return file_mb * 1024 * 1024 + + +def is_path_within_directory(path: Path, directory: Path) -> bool: + resolved_path = path.resolve(strict=False) + resolved_directory = directory.resolve(strict=False) + return resolved_path == resolved_directory or resolved_directory in resolved_path.parents + + +def resolve_session_path(path_value: str | Path, session_dir: Path, must_exist: bool = True) -> Path: + candidate = Path(path_value) + if not candidate.is_absolute(): + candidate = session_dir / candidate + + resolved = candidate.resolve(strict=False) + if not is_path_within_directory(resolved, session_dir): + raise ValueError( + f"Path '{path_value}' is outside the allowed session directory '{session_dir}'." + ) + + if must_exist and not resolved.exists(): + raise ValueError(f"Path '{path_value}' does not exist in the current session.") + + return resolved diff --git a/app/core/tests/test_security.py b/app/core/tests/test_security.py new file mode 100644 index 0000000..4691b5c --- /dev/null +++ b/app/core/tests/test_security.py @@ -0,0 +1,43 @@ +import pytest + +from app.core.security import resolve_session_path +from app.core.utils import load_config + + +def test_load_config_excludes_interpreter_by_default(monkeypatch): + monkeypatch.delenv("METABOT_TRUSTED_MODE", raising=False) + + config = load_config() + + assert "Interpreter_agent" not in config["supervisor"]["members"] + assert all(agent["name"] != "Interpreter_agent" for agent in config["agents"]) + + +def test_load_config_includes_interpreter_in_trusted_mode(monkeypatch): + monkeypatch.setenv("METABOT_TRUSTED_MODE", "true") + + config = load_config() + + assert "Interpreter_agent" in config["supervisor"]["members"] + assert any(agent["name"] == "Interpreter_agent" for agent in config["agents"]) + + +def test_resolve_session_path_allows_current_session_files(tmp_path): + session_dir = tmp_path / "session" + session_dir.mkdir() + allowed_file = session_dir / "input.csv" + allowed_file.write_text("a,b\n1,2\n", encoding="utf-8") + + resolved = resolve_session_path(allowed_file, session_dir=session_dir) + + assert resolved == allowed_file.resolve() + + +def test_resolve_session_path_rejects_outside_files(tmp_path): + session_dir = tmp_path / "session" + session_dir.mkdir() + outside_file = tmp_path / "outside.csv" + outside_file.write_text("a,b\n1,2\n", encoding="utf-8") + + with pytest.raises(ValueError): + resolve_session_path(outside_file, session_dir=session_dir) diff --git a/app/core/utils.py b/app/core/utils.py index 59c68b2..4b81a5f 100644 --- a/app/core/utils.py +++ b/app/core/utils.py @@ -3,6 +3,7 @@ import logging import logging.config import os +from copy import deepcopy from pathlib import Path from typing import List, Optional, Type from langchain.tools import BaseTool @@ -10,6 +11,7 @@ import inspect import tempfile from uuid import uuid4 +from app.core.security import is_trusted_mode_enabled from app.core.session import setup_logger logger = setup_logger(__name__) @@ -18,7 +20,42 @@ def load_config(): config_path = Path(__file__).resolve().parent.parent / "config" / "langgraph.json" with open(config_path, "r") as file: - return json.load(file) + config = json.load(file) + + return _with_optional_interpreter(config) + + +def _with_optional_interpreter(config: dict) -> dict: + if not is_trusted_mode_enabled(): + return config + + trusted_config = deepcopy(config) + interpreter_agent = { + "name": "Interpreter_agent", + "path": "app.core.agents.interpreter.agent", + "llm_choice": "llm_o", + } + interpreter_edge = {"source": "Interpreter_agent", "target": "supervisor"} + interpreter_target = { + "condition_value": "Interpreter_agent", + "target": "Interpreter_agent", + } + + if interpreter_agent not in trusted_config["agents"]: + trusted_config["agents"].insert(3, interpreter_agent) + + if interpreter_edge not in trusted_config["edges"]: + trusted_config["edges"].append(interpreter_edge) + + for edge in trusted_config["conditional_edges"]: + if edge["source"] == "supervisor" and interpreter_target not in edge["targets"]: + edge["targets"].insert(-1, interpreter_target) + + members = trusted_config["supervisor"]["members"] + if "Interpreter_agent" not in members: + members.append("Interpreter_agent") + + return trusted_config def import_tools(directory: str, module_prefix: str, **kwargs) -> List: diff --git a/streamlit_webapp/streamlit_app.py b/streamlit_webapp/streamlit_app.py index 5de1760..f399f55 100644 --- a/streamlit_webapp/streamlit_app.py +++ b/streamlit_webapp/streamlit_app.py @@ -26,18 +26,19 @@ st.session_state.logger = logger # Following normal code execution -import os -from pathlib import Path -import logging -import time -from openai import OpenAI -from langchain_core.messages import HumanMessage -from langsmith import Client -from app.core.memory.database_manager import tools_database, memory_database -from langchain.callbacks.manager import tracing_v2_enabled -from streamlit_utils import check_characters_api_key, test_sparql_endpoint, test_openai_key, new_process_langgraph_output, create_zip_buffer, is_true -from app.core.workflow.langraph_workflow import create_workflow -from app.core.main import llm_creation +import os +from pathlib import Path +import logging +import time +from openai import OpenAI +from langchain_core.messages import HumanMessage +from langsmith import Client +from app.core.memory.database_manager import tools_database, memory_database +from app.core.security import is_trusted_mode_enabled +from langchain.callbacks.manager import tracing_v2_enabled +from streamlit_utils import check_characters_api_key, test_sparql_endpoint, test_openai_key, new_process_langgraph_output, create_zip_buffer, is_true +from app.core.workflow.langraph_workflow import create_workflow +from app.core.main import llm_creation def add_videos_to_content(): @@ -115,8 +116,11 @@ def add_videos_to_content(): if "preselected_question" not in st.session_state: st.session_state.preselected_question = None -if "set_standard_endpoint" not in st.session_state: - st.session_state.set_standard_endpoint = True +if "set_standard_endpoint" not in st.session_state: + st.session_state.set_standard_endpoint = True + +if "trusted_mode_enabled" not in st.session_state: + st.session_state.trusted_mode_enabled = is_trusted_mode_enabled() if "langgraph_app_created" not in st.session_state: st.session_state.langgraph_app_created = False @@ -366,11 +370,11 @@ def clear_dialog(): st.write(splash_text) add_videos_to_content() -if st.session_state.openai_key_success == True and st.session_state.endpoint_url_success == True: - if st.session_state.langgraph_app_created == False: - st.warning("Initializing the LangGraph... Please wait") - st.session_state.logger.info("Initializing the LangGraph") - try: +if st.session_state.openai_key_success == True and st.session_state.endpoint_url_success == True: + if st.session_state.langgraph_app_created == False: + st.warning("Initializing the LangGraph... Please wait") + st.session_state.logger.info("Initializing the LangGraph") + try: st.session_state.models = llm_creation(api_key=st.session_state.OPENAI_API_KEY) @@ -379,29 +383,36 @@ def clear_dialog(): st.session_state.logger.info("LangGraph initialized") st.rerun() except Exception as e: - st.error(f"An error occurred while initializing the LangGraph: {e}") - st.session_state.logger.error(f"An error occurred while initializing the LangGraph: {e}") - - else: - # Variables to track submission status of predefined and custom question - select_submit_clicked = False - custom_submit_clicked = False - - with st.expander("Input your own file", expanded=False): - user_files = st.file_uploader("Choose your files", accept_multiple_files=True) - if user_files: - filenames = [file.name for file in user_files] - try: - for file in user_files: - file_path = st.session_state.user_input_dir / file.name - with file_path.open("wb") as f: - f.write(file.getbuffer()) - st.success(f"File(s) uploaded successfully: {filenames}") - st.session_state.logger.info(f"File(s) uploaded successfully: {filenames}") - except Exception as e: - st.error(f"An error occurred while processing the file: {e}") - - with st.expander ("Try one our standard questions:", expanded=False): + st.error(f"An error occurred while initializing the LangGraph: {e}") + st.session_state.logger.error(f"An error occurred while initializing the LangGraph: {e}") + + else: + if st.session_state.trusted_mode_enabled == False: + st.info( + "Trusted mode is disabled in this deployment. " + "Interpreter-based file analysis and LLM-generated visualizations are not exposed to public users." + ) + + # Variables to track submission status of predefined and custom question + select_submit_clicked = False + custom_submit_clicked = False + + if st.session_state.trusted_mode_enabled == True: + with st.expander("Input your own file", expanded=False): + user_files = st.file_uploader("Choose your files", accept_multiple_files=True) + if user_files: + filenames = [file.name for file in user_files] + try: + for file in user_files: + file_path = st.session_state.user_input_dir / file.name + with file_path.open("wb") as f: + f.write(file.getbuffer()) + st.success(f"File(s) uploaded successfully: {filenames}") + st.session_state.logger.info(f"File(s) uploaded successfully: {filenames}") + except Exception as e: + st.error(f"An error occurred while processing the file: {e}") + + with st.expander ("Try one our standard questions:", expanded=False): # Form for selecting a predefined question with st.form(key="select_form"): # Dynamically generated list of questions for user selection From 115678a6f099fd5a7077ad334d7827d3a1ee2564 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Louis-F=C3=A9lix=20Nothias?= Date: Sat, 25 Apr 2026 00:18:11 +0200 Subject: [PATCH 13/21] Harden interpreter file handling and output tracking Improve interpreter free-text filepath extraction to handle quoted paths, spaces, and dotted filenames, and track files updated in place as well as newly created outputs. Add focused tests for path parsing, DB fallback behavior, and changed-file detection. --- app/core/agents/interpreter/prompt.py | 3 +- .../agents/interpreter/tool_interpreter.py | 79 ++++++++--- app/core/tests/test_interpreter_tool.py | 132 ++++++++++++++++++ 3 files changed, 196 insertions(+), 18 deletions(-) create mode 100644 app/core/tests/test_interpreter_tool.py diff --git a/app/core/agents/interpreter/prompt.py b/app/core/agents/interpreter/prompt.py index 5190e5c..98a3dc5 100644 --- a/app/core/agents/interpreter/prompt.py +++ b/app/core/agents/interpreter/prompt.py @@ -23,9 +23,10 @@ For using INTERPRETER_TOOL tool, you have to provide all the information needed for the interpretation of the question.This allows INTERPRETER_TOOL to analyze the information thoroughly and provide a clear, concise answer to the initial question. Your input for the tool should be a dictionary with one key, __arg1, and the value of this key should be a string contain at least all relevant complete filepaths, the user question and your demands for the tool. You should include, if any, the generated SPARQL query or other relevant information. + Wrap every filepath in quotes. This is mandatory for any filepath containing spaces and preferred for all filepaths. Your demand should explicitly say what you want from the tool. Please include Example: - Filepath: Filepath provided by SPARQL_QUERY_RUNNER + Filepath: "Filepath provided by SPARQL_QUERY_RUNNER" User question: User question Demand: Interpret the file to give x and y information and provide a chart Other relevant information: Other information here diff --git a/app/core/agents/interpreter/tool_interpreter.py b/app/core/agents/interpreter/tool_interpreter.py index f595210..8895ce6 100644 --- a/app/core/agents/interpreter/tool_interpreter.py +++ b/app/core/agents/interpreter/tool_interpreter.py @@ -31,6 +31,17 @@ logger = setup_logger(__name__) _ALLOWED_INPUT_SUFFIXES = {".csv", ".tsv", ".mgf", ".txt", ".xlsx", ".xls", ".json"} +_ALLOWED_INPUT_SUFFIXES_PATTERN = "|".join( + sorted(suffix.removeprefix(".") for suffix in _ALLOWED_INPUT_SUFFIXES) +) +_QUOTED_FILE_PATH_PATTERN = re.compile( + rf"""(?P["'])(?P[^"']+\.(?:{_ALLOWED_INPUT_SUFFIXES_PATTERN}))(?P=quote)""", + re.IGNORECASE, +) +_UNQUOTED_FILE_PATH_PATTERN = re.compile( + rf"""(?P[A-Za-z0-9_./\\-]+\.(?:{_ALLOWED_INPUT_SUFFIXES_PATTERN}))""", + re.IGNORECASE, +) _MAX_OUTPUT_CHARS = 2000 @@ -121,7 +132,7 @@ def _run( logger.info(f"Generated code:\n{code}") - existing_files = self.list_session_files(session_dir) + existing_files = self.capture_session_file_metadata(session_dir) execution_result = self.execute_in_subprocess( code=code, session_dir=session_dir, @@ -137,21 +148,22 @@ def _run( combined_output = self.truncate_output(combined_output) logger.info(f"Execution output: {combined_output}") - new_files = sorted( + current_files = self.capture_session_file_metadata(session_dir) + changed_or_new_files = sorted( str(path) - for path in (self.list_session_files(session_dir) - existing_files) + for path in self.find_changed_or_new_files(existing_files, current_files) ) - logger.info(f"Files generated: {new_files}") + logger.info(f"Files generated or updated: {changed_or_new_files}") - output_data = {"output": {"paths": new_files}} + output_data = {"output": {"paths": changed_or_new_files}} try: db_manager.put(data=json.dumps(output_data), tool_name="tool_interpreter") except Exception as e: logger.error(f"Error saving to database: {e}") suffix = ( - "\n\nThe full path of the files generated are:\n" + "\n".join(new_files) - if new_files + "\n\nThe full path of the files generated or updated are:\n" + "\n".join(changed_or_new_files) + if changed_or_new_files else "" ) return f"{combined_output}{suffix}" @@ -353,17 +365,38 @@ def extract_python_code(self, text: str) -> str | None: return code_blocks[0] if code_blocks else None def extract_file_paths(self, text: str) -> list[str]: - regex = r"['\"]?([^'\"\s]+(?:\.csv|\.tsv|\.mgf|\.txt|\.xlsx|\.xls|\.json))['\"]?" - return [ - match.group(1).replace("'", "").replace('"', "") - for match in re.finditer(regex, text) - ] - - def list_session_files(self, session_dir: Path) -> set[Path]: + candidates: list[str] = [] + quoted_spans: list[tuple[int, int]] = [] + + for match in _QUOTED_FILE_PATH_PATTERN.finditer(text): + candidates.append(match.group("path")) + quoted_spans.append(match.span()) + + for match in _UNQUOTED_FILE_PATH_PATTERN.finditer(text): + if self.is_overlapping_span(match.span(), quoted_spans): + continue + candidates.append(match.group("path")) + + return self.deduplicate_paths(candidates) + + def capture_session_file_metadata(self, session_dir: Path) -> dict[Path, tuple[int, int]]: + metadata: dict[Path, tuple[int, int]] = {} + for path in session_dir.rglob("*"): + if not path.is_file(): + continue + stat = path.stat() + metadata[path.resolve(strict=False)] = (stat.st_mtime_ns, stat.st_size) + return metadata + + def find_changed_or_new_files( + self, + previous_metadata: dict[Path, tuple[int, int]], + current_metadata: dict[Path, tuple[int, int]], + ) -> set[Path]: return { - path.resolve(strict=False) - for path in session_dir.rglob("*") - if path.is_file() + path + for path, metadata in current_metadata.items() + if previous_metadata.get(path) != metadata } def truncate_output(self, output: str) -> str: @@ -376,3 +409,15 @@ def truncate_cell(value: Any) -> Any: if isinstance(value, str) and len(value) > 80: return value[:80] + "..." return value + + @staticmethod + def deduplicate_paths(paths: list[str]) -> list[str]: + deduplicated_paths: list[str] = [] + for path in paths: + if path not in deduplicated_paths: + deduplicated_paths.append(path) + return deduplicated_paths + + @staticmethod + def is_overlapping_span(span: tuple[int, int], other_spans: list[tuple[int, int]]) -> bool: + return any(span[0] < other[1] and other[0] < span[1] for other in other_spans) diff --git a/app/core/tests/test_interpreter_tool.py b/app/core/tests/test_interpreter_tool.py new file mode 100644 index 0000000..c307408 --- /dev/null +++ b/app/core/tests/test_interpreter_tool.py @@ -0,0 +1,132 @@ +from __future__ import annotations + +import json +import time +from pathlib import Path + +from app.core.agents.interpreter.tool_interpreter import Interpreter + + +class StubDBManager: + def __init__(self, payloads: dict[str, str] | None = None): + self.payloads = payloads or {} + + def get(self, tool_name: str): + return self.payloads.get(tool_name) + + +def build_interpreter() -> Interpreter: + return Interpreter(llm_instance=object()) + + +def test_extract_file_paths_allows_dots_in_file_stem(): + interpreter = build_interpreter() + + paths = interpreter.extract_file_paths("Use /tmp/session/foo.v1.csv for the analysis.") + + assert paths == ["/tmp/session/foo.v1.csv"] + + +def test_extract_file_paths_supports_quoted_paths_with_spaces(): + interpreter = build_interpreter() + + paths = interpreter.extract_file_paths('Filepath: "/tmp/session/my file.v1.csv"') + + assert paths == ["/tmp/session/my file.v1.csv"] + + +def test_extract_file_paths_supports_multiple_paths(): + interpreter = build_interpreter() + + paths = interpreter.extract_file_paths( + 'Compare "/tmp/session/my file.v1.csv" with /tmp/session/other_file.tsv' + ) + + assert paths == ["/tmp/session/my file.v1.csv", "/tmp/session/other_file.tsv"] + + +def test_collect_allowed_file_paths_falls_back_to_db_when_no_explicit_paths(tmp_path): + interpreter = build_interpreter() + session_dir = tmp_path / "session" + session_dir.mkdir() + input_file = session_dir / "from_db.csv" + input_file.write_text("a,b\n1,2\n", encoding="utf-8") + db_manager = StubDBManager( + { + "tool_sparql": json.dumps( + {"output": {"paths": [str(input_file)]}} + ) + } + ) + + paths = interpreter.collect_allowed_file_paths( + input_text="Interpret the latest results.", + session_dir=session_dir, + db_manager=db_manager, + ) + + assert paths == [input_file.resolve()] + + +def test_collect_allowed_file_paths_rejects_invalid_path_without_suppressing_valid_explicit_path(tmp_path): + interpreter = build_interpreter() + session_dir = tmp_path / "session" + session_dir.mkdir() + valid_file = session_dir / "valid file.csv" + valid_file.write_text("a,b\n1,2\n", encoding="utf-8") + outside_file = tmp_path / "outside.csv" + outside_file.write_text("a,b\n9,9\n", encoding="utf-8") + + paths = interpreter.collect_allowed_file_paths( + input_text=f'Use "{outside_file}" and "{valid_file}"', + session_dir=session_dir, + db_manager=StubDBManager(), + ) + + assert paths == [valid_file.resolve()] + + +def test_find_changed_or_new_files_includes_new_file(tmp_path): + interpreter = build_interpreter() + session_dir = tmp_path / "session" + session_dir.mkdir() + before = interpreter.capture_session_file_metadata(session_dir) + + created_file = session_dir / "created.csv" + created_file.write_text("a,b\n1,2\n", encoding="utf-8") + after = interpreter.capture_session_file_metadata(session_dir) + + changed_files = interpreter.find_changed_or_new_files(before, after) + + assert changed_files == {created_file.resolve()} + + +def test_find_changed_or_new_files_includes_overwritten_file(tmp_path): + interpreter = build_interpreter() + session_dir = tmp_path / "session" + session_dir.mkdir() + existing_file = session_dir / "updated.csv" + existing_file.write_text("a,b\n1,2\n", encoding="utf-8") + before = interpreter.capture_session_file_metadata(session_dir) + + time.sleep(0.001) + existing_file.write_text("a,b\n3,4\n", encoding="utf-8") + after = interpreter.capture_session_file_metadata(session_dir) + + changed_files = interpreter.find_changed_or_new_files(before, after) + + assert changed_files == {existing_file.resolve()} + + +def test_find_changed_or_new_files_ignores_unchanged_files(tmp_path): + interpreter = build_interpreter() + session_dir = tmp_path / "session" + session_dir.mkdir() + existing_file = session_dir / "unchanged.csv" + existing_file.write_text("a,b\n1,2\n", encoding="utf-8") + before = interpreter.capture_session_file_metadata(session_dir) + after = interpreter.capture_session_file_metadata(session_dir) + + changed_files = interpreter.find_changed_or_new_files(before, after) + + assert changed_files == set() From 1515fec90b52ec47948bc2e624dba7562bf90f2d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Louis-F=C3=A9lix=20Nothias?= Date: Sat, 25 Apr 2026 00:27:12 +0200 Subject: [PATCH 14/21] Add session file preparation error handling and corresponding tests - Introduced SessionFilePreparationError for better error management during file staging. - Enhanced _prepare_session_files function to handle various file-related errors. - Added unit tests for session file preparation, covering valid file copying and error scenarios. - Documented changes in the agentic engineering log. --- app/core/main.py | 70 ++++++++++++--- app/core/tests/test_main.py | 172 ++++++++++++++++++++++++++++++++++++ 2 files changed, 232 insertions(+), 10 deletions(-) create mode 100644 app/core/tests/test_main.py diff --git a/app/core/main.py b/app/core/main.py index 691928c..db2c3e1 100644 --- a/app/core/main.py +++ b/app/core/main.py @@ -36,6 +36,14 @@ } +class SessionFilePreparationError(ValueError): + """Raised when CLI input files cannot be staged into the session directory.""" + + def __init__(self, source_path: Path, message: str): + super().__init__(message) + self.source_path = source_path + + def get_api_key(provider: str) -> Optional[str]: """ Get API key for specified provider from environment variables. @@ -215,16 +223,55 @@ def _prepare_session_files(session_id: str, file_paths: List[str]) -> Path: Path to the session input directory. Raises: - FileNotFoundError: If any of the supplied paths do not exist. + SessionFilePreparationError: If any supplied path cannot be staged safely. """ input_dir = create_user_session(session_id, input_dir=True) + staged_destinations: dict[Path, Path] = {} for raw_path in file_paths: - src = Path(raw_path).resolve() + src = Path(raw_path).expanduser().resolve(strict=False) if not src.exists(): - raise FileNotFoundError(f"File not found: {src}") + raise SessionFilePreparationError(src, f"File not found: {src}") + if not src.is_file(): + raise SessionFilePreparationError(src, f"Input path is not a file: {src}") + dest = input_dir / src.name - shutil.copy2(str(src), str(dest)) + previous_src = staged_destinations.get(dest) + if previous_src is not None: + if previous_src == src: + raise SessionFilePreparationError( + src, + f"Input file was provided more than once: {src}", + ) + raise SessionFilePreparationError( + src, + ( + f"Cannot stage '{src}' because it would overwrite '{previous_src}' in " + f"the session input directory. Rename one of the files or choose a different path." + ), + ) + + if src == dest.resolve(strict=False): + raise SessionFilePreparationError( + src, + f"Input file is already staged in the session directory: {src}", + ) + + if dest.exists(): + raise SessionFilePreparationError( + src, + f"Cannot stage '{src}' because destination '{dest}' already exists.", + ) + + try: + shutil.copy2(str(src), str(dest)) + except (shutil.SameFileError, OSError) as exc: + raise SessionFilePreparationError( + src, + f"Failed to stage '{src}' into '{dest}': {exc}", + ) from exc + + staged_destinations[dest] = src logger.info(f"Copied '{src}' -> '{dest}'") return input_dir @@ -279,6 +326,13 @@ def main(): print("You must provide either a standard question number (-q) or a custom question (-c).") return + # Create a user session (mirrors the Streamlit session lifecycle) and + # reconfigure the logger so subsequent CLI logs land in the session file. + session_id = create_user_session() + initialize_session_context(session_id) + global logger + logger = setup_logger(__name__) + # Initialize LangSmith if available langsmith_setup() @@ -290,17 +344,13 @@ def main(): ) # Initialize language models - models = llm_creation() - - # Create a user session (mirrors the Streamlit session lifecycle) - session_id = create_user_session() - initialize_session_context(session_id) + models = llm_creation(api_key=args.api_key) # Stage user-provided files into the session's input directory if args.file: try: _prepare_session_files(session_id, args.file) - except FileNotFoundError as exc: + except SessionFilePreparationError as exc: logger.error(str(exc)) print(f"Error: {exc}") return diff --git a/app/core/tests/test_main.py b/app/core/tests/test_main.py new file mode 100644 index 0000000..fbdd20b --- /dev/null +++ b/app/core/tests/test_main.py @@ -0,0 +1,172 @@ +from __future__ import annotations + +import sys +from pathlib import Path + +import app.core.main as main_module + + +class DummyLogger: + def __init__(self): + self.info_messages = [] + self.error_messages = [] + + def info(self, message, *args): + if args: + message = message % args + self.info_messages.append(message) + + def error(self, message, *args): + if args: + message = message % args + self.error_messages.append(message) + + +def test_prepare_session_files_copies_valid_file(tmp_path, monkeypatch): + input_dir = tmp_path / "input_files" + input_dir.mkdir() + source_file = tmp_path / "source.csv" + source_file.write_text("a,b\n1,2\n", encoding="utf-8") + dummy_logger = DummyLogger() + input_dir_path = input_dir + + monkeypatch.setattr(main_module, "create_user_session", lambda session_id, input_dir=False: input_dir_path) + monkeypatch.setattr(main_module, "logger", dummy_logger) + + staged_dir = main_module._prepare_session_files("session-id", [str(source_file)]) + + copied_file = input_dir / source_file.name + assert staged_dir == input_dir + assert copied_file.read_text(encoding="utf-8") == source_file.read_text(encoding="utf-8") + + +def test_prepare_session_files_rejects_directory(tmp_path, monkeypatch): + input_dir = tmp_path / "input_files" + input_dir.mkdir() + source_dir = tmp_path / "source_dir" + source_dir.mkdir() + input_dir_path = input_dir + + monkeypatch.setattr(main_module, "create_user_session", lambda session_id, input_dir=False: input_dir_path) + + try: + main_module._prepare_session_files("session-id", [str(source_dir)]) + assert False, "Expected SessionFilePreparationError" + except main_module.SessionFilePreparationError as exc: + assert "not a file" in str(exc) + + +def test_prepare_session_files_rejects_colliding_basenames(tmp_path, monkeypatch): + input_dir = tmp_path / "input_files" + input_dir.mkdir() + first_dir = tmp_path / "first" + second_dir = tmp_path / "second" + first_dir.mkdir() + second_dir.mkdir() + first_file = first_dir / "results.csv" + second_file = second_dir / "results.csv" + first_file.write_text("a,b\n1,2\n", encoding="utf-8") + second_file.write_text("a,b\n3,4\n", encoding="utf-8") + dummy_logger = DummyLogger() + input_dir_path = input_dir + + monkeypatch.setattr(main_module, "create_user_session", lambda session_id, input_dir=False: input_dir_path) + monkeypatch.setattr(main_module, "logger", dummy_logger) + + try: + main_module._prepare_session_files("session-id", [str(first_file), str(second_file)]) + assert False, "Expected SessionFilePreparationError" + except main_module.SessionFilePreparationError as exc: + assert "would overwrite" in str(exc) + + +def test_prepare_session_files_rejects_same_file_destination(tmp_path, monkeypatch): + input_dir = tmp_path / "input_files" + input_dir.mkdir() + staged_file = input_dir / "already_staged.csv" + staged_file.write_text("a,b\n1,2\n", encoding="utf-8") + input_dir_path = input_dir + + monkeypatch.setattr(main_module, "create_user_session", lambda session_id, input_dir=False: input_dir_path) + + try: + main_module._prepare_session_files("session-id", [str(staged_file)]) + assert False, "Expected SessionFilePreparationError" + except main_module.SessionFilePreparationError as exc: + assert "already staged" in str(exc) + + +def test_main_passes_cli_api_key_and_reconfigures_logger(monkeypatch): + original_argv = sys.argv[:] + state = {"session_initialized": False, "setup_logger_states": []} + captured = {} + + def fake_setup_logger(name): + state["setup_logger_states"].append(state["session_initialized"]) + return DummyLogger() + + def fake_initialize_session_context(session_id): + state["session_initialized"] = True + + def fake_llm_creation(api_key=None, params_file=None): + captured["llm_api_key"] = api_key + return {"llm_o": object()} + + def fake_create_workflow(models, session_id=None, endpoint_url=None, evaluation=False, api_key=None): + captured["workflow_api_key"] = api_key + captured["session_id"] = session_id + return "workflow" + + def fake_process_workflow(workflow, question): + captured["workflow"] = workflow + captured["question"] = question + + monkeypatch.setattr(sys, "argv", ["prog", "-c", "hello", "--api-key", "cli-key"]) + monkeypatch.setattr(main_module, "logger", DummyLogger()) + monkeypatch.setattr(main_module, "setup_logger", fake_setup_logger) + monkeypatch.setattr(main_module, "initialize_session_context", fake_initialize_session_context) + monkeypatch.setattr(main_module, "create_user_session", lambda session_id=None, user_session_dir=False, input_dir=False: "session-123") + monkeypatch.setattr(main_module, "langsmith_setup", lambda: None) + monkeypatch.setattr(main_module, "llm_creation", fake_llm_creation) + monkeypatch.setattr(main_module, "create_workflow", fake_create_workflow) + monkeypatch.setattr(main_module, "process_workflow", fake_process_workflow) + + try: + main_module.main() + finally: + monkeypatch.setattr(sys, "argv", original_argv) + + assert captured["llm_api_key"] == "cli-key" + assert captured["workflow_api_key"] == "cli-key" + assert state["setup_logger_states"] == [True] + assert captured["session_id"] == "session-123" + assert captured["workflow"] == "workflow" + assert captured["question"] == "hello" + + +def test_main_prints_user_friendly_error_for_bad_staged_file(monkeypatch, capsys): + original_argv = sys.argv[:] + + monkeypatch.setattr(sys, "argv", ["prog", "-c", "hello", "-f", "/missing/file.csv"]) + monkeypatch.setattr(main_module, "logger", DummyLogger()) + monkeypatch.setattr(main_module, "setup_logger", lambda name: DummyLogger()) + monkeypatch.setattr(main_module, "initialize_session_context", lambda session_id: None) + monkeypatch.setattr(main_module, "create_user_session", lambda session_id=None, user_session_dir=False, input_dir=False: "session-123") + monkeypatch.setattr(main_module, "langsmith_setup", lambda: None) + monkeypatch.setattr(main_module, "llm_creation", lambda api_key=None, params_file=None: {"llm_o": object()}) + monkeypatch.setattr( + main_module, + "_prepare_session_files", + lambda session_id, file_paths: (_ for _ in ()).throw( + main_module.SessionFilePreparationError(Path(file_paths[0]), f"File not found: {file_paths[0]}") + ), + ) + monkeypatch.setattr(main_module, "create_workflow", lambda *args, **kwargs: (_ for _ in ()).throw(AssertionError("workflow should not run"))) + + try: + main_module.main() + finally: + monkeypatch.setattr(sys, "argv", original_argv) + + captured = capsys.readouterr() + assert "Error: File not found: /missing/file.csv" in captured.out From 524c2b14184fceefbea3498a7e8bdcf46cde2b59 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Louis-F=C3=A9lix=20Nothias?= Date: Sat, 25 Apr 2026 00:35:40 +0200 Subject: [PATCH 15/21] Harden interpreter sandbox error handling and upload safety Improve interpreter sandbox validation and failure handling by blocking dunder attribute access, preserving captured output on execution errors, softening malformed DB payload parsing, and cleaning up subprocess tempfiles on all error paths. Also sanitize uploaded filenames in Streamlit, fix the supervisor prompt typo, and add focused regression tests for the sandbox runner and interpreter tool. --- app/core/agents/interpreter/sandbox_runner.py | 37 ++++++-- .../agents/interpreter/tool_interpreter.py | 94 ++++++++++++++----- app/core/agents/supervisor/prompt.py | 2 +- app/core/tests/test_interpreter_tool.py | 89 +++++++++++++++++- app/core/tests/test_sandbox_runner.py | 35 +++++++ streamlit_webapp/streamlit_app.py | 10 +- 6 files changed, 232 insertions(+), 35 deletions(-) create mode 100644 app/core/tests/test_sandbox_runner.py diff --git a/app/core/agents/interpreter/sandbox_runner.py b/app/core/agents/interpreter/sandbox_runner.py index e59112b..011d3b6 100644 --- a/app/core/agents/interpreter/sandbox_runner.py +++ b/app/core/agents/interpreter/sandbox_runner.py @@ -32,6 +32,7 @@ REAL_OPEN = builtins.open REAL_IO_OPEN = io.open REAL_OS_OPEN = os.open +REAL_PATH_OPEN = Path.open def parse_args() -> argparse.Namespace: @@ -50,6 +51,8 @@ def validate_code(code: str) -> None: tree = ast.parse(code, mode="exec") blocked_names = {"__import__", "eval", "exec", "compile", "globals", "locals", "vars", "breakpoint"} + # Defense in depth only: AST validation helps reduce accidental footguns, + # but the subprocess sandbox remains the primary containment layer. for node in ast.walk(tree): if isinstance(node, ast.Import): for alias in node.names: @@ -61,6 +64,9 @@ def validate_code(code: str) -> None: raise ValueError(f"Import from '{module}' is not allowed.") elif isinstance(node, ast.Name) and node.id in blocked_names: raise ValueError(f"Use of '{node.id}' is not allowed.") + elif isinstance(node, ast.Attribute): + if node.attr.startswith("__") and node.attr.endswith("__"): + raise ValueError("Dunder attribute access is not allowed.") def apply_resource_limits(config: dict) -> None: @@ -169,6 +175,13 @@ def safe_os_open(file, flags, mode=0o777, *args, **kwargs): return safe_open +def restore_filesystem_access() -> None: + builtins.open = REAL_OPEN + io.open = REAL_IO_OPEN + os.open = REAL_OS_OPEN + Path.open = REAL_PATH_OPEN + + def is_within_directory(path: Path, directory: Path) -> bool: resolved_path = path.resolve(strict=False) resolved_directory = directory.resolve(strict=False) @@ -247,15 +260,25 @@ def execute_user_code(config: dict) -> dict: try: with contextlib.redirect_stdout(stdout_buffer), contextlib.redirect_stderr(stderr_buffer): - exec(compile(config["code"], "", "exec"), sandbox_globals, sandbox_globals) - return { - "success": True, - "stdout": stdout_buffer.getvalue(), - "stderr": stderr_buffer.getvalue(), - "error": "", - } + try: + exec(compile(config["code"], "", "exec"), sandbox_globals, sandbox_globals) + except BaseException as exc: + return { + "success": False, + "stdout": stdout_buffer.getvalue(), + "stderr": stderr_buffer.getvalue(), + "error": str(exc), + "traceback": traceback.format_exc(), + } + return { + "success": True, + "stdout": stdout_buffer.getvalue(), + "stderr": stderr_buffer.getvalue(), + "error": "", + } finally: signal.alarm(0) + restore_filesystem_access() def main() -> int: diff --git a/app/core/agents/interpreter/tool_interpreter.py b/app/core/agents/interpreter/tool_interpreter.py index 8895ce6..b1259f4 100644 --- a/app/core/agents/interpreter/tool_interpreter.py +++ b/app/core/agents/interpreter/tool_interpreter.py @@ -121,11 +121,17 @@ def _run( user_message = f"File previews:\n{preview_block}\n\nRequest: {input}" logger.info("Requesting analysis code from LLM") - response = llm.invoke( - [SystemMessage(content=system_prompt), HumanMessage(content=user_message)] - ) + try: + response = llm.invoke( + [SystemMessage(content=system_prompt), HumanMessage(content=user_message)] + ) + except Exception as exc: + logger.error(f"Interpreter LLM invocation failed: {exc}") + return "Interpreter could not generate analysis code for this request." - code = self.extract_python_code(response.content) + response_content = self.normalize_response_content(response.content) + + code = self.extract_python_code(response_content) if code is None: logger.error("LLM did not return a Python code block") return "Interpreter could not generate analysis code for this request." @@ -211,7 +217,37 @@ def extract_paths_from_db(self, db_manager, tool_name: str) -> list[str]: logger.warning(f"Could not decode tool payload for {tool_name}: {exc}") return [] - return payload_json.get("output", {}).get("paths", []) + if not isinstance(payload_json, dict): + logger.warning( + "Unexpected payload shape for %s: top-level payload type is %s", + tool_name, + type(payload_json).__name__, + ) + return [] + + output = payload_json.get("output") + if isinstance(output, dict): + output_paths = output.get("paths", []) + if isinstance(output_paths, list): + return output_paths + logger.warning( + "Unexpected payload shape for %s: output.paths type is %s", + tool_name, + type(output_paths).__name__, + ) + return [] + + top_level_paths = payload_json.get("paths") + if isinstance(top_level_paths, list): + return top_level_paths + + logger.warning( + "Unexpected payload shape for %s: output type is %s and top-level paths type is %s", + tool_name, + type(output).__name__, + type(top_level_paths).__name__, + ) + return [] def build_system_prompt(self, session_dir: Path, file_paths: list[Path]) -> str: allowed_paths = "\n".join(f"- {path}" for path in file_paths) @@ -324,6 +360,22 @@ def execute_in_subprocess( ) stdout, stderr = process.communicate(timeout=timeout_seconds + 2) + + result: dict[str, Any] | None = None + if result_path.exists(): + try: + result = json.loads(result_path.read_text(encoding="utf-8")) + except json.JSONDecodeError as exc: + logger.warning(f"Could not decode sandbox result: {exc}") + + if result is None: + error_message = stderr.strip() or stdout.strip() or "Interpreter execution failed." + return {"success": False, "stdout": "", "error": error_message} + + if process.returncode != 0 and not result.get("error"): + result["error"] = stderr.strip() or "Interpreter subprocess failed." + + return result except subprocess.TimeoutExpired: if process is not None: try: @@ -331,7 +383,6 @@ def execute_in_subprocess( except ProcessLookupError: process.kill() process.wait() - result_path.unlink(missing_ok=True) return { "success": False, "stdout": "", @@ -339,24 +390,7 @@ def execute_in_subprocess( } finally: payload_path.unlink(missing_ok=True) - - result: dict[str, Any] | None = None - if result_path.exists(): - try: - result = json.loads(result_path.read_text(encoding="utf-8")) - except json.JSONDecodeError as exc: - logger.warning(f"Could not decode sandbox result: {exc}") - finally: - result_path.unlink(missing_ok=True) - - if result is None: - error_message = stderr.strip() or stdout.strip() or "Interpreter execution failed." - return {"success": False, "stdout": "", "error": error_message} - - if process.returncode != 0 and not result.get("error"): - result["error"] = stderr.strip() or "Interpreter subprocess failed." - - return result + result_path.unlink(missing_ok=True) def extract_python_code(self, text: str) -> str | None: code_blocks = re.findall(r"```python\s*(.*?)\s*```", text, re.DOTALL) @@ -364,6 +398,18 @@ def extract_python_code(self, text: str) -> str | None: code_blocks = re.findall(r"```\s*(.*?)\s*```", text, re.DOTALL) return code_blocks[0] if code_blocks else None + def normalize_response_content(self, content: Any) -> str: + if isinstance(content, str): + return content + if isinstance(content, list): + return "\n".join( + item if isinstance(item, str) else json.dumps(item, ensure_ascii=True, default=str) + for item in content + ) + if isinstance(content, dict): + return json.dumps(content, ensure_ascii=True, default=str) + return str(content) + def extract_file_paths(self, text: str) -> list[str]: candidates: list[str] = [] quoted_spans: list[tuple[int, int]] = [] diff --git a/app/core/agents/supervisor/prompt.py b/app/core/agents/supervisor/prompt.py index 600d528..bf18fb9 100644 --- a/app/core/agents/supervisor/prompt.py +++ b/app/core/agents/supervisor/prompt.py @@ -30,7 +30,7 @@ Always tell the user the SPARQL query that has been returned by the Sparql_query_runner. -If interpretation of a file is needed, delegate the task directly to interpreter agent only when it is available, unless it needs to resolve any chemical entity or retrieve extra information from sparql_query_runner. In this case, delegate the task to the aproppriate agent. +If interpretation of a file is needed, delegate the task directly to interpreter agent only when it is available, unless it needs to resolve any chemical entity or retrieve extra information from sparql_query_runner. In this case, delegate the task to the appropriate agent. If the agent does not provide the expected output mark the process as FINISH. diff --git a/app/core/tests/test_interpreter_tool.py b/app/core/tests/test_interpreter_tool.py index c307408..c40b938 100644 --- a/app/core/tests/test_interpreter_tool.py +++ b/app/core/tests/test_interpreter_tool.py @@ -15,6 +15,22 @@ def get(self, tool_name: str): return self.payloads.get(tool_name) +class StubLLMResponse: + def __init__(self, content): + self.content = content + + +class StubLLM: + def __init__(self, content=None, error: Exception | None = None): + self.content = content + self.error = error + + def invoke(self, messages): + if self.error is not None: + raise self.error + return StubLLMResponse(self.content) + + def build_interpreter() -> Interpreter: return Interpreter(llm_instance=object()) @@ -86,6 +102,28 @@ def test_collect_allowed_file_paths_rejects_invalid_path_without_suppressing_val assert paths == [valid_file.resolve()] +def test_extract_paths_from_db_handles_unexpected_payload_shape(): + interpreter = build_interpreter() + + paths = interpreter.extract_paths_from_db( + StubDBManager({"tool_sparql": json.dumps(["not-a-dict"])}), + "tool_sparql", + ) + + assert paths == [] + + +def test_extract_paths_from_db_supports_top_level_paths_list(): + interpreter = build_interpreter() + + paths = interpreter.extract_paths_from_db( + StubDBManager({"tool_sparql": json.dumps({"paths": ["a.csv"]})}), + "tool_sparql", + ) + + assert paths == ["a.csv"] + + def test_find_changed_or_new_files_includes_new_file(tmp_path): interpreter = build_interpreter() session_dir = tmp_path / "session" @@ -110,7 +148,7 @@ def test_find_changed_or_new_files_includes_overwritten_file(tmp_path): before = interpreter.capture_session_file_metadata(session_dir) time.sleep(0.001) - existing_file.write_text("a,b\n3,4\n", encoding="utf-8") + existing_file.write_text("a,b\n30,400\n", encoding="utf-8") after = interpreter.capture_session_file_metadata(session_dir) changed_files = interpreter.find_changed_or_new_files(before, after) @@ -130,3 +168,52 @@ def test_find_changed_or_new_files_ignores_unchanged_files(tmp_path): changed_files = interpreter.find_changed_or_new_files(before, after) assert changed_files == set() + + +def test_run_returns_user_friendly_error_when_llm_invoke_fails(tmp_path, monkeypatch): + session_dir = tmp_path / "session" + session_dir.mkdir() + input_file = session_dir / "input.csv" + input_file.write_text("a,b\n1,2\n", encoding="utf-8") + interpreter = Interpreter(llm_instance=StubLLM(error=RuntimeError("boom"))) + + monkeypatch.setenv("METABOT_TRUSTED_MODE", "true") + monkeypatch.setattr( + "app.core.agents.interpreter.tool_interpreter.create_user_session", + lambda session_id, user_session_dir=False, input_dir=False: session_dir, + ) + monkeypatch.setattr( + "app.core.agents.interpreter.tool_interpreter.tools_database", + lambda: StubDBManager(), + ) + + result = interpreter._run(f'Filepath: "{input_file}"') + + assert result == "Interpreter could not generate analysis code for this request." + + +def test_run_normalizes_non_string_llm_content(tmp_path, monkeypatch): + session_dir = tmp_path / "session" + session_dir.mkdir() + input_file = session_dir / "input.csv" + input_file.write_text("a,b\n1,2\n", encoding="utf-8") + interpreter = Interpreter(llm_instance=StubLLM(content=["```python\nprint('ok')\n```"])) + + monkeypatch.setenv("METABOT_TRUSTED_MODE", "true") + monkeypatch.setattr( + "app.core.agents.interpreter.tool_interpreter.create_user_session", + lambda session_id, user_session_dir=False, input_dir=False: session_dir, + ) + monkeypatch.setattr( + "app.core.agents.interpreter.tool_interpreter.tools_database", + lambda: StubDBManager(), + ) + monkeypatch.setattr( + Interpreter, + "execute_in_subprocess", + lambda self, code, session_dir, allowed_input_paths: {"stdout": "ok", "error": ""}, + ) + + result = interpreter._run(f'Filepath: "{input_file}"') + + assert result == "ok" diff --git a/app/core/tests/test_sandbox_runner.py b/app/core/tests/test_sandbox_runner.py new file mode 100644 index 0000000..11933ee --- /dev/null +++ b/app/core/tests/test_sandbox_runner.py @@ -0,0 +1,35 @@ +from __future__ import annotations + +from app.core.agents.interpreter.sandbox_runner import execute_user_code, validate_code + + +def test_validate_code_rejects_dunder_attribute_access(): + try: + validate_code("x = object().__class__") + assert False, "Expected ValueError" + except ValueError as exc: + assert "Dunder attribute access" in str(exc) + + +def test_execute_user_code_returns_captured_output_on_exception(tmp_path): + session_dir = tmp_path / "session" + session_dir.mkdir() + input_file = session_dir / "input.csv" + input_file.write_text("a,b\n1,2\n", encoding="utf-8") + + result = execute_user_code( + { + "code": "print('hello before error')\nraise ValueError('boom')", + "session_dir": str(session_dir), + "allowed_input_paths": [str(input_file)], + "timeout_seconds": 5, + "cpu_seconds": 5, + "memory_bytes": 128 * 1024 * 1024, + "file_size_bytes": 10 * 1024 * 1024, + } + ) + + assert result["success"] is False + assert "hello before error" in result["stdout"] + assert result["error"] == "boom" + assert "ValueError: boom" in result["traceback"] diff --git a/streamlit_webapp/streamlit_app.py b/streamlit_webapp/streamlit_app.py index f399f55..7fd8c30 100644 --- a/streamlit_webapp/streamlit_app.py +++ b/streamlit_webapp/streamlit_app.py @@ -35,6 +35,7 @@ from langsmith import Client from app.core.memory.database_manager import tools_database, memory_database from app.core.security import is_trusted_mode_enabled +from app.core.security import resolve_session_path from langchain.callbacks.manager import tracing_v2_enabled from streamlit_utils import check_characters_api_key, test_sparql_endpoint, test_openai_key, new_process_langgraph_output, create_zip_buffer, is_true from app.core.workflow.langraph_workflow import create_workflow @@ -401,10 +402,15 @@ def clear_dialog(): with st.expander("Input your own file", expanded=False): user_files = st.file_uploader("Choose your files", accept_multiple_files=True) if user_files: - filenames = [file.name for file in user_files] + filenames = [Path(file.name).name for file in user_files] try: for file in user_files: - file_path = st.session_state.user_input_dir / file.name + safe_filename = Path(file.name).name + file_path = resolve_session_path( + safe_filename, + session_dir=st.session_state.user_input_dir, + must_exist=False, + ) with file_path.open("wb") as f: f.write(file.getbuffer()) st.success(f"File(s) uploaded successfully: {filenames}") From c72de3990c1a5ae8bbac4849dafef8a18ad4f585 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Louis-F=C3=A9lix=20Nothias?= Date: Sat, 25 Apr 2026 00:49:53 +0200 Subject: [PATCH 16/21] feat: Add OpenAI key support to agent creation and enhance tool functionality --- app/core/agents/sparql/agent.py | 5 +- app/core/agents/sparql/tool_sparql.py | 13 ++++- app/core/tests/test_sparql_tool.py | 84 +++++++++++++++++++++++++++ 3 files changed, 98 insertions(+), 4 deletions(-) create mode 100644 app/core/tests/test_sparql_tool.py diff --git a/app/core/agents/sparql/agent.py b/app/core/agents/sparql/agent.py index 013e32f..face7a5 100644 --- a/app/core/agents/sparql/agent.py +++ b/app/core/agents/sparql/agent.py @@ -11,7 +11,7 @@ logger = setup_logger(__name__) -def create_agent(llms, graph, session_id, llm_instance=None) -> AgentExecutor: +def create_agent(llms, graph, session_id, openai_key=None, llm_instance=None) -> AgentExecutor: logger.info("Creating agent with tools...") directory = os.path.dirname(__file__) module_prefix = get_module_prefix(__name__) @@ -19,7 +19,8 @@ def create_agent(llms, graph, session_id, llm_instance=None) -> AgentExecutor: tool_parameters = { "graph": graph, "llm": llms, - "session_id": session_id + "session_id": session_id, + "openai_key": openai_key, } tools = import_tools(directory, module_prefix, **tool_parameters) diff --git a/app/core/agents/sparql/tool_sparql.py b/app/core/agents/sparql/tool_sparql.py index efa51f0..d95e7b5 100644 --- a/app/core/agents/sparql/tool_sparql.py +++ b/app/core/agents/sparql/tool_sparql.py @@ -232,8 +232,16 @@ class GraphSparqlQAChain(BaseTool): requires_params: bool = True graph: RdfGraph = None session_id: str = None + openai_key: Optional[str] = None - def __init__(self, llm: dict, graph: RdfGraph, session_id: str, **kwargs): + def __init__( + self, + llm: dict, + graph: RdfGraph, + session_id: str, + openai_key: Optional[str] = None, + **kwargs, + ): super().__init__(**kwargs) try: self.sparql_generation_select_chain = LLMChain( @@ -250,6 +258,7 @@ def __init__(self, llm: dict, graph: RdfGraph, session_id: str, **kwargs): raise self.graph = graph self.session_id = session_id + self.openai_key = openai_key or os.getenv("OPENAI_API_KEY") def _run( self, @@ -385,7 +394,7 @@ def search_nodes(self, query): # Construct the path to the faiss_db directory db_path = os.path.abspath(os.path.join(current_dir, '..', '..', '..', 'data', 'faiss_db')) - embeddings = OpenAIEmbeddings() + embeddings = OpenAIEmbeddings(api_key=self.openai_key) db = FAISS.load_local(db_path, embeddings, allow_dangerous_deserialization=True) related_nodes = db.similarity_search(query, 12) return related_nodes diff --git a/app/core/tests/test_sparql_tool.py b/app/core/tests/test_sparql_tool.py new file mode 100644 index 0000000..840b29a --- /dev/null +++ b/app/core/tests/test_sparql_tool.py @@ -0,0 +1,84 @@ +from app.core.agents.sparql import agent as sparql_agent +from app.core.agents.sparql import tool_sparql + + +def _construct_tool(**kwargs): + if hasattr(tool_sparql.GraphSparqlQAChain, "model_construct"): + return tool_sparql.GraphSparqlQAChain.model_construct(**kwargs) + return tool_sparql.GraphSparqlQAChain.construct(**kwargs) + + +def test_create_agent_passes_openai_key_to_import_tools(monkeypatch): + captured = {} + + def fake_import_tools(directory, module_prefix, **kwargs): + captured["tool_kwargs"] = kwargs + return ["tool"] + + def fake_create_openai_tools_agent(model, tools, prompt): + captured["agent_model"] = model + captured["agent_tools"] = tools + return "agent" + + def fake_agent_executor(agent, tools): + captured["executor_agent"] = agent + captured["executor_tools"] = tools + return "executor" + + monkeypatch.setattr(sparql_agent, "import_tools", fake_import_tools) + monkeypatch.setattr( + sparql_agent, + "create_openai_tools_agent", + fake_create_openai_tools_agent, + ) + monkeypatch.setattr(sparql_agent, "AgentExecutor", fake_agent_executor) + + executor = sparql_agent.create_agent( + llms={sparql_agent.MODEL_CHOICE: "model"}, + graph="graph", + session_id="session-123", + openai_key="cli-key", + ) + + assert executor == "executor" + assert captured["tool_kwargs"] == { + "graph": "graph", + "llm": {sparql_agent.MODEL_CHOICE: "model"}, + "session_id": "session-123", + "openai_key": "cli-key", + } + assert captured["agent_model"] == "model" + assert captured["executor_agent"] == "agent" + assert captured["executor_tools"] == ["tool"] + + +def test_search_nodes_uses_explicit_openai_key(monkeypatch): + captured = {} + + class FakeEmbeddings: + def __init__(self, api_key=None): + captured["api_key"] = api_key + + class FakeVectorStore: + def similarity_search(self, query, limit): + captured["query"] = query + captured["limit"] = limit + return ["related-node"] + + def fake_load_local(path, embeddings, allow_dangerous_deserialization=False): + captured["db_path"] = path + captured["embeddings"] = embeddings + captured["allow_dangerous_deserialization"] = allow_dangerous_deserialization + return FakeVectorStore() + + monkeypatch.setattr(tool_sparql, "OpenAIEmbeddings", FakeEmbeddings) + monkeypatch.setattr(tool_sparql.FAISS, "load_local", fake_load_local) + + tool = _construct_tool(openai_key="cli-key") + result = tool.search_nodes("SELECT * WHERE { ?s ?p ?o }") + + assert captured["api_key"] == "cli-key" + assert captured["query"] == "SELECT * WHERE { ?s ?p ?o }" + assert captured["limit"] == 12 + assert captured["allow_dangerous_deserialization"] is True + assert result == ["related-node"] From 94c7840fcf674b74603cf5a5c91be308d1a36b3a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Louis-F=C3=A9lix=20Nothias?= Date: Sat, 25 Apr 2026 01:18:47 +0200 Subject: [PATCH 17/21] feat: Update README for v1.1.0 highlights and improve Streamlit app launch instructions; add test for Streamlit app rendering --- README.md | 20 ++++----- streamlit_webapp/README.md | 36 ++++++++++------ streamlit_webapp/streamlit_app.py | 60 +++++++++++++++----------- streamlit_webapp/test_streamlit_app.py | 21 +++++++++ 4 files changed, 86 insertions(+), 51 deletions(-) create mode 100644 streamlit_webapp/test_streamlit_app.py diff --git a/README.md b/README.md index 4f1f5f5..4802fd5 100644 --- a/README.md +++ b/README.md @@ -17,6 +17,12 @@ Take a break, brew a cup of tea while 🧪 MetaboT 🍵 digs into mass spec data Take a break, brew a cup of tea 🍵, and have some fun with words while 🧪 MetaboT 🍵 digs into mass spec data! Enjoy your brew and happy puzzling! +## v1.1.0 Highlights + +- Hardened the interpreter path by isolating LLM-generated Python behind trusted-mode controls and a subprocess runner instead of in-process execution. +- Improved CLI safety with better file staging validation, collision handling, session-scoped logging, and consistent `--api-key` propagation through fallback SPARQL paths. +- Made the Streamlit app easier to launch locally by stabilizing its import path handling and aligning the recommended startup command with the tested repo-root workflow. + ## Documentation Comprehensive documentation is available at [https://holobiomicslab.github.io/MetaboT/](https://holobiomicslab.github.io/MetaboT/). It includes: @@ -217,22 +223,14 @@ python -m app.core.main -c "Your custom question" ``` ### Running via Streamlit -To launch the application through Streamlit, set the required environment variables, install the dependencies, and run the app. In your terminal, execute: +To launch the application through Streamlit, install the dependencies and run the app from the repository root. In your terminal, execute: ```bash -export ADMIN_OPENAI_KEY=your_openai_api_key -export LANGCHAIN_API_KEY=your_langchain_api_key pip install -r requirements.txt -streamlit run streamlit_webapp/streamlit_app.py -``` - -If you encounter an error stating that the `app` directory cannot be found (e.g., "ModuleNotFoundError: No module named 'app'"), it means Python is unable to locate the module. To resolve this, add the current directory to your `PYTHONPATH` by running: - -```bash -export PYTHONPATH="${PYTHONPATH}:$(pwd)" +python -m streamlit run streamlit_webapp/streamlit_app.py ``` -This command ensures that Python can locate the `app` directory. +This repo-root launch path is the recommended setup for local development and matches the Streamlit smoke-tested workflow used in `v1.1.0`. You can provide your OpenAI key in the sidebar once the app starts, or preconfigure contributor/admin keys through environment variables if you use those deployment paths. --- ### Running in Docker diff --git a/streamlit_webapp/README.md b/streamlit_webapp/README.md index 0352a09..bfc9395 100644 --- a/streamlit_webapp/README.md +++ b/streamlit_webapp/README.md @@ -6,17 +6,23 @@ A Streamlit-based web application for exploring and analyzing metabolomics data MetaboT is an AI-powered system designed to facilitate the exploration of metabolomics data. It integrates with OpenAI's GPT models and SPARQL endpoints to provide intelligent analysis and querying capabilities for metabolomics datasets. -## Features - -- Interactive chat interface for querying metabolomics data -- Integration with OpenAI GPT models -- SPARQL endpoint connectivity for data retrieval +## Features + +- Interactive chat interface for querying metabolomics data +- Integration with OpenAI GPT models +- SPARQL endpoint connectivity for data retrieval - Real-time data visualization - File upload capabilities for custom data analysis -- Session management and data persistence -- Automated file cleanup system - -## Project Structure +- Session management and data persistence +- Automated file cleanup system + +## v1.1.0 Notes + +- Streamlit startup is now resilient to the working directory used to launch the app. +- Public-facing deployments keep the interpreter workflow disabled unless trusted mode is explicitly enabled. +- Safer file handling and API-key propagation fixes from the CLI now align better with Streamlit-backed workflows. + +## Project Structure ``` streamlit_webapp/ @@ -61,10 +67,12 @@ pip install -r requirements.txt 2. Set up the PostgreSQL database and configure the environment variables. -3. Run the Streamlit application: -```bash -streamlit run streamlit_app.py -``` +3. Run the Streamlit application from the repository root: +```bash +python -m streamlit run streamlit_webapp/streamlit_app.py +``` + +If you are currently inside `streamlit_webapp/`, run `cd ..` first and then use the command above. ## Usage @@ -111,4 +119,4 @@ For contributors with access keys, the application provides: ## Support -[Include support contact information here] \ No newline at end of file +[Include support contact information here] diff --git a/streamlit_webapp/streamlit_app.py b/streamlit_webapp/streamlit_app.py index 7fd8c30..bffcd1a 100644 --- a/streamlit_webapp/streamlit_app.py +++ b/streamlit_webapp/streamlit_app.py @@ -1,10 +1,20 @@ -# Setting up Session for streamlit app -# It's ugly but it has to be done in the very beginning of the script -import streamlit as st -import os -from uuid import uuid4 - -from app.core.session import create_user_session, initialize_session_context, initialize_thread_id, setup_logger +# Setting up Session for streamlit app +# It's ugly but it has to be done in the very beginning of the script +import streamlit as st +import os +import sys +from pathlib import Path +from uuid import uuid4 + +STREAMLIT_APP_DIR = Path(__file__).resolve().parent +REPO_ROOT = STREAMLIT_APP_DIR.parent + +# Support running via `streamlit run streamlit_webapp/streamlit_app.py` from the +# repo root as well as from inside `streamlit_webapp/`. +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) + +from app.core.session import create_user_session, initialize_session_context, initialize_thread_id, setup_logger if "session_id" not in st.session_state: st.session_state.session_id = create_user_session() @@ -25,9 +35,7 @@ logger = setup_logger(__name__) st.session_state.logger = logger -# Following normal code execution -import os -from pathlib import Path +# Following normal code execution import logging import time from openai import OpenAI @@ -37,7 +45,7 @@ from app.core.security import is_trusted_mode_enabled from app.core.security import resolve_session_path from langchain.callbacks.manager import tracing_v2_enabled -from streamlit_utils import check_characters_api_key, test_sparql_endpoint, test_openai_key, new_process_langgraph_output, create_zip_buffer, is_true +from streamlit_webapp.streamlit_utils import check_characters_api_key, test_sparql_endpoint, test_openai_key, new_process_langgraph_output, create_zip_buffer, is_true from app.core.workflow.langraph_workflow import create_workflow from app.core.main import llm_creation @@ -175,7 +183,7 @@ def terms_and_conditions_dialog(): """) - if st.button("✅ I Agree to the Terms", use_container_width=True): + if st.button("✅ I Agree to the Terms", width="stretch"): st.session_state.terms_accepted = True st.success("Thanks! You have accepted the Terms.") st.rerun() @@ -183,7 +191,7 @@ def terms_and_conditions_dialog(): # --- Button to open the Terms dialog --- st.markdown("## 📘 Please read the Terms and Conditions.") - open_terms_dialog = st.button("📘 Terms and Conditions", use_container_width=True) + open_terms_dialog = st.button("📘 Terms and Conditions", width="stretch") if open_terms_dialog: terms_and_conditions_dialog() # OpenAI API Key Input and Validation @@ -298,7 +306,7 @@ def question_instructions_dialog(): if st.button("Close"): st.rerun() - open_question_instructions_dialog = st.button("Question Instructions", use_container_width=True, disabled=st.session_state.is_processing) + open_question_instructions_dialog = st.button("Question Instructions", width="stretch", disabled=st.session_state.is_processing) if open_question_instructions_dialog: question_instructions_dialog() @@ -321,20 +329,20 @@ def clear_dialog(): st.rerun() - open_help_dialog = st.button("Help", use_container_width=True, disabled=st.session_state.is_processing) + open_help_dialog = st.button("Help", width="stretch", disabled=st.session_state.is_processing) if open_help_dialog: help_dialog() - download = st.download_button( - label="Download files", - data=create_zip_buffer(st.session_state.session_id), - file_name="metabot_files.zip", - mime="application/zip", - use_container_width=True, - disabled=st.session_state.is_processing - ) - - open_clear_dialog = st.button("Clear conversation data", use_container_width=True, disabled=st.session_state.is_processing) + download = st.download_button( + label="Download files", + data=create_zip_buffer(st.session_state.session_id), + file_name="metabot_files.zip", + mime="application/zip", + width="stretch", + disabled=st.session_state.is_processing + ) + + open_clear_dialog = st.button("Clear conversation data", width="stretch", disabled=st.session_state.is_processing) if open_clear_dialog: clear_dialog() @@ -351,7 +359,7 @@ def clear_dialog(): st.markdown("---") st.subheader("Holobiomics Lab - CNRS, Université Côte d'Azur, Interdisciplinary Institute for Artificial Intelligence (3iA) Côte d'Azur") holobiomics_logo_path = str(Path(__file__).parent / "misc" / "HolobiomicsLab_graphics_v1_logos_small.png") - st.image(holobiomics_logo_path, use_container_width=True) + st.image(holobiomics_logo_path, width="stretch") if st.session_state.openai_key_success == False and st.session_state.endpoint_url_success == False: st.warning("You haven't provided a valid OpenAI API key and validated the connection to the endpoint. You need to provide a valid OpenAI API Key and connect to an Endpoint server. If you already tried to connect to an endpoint and it was unsucessful, there might be a connection problem. Please investigate further or come back later") diff --git a/streamlit_webapp/test_streamlit_app.py b/streamlit_webapp/test_streamlit_app.py new file mode 100644 index 0000000..d181309 --- /dev/null +++ b/streamlit_webapp/test_streamlit_app.py @@ -0,0 +1,21 @@ +from streamlit.testing.v1 import AppTest + +import app.core.memory.database_manager as database_manager +from streamlit_webapp import streamlit_utils + + +class DummyToolsDatabase: + pass + + +def test_streamlit_app_renders_without_import_errors(monkeypatch): + monkeypatch.setattr(streamlit_utils, "test_sparql_endpoint", lambda endpoint: False) + monkeypatch.setattr(database_manager, "tools_database", lambda: DummyToolsDatabase()) + monkeypatch.setattr(database_manager, "memory_database", lambda: object()) + + app = AppTest.from_file("streamlit_webapp/streamlit_app.py") + app.run(timeout=30) + + assert not app.exception + assert app.title + assert app.title[0].value == "MetaboT - An AI-system for Metabolomics Data Exploration" From 84892c84383a253d614c7d5461776c93e921128b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Louis-F=C3=A9lix=20Nothias?= Date: Sat, 25 Apr 2026 18:57:24 +0200 Subject: [PATCH 18/21] Tighten Streamlit startup and test isolation Stabilize Streamlit startup/import handling, add a render smoke test, and update the README with v1.1.0 launch guidance. Also harden follow-up test coverage by moving sandbox-runner execution checks into a subprocess, cleaning up pytest assertions, and refining SPARQL/OpenAI key handling to avoid env-only regressions and deprecation warnings. --- app/core/agents/sparql/tool_sparql.py | 3 +- app/core/main.py | 3 +- app/core/tests/test_main.py | 35 ++++---------- app/core/tests/test_sandbox_runner.py | 67 ++++++++++++++++++++------- app/core/tests/test_sparql_tool.py | 48 +++++++++++++++++-- 5 files changed, 109 insertions(+), 47 deletions(-) diff --git a/app/core/agents/sparql/tool_sparql.py b/app/core/agents/sparql/tool_sparql.py index d95e7b5..8721f67 100644 --- a/app/core/agents/sparql/tool_sparql.py +++ b/app/core/agents/sparql/tool_sparql.py @@ -394,7 +394,8 @@ def search_nodes(self, query): # Construct the path to the faiss_db directory db_path = os.path.abspath(os.path.join(current_dir, '..', '..', '..', 'data', 'faiss_db')) - embeddings = OpenAIEmbeddings(api_key=self.openai_key) + embedding_kwargs = {"api_key": self.openai_key} if self.openai_key else {} + embeddings = OpenAIEmbeddings(**embedding_kwargs) db = FAISS.load_local(db_path, embeddings, allow_dangerous_deserialization=True) related_nodes = db.similarity_search(query, 12) return related_nodes diff --git a/app/core/main.py b/app/core/main.py index db2c3e1..4293b2b 100644 --- a/app/core/main.py +++ b/app/core/main.py @@ -7,7 +7,8 @@ from dotenv import load_dotenv from langsmith import Client -from langchain_community.chat_models import ChatOpenAI, ChatLiteLLM +from langchain_community.chat_models import ChatLiteLLM +from langchain_openai import ChatOpenAI from app.core.workflow.langraph_workflow import create_workflow, process_workflow from app.core.session import create_user_session, initialize_session_context diff --git a/app/core/tests/test_main.py b/app/core/tests/test_main.py index fbdd20b..4af8b90 100644 --- a/app/core/tests/test_main.py +++ b/app/core/tests/test_main.py @@ -1,8 +1,9 @@ from __future__ import annotations -import sys from pathlib import Path +import pytest + import app.core.main as main_module @@ -49,11 +50,8 @@ def test_prepare_session_files_rejects_directory(tmp_path, monkeypatch): monkeypatch.setattr(main_module, "create_user_session", lambda session_id, input_dir=False: input_dir_path) - try: + with pytest.raises(main_module.SessionFilePreparationError, match="not a file"): main_module._prepare_session_files("session-id", [str(source_dir)]) - assert False, "Expected SessionFilePreparationError" - except main_module.SessionFilePreparationError as exc: - assert "not a file" in str(exc) def test_prepare_session_files_rejects_colliding_basenames(tmp_path, monkeypatch): @@ -73,11 +71,8 @@ def test_prepare_session_files_rejects_colliding_basenames(tmp_path, monkeypatch monkeypatch.setattr(main_module, "create_user_session", lambda session_id, input_dir=False: input_dir_path) monkeypatch.setattr(main_module, "logger", dummy_logger) - try: + with pytest.raises(main_module.SessionFilePreparationError, match="would overwrite"): main_module._prepare_session_files("session-id", [str(first_file), str(second_file)]) - assert False, "Expected SessionFilePreparationError" - except main_module.SessionFilePreparationError as exc: - assert "would overwrite" in str(exc) def test_prepare_session_files_rejects_same_file_destination(tmp_path, monkeypatch): @@ -89,15 +84,11 @@ def test_prepare_session_files_rejects_same_file_destination(tmp_path, monkeypat monkeypatch.setattr(main_module, "create_user_session", lambda session_id, input_dir=False: input_dir_path) - try: + with pytest.raises(main_module.SessionFilePreparationError, match="already staged"): main_module._prepare_session_files("session-id", [str(staged_file)]) - assert False, "Expected SessionFilePreparationError" - except main_module.SessionFilePreparationError as exc: - assert "already staged" in str(exc) def test_main_passes_cli_api_key_and_reconfigures_logger(monkeypatch): - original_argv = sys.argv[:] state = {"session_initialized": False, "setup_logger_states": []} captured = {} @@ -121,7 +112,7 @@ def fake_process_workflow(workflow, question): captured["workflow"] = workflow captured["question"] = question - monkeypatch.setattr(sys, "argv", ["prog", "-c", "hello", "--api-key", "cli-key"]) + monkeypatch.setattr("sys.argv", ["prog", "-c", "hello", "--api-key", "cli-key"]) monkeypatch.setattr(main_module, "logger", DummyLogger()) monkeypatch.setattr(main_module, "setup_logger", fake_setup_logger) monkeypatch.setattr(main_module, "initialize_session_context", fake_initialize_session_context) @@ -131,10 +122,7 @@ def fake_process_workflow(workflow, question): monkeypatch.setattr(main_module, "create_workflow", fake_create_workflow) monkeypatch.setattr(main_module, "process_workflow", fake_process_workflow) - try: - main_module.main() - finally: - monkeypatch.setattr(sys, "argv", original_argv) + main_module.main() assert captured["llm_api_key"] == "cli-key" assert captured["workflow_api_key"] == "cli-key" @@ -145,9 +133,7 @@ def fake_process_workflow(workflow, question): def test_main_prints_user_friendly_error_for_bad_staged_file(monkeypatch, capsys): - original_argv = sys.argv[:] - - monkeypatch.setattr(sys, "argv", ["prog", "-c", "hello", "-f", "/missing/file.csv"]) + monkeypatch.setattr("sys.argv", ["prog", "-c", "hello", "-f", "/missing/file.csv"]) monkeypatch.setattr(main_module, "logger", DummyLogger()) monkeypatch.setattr(main_module, "setup_logger", lambda name: DummyLogger()) monkeypatch.setattr(main_module, "initialize_session_context", lambda session_id: None) @@ -163,10 +149,7 @@ def test_main_prints_user_friendly_error_for_bad_staged_file(monkeypatch, capsys ) monkeypatch.setattr(main_module, "create_workflow", lambda *args, **kwargs: (_ for _ in ()).throw(AssertionError("workflow should not run"))) - try: - main_module.main() - finally: - monkeypatch.setattr(sys, "argv", original_argv) + main_module.main() captured = capsys.readouterr() assert "Error: File not found: /missing/file.csv" in captured.out diff --git a/app/core/tests/test_sandbox_runner.py b/app/core/tests/test_sandbox_runner.py index 11933ee..ddaa290 100644 --- a/app/core/tests/test_sandbox_runner.py +++ b/app/core/tests/test_sandbox_runner.py @@ -1,34 +1,69 @@ from __future__ import annotations -from app.core.agents.interpreter.sandbox_runner import execute_user_code, validate_code +import json +import subprocess +import sys +from pathlib import Path + +import pytest + +from app.core.agents.interpreter import sandbox_runner +from app.core.agents.interpreter.sandbox_runner import validate_code def test_validate_code_rejects_dunder_attribute_access(): - try: + with pytest.raises(ValueError) as excinfo: validate_code("x = object().__class__") - assert False, "Expected ValueError" - except ValueError as exc: - assert "Dunder attribute access" in str(exc) + assert "Dunder attribute access" in str(excinfo.value) -def test_execute_user_code_returns_captured_output_on_exception(tmp_path): +def test_execute_user_code_returns_captured_output_on_exception_from_subprocess(tmp_path): session_dir = tmp_path / "session" session_dir.mkdir() input_file = session_dir / "input.csv" input_file.write_text("a,b\n1,2\n", encoding="utf-8") - result = execute_user_code( - { - "code": "print('hello before error')\nraise ValueError('boom')", - "session_dir": str(session_dir), - "allowed_input_paths": [str(input_file)], - "timeout_seconds": 5, - "cpu_seconds": 5, - "memory_bytes": 128 * 1024 * 1024, - "file_size_bytes": 10 * 1024 * 1024, - } + payload_path = tmp_path / "payload.json" + result_path = tmp_path / "result.json" + payload_path.write_text( + json.dumps( + { + "code": "print('hello before error')\nraise ValueError('boom')", + "session_dir": str(session_dir), + "allowed_input_paths": [str(input_file)], + "timeout_seconds": 5, + "cpu_seconds": 5, + "memory_bytes": 128 * 1024 * 1024, + "file_size_bytes": 10 * 1024 * 1024, + } + ), + encoding="utf-8", ) + completed = subprocess.run( + [ + sys.executable, + "-I", + str(Path(sandbox_runner.__file__).resolve()), + "--payload", + str(payload_path), + "--result", + str(result_path), + ], + cwd=session_dir, + env={ + "HOME": str(session_dir), + "PYTHONIOENCODING": "utf-8", + "TMPDIR": str(session_dir), + }, + capture_output=True, + text=True, + ) + + assert completed.returncode == 1 + assert result_path.exists(), completed.stderr or completed.stdout + + result = json.loads(result_path.read_text(encoding="utf-8")) assert result["success"] is False assert "hello before error" in result["stdout"] assert result["error"] == "boom" diff --git a/app/core/tests/test_sparql_tool.py b/app/core/tests/test_sparql_tool.py index 840b29a..6b95ae0 100644 --- a/app/core/tests/test_sparql_tool.py +++ b/app/core/tests/test_sparql_tool.py @@ -3,9 +3,7 @@ def _construct_tool(**kwargs): - if hasattr(tool_sparql.GraphSparqlQAChain, "model_construct"): - return tool_sparql.GraphSparqlQAChain.model_construct(**kwargs) - return tool_sparql.GraphSparqlQAChain.construct(**kwargs) + return tool_sparql.GraphSparqlQAChain.model_construct(**kwargs) def test_create_agent_passes_openai_key_to_import_tools(monkeypatch): @@ -82,3 +80,47 @@ def fake_load_local(path, embeddings, allow_dangerous_deserialization=False): assert captured["limit"] == 12 assert captured["allow_dangerous_deserialization"] is True assert result == ["related-node"] + + +def test_search_nodes_falls_back_to_env_openai_key(monkeypatch): + captured = {} + + class FakeLLMChain: + def __init__(self, llm, prompt): + self.llm = llm + self.prompt = prompt + + class FakeEmbeddings: + def __init__(self, api_key=None): + captured["api_key"] = api_key + + class FakeVectorStore: + def similarity_search(self, query, limit): + captured["query"] = query + captured["limit"] = limit + return ["related-node"] + + def fake_load_local(path, embeddings, allow_dangerous_deserialization=False): + captured["db_path"] = path + captured["embeddings"] = embeddings + captured["allow_dangerous_deserialization"] = allow_dangerous_deserialization + return FakeVectorStore() + + monkeypatch.setenv("OPENAI_API_KEY", "env-key") + monkeypatch.setattr(tool_sparql, "LLMChain", FakeLLMChain) + monkeypatch.setattr(tool_sparql, "OpenAIEmbeddings", FakeEmbeddings) + monkeypatch.setattr(tool_sparql.FAISS, "load_local", fake_load_local) + + tool = tool_sparql.GraphSparqlQAChain( + llm={"llm_o": object(), "llm_o3_mini": object()}, + graph=object(), + session_id="session-123", + openai_key=None, + ) + result = tool.search_nodes("SELECT * WHERE { ?s ?p ?o }") + + assert captured["api_key"] == "env-key" + assert captured["query"] == "SELECT * WHERE { ?s ?p ?o }" + assert captured["limit"] == 12 + assert captured["allow_dangerous_deserialization"] is True + assert result == ["related-node"] From 4bd7a174e0f93361a04fdf3c6e4e974450d26c35 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Louis-F=C3=A9lix=20Nothias?= Date: Sat, 25 Apr 2026 19:00:17 +0200 Subject: [PATCH 19/21] Update pr_agent.yml --- .github/workflows/pr_agent.yml | 3 --- 1 file changed, 3 deletions(-) diff --git a/.github/workflows/pr_agent.yml b/.github/workflows/pr_agent.yml index b5a782c..8fd3f8f 100644 --- a/.github/workflows/pr_agent.yml +++ b/.github/workflows/pr_agent.yml @@ -6,9 +6,6 @@ on: branches: - main - dev - - dev_lfx - - dev_madina - - dev_Mkdocs workflow_dispatch: inputs: branch: From 10af7b651ce8f93bef5e3353dab2c2986acaab28 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Louis-F=C3=A9lix=20Nothias?= Date: Sat, 25 Apr 2026 21:28:50 +0200 Subject: [PATCH 20/21] fix: Add timeout handling for subprocess in test_execute_user_code --- app/core/tests/test_sandbox_runner.py | 42 +++++++++++++++------------ 1 file changed, 23 insertions(+), 19 deletions(-) diff --git a/app/core/tests/test_sandbox_runner.py b/app/core/tests/test_sandbox_runner.py index ddaa290..8a15d88 100644 --- a/app/core/tests/test_sandbox_runner.py +++ b/app/core/tests/test_sandbox_runner.py @@ -40,25 +40,29 @@ def test_execute_user_code_returns_captured_output_on_exception_from_subprocess( encoding="utf-8", ) - completed = subprocess.run( - [ - sys.executable, - "-I", - str(Path(sandbox_runner.__file__).resolve()), - "--payload", - str(payload_path), - "--result", - str(result_path), - ], - cwd=session_dir, - env={ - "HOME": str(session_dir), - "PYTHONIOENCODING": "utf-8", - "TMPDIR": str(session_dir), - }, - capture_output=True, - text=True, - ) + try: + completed = subprocess.run( + [ + sys.executable, + "-I", + str(Path(sandbox_runner.__file__).resolve()), + "--payload", + str(payload_path), + "--result", + str(result_path), + ], + cwd=session_dir, + env={ + "HOME": str(session_dir), + "PYTHONIOENCODING": "utf-8", + "TMPDIR": str(session_dir), + }, + capture_output=True, + text=True, + timeout=8, + ) + except subprocess.TimeoutExpired as exc: + pytest.fail(f"Sandbox runner subprocess hung unexpectedly: {exc}") assert completed.returncode == 1 assert result_path.exists(), completed.stderr or completed.stdout From b70828053edb886145f48d8fa1218e690dead397 Mon Sep 17 00:00:00 2001 From: madina1203 Date: Sun, 26 Apr 2026 20:10:10 +0200 Subject: [PATCH 21/21] Add how to run evaluation.py (LangSmith api key is needed) --- README.md | 4 ++++ app/core/tests/evaluation.py | 2 +- docs/examples/langsmith-evaluation.md | 20 ++++++++++++++++++++ docs/user-guide/configuration.md | 2 ++ 4 files changed, 27 insertions(+), 1 deletion(-) create mode 100644 docs/examples/langsmith-evaluation.md diff --git a/README.md b/README.md index a589a7d..3128c0c 100644 --- a/README.md +++ b/README.md @@ -168,6 +168,10 @@ python -m app.core.main -c "Which lab extracts show inhibition above 50% against MetaboT saves all result sets to CSV files in a temporary folder and returns the file path. When results are small, they are also displayed inline; for large result sets, only the file path is returned to avoid exceeding the LLM context window. +### LangSmith automated evaluation (benchmark) + +This repository includes a LangSmith-based automated evaluation script at `app/core/tests/evaluation.py`. To run it locally you need a LangSmith API key (`LANGCHAIN_API_KEY` or `LANGSMITH_API_KEY`) and an LLM provider key (e.g. `OPENAI_API_KEY`). See [docs/examples/langsmith-evaluation.md](docs/examples/langsmith-evaluation.md). + ### Streamlit web app The repository also includes a Streamlit interface: diff --git a/app/core/tests/evaluation.py b/app/core/tests/evaluation.py index 2e06f56..ccd171e 100644 --- a/app/core/tests/evaluation.py +++ b/app/core/tests/evaluation.py @@ -24,7 +24,7 @@ openai_key = os.getenv("OPENAI_API_KEY") if not api_key: - raise ValueError("Missing LANGCHAIN_API_KEY. Please copy .env.template to .env and add your key.") + raise ValueError("Missing LANGCHAIN_API_KEY (or LANGSMITH_API_KEY). Please add it to your repo-root .env (see docs/user-guide/configuration.md).") if not openai_key: raise ValueError("Missing OPENAI_API_KEY. Please copy .env.template to .env and add your key.") diff --git a/docs/examples/langsmith-evaluation.md b/docs/examples/langsmith-evaluation.md new file mode 100644 index 0000000..c6a489c --- /dev/null +++ b/docs/examples/langsmith-evaluation.md @@ -0,0 +1,20 @@ +# LangSmith automated evaluation + +To run the automated evaluation you need a **LangSmith API key**. Create an account and generate an API key using the LangSmith docs: [Create an account and API key](https://docs.langchain.com/langsmith/create-account-api-key). + +Add the key to your repo-root `.env` (do not commit it), along with your LLM provider key: + +```env +LANGCHAIN_API_KEY=your_langsmith_key # or LANGSMITH_API_KEY +LANGCHAIN_PROJECT=MetaboT_Test_Run +OPENAI_API_KEY=your_openai_key +``` + +Then run (from the repository root): + +```bash +python -m app.core.tests.evaluation +``` + +This starts the benchmark evaluation (running MetaboT on ~50 questions). LLM-based evaluators compare the generated answers with the reference answers. A link to the evaluation run will appear once the evaluation starts, and you can also find it later in your LangSmith account. + diff --git a/docs/user-guide/configuration.md b/docs/user-guide/configuration.md index 85afe7b..f225e74 100644 --- a/docs/user-guide/configuration.md +++ b/docs/user-guide/configuration.md @@ -142,6 +142,8 @@ LANGCHAIN_ENDPOINT=https://api.smith.langchain.com If no tracing key is present, MetaboT disables tracing automatically. +For running the repository's LangSmith-based automated benchmark evaluation, see [docs/examples/langsmith-evaluation.md](../examples/langsmith-evaluation.md). + ## SPARQL and Schema Configuration `app/config/sparql.ini` contains helper queries used to inspect or work with the graph schema. These are especially important when MetaboT needs to construct schema-aware prompts from an endpoint rather than from assumptions.