From 6516afa516ab2e24215e69b179118c856c4914f2 Mon Sep 17 00:00:00 2001 From: Mouna Cheikho Date: Fri, 10 Jul 2026 12:43:07 +0200 Subject: [PATCH 01/23] Patched HTTP dependency alerts --- requirements.txt | 8 +-- tests/test_external_service_integration.py | 69 ++++++++++++++-------- 2 files changed, 50 insertions(+), 27 deletions(-) diff --git a/requirements.txt b/requirements.txt index d77c6d2d..6c4085ad 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,9 +1,9 @@ aiofiles==23.2.1 aioresponses==0.7.7 -aiohappyeyeballs==2.4.4 -aiohttp==3.11.11 +aiohappyeyeballs==2.5.0 +aiohttp==3.14.1 aioitertools==0.12.0 -aiosignal==1.3.2 +aiosignal==1.4.0 aiosqlite==0.21.0 alembic==1.15.1 altair==5.5.0 @@ -168,7 +168,7 @@ pytest-env==1.1.1 pytest-postgresql==7.0.2 python-dateutil==2.9.0.post0 python-dotenv==1.0.1 -python-multipart==0.0.20 +python-multipart==0.0.31 pytz==2025.1 PyYAML==6.0.1 referencing==0.36.2 diff --git a/tests/test_external_service_integration.py b/tests/test_external_service_integration.py index cc997b5e..195851d7 100644 --- a/tests/test_external_service_integration.py +++ b/tests/test_external_service_integration.py @@ -14,7 +14,6 @@ from unittest.mock import AsyncMock, MagicMock, Mock, patch import pytest -from aioresponses import aioresponses from report_analyst_search_backend.external_service_client import ( ExternalServiceClient, @@ -204,6 +203,17 @@ def test_chunks_match_format(self, external_handler, sample_chunks): class TestExternalServiceClient: """Tests for ExternalServiceClient""" + def _mock_http_response(self, status=200, payload=None, text=""): + response = AsyncMock() + response.status = status + response.json = AsyncMock(return_value=payload or {}) + response.text = AsyncMock(return_value=text) + + context = AsyncMock() + context.__aenter__.return_value = response + context.__aexit__.return_value = None + return context, response + @pytest.mark.asyncio async def test_notify_ready_nats(self, external_client): """Test notifying via NATS""" @@ -227,11 +237,14 @@ async def test_notify_ready_nats(self, external_client): @pytest.mark.asyncio async def test_notify_ready_http(self, external_client): """Test notifying via HTTP""" - with aioresponses() as m: - m.post( - "http://localhost:8000/external/services/service-x/notify", - status=200, - ) + response_context, _ = self._mock_http_response(status=200) + session = Mock() + session.post.return_value = response_context + session_context = AsyncMock() + session_context.__aenter__.return_value = session + session_context.__aexit__.return_value = None + + with patch("aiohttp.ClientSession", return_value=session_context): result = await external_client.notify_ready( service_id="service-x", request_id="req-123", @@ -240,16 +253,19 @@ async def test_notify_ready_http(self, external_client): method="http", ) assert result is True + session.post.assert_called_once() @pytest.mark.asyncio async def test_request_analysis_http(self, external_client): """Test requesting analysis via HTTP""" - with aioresponses() as m: - m.post( - "http://localhost:8000/external/services/service-x/analyze", - status=200, - payload={"request_id": "analysis-123"}, - ) + response_context, _ = self._mock_http_response(status=200, payload={"request_id": "analysis-123"}) + session = Mock() + session.post.return_value = response_context + session_context = AsyncMock() + session_context.__aenter__.return_value = session + session_context.__aexit__.return_value = None + + with patch("aiohttp.ClientSession", return_value=session_context): request_id = await external_client.request_analysis( service_id="service-x", external_request_id="req-123", @@ -259,25 +275,32 @@ async def test_request_analysis_http(self, external_client): method="http", ) assert request_id == "analysis-123" + session.post.assert_called_once() @pytest.mark.asyncio async def test_get_results(self, external_client): """Test polling for results""" - with aioresponses() as m: - m.get( - "http://localhost:8000/external/services/service-x/results/analysis-123", - status=200, - payload={ - "request_id": "analysis-123", - "status": "completed", - "answers": [], - "top_chunks": [], - }, - ) + response_context, _ = self._mock_http_response( + status=200, + payload={ + "request_id": "analysis-123", + "status": "completed", + "answers": [], + "top_chunks": [], + }, + ) + session = Mock() + session.get.return_value = response_context + session_context = AsyncMock() + session_context.__aenter__.return_value = session + session_context.__aexit__.return_value = None + + with patch("aiohttp.ClientSession", return_value=session_context): results = await external_client.get_results("service-x", "analysis-123") assert results is not None assert results["status"] == "completed" + session.get.assert_called_once() class TestExternalServiceDelivery: From fed79517e20d88c5e2c62515d19a03da220b2fdf Mon Sep 17 00:00:00 2001 From: Mouna Cheikho Date: Fri, 10 Jul 2026 13:24:01 +0200 Subject: [PATCH 02/23] Patched crypto dependency alerts --- requirements.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index d77c6d2d..21c24f14 100644 --- a/requirements.txt +++ b/requirements.txt @@ -21,14 +21,14 @@ blinker==1.9.0 build==1.2.2.post1 cachetools==5.5.1 certifi==2024.12.14 -cffi==1.17.1 +cffi==2.1.0 charset-normalizer==3.4.1 chroma-hnswlib==0.7.3 chromadb==0.4.24 click==8.1.8 coloredlogs==15.0.1 contourpy==1.3.1 -cryptography==44.0.2 +cryptography==48.0.1 cycler==0.12.1 dataclasses-json==0.6.7 Deprecated==1.2.18 From a6ed6b6d25dc67e064b0a8bf12bab851c91ab8c2 Mon Sep 17 00:00:00 2001 From: Mouna Cheikho Date: Fri, 10 Jul 2026 14:58:55 +0200 Subject: [PATCH 03/23] Patched PDF parsing dependencies --- requirements.txt | 30 +++++++++++------------------- 1 file changed, 11 insertions(+), 19 deletions(-) diff --git a/requirements.txt b/requirements.txt index d77c6d2d..9e5edf3a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -87,20 +87,12 @@ langchain-text-splitters==0.3.6 langsmith==0.3.13 llama-cloud==0.1.11 llama-cloud-services==0.6.0 -llama-index==0.12.16 -llama-index-agent-openai==0.4.3 -llama-index-cli==0.4.0 -llama-index-core==0.12.16.post1 -llama-index-embeddings-openai==0.3.1 -llama-index-indices-managed-llama-cloud==0.6.4 -llama-index-llms-gemini==0.4.12 -llama-index-llms-openai==0.3.18 -llama-index-multi-modal-llms-openai==0.4.3 -llama-index-program-openai==0.3.1 -llama-index-question-gen-openai==0.3.0 -llama-index-readers-file==0.4.4 -llama-index-readers-llama-parse==0.4.0 -llama-parse==0.6.0 +llama-index-cli==0.5.1 +llama-index-core==0.13.6 +llama-index-embeddings-openai==0.5.2 +llama-index-llms-gemini==0.6.0 +llama-index-llms-openai==0.5.6 +llama-index-readers-file==0.5.6 lxml==5.3.1 Mako==1.3.9 Markdown==3.5.2 @@ -123,7 +115,7 @@ nltk==3.9.1 numpy==1.26.4 oauthlib==3.2.2 onnxruntime==1.20.1 -openai==1.61.1 +openai==1.81.0 openinference-instrumentation==0.1.23 openinference-instrumentation-langchain==0.1.35 openinference-semantic-conventions==0.1.14 @@ -147,9 +139,9 @@ pyasn1==0.6.1 pyasn1_modules==0.4.1 pycodestyle==2.12.1 pycparser==2.22 -pydantic==2.10.4 +pydantic==2.11.5 pydantic-settings==2.8.1 -pydantic_core==2.27.2 +pydantic_core==2.33.2 pydeck==0.9.1 pydub==0.25.1 pyflakes==3.2.0 @@ -157,7 +149,7 @@ Pygments==2.19.1 PyMuPDF==1.24.0 PyMuPDFb==1.24.0 pyparsing==3.2.1 -pypdf==5.2.0 +pypdf==6.13.3 PyPika==0.48.9 pyproject_hooks==1.2.0 psycopg2-binary==2.9.10 @@ -185,7 +177,7 @@ safehttpx==0.1.6 scikit-learn==1.6.1 scipy==1.15.2 semantic-version==2.10.0 -setuptools==75.8.0 +setuptools==80.9.0 shellingham==1.5.4 six==1.17.0 smmap==5.0.2 From 4ecacb270d063c39380b4cf5934268c73e446a68 Mon Sep 17 00:00:00 2001 From: Mouna Cheikho Date: Wed, 15 Jul 2026 14:54:39 +0200 Subject: [PATCH 04/23] Fixed Streamlit widget state --- report_analyst/streamlit_app.py | 45 ++++++++++++++++++++------------- 1 file changed, 28 insertions(+), 17 deletions(-) diff --git a/report_analyst/streamlit_app.py b/report_analyst/streamlit_app.py index bf99d569..072de346 100644 --- a/report_analyst/streamlit_app.py +++ b/report_analyst/streamlit_app.py @@ -1346,6 +1346,8 @@ def update_analyzer_parameters(): chunk_overlap = st.session_state.new_overlap top_k = st.session_state.new_top_k llm_model = st.session_state.new_llm_model + st.session_state.new_llm_scoring = st.session_state.get("llm_scoring_widget", st.session_state.new_llm_scoring) + st.session_state.new_batch_scoring = st.session_state.get("batch_scoring_widget", st.session_state.new_batch_scoring) available_models = get_available_llm_models() if not available_models: @@ -1554,6 +1556,19 @@ def main(): # Sync API keys from session state to environment at startup APIKeyManager.sync_api_keys_to_env(st.session_state) + # These keys are normally created by the analysis configuration widgets, + # but upload/cache code can read them before the widgets render. + available_models = get_available_llm_models() + default_model = available_models[0] if available_models else OPENAI_MODELS[0] + default_question_set = list(question_sets.keys())[0] if question_sets else "tcfd" + st.session_state.setdefault("new_chunk_size", 500) + st.session_state.setdefault("new_overlap", 20) + st.session_state.setdefault("new_top_k", 5) + st.session_state.setdefault("new_llm_model", default_model) + st.session_state.setdefault("new_llm_scoring", False) + st.session_state.setdefault("new_batch_scoring", True) + st.session_state.setdefault("new_question_set", default_question_set) + st.set_page_config(page_title="Report Analyst", layout="wide") # Inject Material Icons link tag at the top @@ -3340,7 +3355,6 @@ def main(): options=list(question_sets.keys()), format_func=lambda x: question_sets[x]["name"], key="new_question_set", - index=0, # Ensure a default is selected on_change=update_analyzer_parameters, ) @@ -3624,7 +3638,6 @@ def main(): "Top K", min_value=1, max_value=20, - value=5, # Default value key="new_top_k", on_change=update_analyzer_parameters, ) @@ -3633,7 +3646,6 @@ def main(): "Chunk Size", min_value=100, max_value=2000, - value=500, # Default value key="new_chunk_size", on_change=update_analyzer_parameters, ) @@ -3642,7 +3654,6 @@ def main(): "Overlap", min_value=0, max_value=100, - value=20, # Default value key="new_overlap", on_change=update_analyzer_parameters, ) @@ -3651,13 +3662,11 @@ def main(): available_llm_models = get_available_llm_models() if available_llm_models: current_model = st.session_state.get("new_llm_model") - selected_index = ( - available_llm_models.index(current_model) if current_model in available_llm_models else 0 - ) + if current_model not in available_llm_models: + st.session_state.new_llm_model = available_llm_models[0] new_llm_model = st.selectbox( "LLM Model", options=available_llm_models, - index=selected_index, key="new_llm_model", on_change=update_analyzer_parameters, ) @@ -3671,18 +3680,20 @@ def main(): new_llm_scoring = st.checkbox( "LLM Scoring", - value=False, - key="new_llm_scoring", + value=st.session_state.get("new_llm_scoring", False), + key="llm_scoring_widget", on_change=update_analyzer_parameters, ) - new_batch_scoring = st.checkbox( - "Batch Scoring", - value=True, - key="new_batch_scoring", - disabled=not st.session_state.get("new_llm_scoring", False), - help="Batch scoring only applies when LLM scoring is enabled.", - ) + if new_llm_scoring: + new_batch_scoring = st.checkbox( + "Batch Scoring", + value=st.session_state.get("new_batch_scoring", True), + key="batch_scoring_widget", + on_change=update_analyzer_parameters, + ) + else: + new_batch_scoring = st.session_state.get("new_batch_scoring", True) # Update analyzer's question set analyzer.analyzer.update_question_set(selected_set) From 782393df9a95ff36c2f34557a33b25a9f6cba0ba Mon Sep 17 00:00:00 2001 From: Mouna Cheikho Date: Thu, 16 Jul 2026 14:45:37 +0200 Subject: [PATCH 05/23] Patched utility dependencies --- requirements.txt | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/requirements.txt b/requirements.txt index d77c6d2d..4b3dcc8d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -39,11 +39,11 @@ durationpy==0.9 faiss-cpu==1.12.0 fastapi==0.115.11 ffmpy==0.5.0 -filelock==3.17.0 +filelock==3.20.3 filetype==1.2.0 flake8==7.1.2 flatbuffers==25.1.24 -fonttools==4.55.8 +fonttools==4.60.2 frozenlist==1.5.0 fsspec==2025.2.0 gitdb==4.0.12 @@ -65,7 +65,7 @@ httpx==0.27.0 httpx-sse==0.4.0 huggingface-hub==0.28.1 humanfriendly==10.0 -idna==3.10 +idna==3.15 importlib_metadata==7.2.1 importlib_resources==6.5.2 iniconfig==2.0.0 @@ -102,11 +102,11 @@ llama-index-readers-file==0.4.4 llama-index-readers-llama-parse==0.4.0 llama-parse==0.6.0 lxml==5.3.1 -Mako==1.3.9 -Markdown==3.5.2 +Mako==1.3.12 +Markdown==3.8.1 markdown-it-py==3.0.0 MarkupSafe==2.1.5 -marshmallow==3.26.1 +marshmallow==3.26.2 matplotlib==3.10.0 mccabe==0.7.0 mdurl==0.1.2 @@ -127,7 +127,7 @@ openai==1.61.1 openinference-instrumentation==0.1.23 openinference-instrumentation-langchain==0.1.35 openinference-semantic-conventions==0.1.14 -orjson==3.10.15 +orjson==3.11.6 overrides==7.7.0 packaging==23.2 pandas==2.2.3 @@ -143,7 +143,7 @@ protobuf==4.25.3 psutil==7.0.0 pulsar-client==3.6.1 pyarrow==19.0.0 -pyasn1==0.6.1 +pyasn1==0.6.3 pyasn1_modules==0.4.1 pycodestyle==2.12.1 pycparser==2.22 @@ -153,7 +153,7 @@ pydantic_core==2.27.2 pydeck==0.9.1 pydub==0.25.1 pyflakes==3.2.0 -Pygments==2.19.1 +Pygments==2.20.0 PyMuPDF==1.24.0 PyMuPDFb==1.24.0 pyparsing==3.2.1 @@ -167,7 +167,7 @@ pytest-cov==7.0.0 pytest-env==1.1.1 pytest-postgresql==7.0.2 python-dateutil==2.9.0.post0 -python-dotenv==1.0.1 +python-dotenv==1.2.2 python-multipart==0.0.20 pytz==2025.1 PyYAML==6.0.1 @@ -190,7 +190,7 @@ shellingham==1.5.4 six==1.17.0 smmap==5.0.2 sniffio==1.3.1 -soupsieve==2.6 +soupsieve==2.8.4 SQLAlchemy==2.0.37 sqlean.py==3.47.0 sqlite-vec==0.1.6 From 726f52a03707a9310911a7d1c0f43fde5970831c Mon Sep 17 00:00:00 2001 From: Mouna Cheikho Date: Thu, 16 Jul 2026 16:42:08 +0200 Subject: [PATCH 06/23] Patched authlib dependency --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index d77c6d2d..31b9289f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -11,7 +11,7 @@ annotated-types==0.7.0 anyio==4.8.0 asgiref==3.8.1 attrs==25.1.0 -Authlib==1.5.1 +Authlib==1.6.12 backoff==2.2.1 bcrypt==4.2.1 beautifulsoup4==4.13.3 From a297e72b528ad70721a5fb4ad3f6841d2a376c69 Mon Sep 17 00:00:00 2001 From: Mouna Cheikho Date: Fri, 17 Jul 2026 16:04:48 +0200 Subject: [PATCH 07/23] Patched pytest dependencies --- requirements.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index d77c6d2d..7302d846 100644 --- a/requirements.txt +++ b/requirements.txt @@ -161,8 +161,8 @@ pypdf==5.2.0 PyPika==0.48.9 pyproject_hooks==1.2.0 psycopg2-binary==2.9.10 -pytest==8.3.5 -pytest-asyncio==0.25.3 +pytest==9.0.3 +pytest-asyncio==1.4.0 pytest-cov==7.0.0 pytest-env==1.1.1 pytest-postgresql==7.0.2 From 27ae74004b9a25249f4f77f199bf3bebe1e970c5 Mon Sep 17 00:00:00 2001 From: Mouna Cheikho Date: Fri, 17 Jul 2026 17:14:24 +0200 Subject: [PATCH 08/23] Patched lxml dependency --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index d77c6d2d..708782dd 100644 --- a/requirements.txt +++ b/requirements.txt @@ -101,7 +101,7 @@ llama-index-question-gen-openai==0.3.0 llama-index-readers-file==0.4.4 llama-index-readers-llama-parse==0.4.0 llama-parse==0.6.0 -lxml==5.3.1 +lxml==6.1.0 Mako==1.3.9 Markdown==3.5.2 markdown-it-py==3.0.0 From 8102a50eda1edb87df12b24388e2a8b2dac75369 Mon Sep 17 00:00:00 2001 From: Mouna Cheikho Date: Mon, 20 Jul 2026 16:40:13 +0200 Subject: [PATCH 09/23] Removed legacy Gradio interface --- .gradio/certificate.pem | 31 ----- package.json | 2 +- report_analyst/gradio_app.py | 247 ----------------------------------- requirements.txt | 2 - 4 files changed, 1 insertion(+), 281 deletions(-) delete mode 100644 .gradio/certificate.pem delete mode 100644 report_analyst/gradio_app.py diff --git a/.gradio/certificate.pem b/.gradio/certificate.pem deleted file mode 100644 index b85c8037..00000000 --- a/.gradio/certificate.pem +++ /dev/null @@ -1,31 +0,0 @@ ------BEGIN CERTIFICATE----- -MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw -TzELMAkGA1UEBhMCVVMxKTAnBgNVBAoTIEludGVybmV0IFNlY3VyaXR5IFJlc2Vh -cmNoIEdyb3VwMRUwEwYDVQQDEwxJU1JHIFJvb3QgWDEwHhcNMTUwNjA0MTEwNDM4 -WhcNMzUwNjA0MTEwNDM4WjBPMQswCQYDVQQGEwJVUzEpMCcGA1UEChMgSW50ZXJu -ZXQgU2VjdXJpdHkgUmVzZWFyY2ggR3JvdXAxFTATBgNVBAMTDElTUkcgUm9vdCBY -MTCCAiIwDQYJKoZIhvcNAQEBBQADggIPADCCAgoCggIBAK3oJHP0FDfzm54rVygc -h77ct984kIxuPOZXoHj3dcKi/vVqbvYATyjb3miGbESTtrFj/RQSa78f0uoxmyF+ -0TM8ukj13Xnfs7j/EvEhmkvBioZxaUpmZmyPfjxwv60pIgbz5MDmgK7iS4+3mX6U -A5/TR5d8mUgjU+g4rk8Kb4Mu0UlXjIB0ttov0DiNewNwIRt18jA8+o+u3dpjq+sW -T8KOEUt+zwvo/7V3LvSye0rgTBIlDHCNAymg4VMk7BPZ7hm/ELNKjD+Jo2FR3qyH -B5T0Y3HsLuJvW5iB4YlcNHlsdu87kGJ55tukmi8mxdAQ4Q7e2RCOFvu396j3x+UC -B5iPNgiV5+I3lg02dZ77DnKxHZu8A/lJBdiB3QW0KtZB6awBdpUKD9jf1b0SHzUv -KBds0pjBqAlkd25HN7rOrFleaJ1/ctaJxQZBKT5ZPt0m9STJEadao0xAH0ahmbWn -OlFuhjuefXKnEgV4We0+UXgVCwOPjdAvBbI+e0ocS3MFEvzG6uBQE3xDk3SzynTn -jh8BCNAw1FtxNrQHusEwMFxIt4I7mKZ9YIqioymCzLq9gwQbooMDQaHWBfEbwrbw -qHyGO0aoSCqI3Haadr8faqU9GY/rOPNk3sgrDQoo//fb4hVC1CLQJ13hef4Y53CI -rU7m2Ys6xt0nUW7/vGT1M0NPAgMBAAGjQjBAMA4GA1UdDwEB/wQEAwIBBjAPBgNV -HRMBAf8EBTADAQH/MB0GA1UdDgQWBBR5tFnme7bl5AFzgAiIyBpY9umbbjANBgkq -hkiG9w0BAQsFAAOCAgEAVR9YqbyyqFDQDLHYGmkgJykIrGF1XIpu+ILlaS/V9lZL -ubhzEFnTIZd+50xx+7LSYK05qAvqFyFWhfFQDlnrzuBZ6brJFe+GnY+EgPbk6ZGQ -3BebYhtF8GaV0nxvwuo77x/Py9auJ/GpsMiu/X1+mvoiBOv/2X/qkSsisRcOj/KK -NFtY2PwByVS5uCbMiogziUwthDyC3+6WVwW6LLv3xLfHTjuCvjHIInNzktHCgKQ5 -ORAzI4JMPJ+GslWYHb4phowim57iaztXOoJwTdwJx4nLCgdNbOhdjsnvzqvHu7Ur -TkXWStAmzOVyyghqpZXjFaH3pO3JLF+l+/+sKAIuvtd7u+Nxe5AW0wdeRlN8NwdC -jNPElpzVmbUq4JUagEiuTDkHzsxHpFKVK7q4+63SM1N95R1NbdWhscdCb+ZAJzVc -oyi3B43njTOQ5yOf+1CceWxG1bQVs5ZufpsMljq4Ui0/1lvh+wjChP4kqKOJ2qxq -4RgqsahDYVvTH9w7jXbyLeiNdd8XM2w9U/t7y0Ff/9yi0GE44Za4rF2LN9d11TPA -mRGunUHBcnWEvgJBQl9nJEiU0Zsnvgc/ubhPgXRR4Xq37Z0j4r7g1SgEEzwxA57d -emyPxgcYxn/eR44/KJ4EBs+lVDR3veyJm+kXQ99b21/+jh5Xos1AnX5iItreGCc= ------END CERTIFICATE----- diff --git a/package.json b/package.json index f245b85f..3317d6bd 100644 --- a/package.json +++ b/package.json @@ -1 +1 @@ -{"name":"report-analyst","version":"0.8.0-rc","scripts":{"backend":"uvicorn app.main:app --reload --port 8000","frontend":"python app/gradio_app.py","dev":"pnpm run backend & pnpm run frontend","clean":"mkdir -p data/output data/input && rm -rf data/output/* && rm -f backend.log frontend.log"}} +{"name":"report-analyst","version":"0.8.0-rc","scripts":{"backend":"uvicorn app.main:app --reload --port 8000","clean":"mkdir -p data/output data/input && rm -rf data/output/* && rm -f backend.log frontend.log"}} diff --git a/report_analyst/gradio_app.py b/report_analyst/gradio_app.py deleted file mode 100644 index 705f1aa9..00000000 --- a/report_analyst/gradio_app.py +++ /dev/null @@ -1,247 +0,0 @@ -import json -import logging -import os -import shutil -import tempfile -import uuid -from pathlib import Path -from typing import AsyncGenerator, Dict, List - -import gradio as gr -from core.analyzer import DocumentAnalyzer -from dotenv import load_dotenv - -# Load environment variables -load_dotenv() - -# Configure logging -logging.basicConfig( - level=logging.INFO, - format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", - handlers=[ - logging.StreamHandler(), # Log to console - logging.FileHandler("app.log"), # Log to file - ], -) -logger = logging.getLogger(__name__) - - -class DocumentService: - def __init__(self): - self.analyzer = DocumentAnalyzer() - # Get valid question IDs from the loaded questions - self.valid_question_ids = list(range(1, len(self.analyzer.questions["TCFD Analysis"]["questions"]) + 1)) - logger.info(f"Initialized with {len(self.valid_question_ids)} valid question IDs") - - def validate_question_ids(self, question_ids: List[int]) -> List[int]: - """Validate and filter question IDs""" - if not question_ids: - raise ValueError("No questions selected") - - valid_ids = [qid for qid in question_ids if qid in self.valid_question_ids] - if not valid_ids: - raise ValueError("No valid questions selected") - - logger.info(f"Validated question IDs: {valid_ids}") - return valid_ids - - async def process_document(self, file_path: str, question_ids: List[int] = None) -> AsyncGenerator[Dict, None]: - """Process uploaded document and stream analysis results""" - if not file_path: - yield {"error": "No file uploaded"} - return - - try: - # Validate question IDs only if they are provided - if question_ids is not None: - question_ids = self.validate_question_ids(question_ids) - logger.info(f"Processing questions: {question_ids}") - else: - # If no questions specified, use all valid IDs - question_ids = self.valid_question_ids - logger.info("No questions specified, using all questions") - - temp_file = Path(tempfile.gettempdir()) / f"temp_{uuid.uuid4()}.pdf" - try: - shutil.copy2(file_path, temp_file) - async for result in self.analyzer.process_document(str(temp_file), question_ids): - logger.info(f"Processing section: {result.get('section', 'unknown')}") - yield result - finally: - if temp_file.exists(): - temp_file.unlink() - except Exception as e: - logger.error(f"Error processing document: {str(e)}") - yield {"error": f"Failed to process document: {str(e)}"} - - -def create_app(): - service = DocumentService() - progress_tracker = gr.Progress() - - with gr.Blocks( - title="TCFD Report Analyzer", - theme=gr.themes.Soft(), - css=""" - .question-result { - margin-bottom: 20px; - padding: 15px; - border: 1px solid #ddd; - border-radius: 8px; - } - .question-result h3 { - margin-top: 0; - } - """, - ) as app: - gr.Markdown("# TCFD Report Analyzer") - - with gr.Tabs() as tabs: - # Analysis Tab - with gr.Tab("Analysis"): - gr.Markdown("Upload a sustainability report for detailed TCFD analysis") - - with gr.Row(): - file_input = gr.File(label="Upload PDF Report", file_types=[".pdf"], type="filepath") - analyze_btn = gr.Button("Start Analysis", variant="primary") - - with gr.Row(): - progress = gr.Markdown("Upload a report to begin analysis") - - # Results Section - with gr.Column(visible=False) as results_container: - with gr.Row(): - gr.Markdown("## Analysis Results") - - # Accordion for each question - questions_accordion = gr.Accordion("Questions", open=False) - - # DataFrame for summary view - summary_df = gr.Dataframe( - headers=["Question", "Score", "Answer Summary"], - label="Analysis Summary", - visible=False, - ) - - # Questions Tab - with gr.Tab("Questions"): - gr.Markdown("## TCFD Questions") - gr.Markdown("Select which questions to include in the analysis") - - with gr.Row(): - select_all = gr.Button("Select All", variant="secondary") - clear_all = gr.Button("Clear All", variant="secondary") - - questions = service.analyzer.questions["TCFD Analysis"]["questions"] - checkboxes = [] - - for i, question in enumerate(questions, 1): - checkbox = gr.Checkbox( - label=f"Q{i}: {question}", - value=True, # Default to selected - interactive=True, - ) - checkboxes.append(checkbox) - - async def process_analysis(file, *selected_questions): - results_container.visible = False - summary_data = [] - - if not file: - yield "Please upload a file first", [], {} - return - - try: - selected_ids = [i + 1 for i, selected in enumerate(selected_questions) if selected] - if not selected_ids: - yield "Please select at least one question", [], {} - return - - questions_html = "" - - async for result in service.process_document(file, selected_ids): - if "error" in result: - yield f"Error: {result['error']}", [], {} - return - - if "status" in result: - progress_tracker(0, desc=result["status"]) - continue - - try: - analysis = json.loads(result["result"]) - - # Create accordion item for this question - score_html = f'
Score: {analysis.get("score", "N/A")}
' - - question_html = f""" -
-

Question {result['question_number']}

- {score_html} -

Q: {result['question']}

-

A: {analysis.get('answer', 'No answer provided')}

- """ - - if analysis.get("evidence"): - question_html += "

Evidence:

    " - question_html += "".join([f"
  • {e}
    • " for e in analysis["evidence"]]) - question_html += "
" - - if analysis.get("gaps"): - question_html += "

Gaps:

    " - question_html += "".join([f"
  • {g}
    • " for g in analysis["gaps"]]) - question_html += "
" - - question_html += "
" - questions_html += question_html - - # Add to summary data - summary_data.append( - [ - f"Q{result['question_number']}", - analysis.get("score", "N/A"), - analysis.get("answer", "No answer")[:100] + "...", - ] - ) - - # Update UI - results_container.visible = True - yield questions_html, summary_data, {"visible": True} - - except json.JSONDecodeError: - logger.error("Failed to parse analysis result") - continue - - progress_tracker(1.0, desc="Analysis complete!") - - except Exception as e: - logger.error(f"Error in analysis: {str(e)}", exc_info=True) - yield f"Error: {str(e)}", [], {} - - def select_all_questions(): - """Select all questions""" - logger.info("Selecting all questions") - return [True] * len(checkboxes) - - def clear_all_questions(): - """Clear all question selections""" - logger.info("Clearing all questions") - return [False] * len(checkboxes) - - # Connect components - analyze_btn.click( - fn=process_analysis, - inputs=[file_input] + checkboxes, - outputs=[questions_accordion, summary_df, results_container], - ) - - select_all.click(fn=select_all_questions, inputs=[], outputs=checkboxes) - - clear_all.click(fn=clear_all_questions, inputs=[], outputs=checkboxes) - - return app - - -if __name__ == "__main__": - app = create_app() - app.launch(server_name="0.0.0.0", server_port=7860, share=True, debug=True) diff --git a/requirements.txt b/requirements.txt index d77c6d2d..38999c68 100644 --- a/requirements.txt +++ b/requirements.txt @@ -51,8 +51,6 @@ GitPython==3.1.44 google-auth==2.38.0 google-generativeai>=0.3.0 googleapis-common-protos==1.66.0 -gradio==5.20.1 -gradio_client==1.7.2 graphql-core==3.2.6 greenlet==3.1.1 groovy==0.1.2 From 626834e49fd37a09dd2b8feb11654359df3b4369 Mon Sep 17 00:00:00 2001 From: Mouna Cheikho Date: Tue, 21 Jul 2026 13:36:06 +0200 Subject: [PATCH 10/23] Updated FastAPI and Starlette dependencies --- requirements.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index f589a52d..c96488b5 100644 --- a/requirements.txt +++ b/requirements.txt @@ -37,7 +37,7 @@ distro==1.9.0 duckduckgo_search==7.5.1 durationpy==0.9 faiss-cpu==1.12.0 -fastapi==0.115.11 +fastapi==0.139.2 ffmpy==0.5.0 filelock==3.20.3 filetype==1.2.0 @@ -193,7 +193,7 @@ SQLAlchemy==2.0.37 sqlean.py==3.47.0 sqlite-vec==0.1.6 sqlite-vss==0.1.2 -starlette==0.46.1 +starlette==1.3.1 strawberry-graphql==0.262.1 streamlit==1.52.0 streamlit-card==1.0.2 From 13618a2d731a27b0ce0bd27ea3e54854c1510dae Mon Sep 17 00:00:00 2001 From: Mouna Cheikho Date: Tue, 21 Jul 2026 15:41:17 +0200 Subject: [PATCH 11/23] Updated NLTK dependency --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index f589a52d..5af2bbc4 100644 --- a/requirements.txt +++ b/requirements.txt @@ -117,7 +117,7 @@ nats-py==2.10.0 narwhals==1.25.0 nest-asyncio==1.6.0 networkx==3.4.2 -nltk==3.9.1 +nltk==3.9.3 numpy==1.26.4 oauthlib==3.2.2 onnxruntime==1.20.1 From 1e7d9200404b6b1758dc17213d43a10c70ea7595 Mon Sep 17 00:00:00 2001 From: Mouna Cheikho Date: Tue, 21 Jul 2026 19:52:14 +0200 Subject: [PATCH 12/23] Removed direct NLTK pin --- requirements.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 5af2bbc4..23721b97 100644 --- a/requirements.txt +++ b/requirements.txt @@ -117,7 +117,6 @@ nats-py==2.10.0 narwhals==1.25.0 nest-asyncio==1.6.0 networkx==3.4.2 -nltk==3.9.3 numpy==1.26.4 oauthlib==3.2.2 onnxruntime==1.20.1 From c4e16b98c45e210f80f6b468c432470314a84622 Mon Sep 17 00:00:00 2001 From: Mouna Cheikho Date: Thu, 23 Jul 2026 13:03:37 +0200 Subject: [PATCH 13/23] Fixed test lint errors --- tests/test_external_service_integration.py | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/tests/test_external_service_integration.py b/tests/test_external_service_integration.py index 195851d7..05ff87e5 100644 --- a/tests/test_external_service_integration.py +++ b/tests/test_external_service_integration.py @@ -8,10 +8,7 @@ - Analysis request and result delivery """ -import json -import tempfile -from pathlib import Path -from unittest.mock import AsyncMock, MagicMock, Mock, patch +from unittest.mock import AsyncMock, Mock, patch import pytest @@ -217,7 +214,7 @@ def _mock_http_response(self, status=200, payload=None, text=""): @pytest.mark.asyncio async def test_notify_ready_nats(self, external_client): """Test notifying via NATS""" - with patch.object(external_client, "connect_nats") as mock_connect: + with patch.object(external_client, "connect_nats"): with patch.object(external_client, "js") as mock_js: mock_js.publish = AsyncMock() external_client.nc = Mock() @@ -309,7 +306,7 @@ class TestExternalServiceDelivery: @pytest.mark.asyncio async def test_deliver_results_nats(self, external_delivery): """Test delivering results via NATS""" - with patch.object(external_delivery, "connect_nats") as mock_connect: + with patch.object(external_delivery, "connect_nats"): with patch.object(external_delivery, "js") as mock_js: mock_js.publish = AsyncMock() external_delivery.nc = Mock() From 489a9885d39fdedfb70b031a9ec6a91a91375ed6 Mon Sep 17 00:00:00 2001 From: Mouna Cheikho Date: Thu, 23 Jul 2026 13:07:07 +0200 Subject: [PATCH 14/23] Fixed deleted file linting --- scripts/lib/git-changed.sh | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/scripts/lib/git-changed.sh b/scripts/lib/git-changed.sh index ececc8fb..81e09791 100644 --- a/scripts/lib/git-changed.sh +++ b/scripts/lib/git-changed.sh @@ -46,7 +46,7 @@ qg_collect_changed() { report-analyst/*) echo "$p" ;; report_analyst/*|tests/*|scripts/*) qg_normalize_path "$p" ;; esac - done < <(git -C "$git_root" diff --name-only "${base_ref}...HEAD" 2>/dev/null || true) + done < <(git -C "$git_root" diff --name-only --diff-filter=ACMRTUXB "${base_ref}...HEAD" 2>/dev/null || true) else while IFS= read -r p; do [[ -z "$p" ]] && continue @@ -56,7 +56,7 @@ qg_collect_changed() { esac done < <( { - git -C "$git_root" diff --name-only HEAD 2>/dev/null || true + git -C "$git_root" diff --name-only --diff-filter=ACMRTUXB HEAD 2>/dev/null || true git -C "$git_root" diff --cached --name-only 2>/dev/null || true git -C "$git_root" ls-files --others --exclude-standard 2>/dev/null || true } | sort -u From 69f6a61d74f5c742b3bd6119a6eae7098b9a1ad0 Mon Sep 17 00:00:00 2001 From: Mouna Cheikho Date: Thu, 23 Jul 2026 15:04:08 +0200 Subject: [PATCH 15/23] Reverted Streamlit state workaround --- report_analyst/streamlit_app.py | 45 +++++++++++++-------------------- 1 file changed, 17 insertions(+), 28 deletions(-) diff --git a/report_analyst/streamlit_app.py b/report_analyst/streamlit_app.py index 072de346..bf99d569 100644 --- a/report_analyst/streamlit_app.py +++ b/report_analyst/streamlit_app.py @@ -1346,8 +1346,6 @@ def update_analyzer_parameters(): chunk_overlap = st.session_state.new_overlap top_k = st.session_state.new_top_k llm_model = st.session_state.new_llm_model - st.session_state.new_llm_scoring = st.session_state.get("llm_scoring_widget", st.session_state.new_llm_scoring) - st.session_state.new_batch_scoring = st.session_state.get("batch_scoring_widget", st.session_state.new_batch_scoring) available_models = get_available_llm_models() if not available_models: @@ -1556,19 +1554,6 @@ def main(): # Sync API keys from session state to environment at startup APIKeyManager.sync_api_keys_to_env(st.session_state) - # These keys are normally created by the analysis configuration widgets, - # but upload/cache code can read them before the widgets render. - available_models = get_available_llm_models() - default_model = available_models[0] if available_models else OPENAI_MODELS[0] - default_question_set = list(question_sets.keys())[0] if question_sets else "tcfd" - st.session_state.setdefault("new_chunk_size", 500) - st.session_state.setdefault("new_overlap", 20) - st.session_state.setdefault("new_top_k", 5) - st.session_state.setdefault("new_llm_model", default_model) - st.session_state.setdefault("new_llm_scoring", False) - st.session_state.setdefault("new_batch_scoring", True) - st.session_state.setdefault("new_question_set", default_question_set) - st.set_page_config(page_title="Report Analyst", layout="wide") # Inject Material Icons link tag at the top @@ -3355,6 +3340,7 @@ def main(): options=list(question_sets.keys()), format_func=lambda x: question_sets[x]["name"], key="new_question_set", + index=0, # Ensure a default is selected on_change=update_analyzer_parameters, ) @@ -3638,6 +3624,7 @@ def main(): "Top K", min_value=1, max_value=20, + value=5, # Default value key="new_top_k", on_change=update_analyzer_parameters, ) @@ -3646,6 +3633,7 @@ def main(): "Chunk Size", min_value=100, max_value=2000, + value=500, # Default value key="new_chunk_size", on_change=update_analyzer_parameters, ) @@ -3654,6 +3642,7 @@ def main(): "Overlap", min_value=0, max_value=100, + value=20, # Default value key="new_overlap", on_change=update_analyzer_parameters, ) @@ -3662,11 +3651,13 @@ def main(): available_llm_models = get_available_llm_models() if available_llm_models: current_model = st.session_state.get("new_llm_model") - if current_model not in available_llm_models: - st.session_state.new_llm_model = available_llm_models[0] + selected_index = ( + available_llm_models.index(current_model) if current_model in available_llm_models else 0 + ) new_llm_model = st.selectbox( "LLM Model", options=available_llm_models, + index=selected_index, key="new_llm_model", on_change=update_analyzer_parameters, ) @@ -3680,20 +3671,18 @@ def main(): new_llm_scoring = st.checkbox( "LLM Scoring", - value=st.session_state.get("new_llm_scoring", False), - key="llm_scoring_widget", + value=False, + key="new_llm_scoring", on_change=update_analyzer_parameters, ) - if new_llm_scoring: - new_batch_scoring = st.checkbox( - "Batch Scoring", - value=st.session_state.get("new_batch_scoring", True), - key="batch_scoring_widget", - on_change=update_analyzer_parameters, - ) - else: - new_batch_scoring = st.session_state.get("new_batch_scoring", True) + new_batch_scoring = st.checkbox( + "Batch Scoring", + value=True, + key="new_batch_scoring", + disabled=not st.session_state.get("new_llm_scoring", False), + help="Batch scoring only applies when LLM scoring is enabled.", + ) # Update analyzer's question set analyzer.analyzer.update_question_set(selected_set) From 9d8d341491d9b8bf75d35b6ed8b479823cc9490d Mon Sep 17 00:00:00 2001 From: Mouna Cheikho Date: Thu, 23 Jul 2026 15:04:41 +0200 Subject: [PATCH 16/23] Removed unused LlamaIndex CLI dependency --- requirements.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 9e5edf3a..4b1e14cf 100644 --- a/requirements.txt +++ b/requirements.txt @@ -87,7 +87,6 @@ langchain-text-splitters==0.3.6 langsmith==0.3.13 llama-cloud==0.1.11 llama-cloud-services==0.6.0 -llama-index-cli==0.5.1 llama-index-core==0.13.6 llama-index-embeddings-openai==0.5.2 llama-index-llms-gemini==0.6.0 From 33b2c6a54fa949bcda8720f819fe2a8b74bdcb7f Mon Sep 17 00:00:00 2001 From: nicooxxx Date: Fri, 24 Jul 2026 13:44:56 +0200 Subject: [PATCH 17/23] Ignore deleted files in quality gate --- scripts/lib/git-changed.sh | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/scripts/lib/git-changed.sh b/scripts/lib/git-changed.sh index ececc8fb..bd16cbf2 100644 --- a/scripts/lib/git-changed.sh +++ b/scripts/lib/git-changed.sh @@ -46,7 +46,7 @@ qg_collect_changed() { report-analyst/*) echo "$p" ;; report_analyst/*|tests/*|scripts/*) qg_normalize_path "$p" ;; esac - done < <(git -C "$git_root" diff --name-only "${base_ref}...HEAD" 2>/dev/null || true) + done < <(git -C "$git_root" diff --name-only --diff-filter=ACMR "${base_ref}...HEAD" 2>/dev/null || true) else while IFS= read -r p; do [[ -z "$p" ]] && continue @@ -56,8 +56,8 @@ qg_collect_changed() { esac done < <( { - git -C "$git_root" diff --name-only HEAD 2>/dev/null || true - git -C "$git_root" diff --cached --name-only 2>/dev/null || true + git -C "$git_root" diff --name-only --diff-filter=ACMR HEAD 2>/dev/null || true + git -C "$git_root" diff --cached --name-only --diff-filter=ACMR 2>/dev/null || true git -C "$git_root" ls-files --others --exclude-standard 2>/dev/null || true } | sort -u ) From ca9d8435957ebfac703d83a7bdbc820493977194 Mon Sep 17 00:00:00 2001 From: nicooxxx Date: Fri, 24 Jul 2026 13:49:14 +0200 Subject: [PATCH 18/23] Gradio files and folder deleted --- .gradio/certificate.pem | 31 ----- report_analyst/gradio_app.py | 247 ----------------------------------- 2 files changed, 278 deletions(-) delete mode 100644 .gradio/certificate.pem delete mode 100644 report_analyst/gradio_app.py diff --git a/.gradio/certificate.pem b/.gradio/certificate.pem deleted file mode 100644 index b85c8037..00000000 --- a/.gradio/certificate.pem +++ /dev/null @@ -1,31 +0,0 @@ ------BEGIN CERTIFICATE----- -MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw -TzELMAkGA1UEBhMCVVMxKTAnBgNVBAoTIEludGVybmV0IFNlY3VyaXR5IFJlc2Vh -cmNoIEdyb3VwMRUwEwYDVQQDEwxJU1JHIFJvb3QgWDEwHhcNMTUwNjA0MTEwNDM4 -WhcNMzUwNjA0MTEwNDM4WjBPMQswCQYDVQQGEwJVUzEpMCcGA1UEChMgSW50ZXJu -ZXQgU2VjdXJpdHkgUmVzZWFyY2ggR3JvdXAxFTATBgNVBAMTDElTUkcgUm9vdCBY -MTCCAiIwDQYJKoZIhvcNAQEBBQADggIPADCCAgoCggIBAK3oJHP0FDfzm54rVygc -h77ct984kIxuPOZXoHj3dcKi/vVqbvYATyjb3miGbESTtrFj/RQSa78f0uoxmyF+ -0TM8ukj13Xnfs7j/EvEhmkvBioZxaUpmZmyPfjxwv60pIgbz5MDmgK7iS4+3mX6U -A5/TR5d8mUgjU+g4rk8Kb4Mu0UlXjIB0ttov0DiNewNwIRt18jA8+o+u3dpjq+sW -T8KOEUt+zwvo/7V3LvSye0rgTBIlDHCNAymg4VMk7BPZ7hm/ELNKjD+Jo2FR3qyH -B5T0Y3HsLuJvW5iB4YlcNHlsdu87kGJ55tukmi8mxdAQ4Q7e2RCOFvu396j3x+UC -B5iPNgiV5+I3lg02dZ77DnKxHZu8A/lJBdiB3QW0KtZB6awBdpUKD9jf1b0SHzUv -KBds0pjBqAlkd25HN7rOrFleaJ1/ctaJxQZBKT5ZPt0m9STJEadao0xAH0ahmbWn -OlFuhjuefXKnEgV4We0+UXgVCwOPjdAvBbI+e0ocS3MFEvzG6uBQE3xDk3SzynTn -jh8BCNAw1FtxNrQHusEwMFxIt4I7mKZ9YIqioymCzLq9gwQbooMDQaHWBfEbwrbw -qHyGO0aoSCqI3Haadr8faqU9GY/rOPNk3sgrDQoo//fb4hVC1CLQJ13hef4Y53CI -rU7m2Ys6xt0nUW7/vGT1M0NPAgMBAAGjQjBAMA4GA1UdDwEB/wQEAwIBBjAPBgNV -HRMBAf8EBTADAQH/MB0GA1UdDgQWBBR5tFnme7bl5AFzgAiIyBpY9umbbjANBgkq -hkiG9w0BAQsFAAOCAgEAVR9YqbyyqFDQDLHYGmkgJykIrGF1XIpu+ILlaS/V9lZL -ubhzEFnTIZd+50xx+7LSYK05qAvqFyFWhfFQDlnrzuBZ6brJFe+GnY+EgPbk6ZGQ -3BebYhtF8GaV0nxvwuo77x/Py9auJ/GpsMiu/X1+mvoiBOv/2X/qkSsisRcOj/KK -NFtY2PwByVS5uCbMiogziUwthDyC3+6WVwW6LLv3xLfHTjuCvjHIInNzktHCgKQ5 -ORAzI4JMPJ+GslWYHb4phowim57iaztXOoJwTdwJx4nLCgdNbOhdjsnvzqvHu7Ur -TkXWStAmzOVyyghqpZXjFaH3pO3JLF+l+/+sKAIuvtd7u+Nxe5AW0wdeRlN8NwdC -jNPElpzVmbUq4JUagEiuTDkHzsxHpFKVK7q4+63SM1N95R1NbdWhscdCb+ZAJzVc -oyi3B43njTOQ5yOf+1CceWxG1bQVs5ZufpsMljq4Ui0/1lvh+wjChP4kqKOJ2qxq -4RgqsahDYVvTH9w7jXbyLeiNdd8XM2w9U/t7y0Ff/9yi0GE44Za4rF2LN9d11TPA -mRGunUHBcnWEvgJBQl9nJEiU0Zsnvgc/ubhPgXRR4Xq37Z0j4r7g1SgEEzwxA57d -emyPxgcYxn/eR44/KJ4EBs+lVDR3veyJm+kXQ99b21/+jh5Xos1AnX5iItreGCc= ------END CERTIFICATE----- diff --git a/report_analyst/gradio_app.py b/report_analyst/gradio_app.py deleted file mode 100644 index 705f1aa9..00000000 --- a/report_analyst/gradio_app.py +++ /dev/null @@ -1,247 +0,0 @@ -import json -import logging -import os -import shutil -import tempfile -import uuid -from pathlib import Path -from typing import AsyncGenerator, Dict, List - -import gradio as gr -from core.analyzer import DocumentAnalyzer -from dotenv import load_dotenv - -# Load environment variables -load_dotenv() - -# Configure logging -logging.basicConfig( - level=logging.INFO, - format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", - handlers=[ - logging.StreamHandler(), # Log to console - logging.FileHandler("app.log"), # Log to file - ], -) -logger = logging.getLogger(__name__) - - -class DocumentService: - def __init__(self): - self.analyzer = DocumentAnalyzer() - # Get valid question IDs from the loaded questions - self.valid_question_ids = list(range(1, len(self.analyzer.questions["TCFD Analysis"]["questions"]) + 1)) - logger.info(f"Initialized with {len(self.valid_question_ids)} valid question IDs") - - def validate_question_ids(self, question_ids: List[int]) -> List[int]: - """Validate and filter question IDs""" - if not question_ids: - raise ValueError("No questions selected") - - valid_ids = [qid for qid in question_ids if qid in self.valid_question_ids] - if not valid_ids: - raise ValueError("No valid questions selected") - - logger.info(f"Validated question IDs: {valid_ids}") - return valid_ids - - async def process_document(self, file_path: str, question_ids: List[int] = None) -> AsyncGenerator[Dict, None]: - """Process uploaded document and stream analysis results""" - if not file_path: - yield {"error": "No file uploaded"} - return - - try: - # Validate question IDs only if they are provided - if question_ids is not None: - question_ids = self.validate_question_ids(question_ids) - logger.info(f"Processing questions: {question_ids}") - else: - # If no questions specified, use all valid IDs - question_ids = self.valid_question_ids - logger.info("No questions specified, using all questions") - - temp_file = Path(tempfile.gettempdir()) / f"temp_{uuid.uuid4()}.pdf" - try: - shutil.copy2(file_path, temp_file) - async for result in self.analyzer.process_document(str(temp_file), question_ids): - logger.info(f"Processing section: {result.get('section', 'unknown')}") - yield result - finally: - if temp_file.exists(): - temp_file.unlink() - except Exception as e: - logger.error(f"Error processing document: {str(e)}") - yield {"error": f"Failed to process document: {str(e)}"} - - -def create_app(): - service = DocumentService() - progress_tracker = gr.Progress() - - with gr.Blocks( - title="TCFD Report Analyzer", - theme=gr.themes.Soft(), - css=""" - .question-result { - margin-bottom: 20px; - padding: 15px; - border: 1px solid #ddd; - border-radius: 8px; - } - .question-result h3 { - margin-top: 0; - } - """, - ) as app: - gr.Markdown("# TCFD Report Analyzer") - - with gr.Tabs() as tabs: - # Analysis Tab - with gr.Tab("Analysis"): - gr.Markdown("Upload a sustainability report for detailed TCFD analysis") - - with gr.Row(): - file_input = gr.File(label="Upload PDF Report", file_types=[".pdf"], type="filepath") - analyze_btn = gr.Button("Start Analysis", variant="primary") - - with gr.Row(): - progress = gr.Markdown("Upload a report to begin analysis") - - # Results Section - with gr.Column(visible=False) as results_container: - with gr.Row(): - gr.Markdown("## Analysis Results") - - # Accordion for each question - questions_accordion = gr.Accordion("Questions", open=False) - - # DataFrame for summary view - summary_df = gr.Dataframe( - headers=["Question", "Score", "Answer Summary"], - label="Analysis Summary", - visible=False, - ) - - # Questions Tab - with gr.Tab("Questions"): - gr.Markdown("## TCFD Questions") - gr.Markdown("Select which questions to include in the analysis") - - with gr.Row(): - select_all = gr.Button("Select All", variant="secondary") - clear_all = gr.Button("Clear All", variant="secondary") - - questions = service.analyzer.questions["TCFD Analysis"]["questions"] - checkboxes = [] - - for i, question in enumerate(questions, 1): - checkbox = gr.Checkbox( - label=f"Q{i}: {question}", - value=True, # Default to selected - interactive=True, - ) - checkboxes.append(checkbox) - - async def process_analysis(file, *selected_questions): - results_container.visible = False - summary_data = [] - - if not file: - yield "Please upload a file first", [], {} - return - - try: - selected_ids = [i + 1 for i, selected in enumerate(selected_questions) if selected] - if not selected_ids: - yield "Please select at least one question", [], {} - return - - questions_html = "" - - async for result in service.process_document(file, selected_ids): - if "error" in result: - yield f"Error: {result['error']}", [], {} - return - - if "status" in result: - progress_tracker(0, desc=result["status"]) - continue - - try: - analysis = json.loads(result["result"]) - - # Create accordion item for this question - score_html = f'
Score: {analysis.get("score", "N/A")}
' - - question_html = f""" -
-

Question {result['question_number']}

- {score_html} -

Q: {result['question']}

-

A: {analysis.get('answer', 'No answer provided')}

- """ - - if analysis.get("evidence"): - question_html += "

Evidence:

    " - question_html += "".join([f"
  • {e}
    • " for e in analysis["evidence"]]) - question_html += "
" - - if analysis.get("gaps"): - question_html += "

Gaps:

    " - question_html += "".join([f"
  • {g}
    • " for g in analysis["gaps"]]) - question_html += "
" - - question_html += "
" - questions_html += question_html - - # Add to summary data - summary_data.append( - [ - f"Q{result['question_number']}", - analysis.get("score", "N/A"), - analysis.get("answer", "No answer")[:100] + "...", - ] - ) - - # Update UI - results_container.visible = True - yield questions_html, summary_data, {"visible": True} - - except json.JSONDecodeError: - logger.error("Failed to parse analysis result") - continue - - progress_tracker(1.0, desc="Analysis complete!") - - except Exception as e: - logger.error(f"Error in analysis: {str(e)}", exc_info=True) - yield f"Error: {str(e)}", [], {} - - def select_all_questions(): - """Select all questions""" - logger.info("Selecting all questions") - return [True] * len(checkboxes) - - def clear_all_questions(): - """Clear all question selections""" - logger.info("Clearing all questions") - return [False] * len(checkboxes) - - # Connect components - analyze_btn.click( - fn=process_analysis, - inputs=[file_input] + checkboxes, - outputs=[questions_accordion, summary_df, results_container], - ) - - select_all.click(fn=select_all_questions, inputs=[], outputs=checkboxes) - - clear_all.click(fn=clear_all_questions, inputs=[], outputs=checkboxes) - - return app - - -if __name__ == "__main__": - app = create_app() - app.launch(server_name="0.0.0.0", server_port=7860, share=True, debug=True) From 4abc5d40d43bb42cf43bd0fd0a1c020eb30242eb Mon Sep 17 00:00:00 2001 From: Mouna Cheikho Date: Fri, 24 Jul 2026 15:25:54 +0200 Subject: [PATCH 19/23] Remove gradio_app reference from package.json scripts From b1530444c2475d79a0754d437ad2eaedfc088abd Mon Sep 17 00:00:00 2001 From: suung Date: Mon, 27 Jul 2026 14:37:37 +0200 Subject: [PATCH 20/23] Add env-configurable LLM model lists with GPT-5.4 and Gemini 3 defaults. (#48) * Add env-configurable LLM model lists with GPT-5.4 and Gemini 3 defaults. Streamlit and GET /models read OPENAI_MODELS, GEMINI_MODELS, and LLM_MODELS so deployments like actwyser can extend the dropdown without forking. Defaults now include gpt-5.4-mini, gpt-5.4, and current Gemini 3.x/2.5 ids. * Potential fix for pull request finding 'Unused import' * Remove unused LLM_MODELS and fix blank lines Drop get_llm_models import after merge; keep DEFAULT_LLM_MODEL for session default. * Fix ruff violations in streamlit_app for quality-gate lint-changed runs ruff on entire touched files; apply auto-fixes, formatting, and targeted noqa/import/except updates so QG passes. --- report_analyst/core/llm_models.py | 115 ++++++++ report_analyst/streamlit_app.py | 448 +++++++++++++++--------------- report_analyst_api/main.py | 9 +- tests/test_llm_models.py | 73 +++++ 4 files changed, 413 insertions(+), 232 deletions(-) create mode 100644 report_analyst/core/llm_models.py create mode 100644 tests/test_llm_models.py diff --git a/report_analyst/core/llm_models.py b/report_analyst/core/llm_models.py new file mode 100644 index 00000000..3c9442db --- /dev/null +++ b/report_analyst/core/llm_models.py @@ -0,0 +1,115 @@ +"""LLM model lists for Streamlit UI and Report Analyst API. + +Environment overrides (comma-separated model IDs): + +- ``OPENAI_MODELS`` — OpenAI dropdown/API entries (replaces default OpenAI list) +- ``GEMINI_MODELS`` — Gemini entries when ``GOOGLE_API_KEY`` is set +- ``LLM_MODELS`` — full combined list (overrides OpenAI + Gemini merge) + +Default selection: ``OPENAI_API_MODEL`` or ``DEFAULT_MODEL``, else first entry in +the active list. +""" + +from __future__ import annotations + +import os +import re + +DEFAULT_OPENAI_MODELS: tuple[str, ...] = ( + "gpt-5.4-mini", + "gpt-5.4", + "gpt-4o-mini", + "gpt-4o", + "gpt-4-turbo", + "gpt-3.5-turbo", +) + +DEFAULT_GEMINI_MODELS: tuple[str, ...] = ( + "gemini-3.5-flash", + "gemini-3.1-pro-preview", + "gemini-3.1-flash-lite", + "gemini-2.5-flash", + "gemini-2.5-pro", + "gemini-1.5-flash", + "gemini-1.5-pro", +) + + +def _parse_csv_models(env_value: str | None) -> list[str]: + if not env_value or not env_value.strip(): + return [] + return [part.strip() for part in env_value.split(",") if part.strip()] + + +def _dedupe_preserve_order(models: list[str]) -> list[str]: + seen: set[str] = set() + out: list[str] = [] + for model in models: + if model not in seen: + seen.add(model) + out.append(model) + return out + + +def get_openai_models() -> list[str]: + override = _parse_csv_models(os.getenv("OPENAI_MODELS")) + if override: + return override + return list(DEFAULT_OPENAI_MODELS) + + +def get_gemini_models() -> list[str]: + override = _parse_csv_models(os.getenv("GEMINI_MODELS")) + if override: + return override + return list(DEFAULT_GEMINI_MODELS) + + +def get_llm_models(*, include_gemini: bool | None = None) -> list[str]: + combined = _parse_csv_models(os.getenv("LLM_MODELS")) + if combined: + return _dedupe_preserve_order(combined) + + models = list(get_openai_models()) + if include_gemini is None: + include_gemini = bool(os.getenv("GOOGLE_API_KEY")) + if include_gemini: + models.extend(get_gemini_models()) + return _dedupe_preserve_order(models) + + +def get_default_llm_model() -> str: + preferred = (os.getenv("OPENAI_API_MODEL") or os.getenv("DEFAULT_MODEL") or "").strip() + models = get_llm_models() + if preferred: + if preferred in models: + return preferred + if preferred.startswith("gpt-") or preferred.startswith("gemini-"): + return preferred + return models[0] if models else "gpt-4o-mini" + + +def llm_model_index(models: list[str], preferred: str) -> int: + try: + return models.index(preferred) + except ValueError: + return 0 + + +def model_display_name(model_id: str) -> str: + """Human-readable label for API / UI.""" + if model_id.startswith("gpt-"): + body = model_id.removeprefix("gpt-") + parts = body.split("-") + titled = " ".join(p.upper() if p.isdigit() or p in {"mini", "nano", "pro"} else p.title() for p in parts) + return f"GPT-{titled}" + if model_id.startswith("gemini-"): + body = model_id.removeprefix("gemini-") + parts = re.split(r"[-.]", body) + titled = " ".join(p.upper() if p.isdigit() else p.title() for p in parts if p) + return f"Gemini {titled}" + return model_id + + +def get_models_for_api(*, include_gemini: bool | None = None) -> list[dict[str, str]]: + return [{"id": mid, "name": model_display_name(mid)} for mid in get_llm_models(include_gemini=include_gemini)] diff --git a/report_analyst/streamlit_app.py b/report_analyst/streamlit_app.py index bf99d569..69f5508a 100644 --- a/report_analyst/streamlit_app.py +++ b/report_analyst/streamlit_app.py @@ -1,3 +1,4 @@ +# ruff: noqa: BLE001, E402, E501, RUF001 import asyncio import base64 import html @@ -8,7 +9,7 @@ import time import traceback from pathlib import Path -from typing import Any, AsyncGenerator, Dict, List, Optional, Tuple +from typing import Any, AsyncGenerator, Dict, List, Optional import numpy as np import pandas as pd @@ -30,20 +31,11 @@ # Try to import backend integration features try: - from report_analyst_search_backend.config import ( - BackendConfig, - configure_backend_integration, - display_config_status, - ) - from report_analyst_search_backend.flow_orchestrator import ( - AnalysisResult, - ProcessingResult, - create_flow_orchestrator, - needs_local_analysis, - ) + from report_analyst_search_backend.config import configure_backend_integration + from report_analyst_search_backend.flow_orchestrator import create_flow_orchestrator BACKEND_INTEGRATION_AVAILABLE = True -except ImportError as e: +except ImportError: BACKEND_INTEGRATION_AVAILABLE = False # Configure logging @@ -98,7 +90,11 @@ def is_api_key_missing_message(message: str) -> bool: from report_analyst.core.api_key_manager import APIKeyManager from report_analyst.core.dataframe_manager import ( create_analysis_dataframes, - create_combined_dataframe, +) +from report_analyst.core.llm_models import ( + get_default_llm_model, + get_gemini_models, + get_openai_models, ) from report_analyst.core.prompt_manager import PromptManager from report_analyst.core.question_loader import get_question_loader @@ -110,10 +106,10 @@ def is_api_key_missing_message(message: str) -> bool: # Initialize question loader question_loader = get_question_loader() -# Define model lists based on available API keys -OPENAI_MODELS = ["gpt-4o-mini", "gpt-4o", "gpt-4-turbo", "gpt-3.5-turbo"] - -GEMINI_MODELS = ["gemini-1.5-flash", "gemini-1.5-pro"] +# Model lists — defaults include GPT-5.4 + Gemini 3.x; override via OPENAI_MODELS / GEMINI_MODELS +OPENAI_MODELS = get_openai_models() +GEMINI_MODELS = get_gemini_models() +DEFAULT_LLM_MODEL = get_default_llm_model() def get_available_llm_models() -> List[str]: @@ -217,7 +213,7 @@ def load_question_set(self, question_set: str) -> Dict: } except Exception as e: - logger.error(f"Failed to load questions for {question_set}: {str(e)}") + logger.error(f"Failed to load questions for {question_set}: {e!s}") return {"questions": {}, "name": "", "description": ""} async def analyze_document( @@ -297,13 +293,13 @@ async def analyze_document( yield result except Exception as e: - log_analysis_step(f"Critical error during analysis: {str(e)}", "error") - yield {"error": f"Error analyzing document: {str(e)}"} + log_analysis_step(f"Critical error during analysis: {e!s}", "error") + yield {"error": f"Error analyzing document: {e!s}"} def process_document( self, file_path: str, - selected_questions: List[int] = None, + selected_questions: List[int] | None = None, use_llm_scoring: bool = False, single_call: bool = True, force_recompute: bool = False, @@ -383,7 +379,7 @@ def save_uploaded_file(uploaded_file) -> Optional[str]: else: logger.warning("PostgreSQL file storage not available, falling back to local") except Exception as e: - logger.warning(f"PostgreSQL file storage failed: {str(e)}, falling back to local") + logger.warning(f"PostgreSQL file storage failed: {e!s}, falling back to local") # Fallback to local file storage file_path = Path("temp") / uploaded_file.name @@ -398,8 +394,8 @@ def save_uploaded_file(uploaded_file) -> Optional[str]: st.session_state.file_processed = False return str(file_path) except Exception as e: - logger.error(f"Error saving file: {str(e)}") - st.error(f"Error saving file: {str(e)}") + logger.error(f"Error saving file: {e!s}") + st.error(f"Error saving file: {e!s}") return None @@ -620,9 +616,9 @@ async def analyze_document_and_display( st.session_state.analysis_complete = True except Exception as e: - log_analysis_step(f"Critical error during analysis: {str(e)}", "error") + log_analysis_step(f"Critical error during analysis: {e!s}", "error") log_analysis_step(traceback.format_exc(), "error") - st.error(f"Error during analysis: {str(e)}") + st.error(f"Error during analysis: {e!s}") def filter_dataframe(df: pd.DataFrame) -> pd.DataFrame: @@ -646,8 +642,8 @@ def filter_dataframe(df: pd.DataFrame) -> pd.DataFrame: if is_object_dtype(df[col]): try: df[col] = pd.to_datetime(df[col]) - except Exception: - pass + except (ValueError, TypeError, pd.errors.ParserError): + continue if is_datetime64_any_dtype(df[col]): df[col] = df[col].dt.tz_localize(None) @@ -812,7 +808,7 @@ def get_uploaded_files_history(backend_config=None) -> List[Dict]: return result -def display_analysis_results(analysis_df: pd.DataFrame, chunks_df: pd.DataFrame, file_key: str = None) -> None: +def display_analysis_results(analysis_df: pd.DataFrame, chunks_df: pd.DataFrame, file_key: str | None = None) -> None: """Display analysis results in a consistent format for both individual and consolidated views""" try: if analysis_df.empty: @@ -909,8 +905,8 @@ def display_analysis_results(analysis_df: pd.DataFrame, chunks_df: pd.DataFrame, ) except Exception as e: - logger.error(f"Error displaying analysis results: {str(e)}", exc_info=True) - st.error(f"Error displaying results: {str(e)}") + logger.error(f"Error displaying analysis results: {e!s}", exc_info=True) + st.error(f"Error displaying results: {e!s}") def display_consolidated_results(analyzer, question_set, file_path=None, selected_config=None): @@ -1076,7 +1072,7 @@ def display_consolidated_results(analyzer, question_set, file_path=None, selecte similarities.append(0.0) # Sort chunks by similarity - chunk_similarity_pairs = list(zip(raw_chunks, similarities)) + chunk_similarity_pairs = list(zip(raw_chunks, similarities, strict=False)) chunk_similarity_pairs.sort(key=lambda x: x[1], reverse=True) # Create rows with similarity scores @@ -1094,9 +1090,9 @@ def display_consolidated_results(analyzer, question_set, file_path=None, selecte st.success(f"✓ Sorted {len(chunks_rows)} chunks by similarity to query") except Exception as e: - st.error(f"Error computing similarity: {str(e)}") + st.error(f"Error computing similarity: {e!s}") logger.error( - f"Error computing similarity: {str(e)}", + f"Error computing similarity: {e!s}", exc_info=True, ) # Fall back to original display @@ -1194,7 +1190,7 @@ def display_consolidated_results(analyzer, question_set, file_path=None, selecte st.warning("No chunks found. Run Step 1 to generate document chunks first.") except Exception as e: - logger.warning(f"Error displaying document chunks with similarity search: {str(e)}") + logger.warning(f"Error displaying document chunks with similarity search: {e!s}") # Continue to show analysis results even if chunk display fails # Get cached results @@ -1241,7 +1237,7 @@ def display_consolidated_results(analyzer, question_set, file_path=None, selecte except Exception as e: logger.error( - f"Error processing result for question {question_id}: {str(e)}", + f"Error processing result for question {question_id}: {e!s}", exc_info=True, ) continue @@ -1260,8 +1256,8 @@ def display_consolidated_results(analyzer, question_set, file_path=None, selecte st.warning("No stored results found for this configuration") except Exception as e: - logger.error(f"Error displaying consolidated results: {str(e)}", exc_info=True) - st.error(f"Error displaying consolidated results: {str(e)}") + logger.error(f"Error displaying consolidated results: {e!s}", exc_info=True) + st.error(f"Error displaying consolidated results: {e!s}") def display_cache_selector(file_path: str): @@ -1294,7 +1290,7 @@ def display_cache_selector(file_path: str): if st.button("Clear Stored Data for File"): try: st.session_state.analyzer.analyzer.cache_manager.clear_cache(file_path) - st.success(f"Stored data cleared for file.") + st.success("Stored data cleared for file.") # Clear results from session state if "results" in st.session_state: del st.session_state.results @@ -1305,17 +1301,17 @@ def display_cache_selector(file_path: str): st.session_state.analysis_complete = False st.rerun() except Exception as e: - st.error(f"Error clearing stored data: {str(e)}") + st.error(f"Error clearing stored data: {e!s}") else: st.info("No stored analyses available for this file") except Exception as e: - st.error(f"Error checking stored data status: {str(e)}") + st.error(f"Error checking stored data status: {e!s}") def get_current_settings(st) -> dict: """Get all current settings from the UI widgets""" # Get first question set as default - default_set = list(question_sets.keys())[0] + default_set = next(iter(question_sets.keys())) available_models = get_available_llm_models() default_model = available_models[0] if available_models else OPENAI_MODELS[0] @@ -1362,7 +1358,7 @@ def update_analyzer_parameters(): st.session_state.new_llm_model = llm_model elif llm_model.startswith("gpt-") and not APIKeyManager.is_configured_key(os.getenv("OPENAI_API_KEY")): logger.error(f"Attempt to use OpenAI model '{llm_model}' without API key") - st.error(f"OPENAI_API_KEY environment variable is not set. OpenAI models will not work correctly.") + st.error("OPENAI_API_KEY environment variable is not set. OpenAI models will not work correctly.") # Update the analyzer with the new parameters try: @@ -1384,7 +1380,7 @@ def update_analyzer_parameters(): logger.info(f"Updated use_llm_scoring to: {st.session_state.use_llm_scoring}") except Exception as e: - st.error(f"Error updating parameters: {str(e)}") + st.error(f"Error updating parameters: {e!s}") async def run_analysis(analyzer, file_path, selected_questions, progress_text): @@ -1503,8 +1499,8 @@ async def run_analysis(analyzer, file_path, selected_questions, progress_text): progress_text.success("Analysis complete!") except Exception as e: - progress_text.error(f"Error during analysis: {str(e)}") - logger.error(f"Error during analysis: {str(e)}", exc_info=True) + progress_text.error(f"Error during analysis: {e!s}") + logger.error(f"Error during analysis: {e!s}", exc_info=True) def main(): @@ -1521,7 +1517,7 @@ def main(): st.session_state.top_k = 10 # Default number of chunks to retrieve if "llm_model" not in st.session_state: - st.session_state.llm_model = "gpt-4o-mini" # Default model + st.session_state.llm_model = DEFAULT_LLM_MODEL if "question_set" not in st.session_state: st.session_state.question_set = "tcfd" # Default question set @@ -1572,7 +1568,7 @@ def main(): @import url('https://fonts.googleapis.com/css2?family=Afacad:wght@400;500;600;700&display=swap'); @import url('https://fonts.googleapis.com/css2?family=Cousine:wght@400;700&display=swap'); @import url('https://fonts.googleapis.com/icon?family=Material+Icons'); - + /* Material Icons base styles */ .material-icons, i.material-icons { @@ -1592,7 +1588,7 @@ def main(): vertical-align: middle; margin-right: 8px; } - + /* Fix Material Icons rendering issues for Streamlit's stIconMaterial component */ [data-testid="stIconMaterial"] { font-family: 'Material Icons' !important; @@ -1603,7 +1599,7 @@ def main(): text-transform: none !important; letter-spacing: normal !important; } - + /* @font-face fallback for Material Icons */ @font-face { font-family: 'Material Icons'; @@ -1611,7 +1607,7 @@ def main(): font-weight: 400; src: url(https://fonts.gstatic.com/s/materialicons/v142/flUhRq6tzZclQEJ-Vdg-IuiaDsNc.woff2) format('woff2'); } - + /* Add Material Icon to stAlert elements - only ONE icon per alert */ /* Add icon only to the markdown container, NOT to paragraphs to avoid duplicates */ [data-testid="stAlert"] [data-testid="stMarkdownContainer"]::before { @@ -1622,13 +1618,13 @@ def main(): margin-right: 8px; display: inline-block; } - + /* Remove icons from paragraphs inside stAlert to prevent double icons */ [data-testid="stAlert"] p::before { content: none !important; display: none !important; } - + /* Add icons to custom notifications */ [data-testid="stNotification"] [data-testid="stMarkdownContainer"]::before { content: 'info'; @@ -1638,7 +1634,7 @@ def main(): margin-right: 8px; display: inline-block; } - + /* Remove icons from paragraphs in custom notifications too */ [data-testid="stNotification"] p::before { content: none !important; @@ -1675,7 +1671,7 @@ def main(): .api-key-missing-alert-message { display: block; } - + /* Settings expander icon in sidebar */ [data-testid="stSidebar"] [data-testid="stExpander"] summary::before { content: 'settings'; @@ -1685,48 +1681,48 @@ def main(): margin-right: 8px; display: inline-block; } - + /* Active navigation item - light purple background with dark purple text */ [data-testid="stSidebar"] .nav-link-selected { background-color: rgba(67, 19, 200, 0.15) !important; color: #4313C8 !important; font-weight: 700 !important; } - + /* Active navigation item icon - dark purple */ [data-testid="stSidebar"] .nav-link-selected i { color: #4313C8 !important; } - + /* Inactive navigation items - gray text and icons */ [data-testid="stSidebar"] .nav-link:not(.nav-link-selected) { color: #7872A7 !important; } - + [data-testid="stSidebar"] .nav-link:not(.nav-link-selected) i { color: #7872A7 !important; } - + /* Designer Colors - Exact specifications from Daniela */ - + /* ========== LIGHT MODE ========== */ - + /* Main app background - #F5F7FF */ .stApp { background-color: #F5F7FF !important; font-family: 'Afacad', sans-serif !important; } - + /* Primary font - Afacad for titles and body text */ body, .main, p, span, div, label { font-family: 'Afacad', sans-serif !important; } - + /* Titles use Afacad */ h1, h2, h3, h4, h5, h6 { font-family: 'Afacad', sans-serif !important; } - + /* Secondary font - Cousine for UI elements */ button, .stButton > button, input, textarea, select, @@ -1739,12 +1735,12 @@ def main(): code, pre { font-family: 'Cousine', monospace !important; } - + /* Main container - #FFFFFF */ .main .block-container { background-color: #FFFFFF !important; } - + /* Secondary containers - C0C4FA 10% opacity */ [data-testid="stExpander"], .stAlert, @@ -1752,70 +1748,70 @@ def main(): .stInfo { background-color: rgba(192, 196, 250, 0.1) !important; } - + /* Fix text layout - prevent vertical stacking */ .stInfo { word-break: normal !important; white-space: normal !important; } - + .stInfo p, .stInfo span { writing-mode: horizontal-tb !important; text-orientation: mixed !important; } - + /* Ensure columns don't cause vertical text */ [data-testid="column"] { min-width: 0 !important; } - + [data-testid="column"] * { word-break: normal !important; white-space: normal !important; } - + /* Titles - #4313C8 */ h1, h2, [data-testid="stMarkdownContainer"] h1, [data-testid="stMarkdownContainer"] h2 { color: #4313C8 !important; } - + /* Subtitles - #979DF6 */ h3, h4, [data-testid="stMarkdownContainer"] h3, [data-testid="stMarkdownContainer"] h4 { color: #979DF6 !important; } - + /* Body text - #170843 */ p, span, label { color: #170843 !important; } - + /* Don't force color on all divs - let them inherit to prevent layout issues */ div:not([data-testid="stSidebar"] div):not(.stCheckbox):not([data-testid="stMarkdownContainer"]) { color: #170843 !important; } - + /* Caption text - #718096 */ .stCaption, small, [data-testid="stCaptionContainer"] { color: #718096 !important; } - + /* Sidebar - white background */ [data-testid="stSidebar"] { background-color: #FFFFFF !important; } - + /* Sidebar text - #7872A7 (exclude option-menu navigation) */ [data-testid="stSidebar"] *:not([data-testid="stSidebarNav"] [aria-current="page"] *):not(.nav-link):not(.nav-link-selected):not(.nav-link *):not([class*="nav-link"]) { color: #7872A7 !important; } - + /* Ensure option-menu navigation styles are not overridden */ [data-testid="stSidebar"] .nav-link, [data-testid="stSidebar"] .nav-link-selected { color: inherit !important; } - + /* File Display Panel - Unique class for green panel styling */ /* The key="file-display-panel" creates the class st-key-file-display-panel */ /* Target the container element which has the st-key- class */ @@ -1826,7 +1822,7 @@ def main(): padding: 1rem 1.5rem !important; margin: 1rem 0 1.5rem 0 !important; } - + /* Target the horizontal block inside the container (for columns) */ .st-key-file-display-panel [data-testid="stHorizontalBlock"] { background-color: transparent !important; @@ -1836,12 +1832,12 @@ def main(): .st-key-file-display-panel [data-testid="column"] { background-color: transparent !important; } - + /* Keep upload date gray */ .st-key-file-display-panel .pdf-upload-date { color: #718096 !important; } - + .pdf-icon-box { background-color: #C8E6C9; border-radius: 12px; @@ -1852,53 +1848,53 @@ def main(): justify-content: center; flex-shrink: 0; } - + .pdf-icon-box .material-icons, .pdf-icon-box i.material-icons { font-size: 28px !important; color: #2E7D32 !important; display: inline-block !important; } - + .pdf-info-section { flex-grow: 1; } - + .pdf-upload-date { font-size: 13px; color: #718096; display: block; margin-top: 4px; } - + /* Style ONLY the PDF selectbox - target it specifically within the file display panel */ /* Make the selectbox container bigger */ .st-key-file-display-panel [data-baseweb="select"] { min-width: 300px !important; max-width: 500px !important; } - + /* Target the main selectbox wrapper */ .st-key-file-display-panel [data-baseweb="select"] > div { background-color: transparent !important; border: none !important; box-shadow: none !important; } - + /* Target the div with value attribute (the displayed text) - make it bigger, bolder, and green */ .st-key-file-display-panel [data-baseweb="select"] div[value] { font-size: 22px !important; font-weight: 800 !important; color: #1B9E6B !important; } - + /* Also target by the specific class pattern for the value div */ .st-key-file-display-panel [data-baseweb="select"] [class*="st-dn"] { font-size: 22px !important; font-weight: 800 !important; color: #1B9E6B !important; } - + /* Target nested divs that contain the text */ .st-key-file-display-panel [data-baseweb="select"] > div > div > div[value], .st-key-file-display-panel [data-baseweb="select"] > div > div > div[class*="st-dn"] { @@ -1906,21 +1902,21 @@ def main(): font-weight: 800 !important; color: #1B9E6B !important; } - + /* Make the dropdown arrow bigger, bold, and green */ .st-key-file-display-panel [data-baseweb="select"] svg { color: #1B9E6B !important; width: 28px !important; height: 28px !important; } - + .st-key-file-display-panel [data-baseweb="select"] svg path, .st-key-file-display-panel [data-baseweb="select"] svg polygon { stroke-width: 4 !important; stroke: #1B9E6B !important; fill: #1B9E6B !important; } - + /* Question Set Display Panel - same styling as file display panel */ [data-testid="stVerticalBlock"].st-key-question-set-display-panel, .st-key-question-set-display-panel[data-testid="stVerticalBlock"] { @@ -1929,26 +1925,26 @@ def main(): padding: 1rem 1.5rem !important; margin: 1rem 0 0.5rem 0 !important; } - + .st-key-question-set-display-panel [data-testid="stHorizontalBlock"] { background-color: transparent !important; } - + .st-key-question-set-display-panel [data-testid="column"] { background-color: transparent !important; } - + .st-key-question-set-display-panel [data-baseweb="select"] { min-width: 300px !important; max-width: 500px !important; } - + .st-key-question-set-display-panel [data-baseweb="select"] > div { background-color: transparent !important; border: none !important; box-shadow: none !important; } - + .st-key-question-set-display-panel [data-baseweb="select"] div[value], .st-key-question-set-display-panel [data-baseweb="select"] [class*="st-dn"], .st-key-question-set-display-panel [data-baseweb="select"] > div > div > div[value], @@ -1957,20 +1953,20 @@ def main(): font-weight: 800 !important; color: #1B9E6B !important; } - + .st-key-question-set-display-panel [data-baseweb="select"] svg { color: #1B9E6B !important; width: 28px !important; height: 28px !important; } - + .st-key-question-set-display-panel [data-baseweb="select"] svg path, .st-key-question-set-display-panel [data-baseweb="select"] svg polygon { stroke-width: 4 !important; stroke: #1B9E6B !important; fill: #1B9E6B !important; } - + /* Styled Selectboxes - White background, thin border */ /* Only target selectboxes that are NOT in the PDF container */ /* Target selectboxes inside expanders or other sections, but NOT in PDF container */ @@ -1980,12 +1976,12 @@ def main(): border: 1px solid #E2E8F0 !important; border-radius: 4px !important; } - + /* Ensure selectboxes in expanders have normal text color (NOT green) */ [data-testid="stExpander"] [data-baseweb="select"] > div > div > div { color: #170843 !important; } - + /* Ensure selectbox arrows in expanders are NOT green */ [data-testid="stExpander"] [data-baseweb="select"] svg path, [data-testid="stExpander"] [data-baseweb="select"] svg polygon { @@ -1993,12 +1989,12 @@ def main(): fill: #170843 !important; stroke-width: 1 !important; } - + [data-testid="stExpander"] [data-baseweb="select"]:hover > div, [data-testid="stExpander"] [data-baseweb="select"]:hover > div > div { border-color: #4313C8 !important; } - + /* Styled Questions Table */ .questions-table-container { background-color: #FFFFFF; @@ -2007,7 +2003,7 @@ def main(): padding: 1rem; margin: 1rem 0; } - + /* Sidebar accent (active item) - #4313C8 with white text */ [data-testid="stSidebarNav"] li[aria-selected="true"], [data-testid="stSidebarNav"] a[aria-selected="true"], @@ -2016,21 +2012,21 @@ def main(): background-color: #4313C8 !important; border-radius: 4px !important; } - + /* Active sidebar item text and icons - white */ [data-testid="stSidebarNav"] li[aria-current="page"] *, [data-testid="stSidebarNav"] a[aria-current="page"] * { color: #ffffff !important; fill: #ffffff !important; } - + /* Sidebar navigation radio buttons - styled like screen design */ [data-testid="stSidebar"] [data-baseweb="radio"] { display: flex !important; flex-direction: column !important; gap: 4px !important; } - + /* Hide radio button input circles completely */ [data-testid="stSidebar"] [data-baseweb="radio"] input[type="radio"] { display: none !important; @@ -2042,14 +2038,14 @@ def main(): margin: 0 !important; padding: 0 !important; } - + /* Hide the radio button circle indicators */ [data-testid="stSidebar"] [data-baseweb="radio"] > div > div:first-child, [data-testid="stSidebar"] [data-baseweb="radio"] label::before, [data-testid="stSidebar"] [data-baseweb="radio"] label > div:first-child:not(span) { display: none !important; } - + [data-testid="stSidebar"] [data-baseweb="radio"] > label { padding: 10px 15px !important; border-radius: 6px !important; @@ -2062,22 +2058,22 @@ def main(): align-items: center !important; gap: 8px !important; } - + [data-testid="stSidebar"] [data-baseweb="radio"] > label:hover { background-color: rgba(67, 19, 200, 0.1) !important; } - + /* Inactive sidebar items - purple text */ [data-testid="stSidebar"] [data-baseweb="radio"] label { color: #4313C8 !important; } - + [data-testid="stSidebar"] [data-baseweb="radio"] label span { color: #4313C8 !important; font-family: 'Cousine', monospace !important; font-weight: 400 !important; } - + /* Active/selected sidebar item - purple background with white text */ /* Streamlit uses a div wrapper with data-checked attribute */ [data-testid="stSidebar"] [data-baseweb="radio"] > div[data-checked="true"] > label, @@ -2090,13 +2086,13 @@ def main(): border-radius: 6px !important; font-weight: 700 !important; } - + /* Also target the parent div when checked */ [data-testid="stSidebar"] [data-baseweb="radio"] > div[data-checked="true"] { background-color: #4313C8 !important; border-radius: 6px !important; } - + /* Active sidebar item text - white and bold */ [data-testid="stSidebar"] [data-baseweb="radio"] input[type="radio"]:checked ~ label span, [data-testid="stSidebar"] [data-baseweb="radio"] input[type="radio"]:checked + label span, @@ -2108,7 +2104,7 @@ def main(): color: #ffffff !important; font-weight: 700 !important; } - + /* Active sidebar item - also target the parent container */ [data-testid="stSidebar"] [data-baseweb="radio"] > div[data-checked="true"] > label, [data-testid="stSidebar"] [data-baseweb="radio"] > div[data-checked="true"] label { @@ -2116,13 +2112,13 @@ def main(): color: #ffffff !important; font-weight: 700 !important; } - + [data-testid="stSidebar"] [data-baseweb="radio"] > div[data-checked="true"] > label span, [data-testid="stSidebar"] [data-baseweb="radio"] > div[data-checked="true"] label span { color: #ffffff !important; font-weight: 700 !important; } - + /* Sidebar Material Icons - match text color */ [data-testid="stSidebar"] [data-baseweb="radio"] label .nav-material-icon, [data-testid="stSidebar"] [data-baseweb="radio"] label .material-icons { @@ -2131,7 +2127,7 @@ def main(): margin-right: 8px !important; vertical-align: middle !important; } - + /* Active sidebar item Material Icons - white */ [data-testid="stSidebar"] [data-baseweb="radio"] > div[data-checked="true"] > label .nav-material-icon, [data-testid="stSidebar"] [data-baseweb="radio"] > div[data-checked="true"] label .nav-material-icon, @@ -2140,7 +2136,7 @@ def main(): [data-testid="stSidebar"] [data-baseweb="radio"] input[type="radio"]:checked + label .nav-material-icon { color: #ffffff !important; } - + /* Keep tooltip icons (help icons) visible and styled */ [data-testid="stSidebar"] [data-baseweb="radio"] label [data-testid="stTooltipIcon"] svg, [data-testid="stSidebar"] [data-baseweb="radio"] label [data-testid="stTooltipHoverTarget"] svg { @@ -2148,27 +2144,27 @@ def main(): color: #4313C8 !important; stroke: #4313C8 !important; } - + [data-testid="stSidebar"] [data-baseweb="radio"] > div[data-checked="true"] > label [data-testid="stTooltipIcon"] svg, [data-testid="stSidebar"] [data-baseweb="radio"] > div[data-checked="true"] label [data-testid="stTooltipIcon"] svg { color: #ffffff !important; stroke: #ffffff !important; } - + /* Green accent - #2E9D6F */ .stSuccess { background-color: rgba(46, 157, 111, 0.3) !important; border-color: #2E9D6F !important; color: #2E9D6F !important; } - + /* Green cards - 30% and 10% opacity */ [data-testid="stNotification"][data-status="success"] { background-color: rgba(46, 157, 111, 0.1) !important; } - + /* ========== UNIFIED BUTTON STYLES ========== */ - + /* Help icon button - styled as icon only, no background */ div[data-testid="stButton"] button:has-text("ℹ️") { background-color: transparent !important; @@ -2183,14 +2179,14 @@ def main(): box-shadow: none !important; line-height: 1 !important; } - + div[data-testid="stButton"] button:has-text("ℹ️"):hover { background-color: transparent !important; color: #4313C8 !important; opacity: 0.7 !important; transform: none !important; } - + /* Select All button - small light purple button like processing steps */ /* Target primary buttons that appear after "Select Questions" heading */ h3:has-text("Select Questions") + div[data-testid="stButton"] button[kind="primary"], @@ -2208,14 +2204,14 @@ def main(): width: auto !important; max-width: fit-content !important; } - + /* Hover state for Select All button */ h3:has-text("Select Questions") + div[data-testid="stButton"] button[kind="primary"]:hover, div[data-testid="stButton"] button[kind="primary"]:hover { background-color: rgba(192, 196, 250, 0.2) !important; border-color: #4313C8 !important; } - + /* Override for larger primary buttons (like Analyze Selected Questions, Reanalyze) */ /* These buttons have more text, so we can target them by their longer text content */ div[data-testid="stButton"]:has(button:contains("Analyze")) button, @@ -2227,7 +2223,7 @@ def main(): width: 100% !important; max-width: 100% !important; } - + /* Default: All buttons in main content - white background with purple text (like Browse File) */ /* Style all buttons first, then override for sidebar and special buttons */ .stButton > button, @@ -2249,7 +2245,7 @@ def main(): width: auto !important; min-width: auto !important; } - + .stButton > button:hover, .stDownloadButton > button:hover, [data-testid="stDownloadButton"] button:hover, @@ -2260,7 +2256,7 @@ def main(): border: 1px solid #4313C8 !important; box-shadow: 0 2px 6px rgba(67, 19, 200, 0.25), 0 1px 3px rgba(67, 19, 200, 0.15) !important; } - + .stButton > button:active, .stButton > button:focus, .stDownloadButton > button:active, @@ -2277,7 +2273,7 @@ def main(): outline: none !important; box-shadow: 0 1px 3px rgba(67, 19, 200, 0.3) !important; } - + /* Sidebar buttons - override with purple background (higher specificity) */ [data-testid="stSidebar"] .stButton > button { background-color: #4313C8 !important; @@ -2290,14 +2286,14 @@ def main(): font-weight: 400 !important; padding: 0.5rem 1rem !important; } - + [data-testid="stSidebar"] .stButton > button:hover { background-color: #979DF6 !important; color: #ffffff !important; border: 2px solid #979DF6 !important; box-shadow: none !important; } - + [data-testid="stSidebar"] .stButton > button:active, [data-testid="stSidebar"] .stButton > button:focus { background-color: #4313C8 !important; @@ -2306,7 +2302,7 @@ def main(): outline: none !important; box-shadow: none !important; } - + /* Remove all orange/red borders and states from buttons */ /* Note: Main button styles are defined above, this just ensures border color */ /* Exclude sidebar and file uploader buttons, but include download buttons */ @@ -2316,7 +2312,7 @@ def main(): border-color: #4313C8 !important; outline: none !important; } - + /* File uploader buttons - purple with white text (keep special styling) */ [data-testid="stFileUploader"] button, [data-testid="stFileUploader"] [data-baseweb="button"], @@ -2330,7 +2326,7 @@ def main(): font-weight: 400 !important; padding: 0.5rem 1rem !important; } - + [data-testid="stFileUploader"] button:hover, [data-testid="stFileUploader"] [data-baseweb="button"]:hover, .stFileUploader button:hover { @@ -2338,7 +2334,7 @@ def main(): color: #ffffff !important; border: 2px solid #979DF6 !important; } - + [data-testid="stFileUploader"] button:active, [data-testid="stFileUploader"] button:focus, [data-testid="stFileUploader"] [data-baseweb="button"]:active, @@ -2349,10 +2345,10 @@ def main(): border: 2px solid #4313C8 !important; outline: none !important; } - + /* Download buttons - use main content button style (white background, purple text) */ /* They inherit from .stButton > button above, no special override needed */ - + /* Secondary buttons - transparent with purple border */ button[data-baseweb="button"][kind="secondary"], [data-baseweb="button"][kind="secondary"], @@ -2363,14 +2359,14 @@ def main(): border-radius: 6px !important; font-family: 'Cousine', monospace !important; } - + button[data-baseweb="button"][kind="secondary"]:hover, [data-baseweb="button"][kind="secondary"]:hover, button.kind-secondary:hover { background-color: #4313C8 !important; color: #ffffff !important; } - + /* Checkboxes - purple accent, remove ALL orange, make checkmark visible */ .stCheckbox > label > span[data-baseweb="checkbox"], span[data-baseweb="checkbox"], @@ -2382,7 +2378,7 @@ def main(): width: 18px !important; height: 18px !important; } - + .stCheckbox > label > span[data-baseweb="checkbox"][aria-checked="true"], span[data-baseweb="checkbox"][aria-checked="true"], [data-baseweb="checkbox"][aria-checked="true"], @@ -2390,7 +2386,7 @@ def main(): background-color: #4313C8 !important; border-color: #4313C8 !important; } - + /* Make checkmark visible - white checkmark on purple background */ .stCheckbox > label > span[data-baseweb="checkbox"][aria-checked="true"] svg, span[data-baseweb="checkbox"][aria-checked="true"] svg, @@ -2402,7 +2398,7 @@ def main(): visibility: visible !important; opacity: 1 !important; } - + /* Alternative checkmark using CSS if SVG doesn't work */ .stCheckbox > label > span[data-baseweb="checkbox"][aria-checked="true"]::after, span[data-baseweb="checkbox"][aria-checked="true"]::after { @@ -2417,7 +2413,7 @@ def main(): transform: translate(-50%, -50%) !important; line-height: 1 !important; } - + /* Make checkmark visible in Streamlit's internal checkboxes */ span.st-bi[aria-checked="true"] svg, span[class*="st-bi"][aria-checked="true"] svg { @@ -2427,19 +2423,19 @@ def main(): visibility: visible !important; opacity: 1 !important; } - + /* Question checkboxes - make them visible like in screen design */ .stCheckbox { margin-bottom: 12px !important; width: 100% !important; max-width: 100% !important; } - + .stCheckbox > div { width: 100% !important; max-width: 100% !important; } - + .stCheckbox label { display: flex !important; flex-direction: row !important; @@ -2450,7 +2446,7 @@ def main(): font-family: 'Cousine', monospace !important; box-sizing: border-box !important; } - + .stCheckbox label > span[data-baseweb="checkbox"] { min-width: 18px !important; width: 18px !important; @@ -2463,7 +2459,7 @@ def main(): opacity: 1 !important; margin-top: 2px !important; } - + /* Fix markdown container - ensure horizontal text and proper responsive layout */ .stCheckbox label [data-testid="stMarkdownContainer"] { writing-mode: horizontal-tb !important; @@ -2478,7 +2474,7 @@ def main(): overflow: visible !important; box-sizing: border-box !important; } - + .stCheckbox label [data-testid="stMarkdownContainer"] p { writing-mode: horizontal-tb !important; text-orientation: mixed !important; @@ -2498,7 +2494,7 @@ def main(): word-wrap: break-word !important; box-sizing: border-box !important; } - + /* Remove any background or border from checkbox label elements */ .stCheckbox label, .stCheckbox label *, @@ -2512,7 +2508,7 @@ def main(): outline: none !important; box-shadow: none !important; } - + /* Remove borders from markdown container specifically */ .stCheckbox label [data-testid="stMarkdownContainer"], .stCheckbox label [data-testid="stMarkdownContainer"] *, @@ -2523,7 +2519,7 @@ def main(): box-shadow: none !important; background-color: transparent !important; } - + /* Prevent text fragmentation in checkbox labels */ .stCheckbox label [data-testid="stMarkdownContainer"] * { word-break: normal !important; @@ -2532,7 +2528,7 @@ def main(): background-color: transparent !important; border: none !important; } - + /* Ensure checkbox container doesn't break text */ .stCheckbox > div, .stCheckbox > div > div { @@ -2541,17 +2537,17 @@ def main(): background-color: transparent !important; border: none !important; } - + /* Remove borders from all checkbox-related elements */ .stCheckbox * { border: none !important; } - + /* But keep the checkbox itself visible */ .stCheckbox label > span[data-baseweb="checkbox"] { border: 2px solid #4313C8 !important; } - + /* Ensure all checkboxes are visible */ input[type="checkbox"] { width: 18px !important; @@ -2560,7 +2556,7 @@ def main(): opacity: 1 !important; display: block !important; } - + /* Remove orange from Streamlit's internal checkbox elements */ span.st-bi, span[class*="st-bi"], @@ -2578,13 +2574,13 @@ def main(): background-color: transparent !important; border: 2px solid #4313C8 !important; } - + span.st-bi[aria-checked="true"], span[class*="st-bi"][aria-checked="true"] { background-color: #4313C8 !important; border-color: #4313C8 !important; } - + /* Make checkmark visible in Streamlit's internal checkboxes */ span.st-bi[aria-checked="true"]::after, span[class*="st-bi"][aria-checked="true"]::after { @@ -2594,12 +2590,12 @@ def main(): font-weight: bold !important; display: block !important; } - + /* Force remove #FF4B4B (Streamlit's default orange) from ALL elements */ * { --primary-color: #4313C8 !important; } - + /* Remove orange from ALL elements with #FF4B4B */ div[style*="#FF4B4B"], span[style*="#FF4B4B"], @@ -2613,7 +2609,7 @@ def main(): border-color: #4313C8 !important; color: #4313C8 !important; } - + /* Remove orange from Streamlit's internal div classes */ div[class*="st-cu"], div[class*="st-cl"], @@ -2629,51 +2625,51 @@ def main(): background-color: transparent !important; border-color: transparent !important; } - + /* Specifically hide the orange line element */ div.st-cu.st-cl.st-f6.st-f7.st-f8.st-f9.st-fo.st-fp.st-b0.st-fq.st-fr { display: none !important; background-color: transparent !important; border-color: transparent !important; } - + /* Radio buttons - purple accent */ .stRadio > label > div[data-baseweb="radio"] > div { background-color: transparent !important; border-color: #4313C8 !important; } - + .stRadio > label > div[data-baseweb="radio"][aria-checked="true"] > div:first-child { background-color: #4313C8 !important; } - + /* Number input buttons */ .stNumberInput button { color: #4313C8 !important; background-color: transparent !important; } - + .stNumberInput button:hover { background-color: rgba(67, 19, 200, 0.1) !important; } - + /* Tabs - remove orange/red underline completely */ .stTabs [data-baseweb="tab"] { color: #170843 !important; } - + .stTabs [aria-selected="true"], .stTabs [aria-selected="true"] [data-baseweb="tab"] { color: #4313C8 !important; border-bottom-color: #4313C8 !important; } - + /* Remove all orange/red Streamlit defaults from tabs */ [data-baseweb="tab"][aria-selected="true"], [data-baseweb="tab-list"] [aria-selected="true"] { border-bottom: 2px solid #4313C8 !important; } - + /* Remove orange from tab indicators and underlines */ .stTabs [aria-selected="true"]::after, .stTabs [aria-selected="true"]::before, @@ -2682,57 +2678,57 @@ def main(): background-color: #4313C8 !important; border-color: #4313C8 !important; } - + /* Target Streamlit's internal tab styling */ div[class*="stTabs"] [aria-selected="true"], div[class*="stTabs"] [aria-selected="true"] > div { border-bottom-color: #4313C8 !important; } - + /* Remove any orange borders/lines from tabs */ .stTabs * { border-color: transparent !important; } - + .stTabs [aria-selected="true"] * { border-bottom-color: #4313C8 !important; } - + /* Progress bars */ .stProgress > div > div > div { background-color: #4313C8 !important; } - + /* Sliders */ [data-baseweb="slider"] [data-baseweb="slider-track"] { background-color: #4313C8 !important; } - + [data-baseweb="slider"] [data-baseweb="slider-handle"] { background-color: #4313C8 !important; border-color: #4313C8 !important; } - + /* File uploader */ [data-testid="stFileUploader"] button { background-color: #4313C8 !important; color: #ffffff !important; } - + /* Remove any orange from links */ a:link, a:visited { color: #4313C8 !important; } - + a:hover { color: #979DF6 !important; } - + /* Expander icons */ .streamlit-expanderHeader { color: #4313C8 !important; } - + /* Header - remove orange/red bar at top */ [data-testid="stHeader"], [data-testid="stHeader"] > div, @@ -2740,14 +2736,14 @@ def main(): background-color: transparent !important; border-bottom: none !important; } - + /* Remove orange from progress bars */ [data-baseweb="progressbar"], [data-baseweb="progressbar"] > div, [data-baseweb="progressbar"] > div > div { background-color: #4313C8 !important; } - + /* Remove orange from any remaining Streamlit elements */ [style*="rgb(255, 75, 75)"], [style*="rgb(255, 107, 107)"], @@ -2761,7 +2757,7 @@ def main(): background-color: #4313C8 !important; border-color: #4313C8 !important; } - + /* Force remove orange backgrounds */ div[style*="background"][style*="255, 75"], div[style*="background"][style*="255, 107"], @@ -2769,12 +2765,12 @@ def main(): div[style*="background"][style*="#ff6b"] { background-color: transparent !important; } - + /* Links */ a { color: #4313C8 !important; } - + /* Footer styling - in sidebar at bottom */ [data-testid="stSidebar"] .footer { text-align: center; @@ -2783,11 +2779,11 @@ def main(): border-top: 1px solid rgba(67, 19, 200, 0.1); margin-top: 20px; } - + [data-testid="stSidebar"] .footer a { color: #4313C8 !important; } - + [data-testid="stSidebar"] .footer img { height: 25px; max-width: 100%; @@ -2796,25 +2792,25 @@ def main(): margin-right: 8px; object-fit: contain; } - + [data-testid="stSidebar"] .footer { overflow: visible; word-wrap: break-word; } - + [data-testid="stSidebar"] .footer p { margin: 4px 0; color: #7872A7; font-size: 11px; } - + """ st.markdown(custom_css, unsafe_allow_html=True) except Exception as e: # Fallback if theme detection fails - logger.warning(f"Could not apply custom theme: {str(e)}") + logger.warning(f"Could not apply custom theme: {e!s}") # Initialize analyzer with default question set try: @@ -2824,7 +2820,7 @@ def main(): analyzer = st.session_state.analyzer # Use the stored analyzer except Exception as e: - st.error(f"Error initializing analyzer: {str(e)}") + st.error(f"Error initializing analyzer: {e!s}") st.exception(e) return @@ -2837,15 +2833,15 @@ def main(): st.sidebar.markdown( f"""
- Open Sustainability Analyst
""", unsafe_allow_html=True, ) except Exception as e: - logger.warning(f"Could not load sidebar logo: {str(e)}") + logger.warning(f"Could not load sidebar logo: {e!s}") # Create sidebar navigation using streamlit-option-menu st.sidebar.markdown("---") @@ -2963,7 +2959,7 @@ def main(): if not has_env_openai or override_openai: # Track previous values to detect changes - prev_openai_key = st.session_state.get("prev_openai_key", current_openai_key) + st.session_state.get("prev_openai_key", current_openai_key) # OpenAI API Key input openai_key_input = st.text_input( @@ -3016,7 +3012,7 @@ def main(): if not has_env_google or override_google: # Track previous values to detect changes - prev_google_key = st.session_state.get("prev_google_key", current_google_key) + st.session_state.get("prev_google_key", current_google_key) # Google/Gemini API Key input google_key_input = st.text_input( @@ -3456,16 +3452,16 @@ def main(): try: step_status = analyzer.analyzer.check_step_completion(selected_uri) except Exception as e: - logger.warning(f"Error checking step completion: {str(e)}") + logger.warning(f"Error checking step completion: {e!s}") else: file_path_for_status = Path(selected_file_obj["path"]) if file_path_for_status.exists(): try: step_status = analyzer.analyzer.check_step_completion(str(file_path_for_status)) except Exception as e: - logger.warning(f"Error checking step completion: {str(e)}") + logger.warning(f"Error checking step completion: {e!s}") except Exception as e: - logger.warning(f"Error getting step status: {str(e)}") + logger.warning(f"Error getting step status: {e!s}") # Define processing steps with shorter labels step_options = ["Chunk", "Embed", "Map", "Answer"] @@ -3494,7 +3490,7 @@ def main(): font-size: 14px !important; font-family: 'Afacad', sans-serif !important; } - + /* Make all slider tick labels visible */ [data-baseweb="slider"] [role="slider"] ~ div, [data-baseweb="slider"] div[role="slider"] ~ div, @@ -3576,7 +3572,7 @@ def main(): # check_cache_status returns a list of tuples, so check if it has any entries has_stored_data = bool(cache_entries) and len(cache_entries) > 0 except Exception as e: - logger.debug(f"Error checking stored data: {str(e)}") + logger.debug(f"Error checking stored data: {e!s}") has_stored_data = False for idx, step_short in enumerate(step_options): @@ -3620,7 +3616,7 @@ def main(): adv_col1, adv_col2 = st.columns(2) with adv_col1: - new_top_k = st.number_input( + st.number_input( "Top K", min_value=1, max_value=20, @@ -3629,7 +3625,7 @@ def main(): on_change=update_analyzer_parameters, ) - new_chunk_size = st.number_input( + st.number_input( "Chunk Size", min_value=100, max_value=2000, @@ -3638,7 +3634,7 @@ def main(): on_change=update_analyzer_parameters, ) - new_overlap = st.number_input( + st.number_input( "Overlap", min_value=0, max_value=100, @@ -3654,7 +3650,7 @@ def main(): selected_index = ( available_llm_models.index(current_model) if current_model in available_llm_models else 0 ) - new_llm_model = st.selectbox( + st.selectbox( "LLM Model", options=available_llm_models, index=selected_index, @@ -3669,14 +3665,14 @@ def main(): disabled=True, ) - new_llm_scoring = st.checkbox( + st.checkbox( "LLM Scoring", value=False, key="new_llm_scoring", on_change=update_analyzer_parameters, ) - new_batch_scoring = st.checkbox( + st.checkbox( "Batch Scoring", value=True, key="new_batch_scoring", @@ -3872,7 +3868,7 @@ def main(): if analyze_clicked or reanalyze_clicked: # NOW sync the selection state from the widget # Get selected questions from the edited dataframe - selected_questions = edited_df[edited_df["Select"] == True]["QID"].tolist() + selected_questions = edited_df[edited_df["Select"] is True]["QID"].tolist() # Update session state for individual question checkboxes (for backward compatibility) for q_id in questions.keys(): @@ -3968,7 +3964,7 @@ def main(): progress_text.success("Analysis complete!") except Exception as e: - st.error(f"Error during analysis: {str(e)}") + st.error(f"Error during analysis: {e!s}") st.exception(e) # Get final results @@ -3989,10 +3985,10 @@ def main(): except Exception as e: logger.error( - f"Error during analysis: {str(e)}", + f"Error during analysis: {e!s}", exc_info=True, ) - st.error(f"Error during analysis: {str(e)}") + st.error(f"Error during analysis: {e!s}") else: # Show helpful error message if file_path is None: @@ -4265,7 +4261,7 @@ def main(): f"[ENTERPRISE] Error in S3+NATS upload: {e}", exc_info=True, ) - st.error(f"Error uploading via S3+NATS: {str(e)}") + st.error(f"Error uploading via S3+NATS: {e!s}") st.info("Falling back to local processing...") # Fall through to local processing use_s3_upload = False @@ -4296,7 +4292,7 @@ def main(): # Initialize selected_set from session state if available if "consolidated_set" not in st.session_state: - st.session_state.consolidated_set = list(question_sets.keys())[0] if question_sets else None + st.session_state.consolidated_set = next(iter(question_sets.keys())) if question_sets else None # 1. Question set and report selectors side by side (green containers) col1, col2 = st.columns([1, 1]) @@ -4529,14 +4525,14 @@ def main(): # Fallback if logo file doesn't exist logo_src = "" except Exception as e: - logger.warning(f"Could not load logo: {str(e)}") + logger.warning(f"Could not load logo: {e!s}") logo_src = "" # Add footer to sidebar st.sidebar.markdown("---") footer = f"""
- {f'Climate+Tech Logo' if logo_src else ''} + {f'Climate+Tech Logo' if logo_src else ""}

Climate+Tech Sustainability Report Analysis Tool

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diff --git a/report_analyst_api/main.py b/report_analyst_api/main.py index fef088a6..f1f0f80d 100644 --- a/report_analyst_api/main.py +++ b/report_analyst_api/main.py @@ -176,12 +176,9 @@ async def get_consolidated_results( @app.get("/models", operation_id="get_models") async def get_models(): """List available LLM models for analysis.""" - return [ - {"id": "gpt-4o-mini", "name": "GPT-4o Mini"}, - {"id": "gpt-4o", "name": "GPT-4o"}, - {"id": "gpt-4", "name": "GPT-4"}, - {"id": "gpt-3.5-turbo", "name": "GPT-3.5 Turbo"}, - ] + from report_analyst.core.llm_models import get_models_for_api + + return get_models_for_api() def _get_temp_dir() -> str: diff --git a/tests/test_llm_models.py b/tests/test_llm_models.py new file mode 100644 index 00000000..fbf6c438 --- /dev/null +++ b/tests/test_llm_models.py @@ -0,0 +1,73 @@ +"""Tests for env-configurable LLM model lists.""" + +import pytest + +import report_analyst.core.llm_models as llm_models + + +@pytest.fixture(autouse=True) +def _clear_model_env(monkeypatch): + for key in ("OPENAI_MODELS", "GEMINI_MODELS", "LLM_MODELS", "OPENAI_API_MODEL", "DEFAULT_MODEL", "GOOGLE_API_KEY"): + monkeypatch.delenv(key, raising=False) + + +def test_default_openai_models_include_gpt_54(): + models = llm_models.get_openai_models() + assert models[0] == "gpt-5.4-mini" + assert "gpt-5.4" in models + assert "gpt-4o-mini" in models + + +def test_default_gemini_models_include_latest(): + models = llm_models.get_gemini_models() + assert models[0] == "gemini-3.5-flash" + assert "gemini-2.5-flash" in models + assert "gemini-1.5-flash" in models + + +def test_openai_models_env_override(monkeypatch): + monkeypatch.setenv("OPENAI_MODELS", "gpt-5.4-mini,custom-model") + assert llm_models.get_openai_models() == ["gpt-5.4-mini", "custom-model"] + + +def test_gemini_models_env_override(monkeypatch): + monkeypatch.setenv("GEMINI_MODELS", "gemini-3.5-flash") + assert llm_models.get_gemini_models() == ["gemini-3.5-flash"] + + +def test_llm_models_combined_env_override(monkeypatch): + monkeypatch.setenv("LLM_MODELS", "gpt-5.4,gemini-3.5-flash") + assert llm_models.get_llm_models(include_gemini=False) == ["gpt-5.4", "gemini-3.5-flash"] + + +def test_llm_models_merges_gemini_when_key_set(monkeypatch): + monkeypatch.setenv("GOOGLE_API_KEY", "test-key") + models = llm_models.get_llm_models() + assert models[0].startswith("gpt-") + assert any(m.startswith("gemini-") for m in models) + + +def test_llm_models_omits_gemini_without_key(monkeypatch): + models = llm_models.get_llm_models() + assert all(not m.startswith("gemini-") for m in models) + + +def test_default_llm_model_from_openai_api_model(monkeypatch): + monkeypatch.setenv("OPENAI_API_MODEL", "gpt-5.4-mini") + assert llm_models.get_default_llm_model() == "gpt-5.4-mini" + + +def test_default_llm_model_falls_back_to_first_list_entry(monkeypatch): + assert llm_models.get_default_llm_model() == "gpt-5.4-mini" + + +def test_models_for_api_shape(monkeypatch): + rows = llm_models.get_models_for_api(include_gemini=False) + assert rows[0]["id"] == "gpt-5.4-mini" + assert "GPT" in rows[0]["name"] + + +def test_models_for_api_respects_openai_env(monkeypatch): + monkeypatch.setenv("OPENAI_MODELS", "gpt-5.4-mini,gpt-4o-mini") + rows = llm_models.get_models_for_api(include_gemini=False) + assert [row["id"] for row in rows] == ["gpt-5.4-mini", "gpt-4o-mini"] From 73d5469f140dd620421c05ef2a76c795056ff7b2 Mon Sep 17 00:00:00 2001 From: suung Date: Thu, 30 Jul 2026 12:09:45 +0200 Subject: [PATCH 21/23] Add complete benchmarking system for information retrieval evaluation (#4) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Add complete benchmarking system for information retrieval evaluation • Extended database schema with 4 new tables for benchmarking data • Created comprehensive data models with Pydantic validation • Implemented dataset loader with YAML/JSON support and validation • Built evaluation engine with precision, recall, F1, NDCG, MAP, MRR metrics • Added full Streamlit UI integration with 4 sub-tabs for benchmarking workflow • Included comprehensive test suite with 31 passing tests • Added plotly dependency for metrics visualization • Created sample TCFD benchmark dataset for testing • Updated .gitignore to exclude Cursor IDE files * Update pulsar-client version to 3.6.1 - Fix pulsar-client version from 3.6.0 to 3.6.1 (3.6.0 not available) - Add venv2/ to .gitignore * Add flexible benchmark dataset support and ClimRetrieve integration - Unify BenchmarkDataset and FlexibleBenchmarkDataset into single flexible class - Add DatasetType enum for IR and IE datasets - Add FlexibleDatasetRow with get_X() helper methods for variant column names - Create flexible CSV and SQLite loaders with automatic type detection - Update evaluation engine to compare flexible IR and IE datasets - Add comprehensive tests for flexible dataset loaders and evaluation - Create ClimRetrieve benchmark script with Excel support and content-based matching - Update benchmark_store to handle optional fields in unified model - Add documentation for flexible dataset flow - Update README with benchmark running instructions * Add flexible benchmark dataset support and ClimRetrieve integration - Unify BenchmarkDataset and FlexibleBenchmarkDataset into single flexible class - Add DatasetType enum for IR and IE datasets - Add FlexibleDatasetRow with get_X() helper methods for variant column names - Create flexible CSV and SQLite loaders with automatic type detection - Update evaluation engine to compare flexible IR and IE datasets - Add comprehensive tests for flexible dataset loaders and evaluation - Create ClimRetrieve benchmark script with Excel support and content-based matching - Update benchmark_store to handle optional fields in unified model - Add documentation for flexible dataset flow - Update README with benchmark running instructions * Format benchmark code and add merge assessment - Format all benchmark files with black and isort - Add assessment document for benchmark_zhenya merge requirements - Fix import formatting in test files * Add .devnotes/ to .gitignore for development assessments * Exclude report_analyst_enterprise from black and isort linting - Add report_analyst_enterprise to black exclude pattern - Add report_analyst_enterprise to isort skip list - Update test_linting.py to filter out report_analyst_enterprise errors - Fixes CI linting failure on node_modules files in report_analyst_enterprise * Format test_linting.py with black - Fix black formatting issues in test_linting.py - All linting checks now pass * Remove emojis from benchmarking feature - Remove emojis from benchmarking UI headers and tab labels - Remove emoji from benchmarking page header in streamlit app * Bugs identified, chunk dataset created, cliretrieve yaml created * Benchmarking tab added * added analysis against climretrieve question set, removed unnecessary logs * download error analysis dataset implementation, metrics evaluation bug fixed * creating a possibility for report analyst to be a package * fixing toml file * fixing toml file 2 * fixing toml file 3 * fixing toml file 4 * Remove accidental frontend artifacts from benchmark branch - Stop tracking committed node_modules and generated build outputs - Ignore artifact directories to prevent accidental recommits * Ignore coverage.xml artifact - Keep generated coverage report out of version control * Format files for latest Black CI - Apply Black-compatible formatting updates in benchmark storage and cache modules - Normalize formatting in affected benchmark and integration tests * implementing alignment option for the datasets of various structures * fixing missing climretrieve test * fixing black test * fixing flake8 bug * fixing warning issue * fixing warning issue * untracking data folder, modifying COLAB.md * Fix CI: benchmark_store fixture, black format, pytest-asyncio - Fix merge conflict in test_benchmark_store: use BenchmarkStore for schema only, single yield with try/finally - Apply black formatting to multi-line execute()/st.info() in 7 files - Fix pyproject.toml exclude typo and set asyncio_default_fixture_loop_scope * edited readme files, added a feature to download aligned datasets from UI * Resolve conflicts after syncing with remote * deleting merge conflict marker * Potential fix for pull request finding 'Unused import' Co-authored-by: Copilot Autofix powered by AI <223894421+github-code-quality[bot]@users.noreply.github.com> * Feature/benchmarking bugfixes (#38) * flexible dataset alignment implementation * Add unit tests for flexible benchmark modules. Cover flexible_alignment, dataset_normalizer, and classification_calibration to address PR #38 review feedback requesting tests for new classes. * Fix CI lint formatting on benchmark and integration tests. Apply black to new flexible benchmark test modules and isort fix for external service integration imports so the lint job passes. --------- Co-authored-by: suung * Fix duplicate Streamlit keys in classification panel * Format benchmark.py with Black * Fix CI ruff and raise benchmarking module coverage. - Clear quality-gate ruff failures on changed files after main merge - Add unit tests for error_analysis, UI, mapper/loader/engine edges - Fix error_analysis field access; drop dead upload UI and debug log - Fix cache_manager double-text() execute; use package-level --cov in QG * Remove unused BLE001 noqa flagged by CI ruff. * Use MockEmbedding when reloading vector store from cached vectors. VectorStoreIndex.from_documents no longer requires OPENAI_API_KEY when chunks already include embeddings, so CI cache tests pass without a real key. * Enforce E501 in streamlit_app and wrap long lines Drop file-level E501 noqa so quality-gate catches line length; split long f-strings/help/CSS/HTML for PR review. * Format streamlit_app with black after E501 wraps * Fix DocumentAnalyzer construction on Benchmarking page Add regression test that forbids DocumentAnalyzer(cache_manager=). Use ReportAnalyzer() like other nav pages; it already owns cache_manager. * Clear minor GitHub Code Quality notes on benchmarking Drop unused IGNORE_ACTION from conftest, document soft DB/OS excepts, use generate_chunk_id directly in apply, and satisfy mixed-return analysis after pytest.skip. --------- Co-authored-by: Huryn Yauheni Co-authored-by: YauheniHuryn <126465715+YauheniHuryn@users.noreply.github.com> Co-authored-by: nicooxxx Co-authored-by: Nico <150376360+nicooxxx@users.noreply.github.com> --- .env.example | 3 + .gitignore | 16 +- COLAB.md | 281 +++ EXPECTED_FILE_FORMATS.md | 110 + README.md | 128 +- docs/BENCHMARK_DATASET_FLOW.md | 257 +++ docs/benchmarking_plan.md | 283 +++ pyproject.toml | 33 +- .../config/datasets/climretrieve.yaml | 19 + report_analyst/core/analyzer.py | 374 ++-- report_analyst/core/benchmark/__init__.py | 1 + .../benchmark/classification_calibration.py | 233 ++ .../core/benchmark/dataset_loader.py | 184 ++ .../core/benchmark/dataset_mapper.py | 462 ++++ .../core/benchmark/dataset_normalizer.py | 188 ++ .../core/benchmark/error_analysis.py | 453 ++++ .../core/benchmark/evaluation_engine.py | 650 ++++++ .../core/benchmark/flexible_alignment.py | 240 +++ .../benchmark/retrieval_results_loader.py | 609 ++++++ report_analyst/core/cache_manager.py | 315 ++- .../core/storage/benchmark_store.py | 402 ++++ report_analyst/data/cache/analysis.db | Bin 4055040 -> 0 bytes report_analyst/models/benchmark.py | 235 ++ .../questionsets/climretrieve_questions.yaml | 793 +++++++ report_analyst/streamlit_app.py | 150 +- report_analyst/ui/__init__.py | 0 report_analyst/ui/benchmarking.py | 1892 +++++++++++++++++ .../frontend/public/pdf-viewer.es.js | 985 +++++++++ sample_datasets/tcfd_sample_benchmark.yaml | 77 + scripts/README_CLIMRETRIEVE.md | 130 ++ scripts/__init__.py | 11 + scripts/align_benchmark_datasets.py | 101 + scripts/evaluate_benchmark_from_csv.py | 155 ++ scripts/lib/affected-tests.sh | 8 + scripts/lib/git-changed.sh | 2 +- scripts/lint-changed.sh | 6 +- scripts/test_climretrieve_benchmark.py | 468 ++++ tests/conftest.py | 38 +- tests/test_analyzer.py | 238 ++- tests/test_benchmark_dataset_loader.py | 256 +++ tests/test_benchmark_evaluation_engine.py | 281 +++ tests/test_benchmark_store.py | 310 +++ tests/test_benchmarking_ui.py | 612 ++++++ tests/test_cache_manager.py | 116 +- tests/test_classification_calibration.py | 105 + tests/test_climretrieve_integration.py | 224 ++ tests/test_dataset_mapper.py | 82 + tests/test_dataset_mapper_coverage.py | 77 + tests/test_dataset_normalizer.py | 106 + tests/test_document_analyzer_construction.py | 16 + tests/test_error_analysis.py | 294 +++ tests/test_evaluation_engine_coverage.py | 104 + tests/test_external_service_integration.py | 4 +- tests/test_flexible_alignment.py | 142 ++ tests/test_flexible_dataset_evaluation.py | 142 ++ tests/test_question_loader.py | 41 +- tests/test_retrieval_results_loader.py | 290 +++ .../test_retrieval_results_loader_coverage.py | 109 + 58 files changed, 13513 insertions(+), 328 deletions(-) create mode 100644 COLAB.md create mode 100644 EXPECTED_FILE_FORMATS.md create mode 100644 docs/BENCHMARK_DATASET_FLOW.md create mode 100644 docs/benchmarking_plan.md create mode 100644 report_analyst/config/datasets/climretrieve.yaml create mode 100644 report_analyst/core/benchmark/__init__.py create mode 100644 report_analyst/core/benchmark/classification_calibration.py create mode 100644 report_analyst/core/benchmark/dataset_loader.py create mode 100644 report_analyst/core/benchmark/dataset_mapper.py create mode 100644 report_analyst/core/benchmark/dataset_normalizer.py create mode 100644 report_analyst/core/benchmark/error_analysis.py create mode 100644 report_analyst/core/benchmark/evaluation_engine.py create mode 100644 report_analyst/core/benchmark/flexible_alignment.py create mode 100644 report_analyst/core/benchmark/retrieval_results_loader.py create mode 100644 report_analyst/core/storage/benchmark_store.py delete mode 100644 report_analyst/data/cache/analysis.db create mode 100644 report_analyst/models/benchmark.py create mode 100644 report_analyst/questionsets/climretrieve_questions.yaml create mode 100644 report_analyst/ui/__init__.py create mode 100644 report_analyst/ui/benchmarking.py create mode 100644 report_analyst_enterprise/components/streamlit_component/frontend/public/pdf-viewer.es.js create mode 100644 sample_datasets/tcfd_sample_benchmark.yaml create mode 100644 scripts/README_CLIMRETRIEVE.md create mode 100644 scripts/__init__.py create mode 100755 scripts/align_benchmark_datasets.py create mode 100644 scripts/evaluate_benchmark_from_csv.py create mode 100755 scripts/test_climretrieve_benchmark.py create mode 100644 tests/test_benchmark_dataset_loader.py create mode 100644 tests/test_benchmark_evaluation_engine.py create mode 100644 tests/test_benchmark_store.py create mode 100644 tests/test_benchmarking_ui.py create mode 100644 tests/test_classification_calibration.py create mode 100644 tests/test_climretrieve_integration.py create mode 100644 tests/test_dataset_mapper.py create mode 100644 tests/test_dataset_mapper_coverage.py create mode 100644 tests/test_dataset_normalizer.py create mode 100644 tests/test_document_analyzer_construction.py create mode 100644 tests/test_error_analysis.py create mode 100644 tests/test_evaluation_engine_coverage.py create mode 100644 tests/test_flexible_alignment.py create mode 100644 tests/test_flexible_dataset_evaluation.py create mode 100644 tests/test_retrieval_results_loader.py create mode 100644 tests/test_retrieval_results_loader_coverage.py diff --git a/.env.example b/.env.example index 67f33ba6..19cbdc8d 100644 --- a/.env.example +++ b/.env.example @@ -15,3 +15,6 @@ STORAGE_PATH=./storage # Optional: Custom API Base URL # OPENAI_API_BASE=your_custom_api_base_url + +# Optional: search backend URL (if using report_analyst_search_backend) +# SEARCH_BACKEND_URL=http://localhost:8001 diff --git a/.gitignore b/.gitignore index 95afcd34..821a1f31 100644 --- a/.gitignore +++ b/.gitignore @@ -1,7 +1,8 @@ -/data +report_analyst/data .env *.log venv/ +venv2/ .gradio __pycache__/ @@ -21,6 +22,7 @@ report_analyst/temp/* # Coverage and test artifacts .coverage +coverage.xml .pytest_cache/ # Temporary files and diffs @@ -39,6 +41,9 @@ inputlucia # macOS system files .DS_Store +# Development notes and assessments (keep local, don't commit) +.devnotes/ + # Deployment guides and assessments (keep local, don't commit) CLOUDFLARE_ASSESSMENT.md CLOUDFLARE_QUICK_SUMMARY.md @@ -50,7 +55,14 @@ THEME_SWITCHING.md VERCEL_CHANGES_SUMMARY.md VERCEL_DEPLOYMENT_ASSESSMENT.md VERCEL_MIGRATION_GUIDE.md +.env +reports/ + +# Frontend generated artifacts +report_analyst_enterprise/components/web/node_modules/ +report_analyst_enterprise/components/web/dist/ +report_analyst_enterprise/components/streamlit_component/frontend/build/ node_modules Agents.md report-analyst.code-workspace -.vscode/launch.json \ No newline at end of file +.vscode/launch.json diff --git a/COLAB.md b/COLAB.md new file mode 100644 index 00000000..39daea13 --- /dev/null +++ b/COLAB.md @@ -0,0 +1,281 @@ +# Using report-analyst in Google Colab + +This guide explains how to use the **benchmarking and diagnostics** parts of +`report-analyst` from Google Colab (or any notebook environment). + +You can use it to: + +- **Evaluate retrieval systems from CSV files** (Precision@K, Recall@K, F1@K, + NDCG@K, MAP, MRR). +- **Generate detailed error-analysis tables** for retrieved chunks. +- **Align heterogeneous benchmark CSVs** (e.g. ClimRetrieve) to a common + internal schema using YAML configs. +- **Run PDF chunk subset diagnostics** to understand how different chunk sizes + relate to each other. + +The examples below focus on **offline evaluation and diagnostics**. They do not +run the Streamlit UI or the full backend. + +## 1. Installation in Colab + +Install directly from GitHub. For a clean install (e.g. after changing branches), you can uninstall and clear the pip cache first: + +```python +!pip uninstall report-analyst -y +!pip cache purge +``` + +Then install from your repo and branch (replace ``, ``, and `` with your values): + +```python +# Public repo – no token needed +!pip install --no-cache-dir "git+https://github.com//.git@#subdirectory=report-analyst" +``` + +If the repo is **private**, use a GitHub personal access token. Store it in Colab secrets or as an environment variable (e.g. `GITHUB_TOKEN`) and do **not** commit it: + +```python +import os +token = os.environ.get("GITHUB_TOKEN", "") # or from Colab secrets +if token: + !pip install --no-cache-dir "git+https://{token}@github.com//.git@#subdirectory=report-analyst" +else: + !pip install --no-cache-dir "git+https://github.com//.git@#subdirectory=report-analyst" +``` + +If the package lives at the repo root (no `report-analyst` subdirectory), omit `#subdirectory=report-analyst`. + +Optional boilerplate imports: + +```python +import pandas as pd +from pathlib import Path + +from report_analyst.core.benchmark.evaluation_engine import EvaluationEngine +from report_analyst.core.benchmark.retrieval_results_loader import load_flexible_dataset_from_csv +from report_analyst.core.benchmark import error_analysis +from report_analyst.core.benchmark.dataset_mapper import ( + DatasetMapperFactory, + list_available_dataset_ids, +) +``` + +If your CSVs are on Google Drive, mount it first: + +```python +from google.colab import drive +drive.mount("/content/drive") +``` + +### Loading datasets from a Git repository + +You can load ground truth and benchmark CSVs from raw GitHub URLs. Use **raw** URLs (e.g. `raw.githubusercontent.com`), not blob/view URLs, so you get CSV content, not HTML. + +**Public repo:** + +```python +import requests + +GROUND_TRUTH_URL = "https://raw.githubusercontent.com////path/to/your_ground_truth.csv" +BENCHMARK_URL = "https://raw.githubusercontent.com////path/to/your_benchmark.csv" + +ground_truth_csv = requests.get(GROUND_TRUTH_URL).text +benchmark_csv = requests.get(BENCHMARK_URL).text +``` + +**Private repo:** pass your token in the `Authorization` header (use Colab secrets or env, never hardcode): + +```python +import os +import requests + +token = os.environ.get("GITHUB_TOKEN", "") # set in Colab environment or secrets +headers = {"Authorization": f"token {token}"} if token else {} + +ground_truth_csv = requests.get(GROUND_TRUTH_URL, headers=headers).text +benchmark_csv = requests.get(BENCHMARK_URL, headers=headers).text +``` + +Then pass these **strings** to `load_flexible_dataset_from_csv` using `csv_content=`, as in section 2. + +**Alternative – clone the repo:** if you prefer a local path, clone the repo (with token in the URL for private repos) and pass file paths to the loader instead of `csv_content`. + +## 2. Core workflow: evaluate retrieval from CSVs + +Load ground truth and benchmark from CSV **content** (strings from URLs or file reads), then evaluate with the flexible-dataset API. Use `compare_flexible_datasets`, not `compare_datasets`. + +```python +from pathlib import Path +import pandas as pd + +from report_analyst.core.benchmark.evaluation_engine import EvaluationEngine +from report_analyst.core.benchmark.retrieval_results_loader import load_flexible_dataset_from_csv + +# If you loaded CSV strings from URLs (section 1), use csv_content= +ground_truth_ds = load_flexible_dataset_from_csv( + csv_content=ground_truth_csv, + dataset_name="ground_truth", +) +benchmark_ds = load_flexible_dataset_from_csv( + csv_content=benchmark_csv, + dataset_name="benchmark", +) + +# If you have local paths instead: +# ground_truth_ds = load_flexible_dataset_from_csv(csv_path="ground_truth_aligned.csv", dataset_name="ground_truth") +# benchmark_ds = load_flexible_dataset_from_csv(csv_path="benchmark_aligned.csv", dataset_name="benchmark") + +engine = EvaluationEngine() +k_values = [1, 3, 5, 10] +metrics = engine.compare_flexible_datasets(ground_truth_ds, benchmark_ds, k_values=k_values) + +# Build a metrics table (pivot) +rows = [] +for k, v in metrics.precision_at_k.items(): + rows.append({"k": k, "metric": "precision", "value": v}) +for k, v in metrics.recall_at_k.items(): + rows.append({"k": k, "metric": "recall", "value": v}) +for k, v in metrics.f1_at_k.items(): + rows.append({"k": k, "metric": "f1", "value": v}) +for k, v in metrics.ndcg_at_k.items(): + rows.append({"k": k, "metric": "ndcg", "value": v}) +rows.append({"k": None, "metric": "MAP", "value": metrics.mean_average_precision}) +rows.append({"k": None, "metric": "MRR", "value": metrics.mean_reciprocal_rank}) + +metrics_df = pd.DataFrame(rows) +metrics_df.pivot(index="metric", columns="k", values="value").round(4) +``` + +## 3. Error analysis in notebooks + +You can build a detailed, per-chunk error-analysis table similar to what the +Streamlit app exports, but directly inside a notebook. + +Use the flexible version of the helper so you can pass the same +`BenchmarkDataset` objects used above: + +```python +from report_analyst.core.benchmark import error_analysis + +top_k = 10 +df_error = error_analysis.build_error_analysis_dataframe_from_flexible( + ground_truth_dataset=ground_truth_ds, + benchmark_dataset=benchmark_ds, + top_k=top_k, +) + +# Inspect the first rows +display(df_error.head()) + +# Example: show only non-relevant retrieved chunks for manual inspection +df_false_positives = df_error[df_error["is_really_relevant"] == False] +display(df_false_positives.head()) +``` + +Columns in `df_error` include (depending on your data): + +- `report_name`, `question_id`, `question` +- `relevant_part_text` (from ground truth) +- `retrieved_chunk_text` (from benchmark) +- `position_in_top_k`, `model_score` +- `expert_relevance_label`, `is_really_relevant` +- `chunk_id`, `query_id` (identifiers) + +## 4. Dataset alignment via DatasetMapper + +If your raw CSVs do not yet match the internal benchmark schema, you can align +them using the same mapping logic that powers the CLI and Streamlit UI. + +Each dataset has a YAML config under `report_analyst/config/datasets/`. For +example, `climretrieve.yaml` defines how to map the ClimRetrieve CSVs. + +```python +import pandas as pd +from report_analyst.core.benchmark.dataset_mapper import ( + DatasetMapperFactory, + list_available_dataset_ids, +) + +print("Available dataset mapping IDs:", list_available_dataset_ids()) + +dataset_id = "climretrieve" # or another ID present in config/datasets +mapper = DatasetMapperFactory.get_mapper(dataset_id) + +# Raw CSVs from the original benchmark +df_gt_raw = pd.read_csv("climretrieve_ground_truth.csv") +df_bm_raw = pd.read_csv("climretrieve_benchmark.csv") + +# Align to the internal schema +df_gt_aligned = mapper.align_ground_truth(df_gt_raw) +df_bm_aligned = mapper.align_benchmark(df_bm_raw) + +display(df_gt_aligned.head()) +display(df_bm_aligned.head()) + +# Optionally, save aligned CSVs for later reuse +df_gt_aligned.to_csv("ground_truth_aligned.csv", index=False) +df_bm_aligned.to_csv("benchmark_results_aligned.csv", index=False) +``` + +You can then feed the aligned CSVs into the evaluation workflow described in +section 2. + +## 5. Chunk subset analysis (optional diagnostic) + +You can inspect how different chunk sizes relate to each other for a given PDF, +using the same `SentenceSplitter` logic as the analyzer but without embeddings. +This is independent from the IR metrics in `EvaluationEngine`, but can help you +understand whether smaller chunks are strict refinements or often fully +contained inside larger chunks. + +### From Python + +```python +from report_analyst.core.benchmark.chunk_subset_analysis import ( + analyze_pdf_chunk_subsets, + analyze_multiple_pdfs_chunk_subsets, +) + +# Single PDF, subset ratios between sizes 250, 440, 770 +result = analyze_pdf_chunk_subsets( + pdf_path="my_report.pdf", + chunk_sizes=[250, 440, 770], + chunk_overlap=20, +) + +# result.summary is a pandas DataFrame with: +# chunk_size_small, chunk_size_large, num_small, num_large, +# num_small_subsets, subset_ratio +display(result.summary) + +# Multiple PDFs (aggregated statistics across all files) +multi_result = analyze_multiple_pdfs_chunk_subsets( + pdf_paths=["report1.pdf", "report2.pdf"], + chunk_sizes=[250, 440, 770], + chunk_overlap=20, +) +display(multi_result.summary) +``` + +## 6. Scripts and local usage + +Scripts in `scripts/` (for example `align_benchmark_datasets.py`, +`evaluate_benchmark_from_csv.py`, `find_missing_relevant_parts.py`) are **not** +installed as commands when you `pip install` the library. + +To run them directly you should: + +1. Clone the repository locally. +2. Create a virtual environment and install dependencies. +3. Run the scripts from the project root, for example: + + ```bash + python scripts/evaluate_benchmark_from_csv.py \ + --reference path/to/ground_truth_aligned.csv \ + --input path/to/benchmark_results_aligned.csv \ + --k-values 1 3 5 10 + ``` + +In Colab, it is usually more convenient to **call the underlying Python +functions directly** (as shown in the examples above) instead of invoking these +scripts via shell commands. diff --git a/EXPECTED_FILE_FORMATS.md b/EXPECTED_FILE_FORMATS.md new file mode 100644 index 00000000..e4418f0f --- /dev/null +++ b/EXPECTED_FILE_FORMATS.md @@ -0,0 +1,110 @@ +# Expected File Formats for Datasets + +This document describes the structure expected for **ground truth** and **benchmark** datasets when using the Benchmarking / Evaluate features. Alignment (CSV/Excel only) maps your columns to these conventions. + +--- + +## CSV and Excel (information retrieval) + +Used for both ground truth and benchmark. Column names are **case-insensitive**; the loader accepts common variants. + +### After alignment (evaluation engine expects) + +| Role | Required columns | Optional / accepted variants | +|------|------------------|------------------------------| +| **Query** | `query_id` | `question_id`, `qid`, `query` | +| **Chunk** | `chunk_id` | `chunk`, `cid` | +| **Rank** | `position` | `rank`, `order`, `pos` | +| **Score** | `score` | `relevance_score`, `confidence_score`, `similarity_score` | +| **Document** | — | `report_id`, `document_id`, `doc_id`, `report` | + +For **ground truth** alignment (e.g. ClimRetrieve mapper), your CSV/Excel should have: + +- **document** (or `report`) — document/report identifier +- **question** — question text +- **context** or **relevant** — chunk/relevant text +- **relevance_label** (or `Source Relevance Score`, `relevance`, `label`) — relevance score or label + +For **benchmark** alignment: + +- **report** (or `document`) — report identifier +- **question** — question text +- **paragraph** (or `chunk`) — retrieved paragraph text +- **relevant_text** (or `relevant`) — optional; used for matching to ground truth +- **position** / **number** — optional; rank or paragraph number +- **relevant_text_sim** — optional; similarity score for ranking +- **label** / **relevance** — optional; relevance label + +If your file already has `query_id`, `chunk_id`, `position`, and `score` (or the accepted variants), it can be used without alignment. Otherwise you can: + +1. **Use a preset mapping** — Choose a dataset config (e.g. ClimRetrieve) under **Dataset Alignment** (CSV or Excel only) to convert to the expected structure. +2. **Flexible alignment wizard** — In the **Datasets** tab, use **Flexible Dataset Alignment (Wizard)** to:\n - Select which columns represent the query/criteria (single description or document + question).\n - Choose the chunk text column and, optionally, expert-annotated relevant parts.\n - Choose one or more ground-truth label columns (e.g. `relevance`, `usefulness`).\n - Choose one or more prediction/similarity columns on the benchmark side (e.g. model scores, `relevant_text_sim`).\n\n The wizard produces aligned ground truth and benchmark tables with a unified schema (`query_id`, `chunk_id`, optional `relevant_part_id`, text fields, labels, and predictions) so that both **ranking** and **classification** metrics can be derived from the same aligned data.\n3. **Configure columns manually** — In the upload flow, you can still use **Or configure columns manually** to map your columns directly to `query_id`, `chunk_id`, `position`, `score`, and `document` when you only need the basic IR schema. This is lighter than the wizard but provides fewer guarantees about compatibility between ground truth and benchmark datasets. + +--- + +## YAML and JSON (benchmark content schema) + +Used for **benchmark datasets** that follow the internal content schema (e.g. question sets with ground truth chunks). **Not** used for alignment; alignment works only for CSV and Excel. + +### Expected top-level structure + +```yaml +dataset_id: string +name: string +description: string +version: string +question_set: string +questions: array +``` + +### Each item in `questions` + +```yaml +question_id: string +question_text: string +ground_truth_chunks: array +``` + +### Each item in `ground_truth_chunks` + +```yaml +chunk_id: string +relevance_score: number # 0.0 to 1.0 +is_evidence: boolean +evidence_order: number # optional +annotation_notes: string # optional +``` + +Example (YAML): + +```yaml +dataset_id: my-benchmark +name: My Benchmark +description: Optional description +version: "1.0" +question_set: default +questions: + - question_id: q1 + question_text: "What are the company's emissions targets?" + ground_truth_chunks: + - chunk_id: c1 + relevance_score: 1.0 + is_evidence: true + evidence_order: 1 +``` + +**Note:** Report-level YAML (e.g. ClimRetrieve with `reports` → `questions` → `paragraphs`) is handled by a separate loader and is converted to the same internal format when supported. + +--- + +## Summary + +| Format | Use case | Alignment | +|--------|----------|-----------| +| **CSV** | Ground truth or benchmark (IR) | Yes — use Dataset Alignment (preset), the **Flexible alignment wizard**, or **Configure columns manually** if your columns differ. | +| **Excel** | Ground truth or benchmark (IR) | Yes — same as CSV. | +| **YAML** | Benchmark (content schema) | No — must match the schema above. | +| **JSON** | Benchmark (content schema) | No — same structure as YAML. | + +- For **preset** column mapping (e.g. ClimRetrieve), see `report_analyst/config/datasets/climretrieve.yaml` and add configs under `report_analyst/config/datasets/` for other conventions. +- For **custom** column names (e.g. `description`, `chunk_text`, `relevance`, `usefulness`, model prediction columns), you can either use the **Flexible alignment wizard** (recommended when you want both ranking and classification metrics) or **Configure columns manually** in the Dataset Management upload flow when you only need the basic IR schema. diff --git a/README.md b/README.md index c2118f59..b903cec3 100644 --- a/README.md +++ b/README.md @@ -93,11 +93,118 @@ python3 -m streamlit run report_analyst/streamlit_app.py In the web UI you can: - Upload a PDF sustainability report. -- Select a question set (e.g. TCFD, Lucia, Everest). +- Select a question set (e.g. TCFD, Lucia, Everest, ClimRetrieve). - Run the analysis and view: - Answers - Evidence and sources - Gaps and uncertainties +- Use the **Benchmarking** page to evaluate retrieval or extraction results against reference datasets (upload CSVs, align formats, run metrics, view results). + +## Running With Docker + +If you prefer to run the app in Docker instead of installing Python locally: + +1. **Build the image** + +```bash +docker build -t report-analyst . +``` + +2. **Run the Streamlit app** + +Make sure you have a `.env` file (for example by copying `.env.example`), then: + +```bash +docker run --rm -p 8501:8501 --env-file .env report-analyst +``` + +Open `http://localhost:8501` in your browser. + +3. **(Optional) Use Docker Compose** + +```bash +docker compose up app +``` + +You can also run CLI tools inside the same image, for example: + +```bash +docker run --rm --env-file .env report-analyst python scripts/test_climretrieve_benchmark.py --help +# Or with Compose: +docker compose run --rm cli python scripts/test_climretrieve_benchmark.py --help +``` + +The Dockerfile is configured to use Python 3.12 and installs system dependencies +required by the PDF and benchmarking stack (`libpoppler-cpp-dev`, `pkg-config`, etc.). + +## Running Benchmarks + +The project includes support for benchmarking retrieval and extraction systems against reference datasets. You can run benchmarks **from the Streamlit app** or via **command-line scripts**. + +### Benchmarking in the Streamlit App + +In the web UI, open the **Benchmarking** page from the sidebar. It provides four tabs: + +| Tab | Functionality | +|-----|----------------| +| **Datasets** | Upload ground truth and benchmark datasets (CSV, Excel, YAML, JSON). List and manage stored datasets. **Dataset Alignment (presets):** CSV/Excel can be aligned via configurable mappers (`report_analyst/config/datasets/`, e.g. ClimRetrieve). YAML/JSON that don’t match the app schema (e.g. ClimRetrieve Report-Level) are **aligned automatically** when possible (list-of-records with document, question, paragraph/relevance). **Flexible alignment (wizard):** for custom CSV/Excel files with expert labels and model predictions, use the **Flexible Dataset Alignment (Wizard)** section to interactively map query/criteria, chunk text, relevant parts, and label/prediction columns into a unified schema. Expected structures for CSV, Excel, YAML, and JSON: see [Expected file formats](EXPECTED_FILE_FORMATS.md). | +| **Evaluate** | Select a reference (ground truth) and a benchmark dataset, set K values and evaluation name, and run the evaluation. Metrics are computed with the same engine as the CLI (Precision@K, Recall@K, F1@K, NDCG@K, MAP, MRR). | +| **Results** | View and compare past evaluations, filter by dataset or date, and inspect metrics and charts. When a benchmark dataset also contains `relevance` / `usefulness` labels and corresponding `*_score_*` columns, this tab additionally shows a **Classification calibration (relevance/usefulness)** panel with calibration metrics (ECE, Brier scores, ROC AUC, F1) and a 3-class classification report for a selected model. | +| **Annotate** | Attach human annotations to evaluation results for later analysis. | + +To use the classification calibration view, prepare a benchmark dataset that includes: + +- Label columns: `relevance` and/or `usefulness` (ordinal labels 0–2). +- Model score columns: `relevance_score_` / `usefulness_score_` (continuous scores from your model or a notebook, for example as in `report_analyst/core/benchmark/02_classification_calibration.py`). + +Upload this dataset as a **Benchmark Dataset**. In the **Results** tab, pick the dataset, choose the label type and one or more score columns, then run the calibration analysis to inspect reliability and per-class performance. + +### ClimRetrieve Benchmark (CLI) + +Run the ClimRetrieve benchmark to evaluate your retrieval system against expert-annotated datasets: + +```bash +# Activate virtual environment +source venv/bin/activate # On Windows: venv\Scripts\activate + +# Run the benchmark (downloads datasets automatically) +python scripts/test_climretrieve_benchmark.py +``` + +This will: +1. Download ClimRetrieve datasets from GitHub to `data/climretrieve/` +2. Transform datasets to the required format +3. Compare reference (ground truth) vs input (your results) datasets +4. Display evaluation metrics (MAP, MRR, Precision@K, Recall@K, F1@K, NDCG@K) + +**Options:** +- `--skip-download`: Use existing downloaded datasets +- `--reference-path PATH`: Specify custom reference dataset path +- `--input-path PATH`: Specify custom input dataset path +- `--k-values 1 5 10 20`: Custom K values for metrics + +**Requirements:** +- `openpyxl` for Excel file support (install with `pip install openpyxl`) +- Internet connection for initial download + +### Generic CSV Evaluation + +For **any** pair of ground-truth and results CSVs (not only ClimRetrieve), use the flexible loader and evaluation engine: + +- **Script:** `scripts/evaluate_benchmark_from_csv.py` — load two CSVs, run evaluation, print metrics and optionally write JSON. +- **Loader:** `load_flexible_dataset_from_csv` auto-detects IR vs IE datasets and accepts common column names (`query_id`/`question_id`, `chunk_id`, `position`/`rank`, `score`, etc.). +- **Engine:** `EvaluationEngine.compare_flexible_datasets` computes Precision@K, Recall@K, F1@K, NDCG@K, MAP, MRR. + +Example: + +```bash +python -m scripts.evaluate_benchmark_from_csv \ + --reference path/to/ground_truth.csv \ + --input path/to/benchmark_results.csv \ + --k-values 1 3 5 10 +``` + +For expected CSV formats (IR vs IE), script options, and ClimRetrieve-specific details, see `scripts/README_CLIMRETRIEVE.md`. For using the same workflow in **Google Colab** (install from Git, load CSVs from URLs, run evaluation and error analysis), see `COLAB.md`. For more detailed setup options (API, search backend, jobs), see `INSTALL.md`. @@ -126,7 +233,7 @@ Open Sustainability Analyst is used by various organizations and research instit | Feature | Description | |---------|-------------| -| Preset Question Sets | Use research-validated frameworks (TCFD, Lucia, Everest, Denali, Kilimanjaro) | +| Preset Question Sets | Use research-validated frameworks (TCFD, Lucia, Everest, ClimRetrieve, Denali, Kilimanjaro) | | Custom Question Selection | Choose specific questions from any framework | | Framework Comparison | Analyze the same document with different frameworks side-by-side | | Framework Extensibility | Add your own question sets via YAML files | @@ -169,6 +276,16 @@ Open Sustainability Analyst is used by various organizations and research instit | REST API | Optional FastAPI integration for other systems | | Backend Integration | Optional enterprise S3+NATS upload and processing | +### Benchmarking + +| Feature | Description | +|---------|-------------| +| Streamlit Benchmarking UI | Upload ground truth and benchmark CSV/Excel/YAML/JSON, align datasets (e.g. ClimRetrieve), run evaluations, view results and charts, attach human annotations | +| Flexible CSV Loader | Auto-detect IR vs IE datasets; accept common column names (query_id, chunk_id, position, score, etc.) | +| Evaluation Metrics | Precision@K, Recall@K, F1@K, NDCG@K, MAP, MRR via `EvaluationEngine`; calibration and classification metrics (ECE, Brier, ROC AUC, F1, 3-class report) via `classification_calibration` | +| Dataset Alignment | **Preset mappers:** CSV/Excel can be mapped via YAML configs (e.g. `config/datasets/climretrieve.yaml`). **Flexible wizard:** CSV/Excel can be aligned through a guided wizard that defines `query_id`, `chunk_id`, optional relevant-part IDs, and ground-truth/prediction columns so that both ranking and classification metrics can be computed from the same aligned data. **YAML/JSON:** when the file does not match the app schema, alignment is attempted automatically (list-of-records with document, question, chunk, score). | +| Colab Support | Run evaluation and error analysis from Google Colab; see `COLAB.md` | + ### Advanced Features | Feature | Description | @@ -189,6 +306,8 @@ Current core question sets (in `report_analyst/questionsets/`): Comprehensive sustainability labeling and gap analysis framework (35+ questions). - **TCFD** – `tcfd_questions.yaml` Climate-related financial disclosure questions aligned with TCFD. +- **ClimRetrieve** – `climretrieve_questions.yaml` + Climate-focused questions aligned with the [ClimRetrieve](https://arxiv.org/abs/2406.09818) benchmark (adaptation, scenarios, targets, risks, etc.). Use this set when evaluating retrieval or analysis against ClimRetrieve datasets. - **Denali** – `denali_questions.yaml` Deeper sustainability analysis for specific focus areas. - **Kilimanjaro** – `kilimanjaro_questions.yaml` @@ -247,7 +366,10 @@ This repository is intentionally modular. The separation also reflects **differe report-analyst/ ├── report_analyst/ # Core open-source analysis engine (RPL) │ ├── core/ # Chunking, analysis, caching, workflows +│ │ └── benchmark/ # Evaluation engine, CSV loader, error analysis, dataset mapper +│ ├── config/datasets/ # YAML configs for dataset alignment (e.g. climretrieve.yaml) │ ├── questionsets/ # Question set YAML files (frameworks) +│ ├── ui/benchmarking.py # Streamlit benchmarking UI (datasets, evaluate, results, annotate) │ ├── streamlit_app.py # Main Streamlit application │ └── streamlit_app_backend.py # Legacy / backend-focused UI ├── report_analyst_api/ # FastAPI REST API (Climate+Tech Open License for Good) @@ -381,6 +503,8 @@ pytest tests/ -v --cov=report_analyst --cov-report=term-missing For detailed deployment patterns (Docker, Kubernetes, NATS workers, etc.), see: - `INSTALL.md` – Installation and configuration options +- `COLAB.md` – Using benchmarking and evaluation in Google Colab +- `scripts/README_CLIMRETRIEVE.md` – ClimRetrieve benchmark and generic CSV evaluation - `report_analyst_jobs/README.md` – Job processing and worker patterns - `docs/CI.md` – Continuous integration and testing diff --git a/docs/BENCHMARK_DATASET_FLOW.md b/docs/BENCHMARK_DATASET_FLOW.md new file mode 100644 index 00000000..503da85a --- /dev/null +++ b/docs/BENCHMARK_DATASET_FLOW.md @@ -0,0 +1,257 @@ +# Benchmark Dataset Flow + +This document describes the flexible dataset flow for benchmarking, supporting both Information Retrieval (IR) and Information Extraction (IE) datasets. + +## Overview + +The benchmarking system supports comparing two datasets with flexible column names: +- **Reference Dataset**: Ground truth (e.g., "climretrieve" for IR, "chatreport" for IE) +- **Input Dataset**: Actual results to evaluate + +### Dataset Types + +1. **Information Retrieval (IR)**: Compare retrieved chunks + - Example: climretrieve dataset + - Compares: chunk_id, position, relevance scores + +2. **Information Extraction (IE)**: Compare analysis/answers + - Example: chatreport, climatefinancebench datasets + - Compares: answers, categories, extracted values + +## Data Format + +The system is flexible and supports different column names. It automatically detects dataset type (IR vs IE) and maps columns. + +### Information Retrieval (IR) CSV Format + +```csv +query_id,report_id,chunk_id,chunk_text,position,score,similarity_score,llm_score +tcfd_1,report_001,chunk_001,"Climate risks include...",1,0.95,0.92,0.88 +tcfd_1,report_001,chunk_015,"Risk assessment...",2,0.89,0.87,0.85 +``` + +**Common column name variations (all supported):** +- Query ID: `query_id`, `question_id`, `qid`, `query` +- Chunk ID: `chunk_id`, `chunk`, `cid` +- Position: `position`, `rank`, `order`, `pos` +- Score: `score`, `relevance_score`, `confidence_score`, `similarity_score` +- Report ID: `report_id`, `document_id`, `doc_id`, `report` + +### Information Extraction (IE) CSV Format + +```csv +question_id,answer,category,confidence_score,extracted_value +tcfd_1,"The company identifies climate risks...","risk_identification",0.92,"High" +tcfd_2,"Strategy includes...","strategy",0.88,"Medium" +``` + +**Common column name variations (all supported):** +- Query ID: `query_id`, `question_id`, `qid`, `query` +- Answer: `answer`, `analysis`, `response`, `text` +- Category: `category`, `class`, `label`, `type` +- Score: `score`, `confidence_score`, `relevance_score` + +### SQLite Format + +Same structure as CSV, stored in a table (default: `retrieval_results`). + +## Flow A: Export Our Retrieval Results + +Export retrieval results from our internal tools to CSV/SQLite format. + +### Example: Export to CSV + +```python +from report_analyst.core.benchmark.retrieval_results_loader import export_retrieval_results_to_csv +from report_analyst.models.benchmark import RetrievalResultsDataset, RetrievalResultRow + +# Create dataset from our retrieval results +results = [ + RetrievalResultRow( + query_id="tcfd_1", + report_id="report_001", + chunk_id="chunk_001", + chunk_text="Climate risks...", + position=1, + score=0.95, + similarity_score=0.92 + ), + # ... more results +] + +dataset = RetrievalResultsDataset( + dataset_id="our_results_v1", + name="Our Retrieval Results", + source="internal", + results=results +) + +# Export to CSV +export_retrieval_results_to_csv(dataset, "our_results.csv") +``` + +## Flow B: Load User-Provided CSV (Webhook-Ready) + +Load retrieval results from CSV file or content. This function is designed to be webhook-ready. + +### Function: `load_retrieval_results_from_csv` + +```python +from report_analyst.core.benchmark.retrieval_results_loader import load_retrieval_results_from_csv + +# Option 1: Load from file path +dataset = load_retrieval_results_from_csv( + csv_path="user_results.csv", + dataset_id="user_results_v1", + dataset_name="User Uploaded Results" +) + +# Option 2: Load from CSV content (webhook-ready) +csv_content = """query_id,chunk_id,position,score +tcfd_1,chunk_001,1,0.95 +tcfd_1,chunk_015,2,0.89""" + +dataset = load_retrieval_results_from_csv( + csv_content=csv_content, + dataset_id="webhook_results", + dataset_name="Webhook Results" +) +``` + +### Webhook Example + +```python +from fastapi import FastAPI, UploadFile, File +from report_analyst.core.benchmark.retrieval_results_loader import load_retrieval_results_from_csv + +app = FastAPI() + +@app.post("/api/benchmark/upload-results") +async def upload_retrieval_results(file: UploadFile = File(...)): + """Webhook endpoint to upload retrieval results CSV""" + csv_content = await file.read() + + dataset = load_retrieval_results_from_csv( + csv_content=csv_content, + dataset_id=f"upload_{pd.Timestamp.now().strftime('%Y%m%d_%H%M%S')}", + dataset_name=file.filename + ) + + return {"dataset_id": dataset.dataset_id, "num_results": len(dataset.results)} +``` + +## Flow C: Load from SQLite + +```python +from report_analyst.core.benchmark.retrieval_results_loader import load_retrieval_results_from_sqlite + +dataset = load_retrieval_results_from_sqlite( + db_path="retrieval_results.db", + table_name="retrieval_results", + dataset_id="sqlite_results", + dataset_name="SQLite Results", + query_filter="report_id = 'report_001'" # Optional filter +) +``` + +## Comparing Datasets + +Compare a reference dataset (ground truth) against an input dataset (actual results). + +### Example + +```python +from report_analyst.core.benchmark.evaluation_engine import EvaluationEngine +from report_analyst.core.benchmark.retrieval_results_loader import ( + load_retrieval_results_from_csv, + load_retrieval_results_from_sqlite +) + +# Load reference dataset (e.g., climretrieve) +reference = load_retrieval_results_from_csv( + csv_path="climretrieve_reference.csv", + dataset_id="climretrieve_v1", + dataset_name="ClimRetrieve Reference" +) + +# Load input dataset (our results or user upload) +input_dataset = load_retrieval_results_from_csv( + csv_path="our_results.csv", + dataset_id="our_results_v1", + dataset_name="Our Results" +) + +# Compare datasets +engine = EvaluationEngine() +metrics = engine.compare_datasets( + reference_dataset=reference, + input_dataset=input_dataset, + k_values=[1, 3, 5, 10] +) + +print(f"MAP: {metrics.mean_average_precision:.3f}") +print(f"MRR: {metrics.mean_reciprocal_rank:.3f}") +print(f"Precision@5: {metrics.precision_at_k[5]:.3f}") +``` + +## Models + +### RetrievalResultRow + +Single row in a retrieval results dataset. + +```python +class RetrievalResultRow(BaseModel): + query_id: str + report_id: Optional[str] + chunk_id: str + chunk_text: Optional[str] + position: int # 1-indexed + score: float + similarity_score: Optional[float] + llm_score: Optional[float] + metadata: Dict[str, Any] +``` + +### RetrievalResultsDataset + +Collection of retrieval results. + +```python +class RetrievalResultsDataset(BaseModel): + dataset_id: str + name: str + description: Optional[str] + source: str # 'csv', 'sqlite', 'internal' + source_path: Optional[str] + results: List[RetrievalResultRow] + + def get_results_by_query(self, query_id: str) -> List[RetrievalResultRow] + def get_unique_queries(self) -> List[str] + def get_unique_reports(self) -> List[str] +``` + +## Key Functions + +### Loaders + +- `load_retrieval_results_from_csv()` - Load from CSV file or content (webhook-ready) +- `load_retrieval_results_from_sqlite()` - Load from SQLite database + +### Exporters + +- `export_retrieval_results_to_csv()` - Export dataset to CSV file + +### Evaluation + +- `EvaluationEngine.compare_datasets()` - Compare reference vs input datasets + +## Migration from Old Format + +The old format (YAML with ground truth chunks) is still supported through `BenchmarkDatasetContent`. The new format (`RetrievalResultsDataset`) is for comparing two retrieval result sets directly. + +## Next Steps + +1. Update UI to support CSV upload +2. Add export functionality to convert our retrieval results to CSV +3. Integrate with existing analyzer to export results automatically diff --git a/docs/benchmarking_plan.md b/docs/benchmarking_plan.md new file mode 100644 index 00000000..0d44954c --- /dev/null +++ b/docs/benchmarking_plan.md @@ -0,0 +1,283 @@ +# Information Retrieval Benchmarking Plan + +## Overview + +This document outlines the plan for implementing dataset upload capabilities, benchmarking the information retrieval pipeline, and adding human annotation functionality to the report-analyst system. + +## 1. Dataset Upload and Management + +### Data Structure +Benchmark datasets will be stored in YAML format: + +```yaml +dataset_id: "tcfd_climate_risks_v1" +name: "TCFD Climate Risks Benchmark Dataset" +description: "Ground truth chunk relevance for TCFD climate risk questions" +version: "1.0" +created_at: "2024-01-15" +question_set: "tcfd" +questions: + - question_id: "tcfd_1" + question_text: "What are the climate-related risks?" + ground_truth_chunks: + - chunk_id: "chunk_001" + relevance_score: 1.0 # Ground truth relevance (0.0-1.0) + is_evidence: true + evidence_order: 1 + annotation_notes: "Contains specific climate risk metrics" +``` + +### Database Schema Extensions + +```sql +-- Benchmark datasets table +CREATE TABLE benchmark_datasets ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + dataset_id TEXT UNIQUE, + name TEXT, + description TEXT, + version TEXT, + question_set TEXT, + file_path TEXT, -- Path to the dataset file + created_at TIMESTAMP, + updated_at TIMESTAMP +); + +-- Ground truth chunk relevance +CREATE TABLE ground_truth_chunks ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + dataset_id TEXT, + question_id TEXT, + chunk_id TEXT, + relevance_score REAL, + is_evidence BOOLEAN, + evidence_order INTEGER, + annotation_notes TEXT, + created_at TIMESTAMP, + FOREIGN KEY(dataset_id) REFERENCES benchmark_datasets(dataset_id) +); + +-- Benchmark evaluation results +CREATE TABLE benchmark_evaluations ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + dataset_id TEXT, + evaluation_name TEXT, + config_hash TEXT, -- Hash of retrieval configuration + retrieval_config TEXT, -- JSON of retrieval parameters + evaluation_metrics TEXT, -- JSON of computed metrics + created_at TIMESTAMP, + FOREIGN KEY(dataset_id) REFERENCES benchmark_datasets(dataset_id) +); + +-- Human annotations table +CREATE TABLE human_annotations ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + evaluation_id INTEGER, + question_id TEXT, + chunk_id TEXT, + human_relevance_score REAL, + human_is_evidence BOOLEAN, + human_evidence_order INTEGER, + annotation_notes TEXT, + annotator_id TEXT, + created_at TIMESTAMP, + FOREIGN KEY(evaluation_id) REFERENCES benchmark_evaluations(id) +); +``` + +## 2. Benchmarking Engine + +### Core Components + +1. **Dataset Loader**: Parse and validate benchmark datasets +2. **Evaluation Engine**: Compare retrieval results against ground truth +3. **Metrics Calculator**: Compute various evaluation metrics +4. **Configuration Manager**: Track different retrieval configurations + +### Evaluation Metrics + +- **Precision@K**: Percentage of retrieved chunks that are relevant +- **Recall@K**: Percentage of relevant chunks that were retrieved +- **F1@K**: Harmonic mean of precision and recall +- **Mean Reciprocal Rank (MRR)**: Average of reciprocal ranks of first relevant chunk +- **Normalized Discounted Cumulative Gain (NDCG)**: Considers relevance scores and ranking +- **Mean Average Precision (MAP)**: Average precision across all questions + +### Configuration Management + +```python +class RetrievalConfig: + chunk_size: int + chunk_overlap: int + top_k: int + use_llm_scoring: bool + embedding_model: str + similarity_threshold: float + llm_model: str +``` + +## 3. Human Annotation Interface + +### Annotation Workflow + +1. **Batch Annotation**: Annotate multiple chunks at once +2. **Individual Annotation**: Fine-tune individual chunk scores +3. **Evidence Assignment**: Mark chunks as evidence and assign order +4. **Notes and Comments**: Add contextual notes for annotations + +### Interface Features + +- Side-by-side comparison of retrieved vs. ground truth chunks +- Slider controls for relevance scores (0.0-1.0) +- Checkbox for evidence assignment +- Text areas for annotation notes +- Bulk operations for similar chunks +- Export/import annotation data + +## 4. UI Integration + +### New Streamlit Pages + +1. **Dataset Management Page** + - Upload benchmark datasets + - View existing datasets + - Dataset validation and preview + - Dataset versioning + +2. **Benchmarking Page** + - Select dataset and retrieval configuration + - Run benchmark evaluations + - View evaluation results and metrics + - Compare different configurations + - Export evaluation reports + +3. **Annotation Page** + - Select evaluation results to annotate + - Interactive annotation interface + - Progress tracking + - Annotation quality metrics + +4. **Results Dashboard** + - Historical evaluation trends + - Configuration performance comparison + - Annotation statistics + - Model improvement tracking + +## 5. API Extensions + +### New Endpoints + +```python +# Dataset management +POST /api/datasets/upload +GET /api/datasets +GET /api/datasets/{dataset_id} +DELETE /api/datasets/{dataset_id} + +# Benchmarking +POST /api/benchmarks/evaluate +GET /api/benchmarks/results +GET /api/benchmarks/{evaluation_id}/metrics + +# Annotations +POST /api/annotations/batch +GET /api/annotations/{evaluation_id} +PUT /api/annotations/{annotation_id} +``` + +## 6. File Structure + +``` +app/ +├── core/ +│ ├── benchmark/ +│ │ ├── __init__.py +│ │ ├── dataset_loader.py +│ │ ├── evaluation_engine.py +│ │ ├── metrics_calculator.py +│ │ └── config_manager.py +│ ├── annotation/ +│ │ ├── __init__.py +│ │ ├── annotation_manager.py +│ │ └── annotation_validator.py +│ └── storage/ +│ ├── benchmark_store.py +│ └── annotation_store.py +├── models/ +│ ├── benchmark.py +│ └── annotation.py +├── ui/ +│ ├── dataset_management.py +│ ├── benchmarking.py +│ └── annotation_interface.py +└── api/ + ├── benchmark_routes.py + └── annotation_routes.py +``` + +## 7. Implementation Phases + +### Phase 1: Core Infrastructure (Week 1-2) +- Database schema extensions +- Dataset loader and validator +- Basic evaluation engine +- Core metrics calculation + +### Phase 2: Benchmarking Engine (Week 3-4) +- Configuration management +- Evaluation workflow +- Results storage and retrieval +- Basic UI for running benchmarks + +### Phase 3: Annotation System (Week 5-6) +- Annotation data model +- Annotation interface +- Batch annotation capabilities +- Annotation validation + +### Phase 4: UI Integration (Week 7-8) +- Streamlit page integration +- Results visualization +- Configuration comparison +- Export/import functionality + +### Phase 5: Advanced Features (Week 9-10) +- Advanced metrics (NDCG, MAP) +- Annotation quality assessment +- Automated improvement suggestions +- Performance optimization + +## 8. Data Flow + +1. **Dataset Upload**: User uploads benchmark dataset → Validation → Storage +2. **Benchmark Execution**: Select dataset + config → Run retrieval → Compare with ground truth → Calculate metrics → Store results +3. **Annotation Process**: Select evaluation results → Annotate chunks → Store annotations → Update evaluation metrics +4. **Analysis**: View historical trends → Compare configurations → Identify improvement opportunities + +## 9. Configuration Management + +### Retrieval Configurations +- Predefined configurations (fast, balanced, accurate) +- Custom configuration builder +- Configuration templates +- A/B testing framework + +### Evaluation Settings +- Metrics to compute +- Evaluation thresholds +- Comparison baselines +- Export formats + +## 10. Quality Assurance + +### Dataset Validation +- Schema validation +- Data consistency checks +- Coverage analysis +- Duplicate detection + +### Annotation Quality +- Inter-annotator agreement +- Annotation consistency checks +- Quality metrics +- Automated validation \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index f22dafe3..fafdfc16 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,7 +1,31 @@ +[build-system] +requires = ["setuptools>=61", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "report-analyst" +version = "0.8.0rc" +description = "Open Sustainability Analyst - benchmark evaluation, error analysis, and dataset loading for retrieval evaluation" +readme = "README.md" +requires-python = ">=3.8" +dependencies = [ + "pandas", + "numpy", + "pydantic", + "pyyaml", +] + +[tool.setuptools.packages.find] +where = ["."] +include = ["report_analyst", "report_analyst.*"] + +[tool.setuptools.package-data] +"report_analyst" = ["data/*.csv", "config/datasets/*.yaml"] + [tool.isort] profile = "black" line_length = 127 -skip = [".git", ".hg", "venv", "venv2", "node_modules", "__pycache__"] +skip = [".git", ".hg", "venv", "venv2", "node_modules", "report_analyst_enterprise", "__pycache__", "htmlcov", "*.egg-info", "report_analyst/core/benchmark/02_classification_calibration.py"] [tool.black] line-length = 127 @@ -10,13 +34,20 @@ exclude = ''' /( \.git | \.hg + | \.venv | venv | venv2 | node_modules + | report_analyst_enterprise | __pycache__ + | \.pytest_cache + | htmlcov )/ ''' +[tool.pytest.ini_options] +asyncio_default_fixture_loop_scope = "function" + [tool.ruff] target-version = "py312" line-length = 127 diff --git a/report_analyst/config/datasets/climretrieve.yaml b/report_analyst/config/datasets/climretrieve.yaml new file mode 100644 index 00000000..56edd7a5 --- /dev/null +++ b/report_analyst/config/datasets/climretrieve.yaml @@ -0,0 +1,19 @@ +id: climretrieve + +ground_truth: + columns: + document: document + question: question + context: context + relevant: relevant + page_number: page_number + relevance_label: "Source Relevance Score" + +benchmark: + columns: + report_id: report + question: question + paragraph: paragraph + relevant_text: relevant_text + relevance_label: label + diff --git a/report_analyst/core/analyzer.py b/report_analyst/core/analyzer.py index cef5ae6e..92c9cb45 100644 --- a/report_analyst/core/analyzer.py +++ b/report_analyst/core/analyzer.py @@ -2,25 +2,17 @@ import json import logging import os -import pickle import re -import shutil -import sqlite3 -import sys from pathlib import Path -from typing import Any, AsyncGenerator, Dict, List, Optional, Tuple +from typing import Any, AsyncGenerator, Dict, List, Optional import numpy as np import pandas as pd import yaml from dotenv import load_dotenv -from langchain.chains import RetrievalQA -from langchain.chains.summarize import load_summarize_chain -from langchain.prompts import PromptTemplate -from langchain_openai import ChatOpenAI from llama_index.core import Document, Settings from llama_index.core.ingestion import IngestionCache -from llama_index.core.llms import ChatMessage, MessageRole +from llama_index.core.llms import ChatMessage from llama_index.core.node_parser import SentenceSplitter from llama_index.embeddings.openai import OpenAIEmbedding from llama_index.readers.file import PyMuPDFReader @@ -50,7 +42,10 @@ # Log available model keys logger.info(f"API Keys available - OpenAI: {bool(openai_key)}, Gemini: {bool(gemini_key)}") logger.info( - f"Backend mode - USE_BACKEND: {use_backend}, USE_CENTRALIZED_LLM: {use_centralized_llm}, USE_FULL_BACKEND_ANALYSIS: {use_full_backend_analysis}" + "Backend mode - USE_BACKEND: %s, USE_CENTRALIZED_LLM: %s, USE_FULL_BACKEND_ANALYSIS: %s", + use_backend, + use_centralized_llm, + use_full_backend_analysis, ) # If using backend for LLM, don't require local API keys @@ -137,10 +132,6 @@ def __init__(self): self.cache_path.mkdir(parents=True, exist_ok=True) self.llm_cache_path.mkdir(parents=True, exist_ok=True) - log_analysis_step(f"Storage path: {self.storage_path.resolve()}", "debug") - log_analysis_step(f"Cache path: {self.cache_path.resolve()}", "debug") - log_analysis_step(f"LLM cache path: {self.llm_cache_path.resolve()}", "debug") - # Set default question set self.question_set = "tcfd" self.questions = self._load_questions() @@ -153,10 +144,6 @@ def __init__(self): self.use_backend_llm = use_backend and (use_centralized_llm or use_full_backend_analysis) if self.use_backend_llm: - log_analysis_step( - "Skipping local LLM initialization - using backend for all LLM functionality", - "info", - ) # Set minimal placeholders for compatibility self.llm = None self.embeddings = None @@ -230,11 +217,11 @@ def _initialize_llm_clients(self) -> None: else: logger.warning("No OpenAI API key - embedding functionality will be limited") except ValueError as e: - log_analysis_step(f"LLM initialization deferred: {str(e)}", "warning") + log_analysis_step(f"LLM initialization deferred: {e!s}", "warning") self.llm = None self.embeddings = None except Exception as e: - log_analysis_step(f"Error initializing local LLM clients: {str(e)}", "error") + log_analysis_step(f"Error initializing local LLM clients: {e!s}", "error") raise def _get_cache_key(self, file_path: str) -> str: @@ -275,7 +262,7 @@ def _get_cache_key(self, file_path: str) -> str: else: # Local file path (existing behavior - maintain backwards compatibility) return f"{Path(file_path).stem}_{params_str}" - except Exception as e: + except Exception as e: # noqa: BLE001 logger.warning(f"[ANALYSIS] Cache ERROR: Failed to generate cache key: {e}") # Fallback: try to extract identifier safely if file_path.startswith("urn:report-analyst:backend:"): @@ -284,8 +271,8 @@ def _get_cache_key(self, file_path: str) -> str: else: try: return f"{Path(file_path).stem}_fallback" - except: - return f"unknown_fallback" + except Exception: # noqa: BLE001 + return "unknown_fallback" def _get_vector_store_collection_name(self, cache_key: str) -> str: """Generate a valid collection name from cache key.""" @@ -312,7 +299,7 @@ def _load_chunks_cache(self, cache_key: str) -> Optional[List]: return chunks logger.info("[ANALYSIS] Cache MISS: No cached chunks found") return None - except Exception as e: + except Exception as e: # noqa: BLE001 logger.warning(f"[ANALYSIS] Cache ERROR: Failed to load chunks cache: {e}") return None @@ -331,7 +318,7 @@ def _save_chunks_cache(self, cache_key: str, chunks: List) -> None: with open(cache_file, "w", encoding="utf-8") as f: json.dump(serializable_chunks, f) logger.info(f"[ANALYSIS] ✓ Cache SAVE: Saved {len(chunks)} chunks to cache") - except Exception as e: + except Exception as e: # noqa: BLE001 logger.warning(f"[ANALYSIS] Cache ERROR: Failed to save chunks cache: {e}") def _load_vector_store(self, cache_key: str, chunks: List) -> Optional[LlamaVectorStore]: @@ -346,7 +333,7 @@ def _load_vector_store(self, cache_key: str, chunks: List) -> Optional[LlamaVect vector_store = LlamaVectorStore(store_dir) # Try to load the store - this will verify if it's valid if vector_store.load(): - logger.info(f"[ANALYSIS] ✓ Cache HIT: Loaded vector store from cache") + logger.info("[ANALYSIS] ✓ Cache HIT: Loaded vector store from cache") return vector_store except Exception as inner_e: logger.error( @@ -358,7 +345,7 @@ def _load_vector_store(self, cache_key: str, chunks: List) -> Optional[LlamaVect return None except Exception as e: logger.warning(f"[ANALYSIS] Cache ERROR: Failed to load vector store cache: {e}") - logger.debug(f"Full vector store cache error: {str(e)}", exc_info=True) + logger.debug(f"Full vector store cache error: {e!s}", exc_info=True) return None async def score_chunk_relevance(self, question: str, chunk_text: str) -> float: @@ -366,59 +353,51 @@ async def score_chunk_relevance(self, question: str, chunk_text: str) -> float: if not self.use_cache: Settings.ingestion_cache = None - log_analysis_step(f"Computing relevance score for chunk: {chunk_text[:100]}...") - try: response = await self.llm.achat( - prompt=f"""As a senior equity analyst with expertise in climate science evaluating a company's sustainability report, you are tasked with evaluating text fragments for their usefulness in answering specific TCFD questions. - -Your task is to score the relevance and quality of evidence in each text fragment. Consider: - -1. Specificity and Concreteness: - - Quantitative data and specific metrics (highest value) - - Concrete policies and procedures - - Specific commitments with timelines - - General statements or vague claims (lowest value) - -2. Evidence Quality: - - Verifiable data and third-party verification - - Clear methodologies and frameworks - - Specific examples and case studies - - Unsubstantiated claims (lowest value) - -3. Direct Relevance: - - Direct answers to the question components - - Related but indirect information - - Contextual background - - Unrelated information (lowest value) - -4. Disclosure Quality: - - Comprehensive and transparent disclosure - - Balanced reporting (both positive and negative) - - Clear acknowledgment of limitations - - Potential greenwashing or selective disclosure (lowest value) - -Score from 0.0 to 1.0 where: -0.0 = Not useful (generic statements, unrelated content) -0.3 = Contains relevant context but no specific evidence -0.6 = Contains useful specific information but requires additional context -1.0 = Contains critical evidence or specific details that directly answer the question - -Question: {question} - -Text to evaluate: -{chunk_text} - -Output only the numeric score (0.0-1.0):""" + prompt=( + "As a senior equity analyst with expertise in climate science evaluating a company's " + "sustainability report, you are tasked with evaluating text fragments for their " + "usefulness in answering specific TCFD questions.\n\n" + "Your task is to score the relevance and quality of evidence in each text fragment. Consider:\n\n" + "1. Specificity and Concreteness:\n" + " - Quantitative data and specific metrics (highest value)\n" + " - Concrete policies and procedures\n" + " - Specific commitments with timelines\n" + " - General statements or vague claims (lowest value)\n\n" + "2. Evidence Quality:\n" + " - Verifiable data and third-party verification\n" + " - Clear methodologies and frameworks\n" + " - Specific examples and case studies\n" + " - Unsubstantiated claims (lowest value)\n\n" + "3. Direct Relevance:\n" + " - Direct answers to the question components\n" + " - Related but indirect information\n" + " - Contextual background\n" + " - Unrelated information (lowest value)\n\n" + "4. Disclosure Quality:\n" + " - Comprehensive and transparent disclosure\n" + " - Balanced reporting (both positive and negative)\n" + " - Clear acknowledgment of limitations\n" + " - Potential greenwashing or selective disclosure (lowest value)\n\n" + "Score from 0.0 to 1.0 where:\n" + "0.0 = Not useful (generic statements, unrelated content)\n" + "0.3 = Contains relevant context but no specific evidence\n" + "0.6 = Contains useful specific information but requires additional context\n" + "1.0 = Contains critical evidence or specific details that directly answer the question\n\n" + f"Question: {question}\n\n" + f"Text to evaluate:\n{chunk_text}\n\n" + "Output only the numeric score (0.0-1.0):" + ) ) score = float(response.message.content.strip()) score = max(0.0, min(1.0, score)) - log_analysis_step(f"Computed relevance score: {score:.4f}") + logger.debug(f"[ANALYSIS] Computed relevance score: {score:.4f}") return score - except Exception as e: - log_analysis_step(f"Error scoring chunk relevance: {str(e)}", "error") + except Exception as e: # noqa: BLE001 + logger.error(f"[ANALYSIS] Error scoring chunk relevance: {e!s}") return 0.0 async def score_chunk_relevance_batch(self, question: str, chunks: List[Dict], single_call: bool = True) -> List[float]: @@ -432,49 +411,44 @@ async def score_chunk_relevance_batch(self, question: str, chunks: List[Dict], s try: if single_call: # Batch scoring - all chunks in one call - chunks_text = "\n\n".join([f"[CHUNK {i+1}]\n{chunk['text']}" for i, chunk in enumerate(chunks)]) + chunks_text = "\n\n".join([f"[CHUNK {i + 1}]\n{chunk['text']}" for i, chunk in enumerate(chunks)]) response = await self.llm.achat( - prompt=f"""As a senior equity analyst with expertise in climate science evaluating a company's sustainability report, you are tasked with evaluating text fragments for their usefulness in answering specific TCFD questions. - -Your task is to score the relevance and quality of evidence in each text fragment marked as [CHUNK X]. Consider: - -1. Specificity and Concreteness: - - Quantitative data and specific metrics (highest value) - - Concrete policies and procedures - - Specific commitments with timelines - - General statements or vague claims (lowest value) - -2. Evidence Quality: - - Verifiable data and third-party verification - - Clear methodologies and frameworks - - Specific examples and case studies - - Unsubstantiated claims (lowest value) - -3. Direct Relevance: - - Direct answers to the question components - - Related but indirect information - - Contextual background - - Unrelated information (lowest value) - -4. Disclosure Quality: - - Comprehensive and transparent disclosure - - Balanced reporting (both positive and negative) - - Clear acknowledgment of limitations - - Potential greenwashing or selective disclosure (lowest value) - -For each chunk marked [CHUNK X], provide a score from 0.0 to 1.0 where: -0.0 = Not useful (generic statements, unrelated content) -0.3 = Contains relevant context but no specific evidence -0.6 = Contains useful specific information but requires additional context -1.0 = Contains critical evidence or specific details that directly answer the question - -Question: {question} - -Text fragments to evaluate: -{chunks_text} - -Output only the scores, one per line, in order:""" + prompt=( + "As a senior equity analyst with expertise in climate science evaluating a company's " + "sustainability report, you are tasked with evaluating text fragments for their " + "usefulness in answering specific TCFD questions.\n\n" + "Your task is to score the relevance and quality of evidence in each text fragment " + "marked as [CHUNK X]. Consider:\n\n" + "1. Specificity and Concreteness:\n" + " - Quantitative data and specific metrics (highest value)\n" + " - Concrete policies and procedures\n" + " - Specific commitments with timelines\n" + " - General statements or vague claims (lowest value)\n\n" + "2. Evidence Quality:\n" + " - Verifiable data and third-party verification\n" + " - Clear methodologies and frameworks\n" + " - Specific examples and case studies\n" + " - Unsubstantiated claims (lowest value)\n\n" + "3. Direct Relevance:\n" + " - Direct answers to the question components\n" + " - Related but indirect information\n" + " - Contextual background\n" + " - Unrelated information (lowest value)\n\n" + "4. Disclosure Quality:\n" + " - Comprehensive and transparent disclosure\n" + " - Balanced reporting (both positive and negative)\n" + " - Clear acknowledgment of limitations\n" + " - Potential greenwashing or selective disclosure (lowest value)\n\n" + "For each chunk marked [CHUNK X], provide a score from 0.0 to 1.0 where:\n" + "0.0 = Not useful (generic statements, unrelated content)\n" + "0.3 = Contains relevant context but no specific evidence\n" + "0.6 = Contains useful specific information but requires additional context\n" + "1.0 = Contains critical evidence or specific details that directly answer the question\n\n" + f"Question: {question}\n\n" + f"Text fragments to evaluate:\n{chunks_text}\n\n" + "Output only the scores, one per line, in order:" + ) ) # Parse scores from response @@ -483,8 +457,8 @@ async def score_chunk_relevance_batch(self, question: str, chunks: List[Dict], s if len(scores) != len(chunks): raise ValueError(f"Got {len(scores)} scores for {len(chunks)} chunks") return scores - except Exception as e: - log_analysis_step(f"Error parsing batch scores: {str(e)}", "error") + except Exception as e: # noqa: BLE001 + logger.error(f"[ANALYSIS] Error parsing batch scores: {e!s}") return [0.0] * len(chunks) else: @@ -493,18 +467,18 @@ async def score_chunk_relevance_batch(self, question: str, chunks: List[Dict], s for i, chunk in enumerate(chunks): score = await self.score_chunk_relevance(question, chunk["text"]) scores.append(score) - log_analysis_step(f"Scored chunk {i+1}/{len(chunks)}: {score:.2f}") + logger.debug(f"[ANALYSIS] Scored chunk {i + 1}/{len(chunks)}: {score:.2f}") return scores - except Exception as e: - log_analysis_step(f"Error in batch scoring: {str(e)}", "error") + except Exception as e: # noqa: BLE001 + logger.error(f"[ANALYSIS] Error in batch scoring: {e!s}") return [0.0] * len(chunks) def _load_cached_answers(self, file_path: str) -> Dict: """Load cached answers for a file with exact configuration match""" try: # Log current configuration - logger.info(f"Current configuration:") + logger.info("Current configuration:") logger.info(f"- Chunk size: {self.chunk_params['chunk_size']}") logger.info(f"- Overlap: {self.chunk_params['chunk_overlap']}") logger.info(f"- Top K: {self.chunk_params['top_k']}") @@ -521,24 +495,31 @@ def _load_cached_answers(self, file_path: str) -> Dict: logger.info(f"- {cf.name}") # Generate cache key for current configuration - cache_key = f"cs{self.chunk_params['chunk_size']}_ov{self.chunk_params['chunk_overlap']}_tk{self.chunk_params['top_k']}_m{model_name}_qs{self.question_set}" + cache_key = ( + f"cs{self.chunk_params['chunk_size']}" + f"_ov{self.chunk_params['chunk_overlap']}" + f"_tk{self.chunk_params['top_k']}" + f"_m{model_name}" + f"_qs{self.question_set}" + ) file_stem = Path(file_path).stem cache_file = Path(self.cache_path) / f"{file_stem}_{cache_key}.json" logger.info(f"Looking for cache file: {cache_file}") if not cache_file.exists(): - logger.info(f"No cache file found for current configuration") + logger.info("No cache file found for current configuration") return {} with open(cache_file, "r") as f: cached_data = json.load(f) logger.info(f"Loaded cache data with keys: {list(cached_data.keys())}") - logger.info(f"Cache data structure: {json.dumps(cached_data, indent=2)[:500]}...") # Show first 500 chars + cache_preview = json.dumps(cached_data, indent=2)[:500] + logger.info("Cache data structure: %s...", cache_preview) # Show first 500 chars return cached_data - except Exception as e: - logger.error(f"Error loading cache: {str(e)}") + except Exception as e: # noqa: BLE001 + logger.error(f"Error loading cache: {e!s}") return {} def _validate_cache_filename(self, filename: str) -> bool: @@ -560,7 +541,7 @@ def _save_cached_answers(self, file_path: str, answers: Dict) -> None: self._answers_cache[cache_key] = answers logger.info(f"[ANALYSIS] ✓ Cache SAVE: Saved answers to {cache_path}") - except Exception as e: + except Exception as e: # noqa: BLE001 logger.warning(f"[ANALYSIS] Cache ERROR: Failed to save answers: {e}") async def process_document( @@ -587,10 +568,16 @@ async def process_document( logger.info(f"[ANALYSIS] Starting document processing for {file_path}") logger.info(f"[ANALYSIS] Selected questions: {selected_questions}") logger.info( - f"[ANALYSIS] LLM scoring: {use_llm_scoring}, Single call: {single_call}, Force recompute: {force_recompute}" + "[ANALYSIS] LLM scoring: %s, Single call: %s, Force recompute: %s", + use_llm_scoring, + single_call, + force_recompute, ) logger.info( - f"[ANALYSIS] Current chunk parameters: size={self.chunk_params['chunk_size']}, overlap={self.chunk_params['chunk_overlap']}, top_k={self.chunk_params['top_k']}" + "[ANALYSIS] Current chunk parameters: size=%s, overlap=%s, top_k=%s", + self.chunk_params["chunk_size"], + self.chunk_params["chunk_overlap"], + self.chunk_params["top_k"], ) if not self.use_backend_llm and self.llm is None: @@ -631,7 +618,10 @@ async def process_document( logger.info(f"[ANALYSIS] Saved {len(chunks)} pre-retrieved chunks to cache") else: logger.info( - f"[ANALYSIS] Getting document chunks from cache for {file_path} with size={self.chunk_params['chunk_size']}, overlap={self.chunk_params['chunk_overlap']}" + "[ANALYSIS] Getting document chunks from cache for %s with size=%s, overlap=%s", + file_path, + self.chunk_params["chunk_size"], + self.chunk_params["chunk_overlap"], ) chunks = self.cache_manager.get_document_chunks( file_path=file_path, @@ -641,15 +631,19 @@ async def process_document( logger.info(f"[ANALYSIS] Retrieved {len(chunks)} chunks from cache") if not chunks: - logger.info(f"[ANALYSIS] No chunks found in cache with current parameters, creating new chunks") + logger.info("[ANALYSIS] No chunks found in cache with current parameters, creating new chunks") # If no chunks in cache with current parameters, create them # Check if file_path is a URN (backend resource) if file_path.startswith("urn:report-analyst:backend:"): logger.warning( - f"[ANALYSIS] URN detected but no pre-retrieved chunks provided. Cannot process backend resource without chunks." + "[ANALYSIS] URN detected but no pre-retrieved chunks provided. " + "Cannot process backend resource without chunks." ) yield { - "error": "Backend resource requires pre-retrieved chunks. Please ensure chunks are retrieved from backend first." + "error": ( + "Backend resource requires pre-retrieved chunks. " + "Please ensure chunks are retrieved from backend first." + ) } return chunks = self._create_chunks(file_path) @@ -676,7 +670,22 @@ async def process_document( yield {"error": f"Question {question_number} not found"} continue - question_id = f"{self.question_set}_{question_number}" + # Use the same prefix extraction logic as get_question_by_number + question_set_mapping = { + "everest": "ev", + "tcfd": "tcfd", + "s4m": "s4m", + "lucia": "lucia", + "climretrieve": "climretr", + } + question_prefix = question_set_mapping.get(self.question_set, self.question_set) + # If still not found in mapping, try to extract prefix from actual question IDs + if question_prefix == self.question_set and self.questions: + first_qid = next(iter(self.questions.keys()), "") + if first_qid and "_" in first_qid: + question_prefix = first_qid.split("_")[0] + + question_id = f"{question_prefix}_{question_number}" logger.info(f"[ANALYSIS] Question ID: {question_id}") yield {"status": f"Processing question {question_number}: {question_data['text'][:50]}..."} @@ -704,16 +713,16 @@ async def process_document( for i, chunk in enumerate(similar_chunks): if i < len(llm_scores): chunk["llm_score"] = llm_scores[i] - logger.debug(f"Applied LLM score {llm_scores[i]:.3f} to chunk {i+1}") + logger.debug(f"Applied LLM score {llm_scores[i]:.3f} to chunk {i + 1}") else: chunk["llm_score"] = 0.0 - logger.warning(f"No LLM score available for chunk {i+1}") + logger.warning(f"No LLM score available for chunk {i + 1}") logger.info(f"[ANALYSIS] Applied LLM scores to {len(similar_chunks)} chunks") except Exception as e: logger.error( - f"[ANALYSIS] Error applying LLM scores: {str(e)}", + f"[ANALYSIS] Error applying LLM scores: {e!s}", exc_info=True, ) # Set default scores if LLM scoring fails @@ -761,8 +770,12 @@ async def process_document( "metadata": similar_chunks[chunk_idx]["metadata"], } ) + evidence_preview = evidence.get("text", "")[:100] logger.info( - f"Added evidence {evidence_idx + 1} from chunk {chunk_num}: {evidence.get('text', '')[:100]}..." + "Added evidence %s from chunk %s: %s...", + evidence_idx + 1, + chunk_num, + evidence_preview, ) # Replace evidence array with processed items @@ -804,14 +817,14 @@ async def process_document( except Exception as e: logger.error( - f"[ANALYSIS] Error processing question {question_number}: {str(e)}", + f"[ANALYSIS] Error processing question {question_number}: {e!s}", exc_info=True, ) - yield {"error": f"Error processing question {question_number}: {str(e)}"} + yield {"error": f"Error processing question {question_number}: {e!s}"} except Exception as e: - logger.error(f"[ANALYSIS] Error processing document: {str(e)}", exc_info=True) - yield {"error": f"Error processing document: {str(e)}"} + logger.error(f"[ANALYSIS] Error processing document: {e!s}", exc_info=True) + yield {"error": f"Error processing document: {e!s}"} def _create_chunks(self, file_path: str) -> List[Dict[str, Any]]: """Create document chunks with embeddings""" @@ -847,7 +860,7 @@ def _create_chunks(self, file_path: str) -> List[Dict[str, Any]]: for i in range(0, len(text_chunks), BATCH_SIZE): batch = text_chunks[i : i + BATCH_SIZE] - logger.info(f"Computing embeddings for batch {i//BATCH_SIZE + 1}/{(len(text_chunks)-1)//BATCH_SIZE + 1}") + logger.info(f"Computing embeddings for batch {i // BATCH_SIZE + 1}/{(len(text_chunks) - 1) // BATCH_SIZE + 1}") # Get text from batch and clean it batch_texts = [] @@ -859,7 +872,7 @@ def _create_chunks(self, file_path: str) -> List[Dict[str, Any]]: text = " ".join(text.replace("\x00", "").split()) batch_texts.append(text) else: - logger.warning(f"Skipping empty or invalid chunk") + logger.warning("Skipping empty or invalid chunk") continue try: @@ -870,7 +883,7 @@ def _create_chunks(self, file_path: str) -> List[Dict[str, Any]]: logger.info(f"Successfully computed {len(batch_embeddings)} embeddings") # Create chunk dictionaries with embeddings - for chunk, embedding in zip(batch, batch_embeddings): + for chunk, embedding in zip(batch, batch_embeddings, strict=False): if embedding is not None: # Only add chunks with valid embeddings chunk_dict = { "text": chunk.text, @@ -882,10 +895,10 @@ def _create_chunks(self, file_path: str) -> List[Dict[str, Any]]: chunks_data.append(chunk_dict) logger.debug(f"Added chunk with text length {len(chunk.text)}") else: - logger.warning(f"Skipping chunk - embedding is None") + logger.warning("Skipping chunk - embedding is None") except Exception as e: - logger.error(f"Error computing embeddings for batch: {str(e)}", exc_info=True) + logger.error(f"Error computing embeddings for batch: {e!s}", exc_info=True) # Continue with next batch, storing chunks without embeddings for chunk in batch: chunk_dict = { @@ -896,7 +909,7 @@ def _create_chunks(self, file_path: str) -> List[Dict[str, Any]]: "computed_score": 0.0, } chunks_data.append(chunk_dict) - logger.warning(f"Added chunk without embedding due to error") + logger.warning("Added chunk without embedding due to error") # Log embedding statistics chunks_with_embeddings = sum(1 for c in chunks_data if c["embedding"] is not None) @@ -908,16 +921,16 @@ def _create_chunks(self, file_path: str) -> List[Dict[str, Any]]: logger.info(f"Saving {len(valid_chunks)} valid chunks to cache") try: self.cache_manager.save_vectors(file_path, valid_chunks) - logger.info(f"Successfully saved chunks and vectors to cache") + logger.info("Successfully saved chunks and vectors to cache") except Exception as e: - logger.error(f"Failed to save vectors to cache: {str(e)}", exc_info=True) + logger.error(f"Failed to save vectors to cache: {e!s}", exc_info=True) else: logger.warning("No valid chunks to save to cache") return chunks_data except Exception as e: - logger.error(f"Error creating chunks: {str(e)}", exc_info=True) + logger.error(f"Error creating chunks: {e!s}", exc_info=True) raise async def _analyze_chunks( @@ -933,7 +946,7 @@ async def _analyze_chunks( # Process chunks first processed_chunks = [] for i, chunk in enumerate(chunks): - logger.debug(f"Processing chunk {i+1}/{len(chunks)}") + logger.debug(f"Processing chunk {i + 1}/{len(chunks)}") # Get similarity score from either 'score' (from vector store) or 'similarity_score' (from cache) similarity_score = chunk.get("score", chunk.get("similarity_score", 0.0)) @@ -987,7 +1000,7 @@ async def _analyze_chunks( logger.info(response_text) logger.info("=== End LLM Response ===") except Exception as e: - logger.error(f"Error getting LLM response: {str(e)}") + logger.error(f"Error getting LLM response: {e!s}") raise # Parse the response @@ -1058,8 +1071,12 @@ async def _analyze_chunks( "metadata": processed_chunks[chunk_idx]["metadata"], } ) + evidence_preview = evidence.get("text", "")[:100] logger.info( - f"Added evidence {evidence_idx + 1} from chunk {chunk_num}: {evidence.get('text', '')[:100]}..." + "Added evidence %s from chunk %s: %s...", + evidence_idx + 1, + chunk_num, + evidence_preview, ) # Replace evidence array with processed items @@ -1074,9 +1091,9 @@ async def _analyze_chunks( return result except Exception as e: - logger.error(f"Error analyzing chunks: {str(e)}", exc_info=True) + logger.error(f"Error analyzing chunks: {e!s}", exc_info=True) return { - "ANSWER": f"Error analyzing document: {str(e)}", + "ANSWER": f"Error analyzing document: {e!s}", "SCORE": 0, "EVIDENCE": [], "GAPS": ["Error during analysis"], @@ -1095,21 +1112,15 @@ def _load_questions(self) -> dict: Path.cwd() / "questionsets" / f"{self.question_set}_questions.yaml", # current working directory ] - log_analysis_step(f"Looking for {self.question_set}_questions.yaml in:") - for path in possible_paths: - log_analysis_step(f"- {path.resolve()}") - yaml_file = None for path in possible_paths: if path.exists(): yaml_file = path - log_analysis_step(f"✓ Found questions file at: {path.resolve()}") break if not yaml_file: - log_analysis_step( - f"Could not find questions file for {self.question_set} in any of: {[str(p) for p in possible_paths]}", - "error", + logger.error( + f"Could not find questions file for {self.question_set} in any of: {[str(p) for p in possible_paths]}" ) return {} @@ -1129,11 +1140,11 @@ def _load_questions(self) -> dict: } log_analysis_step(f"Added question {q_id}: {questions[q_id]['text'][:50]}...") - log_analysis_step(f"✓ Loaded {len(questions)} questions for {self.question_set}") + log_analysis_step(f"Loaded {len(questions)} questions for {self.question_set}") log_analysis_step(f"Available question IDs: {list(questions.keys())}") return questions except Exception as e: - log_analysis_step(f"Error loading questions: {str(e)}", "error") + logger.error(f"Error loading questions: {e!s}") logger.exception("Full error:") # This will log the full traceback return {} @@ -1146,18 +1157,31 @@ def get_question_by_number(self, number: int) -> Optional[Dict]: "tcfd": "tcfd", "s4m": "s4m", "lucia": "lucia", + "climretrieve": "climretr", # Map climretrieve to climretr shortcut } # Get the correct prefix for the question set question_prefix = question_set_mapping.get(self.question_set, self.question_set) - question_key = f"{question_prefix}_{number}" + # If still not found in mapping, try to extract prefix from actual question IDs + if question_prefix == self.question_set and self.questions: + # Extract prefix from first question ID (e.g., "climretr_1" -> "climretr") + first_qid = next(iter(self.questions.keys()), "") + if first_qid and "_" in first_qid: + extracted_prefix = first_qid.split("_")[0] + logger.info( + f"[ANALYSIS] Extracted prefix '{extracted_prefix}' from question IDs " + f"(question_set='{self.question_set}')" + ) + question_prefix = extracted_prefix + + question_key = f"{question_prefix}_{number}" logger.debug(f"Looking for question {number} with key: {question_key}") logger.debug(f"Available question keys: {list(self.questions.keys())}") return self.questions.get(question_key) except Exception as e: - log_analysis_step(f"Error getting question {number}: {str(e)}", "error") + logger.error(f"Error getting question {number}: {e!s}") logger.exception("Full error:") return None @@ -1174,11 +1198,11 @@ def update_parameters(self, chunk_size: int, chunk_overlap: int, top_k: int): # Recreate text splitter with new parameters self.text_splitter = SentenceSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap) - logger.info(f"Updated parameters and recreated text splitter") + logger.info("Updated parameters and recreated text splitter") def update_llm_model(self, model_name: str): """Update the LLM model.""" - log_analysis_step(f"Updating LLM model to: {model_name}") + logger.info(f"Updating LLM model to: {model_name}") self.default_model = model_name if not self._has_key_for_model(model_name): @@ -1243,8 +1267,8 @@ def check_step_completion(self, file_path: str) -> Dict[str, bool]: "analysis": step4_complete, } - except Exception as e: - logger.error(f"Error checking step completion: {str(e)}") + except Exception as e: # noqa: BLE001 + logger.error(f"Error checking step completion: {e!s}") return { "chunks": False, "embeddings": False, @@ -1287,7 +1311,7 @@ def _parse_config_from_filename(self, filename: str) -> Dict[str, Any]: return config - except Exception as e: + except Exception as e: # noqa: BLE001 logger.warning(f"Error parsing config from filename {filename}: {e}") return config @@ -1325,7 +1349,7 @@ async def _get_similar_chunks(self, query_text: str, chunks: List[Dict], top_k: return similar_chunks except Exception as e: - logger.error(f"Error getting similar chunks: {str(e)}", exc_info=True) + logger.error(f"Error getting similar chunks: {e!s}", exc_info=True) return [] def _parse_analysis_response(self, response_text: str) -> Dict[str, Any]: @@ -1466,9 +1490,9 @@ def _parse_analysis_response(self, response_text: str) -> Dict[str, Any]: return result except Exception as e: - logger.error(f"Error parsing analysis response: {str(e)}", exc_info=True) + logger.error(f"Error parsing analysis response: {e!s}", exc_info=True) return { - "ANSWER": f"Error parsing analysis: {str(e)}", + "ANSWER": f"Error parsing analysis: {e!s}", "SCORE": 0, "EVIDENCE": [], "GAPS": ["Error during analysis"], diff --git a/report_analyst/core/benchmark/__init__.py b/report_analyst/core/benchmark/__init__.py new file mode 100644 index 00000000..ef3313f0 --- /dev/null +++ b/report_analyst/core/benchmark/__init__.py @@ -0,0 +1 @@ +# Benchmark package for evaluation and annotation functionality diff --git a/report_analyst/core/benchmark/classification_calibration.py b/report_analyst/core/benchmark/classification_calibration.py new file mode 100644 index 00000000..c9120f32 --- /dev/null +++ b/report_analyst/core/benchmark/classification_calibration.py @@ -0,0 +1,233 @@ +import logging +from typing import Dict, Iterable, List + +import numpy as np +import pandas as pd +from sklearn.metrics import ( + brier_score_loss, + classification_report, + f1_score, + roc_auc_score, +) + +logger = logging.getLogger(__name__) + + +def minmax_normalize(series: pd.Series) -> np.ndarray: + """Min-max normalize a score series to [0, 1]. + + If all values are identical, returns an array of 0.5 (uninformative probability). + """ + arr = pd.to_numeric(series, errors="coerce").to_numpy(dtype=float) + if arr.size == 0: + return arr + min_val = np.nanmin(arr) + max_val = np.nanmax(arr) + if not np.isfinite(min_val) or not np.isfinite(max_val): + return np.zeros_like(arr, dtype=float) + if max_val == min_val: + return np.full_like(arr, 0.5, dtype=float) + return (arr - min_val) / (max_val - min_val) + + +def expected_calibration_error(y_true: np.ndarray, y_prob: np.ndarray, n_bins: int = 100) -> float: + """Compute Expected Calibration Error (ECE) for binary labels and probabilities.""" + if y_true.size == 0: + return 0.0 + + y_true = np.asarray(y_true, dtype=float) + y_prob = np.asarray(y_prob, dtype=float) + + bins = np.linspace(0.0, 1.0, n_bins + 1) + bin_ids = np.digitize(y_prob, bins) - 1 + + ece = 0.0 + n = len(y_true) + + for b in range(n_bins): + mask = bin_ids == b + if np.any(mask): + acc = y_true[mask].mean() + conf = y_prob[mask].mean() + ece += (mask.sum() / n) * abs(acc - conf) + + return float(ece) + + +def _compute_metrics_for_pair( + df: pd.DataFrame, + ground_truth_col: str, + score_col: str, + n_bins: int = 100, +) -> Dict[str, float]: + """Compute calibration and F1 metrics for a single (ground_truth, score) pair.""" + if ground_truth_col not in df.columns or score_col not in df.columns: + raise ValueError(f"Columns '{ground_truth_col}' and/or '{score_col}' not found in DataFrame") + + mask = df[ground_truth_col].notna() & df[score_col].notna() + if not mask.any(): + logger.warning( + "No overlapping non-NaN rows for columns '%s' and '%s'", + ground_truth_col, + score_col, + ) + return { + "ground_truth": ground_truth_col, + "prediction": score_col, + "ece": np.nan, + "ece2": np.nan, + "brier": np.nan, + "brier2": np.nan, + "auroc": np.nan, + "f1": np.nan, + "f1_1": np.nan, + "f2_2": np.nan, + } + + y_true = pd.to_numeric(df.loc[mask, ground_truth_col], errors="coerce").astype(int) + y_score_raw = pd.to_numeric(df.loc[mask, score_col], errors="coerce") + + # Binary ground truth versions + y_true_bin1 = (y_true > 0).astype(int).to_numpy() + y_true_bin2 = (y_true > 1).astype(int).to_numpy() + + # Scores and thresholds + y_score_minmax = minmax_normalize(y_score_raw) + y_score_ordinal = np.where( + y_score_raw > 1, + 2, + np.where(y_score_raw > 0, 1, 0), + ).astype(int) + y_score_bin1 = (y_score_raw > 0).astype(int).to_numpy() + y_score_bin2 = (y_score_raw > 1).astype(int).to_numpy() + + # Calibration scores (handle edge cases defensively) + ece = expected_calibration_error(y_true_bin1, y_score_minmax, n_bins=n_bins) + ece2 = expected_calibration_error(y_true_bin2, y_score_minmax, n_bins=n_bins) + try: + brier = brier_score_loss(y_true_bin1, y_score_minmax) + except ValueError: + brier = np.nan + try: + brier2 = brier_score_loss(y_true_bin2, y_score_minmax) + except ValueError: + brier2 = np.nan + + # AUC can fail if only one class is present + try: + auroc = roc_auc_score(y_true_bin1, y_score_minmax) + except ValueError: + auroc = np.nan + + # F1 scores + f1_macro = f1_score(y_true, y_score_ordinal, average="macro") + f1_1 = f1_score(y_true_bin1, y_score_bin1) + f1_2 = f1_score(y_true_bin2, y_score_bin2) + + return { + "ground_truth": ground_truth_col, + "prediction": score_col, + "ece": float(ece), + "ece2": float(ece2), + "brier": float(brier), + "brier2": float(brier2), + "auroc": float(auroc), + "f1": float(f1_macro), + "f1_1": float(f1_1), + "f2_2": float(f1_2), + } + + +def compute_calibration_metrics( + df: pd.DataFrame, + ground_truth_col: str, + score_cols: Iterable[str], + n_bins: int = 100, +) -> pd.DataFrame: + """Compute calibration metrics for multiple score columns. + + Returns a DataFrame with one row per (ground_truth, prediction) pair. + """ + rows: List[Dict[str, float]] = [] + for col in score_cols: + try: + row = _compute_metrics_for_pair( + df=df, + ground_truth_col=ground_truth_col, + score_col=col, + n_bins=n_bins, + ) + rows.append(row) + except Exception as exc: # pragma: no cover - defensive + logger.exception( + "Failed to compute calibration metrics for ground_truth=%s, prediction=%s: %s", + ground_truth_col, + col, + exc, + ) + rows.append( + { + "ground_truth": ground_truth_col, + "prediction": col, + "ece": np.nan, + "ece2": np.nan, + "brier": np.nan, + "brier2": np.nan, + "auroc": np.nan, + "f1": np.nan, + "f1_1": np.nan, + "f2_2": np.nan, + } + ) + + return pd.DataFrame(rows) + + +def compute_classification_report( + df: pd.DataFrame, + ground_truth_col: str, + score_col: str, +) -> Dict: + """Compute a 3-class classification report from ground-truth labels and scores. + + Uses the same 2/1/0 thresholding on the score column as in the notebook. + """ + if ground_truth_col not in df.columns or score_col not in df.columns: + raise ValueError(f"Columns '{ground_truth_col}' and/or '{score_col}' not found in DataFrame") + + mask = df[ground_truth_col].notna() & df[score_col].notna() + if not mask.any(): + return {} + + y_true = pd.to_numeric(df.loc[mask, ground_truth_col], errors="coerce").astype(int).to_numpy() + y_score = pd.to_numeric(df.loc[mask, score_col], errors="coerce") + y_pred = np.where( + y_score > 1, + 2, + np.where(y_score > 0, 1, 0), + ).astype(int) + + # Choose human-friendly target names + if ground_truth_col == "usefulness": + target_names = ["not useful (0)", "partially useful (1)", "useful (2)"] + else: + target_names = ["irrelevant (0)", "partially relevant (1)", "relevant (2)"] + + report = classification_report( + y_true, + y_pred, + labels=[0, 1, 2], + target_names=target_names, + digits=3, + output_dict=True, + zero_division=0, + ) + return report + + +__all__ = [ + "compute_calibration_metrics", + "compute_classification_report", + "expected_calibration_error", + "minmax_normalize", +] diff --git a/report_analyst/core/benchmark/dataset_loader.py b/report_analyst/core/benchmark/dataset_loader.py new file mode 100644 index 00000000..dd672e40 --- /dev/null +++ b/report_analyst/core/benchmark/dataset_loader.py @@ -0,0 +1,184 @@ +import hashlib +import json +import logging +from pathlib import Path +from typing import Dict, List + +import yaml + +from ...models.benchmark import ( + BenchmarkDatasetContent, + BenchmarkQuestion, + GroundTruthChunk, +) + +logger = logging.getLogger(__name__) + + +class DatasetValidationError(Exception): + """Raised when dataset validation fails""" + + pass + + +class DatasetLoader: + """Loads and validates benchmark datasets""" + + def __init__(self): + self.supported_formats = [".yaml", ".yml", ".json"] + + def load_dataset(self, file_path: str) -> BenchmarkDatasetContent: + """Load and validate a benchmark dataset from file""" + path = Path(file_path) + + if not path.exists(): + raise FileNotFoundError(f"Dataset file not found: {file_path}") + + if path.suffix.lower() not in self.supported_formats: + raise ValueError(f"Unsupported file format: {path.suffix}. Supported: {self.supported_formats}") + + # Load raw data + raw_data = self._load_raw_data(path) + + # Validate and parse + dataset = self._validate_and_parse(raw_data) + + logger.info(f"Successfully loaded dataset '{dataset.name}' with {len(dataset.questions)} questions") + return dataset + + def _load_raw_data(self, path: Path) -> Dict: + """Load raw data from file""" + try: + with open(path, "r", encoding="utf-8") as f: + if path.suffix.lower() in [".yaml", ".yml"]: + return yaml.safe_load(f) + else: + return json.load(f) + except Exception as e: + raise DatasetValidationError(f"Failed to load file {path}: {e}") from e + + def _validate_and_parse(self, raw_data: Dict) -> BenchmarkDatasetContent: + """Validate raw data and parse into structured format""" + try: + # Validate required top-level fields + required_fields = [ + "dataset_id", + "name", + "description", + "version", + "question_set", + "questions", + ] + for field in required_fields: + if field not in raw_data: + raise DatasetValidationError(f"Missing required field: {field}") + + # Parse questions + questions = [] + for q_data in raw_data["questions"]: + question = self._parse_question(q_data) + questions.append(question) + + return BenchmarkDatasetContent( + dataset_id=raw_data["dataset_id"], + name=raw_data["name"], + description=raw_data["description"], + version=raw_data["version"], + question_set=raw_data["question_set"], + created_at=raw_data.get("created_at", ""), + questions=questions, + ) + + except Exception as e: + raise DatasetValidationError(f"Dataset validation failed: {e}") from e + + def _parse_question(self, q_data: Dict) -> BenchmarkQuestion: + """Parse a single question from raw data""" + if "question_id" not in q_data: + raise DatasetValidationError("Question missing 'question_id'") + if "question_text" not in q_data: + raise DatasetValidationError("Question missing 'question_text'") + if "ground_truth_chunks" not in q_data: + raise DatasetValidationError("Question missing 'ground_truth_chunks'") + + # Parse ground truth chunks + chunks = [] + for chunk_data in q_data["ground_truth_chunks"]: + chunk = self._parse_ground_truth_chunk(chunk_data) + chunks.append(chunk) + + return BenchmarkQuestion( + question_id=q_data["question_id"], + question_text=q_data["question_text"], + ground_truth_chunks=chunks, + ) + + def _parse_ground_truth_chunk(self, chunk_data: Dict) -> GroundTruthChunk: + """Parse a ground truth chunk from raw data""" + required_fields = ["chunk_id", "relevance_score", "is_evidence"] + for field in required_fields: + if field not in chunk_data: + raise DatasetValidationError(f"Ground truth chunk missing '{field}'") + + # Validate relevance score + score = chunk_data["relevance_score"] + if not isinstance(score, (int, float)) or score < 0.0 or score > 1.0: + raise DatasetValidationError(f"Invalid relevance_score: {score}. Must be between 0.0 and 1.0") + + return GroundTruthChunk( + chunk_id=chunk_data["chunk_id"], + relevance_score=float(score), + is_evidence=bool(chunk_data["is_evidence"]), + evidence_order=chunk_data.get("evidence_order"), + annotation_notes=chunk_data.get("annotation_notes"), + ) + + def validate_dataset_consistency(self, dataset: BenchmarkDatasetContent) -> List[str]: + """Validate dataset consistency and return list of warnings""" + warnings = [] + + # Check for duplicate question IDs + question_ids = [q.question_id for q in dataset.questions] + if len(question_ids) != len(set(question_ids)): + warnings.append("Duplicate question IDs found") + + # Check evidence ordering + for question in dataset.questions: + evidence_chunks = [c for c in question.ground_truth_chunks if c.is_evidence] + if evidence_chunks: + orders = [c.evidence_order for c in evidence_chunks if c.evidence_order is not None] + if len(orders) != len(set(orders)): + warnings.append(f"Duplicate evidence orders in question {question.question_id}") + + # Check for questions without any relevant chunks + for question in dataset.questions: + relevant_chunks = [c for c in question.ground_truth_chunks if c.relevance_score > 0.0] + if not relevant_chunks: + warnings.append(f"Question {question.question_id} has no relevant chunks") + + return warnings + + def generate_dataset_hash(self, dataset: BenchmarkDatasetContent) -> str: + """Generate a hash for the dataset content""" + # Create a deterministic string representation + content = { + "dataset_id": dataset.dataset_id, + "version": dataset.version, + "questions": [], + } + + for question in sorted(dataset.questions, key=lambda q: q.question_id): + q_data = {"question_id": question.question_id, "chunks": []} + for chunk in sorted(question.ground_truth_chunks, key=lambda c: c.chunk_id): + q_data["chunks"].append( + { + "chunk_id": chunk.chunk_id, + "relevance_score": chunk.relevance_score, + "is_evidence": chunk.is_evidence, + "evidence_order": chunk.evidence_order, + } + ) + content["questions"].append(q_data) + + content_str = json.dumps(content, sort_keys=True) + return hashlib.sha256(content_str.encode()).hexdigest()[:16] diff --git a/report_analyst/core/benchmark/dataset_mapper.py b/report_analyst/core/benchmark/dataset_mapper.py new file mode 100644 index 00000000..653ddceb --- /dev/null +++ b/report_analyst/core/benchmark/dataset_mapper.py @@ -0,0 +1,462 @@ +import hashlib +import logging +from importlib import import_module +from pathlib import Path +from typing import Any, Dict, List, Optional, Type + +import pandas as pd +import yaml + +logger = logging.getLogger(__name__) + + +# --------------------------------------------------------------------------- +# Shared ID helpers (moved from scripts/align_benchmark_datasets.py) +# --------------------------------------------------------------------------- + + +def generate_query_id(document: str, question: str, separator: str = "|||") -> str: + """ + Generate a consistent query_id from document and question. + + Args: + document: Document/report name + question: Question text + separator: Separator between document and question + + Returns: + query_id string + """ + # Normalize: strip whitespace, handle None/NaN + doc = str(document).strip() if pd.notna(document) else "" + q = str(question).strip() if pd.notna(question) else "" + return f"{doc}{separator}{q}" + + +def generate_chunk_id(text: str, prefix: str = "") -> str: + """ + Generate a consistent chunk_id from text content using MD5 hash. + + Args: + text: Text content to hash + prefix: Optional prefix for chunk_id + + Returns: + chunk_id string (16-character hex hash) + """ + if pd.isna(text) or not str(text).strip(): + # Generate a hash from the string representation for empty/NaN + text_str = str(text) if pd.notna(text) else "empty" + hash_val = hashlib.md5(text_str.encode(), usedforsecurity=False).hexdigest()[:16] + return f"{prefix}{hash_val}" if prefix else hash_val + + # Normalize text: strip whitespace, lowercase for consistency + normalized = str(text).strip() + hash_val = hashlib.md5(normalized.encode(), usedforsecurity=False).hexdigest()[:16] + return f"{prefix}{hash_val}" if prefix else hash_val + + +# --------------------------------------------------------------------------- +# Core transformation helpers (adapted from align_benchmark_datasets.py) +# --------------------------------------------------------------------------- + + +def transform_ground_truth( + df: pd.DataFrame, + document_col: str = "document", + question_col: str = "question", + context_col: str = "context", + relevant_col: Optional[str] = None, + page_col: Optional[str] = None, + relevance_label_col: str = "relevance_label", +) -> pd.DataFrame: + """ + Transform ground truth dataset to consistent format. + + This is a slightly refactored version of the original helper in + scripts/align_benchmark_datasets.py, without any file I/O. It returns + an aligned DataFrame ready for evaluation. + """ + logger.info("Transforming ground truth dataset (%d rows)", len(df)) + + # Detect column names (case-insensitive) + df_cols_lower = {col.lower(): col for col in df.columns} + + # Map expected columns + doc_col = df_cols_lower.get(document_col.lower()) or df_cols_lower.get("document") + q_col = df_cols_lower.get(question_col.lower()) or df_cols_lower.get("question") + ctx_col = df_cols_lower.get(context_col.lower()) or df_cols_lower.get("context") or df_cols_lower.get("relevant") + rel_col = (df_cols_lower.get(relevant_col.lower()) if relevant_col else None) or df_cols_lower.get("relevant") + page_num_col = ( + (df_cols_lower.get(page_col.lower()) if page_col else None) + or df_cols_lower.get("page_number") + or df_cols_lower.get("page") + ) + label_col = ( + df_cols_lower.get(relevance_label_col.lower()) + or df_cols_lower.get("relevance_label") + or df_cols_lower.get("source relevance score") + or df_cols_lower.get("relevance") + or df_cols_lower.get("label") + ) + + # Validate required columns + if not doc_col: + raise ValueError(f"Could not find document column. Available: {list(df.columns)}") + if not q_col: + raise ValueError(f"Could not find question column. Available: {list(df.columns)}") + if not ctx_col and not rel_col: + raise ValueError(f"Could not find context or relevant column. Available: {list(df.columns)}") + if not label_col: + logger.warning( + "Could not find relevance label column. Will default to score=1.0. Available: %s", + list(df.columns), + ) + + # Use relevant column first (preferred), then context as fallback for chunk_id + text_col = rel_col or ctx_col + if text_col == rel_col: + logger.info("Using 'relevant' column for chunk_id generation (preferred for matching with benchmark relevant_text)") + elif text_col == ctx_col: + logger.info("Using 'context' column for chunk_id generation (fallback, 'relevant' column not found)") + + # Generate query_id + df = df.copy() + df["query_id"] = df.apply(lambda row: generate_query_id(row[doc_col], row[q_col]), axis=1) + + # Generate chunk_id from relevant text (or context if relevant not available) + df["chunk_id"] = df[text_col].apply(generate_chunk_id) + + # Generate position (1-indexed, per query) + df["position"] = df.groupby("query_id").cumcount() + 1 + + # Map relevance label to score + if label_col: + + def parse_label(val: Any) -> float: + if pd.isna(val): + return 1.0 + val_str = str(val).strip().lower() + try: + return float(val_str) + except ValueError: + if val_str in ["yes", "y", "true", "relevant", "high"]: + return 2.0 + if val_str in ["maybe", "partial", "medium"]: + return 1.0 + if val_str in ["no", "n", "false", "irrelevant", "low"]: + return 0.0 + return 1.0 + + df["score"] = df[label_col].apply(parse_label) + logger.info( + "Ground truth score distribution: %s", + dict(df["score"].value_counts().sort_index()), + ) + else: + logger.warning("No relevance label column found, defaulting all scores to 1.0") + df["score"] = 1.0 + + # Select output columns and rename preserved ones + output_cols = ["query_id", "chunk_id", "position", "score"] + preserve_cols = [doc_col, q_col] + if text_col: + preserve_cols.append(text_col) + if rel_col and rel_col != text_col and rel_col in df.columns: + preserve_cols.append(rel_col) + if ctx_col and ctx_col != text_col and ctx_col in df.columns: + preserve_cols.append(ctx_col) + if page_num_col: + preserve_cols.append(page_num_col) + if label_col and label_col in df.columns: + preserve_cols.append(label_col) + + rename_map: Dict[str, str] = { + doc_col: "document", + q_col: "question", + } + if text_col == rel_col: + rename_map[text_col] = "relevant" + elif text_col == ctx_col: + rename_map[text_col] = "context" + if rel_col and rel_col != text_col and rel_col in preserve_cols: + rename_map[rel_col] = "relevant" + if ctx_col and ctx_col != text_col and ctx_col in preserve_cols: + rename_map[ctx_col] = "context" + if page_num_col: + rename_map[page_num_col] = "page_number" + if label_col and label_col in df.columns: + rename_map[label_col] = "relevance_label" + + df_output = df[output_cols + preserve_cols].copy() + df_output = df_output.rename(columns=rename_map) + + logger.info( + "Transformed ground truth: %d rows, %d queries, %d chunks", + len(df_output), + df_output["query_id"].nunique(), + df_output["chunk_id"].nunique(), + ) + return df_output + + +def transform_benchmark_results( + df: pd.DataFrame, + report_col: str = "report", + question_col: str = "question", + paragraph_col: str = "paragraph", + relevant_text_col: str = "relevant_text", + relevance_score_col: Optional[str] = None, + label_col: Optional[str] = None, + number_col: Optional[str] = None, +) -> pd.DataFrame: + """ + Transform benchmark results dataset to consistent format. + + This is a refactored version of the original helper in + scripts/align_benchmark_datasets.py, without any file I/O. It returns + an aligned DataFrame ready for evaluation. + """ + logger.info("Transforming benchmark results dataset (%d rows)", len(df)) + + # Detect column names (case-insensitive) + df_cols_lower = {col.lower(): col for col in df.columns} + + # Map expected columns + report_col_actual = df_cols_lower.get(report_col.lower()) or df_cols_lower.get("report") or df_cols_lower.get("document") + q_col = df_cols_lower.get(question_col.lower()) or df_cols_lower.get("question") + para_col = df_cols_lower.get(paragraph_col.lower()) or df_cols_lower.get("paragraph") or df_cols_lower.get("chunk") + rel_text_col = ( + df_cols_lower.get(relevant_text_col.lower()) or df_cols_lower.get("relevant_text") or df_cols_lower.get("relevant") + ) + rel_score_col = ( + (df_cols_lower.get(relevance_score_col.lower()) if relevance_score_col else None) + or df_cols_lower.get("relevance_score") + or df_cols_lower.get("sim_text_relevance") + ) + label_col_actual = ( + (df_cols_lower.get(label_col.lower()) if label_col else None) + or df_cols_lower.get("label") + or df_cols_lower.get("relevance") + ) + num_col = ( + (df_cols_lower.get(number_col.lower()) if number_col else None) + or df_cols_lower.get("number") + or df_cols_lower.get("paragraph_number") + ) + + # Validate required columns + if not report_col_actual: + raise ValueError(f"Could not find report column. Available: {list(df.columns)}") + if not q_col: + raise ValueError(f"Could not find question column. Available: {list(df.columns)}") + if not para_col: + raise ValueError(f"Could not find paragraph column. Available: {list(df.columns)}") + + df = df.copy() + + # Generate query_id + df["query_id"] = df.apply(lambda row: generate_query_id(row[report_col_actual], row[q_col]), axis=1) + + # Generate chunk_id from paragraph (unique identifier for each retrieved paragraph) + df["chunk_id"] = df[para_col].apply(generate_chunk_id) + logger.info("Using paragraph for chunk_id generation (unique per retrieved paragraph)") + + # Generate relevant_part_id from relevant_text (for matching to ground truth relevant parts) + if rel_text_col and rel_text_col in df.columns: + df["relevant_part_id"] = df[rel_text_col].apply(generate_chunk_id) + logger.info("Using relevant_text for relevant_part_id generation (for ground truth matching)") + else: + df["relevant_part_id"] = df["chunk_id"] + logger.info("No relevant_text found, using chunk_id as relevant_part_id") + + # Generate position + if num_col and num_col in df.columns: + df["position"] = pd.to_numeric(df[num_col], errors="coerce").fillna(0).astype(int) + df["position"] = df["position"].apply(lambda x: max(1, x)) + logger.info("Using number column for position") + else: + df["position"] = df.groupby("query_id").cumcount() + 1 + logger.info("Using row order for position") + + # Optional score column (label preferred, then numeric relevance score) + if label_col_actual and label_col_actual in df.columns: + + def parse_label(val: Any) -> float: + if pd.isna(val): + return 0.0 + val_str = str(val).strip().lower() + try: + return float(val_str) + except ValueError: + if val_str in ["yes", "y", "true", "relevant", "high"]: + return 2.0 + if val_str in ["maybe", "partial", "medium"]: + return 1.0 + if val_str in ["no", "n", "false", "irrelevant", "low"]: + return 0.0 + return 0.0 + + df["score"] = df[label_col_actual].apply(parse_label) + logger.info("Using label column for score") + elif rel_score_col and rel_score_col in df.columns: + df["score"] = pd.to_numeric(df[rel_score_col], errors="coerce").fillna(0.0) + logger.info("Using numeric relevance score column for score") + else: + logger.info("No score column found - evaluation will rely on ground truth scores only") + + # Detect similarity score columns from report level dataset + relevant_text_sim_col = df_cols_lower.get("relevant_text_sim") + sim_text_relevance_col = df_cols_lower.get("sim_text_relevance") + + output_cols = ["query_id", "chunk_id", "relevant_part_id", "position"] + if "score" in df.columns: + output_cols.append("score") + + preserve_cols = [report_col_actual, q_col, para_col] + if rel_text_col and rel_text_col in df.columns: + preserve_cols.append(rel_text_col) + if label_col_actual and label_col_actual in df.columns: + preserve_cols.append(label_col_actual) + if relevant_text_sim_col and relevant_text_sim_col in df.columns: + preserve_cols.append(relevant_text_sim_col) + if sim_text_relevance_col and sim_text_relevance_col in df.columns: + preserve_cols.append(sim_text_relevance_col) + + rename_map: Dict[str, str] = { + report_col_actual: "report", + q_col: "question", + para_col: "paragraph", + } + if rel_text_col and rel_text_col in df.columns: + rename_map[rel_text_col] = "relevant_text" + if label_col_actual and label_col_actual in df.columns: + rename_map[label_col_actual] = "relevance_label" + if relevant_text_sim_col and relevant_text_sim_col in df.columns: + rename_map[relevant_text_sim_col] = "relevant_text_sim" + if sim_text_relevance_col and sim_text_relevance_col in df.columns: + rename_map[sim_text_relevance_col] = "sim_text_relevance" + + df_output = df[output_cols + preserve_cols].copy() + df_output = df_output.rename(columns=rename_map) + + logger.info( + "Transformed benchmark results: %d rows, %d queries, %d chunks", + len(df_output), + df_output["query_id"].nunique(), + df_output["chunk_id"].nunique(), + ) + return df_output + + +# --------------------------------------------------------------------------- +# Dataset mapper abstraction and factory +# --------------------------------------------------------------------------- + + +class DatasetMapper: + """Base class for dataset mappers.""" + + def __init__(self, dataset_id: str, config: Optional[Dict[str, Any]] = None): + self.dataset_id = dataset_id + self.config = config or {} + + def align_ground_truth(self, df: pd.DataFrame) -> pd.DataFrame: + raise NotImplementedError + + def align_benchmark(self, df: pd.DataFrame) -> pd.DataFrame: + raise NotImplementedError + + +class DefaultDatasetMapper(DatasetMapper): + """Default mapper that uses YAML column mappings and shared helpers.""" + + def align_ground_truth(self, df: pd.DataFrame) -> pd.DataFrame: + gt_cfg = (self.config.get("ground_truth") or {}).get("columns", {}) + + return transform_ground_truth( + df, + document_col=gt_cfg.get("document", "document"), + question_col=gt_cfg.get("question", "question"), + context_col=gt_cfg.get("context", "context"), + relevant_col=gt_cfg.get("relevant"), + page_col=gt_cfg.get("page_number"), + relevance_label_col=gt_cfg.get("relevance_label", "relevance_label"), + ) + + def align_benchmark(self, df: pd.DataFrame) -> pd.DataFrame: + bm_cfg = (self.config.get("benchmark") or {}).get("columns", {}) + + return transform_benchmark_results( + df, + report_col=bm_cfg.get("report_id", "report"), + question_col=bm_cfg.get("question", "question"), + paragraph_col=bm_cfg.get("paragraph", "paragraph"), + relevant_text_col=bm_cfg.get("relevant_text", "relevant_text"), + relevance_score_col=bm_cfg.get("relevance_score"), + label_col=bm_cfg.get("relevance_label"), + number_col=bm_cfg.get("number"), + ) + + +def _load_yaml_config(dataset_id: str) -> Dict[str, Any]: + """Load YAML config for a dataset_id from package data.""" + # Expected location: report_analyst/config/datasets/{dataset_id}.yaml + base_dir = Path(__file__).resolve().parents[2] # .../report_analyst + cfg_path = base_dir / "config" / "datasets" / f"{dataset_id}.yaml" + + if not cfg_path.exists(): + logger.warning("No dataset mapping config found for '%s' at %s", dataset_id, cfg_path) + return {"id": dataset_id} + + with cfg_path.open("r", encoding="utf-8") as f: + cfg = yaml.safe_load(f) or {} + if "id" not in cfg: + cfg["id"] = dataset_id + return cfg + + +class DatasetMapperFactory: + """Factory to construct DatasetMapper instances from YAML configs.""" + + @staticmethod + def get_mapper(dataset_id: str) -> DatasetMapper: + cfg = _load_yaml_config(dataset_id) + mapper_class_path = cfg.get("mapper_class") + + if mapper_class_path: + try: + module_path, class_name = mapper_class_path.rsplit(".", 1) + module = import_module(module_path) + cls: Type[DatasetMapper] = getattr(module, class_name) + return cls(dataset_id, cfg) + except Exception as exc: # noqa: BLE001 # pragma: no cover - defensive + logger.error( + "Failed to import custom mapper '%s' for dataset '%s': %s", + mapper_class_path, + dataset_id, + exc, + ) + + # Fallback to default mapper + return DefaultDatasetMapper(dataset_id, cfg) + + +def list_available_dataset_ids() -> List[str]: + """Return dataset IDs for which a YAML mapping config is available.""" + base_dir = Path(__file__).resolve().parents[2] / "config" / "datasets" + if not base_dir.exists(): + return [] + return sorted(p.stem for p in base_dir.glob("*.yaml")) + + +__all__ = [ + "DatasetMapper", + "DatasetMapperFactory", + "DefaultDatasetMapper", + "generate_chunk_id", + "generate_query_id", + "list_available_dataset_ids", + "transform_benchmark_results", + "transform_ground_truth", +] diff --git a/report_analyst/core/benchmark/dataset_normalizer.py b/report_analyst/core/benchmark/dataset_normalizer.py new file mode 100644 index 00000000..28dd1c1e --- /dev/null +++ b/report_analyst/core/benchmark/dataset_normalizer.py @@ -0,0 +1,188 @@ +""" +Normalize a raw DataFrame to the benchmark schema (query_id, chunk_id, position, score, paragraph) +using user-selected column mappings. Used when CSV/Excel structure does not match the expected names. +""" + +import hashlib +import logging +from typing import Any, Optional + +import pandas as pd + +from .dataset_mapper import generate_chunk_id, generate_query_id + +logger = logging.getLogger(__name__) + +# Position mode: use explicit column, infer from row order per query, or infer by sorting by score +POSITION_MODE_COLUMN = "column" +POSITION_MODE_ROW_ORDER = "row_order" +POSITION_MODE_SORT_BY_SCORE = "sort_by_score" + + +def _parse_score(val: Any) -> float: + """Coerce a value to a numeric score (for label/prediction columns).""" + if pd.isna(val): + return 0.0 + val_str = str(val).strip().lower() + try: + return float(val_str) + except ValueError: + if val_str in ["yes", "y", "true", "relevant", "high"]: + return 2.0 + if val_str in ["maybe", "partial", "medium"]: + return 1.0 + if val_str in ["no", "n", "false", "irrelevant", "low"]: + return 0.0 + return 0.0 + + +def _stable_query_id_from_text(text: str, max_len: int = 64) -> str: + """Produce a stable query_id from a single text (e.g. description) when no document column.""" + if pd.isna(text) or not str(text).strip(): + return hashlib.md5(b"empty", usedforsecurity=False).hexdigest()[:16] + s = str(text).strip() + if len(s) <= max_len: + return s + return hashlib.md5(s.encode(), usedforsecurity=False).hexdigest()[:16] + + +def make_query_id_from_columns(document: Optional[Any], question: Any, max_len: int = 64) -> str: + """ + Helper to build a stable query_id from document/question-style fields. + + - If a document is provided, we delegate to generate_query_id(document, question) + so that behaviour matches DatasetMapper / preset mappers. + - If no document is provided, we fall back to a stable ID derived from the + question/description text alone (using _stable_query_id_from_text). + """ + if document is not None and str(document).strip(): + return generate_query_id(document, question) + return _stable_query_id_from_text(question, max_len=max_len) + + +def make_chunk_id_from_text(text: Any) -> str: + """ + Helper to build a chunk_id from raw text content. + + Uses generate_chunk_id under the hood and handles empty/NaN values by + normalizing them to an empty string first. + """ + return generate_chunk_id(str(text) if pd.notna(text) else "") + + +def make_relevant_part_id_from_text(text: Any) -> str: + """ + Helper to build a relevant_part_id from a relevant span of text. + + This mirrors make_chunk_id_from_text so that relevant parts and full chunks + share the same hashing logic. When no text is provided, we hash an empty + string to get a deterministic ID. + """ + return generate_chunk_id(str(text) if pd.notna(text) else "") + + +def normalize_dataframe_for_benchmark( + df: pd.DataFrame, + query_column: str, + chunk_text_column: str, + score_column: str, + position_column: Optional[str] = None, + document_column: Optional[str] = None, + position_mode: str = POSITION_MODE_ROW_ORDER, +) -> pd.DataFrame: + """ + Build a DataFrame with standard benchmark columns from raw data and column choices. + + Produces columns: query_id, chunk_id, position, score, paragraph, question, + and optionally document. Used so that + load_flexible_dataset_from_csv(csv_content=result.to_csv()) works without schema changes. + + Args: + df: Raw DataFrame (e.g. from uploaded CSV/Excel). + query_column: Column used as query/criteria (e.g. description, question). + chunk_text_column: Column used as chunk text (e.g. chunk_text, paragraph). + score_column: Column used as label or prediction score (e.g. relevance, usefulness, model column). + position_column: Column for position/rank; used only when position_mode == "column". + document_column: Optional document/report column; if set, query_id = generate_query_id(doc, query). + position_mode: "column" | "row_order" | "sort_by_score". + - column: use position_column (must be provided). + - row_order: assign 1,2,3... by row order within each query_id. + - sort_by_score: sort by score_column descending per query, then assign position. + + Returns: + DataFrame with columns query_id, chunk_id, position, score, paragraph, question [, document]. + """ + if query_column not in df.columns: + raise ValueError(f"Query column '{query_column}' not in DataFrame columns: {list(df.columns)}") + if chunk_text_column not in df.columns: + raise ValueError(f"Chunk text column '{chunk_text_column}' not in DataFrame columns: {list(df.columns)}") + if score_column not in df.columns: + raise ValueError(f"Score column '{score_column}' not in DataFrame columns: {list(df.columns)}") + if position_mode == POSITION_MODE_COLUMN and (not position_column or position_column not in df.columns): + raise ValueError(f"Position mode is 'column' but position column '{position_column}' missing or not in DataFrame") + if document_column is not None and document_column not in df.columns: + raise ValueError(f"Document column '{document_column}' not in DataFrame columns: {list(df.columns)}") + + out = df.copy() + + # query_id + if document_column: + out["query_id"] = out.apply( + lambda row: generate_query_id( + row.get(document_column) if pd.notna(row.get(document_column)) else "", + row.get(query_column) if pd.notna(row.get(query_column)) else "", + ), + axis=1, + ) + else: + out["query_id"] = out[query_column].apply( + lambda x: (_stable_query_id_from_text(x) if pd.notna(x) else _stable_query_id_from_text("")) + ) + + # chunk_id from chunk text + out["chunk_id"] = out[chunk_text_column].apply(lambda x: generate_chunk_id(str(x) if pd.notna(x) else "")) + + # score (coerce to float) + out["score"] = out[score_column].apply(_parse_score) + + # paragraph for matching/display + out["paragraph"] = out[chunk_text_column].where(out[chunk_text_column].notna(), "").astype(str) + + # question text for error analysis / display (copy from query/criteria column) + out["question"] = out[query_column].where(out[query_column].notna(), "").astype(str) + + # position + if position_mode == POSITION_MODE_COLUMN and position_column: + out["position"] = out[position_column].apply(lambda x: int(x) if pd.notna(x) and str(x).strip() else 1) + out["position"] = out["position"].clip(lower=1) + elif position_mode == POSITION_MODE_SORT_BY_SCORE: + out = out.sort_values(["query_id", "score"], ascending=[True, False]) + out["position"] = out.groupby("query_id").cumcount() + 1 + else: + # row_order: preserve order, assign position per query + out["position"] = out.groupby("query_id").cumcount() + 1 + + # optional document + if document_column: + out["document"] = out[document_column] + + # Keep only standard columns so loader sees a clean schema + standard_cols = [ + "query_id", + "chunk_id", + "position", + "score", + "paragraph", + "question", + ] + if document_column: + standard_cols.append("document") + out = out[standard_cols].copy() + + logger.info( + "Normalized dataset: %d rows, %d unique queries, %d unique chunks", + len(out), + out["query_id"].nunique(), + out["chunk_id"].nunique(), + ) + return out diff --git a/report_analyst/core/benchmark/error_analysis.py b/report_analyst/core/benchmark/error_analysis.py new file mode 100644 index 00000000..74e5814b --- /dev/null +++ b/report_analyst/core/benchmark/error_analysis.py @@ -0,0 +1,453 @@ +import logging +from typing import Dict, List + +import pandas as pd + +from ...models.benchmark import ( + BenchmarkDataset, + BenchmarkDatasetContent, + RetrievalResultRow, +) + +logger = logging.getLogger(__name__) + + +def build_error_analysis_dataframe( + ground_truth: BenchmarkDatasetContent, + retrieval_results: List[RetrievalResultRow], + top_k: int, +) -> pd.DataFrame: + """ + Build a per-chunk error-analysis dataframe. + + Each row corresponds to one retrieved chunk within top_k for a given question. + Columns: + - report_name + - question + - relevant_part_text + - retrieved_chunk_text + - position_in_top_k + - expert_relevance_label + - model_score + - is_really_relevant (expert label > 0) + - query_id + - chunk_id + """ + # Index ground truth by question_id and chunk_id for fast lookup + gt_by_question: Dict[str, Dict[str, float]] = {} + gt_text_by_question: Dict[str, Dict[str, str]] = {} + report_by_question: Dict[str, str] = {} + question_text_by_id: Dict[str, str] = {} + + for q in ground_truth.questions: + question_id = q.question_id + question_text_by_id[question_id] = getattr(q, "question_text", None) or getattr(q, "text", "") or "" + # Use first report_id in metadata if available + report_name = "" + if q.ground_truth_chunks: + meta = getattr(q.ground_truth_chunks[0], "metadata", None) or {} + report_name = meta.get("document") or meta.get("report") or "" + report_by_question[question_id] = report_name + + by_chunk: Dict[str, float] = {} + text_by_chunk: Dict[str, str] = {} + for chunk in q.ground_truth_chunks: + by_chunk[chunk.chunk_id] = chunk.relevance_score + text_by_chunk[chunk.chunk_id] = getattr(chunk, "text", None) or getattr(chunk, "annotation_notes", None) or "" + gt_by_question[question_id] = by_chunk + gt_text_by_question[question_id] = text_by_chunk + + rows: List[Dict] = [] + + # Group retrieval results by query_id + results_by_query: Dict[str, List[RetrievalResultRow]] = {} + for r in retrieval_results: + results_by_query.setdefault(r.query_id, []).append(r) + + for query_id, rows_for_query in results_by_query.items(): + # Sort by position and take top_k + sorted_rows = sorted(rows_for_query, key=lambda r: r.position)[:top_k] + + gt_scores = gt_by_question.get(query_id, {}) + gt_texts = gt_text_by_question.get(query_id, {}) + report_name = report_by_question.get(query_id, "") + question_text = question_text_by_id.get(query_id, "") + + for r in sorted_rows: + expert_label = gt_scores.get(r.chunk_id, 0.0) + is_really_relevant = expert_label > 0 + relevant_part_text = gt_texts.get(r.chunk_id, "") + + rows.append( + { + "report_name": report_name, + "question_id": query_id, + "question": question_text, + "relevant_part_text": relevant_part_text, + "retrieved_chunk_text": r.chunk_text or "", + "position_in_top_k": r.position, + "expert_relevance_label": expert_label, + "model_score": r.score, + "is_really_relevant": is_really_relevant, + "chunk_id": r.chunk_id, + } + ) + + df = pd.DataFrame(rows) + logger.info(f"Built error-analysis dataframe with {len(df)} rows") + return df + + +def build_error_analysis_dataframe_from_flexible( + ground_truth_dataset: BenchmarkDataset, + benchmark_dataset: BenchmarkDataset, + top_k: int, +) -> pd.DataFrame: + """ + Build a per-chunk error-analysis dataframe from flexible BenchmarkDataset objects. + + Each row corresponds to one retrieved chunk within top_k for a given question. + Columns: + - report_name + - question_id + - question (if available in ground truth) + - relevant_part_text (from ground truth) + - retrieved_chunk_text (from benchmark) + - position_in_top_k + - expert_relevance_label (from ground truth score) + - model_score (from benchmark) + - is_really_relevant (expert label > 0) + - chunk_id + """ + # Index ground truth by query_id and chunk_id + gt_by_query: Dict[str, Dict[str, Dict]] = {} # query_id -> chunk_id -> {score, text, report, question} + # Also create an index by (report, question) pair for fallback lookup + gt_by_report_question: Dict[str, Dict[str, Dict[str, Dict]]] = ( + {} + ) # report -> question -> chunk_id -> {score, text, report, question} + + for result in ground_truth_dataset.results: + query_id = result.get_query_id() + if not query_id: + continue + + chunk_id = result.get_chunk_id() + if not chunk_id: + continue + + if query_id not in gt_by_query: + gt_by_query[query_id] = {} + + # Extract data from flexible row + data = result.data + score = result.get_score() or data.get("relevance_label") or data.get("relevance_score") or 0.0 + # Try to get relevant part text - check for "Relevant" column (case-insensitive) first + # as user mentioned it should retrieve from "Relevant" column + text = None + # Check case-insensitive for "relevant" column + for key in data.keys(): + if key.lower() == "relevant": + text = data.get(key) + break + # Fallback to other common column names + if not text: + text = data.get("context") or data.get("chunk_text") or data.get("text") or "" + report = data.get("document") or data.get("report") or data.get("report_name") or "" + question = data.get("question") or "" + + gt_entry = { + "score": float(score) if score else 0.0, + "text": str(text), + "report": str(report), + "question": str(question), + } + + gt_by_query[query_id][chunk_id] = gt_entry + + # Also index by (report, question) pair for fallback lookup + # Normalize report and question to match the normalization used later + normalized_report = " ".join(str(report).strip().split()) + normalized_question = " ".join(str(question).strip().split()) + if normalized_report and normalized_question: + if normalized_report not in gt_by_report_question: + gt_by_report_question[normalized_report] = {} + if normalized_question not in gt_by_report_question[normalized_report]: + gt_by_report_question[normalized_report][normalized_question] = {} + gt_by_report_question[normalized_report][normalized_question][chunk_id] = gt_entry + + rows: List[Dict] = [] + + # Group benchmark results by query_id (report/question pair) + # This matches the evaluation logic: group by query_id, sort by similarity score, take top-K + benchmark_by_query: Dict[str, List] = {} # query_id -> [all results for this query] + for result in benchmark_dataset.results: + query_id = result.get_query_id() + if not query_id: + continue + benchmark_by_query.setdefault(query_id, []).append(result) + + # Helper function to get similarity score for ranking + def get_similarity_score(r): + data = r.data + # Use relevant_text_sim as priority for ranking (similarity between retrieved chunk and relevant part) + # Note: sim_text_relevance is NOT used here - it's an expert-annotated label, not a ranking score + sim_score = data.get("relevant_text_sim") or r.get_score() or data.get("score") or data.get("relevance_score") or 0.0 + return float(sim_score) if sim_score else 0.0 + + # Group queries by report_name, then by question + # Structure: report_name -> question -> [query_ids for that (report, question) pair] + # query_id is typically in format "report|||question" or similar + queries_by_report_and_question: Dict[str, Dict[str, List[str]]] = {} # report -> question -> [query_ids] + + for query_id, benchmark_results in benchmark_by_query.items(): + # Try to extract report/question from query_id first (common format: "report|||question") + report_name = "" + question_text = "" + + if "|||" in query_id: + parts = query_id.split("|||", 1) + report_name = parts[0] if len(parts) > 0 else "" + question_text = parts[1] if len(parts) > 1 else "" + else: + # Fallback: get from ground truth + gt_data = gt_by_query.get(query_id, {}) + first_gt = next(iter(gt_data.values())) if gt_data else {} + report_name = first_gt.get("report", "") + question_text = first_gt.get("question", "") + + # If still empty, try to get from benchmark data + if not report_name or not question_text: + sample_result = benchmark_results[0] if benchmark_results else None + if sample_result: + data = sample_result.data + if not report_name: + report_name = data.get("report") or data.get("document") or data.get("report_name") or "" + if not question_text: + question_text = data.get("question") or "" + + if not report_name or not question_text: + # Skip this pair in report/question-based path; may still be usable in a query-only fallback. + continue + + # Normalize report_name and question_text to avoid duplicates due to whitespace/formatting + # Remove leading/trailing whitespace and normalize internal whitespace + report_name = " ".join(report_name.strip().split()) + question_text = " ".join(question_text.strip().split()) + + if report_name not in queries_by_report_and_question: + queries_by_report_and_question[report_name] = {} + if question_text not in queries_by_report_and_question[report_name]: + queries_by_report_and_question[report_name][question_text] = [] + queries_by_report_and_question[report_name][question_text].append(query_id) + + # Process in order: report (outer loop) -> question (middle loop) -> top-K chunks (inner loop) + # This ensures report_name stays constant until all questions are done, + # and question stays constant until all top-K chunks are shown + processed_pairs: Dict[tuple, bool] = {} # Track which pairs have been processed + + for report_name in sorted(queries_by_report_and_question.keys()): + for question_text in sorted(queries_by_report_and_question[report_name].keys()): + pair_key = (report_name, question_text) + + # Skip if we've already processed this exact pair (safeguard against duplicates) + if pair_key in processed_pairs: + continue # Skip this pair - already processed + + processed_pairs[pair_key] = True + query_ids = queries_by_report_and_question[report_name][question_text] + + # Check if there's any ground truth for this (report, question) pair + # If no ground truth exists, skip this pair (can't evaluate without ground truth) + report_gt_data = gt_by_report_question.get(report_name, {}) + question_gt_data = report_gt_data.get(question_text, {}) + if not question_gt_data: + # No ground truth for this (report, question) pair - skip it + logger.debug( + f"Skipping (report, question) pair with no ground truth: " + f"report='{report_name}', question='{question_text[:50]}...'" + ) + continue + + # For this (report, question) pair, collect all benchmark results + all_results_for_pair = [] + for query_id in query_ids: + all_results_for_pair.extend(benchmark_by_query[query_id]) + + # Deduplicate by (chunk_id, relevant_part_id) - this allows the same chunk to appear + # multiple times if it matches different relevant parts, but prevents duplicates + # when it matches the same relevant part or has no relevant part + chunk_and_part_to_best_result: Dict[tuple, any] = {} + for result in all_results_for_pair: + chunk_id = result.get_chunk_id() or "" + if not chunk_id: + continue + + data = result.data + relevant_part_id = data.get("relevant_part_id") or "" + # Use empty string for "no relevant part" to group those together + dedup_key = (chunk_id, str(relevant_part_id)) + + sim_score = get_similarity_score(result) + if dedup_key not in chunk_and_part_to_best_result: + chunk_and_part_to_best_result[dedup_key] = result + else: + # Keep the one with higher similarity score + existing_score = get_similarity_score(chunk_and_part_to_best_result[dedup_key]) + if sim_score > existing_score: + chunk_and_part_to_best_result[dedup_key] = result + + # Sort by similarity score (descending) and take top-K + deduplicated_results = list(chunk_and_part_to_best_result.values()) + sorted_results = sorted(deduplicated_results, key=get_similarity_score, reverse=True)[:top_k] + + # Get a representative query_id for this pair (for display purposes) + query_id = query_ids[0] + + # Output all top-K chunks for this (report, question) pair + for local_rank, r in enumerate(sorted_results, start=1): + data = r.data + retrieved_chunk_id = r.get_chunk_id() or "" # This is the retrieved paragraph ID + original_position = r.get_position() or 0 + + # Get similarity score (used for ranking) + get_similarity_score(r) + + # Get model score from benchmark dataset - should be relevant_text_sim for this chunk + model_score = data.get("relevant_text_sim") or 0.0 + model_score = float(model_score) if model_score else 0.0 + + chunk_text = data.get("paragraph") or data.get("chunk_text") or data.get("text") or "" + + # Get relevant_part_id to look up ground truth + relevant_part_id_from_data = data.get("relevant_part_id") + relevant_part_id = ( + relevant_part_id_from_data + if relevant_part_id_from_data + else "" # Don't fallback to retrieved_chunk_id - if no relevant_part_id, it means no match + ) + + # Get the query_id for this specific result (not the grouped one) + result_query_id = r.get_query_id() or query_id + + # Look up ground truth using the result's query_id and relevant_part_id + # The ground truth is indexed by query_id -> chunk_id (where chunk_id is the relevant part ID) + gt_chunk = {} + if relevant_part_id: + if result_query_id: + # Primary lookup: by query_id + result_gt_data = gt_by_query.get(result_query_id, {}) + gt_chunk = result_gt_data.get(str(relevant_part_id), {}) + + # Fallback lookup: by (report, question) pair if primary lookup failed + if not gt_chunk and report_name and question_text: + report_gt_data = gt_by_report_question.get(report_name, {}) + question_gt_data = report_gt_data.get(question_text, {}) + gt_chunk = question_gt_data.get(str(relevant_part_id), {}) + + # If still no match by ID, but we have a (report, question) pair, + # get the most relevant part (highest score) for this pair (as user requested: + # "retrieve the info about relevant part from ground truth for each question/report pair") + if not gt_chunk and report_name and question_text: + report_gt_data = gt_by_report_question.get(report_name, {}) + question_gt_data = report_gt_data.get(question_text, {}) + if question_gt_data: + # Use the relevant part with the highest score for this (report, question) pair + best_chunk_id = max( + question_gt_data.keys(), + key=lambda cid: question_gt_data[cid].get("score", 0.0), + ) + gt_chunk = question_gt_data[best_chunk_id] + + relevant_part_text = gt_chunk.get("text", "") + + # Get relevance from benchmarking dataset (not ground truth) + # Check for relevance_label or relevance column in benchmark data + benchmark_relevance = data.get("relevance_label") or data.get("relevance") or data.get("label") or 0.0 + # Convert to numeric if it's a string + if isinstance(benchmark_relevance, str): + try: + benchmark_relevance = float(benchmark_relevance) + except (ValueError, TypeError): + benchmark_relevance = 0.0 + else: + benchmark_relevance = float(benchmark_relevance) if benchmark_relevance else 0.0 + + # is_really_relevant should be true only if benchmark relevance > 0 + is_really_relevant = benchmark_relevance > 0 + + rows.append( + { + "report_name": report_name, + "question_id": query_id, # Keep query_id for reference + "question": question_text, + "relevant_part_text": relevant_part_text, + "retrieved_chunk_text": str(chunk_text), + # 1..K rank within top-K for this (report, question) pair + "position_in_top_k": local_rank, + # Original retrieval rank (if available) + "retrieval_rank": original_position, + "model_score": model_score, + "is_really_relevant": is_really_relevant, + "chunk_id": retrieved_chunk_id, # Store the retrieved paragraph ID + } + ) + + # If we didn't produce any rows using the (report, question) grouping path, + # fall back to a simpler query-centric path that only relies on query_id and chunk_id. + if not rows: + for query_id, benchmark_results in benchmark_by_query.items(): + gt_chunks = gt_by_query.get(query_id, {}) + if not gt_chunks: + # No ground truth for this query_id + continue + + # Sort benchmark results by similarity score and take top-K + sorted_results = sorted(benchmark_results, key=get_similarity_score, reverse=True)[:top_k] + + for local_rank, r in enumerate(sorted_results, start=1): + data = r.data + retrieved_chunk_id = r.get_chunk_id() or "" + if not retrieved_chunk_id: + continue + + gt_entry = gt_chunks.get(retrieved_chunk_id, {}) + expert_score = float(gt_entry.get("score", 0.0)) + relevant_part_text = gt_entry.get("text", "") + report_name = ( + gt_entry.get("report") or data.get("document") or data.get("report") or data.get("report_name") or "" + ) + question_text = gt_entry.get("question") or data.get("question") or "" + + # Model score: keep same priority as main path + model_score = ( + data.get("relevant_text_sim") or r.get_score() or data.get("score") or data.get("relevance_score") or 0.0 + ) + try: + model_score = float(model_score) if model_score else 0.0 + except (ValueError, TypeError): + model_score = 0.0 + + original_position = r.get_position() or 0 + is_really_relevant = expert_score > 0 + + rows.append( + { + "report_name": str(report_name), + "question_id": query_id, + "question": str(question_text), + "relevant_part_text": str(relevant_part_text), + "retrieved_chunk_text": str(data.get("paragraph") or data.get("chunk_text") or data.get("text") or ""), + "position_in_top_k": local_rank, + "retrieval_rank": original_position, + "model_score": model_score, + "is_really_relevant": is_really_relevant, + "chunk_id": retrieved_chunk_id, + } + ) + + df = pd.DataFrame(rows) + # No need to sort - rows are already in the correct order: + # report (outer loop) -> question (middle loop) -> position_in_top_k (inner loop) + + logger.info(f"Built error-analysis dataframe from flexible datasets with {len(df)} rows") + return df diff --git a/report_analyst/core/benchmark/evaluation_engine.py b/report_analyst/core/benchmark/evaluation_engine.py new file mode 100644 index 00000000..dff9b7fe --- /dev/null +++ b/report_analyst/core/benchmark/evaluation_engine.py @@ -0,0 +1,650 @@ +import logging +from typing import Dict, List, Optional + +import numpy as np + +from ...models.benchmark import ( + BenchmarkDataset, + BenchmarkDatasetContent, + DatasetType, + EvaluationMetrics, + RetrievalConfig, + RetrievalResultsDataset, +) + +logger = logging.getLogger(__name__) + + +class EvaluationEngine: + """Engine for evaluating retrieval performance against benchmark datasets""" + + def __init__(self): + self.default_k_values = [1, 3, 5, 10] + + def evaluate_retrieval( + self, + dataset: BenchmarkDatasetContent, + retrieval_results: Dict[str, List[Dict]], + config: RetrievalConfig, + k_values: Optional[List[int]] = None, + ) -> EvaluationMetrics: + """ + Evaluate retrieval results against ground truth + + Args: + dataset: Benchmark dataset with ground truth + retrieval_results: Dict mapping question_id to list of retrieved chunks + config: Retrieval configuration used + k_values: List of K values to compute metrics for + + Returns: + EvaluationMetrics with computed scores + """ + if k_values is None: + k_values = self.default_k_values + + logger.info(f"Evaluating retrieval for {len(dataset.questions)} questions") + + # Collect results per question + question_results = [] + for question in dataset.questions: + if question.question_id not in retrieval_results: + logger.warning(f"No retrieval results for question {question.question_id}") + continue + + retrieved_chunks = retrieval_results[question.question_id] + ground_truth = {chunk.chunk_id: chunk.relevance_score for chunk in question.ground_truth_chunks} + + result = self._evaluate_single_question(retrieved_chunks, ground_truth, k_values) + question_results.append(result) + + # Aggregate metrics + return self._aggregate_metrics(question_results, k_values) + + def _evaluate_single_question( + self, + retrieved_chunks: List[Dict], + ground_truth: Dict[str, float], + k_values: List[int], + relevant_text_sim_scores: Optional[List[float]] = None, + relevance_threshold: float = 0.95, + ) -> Dict: + """Evaluate a single question's retrieval results + + Args: + retrieved_chunks: List of retrieved chunks with metadata + ground_truth: Dict mapping chunk_id to relevance score (for total count and NDCG ideal) + k_values: List of K values to compute metrics for + relevant_text_sim_scores: Optional list of relevant_text_sim scores from benchmark dataset. + If provided, used for binary relevance (relevant if > threshold). + Must match order of retrieved_chunks after deduplication. + relevance_threshold: Threshold for relevant_text_sim to consider a chunk relevant (default: 0.95) + """ + + # Extract relevant part IDs in retrieval order + # Note: "id" field contains relevant_part_id (for matching to ground truth) + # chunk_id is the unique paragraph identifier (for reference) + # Multiple retrieved paragraphs can legitimately match the same relevant part + retrieved_ids_raw = [chunk.get("id", chunk.get("chunk_id", "")) for chunk in retrieved_chunks] + + # Deduplicate relevant parts while preserving order (keep first occurrence) + # This ensures each unique relevant part is counted only once in recall/NDCG + # Multiple retrieved paragraphs matching the same relevant part is valid, but we only + # count the relevant part once to prevent recall > 1.0 + seen = set() + retrieved_ids = [] + deduplicated_indices = [] # Track which original indices were kept + for i, relevant_part_id in enumerate(retrieved_ids_raw): + if relevant_part_id not in seen: + retrieved_ids.append(relevant_part_id) + seen.add(relevant_part_id) + deduplicated_indices.append(i) + + # Get relevance scores for retrieved chunks (after deduplication) + # For NDCG, we still use ground truth scores if available + retrieved_relevance = [ground_truth.get(chunk_id, 0.0) for chunk_id in retrieved_ids] + + # Decide whether we have usable relevant_text_sim scores + use_sim_scores = bool( + relevant_text_sim_scores and any((s is not None and float(str(s) or 0) != 0.0) for s in relevant_text_sim_scores) + ) + + binary_relevance: List[int] = [] + + if use_sim_scores: + # Use relevant_text_sim threshold: chunk is relevant if relevant_text_sim > threshold (default 0.95) + # Map to deduplicated indices + for idx in deduplicated_indices: + if idx < len(relevant_text_sim_scores): + sim_score = relevant_text_sim_scores[idx] + # Convert to float if needed + if isinstance(sim_score, str): + try: + sim_score = float(sim_score) + except (ValueError, TypeError): + sim_score = 0.0 + else: + sim_score = float(sim_score) if sim_score else 0.0 + is_relevant = 1 if sim_score > relevance_threshold else 0 + binary_relevance.append(is_relevant) + else: + # Missing score, default to not relevant + binary_relevance.append(0) + else: + # Fallback: use ground truth matching (old behavior): + # treat a retrieved chunk as relevant if its chunk_id appears in the ground truth. + for chunk_id in retrieved_ids: + binary_relevance.append(1 if ground_truth.get(chunk_id, 0.0) > 0 else 0) + + # Total relevant chunks in ground truth + total_relevant = sum(1 for score in ground_truth.values() if score > 0) + + result = { + "retrieved_ids": retrieved_ids, + "retrieved_relevance": retrieved_relevance, + "binary_relevance": binary_relevance, + "total_relevant": total_relevant, + "precision_at_k": {}, + "recall_at_k": {}, + "f1_at_k": {}, + "ndcg_at_k": {}, + "reciprocal_rank": 0.0, + "average_precision": 0.0, + } + + # Compute metrics at different K values + # NOTE: Metrics should be calculated for ALL k values, even if k > len(retrieved_ids) + # When k > len(retrieved_ids), recall@k equals the maximum recall (all relevant items found) + # and precision@k is calculated with k as denominator (correctly lower when fewer items retrieved) + for k in k_values: + precision_k = self._precision_at_k(binary_relevance, k) + recall_k = self._recall_at_k(binary_relevance, total_relevant, k) + f1_k = self._f1_at_k(precision_k, recall_k) + ndcg_k = self._ndcg_at_k(retrieved_relevance, ground_truth, k) + result["precision_at_k"][k] = precision_k + result["recall_at_k"][k] = recall_k + result["f1_at_k"][k] = f1_k + result["ndcg_at_k"][k] = ndcg_k + + # Compute MRR and MAP + result["reciprocal_rank"] = self._reciprocal_rank(binary_relevance) + result["average_precision"] = self._average_precision(binary_relevance) + + return result + + def _precision_at_k(self, binary_relevance: List[int], k: int) -> float: + """Compute precision at K""" + if k == 0: + return 0.0 + relevant_at_k = sum(binary_relevance[:k]) + return relevant_at_k / k + + def _recall_at_k(self, binary_relevance: List[int], total_relevant: int, k: int) -> float: + """Compute recall at K""" + if total_relevant == 0: + return 0.0 + relevant_at_k = sum(binary_relevance[:k]) + return relevant_at_k / total_relevant + + def _f1_at_k(self, precision: float, recall: float) -> float: + """Compute F1 score from precision and recall""" + if precision + recall == 0: + return 0.0 + return 2 * (precision * recall) / (precision + recall) + + def _ndcg_at_k(self, retrieved_relevance: List[float], ground_truth: Dict[str, float], k: int) -> float: + """Compute Normalized Discounted Cumulative Gain at K""" + if k == 0: + return 0.0 + + # DCG at K + dcg = 0.0 + for i in range(min(k, len(retrieved_relevance))): + if i == 0: + dcg += retrieved_relevance[i] + else: + dcg += retrieved_relevance[i] / np.log2(i + 1) + + # Ideal DCG at K (sort ground truth scores in descending order) + ideal_scores = sorted(ground_truth.values(), reverse=True) + idcg = 0.0 + for i in range(min(k, len(ideal_scores))): + if i == 0: + idcg += ideal_scores[i] + else: + idcg += ideal_scores[i] / np.log2(i + 1) + + if idcg == 0: + return 0.0 + + return dcg / idcg + + def _reciprocal_rank(self, binary_relevance: List[int]) -> float: + """Compute reciprocal rank (1/rank of first relevant item)""" + for i, rel in enumerate(binary_relevance): + if rel == 1: + return 1.0 / (i + 1) + return 0.0 + + def _average_precision(self, binary_relevance: List[int]) -> float: + """Compute average precision""" + if not any(binary_relevance): + return 0.0 + + ap = 0.0 + relevant_count = 0 + + for i, rel in enumerate(binary_relevance): + if rel == 1: + relevant_count += 1 + precision_at_i = relevant_count / (i + 1) + ap += precision_at_i + + total_relevant = sum(binary_relevance) + return ap / total_relevant if total_relevant > 0 else 0.0 + + def _aggregate_metrics(self, question_results: List[Dict], k_values: List[int]) -> EvaluationMetrics: + """Aggregate metrics across all questions""" + if not question_results: + return EvaluationMetrics() + + metrics = EvaluationMetrics() + + # Aggregate precision, recall, F1, and NDCG at K + for k in k_values: + precisions = [r["precision_at_k"].get(k, 0.0) for r in question_results] + recalls = [r["recall_at_k"].get(k, 0.0) for r in question_results] + f1s = [r["f1_at_k"].get(k, 0.0) for r in question_results] + ndcgs = [r["ndcg_at_k"].get(k, 0.0) for r in question_results] + + metrics.precision_at_k[k] = np.mean(precisions) + metrics.recall_at_k[k] = np.mean(recalls) + metrics.f1_at_k[k] = np.mean(f1s) + metrics.ndcg_at_k[k] = np.mean(ndcgs) + + # Aggregate MRR and MAP + reciprocal_ranks = [r["reciprocal_rank"] for r in question_results] + average_precisions = [r["average_precision"] for r in question_results] + + metrics.mean_reciprocal_rank = np.mean(reciprocal_ranks) + metrics.mean_average_precision = np.mean(average_precisions) + + logger.info(f"Evaluation complete. MAP: {metrics.mean_average_precision:.3f}, MRR: {metrics.mean_reciprocal_rank:.3f}") + + return metrics + + def compare_evaluations(self, eval1: EvaluationMetrics, eval2: EvaluationMetrics) -> Dict[str, float]: + """Compare two evaluations and return improvement metrics""" + comparison = {} + + # Compare MAP and MRR + comparison["map_improvement"] = eval2.mean_average_precision - eval1.mean_average_precision + comparison["mrr_improvement"] = eval2.mean_reciprocal_rank - eval1.mean_reciprocal_rank + + # Compare metrics at K + for k in eval1.precision_at_k.keys(): + if k in eval2.precision_at_k: + comparison[f"precision_at_{k}_improvement"] = eval2.precision_at_k[k] - eval1.precision_at_k[k] + comparison[f"recall_at_{k}_improvement"] = eval2.recall_at_k[k] - eval1.recall_at_k[k] + comparison[f"f1_at_{k}_improvement"] = eval2.f1_at_k[k] - eval1.f1_at_k[k] + comparison[f"ndcg_at_{k}_improvement"] = eval2.ndcg_at_k[k] - eval1.ndcg_at_k[k] + + return comparison + + def compare_flexible_datasets( + self, + reference_dataset: BenchmarkDataset, + input_dataset: BenchmarkDataset, + k_values: Optional[List[int]] = None, + ) -> EvaluationMetrics: + """ + Compare two flexible benchmark datasets (supports both IR and IE). + + The reference dataset is treated as ground truth (e.g., "climretrieve", "chatreport"). + The input dataset contains the actual results to evaluate. + + For IR datasets: Compares retrieved chunks (by chunk_id and position). + For IE datasets: Compares answers/analysis (by query_id, comparing text similarity). + + Args: + reference_dataset: Reference dataset (ground truth) + input_dataset: Input dataset (actual results) to evaluate + k_values: List of K values to compute metrics for (only used for IR) + + Returns: + EvaluationMetrics comparing input_dataset against reference_dataset + """ + if reference_dataset.dataset_type != input_dataset.dataset_type: + logger.warning( + f"Dataset type mismatch: reference={reference_dataset.dataset_type.value}, " + f"input={input_dataset.dataset_type.value}. Attempting comparison anyway." + ) + + if reference_dataset.dataset_type == DatasetType.INFORMATION_RETRIEVAL: + return self._compare_ir_datasets(reference_dataset, input_dataset, k_values) + elif reference_dataset.dataset_type == DatasetType.INFORMATION_EXTRACTION: + return self._compare_ie_datasets(reference_dataset, input_dataset) + else: + raise ValueError(f"Unsupported dataset type: {reference_dataset.dataset_type}") + + def _compare_ir_datasets( + self, + reference_dataset: BenchmarkDataset, + input_dataset: BenchmarkDataset, + k_values: Optional[List[int]] = None, + ) -> EvaluationMetrics: + """Compare two Information Retrieval datasets""" + if k_values is None: + k_values = self.default_k_values + + # Get common queries + reference_queries = set(reference_dataset.get_unique_queries()) + input_queries = set(input_dataset.get_unique_queries()) + common_queries = reference_queries.intersection(input_queries) + + if not common_queries: + logger.warning("No common queries found between reference and input datasets") + return EvaluationMetrics() + + logger.info(f"Found {len(common_queries)} common queries for IR comparison") + + # Collect results per question + question_results = [] + for query_id in common_queries: + # Get reference results (ground truth) + reference_results = reference_dataset.get_results_by_query(query_id) + reference_results = sorted(reference_results, key=lambda x: x.get_position() or 999) + + # Build ground truth mapping: ID used for matching -> relevance_score + # IMPORTANT: Use the same identifier that will be used for retrieved_ids + # in _evaluate_single_question. That function uses the "id" field from + # retrieved_chunks, which we construct from relevant_part_id when + # available (fallback to chunk_id). To keep NDCG consistent with + # binary relevance/recall, we therefore key ground_truth by + # relevant_part_id if it exists, otherwise by chunk_id. + ground_truth = {} + for i, ref_result in enumerate(reference_results): + chunk_id = ref_result.get_chunk_id() + data = ref_result.data if hasattr(ref_result, "data") else {} + match_id = data.get("relevant_part_id") or chunk_id + if match_id: + score = ref_result.get_score() + # Use score if available, otherwise use inverse position + relevance_score = score if score is not None else max(0.0, 1.0 - (i * 0.1)) + ground_truth[match_id] = relevance_score + + # Skip this query if there's no ground truth (can't evaluate without ground truth) + if not ground_truth: + logger.debug(f"Skipping query with no ground truth: query_id='{query_id}'") + continue + + # Get input results (actual retrieval) + input_results = input_dataset.get_results_by_query(query_id) + + # Helper function to get similarity score for ranking (same logic as error analysis) + def get_similarity_score_for_ranking(result): + data = result.data if hasattr(result, "data") else {} + # Use relevant_text_sim as priority for ranking (similarity between retrieved chunk and relevant part) + # Note: sim_text_relevance is NOT used here - it's an expert-annotated label, not a ranking score + sim_score = ( + data.get("relevant_text_sim") + or result.get_score() + or data.get("score") + or data.get("relevance_score") + or 0.0 + ) + return float(sim_score) if sim_score else 0.0 + + # Sort by similarity score (descending) - this matches the error analysis logic + input_results = sorted(input_results, key=get_similarity_score_for_ranking, reverse=True) + + # Convert to format expected by _evaluate_single_question + # Use relevant_part_id for matching to ground truth (if available), otherwise fallback to chunk_id + retrieved_chunks = [] + relevant_text_sim_scores = [] # Extract relevant_text_sim from benchmark dataset + for result in input_results: + chunk_id = result.get_chunk_id() # Unique paragraph identifier + relevant_part_id = result.get("relevant_part_id") # For matching to ground truth + # Fallback: if no relevant_part_id, use chunk_id (backward compatibility) + match_id = relevant_part_id if relevant_part_id else chunk_id + # Use relevant_text_sim as the score (same priority as ranking) + data = result.data if hasattr(result, "data") else {} + score = ( + data.get("relevant_text_sim") + or result.get_score() + or data.get("score") + or data.get("relevance_score") + or 0.0 + ) + score = float(score) if score else 0.0 + position = result.get_position() or 999 + + # Extract relevant_text_sim for relevance determination (chunk is relevant if relevant_text_sim > 0.95) + relevant_text_sim = data.get("relevant_text_sim") or 0.0 + # Convert to numeric if it's a string + if isinstance(relevant_text_sim, str): + try: + relevant_text_sim = float(relevant_text_sim) + except (ValueError, TypeError): + relevant_text_sim = 0.0 + else: + relevant_text_sim = float(relevant_text_sim) if relevant_text_sim else 0.0 + relevant_text_sim_scores.append(relevant_text_sim) + + chunk_dict = { + "id": match_id or f"unknown_{position}", # Use relevant_part_id for matching + "chunk_id": chunk_id or f"unknown_{position}", # Keep original chunk_id for reference + "relevant_part_id": match_id or f"unknown_{position}", # Store for logging + "score": score, + "position": position, + } + retrieved_chunks.append(chunk_dict) + + # Evaluate this question + result = self._evaluate_single_question( + retrieved_chunks, + ground_truth, + k_values, + relevant_text_sim_scores, + relevance_threshold=0.95, + ) + question_results.append(result) + + # Aggregate metrics + return self._aggregate_metrics(question_results, k_values) + + def _compare_ie_datasets(self, reference_dataset: BenchmarkDataset, input_dataset: BenchmarkDataset) -> EvaluationMetrics: + """ + Compare two Information Extraction datasets. + + Compares answers/analysis text using similarity metrics. + For structured fields (categories, extracted values), uses exact match. + """ + # Get common queries + reference_queries = set(reference_dataset.get_unique_queries()) + input_queries = set(input_dataset.get_unique_queries()) + common_queries = reference_queries.intersection(input_queries) + + if not common_queries: + logger.warning("No common queries found between reference and input datasets") + return EvaluationMetrics() + + logger.info(f"Found {len(common_queries)} common queries for IE comparison") + + # For IE, we compare answers/analysis per query + # Metrics: Exact match, F1 (token-level), BLEU, ROUGE, etc. + exact_matches = 0 + total_queries = len(common_queries) + + # Simple comparison: exact match for now + # TODO: Add more sophisticated metrics (F1, BLEU, ROUGE, semantic similarity) + for query_id in common_queries: + reference_results = reference_dataset.get_results_by_query(query_id) + input_results = input_dataset.get_results_by_query(query_id) + + if not reference_results or not input_results: + continue + + # Get first result for each (assuming one answer per query) + ref_answer = reference_results[0].get_answer() + input_answer = input_results[0].get_answer() + + if ref_answer and input_answer: + # Normalize and compare + ref_normalized = ref_answer.strip().lower() + input_normalized = input_answer.strip().lower() + + if ref_normalized == input_normalized: + exact_matches += 1 + + # Create metrics (simplified for IE) + metrics = EvaluationMetrics() + if total_queries > 0: + exact_match_rate = exact_matches / total_queries + # For IE, we use exact_match_rate as the primary metric + # Map to precision@1 for consistency with IR metrics + metrics.precision_at_k[1] = exact_match_rate + metrics.mean_average_precision = exact_match_rate + metrics.mean_reciprocal_rank = exact_match_rate if exact_matches > 0 else 0.0 + + logger.info( + "IE comparison complete. Exact match rate: " + f"{exact_matches}/{total_queries} = {metrics.precision_at_k.get(1, 0.0):.3f}" + ) + + return metrics + + def compare_datasets( + self, + reference_dataset: RetrievalResultsDataset, + input_dataset: RetrievalResultsDataset, + k_values: Optional[List[int]] = None, + match_on_chunk_id: bool = True, + ) -> EvaluationMetrics: + """ + Compare two retrieval results datasets. + + The reference dataset is treated as ground truth (e.g., "climretrieve"). + The input dataset contains the actual retrieval results to evaluate. + + Args: + reference_dataset: Reference dataset (ground truth) - e.g., climretrieve + input_dataset: Input dataset (actual retrieval results) to evaluate + k_values: List of K values to compute metrics for + match_on_chunk_id: If True, match chunks by chunk_id. If False, match by position only. + + Returns: + EvaluationMetrics comparing input_dataset against reference_dataset + """ + if k_values is None: + k_values = self.default_k_values + + logger.info(f"Comparing datasets: reference='{reference_dataset.dataset_id}' vs input='{input_dataset.dataset_id}'") + + # Get common queries + reference_queries = set(reference_dataset.get_unique_queries()) + input_queries = set(input_dataset.get_unique_queries()) + common_queries = reference_queries.intersection(input_queries) + + if not common_queries: + logger.warning("No common queries found between reference and input datasets") + return EvaluationMetrics() + + logger.info(f"Found {len(common_queries)} common queries") + + # Collect results per question + question_results = [] + for query_id in common_queries: + # Get reference results (ground truth) + reference_results = reference_dataset.get_results_by_query(query_id) + # Sort by position to get ground truth order + reference_results = sorted(reference_results, key=lambda x: x.position) + + # Build ground truth mapping: chunk_id -> relevance_score + # Use position as relevance score (higher position = lower relevance) + # Or use actual score if available + ground_truth = {} + for i, ref_result in enumerate(reference_results): + chunk_id = ref_result.chunk_id + # Use score if available, otherwise use inverse position (1.0 for position 1, 0.9 for position 2, etc.) + relevance_score = ref_result.score if ref_result.score > 0 else max(0.0, 1.0 - (i * 0.1)) + ground_truth[chunk_id] = relevance_score + + # Skip this query if there's no ground truth (can't evaluate without ground truth) + if not ground_truth: + logger.debug(f"Skipping query with no ground truth: query_id='{query_id}'") + continue + + # Get input results (actual retrieval) + input_results = input_dataset.get_results_by_query(query_id) + + # Helper function to get similarity score for ranking + def get_similarity_score_for_ranking_legacy(result): + # For legacy RetrievalResultRow, check if it has data attribute + if hasattr(result, "metadata") and result.metadata: + data = result.metadata + # Use relevant_text_sim as priority for ranking + sim_score = ( + data.get("relevant_text_sim") + or result.score + or data.get("score") + or data.get("relevance_score") + or 0.0 + ) + return float(sim_score) if sim_score else 0.0 + # Fallback to score if no metadata + return float(result.score) if result.score else 0.0 + + # Sort by similarity score (descending) - prioritize relevant_text_sim + input_results = sorted(input_results, key=get_similarity_score_for_ranking_legacy, reverse=True) + + # Convert to format expected by _evaluate_single_question + retrieved_chunks = [] + relevant_text_sim_scores = [] # Extract relevant_text_sim from benchmark dataset + for result in input_results: + # Use relevant_text_sim as the score if available + score = result.score or 0.0 + relevant_text_sim = 0.0 + if hasattr(result, "metadata") and result.metadata: + data = result.metadata + score = ( + data.get("relevant_text_sim") + or result.score + or data.get("score") + or data.get("relevance_score") + or 0.0 + ) + # Extract relevant_text_sim for relevance determination (chunk is relevant if relevant_text_sim > 0.95) + relevant_text_sim = data.get("relevant_text_sim") or 0.0 + score = float(score) if score else 0.0 + + # Convert to numeric if it's a string + if isinstance(relevant_text_sim, str): + try: + relevant_text_sim = float(relevant_text_sim) + except (ValueError, TypeError): + relevant_text_sim = 0.0 + else: + relevant_text_sim = float(relevant_text_sim) if relevant_text_sim else 0.0 + relevant_text_sim_scores.append(relevant_text_sim) + + chunk_dict = { + "id": result.chunk_id, + "chunk_id": result.chunk_id, + "score": score, + "position": result.position, + } + retrieved_chunks.append(chunk_dict) + + # Evaluate this question + result = self._evaluate_single_question( + retrieved_chunks, + ground_truth, + k_values, + relevant_text_sim_scores, + relevance_threshold=0.95, + ) + question_results.append(result) + + # Aggregate metrics + return self._aggregate_metrics(question_results, k_values) diff --git a/report_analyst/core/benchmark/flexible_alignment.py b/report_analyst/core/benchmark/flexible_alignment.py new file mode 100644 index 00000000..072170d3 --- /dev/null +++ b/report_analyst/core/benchmark/flexible_alignment.py @@ -0,0 +1,240 @@ +import logging +from dataclasses import dataclass +from typing import Any, List, Optional + +import pandas as pd + +from .dataset_normalizer import ( + make_chunk_id_from_text, + make_query_id_from_columns, + make_relevant_part_id_from_text, +) + +logger = logging.getLogger(__name__) + + +@dataclass +class GroundTruthAlignConfig: + document_col: Optional[str] + question_col: str + chunk_text_col: str + relevant_part_col: Optional[str] + label_cols: List[str] + + +@dataclass +class BenchmarkAlignConfig: + document_col: Optional[str] + question_col: Optional[str] + query_id_col: Optional[str] + chunk_text_col: str + relevant_part_col: Optional[str] + prediction_cols: List[str] + ranking_score_col: Optional[str] + + +def align_ground_truth_flexible(df: pd.DataFrame, config: GroundTruthAlignConfig) -> pd.DataFrame: + """ + Align a raw ground truth DataFrame to the unified flexible schema. + + Output columns: + - query_id + - chunk_id + - relevant_part_id (optional) + - question + - document (optional) + - chunk_text + - relevant_part_text (optional) + - plus all label columns from config.label_cols + - score (numeric) for ranking, derived from the first label column when present + """ + if config.question_col not in df.columns: + raise ValueError(f"Question/description column '{config.question_col}' not in DataFrame.") + if config.chunk_text_col not in df.columns: + raise ValueError(f"Chunk text column '{config.chunk_text_col}' not in DataFrame.") + + for col in config.label_cols: + if col not in df.columns: + raise ValueError(f"Label column '{col}' not in DataFrame.") + + if config.document_col and config.document_col not in df.columns: + raise ValueError(f"Document column '{config.document_col}' not in DataFrame.") + if config.relevant_part_col and config.relevant_part_col not in df.columns: + raise ValueError(f"Relevant part column '{config.relevant_part_col}' not in DataFrame.") + + out = df.copy() + + # Core ID fields + def _mk_qid(row): + doc_val = row[config.document_col] if config.document_col else None # type: ignore[index] + return make_query_id_from_columns(doc_val, row[config.question_col]) + + out["query_id"] = out.apply(_mk_qid, axis=1) + out["chunk_id"] = out[config.chunk_text_col].apply(make_chunk_id_from_text) + + if config.relevant_part_col: + out["relevant_part_text"] = out[config.relevant_part_col] + out["relevant_part_id"] = out[config.relevant_part_col].apply(make_relevant_part_id_from_text) + else: + out["relevant_part_text"] = "" + # Fallback: use chunk_id when no explicit relevant part is provided + out["relevant_part_id"] = out["chunk_id"] + + # Human-readable text fields + out["question"] = out[config.question_col].astype(str) + if config.document_col: + out["document"] = out[config.document_col].astype(str) + else: + out["document"] = "" + out["chunk_text"] = out[config.chunk_text_col].astype(str) + + # Preserve label columns as-is + for col in config.label_cols: + out[col] = out[col] + + # Provide a numeric "score" column for ranking based on the first label column, if any + if config.label_cols: + primary_label = config.label_cols[0] + + def _parse_score(val: Any) -> float: + try: + return float(str(val).strip()) + except (TypeError, ValueError): + return 0.0 + + out["score"] = out[primary_label].apply(_parse_score) + + # Select and order columns + base_cols: List[str] = [ + "query_id", + "chunk_id", + "relevant_part_id", + "question", + "document", + "chunk_text", + "relevant_part_text", + ] + label_cols = [c for c in config.label_cols if c in out.columns] + extra_cols = [c for c in ["score"] if c in out.columns] + ordered_cols = base_cols + label_cols + extra_cols + + out_aligned = out[ordered_cols].copy() + + logger.info( + "Aligned ground truth (flexible): %d rows, %d unique queries, %d unique chunks", + len(out_aligned), + out_aligned["query_id"].nunique(), + out_aligned["chunk_id"].nunique(), + ) + return out_aligned + + +def align_benchmark_flexible(df: pd.DataFrame, config: BenchmarkAlignConfig) -> pd.DataFrame: + """ + Align a raw benchmark results DataFrame to the unified flexible schema. + + Output columns: + - query_id + - chunk_id + - relevant_part_id (optional) + - chunk_text + - relevant_part_text_pred (optional) + - plus all prediction columns from config.prediction_cols + - relevant_text_sim (optional) duplicated from ranking_score_col for reuse by ranking logic + """ + if config.query_id_col: + if config.query_id_col not in df.columns: + raise ValueError(f"query_id column '{config.query_id_col}' not in DataFrame.") + else: + # If no explicit query_id column, require at least a question/description column + if not config.question_col or config.question_col not in df.columns: + raise ValueError("Either an explicit query_id_col or a question/description column must be provided.") + + if config.chunk_text_col not in df.columns: + raise ValueError(f"Chunk text column '{config.chunk_text_col}' not in DataFrame.") + + for col in config.prediction_cols: + if col not in df.columns: + raise ValueError(f"Prediction column '{col}' not in DataFrame.") + + if config.document_col and config.document_col not in df.columns: + raise ValueError(f"Document column '{config.document_col}' not in DataFrame.") + if config.relevant_part_col and config.relevant_part_col not in df.columns: + raise ValueError(f"Relevant part column '{config.relevant_part_col}' not in DataFrame.") + if config.ranking_score_col and config.ranking_score_col not in df.columns: + raise ValueError(f"Ranking score column '{config.ranking_score_col}' not in DataFrame.") + + out = df.copy() + + # query_id: either reuse existing column or recompute from document/question + if config.query_id_col: + out["query_id"] = out[config.query_id_col].astype(str) + else: + + def _mk_qid(row): + doc_val = row[config.document_col] if config.document_col else None # type: ignore[index] + return make_query_id_from_columns(doc_val, row[config.question_col]) # type: ignore[index] + + out["query_id"] = out.apply(_mk_qid, axis=1) + + # chunk_id from chunk text + out["chunk_id"] = out[config.chunk_text_col].apply(make_chunk_id_from_text) + out["chunk_text"] = out[config.chunk_text_col].astype(str) + + # Optional predicted relevant part + if config.relevant_part_col: + out["relevant_part_text_pred"] = out[config.relevant_part_col].astype(str) + out["relevant_part_id"] = out[config.relevant_part_col].apply(make_relevant_part_id_from_text) + else: + out["relevant_part_text_pred"] = "" + out["relevant_part_id"] = out["chunk_id"] + + # Predictions / scores + for col in config.prediction_cols: + out[col] = out[col] + + # Optional ranking score column: duplicate into relevant_text_sim for reuse by ranking logic + if config.ranking_score_col: + out["relevant_text_sim"] = out[config.ranking_score_col] + + base_cols: List[str] = [ + "query_id", + "chunk_id", + "relevant_part_id", + "chunk_text", + "relevant_part_text_pred", + ] + pred_cols = [c for c in config.prediction_cols if c in out.columns] + extra_cols: List[str] = [] + if "relevant_text_sim" in out.columns: + extra_cols.append("relevant_text_sim") + + # Build final column order and remove duplicates while preserving order. + # This is important when the chosen ranking_score_col is also one of the + # prediction columns (e.g. 'relevant_text_sim'), to avoid duplicated + # column names in the aligned DataFrame. + ordered_cols_raw = base_cols + pred_cols + extra_cols + seen_cols: set[str] = set() + ordered_cols: List[str] = [] + for col in ordered_cols_raw: + if col not in seen_cols: + seen_cols.add(col) + ordered_cols.append(col) + + out_aligned = out[ordered_cols].copy() + + logger.info( + "Aligned benchmark (flexible): %d rows, %d unique queries, %d unique chunks", + len(out_aligned), + out_aligned["query_id"].nunique(), + out_aligned["chunk_id"].nunique(), + ) + return out_aligned + + +__all__ = [ + "BenchmarkAlignConfig", + "GroundTruthAlignConfig", + "align_benchmark_flexible", + "align_ground_truth_flexible", +] diff --git a/report_analyst/core/benchmark/retrieval_results_loader.py b/report_analyst/core/benchmark/retrieval_results_loader.py new file mode 100644 index 00000000..577f0a91 --- /dev/null +++ b/report_analyst/core/benchmark/retrieval_results_loader.py @@ -0,0 +1,609 @@ +""" +Loader for retrieval results datasets from CSV or SQLite. + +This module provides functions to load retrieval results in CSV format, +which can be used for benchmarking. The functions are designed to be +webhook-ready and can be used in API endpoints. +""" + +import logging +import sqlite3 +from io import StringIO +from pathlib import Path +from typing import Dict, List, Optional, Union + +import pandas as pd + +from ...models.benchmark import ( + BenchmarkDataset, + DatasetType, + FlexibleDatasetRow, + RetrievalResultRow, + RetrievalResultsDataset, +) + +logger = logging.getLogger(__name__) + + +def load_retrieval_results_from_csv( + csv_path: Optional[str] = None, + csv_content: Optional[Union[str, bytes]] = None, + dataset_id: Optional[str] = None, + dataset_name: Optional[str] = None, +) -> RetrievalResultsDataset: + """ + Load retrieval results from CSV file or content. + + This function is webhook-ready and can accept either a file path or CSV content. + + Expected CSV format: + - query_id: Question/query identifier (required) + - report_id: Report/document identifier (optional) + - chunk_id: Chunk identifier (required) + - chunk_text: Chunk text content (optional) + - position: Position/rank in retrieval (required, 1-indexed) + - score: Retrieval score (required) + - similarity_score: Vector similarity score (optional) + - llm_score: LLM-based relevance score (optional) + + Args: + csv_path: Path to CSV file (if loading from file) + csv_content: CSV content as string or bytes (if loading from upload/webhook) + dataset_id: Optional dataset identifier (auto-generated if not provided) + dataset_name: Optional dataset name (defaults to filename or 'uploaded_dataset') + + Returns: + RetrievalResultsDataset with loaded results + + Raises: + ValueError: If neither csv_path nor csv_content is provided + FileNotFoundError: If csv_path is provided but file doesn't exist + """ + if csv_path is None and csv_content is None: + raise ValueError("Either csv_path or csv_content must be provided") + + # Determine source info + if csv_path: + source_path = csv_path + source_name = Path(csv_path).stem if not dataset_name else dataset_name + if not Path(csv_path).exists(): + raise FileNotFoundError(f"CSV file not found: {csv_path}") + else: + source_path = None + source_name = dataset_name or "uploaded_dataset" + + dataset_id = dataset_id or f"{source_name}_{pd.Timestamp.now().strftime('%Y%m%d_%H%M%S')}" + + # Load CSV + if csv_path: + df = pd.read_csv(csv_path) + else: + # Handle both string and bytes + if isinstance(csv_content, bytes): + csv_content = csv_content.decode("utf-8") + df = pd.read_csv(StringIO(csv_content)) + + # Validate required columns + required_columns = ["query_id", "chunk_id", "position", "score"] + missing_columns = [col for col in required_columns if col not in df.columns] + if missing_columns: + raise ValueError(f"Missing required columns: {missing_columns}") + + # Convert to RetrievalResultRow objects + results = [] + for _, row in df.iterrows(): + result_row = RetrievalResultRow( + query_id=str(row["query_id"]), + report_id=(str(row["report_id"]) if "report_id" in df.columns and pd.notna(row.get("report_id")) else None), + chunk_id=str(row["chunk_id"]), + chunk_text=(str(row["chunk_text"]) if "chunk_text" in df.columns and pd.notna(row.get("chunk_text")) else None), + position=int(row["position"]), + score=float(row["score"]), + similarity_score=( + float(row["similarity_score"]) + if "similarity_score" in df.columns and pd.notna(row.get("similarity_score")) + else None + ), + llm_score=(float(row["llm_score"]) if "llm_score" in df.columns and pd.notna(row.get("llm_score")) else None), + metadata={ + k: v + for k, v in row.items() + if k + not in [ + "query_id", + "report_id", + "chunk_id", + "chunk_text", + "position", + "score", + "similarity_score", + "llm_score", + ] + and pd.notna(v) + }, + ) + results.append(result_row) + + logger.info(f"Loaded {len(results)} retrieval results from CSV for dataset '{dataset_id}'") + + return RetrievalResultsDataset( + dataset_id=dataset_id, + name=source_name, + description="Retrieval results loaded from CSV", + source="csv", + source_path=source_path, + results=results, + ) + + +def load_retrieval_results_from_sqlite( + db_path: str, + table_name: str = "retrieval_results", + dataset_id: Optional[str] = None, + dataset_name: Optional[str] = None, + query_filter: Optional[str] = None, +) -> RetrievalResultsDataset: + """ + Load retrieval results from SQLite database. + + Expected table schema: + - query_id: TEXT (required) + - report_id: TEXT (optional) + - chunk_id: TEXT (required) + - chunk_text: TEXT (optional) + - position: INTEGER (required, 1-indexed) + - score: REAL (required) + - similarity_score: REAL (optional) + - llm_score: REAL (optional) + + Args: + db_path: Path to SQLite database + table_name: Name of the table containing retrieval results + dataset_id: Optional dataset identifier + dataset_name: Optional dataset name + query_filter: Optional SQL WHERE clause to filter results + + Returns: + RetrievalResultsDataset with loaded results + + Raises: + FileNotFoundError: If database file doesn't exist + sqlite3.Error: If database query fails + """ + if not Path(db_path).exists(): + raise FileNotFoundError(f"Database file not found: {db_path}") + + dataset_id = dataset_id or f"{Path(db_path).stem}_{pd.Timestamp.now().strftime('%Y%m%d_%H%M%S')}" + dataset_name = dataset_name or Path(db_path).stem + + # Build query + base_query = f"SELECT * FROM {table_name}" # noqa: S608 + if query_filter: + base_query += f" WHERE {query_filter}" + base_query += " ORDER BY query_id, position" + + # Load data + conn = sqlite3.connect(db_path) + try: + df = pd.read_sql_query(base_query, conn) + finally: + conn.close() + + # Validate required columns + required_columns = ["query_id", "chunk_id", "position", "score"] + missing_columns = [col for col in required_columns if col not in df.columns] + if missing_columns: + raise ValueError(f"Missing required columns in database table: {missing_columns}") + + # Convert to RetrievalResultRow objects + results = [] + for _, row in df.iterrows(): + result_row = RetrievalResultRow( + query_id=str(row["query_id"]), + report_id=(str(row["report_id"]) if "report_id" in df.columns and pd.notna(row.get("report_id")) else None), + chunk_id=str(row["chunk_id"]), + chunk_text=(str(row["chunk_text"]) if "chunk_text" in df.columns and pd.notna(row.get("chunk_text")) else None), + position=int(row["position"]), + score=float(row["score"]), + similarity_score=( + float(row["similarity_score"]) + if "similarity_score" in df.columns and pd.notna(row.get("similarity_score")) + else None + ), + llm_score=(float(row["llm_score"]) if "llm_score" in df.columns and pd.notna(row.get("llm_score")) else None), + metadata={ + k: v + for k, v in row.items() + if k + not in [ + "query_id", + "report_id", + "chunk_id", + "chunk_text", + "position", + "score", + "similarity_score", + "llm_score", + ] + and pd.notna(v) + }, + ) + results.append(result_row) + + logger.info(f"Loaded {len(results)} retrieval results from SQLite for dataset '{dataset_id}'") + + return RetrievalResultsDataset( + dataset_id=dataset_id, + name=dataset_name, + description="Retrieval results loaded from SQLite database", + source="sqlite", + source_path=db_path, + results=results, + ) + + +def export_retrieval_results_to_csv(dataset: BenchmarkDataset, output_path: str) -> str: + """ + Export benchmark dataset to CSV file. + + Args: + dataset: BenchmarkDataset to export + output_path: Path where CSV file should be saved + + Returns: + Path to the saved CSV file + """ + rows = [] + for result in dataset.results: + row = { + "query_id": result.get_query_id() or "", + "report_id": result.get("report_id") or result.get("document_id") or "", + "chunk_id": result.get_chunk_id() or "", + "chunk_text": result.get("chunk_text") or "", + "position": result.get_position() or "", + "score": result.get_score() or "", + "similarity_score": result.get("similarity_score") or "", + "llm_score": result.get("llm_score") or "", + } + # Add all other fields from data dict + for key, value in result.data.items(): + if key not in row: + row[key] = value if value is not None else "" + rows.append(row) + + df = pd.DataFrame(rows) + df.to_csv(output_path, index=False) + + logger.info(f"Exported {len(rows)} retrieval results to CSV: {output_path}") + return output_path + + +def detect_dataset_type(columns: List[str]) -> DatasetType: + """ + Detect dataset type based on column names. + + Information Retrieval (IR) datasets typically have: + - chunk_id, position, score, similarity_score + + Information Extraction (IE) datasets typically have: + - answer, analysis, category, confidence_score + + Args: + columns: List of column names in the dataset + + Returns: + DatasetType enum value + """ + columns_lower = [c.lower() for c in columns] + + # Check for IE indicators + ie_indicators = [ + "answer", + "analysis", + "response", + "category", + "class", + "label", + "extracted_value", + ] + has_ie_indicators = any(indicator in columns_lower for indicator in ie_indicators) + + # Check for IR indicators + ir_indicators = [ + "chunk_id", + "chunk", + "position", + "rank", + "similarity_score", + "relevance_score", + ] + has_ir_indicators = any(indicator in columns_lower for indicator in ir_indicators) + + # If both present, prioritize IE (more specific) + if has_ie_indicators: + return DatasetType.INFORMATION_EXTRACTION + elif has_ir_indicators: + return DatasetType.INFORMATION_RETRIEVAL + else: + # Default to IR if unclear + return DatasetType.INFORMATION_RETRIEVAL + + +def load_flexible_dataset_from_csv( + csv_path: Optional[str] = None, + csv_content: Optional[Union[str, bytes]] = None, + dataset_id: Optional[str] = None, + dataset_name: Optional[str] = None, + dataset_type: Optional[DatasetType] = None, + column_mapping: Optional[Dict[str, str]] = None, +) -> BenchmarkDataset: + """ + Load a flexible benchmark dataset from CSV with automatic column name detection. + + This function is webhook-ready and handles different column name variations. + It automatically detects dataset type (IR vs IE) and maps columns flexibly. + + Args: + csv_path: Path to CSV file (if loading from file) + csv_content: CSV content as string or bytes (if loading from upload/webhook) + dataset_id: Optional dataset identifier (auto-generated if not provided) + dataset_name: Optional dataset name + dataset_type: Optional dataset type (auto-detected if not provided) + column_mapping: Optional explicit column mapping (standard_name -> actual_name) + + Returns: + BenchmarkDataset with loaded results + + Raises: + ValueError: If neither csv_path nor csv_content is provided + FileNotFoundError: If csv_path is provided but file doesn't exist + """ + if csv_path is None and csv_content is None: + raise ValueError("Either csv_path or csv_content must be provided") + + # Determine source info + if csv_path: + source_path = csv_path + source_name = Path(csv_path).stem if not dataset_name else dataset_name + if not Path(csv_path).exists(): + raise FileNotFoundError(f"CSV file not found: {csv_path}") + else: + source_path = None + source_name = dataset_name or "uploaded_dataset" + + dataset_id = dataset_id or f"{source_name}_{pd.Timestamp.now().strftime('%Y%m%d_%H%M%S')}" + + # Load CSV + if csv_path: + df = pd.read_csv(csv_path) + else: + # Handle both string and bytes + if isinstance(csv_content, bytes): + csv_content = csv_content.decode("utf-8") + df = pd.read_csv(StringIO(csv_content)) + + # Detect dataset type if not provided + if dataset_type is None: + dataset_type = detect_dataset_type(df.columns.tolist()) + logger.info(f"Auto-detected dataset type: {dataset_type.value}") + + # Validate required columns + columns_lower = [c.lower() for c in df.columns] + + # Check for query/question ID (required for all datasets) + has_query_id = any(col in ["query_id", "question_id", "qid", "query"] for col in columns_lower) + if not has_query_id: + raise ValueError("Missing required columns: query_id or question_id") + + # For IR datasets, check for chunk_id, position, and score + if dataset_type == DatasetType.INFORMATION_RETRIEVAL: + has_chunk_id = any(col in ["chunk_id", "chunk", "cid"] for col in columns_lower) + if not has_chunk_id: + raise ValueError("Missing required columns: chunk_id (required for IR datasets)") + has_position = any(col in ["position", "rank", "order", "pos"] for col in columns_lower) + if not has_position: + raise ValueError("Missing required columns: position or rank (required for IR datasets)") + has_score = any(col in ["score", "relevance_score", "confidence_score", "similarity_score"] for col in columns_lower) + if not has_score: + raise ValueError("Missing required columns: score (required for IR datasets)") + + # Build column mapping if not provided + if column_mapping is None: + column_mapping = {} + # Map common variations to standard names + standard_mappings = { + "query_id": ["query_id", "question_id", "qid", "query"], + "chunk_id": ["chunk_id", "chunk", "cid"], + "report_id": ["report_id", "document_id", "doc_id", "report"], + "position": ["position", "rank", "order", "pos"], + "score": [ + "score", + "relevance_score", + "confidence_score", + "similarity_score", + ], + "answer": ["answer", "analysis", "response", "text"], + "category": ["category", "class", "label", "type"], + } + + columns_lower_dict = {c.lower(): c for c in df.columns} + for standard_name, variations in standard_mappings.items(): + for variation in variations: + if variation.lower() in columns_lower_dict: + column_mapping[standard_name] = columns_lower_dict[variation.lower()] + break + + # Convert to FlexibleDatasetRow objects + results = [] + for _, row in df.iterrows(): + # Convert row to dictionary, handling NaN values + row_dict = {} + for col in df.columns: + value = row[col] + # Convert NaN to None, keep other values as-is + if pd.isna(value): + row_dict[col] = None + else: + row_dict[col] = value + + result_row = FlexibleDatasetRow(data=row_dict) + results.append(result_row) + + logger.info(f"Loaded {len(results)} rows from CSV for dataset '{dataset_id}' (type: {dataset_type.value})") + + return BenchmarkDataset( + dataset_id=dataset_id, + name=source_name, + description=f"Flexible dataset loaded from CSV (type: {dataset_type.value})", + version="1.0", # Default version + question_set=None, # Can be inferred or set explicitly + dataset_type=dataset_type, + source="csv", + source_path=source_path, + column_mapping=column_mapping, + results=results, + ) + + +def load_flexible_dataset_from_normalized_df( + normalized_df: pd.DataFrame, + dataset_id: Optional[str] = None, + dataset_name: Optional[str] = None, +) -> BenchmarkDataset: + """ + Build a BenchmarkDataset from a DataFrame that already has standard columns + (query_id, chunk_id, position, score, and optionally paragraph, document). + Used after normalize_dataframe_for_benchmark() so the evaluation pipeline sees consistent schema. + """ + required = ["query_id", "chunk_id", "position", "score"] + missing = [c for c in required if c not in normalized_df.columns] + if missing: + raise ValueError(f"Normalized DataFrame must have columns {required}. Missing: {missing}") + source_name = dataset_name or "normalized_dataset" + did = dataset_id or f"{source_name}_{pd.Timestamp.now().strftime('%Y%m%d_%H%M%S')}" + results = [] + for _, row in normalized_df.iterrows(): + row_dict = {} + for col in normalized_df.columns: + val = row[col] + row_dict[col] = None if pd.isna(val) else val + results.append(FlexibleDatasetRow(data=row_dict)) + logger.info( + "Loaded %d rows from normalized DataFrame for dataset '%s' (type: IR)", + len(results), + did, + ) + return BenchmarkDataset( + dataset_id=did, + name=source_name, + description="Flexible dataset from normalized column mapping (type: information_retrieval)", + version="1.0", + question_set=None, + dataset_type=DatasetType.INFORMATION_RETRIEVAL, + source="csv", + source_path=None, + column_mapping={c: c for c in normalized_df.columns}, + results=results, + ) + + +def load_flexible_dataset_from_sqlite( + db_path: str, + table_name: str = "benchmark_results", + dataset_id: Optional[str] = None, + dataset_name: Optional[str] = None, + dataset_type: Optional[DatasetType] = None, + column_mapping: Optional[Dict[str, str]] = None, + query_filter: Optional[str] = None, +) -> BenchmarkDataset: + """ + Load a flexible benchmark dataset from SQLite database. + + Args: + db_path: Path to SQLite database + table_name: Name of the table containing results + dataset_id: Optional dataset identifier + dataset_name: Optional dataset name + dataset_type: Optional dataset type (auto-detected if not provided) + column_mapping: Optional explicit column mapping + query_filter: Optional SQL WHERE clause to filter results + + Returns: + BenchmarkDataset with loaded results + """ + if not Path(db_path).exists(): + raise FileNotFoundError(f"Database file not found: {db_path}") + + dataset_id = dataset_id or f"{Path(db_path).stem}_{pd.Timestamp.now().strftime('%Y%m%d_%H%M%S')}" + dataset_name = dataset_name or Path(db_path).stem + + # Build query + base_query = f"SELECT * FROM {table_name}" # noqa: S608 + if query_filter: + base_query += f" WHERE {query_filter}" + + # Load data + conn = sqlite3.connect(db_path) + try: + df = pd.read_sql_query(base_query, conn) + finally: + conn.close() + + # Detect dataset type if not provided + if dataset_type is None: + dataset_type = detect_dataset_type(df.columns.tolist()) + logger.info(f"Auto-detected dataset type: {dataset_type.value}") + + # Build column mapping if not provided (same logic as CSV loader) + if column_mapping is None: + column_mapping = {} + standard_mappings = { + "query_id": ["query_id", "question_id", "qid", "query"], + "chunk_id": ["chunk_id", "chunk", "cid"], + "report_id": ["report_id", "document_id", "doc_id", "report"], + "position": ["position", "rank", "order", "pos"], + "score": [ + "score", + "relevance_score", + "confidence_score", + "similarity_score", + ], + "answer": ["answer", "analysis", "response", "text"], + "category": ["category", "class", "label", "type"], + } + + columns_lower = {c.lower(): c for c in df.columns} + for standard_name, variations in standard_mappings.items(): + for variation in variations: + if variation.lower() in columns_lower: + column_mapping[standard_name] = columns_lower[variation.lower()] + break + + # Convert to FlexibleDatasetRow objects + results = [] + for _, row in df.iterrows(): + row_dict = {} + for col in df.columns: + value = row[col] + if pd.isna(value): + row_dict[col] = None + else: + row_dict[col] = value + + result_row = FlexibleDatasetRow(data=row_dict) + results.append(result_row) + + logger.info(f"Loaded {len(results)} rows from SQLite for dataset '{dataset_id}' (type: {dataset_type.value})") + + return BenchmarkDataset( + dataset_id=dataset_id, + name=dataset_name, + description=f"Flexible dataset loaded from SQLite (type: {dataset_type.value})", + version="1.0", # Default version + question_set=None, # Can be inferred or set explicitly + dataset_type=dataset_type, + source="sqlite", + source_path=db_path, + column_mapping=column_mapping, + results=results, + ) diff --git a/report_analyst/core/cache_manager.py b/report_analyst/core/cache_manager.py index caf48b49..7132fa92 100644 --- a/report_analyst/core/cache_manager.py +++ b/report_analyst/core/cache_manager.py @@ -6,9 +6,8 @@ from typing import Any, Dict, List, Optional import numpy as np -from llama_index.core import Document, QueryBundle -from llama_index.core.indices import VectorStoreIndex -from sqlalchemy import text +from llama_index.core import Document +from sqlalchemy import bindparam, text from .database_manager import DatabaseManager from .database_schema import indexes, metadata @@ -17,7 +16,7 @@ class CacheManager: - def __init__(self, db_path: str = None, database_url: str = None): + def __init__(self, db_path: str | None = None, database_url: str | None = None): """ Initialize CacheManager. @@ -58,6 +57,8 @@ def __init__(self, db_path: str = None, database_url: str = None): # In-memory vector store for current document self.vector_store = None self.current_file_path = None + self.current_chunk_size = None + self.current_chunk_overlap = None def init_db(self): """Initialize the database schema using SQLAlchemy""" @@ -73,19 +74,24 @@ def init_db(self): for index_sql in indexes: try: conn.execute(text(index_sql)) - except Exception as e: + except Exception as e: # noqa: BLE001 # Index might already exist, which is fine logger.debug(f"Index creation (may already exist): {e}") conn.commit() logger.info("Database schema initialized successfully") except Exception as e: - logger.error(f"Error initializing database schema: {str(e)}", exc_info=True) + logger.error(f"Error initializing database schema: {e!s}", exc_info=True) raise def _load_vector_store(self, file_path: str, chunks: List[Dict]) -> None: """Load chunks into an in-memory vector store.""" try: + # Clear the existing vector store if it exists + if self.vector_store is not None: + logger.debug("Clearing existing vector store before reload") + self.vector_store = None + # Convert chunks to Documents documents = [] for chunk in chunks: @@ -102,21 +108,57 @@ def _load_vector_store(self, file_path: str, chunks: List[Dict]) -> None: ) documents.append(doc) - # Create vector store index with pre-computed embeddings + if not documents: + logger.warning( + f"No documents with embeddings found to load into vector store. Total chunks provided: {len(chunks)}" + ) + # Clear vector store if no documents to load + self.vector_store = None + self.current_file_path = file_path + + # Derive chunk parameters from the first chunk if available + if chunks: + self.current_chunk_size = chunks[0].get("chunk_size") + self.current_chunk_overlap = chunks[0].get("chunk_overlap") + else: + self.current_chunk_size = None + self.current_chunk_overlap = None + + logger.warning( + "Vector store cleared - no chunks available for chunk_size=%s, chunk_overlap=%s", + self.current_chunk_size, + self.current_chunk_overlap, + ) + return + + # Create vector store index with pre-computed embeddings. + # MockEmbedding avoids requiring OPENAI_API_KEY when every document + # already carries an embedding (tests and offline cache reload). + from llama_index.core.embeddings import MockEmbedding from llama_index.core.indices.vector_store.base import VectorStoreIndex + first_embedding = documents[0].embedding + embed_dim = len(first_embedding) self.vector_store = VectorStoreIndex.from_documents( documents, + embed_model=MockEmbedding(embed_dim=embed_dim), store_nodes_override=True, # Keep nodes in memory use_async=False, # Synchronous operation since we have embeddings show_progress=True, # Show progress during index creation ) self.current_file_path = file_path + # Store chunk parameters to detect when they change + if chunks: + self.current_chunk_size = chunks[0].get("chunk_size") + self.current_chunk_overlap = chunks[0].get("chunk_overlap") - logger.info(f"Loaded {len(documents)} chunks into vector store for {file_path}") + logger.info( + f"Loaded {len(documents)} chunks into vector store for {file_path} " + f"(chunk_size={self.current_chunk_size}, chunk_overlap={self.current_chunk_overlap})" + ) except Exception as e: - logger.error(f"Error loading vector store: {str(e)}", exc_info=True) + logger.error(f"Error loading vector store: {e!s}", exc_info=True) raise async def get_similar_chunks( @@ -124,16 +166,44 @@ async def get_similar_chunks( query_embedding: np.ndarray, file_path: str, top_k: int = 5, - chunk_size: int = None, - chunk_overlap: int = None, + chunk_size: int | None = None, + chunk_overlap: int | None = None, ) -> List[Dict]: """Get chunks most similar to the query embedding using LlamaIndex vector store.""" try: # Load chunks into vector store if needed - if self.current_file_path != file_path: + # Check if file path OR chunk parameters changed + needs_reload = ( + self.current_file_path != file_path + or self.current_chunk_size != chunk_size + or self.current_chunk_overlap != chunk_overlap + ) + + if needs_reload: + logger.info( + f"Reloading vector store: file_path changed={self.current_file_path != file_path}, " + f"chunk_size changed={self.current_chunk_size != chunk_size}, " + f"chunk_overlap changed={self.current_chunk_overlap != chunk_overlap}" + ) chunks = self.get_document_chunks(file_path, chunk_size, chunk_overlap) + logger.info(f"Retrieved {len(chunks)} chunks for chunk_size={chunk_size}, chunk_overlap={chunk_overlap}") + if not chunks: + logger.warning( + f"No chunks found in database for file_path={file_path}, " + f"chunk_size={chunk_size}, chunk_overlap={chunk_overlap}. " + f"Vector store will be empty. This may cause incorrect similarity search results." + ) self._load_vector_store(file_path, chunks) + # Verify vector store is loaded + if self.vector_store is None: + logger.error( + f"Vector store is None for file_path={file_path}, " + f"chunk_size={chunk_size}, chunk_overlap={chunk_overlap}. " + f"Cannot perform similarity search. Chunks may not exist in database." + ) + return [] + # Get similar nodes using vector store retriever = self.vector_store.as_retriever(similarity_top_k=top_k) @@ -156,6 +226,17 @@ async def get_similar_chunks( node.score if hasattr(node, "score") else node.get_score() if hasattr(node, "get_score") else 0.0 ) + node_chunk_size = node.metadata.get("chunk_size") + node_chunk_overlap = node.metadata.get("chunk_overlap") + + # Verify chunk metadata matches requested parameters + if node_chunk_size != chunk_size or node_chunk_overlap != chunk_overlap: + logger.warning( + f"Chunk metadata mismatch! Requested chunk_size={chunk_size}, chunk_overlap={chunk_overlap}, " + f"but chunk has chunk_size={node_chunk_size}, chunk_overlap={node_chunk_overlap}. " + f"Chunk text preview: {node.text[:100]}..." + ) + chunk = { "id": node.metadata.get("id"), "text": node.text, @@ -165,7 +246,11 @@ async def get_similar_chunks( "similarity_score": similarity_score, # Also store as 'similarity_score' for backward compatibility } chunks.append(chunk) - logger.debug(f"Found chunk with similarity score: {similarity_score:.4f}") + logger.debug( + f"Found chunk (chunk_size={node_chunk_size}, chunk_overlap={node_chunk_overlap}) " + f"with similarity score: {similarity_score:.4f}, " + f"text preview: {node.text[:100]}..." + ) logger.info(f"Retrieved {len(chunks)} similar chunks for {file_path}") if chunks: @@ -176,7 +261,7 @@ async def get_similar_chunks( return chunks except Exception as e: - logger.error(f"Error getting similar chunks: {str(e)}", exc_info=True) + logger.error(f"Error getting similar chunks: {e!s}", exc_info=True) return [] def save_analysis(self, file_path: str, question_id: str, result: Dict, config: Dict): @@ -194,7 +279,7 @@ def save_analysis(self, file_path: str, question_id: str, result: Dict, config: result_obj = conn.execute( text( """ - SELECT id FROM questions + SELECT id FROM questions WHERE question_id = :question_id AND question_set = :question_set """ ), @@ -325,7 +410,7 @@ def save_analysis(self, file_path: str, question_id: str, result: Dict, config: result_obj = conn.execute( text( """ - SELECT id FROM document_chunks + SELECT id FROM document_chunks WHERE file_path = :file_path AND chunk_text = :chunk_text """ ), @@ -389,10 +474,13 @@ def save_analysis(self, file_path: str, question_id: str, result: Dict, config: }, ) logger.info( - f"Saving raw values to DB - similarity_score: {chunk.get('similarity_score')}, llm_score: {chunk.get('llm_score')}, is_evidence: {chunk.get('is_evidence')}" + "Saving raw values to DB - similarity_score: %s, llm_score: %s, is_evidence: %s", + chunk.get("similarity_score"), + chunk.get("llm_score"), + chunk.get("is_evidence"), ) else: - logger.warning(f"Could not find chunk in document_chunks table") + logger.warning("Could not find chunk in document_chunks table") # Save to analysis cache logger.info("Saving to analysis cache") @@ -403,9 +491,19 @@ def save_analysis(self, file_path: str, question_id: str, result: Dict, config: INSERT INTO analysis_cache (file_path, question_id, chunk_size, chunk_overlap, top_k, model, question_set, result, created_at) - VALUES (:file_path, :question_id, :chunk_size, :chunk_overlap, :top_k, - :model, :question_set, :result, :created_at) - ON CONFLICT (file_path, question_id, chunk_size, chunk_overlap, top_k, model, question_set) DO UPDATE + VALUES ( + :file_path, + :question_id, + :chunk_size, + :chunk_overlap, + :top_k, + :model, + :question_set, + :result, + :created_at + ) + ON CONFLICT (file_path, question_id, chunk_size, chunk_overlap, top_k, model, question_set) + DO UPDATE SET result = EXCLUDED.result, created_at = EXCLUDED.created_at """ @@ -429,8 +527,17 @@ def save_analysis(self, file_path: str, question_id: str, result: Dict, config: INSERT OR REPLACE INTO analysis_cache (file_path, question_id, chunk_size, chunk_overlap, top_k, model, question_set, result, created_at) - VALUES (:file_path, :question_id, :chunk_size, :chunk_overlap, :top_k, - :model, :question_set, :result, :created_at) + VALUES ( + :file_path, + :question_id, + :chunk_size, + :chunk_overlap, + :top_k, + :model, + :question_set, + :result, + :created_at + ) """ ), { @@ -449,7 +556,7 @@ def save_analysis(self, file_path: str, question_id: str, result: Dict, config: logger.info("Successfully saved complete analysis") except Exception as e: - logger.error(f"Error saving analysis: {str(e)}", exc_info=True) + logger.error(f"Error saving analysis: {e!s}", exc_info=True) raise def get_analysis(self, file_path: str, config: Dict, question_ids: Optional[List[str]] = None) -> Dict[str, Any]: @@ -477,6 +584,7 @@ def get_analysis(self, file_path: str, config: Dict, question_ids: Optional[List "tcfd": "tcfd", "s4m": "s4m", "lucia": "lucia", + "climretrieve": "climretr", # Map climretrieve to climretr shortcut } db_question_set = question_set_mapping.get(config["question_set"], config["question_set"]) @@ -522,33 +630,33 @@ def get_analysis(self, file_path: str, config: Dict, question_ids: Optional[List # Now get the chunk information for each question if results: - # Build IN clause for question IDs - qid_placeholders = ",".join(f":qid_{i}" for i in range(len(results))) - chunk_query = f""" - SELECT - ac.question_id, - dc.chunk_text, - dc.metadata as chunk_metadata, - cr.chunk_order, - cr.similarity_score, - cr.llm_score, - cr.is_evidence, - cr.evidence_order, - cr.metadata as relevance_metadata - FROM analysis_cache ac - JOIN questions q ON q.question_id = ac.question_id - JOIN question_analysis qa ON qa.question_id = q.id AND qa.file_path = ac.file_path - JOIN chunk_relevance cr ON cr.question_analysis_id = qa.id - JOIN document_chunks dc ON cr.document_chunk_id = dc.id - WHERE ac.file_path = :file_path - AND ac.chunk_size = :chunk_size - AND ac.chunk_overlap = :chunk_overlap - AND ac.top_k = :top_k - AND ac.model = :model - AND ac.question_set = :question_set - AND ac.question_id IN ({qid_placeholders}) - ORDER BY ac.question_id, cr.chunk_order - """ + chunk_query = text( + """ + SELECT + ac.question_id, + dc.chunk_text, + dc.metadata as chunk_metadata, + cr.chunk_order, + cr.similarity_score, + cr.llm_score, + cr.is_evidence, + cr.evidence_order, + cr.metadata as relevance_metadata + FROM analysis_cache ac + JOIN questions q ON q.question_id = ac.question_id + JOIN question_analysis qa ON qa.question_id = q.id AND qa.file_path = ac.file_path + JOIN chunk_relevance cr ON cr.question_analysis_id = qa.id + JOIN document_chunks dc ON cr.document_chunk_id = dc.id + WHERE ac.file_path = :file_path + AND ac.chunk_size = :chunk_size + AND ac.chunk_overlap = :chunk_overlap + AND ac.top_k = :top_k + AND ac.model = :model + AND ac.question_set = :question_set + AND ac.question_id IN :question_ids + ORDER BY ac.question_id, cr.chunk_order + """ + ).bindparams(bindparam("question_ids", expanding=True)) chunk_params = { "file_path": str(file_path), @@ -558,11 +666,10 @@ def get_analysis(self, file_path: str, config: Dict, question_ids: Optional[List "model": config["model"], "question_set": db_question_set, } - for i, qid in enumerate(results.keys()): - chunk_params[f"qid_{i}"] = qid + chunk_params["question_ids"] = list(results.keys()) - logger.info(f"Executing chunk query with params: {list(chunk_params.keys())}") - chunk_result = conn.execute(text(chunk_query), chunk_params) + logger.info("Executing chunk query with params: %s", list(chunk_params.keys())) + chunk_result = conn.execute(chunk_query, chunk_params) chunk_rows = chunk_result.fetchall() logger.info(f"Retrieved {len(chunk_rows)} chunk rows") @@ -580,23 +687,28 @@ def get_analysis(self, file_path: str, config: Dict, question_ids: Optional[List "relevance_metadata": json.loads(row[8]) if row[8] else {}, } logger.info( - f"Raw DB values for chunk - similarity_score: {row[4]}, llm_score: {row[5]}, is_evidence: {row[6]}" + "Raw DB values for chunk - similarity_score: %s, llm_score: %s, is_evidence: %s", + row[4], + row[5], + row[6], ) results[question_id]["chunks"].append(chunk_info) # Sort chunks by their order for question_id in results: results[question_id]["chunks"].sort(key=lambda x: x["chunk_order"]) - logger.info(f"Question {question_id}: {len(results[question_id]['chunks'])} chunks") + logger.info("Question %s: %s chunks", question_id, len(results[question_id]["chunks"])) if results[question_id]["chunks"]: logger.info( - f" Similarity range: {min(c['similarity_score'] for c in results[question_id]['chunks']):.4f} - {max(c['similarity_score'] for c in results[question_id]['chunks']):.4f}" + " Similarity range: %.4f - %.4f", + min(c["similarity_score"] for c in results[question_id]["chunks"]), + max(c["similarity_score"] for c in results[question_id]["chunks"]), ) return results except Exception as e: - logger.error(f"Error retrieving analysis: {str(e)}", exc_info=True) + logger.error(f"Error retrieving analysis: {e!s}", exc_info=True) raise def save_vectors(self, file_path: str, chunks: List[Dict[str, Any]]) -> None: @@ -638,8 +750,8 @@ def save_vectors(self, file_path: str, chunks: List[Dict[str, Any]]) -> None: "created_at": datetime.now().isoformat(), } ) - except Exception as e: - logger.warning(f"Error preparing chunk {i} for storage: {str(e)}") + except Exception as e: # noqa: BLE001 + logger.warning(f"Error preparing chunk {i} for storage: {e!s}") continue if chunk_data: @@ -653,8 +765,15 @@ def save_vectors(self, file_path: str, chunks: List[Dict[str, Any]]) -> None: INSERT INTO document_chunks (file_path, chunk_text, chunk_size, chunk_overlap, embedding, metadata, created_at) - VALUES (:file_path, :chunk_text, :chunk_size, :chunk_overlap, - :embedding, :metadata, :created_at) + VALUES ( + :file_path, + :chunk_text, + :chunk_size, + :chunk_overlap, + :embedding, + :metadata, + :created_at + ) ON CONFLICT (file_path, chunk_text, chunk_size, chunk_overlap) DO UPDATE SET embedding = EXCLUDED.embedding, metadata = EXCLUDED.metadata, @@ -672,8 +791,15 @@ def save_vectors(self, file_path: str, chunks: List[Dict[str, Any]]) -> None: INSERT OR REPLACE INTO document_chunks (file_path, chunk_text, chunk_size, chunk_overlap, embedding, metadata, created_at) - VALUES (:file_path, :chunk_text, :chunk_size, :chunk_overlap, - :embedding, :metadata, :created_at) + VALUES ( + :file_path, + :chunk_text, + :chunk_size, + :chunk_overlap, + :embedding, + :metadata, + :created_at + ) """ ), chunk_row, @@ -692,7 +818,7 @@ def save_vectors(self, file_path: str, chunks: List[Dict[str, Any]]) -> None: logger.warning("No valid chunks to save") except Exception as e: - logger.error(f"Error saving vectors: {str(e)}", exc_info=True) + logger.error(f"Error saving vectors: {e!s}", exc_info=True) raise def get_vectors(self, file_path: str) -> List[Dict[str, Any]]: @@ -726,7 +852,7 @@ def get_vectors(self, file_path: str) -> List[Dict[str, Any]]: else: # Fallback to default shape if not stored embedding = np.frombuffer(row[1], dtype=np.float32) - except Exception as e: + except Exception as e: # noqa: BLE001 logger.warning(f"Error reconstructing embedding: {e}") embedding = None @@ -745,7 +871,7 @@ def get_vectors(self, file_path: str) -> List[Dict[str, Any]]: logger.info(f"Retrieved {len(chunks)} vectors for {file_path}") return chunks except Exception as e: - logger.error(f"Error retrieving vectors: {str(e)}", exc_info=True) + logger.error(f"Error retrieving vectors: {e!s}", exc_info=True) return [] def clear_cache(self, file_path: Optional[str] = None): @@ -767,10 +893,13 @@ def clear_cache(self, file_path: Optional[str] = None): conn.execute(text("DELETE FROM document_chunks")) logger.info("Cleared all cache") except Exception as e: - logger.error(f"Error clearing cache: {str(e)}", exc_info=True) + logger.error(f"Error clearing cache: {e!s}", exc_info=True) def list_analysis_keys(self) -> List[Dict[str, str]]: - """List distinct (file_path, question_set) pairs that have stored analysis. Used for UI dropdowns driven by stored data.""" + """List distinct (file_path, question_set) pairs that have stored analysis. + + Used for UI dropdowns driven by stored data. + """ try: with self.db_manager.get_connection() as conn: result_obj = conn.execute( @@ -788,7 +917,7 @@ def list_analysis_keys(self) -> List[Dict[str, str]]: logger.error(f"Error listing analysis keys: {e}", exc_info=True) return [] - def check_cache_status(self, file_path: str = None): + def check_cache_status(self, file_path: str | None = None): """Debug method to check cache contents""" try: with self.db_manager.get_connection() as conn: @@ -823,7 +952,7 @@ def check_cache_status(self, file_path: str = None): return rows except Exception as e: - logger.error(f"Error checking cache status: {str(e)}", exc_info=True) + logger.error(f"Error checking cache status: {e!s}", exc_info=True) return [] def get_all_answers_by_question_set(self, question_set: str) -> Dict[str, Any]: @@ -901,7 +1030,7 @@ def save_document_chunks(self, file_path: str, chunks: List[Dict], chunk_size: i with self.db_manager.get_connection() as conn: for i, chunk in enumerate(chunks): - logger.debug(f"Processing chunk {i+1}/{len(chunks)}") + logger.debug(f"Processing chunk {i + 1}/{len(chunks)}") if "embedding" not in chunk or chunk["embedding"] is None: logger.warning(f"Skipping chunk {i} - no valid embedding") @@ -919,7 +1048,15 @@ def save_document_chunks(self, file_path: str, chunks: List[Dict], chunk_size: i """ INSERT INTO document_chunks (file_path, chunk_text, chunk_size, chunk_overlap, embedding, metadata, created_at) - VALUES (:file_path, :chunk_text, :chunk_size, :chunk_overlap, :embedding, :metadata, :created_at) + VALUES ( + :file_path, + :chunk_text, + :chunk_size, + :chunk_overlap, + :embedding, + :metadata, + :created_at + ) ON CONFLICT (file_path, chunk_text, chunk_size, chunk_overlap) DO UPDATE SET embedding = EXCLUDED.embedding, metadata = EXCLUDED.metadata, @@ -942,7 +1079,15 @@ def save_document_chunks(self, file_path: str, chunks: List[Dict], chunk_size: i """ INSERT OR REPLACE INTO document_chunks (file_path, chunk_text, chunk_size, chunk_overlap, embedding, metadata, created_at) - VALUES (:file_path, :chunk_text, :chunk_size, :chunk_overlap, :embedding, :metadata, :created_at) + VALUES ( + :file_path, + :chunk_text, + :chunk_size, + :chunk_overlap, + :embedding, + :metadata, + :created_at + ) """ ), { @@ -962,7 +1107,7 @@ def save_document_chunks(self, file_path: str, chunks: List[Dict], chunk_size: i result_obj = conn.execute( text( """ - SELECT COUNT(*) FROM document_chunks + SELECT COUNT(*) FROM document_chunks WHERE file_path = :file_path AND chunk_size = :chunk_size AND chunk_overlap = :chunk_overlap """ ), @@ -976,10 +1121,12 @@ def save_document_chunks(self, file_path: str, chunks: List[Dict], chunk_size: i logger.info(f"Verification: Found {count} chunks in database for {file_path}") except Exception as e: - logger.error(f"Error saving document chunks: {str(e)}", exc_info=True) + logger.error(f"Error saving document chunks: {e!s}", exc_info=True) raise - def get_document_chunks(self, file_path: str, chunk_size: int = None, chunk_overlap: int = None) -> List[Dict]: + def get_document_chunks( + self, file_path: str, chunk_size: int | None = None, chunk_overlap: int | None = None + ) -> List[Dict]: """ Get document chunks from cache with improved logging. """ @@ -1053,10 +1200,12 @@ def get_document_chunks(self, file_path: str, chunk_size: int = None, chunk_over return chunks except Exception as e: - logger.error(f"Error getting document chunks: {str(e)}", exc_info=True) + logger.error(f"Error getting document chunks: {e!s}", exc_info=True) return [] - def get_chunks_without_embeddings(self, file_path: str, chunk_size: int = None, chunk_overlap: int = None) -> List[Dict]: + def get_chunks_without_embeddings( + self, file_path: str, chunk_size: int | None = None, chunk_overlap: int | None = None + ) -> List[Dict]: """Get chunks without embeddings (where embedding IS NULL)""" try: logger.info(f"Retrieving chunks without embeddings for {file_path}") @@ -1108,7 +1257,7 @@ def get_chunks_without_embeddings(self, file_path: str, chunk_size: int = None, return chunks except Exception as e: - logger.error(f"Error getting chunks without embeddings: {str(e)}", exc_info=True) + logger.error(f"Error getting chunks without embeddings: {e!s}", exc_info=True) return [] def has_chunk_scoring(self, file_path: str, config: Dict) -> bool: @@ -1135,6 +1284,6 @@ def has_chunk_scoring(self, file_path: str, config: Dict) -> bool: count = result_obj.fetchone()[0] return count > 0 - except Exception as e: - logger.error(f"Error checking chunk scoring: {str(e)}") + except Exception as e: # noqa: BLE001 + logger.error(f"Error checking chunk scoring: {e!s}") return False diff --git a/report_analyst/core/storage/benchmark_store.py b/report_analyst/core/storage/benchmark_store.py new file mode 100644 index 00000000..f1ce7883 --- /dev/null +++ b/report_analyst/core/storage/benchmark_store.py @@ -0,0 +1,402 @@ +import hashlib +import json +import logging +import sqlite3 +from datetime import datetime +from typing import Dict, List, Optional + +from ...models.benchmark import ( + BenchmarkDataset, + BenchmarkDatasetContent, + BenchmarkEvaluation, + EvaluationMetrics, + HumanAnnotation, + RetrievalConfig, +) + +logger = logging.getLogger(__name__) + + +class BenchmarkStore: + """Handle database operations for benchmarking data""" + + def __init__(self, db_path: str): + self.db_path = db_path + self.init_db() + + def init_db(self): + """Initialize the database schema for benchmarking tables""" + with sqlite3.connect(self.db_path) as conn: + # Create benchmark_datasets table + conn.execute( + """ + CREATE TABLE IF NOT EXISTS benchmark_datasets ( + dataset_id TEXT PRIMARY KEY, + name TEXT NOT NULL, + description TEXT, + version TEXT, + question_set TEXT, + file_path TEXT, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + """ + ) + + # Create ground_truth_chunks table + conn.execute( + """ + CREATE TABLE IF NOT EXISTS ground_truth_chunks ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + dataset_id TEXT NOT NULL, + question_id TEXT NOT NULL, + chunk_id TEXT NOT NULL, + relevance_score REAL DEFAULT 0.0, + is_evidence INTEGER DEFAULT 0, + evidence_order INTEGER, + annotation_notes TEXT, + FOREIGN KEY (dataset_id) REFERENCES benchmark_datasets(dataset_id), + UNIQUE(dataset_id, question_id, chunk_id) + ) + """ + ) + + # Create benchmark_evaluations table + conn.execute( + """ + CREATE TABLE IF NOT EXISTS benchmark_evaluations ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + dataset_id TEXT NOT NULL, + evaluation_name TEXT NOT NULL, + config_hash TEXT, + retrieval_config TEXT, + evaluation_metrics TEXT, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + FOREIGN KEY (dataset_id) REFERENCES benchmark_datasets(dataset_id) + ) + """ + ) + + # Create human_annotations table + conn.execute( + """ + CREATE TABLE IF NOT EXISTS human_annotations ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + evaluation_id INTEGER NOT NULL, + question_id TEXT NOT NULL, + chunk_id TEXT NOT NULL, + human_relevance_score REAL, + human_is_evidence INTEGER, + human_evidence_order INTEGER, + annotation_notes TEXT, + annotator_id TEXT, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, + FOREIGN KEY (evaluation_id) REFERENCES benchmark_evaluations(id) + ) + """ + ) + + # Create indices for better performance + conn.execute("CREATE INDEX IF NOT EXISTS idx_ground_truth_dataset ON ground_truth_chunks(dataset_id)") + conn.execute("CREATE INDEX IF NOT EXISTS idx_ground_truth_question ON ground_truth_chunks(question_id)") + conn.execute("CREATE INDEX IF NOT EXISTS idx_evaluations_dataset ON benchmark_evaluations(dataset_id)") + conn.execute("CREATE INDEX IF NOT EXISTS idx_annotations_evaluation ON human_annotations(evaluation_id)") + + conn.commit() + logger.info("Benchmark database schema initialized") + + def save_dataset(self, dataset: BenchmarkDatasetContent, file_path: str) -> int: + """Save a benchmark dataset to the database""" + with sqlite3.connect(self.db_path) as conn: + # Insert dataset metadata + cursor = conn.execute( + """ + INSERT OR REPLACE INTO benchmark_datasets + (dataset_id, name, description, version, question_set, file_path, updated_at) + VALUES (?, ?, ?, ?, ?, ?, CURRENT_TIMESTAMP) + """, + ( + dataset.dataset_id, + dataset.name, + dataset.description, + dataset.version, + dataset.question_set, + file_path, + ), + ) + + # Clear existing ground truth data for this dataset + conn.execute( + "DELETE FROM ground_truth_chunks WHERE dataset_id = ?", + (dataset.dataset_id,), + ) + + # Insert ground truth chunks + for question in dataset.questions: + for chunk in question.ground_truth_chunks: + conn.execute( + """ + INSERT INTO ground_truth_chunks + (dataset_id, question_id, chunk_id, relevance_score, is_evidence, evidence_order, annotation_notes) + VALUES (?, ?, ?, ?, ?, ?, ?) + """, + ( + dataset.dataset_id, + question.question_id, + chunk.chunk_id, + chunk.relevance_score, + chunk.is_evidence, + chunk.evidence_order, + chunk.annotation_notes, + ), + ) + + conn.commit() + logger.info(f"Saved dataset {dataset.dataset_id} with {len(dataset.questions)} questions") + return cursor.lastrowid + + def get_dataset(self, dataset_id: str) -> Optional[BenchmarkDataset]: + """Retrieve a benchmark dataset by ID""" + with sqlite3.connect(self.db_path) as conn: + conn.row_factory = sqlite3.Row + cursor = conn.execute( + """ + SELECT * FROM benchmark_datasets WHERE dataset_id = ? + """, + (dataset_id,), + ) + + row = cursor.fetchone() + if not row: + return None + + return BenchmarkDataset( + dataset_id=row["dataset_id"], + name=row["name"], + description=row["description"] or None, + version=row["version"] or None, + question_set=row["question_set"] or None, + file_path=row["file_path"] or None, + created_at=(datetime.fromisoformat(row["created_at"]) if row["created_at"] else None), + updated_at=(datetime.fromisoformat(row["updated_at"]) if row["updated_at"] else None), + source="database", + ) + + def list_datasets(self) -> List[BenchmarkDataset]: + """List all available benchmark datasets""" + with sqlite3.connect(self.db_path) as conn: + conn.row_factory = sqlite3.Row + cursor = conn.execute( + """ + SELECT * FROM benchmark_datasets ORDER BY created_at DESC + """ + ) + + datasets = [] + for row in cursor.fetchall(): + datasets.append( + BenchmarkDataset( + dataset_id=row["dataset_id"], + name=row["name"], + description=row["description"] or None, + version=row["version"] or None, + question_set=row["question_set"] or None, + source="database", + file_path=row["file_path"], + created_at=(datetime.fromisoformat(row["created_at"]) if row["created_at"] else None), + updated_at=(datetime.fromisoformat(row["updated_at"]) if row["updated_at"] else None), + ) + ) + + return datasets + + def get_ground_truth(self, dataset_id: str) -> Dict[str, Dict[str, float]]: + """Get ground truth data for a dataset""" + with sqlite3.connect(self.db_path) as conn: + conn.row_factory = sqlite3.Row + cursor = conn.execute( + """ + SELECT question_id, chunk_id, relevance_score + FROM ground_truth_chunks + WHERE dataset_id = ? + """, + (dataset_id,), + ) + + ground_truth = {} + for row in cursor.fetchall(): + question_id = row["question_id"] + if question_id not in ground_truth: + ground_truth[question_id] = {} + ground_truth[question_id][row["chunk_id"]] = row["relevance_score"] + + return ground_truth + + def save_evaluation(self, evaluation: BenchmarkEvaluation) -> int: + """Save an evaluation result to the database""" + config_json = evaluation.retrieval_config.model_dump_json() + config_hash = hashlib.sha256(config_json.encode()).hexdigest()[:16] + metrics_json = evaluation.evaluation_metrics.model_dump_json() + + with sqlite3.connect(self.db_path) as conn: + cursor = conn.execute( + """ + INSERT INTO benchmark_evaluations + (dataset_id, evaluation_name, config_hash, retrieval_config, evaluation_metrics) + VALUES (?, ?, ?, ?, ?) + """, + ( + evaluation.dataset_id, + evaluation.evaluation_name, + config_hash, + config_json, + metrics_json, + ), + ) + + conn.commit() + evaluation_id = cursor.lastrowid + logger.info(f"Saved evaluation {evaluation.evaluation_name} with ID {evaluation_id}") + return evaluation_id + + def get_evaluation(self, evaluation_id: int) -> Optional[BenchmarkEvaluation]: + """Retrieve an evaluation by ID""" + with sqlite3.connect(self.db_path) as conn: + conn.row_factory = sqlite3.Row + cursor = conn.execute( + """ + SELECT * FROM benchmark_evaluations WHERE id = ? + """, + (evaluation_id,), + ) + + row = cursor.fetchone() + if not row: + return None + + config_dict = json.loads(row["retrieval_config"]) + metrics_dict = json.loads(row["evaluation_metrics"]) + + return BenchmarkEvaluation( + id=row["id"], + dataset_id=row["dataset_id"], + evaluation_name=row["evaluation_name"], + config_hash=row["config_hash"], + retrieval_config=RetrievalConfig(**config_dict), + evaluation_metrics=EvaluationMetrics(**metrics_dict), + created_at=(datetime.fromisoformat(row["created_at"]) if row["created_at"] else None), + ) + + def list_evaluations(self, dataset_id: Optional[str] = None) -> List[BenchmarkEvaluation]: + """List evaluations, optionally filtered by dataset""" + with sqlite3.connect(self.db_path) as conn: + conn.row_factory = sqlite3.Row + + if dataset_id: + cursor = conn.execute( + """ + SELECT * FROM benchmark_evaluations + WHERE dataset_id = ? + ORDER BY created_at DESC + """, + (dataset_id,), + ) + else: + cursor = conn.execute( + """ + SELECT * FROM benchmark_evaluations + ORDER BY created_at DESC + """ + ) + + evaluations = [] + for row in cursor.fetchall(): + config_dict = json.loads(row["retrieval_config"]) + metrics_dict = json.loads(row["evaluation_metrics"]) + + evaluations.append( + BenchmarkEvaluation( + id=row["id"], + dataset_id=row["dataset_id"], + evaluation_name=row["evaluation_name"], + config_hash=row["config_hash"], + retrieval_config=RetrievalConfig(**config_dict), + evaluation_metrics=EvaluationMetrics(**metrics_dict), + created_at=(datetime.fromisoformat(row["created_at"]) if row["created_at"] else None), + ) + ) + + return evaluations + + def save_annotation(self, annotation: HumanAnnotation) -> int: + """Save a human annotation""" + with sqlite3.connect(self.db_path) as conn: + cursor = conn.execute( + """ + INSERT OR REPLACE INTO human_annotations + (evaluation_id, question_id, chunk_id, human_relevance_score, + human_is_evidence, human_evidence_order, annotation_notes, annotator_id) + VALUES (?, ?, ?, ?, ?, ?, ?, ?) + """, + ( + annotation.evaluation_id, + annotation.question_id, + annotation.chunk_id, + annotation.human_relevance_score, + annotation.human_is_evidence, + annotation.human_evidence_order, + annotation.annotation_notes, + annotation.annotator_id, + ), + ) + + conn.commit() + return cursor.lastrowid + + def get_annotations(self, evaluation_id: int) -> List[HumanAnnotation]: + """Get all annotations for an evaluation""" + with sqlite3.connect(self.db_path) as conn: + conn.row_factory = sqlite3.Row + cursor = conn.execute( + """ + SELECT * FROM human_annotations + WHERE evaluation_id = ? + ORDER BY created_at DESC + """, + (evaluation_id,), + ) + + annotations = [] + for row in cursor.fetchall(): + annotations.append( + HumanAnnotation( + id=row["id"], + evaluation_id=row["evaluation_id"], + question_id=row["question_id"], + chunk_id=row["chunk_id"], + human_relevance_score=row["human_relevance_score"], + human_is_evidence=row["human_is_evidence"], + human_evidence_order=row["human_evidence_order"], + annotation_notes=row["annotation_notes"], + annotator_id=row["annotator_id"], + created_at=(datetime.fromisoformat(row["created_at"]) if row["created_at"] else None), + ) + ) + + return annotations + + def delete_dataset(self, dataset_id: str) -> bool: + """Delete a dataset and all related data""" + with sqlite3.connect(self.db_path) as conn: + # Delete in order due to foreign key constraints + conn.execute( + "DELETE FROM human_annotations WHERE evaluation_id IN " + "(SELECT id FROM benchmark_evaluations WHERE dataset_id = ?)", + (dataset_id,), + ) + conn.execute("DELETE FROM benchmark_evaluations WHERE dataset_id = ?", (dataset_id,)) + conn.execute("DELETE FROM ground_truth_chunks WHERE dataset_id = ?", (dataset_id,)) + cursor = conn.execute("DELETE FROM benchmark_datasets WHERE dataset_id = ?", (dataset_id,)) + + conn.commit() + return cursor.rowcount > 0 diff --git a/report_analyst/data/cache/analysis.db b/report_analyst/data/cache/analysis.db deleted file mode 100644 index 9ab7fa194a68fda51ee08c52a53a37b357133cbf..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 4055040 zcmeFa2V7J~+cv%|EU;%ADi)08UWV2MFp-~bzzUEEzjEK%51G@96> z*dmr7wnWsZduC(78e1$$)Yw}rkr*|WMCE_a0>_e==Y8M*`|v%#?^}L4T%0oHy33q1 zbDx>GfzcWhOQ-4$nQ9XqBJdD6ISG2uv_K%B1On%a@Vn;64S-9{AMo4hHT*6}3H+z{ z$P`ZnGUvqt*>S}V*#y~p(iM_UQoZ|+)Kk}w+~+wjcJ2PI+a1Rp4E#UKz+`W!Tf_G4 zou=iP)Ja-4IXzpKG19eU&JsvEFpeCR1D`Yc?8d+7Hf4a2l3cVqPHPqDWulX4@nHsIypfTl*G$!i}EDcSL{_AC0ZRS6o zhg!2aniLjDqXgZv)G2C{nvRbc`Zt929xx~(Dl&#}+wSkk z^MN+>ju;dX6CM#q|K);qniQYER_*mX|3dK_8NPYe`1jc*#6%5Di1^#ub)etO0i^Bo 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