From 758f0df6ba02a2c229c0992ad42fd5a6097fc2b8 Mon Sep 17 00:00:00 2001
From: Evgeniya Sukhodolskaya <suxodolskaya97@gmail.com>
Date: Wed, 11 Feb 2026 14:12:48 +0100
Subject: [PATCH 1/3] db to engine

---
 Makefile                                                    | 6 +++---
 .../application/services/hybrid_service/qdrant_query.py     | 2 +-
 .../services/query_vectorstore_service/qdrant_query.py      | 2 +-
 .../infrastructure/{qdrant_db => qdrant_engine}/__init__.py | 0
 .../{qdrant_db => qdrant_engine}/create_collection.py       | 2 +-
 .../{qdrant_db => qdrant_engine}/delete_collection.py       | 2 +-
 .../{qdrant_db => qdrant_engine}/qdrant_ingestion.py        | 2 +-
 .../{qdrant_db => qdrant_engine}/qdrant_vectorstore.py      | 0
 tests/integration/test_databases.py                         | 6 +++---
 9 files changed, 11 insertions(+), 11 deletions(-)
 rename src/biomedical_graphrag/infrastructure/{qdrant_db => qdrant_engine}/__init__.py (100%)
 rename src/biomedical_graphrag/infrastructure/{qdrant_db => qdrant_engine}/create_collection.py (79%)
 rename src/biomedical_graphrag/infrastructure/{qdrant_db => qdrant_engine}/delete_collection.py (79%)
 rename src/biomedical_graphrag/infrastructure/{qdrant_db => qdrant_engine}/qdrant_ingestion.py (95%)
 rename src/biomedical_graphrag/infrastructure/{qdrant_db => qdrant_engine}/qdrant_vectorstore.py (100%)

diff --git a/Makefile b/Makefile
index 5c743ea..67a56eb 100644
--- a/Makefile
+++ b/Makefile
@@ -63,17 +63,17 @@ custom-graph-query: ## Run a custom natural language query using Neo4j GraphRAG
 #################################################################################
 create-qdrant-collection: ## Create the Qdrant collection for embeddings
 	@echo "Creating Qdrant collection for embeddings..."
-	uv run src/biomedical_graphrag/infrastructure/qdrant_db/create_collection.py
+	uv run src/biomedical_graphrag/infrastructure/qdrant_engine/create_collection.py
 	@echo "Qdrant collection creation complete."
 
 delete-qdrant-collection: ## Delete the Qdrant collection for embeddings
 	@echo "Deleting Qdrant collection for embeddings..."
-	uv run src/biomedical_graphrag/infrastructure/qdrant_db/delete_collection.py
+	uv run src/biomedical_graphrag/infrastructure/qdrant_engine/delete_collection.py
 	@echo "Qdrant collection deletion complete."
 
 ingest-qdrant-data: ## Ingest embeddings into the Qdrant collection
 	@echo "Ingesting embeddings into the Qdrant collection..."
-	uv run src/biomedical_graphrag/infrastructure/qdrant_db/qdrant_ingestion.py
+	uv run src/biomedical_graphrag/infrastructure/qdrant_engine/qdrant_ingestion.py
 	@echo "Embeddings ingestion complete."
 
 custom-qdrant-query: ## Run a custom query on the Qdrant collection (modify the --ask parameter as needed)
diff --git a/src/biomedical_graphrag/application/services/hybrid_service/qdrant_query.py b/src/biomedical_graphrag/application/services/hybrid_service/qdrant_query.py
index 9632476..5cdebe5 100644
--- a/src/biomedical_graphrag/application/services/hybrid_service/qdrant_query.py
+++ b/src/biomedical_graphrag/application/services/hybrid_service/qdrant_query.py
@@ -1,6 +1,6 @@
 from qdrant_client.models import models
 
-from biomedical_graphrag.infrastructure.qdrant_db.qdrant_vectorstore import AsyncQdrantVectorStore
+from biomedical_graphrag.infrastructure.qdrant_engine.qdrant_vectorstore import AsyncQdrantVectorStore
 from biomedical_graphrag.utils.logger_util import setup_logging
 
 logger = setup_logging()
diff --git a/src/biomedical_graphrag/application/services/query_vectorstore_service/qdrant_query.py b/src/biomedical_graphrag/application/services/query_vectorstore_service/qdrant_query.py
index 642fa71..6d081c2 100644
--- a/src/biomedical_graphrag/application/services/query_vectorstore_service/qdrant_query.py
+++ b/src/biomedical_graphrag/application/services/query_vectorstore_service/qdrant_query.py
@@ -4,7 +4,7 @@
     QDRANT_GENERATION_PROMPT,
 )
 from biomedical_graphrag.config import settings
-from biomedical_graphrag.infrastructure.qdrant_db.qdrant_vectorstore import AsyncQdrantVectorStore
+from biomedical_graphrag.infrastructure.qdrant_engine.qdrant_vectorstore import AsyncQdrantVectorStore
 from biomedical_graphrag.utils.logger_util import setup_logging
 
 from qdrant_client.models import models
diff --git a/src/biomedical_graphrag/infrastructure/qdrant_db/__init__.py b/src/biomedical_graphrag/infrastructure/qdrant_engine/__init__.py
similarity index 100%
rename from src/biomedical_graphrag/infrastructure/qdrant_db/__init__.py
rename to src/biomedical_graphrag/infrastructure/qdrant_engine/__init__.py
diff --git a/src/biomedical_graphrag/infrastructure/qdrant_db/create_collection.py b/src/biomedical_graphrag/infrastructure/qdrant_engine/create_collection.py
similarity index 79%
rename from src/biomedical_graphrag/infrastructure/qdrant_db/create_collection.py
rename to src/biomedical_graphrag/infrastructure/qdrant_engine/create_collection.py
index 841fe6f..3f08b30 100644
--- a/src/biomedical_graphrag/infrastructure/qdrant_db/create_collection.py
+++ b/src/biomedical_graphrag/infrastructure/qdrant_engine/create_collection.py
@@ -1,6 +1,6 @@
 import asyncio
 
-from biomedical_graphrag.infrastructure.qdrant_db.qdrant_vectorstore import AsyncQdrantVectorStore
+from biomedical_graphrag.infrastructure.qdrant_engine.qdrant_vectorstore import AsyncQdrantVectorStore
 
 
 async def create_collection() -> None:
diff --git a/src/biomedical_graphrag/infrastructure/qdrant_db/delete_collection.py b/src/biomedical_graphrag/infrastructure/qdrant_engine/delete_collection.py
similarity index 79%
rename from src/biomedical_graphrag/infrastructure/qdrant_db/delete_collection.py
rename to src/biomedical_graphrag/infrastructure/qdrant_engine/delete_collection.py
index 0cfe57e..301a5f1 100644
--- a/src/biomedical_graphrag/infrastructure/qdrant_db/delete_collection.py
+++ b/src/biomedical_graphrag/infrastructure/qdrant_engine/delete_collection.py
@@ -1,6 +1,6 @@
 import asyncio
 
-from biomedical_graphrag.infrastructure.qdrant_db.qdrant_vectorstore import AsyncQdrantVectorStore
+from biomedical_graphrag.infrastructure.qdrant_engine.qdrant_vectorstore import AsyncQdrantVectorStore
 
 
 async def delete_collection() -> None:
diff --git a/src/biomedical_graphrag/infrastructure/qdrant_db/qdrant_ingestion.py b/src/biomedical_graphrag/infrastructure/qdrant_engine/qdrant_ingestion.py
similarity index 95%
rename from src/biomedical_graphrag/infrastructure/qdrant_db/qdrant_ingestion.py
rename to src/biomedical_graphrag/infrastructure/qdrant_engine/qdrant_ingestion.py
index 485c323..2fd3210 100644
--- a/src/biomedical_graphrag/infrastructure/qdrant_db/qdrant_ingestion.py
+++ b/src/biomedical_graphrag/infrastructure/qdrant_engine/qdrant_ingestion.py
@@ -1,6 +1,6 @@
 import asyncio
 
-from biomedical_graphrag.infrastructure.qdrant_db.qdrant_vectorstore import AsyncQdrantVectorStore
+from biomedical_graphrag.infrastructure.qdrant_engine.qdrant_vectorstore import AsyncQdrantVectorStore
 from biomedical_graphrag.utils.json_util import load_gene_json, load_pubmed_json
 from biomedical_graphrag.utils.logger_util import setup_logging
 
diff --git a/src/biomedical_graphrag/infrastructure/qdrant_db/qdrant_vectorstore.py b/src/biomedical_graphrag/infrastructure/qdrant_engine/qdrant_vectorstore.py
similarity index 100%
rename from src/biomedical_graphrag/infrastructure/qdrant_db/qdrant_vectorstore.py
rename to src/biomedical_graphrag/infrastructure/qdrant_engine/qdrant_vectorstore.py
diff --git a/tests/integration/test_databases.py b/tests/integration/test_databases.py
index 12a0b8e..2670cad 100644
--- a/tests/integration/test_databases.py
+++ b/tests/integration/test_databases.py
@@ -6,7 +6,7 @@
 
 from biomedical_graphrag.config import Neo4jSettings, QdrantSettings
 from biomedical_graphrag.infrastructure.neo4j_db.neo4j_client import AsyncNeo4jClient
-from biomedical_graphrag.infrastructure.qdrant_db.qdrant_vectorstore import AsyncQdrantVectorStore
+from biomedical_graphrag.infrastructure.qdrant_engine.qdrant_vectorstore import AsyncQdrantVectorStore
 
 
 class TestQdrantIntegration:
@@ -16,7 +16,7 @@ class TestQdrantIntegration:
     async def test_qdrant_vectorstore_initialization(self) -> None:
         """Test that QdrantVectorStore can be initialized with settings."""
         with patch(
-            "biomedical_graphrag.infrastructure.qdrant_db.qdrant_vectorstore.settings"
+            "biomedical_graphrag.infrastructure.qdrant_engine.qdrant_vectorstore.settings"
         ) as mock_settings:
             mock_settings.qdrant.url = "http://localhost:6333"
             mock_settings.qdrant.api_key.get_secret_value.return_value = "test-key"
@@ -27,7 +27,7 @@ async def test_qdrant_vectorstore_initialization(self) -> None:
             mock_settings.openai.api_key.get_secret_value.return_value = "test-openai-key"
 
             with patch(
-                "biomedical_graphrag.infrastructure.qdrant_db.qdrant_vectorstore.AsyncQdrantClient"
+                "biomedical_graphrag.infrastructure.qdrant_engine.qdrant_vectorstore.AsyncQdrantClient"
             ) as mock_client_class:
                 mock_client = AsyncMock()
                 mock_client_class.return_value = mock_client

From a91c59fdf4efd76c1e2d0c705ae041a61f4423cc Mon Sep 17 00:00:00 2001
From: Evgeniya Sukhodolskaya <suxodolskaya97@gmail.com>
Date: Wed, 11 Feb 2026 15:21:17 +0100
Subject: [PATCH 2/3] fixed ingestion bug

---
 .../infrastructure/qdrant_engine/qdrant_vectorstore.py      | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/src/biomedical_graphrag/infrastructure/qdrant_engine/qdrant_vectorstore.py b/src/biomedical_graphrag/infrastructure/qdrant_engine/qdrant_vectorstore.py
index 47a5af0..cf586d1 100644
--- a/src/biomedical_graphrag/infrastructure/qdrant_engine/qdrant_vectorstore.py
+++ b/src/biomedical_graphrag/infrastructure/qdrant_engine/qdrant_vectorstore.py
@@ -139,7 +139,7 @@ def _define_bm25_vectors(self, text: str, avg_len: int = 256) -> models.Document
         )
 
     async def upsert_points(
-        self, pubmed_data: dict[str, Any], gene_data: dict[str, Any] | None = None, only_new: bool = False, batch_size: int = 30
+        self, pubmed_data: dict[str, Any], gene_data: dict[str, Any] | None = None, only_new: bool = False, batch_size: int = 32
     ) -> None:
         """
         Upsert points into a collection from pubmed_dataset.json structure,
@@ -324,9 +324,7 @@ async def upsert_points(
                         try:
                             await self.client.upsert(
                                 collection_name=self.collection_name,
-                                points=PointsList(
-                                    points=batch_points,
-                                ),
+                                points=batch_points,
                             )
                             total_skipped += batch_skipped
                             total_processed += len(batch_points)

From 772d9b6046fd9da6f8af78c367607daf9cfaf787 Mon Sep 17 00:00:00 2001
From: Evgeniya Sukhodolskaya <suxodolskaya97@gmail.com>
Date: Wed, 25 Feb 2026 20:12:19 +0100
Subject: [PATCH 3/3] Claude & I updated README + make file + terms & dropped
 stale Qdrant service part

---
 .env.example                                  |  20 +--
 Makefile                                      |  16 +-
 README.md                                     | 104 ++++-------
 pyproject.toml                                |   4 +-
 src/biomedical_graphrag/api/server.py         |  10 +-
 .../application/cli/fusion_query.py           |   8 +-
 .../application/cli/query_vectorstore.py      |  47 -----
 .../hybrid_service/prompts/__init__.py        |   2 +-
 .../hybrid_service/prompts/hybrid_prompts.py  |   2 +-
 .../services/hybrid_service/qdrant_query.py   |   2 +-
 .../services/hybrid_service/tool_calling.py   |   8 +-
 .../services/hybrid_service/tools/__init__.py |   2 +-
 .../hybrid_service/tools/qdrant_tools.py      |   4 +-
 .../query_vectorstore_service/__init__.py     |   5 -
 .../prompts/__init__.py                       |   5 -
 .../prompts/qdrant_prompts.py                 |  13 --
 .../query_vectorstore_service/qdrant_query.py | 163 -----------------
 .../qdrant_engine/qdrant_ingestion.py         |   2 +-
 uv.lock                                       | 166 ------------------
 19 files changed, 71 insertions(+), 512 deletions(-)
 delete mode 100644 src/biomedical_graphrag/application/cli/query_vectorstore.py
 delete mode 100644 src/biomedical_graphrag/application/services/query_vectorstore_service/__init__.py
 delete mode 100644 src/biomedical_graphrag/application/services/query_vectorstore_service/prompts/__init__.py
 delete mode 100644 src/biomedical_graphrag/application/services/query_vectorstore_service/prompts/qdrant_prompts.py
 delete mode 100644 src/biomedical_graphrag/application/services/query_vectorstore_service/qdrant_query.py

diff --git a/.env.example b/.env.example
index 07c0b37..9ecfa4b 100644
--- a/.env.example
+++ b/.env.example
@@ -1,5 +1,8 @@
 # OpenAI Configuration
 OPENAI__API_KEY=your_openai_api_key_here
+OPENAI__MODEL=gpt-4o-mini
+OPENAI__TEMPERATURE=0.0
+OPENAI__MAX_TOKENS=1500
 
 # Neo4j Configuration
 NEO4J__URI=your_neo4j_uri_here
@@ -11,23 +14,16 @@ NEO4J__DATABASE=neo4j
 QDRANT__URL=your_qdrant_url_here
 QDRANT__API_KEY=your_qdrant_api_key_here
 QDRANT__COLLECTION_NAME=biomedical_papers
+QDRANT__EMBEDDING_MODEL=text-embedding-3-large
 QDRANT__EMBEDDING_DIMENSION=1536
 QDRANT__RERANKER_EMBEDDING_DIMENSION=3072
-QDRANT__EMBEDDING_MODEL=text-embedding-3-large
-QDRANT__ESTIMATE_BM25_AVG_LEN_ON_X_DOCS=500
+QDRANT__ESTIMATE_BM25_AVG_LEN_ON_X_DOCS=300
 QDRANT__CLOUD_INFERENCE=true
-JSON__DATA_PATH=data/pubmed_sample.json
 
 # PubMed Configuration
 PUBMED__API_KEY=your_pubmed_api_key_here
 PUBMED__EMAIL=your_email@example.com
 
-# OpenAI Settings
-OPENAI__API_KEY=your_openai_api_key_here
-OPENAI__MODEL=gpt-4o-mini
-OPENAI__TEMPERATURE=0.0
-OPENAI__MAX_TOKENS=1500
-
-# JSON Paths
-JSON__PUBMED_JSON_PATH=data/pubmed_dataset.json
-JSON__GENE_JSON_PATH=data/gene_dataset.json
+# JSON Data Paths (optional — defaults are data/pubmed_dataset.json and data/gene_dataset.json)
+JSON_DATA__PUBMED_JSON_PATH=data/pubmed_dataset.json
+JSON_DATA__GENE_JSON_PATH=data/gene_dataset.json
diff --git a/Makefile b/Makefile
index 67a56eb..5600da0 100644
--- a/Makefile
+++ b/Makefile
@@ -48,14 +48,9 @@ delete-graph: ## Delete all nodes and relationships in the Neo4j graph
 	uv run src/biomedical_graphrag/infrastructure/neo4j_db/delete_graph.py
 	@echo "Neo4j graph deletion complete."
 
-example-graph-query: ## Run example queries on the Neo4j graph using GraphRAG
-	@echo "Running example queries on the Neo4j graph..."
-	uv run src/biomedical_graphrag/application/cli/fusion_query.py --examples
-	@echo "Example queries complete."
-
-custom-graph-query: ## Run a custom natural language query using Neo4j GraphRAG (use QUESTION="your question")
-	@echo "Running custom query on the Neo4j graph with GraphRAG..."
-	uv run src/biomedical_graphrag/application/cli/fusion_query.py $(if $(QUESTION),--ask "$(QUESTION)")
+custom-graph-query: ## Run a custom natural language query (use QUESTION="your question")
+	@echo "Running custom query..."
+	uv run src/biomedical_graphrag/application/cli/fusion_query.py "$(QUESTION)"
 	@echo "Custom query complete."
 
 #################################################################################
@@ -76,11 +71,6 @@ ingest-qdrant-data: ## Ingest embeddings into the Qdrant collection
 	uv run src/biomedical_graphrag/infrastructure/qdrant_engine/qdrant_ingestion.py
 	@echo "Embeddings ingestion complete."
 
-custom-qdrant-query: ## Run a custom query on the Qdrant collection (modify the --ask parameter as needed)
-	@echo "Running custom query on the Qdrant collection..."
-	uv run src/biomedical_graphrag/application/cli/query_vectorstore.py $(if $(QUESTION),--ask "$(QUESTION)")
-	@echo "Custom query complete."
-
 #################################################################################
 ## API Server Commands
 #################################################################################
diff --git a/README.md b/README.md
index 9fcd7a9..49816ac 100644
--- a/README.md
+++ b/README.md
@@ -31,11 +31,9 @@
     - [Data Collection](#data-collection)
     - [Infrastructure Setup](#infrastructure-setup)
       - [Neo4j Graph Database](#neo4j-graph-database)
-      - [Qdrant Vector Database](#qdrant-vector-database)
+      - [Qdrant Vector Search Engine](#qdrant-vector-search-engine)
     - [Query Commands](#query-commands)
-      - [Qdrant Vector Search](#qdrant-vector-search)
       - [Hybrid Neo4j + Qdrant Queries](#hybrid-neo4j--qdrant-queries)
-      - [Available Query Types](#available-query-types)
       - [Sample Queries](#sample-queries)
     - [API Server](#api-server)
     - [Frontend](#frontend)
@@ -46,15 +44,20 @@
 
 ## Overview
 
-A comprehensive GraphRAG (Graph Retrieval-Augmented Generation) system designed for biomedical research. It combines knowledge graphs with vector search engine to provide intelligent querying and analysis of biomedical literature and genomic data.
+A biomedical context engineering system. An agent uses Qdrant vector search engine tools (hybrid retrieval, recommendations) and Neo4j graph database tools (graph enrichment) to gather context, then fuses it into a single biomedical answer.
 
-Article: [Building a Biomedical GraphRAG: When Knowledge Graphs Meet Vector Search](https://aiechoes.substack.com/p/building-a-biomedical-graphrag-when)
+> Originally forked from [benitomartin/biomedical-graphrag](https://github.com/benitomartin/biomedical-graphrag).
+
+**References:**
+- Video: [PubMed Navigator](https://www.youtube.com/watch?v=3NWTi90i6C4)
+- Article: [Building a Biomedical GraphRAG: When Knowledge Graphs Meet Vector Search](https://aiechoes.substack.com/p/building-a-biomedical-graphrag-when)
 
 **Key Features:**
 
-- **Hybrid Query System**: Combines Neo4j graph database with Qdrant vector search engine for comprehensive biomedical insights
+- **Context Engineering**: Agent orchestrates Qdrant and Neo4j tools, fusing results into a single answer
+- **Qdrant Vector Search Engine**: Hybrid retrieval (dense + BM25 with reranking) and constraint-based recommendations
+- **Neo4j Graph Database**: Graph enrichment via ontology-based tools (collaborator networks, MeSH relations, gene co-mentions)
 - **Data Integration**: Processes PubMed papers, gene data, and research citations
-- **Intelligent Querying**: Uses LLM-powered tool selection for graph enrichment and hybrid (semantic + lexical) search
 - **Biomedical Schema**: Specialized graph schema for papers, authors, institutions, genes, and MeSH terms
 - **Async Processing**: High-performance async data collection and processing
 
@@ -67,7 +70,7 @@ biomedical-graphrag/
 ├── src/
 │   └── biomedical_graphrag/
 │       ├── api/                # FastAPI server
-│       │   └── server.py       # GraphRAG API endpoints
+│       │   └── server.py       # PubMed Navigator API endpoints
 │       ├── application/        # Application layer
 │       │   ├── cli/            # Command-line interfaces
 │       │   └── services/       # Business logic services
@@ -102,7 +105,7 @@ biomedical-graphrag/
 1. Clone the repository:
 
    ```bash
-   git clone git@github.com:benitomartin/biomedical-graphrag.git
+   git clone git@github.com:thierrypdamiba/biomedical-graphrag.git
    cd biomedical-graphrag
    ```
 
@@ -127,7 +130,7 @@ biomedical-graphrag/
 1. Create a `.env` file in the root directory:
 
    ```bash
-    cp env.example .env
+    cp .env.example .env
    ```
 
 ## Usage
@@ -221,7 +224,7 @@ make delete-qdrant-collection
 Notes:
 
 - Embeddings are built from **PubMed paper abstracts**.
-- This project uses OpenAI eembeddings **Matryoshka Representation Learning (MRL)** feature:
+- This project uses OpenAI embeddings **Matryoshka Representation Learning (MRL)** feature:
   - `QDRANT__EMBEDDING_DIMENSION` is the prefix dimension used for **retrieval** (stored in Qdrant as the `Dense` vector).
   - `QDRANT__RERANKER_EMBEDDING_DIMENSION` is the (larger) prefix dimension used for **reranking** (stored in Qdrant as the `Reranker` vector).
 - `make ingest-qdrant-data` currently recreates the collection each run (see `qdrant_ingestion.py`).
@@ -233,48 +236,27 @@ Notes:
 
 ### Query Commands
 
-#### Qdrant Vector Search
-
-```bash
-# Run a custom query on the Qdrant vector store
-make custom-qdrant-query QUESTION="Which institutions have collaborated most frequently on papers about 'Gene Editing' and 'Immunotherapy'?"
-
-# Or run directly with the CLI
-uv run src/biomedical_graphrag/application/cli/query_vectorstore.py --ask "Which institutions have collaborated most frequently on papers about 'Gene Editing' and 'Immunotherapy'?"
-```
-
 #### Hybrid Neo4j + Qdrant Queries
 
 ```bash
-# Run example queries on the Neo4j graph using GraphRAG
-make example-graph-query
-
-# Run a custom natural language query using hybrid GraphRAG
+# Run a custom natural language query
 make custom-graph-query QUESTION="What are the latest research trends in cancer immunotherapy?"
 
 # Or run directly with the CLI (positional args)
 uv run src/biomedical_graphrag/application/cli/fusion_query.py "What are the latest research trends in cancer immunotherapy?"
 ```
 
-#### Available Query Types
-
-**Qdrant Queries:**
-
-- Semantic search across paper abstracts and content
-- Similarity-based retrieval using embeddings and BM25 fusion
+The hybrid query system combines vector search engine (Qdrant) with graph enrichment (Neo4j):
+- Author collaboration networks
+- Citation analysis and paper relationships
+- Gene-paper associations
+- MeSH term relationships
+- Institution affiliations
 
-**Hybrid Queries:**
-
-- Combines vector search engine (Qdrant) with graph enrichment (Neo4j):
-  - Author collaboration networks
-  - Citation analysis and paper relationships
-  - Gene-paper associations
-  - MeSH term relationships
-  - Institution affiliations
-- LLM-powered tool selection & fusion:
-  - Runs one Qdrant tool: hybrid retrieval (BM25 + dense & reranking) or recommendations with contraints, - to fetch relevant papers.
-  - Calls Neo4j enrichment tools for graph evidence.
-  - Produces one fused answer from both sources.
+LLM-powered tool selection & fusion:
+- Runs one Qdrant tool: hybrid retrieval (BM25 + dense & reranking) or recommendations with constraints — to fetch relevant papers.
+- Calls Neo4j enrichment tools for graph evidence.
+- Produces one fused answer from both sources.
 
 Output:
 
@@ -283,27 +265,12 @@ Output:
 #### Sample Queries
 
 - Who collaborates with Jennifer Doudna on CRISPR research?
-  Which researchers work with Emmanuelle Charpentier on gene editing or genome engineering papers?
-
-- Who are George Church’s collaborators publishing on synthetic biology and genome sequencing?
-
-- List scientists collaborating with Feng Zhang on neuroscience studies
-
-- Which papers are related to PMID 31295471 based on shared MeSH terms?
-
-- Find papers similar to the CRISPR-Cas9 genome editing study with PMID 31295471
-
-- Show other studies linked by MeSH terms to PMID 27562951
-
-- Which genes are mentioned in the same papers as gag?
-
-- What genes appear together with HIF1A in cancer research?
 
 - Which genes are frequently co-mentioned with TP53?
 
 ### API Server
 
-The project includes a FastAPI server that exposes the GraphRAG functionality via HTTP endpoints:
+The project includes a FastAPI server (PubMed Navigator) that exposes the context engineering pipeline via HTTP endpoints:
 
 ```bash
 # Start the API server (runs on port 8765)
@@ -316,21 +283,28 @@ make run-api
 |--------|----------|-------------|
 | GET | `/health` | Health check |
 | GET | `/api/neo4j/stats` | Neo4j graph statistics (node/relationship counts) |
-| POST | `/api/search` | Hybrid GraphRAG search |
+| POST | `/api/graphrag-query` | Context engineering search (Qdrant + Neo4j) |
 
 **Search Request Example:**
 
 ```bash
-curl -X POST http://localhost:8765/api/search \
+curl -X POST http://localhost:8765/api/graphrag-query \
   -H "Content-Type: application/json" \
-  -d '{"query": "What genes are associated with breast cancer?", "limit": 10}'
+  -d '{"query": "What genes are associated with breast cancer?", "limit": 5}'
 ```
 
 ### Frontend
 
-The frontend is maintained in a separate repository:
+The frontend is maintained in a separate repository: **[biomedical-graphrag-frontend](https://github.com/thierrypdamiba/biomedical-graphrag-frontend)**
+
+The quickest way to run it locally:
+
+```bash
+# Auto-clones the frontend repo and starts it (requires pnpm)
+make run-frontend
+```
 
-**[biomedical-graphrag-frontend](https://github.com/thierrypdamiba/biomedical-graphrag-frontend)**
+Or manually:
 
 ```bash
 git clone https://github.com/thierrypdamiba/biomedical-graphrag-frontend.git
@@ -359,7 +333,7 @@ curl http://localhost:8765/health
 ### Troubleshooting
 
 - **Make fails immediately with ".env file is missing"**
-  - Create it with `cp env.example .env` and fill in required values.
+  - Create it with `cp .env.example .env` and fill in required values.
 
 - **Qdrant ingestion/query fails**
   - Confirm Qdrant is running and `QDRANT__URL` points to it.
diff --git a/pyproject.toml b/pyproject.toml
index 9943099..78ba0e6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,7 @@
 [project]
 name = "biomedical-graphrag"
 version = "0.1.0"
-description = "GraphRAG system for biomedical research, combining knowledge graphs and vector search"
+description = "Context engineering for biomedical research, combining Qdrant vector search engine and Neo4j graph database"
 readme = "README.md"
 authors = [
     {name = "Benito Martin"}
@@ -14,8 +14,6 @@ dependencies = [
     "fastapi>=0.115.0",
     "loguru>=0.7.3",
     "neo4j>=5.28.2",
-    "neo4j-graphrag>=1.10.0",
-    "nest-asyncio>=1.5.8",
     "openai>=1.0.0",
     "pydantic>=2.12.0",
     "pydantic-settings>=2.11.0",
diff --git a/src/biomedical_graphrag/api/server.py b/src/biomedical_graphrag/api/server.py
index 3e5eae9..47b4c68 100644
--- a/src/biomedical_graphrag/api/server.py
+++ b/src/biomedical_graphrag/api/server.py
@@ -64,7 +64,7 @@ async def _preload_services() -> None:
 
 app = FastAPI(
     title="PubMed Navigator API",
-    description="Hybrid search API combining Qdrant vector search with Neo4j knowledge graph",
+    description="Context engineering API combining Qdrant vector search engine with Neo4j graph database",
     version="0.1.0",
     lifespan=lifespan,
 )
@@ -84,8 +84,8 @@ class SearchRequest(BaseModel):
     """Search request body."""
 
     query: str = Field(..., description="The search query")
-    limit: int = Field(default=5, ge=1, le=5, description="Maximum number of results")
-    mode: str = Field(default="graphrag", description="Search mode: graphrag, dense, sparse, hybrid")
+    limit: int = Field(default=5, ge=1, le=5, description="Maximum number of results (vector search)")
+    mode: str = Field(default="graphrag", description="Search mode: graphrag (Qdrant + Neo4j context engineering)")
 
 
 class TraceStep(BaseModel):
@@ -188,9 +188,9 @@ async def get_neo4j_stats() -> Neo4jStatsResponse:
 @app.post("/api/graphrag-query", response_model=SearchResponse)
 async def search(request: SearchRequest) -> SearchResponse:
     """
-    Perform hybrid GraphRAG search.
+    Run context engineering pipeline.
 
-    Combines Qdrant vector search with Neo4j knowledge graph enrichment.
+    Combines Qdrant vector search engine with Neo4j graph enrichment and fuses the results.
     """
     try:
         _load_services()
diff --git a/src/biomedical_graphrag/application/cli/fusion_query.py b/src/biomedical_graphrag/application/cli/fusion_query.py
index ba2ffb0..5d45460 100644
--- a/src/biomedical_graphrag/application/cli/fusion_query.py
+++ b/src/biomedical_graphrag/application/cli/fusion_query.py
@@ -1,7 +1,7 @@
 """
-CLI for hybrid GraphRAG querying:
-1. Retrieve relevant papers from Qdrant based on the tool selected.
-2. Use LLM to select and run Neo4j enrichment tools.
+CLI for context engineering pipeline:
+1. Retrieve relevant papers from Qdrant (hybrid retrieval or recommendations).
+2. Use LLM to select and run Neo4j graph enrichment tools.
 3. Fuse both sources into one concise biomedical summary.
 """
 
@@ -36,7 +36,7 @@ async def main() -> None:
         answer = await run_tools_sequence_and_summarize(question)
 
         print("\n=== Unified Biomedical Answer ===\n")
-        print(answer)
+        print(answer.summary)
     except Exception as e:
         logger.error(f"Error during query processing: {e}")
         raise
diff --git a/src/biomedical_graphrag/application/cli/query_vectorstore.py b/src/biomedical_graphrag/application/cli/query_vectorstore.py
deleted file mode 100644
index 22788fd..0000000
--- a/src/biomedical_graphrag/application/cli/query_vectorstore.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import argparse
-import asyncio
-
-from biomedical_graphrag.application.services.query_vectorstore_service.qdrant_query import (
-    AsyncQdrantQuery,
-)
-from biomedical_graphrag.utils.logger_util import setup_logging
-
-logger = setup_logging()
-
-
-async def main() -> None:
-    """
-    Command-line interface to query the biomedical vector store (async).
-    Args:
-        --ask: The question to ask (default: example question)
-
-    Returns:
-        Prints the answer to the console.
-
-    """
-    parser = argparse.ArgumentParser(description="Query the biomedical vector store.")
-
-    parser.add_argument(
-        "--ask",
-        type=str,
-        default=(
-            "Which institutions have collaborated most"
-            "frequently on papers about 'Gene Editing' and 'Immunotherapy'?"
-        ),
-        help="The question to ask.",
-    )
-
-    args = parser.parse_args()
-
-    # Initialize AsyncQdrantQuery
-    qdrant_query = AsyncQdrantQuery()
-    try:
-        # Perform the query
-        answer = await qdrant_query.get_answer(args.ask)
-        logger.info(f"Answer: {answer}")
-    finally:
-        await qdrant_query.close()
-
-
-if __name__ == "__main__":
-    asyncio.run(main())
diff --git a/src/biomedical_graphrag/application/services/hybrid_service/prompts/__init__.py b/src/biomedical_graphrag/application/services/hybrid_service/prompts/__init__.py
index 4e56ffb..3ec8e45 100644
--- a/src/biomedical_graphrag/application/services/hybrid_service/prompts/__init__.py
+++ b/src/biomedical_graphrag/application/services/hybrid_service/prompts/__init__.py
@@ -1,4 +1,4 @@
-"""Prompts for hybrid GraphRAG querying.
+"""Prompts for context engineering.
 
 This package exports the prompt templates/helpers used by the hybrid service.
 """
diff --git a/src/biomedical_graphrag/application/services/hybrid_service/prompts/hybrid_prompts.py b/src/biomedical_graphrag/application/services/hybrid_service/prompts/hybrid_prompts.py
index f7c0f3c..399134b 100644
--- a/src/biomedical_graphrag/application/services/hybrid_service/prompts/hybrid_prompts.py
+++ b/src/biomedical_graphrag/application/services/hybrid_service/prompts/hybrid_prompts.py
@@ -1,4 +1,4 @@
-"""Prompt templates for hybrid GraphRAG querying."""
+"""Prompt templates for context engineering."""
 
 import json
 from typing import Any
diff --git a/src/biomedical_graphrag/application/services/hybrid_service/qdrant_query.py b/src/biomedical_graphrag/application/services/hybrid_service/qdrant_query.py
index 5cdebe5..dbce3f1 100644
--- a/src/biomedical_graphrag/application/services/hybrid_service/qdrant_query.py
+++ b/src/biomedical_graphrag/application/services/hybrid_service/qdrant_query.py
@@ -21,7 +21,7 @@ async def close(self) -> None:
 
     async def retrieve_papers_dense(self, query: str, top_k: int = 5) -> list[dict]:
         """
-        Query the Qdrant vector store for similar papers (async).
+        Query the Qdrant vector search engine for similar papers (async).
         Vanilla dense search on quantized openAI embeddings.
 
         Args:
diff --git a/src/biomedical_graphrag/application/services/hybrid_service/tool_calling.py b/src/biomedical_graphrag/application/services/hybrid_service/tool_calling.py
index a63c7ca..86f0176 100644
--- a/src/biomedical_graphrag/application/services/hybrid_service/tool_calling.py
+++ b/src/biomedical_graphrag/application/services/hybrid_service/tool_calling.py
@@ -28,7 +28,7 @@
 openai_client = OpenAI(api_key=settings.openai.api_key.get_secret_value())
 
 
-def _extract_qdrant_context(qdrant_results: list[dict]) -> dict[str, list[str]]:
+def _extract_qdrant_context(qdrant_results: list[dict]) -> dict[str, list[str]]: #Is it a reverse engineering approach of Paper class?
     """Extract structured entities from Qdrant results for Neo4j tool pre-fill."""
     pmids: list[str] = []
     authors: list[str] = []
@@ -58,7 +58,7 @@ def _extract_qdrant_context(qdrant_results: list[dict]) -> dict[str, list[str]]:
     }
 
 
-def _score_authors(neo4j: Neo4jGraphQuery, authors: list[str], mesh_terms: list[str]) -> list[str]:
+def _score_authors(neo4j: Neo4jGraphQuery, authors: list[str], mesh_terms: list[str]) -> list[str]: #Might be problematic because terms have different importance to a person using the assistant
     """Score authors by paper count on relevant topics. Returns 'Name (N papers)' sorted by count."""
     if not authors:
         return []
@@ -212,7 +212,7 @@ def run_graph_enrichment(question: str, qdrant_results: list[dict]) -> Neo4jEnri
 
         results: dict[str, Any] = {}
         tool_call_counts: dict[str, int] = {}
-        max_calls_per_tool = 3
+        max_calls_per_tool = 3 #TBD
 
         if response.output:
             for tool_call in response.output:
@@ -243,7 +243,7 @@ def run_graph_enrichment(question: str, qdrant_results: list[dict]) -> Neo4jEnri
                             count = len(result) if isinstance(result, list) else None
                         except Exception as e:
                             logger.error(f"Neo4j tool {name} failed: {e}")
-                            results[name] = f"Error: {e}"
+                            results[name] = f"Error: {e}" #This is dangerous, we leak errors which can be showing some sensitive information
                             result = None
                             count = 0
                         tools_executed.append(ToolExecution(name=name, arguments=args, result_count=count, results=result))
diff --git a/src/biomedical_graphrag/application/services/hybrid_service/tools/__init__.py b/src/biomedical_graphrag/application/services/hybrid_service/tools/__init__.py
index 99785a9..0bf6ea9 100644
--- a/src/biomedical_graphrag/application/services/hybrid_service/tools/__init__.py
+++ b/src/biomedical_graphrag/application/services/hybrid_service/tools/__init__.py
@@ -1,4 +1,4 @@
-"""Tools for hybrid GraphRAG querying."""
+"""Tool definitions for context engineering."""
 
 from .enrichment_tools import NEO4J_ENRICHMENT_TOOLS
 from .qdrant_tools import QDRANT_TOOLS
diff --git a/src/biomedical_graphrag/application/services/hybrid_service/tools/qdrant_tools.py b/src/biomedical_graphrag/application/services/hybrid_service/tools/qdrant_tools.py
index 3012adc..843507d 100644
--- a/src/biomedical_graphrag/application/services/hybrid_service/tools/qdrant_tools.py
+++ b/src/biomedical_graphrag/application/services/hybrid_service/tools/qdrant_tools.py
@@ -1,9 +1,9 @@
 """
-Qdrant vector-search tool definitions.
+Qdrant tool definitions for hybrid retrieval and recommendations.
 """
 
 # ----------------------------
-# Qdrant Vector Search tools definition
+# Qdrant tools definition
 # ----------------------------
 
 QDRANT_TOOLS = [
diff --git a/src/biomedical_graphrag/application/services/query_vectorstore_service/__init__.py b/src/biomedical_graphrag/application/services/query_vectorstore_service/__init__.py
deleted file mode 100644
index 9cf0d1a..0000000
--- a/src/biomedical_graphrag/application/services/query_vectorstore_service/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-"""Query vectorstore service for Qdrant operations."""
-
-from .qdrant_query import AsyncQdrantQuery
-
-__all__ = ["AsyncQdrantQuery"]
diff --git a/src/biomedical_graphrag/application/services/query_vectorstore_service/prompts/__init__.py b/src/biomedical_graphrag/application/services/query_vectorstore_service/prompts/__init__.py
deleted file mode 100644
index 52bf0b0..0000000
--- a/src/biomedical_graphrag/application/services/query_vectorstore_service/prompts/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-"""Prompts for Qdrant vectorstore querying."""
-
-from .qdrant_prompts import QDRANT_GENERATION_PROMPT
-
-__all__ = ["QDRANT_GENERATION_PROMPT"]
diff --git a/src/biomedical_graphrag/application/services/query_vectorstore_service/prompts/qdrant_prompts.py b/src/biomedical_graphrag/application/services/query_vectorstore_service/prompts/qdrant_prompts.py
deleted file mode 100644
index fcd8529..0000000
--- a/src/biomedical_graphrag/application/services/query_vectorstore_service/prompts/qdrant_prompts.py
+++ /dev/null
@@ -1,13 +0,0 @@
-"""Prompt templates for OpenAI Qdrant generation."""
-
-QDRANT_GENERATION_PROMPT = """
-You are a biomedical research assistant.
-
-Generate an answer based on the following context from biomedical research papers.
-
-Use the context to answer the question as accurately as possible.
-
-Context: {context}
-Question: {question}
-Answer:
-"""
diff --git a/src/biomedical_graphrag/application/services/query_vectorstore_service/qdrant_query.py b/src/biomedical_graphrag/application/services/query_vectorstore_service/qdrant_query.py
deleted file mode 100644
index 6d081c2..0000000
--- a/src/biomedical_graphrag/application/services/query_vectorstore_service/qdrant_query.py
+++ /dev/null
@@ -1,163 +0,0 @@
-from openai import AsyncOpenAI
-
-from biomedical_graphrag.application.services.query_vectorstore_service.prompts.qdrant_prompts import (
-    QDRANT_GENERATION_PROMPT,
-)
-from biomedical_graphrag.config import settings
-from biomedical_graphrag.infrastructure.qdrant_engine.qdrant_vectorstore import AsyncQdrantVectorStore
-from biomedical_graphrag.utils.logger_util import setup_logging
-
-from qdrant_client.models import models
-
-logger = setup_logging()
-
-
-class AsyncQdrantQuery:
-    """Handles querying Qdrant vector store for natural language questions (async)."""
-
-    def __init__(self) -> None:
-        """
-        Initialize the async Qdrant client with connection parameters.
-        """
-        self.url = settings.qdrant.url
-        self.api_key = settings.qdrant.api_key
-        self.collection_name = settings.qdrant.collection_name
-        self.embedding_dimension = settings.qdrant.embedding_dimension
-        self.reranker_embedding_dimension = settings.qdrant.reranker_embedding_dimension
-        self.cloud_inference = settings.qdrant.cloud_inference
-
-        self.openai_client = AsyncOpenAI(api_key=settings.openai.api_key.get_secret_value())
-
-        self.qdrant_client = AsyncQdrantVectorStore()
-
-    async def close(self) -> None:
-        """Close the async Qdrant client."""
-        await self.qdrant_client.close()
-
-    async def retrieve_documents_dense(self, question: str, top_k: int = 3) -> list[dict]:
-        """
-        Query the Qdrant vector store for similar documents (async).
-        Vanilla dense search on quantized openAI embeddings.
-
-        Args:
-            question (str): Input question to query.
-            top_k (int): Number of top similar documents to retrieve.
-        Returns:
-            List of dictionaries containing the top_k similar documents.
-        """
-        if self.cloud_inference:
-            dense_vector = self.qdrant_client._define_openai_vectors(question, dimensions=self.embedding_dimension)
-        else:
-            dense_vector = await self.qdrant_client._get_openai_vectors(question, dimensions=self.embedding_dimension)
-
-        search_result = await self.qdrant_client.client.query_points(
-            collection_name=self.collection_name,
-            query=dense_vector,
-            using="Dense",
-            search_params=models.SearchParams(
-                quantization=models.QuantizationSearchParams(
-                    oversampling=3.0,  # retrieve 3 * top_k quantized vectors
-                    rescore=True,  # to rescore with original vectors
-                )
-            ),
-            limit=top_k,
-            with_payload=True,
-        )
-        results = [
-            {
-                "id": point.id,
-                "score": point.score,
-                "payload": point.payload,
-            }
-            for point in search_result.points
-        ]
-        return results
-
-    async def retrieve_documents_hybrid(self, question: str, top_k: int = 3) -> list[dict]:
-        """
-        Query the Qdrant vector search engine (async).
-        Hybrid dense + lexical search, fused by reranking with text-embedding-3-large.
-
-        Args:
-            question (str): Input question - query.
-            top_k (int): Number of top similar documents to retrieve.
-        Returns:
-            List of dictionaries containing the top_k similar documents.
-        """
-        if self.cloud_inference:
-            retriever_vector = self.qdrant_client._define_openai_vectors(
-                question, dimensions=self.embedding_dimension
-            )
-            reranker_vector = self.qdrant_client._define_openai_vectors(
-                question, dimensions=self.reranker_embedding_dimension
-            )
-        else:
-            openai_vector = await self.qdrant_client._get_openai_vectors(
-                question, dimensions=self.reranker_embedding_dimension
-            )
-            retriever_vector = openai_vector[:self.embedding_dimension] # Qdrant normalizes vectors used with COSINE automatically on upsert/query
-            reranker_vector = openai_vector # full precision for reranking
-
-        sparse_vector = self.qdrant_client._define_bm25_vectors(question)
-
-        search_result = await self.qdrant_client.client.query_points(
-            collection_name=self.collection_name,
-            prefetch=[
-                models.Prefetch(
-                    query=retriever_vector,
-                    using="Dense",
-                    params=models.SearchParams(
-                        quantization=models.QuantizationSearchParams(
-                            oversampling=3.0,  # retrieve 3 * top_k quantized vectors
-                            rescore=True,  # to rescore with original vectors
-                        )
-                    ),
-                    limit=top_k,
-                ),
-                models.Prefetch(
-                    query=sparse_vector,
-                    using="Lexical",
-                    limit=top_k,
-                ),
-            ],
-            # query=models.RrfQuery(rrf=models.Rrf(k=60)),
-            query=reranker_vector,
-            using="Reranker",
-            limit=top_k,
-            with_payload=True,
-        )
-
-        results = [
-            {
-                "id": point.id,
-                "score": point.score,
-                "payload": point.payload,
-            }
-            for point in search_result.points
-        ]
-        return results
-
-    async def get_answer(self, question: str) -> str:
-        """
-        Get an answer to the question by retrieving relevant documents from Qdrant (async).
-
-        Args:
-            question (str): The question to answer.
-        Returns:
-            str: The answer generated from the retrieved documents.
-        """
-
-        context = QDRANT_GENERATION_PROMPT.format(
-            question=question,
-            context="\n".join(
-                f"Content: {doc['payload']}" for doc in await self.retrieve_documents_dense(question)
-            ),
-        )
-        logger.debug(f"Qdrant context: {context}")
-
-        response = await self.openai_client.chat.completions.create(
-            model=settings.openai.model,
-            messages=[{"role": "user", "content": context}],
-            max_tokens=settings.openai.max_tokens,
-        )
-        return response.choices[0].message.content or ""
diff --git a/src/biomedical_graphrag/infrastructure/qdrant_engine/qdrant_ingestion.py b/src/biomedical_graphrag/infrastructure/qdrant_engine/qdrant_ingestion.py
index 2fd3210..1051da0 100644
--- a/src/biomedical_graphrag/infrastructure/qdrant_engine/qdrant_ingestion.py
+++ b/src/biomedical_graphrag/infrastructure/qdrant_engine/qdrant_ingestion.py
@@ -52,4 +52,4 @@ async def ingest_data(recreate: bool = False, only_new: bool = True) -> None:
 
 
 if __name__ == "__main__":
-    asyncio.run(ingest_data(recreate=False, only_new=True))
+    asyncio.run(ingest_data(recreate=True, only_new=False))
diff --git a/uv.lock b/uv.lock
index 54afc80..0fd5ce6 100644
--- a/uv.lock
+++ b/uv.lock
@@ -46,8 +46,6 @@ dependencies = [
     { name = "fastapi" },
     { name = "loguru" },
     { name = "neo4j" },
-    { name = "neo4j-graphrag" },
-    { name = "nest-asyncio" },
     { name = "openai" },
     { name = "pydantic" },
     { name = "pydantic-settings" },
@@ -74,8 +72,6 @@ requires-dist = [
     { name = "fastapi", specifier = ">=0.115.0" },
     { name = "loguru", specifier = ">=0.7.3" },
     { name = "neo4j", specifier = ">=5.28.2" },
-    { name = "neo4j-graphrag", specifier = ">=1.10.0" },
-    { name = "nest-asyncio", specifier = ">=1.5.8" },
     { name = "openai", specifier = ">=1.0.0" },
     { name = "pydantic", specifier = ">=2.12.0" },
     { name = "pydantic-settings", specifier = ">=2.11.0" },
@@ -166,15 +162,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/5c/05/5cbb59154b093548acd0f4c7c474a118eda06da25aa75c616b72d8fcd92a/fastapi-0.128.0-py3-none-any.whl", hash = "sha256:aebd93f9716ee3b4f4fcfe13ffb7cf308d99c9f3ab5622d8877441072561582d", size = 103094, upload-time = "2025-12-27T15:21:12.154Z" },
 ]
 
-[[package]]
-name = "fsspec"
-version = "2024.12.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/ee/11/de70dee31455c546fbc88301971ec03c328f3d1138cfba14263f651e9551/fsspec-2024.12.0.tar.gz", hash = "sha256:670700c977ed2fb51e0d9f9253177ed20cbde4a3e5c0283cc5385b5870c8533f", size = 291600, upload-time = "2024-12-19T19:57:30.333Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/de/86/5486b0188d08aa643e127774a99bac51ffa6cf343e3deb0583956dca5b22/fsspec-2024.12.0-py3-none-any.whl", hash = "sha256:b520aed47ad9804237ff878b504267a3b0b441e97508bd6d2d8774e3db85cee2", size = 183862, upload-time = "2024-12-19T19:57:28.258Z" },
-]
-
 [[package]]
 name = "grpcio"
 version = "1.75.1"
@@ -333,15 +320,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/af/22/7ab7b4ec3a1c1f03aef376af11d23b05abcca3fb31fbca1e7557053b1ba2/jiter-0.11.0-cp314-cp314t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6e2bbf24f16ba5ad4441a9845e40e4ea0cb9eed00e76ba94050664ef53ef4406", size = 347102, upload-time = "2025-09-15T09:20:20.16Z" },
 ]
 
-[[package]]
-name = "json-repair"
-version = "0.44.1"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/a8/6b/ed6e92efc5acfbc9c35ccae1676b70e4adb1552421e64f838c2a3f097d9a/json_repair-0.44.1.tar.gz", hash = "sha256:1130eb9733b868dac1340b43cb2effebb519ae6d52dd2d0728c6cca517f1e0b4", size = 32886, upload-time = "2025-04-30T16:09:38.54Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/82/b4/3cbd27a3240b2962c3b87bbb1c20eb6c56e5b26cde61f141f86ca98e2f68/json_repair-0.44.1-py3-none-any.whl", hash = "sha256:51d82532c3b8263782a301eb7904c75dce5fee8c0d1aba490287fc0ab779ac50", size = 22478, upload-time = "2025-04-30T16:09:37.303Z" },
-]
-
 [[package]]
 name = "loguru"
 version = "0.7.3"
@@ -402,36 +380,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/04/00/1f74089c06aec1fac9390e2300a6a6b2381e0dac281783d64ccca9d681fd/neo4j-5.28.2-py3-none-any.whl", hash = "sha256:5c53b5c3eee6dee7e920c9724391aa38d7135a651e71b766da00533b92a91a94", size = 313156, upload-time = "2025-07-30T06:04:31.438Z" },
 ]
 
-[[package]]
-name = "neo4j-graphrag"
-version = "1.10.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "fsspec" },
-    { name = "json-repair" },
-    { name = "neo4j" },
-    { name = "numpy", marker = "python_full_version < '3.14'" },
-    { name = "pydantic" },
-    { name = "pypdf" },
-    { name = "pyyaml" },
-    { name = "scipy", marker = "python_full_version < '3.14'" },
-    { name = "tenacity" },
-    { name = "types-pyyaml" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/20/ea/472d83d19a5da46957ff8a427861aa3954a424e8f4273023e87a940ee0f4/neo4j_graphrag-1.10.0.tar.gz", hash = "sha256:b74d57ebcaebb64b8496f7cb3cb8a3a3dd8d8f3b0df277635eade4584e7606a8", size = 121640, upload-time = "2025-09-04T13:10:05.673Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/be/38/71757c01c627402647c13a87f342b697e445623e21ad2aace3a4142b6bf9/neo4j_graphrag-1.10.0-py3-none-any.whl", hash = "sha256:7e016c5bf281df545309ff139c04b9a3ac426056bb68ba6a5508fa6578650f9d", size = 201746, upload-time = "2025-09-04T13:10:03.888Z" },
-]
-
-[[package]]
-name = "nest-asyncio"
-version = "1.6.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/83/f8/51569ac65d696c8ecbee95938f89d4abf00f47d58d48f6fbabfe8f0baefe/nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe", size = 7418, upload-time = "2024-01-21T14:25:19.227Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/a0/c4/c2971a3ba4c6103a3d10c4b0f24f461ddc027f0f09763220cf35ca1401b3/nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c", size = 5195, upload-time = "2024-01-21T14:25:17.223Z" },
-]
-
 [[package]]
 name = "numpy"
 version = "2.3.3"
@@ -665,15 +613,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c7/21/705964c7812476f378728bdf590ca4b771ec72385c533964653c68e86bdc/pygments-2.19.2-py3-none-any.whl", hash = "sha256:86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b", size = 1225217, upload-time = "2025-06-21T13:39:07.939Z" },
 ]
 
-[[package]]
-name = "pypdf"
-version = "6.1.1"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/a6/85/4c0f12616db83c2e3ef580c3cfa98bd082e88fc8d02e136bad3bede1e3fa/pypdf-6.1.1.tar.gz", hash = "sha256:10f44d49bf2a82e54c3c5ba3cdcbb118f2a44fc57df8ce51d6fb9b1ed9bfbe8b", size = 5074507, upload-time = "2025-09-28T13:29:16.165Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/07/ed/adae13756d9dabdddee483fc7712905bb5585fbf6e922b1a19aca3a29cd1/pypdf-6.1.1-py3-none-any.whl", hash = "sha256:7781f99493208a37a7d4275601d883e19af24e62a525c25844d22157c2e4cde7", size = 323455, upload-time = "2025-09-28T13:29:14.392Z" },
-]
-
 [[package]]
 name = "pytest"
 version = "8.4.2"
@@ -733,42 +672,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c0/d2/21af5c535501a7233e734b8af901574572da66fcc254cb35d0609c9080dd/pywin32-311-cp314-cp314-win_arm64.whl", hash = "sha256:a508e2d9025764a8270f93111a970e1d0fbfc33f4153b388bb649b7eec4f9b42", size = 8932540, upload-time = "2025-07-14T20:13:36.379Z" },
 ]
 
-[[package]]
-name = "pyyaml"
-version = "6.0.3"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/05/8e/961c0007c59b8dd7729d542c61a4d537767a59645b82a0b521206e1e25c2/pyyaml-6.0.3.tar.gz", hash = "sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f", size = 130960, upload-time = "2025-09-25T21:33:16.546Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/d1/11/0fd08f8192109f7169db964b5707a2f1e8b745d4e239b784a5a1dd80d1db/pyyaml-6.0.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:8da9669d359f02c0b91ccc01cac4a67f16afec0dac22c2ad09f46bee0697eba8", size = 181669, upload-time = "2025-09-25T21:32:23.673Z" },
-    { url = "https://files.pythonhosted.org/packages/b1/16/95309993f1d3748cd644e02e38b75d50cbc0d9561d21f390a76242ce073f/pyyaml-6.0.3-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:2283a07e2c21a2aa78d9c4442724ec1eb15f5e42a723b99cb3d822d48f5f7ad1", size = 173252, upload-time = "2025-09-25T21:32:25.149Z" },
-    { url = "https://files.pythonhosted.org/packages/50/31/b20f376d3f810b9b2371e72ef5adb33879b25edb7a6d072cb7ca0c486398/pyyaml-6.0.3-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ee2922902c45ae8ccada2c5b501ab86c36525b883eff4255313a253a3160861c", size = 767081, upload-time = "2025-09-25T21:32:26.575Z" },
-    { url = "https://files.pythonhosted.org/packages/49/1e/a55ca81e949270d5d4432fbbd19dfea5321eda7c41a849d443dc92fd1ff7/pyyaml-6.0.3-cp313-cp313-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a33284e20b78bd4a18c8c2282d549d10bc8408a2a7ff57653c0cf0b9be0afce5", size = 841159, upload-time = "2025-09-25T21:32:27.727Z" },
-    { url = "https://files.pythonhosted.org/packages/74/27/e5b8f34d02d9995b80abcef563ea1f8b56d20134d8f4e5e81733b1feceb2/pyyaml-6.0.3-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:0f29edc409a6392443abf94b9cf89ce99889a1dd5376d94316ae5145dfedd5d6", size = 801626, upload-time = "2025-09-25T21:32:28.878Z" },
-    { url = "https://files.pythonhosted.org/packages/f9/11/ba845c23988798f40e52ba45f34849aa8a1f2d4af4b798588010792ebad6/pyyaml-6.0.3-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:f7057c9a337546edc7973c0d3ba84ddcdf0daa14533c2065749c9075001090e6", size = 753613, upload-time = "2025-09-25T21:32:30.178Z" },
-    { url = "https://files.pythonhosted.org/packages/3d/e0/7966e1a7bfc0a45bf0a7fb6b98ea03fc9b8d84fa7f2229e9659680b69ee3/pyyaml-6.0.3-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:eda16858a3cab07b80edaf74336ece1f986ba330fdb8ee0d6c0d68fe82bc96be", size = 794115, upload-time = "2025-09-25T21:32:31.353Z" },
-    { url = "https://files.pythonhosted.org/packages/de/94/980b50a6531b3019e45ddeada0626d45fa85cbe22300844a7983285bed3b/pyyaml-6.0.3-cp313-cp313-win32.whl", hash = "sha256:d0eae10f8159e8fdad514efdc92d74fd8d682c933a6dd088030f3834bc8e6b26", size = 137427, upload-time = "2025-09-25T21:32:32.58Z" },
-    { url = "https://files.pythonhosted.org/packages/97/c9/39d5b874e8b28845e4ec2202b5da735d0199dbe5b8fb85f91398814a9a46/pyyaml-6.0.3-cp313-cp313-win_amd64.whl", hash = "sha256:79005a0d97d5ddabfeeea4cf676af11e647e41d81c9a7722a193022accdb6b7c", size = 154090, upload-time = "2025-09-25T21:32:33.659Z" },
-    { url = "https://files.pythonhosted.org/packages/73/e8/2bdf3ca2090f68bb3d75b44da7bbc71843b19c9f2b9cb9b0f4ab7a5a4329/pyyaml-6.0.3-cp313-cp313-win_arm64.whl", hash = "sha256:5498cd1645aa724a7c71c8f378eb29ebe23da2fc0d7a08071d89469bf1d2defb", size = 140246, upload-time = "2025-09-25T21:32:34.663Z" },
-    { url = "https://files.pythonhosted.org/packages/9d/8c/f4bd7f6465179953d3ac9bc44ac1a8a3e6122cf8ada906b4f96c60172d43/pyyaml-6.0.3-cp314-cp314-macosx_10_13_x86_64.whl", hash = "sha256:8d1fab6bb153a416f9aeb4b8763bc0f22a5586065f86f7664fc23339fc1c1fac", size = 181814, upload-time = "2025-09-25T21:32:35.712Z" },
-    { url = "https://files.pythonhosted.org/packages/bd/9c/4d95bb87eb2063d20db7b60faa3840c1b18025517ae857371c4dd55a6b3a/pyyaml-6.0.3-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:34d5fcd24b8445fadc33f9cf348c1047101756fd760b4dacb5c3e99755703310", size = 173809, upload-time = "2025-09-25T21:32:36.789Z" },
-    { url = "https://files.pythonhosted.org/packages/92/b5/47e807c2623074914e29dabd16cbbdd4bf5e9b2db9f8090fa64411fc5382/pyyaml-6.0.3-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:501a031947e3a9025ed4405a168e6ef5ae3126c59f90ce0cd6f2bfc477be31b7", size = 766454, upload-time = "2025-09-25T21:32:37.966Z" },
-    { url = "https://files.pythonhosted.org/packages/02/9e/e5e9b168be58564121efb3de6859c452fccde0ab093d8438905899a3a483/pyyaml-6.0.3-cp314-cp314-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:b3bc83488de33889877a0f2543ade9f70c67d66d9ebb4ac959502e12de895788", size = 836355, upload-time = "2025-09-25T21:32:39.178Z" },
-    { url = "https://files.pythonhosted.org/packages/88/f9/16491d7ed2a919954993e48aa941b200f38040928474c9e85ea9e64222c3/pyyaml-6.0.3-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c458b6d084f9b935061bc36216e8a69a7e293a2f1e68bf956dcd9e6cbcd143f5", size = 794175, upload-time = "2025-09-25T21:32:40.865Z" },
-    { url = "https://files.pythonhosted.org/packages/dd/3f/5989debef34dc6397317802b527dbbafb2b4760878a53d4166579111411e/pyyaml-6.0.3-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:7c6610def4f163542a622a73fb39f534f8c101d690126992300bf3207eab9764", size = 755228, upload-time = "2025-09-25T21:32:42.084Z" },
-    { url = "https://files.pythonhosted.org/packages/d7/ce/af88a49043cd2e265be63d083fc75b27b6ed062f5f9fd6cdc223ad62f03e/pyyaml-6.0.3-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:5190d403f121660ce8d1d2c1bb2ef1bd05b5f68533fc5c2ea899bd15f4399b35", size = 789194, upload-time = "2025-09-25T21:32:43.362Z" },
-    { url = "https://files.pythonhosted.org/packages/23/20/bb6982b26a40bb43951265ba29d4c246ef0ff59c9fdcdf0ed04e0687de4d/pyyaml-6.0.3-cp314-cp314-win_amd64.whl", hash = "sha256:4a2e8cebe2ff6ab7d1050ecd59c25d4c8bd7e6f400f5f82b96557ac0abafd0ac", size = 156429, upload-time = "2025-09-25T21:32:57.844Z" },
-    { url = "https://files.pythonhosted.org/packages/f4/f4/a4541072bb9422c8a883ab55255f918fa378ecf083f5b85e87fc2b4eda1b/pyyaml-6.0.3-cp314-cp314-win_arm64.whl", hash = "sha256:93dda82c9c22deb0a405ea4dc5f2d0cda384168e466364dec6255b293923b2f3", size = 143912, upload-time = "2025-09-25T21:32:59.247Z" },
-    { url = "https://files.pythonhosted.org/packages/7c/f9/07dd09ae774e4616edf6cda684ee78f97777bdd15847253637a6f052a62f/pyyaml-6.0.3-cp314-cp314t-macosx_10_13_x86_64.whl", hash = "sha256:02893d100e99e03eda1c8fd5c441d8c60103fd175728e23e431db1b589cf5ab3", size = 189108, upload-time = "2025-09-25T21:32:44.377Z" },
-    { url = "https://files.pythonhosted.org/packages/4e/78/8d08c9fb7ce09ad8c38ad533c1191cf27f7ae1effe5bb9400a46d9437fcf/pyyaml-6.0.3-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:c1ff362665ae507275af2853520967820d9124984e0f7466736aea23d8611fba", size = 183641, upload-time = "2025-09-25T21:32:45.407Z" },
-    { url = "https://files.pythonhosted.org/packages/7b/5b/3babb19104a46945cf816d047db2788bcaf8c94527a805610b0289a01c6b/pyyaml-6.0.3-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:6adc77889b628398debc7b65c073bcb99c4a0237b248cacaf3fe8a557563ef6c", size = 831901, upload-time = "2025-09-25T21:32:48.83Z" },
-    { url = "https://files.pythonhosted.org/packages/8b/cc/dff0684d8dc44da4d22a13f35f073d558c268780ce3c6ba1b87055bb0b87/pyyaml-6.0.3-cp314-cp314t-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:a80cb027f6b349846a3bf6d73b5e95e782175e52f22108cfa17876aaeff93702", size = 861132, upload-time = "2025-09-25T21:32:50.149Z" },
-    { url = "https://files.pythonhosted.org/packages/b1/5e/f77dc6b9036943e285ba76b49e118d9ea929885becb0a29ba8a7c75e29fe/pyyaml-6.0.3-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:00c4bdeba853cc34e7dd471f16b4114f4162dc03e6b7afcc2128711f0eca823c", size = 839261, upload-time = "2025-09-25T21:32:51.808Z" },
-    { url = "https://files.pythonhosted.org/packages/ce/88/a9db1376aa2a228197c58b37302f284b5617f56a5d959fd1763fb1675ce6/pyyaml-6.0.3-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:66e1674c3ef6f541c35191caae2d429b967b99e02040f5ba928632d9a7f0f065", size = 805272, upload-time = "2025-09-25T21:32:52.941Z" },
-    { url = "https://files.pythonhosted.org/packages/da/92/1446574745d74df0c92e6aa4a7b0b3130706a4142b2d1a5869f2eaa423c6/pyyaml-6.0.3-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:16249ee61e95f858e83976573de0f5b2893b3677ba71c9dd36b9cf8be9ac6d65", size = 829923, upload-time = "2025-09-25T21:32:54.537Z" },
-    { url = "https://files.pythonhosted.org/packages/f0/7a/1c7270340330e575b92f397352af856a8c06f230aa3e76f86b39d01b416a/pyyaml-6.0.3-cp314-cp314t-win_amd64.whl", hash = "sha256:4ad1906908f2f5ae4e5a8ddfce73c320c2a1429ec52eafd27138b7f1cbe341c9", size = 174062, upload-time = "2025-09-25T21:32:55.767Z" },
-    { url = "https://files.pythonhosted.org/packages/f1/12/de94a39c2ef588c7e6455cfbe7343d3b2dc9d6b6b2f40c4c6565744c873d/pyyaml-6.0.3-cp314-cp314t-win_arm64.whl", hash = "sha256:ebc55a14a21cb14062aa4162f906cd962b28e2e9ea38f9b4391244cd8de4ae0b", size = 149341, upload-time = "2025-09-25T21:32:56.828Z" },
-]
-
 [[package]]
 name = "qdrant-client"
 version = "1.16.2"
@@ -813,57 +716,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c6/2a/65880dfd0e13f7f13a775998f34703674a4554906167dce02daf7865b954/ruff-0.14.0-py3-none-win_arm64.whl", hash = "sha256:f42c9495f5c13ff841b1da4cb3c2a42075409592825dada7c5885c2c844ac730", size = 12565142, upload-time = "2025-10-07T18:21:53.577Z" },
 ]
 
-[[package]]
-name = "scipy"
-version = "1.16.2"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "numpy", marker = "python_full_version < '3.14'" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/4c/3b/546a6f0bfe791bbb7f8d591613454d15097e53f906308ec6f7c1ce588e8e/scipy-1.16.2.tar.gz", hash = "sha256:af029b153d243a80afb6eabe40b0a07f8e35c9adc269c019f364ad747f826a6b", size = 30580599, upload-time = "2025-09-11T17:48:08.271Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/c1/27/c5b52f1ee81727a9fc457f5ac1e9bf3d6eab311805ea615c83c27ba06400/scipy-1.16.2-cp313-cp313-macosx_10_14_x86_64.whl", hash = "sha256:84f7bf944b43e20b8a894f5fe593976926744f6c185bacfcbdfbb62736b5cc70", size = 36604856, upload-time = "2025-09-11T17:41:47.695Z" },
-    { url = "https://files.pythonhosted.org/packages/32/a9/15c20d08e950b540184caa8ced675ba1128accb0e09c653780ba023a4110/scipy-1.16.2-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:5c39026d12edc826a1ef2ad35ad1e6d7f087f934bb868fc43fa3049c8b8508f9", size = 28864626, upload-time = "2025-09-11T17:41:52.642Z" },
-    { url = "https://files.pythonhosted.org/packages/4c/fc/ea36098df653cca26062a627c1a94b0de659e97127c8491e18713ca0e3b9/scipy-1.16.2-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:e52729ffd45b68777c5319560014d6fd251294200625d9d70fd8626516fc49f5", size = 20855689, upload-time = "2025-09-11T17:41:57.886Z" },
-    { url = "https://files.pythonhosted.org/packages/dc/6f/d0b53be55727f3e6d7c72687ec18ea6d0047cf95f1f77488b99a2bafaee1/scipy-1.16.2-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:024dd4a118cccec09ca3209b7e8e614931a6ffb804b2a601839499cb88bdf925", size = 23512151, upload-time = "2025-09-11T17:42:02.303Z" },
-    { url = "https://files.pythonhosted.org/packages/11/85/bf7dab56e5c4b1d3d8eef92ca8ede788418ad38a7dc3ff50262f00808760/scipy-1.16.2-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:7a5dc7ee9c33019973a470556081b0fd3c9f4c44019191039f9769183141a4d9", size = 33329824, upload-time = "2025-09-11T17:42:07.549Z" },
-    { url = "https://files.pythonhosted.org/packages/da/6a/1a927b14ddc7714111ea51f4e568203b2bb6ed59bdd036d62127c1a360c8/scipy-1.16.2-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c2275ff105e508942f99d4e3bc56b6ef5e4b3c0af970386ca56b777608ce95b7", size = 35681881, upload-time = "2025-09-11T17:42:13.255Z" },
-    { url = "https://files.pythonhosted.org/packages/c1/5f/331148ea5780b4fcc7007a4a6a6ee0a0c1507a796365cc642d4d226e1c3a/scipy-1.16.2-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:af80196eaa84f033e48444d2e0786ec47d328ba00c71e4299b602235ffef9acb", size = 36006219, upload-time = "2025-09-11T17:42:18.765Z" },
-    { url = "https://files.pythonhosted.org/packages/46/3a/e991aa9d2aec723b4a8dcfbfc8365edec5d5e5f9f133888067f1cbb7dfc1/scipy-1.16.2-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:9fb1eb735fe3d6ed1f89918224e3385fbf6f9e23757cacc35f9c78d3b712dd6e", size = 38682147, upload-time = "2025-09-11T17:42:25.177Z" },
-    { url = "https://files.pythonhosted.org/packages/a1/57/0f38e396ad19e41b4c5db66130167eef8ee620a49bc7d0512e3bb67e0cab/scipy-1.16.2-cp313-cp313-win_amd64.whl", hash = "sha256:fda714cf45ba43c9d3bae8f2585c777f64e3f89a2e073b668b32ede412d8f52c", size = 38520766, upload-time = "2025-09-11T17:43:25.342Z" },
-    { url = "https://files.pythonhosted.org/packages/1b/a5/85d3e867b6822d331e26c862a91375bb7746a0b458db5effa093d34cdb89/scipy-1.16.2-cp313-cp313-win_arm64.whl", hash = "sha256:2f5350da923ccfd0b00e07c3e5cfb316c1c0d6c1d864c07a72d092e9f20db104", size = 25451169, upload-time = "2025-09-11T17:43:30.198Z" },
-    { url = "https://files.pythonhosted.org/packages/09/d9/60679189bcebda55992d1a45498de6d080dcaf21ce0c8f24f888117e0c2d/scipy-1.16.2-cp313-cp313t-macosx_10_14_x86_64.whl", hash = "sha256:53d8d2ee29b925344c13bda64ab51785f016b1b9617849dac10897f0701b20c1", size = 37012682, upload-time = "2025-09-11T17:42:30.677Z" },
-    { url = "https://files.pythonhosted.org/packages/83/be/a99d13ee4d3b7887a96f8c71361b9659ba4ef34da0338f14891e102a127f/scipy-1.16.2-cp313-cp313t-macosx_12_0_arm64.whl", hash = "sha256:9e05e33657efb4c6a9d23bd8300101536abd99c85cca82da0bffff8d8764d08a", size = 29389926, upload-time = "2025-09-11T17:42:35.845Z" },
-    { url = "https://files.pythonhosted.org/packages/bf/0a/130164a4881cec6ca8c00faf3b57926f28ed429cd6001a673f83c7c2a579/scipy-1.16.2-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:7fe65b36036357003b3ef9d37547abeefaa353b237e989c21027b8ed62b12d4f", size = 21381152, upload-time = "2025-09-11T17:42:40.07Z" },
-    { url = "https://files.pythonhosted.org/packages/47/a6/503ffb0310ae77fba874e10cddfc4a1280bdcca1d13c3751b8c3c2996cf8/scipy-1.16.2-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:6406d2ac6d40b861cccf57f49592f9779071655e9f75cd4f977fa0bdd09cb2e4", size = 23914410, upload-time = "2025-09-11T17:42:44.313Z" },
-    { url = "https://files.pythonhosted.org/packages/fa/c7/1147774bcea50d00c02600aadaa919facbd8537997a62496270133536ed6/scipy-1.16.2-cp313-cp313t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:ff4dc42bd321991fbf611c23fc35912d690f731c9914bf3af8f417e64aca0f21", size = 33481880, upload-time = "2025-09-11T17:42:49.325Z" },
-    { url = "https://files.pythonhosted.org/packages/6a/74/99d5415e4c3e46b2586f30cdbecb95e101c7192628a484a40dd0d163811a/scipy-1.16.2-cp313-cp313t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:654324826654d4d9133e10675325708fb954bc84dae6e9ad0a52e75c6b1a01d7", size = 35791425, upload-time = "2025-09-11T17:42:54.711Z" },
-    { url = "https://files.pythonhosted.org/packages/1b/ee/a6559de7c1cc710e938c0355d9d4fbcd732dac4d0d131959d1f3b63eb29c/scipy-1.16.2-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:63870a84cd15c44e65220eaed2dac0e8f8b26bbb991456a033c1d9abfe8a94f8", size = 36178622, upload-time = "2025-09-11T17:43:00.375Z" },
-    { url = "https://files.pythonhosted.org/packages/4e/7b/f127a5795d5ba8ece4e0dce7d4a9fb7cb9e4f4757137757d7a69ab7d4f1a/scipy-1.16.2-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:fa01f0f6a3050fa6a9771a95d5faccc8e2f5a92b4a2e5440a0fa7264a2398472", size = 38783985, upload-time = "2025-09-11T17:43:06.661Z" },
-    { url = "https://files.pythonhosted.org/packages/3e/9f/bc81c1d1e033951eb5912cd3750cc005943afa3e65a725d2443a3b3c4347/scipy-1.16.2-cp313-cp313t-win_amd64.whl", hash = "sha256:116296e89fba96f76353a8579820c2512f6e55835d3fad7780fece04367de351", size = 38631367, upload-time = "2025-09-11T17:43:14.44Z" },
-    { url = "https://files.pythonhosted.org/packages/d6/5e/2cc7555fd81d01814271412a1d59a289d25f8b63208a0a16c21069d55d3e/scipy-1.16.2-cp313-cp313t-win_arm64.whl", hash = "sha256:98e22834650be81d42982360382b43b17f7ba95e0e6993e2a4f5b9ad9283a94d", size = 25787992, upload-time = "2025-09-11T17:43:19.745Z" },
-    { url = "https://files.pythonhosted.org/packages/8b/ac/ad8951250516db71619f0bd3b2eb2448db04b720a003dd98619b78b692c0/scipy-1.16.2-cp314-cp314-macosx_10_14_x86_64.whl", hash = "sha256:567e77755019bb7461513c87f02bb73fb65b11f049aaaa8ca17cfaa5a5c45d77", size = 36595109, upload-time = "2025-09-11T17:43:35.713Z" },
-    { url = "https://files.pythonhosted.org/packages/ff/f6/5779049ed119c5b503b0f3dc6d6f3f68eefc3a9190d4ad4c276f854f051b/scipy-1.16.2-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:17d9bb346194e8967296621208fcdfd39b55498ef7d2f376884d5ac47cec1a70", size = 28859110, upload-time = "2025-09-11T17:43:40.814Z" },
-    { url = "https://files.pythonhosted.org/packages/82/09/9986e410ae38bf0a0c737ff8189ac81a93b8e42349aac009891c054403d7/scipy-1.16.2-cp314-cp314-macosx_14_0_arm64.whl", hash = "sha256:0a17541827a9b78b777d33b623a6dcfe2ef4a25806204d08ead0768f4e529a88", size = 20850110, upload-time = "2025-09-11T17:43:44.981Z" },
-    { url = "https://files.pythonhosted.org/packages/0d/ad/485cdef2d9215e2a7df6d61b81d2ac073dfacf6ae24b9ae87274c4e936ae/scipy-1.16.2-cp314-cp314-macosx_14_0_x86_64.whl", hash = "sha256:d7d4c6ba016ffc0f9568d012f5f1eb77ddd99412aea121e6fa8b4c3b7cbad91f", size = 23497014, upload-time = "2025-09-11T17:43:49.074Z" },
-    { url = "https://files.pythonhosted.org/packages/a7/74/f6a852e5d581122b8f0f831f1d1e32fb8987776ed3658e95c377d308ed86/scipy-1.16.2-cp314-cp314-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:9702c4c023227785c779cba2e1d6f7635dbb5b2e0936cdd3a4ecb98d78fd41eb", size = 33401155, upload-time = "2025-09-11T17:43:54.661Z" },
-    { url = "https://files.pythonhosted.org/packages/d9/f5/61d243bbc7c6e5e4e13dde9887e84a5cbe9e0f75fd09843044af1590844e/scipy-1.16.2-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:d1cdf0ac28948d225decdefcc45ad7dd91716c29ab56ef32f8e0d50657dffcc7", size = 35691174, upload-time = "2025-09-11T17:44:00.101Z" },
-    { url = "https://files.pythonhosted.org/packages/03/99/59933956331f8cc57e406cdb7a483906c74706b156998f322913e789c7e1/scipy-1.16.2-cp314-cp314-musllinux_1_2_aarch64.whl", hash = "sha256:70327d6aa572a17c2941cdfb20673f82e536e91850a2e4cb0c5b858b690e1548", size = 36070752, upload-time = "2025-09-11T17:44:05.619Z" },
-    { url = "https://files.pythonhosted.org/packages/c6/7d/00f825cfb47ee19ef74ecf01244b43e95eae74e7e0ff796026ea7cd98456/scipy-1.16.2-cp314-cp314-musllinux_1_2_x86_64.whl", hash = "sha256:5221c0b2a4b58aa7c4ed0387d360fd90ee9086d383bb34d9f2789fafddc8a936", size = 38701010, upload-time = "2025-09-11T17:44:11.322Z" },
-    { url = "https://files.pythonhosted.org/packages/e4/9f/b62587029980378304ba5a8563d376c96f40b1e133daacee76efdcae32de/scipy-1.16.2-cp314-cp314-win_amd64.whl", hash = "sha256:f5a85d7b2b708025af08f060a496dd261055b617d776fc05a1a1cc69e09fe9ff", size = 39360061, upload-time = "2025-09-11T17:45:09.814Z" },
-    { url = "https://files.pythonhosted.org/packages/82/04/7a2f1609921352c7fbee0815811b5050582f67f19983096c4769867ca45f/scipy-1.16.2-cp314-cp314-win_arm64.whl", hash = "sha256:2cc73a33305b4b24556957d5857d6253ce1e2dcd67fa0ff46d87d1670b3e1e1d", size = 26126914, upload-time = "2025-09-11T17:45:14.73Z" },
-    { url = "https://files.pythonhosted.org/packages/51/b9/60929ce350c16b221928725d2d1d7f86cf96b8bc07415547057d1196dc92/scipy-1.16.2-cp314-cp314t-macosx_10_14_x86_64.whl", hash = "sha256:9ea2a3fed83065d77367775d689401a703d0f697420719ee10c0780bcab594d8", size = 37013193, upload-time = "2025-09-11T17:44:16.757Z" },
-    { url = "https://files.pythonhosted.org/packages/2a/41/ed80e67782d4bc5fc85a966bc356c601afddd175856ba7c7bb6d9490607e/scipy-1.16.2-cp314-cp314t-macosx_12_0_arm64.whl", hash = "sha256:7280d926f11ca945c3ef92ba960fa924e1465f8d07ce3a9923080363390624c4", size = 29390172, upload-time = "2025-09-11T17:44:21.783Z" },
-    { url = "https://files.pythonhosted.org/packages/c4/a3/2f673ace4090452696ccded5f5f8efffb353b8f3628f823a110e0170b605/scipy-1.16.2-cp314-cp314t-macosx_14_0_arm64.whl", hash = "sha256:8afae1756f6a1fe04636407ef7dbece33d826a5d462b74f3d0eb82deabefd831", size = 21381326, upload-time = "2025-09-11T17:44:25.982Z" },
-    { url = "https://files.pythonhosted.org/packages/42/bf/59df61c5d51395066c35836b78136accf506197617c8662e60ea209881e1/scipy-1.16.2-cp314-cp314t-macosx_14_0_x86_64.whl", hash = "sha256:5c66511f29aa8d233388e7416a3f20d5cae7a2744d5cee2ecd38c081f4e861b3", size = 23915036, upload-time = "2025-09-11T17:44:30.527Z" },
-    { url = "https://files.pythonhosted.org/packages/91/c3/edc7b300dc16847ad3672f1a6f3f7c5d13522b21b84b81c265f4f2760d4a/scipy-1.16.2-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:efe6305aeaa0e96b0ccca5ff647a43737d9a092064a3894e46c414db84bc54ac", size = 33484341, upload-time = "2025-09-11T17:44:35.981Z" },
-    { url = "https://files.pythonhosted.org/packages/26/c7/24d1524e72f06ff141e8d04b833c20db3021020563272ccb1b83860082a9/scipy-1.16.2-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7f3a337d9ae06a1e8d655ee9d8ecb835ea5ddcdcbd8d23012afa055ab014f374", size = 35790840, upload-time = "2025-09-11T17:44:41.76Z" },
-    { url = "https://files.pythonhosted.org/packages/aa/b7/5aaad984eeedd56858dc33d75efa59e8ce798d918e1033ef62d2708f2c3d/scipy-1.16.2-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:bab3605795d269067d8ce78a910220262711b753de8913d3deeaedb5dded3bb6", size = 36174716, upload-time = "2025-09-11T17:44:47.316Z" },
-    { url = "https://files.pythonhosted.org/packages/fd/c2/e276a237acb09824822b0ada11b028ed4067fdc367a946730979feacb870/scipy-1.16.2-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:b0348d8ddb55be2a844c518cd8cc8deeeb8aeba707cf834db5758fc89b476a2c", size = 38790088, upload-time = "2025-09-11T17:44:53.011Z" },
-    { url = "https://files.pythonhosted.org/packages/c6/b4/5c18a766e8353015439f3780f5fc473f36f9762edc1a2e45da3ff5a31b21/scipy-1.16.2-cp314-cp314t-win_amd64.whl", hash = "sha256:26284797e38b8a75e14ea6631d29bda11e76ceaa6ddb6fdebbfe4c4d90faf2f9", size = 39457455, upload-time = "2025-09-11T17:44:58.899Z" },
-    { url = "https://files.pythonhosted.org/packages/97/30/2f9a5243008f76dfc5dee9a53dfb939d9b31e16ce4bd4f2e628bfc5d89d2/scipy-1.16.2-cp314-cp314t-win_arm64.whl", hash = "sha256:d2a4472c231328d4de38d5f1f68fdd6d28a615138f842580a8a321b5845cf779", size = 26448374, upload-time = "2025-09-11T17:45:03.45Z" },
-]
-
 [[package]]
 name = "sniffio"
 version = "1.3.1"
@@ -885,15 +737,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d9/52/1064f510b141bd54025f9b55105e26d1fa970b9be67ad766380a3c9b74b0/starlette-0.50.0-py3-none-any.whl", hash = "sha256:9e5391843ec9b6e472eed1365a78c8098cfceb7a74bfd4d6b1c0c0095efb3bca", size = 74033, upload-time = "2025-11-01T15:25:25.461Z" },
 ]
 
-[[package]]
-name = "tenacity"
-version = "9.1.2"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/0a/d4/2b0cd0fe285e14b36db076e78c93766ff1d529d70408bd1d2a5a84f1d929/tenacity-9.1.2.tar.gz", hash = "sha256:1169d376c297e7de388d18b4481760d478b0e99a777cad3a9c86e556f4b697cb", size = 48036, upload-time = "2025-04-02T08:25:09.966Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/e5/30/643397144bfbfec6f6ef821f36f33e57d35946c44a2352d3c9f0ae847619/tenacity-9.1.2-py3-none-any.whl", hash = "sha256:f77bf36710d8b73a50b2dd155c97b870017ad21afe6ab300326b0371b3b05138", size = 28248, upload-time = "2025-04-02T08:25:07.678Z" },
-]
-
 [[package]]
 name = "tqdm"
 version = "4.67.1"
@@ -906,15 +749,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d0/30/dc54f88dd4a2b5dc8a0279bdd7270e735851848b762aeb1c1184ed1f6b14/tqdm-4.67.1-py3-none-any.whl", hash = "sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2", size = 78540, upload-time = "2024-11-24T20:12:19.698Z" },
 ]
 
-[[package]]
-name = "types-pyyaml"
-version = "6.0.12.20250915"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/7e/69/3c51b36d04da19b92f9e815be12753125bd8bc247ba0470a982e6979e71c/types_pyyaml-6.0.12.20250915.tar.gz", hash = "sha256:0f8b54a528c303f0e6f7165687dd33fafa81c807fcac23f632b63aa624ced1d3", size = 17522, upload-time = "2025-09-15T03:01:00.728Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/bd/e0/1eed384f02555dde685fff1a1ac805c1c7dcb6dd019c916fe659b1c1f9ec/types_pyyaml-6.0.12.20250915-py3-none-any.whl", hash = "sha256:e7d4d9e064e89a3b3cae120b4990cd370874d2bf12fa5f46c97018dd5d3c9ab6", size = 20338, upload-time = "2025-09-15T03:00:59.218Z" },
-]
-
 [[package]]
 name = "typing-extensions"
 version = "4.15.0"