neo4j-graphrag-python and biomedical-graphrag
Neo4j's official GraphRAG implementation provides the core framework and Neo4j database integration that biomedical-graphrag builds upon as a specialized domain application, making them ecosystem components where the latter extends the former for biomedical use cases.
About neo4j-graphrag-python
neo4j/neo4j-graphrag-python
Neo4j GraphRAG for Python
Supports automated knowledge graph construction from unstructured text and PDFs via LLM-powered entity/relation extraction, alongside multiple retrieval strategies (vector search, graph traversal, hybrid, and Text2Cypher). Integrates with major LLM providers (OpenAI, Anthropic, Google, Cohere, Ollama, MistralAI) and optional external vector stores (Weaviate, Pinecone, Qdrant), with experimental NLP components using spaCy for semantic resolution.
About biomedical-graphrag
benitomartin/biomedical-graphrag
A comprehensive GraphRAG (Graph Retrieval-Augmented Generation) system designed for biomedical research
Combines Neo4j knowledge graphs with Qdrant vector embeddings for hybrid biomedical retrieval, ingesting PubMed papers, gene data, and citation networks into a specialized schema covering papers, authors, institutions, genes, and MeSH terms. LLM-powered tool selection routes queries to semantic search or graph traversal, while async processing handles high-volume data collection from external biomedical APIs.
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