agent-framework/python/samples/05-end-to-end/neo4j_graphrag

Neo4j GraphRAG Context Provider

The Neo4j GraphRAG context provider adds read-only retrieval from a Neo4j knowledge graph to an Agent Framework agent. It supports vector, fulltext, and hybrid retrieval, and can enrich search results by traversing graph relationships with a Cypher retrieval_query.

This sample keeps setup lightweight by using a pre-built Neo4j fulltext index plus a graph-enrichment query.

Example

File	Description
main.py	Runnable GraphRAG sample using a Neo4j fulltext index and a Cypher enrichment query to surface related companies, products, and risk factors.

Prerequisites

1. A Neo4j database with document chunks already loaded
2. A Neo4j fulltext index over chunk text, such as search_chunks
3. An Azure AI Foundry project endpoint and chat deployment
4. Azure CLI authentication via az login

Environment variables

This sample expects:

• FOUNDRY_PROJECT_ENDPOINT
• FOUNDRY_MODEL
• NEO4J_URI
• NEO4J_USERNAME
• NEO4J_PASSWORD
• NEO4J_FULLTEXT_INDEX_NAME (optional, defaults to search_chunks)

Run with uv

From the python/ directory:

uv run samples/05-end-to-end/neo4j_graphrag/main.py

Notes

• This sample uses the published agent-framework-neo4j package rather than code from this repository.
• The package also supports vector and hybrid retrieval when you configure embeddings and indexes in Neo4j.
• For memory-oriented scenarios, the Neo4j project also maintains companion examples in the external provider repository.