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46 lines
1.7 KiB
Markdown
46 lines
1.7 KiB
Markdown
# Neo4j GraphRAG Context Provider
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The [Neo4j GraphRAG context provider](https://github.com/neo4j-labs/neo4j-maf-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`.
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This sample keeps setup lightweight by using a pre-built Neo4j fulltext index plus a graph-enrichment query.
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For full documentation, see the [Neo4j GraphRAG integration guide on Microsoft Learn](https://learn.microsoft.com/agent-framework/integrations/neo4j-graphrag).
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## Example
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| File | Description |
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|---|---|
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| [`main.py`](main.py) | Runnable GraphRAG sample using a Neo4j fulltext index and a Cypher enrichment query to surface related companies, products, and risk factors. |
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## Prerequisites
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1. A Neo4j database with document chunks already loaded
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2. A Neo4j fulltext index over chunk text, such as `search_chunks`
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3. An Azure AI Foundry project endpoint and chat deployment
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4. Azure CLI authentication via `az login`
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## Environment variables
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This sample expects:
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- `FOUNDRY_PROJECT_ENDPOINT`
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- `FOUNDRY_MODEL`
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- `NEO4J_URI`
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- `NEO4J_USERNAME`
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- `NEO4J_PASSWORD`
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- `NEO4J_FULLTEXT_INDEX_NAME` (optional, defaults to `search_chunks`)
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## Run with uv
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From the `python/` directory:
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```bash
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uv run samples/05-end-to-end/neo4j_graphrag/main.py
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```
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## Notes
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- This sample uses the published `agent-framework-neo4j` package rather than code from this repository.
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- The package also supports vector and hybrid retrieval when you configure embeddings and indexes in Neo4j.
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- For memory-oriented scenarios, the Neo4j project also maintains companion examples in the external provider repository.
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