mirror of
https://github.com/microsoft/agent-framework.git
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Christian Glessner
34329840e1
* Add Neo4j GraphRAG samples * Fix sample CI issues * Address sample review feedback * Move Neo4j Python sample to end-to-end * Make Neo4j GraphRAG sample self-contained * Remove unused central package versions
113 lines
3.8 KiB
Python
113 lines
3.8 KiB
Python
# /// script
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# requires-python = ">=3.10"
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# dependencies = [
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# "agent-framework-foundry",
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# "agent-framework-neo4j",
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# ]
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# ///
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import os
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from agent_framework import Agent
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from agent_framework.foundry import FoundryChatClient
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from agent_framework_neo4j import Neo4jContextProvider, Neo4jSettings
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from azure.identity.aio import AzureCliCredential
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from dotenv import load_dotenv
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load_dotenv()
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"""
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This sample demonstrates how to use the Neo4j GraphRAG context provider with
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Agent Framework and Azure AI Foundry.
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Environment variables:
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FOUNDRY_PROJECT_ENDPOINT — Azure AI Foundry project endpoint
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FOUNDRY_MODEL — Model deployment name (e.g. gpt-4o)
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NEO4J_URI — Neo4j connection URI
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NEO4J_USERNAME — Neo4j username
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NEO4J_PASSWORD — Neo4j password
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NEO4J_FULLTEXT_INDEX_NAME — Optional fulltext index name (defaults to search_chunks)
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"""
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USER_INPUTS = [
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"What products does Microsoft offer?",
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"What risks does Apple face?",
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"Tell me about NVIDIA's AI business and risk factors.",
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]
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# Optional graph-enrichment query: retrieval works without this, but supplying
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# a query lets the sample attach related company, product, and risk metadata to
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# each retrieved chunk.
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RETRIEVAL_QUERY = """
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MATCH (node)-[:FROM_DOCUMENT]->(doc:Document)<-[:FILED]-(company:Company)
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OPTIONAL MATCH (company)-[:FACES_RISK]->(risk:RiskFactor)
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WITH node, score, company, doc, collect(DISTINCT risk.name)[0..5] AS risks
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OPTIONAL MATCH (company)-[:MENTIONS]->(product:Product)
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WITH node, score, company, doc, risks, collect(DISTINCT product.name)[0..5] AS products
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RETURN
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node.text AS text,
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score,
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company.name AS company,
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company.ticker AS ticker,
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doc.title AS title,
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risks,
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products
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ORDER BY score DESC
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"""
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async def main() -> None:
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# 1. Load and validate the Neo4j connection settings.
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settings = Neo4jSettings()
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if not settings.is_configured:
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raise RuntimeError("Set NEO4J_URI, NEO4J_USERNAME, and NEO4J_PASSWORD before running this sample.")
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# 2. Read the Azure AI Foundry project endpoint and model configuration.
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project_endpoint = os.environ.get("FOUNDRY_PROJECT_ENDPOINT")
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if not project_endpoint:
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raise RuntimeError("Set FOUNDRY_PROJECT_ENDPOINT before running this sample.")
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model = os.environ.get("FOUNDRY_MODEL") or "gpt-4o"
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# 3. Create the Neo4j context provider and Foundry-backed agent, then ask sample questions.
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async with (
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AzureCliCredential() as credential,
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Neo4jContextProvider(
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source_id="neo4j_graphrag",
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uri=settings.uri,
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username=settings.username,
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password=settings.get_password(),
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index_name=settings.fulltext_index_name,
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index_type="fulltext",
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retrieval_query=RETRIEVAL_QUERY,
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top_k=5,
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) as provider,
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Agent(
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client=FoundryChatClient(
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project_endpoint=project_endpoint,
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model=model,
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credential=credential,
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),
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name="Neo4jGraphRAGAgent",
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instructions=(
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"You are a helpful assistant. Use the Neo4j context provider results to answer accurately. "
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"If the retrieved context is insufficient, say so plainly."
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),
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context_providers=[provider],
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) as agent,
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):
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session = agent.create_session()
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print("=== Neo4j GraphRAG Context Provider ===\n")
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for user_input in USER_INPUTS:
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print(f"User: {user_input}")
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result = await agent.run(user_input, session=session)
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print(f"Agent: {getattr(result, 'text', result)}\n")
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if __name__ == "__main__":
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asyncio.run(main())
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