Add Neo4j GraphRAG samples (#4994)

* 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

python/samples/05-end-to-end/neo4j_graphrag/README.md

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+# Neo4j GraphRAG Context Provider
+
+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`.
+
+This sample keeps setup lightweight by using a pre-built Neo4j fulltext index plus a graph-enrichment query.
+
+## Example
+
+| File | Description |
+|---|---|
+| [`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. |
+
+## 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:
+
+```bash
+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.

python/samples/05-end-to-end/neo4j_graphrag/main.py

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