docs: Neo4j integration

Signed-off-by: Anush008 <anushshetty90@gmail.com>
2026-10-01 00:48:32 +02:00 · 2024-11-07 02:58:58 +05:30
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+---
+title: Neo4j GraphRAG
+---
+
+# Neo4j GraphRAG
+
+[Neo4j GraphRAG](https://neo4j.com/docs/neo4j-graphrag-python/current/) is a Python package to build graph retrieval augmented generation (GraphRAG) applications using Neo4j and Python. As a first-party library, it offers a robust, feature-rich, and high-performance solution, with the added assurance of long-term support and maintenance directly from Neo4j. It offers a Qdrant retriever natively to search for vectors stored in a Qdrant collection.
+
+## Installation
+
+```bash
+pip install neo4j-graphrag[qdrant]
+```
+
+## Usage
+
+A vector query with Neo4j and Qdrant could look like:
+
+```python
+from neo4j import GraphDatabase
+from neo4j_graphrag.retrievers import QdrantNeo4jRetriever
+from qdrant_client import QdrantClient
+from examples.embedding_biology import EMBEDDING_BIOLOGY
+
+NEO4J_URL = "neo4j://localhost:7687"
+NEO4J_AUTH = ("neo4j", "password")
+
+with GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) as neo4j_driver:
+    retriever = QdrantNeo4jRetriever(
+        driver=neo4j_driver,
+        client=QdrantClient(url="http://localhost:6333"),
+        collection_name="{collection_name}",
+        id_property_external="neo4j_id",
+        id_property_neo4j="id",
+    )
+
+retriever.search(query_vector=[0.5523, 0.523, 0.132, 0.523, ...], top_k=5)
+```
+
+Alternatively, you can use any [Langchain embeddings providers](https://python.langchain.com/docs/integrations/text_embedding/), to vectorize text queries automatically.
+
+```python
+from langchain_huggingface.embeddings import HuggingFaceEmbeddings
+from neo4j import GraphDatabase
+from neo4j_graphrag.retrievers import QdrantNeo4jRetriever
+from qdrant_client import QdrantClient
+
+NEO4J_URL = "neo4j://localhost:7687"
+NEO4J_AUTH = ("neo4j", "password")
+
+with GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) as neo4j_driver:
+    embedder = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+    retriever = QdrantNeo4jRetriever(
+        driver=neo4j_driver,
+        client=QdrantClient(url="http://localhost:6333"),
+        collection_name="{collection_name}",
+        id_property_external="neo4j_id",
+        id_property_neo4j="id",
+        embedder=embedder,
+    )
+
+retriever.search(query_text="my user query", top_k=10)
+```
+
+## Further Reading
+
+- [Neo4j GraphRAG Reference](https://neo4j.com/docs/neo4j-graphrag-python/current/index.html)
+- [Qdrant Retriever Reference](https://neo4j.com/docs/neo4j-graphrag-python/current/user_guide_rag.html#qdrant-neo4j-retriever-user-guide)
+- [Source](https://github.com/neo4j/neo4j-graphrag-python/tree/main/src/neo4j_graphrag/retrievers/external/qdrant)