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70 lines
2.5 KiB
Markdown
70 lines
2.5 KiB
Markdown
---
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title: Neo4j GraphRAG
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---
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# Neo4j GraphRAG
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[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.
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## Installation
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```bash
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pip install neo4j-graphrag[qdrant]
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```
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## Usage
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A vector query with Neo4j and Qdrant could look like:
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```python
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from neo4j import GraphDatabase
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from neo4j_graphrag.retrievers import QdrantNeo4jRetriever
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from qdrant_client import QdrantClient
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from examples.embedding_biology import EMBEDDING_BIOLOGY
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NEO4J_URL = "neo4j://localhost:7687"
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NEO4J_AUTH = ("neo4j", "password")
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with GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) as neo4j_driver:
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retriever = QdrantNeo4jRetriever(
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driver=neo4j_driver,
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client=QdrantClient(url="http://localhost:6333"),
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collection_name="{collection_name}",
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id_property_external="neo4j_id",
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id_property_neo4j="id",
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)
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retriever.search(query_vector=[0.5523, 0.523, 0.132, 0.523, ...], top_k=5)
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```
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Alternatively, you can use any [Langchain embeddings providers](https://python.langchain.com/docs/integrations/text_embedding/), to vectorize text queries automatically.
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```python
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from langchain_huggingface.embeddings import HuggingFaceEmbeddings
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from neo4j import GraphDatabase
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from neo4j_graphrag.retrievers import QdrantNeo4jRetriever
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from qdrant_client import QdrantClient
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NEO4J_URL = "neo4j://localhost:7687"
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NEO4J_AUTH = ("neo4j", "password")
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with GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) as neo4j_driver:
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embedder = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
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retriever = QdrantNeo4jRetriever(
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driver=neo4j_driver,
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client=QdrantClient(url="http://localhost:6333"),
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collection_name="{collection_name}",
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id_property_external="neo4j_id",
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id_property_neo4j="id",
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embedder=embedder,
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)
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retriever.search(query_text="my user query", top_k=10)
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```
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## Further Reading
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- [Neo4j GraphRAG Reference](https://neo4j.com/docs/neo4j-graphrag-python/current/index.html)
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- [Qdrant Retriever Reference](https://neo4j.com/docs/neo4j-graphrag-python/current/user_guide_rag.html#qdrant-neo4j-retriever-user-guide)
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- [Source](https://github.com/neo4j/neo4j-graphrag-python/tree/main/src/neo4j_graphrag/retrievers/external/qdrant)
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