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qdrant-landing/content/documentation/frameworks/neo4j-graphrag.md
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| --- | ||
| title: Neo4j GraphRAG | ||
| --- | ||
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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 | ||
| pip install neo4j-graphrag[qdrant] | ||
| ``` | ||
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| ## Usage | ||
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| A vector query with Neo4j and Qdrant could look like: | ||
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| ```python | ||
| from neo4j import GraphDatabase | ||
| from neo4j_graphrag.retrievers import QdrantNeo4jRetriever | ||
| from qdrant_client import QdrantClient | ||
| from examples.embedding_biology import EMBEDDING_BIOLOGY | ||
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| NEO4J_URL = "neo4j://localhost:7687" | ||
| NEO4J_AUTH = ("neo4j", "password") | ||
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| 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", | ||
| ) | ||
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| retriever.search(query_vector=[0.5523, 0.523, 0.132, 0.523, ...], top_k=5) | ||
| ``` | ||
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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 | ||
| from langchain_huggingface.embeddings import HuggingFaceEmbeddings | ||
| from neo4j import GraphDatabase | ||
| from neo4j_graphrag.retrievers import QdrantNeo4jRetriever | ||
| from qdrant_client import QdrantClient | ||
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| NEO4J_URL = "neo4j://localhost:7687" | ||
| NEO4J_AUTH = ("neo4j", "password") | ||
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| 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, | ||
| ) | ||
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| retriever.search(query_text="my user query", top_k=10) | ||
| ``` | ||
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| ## Further Reading | ||
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| - [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) |