Neo4j GraphRAG
Neo4j GraphRAG 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
pip install neo4j-graphrag[qdrant]
Usage
A vector query with Neo4j and Qdrant could look like:
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, to vectorize text queries automatically.
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)