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Vector DBFreemiumOpen Source
NEO4J
Graph database with vector search for AI-powered knowledge graphs
GPL-3.0
ABOUT
Traditional RAG pipelines treat documents as isolated chunks, missing the rich relationships between entities, concepts, and data points. Neo4j solves this by combining a native graph database with vector search capabilities, enabling GraphRAG architectures that leverage both semantic similarity and relationship traversal. This allows AI applications to answer questions that require multi-hop reasoning across connected data — something flat vector stores cannot do efficiently.
INSTALL
docker run --publish=7474:7474 --publish=7687:7687 neo4j:latest
INTEGRATION GUIDE
1. Build a GraphRAG system that traverses entity relationships for multi-hop question answering
2. Store and query knowledge graphs alongside vector embeddings for hybrid AI retrieval
3. Implement recommendation systems that combine collaborative filtering with semantic similarity
4. Create a fraud detection pipeline that analyzes transaction patterns and entity relationships in real time
5. Power a conversational AI that understands connections between people, places, and events in your data
TAGS
graph-databasevector-searchknowledge-graphraggraph-ragcypherdockerenterprise