A hybrid AI search and retrieval system combining Neo4j graph database with Pinecone vector search, orchestrated by LangChain and deployed on AWS.
A hybrid AI search and retrieval system combining Neo4j graph database with Pinecone vector search, deployed on AWS. Uses LangChain for orchestration, enabling queries that require both semantic understanding and relationship traversal simultaneously.
Pure vector search finds semantically similar content but misses structural relationships. Pure graph search captures relationships but struggles with semantic similarity. Enterprise AI applications need both: find documents semantically AND traverse the relationship graph to find connected entities — a capability neither database achieves alone.
Built a hybrid retrieval architecture using Pinecone for vector-based semantic search and Neo4j Aura for relationship-based graph traversal. LangChain orchestrates the pipeline, deciding when to use vector search vs. graph traversal based on query type. AWS hosts the infrastructure. The system answers complex questions that require both semantic understanding and graph reasoning.
Tell us about your problem. We'll tell you honestly how we'd approach it — and whether we're the right team.