How It Works
1
Chunk
Documents are split into smaller pieces for more precise retrieval.
2
Embed
Each chunk is converted to a vector embedding and stored in the database.
3
Search
Queries are embedded and matched against stored vectors to find similar content.
Hybrid Search
Many vector databases support a hybrid search mode that combines vector similarity with a provider-specific keyword or lexical ranking. Hybrid search works by:- Finding semantically similar content via vector search
- Finding lexical matches with the database’s keyword-search implementation
- Combining results using ranked fusion
Supported Databases
Azure Cosmos DB
MongoDB vCore vector search
Cassandra
Distributed database with vector search
Chroma
Open-source embedding database
ClickHouse
Analytical database with vector search
Couchbase
NoSQL with vector search
LanceDB
Local, serverless, hybrid search
LangChain
Use any LangChain vector store
LightRAG
Graph-based RAG
LlamaIndex
Search existing LlamaIndex indexes
Milvus
Scalable vector database
MongoDB
Atlas vector search
PgVector
PostgreSQL extension, hybrid search
Pinecone
Managed vector database
Qdrant
High-performance vector search
Redis
In-memory with vector search
Valkey
In-memory with vector search
SingleStore
Real-time analytics with vectors
SurrealDB
Multi-model database
Upstash
Serverless vector search
Weaviate
Vector search with modules
Choosing a Database
Local Development
LanceDB or ChromaDB for zero-setup local development
Production
PgVector if you already use PostgreSQL
Managed Service
Pinecone or Weaviate Cloud for vendor-operated infrastructure
Distributed Deployment
Qdrant or Milvus for a separately operated vector service
Async Support
Vector databases with async support let an async application await ingestion and search I/O. Useainsert and asearch inside an existing event loop.