Key infrastructure for AI, RAG and semantic search.
Principle¶
Data → embedding model → vector → storage. Query → embedding → nearest neighbor → results.
Algorithms¶
- HNSW — most popular
- IVF — partitioning
- Flat — brute force
Databases¶
- Pinecone — managed
- ChromaDB — OSS embedded
- Weaviate — hybrid search
- Qdrant — Rust, performance
- pgvector — PG extension
Use cases: - RAG - Semantic search - Recommendations - Image similarity
Vector DB for AI¶
Essential for RAG and semantic search.
vector dbaiembeddings