embedding store

Embedding stores are databases optimized for storing, indexing, and retrieving high-dimensional embedding vectors efficiently. **Purpose**: Store embeddings from ML models (text, images, users), enable fast similarity search, power retrieval and recommendation. **Core operations**: **Insert**: Add embedding with metadata and ID. **Search**: Find k nearest neighbors to query embedding. **Update/Delete**: Manage embeddings over time. **Relation to vector databases**: Often synonymous. Embedding store emphasizes ML workflow, vector DB emphasizes database features. **Index structures**: HNSW (graph-based), IVF (inverted file), PQ (product quantization), flat (exact but slow). **Scale considerations**: Billions of embeddings require distributed systems, approximate search, and careful index tuning. **Filtering**: Many stores support metadata filtering combined with vector search (hybrid search). **Popular options**: Pinecone, Weaviate, Milvus, Qdrant, Chroma, pgvector. **Integration with ML**: Store embeddings from CLIP, sentence transformers, or custom models. Update as models change. **Use cases**: Semantic search, RAG retrieval, recommendation, deduplication, clustering. Foundational for modern AI applications.