vector-db

name	vector-db
description	Use when operating a vector store as a data layer — choosing or migrating between Pinecone, Qdrant, Weaviate and pgvector; designing a collection or index (distance metric, dimensions, HNSW parameters, named vectors); filtering on metadata; hybrid dense-plus-sparse search; and quantization to cut RAM and cost. Covers garbage results, silently ignored filters, low recall, slow queries, and filtered queries returning fewer than k rows. NOT producing, chunking or judging embeddings (that is `embeddings-search`).
tags	["vector-database","pinecone","qdrant","weaviate","pgvector","hnsw","hybrid-search","metadata-filter","quantization"]
recommends	["embeddings-search","rag","postgresdb","redis","supabase"]
origin	risco

vector-db — operate the store, not the embeddings

You own the store: the collection schema, the index, the filter path, recall-vs-latency tuning, hybrid fusion, quantization, and the production knobs (upserts, namespaces, deletes, backups). You operate it across the four engines a Claude agent actually meets: pgvector (Postgres extension), Qdrant, Weaviate, Pinecone (serverless).

Three things are not yours, and pretending they are produces wrong advice: