Embeddings & Vector Databases · TRACK 03/04
Vector Database Guides
One page per major vector store: pgvector, Pinecone, Qdrant, Weaviate, Chroma, Milvus, FAISS.
// THE TRACK
01 · START HEREVector DatabasesGet the core mental model: what a vector database stores, the queries it answers, and how it slots into a RAG or search stack.BEGINNERPinecone vs Weaviate vs QdrantUnderstand the real trade-offs between the four leading vector databases so you can pick the right one for your project’s scale, budget, and ops tolerance.INTERMEDIATEpgvectorLearn how a single Postgres extension gives you production vector search right next to the data you already have.BEGINNERPineconeUnderstand Pinecone's serverless model — indexes, namespaces, upserts, and pricing — without touching a single server.BEGINNERQdrantLearn Qdrant's core concepts — collections, points, payloads — and the filtered HNSW search that sets it apart.INTERMEDIATEWeaviateUnderstand Weaviate's schema-first approach, built-in embedding modules, and native hybrid search in one read.INTERMEDIATEFAISSLearn when an in-process index beats a database, and how FAISS's index zoo — Flat, IVF, HNSW, PQ — fits together.ADVANCEDChromaSpin up vector search in three lines of Python and learn where Chroma shines — and where it stops scaling.BEGINNERMilvusLearn what Milvus is, how its distributed architecture targets billion-scale search, and when it is the right pick over lighter vector stores.INTERMEDIATEsqlite-vecLearn how sqlite-vec adds embedding storage and similarity search to an ordinary SQLite file, and when that is all the vector search you need.BEGINNERLanceDBUnderstand what LanceDB is, how its on-disk Lance columnar format enables fast search without a server, and where it fits between Chroma and Pinecone.INTERMEDIATE