Weaviate

Weaviate is an open-source vector database written in Go that thinks of itself as more than an index: it’s an AI-native database with a schema of typed collections, built-in hybrid search that fuses vector and BM25 keyword scoring in one query, and a modular vectorizer system that can call OpenAI, Cohere, Google, or local models to embed your data on the way in. You can self-host it with Docker or Kubernetes, or use the managed Weaviate Cloud.

Its differentiator in this module’s lineup is that batteries-included integration layer. Where FAISS gives you a bare index and Pinecone gives you managed storage, Weaviate can own the whole retrieval pipeline: define a collection with text2vec-openai, insert raw objects, and querying, embedding, keyword fusion, and even generative search (retrieve-then-summarize with an LLM) happen server-side. That makes it a strong fit for search-heavy products where hybrid relevance matters — pure vector search famously fumbles exact part numbers and names, and Weaviate’s fused scoring handles both in a single call.

You’ll connect with the client (weaviate.connect_to_local() or to the cloud), create a collection with a vectorizer config, insert objects, and query with collection.query.hybrid("your question", limit=5) or near_text. Filters, multi-tenancy, and RBAC are first-class. Try the hybrid alpha parameter — sliding between pure keyword and pure vector — to feel how much retrieval quality it buys on realistic queries.