Weaviate is an open-source vector database with a GraphQL API. Data is organised into classes (like tables) with typed properties. Built-in vectorizer modules (text2vec-openai, text2vec-cohere) automatically embed text on import. HNSW with Binary Quantisation (HNSW+BQ) compresses index memory 32x. Generative search (RAG) returns LLM-synthesised answers from retrieved objects. Supports hybrid search, multi-tenancy, and replication.
Each stage in order — click any step to read what it does.
Weaviate components: schema, vectorizer modules, HNSW index.
The trade-offs worth knowing before you build this.
text2vec-openai auto-embeds on import — no separate embedding pipeline. Configure moduleConfig per class to control model and which properties to vectorize.
HNSW+BQ compresses 1536-dim float32 vectors (6KB each) to 192 bytes — 32x reduction. Minimal recall loss with rescore enabled. Essential for large-scale deployments.
Weaviate's generative module does retrieval + synthesis in one GraphQL query. No application-level prompt assembly needed for standard RAG patterns.
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