All Tools
W
Vector DBFreemiumOpen Source
WEAVIATE
The AI-native database for building production RAG and search
BSD-3-Clause
ABOUT
Building a vector search system from scratch means managing embedding models separately from the database, writing custom serialization, and handling schema drift. Weaviate solves this with native vectorizer modules (OpenAI, Cohere, HuggingFace) that auto-embed on write, a schema that enforces data consistency, and GraphQL for flexible retrieval queries.
INSTALL
npm install weaviate-clientINTEGRATION GUIDE
1. Build a multi-modal search engine that searches both product images and text descriptions with a single query
2. Create a knowledge base with automatic embedding generation — write text, Weaviate embeds it
3. Build a generative search system where retrieved results are fed directly into an LLM answer
4. Implement hybrid keyword + semantic search for e-commerce product discovery
5. Power a question-answering system over a private document corpus
TAGS
pythontypescriptgraphqlmulti-modalembeddingsraghybrid-search