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Weaviate

Open-source vector database with built-in embedding and hybrid search.

0Open Source
Vector Database
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Overview

Weaviate is an open-source vector database with built-in embedding generation, hybrid search (BM25 + vector), and a GraphQL/REST API.

Key Features

  • Built-in vectorization modules (OpenAI, Cohere, HuggingFace)
  • Hybrid search combining BM25 keyword and vector search
  • GraphQL and REST APIs
  • Multi-tenancy for SaaS applications
  • Generative search — query + LLM response in one call
  • Cloud-managed or self-hosted with Docker/Kubernetes

Real-World Workflows

Build generative search over your knowledge base

  1. 1Start Weaviate with docker-compose (includes vectorizer module)
  2. 2Import objects — Weaviate auto-vectorizes text fields
  3. 3Use nearText query to find semantically similar objects
  4. 4Add .withGenerate() to get LLM-summarized answers

Getting Started

docker run -d -p 8080:8080 \
  -e ENABLE_MODULES='text2vec-openai,generative-openai' \
  -e OPENAI_APIKEY=$OPENAI_API_KEY \
  cr.weaviate.io/semitechnologies/weaviate:latest

pip install weaviate-client

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