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Weaviate

Open-source vector database combining vector search with structured filtering for AI applications

★ 16,448 GitHub BSD-3-Clause vector-databasevector-searchsemantic-searchraggenerative-searchhybrid-searchhnsw RAG & Knowledge

Overview

Weaviate is an open-source vector database that stores both objects and vectors, allowing vector search with structured filtering, with the fault tolerance and scalability of a cloud-native database. It supports hybrid search (vector + keyword), generative search, and comes with built-in modules for OpenAI, Cohere, Hugging Face, and more. Written in Go, it uses HNSW for ANN indexing and provides a GraphQL API for seamless integration with AI and ML pipelines.

Requirements

Min vCPU
1
Min RAM
4096 MB
Min Disk
10 GB
Rec vCPU
2
Rec RAM
4096 MB
Rec Disk
20 GB

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Docker Compose

# Generated by Run This Ai — docker-compose.yml
services:
  weaviate:
    image: semitechnologies/weaviate:latest
    restart: unless-stopped
    ports:
      - 8080:8080
    volumes:
      - ./data/weaviate:/data

Best VPS for Weaviate →

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Guides & articles

How to Deploy Weaviate Vector Database with Docker: A Quick Start Guide

Learn how to deploy Weaviate, the open-source vector database, with Docker in minutes. Step-by-step guide with curl examples, Docker Compose setup, and production tips.

Jun 27, 2026

Weaviate: Open-Source Vector Database for AI-Powered Semantic Search

Discover Weaviate - the open-source vector database that combines semantic search, hybrid search, and generative AI in one self-hosted package. Perfect for RAG, recommendations, and AI agents.

Jun 27, 2026