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txtai

All-in-one embeddings, semantic search, and RAG framework for AI applications

★ 9,000 GitHub Apache-2.0 ragembeddingssemantic-searchvector-databasepythonnlp RAG & Knowledge

Overview

txtai is an all-in-one open-source embeddings database and semantic search platform that brings together vector indexing, text embeddings, and retrieval-augmented generation (RAG) in a unified framework. Built with Python, txtai enables building AI-powered search applications, knowledge bases, and LLM workflows with zero external dependencies. It supports multiple backends (Transformers, Sentence Transformers, ONNX), provides a full REST API, and includes built-in workflows for content processing, translation, and summarization. With 9k+ stars and Apache-2.0 license, txtai is trusted for production RAG deployments.

Requirements

Min vCPU
1
Min RAM
4096 MB
Min Disk
10 GB
Rec vCPU
4
Rec RAM
8192 MB
Rec Disk
20 GB

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Affiliate disclosure

Docker Compose

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

Best VPS for txtai →

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Guides & articles

Getting Started with txtai: Docker Deployment and RAG Quick Start

Deploy txtai with Docker in 5 minutes. Step-by-step guide to running the embeddings database, indexing documents, and running semantic search and RAG queries.

Jun 27, 2026

txtai: The All-in-One Embeddings Database for AI-Powered Semantic Search and RAG

Discover txtai — the all-in-one open-source embeddings database for semantic search, vector indexing, and RAG. Self-hosted, Python-native, and zero external dependencies.

Jun 27, 2026