txtai

// Overview

txtai is an open-source AI framework built around an embeddings database that combines vector indexes (sparse and dense), graph networks, and relational databases. You can index text, documents, audio, images, and video, then run vector search over them or use the index as a knowledge source for LLM applications.

It is aimed at Python developers who want to build semantic search, retrieval augmented generation (RAG), and multi-model workflows without wiring together several separate services. Pipelines wrap language-model tasks such as question answering, summarization, transcription, and translation, and workflows join those pipelines into larger processes.

Within the RAG frameworks category, txtai bundles the vector store, retrieval, pipelines, and agents into one package. It runs locally so your data stays on your machine, and it ships with sensible defaults plus a web and Model Context Protocol (MCP) API for use from other languages.

// What it does

• Embeddings database that unites sparse and dense vector indexes, graph networks, and relational storage
• Vector search with SQL, object storage, topic modeling, and graph analysis, plus multimodal indexing across text, documents, audio, images, and video
• Pipelines for LLM prompts, question answering, labeling, transcription, translation, and summarization
• Workflows that chain pipelines together to build microservices or multi-model processes
• Agents that connect embeddings, pipelines, workflows, and other agents to solve multi-step tasks
• Web and Model Context Protocol (MCP) APIs with bindings for JavaScript, Java, Rust, and Go

// Getting started

Install txtai with pip, then build an embeddings index and run a search in a few lines of Python.

pip install txtai

import txtai

embeddings = txtai.Embeddings()
embeddings.index(["Correct", "Not what we hoped"])
embeddings.search("positive", 1)
#[(0, 0.29862046241760254)]

# app.yml
embeddings:
    path: sentence-transformers/all-MiniLM-L6-v2

CONFIG=app.yml uvicorn "txtai.api:app"
curl -X GET "http://localhost:8000/search?query=positive"

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Build a semantic or similarity search application that matches on meaning instead of exact keywords
• Power a RAG pipeline by using the embeddings database as the retrieval source for an LLM
• Run language-model pipelines for summarization, translation, transcription, or question answering
• Chain pipelines into workflows or autonomous agents that solve multi-step tasks

// How txtai compares

txtai alongside other open-source rag frameworks & platforms tools AI/TLDR tracks, ranked by GitHub stars.