neuml/txtai

★ 12,483PythonAudience · developerComplexity · 3/5LicenseSetup · moderate

Mindmap

mindmap
  root((txtai))
    What it does
      Semantic search
      RAG pipelines
      Autonomous agents
    Inputs
      Text documents
      Images audio video
    AI Tasks
      Summarize translate
      Transcribe label
      Question answering
    Audience
      Developers
      Data teams
      Researchers

mindmap root((txtai)) What it does Semantic search RAG pipelines Autonomous agents Inputs Text documents Images audio video AI Tasks Summarize translate Transcribe label Question answering Audience Developers Data teams Researchers

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Things people build with this

USE CASE 1

Build a search system over your own documents that finds relevant results even when the exact words don't match.

USE CASE 2

Set up a retrieval-augmented generation pipeline that answers questions using your private data fed to a language model.

USE CASE 3

Create an autonomous agent that decides which data sources and tools to query to answer complex multi-step questions.

USE CASE 4

Transcribe audio, summarize text, and translate content in a single local pipeline without external API calls.

Tech stack

Python

Getting it running

Difficulty · moderate Time to first run · 30min

Requires Python 3.10+, local model downloads can be several gigabytes depending on the pipeline selected.

Use freely for any purpose including commercial use under the Apache 2.0 license, as long as you keep the copyright notice.

In plain English

Txtai is a Python library for building search systems and AI-powered workflows. Its core feature is an embeddings database, which indexes content so you can search by meaning rather than by keywords. Traditional search finds documents that contain the exact words you typed, semantic search finds content that means the same thing, even if the wording is different. Txtai handles this by converting text, images, audio, or video into numerical representations (called vectors or embeddings) that capture meaning and can be compared mathematically. On top of that search foundation, txtai provides building blocks for connecting language models to your data. Retrieval augmented generation (RAG) is a pattern where a system retrieves relevant information from your own content and feeds it to a language model to produce a response grounded in that data rather than general training knowledge. Txtai supports this pattern, along with multi-step pipelines for tasks like summarizing documents, translating text, transcribing audio, labeling content, and answering questions. The library also supports autonomous agents: systems that decide on their own which tools or data sources to consult in order to answer a question or complete a task. Agents in txtai can chain together search, language models, and other tools to handle more complex problems without manual step-by-step instructions. Txtai can run entirely on a local machine without sending data to outside services, which matters for private or sensitive content. It exposes a web API so that applications written in JavaScript, Java, Rust, or Go can connect to a txtai instance running in Python. Over 70 example notebooks cover the range of functionality. The library requires Python 3.10 or later and is open source under an Apache 2.0 license. The company behind it, NeuML, also offers consulting services and a hosted cloud version.

Copy-paste prompts

Prompt 1

I have a folder of PDF documents and want to search them by meaning using txtai. Write me the Python code to index them and run a semantic query.

Prompt 2

Set up a RAG pipeline with txtai that retrieves relevant chunks from my private document collection and feeds them to a local language model to answer a user question.

Prompt 3

I want a txtai agent that can search my knowledge base and use a calculator tool to answer data questions. Show me the setup code.

Prompt 4

Show me how to expose a txtai embeddings database as a REST API so my JavaScript frontend can send queries and get results.

Prompt 5

Write a txtai pipeline that transcribes an audio file, summarizes the transcript, and labels it by topic, all running locally without any API keys.

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