RAG et connaissances
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AST-powered code intelligence engine — graph database plus hybrid semantic search across 62 languages, zero LLM dependency.
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Voir le guide →txtai est un projet de RAG et connaissances open source, créé par neuml (neuml/txtai sur GitHub). Il compte 13 000 étoiles sur GitHub et 922 forks, il est publié sous la licence Apache-2.0 et a été mis à jour pour la dernière fois sur GitHub le 8 octobre 2026.
All-in-one AI framework for semantic search, LLM orchestration and language model workflows.
Decide whether your first job is semantic search over content, a RAG pipeline, or a language-model workflow such as summarization or transcription.
Run pip install txtai (Python 3.11+); install only the optional dependencies your components need to keep the footprint low.
Create an Embeddings instance and index text, documents, audio, images or video - sensible defaults get you running in a couple of lines.
Chain pipelines into workflows, ground an LLM with your embeddings for RAG, or connect components into an agent; optionally serve it all through the built-in API.
A union of sparse and dense vector indexes, graph networks and a relational database, enabling vector search with SQL, topic modeling, graph analysis and multimodal indexing.
Create embeddings for text, documents, audio, images and video, so mixed content lives in one searchable index.
Ready pipelines run LLM prompts, question-answering, labeling, transcription, translation, summarization and more.
Join pipelines together and aggregate business logic - from simple microservices to multi-model workflows.
Agents built on smolagents connect embeddings, pipelines, workflows and other agents, supporting Hugging Face, llama.cpp and LiteLLM-backed LLMs (OpenAI, Claude, Bedrock).
A built-in FastAPI web API and MCP API, with bindings for JavaScript, Java, Rust and Go, so applications in any language can use a txtai service.
Clone txtai for the examples and source; most users install from PyPI instead.
git clone https://github.com/neuml/txtai.git
cd txtaiInstall txtai into a Python 3.11+ virtual environment.
pip install txtaiPoint the built-in API at a YAML config and query it over HTTP.
CONFIG=app.yml uvicorn "txtai.api:app"
# then: curl "http://localhost:8000/search?query=positive"txtai is developed by NeuML, the company behind the project, with open-source contributors; NeuML also offers consulting and a hosted txtai.cloud.
Yes. It is open source under the Apache-2.0 license.
Not for local models: txtai runs Hugging Face and llama.cpp models on your machine. Cloud LLMs reached through LiteLLM (OpenAI, Claude, AWS Bedrock) use your own provider keys.
A vector database only stores and searches embeddings, and LangChain orchestrates external components; txtai bundles the embeddings database, pipelines, workflows and agents into one framework with batteries included.
Run pip install txtai, create an Embeddings index in a few lines, and work through the introductory notebook.
AST-powered code intelligence engine — graph database plus hybrid semantic search across 62 languages, zero LLM dependency.
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Gratuit et ouvert : récupérez le code source, mettez une étoile au dépôt et commencez à créer.