README

All-in-one AI framework



    
        
    
    
        
    
    
        
    
    
        
    
    
        
    
    
        
    


txtai is an all-in-one AI framework for semantic search, LLM orchestration and language model workflows.

!architecture
!architecture

The key component of txtai is an embeddings database, which is a union of vector indexes (sparse and dense), graph networks and relational databases.

This foundation enables vector search and/or serves as a powerful knowledge source for large language model (LLM) applications.

Build autonomous agents, retrieval augmented generation (RAG) processes, multi-model workflows and more.

Summary of txtai features:

- 🔎 Vector search with SQL, object storage, topic modeling, graph analysis and multimodal indexing
- 📄 Create embeddings for text, documents, audio, images and video
- 💡 Pipelines powered by language models that run LLM prompts, question-answering, labeling, transcription, translation, summarization and more
- ↪️️ Workflows to join pipelines together and aggregate business logic. txtai processes can be simple microservices or multi-model workflows.
- 🤖 Agents that intelligently connect embeddings, pipelines, workflows and other agents together to autonomously solve complex problems
- ⚙️ Web and Model Context Protocol (MCP) APIs. Bindings available for JavaScript, Java, Rust and Go.
- 🔋 Batteries included with defaults to get up and running fast
- ☁️ Run local or scale out with container orchestration

txtai is built with Python 3.11+, Hugging Face Transformers, Sentence Transformers and FastAPI. txtai is open-source under an Apache 2.0 license.

 [!NOTE]

 NeuML is the company behind txtai and we provide AI consulting services around our stack. Schedule a meeting or send a message to learn more.

 We're also building an easy and secure way to run hosted txtai applications with txtai.cloud.

 Why txtai?

!why
!why

New vector databases, LLM frameworks and everything in between are sprouting up daily. Why build with txtai?

- Up and running in minutes with pip or Docker
python
 Get started in a couple lines
import txtai

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

- Built-in API makes it easy to develop applications using your programming language of choice
yaml
 app.yml
embeddings:
    path: sentence-transformers/all-MiniLM-L6-v2

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

- Run local - no need to ship data off to disparate remote services
- Work with micromodels all the way up to large language models (LLMs)
- Low footprint - install additional dependencies and scale up when needed
- Learn by example - notebooks cover all available functionality

 Use Cases

The following sections introduce common txtai use cases. A comprehensive set of over 70 example notebooks and applications are also available.

 Semantic S

Top issues

Latest release

This release adds new ANN backends for RaBitQ and Zvec Sparse, native support for ONNX vectors along with a bonanza of improvements and bug fixes

See below for full details on the new features, improvements and bug fixes.

New Features
--------------------------

- Add ONNX vectors backend (1211, 1212) @joseph-bergin
- Add RaBitQ as an ANN backend (1244, 1251) @pma1999
- Add Zvec Sparse ANN (1280, 1307) @Ni0ri
- Require Python 3.11 (1273)

Improvements
--------------------------

- Update vectors package to standardize fallback logic (1221)
- Support pooling_mode with models t