txtai

All-in-one AI framework

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

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 3.10+, 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?

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

# 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 cho