What problem it solves

Developers need a unified way to build semantic search applications, ground language models with knowledge sources, and connect models into larger workflows. They also need support for different data types, models, deployment modes, and application interfaces without assembling each capability separately.

Key features

• Unified embeddings database — The core component combines sparse and dense vector indexes, graph networks, and relational databases. It supports vector search and can act as a knowledge source for LLM applications.

  Source: README

• Multimodal content indexing — Create embeddings for text, documents, audio, images, and video. Search features also include SQL, object storage, topic modeling, and graph analysis.

  Source: README

• Language model pipelines and workflows — Pipelines run language model tasks such as prompting, question-answering, labeling, transcription, translation, and summarization. Workflows join pipelines together and aggregate business logic, ranging from simple microservices to multi-model workflows.

  Source: README

• Agents and retrieval-augmented generation — Agents connect embeddings, pipelines, workflows, and other agents to work on complex problems. RAG workflows constrain model output with a knowledge base and can retrieve context from vector, Web, SQL, and other sources.

  Source: README

• APIs and flexible deployment — Expose functionality through Web and Model Context Protocol APIs, with bindings available for several programming languages. Applications can run locally or scale out with container orchestration.

  Source: README

Quick start

Copied verbatim from the project's README.

pip install txtai

# 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)]

Who it may suit

This may suit Python developers building semantic search, RAG, agent, pipeline, or multi-model applications. It may also suit teams that need local execution, API access, or container-based deployment.

Topics to discuss

• Which embeddings database configuration best fits a particular search workload?

• How should teams structure RAG retrieval across vector, graph, Web, and SQL sources?

• What are the practical tradeoffs between local execution and container orchestration?

Written by ReadmeX AI from the project's README, source and public GitHub data; it may contain mistakes.

Read the original README https://github.com/neuml/txtai#readme

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