Biblos supports semantic Bible search and layered analysis through a GitHub project.
neuml/txtai
txtai combines semantic search, language model orchestration, and AI workflow tools in one framework.
About neuml/txtai
txtai is an open-source Python framework for semantic search, LLM orchestration and language model workflows. Its core is an embeddings database that combines sparse and dense vector indexes with graph networks and relational storage, used for vector search or as a knowledge source for LLM applications such as retrieval augmented generation.
It provides pipelines for question-answering, summarization, translation, transcription and labeling, with workflows and agents to connect pipelines and models. It offers Web and Model Context Protocol APIs and can run locally or with container orchestration. It requires Python 3.11+ and additional dependencies for some uses.
Key features
- Semantic search over text, documents, audio, images and video
- Embeddings database combining vector indexes, graphs and relational storage
- Language-model pipelines for QA, summarization, translation, transcription and labeling
- Workflows joining pipelines into microservices or multi-model processes
- Agents connecting embeddings, pipelines and workflows for RAG tasks
- Web and MCP APIs with bindings for JavaScript, Java, Rust and Go
Best for: Developers building semantic search, RAG and multi-model LLM workflows in Python.
Good for
- Open source under Apache 2.0 license
- Runs locally without sending data to remote services
- Supports text, documents, audio, images and video
Watch out for
- Requires Python 3.11 or later
- Additional dependencies must be installed to scale up functionality
neuml/txtai at a glance
- Website
- github.com/neuml/txtai
- Category
- AI & Machine Learning › AI Search & Research
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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neuml/txtai FAQ
Can txtai run locally?
Yes. It can run locally or scale out with container orchestration.
Which APIs and language bindings does it support?
It provides Web and Model Context Protocol APIs, with bindings for JavaScript, Java, Rust and Go.
What is the core data component?
An embeddings database that combines sparse and dense vector indexes with graph networks and relational storage.
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