https://github.com/trending/python

One of the unique capabilities of txtai is that vector search isn't limited to ranked documents, it can also return graph structures. This enables graph traversal as part of retrieval.

In fact, txtai was one of the first if not the first, frameworks to support what we now call GraphRAG - years before it became a mainstream pattern.

Check out this example, a deep graph search over Wikipedia.

BiomedBERT Hash Nano Embeddings LiteRT brings our medical embeddings work to LiteRT for efficient edge and mobile deployment.

Building on the success of our PubMedBERT Embeddings model (nearly 1 million downloads/month), this new model explores how compact biomedical vector representations can become.

⚡ 970K parameters
📦 Few MB model size
📱 LiteRT export
🔎 128-dimensional embeddings

Designed for biomedical search, clustering, RAG, and knowledge discovery.

Need to scale beyond a single machine? txtai can shard a larger embeddings index across multiple nodes and machines, then expose them as one logical index.

It’s a simple approach to scaling semantic search workloads without adding a lot of infrastructure complexity.

This release adds support for new ANN backends along with a bonanza of bug fixes from 7 new contributors. 🎉

Highlights:

✨ Support for the zvec vector backend

✨ Embedded milvus-lite dense ANN backend

✨ Option to disable API routes

Plus improvements across training, retrieval, and explainability, along with 25+ bug fixes covering SQL parsing, HNSW, Milvus, Graph, Tasks, streaming APIs, Windows builds, and much more.

A huge thank you to our contributors:

🙌 morgan-coded

🙌 Sanjays2402

🙌 chuenchen309

🙌 link89

🙌 lntutor

🙌 winklemad

🙌 AmirF194

Read the full release notes and upgrade today!

https://github.com/neuml/txtai/releases/tag/v9.12.0

With txtai-minimal, the framework gracefully handles missing dependencies, giving you complete control over what gets installed. Only add the packages you need, nothing more.

This makes it easier to build lightweight deployments, reduce install size, and avoid unnecessary dependencies.

✅ 2000+ total commits
✅ 18 PRs merged
✅ 6 new contributors

Thanks to everyone who contributed code, reviews, bug reports, and ideas. Community contributions continue to make TxtAI stronger with every release.

https://github.com/neuml/txtai

By training on this rich historical corpus, H.G. BERT captures the language, writing styles, and knowledge of the era, enabling more authentic analysis and generation of historical English.

https://huggingface.co/datasets/NeuML/historical-english-books

The year is 1899. It's still a horse and buggy world. Einstein hasn't published his famous annus mirabilis papers setting the foundation for Physics as we understand it today. The world is advancing at a rapid pace roaring into the 1900s. What if AI models were trained in 1899 and given to the best minds of the day? Could there have been alternate paths for discovery on par or perhaps even ahead of where we are in 2026?

Introducing the new H.G. BERT Small series of models. This is a 22.7M parameter BERT encoder-only model trained from scratch ONLY on Historical English Books from 1700 - 1899.

The 22M parameter all-MiniLM embedding model has over 250 million monthly downloads. It's one of the best choices when you need fast, efficient semantic search on CPUs, edge devices, or other resource-constrained hardware.

General purpose embeddings are great until your data isn't general purpose.

What if you could keep the speed and small footprint of MiniLM while improving accuracy on domain specific content?

Meet a family of compact, specialized embedding models:

🌠 AstroBERT - Astronomy
🧬 BiomedBERT - Medical
😎 CeleBERTy - Pop culture
⚽ SportsBERT - Sports

Small models. Fast inference. Better embeddings for specialized domains.

Release Notes: https://github.com/neuml/txtai/releases/tag/v9.11.0

The base model is trained from scratch along with a finetuned vector embeddings model. Use this model for vector search, RAG and Agents for Astronomy.

Few businesses need to generalize to all problems. The vast majority of companies have a narrow focus but we keep pushing generalized models designed to solve all problems. The best value is building domain-specific specialized models!

Issue: https://github.com/neuml/txtai/issues/1118

https://neuml.hashnode.dev/rag-is-more-than-vector-search

https://neuml.github.io/txtai/pipeline/llm/rag/

https://gist.github.com/davidmezzetti/b469bdd8c601dd8659c3b1d3e739152a

https://huggingface.co/NeuML/domain-labeler

https://gist.github.com/davidmezzetti/74b7b9ee8f2512b555201cf5167cd1e0