NeuML

⚕️🧬🔬 PubMedBERT Embeddings gets over 1 million downloads a month and has been cited in over 50 articles over the last year! Google Scholar: https://lnkd.in/eBcn8SDM Model: https://lnkd.in/egnEKcqd

💥 txtai v9.14.0 is out! 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. There were 25 contributors with 20 new ones. These contributors added solid new features and are experts in their field. What a great and growing community! Release Notes: https://lnkd.in/esvAu7j6 GitHub: https://lnkd.in/dxWDeey

Long scrolling window of dependencies stink if you don't need them! Did you know that txtai supports a minimal no-dependency install? https://lnkd.in/ee_MqKXD

Why the large uptick in txtai usage, PRs and overall interest? Well the "Why txtai?" page that's been around forever covers it best. - Up and running in minutes with pip or Docker  - Built-in API makes it easy to develop applications using your programming language of choice  - 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 https://lnkd.in/dZM3hEiX

🔥 Who's interested in this: - Sub 100K parameter vector embeddings model - 90% of the NDCG performance of all-MiniLM-L6-v2 at 0.4% the size - Crushes the bert-hash series on retrieval accuracy at 10x less the size - Not a Transformer: trains significantly faster with less data - ColBERT-style multi-vector clarity packed into a single dense vector - Scales up to larger sizes Stay tuned for more!

🚀 txtai v9.13.0 is out! This release adds LEMUR (Learned Multi-Vector Retrieval), expands late-interaction retrieval, improves MUVERA and pooling, and includes a number of bug fixes and performance improvements. A big thank you to Morgan Carr, Aryan Pardeshi, @LHMQ878, @Anai-Guo, @FU-max-boop, Arnaud Thery, @arose26, Amir Fathi, Serhii Zghama, sainikhil juluri, @Evhye38496, Yash Raj Pandey for their contributions! 🙌 Release Notes: https://lnkd.in/e6MgPvxi GitHub: https://lnkd.in/dxWDeey

The last month plus has seen such a great increase in contribution from the txtai community. In a little over the last month, we had 41 PRs successfully merged from 19 contributors. To give perspective, before that only 25 PRs had been merged into the project since 2020! https://lnkd.in/ev8AD6it

Exciting addition coming with the next txtai release: LEMUR for ColBERT-style Late-Interaction Retrieval! 🎉 Contributor Morgan Carr introduced LEMUR to txtai, making it, as far as we know, the first framework to incorporate LEMUR for late-interaction retrieval using standard, fixed-vector indexes. Key benefits: 🚀 Significant boost: 49–62% higher NDCG@10 than 2,048-dimensional MUVERA 💾 5x less storage: 2,048 dimensions vs. MUVERA’s default 10,240 📐 Better geometry: Optional batch mean centering addresses anisotropy in token embeddings A promising step toward making ColBERT-style retrieval more practical with conventional vector search. Read the full breakdown: https://lnkd.in/eSHvqqjc

🎂 Happy 6th Birthday to txtai! The initial release stated: "txtai builds an AI-powered index over sections of text. txtai supports building text indices to perform similarity searches and create extractive question-answering based systems." While much has changed, much has stayed the same. We're still in a world where the best search makes the best products. https://lnkd.in/e_YaCZEd

🔥 TxtAI is a trending Python project on GitHub today. First time since early 2025. Getting a ton of new contributors and PRs lately. Why now? Because TxtAI has always been here doing the right thing - local AI. No gimmicks and hype. https://lnkd.in/dUixHvq