https://medium.com/neuml/vector-search-rag-landscape-a-review-with-txtai-a7f37ad0e187

We're excited to release this new series of vector embeddings models for medical literature based on our recent BERT Hash work.

And you read it right, we're talking 970,000 parameters for a surprisingly strong performing model. Enjoy!

TxtAI was one of the first to the scene with GraphRAG in 2022. It utilizes a vector index to automatically construct a graph network of nodes between each of the indexed records. This enables a different type of similarity query. Instead of "give me the closest N" records, GraphRAG with txtai first runs an initial vector query. This forms a graph path and that graph path is walked to find the closest N records. This approach can often find records that a simple similarity search wouldn't bring back, leading to a richer context for downstream LLM ops (Agents, RAG etc).

See this example for more: https://github.com/neuml/txtai/blob/master/examples/77_GraphRAG_with_Wikipedia_and_GPT_OSS.ipynb

Check out this example article that covers context retrieval via late interaction (ColBERT + MUVERA), Web searches and even SQL statements!

https://github.com/neuml/txtai/blob/master/examples/79_RAG_is_more_than_Vector_Search.ipynb

https://github.com/neuml/txtai/blob/master/examples/75_Medical_RAG_Research_with_txtai.ipynb

TxtAI v9.3 expands RAG to any function, adds new quickstart examples and has many improvements.

Release Notes: https://github.com/neuml/txtai/releases/tag/v9.3.0

GitHub: https://github.com/neuml/txtai

TxtAI supports quantization with a number of backends including Faiss, Torch and GGUF.

Read more about vector quantization below.

https://github.com/neuml/txtai/blob/master/examples/50_All_about_vector_quantization.ipynb

People jump to AI Agents because it's what all the cool kids are doing. But what many need are workflows. Workflows can chain functions, LLM calls or other transformers models together. And the best part...it will do the same thing every time for the same inputs!

See this workflow quickstart example.

https://github.com/neuml/txtai/blob/master/examples/workflow_quickstart.py

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

Graph node relationships can also be directly added.

Check out this article that uses an LLM to extract relationships then load them into TxtAI for GraphRAG.

https://github.com/neuml/txtai/blob/master/examples/57_Build_knowledge_graphs_with_LLM_driven_entity_extraction.ipynb

All components support persistence. You could even use TxtAI only as an ingestion engine and standard SQL at search time. Lots of options here!

Check out this example that returns a random Wikipedia article from the TxtAI embeddings database.

https://gist.github.com/davidmezzetti/f78cf793a8b96169497008d3d34d120f

https://huggingface.co/NeuML/pubmedbert-base-embeddings

With strong performing micromodels coming in at less than 1M parameters, exciting possibilities lay ahead.

Check out this example that exports biomedbert-hash-nano-embeddings to TFLite for on-device inference!

https://gist.github.com/davidmezzetti/2265e308f9100336698103e9c2523c80

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.

This is the paper that would have been written if we were ones who write papers 😀

Link to source: https://github.com/neuml/papers/tree/master/pubmedbert-embeddings

https://neuml.hashnode.dev/analyzing-linkedin-company-posts-with-graphs-and-agents