tldrstory VS paperai

['Introducing txtai, an AI-powered search engine built on Transformers Add Natural Language Understanding to any application Search is the base of many applications.', 'Once data starts to pile up, users want to be able to find it.', 'It’s the foundation of the internet and an ever-growing challenge that is never solved or done.', 'The field of Natural Language Processing (NLP) is rapidly evolving with a number of new developments.', 'Large-scale general language models are an exciting new capability allowing us to add amazing functionality quickly with limited compute and people.', 'Innovation continues with new models and advancements coming in at what seems a weekly basis.', 'This article introduces txtai, an AI-powered search engine that enables Natural Language Understanding (NLU) based search in any application.', 'Introducing txtai 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.', 'txtai also has functionality for zero-shot classification.', 'txtai is open source and available on GitHub.', 'txtai and/or the concepts behind it has already been used to power the Natural Language Processing (NLP) applications listed below: • paperai — AI-powered literature discovery and review engine for medical/scientific papers • tldrstory — AI-powered understanding of headlines and story text • neuspo — Fact-driven, real-time sports event and news site • codequestion — Ask coding questions directly from the terminal Build an Embeddings index For small lists of texts, the method above works.', 'But for larger repositories of documents, it doesn’t make sense to tokenize and convert all embeddings for each query.', 'txtai supports building pre- computed indices which significantly improves performance.', 'Building on the previous example, the following example runs an index method to build and store the text embeddings.', 'In this case, only the query is converted to an embeddings vector each search.', 'https://github.com/neuml/codequestion https://neuspo.com/ https://github.com/neuml/tldrstory https://github.com/neuml/paperai Introducing txtai, an AI-powered search engine built on Transformers Add Natural Language Understanding to any application Introducing txtai Build an Embeddings index']

tldrstory - AI-powered understanding of headlines and story text

tldrstory has full-stack implementation of a zero-shot classification system using Streamlit, FastAPI and Hugging Face Transformers. There is also a Medium article describing tldrstory and zero-shot classification.

Nice project!

I've spent quite a lot of time in the medical/scientific literature space. With regards to LLMs, specifically RAG, how the data is chunked is quite important. With that, I have a couple projects that might be beneficial additions.

paperetl (https://github.com/neuml/paperetl) - supports parsing arXiv, PubMed and integrates with GROBID to handle parsing metadata and text from arbitrary papers.

paperai (https://github.com/neuml/paperai) - builds embeddings databases of medical/scientific papers. Supports LLM prompting, semantic workflows and vector search. Built with txtai (https://github.com/neuml/txtai).

While arbitrary chunking/splitting can work, I've found that integrating parsing that has knowledge of medical/scientific paper structure increases the overall accuracy and experience of downstream applications.

https://github.com/neuml/paperai

Disclaimer: I am the author of both

paperai | paperetl

action: translate input: txtai executes machine-learning workflows to transform data and build AI-powered semantic search applications. output: txtai exécute des workflows d'apprentissage automatique pour transformer les données et construire des applications de recherche sémantique alimentées par l'IA. action: translate input: Traditional search systems use keywords to find data output: Les systèmes de recherche traditionnels utilisent des mots-clés pour trouver des données action: summary input: https://github.com/neuml/txtai output: txtai executes machine-learning workflows to transform data and build AI-powered semantic search applications. Semantic search applications have an understanding of natural language and identify results that have the same meaning, not necessarily the same keywords. API bindings for JavaScript, Java, Rust and Go. Cloud-native architecture scales out with container orchestration systems (e. g. Kubernetes) action: summary input: https://github.com/neuml/paperai output: paperai is an AI-powered literature discovery and review engine for medical/scientific papers. Paperai was used to analyze the COVID-19 Open Research Dataset (CORD-19) paperai and NeuML have been recognized in the following articles: Cord-19 Kaggle Challenge Awards Machine-Learning Experts Delve Into 47,000 Papers on Coronavirus Family. real 0m22.478s user 0m13.776s sys 0m3.218s