paperai
openai-cookbook
paperai | openai-cookbook | |
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19 | 215 | |
1,198 | 55,954 | |
1.3% | 1.0% | |
5.9 | 9.5 | |
5 months ago | 6 days ago | |
Python | MDX | |
Apache License 2.0 | MIT License |
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paperai
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Oracle of Zotero: LLM QA of Your Research Library
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.
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Build Personal ChatGPT Using Your Data
https://github.com/neuml/paperai
Disclaimer: I am the author of both
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[P] Parse research papers into structured data
paperai | paperetl
- Show HN: Semantic search and workflows for medical/scientific papers
- Semantic search and workflows for medical/scientific papers
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# Run txtai in native code
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
openai-cookbook
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Question-Answer System Architectures using LLMs
A pretrained LLM is a closed-book system: It can only access information that it was trained on. With domain fine-tuning, the system manifests additional material. An early prototype of this technique was shown in this OpenAi cookbook: For the target domain, text was embedded using an API, and then when using the LLM, embeddings were retrieved using semantic similarity search to formulate an answer. Although this approach evolved to retrieval-augmented generation, its still a technique to adapt a Gen2 (2020) or Gen3 (2022) LLM into a question-answering system.
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Ask HN: High quality Python scripts or small libraries to learn from
https://github.com/openai/openai-cookbook/blob/main/examples...
- Collection of notebooks showcasing some fun and effective ways of using Claude
- OpenAI Cookbook: Techniques to improve reliability
- OpenAI Cookbooks
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How to fine tune vit/convnet to focus on the layout of the input room image and ignore other things ?
It sounds like you are trying to tweak embeddings for similarity search. Rather than fine-tune the model's layers, you may want to try training a linear transformation the existing model's output embedding. Openai has a cookbook on how to do that. You will need some data though - but I think you can try it with ~20 pieces of synthetically generated data.
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Best base model 1B or 7B for full finetuning
tutorial from OpenAI https://github.com/openai/openai-cookbook/blob/main/examples/Question_answering_using_embeddings.ipynb
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Resources to learn ChatGPT and the OpenAI API
OpenAI Cookbook
- OpenAI Cookbook
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Another Major Outage Across ChatGPT and API
OpenAI community repo with lots of examples: https://github.com/openai/openai-cookbook
What are some alternatives?
txtai - đź’ˇ All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
tika-python - Tika-Python is a Python binding to the Apache Tika™ REST services allowing Tika to be called natively in the Python community.
gpt4-pdf-chatbot-langchain - GPT4 & LangChain Chatbot for large PDF docs
SciencePlots - Matplotlib styles for scientific plotting
chatgpt-retrieval-plugin - The ChatGPT Retrieval Plugin lets you easily find personal or work documents by asking questions in natural language.
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
askai - Command Line Interface for OpenAi ChatGPT
faiss - A library for efficient similarity search and clustering of dense vectors.
gpt_index - LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data. [Moved to: https://github.com/jerryjliu/llama_index]
scibert - A BERT model for scientific text.