paper-qa
txtai
paper-qa | txtai | |
---|---|---|
10 | 356 | |
3,664 | 7,080 | |
- | 3.8% | |
8.7 | 9.3 | |
15 days ago | 8 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
paper-qa
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Oracle of Zotero: LLM QA of Your Research Library
Why does this post link to a renamed fork of Paper-QA (https://github.com/whitead/paper-qa) which has made zero changes and is 19 commits behind the original?
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[P] A Large Language Model for Healthcare | NHS-LLM and OpenGPT
To be honest, I'm not too sure about this part, and think that it is probably not the best approach to have the model itself generate references. I prefer the approach used in e.g. paperqa, but wanted to explore both options.
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Looking for a paper summarizer
I’ve come across Paper QA (github page) and as a graduate student I loved the idea that when I do literature review and find tons of papers I can just ask the AI to find the info I’m looking for in the paper. However, this service requires OpenAI API key, which I’ve acquired but turns out it’s a paid service. Free key doesn’t get me anything. Is there a service/software like this that is free? Or something that I can host on my PC instead of using people’s servers so it’s cheaper/free?
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ChatPDF – Chat with Any PDF
I tried it [1] a lot, but I must say it confuses me most of the time and I need to read the original text to check if it makes sense. Lots of times it doesn't.
[1] https://github.com/whitead/paper-qa
- Alternatives to Pinecone? (Vector databases) [D]
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DIY natural language processing - How to start, techniques guidance
Have a look at this: https://github.com/whitead/paper-qa
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Show HN: Document Q&A with GPT: web, .pdf, .docx, etc.
1: We are finding out. Someone else mentioned: https://github.com/whitead/paper-qa We're hoping to keep our service be accessible and easy to use, and add features. Such as from your other questions...
2: We are thinking of the website integration. Do you think OpenAI may release this too? Questions received by email is a new idea that sounds interesting!
3: Thanks for the suggestion – we will look into it.
- GitHub - whitead/paper-qa: LLM Chain for answering questions from documents with citations
- Paper QA: LLM Chain for answering questions from documents with citations
txtai
- Show HN: FileKitty – Combine and label text files for LLM prompt contexts
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What contributing to Open-source is, and what it isn't
I tend to agree with this sentiment. Many junior devs and/or those in college want to contribute. Then they feel entitled to merge a PR that they worked hard on often without guidance. I'm all for working with people but projects have standards and not all ideas make sense. In many cases, especially with commercial open source, the project is the base of a companies identity. So it's not just for drive-by ideas to pad a resume or finish a school project.
For those who do want to do this, I'd recommend writing an issue and/or reaching out to the developers to engage in a dialogue. This takes work but it will increase the likelihood of a PR being merged.
Disclaimer: I'm the primary developer of txtai (https://github.com/neuml/txtai), an open-source vector database + RAG framework
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Build knowledge graphs with LLM-driven entity extraction
txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows.
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Bootstrap or VC?
Bootstrapping only works if you have the runway to do it and you don't feel the need to grow fast.
With NeuML (https://neuml.com), I've went the bootstrapping route. I've been able to build a fairly successful open source project (txtai 6K stars https://github.com/neuml/txtai) and a revenue positive company. It's a "live within your means" strategy.
VC funding can have a snowball effect where you need more and more. Then you're in the loop of needing funding rounds to survive. The hope is someday you're acquired or start turning a profit.
I would say both have their pros and cons. Not all ideas have the luxury of time.
- txtai: An embeddings database for semantic search, graph networks and RAG
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Ask HN: What happened to startups, why is everything so polished?
I agree that in many cases people are puffing their feathers to try to be something they're not (at least not yet). Some believe in the fake it until you make it mentality.
With NeuML (https://neuml.com), the website is a simple HTML page. On social media, I'm honest about what NeuML is, that I'm in my 40s with a family and not striving to be the next Steve Jobs. I've been able to build a fairly successful open source project (txtai 6K stars https://github.com/neuml/txtai) and a revenue positive company. For me, authenticity and being genuine is most important. I would say that being genuine has been way more of an asset than liability.
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Are we at peak vector database?
I'll add txtai (https://github.com/neuml/txtai) to the list.
There is still plenty of room for innovation in this space. Just need to focus on the right projects that are innovating and not the ones (re)working on problems solved in 2020/2021.
- Txtai: An all-in-one embeddings database for semantic search and LLM workflows
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Generate knowledge with Semantic Graphs and RAG
txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows.
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Show HN: Open-source Rule-based PDF parser for RAG
Nice project! I've long used Tika for document parsing given it's maturity and wide number of formats supported. The XHTML output helps with chunking documents for RAG.
Here's a couple examples:
- https://neuml.hashnode.dev/build-rag-pipelines-with-txtai
- https://neuml.hashnode.dev/extract-text-from-documents
Disclaimer: I'm the primary author of txtai (https://github.com/neuml/txtai).
What are some alternatives?
vault-ai - OP Vault ChatGPT: Give ChatGPT long-term memory using the OP Stack (OpenAI + Pinecone Vector Database). Upload your own custom knowledge base files (PDF, txt, epub, etc) using a simple React frontend.
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
simple-llm-finetuner - Simple UI for LLM Model Finetuning
tika-python - Tika-Python is a Python binding to the Apache Tikaâ„¢ REST services allowing Tika to be called natively in the Python community.
gpt4-pdf-chatbot-langchain - GPT4 & LangChain Chatbot for large PDF docs
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
langchain - âš¡ Building applications with LLMs through composability âš¡ [Moved to: https://github.com/langchain-ai/langchain]
faiss - A library for efficient similarity search and clustering of dense vectors.
google-research - Google Research
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
OpenGPT - A framework for creating grounded instruction based datasets and training conversational domain expert Large Language Models (LLMs).
paperai - 📄 🤖 Semantic search and workflows for medical/scientific papers