mychatGPT
gpt4-pdf-chatbot-langchain
mychatGPT | gpt4-pdf-chatbot-langchain | |
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10 | 32 | |
123 | 14,594 | |
- | - | |
8.1 | 3.9 | |
about 1 month ago | about 2 months ago | |
Python | TypeScript | |
Apache License 2.0 | - |
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mychatGPT
- Can I let AI read a group of information from books and what not and then let it answer questions?
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How do I ask a meta question to a document? (Retrieval augmented generation, langchain, pinecone)
Try this, just doesnt have source snippets but youll have great results. jlonge4/gpt_chatwithPDF: GPT chat with your docs! (github.com)
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Textgen webui for gpt_chatwithPDF
I would like to use this tool https://github.com/jlonge4/gpt_chatwithPDF/blob/main/gpt_chat_api.py but unfortunately the local version (https://github.com/jlonge4/local_llama) is bound to the CPU and thus quiet slow. Is there any way i could get textgenwebui working with the above stated tool?
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Ways to integrate PDF file content into my own Chatbot powered by GPT API
I just built this last week here
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Offline llama
The imports use openAI as well as openAI embeddings. Similar to my project here . This post is free from openAI use or the internet
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Help with long PDF and processing
Try this https://github.com/jlonge4/gpt_chatwithPDF I’ve used it on up to 1k page PDF no issue
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GPT->PDF chat completed!
So after much interest, especially after my video post demonstrating it, I have finally finished my GPT chat with your PDFs project and posted it on my github https://github.com/jlonge4/gpt_chatwithPDF for everyone to use! Drop a star if you enjoy it 😎
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Llama_index PDF chatbot
Check the updated project https://github.com/jlonge4/gpt_chatwithPDF
gpt4-pdf-chatbot-langchain
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Back and forth conversations before a vector search?
I am playing around with this github project, which takes a user question as input and immediately runs a vector search on it to find relevant storied information before delivering an answer.
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How do I ask a meta question to a document? (Retrieval augmented generation, langchain, pinecone)
I am using this https://github.com/mayooear/gpt4-pdf-chatbot-langchain as a reference to ingest PDFs into pinecone and chat with a document, but my results aren’t good. Since it’s looking for related documents, there’s no good relation to the meta question: “What questions were asked in this interview?”
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Recently I launched dataspot Ai tool for students and academics, that turns any type of content such as research paper, website, or YouTube video into interactive chatbot. You can effortlessly retrieve information, obtain summaries. Google "dataspot ai" & let me know what you think :)
Anyone can already do this locally with their own API keys for free, with no technical knowledge by cloning a github repo (e.g. https://github.com/mayooear/gpt4-pdf-chatbot-langchain - this one can also chat with multiple pdfs which is much better). Even with gpt-4, I just don't find the responses useful usually. I find the model doesn't do great with scientific stuff aside from asking very basic things. Might have to wait for gpt-5.
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Chat with Documents using Open source LLMs
https://github.com/mayooear/gpt4-pdf-chatbot-langchain this repo uses gpt-3.5/4 which uses OpenAI API. Is there any work donw with free/open-source LLMs
- Using ChatGPT to read multiple PDFs and create writing using them as sources
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How do you train GPT on your own documents?
Follow this guide https://github.com/mayooear/gpt4-pdf-chatbot-langchain
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Best GPT-based tool for summarizing PDFs/long docs
I am using this one on windows 10. Took 2 evenings to set up: https://github.com/mayooear/gpt4-pdf-chatbot-langchain
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Earthling Ed ChatGPT type AI
Thanks for your take on the subject. I agree that starting from scratch would be too much. I think my post above might be misleading in regard to training. I wouldn't suggest to start from scratch but to provide additional data to a pretrained AI. But you can use GPT-4 (through API) in combination with pinecone to provide data. Here is a project, where someone implemented this to work with large PDF files. I don't think it would be too hard, to start from there and adapt the project to the requirements of OP. Obviously this would require paid for API keys. LLama could be also a good starting point, with a lot of resources available.
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Seeking Cost-Effective Alternatives and Optimization Tips for a GPT-based PDF Chatbot
I'm currently developing a chatbot application that interacts with PDF documents using GPT API, Langchain, and a Pinecone vector database. The project is built on this repository: mayooear/gpt4-pdf-chatbot-langchain.
- ChatGPT for your files - Discovered an AI research tool that allows you to ask questions across multiple files all at once and get instant answers with highlighted references
What are some alternatives?
pdfGPT - PDF GPT allows you to chat with the contents of your PDF file by using GPT capabilities. The most effective open source solution to turn your pdf files in a chatbot!
openai-cookbook - Examples and guides for using the OpenAI API
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
localGPT - Chat with your documents on your local device using GPT models. No data leaves your device and 100% private.
local_llama - This repo is to showcase how you can run a model locally and offline, free of OpenAI dependencies.
privateGPT - Interact with your documents using the power of GPT, 100% privately, no data leaks [Moved to: https://github.com/zylon-ai/private-gpt]
marqo - Unified embedding generation and search engine. Also available on cloud - cloud.marqo.ai
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.
chatpdf-gpt - ChatPDF-GPT is an innovative chat interface application powered by LangChain and OpenAI, allowing users to upload and chat with PDF documents, stored in Pinecone vector database and Supabase storage.
Parsr - Transforms PDF, Documents and Images into Enriched Structured Data
evals - Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.