gpt4-pdf-chatbot-langchain
semantic-search-tweets
gpt4-pdf-chatbot-langchain | semantic-search-tweets | |
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32 | 2 | |
14,573 | 38 | |
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3.9 | 1.1 | |
about 1 month ago | about 1 year ago | |
TypeScript | Python | |
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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
semantic-search-tweets
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You probably shouldn't use OpenAI's embeddings
It's in the repo:
You first create embeddings. What is this? It's an n-dimensional vector space with your tweets 'embedded' in that space. Each word is an n-dimensional vector in this space. The vectorization is supposed to maintain 'semantic distance'. Basically, if two words are very close in meaning or related (by say frequently appearing next to each other in corpus) they should be 'close' in some of those n-dimensions as well. The result at the end is the '.bin' file, the 'semantic model' of your corpus.
https://github.com/dbasch/semantic-search-tweets/blob/main/e...
For semantic search, you run the same embedding algorithm against the query and take the resultant vectors and do similarity search via matrix ops, resulting in a set of results, with probabilities. These point back to the original source, here the tweets, and you just print the tweet(s) that you select from that result set.
https://github.com/dbasch/semantic-search-tweets/blob/main/s...
Experts can chime in here but there are knobs such as 'batch size' and the functions you use to index. (cosine was used here.)
So the various performance dimensions of the process should also be clear. There is a fixed cost of making the embeddings of your data. There is a per-op embedding of your query, and then running the similarity algorithm to find the result set.
What are some alternatives?
openai-cookbook - Examples and guides for using the OpenAI API
localGPT - Chat with your documents on your local device using GPT models. No data leaves your device and 100% private.
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
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.
BrainChulo - Harnessing the Memory Power of the Camelids
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!
unstructured - Open source libraries and APIs to build custom preprocessing pipelines for labeling, training, or production machine learning pipelines.
babyagi-asi - BabyAGI: an Autonomous and Self-Improving agent, or BASI