60 lines
2.2 KiB
Markdown
60 lines
2.2 KiB
Markdown
Chat locally with any PDF
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Ask questions, get answer with usefull references
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Work well with math pdfs (convert them to LaTex, a math syntax comprehensible by computer)
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## Work flow chart
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## Demos
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chatbot test with some US Laws pdf
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<video src="https://git.rufous-trench.ts.net/Crizomb/Medias/raw/branch/main/ai_pdf_test_law.mp4" controls></video>
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chatbot test with math pdf (interpereted as latex by the LLM)
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<video src="https://git.rufous-trench.ts.net/Crizomb/Medias/raw/branch/main/ai_pdf_test_math.mp4" controls></video>
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full length process of converting pdf to latex, then using the chat bot
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<video src="https://git.rufous-trench.ts.net/Crizomb/Medias/raw/branch/main/ai_pdf_full_length.mp4" controls></video>
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## How to use
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* Clone the project to some location that we will call 'x'
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* install requierements listed in the requirements.txt file
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* (open terminal, go to the 'x' location, run pip install -r requirements.txt)
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* ([OPTIONAL] for better performance during embedding, install pytorch with cuda, go to https://pytorch.org/get-started/locally/)
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* Put your pdfs in x/ai_pdf/documents/pdfs
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* Run x/ai_pdf/front_end/main.py
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* Select or not math mode
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* Choose the pdf you want to work on (those documents must be on x/ai_pdf/documents/pdfs to work well)
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* Wait a little bit for the pdf to get vectorized (check task manager to see if your gpu is going vrum)
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* Launch LM Studio, Go to the local Server tab, choose the model you want to run, choose 1234 as server port, start server
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* (If you want to use open-ai or any other cloud LLM services, change line 10 of x/ai_pdf/back_end/inference.py with your api_key and your provider url)
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* Ask questions to the chatbot
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* Get answer
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* Go eat cookies
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### TODO
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- [ ] Option tabs
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- [ ] add more different embedding models
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- [ ] add menu to choose how many relevant chunk of information the vector search should get from the vector db
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- [ ] menu to configure api url and api key
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## Maybe in the futur
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- [ ] Add special support for code PDF (with specialized langchain code spliter)
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- [ ] Add Multimodality
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