Chat locally with any PDF

Ask questions, get answer with usefull references 

Work well with math pdfs (convert them to LaTex, a math syntax comprehensible by computer)

## Work flow chart

![RAG_diagrams](https://git.rufous-trench.ts.net/Crizomb/Medias/raw/branch/main/ai_pdf_workflow_chart.png)

## Demos

chatbot test with some US Laws pdf

<video src="https://git.rufous-trench.ts.net/Crizomb/Medias/raw/branch/main/ai_pdf_test_law.mp4" controls>

chatbot test with math pdf (interpereted as latex by the LLM)

<video src="https://git.rufous-trench.ts.net/Crizomb/Medias/raw/branch/main/ai_pdf_test_math.mp4" controls>

full length process of converting pdf to latex, then using the chat bot

<video src="https://git.rufous-trench.ts.net/Crizomb/Medias/raw/branch/main/ai_pdf_full_length.mp4" controls>



## How to use 

* Clone the project to some location that we will call 'x'
* install requierements listed in the requirements.txt file
* (open terminal, go to the 'x' location, run pip install -r requirements.txt)
* ([OPTIONAL] for better performance during embedding, install pytorch with cuda, go to https://pytorch.org/get-started/locally/) 

* Put your pdfs in x/ai_pdf/documents/pdfs
* Run x/ai_pdf/front_end/main.py
* Select or not math mode
* Choose the pdf you want to work on (those documents must be on x/ai_pdf/documents/pdfs to work well)
* Wait a little bit for the pdf to get vectorized (check task manager to see if your gpu is going vrum)

* Launch LM Studio, Go to the local Server tab, choose the model you want to run, choose 1234 as server port, start server
* (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)

* Ask questions to the chatbot
* Get answer
* Go eat cookies


### TODO 

- [ ] Option tabs
    - [ ] add more different embedding models
    - [ ] add menu to choose how many relevant chunk of information the vector search should get from the vector db
    - [ ] menu to configure api url and api key
     
## Maybe in the futur

- [ ] Add special support for code PDF (with specialized langchain code spliter)
- [ ] Add Multimodality
      

