This is a project to make an interface to use RAG easily

To install this app on your local machine, follow these steps:

• Install python 3.10 or higher
• Install git if you don't have
• Open a terminal in your specified folder
• Clone the project with this command git clone https://github.com/HolzerSoahita/Generative_AI_RAG_Project.git
• Navigate to the app directory with the command cd Generative_AI_RAG_Project
• Create a virtual environment named ragvenv using the command: python -m venv ragvenv
• Activate the ragvenv environment by running the following command:
  • On Windows: ragvenv\Scripts\activate.bat
  • On Linux or Mac: source ragvenv/bin/activate
• Install the requirements.txt file using the command: pip install -r requirements.txt
• Run the app with the command: python main.py. This will launch a web server on your local machine and click the link generated in the console to access the gradio app.
• To stop the server press CTRL+C twice

• Download the file Test_RAG_Generative_AI.ipynb
• Upload it on google colab
• Run the cell of this notebook until the cell of !pip install -r requirements.txt
• You need to open the file main.py in the file explorer(It is in the folder Generative_AI_RAG_Project)
• Change the code demo.launch(debug=True) at the line 456 to demo.launch(debug=True,share=True)
• Now you can run the cell !python main.py
• Click on the public URL not the local URL, because the local URL is blocked by google colab
• NB: The process is longer than the local installation because it is shared gradio link (for example, if we upload a file, you will see that nothing is happen but after a few second the file is uploaded). I really suggest you to make local machine installation to avoid this problem.

• This experience is released with the model TheBloke/Mistral-7B-Instruct-v0.2-GGUF.
• We use size input 256 and overlap 20 on the chunking
• The folder of used dataset(one folder by use case) and the related questions can be found in this link
• The result of the RAG experience is sum up with this following table :

NB: I recommend to choose a text generation model that can support chat format to have better result

The user documentation of this app can be found here

The main file is main.py. In the folder library , you can find all the libraries needed for this app. These libraries are:

• agentic_chunker.py : File class to make the agentic chunking
• chunking.py : all the functions needed for the chunking and saving vector database
• FileSaver.py : File class for saving an html code to file format(html,pdf,docx)
• rag.py : File class to make the RAG(Retrieval Augmented Generation)

SOAHITA Salvoldi Holzer

PGE 5 Student at Aivancity University Cachan Paris