Fine-tuned RAG with Local Embeddings, Qdrant, and OpenAI LLM

This project implements a Retrieval-Augmented Generation (RAG) system using fine-tuned local embeddings, Qdrant vector storage, GPT-4o.

Installation

1. Clone the repository and navigate to the project folder:

   git clone https://github.com/s-smits/RAG-finetuned-embeddings
   cd RAG-finetuned-embeddings
   

2. Create a virtual environment named venv_RAG_finetuned_embeddings and activate it:

   • For macOS and Linux:

     python3 -m venv venv_RAG_finetuned_embeddings
     source venv_RAG_finetuned_embeddings/bin/activate
     
   • For Windows:

     python -m venv venv_RAG_finetuned_embeddings
     venv_RAG_finetuned_embeddings\Scripts\activate
     

3. Install the required dependencies:

   pip install -r requirements.txt
   

4. Rename .env.example to .env in the project's root directory and add your OpenAI API key:

   OPENAI_API_KEY=your_api_key_here
   

   Make sure to enter your own OpenAI API key here. This key is necessary for using GPT-4 in this project.

Usage

Start the script:

   python rag_finetune.py

This opens a Gradio interface where you can:

1. Upload PDF files
2. Process the PDFs
3. Start fine-tuning the embeddings
4. Ask questions based on the processed documents while using the fine-tuned embeddings

Requirements

See requirements.txt for a full list of dependencies.

License

MIT License

Demo

output_3x_speed.mp4