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RAG PDF Deep Learning

A simple Retrieval-Augmented Generation (RAG) project using LangChain, FAISS, and OpenAI GPT on a custom PDF.

Features

• Extracts text from PDF
• Chunks and indexes text with FAISS
• Retrieves relevant context and answers questions using GPT

Setup

1. Clone the repo:

   git clone https://github.com/your-username/your-repo.git
   cd your-repo
   

2. Create and activate a virtual environment:

   python -m venv venv
   venv\Scripts\activate
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Add your OpenAI API key to a .env file:

   OPENAI_API_KEY=your_openai_api_key
   

5. Place your PDF (e.g., deep learning.pdf) in the project folder.

Usage

1. Extract text from PDF:

   python extract_pdf.py
   

2. Chunk the text:

   python chunk_text.py
   

3. Build the FAISS index:

   python build_faiss_index.py
   

4. Ask questions using GPT:

   python rag_with_gpt.py
   

Notes

• Do not commit your .env file or API keys.
• The faiss_index/ folder is ignored for security and size reasons.

License

MIT

rag-pdf-deep-learning

A simple RAG system

4599b97c5d8bf431e46de393882aef4638306595