ChatPDF

This project is a Document Retrieval application that utilizes Retrieval-Augmented Generation (RAG) techniques to enable users to interact with uploaded PDF documents. By leveraging a Large Language Model (LLM), users can ask questions about the content of the documents and receive accurate answers based on the information retrieved.

Features

• PDF Upload: Users can upload PDF files for processing.
• AI Interaction: Ask questions about the content of the uploaded PDFs.
• Machine Learning Integration: Utilizes advanced machine learning models for document processing and question answering.

Technologies Used

• Backend: FastAPI
• Frontend: Streamlit
• Machine Learning: Langchain, Hugging Face Transformers
• Vector Store: FAISS for efficient similarity search

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/chatpdf.git
   cd chatpdf

2. Create a virtual environment and activate it:

   python -m venv .venv
   source .venv/bin/activate  # On Windows use .venv\Scripts\activate

3. Install the required packages:

   pip install -r requirements.txt

Usage

1. Start the FastAPI server:

   uvicorn app.main:app --reload

2. Open the Streamlit app in another terminal:

   streamlit run app/streamlit_app.py

3. Navigate to http://localhost:8501 in your web browser to access the application.

API Endpoints

• GET /: Returns a welcome message.

• POST /upload_pdf/: Uploads a PDF file for processing.

  • Request: Multipart form data with the PDF file.
  • Response: Success message upon successful upload and processing.

• POST /ask/: Asks a question about the uploaded PDF.

  • Request: JSON body with the question.
  • Response: The answer to the question based on the PDF content.

Testing

4. To run the tests, use:

   streamlit run app/streamlit_app.py