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

Clone the repository:

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

Create a virtual environment and activate it:

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

Install the required packages:
```
pip install -r requirements.txt
```

Usage

Start the FastAPI server:
```
uvicorn app.main:app --reload
```
Open the Streamlit app in another terminal:
```
streamlit run app/streamlit_app.py
```
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

To run the tests, use:
```
streamlit run app/streamlit_app.py
```