This is an AI-powered chatbot that allows users to upload PDF documents and ask questions about the content. The chatbot extracts text from the PDF, creates a vector store using FAISS, and generates responses using OpenAI GPT models.

• Upload PDF files and extract text.
• Store and retrieve text using FAISS vector search.
• Ask questions based on the document content.
• Uses LangChain and OpenAI API for question answering.
• FastAPI backend with a React.js frontend.
• Stylish UI with dark mode and modern design.

• FastAPI (for API endpoints)
• FAISS (for vector storage and similarity search)
• LangChain (for text chunking and embedding generation)
• OpenAI GPT-4 (for answering questions)
• pdfplumber & pytesseract (for text extraction)
• Uvicorn (for running the FastAPI server)

• React.js (for UI)
• Axios (for API calls)
• Styled Components / CSS (for UI styling)
• React Loader Spinner (for better user experience)

AI-PDF-CHATBOT/
│── backend/
│   ├── main.py              # FastAPI backend logic
│   ├── pdf_processing.py    # PDF text extraction logic
│   ├── vector_store.py      # FAISS vector storage and retrieval
│   ├── settings.py          # Centralized configuration (env-based)
│   ├── uploads/             # Directory to store uploaded PDFs
│   ├── cache/               # Directory to store cached embeddings
│   ├── .env                 # API keys and environment variables (not committed)
│   ├── .env.example         # Example backend environment variables
│   ├── venv/                # Virtual environment (ignored in Git)
│
│── frontend/
│   ├── src/
│   │   ├── components/
│   │   │   ├── FileUpload.js  # Component to upload PDF
│   │   │   ├── Chat.js        # Component for asking questions
│   │   │   ├── ErrorMessage.js# Shared error display component
│   │   ├── App.js             # Main React app logic
│   │   ├── styles/App.css     # Styling for UI
│   ├── public/
│   ├── package.json           # Frontend dependencies
│   ├── .env.example           # Example frontend environment variables
│
│── README.md                 # Project documentation
│── requirements.txt           # Python dependencies
│── package.json               # React dependencies

1. Clone the repository:

   git clone https://github.com/yourusername/AI-PDF-CHATBOT.git
   cd AI-PDF-CHATBOT/backend

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # macOS/Linux
   venv\Scripts\activate     # Windows

3. Install dependencies:

   pip install -r requirements.txt

4. Set up your .env file (based on .env.example):

   cp .env.example .env
   # Then edit .env to include your real values

5. Run the FastAPI server (development):

   uvicorn main:app --host 0.0.0.0 --port 8000 --reload

6. Visit http://127.0.0.1:8000/docs to test the API, or http://127.0.0.1:8000/health for a simple health check.

For a production-style run (no auto-reload), you can omit the --reload flag:

uvicorn main:app --host 0.0.0.0 --port 8000

1. Navigate to the frontend folder:

   cd ../frontend

2. Install dependencies:

   npm install

3. (Optional) Create a .env file from the example to point to a non-local backend:

   cd src/..
   cp .env.example .env
   # REACT_APP_API_URL defaults to http://localhost:8000 if not set

4. Start the React app:

   npm start

5. Open http://localhost:3000 in your browser.

Backend (backend/.env based on backend/.env.example):

• OPENAI_API_KEY – your OpenAI API key (required).
• OPENAI_MODEL – OpenAI model name to use (default: gpt-4-turbo).
• ALLOWED_ORIGINS – comma-separated list of allowed frontend origins for CORS (default includes http://localhost:3000).
• UPLOAD_DIR – directory for uploaded PDFs (default: uploads).
• CACHE_DIR – directory for cached embeddings (default: cache).
• MAX_PDF_SIZE_MB – maximum allowed PDF size in megabytes (default: 20).
• MAX_PDF_PAGES – maximum number of pages to process (default: 200).
• FAISS_K – number of chunks to retrieve from FAISS (default: 5).
• CONTEXT_MAX_CHARS – maximum number of characters of context sent to the language model (default: 8000).

Frontend (frontend/.env based on frontend/.env.example):

• REACT_APP_API_URL – base URL of the backend API (default: http://localhost:8000).

1. Upload a PDF file using the Upload PDF button.
2. Once uploaded, ask a question related to the PDF content.
3. The chatbot will retrieve the most relevant context and generate an AI-powered response.
4. You can clear chat history and re-upload different PDFs.

• If FastAPI does not start, ensure your virtual environment is activated.
• If you get an OpenAI API key error, set your .env file correctly.
• If dependencies are missing, install them again:

  pip install -r requirements.txt

• If React app does not start, ensure all dependencies are installed:

  npm install

• If the backend is not reachable, ensure FastAPI is running at http://127.0.0.1:8000.
• If CORS issues occur, make sure CORS is enabled in main.py.

• Multi-PDF Support: Handle multiple document uploads and queries.
• Better Search Optimization: Improve FAISS vector search accuracy.
• UI Enhancements: Dark mode toggle, better animations.
• Deploy to Cloud: Host on AWS, Vercel, or Heroku.

Developed by Rohan Jain. Contributions are welcome! Feel free to fork and improve the project.