Ai Pdf Analysis and Question Answer Bot

Hosted Client : https://ai-pdf-analyzer-questioner.vercel.app/

Hosted API : https://aipdf-analyzer.onrender.com

Disclaimer : server is a spin down server so it will be sleeping on first request , it takes 1 - 2 minutes to go live again ! So , please be patient while testing.

This project consists of a FastAPI server for uploading PDF documents, storing their content in a vector database, and querying them using natural language. Additionally, it includes a frontend client built with Vite, React, TypeScript, and Tailwind CSS that provides a user-friendly interface for interacting with the API.

Features

Server Features:

• Upload PDF documents via API and store their metadata in a PostgreSQL database.
• Process PDFs into vector embeddings using HuggingFace embeddings and store them in FAISS for efficient querying.
• Query uploaded PDFs with natural language and receive context-based answers.

Client Features:

• Intuitive interface for uploading PDF files and asking questions.
• Real-time feedback and responses displayed to the user.
• Built with modern technologies including Vite, TypeScript, and Tailwind CSS for a sleek, responsive UI.

Innovations

• Added Chat Scroll Option for going through chat history.
• Added Indicator to tell if server is ready or not for questions.
• Added Chat Saving through PDF so that users can save their chat history for future references.

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Technologies Used

Server:

• FastAPI: For building the backend API.
• PostgreSQL: To store metadata about uploaded PDFs.
• FAISS: For efficient vector storage and retrieval.
• Langchain: Framework for language model integrations.
• HuggingFace Embeddings: For generating vector representations of PDF content.

Client:

• React: For building the UI.
• TypeScript: For a type-safe codebase.
• Vite: For fast development and bundling.
• Tailwind CSS: For styling and responsive design.

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Installation and Setup

Prerequisites

• Python 3.8+
• Node.js and npm
• PostgreSQL database (local or remote)

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Backend Setup (Server)

1. Clone the repository:

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

2. Create a virtual environment and activate it:

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

3. Install backend dependencies:

   pip install -r requirements.txt

4. Configure environment variables by creating a .env file in the root directory with the following content:

   DB_NAME=your_database_name
   DB_USER=your_database_user
   DB_PASSWORD=your_database_password
   DB_HOST=your_database_host
   DB_PORT=your_database_port
   groq_api=your_groq_api_key

5. Start the server:

   uvicorn main:app --host 0.0.0.0 --port 5000 --reload 

The server will run at http://0.0.0.0:5000.

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Frontend Setup (Client)

1. Navigate to the client directory:

   cd client

2. Install dependencies:

   npm install

3. Start the development server:

   npm run dev

The client will be accessible at http://localhost:5173.

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API Endpoints

1. /upload_pdf/ (Server)

• Method: POST
• Description: Upload a PDF document, store its metadata in the database, and save its content in a vector store.
• Parameters:
  • file: The PDF file to upload (Multipart/Form-Data).

Example Request:

curl -X 'POST' \
  'http://0.0.0.0:5000/upload_pdf/' \
  -H 'accept: application/json' \
  -H 'Content-Type: multipart/form-data' \
  -F 'file=@path_to_your_pdf.pdf'

Response:

{
  "message": "Vector database created successfully and metadata saved."
}

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2. /ask_question/ (Server)

• Method: POST
• Description: Ask a question based on the uploaded PDFs, and receive an answer.
• Parameters:
  • question: The question to ask (Form-Data).

Example Request:

curl -X 'POST' \
  'http://0.0.0.0:5000/ask_question/' \
  -H 'accept: application/json' \
  -H 'Content-Type:  multipart/form-data' \
  -d 'question=What is the purpose of this document?'

Response:

{
  "answer": "This document is a guide to using the API for PDF uploading and querying."
}

Database Schema

The application uses a PostgreSQL database to store metadata about the uploaded PDF documents. The table schema is as follows:

pdf_documents Table

Column	Type	Description
id	SERIAL	Primary Key, auto-incremented ID
filename	VARCHAR(255)	The name of the uploaded PDF file
upload_date	TIMESTAMP	The date and time when the PDF was uploaded

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Client Functionality

The client provides a bot-like interface where users can:

1. Upload a PDF document directly from their browser.
2. Input questions related to the uploaded document.
3. Receive context-aware answers from the server.
4. Export or save chat history as a pdf.

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Folder Structure

AiPdf_Analyzer/
├── client/                 # Frontend application
│   ├── public/             # Public assets
│   ├── src/                # Source code
│   │   ├── components/     # React components
│   │   ├── types/          # Application Interfaces
│   │   ├── App.tsx         # Main application component
│   │   ├── main.tsx        # Entry point
│   │   └── ...             # Other source files
│   ├── index.html          # HTML template
│   ├── package.json        # NPM package configuration
│   ├── postcss.config.js   # PostCSS configuration
│   ├── tailwind.config.js  # Tailwind CSS configuration
│   └── tsconfig.json       # TypeScript configuration
│
├── server/                 # Backend application
│   ├── main.py             # FastAPI application
│   ├── requirements.txt    # Python dependencies
│   └── ...                 # Other server files
│
├── .env                    # Environment variables
├── .gitignore              # Git ignore file
├── LICENSE                 # License file
└── README.md               # Project documentation

License

This project is licensed under the MIT License. Feel free to use and contribute.

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Handheld UI

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Desktop UI

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Innovations ScreenShots

Server Ready Indicator :-

Chat to PDF :-

Scrolling Feature :-

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Working Sample

Final_Demon_Video_Aryan_Swaroop.mp4

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Contributing

1. Fork the repository.
2. Create a new branch.
3. Make your changes.
4. Submit a pull request.

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Contact

For any issues or questions, feel free to open an issue or reach out to the maintainer.