Interactive PDF Chat Assistant

The Interactive PDF Chat Assistant is a Streamlit-powered application designed to facilitate conversations with a conversational AI model using content extracted from PDF documents. Users can upload multiple PDF files, and the application will extract the text and use it to answer questions through a user-friendly chat interface.

Key Features

• Multi-File Upload: Easily upload and process multiple PDF documents at once.
• Text Extraction: Automatically extract text from uploaded PDF files.
• AI-Powered Conversations: Engage in detailed conversations with a conversational AI model trained on the extracted text.
• Chat Interface: Interact with the AI through an intuitive chat interface.

Running the Application

Using Docker

1. Build and Run the Docker Container:

   docker compose up --build

2. Access the Application:

   Once the container is running, you can access the application at http://localhost:8501.

Local Development

1. Clone the Repository:

   git clone https://github.com/kaifcoder/gemini_multipdf_chat.git
   cd gemini_multipdf_chat

2. Install Dependencies:

   pip install -r requirements.txt

3. Set Up Google API Key:

   Ensure your .env file contains the Google API key.

   GOOGLE_API_KEY=your_api_key_here
   

4. Launch the Application:

   streamlit run app.py

5. Upload and Analyze Documents:

   • Use the sidebar to upload PDF files.
   • Click "Process Uploads" to analyze the documents and generate embeddings.

6. Engage in Conversations:

   • Interact with the AI in the main chat interface to ask questions and receive detailed responses.

Project Structure

• app.py: The main application script that handles document processing, AI interactions, and user interface.
• .env: Stores environment variables, including the Google API key.
• requirements.txt: Lists all the Python packages required for the application.
• README.md: Comprehensive documentation for the project.

Dependencies

• PyPDF2: For extracting text from PDF files.
• langchain: For managing language models and vector stores.
• Streamlit: For building the user interface.
• google.generativeai: For accessing the Gemini AI model.
• dotenv: For loading environment variables from the .env file.

Acknowledgments

• Google Gemini: Providing the advanced language model.
• Streamlit: Enabling the creation of interactive web applications.