Chat with Multiple PDFs using LangChain, Gemini, and Streamlit

This project allows you to have a conversation with the content of multiple uploaded PDF files, leveraging Google's Gemini large language model (LLM) through LangChain. The interactive web interface is built with Streamlit.

Project Structure

└── 📁 utils
    ├── 📄 pdf_handler.py
    └── 📄 embedding_handler.py
📄 app.py
📄 requirements.txt 
📄 .env 

• app.py: The main Streamlit application file. It contains the logic for loading PDFs, processing text, creating embeddings, interacting with Gemini, and displaying the chat interface.
• utils/pdf_handler.py: Contains functions to extract text from PDF files.
• utils/embedding_handler.py: Contains functions to split the text into smaller chunks and create embeddings using language models.
• requirements.txt: Lists the required Python dependencies for the project.
• .env: File to store environment variables, such as your Google Cloud API key.

Setup and Run

1. Enable Google Cloud API and obtain credentials:

   • Go to the Google Cloud Console.
   • Create a project or select an existing one.
   • Enable the Generative Language API.
   • Create a service account and download the JSON key.

2. Set up environment variables:

   • Create a .env file in the root of your project and add the following:

     GOOGLE_API_KEY="SUBSTITUTE_YOUR_API_KEY_HERE"
     

3. Clone the repository:

   git clone https://github.com/your-username/chat-with-pdfs.git 
   cd chat-with-pdfs

4. Create and activate a virtual environment:

   python3 -m venv .venv
   source .venv/bin/activate

5. Install the dependencies:

   pip install -r requirements.txt

6. Run the application:

   streamlit run app.py

7. Access the application: Open your web browser and go to http://localhost:8501/.

Usage

1. Upload PDFs: Use the file upload widget in the sidebar to upload one or more PDF files.
2. Process: Click the "Process" button to extract text from the PDFs, split it into chunks, and create embeddings.
3. Converse: Type your question in the text box and press Enter. The chatbot will respond based on the information extracted from the PDFs.

Notes

• Make sure you have enabled billing for your project on Google Cloud, as using the Gemini API may incur charges.
• The accuracy and quality of the chatbot's responses depend on the quality of the uploaded PDFs, the Gemini model being used, and how the questions are phrased.
• This is a basic project and can be extended with additional features such as support for different file formats, model customization options, enhanced user interface, and more.
• Refer to the LangChain and Google Gemini documentation for more detailed information on customization and advanced usage.