Advanced PDF Chatbot

This project provides an advanced chatbot powered by machine learning, capable of processing PDF documents and answering questions based on their contents. It uses Sentence Transformers for embedding and TinyLlama for generating responses, along with Gradio for the user interface.

Features

• PDF Processing: Upload PDF files to extract and embed text chunks from them.
• Context Retrieval: For a given query, the system retrieves the most relevant context from the PDF.
• Question Answering: The chatbot uses the retrieved context to generate a concise answer to the user's question based on the document's contents.
• Gradio Interface: A user-friendly web interface where users can upload PDFs, ask questions, and receive responses.

Installation

Step 1: Set Up a Python Virtual Environment

It is recommended to use a virtual environment to manage the dependencies for this project. To set up a virtual environment, run the following commands:

1. Create a virtual environment:

   python3 -m venv venv

2. Activate the virtual environment:

   • On macOS/Linux:

     source venv/bin/activate

   • On Windows:

     .\venv\Scripts\activate

Step 2: Install Dependencies

With the virtual environment activated, install the required dependencies from requirements.txt. Additionally, if you are running this in a server or production environment, install uvicorn to serve the application:

pip install -r requirements.txt

Step 3: Run the Application

python main.py

This will start a local development server, allowing you to interact with the chatbot via the Gradio interface at http://127.0.0.1:7861 (can differ too).

How to Use

1. Upload PDF: Click the "Upload PDF" button to upload a PDF document. The system will process the document and extract text from it.

2. Ask a Question: After the document is processed, enter your question in the chat box. The chatbot will retrieve the relevant context from the document and generate a response based on the content.

3. Response: The chatbot will provide a clear, concise answer, or inform you if the document does not contain enough information to answer the question.

Customizing Response Generation and Model

• Change the Response Attribute: The model used for generating responses can be modified by adjusting the tinyllama_api_url in the code to point to another API or a different model endpoint.

• Switch to a Different Model: If you wish to use a different model for response generation, simply change the tinyllama_api_url to the appropriate model URL and update the model field in the API call within the generate_response method to the new model’s name.

Example:

self.tinyllama_api_url = "http://localhost:12345/api/generate"  # Replace with your model's API URL

"model": "new-model-name"  # Replace with the desired model name

These adjustments allow you to switch between different models or APIs as required.

Requirements

• Python 3.7+
• CUDA (optional for GPU acceleration)

Troubleshooting

• TinyLlama API Not Found: Ensure that the TinyLlama API is running locally on http://localhost:11434. The chatbot requires the TinyLlama API to generate responses.

• CUDA Issues: If you're using a GPU, ensure you have the proper CUDA version installed for PyTorch. If no GPU is available, the code will fall back to using CPU.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Requirements for requirements.txt

Ensure the following packages are included in the requirements.txt for the project:

gradio
torch
sentence-transformers
numpy
pandas
requests
langchain
langchain_community
scikit-learn
pypdf