Advanced RAG Q&A Chatbot with LangChain

This project implements a powerful Question-Answering chatbot using LangChain's Retrieval-Augmented Generation (RAG) concepts. The chatbot can process uploaded PDF documents, answer complex questions based on their content, and leverage advanced techniques like chains and retrievers.

Key Features

• PDF Document Processing: Ability to upload and extract knowledge from PDF files.
• Retrieval-Augmented Generation (RAG): Employs retrievers to access relevant information and chains to structure the question answering process.
• Streamlit Integration: User-friendly web interface powered by Streamlit.
• LangChain: Built on the flexible and powerful LangChain framework.
• OpenAI Integration (Optional): Support for integrating OpenAI's language models like GPT-3.5.

Prerequisites

• Python 3.x
• A LangChain API Key (if specified in the code)
• An OpenAI API Key (if you're using OpenAI models)

Installation

1. Clone this repository:

   git clone [https://github.com/](https://github.com/)<your-username>/<your-repo-name>

2. Navigate to the project directory:

   cd <your-repo-name>

3. Create a virtual environment (recommended):

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

4. Install dependencies:

   pip install -r requirements.txt

Usage

1. Environment Variables:

   • Create a .env file in the project root.
   • Add the following, replacing placeholders with your actual keys:

     OPENAI_API_KEY=<your_openai_api_key>
     LANGCHAIN_API_KEY=<your_langchain_api_key>
     

   • Load the environment variables using the dotenv library (already included in the code).

2. Start the Application:

   streamlit run app.py 

3. Using the Web Interface

   • Open http://localhost:8501 (or the provided Streamlit URL) in your web browser.
   • Upload a PDF file.
   • Type your question in the text box.
   • Click "Submit" to get the answer.

Customization

• LLM Choice: Modify the code to switch between OpenAI and Ollama language models.
• Embedding Techniques: Experiment with other embedding providers.
• Streamlit Enhancements: Add more interactive elements or styling to the web interface.

Contributions

This project welcomes contributions! Feel free to add features, fix bugs, or suggest improvements.

License

[Specify the license of your project, e.g., MIT, Apache 2.0, etc.]