RAG - Enhanced Chatbot Application Using Langchain and Streamlit 📚🤖

Table of Contents

• RAG - Enhanced Chatbot Application Using Langchain and Streamlit 📚🤖
  • Table of Contents
  • Introduction
• Project Goal
  • Features
  • Demo
    • Chat Interface
  • Demo Video
  • Technologies Used
  • Installation
    • Prerequisites
    • Steps
  • Usage
  • Logging
  • Contributing
    • How to Contribute
    • Guidelines
  • License
  • Contact

Introduction

Welcome to the RAG-Enhanced Chatbot Application, a powerful and scalable chatbot solution that leverages Retrieval-Augmented Generation (RAG) techniques to provide intelligent and context-aware responses. Built with Streamlit, Python, and advanced language models from OpenAI, this application is designed to enhance user interactions by integrating document and web-based knowledge sources.

Whether you're developing an e-commerce platform, a real estate service, or any application that requires dynamic and informed conversational agents, our chatbot offers the flexibility and robustness you need.

Project Goal

The goal of this project is to build a highly responsive and intelligent chatbot using Retrieval-Augmented Generation (RAG). The chatbot integrates Large Language Models (LLMs), such as OpenAI's GPT, with a document retriever mechanism powered by ChromaDB. This approach enhances the chatbot’s ability to provide precise, context-aware answers by referring to uploaded documents and web resources. The entire solution is designed to be efficient, scalable, and easily deployable via Docker.

Features

• Real-Time Chat Interface: Seamless AI-driven conversation interface.
• Document Uploads: Upload various file formats (PDF, DOCX, TXT, MD) for data retrieval.
• OpenAI GPT-4 Integration: Utilizes OpenAI's GPT-4 models for advanced language generation.
• RAG Integration: Enhance chatbot responses by uploading documents or providing URLs, enabling the chatbot to retrieve and utilize external knowledge.
• User-Friendly Interface: Intuitive Streamlit-based UI with sidebar controls for API key management, model selection, and RAG source uploads.
• Comprehensive Logging: Detailed logging of user interactions, model selections, and system events stored in a dedicated logs/ folder with log rotation.

Demo

Check out a short demo of the application in action:

Chat Interface

Demo Video

Watch the application in action:

Click the image above to watch the demo video.

Technologies Used

• Streamlit: For building the interactive web application.
• Python: Core programming language.
• LangChain: For integrating language models.
• OpenAI GPT-4: For advanced language generation.
• SQLite: For lightweight database management.
• Logging: Python's built-in logging module for comprehensive logging.
• Docker: For containerized deployments (if applicable).
• Other Libraries: dotenv, uuid, etc.

Installation

Follow these steps to set up the RAG-Enhanced Chatbot Application on your local machine.

Prerequisites

• Python 3.11+
• pip (Python package installer)
• Git (for cloning the repository)
• Docker (for containerized deployments)

Steps

1. Clone the Repository

   git clone [email protected]:abdurrahimcs50/RAG_Chatbot_Project.git
   cd RAG_Chatbot_Project

2. Create a Virtual Environment

   python3 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install Dependencies

   cd src
   pip install -r requirements.txt

4. Set Up Environment Variables

   Create a .env file in the root directory and add your API keys:

   OPENAI_API_KEY=your_openai_api_key

   Ensure that you replace the placeholder values with your actual API keys.

5. Run the Application

   streamlit run app.py

6. Access the App

   Open your web browser and navigate to http://localhost:8501 to interact with the chatbot.

Usage

1. API Key Management

   • Navigate to the sidebar to enter your OpenAI API keys.
   • These keys are essential for authenticating and utilizing the OpenAI GPT-4 models.

2. Model Selection

   • Choose your preferred language model from the dropdown menu in the sidebar.
   • Options include openai/gpt-4o, openai/gpt-4o-mini depending on your API keys.

3. RAG Source Uploads

   • Upload Documents: Click on the "Upload Documents for RAG Processing" button to upload PDFs, TXT, DOCX, or MD files.
   • Add URLs: Enter a URL to integrate web-based content into the chatbot's knowledge base.

4. Chat Interface

   • Type your message in the chat input field and press Enter.
   • The assistant will respond based on your input and the integrated RAG sources.

5. Logging

   • All interactions and system events are logged in the logs/ directory.
   • Logs are rotated to prevent excessive file sizes, ensuring efficient storage management.

6. Clear Chat

   • Use the "Clear Chat" button in the sidebar to reset the conversation history.

Logging

The application employs Python's built-in logging module to capture and store logs systematically.

• Log Directory: All logs are stored in the logs/ folder.
• Log Rotation: Logs are rotated after reaching 5 MB, with up to 5 backup logs maintained to prevent storage issues.
• Log Contents:
  • Session initializations
  • API key inputs (without exposing the keys)
  • Model selections
  • RAG source uploads
  • User messages and assistant responses
  • Error and warning messages

Example log entry:

2024-10-16 14:30:45,123 - INFO - Initialized new session with ID: 123e4567-e89b-12d3-a456-426614174000
2024-10-16 14:31:10,456 - INFO - OpenAI API Key provided by user.
2024-10-16 14:31:15,789 - INFO - Selected model: openai/gpt-4o
2024-10-16 14:32:00,012 - INFO - 2 document(s) uploaded for RAG processing.
2024-10-16 14:32:30,345 - INFO - User input: How can I integrate RAG into my project?
2024-10-16 14:32:45,678 - INFO - Assistant response: To integrate RAG into your project...

Contributing

We welcome contributions from the community! Whether it's bug fixes, feature enhancements, or documentation improvements, your input is valuable.

How to Contribute

1. Fork the Repository

   Click the "Fork" button at the top right of the repository page.

2. Clone Your Fork

   git clone [email protected]:abdurrahimcs50/RAG_Chatbot_Project.git
   cd RAG_Chatbot_Project

3. Create a New Branch

   git checkout -b feature/YourFeatureName

4. Make Your Changes

   Implement your feature or bug fix.

5. Commit Your Changes

   git commit -m "Add your descriptive commit message"

6. Push to Your Fork

   git push origin feature/YourFeatureName

7. Open a Pull Request

   Navigate to the original repository and click "Compare & pull request."

Guidelines

• Code Quality: Ensure your code follows best practices and is well-documented.
• Testing: If applicable, include tests to verify your changes.
• Documentation: Update the README or other documentation if your changes affect usage.

License

This project is licensed under the MIT License.

Contact

For any questions, suggestions, or feedback, feel free to reach out:

• MD Abdur Rahim
• Email: [email protected]
• Website: www.rahim.com.bd
• LinkedIn: https://www.linkedin.com/in/abdurrahimcs50/

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