Generative AI Chatbot with Memory

This repository provides a conversational AI chatbot interface built with LangChain, Google Generative AI, and Gradio. The chatbot retains conversation history and can recall details across multiple interactions within the same session. It demonstrates the use of memory in AI-driven conversations, allowing for context-aware responses based on prior exchanges.

Features

• Google Generative AI Integration: The chatbot is powered by Google’s Generative AI, delivering high-quality responses.
• Session Memory: User-specific conversation history is preserved, enabling the bot to refer back to previous messages within a session.
• Gradio Interface: An interactive, user-friendly interface for real-time conversation, displaying both user queries and AI responses.
• Dynamic Prompting: The chatbot uses LangChain’s prompt templates to customize its conversational style.

Requirements

• Python 3.10+
• API Key for Google Generative AI
• LangChain, dotenv, and Gradio libraries

Installation

1. Clone the repository:

   git clone https://github.com/vinodvpillai/generativeAI-chatbot-with-memory.git
   cd generativeAI-chatbot-with-memory

2. Install dependencies:

   pip install langchain langchain-google-genai gradio pydantic python-dotenv

3. Set up environment variables:

   • Create a .env file in the root directory and add your Google Generative AI API key and model configuration:

   GOOGLE_API_KEY=<your_google_api_key>
   GOOGLE_MODEL=<your_google_model>
   

Usage

Running the Chatbot

1. Start the chatbot by running the main script:

   python your_script.py

2. Open the Gradio interface in your browser (it will provide a link in the terminal).

3. Chat with the Bot:

   • Enter a username and a query in the provided fields.
   • Each query and response will be displayed in the chat history with labels [username] and [AI].

Example Chat Flow

• Username: Vinod
• User Query 1: "Hi, I would like to know a brief overview of AI."
• Bot Response 1: AI provides a brief description of artificial intelligence.
• User Query 2: "Do you remember my name?"
• Bot Response 2: "Yes, your name is Vinod."

The chatbot recalls prior messages, providing a seamless and contextually rich interaction.

Code Overview

1. Environment Setup: Loads the .env file to get API credentials for Google Generative AI.
2. Model and Prompt Initialization: Sets up the Google Generative AI model and a conversational prompt template.
3. Session Memory:
   • Manages conversation history with InMemoryHistory, a custom class to store message sequences.
   • Retrieves or creates new session histories as needed.
4. Gradio Interface:
   • Uses gr.Chatbot to display the chat history sequentially.
   • Displays each exchange with labels indicating whether it was the user's query or the bot's response.

File Structure

.
├── main.py                # Main script to run the chatbot
├── README.md              # Documentation
├── .env                   # Environment variables file
└── requirements.txt       # Python dependencies

Sample .env file

GOOGLE_API_KEY=your_google_api_key
GOOGLE_MODEL=your_google_model_name

Dependencies

This project requires the following libraries:

• langchain - Core library for language model integration and prompt management.
• gradio - For creating a web-based chat interface.
• pydantic - Data validation and settings management.
• python-dotenv - Loads environment variables from a .env file.

Install dependencies using:

pip install -r requirements.txt

Contributing

Feel free to submit issues or pull requests if you’d like to contribute to the project. Contributions are welcome to enhance functionality or improve the user interface.

License

This project is licensed under the MIT License. See LICENSE for details.

Acknowledgments

• LangChain for enabling robust prompt and memory management.
• Gradio for the intuitive and easy-to-use chat interface.
• Google Generative AI for powering high-quality conversational responses.