Persistent AI Chatbot with Google Generative AI, LangChain, and PostgreSQL

This repository provides a memory-powered conversational AI chatbot built with LangChain, Google Generative AI, and Gradio, integrated with PostgreSQL for persistent storage of conversation history. This project demonstrates how to create a chatbot that remembers user interactions, enhancing user experience by maintaining context across multiple conversations.

Features

• Memory-Powered Conversations: The chatbot recalls past user interactions, enabling more context-aware responses.
• Persistent Storage: Uses PostgreSQL to store and retrieve conversation history, making the chatbot robust and scalable.
• Interactive Gradio Interface: A user-friendly interface for seamless interaction with the chatbot.
• Google Generative AI Integration: Leverages Google’s state-of-the-art Generative AI for high-quality responses.

Prerequisites

• Python 3.10+
• PostgreSQL installed and configured
• Google Generative AI API Key

Installation

1. Clone the repository:

   git clone https://github.com/vinodvpillai/persistent-ai-chatbot-with-postgresql.git
   cd persistent-ai-chatbot-with-postgresql

2. Install the required Python packages:

   pip install langchain langchain-google-genai gradio pydantic python-dotenv psycopg2-binary

3. Set up the PostgreSQL database:

   • Create a database named chatdb.
   • Run the following SQL commands to create the chat_history table:

     CREATE DATABASE chatdb;
     
     \c chatdb  -- Connect to the chatdb database
     
     CREATE TABLE chat_history (
         id serial4 NOT NULL, 
         user_id VARCHAR(255),
         conversation_id VARCHAR(255),
         message_type VARCHAR(50),
         content TEXT,
         CONSTRAINT ai_chat_history_pkey PRIMARY KEY (id)
     );
     CREATE INDEX chat_history_user_id ON public.chat_history USING btree (user_id, conversation_id);

4. Set up your environment variables:

   • Create a .env file in the root directory and add:

     GOOGLE_API_KEY=<your_google_api_key>
     GOOGLE_MODEL=<your_google_model>
     
     POSTGRES_HOST=<your_database_host>
     POSTGRES_PORT=<your_database_port>
     POSTGRES_DB=<your_database_name>
     POSTGRES_USER=<your_database_username>
     POSTGRES_PASSWORD=<your_database_password>
     

Usage

Running the Chatbot

1. Run the script:

   python main.py

2. Access the Gradio interface:

   • The terminal will provide a local URL. Open this link in your browser to start interacting with the chatbot.

Example Conversation

• User: "Hi, I would like to know about AI."
• AI: "Artificial Intelligence (AI) refers to the simulation of human intelligence in machines..."
• User: "Can you remember my name?"
• AI: "Yes, your name is Vinod."

Code Overview

Main Components

1. Environment Setup:

   • Loads API keys and model configuration from .env for secure handling.

2. Database Integration:

   • Uses psycopg2 to connect to PostgreSQL and manage persistent chat history.

3. Memory Management:

   • A custom PostgresChatHistory class loads, adds, and clears messages from the database.

4. Gradio Interface:

   • Provides an intuitive web interface for user interaction, displaying chat history as a conversation with labels [username] and [AI].

Functionality Breakdown

• PostgresChatHistory Class:
  • Overrides the default in-memory storage with database-backed methods to add, fetch, and clear messages.
• get_session_history Function:
  • Retrieves or initializes chat history based on user and conversation IDs.
• Gradio UI:
  • Users input their username and query, and the chatbot responds with memory-driven replies.

Configuration

Ensure that your PostgreSQL credentials (user, password, host, port) are securely set in your script or environment.

Dependencies

• LangChain: Framework for integrating language models.
• Gradio: Web-based UI for machine learning models.
• psycopg2-binary: PostgreSQL database adapter for Python.
• Pydantic: Data validation and settings management.
• python-dotenv: Load environment variables from a .env file.

Troubleshooting

• Database Connection Errors: Ensure PostgreSQL is running and credentials are correct.
• API Key Issues: Verify your Google Generative AI API key and permissions.

Future Enhancements

• Add multi-session management with unique conversation IDs.
• Implement user authentication for better security.
• Deploy the chatbot on cloud platforms for broader access.

License

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

Acknowledgments

• LangChain for memory and model management.
• Gradio for the interactive user interface.
• Google Generative AI for powering intelligent responses.