Interactive-Chatbot-with-LangGraph

Welcome to the LangGraph Chatbot repository! This project showcases a cutting-edge implementation of a conversational agent using LangGraph, which was developed to highlight state-driven interactions and dynamic response management in a chatbot environment.

Overview

This contains a Jupyter Notebook demonstrating the creation and operation of an interactive chatbot utilizing LangGraph. Designed for professionals and enthusiasts alike, this chatbot manages conversation states and interactions through sophisticated graph-based techniques, providing a robust solution for real-time dialogue handling.

Key Features

• Real-Time Interaction: Seamlessly handle and respond to user inputs in real-time.
• State Management: Utilize LangGraph’s state graphs to manage complex conversational flows and transitions.
• Customizable Responses: Adapt and extend chatbot responses based on various input scenarios.

Technology Stack

• LangGraph: A powerful tool for state management and graph-based dialogue systems.
• Jupyter Notebook: An interactive environment used to develop, demonstrate, and share the chatbot implementation.
• Python: The Programming language used is Python.
• LLMS: Open source LLMS from groq cloud is utilised to create a custom bot.

Code Snippet of Chatbot

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Results

The LangGraph Chatbot delivers impressive results, showcasing the effectiveness of graph-based state management in conversational AI. Here’s what you can expect from this implementation:

• Enhanced Conversational Flow

• Dynamic Response Management

• Real-Time Interaction

• Customizability and Scalability

  

Conclusion

The LangGraph Chatbot successfully highlights the potential of state-driven conversational agents. Its results demonstrate effective dialogue management, real-time interaction, and the ability to provide customizable and contextually relevant responses.