Overview
This project implements a conversational chatbot using LangGraph, LangChain, and Groq LLM. The chatbot is enhanced with external tools (e.g., Wikipedia) to provide factual answers. The system is designed with modularity, making it easy to integrate additional tools in the future.
Key Features
- LangGraph-based workflow to define chatbot state transitions.
- Groq LLM integration for natural language understanding and response generation.
- Wikipedia tool integration for fetching factual data.
- Streaming response handling for step-by-step event processing.
- Colab-compatible implementation with environment variable management.
Architecture
The chatbot workflow is represented as a directed graph:
- START → Chatbot Node: User input enters the chatbot node.
- Chatbot Node → Tool Node (Conditional): If the chatbot requires external information, it calls the tool node.
- Tool Node → Chatbot Node: The tool fetches the required data and returns it to the chatbot.
- Chatbot Node → END: If no tool call is required, or after processing the tool response, the chatbot produces the final answer.
Architecture Flowchart
