Building Generative UI Chatbot with Langchain

Sep 4, 2024

Generative UI Chatbot with Langchain

Introduction

  • Presenter: Brace
  • Topic: Building a generative UI chatbot
  • Technologies used: Python backend, Next.js frontend
  • Reference: First video covers high-level concepts and use cases of generative UI.
  • Link to JavaScript version of the same chatbot is available.

Architecture Overview

  • Architecture Diagram: Two main sections: server (Python) and client (Next.js).
    • Server handles user input, images, and chat history
    • Uses LLM (Large Language Model) with tools (corresponding to UI components)
  • Flow of information:
    • User input -> LLM -> Tool selection -> Response
    • If no tools are needed, return plain text directly to UI.

Langraph Overview

  • Langraph: A library for constructing graphs (used instead of traditional agents).
    • Nodes represent functions that process input and maintain state.
    • Example flow:
      1. Retrieve node processes the question.
      2. Result passed to grading node.
      3. Conditional edge checks relevance of documents.
      4. Generates an answer or rewrites query based on relevance.
  • Benefits of Langraph: Predictable and configurable flow, unlike traditional agents.

Working with the Langgraph

  • State Management: Define the chain's state (e.g., user input, results).
    • Human message and optional results from LLM or tool calls.
  • Creating Graphs: Implement create graph function with nodes for invoking models and tools.
  • Node Implementation:
    • invoke model for processing inputs and tool calls.
    • invoke tools for handling tool execution and returning results to the client.

Tool Implementation

  • Tools Defined: Examples include GitHub repo tool, invoice tool, weather tool.
    • GitHub Repo Tool:
      • Input schema: owner and repo fields.
      • Logic to hit GitHub API and return data (stars, description, language).
    • Invoice Tool: More complex with image processing.
    • Weather Tool: Fetches current weather based on city/state.

FastAPI Server Setup

  • Setup for FastAPI server with CORS for handling requests from the frontend.
  • Define API endpoints (e.g., /chat) that connect to the Langgraph.
  • Swagger docs for API endpoint visibility.

Frontend Integration

  • Connecting to Backend: Use remote runnable with provided API URL.
  • Implement chat UI to handle user input and display responses streamingly.
  • Streaming Event Handling: Manage event updates for loading states, final responses, and error handling.

Testing the Application

  • Running the App: Start the backend server and frontend.
  • Example inputs: Asking for weather or GitHub repo info.
  • Updates UI dynamically based on events received from the server.

Conclusion

  • Recap the process of building a generative UI application using Python and React.
  • Link to TypeScript version available for reference.
  • Encouragement to explore the capabilities of Langchain for building generative UI applications.