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Streamlit Streaming with LangChain

Lab Overview​

Learn how to implement streaming responses with Streamlit and LangChain, enabling real-time text generation in web applications. This lab demonstrates how to create a responsive chat interface that displays AI responses as they are generated, providing a more engaging user experience.

Lab Materials​

Key Components​

  • Streaming Handler:

    class StreamHandler(BaseCallbackHandler):
        def __init__(self, container, initial_text=""):
            self.container = container
            self.text = initial_text

        def on_llm_new_token(self, token: str, **kwargs) -> None:
            self.text += token
            self.container.markdown(self.text)

  • Chat Interface:

    • Session state management
    • Message history tracking
    • Real-time response display
    • API key management

Technical Implementation​

from langchain.callbacks.base import BaseCallbackHandler
from langchain.chat_models import ChatOpenAI
from langchain.schema import ChatMessage
import streamlit as st

Core Features​

  1. Message Management:

    • Session state initialization
    • Message history storage
    • Role-based message display

  2. Streaming Setup:

    • Custom callback handler
    • Token-by-token display
    • Real-time markdown rendering

  3. Chat Model Integration:

    • OpenAI chat model configuration
    • Streaming callback integration
    • Response processing

Implementation Steps​

  1. Environment Setup:

    pip install langchain openai streamlit cohere tiktoken

  2. Interface Configuration:

    • Set up sidebar for API key
    • Initialize session state
    • Create chat message display

  3. Streaming Implementation:

    • Create StreamHandler class
    • Configure callback system
    • Implement token processing

  4. Chat Integration:

    • Set up chat input
    • Configure message handling
    • Implement response streaming

Features​

  • Real-time text generation
  • Interactive chat interface
  • Message history tracking
  • Secure API key handling
  • Markdown formatting support
  • Role-based message display
  • Token-by-token streaming

Technical Components​

  • StreamHandler for real-time updates
  • Session state management
  • ChatMessage schema implementation
  • OpenAI chat model integration
  • Streamlit UI components

Best Practices​

  • Implement secure API key handling
  • Use session state for persistence
  • Handle streaming callbacks efficiently
  • Implement proper error handling
  • Maintain message history
  • Format responses appropriately

Prerequisites​

  • Google Colab account
  • OpenAI API key
  • Basic Python knowledge
  • Understanding of:
    • Streaming concepts
    • Chat interfaces
    • Callback systems
    • Web development basics

Advanced Features​

  • Custom callback handler implementation
  • Real-time markdown rendering
  • Message role management
  • Stateful chat history
  • Dynamic response display

Resources​