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LangGraph Tutorial: Dynamic Conversation Summarization - Unit 1.2 Exercise 4

This tutorial demonstrates how to maintain context in long-running conversations by implementing intelligent summarization techniques. Learn to manage message histories efficiently, generate summaries dynamically, and integrate summarization logic into LangGraph for scalable conversation processing.

🎯 What You'll Learn Today

LangGraph Tutorial: Dynamic Conversation Summarization - Unit 1.2 Exercise 4

This tutorial is also available in Google Colab here or for download here

Joint Initiative: This tutorial is part of a collaboration between AI Product Engineer and the Nebius Academy.

This tutorial demonstrates how to implement dynamic conversation summarization in LangGraph, enabling efficient context management through automated summary generation. We'll create a system that maintains conversation context through intelligent summarization.

Key Concepts Covered

  1. Dynamic Summary Generation
  2. Message History Processing
  3. State-Based Summary Management
  4. Graph Integration of Summarization Logic
from typing import Annotated, TypedDict
#!pip install langchain-core
#!pip install langgraph
from langchain_core.messages import BaseMessage, HumanMessage
from langgraph.graph import START, StateGraph
from langgraph.graph.message import add_messages

Step 1: State Definition with Summary Support

We define a state structure that combines message management with summary tracking capabilities.

class State(TypedDict):
    """Advanced state container with summary management capabilities.

    This implementation demonstrates three key features:
    1. Message storage with LangGraph annotations
    2. Dynamic summary tracking
    3. Configurable history window

    Attributes:
        messages: Annotated list of conversation messages
        summary: Running summary of conversation context
        window_size: Maximum messages to maintain before summarization

    Note:
        The summary field is crucial for maintaining context when
        older messages are pruned from the window.
    """

    messages: Annotated[list[BaseMessage], add_messages]
    summary: str
    window_size: int

Why This Matters

Dynamic summarization is essential because

  1. It enables long-running conversations while managing memory
  2. Preserves crucial context even after message pruning
  3. Enables more intelligent agent responses
  4. Reduces token usage in LLM calls

Step 2: Summary Generation Implementation

We implement the core summarization logic for conversation tracking.

def summary_generation(state: State) -> State:
    """Generate and maintain dynamic conversation summaries.

    This function implements several advanced concepts:
    1. Threshold-based summary generation
    2. Context preservation through summarization
    3. State-aware summary updates

    The summarization process follows this flow:
    1. Check message threshold
    2. Process message history
    3. Generate contextual summary
    4. Update state with new summary

    Args:
        state: Current conversation state with messages and existing summary

    Returns:
        State: Updated state with new summary

    Example:
        >>> messages = [
        ...     HumanMessage(content="Hello"),
        ...     HumanMessage(content="How are you"),
        ...     HumanMessage(content="Goodbye")
        ... ]
        >>> state = {"messages": messages, "summary": "", "window_size": 3}
        >>> result = summary_generation(state)
        >>> print(result["summary"])
        "Conversation summary: Hello -> How are you -> Goodbye"
    """
    if len(state["messages"]) > 2:
        messages_text = " -> ".join([m.content for m in state["messages"]])
        state["summary"] = f"Conversation summary: {messages_text}"
    return state

Debug Tips

  1. Summary Generation Issues:
  • Log message contents before summarization
  • Verify summary format consistency
  • Check threshold conditions
  1. State Management:
  • Monitor summary field updates
  • Validate message processing
  • Check state preservation
  1. Common Errors:
  • AttributeError: Verify message object structure
  • String formatting issues: Check message content types
  • State mutation problems: Verify proper state handling

Step 3: Graph Integration

We integrate our summarization logic into the LangGraph structure.

Initialize graph with summary support

graph = StateGraph(State)

Add summarizer node

graph.add_node("summarizer", summary_generation)

Configure entry point

graph.add_edge(START, "summarizer")

Why This Matters

Proper graph integration ensures

  1. Automatic summary updates
  2. Consistent state management
  3. Scalable conversation processing

Key Takeaways

  1. Summary Management:
  • Use thresholds for efficient processing
  • Maintain context through summarization
  • Integrate with message windowing
  1. State Handling:
  • Update summaries atomically
  • Preserve message context
  • Maintain state consistency

Common Pitfalls

  1. Missing message validation
  2. Inefficient summary generation
  3. Poor summary format design
  4. Incomplete state updates

Next Steps

  1. Add intelligent summary compression
  2. Implement topic extraction
  3. Add summary-based routing
  4. Integrate with LLM for better summaries

Example Usage


messages = [
    HumanMessage(content="Hello"),
    HumanMessage(content="How are you"),
    HumanMessage(content="Goodbye"),
]

initial_state = {"messages": messages, "summary": "", "window_size": 3}

result = summary_generation(initial_state)

print(f"Generated Summary: {result['summary']}")

Variations and Extensions

  1. Enhanced Summarization:
  • Use LLM for semantic summarization
  • Implement topic-based summary clustering Example use case: Complex conversation tracking
  1. Conditional Summarization:
  • Topic-based summary triggers
  • Importance-based summarization Scenario: Adaptive conversation management

Expected Output

Generated Summary: Conversation summary: Hello -> How are you -> Goodbye

Rod Rivera

🇬🇧 Chapter