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A dataclass that stores comprehensive conversation context including timeline, turns, topics, insights, and references for managing rich conversational state.

Purpose

ConversationContext serves as a structured container for maintaining detailed conversation state across multiple exchanges. It tracks the conversation timeline through turns, maintains active topics, stores summaries and key insights, manages problem-solving chains, and provides a reference mapping system. This class is designed to support context-aware conversational AI systems that need to maintain coherent, long-running dialogues with historical awareness and cross-referencing capabilities.

Source Code

class ConversationContext:
    """Rich conversation context with timeline and references"""
    conversation_id: str
    total_exchanges: int
    conversation_turns: List[ConversationTurn]
    active_topics: List[str]
    conversation_summary: str
    key_insights: List[str]
    problem_solving_chain: List[Dict[str, Any]]
    recent_context: str
    reference_map: Dict[int, List[ConversationReference]]

Parameters

Parameter Details

conversation_id: Unique identifier string for the conversation instance, used to distinguish between different conversation sessions

total_exchanges: Integer count of the total number of message exchanges that have occurred in this conversation

conversation_turns: List of ConversationTurn objects representing the chronological sequence of exchanges in the conversation, each turn containing message details and metadata

active_topics: List of strings representing currently active or relevant topics being discussed in the conversation

conversation_summary: String containing a high-level summary of the conversation content and progression

key_insights: List of strings capturing important insights, conclusions, or notable points extracted from the conversation

problem_solving_chain: List of dictionaries tracking the problem-solving process, where each dictionary contains steps, decisions, or reasoning chains with flexible key-value structure

recent_context: String containing the most recent contextual information, typically used for immediate context window management

reference_map: Dictionary mapping integer indices to lists of ConversationReference objects, enabling cross-referencing between different parts of the conversation

Return Value

As a dataclass, instantiation returns a ConversationContext object with all specified attributes initialized. The class itself does not define explicit methods with return values, but instances can be used to access and modify conversation state through attribute access.

Class Interface

Attributes

Dependencies

• dataclasses
• typing
• datetime
• json
• re
• asyncio
• logging
• pathlib

Required Imports

from dataclasses import dataclass
from typing import List, Dict, Any, Optional, Tuple
from datetime import datetime
import json
import re
import asyncio
import logging
from pathlib import Path

Conditional/Optional Imports

These imports are only needed under specific conditions:

from session_manager import SessionManager

Usage Example

from dataclasses import dataclass
from typing import List, Dict, Any
from datetime import datetime

# Assuming ConversationTurn and ConversationReference are defined
context = ConversationContext(
    conversation_id="conv_12345",
    total_exchanges=5,
    conversation_turns=[],
    active_topics=["machine learning", "data preprocessing"],
    conversation_summary="Discussion about ML model training",
    key_insights=["User needs batch processing", "Performance is critical"],
    problem_solving_chain=[
        {"step": 1, "action": "identify requirements", "outcome": "batch processing needed"},
        {"step": 2, "action": "propose solution", "outcome": "suggested parallel processing"}
    ],
    recent_context="User asked about optimizing data pipeline",
    reference_map={0: [], 1: []}
)

# Access attributes
print(context.conversation_id)
print(context.active_topics)

# Modify attributes
context.total_exchanges += 1
context.active_topics.append("optimization")
context.key_insights.append("Consider using GPU acceleration")

Best Practices

• Initialize all required attributes when creating an instance to avoid AttributeError exceptions
• Use conversation_id consistently across related operations to maintain conversation continuity
• Update total_exchanges incrementally as new turns are added to conversation_turns to keep counts synchronized
• Maintain chronological order in conversation_turns list for accurate timeline representation
• Keep active_topics list updated by removing stale topics and adding new ones as conversation evolves
• Update conversation_summary periodically rather than after every turn to balance detail and performance
• Use problem_solving_chain to track reasoning steps in complex multi-turn problem-solving scenarios
• Populate reference_map with integer keys corresponding to turn indices for efficient cross-referencing
• Keep recent_context concise and focused on the most immediately relevant information for context window management
• Consider implementing serialization methods (to_dict/from_dict) for persistence if needed
• Be mindful of memory usage when storing long conversations with many turns and references
• Validate that ConversationTurn and ConversationReference types are properly defined before instantiation

Tags

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