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45f3fab 208961a 45f3fab | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | """Manages sliding-window conversation history for an assistant session."""
from collections import deque
from dataclasses import dataclass
@dataclass
class Message:
"""Represents a single turn in the conversation."""
role: str # "user" | "assistant" | "system"
content: str
class ConversationMemory:
"""Maintains a bounded deque of Messages with a max-turns sliding window.
Responsibility: store and retrieve conversation history only.
Does not know about models, APIs, or evaluation.
"""
def __init__(self, max_turns: int) -> None:
# Each turn = 1 user + 1 assistant message -> maxlen = max_turns * 2
self._history: deque[Message] = deque(maxlen=max_turns * 2)
self._max_turns = max_turns
def add_user_message(self, content: str) -> None:
"""Append a user message to the history."""
self._history.append(Message(role="user", content=content))
def add_assistant_message(self, content: str) -> None:
"""Append an assistant message to the history."""
self._history.append(Message(role="assistant", content=content))
def get_history(self) -> list[dict]:
"""Return history as a list of role/content dicts suitable for LLM APIs."""
return [{"role": msg.role, "content": msg.content} for msg in self._history]
def clear(self) -> None:
"""Remove all messages from history."""
self._history.clear()
def __len__(self) -> int:
"""Return total number of individual messages stored."""
return len(self._history)
@property
def turn_count(self) -> int:
"""Return number of complete user-assistant turns."""
return len(self._history) // 2
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