ac436b87a2
- MemoryService 异步路径委托 MemoryIngestService / MemoryRetrievalService；富化派发经 MemoryEnrichmentScheduler - WebSocket pipeline 经 ChatTurnService 与显式 DTO 编排单轮对话；回忆录片段入队由 MemoirIngestScheduler 封装 - 新增 LlmGateway（LlmUseCase），各 agent、任务与适配器对齐 ports - 补充 memory 提示适配、runtime 类型、memory-retrieval 文档、ai-touchpoints 说明与扫描脚本及配套测试 Made-with: Cursor
117 lines
3.6 KiB
Python
117 lines
3.6 KiB
Python
"""Conversation chat turn boundary.
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This module gives the WebSocket pipeline a small, explicit contract for one
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user turn. It deliberately keeps the existing ``ChatOrchestrator`` behavior
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intact while making the runtime inputs/outputs visible and testable.
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"""
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from __future__ import annotations
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from collections.abc import Callable
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from dataclasses import dataclass
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from datetime import datetime
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from typing import Any
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.agents.chat import ChatOrchestrator
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from app.agents.chat.agent_turn import AgentChatTurn
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@dataclass(frozen=True)
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class ChatTurnInput:
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"""Transport-neutral input for a single user chat turn."""
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conversation_id: str
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user_message: str
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is_from_voice: bool = False
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voice_session_id: str | None = None
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user_message_timestamp: datetime | None = None
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audio_duration_seconds: int | None = None
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force_skip_tts: bool = False
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@dataclass(frozen=True)
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class ChatTurnContext:
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"""Runtime dependencies needed to execute a turn."""
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db: AsyncSession
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user: Any | None
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conversation: Any | None
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apply_extracted_profile_fn: Callable[..., Any]
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get_missing_profile_fields_fn: Callable[[Any], list[str]]
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get_filled_profile_fields_fn: Callable[[Any], dict[str, Any]]
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@dataclass(frozen=True)
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class ChatTurnDecision:
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"""Observable decision metadata for the chat runtime boundary."""
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engine: str = "ChatOrchestrator"
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route_hint: str = "auto"
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force_skip_tts: bool = False
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@dataclass(frozen=True)
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class ChatTurnResult:
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"""Stable result shape consumed by conversation persistence and delivery."""
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messages: list[str]
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skip_tts: bool
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memory_retrieval_trace: dict[str, Any] | None = None
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interview_state_meta: dict[str, Any] | None = None
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decision: ChatTurnDecision = ChatTurnDecision()
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@classmethod
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def from_agent_turn(
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cls,
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turn: AgentChatTurn,
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*,
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decision: ChatTurnDecision,
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) -> "ChatTurnResult":
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return cls(
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messages=list(turn.messages or []),
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skip_tts=bool(turn.skip_tts or decision.force_skip_tts),
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memory_retrieval_trace=turn.memory_retrieval_trace,
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interview_state_meta=turn.interview_state_meta,
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decision=decision,
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)
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class ChatTurnService:
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"""Executes one chat turn behind an explicit internal contract."""
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def __init__(self, orchestrator: ChatOrchestrator | None = None) -> None:
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self._orchestrator = orchestrator or ChatOrchestrator()
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async def process_turn(
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self,
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turn_input: ChatTurnInput,
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context: ChatTurnContext,
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) -> ChatTurnResult:
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decision = ChatTurnDecision(force_skip_tts=turn_input.force_skip_tts)
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turn = await self._orchestrator.process_user_message(
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conversation_id=turn_input.conversation_id,
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user_message=turn_input.user_message,
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user=context.user,
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conversation=context.conversation,
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is_from_voice=turn_input.is_from_voice,
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voice_session_id=turn_input.voice_session_id,
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db=context.db,
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apply_extracted_profile_fn=context.apply_extracted_profile_fn,
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get_missing_profile_fields_fn=context.get_missing_profile_fields_fn,
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get_filled_profile_fields_fn=context.get_filled_profile_fields_fn,
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user_message_timestamp=turn_input.user_message_timestamp,
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audio_duration_seconds=turn_input.audio_duration_seconds,
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)
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return ChatTurnResult.from_agent_turn(turn, decision=decision)
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__all__ = [
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"ChatTurnContext",
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"ChatTurnDecision",
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"ChatTurnInput",
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"ChatTurnResult",
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"ChatTurnService",
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]
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