213 lines
7.3 KiB
Python
213 lines
7.3 KiB
Python
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"""
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回忆录后台处理器：分析对话、更新状态、生成章节、创意标题
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使用 Celery 进行后台任务处理
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"""
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from __future__ import annotations
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import json
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from dataclasses import dataclass
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from typing import Dict, List
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from app.core.dependencies import get_llm_provider
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from app.core.logging import get_logger
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from app.core.task_tracker import task_tracker
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from app.agents.state_schema import MemoirStateSchema
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from app.agents.prompts.memory_prompts import (
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get_creative_title_prompt,
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get_narrative_prompt,
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get_state_extraction_prompt,
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)
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logger = get_logger(__name__)
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STAGE_KEYWORDS = {
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"childhood": ["童年", "小时候", "出生", "家乡", "小镇"],
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"education": ["上学", "学校", "老师", "同学", "教育", "大学"],
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"career": ["工作", "职业", "事业", "公司", "同事", "创业"],
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"family": ["伴侣", "孩子", "家庭", "家人", "结婚", "父母"],
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"belief": ["信念", "价值观", "座右铭", "坚持", "原则"],
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}
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def _get_langchain_llm():
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try:
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provider = get_llm_provider()
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return getattr(provider, "langchain_llm", None)
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except Exception:
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return None
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@dataclass
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class AnalysisResult:
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detected_stage: str
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extracted_slots: Dict[str, str]
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emotion: str
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is_new_chapter: bool
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class ContentAnalyzer:
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def __init__(self) -> None:
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self.llm = _get_langchain_llm()
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def _detect_stage(self, user_message: str, fallback_stage: str) -> str:
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message = user_message.lower()
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for stage, keywords in STAGE_KEYWORDS.items():
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if any(word in message for word in keywords):
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return stage
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return fallback_stage
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def _fallback_slots(
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self, state: MemoirStateSchema, stage: str, user_message: str
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) -> Dict[str, str]:
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stage_slots = state.slots.get(stage, {})
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for key, value in stage_slots.items():
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if not value.snippet:
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return {key: user_message.strip()[:200]}
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return {}
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async def analyze_message(
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self, user_message: str, current_state: MemoirStateSchema
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) -> AnalysisResult:
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detected_stage = self._detect_stage(
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user_message, current_state.current_stage
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)
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extracted_slots: Dict[str, str] = {}
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emotion = "neutral"
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is_new_chapter = False
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if self.llm:
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try:
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prompt = get_state_extraction_prompt(
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user_message=user_message,
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current_stage=current_state.current_stage,
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stage_slots=current_state.slots.get(detected_stage, {}),
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)
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response = await self.llm.ainvoke(prompt)
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content = response.content.strip()
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parsed = json.loads(content)
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detected_stage = parsed.get("detected_stage", detected_stage)
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extracted_slots = parsed.get("slots", {}) or {}
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emotion = parsed.get("emotion", emotion)
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is_new_chapter = bool(parsed.get("is_new_chapter", is_new_chapter))
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except json.JSONDecodeError:
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extracted_slots = self._fallback_slots(
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current_state, detected_stage, user_message
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)
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except Exception as e:
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logger.error("分析消息失败: %s", e)
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extracted_slots = self._fallback_slots(
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current_state, detected_stage, user_message
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)
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else:
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extracted_slots = self._fallback_slots(
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current_state, detected_stage, user_message
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)
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return AnalysisResult(
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detected_stage=detected_stage,
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extracted_slots=extracted_slots,
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emotion=emotion,
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is_new_chapter=is_new_chapter,
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)
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class MemoirGenerator:
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def __init__(self) -> None:
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self.llm = _get_langchain_llm()
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async def generate_chapter_title(
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self, stage: str, slots: Dict[str, str], emotion: str
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) -> str:
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if not self.llm:
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return f"{stage} 回忆"
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try:
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prompt = get_creative_title_prompt(
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stage=stage, emotion=emotion, slots=slots
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)
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response = await self.llm.ainvoke(prompt)
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return response.content.strip().strip('"')
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except Exception as e:
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logger.error("生成标题失败: %s", e)
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return f"{stage} 回忆"
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async def generate_narrative(
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self,
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stage: str,
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slots: Dict[str, str],
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new_content: str,
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existing_content: str,
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) -> str:
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if not self.llm:
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if existing_content:
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return f"{existing_content}\n\n{new_content}"
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return new_content
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try:
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prompt = get_narrative_prompt(
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stage=stage,
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slots=slots,
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new_content=new_content,
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existing_content=existing_content,
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)
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response = await self.llm.ainvoke(prompt)
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return response.content.strip()
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except Exception as e:
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logger.error("生成叙事失败: %s", e)
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if existing_content:
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return f"{existing_content}\n\n{new_content}"
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return new_content
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class BackgroundTaskRunner:
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def __init__(self, debounce_seconds: int = 5) -> None:
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self.debounce_seconds = debounce_seconds
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self._pending: Dict[str, List[str]] = {}
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self._timers: Dict[str, object] = {}
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self.analyzer = ContentAnalyzer()
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self.generator = MemoirGenerator()
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async def _submit_task(self, user_id: str, segment_ids: List[str]) -> str | None:
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try:
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from app.tasks.memoir_tasks import process_memoir_segments
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result = process_memoir_segments.delay(user_id, segment_ids)
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task_id = result.id
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await task_tracker.add_task(user_id, task_id, "memoir")
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logger.info(
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"已提交 Celery 任务: user_id=%s, task_id=%s, segments=%s",
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user_id,
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task_id,
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len(segment_ids),
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)
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return task_id
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except Exception as e:
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logger.error("提交 Celery 任务失败: %s", e)
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return None
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async def queue_message(self, user_id: str, segment_id: str) -> None:
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import asyncio
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self._pending.setdefault(user_id, []).append(segment_id)
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if user_id in self._timers:
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self._timers[user_id].cancel()
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async def delayed_submit():
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try:
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await asyncio.sleep(self.debounce_seconds)
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segment_ids = self._pending.pop(user_id, [])
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if segment_ids:
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await self._submit_task(user_id, segment_ids)
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except asyncio.CancelledError:
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pass
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except Exception as e:
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logger.error("延迟提交任务失败: %s", e)
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self._timers[user_id] = asyncio.create_task(delayed_submit())
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async def flush_pending(self, user_id: str) -> str | None:
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if user_id in self._timers:
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self._timers[user_id].cancel()
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del self._timers[user_id]
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segment_ids = self._pending.pop(user_id, [])
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if segment_ids:
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return await self._submit_task(user_id, segment_ids)
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return None
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