124 lines
4.5 KiB
Python
124 lines
4.5 KiB
Python
"""
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MemoirOrchestrator：按 segment 编排流水线，调用各 Specialist Agent。
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负责：遍历 segments、按 category 聚合、调用 Specialist、更新 state；
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持久化与章节生成由 process_category 回调完成。
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"""
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from __future__ import annotations
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from typing import Any, Callable, Dict, List, Set, Tuple
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from app.core.logging import get_logger
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from app.features.conversation.models import Segment
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from app.agents.state_schema import MemoirStateSchema
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from app.agents.memoir.extraction_agent import ExtractionAgent, ExtractionResult
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from app.agents.memoir.classification_agent import (
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ClassificationAgent,
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_detect_stage as detect_stage_from_keywords,
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)
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logger = get_logger(__name__)
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class MemoirOrchestrator:
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"""
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回忆录生成编排器。
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遍历 segments → ExtractionAgent → ClassificationAgent → 按 category 聚合 →
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调用 process_category 生成叙事并持久化。
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"""
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def __init__(self) -> None:
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self.extraction_agent = ExtractionAgent()
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self.classification_agent = ClassificationAgent()
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def run(
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self,
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*,
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segments: List[Segment],
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llm: Any,
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user_profile: str = "",
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user_birth_year: Any = None,
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get_or_create_state: Callable[[], MemoirStateSchema],
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update_slot: Callable[[str, str, str, List[str]], MemoirStateSchema],
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acquire_lock: Callable[[str], bool],
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release_lock: Callable[[str], None],
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process_category: Callable[
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[
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str,
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List[Segment],
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MemoirStateSchema,
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str,
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Any,
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Any,
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],
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Tuple[Any, bool],
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],
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raise_retry: Callable[[], None],
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) -> Tuple[Set[str], int]:
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"""
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执行回忆录流水线。
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process_category(category, segments, state, user_profile, user_birth_year, llm)
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返回 (chapter, has_images_to_generate)。
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返回 (chapters_to_enqueue, processed_count)。
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raise_retry 用于锁竞争时抛出 Celery retry。
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"""
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state = get_or_create_state()
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chapters_to_enqueue: Set[str] = set()
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category_to_segments: Dict[str, List[Segment]] = {}
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# 1) 遍历 segments：ExtractionAgent → 更新 slots；ClassificationAgent → 聚合
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for segment in segments:
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text = segment.transcript_text or ""
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# 关键词预检测阶段，用于 slot 查找（与原有逻辑一致）
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initial_stage = detect_stage_from_keywords(
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text, state.current_stage or "childhood"
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)
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stage_slots_raw = state.slots.get(initial_stage, {}) or {}
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result: ExtractionResult = self.extraction_agent.extract(
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user_message=text,
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current_stage=state.current_stage or "childhood",
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stage_slots=stage_slots_raw,
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llm=llm,
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)
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detected_stage = result.detected_stage
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for slot_name, snippet in result.slots.items():
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state = update_slot(detected_stage, slot_name, snippet, [segment.id])
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# ClassificationAgent
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chapter_category = self.classification_agent.classify(
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text=text,
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fallback_stage=detected_stage,
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llm=llm,
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)
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if chapter_category is None:
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logger.info("段落无回忆录价值，跳过: segment_id=%s", segment.id)
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continue
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category_to_segments.setdefault(chapter_category, []).append(segment)
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# 2) 按 category 调用 process_category：内含 NarrativeAgent、PlaceholderInject、持久化
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for chapter_category, category_segments in category_to_segments.items():
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if not acquire_lock(chapter_category):
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logger.warning(
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"章节锁竞争: category=%s, 延迟重试",
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chapter_category,
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)
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raise_retry()
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try:
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chapter, has_images = process_category(
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chapter_category,
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category_segments,
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state,
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user_profile,
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user_birth_year,
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llm,
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)
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if chapter and has_images:
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chapters_to_enqueue.add(chapter.id)
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finally:
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release_lock(chapter_category)
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return chapters_to_enqueue, len(segments)
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