816 lines
32 KiB
Python
816 lines
32 KiB
Python
"""
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回忆录处理 Celery 任务
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"""
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import json
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import logging
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import os
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import uuid
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from io import BytesIO
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from typing import Dict, List
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from datetime import datetime, timezone
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import redis
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from celery import shared_task
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from PIL import Image
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from sqlalchemy import select
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from sqlalchemy.orm import Session
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from database.database import SessionLocal
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from database.models import Book, Chapter, Segment, MemoirState, User
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from services.llm_service import llm_service
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from agents.state_schema import MemoirStateSchema, SlotData, default_state
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from 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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get_chapter_classification_prompt,
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inject_image_placeholder_template,
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STAGE_TO_ORDER,
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CHAPTER_CATEGORIES,
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)
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from agents.prompts.profile_prompts import format_user_profile_context
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import hashlib
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from services.memoir_images.parser import build_initial_image_assets, parse_image_placeholders
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from services.memoir_images.json_payload import extract_json_payload
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from services.memoir_images.prompting import MemoirImagePromptService
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from services.memoir_images.provider import LiblibImageProvider
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from services.memoir_images.schema import (
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completed_image_assets,
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IMAGE_STATUS_COMPLETED,
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IMAGE_STATUS_FAILED,
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IMAGE_STATUS_PENDING,
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IMAGE_STATUS_PROCESSING,
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normalize_image_assets,
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)
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from services.memoir_images.settings import MemoirImageSettings
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from services.memoir_images.storage import TencentCosStorageService
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logger = logging.getLogger(__name__)
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_REDIS_CLIENTS: dict[bool, redis.Redis] = {}
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def _get_redis_client(*, decode_responses: bool = False) -> redis.Redis:
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client = _REDIS_CLIENTS.get(decode_responses)
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if client is None:
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client = redis.from_url(
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os.getenv("REDIS_URL", "redis://localhost:6379/0"),
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decode_responses=decode_responses,
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)
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_REDIS_CLIENTS[decode_responses] = client
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return client
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def _acquire_chapter_lock(user_id: str, stage: str, timeout: int = 120) -> bool:
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"""获取章节分布式锁，防止并发写入同一章节"""
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r = _get_redis_client()
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lock_key = f"lock:chapter:{user_id}:{stage}"
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return r.set(lock_key, "1", nx=True, ex=timeout)
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def _release_chapter_lock(user_id: str, stage: str):
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"""释放章节分布式锁"""
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r = _get_redis_client()
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lock_key = f"lock:chapter:{user_id}:{stage}"
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r.delete(lock_key)
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def _acquire_chapter_image_lock(chapter_id: str, timeout: int = 600) -> bool:
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"""获取章节补图分布式锁，避免同一章节重复补图。"""
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r = _get_redis_client()
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lock_key = f"lock:chapter-images:{chapter_id}"
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return r.set(lock_key, "1", nx=True, ex=timeout)
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def _release_chapter_image_lock(chapter_id: str):
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"""释放章节补图分布式锁。"""
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r = _get_redis_client()
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lock_key = f"lock:chapter-images:{chapter_id}"
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r.delete(lock_key)
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def _update_task_status_sync(user_id: str, task_id: str, status: str, result: Dict = None):
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"""同步更新任务状态（在 Celery 任务中使用）"""
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try:
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r = _get_redis_client(decode_responses=True)
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key = f"task:user:{user_id}:tasks"
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# 获取现有任务信息
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data = r.hget(key, task_id)
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if data:
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task_info = json.loads(data)
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else:
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task_info = {"task_id": task_id}
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task_info["status"] = status
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task_info["updated_at"] = datetime.now(timezone.utc).isoformat()
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if result is not None:
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task_info["result"] = result
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r.hset(key, task_id, json.dumps(task_info))
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r.expire(key, 3600) # 1小时过期
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logger.info(f"任务状态已更新: task_id={task_id}, status={status}")
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except Exception as e:
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logger.error(f"更新任务状态失败: {e}")
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def _merge_chapter_image_assets(
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existing_images: list[dict] | None,
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placeholders: list[dict],
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provider: str,
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style: str,
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size: str,
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now_iso: str,
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) -> list[dict]:
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normalized_existing_images = normalize_image_assets(existing_images)
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existing_by_placeholder = {
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item.get("placeholder"): dict(item)
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for item in normalized_existing_images
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if item.get("placeholder")
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}
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merged_assets: list[dict] = []
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for item in placeholders:
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existing = existing_by_placeholder.get(item["placeholder"])
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if existing:
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merged_item = dict(existing)
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merged_item["index"] = item["index"]
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merged_item["placeholder"] = item["placeholder"]
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merged_item["description"] = item["description"]
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merged_item["provider"] = merged_item.get("provider") or provider
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merged_item["style"] = merged_item.get("style") or style
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merged_item["size"] = merged_item.get("size") or size
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merged_item["created_at"] = merged_item.get("created_at") or now_iso
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merged_item["updated_at"] = merged_item.get("updated_at") or now_iso
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if merged_item.get("status") == IMAGE_STATUS_COMPLETED and not (
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merged_item.get("storage_key") or merged_item.get("url")
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):
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merged_item["status"] = IMAGE_STATUS_FAILED
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merged_item["error"] = merged_item.get("error") or "missing image url"
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else:
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merged_item = build_initial_image_assets(
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placeholders=[item],
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provider=provider,
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style=style,
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size=size,
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now_iso=now_iso,
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)[0]
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merged_assets.append(merged_item)
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return merged_assets
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def chapter_has_images_to_generate(images: list[dict] | None) -> bool:
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return any(
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item.get("status") in {IMAGE_STATUS_PENDING, IMAGE_STATUS_FAILED}
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for item in normalize_image_assets(images)
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)
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def _select_placeholders_for_effective_max(
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placeholders: list[dict],
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existing_images: list[dict] | None,
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effective_max: int,
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) -> list[dict]:
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existing_placeholders = {
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item.get("placeholder")
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for item in normalize_image_assets(existing_images)
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if item.get("placeholder")
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}
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existing_count_in_content = sum(
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1 for item in placeholders if item.get("placeholder") in existing_placeholders
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)
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remaining_new_slots = max(0, effective_max - existing_count_in_content)
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selected: list[dict] = []
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for item in placeholders:
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if item.get("placeholder") in existing_placeholders:
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selected.append(item)
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continue
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if remaining_new_slots <= 0:
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continue
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selected.append(item)
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remaining_new_slots -= 1
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return [{**item, "index": index} for index, item in enumerate(selected)]
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def initialize_chapter_images(chapter) -> list[dict]:
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"""Parse IMAGE placeholders from chapter content and build pending image assets."""
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settings = MemoirImageSettings.from_env()
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if not settings.enabled:
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chapter.images = completed_image_assets(chapter.images)
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logger.info(f"章节图片初始化跳过: chapter={chapter.id}, enabled=false")
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return chapter.images
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prompt_service = MemoirImagePromptService(llm=None, settings=settings)
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effective_max = settings.effective_max_images(len(chapter.content or ""))
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all_placeholders = parse_image_placeholders(chapter.content, max_images=None)
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placeholders = _select_placeholders_for_effective_max(
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placeholders=all_placeholders,
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existing_images=chapter.images,
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effective_max=effective_max,
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)
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style = prompt_service.CATEGORY_STYLE_MAP.get(chapter.category, settings.default_style)
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chapter.images = _merge_chapter_image_assets(
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existing_images=chapter.images,
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placeholders=placeholders,
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provider=settings.provider,
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style=style,
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size=settings.default_size,
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now_iso=datetime.now(timezone.utc).isoformat(),
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)
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logger.info(
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"章节图片初始化完成: chapter=%s, effective_max=%d, total_placeholders=%d, selected_placeholders=%d, images=%d, statuses=%s",
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chapter.id,
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effective_max,
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len(all_placeholders),
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len(placeholders),
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len(chapter.images or []),
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[item.get("status") for item in (chapter.images or [])],
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)
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return chapter.images
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def _normalize_image_bytes_for_storage(image_bytes: bytes) -> bytes:
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with Image.open(BytesIO(image_bytes)) as image:
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output = BytesIO()
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if image.mode in {"RGBA", "LA"}:
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normalized = image
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elif image.mode == "P":
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normalized = image.convert("RGBA")
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else:
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normalized = image.convert("RGB")
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normalized.save(output, format="PNG")
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return output.getvalue()
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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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# 5-stage → 默认 8-category 映射（LLM 分类失败时的兜底）
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_STAGE_TO_DEFAULT_CATEGORY = {
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"childhood": "childhood",
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"education": "education",
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"career": "career_early",
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"family": "family",
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"belief": "beliefs",
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}
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def _detect_stage(user_message: str, fallback_stage: str) -> str:
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"""检测消息所属的 5-stage 阶段（用于状态跟踪）"""
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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 _classify_chapter_category(text: str, fallback_stage: str, llm=None) -> str | None:
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"""
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将内容分类到 8 个章节类别之一。
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优先使用 LLM，失败则按 5-stage 关键词映射到默认类别。
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如果 LLM 判定内容无实质回忆录价值，返回 None。
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"""
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if llm:
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try:
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prompt = get_chapter_classification_prompt(text)
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response = llm.invoke(prompt)
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category = response.content.strip().lower()
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if category == "none":
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logger.info(f"LLM 判定内容无回忆录价值，跳过: {text[:80]}...")
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return None
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if category in CHAPTER_CATEGORIES:
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return category
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except Exception as e:
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logger.warning(f"LLM 章节分类失败: {e}")
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stage = _detect_stage(text, fallback_stage)
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return _STAGE_TO_DEFAULT_CATEGORY.get(stage, _STAGE_TO_DEFAULT_CATEGORY.get(fallback_stage, "childhood"))
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def _coerce_state(model: MemoirState) -> MemoirStateSchema:
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"""将数据库模型转换为 Schema"""
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return MemoirStateSchema.model_validate(
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{
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"stage_order": model.stage_order or default_state().stage_order,
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"current_stage": model.current_stage,
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"covered_stages": model.covered_stages or [],
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"slots": model.slots if isinstance(model.slots, dict) else default_state().slots,
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}
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)
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def _get_or_create_state_sync(user_id: str, db: Session) -> MemoirStateSchema:
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"""同步获取或创建状态"""
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stmt = select(MemoirState).where(MemoirState.user_id == user_id)
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result = db.execute(stmt)
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state = result.scalar_one_or_none()
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if state:
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return _coerce_state(state)
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default = default_state()
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state = MemoirState(
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id=str(uuid.uuid4()),
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user_id=user_id,
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stage_order=default.stage_order,
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current_stage=default.current_stage,
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covered_stages=default.covered_stages,
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slots={k: {sk: sv.model_dump() for sk, sv in v.items()} for k, v in default.slots.items()},
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)
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db.add(state)
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db.commit()
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db.refresh(state)
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return _coerce_state(state)
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def _update_slot_sync(
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user_id: str,
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stage: str,
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slot_name: str,
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snippet: str,
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segment_ids: List[str],
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db: Session,
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) -> MemoirStateSchema:
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"""同步更新 slot"""
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stmt = select(MemoirState).where(MemoirState.user_id == user_id)
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result = db.execute(stmt)
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state = result.scalar_one_or_none()
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if not state:
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_get_or_create_state_sync(user_id, db)
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result = db.execute(stmt)
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state = result.scalar_one()
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slots: Dict[str, Dict] = state.slots or {}
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stage_slots = slots.get(stage, {})
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existing = stage_slots.get(slot_name, {})
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merged_segment_ids = list({*(existing.get("segment_ids") or []), *segment_ids})
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stage_slots[slot_name] = SlotData(snippet=snippet, segment_ids=merged_segment_ids).model_dump()
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slots[stage] = stage_slots
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state.slots = slots
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state.current_stage = state.current_stage or stage
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db.commit()
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db.refresh(state)
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return _coerce_state(state)
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@shared_task(bind=True, max_retries=3, default_retry_delay=60)
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def process_memoir_segments(self, user_id: str, segment_ids: List[str]):
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"""
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处理回忆录段落的 Celery 任务
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Args:
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user_id: 用户 ID
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segment_ids: 段落 ID 列表
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"""
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task_id = self.request.id
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logger.info(f"开始处理回忆录段落: user_id={user_id}, task_id={task_id}, segments={len(segment_ids)}")
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# 更新任务状态为 running
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_update_task_status_sync(user_id, task_id, "running")
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try:
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db = SessionLocal()
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try:
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chapters_to_enqueue: set[str] = set()
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# 获取段落
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stmt = select(Segment).where(Segment.id.in_(segment_ids))
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result = db.execute(stmt)
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segments = result.scalars().all()
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if not segments:
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logger.warning(f"未找到段落: {segment_ids}")
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return {"status": "no_segments"}
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# 获取用户状态和资料
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state = _get_or_create_state_sync(user_id, db)
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llm = llm_service.get_llm()
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image_settings = MemoirImageSettings.from_env()
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user_obj = db.get(User, user_id)
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user_profile = ""
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user_birth_year = None
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if user_obj:
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user_birth_year = user_obj.birth_year
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user_profile = format_user_profile_context(
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birth_year=user_obj.birth_year,
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birth_place=user_obj.birth_place,
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grew_up_place=user_obj.grew_up_place,
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occupation=user_obj.occupation,
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)
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# 分两步处理：
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# 1) 5-stage 状态跟踪（slots）
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# 2) 8-category 章节分类（chapter creation）
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category_to_segments: Dict[str, List[Segment]] = {}
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for segment in segments:
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text = segment.transcript_text
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detected_stage = _detect_stage(text, state.current_stage)
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# 提取 slots（5-stage 状态跟踪）
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extracted_slots = {}
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if llm:
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try:
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prompt = get_state_extraction_prompt(
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user_message=text,
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current_stage=state.current_stage,
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stage_slots=state.slots.get(detected_stage, {}),
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)
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response = llm.invoke(prompt)
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parsed = json.loads(extract_json_payload(response.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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except (json.JSONDecodeError, Exception) as e:
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logger.warning(f"LLM 解析失败: {e}")
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for slot_name, snippet in extracted_slots.items():
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state = _update_slot_sync(
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user_id=user_id,
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stage=detected_stage,
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slot_name=slot_name,
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snippet=snippet,
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segment_ids=[segment.id],
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db=db,
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)
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# 8-category 章节分类
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chapter_category = _classify_chapter_category(text, detected_stage, llm)
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if chapter_category is None:
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logger.info(f"段落无回忆录价值，跳过: segment_id={segment.id}")
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continue
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category_to_segments.setdefault(chapter_category, []).append(segment)
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# 按 8 分类生成章节内容
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for chapter_category, category_segments in category_to_segments.items():
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if not _acquire_chapter_lock(user_id, chapter_category):
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logger.warning(f"章节锁竞争: user={user_id}, category={chapter_category}, 延迟重试")
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raise self.retry(countdown=10)
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try:
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segment_texts = [seg.transcript_text for seg in category_segments]
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combined_text = "\n\n".join(segment_texts)
|
||
source_ids = [seg.id for seg in category_segments]
|
||
|
||
# 查找 active 章节（被清除的章节不继续更新，而是创建新的）
|
||
stmt_chapter = select(Chapter).where(
|
||
Chapter.user_id == user_id,
|
||
Chapter.category == chapter_category,
|
||
Chapter.is_active == True,
|
||
)
|
||
result_chapter = db.execute(stmt_chapter)
|
||
chapter = result_chapter.scalar_one_or_none()
|
||
|
||
# 获取 slot snippets
|
||
slot_snippets = {
|
||
key: value.snippet
|
||
for key, value in (state.slots.get(chapter_category, {}) or {}).items()
|
||
if value.snippet
|
||
}
|
||
|
||
# 生成标题和内容
|
||
title = chapter.title if chapter else f"{chapter_category} 回忆"
|
||
existing_content = chapter.content if chapter else ""
|
||
narrative = combined_text
|
||
|
||
if llm:
|
||
try:
|
||
if not chapter:
|
||
title_prompt = get_creative_title_prompt(
|
||
stage=chapter_category,
|
||
emotion="neutral",
|
||
slots=slot_snippets,
|
||
user_profile=user_profile,
|
||
birth_year=user_birth_year,
|
||
)
|
||
title_response = llm.invoke(title_prompt)
|
||
title = title_response.content.strip().strip('"')
|
||
|
||
narrative_prompt = get_narrative_prompt(
|
||
stage=chapter_category,
|
||
slots=slot_snippets,
|
||
new_content=combined_text,
|
||
existing_content=existing_content,
|
||
user_profile=user_profile,
|
||
birth_year=user_birth_year,
|
||
)
|
||
narrative_response = llm.invoke(narrative_prompt)
|
||
new_narrative = narrative_response.content.strip()
|
||
|
||
# 追加而非替换
|
||
if existing_content:
|
||
narrative = f"{existing_content}\n\n{new_narrative}"
|
||
else:
|
||
narrative = new_narrative
|
||
except Exception as e:
|
||
logger.warning(f"LLM 生成失败: {e}")
|
||
if existing_content:
|
||
narrative = f"{existing_content}\n\n{combined_text}"
|
||
|
||
# 安全检查：新内容不应比旧内容短
|
||
if existing_content and len(narrative) < len(existing_content) * 0.8:
|
||
logger.warning(
|
||
f"内容长度异常: existing={len(existing_content)}, "
|
||
f"new={len(narrative)}, category={chapter_category}. 回退为追加模式"
|
||
)
|
||
narrative = f"{existing_content}\n\n{combined_text}"
|
||
|
||
# 入库前：占位符位置用正则匹配后拼上固定模板
|
||
narrative = inject_image_placeholder_template(narrative)
|
||
|
||
# 更新或创建章节
|
||
if chapter:
|
||
chapter.content = narrative
|
||
chapter.title = title
|
||
chapter.is_new = True
|
||
chapter.source_segments = list({*(chapter.source_segments or []), *source_ids})
|
||
else:
|
||
# 根据 stage 计算正确的排序索引
|
||
calculated_order_index = STAGE_TO_ORDER.get(chapter_category, 999)
|
||
chapter = Chapter(
|
||
id=str(uuid.uuid4()),
|
||
user_id=user_id,
|
||
title=title,
|
||
content=narrative,
|
||
order_index=calculated_order_index,
|
||
status="completed",
|
||
category=chapter_category,
|
||
images=[],
|
||
is_new=True,
|
||
source_segments=source_ids,
|
||
)
|
||
db.add(chapter)
|
||
|
||
db.flush()
|
||
|
||
initialize_chapter_images(chapter)
|
||
if image_settings.enabled and chapter_has_images_to_generate(chapter.images):
|
||
chapters_to_enqueue.add(chapter.id)
|
||
|
||
# 更新 Book
|
||
stmt_book = select(Book).where(Book.user_id == user_id).order_by(Book.updated_at.desc())
|
||
result_book = db.execute(stmt_book)
|
||
book = result_book.scalar_one_or_none()
|
||
if not book:
|
||
book = Book(
|
||
id=str(uuid.uuid4()),
|
||
user_id=user_id,
|
||
title="我的回忆录",
|
||
total_pages=0,
|
||
total_words=0,
|
||
cover_image_url=None,
|
||
)
|
||
db.add(book)
|
||
book.has_update = True
|
||
book.last_update_chapter_id = chapter.id
|
||
finally:
|
||
_release_chapter_lock(user_id, chapter_category)
|
||
|
||
# 标记段落为已处理
|
||
for seg in segments:
|
||
seg.processed = True
|
||
|
||
db.commit()
|
||
|
||
for chapter_id in sorted(chapters_to_enqueue):
|
||
try:
|
||
logger.info(f"派发章节补图任务: chapter={chapter_id}")
|
||
generate_chapter_images.delay(chapter_id)
|
||
except Exception as exc:
|
||
logger.warning(f"补图任务派发失败: chapter={chapter_id}, error={exc}")
|
||
|
||
logger.info(f"回忆录处理完成: user_id={user_id}, task_id={task_id}")
|
||
|
||
# 更新任务状态为成功
|
||
_update_task_status_sync(user_id, task_id, "success", {"processed": len(segments)})
|
||
|
||
return {"status": "success", "processed": len(segments)}
|
||
|
||
finally:
|
||
db.close()
|
||
|
||
except Exception as e:
|
||
logger.error(f"回忆录处理失败: {e}")
|
||
|
||
# 更新任务状态为失败
|
||
_update_task_status_sync(user_id, task_id, "failure", {"error": str(e)})
|
||
|
||
# 重试
|
||
raise self.retry(exc=e)
|
||
|
||
|
||
@shared_task(bind=True, max_retries=3, default_retry_delay=30)
|
||
def generate_chapter_content(self, user_id: str, stage: str, new_content: str):
|
||
"""
|
||
单独生成章节内容的任务（用于实时更新）
|
||
|
||
Args:
|
||
user_id: 用户 ID
|
||
stage: 阶段
|
||
new_content: 新内容
|
||
"""
|
||
logger.info(f"生成章节内容: user_id={user_id}, stage={stage}")
|
||
|
||
try:
|
||
db = SessionLocal()
|
||
try:
|
||
llm = llm_service.get_llm()
|
||
|
||
# 查找 active 章节（被清除的章节不继续更新，而是创建新的）
|
||
stmt = select(Chapter).where(
|
||
Chapter.user_id == user_id,
|
||
Chapter.category == stage,
|
||
Chapter.is_active == True,
|
||
)
|
||
result = db.execute(stmt)
|
||
chapter = result.scalar_one_or_none()
|
||
|
||
existing_content = chapter.content if chapter else ""
|
||
|
||
if llm:
|
||
prompt = get_narrative_prompt(
|
||
stage=stage,
|
||
slots={},
|
||
new_content=new_content,
|
||
existing_content=existing_content,
|
||
)
|
||
response = llm.invoke(prompt)
|
||
new_narrative = response.content.strip()
|
||
# 追加而非替换
|
||
if existing_content:
|
||
narrative = f"{existing_content}\n\n{new_narrative}"
|
||
else:
|
||
narrative = new_narrative
|
||
else:
|
||
narrative = f"{existing_content}\n\n{new_content}" if existing_content else new_content
|
||
|
||
# 安全检查：新内容不应比旧内容短
|
||
if existing_content and len(narrative) < len(existing_content) * 0.8:
|
||
logger.warning(
|
||
f"内容长度异常: existing={len(existing_content)}, "
|
||
f"new={len(narrative)}, stage={stage}. 回退为追加模式"
|
||
)
|
||
narrative = f"{existing_content}\n\n{new_content}"
|
||
|
||
# 入库前：占位符位置用正则匹配后拼上固定模板
|
||
narrative = inject_image_placeholder_template(narrative)
|
||
|
||
if chapter:
|
||
chapter.content = narrative
|
||
chapter.is_new = True
|
||
else:
|
||
# 根据 stage 计算正确的排序索引
|
||
calculated_order_index = STAGE_TO_ORDER.get(stage, 999)
|
||
chapter = Chapter(
|
||
id=str(uuid.uuid4()),
|
||
user_id=user_id,
|
||
title=f"{stage} 回忆",
|
||
content=narrative,
|
||
order_index=calculated_order_index,
|
||
status="completed",
|
||
category=stage,
|
||
images=[],
|
||
is_new=True,
|
||
source_segments=[],
|
||
)
|
||
db.add(chapter)
|
||
|
||
db.commit()
|
||
return {"status": "success"}
|
||
|
||
finally:
|
||
db.close()
|
||
|
||
except Exception as e:
|
||
logger.error(f"章节生成失败: {e}")
|
||
raise self.retry(exc=e)
|
||
|
||
|
||
def build_cos_key(user_id: str, chapter_id: str, index: int, prompt: str) -> str:
|
||
short_hash = hashlib.sha1(prompt.encode("utf-8")).hexdigest()[:10]
|
||
return f"memoirs/{user_id}/{chapter_id}/{index}-{short_hash}.png"
|
||
|
||
|
||
@shared_task(bind=True, max_retries=3, default_retry_delay=30)
|
||
def generate_chapter_images(self, chapter_id: str):
|
||
"""Async task to generate images for a chapter's pending image assets."""
|
||
db = SessionLocal()
|
||
lock_acquired = False
|
||
provider = None
|
||
try:
|
||
chapter = db.get(Chapter, chapter_id)
|
||
if not chapter or not chapter.images:
|
||
logger.info(f"章节补图跳过: chapter={chapter_id}, reason=no_images")
|
||
return {"status": "no_images"}
|
||
|
||
settings = MemoirImageSettings.from_env()
|
||
if not settings.enabled:
|
||
chapter.images = completed_image_assets(chapter.images)
|
||
db.commit()
|
||
logger.info("章节补图跳过: chapter=%s, reason=disabled", chapter_id)
|
||
return {"status": "disabled"}
|
||
|
||
lock_acquired = _acquire_chapter_image_lock(chapter_id)
|
||
if not lock_acquired:
|
||
logger.info("章节补图跳过: chapter=%s, reason=locked", chapter_id)
|
||
return {"status": "locked"}
|
||
|
||
prompt_service = MemoirImagePromptService(llm_service.get_llm(), settings)
|
||
provider = LiblibImageProvider(template_uuid=settings.liblib_template_uuid)
|
||
storage = TencentCosStorageService.from_env()
|
||
images = normalize_image_assets(chapter.images)
|
||
pending_count = sum(
|
||
1 for item in images if item.get("status") in {IMAGE_STATUS_PENDING, IMAGE_STATUS_FAILED}
|
||
)
|
||
logger.info(
|
||
"章节补图开始: chapter=%s, total_images=%d, pending_images=%d",
|
||
chapter_id,
|
||
len(images),
|
||
pending_count,
|
||
)
|
||
failures: list[str] = []
|
||
|
||
for index, item in enumerate(images):
|
||
if item.get("status") == IMAGE_STATUS_COMPLETED and (item.get("storage_key") or item.get("url")):
|
||
continue
|
||
if item.get("status") not in {IMAGE_STATUS_PENDING, IMAGE_STATUS_FAILED}:
|
||
continue
|
||
|
||
current_item = dict(item)
|
||
current_item["status"] = IMAGE_STATUS_PROCESSING
|
||
current_item["updated_at"] = datetime.now(timezone.utc).isoformat()
|
||
images[index] = current_item
|
||
chapter.images = images
|
||
db.commit()
|
||
|
||
try:
|
||
context_lines = (chapter.content or "").split("\n")
|
||
context_excerpt = " ".join(context_lines[:5])[:200]
|
||
|
||
prompt_data = prompt_service.build_prompt(
|
||
chapter_title=chapter.title,
|
||
chapter_category=chapter.category or "",
|
||
description=item.get("description", ""),
|
||
context_excerpt=context_excerpt,
|
||
)
|
||
job = provider.submit_generation(
|
||
prompt=prompt_data["prompt"],
|
||
size=prompt_data["size"],
|
||
style=prompt_data["style"],
|
||
)
|
||
if job["status"] != "completed":
|
||
job = provider.poll_until_complete(
|
||
job,
|
||
poll_interval_seconds=settings.poll_interval_seconds,
|
||
max_attempts=settings.max_attempts,
|
||
)
|
||
image_bytes = _normalize_image_bytes_for_storage(provider.download_image(job))
|
||
key = build_cos_key(chapter.user_id, chapter.id, current_item["index"], prompt_data["prompt"])
|
||
current_item["storage_key"] = key
|
||
current_item["url"] = storage.upload_bytes(image_bytes, key, "image/png")
|
||
current_item["prompt"] = prompt_data["prompt"]
|
||
current_item["style"] = prompt_data["style"]
|
||
current_item["size"] = prompt_data["size"]
|
||
current_item["status"] = IMAGE_STATUS_COMPLETED
|
||
current_item["error"] = None
|
||
logger.info(
|
||
"章节补图成功: chapter=%s, index=%s, url=%s",
|
||
chapter_id,
|
||
current_item.get("index"),
|
||
current_item["url"],
|
||
)
|
||
except Exception as exc:
|
||
current_item["status"] = IMAGE_STATUS_FAILED
|
||
current_item["error"] = str(exc)
|
||
failures.append(f"index={current_item.get('index')}, error={exc}")
|
||
logger.warning(f"图片生成失败: chapter={chapter_id}, index={current_item.get('index')}, error={exc}")
|
||
|
||
current_item["updated_at"] = datetime.now(timezone.utc).isoformat()
|
||
images[index] = current_item
|
||
chapter.images = images
|
||
db.commit()
|
||
|
||
if failures:
|
||
raise RuntimeError(
|
||
f"章节补图存在失败项: chapter={chapter_id}, failures={'; '.join(failures)}"
|
||
)
|
||
|
||
return {"status": "success"}
|
||
except Exception as exc:
|
||
logger.error("章节补图任务失败: chapter=%s, error=%s", chapter_id, exc)
|
||
raise self.retry(exc=exc)
|
||
finally:
|
||
if provider:
|
||
provider.close()
|
||
if lock_acquired:
|
||
_release_chapter_image_lock(chapter_id)
|
||
db.close()
|