feat: add batch AI re-organize for transfer history and search result recommendation

- Implement batch AI re-organize endpoint for transfer history with progress tracking - Add batch_manual_transfer_redo system task template and prompt generation - Refactor agent_manager to support generic background prompt execution - Add AIRecommendChain for search result recommendation using agent background prompt - Update search endpoints to use new AIRecommendChain and remove legacy code - Enhance test cases for batch manual transfer redo - Minor code cleanup and style fixes
2026-09-05 07:27:15 +08:00 · 2026-04-29 22:16:04 +08:00
parent b6f0ef99ab
commit 460d716512
16 changed files with 821 additions and 631 deletions
@@ -1,14 +1,15 @@
 import asyncio
 import time
+from pathlib import Path
 from typing import List, Any, Optional

 import jieba
 from fastapi import APIRouter, Depends
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy.orm import Session
-from pathlib import Path

 from app import schemas
+from app.agent import prompt_manager, agent_manager
 from app.chain.storage import StorageChain
 from app.core.config import settings, global_vars
 from app.core.event import eventmanager
@@ -24,13 +25,99 @@ from app.schemas.types import EventType
 router = APIRouter()


-def _start_ai_redo_task(history_id: int, progress_key: str):
-    from app.agent import agent_manager
+def normalize_history_ids(history_ids: list[int]) -> list[int]:
+    """对输入的历史记录 ID 列表进行规范化处理，去除重复项并保持原有顺序。"""
+    normalized_ids: list[int] = []
+    for history_id in history_ids:
+        if history_id not in normalized_ids:
+            normalized_ids.append(history_id)
+    return normalized_ids

+
+def build_manual_redo_template_context(history: TransferHistory) -> dict[str, int | str]:
+    """仅负责把整理历史对象映射成 System Tasks 需要的模板变量。"""
+    src_fileitem = history.src_fileitem or {}
+    source_path = src_fileitem.get("path") if isinstance(src_fileitem, dict) else ""
+    source_path = source_path or history.src or ""
+    season_episode = f"{history.seasons or ''}{history.episodes or ''}".strip()
+    return {
+        "history_id": history.id,
+        "current_status": "success" if history.status else "failed",
+        "recognized_title": history.title or "unknown",
+        "media_type": history.type or "unknown",
+        "category": history.category or "unknown",
+        "year": history.year or "unknown",
+        "season_episode": season_episode or "unknown",
+        "source_path": source_path or "unknown",
+        "source_storage": history.src_storage or "local",
+        "destination_path": history.dest or "unknown",
+        "destination_storage": history.dest_storage or "unknown",
+        "transfer_mode": history.mode or "unknown",
+        "tmdbid": history.tmdbid or "none",
+        "doubanid": history.doubanid or "none",
+        "error_message": history.errmsg or "none",
+    }
+
+
+def format_manual_redo_record_context(history: Any) -> str:
+    """把单条整理记录格式化为批量任务可直接消费的上下文块。"""
+    context = build_manual_redo_template_context(history)
+    return "\n".join(
+        [
+            f"Record #{context['history_id']}:",
+            f"- Current status: {context['current_status']}",
+            f"- Current recognized title: {context['recognized_title']}",
+            f"- Media type: {context['media_type']}",
+            f"- Category: {context['category']}",
+            f"- Year: {context['year']}",
+            f"- Season/Episode: {context['season_episode']}",
+            f"- Source path: {context['source_path']}",
+            f"- Source storage: {context['source_storage']}",
+            f"- Destination path: {context['destination_path']}",
+            f"- Destination storage: {context['destination_storage']}",
+            f"- Transfer mode: {context['transfer_mode']}",
+            f"- Current TMDB ID: {context['tmdbid']}",
+            f"- Current Douban ID: {context['doubanid']}",
+            f"- Error message: {context['error_message']}",
+        ]
+    )
+
+
+def build_manual_redo_prompt(history: Any) -> str:
+    """构建手动 AI 整理提示词。"""
+    return prompt_manager.render_system_task_message(
+        "manual_transfer_redo",
+        template_context=build_manual_redo_template_context(history),
+    )
+
+
+def build_batch_manual_redo_template_context(
+        histories: list[Any],
+) -> dict[str, int | str]:
+    """仅负责把多条整理历史对象映射成批量 System Tasks 需要的模板变量。"""
+    return {
+        "history_ids_csv": ", ".join(str(history.id) for history in histories),
+        "history_count": len(histories),
+        "records_context": "\n\n".join(
+            format_manual_redo_record_context(history) for history in histories
+        ),
+    }
+
+
+def build_batch_manual_redo_prompt(histories: list[Any]) -> str:
+    """构建批量手动 AI 整理提示词。"""
+    return prompt_manager.render_system_task_message(
+        "batch_manual_transfer_redo",
+        template_context=build_batch_manual_redo_template_context(histories),
+    )
+
+
+def _start_ai_redo_task(history_id: int, prompt: str, progress_key: str):
+    """在后台线程中启动单条 AI 重新整理任务，并通过 ProgressHelper 实时更新进度。"""
    progress = ProgressHelper(progress_key)
    progress.start()
    progress.update(
-        text=f"智能助正在准备整理记录 #{history_id} ...",
+        text=f"智能助手正在准备整理记录 #{history_id} ...",
        data={"history_id": history_id, "success": True},
    )

@@ -39,9 +126,11 @@ def _start_ai_redo_task(history_id: int, progress_key: str):

    async def runner():
        try:
-            await agent_manager.manual_redo_transfer(
-                history_id=history_id,
+            await agent_manager.run_background_prompt(
+                message=prompt,
+                session_prefix=f"__agent_manual_redo_{history_id}",
                output_callback=update_output,
+                suppress_user_reply=True,
            )
            progress.update(
                text="智能助手整理完成",
@@ -63,6 +152,50 @@ def _start_ai_redo_task(history_id: int, progress_key: str):
    asyncio.run_coroutine_threadsafe(runner(), global_vars.loop)


+def _start_batch_ai_redo_task(
+        history_ids: list[int],
+        prompt: str,
+        progress_key: str,
+):
+    """在后台线程中启动批量 AI 重新整理任务，并通过 ProgressHelper 实时更新进度。"""
+    progress = ProgressHelper(progress_key)
+    progress.start()
+    progress.update(
+        text=f"智能助手正在准备批量整理 {len(history_ids)} 条记录 ...",
+        data={"history_ids": history_ids, "success": True},
+    )
+
+    def update_output(text: str):
+        progress.update(text=text, data={"history_ids": history_ids})
+
+    async def runner():
+        try:
+            await agent_manager.run_background_prompt(
+                message=prompt,
+                session_prefix="__agent_manual_redo_batch",
+                output_callback=update_output,
+                suppress_user_reply=True,
+            )
+            progress.update(
+                text="智能助手批量整理完成",
+                data={"history_ids": history_ids, "success": True, "completed": True},
+            )
+        except Exception as e:
+            progress.update(
+                text=f"智能助手批量整理失败：{str(e)}",
+                data={
+                    "history_ids": history_ids,
+                    "success": False,
+                    "completed": True,
+                    "error": str(e),
+                },
+            )
+        finally:
+            progress.end()
+
+    asyncio.run_coroutine_threadsafe(runner(), global_vars.loop)
+
+
@router.get("/download", summary="查询下载历史记录", response_model=List[schemas.DownloadHistory])
 async def download_history(page: Optional[int] = 1,
                           count: Optional[int] = 30,
@@ -159,9 +292,9 @@ def delete_transfer_history(history_in: schemas.TransferHistory,

@router.post("/transfer/{history_id}/ai-redo", summary="智能助手重新整理", response_model=schemas.Response)
 def ai_redo_transfer_history(
-    history_id: int,
-    db: Session = Depends(get_db),
-    _: User = Depends(get_current_active_superuser),
+        history_id: int,
+        db: Session = Depends(get_db),
+        _: User = Depends(get_current_active_superuser),
 ) -> Any:
    """
    手动触发单条历史记录的 AI 重新整理，并返回进度键。
@@ -173,12 +306,62 @@ def ai_redo_transfer_history(
    if not history:
        return schemas.Response(success=False, message="整理记录不存在")

+    prompt = build_manual_redo_prompt(history)
    progress_key = f"ai_redo_transfer_{history_id}_{int(time.time() * 1000)}"
-    _start_ai_redo_task(history_id=history_id, progress_key=progress_key)
+    _start_ai_redo_task(
+        history_id=history_id,
+        prompt=prompt,
+        progress_key=progress_key,
+    )

    return schemas.Response(success=True, data={"progress_key": progress_key})


+@router.post("/transfer/ai-redo", summary="智能助手批量重新整理", response_model=schemas.Response)
+def batch_ai_redo_transfer_history(
+        payload: schemas.BatchTransferHistoryRedoRequest,
+        db: Session = Depends(get_db),
+        _: User = Depends(get_current_active_superuser),
+) -> Any:
+    """
+    手动触发多条历史记录的 AI 批量重新整理，并返回进度键。
+    """
+    if not settings.AI_AGENT_ENABLE:
+        return schemas.Response(success=False, message="MoviePilot智能助手未启用")
+
+    history_ids = normalize_history_ids(payload.history_ids)
+    if not history_ids:
+        return schemas.Response(success=False, message="未提供有效的整理记录")
+
+    histories = []
+    missing_ids = []
+    for history_id in history_ids:
+        history = TransferHistory.get(db, history_id)
+        if not history:
+            missing_ids.append(history_id)
+            continue
+        histories.append(history)
+
+    if missing_ids:
+        return schemas.Response(
+            success=False,
+            message="整理记录不存在: " + ", ".join(str(history_id) for history_id in missing_ids),
+        )
+
+    prompt = build_batch_manual_redo_prompt(histories)
+    progress_key = f"ai_redo_transfer_batch_{int(time.time() * 1000)}"
+    _start_batch_ai_redo_task(
+        history_ids=history_ids,
+        prompt=prompt,
+        progress_key=progress_key,
+    )
+
+    return schemas.Response(
+        success=True,
+        data={"progress_key": progress_key, "history_ids": history_ids},
+    )
+
+
@router.get("/empty/transfer", summary="清空整理记录", response_model=schemas.Response)
 async def empty_transfer_history(db: AsyncSession = Depends(get_async_db),
                                 _: User = Depends(get_current_active_superuser_async)) -> Any: