mirror of
https://github.com/jxxghp/MoviePilot.git
synced 2026-09-04 23:17:20 +08:00
372 lines
12 KiB
Python
372 lines
12 KiB
Python
import re
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from collections.abc import Awaitable, Callable
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from typing import Annotated, NotRequired, TypedDict
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import yaml # noqa
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from anyio import Path as AsyncPath
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from langchain.agents.middleware.types import (
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AgentMiddleware,
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AgentState,
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ContextT,
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ModelRequest,
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ModelResponse,
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PrivateStateAttr, # noqa
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ResponseT,
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)
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from langchain_core.runnables import RunnableConfig
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from langgraph.runtime import Runtime
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from app.agent.middleware.utils import append_to_system_message
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from app.log import logger
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# JOB.md 文件最大限制为 1MB
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MAX_JOB_FILE_SIZE = 1 * 1024 * 1024
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ACTIVE_JOB_STATUSES = ("pending", "in_progress")
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class JobMetadata(TypedDict):
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"""Job 元数据。"""
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path: str
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"""JOB.md 文件路径。"""
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id: str
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"""Job 标识符（目录名）。"""
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name: str
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"""Job 名称。"""
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description: str
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"""Job 描述。"""
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schedule: str
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"""调度类型: once（一次性）/ recurring（重复性）。"""
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status: str
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"""当前状态: pending / in_progress / completed / cancelled。"""
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last_run: str | None
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"""上次执行时间。"""
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class JobsState(AgentState):
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"""jobs 中间件状态。"""
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jobs_metadata: NotRequired[Annotated[list[JobMetadata], PrivateStateAttr]]
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"""已加载的 job 元数据列表，不传播给父 agent。"""
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class JobsStateUpdate(TypedDict):
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"""jobs 中间件状态更新项。"""
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jobs_metadata: list[JobMetadata]
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"""待合并的 job 元数据列表。"""
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def _parse_job_metadata(
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content: str,
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job_path: str,
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job_id: str,
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) -> JobMetadata | None:
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"""从 JOB.md 内容中解析 YAML 前言并验证元数据。"""
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if len(content) > MAX_JOB_FILE_SIZE:
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logger.warning(
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"Skipping %s: content too large (%d bytes)", job_path, len(content)
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)
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return None
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# 匹配 --- 分隔的 YAML 前言
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frontmatter_pattern = r"^---\s*\n(.*?)\n---\s*\n"
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match = re.match(frontmatter_pattern, content, re.DOTALL)
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if not match:
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logger.warning("Skipping %s: no valid YAML frontmatter found", job_path)
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return None
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frontmatter_str = match.group(1)
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# 解析 YAML
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try:
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frontmatter_data = yaml.safe_load(frontmatter_str)
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except yaml.YAMLError as e:
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logger.warning("Invalid YAML in %s: %s", job_path, e)
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return None
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if not isinstance(frontmatter_data, dict):
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logger.warning("Skipping %s: frontmatter is not a mapping", job_path)
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return None
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# Job 名称和描述
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name = str(frontmatter_data.get("name", "")).strip()
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description = str(frontmatter_data.get("description", "")).strip()
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if not name:
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logger.warning("Skipping %s: missing required 'name'", job_path)
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return None
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# 调度类型
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schedule = str(frontmatter_data.get("schedule", "once")).strip().lower()
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if schedule not in ("once", "recurring"):
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schedule = "once"
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# 状态
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status = str(frontmatter_data.get("status", "pending")).strip().lower()
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if status not in ("pending", "in_progress", "completed", "cancelled"):
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status = "pending"
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# 上次执行时间
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last_run = str(frontmatter_data.get("last_run", "")).strip() or None
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return JobMetadata(
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id=job_id,
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name=name,
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description=description,
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path=job_path,
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schedule=schedule,
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status=status,
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last_run=last_run,
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)
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async def _alist_jobs(source_path: AsyncPath) -> list[JobMetadata]:
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"""异步列出指定路径下的所有任务。
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扫描包含 JOB.md 的目录并解析其元数据。
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"""
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jobs: list[JobMetadata] = []
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if not await source_path.exists():
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return []
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# 查找所有任务目录（包含 JOB.md 的目录）
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job_dirs: list[AsyncPath] = []
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async for path in source_path.iterdir():
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if await path.is_dir() and await (path / "JOB.md").is_file():
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job_dirs.append(path)
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if not job_dirs:
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return []
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# 显式按目录名排序，避免文件系统返回顺序不稳定时破坏提示词缓存命中。
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job_dirs.sort(key=lambda p: p.name.casefold())
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# 解析 JOB.md
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for job_path in job_dirs:
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job_md_path = job_path / "JOB.md"
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job_content = await job_md_path.read_text(encoding="utf-8")
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# 解析元数据
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job_metadata = _parse_job_metadata(
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content=job_content,
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job_path=str(job_md_path),
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job_id=job_path.name,
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)
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if job_metadata:
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jobs.append(job_metadata)
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return jobs
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def filter_active_jobs(jobs_metadata: list[JobMetadata]) -> list[JobMetadata]:
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"""筛选需要参与心跳检查的活跃任务。
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这里严格以任务状态为准，只保留 `pending` / `in_progress`。
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`recurring` 任务执行完成后按约定应回写为 `pending`，因此无需再额外放宽
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到 `completed`，避免已结束任务被重复注入后台心跳。
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"""
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return [
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job for job in jobs_metadata if job.get("status") in ACTIVE_JOB_STATUSES
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]
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async def load_jobs_metadata(source_paths: list[str]) -> list[JobMetadata]:
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"""按顺序加载多个 jobs 目录下的任务元数据。"""
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all_jobs: list[JobMetadata] = []
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for source_path_str in source_paths:
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source_path = AsyncPath(source_path_str)
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if not await source_path.exists():
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await source_path.mkdir(parents=True, exist_ok=True)
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continue
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source_jobs = await _alist_jobs(source_path)
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all_jobs.extend(source_jobs)
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return all_jobs
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JOBS_SYSTEM_PROMPT = """
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<jobs_system>
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You have a **scheduled jobs** system that allows you to track and execute long-running or recurring tasks.
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**Jobs Location:** `{jobs_location}`
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**Current Jobs:**
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{jobs_list}
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**Job File Format:**
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Each job is a directory containing a `JOB.md` file with YAML frontmatter followed by task details:
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```markdown
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---
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name: 任务名称（简短中文描述）
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description: 任务的详细描述，说明要做什么
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schedule: once 或 recurring
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status: pending / in_progress / completed / cancelled
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last_run: "YYYY-MM-DD HH:MM"（上次执行时间，可选）
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---
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# 任务详情
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## 目标
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详细描述这个任务要完成的目标。
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## 执行日志
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记录每次执行的情况和结果。
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- **2024-01-15 10:00** - 执行了XXX操作，结果：成功/失败
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- **2024-01-16 10:00** - 继续执行XXX...
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```
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**Job Lifecycle Rules:**
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1. **Creating a Job**: When a user asks you to do something periodically or at a later time:
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- Create a new directory under the jobs location, directory name is the `job-id` (lowercase, hyphens, 1-64 chars)
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- Write a `JOB.md` file with proper frontmatter and detailed task description
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- Set `schedule: once` for one-time tasks, `schedule: recurring` for repeating tasks (e.g., daily sign-in, weekly checks)
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- Set initial `status: pending`
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2. **Executing a Job**: When you work on a job:
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- Update `status: in_progress` in the frontmatter
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- Execute the required actions using your tools
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- Log the execution result in the "执行日志" section with timestamp
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- Update `last_run` in frontmatter to current time
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3. **Completing a Job**:
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- For `schedule: once` tasks: set `status: completed` after successful execution
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- For `schedule: recurring` tasks: keep `status: pending` after execution, only update `last_run` time. The job stays active for the next scheduled run.
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- Set `status: cancelled` if the user explicitly asks to cancel/stop a task
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4. **Heartbeat Check**: You will be periodically woken up to check pending jobs. When woken up:
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- Read the jobs directory to find all active jobs (status: pending or in_progress)
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- Skip jobs with `status: completed` or `status: cancelled`
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- For `schedule: recurring` jobs, check `last_run` to determine if it's time to run again
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- Execute pending jobs and update their status/logs accordingly
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**Important Notes:**
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- Each job MUST have its own separate directory and JOB.md file to avoid conflicts
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- Always update the frontmatter fields (status, last_run) when executing a job
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- Keep execution logs concise but informative
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- For recurring jobs, maintain a rolling log (keep recent entries, you can summarize/remove old entries to keep the file manageable)
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- When creating jobs, make the description detailed enough that you can understand and execute the task in future sessions without additional context
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**When to Create Jobs:**
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- User says "每天帮我..." / "定期..." / "定时..." / "提醒我..." / "以后每次..."
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- User requests a task that should be done repeatedly
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- User asks for monitoring or periodic checking of something
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**When NOT to Create Jobs:**
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- User asks for an immediate one-time action (just do it now)
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- Simple questions or conversations
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- Tasks that are already handled by MoviePilot's built-in scheduler services
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</jobs_system>
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"""
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class JobsMiddleware(AgentMiddleware[JobsState, ContextT, ResponseT]): # noqa
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"""加载并向系统提示词注入 Agent Jobs 的中间件。
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扫描 jobs 目录下的 JOB.md 文件，解析元数据并注入到系统提示词中，
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使智能体了解当前的长期任务及其状态。
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"""
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state_schema = JobsState
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def __init__(self, *, sources: list[str]) -> None:
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"""初始化 Jobs 中间件。"""
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self.sources = sources
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self.system_prompt_template = JOBS_SYSTEM_PROMPT
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@staticmethod
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def _format_jobs_list(jobs: list[JobMetadata]) -> str:
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"""格式化任务元数据列表用于系统提示词。"""
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if not jobs:
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return "(No active jobs. You can create jobs when users request periodic or scheduled tasks.)"
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lines = []
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for job in jobs:
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status_emoji = {
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"pending": "⏳",
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"in_progress": "🔄",
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"completed": "✅",
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"cancelled": "❌",
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}.get(job["status"], "❓")
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schedule_label = (
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"recurring (重复)"
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if job["schedule"] == "recurring"
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else "once (一次性)"
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)
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desc_line = (
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f"- {status_emoji} **{job['id']}**: {job['name']}"
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f" [{schedule_label}] - {job['description']}"
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)
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if job.get("last_run"):
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desc_line += f" (上次执行: {job['last_run']})"
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lines.append(desc_line)
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lines.append(f" -> Read `{job['path']}` for full details")
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return "\n".join(lines)
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def modify_request(self, request: ModelRequest[ContextT]) -> ModelRequest[ContextT]:
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"""将任务文档注入模型请求的系统消息中。"""
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jobs_metadata = request.state.get("jobs_metadata", []) # noqa
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# 仅注入真正活跃的任务，避免把已完成任务继续塞进心跳上下文。
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active_jobs = filter_active_jobs(jobs_metadata)
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jobs_list = self._format_jobs_list(active_jobs)
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jobs_location = self.sources[0] if self.sources else ""
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jobs_section = self.system_prompt_template.format(
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jobs_location=jobs_location,
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jobs_list=jobs_list,
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)
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new_system_message = append_to_system_message(
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request.system_message, jobs_section
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)
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return request.override(system_message=new_system_message)
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async def abefore_agent( # noqa
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self, state: JobsState, runtime: Runtime, config: RunnableConfig
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) -> JobsStateUpdate | None:
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"""在 Agent 执行前异步加载任务元数据。
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每个会话仅加载一次。若 state 中已有则跳过。
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"""
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# 如果 state 中已存在元数据则跳过
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if "jobs_metadata" in state:
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return None
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return JobsStateUpdate(
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jobs_metadata=await load_jobs_metadata(self.sources)
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)
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async def awrap_model_call(
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self,
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request: ModelRequest[ContextT],
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handler: Callable[
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[ModelRequest[ContextT]], Awaitable[ModelResponse[ResponseT]]
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],
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) -> ModelResponse[ResponseT]:
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"""在模型调用时注入任务文档。"""
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modified_request = self.modify_request(request)
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return await handler(modified_request)
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__all__ = [
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"ACTIVE_JOB_STATUSES",
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"JobMetadata",
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"JobsMiddleware",
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"filter_active_jobs",
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"load_jobs_metadata",
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]
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