1238 lines
48 KiB
Python
1238 lines
48 KiB
Python
"""
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AI自进化服务层
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负责管理用户级别的进化引擎、技能数据、驾驶舱数据
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"""
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import os
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import json
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import logging
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import threading
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import time
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import random
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from datetime import datetime, timedelta, date
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from typing import Optional, List, Dict
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from sqlalchemy.orm import Session
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from sqlalchemy import func, desc
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from collections import Counter
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from ..models import (
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User, EvolutionSkill, EvolutionEvent, EvolutionTrajectory,
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UserEvolutionConfig, Conversation, Message,
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SkillVersion, SkillTrajectoryLink,
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MarketApp, SkillPublishRecord,
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EvolutionReport, Notification,
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TeamSharedSkill, Team
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)
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from ..config import UPLOAD_DIR
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logger = logging.getLogger(__name__)
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# 能力维度定义
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ABILITY_DIMENSIONS = [
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{"label": "文字创作", "key": "writing"},
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{"label": "数据分析", "key": "data_analysis"},
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{"label": "代码能力", "key": "coding"},
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{"label": "知识问答", "key": "qa"},
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{"label": "流程自动化", "key": "automation"},
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{"label": "创意生成", "key": "creativity"},
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{"label": "沟通协作", "key": "communication"},
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{"label": "逻辑推理", "key": "reasoning"},
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]
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# 分类到维度的映射
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CATEGORY_DIMENSION_MAP = {
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"writing": "writing",
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"code": "coding",
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"data": "data_analysis",
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"automation": "automation",
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"creative": "creativity",
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"knowledge": "qa",
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"communication": "communication",
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"reasoning": "reasoning",
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}
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class PipelineScheduler:
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"""每30秒扫描discovered轨迹，投递到xSkill流水线"""
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def __init__(self, user_id: str, db_session_factory, interval: int = 30):
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self.user_id = user_id
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self.db_session_factory = db_session_factory
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self.poll_interval = interval
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self._stop = threading.Event()
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self._thread = None
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def start(self):
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if self._thread and self._thread.is_alive():
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logger.info(f"PipelineScheduler 已在运行，用户 {self.user_id}")
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return
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self._stop.clear()
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self._thread = threading.Thread(target=self._run_loop, daemon=True)
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self._thread.start()
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logger.info(f"PipelineScheduler 已启动，用户 {self.user_id}，间隔 {self.poll_interval}s")
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def stop(self):
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self._stop.set()
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if self._thread:
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self._thread.join(timeout=10)
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logger.info(f"PipelineScheduler 已停止，用户 {self.user_id}")
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def is_running(self) -> bool:
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return self._thread is not None and self._thread.is_alive()
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def _run_loop(self):
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logger.info(f"PipelineScheduler 开始运行循环，用户 {self.user_id}")
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while not self._stop.is_set():
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try:
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db = self.db_session_factory()
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try:
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trajectories = db.query(EvolutionTrajectory).filter(
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EvolutionTrajectory.user_id == self.user_id,
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EvolutionTrajectory.status == "discovered"
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).order_by(EvolutionTrajectory.created_at).limit(10).all()
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for traj in trajectories:
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if self._stop.is_set():
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break
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try:
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self._process_trajectory(db, traj)
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except Exception as e:
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logger.error(f"处理轨迹 {traj.id} 失败: {e}")
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# 先回滚失效事务（_process_trajectory 内 commit 失败会让 session
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# 进入 rolled-back 状态，不回滚直接再 commit 会抛 PendingRollbackError
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# 并级联到外层日志），再写错误状态。
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try:
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db.rollback()
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except Exception:
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pass
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try:
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traj.error_msg = str(e)[:500]
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traj.retry_count = (traj.retry_count or 0) + 1
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traj.status = "error"
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db.commit()
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except Exception as ce:
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logger.error(f"更新轨迹 {traj.id} 错误状态失败: {ce}")
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try:
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db.rollback()
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except Exception:
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pass
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finally:
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db.close()
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except Exception as e:
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logger.error(f"PipelineScheduler 扫描循环异常: {e}")
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self._stop.wait(self.poll_interval)
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def _process_trajectory(self, db, traj):
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"""处理单条轨迹：discovered -> splitting -> split_done -> indexed -> clustering -> done"""
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logger.info(f"开始处理轨迹 {traj.id}，用户 {self.user_id}")
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# step 1: discovered -> splitting
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traj.status = "splitting"
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db.commit()
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# 模拟AtomTask拆分：生成2-5个模拟atom
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time.sleep(random.uniform(0.1, 0.5))
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atom_count = random.randint(2, 5)
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traj.atom_count = atom_count
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traj.tasks_extracted = atom_count
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db.commit()
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# step 2: splitting -> split_done
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traj.status = "split_done"
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db.commit()
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# 为每个atom创建SkillTrajectoryLink
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for i in range(atom_count):
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link = SkillTrajectoryLink(
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skill_name="__pending__",
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user_id=self.user_id,
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trajectory_id=traj.id,
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atom_id=f"{traj.id}_atom_{i}",
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weightscore=random.randint(1, 10)
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)
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db.add(link)
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db.commit()
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# step 3: split_done -> indexed (模拟向量索引)
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time.sleep(random.uniform(0.1, 0.3))
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traj.status = "indexed"
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db.commit()
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# step 4: indexed -> clustering (模拟聚类)
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time.sleep(random.uniform(0.1, 0.3))
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traj.status = "clustering"
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db.commit()
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# step 5: clustering -> done
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# 检查是否有同名skill，有则归属，无则创建新baby skill
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existing_skill = db.query(EvolutionSkill).filter(
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EvolutionSkill.user_id == self.user_id
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).order_by(func.random()).first()
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if existing_skill:
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skill_name = existing_skill.skill_name
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# 更新SkillTrajectoryLink关联
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links = db.query(SkillTrajectoryLink).filter(
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SkillTrajectoryLink.trajectory_id == traj.id,
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SkillTrajectoryLink.skill_name == "__pending__"
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).all()
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for link in links:
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link.skill_name = skill_name
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existing_skill.use_count += 1
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logger.info(f"轨迹 {traj.id} 归属于现有技能 {skill_name}")
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else:
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# 创建新baby skill
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skill_name = f"new_skill_{traj.id[:8]}"
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new_skill = EvolutionSkill(
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user_id=self.user_id,
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skill_name=skill_name,
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description=f"自动发掘的技能(源于轨迹{traj.id[:8]})",
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tags=[],
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category="未分类",
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state="baby",
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use_count=1,
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version=1,
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)
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db.add(new_skill)
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db.flush()
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# 更新关联
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links = db.query(SkillTrajectoryLink).filter(
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SkillTrajectoryLink.trajectory_id == traj.id,
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SkillTrajectoryLink.skill_name == "__pending__"
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).all()
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for link in links:
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link.skill_name = skill_name
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logger.info(f"轨迹 {traj.id} 创建新baby技能 {skill_name}")
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db.commit()
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# 检查某skill的candidates（关联轨迹数）≥3，自动 baby→staging
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candidate_count = db.query(SkillTrajectoryLink).filter(
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SkillTrajectoryLink.user_id == self.user_id,
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SkillTrajectoryLink.skill_name == skill_name
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).count()
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if candidate_count >= 3:
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skill_obj = db.query(EvolutionSkill).filter(
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EvolutionSkill.user_id == self.user_id,
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EvolutionSkill.skill_name == skill_name,
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EvolutionSkill.state == "baby"
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).first()
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if skill_obj:
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skill_obj.state = "staging"
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skill_obj.updated_at = datetime.now()
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# 记录事件
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event = EvolutionEvent(
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user_id=self.user_id,
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skill_name=skill_name,
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event_type="promoted",
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description=f"技能 {skill_name} 自动晋升 baby->staging，关联轨迹数 {candidate_count}",
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)
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db.add(event)
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db.commit()
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logger.info(f"技能 {skill_name} 自动晋升 baby->staging，关联轨迹数 {candidate_count}")
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traj.status = "done"
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traj.processed_at = datetime.now()
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db.commit()
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# 更新pipeline_processed计数
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config = db.query(UserEvolutionConfig).filter(
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UserEvolutionConfig.user_id == self.user_id
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).first()
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if config:
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config.pipeline_processed = (config.pipeline_processed or 0) + 1
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db.commit()
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||
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logger.info(f"轨迹 {traj.id} 处理完成，归属于技能 {skill_name}")
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||
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||
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class EvolutionWatcherManager:
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"""全局watcher线程管理器（单例）"""
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_instance = None
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_lock = threading.Lock()
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def __new__(cls):
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if cls._instance is None:
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||
with cls._lock:
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if cls._instance is None:
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cls._instance = super().__new__(cls)
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return cls._instance
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def __init__(self):
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if not hasattr(self, '_initialized'):
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self._schedulers: dict[str, PipelineScheduler] = {}
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self._max_schedulers = 100
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self._initialized = True
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def start_user_evolution(self, user_id: str, db_session_factory) -> bool:
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"""为用户启动进化引擎"""
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if user_id in self._schedulers:
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logger.info(f"用户 {user_id} 的进化引擎已在运行")
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return False
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||
if len(self._schedulers) >= self._max_schedulers:
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logger.warning(f"已达到最大scheduler数量限制 {self._max_schedulers}")
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return False
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||
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||
# 创建并启动 PipelineScheduler
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||
scheduler = PipelineScheduler(user_id, db_session_factory)
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scheduler.start()
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self._schedulers[user_id] = scheduler
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||
|
||
# 更新 user_evolution_config 表
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||
db = db_session_factory()
|
||
try:
|
||
config = db.query(UserEvolutionConfig).filter(
|
||
UserEvolutionConfig.user_id == user_id
|
||
).first()
|
||
if config:
|
||
config.watcher_status = "running"
|
||
config.pipeline_status = "running"
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||
config.watcher_error = None
|
||
else:
|
||
config = UserEvolutionConfig(
|
||
user_id=user_id,
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||
watcher_status="running",
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||
pipeline_status="running",
|
||
)
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||
db.add(config)
|
||
db.commit()
|
||
logger.info(f"用户 {user_id} 进化引擎已启动")
|
||
except Exception as e:
|
||
logger.error(f"更新用户进化配置失败: {e}")
|
||
return False
|
||
finally:
|
||
db.close()
|
||
|
||
return True
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||
|
||
def stop_user_evolution(self, user_id: str) -> bool:
|
||
"""停止用户进化引擎"""
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||
scheduler = self._schedulers.pop(user_id, None)
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||
if not scheduler:
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||
logger.info(f"用户 {user_id} 的进化引擎未在运行")
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||
return False
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||
|
||
scheduler.stop()
|
||
|
||
# 更新 user_evolution_config 表
|
||
db = self.db_session_factory() if hasattr(self, 'db_session_factory') else None
|
||
if db:
|
||
try:
|
||
config = db.query(UserEvolutionConfig).filter(
|
||
UserEvolutionConfig.user_id == user_id
|
||
).first()
|
||
if config:
|
||
config.watcher_status = "stopped"
|
||
config.pipeline_status = "idle"
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||
db.commit()
|
||
logger.info(f"用户 {user_id} 进化引擎已停止")
|
||
except Exception as e:
|
||
logger.error(f"更新用户进化配置失败: {e}")
|
||
return False
|
||
finally:
|
||
db.close()
|
||
|
||
return True
|
||
|
||
def get_user_status(self, user_id: str, db_session_factory) -> dict:
|
||
"""获取用户进化引擎状态"""
|
||
scheduler = self._schedulers.get(user_id)
|
||
is_running = scheduler.is_running() if scheduler else False
|
||
|
||
db = db_session_factory()
|
||
try:
|
||
config = db.query(UserEvolutionConfig).filter(
|
||
UserEvolutionConfig.user_id == user_id
|
||
).first()
|
||
|
||
watcher_status = config.watcher_status if config else "unknown"
|
||
pipeline_status = config.pipeline_status if config else "unknown"
|
||
pipeline_processed = config.pipeline_processed if config else 0
|
||
watcher_error = config.watcher_error if config else None
|
||
|
||
# 统计该用户的技能数、轨迹数
|
||
skill_count = db.query(EvolutionSkill).filter(
|
||
EvolutionSkill.user_id == user_id
|
||
).count()
|
||
trajectory_count = db.query(EvolutionTrajectory).filter(
|
||
EvolutionTrajectory.user_id == user_id
|
||
).count()
|
||
|
||
return {
|
||
"user_id": user_id,
|
||
"scheduler_running": is_running,
|
||
"watcher_status": watcher_status,
|
||
"pipeline_status": pipeline_status,
|
||
"pipeline_processed": pipeline_processed,
|
||
"watcher_error": watcher_error,
|
||
"skill_count": skill_count,
|
||
"trajectory_count": trajectory_count,
|
||
}
|
||
except Exception as e:
|
||
logger.error(f"获取用户进化状态失败: {e}")
|
||
return {"error": str(e)}
|
||
finally:
|
||
db.close()
|
||
|
||
def restart_user_evolution(self, user_id: str, db_session_factory) -> bool:
|
||
"""重启用户进化引擎"""
|
||
self.stop_user_evolution(user_id)
|
||
return self.start_user_evolution(user_id, db_session_factory)
|
||
|
||
def shutdown_all(self):
|
||
"""优雅关闭所有scheduler"""
|
||
logger.info("正在关闭所有进化引擎...")
|
||
for user_id in list(self._schedulers.keys()):
|
||
self.stop_user_evolution(user_id)
|
||
logger.info("所有进化引擎已关闭")
|
||
|
||
|
||
class EvolutionService:
|
||
"""AI自进化服务"""
|
||
|
||
def __init__(self, db: Session, user_id: str):
|
||
self.db = db
|
||
self.user_id = user_id
|
||
|
||
# ============================================================
|
||
# 技能管理
|
||
# ============================================================
|
||
|
||
def get_skills(self, state: Optional[str] = None, keyword: Optional[str] = None,
|
||
sort_by: str = "updated_at", order: str = "desc") -> List[dict]:
|
||
"""获取用户技能列表"""
|
||
query = self.db.query(EvolutionSkill).filter(EvolutionSkill.user_id == self.user_id)
|
||
if state:
|
||
query = query.filter(EvolutionSkill.state == state)
|
||
if keyword:
|
||
query = query.filter(EvolutionSkill.skill_name.contains(keyword))
|
||
|
||
if order == "desc":
|
||
query = query.order_by(desc(getattr(EvolutionSkill, sort_by, EvolutionSkill.updated_at)))
|
||
else:
|
||
query = query.order_by(getattr(EvolutionSkill, sort_by, EvolutionSkill.updated_at))
|
||
|
||
skills = query.all()
|
||
return [self._skill_to_dict(s) for s in skills]
|
||
|
||
def get_skill_detail(self, skill_name: str) -> Optional[dict]:
|
||
"""获取技能详情（含SKILL.md内容）"""
|
||
skill = self.db.query(EvolutionSkill).filter(
|
||
EvolutionSkill.user_id == self.user_id,
|
||
EvolutionSkill.skill_name == skill_name
|
||
).first()
|
||
if not skill:
|
||
return None
|
||
|
||
result = self._skill_to_dict(skill)
|
||
# 读取SKILL.md文件内容
|
||
if skill.skill_md_path and os.path.exists(skill.skill_md_path):
|
||
with open(skill.skill_md_path, 'r') as f:
|
||
result['skill_md_content'] = f.read()
|
||
else:
|
||
result['skill_md_content'] = None
|
||
|
||
return result
|
||
|
||
def update_skill(self, skill_name: str, data: dict) -> Optional[dict]:
|
||
"""编辑技能"""
|
||
skill = self.db.query(EvolutionSkill).filter(
|
||
EvolutionSkill.user_id == self.user_id,
|
||
EvolutionSkill.skill_name == skill_name
|
||
).first()
|
||
if not skill:
|
||
return None
|
||
|
||
for key, value in data.items():
|
||
if hasattr(skill, key) and key not in ('id', 'user_id', 'created_at'):
|
||
setattr(skill, key, value)
|
||
|
||
skill.version += 1
|
||
skill.updated_at = datetime.now()
|
||
|
||
# 记录编辑事件
|
||
self._add_event(skill_name, "edited", f"技能 {skill_name} 已更新",
|
||
version_before=skill.version - 1, version_after=skill.version)
|
||
|
||
self.db.commit()
|
||
self.db.refresh(skill)
|
||
return self._skill_to_dict(skill)
|
||
|
||
def delete_skill(self, skill_name: str) -> bool:
|
||
"""删除技能"""
|
||
skill = self.db.query(EvolutionSkill).filter(
|
||
EvolutionSkill.user_id == self.user_id,
|
||
EvolutionSkill.skill_name == skill_name
|
||
).first()
|
||
if not skill:
|
||
return False
|
||
|
||
self._add_event(skill_name, "deleted", f"技能 {skill_name} 已删除")
|
||
self.db.delete(skill)
|
||
self.db.commit()
|
||
return True
|
||
|
||
def freeze_skill(self, skill_name: str) -> Optional[dict]:
|
||
"""冻结技能"""
|
||
skill = self.db.query(EvolutionSkill).filter(
|
||
EvolutionSkill.user_id == self.user_id,
|
||
EvolutionSkill.skill_name == skill_name
|
||
).first()
|
||
if not skill:
|
||
return None
|
||
|
||
skill.state = "frozen"
|
||
skill.updated_at = datetime.now()
|
||
self._add_event(skill_name, "frozen", f"技能 {skill_name} 已冻结")
|
||
self.db.commit()
|
||
self.db.refresh(skill)
|
||
return self._skill_to_dict(skill)
|
||
|
||
def unfreeze_skill(self, skill_name: str) -> Optional[dict]:
|
||
"""解冻技能"""
|
||
skill = self.db.query(EvolutionSkill).filter(
|
||
EvolutionSkill.user_id == self.user_id,
|
||
EvolutionSkill.skill_name == skill_name
|
||
).first()
|
||
if not skill:
|
||
return None
|
||
|
||
skill.state = "main"
|
||
skill.updated_at = datetime.now()
|
||
self._add_event(skill_name, "unfrozen", f"技能 {skill_name} 已解冻")
|
||
self.db.commit()
|
||
self.db.refresh(skill)
|
||
return self._skill_to_dict(skill)
|
||
|
||
def rollback_skill(self, skill_name: str, target_version: int) -> Optional[dict]:
|
||
"""技能回滚：将技能恢复到指定的历史版本"""
|
||
skill = self.db.query(EvolutionSkill).filter(
|
||
EvolutionSkill.user_id == self.user_id,
|
||
EvolutionSkill.skill_name == skill_name
|
||
).first()
|
||
if not skill:
|
||
logger.warning(f"rollback_skill: 技能 {skill_name} 不存在，用户 {self.user_id}")
|
||
return None
|
||
|
||
# 查询目标版本的SKILL.md内容
|
||
target_sv = self.db.query(SkillVersion).filter(
|
||
SkillVersion.user_id == self.user_id,
|
||
SkillVersion.skill_name == skill_name,
|
||
SkillVersion.version == target_version
|
||
).first()
|
||
if not target_sv:
|
||
logger.warning(f"rollback_skill: 版本 {target_version} 不存在，技能 {skill_name}")
|
||
return None
|
||
|
||
version_before = skill.version
|
||
|
||
# 如果skill_md_path存在，以新内容覆盖写入该文件
|
||
if skill.skill_md_path:
|
||
try:
|
||
os.makedirs(os.path.dirname(skill.skill_md_path), exist_ok=True)
|
||
with open(skill.skill_md_path, 'w', encoding='utf-8') as f:
|
||
f.write(target_sv.skill_md_content or "")
|
||
logger.info(f"rollback_skill: 已覆盖SKILL.md文件 {skill.skill_md_path}")
|
||
except Exception as e:
|
||
logger.error(f"rollback_skill: 写入SKILL.md文件失败: {e}")
|
||
return {"error": f"写入SKILL.md文件失败: {str(e)}"}
|
||
|
||
# 保存回滚前的当前版本作为快照（新SkillVersion记录）
|
||
current_content = ""
|
||
if skill.skill_md_path and os.path.exists(skill.skill_md_path):
|
||
with open(skill.skill_md_path, 'r', encoding='utf-8') as f:
|
||
current_content = f.read()
|
||
else:
|
||
# 尝试从数据库获取当前版本内容
|
||
current_sv = self.db.query(SkillVersion).filter(
|
||
SkillVersion.user_id == self.user_id,
|
||
SkillVersion.skill_name == skill_name,
|
||
SkillVersion.version == version_before
|
||
).first()
|
||
if current_sv:
|
||
current_content = current_sv.skill_md_content or ""
|
||
|
||
snapshot_version = SkillVersion(
|
||
user_id=self.user_id,
|
||
skill_name=skill_name,
|
||
version=version_before,
|
||
skill_md_content=current_content,
|
||
git_commit_sha=None,
|
||
description=f"回滚前快照(版本{version_before})"
|
||
)
|
||
self.db.add(snapshot_version)
|
||
|
||
# 更新evolution_skills
|
||
skill.version += 1
|
||
skill.git_commit_sha = None
|
||
skill.updated_at = datetime.now()
|
||
|
||
# 记录回滚事件
|
||
self._add_event(
|
||
skill_name, "rolled_back",
|
||
f"版本 V{version_before} 回滚至 V{target_version}",
|
||
version_before=version_before,
|
||
version_after=skill.version
|
||
)
|
||
|
||
self.db.commit()
|
||
self.db.refresh(skill)
|
||
|
||
logger.info(f"rollback_skill: 技能 {skill_name} 从版本 {version_before} 回滚至 {target_version}")
|
||
return self._skill_to_dict(skill)
|
||
|
||
def get_skill_history(self, skill_name: str) -> List[dict]:
|
||
"""获取技能事件历史"""
|
||
events = self.db.query(EvolutionEvent).filter(
|
||
EvolutionEvent.user_id == self.user_id,
|
||
EvolutionEvent.skill_name == skill_name
|
||
).order_by(desc(EvolutionEvent.created_at)).all()
|
||
return [self._event_to_dict(e) for e in events]
|
||
|
||
def get_skill_trajectories(self, skill_name: str) -> List[dict]:
|
||
"""获取技能来源轨迹（通过 skill_trajectory_links 表关联）"""
|
||
from ..models import SkillTrajectoryLink
|
||
# 通过关联表查询该技能关联的轨迹ID
|
||
links = self.db.query(SkillTrajectoryLink).filter(
|
||
SkillTrajectoryLink.user_id == self.user_id,
|
||
SkillTrajectoryLink.skill_name == skill_name
|
||
).order_by(desc(SkillTrajectoryLink.created_at)).limit(20).all()
|
||
|
||
if not links:
|
||
return []
|
||
|
||
traj_ids = [link.trajectory_id for link in links if link.trajectory_id]
|
||
trajectories = self.db.query(EvolutionTrajectory).filter(
|
||
EvolutionTrajectory.id.in_(traj_ids)
|
||
).order_by(desc(EvolutionTrajectory.created_at)).all()
|
||
|
||
result = []
|
||
for t in trajectories:
|
||
d = self._trajectory_to_dict(t)
|
||
# 附加权重分
|
||
for link in links:
|
||
if link.trajectory_id == t.id:
|
||
d['weightscore'] = link.weightscore
|
||
d['atom_id'] = link.atom_id
|
||
break
|
||
result.append(d)
|
||
return result
|
||
|
||
# ============================================================
|
||
# 进化事件
|
||
# ============================================================
|
||
|
||
def get_events(self, event_type: Optional[str] = None,
|
||
skill_name: Optional[str] = None,
|
||
limit: int = 50) -> List[dict]:
|
||
"""获取进化事件列表"""
|
||
query = self.db.query(EvolutionEvent).filter(EvolutionEvent.user_id == self.user_id)
|
||
if event_type:
|
||
query = query.filter(EvolutionEvent.event_type == event_type)
|
||
if skill_name:
|
||
query = query.filter(EvolutionEvent.skill_name == skill_name)
|
||
|
||
events = query.order_by(desc(EvolutionEvent.created_at)).limit(limit).all()
|
||
return [self._event_to_dict(e) for e in events]
|
||
|
||
# ============================================================
|
||
# 驾驶舱数据
|
||
# ============================================================
|
||
|
||
def get_skill_tree(self) -> Dict:
|
||
"""获取技能图谱数据"""
|
||
skills = self.db.query(EvolutionSkill).filter(
|
||
EvolutionSkill.user_id == self.user_id
|
||
).all()
|
||
|
||
categories = {}
|
||
for s in skills:
|
||
cat = s.category or "未分类"
|
||
if cat not in categories:
|
||
categories[cat] = []
|
||
categories[cat].append(self._skill_to_dict(s))
|
||
|
||
# 统计
|
||
total = len(skills)
|
||
main_count = sum(1 for s in skills if s.state == "main")
|
||
baby_count = sum(1 for s in skills if s.state == "baby")
|
||
staging_count = sum(1 for s in skills if s.state == "staging")
|
||
frozen_count = sum(1 for s in skills if s.state == "frozen")
|
||
|
||
return {
|
||
"categories": [{"name": k, "skills": v} for k, v in categories.items()],
|
||
"total_count": total,
|
||
"main_count": main_count,
|
||
"baby_count": baby_count,
|
||
"staging_count": staging_count,
|
||
"frozen_count": frozen_count,
|
||
}
|
||
|
||
def get_timeline(self, event_type: Optional[str] = None,
|
||
limit: int = 50) -> List[dict]:
|
||
"""获取进化时间线"""
|
||
return self.get_events(event_type=event_type, limit=limit)
|
||
|
||
def get_statistics(self) -> Dict:
|
||
"""获取使用统计数据"""
|
||
skills = self.db.query(EvolutionSkill).filter(
|
||
EvolutionSkill.user_id == self.user_id
|
||
).all()
|
||
|
||
# Top排名
|
||
sorted_skills = sorted(skills, key=lambda s: s.use_count, reverse=True)
|
||
ranking = []
|
||
for s in sorted_skills[:10]:
|
||
ranking.append({
|
||
"skill_name": s.skill_name,
|
||
"category": s.category or "未分类",
|
||
"use_count": s.use_count,
|
||
"state": s.state,
|
||
"last_used_at": s.last_used_at.isoformat() if s.last_used_at else None,
|
||
})
|
||
|
||
# 类别分布
|
||
category_dist = {}
|
||
for s in skills:
|
||
cat = s.category or "未分类"
|
||
category_dist[cat] = category_dist.get(cat, 0) + s.use_count
|
||
|
||
# 总调用次数
|
||
total_use = sum(s.use_count for s in skills)
|
||
|
||
# 最近7天的趋势（按日期分组）
|
||
seven_days_ago = datetime.now() - timedelta(days=7)
|
||
recent_trajectories = self.db.query(EvolutionTrajectory).filter(
|
||
EvolutionTrajectory.user_id == self.user_id,
|
||
EvolutionTrajectory.created_at >= seven_days_ago,
|
||
EvolutionTrajectory.status == "done"
|
||
).all()
|
||
|
||
# 按天汇总
|
||
daily_counts = {}
|
||
for t in recent_trajectories:
|
||
day = t.created_at.strftime("%m-%d")
|
||
daily_counts[day] = daily_counts.get(day, 0) + 1
|
||
|
||
# 低使用提醒（超过30天未使用）
|
||
thirty_days_ago = datetime.now() - timedelta(days=30)
|
||
zero_use_skills = [
|
||
self._skill_to_dict(s) for s in skills
|
||
if s.use_count < 5 and (s.last_used_at is None or s.last_used_at < thirty_days_ago)
|
||
]
|
||
|
||
return {
|
||
"ranking": ranking,
|
||
"total_use_count": total_use,
|
||
"category_distribution": [{"category": k, "count": v} for k, v in category_dist.items()],
|
||
"daily_trend": [{"date": k, "count": v} for k, v in sorted(daily_counts.items())],
|
||
"low_use_skills": zero_use_skills,
|
||
}
|
||
|
||
def get_profile(self) -> Dict:
|
||
"""获取AI能力画像"""
|
||
skills = self.db.query(EvolutionSkill).filter(
|
||
EvolutionSkill.user_id == self.user_id
|
||
).all()
|
||
|
||
total = len(skills)
|
||
main_count = sum(1 for s in skills if s.state == "main")
|
||
staging_count = sum(1 for s in skills if s.state == "staging")
|
||
baby_count = sum(1 for s in skills if s.state == "baby")
|
||
total_use = sum(s.use_count for s in skills)
|
||
|
||
# 计算能力维度
|
||
dim_scores = {d["key"]: {"label": d["label"], "value": 0, "maxValue": 10} for d in ABILITY_DIMENSIONS}
|
||
for s in skills:
|
||
dim_key = CATEGORY_DIMENSION_MAP.get(s.category, "qa")
|
||
if dim_key in dim_scores:
|
||
# 成熟技能贡献更大
|
||
multiplier = {"main": 1.5, "staging": 1.0, "baby": 0.5, "frozen": 0.3}.get(s.state, 1.0)
|
||
dim_scores[dim_key]["value"] += min(s.use_count * 0.2 * multiplier, 3)
|
||
|
||
dimensions = list(dim_scores.values())
|
||
for d in dimensions:
|
||
d["value"] = round(min(d["value"], 10), 1)
|
||
|
||
# 进化等级计算
|
||
level_score = (total * 2 + main_count * 5 + staging_count * 3 + baby_count * 1 + total_use * 0.01)
|
||
level = min(max(int(level_score / 10) + 1, 1), 10)
|
||
|
||
level_titles = {
|
||
1: "AI初生", 2: "学习幼苗", 3: "技能萌芽", 4: "成长小树",
|
||
5: "能力之林", 6: "智慧果园", 7: "知识大师", 8: "AI专家",
|
||
9: "进化先驱", 10: "超级龙虾"
|
||
}
|
||
|
||
# 能力标签
|
||
all_tags = []
|
||
for s in skills:
|
||
if s.tags:
|
||
all_tags.extend(s.tags)
|
||
# 去重并按技能数量加权排序
|
||
tag_counter = Counter(all_tags)
|
||
top_tags = [tag for tag, _ in tag_counter.most_common(12)]
|
||
if not top_tags:
|
||
top_tags = ["AI进化中", "持续学习", "数据分析", "文字处理", "自动化", "智能助手"]
|
||
|
||
# 成长建议
|
||
suggestions = []
|
||
if total == 0:
|
||
suggestions = [
|
||
"多与AI进行各类对话，让它从实际交互中学习你的需求和风格",
|
||
"尝试在不同领域使用AI：写作、编程、数据分析、创意生成等"
|
||
]
|
||
else:
|
||
dim_v = {d["label"]: d["value"] for d in dimensions}
|
||
weak_dims = [d for d in dimensions if d["value"] < 3]
|
||
if weak_dims:
|
||
suggestions.append(f"建议多进行{weak_dims[0]['label']}类对话，培养该领域技能")
|
||
if baby_count > 0:
|
||
suggestions.append(f"你有{baby_count}个萌芽技能，保持使用帮助它们成长")
|
||
if not any(s.state == "staging" for s in skills):
|
||
suggestions.append("继续积累高质量对话，新技能将自动进入灰度验证")
|
||
suggestions.append("持续在各类场景使用AI，系统会自动发掘你的专属技能")
|
||
|
||
return {
|
||
"level": level,
|
||
"level_title": level_titles.get(level, "进化者"),
|
||
"total_skills": total,
|
||
"active_skills": main_count,
|
||
"staging_skills": staging_count,
|
||
"baby_skills": baby_count,
|
||
"total_use_count": total_use,
|
||
"dimensions": dimensions,
|
||
"tags": top_tags,
|
||
"growth_suggestions": suggestions[:4],
|
||
}
|
||
|
||
# ============================================================
|
||
# 轨迹管理
|
||
# ============================================================
|
||
|
||
def submit_trajectory(self, conversation_id: str, messages_data: List[dict]) -> dict:
|
||
"""提交对话轨迹（对话结束后调用）"""
|
||
user_dir = UPLOAD_DIR.parent / "users" / self.user_id / "trajectories"
|
||
user_dir.mkdir(parents=True, exist_ok=True)
|
||
|
||
import uuid
|
||
traj_id = str(uuid.uuid4())[:8]
|
||
traj_dir = user_dir / f"traj_{traj_id}"
|
||
traj_dir.mkdir(exist_ok=True)
|
||
|
||
# 生成标准化轨迹文件
|
||
traj_md = self._format_trajectory_md(messages_data)
|
||
traj_file = traj_dir / f"traj_{traj_id}.md"
|
||
with open(traj_file, 'w', encoding='utf-8') as f:
|
||
f.write(traj_md)
|
||
|
||
# 记录到数据库
|
||
trajectory = EvolutionTrajectory(
|
||
user_id=self.user_id,
|
||
conversation_id=conversation_id,
|
||
file_path=str(traj_file),
|
||
status="discovered",
|
||
atom_count=0,
|
||
token_used=sum(m.get("tokens", 0) for m in messages_data),
|
||
)
|
||
self.db.add(trajectory)
|
||
self.db.commit()
|
||
self.db.refresh(trajectory)
|
||
|
||
logger.info(f"用户 {self.user_id} 提交轨迹 {traj_id}, 对话 {conversation_id}")
|
||
|
||
return self._trajectory_to_dict(trajectory)
|
||
|
||
def _format_trajectory_md(self, messages: List[dict]) -> str:
|
||
"""将消息列表格式化为xSkill兼容的轨迹Markdown"""
|
||
lines = ["# Conversation Trajectory\n"]
|
||
for msg in messages:
|
||
role = msg.get("role", "unknown")
|
||
content = msg.get("content", "")
|
||
if role == "user":
|
||
lines.append(f"## User\n\n{content}\n")
|
||
elif role == "assistant":
|
||
lines.append(f"## Assistant\n\n{content}\n")
|
||
return "\n".join(lines)
|
||
|
||
# ============================================================
|
||
# P0核心：流水线状态、发布到市场、进化报告、团队共享
|
||
# ============================================================
|
||
|
||
def get_pipeline_status(self) -> dict:
|
||
"""获取流水线运行状态"""
|
||
# scheduler运行状态（通过全局单例）
|
||
watcher = EvolutionWatcherManager()
|
||
scheduler = watcher._schedulers.get(self.user_id)
|
||
scheduler_running = scheduler.is_running() if scheduler else False
|
||
|
||
# 查询 user_evolution_config
|
||
config = self.db.query(UserEvolutionConfig).filter(
|
||
UserEvolutionConfig.user_id == self.user_id
|
||
).first()
|
||
|
||
watcher_status = config.watcher_status if config else "unknown"
|
||
pipeline_status = config.pipeline_status if config else "unknown"
|
||
|
||
# discovered_count（待处理轨迹数）
|
||
discovered_count = self.db.query(EvolutionTrajectory).filter(
|
||
EvolutionTrajectory.user_id == self.user_id,
|
||
EvolutionTrajectory.status == "discovered"
|
||
).count()
|
||
|
||
# done_count（已完成轨迹数）
|
||
done_count = self.db.query(EvolutionTrajectory).filter(
|
||
EvolutionTrajectory.user_id == self.user_id,
|
||
EvolutionTrajectory.status == "done"
|
||
).count()
|
||
|
||
# total_skills
|
||
total_skills = self.db.query(EvolutionSkill).filter(
|
||
EvolutionSkill.user_id == self.user_id
|
||
).count()
|
||
|
||
# total_trajectories
|
||
total_trajectories = self.db.query(EvolutionTrajectory).filter(
|
||
EvolutionTrajectory.user_id == self.user_id
|
||
).count()
|
||
|
||
logger.info(f"get_pipeline_status: 用户 {self.user_id}, scheduler_running={scheduler_running}")
|
||
|
||
return {
|
||
"scheduler_running": scheduler_running,
|
||
"watcher_status": watcher_status,
|
||
"pipeline_status": pipeline_status,
|
||
"discovered_count": discovered_count,
|
||
"done_count": done_count,
|
||
"total_skills": total_skills,
|
||
"total_trajectories": total_trajectories,
|
||
}
|
||
|
||
def publish_to_market(self, skill_name: str, price: float = 0.00, market_description: str = "") -> dict:
|
||
"""将技能发布到应用市场"""
|
||
skill = self.db.query(EvolutionSkill).filter(
|
||
EvolutionSkill.user_id == self.user_id,
|
||
EvolutionSkill.skill_name == skill_name
|
||
).first()
|
||
if not skill:
|
||
logger.warning(f"publish_to_market: 技能 {skill_name} 不存在")
|
||
return {"error": "技能不存在"}
|
||
|
||
# 验证技能 state=main，否则返回错误
|
||
if skill.state != "main":
|
||
logger.warning(f"publish_to_market: 技能 {skill_name} 状态为 {skill.state}，不是main")
|
||
return {"error": f"只有main状态的技能才能发布到市场，当前状态: {skill.state}"}
|
||
|
||
# 查询是否已经发布过
|
||
existing_record = self.db.query(SkillPublishRecord).filter(
|
||
SkillPublishRecord.user_id == self.user_id,
|
||
SkillPublishRecord.skill_name == skill_name,
|
||
SkillPublishRecord.status == "published"
|
||
).first()
|
||
if existing_record:
|
||
logger.warning(f"publish_to_market: 技能 {skill_name} 已发布过")
|
||
return {"error": "该技能已发布到市场，不能重复发布"}
|
||
|
||
# 在 market_apps 表中创建记录
|
||
market_app = MarketApp(
|
||
name=f"{skill_name}(进化)",
|
||
type="skill",
|
||
source="user",
|
||
publisher_id=self.user_id,
|
||
description=market_description or skill.description or "",
|
||
price=price,
|
||
skill_content="来自AI自进化技能的SKILL.md",
|
||
status="published",
|
||
)
|
||
self.db.add(market_app)
|
||
self.db.flush()
|
||
|
||
# 在 skill_publish_records 表中创建记录
|
||
publish_record = SkillPublishRecord(
|
||
skill_id=skill.id,
|
||
skill_name=skill_name,
|
||
user_id=self.user_id,
|
||
market_app_id=market_app.id,
|
||
price=price,
|
||
market_description=market_description,
|
||
status="published",
|
||
)
|
||
self.db.add(publish_record)
|
||
|
||
# 记录事件
|
||
self._add_event(skill_name, "published", f"技能 {skill_name} 已发布到市场，价格 {price}")
|
||
|
||
self.db.commit()
|
||
logger.info(f"publish_to_market: 技能 {skill_name} 已发布到市场，market_app_id={market_app.id}")
|
||
|
||
return {
|
||
"skill_name": skill_name,
|
||
"market_app_id": market_app.id,
|
||
"price": price,
|
||
"status": "published",
|
||
"publish_record_id": publish_record.id,
|
||
}
|
||
|
||
def generate_report(self, report_type: str = "weekly") -> dict:
|
||
"""生成进化报告（weekly/monthly）"""
|
||
now = datetime.now()
|
||
if report_type == "weekly":
|
||
period_start = (now - timedelta(days=7)).date()
|
||
period_end = now.date()
|
||
elif report_type == "monthly":
|
||
period_start = (now - timedelta(days=30)).date()
|
||
period_end = now.date()
|
||
else:
|
||
return {"error": f"不支持的报告类型: {report_type}，仅支持 weekly/monthly"}
|
||
|
||
# 新技能数（skills表中created_at在时间范围内的）
|
||
new_skills_count = self.db.query(EvolutionSkill).filter(
|
||
EvolutionSkill.user_id == self.user_id,
|
||
EvolutionSkill.created_at >= period_start,
|
||
EvolutionSkill.created_at <= period_end
|
||
).count()
|
||
|
||
# 技能升级数（events中state_changed事件，event_type含 promoted）
|
||
upgrades_count = self.db.query(EvolutionEvent).filter(
|
||
EvolutionEvent.user_id == self.user_id,
|
||
EvolutionEvent.created_at >= period_start,
|
||
EvolutionEvent.created_at <= period_end,
|
||
EvolutionEvent.event_type.in_(["promoted", "edited", "milestone"])
|
||
).count()
|
||
|
||
# 最活跃技能TOP5
|
||
top_skills = self.db.query(EvolutionSkill).filter(
|
||
EvolutionSkill.user_id == self.user_id
|
||
).order_by(desc(EvolutionSkill.use_count)).limit(5).all()
|
||
top_skills_data = [self._skill_to_dict(s) for s in top_skills]
|
||
|
||
# 总调用次数
|
||
total_calls_raw = self.db.query(func.sum(EvolutionSkill.use_count)).filter(
|
||
EvolutionSkill.user_id == self.user_id
|
||
).scalar() or 0
|
||
total_calls = int(total_calls_raw) if total_calls_raw else 0
|
||
|
||
# 等级变化（查询报告期内的promoted事件数作为等级变化的近似）
|
||
level_changes = self.db.query(EvolutionEvent).filter(
|
||
EvolutionEvent.user_id == self.user_id,
|
||
EvolutionEvent.created_at >= period_start,
|
||
EvolutionEvent.created_at <= period_end,
|
||
EvolutionEvent.event_type == "promoted"
|
||
).count()
|
||
|
||
# 维度分析（基于当前skills计算）
|
||
skills = self.db.query(EvolutionSkill).filter(
|
||
EvolutionSkill.user_id == self.user_id
|
||
).all()
|
||
dim_scores = {d["key"]: {"label": d["label"], "value": 0, "maxValue": 10} for d in ABILITY_DIMENSIONS}
|
||
for s in skills:
|
||
dim_key = CATEGORY_DIMENSION_MAP.get(s.category, "qa")
|
||
if dim_key in dim_scores:
|
||
multiplier = {"main": 1.5, "staging": 1.0, "baby": 0.5, "frozen": 0.3}.get(s.state, 1.0)
|
||
dim_scores[dim_key]["value"] += min(s.use_count * 0.2 * multiplier, 3)
|
||
dimensions = [{"label": v["label"], "value": round(min(v["value"], 10), 1)} for v in dim_scores.values()]
|
||
|
||
# 构建报告数据
|
||
report_data = {
|
||
"report_type": report_type,
|
||
"period_start": period_start.isoformat(),
|
||
"period_end": period_end.isoformat(),
|
||
"generated_at": now.isoformat(),
|
||
"summary": {
|
||
"new_skills_count": new_skills_count,
|
||
"upgrades_count": upgrades_count,
|
||
"total_skills": len(skills),
|
||
"total_calls": total_calls,
|
||
"level_changes": level_changes,
|
||
},
|
||
"top_skills": top_skills_data,
|
||
"dimensions": dimensions,
|
||
}
|
||
|
||
# 存入 evolution_reports 表
|
||
evolution_report = EvolutionReport(
|
||
user_id=self.user_id,
|
||
report_type=report_type,
|
||
period_start=period_start,
|
||
period_end=period_end,
|
||
report_data=report_data,
|
||
)
|
||
self.db.add(evolution_report)
|
||
|
||
# 发送通知给用户
|
||
notification = Notification(
|
||
user_id=self.user_id,
|
||
type="evolution_report",
|
||
title=f"{'周报' if report_type == 'weekly' else '月报'}已生成",
|
||
content=f"你的AI进化{'周报' if report_type == 'weekly' else '月报'}已生成。"
|
||
f"新增技能 {new_skills_count} 个，技能升级 {upgrades_count} 次，总调用次数 {total_calls}。",
|
||
)
|
||
self.db.add(notification)
|
||
|
||
self.db.commit()
|
||
self.db.refresh(evolution_report)
|
||
|
||
logger.info(f"generate_report: 用户 {self.user_id} 生成 {report_type} 报告, report_id={evolution_report.id}")
|
||
|
||
return report_data
|
||
|
||
def get_reports(self, report_type: Optional[str] = None) -> List[dict]:
|
||
"""获取用户所有报告列表"""
|
||
query = self.db.query(EvolutionReport).filter(
|
||
EvolutionReport.user_id == self.user_id
|
||
)
|
||
if report_type:
|
||
query = query.filter(EvolutionReport.report_type == report_type)
|
||
reports = query.order_by(desc(EvolutionReport.created_at)).all()
|
||
|
||
result = []
|
||
for r in reports:
|
||
result.append({
|
||
"id": r.id,
|
||
"report_type": r.report_type,
|
||
"period_start": r.period_start.isoformat() if r.period_start else None,
|
||
"period_end": r.period_end.isoformat() if r.period_end else None,
|
||
"created_at": r.created_at.isoformat() if r.created_at else None,
|
||
})
|
||
return result
|
||
|
||
def get_report_detail(self, report_id: str) -> Optional[dict]:
|
||
"""获取报告详情"""
|
||
report = self.db.query(EvolutionReport).filter(
|
||
EvolutionReport.user_id == self.user_id,
|
||
EvolutionReport.id == report_id
|
||
).first()
|
||
if not report:
|
||
return None
|
||
|
||
return {
|
||
"id": report.id,
|
||
"user_id": report.user_id,
|
||
"report_type": report.report_type,
|
||
"period_start": report.period_start.isoformat() if report.period_start else None,
|
||
"period_end": report.period_end.isoformat() if report.period_end else None,
|
||
"report_data": report.report_data or {},
|
||
"created_at": report.created_at.isoformat() if report.created_at else None,
|
||
}
|
||
|
||
def share_to_team(self, skill_name: str, team_id: str) -> dict:
|
||
"""将技能分享到团队"""
|
||
# 验证技能存在且属于当前用户
|
||
skill = self.db.query(EvolutionSkill).filter(
|
||
EvolutionSkill.user_id == self.user_id,
|
||
EvolutionSkill.skill_name == skill_name
|
||
).first()
|
||
if not skill:
|
||
logger.warning(f"share_to_team: 技能 {skill_name} 不存在或不属于用户 {self.user_id}")
|
||
return {"error": "技能不存在或不属于当前用户"}
|
||
|
||
# 验证用户属于该team（检查team存在且用户是owner，或者有team_member关系需要检查）
|
||
team = self.db.query(Team).filter(
|
||
Team.id == team_id
|
||
).first()
|
||
if not team:
|
||
logger.warning(f"share_to_team: team {team_id} 不存在")
|
||
return {"error": "团队不存在"}
|
||
|
||
# 检查用户是team的owner或属于该team（根据模型结构，Team有owner_id字段）
|
||
is_owner = team.owner_id == self.user_id
|
||
if not is_owner:
|
||
logger.warning(f"share_to_team: 用户 {self.user_id} 不属于 team {team_id}")
|
||
return {"error": "用户不属于该团队"}
|
||
|
||
# 检查是否已分享
|
||
existing = self.db.query(TeamSharedSkill).filter(
|
||
TeamSharedSkill.team_id == team_id,
|
||
TeamSharedSkill.skill_id == skill.id
|
||
).first()
|
||
if existing:
|
||
logger.warning(f"share_to_team: 技能 {skill_name} 已分享至 team {team_id}")
|
||
return {"error": "该技能已分享至此团队"}
|
||
|
||
# 在 team_shared_skills 表中创建记录
|
||
shared_skill = TeamSharedSkill(
|
||
team_id=team_id,
|
||
skill_id=skill.id,
|
||
skill_name=skill_name,
|
||
shared_by=self.user_id,
|
||
shared_by_name=None,
|
||
is_public=True,
|
||
)
|
||
self.db.add(shared_skill)
|
||
|
||
# 记录事件
|
||
self._add_event(skill_name, "shared", f"技能 {skill_name} 已分享至团队 {team.name}")
|
||
|
||
self.db.commit()
|
||
self.db.refresh(shared_skill)
|
||
|
||
logger.info(f"share_to_team: 技能 {skill_name} 已分享至 team {team_id}, shared_id={shared_skill.id}")
|
||
|
||
return {
|
||
"shared_id": shared_skill.id,
|
||
"skill_name": skill_name,
|
||
"team_id": team_id,
|
||
"team_name": team.name,
|
||
"shared_by": self.user_id,
|
||
"is_public": True,
|
||
}
|
||
|
||
# ============================================================
|
||
# 辅助方法
|
||
# ============================================================
|
||
|
||
def _skill_to_dict(self, skill: EvolutionSkill) -> dict:
|
||
return {
|
||
"id": skill.id,
|
||
"skill_name": skill.skill_name,
|
||
"description": skill.description or "",
|
||
"tags": skill.tags or [],
|
||
"category": skill.category or "未分类",
|
||
"state": skill.state,
|
||
"use_count": skill.use_count,
|
||
"version": skill.version,
|
||
"last_used_at": skill.last_used_at.isoformat() if skill.last_used_at else None,
|
||
"created_at": skill.created_at.isoformat() if skill.created_at else None,
|
||
"updated_at": skill.updated_at.isoformat() if skill.updated_at else None,
|
||
}
|
||
|
||
def _event_to_dict(self, event: EvolutionEvent) -> dict:
|
||
return {
|
||
"id": event.id,
|
||
"skill_name": event.skill_name,
|
||
"event_type": event.event_type,
|
||
"description": event.description or "",
|
||
"version_before": event.version_before,
|
||
"version_after": event.version_after,
|
||
"created_at": event.created_at.isoformat() if event.created_at else None,
|
||
}
|
||
|
||
def _trajectory_to_dict(self, traj: EvolutionTrajectory) -> dict:
|
||
return {
|
||
"id": traj.id,
|
||
"conversation_id": traj.conversation_id,
|
||
"file_path": traj.file_path,
|
||
"status": traj.status,
|
||
"atom_count": traj.atom_count,
|
||
"token_used": traj.token_used,
|
||
"created_at": traj.created_at.isoformat() if traj.created_at else None,
|
||
"processed_at": traj.processed_at.isoformat() if traj.processed_at else None,
|
||
}
|
||
|
||
def _add_event(self, skill_name: str, event_type: str, description: str,
|
||
version_before: Optional[int] = None, version_after: Optional[int] = None):
|
||
event = EvolutionEvent(
|
||
user_id=self.user_id,
|
||
skill_name=skill_name,
|
||
event_type=event_type,
|
||
description=description,
|
||
version_before=version_before,
|
||
version_after=version_after,
|
||
)
|
||
self.db.add(event)
|
||
|
||
|
||
def get_evolution_service(db: Session, user_id: str) -> EvolutionService:
|
||
return EvolutionService(db, user_id)
|