Kris
bd60c15df0
实现了 Teams 机器人所需的全功能组件，包括： - 基础命令解析与帮助卡片生成 - 租户验证与访问控制 - 自定义 UA 与媒体工具 - 消息分块、批注处理与会话管理 - 防抖、缓存与配置路由能力 - 投票、配对、审计与运行时状态管理 - TTS 语音合成与卡片构建工具 - 群组管理与权限控制逻辑
212 lines
6.9 KiB
Python
212 lines
6.9 KiB
Python
"""Microsoft Teams AI 反馈系统。
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在 AI 回复消息中附加 Teams Native 拇指标反馈按钮，
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处理 feedback invoke 交互，以及负面反馈反思流水线。
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"""
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from __future__ import annotations
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import json
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import re
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import time
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from pathlib import Path
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from typing import Any
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from yuxi.utils.logging_config import logger
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FEEDBACK_INVOKE_NAME = "message/submitAction"
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_REFLECTION_PROMPT_TEMPLATE = (
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"You received a 👎 negative feedback on your last response. "
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"Please think about what went wrong and write a brief, constructive self-reflection "
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"to help you improve future responses. Focus on specific improvements.\n\n"
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"The user's message was:\n{user_message}\n\nYour response was:\n{bot_response}"
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)
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_CODE_BLOCK_PATTERN = re.compile(r"```(?:json)?\s*([\s\S]*?)```", re.IGNORECASE)
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_DEFAULT_REFLECTION_COOLDOWN_MS = 300000
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_LEARNINGS_STORE_FILENAME = "msteams-feedback-learnings.json"
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def build_feedback_channel_data(
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feedback_enabled: bool = True,
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feedback_reflection: bool = False,
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feedback_reflection_cooldown_ms: int = _DEFAULT_REFLECTION_COOLDOWN_MS,
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) -> dict[str, Any] | None:
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if not feedback_enabled:
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return None
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data: dict[str, Any] = {"feedbackLoopEnabled": True}
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if feedback_reflection:
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data["feedbackReflection"] = True
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data["feedbackReflectionCooldownMs"] = feedback_reflection_cooldown_ms
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return data
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def build_feedback_activity(text: str, feedback_enabled: bool = True) -> dict[str, Any]:
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activity: dict[str, Any] = {
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"type": "message",
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"text": text,
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"textFormat": "markdown",
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}
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channel_data = build_feedback_channel_data(feedback_enabled=feedback_enabled)
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if channel_data:
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activity["channelData"] = channel_data
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return activity
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def is_feedback_invoke(activity: dict[str, Any]) -> bool:
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return activity.get("name", "") == FEEDBACK_INVOKE_NAME
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def parse_feedback_value(activity: dict[str, Any]) -> str:
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value = activity.get("value", {}) or {}
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return str(value.get("feedbackValue", ""))
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def build_reflection_prompt(user_message: str, bot_response: str) -> str:
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return _REFLECTION_PROMPT_TEMPLATE.format(
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user_message=user_message[:2000],
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bot_response=bot_response[:2000],
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)
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def parse_reflection_response(raw: str) -> str:
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"""多路径解析反思响应：JSON.parse → 代码块提取 → 安全回退。"""
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json_result = _try_parse_json_reflection(raw)
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if json_result:
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return json_result
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block_result = _try_extract_code_block(raw)
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if block_result:
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return block_result
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return raw.strip()[:1000]
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def _try_parse_json_reflection(raw: str) -> str:
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try:
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data = json.loads(raw)
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if isinstance(data, dict):
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return str(data.get("reflection", "") or data.get("thought", "") or "")
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if isinstance(data, list):
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return " ".join(str(item) for item in data)[:1000]
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return str(data)
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except (json.JSONDecodeError, TypeError):
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return ""
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def _try_extract_code_block(raw: str) -> str:
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m = _CODE_BLOCK_PATTERN.search(raw)
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if m:
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inner = m.group(1).strip()
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json_result = _try_parse_json_reflection(inner)
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return json_result or inner[:1000]
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return ""
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class FeedbackLearningsStore:
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"""Session-level 学习存储：持久化用户过反馈的反思结果。"""
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def __init__(self, storage_dir: str | None = None):
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self._storage_dir = Path(storage_dir or str(Path.home() / ".yuxi" / "msteams"))
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self._storage_dir.mkdir(parents=True, exist_ok=True)
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self._learnings: dict[str, list[dict[str, Any]]] = {}
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self._cooldowns: dict[str, float] = {}
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self._load()
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@property
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def file_path(self) -> Path:
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return self._storage_dir / _LEARNINGS_STORE_FILENAME
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def _load(self) -> None:
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if not self.file_path.exists():
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return
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try:
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data = json.loads(self.file_path.read_text(encoding="utf-8"))
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self._learnings = data.get("learnings", {})
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self._cooldowns = data.get("cooldowns", {})
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except (json.JSONDecodeError, OSError):
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pass
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def _save(self) -> None:
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try:
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self.file_path.write_text(
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json.dumps(
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{"learnings": self._learnings, "cooldowns": self._cooldowns},
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ensure_ascii=False,
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indent=2,
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),
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encoding="utf-8",
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)
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except OSError as e:
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logger.error(f"MSTeams learnings: failed to save: {e}")
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def is_reflection_allowed(self, session_key: str, cooldown_ms: int = _DEFAULT_REFLECTION_COOLDOWN_MS) -> bool:
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last = self._cooldowns.get(session_key, 0)
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elapsed = (time.monotonic() - last) * 1000
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return elapsed >= cooldown_ms
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def store_session_learning(self, session_key: str, user_message: str, bot_response: str, reflection: str) -> None:
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entry = {
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"timestamp": time.time(),
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"user_message": user_message[:500],
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"bot_response": bot_response[:500],
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"reflection": reflection[:1000],
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}
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if session_key not in self._learnings:
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self._learnings[session_key] = []
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self._learnings[session_key].append(entry)
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if len(self._learnings[session_key]) > 50:
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self._learnings[session_key] = self._learnings[session_key][-50:]
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self._cooldowns[session_key] = time.monotonic()
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self._save()
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def load_session_learnings(self, session_key: str) -> list[dict[str, Any]]:
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return self._learnings.get(session_key, [])[-10:]
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def clear_reflection_cooldowns(self, session_key: str | None = None) -> None:
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if session_key:
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self._cooldowns.pop(session_key, None)
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else:
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self._cooldowns.clear()
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self._save()
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def clear(self) -> None:
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self._learnings.clear()
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self._cooldowns.clear()
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self._save()
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@property
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def session_count(self) -> int:
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return len(self._learnings)
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def process_feedback(
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activity: dict[str, Any],
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on_positive: Any = None,
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on_negative: Any = None,
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) -> dict[str, Any]:
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feedback_value = parse_feedback_value(activity)
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from_info = activity.get("from", {}) or {}
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user_id = from_info.get("aadObjectId", "") or from_info.get("id", "")
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user_name = from_info.get("name", "")
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is_positive = feedback_value == "positive"
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is_negative = feedback_value == "negative"
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result: dict[str, Any] = {
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"feedback_value": feedback_value,
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"is_positive": is_positive,
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"is_negative": is_negative,
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"user_id": user_id,
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"user_name": user_name,
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"reply_to_id": activity.get("replyToId", ""),
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}
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if is_negative:
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logger.info(f"MSTeams negative feedback received from {user_name} ({user_id})")
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elif is_positive:
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logger.info(f"MSTeams positive feedback received from {user_name} ({user_id})")
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return result
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