chore: initial baseline with P0-safety .gitignore
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Simon
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# =============================================================================
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# 企微IT智能服务台 — AI Wingman 服务（坐席智能副驾驶）
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# =============================================================================
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# 说明：复用 Dify 基础设施，使用独立的 Wingman Agent（Agent 2），
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# 与员工端 AI（Agent 1）共用知识库但 system prompt 不同。
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#
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# 核心能力：
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# 1. 生成 AI 草稿回复 — 基于对话上下文为坐席生成专业回复
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# 2. 生成会话自动摘要 — 结单时自动提取问题/原因/解决方案
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# 3. 生成自动标签建议 — 基于对话内容建议标签分类
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#
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# 降级策略：Wingman Agent 不可用时返回友好错误信息，不抛异常
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# =============================================================================
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import json
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import logging
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from typing import Any, Dict, List, Optional
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import httpx
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from app.config import settings
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logger = logging.getLogger(__name__)
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class WingmanService:
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"""AI Wingman 服务 — 坐席智能副驾驶。
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复用 Dify 基础设施，使用独立的 Wingman Agent（Agent 2），
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与员工端 AI（Agent 1）共用知识库但 system prompt 不同。
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三个核心方法使用不同的 system prompt：
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- 草稿生成：生成坐席可采纳的专业回复
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- 摘要生成：提取结构化的会话摘要
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- 标签建议：建议标签分类和优先级
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"""
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# --------------------------------------------------------------------------
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# System Prompt 定义
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# --------------------------------------------------------------------------
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_DRAFT_SYSTEM_PROMPT: str = (
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"你是一个IT服务坐席助手，基于对话上下文为坐席生成专业、准确的回复草稿。"
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"直接输出回复内容，不要解释。"
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)
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_SUMMARY_SYSTEM_PROMPT: str = (
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"你是一个IT服务分析助手，基于完整对话生成结构化摘要，"
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"包含：问题、原因、解决方案。以JSON格式输出。"
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"输出格式：{\"problem\": \"问题描述\", \"cause\": \"原因\", \"solution\": \"解决方案\"}"
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)
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_TAGS_SYSTEM_PROMPT: str = (
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"你是一个IT服务分类助手，基于对话内容建议标签分类。以JSON格式输出。"
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"输出格式：{\"suggested_tags\": [\"标签1\", \"标签2\"], \"category\": \"分类\", \"priority\": \"low/medium/high\"}"
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)
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def __init__(self):
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"""初始化 Wingman 服务。
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从配置读取 Wingman Agent 的 API 地址和认证信息。
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独立于 AIService，使用自己的 httpx 客户端。
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"""
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# Wingman Agent 专用 API 端点
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self.api_url = settings.dify_wingman_api_url
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# Wingman Agent API Key
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self.api_key = settings.dify_wingman_api_key
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# 请求超时（秒）
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self.timeout = settings.dify_wingman_timeout
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# httpx 异步客户端（复用连接池）
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self._client: Optional[httpx.AsyncClient] = None
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async def _get_client(self) -> httpx.AsyncClient:
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"""获取或创建 httpx 异步客户端（懒加载）。
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复用连接池，避免每次请求都创建新连接。
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Returns:
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httpx.AsyncClient: 异步 HTTP 客户端实例
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"""
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if self._client is None or self._client.is_closed:
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self._client = httpx.AsyncClient(
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timeout=httpx.Timeout(self.timeout),
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headers={
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json",
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}
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)
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return self._client
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async def close(self):
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"""关闭 httpx 客户端，释放连接池资源。"""
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if self._client and not self._client.is_closed:
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await self._client.aclose()
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self._client = None
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logger.debug("WingmanService httpx client closed")
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# --------------------------------------------------------------------------
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# 核心方法 1：生成 AI 草稿回复
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# --------------------------------------------------------------------------
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async def generate_draft(
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self,
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conversation_id: str,
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messages: List[Dict[str, Any]],
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db: Any = None,
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) -> Dict[str, Any]:
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"""生成 AI 草稿回复。
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传入当前会话的完整消息历史，让 Wingman Agent 基于上下文
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生成坐席可以采纳的草稿回复。
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Args:
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conversation_id: 会话ID
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messages: 会话消息历史列表，每条消息包含 sender_type/content 等
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db: 数据库会话（可选，当前未使用）
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Returns:
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Dict: {
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"content": str, # 草稿内容
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"confidence": float, # 置信度（0-1）
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"reasoning": str, # 生成推理说明
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}
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"""
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# 构建对话上下文消息列表
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context_messages = self._build_context_messages(
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messages, self._DRAFT_SYSTEM_PROMPT
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)
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try:
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result = await self._call_wingman_api(context_messages)
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if result is None:
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return {
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"content": "",
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"confidence": 0.0,
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"reasoning": "Wingman 服务暂不可用",
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}
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reply_content = result
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# 基于回复长度和内容质量估算置信度
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confidence = self._estimate_confidence(reply_content)
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return {
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"content": reply_content,
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"confidence": confidence,
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"reasoning": f"基于最近 {len(messages)} 条对话上下文生成",
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}
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except Exception as e:
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logger.error(f"Wingman 草稿生成失败: {e}")
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return {
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"content": "",
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"confidence": 0.0,
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"reasoning": f"AI 服务异常: {str(e)}",
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}
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# --------------------------------------------------------------------------
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# 核心方法 2：生成会话自动摘要
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# --------------------------------------------------------------------------
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async def generate_summary(
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self,
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conversation_id: str,
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messages: List[Dict[str, Any]],
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) -> Dict[str, Any]:
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"""生成会话自动摘要。
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基于完整对话生成结构化摘要，包含问题、原因、解决方案。
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结单时自动调用。
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Args:
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conversation_id: 会话ID
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messages: 会话消息历史列表
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Returns:
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Dict: {
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"problem": str, # 问题描述
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"cause": str, # 原因分析
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"solution": str, # 解决方案
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}
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"""
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context_messages = self._build_context_messages(
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messages, self._SUMMARY_SYSTEM_PROMPT
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)
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# 默认摘要（降级时使用）
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default_summary = {
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"problem": "无法自动生成摘要",
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"cause": "",
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"solution": "",
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}
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try:
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result = await self._call_wingman_api(context_messages)
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if result is None:
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return default_summary
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# 尝试解析 JSON 格式的摘要
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parsed = self._parse_json_response(result, default_summary)
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return {
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"problem": parsed.get("problem", default_summary["problem"]),
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"cause": parsed.get("cause", default_summary["cause"]),
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"solution": parsed.get("solution", default_summary["solution"]),
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}
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except Exception as e:
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logger.error(f"Wingman 摘要生成失败: {e}")
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return default_summary
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# --------------------------------------------------------------------------
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# 核心方法 3：生成自动标签建议
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# --------------------------------------------------------------------------
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async def suggest_tags(
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self,
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conversation_id: str,
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messages: List[Dict[str, Any]],
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existing_tags: Dict[str, Any] = None,
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) -> Dict[str, Any]:
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"""生成自动标签建议。
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基于对话内容建议标签分类，包含标签列表、分类和优先级。
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Args:
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conversation_id: 会话ID
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messages: 会话消息历史列表
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existing_tags: 已有标签（可选，用于避免重复建议）
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Returns:
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Dict: {
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"suggested_tags": list[str], # 建议标签列表
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"category": str, # 分类
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"priority": str, # 优先级: low/medium/high
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}
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"""
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context_messages = self._build_context_messages(
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messages, self._TAGS_SYSTEM_PROMPT
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)
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# 默认标签建议（降级时使用）
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default_tags = {
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"suggested_tags": [],
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"category": "",
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"priority": "medium",
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}
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try:
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result = await self._call_wingman_api(context_messages)
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if result is None:
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return default_tags
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# 尝试解析 JSON 格式的标签建议
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parsed = self._parse_json_response(result, default_tags)
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return {
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"suggested_tags": parsed.get("suggested_tags", []),
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"category": parsed.get("category", ""),
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"priority": parsed.get("priority", "medium"),
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}
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except Exception as e:
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logger.error(f"Wingman 标签建议失败: {e}")
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return default_tags
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# --------------------------------------------------------------------------
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# 内部方法
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# --------------------------------------------------------------------------
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def _build_context_messages(
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self,
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messages: List[Dict[str, Any]],
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system_prompt: str,
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) -> List[Dict[str, str]]:
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"""构建发送给 Wingman Agent 的消息列表。
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将数据库中的消息历史转换为 OpenAI Chat Completions 格式的
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messages 列表，包含 system prompt 和对话上下文。
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Args:
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messages: 数据库消息列表
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system_prompt: 当前场景的 system prompt
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Returns:
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List[Dict]: OpenAI 格式的消息列表
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"""
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# 构建上下文消息列表
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context: List[Dict[str, str]] = [
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{"role": "system", "content": system_prompt}
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]
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# 角色映射：数据库 sender_type → OpenAI role
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role_map = {
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"employee": "user", # 员工消息 → user
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"agent": "assistant", # 坐席消息 → assistant
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"ai": "assistant", # AI消息 → assistant
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"system": "system", # 系统消息 → system
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}
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for msg in messages:
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role = role_map.get(msg.get("sender_type", ""), "user")
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content = msg.get("content", "")
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if content:
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# 跳过系统消息（已有 system prompt）
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if msg.get("sender_type") == "system":
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continue
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context.append({"role": role, "content": content})
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return context
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async def _call_wingman_api(
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self,
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context_messages: List[Dict[str, str]],
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) -> Optional[str]:
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"""调用 Wingman Agent API（非流式）。
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Args:
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context_messages: OpenAI 格式的消息列表
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Returns:
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Optional[str]: AI 回复内容，失败时返回 None
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"""
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payload = {
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"model": "Chat",
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"messages": context_messages,
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"stream": False,
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"temperature": 0.3, # 适中的温度，保证准确性同时有一定灵活性
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}
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try:
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client = await self._get_client()
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logger.info(f"调用 Wingman API: messages_count={len(context_messages)}")
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response = await client.post(self.api_url, json=payload)
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response.raise_for_status()
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data = response.json()
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# 解析 OpenAI 兼容格式的返回
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choices = data.get("choices", [])
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if not choices:
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logger.warning("Wingman API 返回空 choices")
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return None
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reply_content = choices[0]["message"]["content"]
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logger.info(f"Wingman API 返回: content_length={len(reply_content)}")
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return reply_content
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except httpx.TimeoutException:
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logger.error("Wingman API 超时")
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return None
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except httpx.HTTPStatusError as e:
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logger.error(f"Wingman API HTTP 错误: status={e.response.status_code}")
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return None
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except Exception as e:
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logger.error(f"Wingman API 调用失败: {e}")
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return None
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def _parse_json_response(
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self,
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content: str,
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default: Dict[str, Any],
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) -> Dict[str, Any]:
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"""解析 AI 返回的 JSON 内容。
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Wingman Agent 可能返回带 markdown 代码块的 JSON，
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也可能返回纯 JSON。此方法尝试多种解析方式。
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Args:
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content: AI 返回的原始文本
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default: 解析失败时的默认值
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Returns:
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Dict: 解析后的字典，失败时返回默认值
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"""
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if not content:
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return default
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# 尝试 1：直接解析
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try:
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return json.loads(content)
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except json.JSONDecodeError:
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pass
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# 尝试 2：提取 markdown 代码块中的 JSON
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# AI 可能返回 ```json ... ``` 格式
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import re
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json_match = re.search(r'```(?:json)?\s*\n?(.*?)\n?```', content, re.DOTALL)
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if json_match:
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try:
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return json.loads(json_match.group(1).strip())
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except json.JSONDecodeError:
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pass
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# 尝试 3：查找第一个 { 到最后一个 } 之间的内容
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start = content.find('{')
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end = content.rfind('}')
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if start != -1 and end != -1 and end > start:
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try:
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return json.loads(content[start:end + 1])
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except json.JSONDecodeError:
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pass
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logger.warning(f"Wingman JSON 解析失败，使用默认值: {content[:200]}")
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return default
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def _estimate_confidence(self, content: str) -> float:
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"""估算 AI 草稿回复的置信度。
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基于回复长度和内容特征估算一个粗略的置信度值。
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- 回复过短（< 10 字符）：低置信度
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- 回复包含不确定措辞：降低置信度
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- 回复长度适中、内容具体：高置信度
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Args:
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content: AI 回复内容
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Returns:
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float: 置信度（0.0 - 1.0）
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"""
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if not content or len(content.strip()) < 5:
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return 0.2
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confidence = 0.8 # 基础置信度
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# 回复过短降低置信度
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if len(content) < 10:
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confidence -= 0.3
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elif len(content) < 30:
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confidence -= 0.1
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# 包含不确定措辞降低置信度
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uncertain_phrases = ["可能", "大概", "也许", "不确定", "建议您"]
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for phrase in uncertain_phrases:
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if phrase in content:
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confidence -= 0.05
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# 包含具体步骤或链接提高置信度
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confident_phrases = ["步骤", "请按以下", "点击", "打开", "http"]
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for phrase in confident_phrases:
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if phrase in content:
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confidence += 0.05
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# 限制在 0.0 - 1.0 范围内
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return max(0.0, min(1.0, confidence))
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