36a6debcb3
.env: - 移除 HOST/PORT/DEBUG（代码未读取，由 uvicorn 命令行控制） - 移除 WEWORK_BOT_SEARCH_LIMIT（已被 SEARCH_LIMIT 取代） - 补充硅基流动配置注释 .env.example: - 同步移除 HOST/PORT/DEBUG/WEWORK_BOT_SEARCH_LIMIT - 补充 OpenAI 兼容硅基流动的说明 - 更新机器人注释（集成模式自动启动） config.py: - 移除 HOST/PORT/DEBUG 字段定义 - 移除 WEWORK_BOT_SEARCH_LIMIT 字段定义 wework_bot.py: - 改为从 settings 统一读取配置（不再 os.getenv） - _get_search_limit fallback 改为 settings.SEARCH_LIMIT - 更新模块文档注释
492 lines
17 KiB
Python
492 lines
17 KiB
Python
"""
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企业微信智能机器人服务（WebSocket 长连接模式）
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使用官方 SDK (wecom-aibot-python-sdk) 的 WSClient 类，
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封装了连接管理、心跳、断线重连等能力。
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支持两种运行方式：
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1. 集成模式：后端启动时自动拉起（BotManager）
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2. 独立进程：python -m app.wework_bot
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"""
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from __future__ import annotations
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import base64
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import hashlib
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import json
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import logging
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import math
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import os
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import re
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import tempfile
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import time
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import asyncio
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from datetime import datetime
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import httpx
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from aibot import WSClient, WSClientOptions, generate_req_id
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from dotenv import load_dotenv
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# 加载 .env 文件（独立运行时需要）
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load_dotenv()
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from app.config import settings
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logger = logging.getLogger("wework_bot")
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# 分片上传参数（参考官方文档）
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_CHUNK_SIZE = 512 * 1024 # 单个分片最大 512KB（Base64 编码前）
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class WeComBotService:
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"""企业微信智能机器人服务（WebSocket 长连接模式）"""
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def __init__(self):
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self.bot_id = settings.WEWORK_BOT_ID or ""
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self.bot_secret = settings.WEWORK_BOT_SECRET or ""
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self.edubrain_base_url = settings.EDUBRAIN_BASE_URL
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self.enabled = settings.WEWORK_BOT_ENABLED
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self.ws_client: WSClient | None = None
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def start(self):
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"""启动机器人服务（阻塞模式，用于独立进程运行）"""
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if not self.enabled:
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logger.info("企业微信机器人未启用 (WEWORK_BOT_ENABLED=false)")
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return
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if not self.bot_id or not self.bot_secret:
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logger.error("WEWORK_BOT_ID 或 WEWORK_BOT_SECRET 未配置")
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return
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self.ws_client = WSClient(
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WSClientOptions(
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bot_id=self.bot_id,
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secret=self.bot_secret,
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)
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)
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self._register_events()
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logger.info("启动企业微信智能机器人...")
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self.ws_client.run()
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async def start_async(self):
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"""启动机器人服务（异步模式，用于嵌入到其他事件循环）"""
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if not self.enabled:
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logger.info("企业微信机器人未启用 (WEWORK_BOT_ENABLED=false)")
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return
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if not self.bot_id or not self.bot_secret:
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logger.error("WEWORK_BOT_ID 或 WEWORK_BOT_SECRET 未配置")
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return
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self.ws_client = WSClient(
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WSClientOptions(
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bot_id=self.bot_id,
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secret=self.bot_secret,
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)
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)
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self._register_events()
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logger.info("启动企业微信智能机器人（异步模式）...")
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await self.ws_client.connect()
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# 永远等待，保持连接
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await asyncio.Future()
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def _register_events(self):
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"""注册所有事件处理器"""
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self.ws_client.on("authenticated", self._on_authenticated)
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self.ws_client.on("message.text", self._on_text_message)
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self.ws_client.on("message.image", self._on_image_message)
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self.ws_client.on("event.enter_chat", self._on_enter_chat)
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self.ws_client.on("disconnected", self._on_disconnected)
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self.ws_client.on("error", self._on_error)
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# ──────────────────────────── 事件处理 ────────────────────────────
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def _on_authenticated(self):
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"""认证成功回调"""
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logger.info("企业微信机器人认证成功")
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async def _on_enter_chat(self, frame: dict):
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"""进入会话事件 -> 回复欢迎语"""
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await self.ws_client.reply_welcome(
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frame,
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{
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"msgtype": "text",
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"text": {
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"content": "你好！我是知识库助手，支持以下功能：\n\n1. 发送关键词 → 搜索相关图片\n2. 发送图片 → 自动识别并录入知识库"
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},
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},
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)
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async def _on_text_message(self, frame: dict):
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"""收到文本消息 -> 搜索图片 -> 流式回复文本 + 逐张发送图片"""
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body = frame.get("body", {})
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text_content = body.get("text", {}).get("content", "")
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# 去掉 @机器人 的前缀
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text_content = re.sub(r"@.*?\s*", "", text_content).strip()
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if not text_content:
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stream_id = generate_req_id("stream")
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await self.ws_client.reply_stream(
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frame, stream_id, "请输入搜索关键词", True
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)
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return
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# 清理口语化前缀，提取关键词
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keyword = re.sub(
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r"(帮我|请|找|搜|搜索|查|查一下|来|给我|要|想要|的?图|图片|相关|关于|一下)",
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"", text_content,
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).strip()
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if not keyword:
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keyword = text_content # 兜底
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stream_id = generate_req_id("stream")
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# 立即回复：开始搜索
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await self.ws_client.reply_stream(
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frame,
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stream_id,
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f"\U0001f50d 正在搜索「{keyword}」...",
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False,
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)
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# 调用 EduBrain 搜索
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try:
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results = await self._search_images(keyword)
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if not results:
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await self.ws_client.reply_stream(
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frame,
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stream_id,
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f"未找到与「{text_content}」相关的图片",
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True,
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)
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return
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# 构建文本回复内容
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content_parts = [f"找到 {len(results)} 张相关图片："]
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for i, r in enumerate(results, 1):
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preview = r.get("ocr_text_preview", "")[:80]
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content_parts.append(f"\n{i}. {preview}...")
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content = "\n".join(content_parts)
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# 完成流式消息（仅文本，不使用 msg_item）
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await self.ws_client.reply_stream(
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frame,
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stream_id,
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content,
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True,
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)
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# 逐张上传临时素材并发送图片消息
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for i, r in enumerate(results, 1):
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b64 = r.get("image_base64", "")
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if not b64:
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continue
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try:
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# 解码 base64 获取原始图片数据
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image_data = base64.b64decode(b64)
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file_name = r.get("image_url", f"image_{i}.jpg")
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# 上传临时素材（分片上传）
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media_id = await self._upload_temp_media(
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image_data, file_name
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)
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if not media_id:
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logger.warning("第 %d 张图片上传失败，跳过", i)
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continue
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# 发送图片消息（通过 aibot_respond_msg）
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await self.ws_client.reply(
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frame,
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{
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"msgtype": "image",
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"image": {"media_id": media_id},
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},
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)
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logger.info("已发送第 %d 张图片 (media_id: %s)", i, media_id)
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except Exception as e:
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logger.error("发送第 %d 张图片失败: %s", i, e, exc_info=True)
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except Exception as e:
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logger.error("搜索失败: %s", e, exc_info=True)
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await self.ws_client.reply_stream(
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frame,
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stream_id,
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f"搜索出错：{e}",
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True,
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)
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def _on_disconnected(self, reason: str):
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"""连接断开回调"""
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logger.warning("企业微信连接断开: %s", reason)
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async def _on_image_message(self, frame: dict):
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"""收到图片消息 -> 下载解密 -> 调用OCR识别 -> 回复结果"""
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body = frame.get("body", {})
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image_info = body.get("image", {})
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image_url = image_info.get("url", "")
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aes_key = image_info.get("aeskey", "")
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if not image_url:
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logger.warning("收到图片消息但无下载地址")
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return
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stream_id = generate_req_id("stream")
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await self.ws_client.reply_stream(
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frame, stream_id, "收到图片，正在识别...", False
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)
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try:
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# 1. 下载并解密图片
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image_data, original_filename = await self.ws_client.download_file(
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image_url, aes_key
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)
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if not image_data:
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await self.ws_client.reply_stream(
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frame, stream_id, "图片下载失败，请重试", True
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)
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return
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logger.info(
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"图片下载成功: filename=%s, size=%d bytes",
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original_filename, len(image_data),
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)
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# 2. 生成保存文件名（避免重名）
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ext = os.path.splitext(original_filename or "image.jpg")[1] or ".jpg"
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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save_filename = f"wecom_{timestamp}{ext}"
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# 3. 调用后端 recognize-direct 接口
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async with httpx.AsyncClient(timeout=120.0) as client:
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resp = await client.post(
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f"{self.edubrain_base_url}/api/v1/images/recognize-direct",
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files={"file": (save_filename, image_data, "image/jpeg")},
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)
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if resp.status_code != 200:
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logger.error("OCR 识别失败: status=%d, body=%s", resp.status_code, resp.text)
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await self.ws_client.reply_stream(
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frame, stream_id, "图片识别失败，请稍后重试", True
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)
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return
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result = resp.json()
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# 4. 根据识别结果回复
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status = result.get("status", "")
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if status == "duplicate":
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dup_id = result.get("duplicate_of", "?")
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await self.ws_client.reply_stream(
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frame,
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stream_id,
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f"该图片内容已存在于知识库中（ID: {dup_id}），无需重复录入。",
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True,
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)
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elif status == "completed":
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ocr_text = result.get("ocr_text", "")
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preview = ocr_text[:200].replace("\n", " ") if ocr_text else "无识别内容"
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record_id = result.get("id", "?")
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await self.ws_client.reply_stream(
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frame,
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stream_id,
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f"✅ 图片已录入知识库（ID: {record_id}）\n\n识别内容预览：\n{preview}{'...' if len(ocr_text) > 200 else ''}",
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True,
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)
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elif status == "failed":
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error_msg = result.get("message", result.get("detail", "未知错误"))
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await self.ws_client.reply_stream(
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frame,
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stream_id,
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f"图片识别失败：{error_msg}",
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True,
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)
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else:
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await self.ws_client.reply_stream(
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frame,
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stream_id,
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f"图片处理结果：{json.dumps(result, ensure_ascii=False)}",
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True,
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)
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except Exception as e:
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logger.error("处理图片消息失败: %s", e, exc_info=True)
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await self.ws_client.reply_stream(
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frame, stream_id, f"处理图片时出错：{e}", True
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)
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def _on_error(self, error):
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"""错误回调"""
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logger.error("企业微信机器人错误: %s", error)
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# ──────────────────────── 上传临时素材 ────────────────────────
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async def _upload_temp_media(
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self, file_data: bytes, filename: str
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) -> str | None:
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"""
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通过 WebSocket 通道上传临时素材（三步分片上传）。
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流程：aibot_upload_media_init → aibot_upload_media_chunk × N → aibot_upload_media_finish
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Args:
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file_data: 文件原始二进制数据
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filename: 文件名
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Returns:
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成功返回 media_id，失败返回 None
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"""
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total_size = len(file_data)
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total_chunks = math.ceil(total_size / _CHUNK_SIZE)
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md5 = hashlib.md5(file_data).hexdigest()
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ws = self.ws_client._ws_manager
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# 第一步：上传初始化
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init_req_id = generate_req_id("upload")
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try:
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init_result = await ws.send_reply(
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init_req_id,
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{
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"type": "image",
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"filename": filename,
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"total_size": total_size,
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"total_chunks": total_chunks,
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"md5": md5,
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},
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cmd="aibot_upload_media_init",
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)
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except Exception as e:
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logger.error("上传初始化失败: %s", e)
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return None
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upload_id = init_result.get("body", {}).get("upload_id", "")
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if not upload_id:
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logger.error("上传初始化未返回 upload_id: %s", init_result)
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return None
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logger.info(
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"上传初始化成功: upload_id=%s, total_size=%d, chunks=%d",
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upload_id, total_size, total_chunks,
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)
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# 第二步：分片上传
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for chunk_idx in range(total_chunks):
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start = chunk_idx * _CHUNK_SIZE
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end = min(start + _CHUNK_SIZE, total_size)
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chunk_data = file_data[start:end]
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chunk_b64 = base64.b64encode(chunk_data).decode("utf-8")
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chunk_req_id = generate_req_id("upload_chunk")
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try:
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await ws.send_reply(
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chunk_req_id,
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{
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"upload_id": upload_id,
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"chunk_index": chunk_idx, # 分片索引从 0 开始
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"base64_data": chunk_b64,
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},
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cmd="aibot_upload_media_chunk",
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)
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except Exception as e:
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logger.error(
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"分片上传失败 (chunk %d/%d): %s",
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chunk_idx + 1, total_chunks, e,
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)
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return None
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logger.info("所有分片上传完成: upload_id=%s", upload_id)
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# 第三步：完成上传
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finish_req_id = generate_req_id("upload_finish")
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try:
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finish_result = await ws.send_reply(
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finish_req_id,
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{"upload_id": upload_id},
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cmd="aibot_upload_media_finish",
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)
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except Exception as e:
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logger.error("完成上传失败: %s", e)
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return None
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media_id = finish_result.get("body", {}).get("media_id", "")
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if not media_id:
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logger.error("完成上传未返回 media_id: %s", finish_result)
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return None
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logger.info("上传临时素材成功: media_id=%s", media_id)
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return media_id
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# ──────────────────────────── 搜索调用 ────────────────────────────
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async def _get_search_limit(self) -> int:
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"""从后端 API 实时获取当前 search_limit 配置"""
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try:
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async with httpx.AsyncClient(timeout=5.0) as client:
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resp = await client.get(f"{self.edubrain_base_url}/api/v1/settings")
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if resp.status_code == 200:
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data = resp.json()
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limit = data.get("search_limit")
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if limit:
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return limit
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except Exception as e:
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logger.warning("获取 search_limit 失败，使用默认值: %s", e)
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return settings.SEARCH_LIMIT
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async def _search_images(self, keyword: str) -> list:
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"""
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调用 EduBrain 搜索接口（SSE 流式），收集所有匹配结果。
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Args:
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keyword: 搜索关键词
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Returns:
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匹配结果列表
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"""
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results = []
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url = f"{self.edubrain_base_url}/api/v1/images/search"
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limit = await self._get_search_limit()
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async with httpx.AsyncClient(timeout=300.0) as client:
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async with client.stream(
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"GET",
|
||
url,
|
||
params={
|
||
"keyword": keyword,
|
||
"limit": limit,
|
||
"sort": "time_desc",
|
||
},
|
||
) as resp:
|
||
async for line in resp.aiter_lines():
|
||
if not line.startswith("data: "):
|
||
continue
|
||
try:
|
||
data = json.loads(line[6:])
|
||
if data.get("type") == "result":
|
||
results.append(data)
|
||
elif data.get("type") == "done":
|
||
break
|
||
except json.JSONDecodeError:
|
||
continue
|
||
|
||
return results
|
||
|
||
|
||
def run_wework_bot():
|
||
"""入口函数"""
|
||
logging.basicConfig(
|
||
level=logging.INFO,
|
||
format="%(asctime)s [%(name)s] %(levelname)s: %(message)s",
|
||
)
|
||
service = WeComBotService()
|
||
service.start()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
run_wework_bot()
|