356 lines
15 KiB
Python
356 lines
15 KiB
Python
# -*- coding: utf-8 -*-
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"""
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企业微信 AI 智能体消息回调控制器
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接收企业微信用户发送给 AI 应用的消息，异步处理后主动推送回复
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"""
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import logging
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import threading
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from odoo import http, api, SUPERUSER_ID
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from odoo.http import request, Response
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from ..utils.wx_biz_msg_crypt import WXBizMsgCrypt, WXBizMsgCryptError, parse_wecom_message
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_logger = logging.getLogger(__name__)
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class WecomAiCallback(http.Controller):
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"""企业微信 AI 消息回调"""
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def _get_ai_app_config(self):
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"""获取 AI 智能体对应的企业微信应用配置"""
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config = request.env['wecom.apps.config'].sudo().search([
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('wecom_apps_type', '=', 'ai_callback'),
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('wecom_apps_category', '=', 'agent'),
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], limit=1)
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if not config or not config.wecom_apps_id:
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return None
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return config.wecom_apps_id
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def _get_crypt_instance(self, wecom_app):
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"""创建加解密实例"""
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token = wecom_app.callback_token
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aes_key = wecom_app.encoding_aes_key
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corpid = wecom_app.corpid
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if not all([token, aes_key, corpid]):
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_logger.error("企业微信 AI 回调配置不完整: token=%s, aes_key=%s, corpid=%s",
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bool(token), bool(aes_key), bool(corpid))
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return None
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return WXBizMsgCrypt(token, aes_key, corpid)
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@http.route('/wecom/ai/callback', type='http', auth='none',
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methods=['GET', 'POST'], csrf=False)
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def ai_callback(self, msg_signature=None, timestamp=None, nonce=None,
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echostr=None, **kw):
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"""
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企业微信 AI 应用消息回调入口
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GET: URL验证（配置接收消息服务器时企业微信发起的验证请求）
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POST: 接收用户消息
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"""
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# 获取 AI 应用配置
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wecom_app = self._get_ai_app_config()
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if not wecom_app:
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_logger.error("未找到 AI 回调应用配置（wecom_apps_type=ai_callback, category=agent）")
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return Response("config error", content_type='text/plain')
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crypt = self._get_crypt_instance(wecom_app)
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if not crypt:
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return Response("config error", content_type='text/plain')
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# === GET: URL验证 ===
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if request.httprequest.method == 'GET':
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return self._handle_verify(crypt, msg_signature, timestamp, nonce, echostr)
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# === POST: 接收消息 ===
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return self._handle_message(crypt, msg_signature, timestamp, nonce)
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def _handle_verify(self, crypt, msg_signature, timestamp, nonce, echostr):
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"""处理URL验证请求"""
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if not all([msg_signature, timestamp, nonce, echostr]):
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_logger.warning("AI回调URL验证缺少参数: sig=%s, ts=%s, nonce=%s, echo=%s",
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msg_signature, timestamp, nonce, bool(echostr))
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return Response("param error", content_type='text/plain')
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try:
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echo_plain = crypt.verify_url(msg_signature, timestamp, nonce, echostr)
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_logger.info("企业微信 AI 回调URL验证成功")
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return Response(echo_plain, content_type='text/plain')
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except WXBizMsgCryptError as e:
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_logger.error("AI回调URL验证失败: %s", e)
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return Response("verify error", content_type='text/plain')
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def _handle_message(self, crypt, msg_signature, timestamp, nonce):
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"""处理接收消息请求"""
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if not all([msg_signature, timestamp, nonce]):
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_logger.warning("AI回调消息缺少签名参数")
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return Response("", content_type='text/plain')
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post_data = request.httprequest.get_data(as_text=True)
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if not post_data:
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_logger.warning("AI回调消息体为空")
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return Response("", content_type='text/plain')
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# 解密消息
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try:
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xml_plain = crypt.decrypt_msg(msg_signature, timestamp, nonce, post_data)
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except WXBizMsgCryptError as e:
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_logger.error("AI回调消息解密失败: %s", e)
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return ""
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# 解析消息
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msg = parse_wecom_message(xml_plain)
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if not msg:
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_logger.error("AI回调消息XML解析失败")
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return ""
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_logger.info("收到企业微信AI消息: from=%s, type=%s, content=%s",
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msg.get('from_user'), msg.get('msg_type'),
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(msg.get('content') or msg.get('media_id', ''))[:50])
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wecom_userid = msg.get('from_user')
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msg_type = msg.get('msg_type')
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content = msg.get('content', '').strip()
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# 文本消息
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if msg_type == 'text':
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if not wecom_userid or not content:
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return Response("", content_type='text/plain')
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db_name = request.env.cr.dbname
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threading.Thread(
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target=self._async_process_ai_message,
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args=(db_name, wecom_userid, content, None),
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daemon=True,
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).start()
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return Response("", content_type='text/plain')
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# 图片消息：下载 → OCR → 交给 AI
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if msg_type == 'image':
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media_id = msg.get('media_id', '')
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if not wecom_userid or not media_id:
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return Response("", content_type='text/plain')
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_logger.info("收到企微图片消息: user=%s, media_id=%s", wecom_userid, media_id[:20])
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db_name = request.env.cr.dbname
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threading.Thread(
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target=self._async_process_ai_message,
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args=(db_name, wecom_userid, "请帮我看看这张图片里有什么", media_id, 'image'),
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daemon=True,
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).start()
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return Response("", content_type='text/plain')
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# 文件消息（Excel/CSV）：下载 → 解析 → 交给 AI
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if msg_type == 'file':
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media_id = msg.get('media_id', '')
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if not wecom_userid or not media_id:
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return Response("", content_type='text/plain')
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_logger.info("收到企微文件消息: user=%s, media_id=%s", wecom_userid, media_id[:20])
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db_name = request.env.cr.dbname
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threading.Thread(
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target=self._async_process_ai_message,
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args=(db_name, wecom_userid, "请帮我分析这个文件的内容", media_id, 'file'),
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daemon=True,
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).start()
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return Response("", content_type='text/plain')
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# 其他类型暂不处理
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_logger.info("AI回调收到非文本/图片/文件消息类型: %s，暂不处理", msg_type)
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return Response("", content_type='text/plain')
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def _async_process_ai_message(self, db_name, wecom_userid, content, media_id=None, media_type='image'):
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"""
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后台线程: 处理 AI 对话并推送回复
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使用独立的数据库游标和环境
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:param media_id: 可选，图片/文件消息的 media_id
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:param media_type: 'image' | 'file'
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"""
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try:
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registry = api.Registry(db_name)
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with registry.cursor() as cr:
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env = api.Environment(cr, SUPERUSER_ID, {})
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attachment_ids = None
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if media_id:
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if media_type == 'file':
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attachment_ids = self._download_and_parse_file(env, wecom_userid, media_id)
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else:
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attachment_ids = self._download_and_ocr_media(env, wecom_userid, media_id)
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if not attachment_ids:
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# 下载/解析失败，给用户一个提示，不再调AI
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if media_type == 'file':
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env['wecom.ai.chat']._send_wecom_reply(
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wecom_userid, "文件已收到，但解析失败。请确保发送的是 .xlsx/.xls/.csv 格式的 Excel 文件。")
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else:
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env['wecom.ai.chat']._send_wecom_reply(
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wecom_userid, "图片已收到，但未能从中提取到文字信息。请尝试发送更清晰的图片（含印刷文字）。")
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cr.commit()
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return
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env['wecom.ai.chat'].process_ai_message(wecom_userid, content, attachment_ids)
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cr.commit()
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except Exception as e:
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_logger.exception("AI消息异步处理失败 [user=%s]: %s", wecom_userid, e)
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def _download_and_ocr_media(self, env, wecom_userid, media_id):
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"""下载企微图片 → 创建 documents.document → OCR → 返回 [document_id]
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:return: [int] 或 None
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"""
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try:
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# 1. 下载图片
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content, error = env['wecom.apps'].sudo().download_agent_media(media_id)
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if content is None:
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_logger.error('[WECOM-OCR] 下载失败: %s', error)
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return None
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_logger.info('[WECOM-OCR] 下载成功: %d bytes', len(content))
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# 2. 确定 mimetype（根据魔数）
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mimetype = 'image/jpeg'
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if content[:4] == b'\x89PNG':
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mimetype = 'image/png'
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elif content[:2] == b'\xff\xd8':
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mimetype = 'image/jpeg'
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elif content[:4] == b'GIF8':
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mimetype = 'image/gif'
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elif content[:4] == b'%PDF':
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mimetype = 'application/pdf'
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ext_map = {'image/jpeg': '.jpg', 'image/png': '.png', 'image/gif': '.gif', 'application/pdf': '.pdf'}
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ext = ext_map.get(mimetype, '.jpg')
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filename = f'wecom_image_{media_id[:8]}{ext}'
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# 3. 创建 documents.document + OCR
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# 复用 ai_agent 控制器里的逻辑：找/建文件夹 + 存文件 + OCR
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DocFolder = env['documents.folder'].sudo()
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folder = DocFolder.search([('name', '=', 'AI 聊天附件')], limit=1)
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if not folder:
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folder = DocFolder.create({'name': 'AI 聊天附件'})
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document = env['documents.document'].sudo().create({
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'name': filename,
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'folder_id': folder.id,
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'mimetype': mimetype,
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'raw': content,
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})
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_logger.info('[WECOM-OCR] 文档已创建: id=%s', document.id)
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# 4. OCR
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provider = env['ai.provider'].sudo().search([('active', '=', True)], limit=1)
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if provider and provider.ocr_enabled:
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try:
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from odoo.addons.yuthon_ai_agent.models.ocr_provider import get_ocr_instance
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except ImportError:
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_logger.warning('[WECOM-OCR] 无法导入 OCR 模块')
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return [document.id]
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ocr, init_err = get_ocr_instance(provider)
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if ocr is not None:
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result = ocr.recognize_general(content, mimetype)
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if result.get('success'):
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ocr_text = result.get('text') or ''
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if ocr_text:
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document.write({'ai_ocr_content': ocr_text})
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_logger.info('[WECOM-OCR] ✅ 识别成功: %d字符', len(ocr_text))
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else:
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_logger.info('[WECOM-OCR] ⚠️ 图片中无文字')
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else:
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_logger.error('[WECOM-OCR] ❌ %s', result.get('error'))
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else:
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_logger.warning('[WECOM-OCR] OCR 实例创建失败: %s', init_err)
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else:
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_logger.info('[WECOM-OCR] OCR 未启用或没有活跃的 AI 服务商')
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return [document.id]
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except Exception as e:
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_logger.exception('[WECOM-OCR] 下载/OCR 异常: %s', e)
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return None
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def _download_and_parse_file(self, env, wecom_userid, media_id):
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"""下载企微文件 → 创建 documents.document → Excel解析 → 返回 [document_id]
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支持 .xlsx / .xls / .csv
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:return: [int] 或 None
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"""
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try:
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# 1. 下载文件
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content, error = env['wecom.apps'].sudo().download_agent_media(media_id)
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if content is None:
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_logger.error('[WECOM-FILE] 下载失败: %s', error)
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return None
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_logger.info('[WECOM-FILE] 下载成功: %d bytes', len(content))
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# 2. 确定 mimetype（根据魔数）
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mimetype = 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
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ext = '.xlsx'
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if content[:2] == b'PK':
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mimetype = 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet'
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ext = '.xlsx'
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elif content[:2] == b'\xd0\xcf':
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mimetype = 'application/vnd.ms-excel'
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ext = '.xls'
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else:
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# 尝试按文本解码 → CSV
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try:
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_ = content.decode('utf-8-sig')
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mimetype = 'text/csv'
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ext = '.csv'
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except:
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_logger.warning('[WECOM-FILE] 无法识别文件类型，降级为 xlsx')
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filename = f'wecom_file_{media_id[:8]}{ext}'
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# 3. 创建 documents.document
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DocFolder = env['documents.folder'].sudo()
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folder = DocFolder.search([('name', '=', 'AI 聊天附件')], limit=1)
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if not folder:
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folder = DocFolder.create({'name': 'AI 聊天附件'})
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document = env['documents.document'].sudo().create({
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'name': filename,
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'folder_id': folder.id,
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'mimetype': mimetype,
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'raw': content,
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})
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_logger.info('[WECOM-FILE] 文档已创建: id=%s', document.id)
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# 4. Excel 解析
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try:
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from odoo.addons.yuthon_ai_agent.utils.excel_parser import parse_excel_to_markdown
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except ImportError:
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_logger.warning('[WECOM-FILE] 无法导入 Excel 解析器')
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return [document.id]
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markdown_text, meta = parse_excel_to_markdown(content, filename)
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if meta.get('error'):
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_logger.error('[WECOM-FILE] 解析失败: %s', meta['error'])
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return [document.id]
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document.write({'ai_ocr_content': markdown_text})
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_logger.info('[WECOM-FILE] ✅ 解析成功: %d字符 | sheets=%s | rows=%s',
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len(markdown_text), meta.get('sheets'), meta.get('total_rows'))
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# 5. 创建导入记录
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try:
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env['ai.import.record'].sudo().create({
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'document_id': document.id,
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'filename': filename,
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'file_type': 'excel',
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'user_id': env.ref('base.user_root').id,
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'wecom_userid': wecom_userid,
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'ocr_status': 'success' if not meta.get('truncated') else 'success',
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'ocr_content': markdown_text,
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})
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except Exception as e:
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_logger.warning('[WECOM-FILE] 创建导入记录失败: %s', e)
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return [document.id]
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except Exception as e:
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_logger.exception('[WECOM-FILE] 下载/解析异常: %s', e)
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return None
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