企业微信 AI 助理腾讯云搭建完整指南

1同事在企业微信发消息
2        │
3        ▼
4企业微信服务器 ──HTTPS 回调──> 你的云服务器（助理服务）
5        │                            │
6        │                            ├─> 本地大模型（Ollama / GLM / InternLM）
7        │                            └─> 内部知识库（RAGFlow / MaxKB）
8        │                            │
9        ▼                            ▼
10   同事收到回复  <──调用发消息接口──┘

1server {
2    listen 443 ssl;
3    server_name 你的域名;
4
5    ssl_certificate     /path/to/fullchain.pem;
6    ssl_certificate_key /path/to/privkey.pem;
7
8    location /wecom/ {
9        proxy_pass http://127.0.0.1:5000;
10        proxy_set_header Host $host;
11        proxy_set_header X-Real-IP $remote_addr;
12    }
13}

1curl http://127.0.0.1:11434/v1/models

1mkdir -p /opt/wecom-bot && cd /opt/wecom-bot
2python3 -m venv venv
3source venv/bin/activate
4pip install flask requests PyCryptodome

1cat > config.py <<'EOF'
2CORP_ID = "你的CorpID"
3AGENT_ID = "你的AgentID"
4SECRET = "你的Secret"
5TOKEN = "回调配置里的Token"
6AES_KEY = "回调配置里的EncodingAESKey"
7
8# 本地 Ollama（按你的实际标签改）
9LLM_BASE_URL = "http://127.0.0.1:11434/v1"
10LLM_MODEL = "glm4:9b"
11LLM_API_KEY = "ollama"
12EOF

1# app.py
2import base64, hashlib, json, struct, time, xml.etree.ElementTree as ET
3import requests
4from flask import Flask, request, abort
5from Crypto.Cipher import AES
6import config
7
8app = Flask(__name__)
9
10# ---------- 加解密 ----------
11def decrypt_msg(encrypted_b64):
12    aes_key = base64.b64decode(config.AES_KEY + "=")
13    cipher = AES.new(aes_key, AES.MODE_CBC, aes_key[:16])
14    raw = cipher.decrypt(base64.b64decode(encrypted_b64))
15    # 去掉补位
16    pad_len = raw[-1]
17    content = raw[:-pad_len]
18    # 前 16 字节是随机串，接下来 4 字节是消息长度（网络字节序）
19    msg_len = struct.unpack("!I", content[16:20])[0]
20    return content[20:20 + msg_len].decode("utf-8")
21
22def encrypt_msg(reply_xml):
23    aes_key = base64.b64decode(config.AES_KEY + "=")
24    rand = b"1234567890abcdef"           # 16 字节随机串，示例写死，生产请随机生成
25    body = rand + struct.pack("!I", len(reply_xml.encode())) + reply_xml.encode()
26    pad = 32 - len(body) % 32
27    body += bytes([pad]) * pad
28    cipher = AES.new(aes_key, AES.MODE_CBC, aes_key[:16])
29    return base64.b64encode(cipher.encrypt(body)).decode()
30
31def get_access_token():
32    url = "https://qyapi.weixin.qq.com/cgi-bin/gettoken"
33    r = requests.get(url, params={"corpid": config.CORP_ID,
34                                  "corpsecret": config.SECRET}, timeout=10)
35    return r.json().get("access_token")
36
37# ---------- 调模型 ----------
38def ask_llm(question):
39    r = requests.post(
40        config.LLM_BASE_URL + "/chat/completions",
41        headers={"Authorization": "Bearer " + config.LLM_API_KEY},
42        json={"model": config.LLM_MODEL,
43              "messages": [{"role": "user", "content": question}]},
44        timeout=120,
45    )
46    return r.json()["choices"][0]["message"]["content"]
47
48# ---------- 发消息 ----------
49def send_text(user_id, text):
50    token = get_access_token()
51    url = "https://qyapi.weixin.qq.com/cgi-bin/message/send"
52    requests.post(url, params={"access_token": token}, json={
53        "touser": user_id,
54        "msgtype": "text",
55        "agentid": config.AGENT_ID,
56        "text": {"content": text},
57    }, timeout=10)
58
59# ---------- 回调 ----------
60@app.get("/wecom/callback")
61def verify():
62    """企业微信配置回调时的 GET 验证"""
63    msg_signature = request.args.get("msg_signature")
64    timestamp = request.args.get("timestamp")
65    nonce = request.args.get("nonce")
66    echostr = request.args.get("echostr")
67
68    # 校验签名：token / timestamp / nonce / echostr 排序后 sha1
69    sign = hashlib.sha1("".join(sorted([config.TOKEN, timestamp, nonce, echostr]))
70                        .encode()).hexdigest()
71    if sign != msg_signature:
72        abort(403)
73
74    return decrypt_msg(echostr)           # 原样返回解密后的 echostr
75
76@app.post("/wecom/callback")
77def receive():
78    """接收用户消息"""
79    root = ET.fromstring(request.data)
80    encrypted = root.find("Encrypt").text
81
82    xml = decrypt_msg(encrypted)
83    msg = ET.fromstring(xml)
84
85    from_user = msg.find("FromUserName").text
86    content = (msg.findtext("Content") or "").strip()
87
88    if not content:
89        return "success"
90
91    try:
92        answer = ask_llm(content)
93    except Exception as e:
94        answer = f"抱歉，处理出错了：{str(e)[:100]}"
95
96    send_text(from_user, answer)
97    return "success"
98
99if __name__ == "__main__":
100    app.run(host="127.0.0.1", port=5000)

1sudo tee /etc/systemd/system/wecom-bot.service <<'EOF'
2[Unit]
3Description=WeCom AI Assistant
4After=network.target
5
6[Service]
7Type=simple
8User=ubuntu
9WorkingDirectory=/opt/wecom-bot
10ExecStart=/opt/wecom-bot/venv/bin/python app.py
11Restart=on-failure
12RestartSec=5
13
14[Install]
15WantedBy=multi-user.target
16EOF
17
18sudo systemctl daemon-reload
19sudo systemctl enable wecom-bot
20sudo systemctl start wecom-bot
21sudo journalctl -u wecom-bot -f

1curl -I http://127.0.0.1:5000/wecom/callback

1def ask_llm(question):
2    """改为：先查知识库，查不到再走通用模型"""
3    r = requests.post(
4        "http://127.0.0.1:8080/api/application/chat/completions",
5        headers={"Authorization": "Bearer 你的MaxKB应用Key"},
6        json={"message": question, "re_chat": False, "stream": False},
7        timeout=120,
8    )
9    data = r.json()
10    answer = data.get("data", {}).get("content") or ""
11    if not answer.strip():
12        # 知识库没命中，退回通用模型
13        return ask_general(question)
14    return answer