fix: image_daemon 接管飞书交互，禁用 OpenClaw 飞书插件

Dockerfile

@@ -15,8 +15,9 @@ RUN npm install -g openclaw@latest
 RUN mkdir -p /root/.openclaw/workspace /root/.openclaw/extensions /root/.openclaw/credentials /root/.openclaw/skills
 
 # 安装飞书插件
-RUN openclaw plugins install feishu-openclaw 2>/dev/null || true
-RUN cd /root/.openclaw/extensions/feishu-openclaw && npm install @sinclair/typebox 2>/dev/null || true
+# 安装飞书插件 (禁用以避免 WebSocket 竞争)
+# RUN openclaw plugins install feishu-openclaw 2>/dev/null || true
+# RUN cd /root/.openclaw/extensions/feishu-openclaw && npm install @sinclair/typebox 2>/dev/null || true
 
 # 复制文件
 COPY SOUL.md /root/.openclaw/workspace/SOUL.md

entrypoint.sh

@@ -42,7 +42,7 @@ cat > "$OPENCLAW_DIR/openclaw.json" << JSONEOF
   },
   "channels": {
     "feishu": {
-      "enabled": true,
+      "enabled": false,
       "appId": "${FEISHU_APP_ID}",
       "appSecret": "${FEISHU_APP_SECRET}"
     }

image_daemon.py

@@ -10,6 +10,11 @@ FEISHU_BASE = "https://open.feishu.cn/open-apis"
 APP_ID = os.environ.get("FEISHU_APP_ID", "")
 APP_SECRET = os.environ.get("FEISHU_APP_SECRET", "")
 
+# LLM 配置
+API_BASE_URL = os.environ.get("API_BASE_URL", "https://asem12345-cliproxyapi.hf.space/v1")
+API_KEY = os.environ.get("API_KEY", "")
+MODEL_NAME = os.environ.get("MODEL_NAME", "gemini-3-flash")
+
 # ---------- 日志 ----------
 def log(msg):
     ts = time.strftime("%H:%M:%S")
@@ -176,6 +181,55 @@ def handle_image_message(message_id, chat_id, image_key):
             f.write(img_data)
         log(f"⚠️ 图床全部失败，本地保存: {path}")
 
+# ---------- LLM 对话 ----------
+def chat_with_llm(user_text):
+    """调用 LLM API 获取回复"""
+    if not API_KEY:
+        log("❌ API_KEY 未设置，无法对话")
+        return "抱歉，我的大脑连接中断了 (API_KEY missing)"
+
+    try:
+        url = f"{API_BASE_URL}/chat/completions"
+        headers = {
+            "Authorization": f"Bearer {API_KEY}",
+            "Content-Type": "application/json"
+        }
+        payload = {
+            "model": MODEL_NAME,
+            "messages": [
+                {"role": "system", "content": "You are MoltBot, a helpful AI assistant."},
+                {"role": "user", "content": user_text}
+            ],
+            "stream": False
+        }
+        log(f"🤖 调用 LLM: {user_text[:50]}...")
+        resp = requests.post(url, headers=headers, json=payload, timeout=60)
+        
+        if resp.status_code == 200:
+            data = resp.json()
+            reply = data["choices"][0]["message"]["content"]
+            log(f"🤖 LLM 回复: {reply[:50]}...")
+            return reply
+        else:
+            log(f"❌ LLM 错误 {resp.status_code}: {resp.text}")
+            return f"思考时遇到错误 ({resp.status_code})"
+    except Exception as e:
+        log(f"❌ LLM 异常: {e}")
+        return f"大脑短路了: {e}"
+
+# ---------- 处理文本消息 ----------
+def handle_text_message(message_id, chat_id, text):
+    """LLM -> 发送"""
+    token = get_token()
+    if not token:
+        return
+
+    # 简单防重：也许以后需要
+    # 这里直接调用 LLM
+    reply = chat_with_llm(text)
+    if reply:
+        send_text(token, chat_id, reply)
+
 # ---------- 事件处理 ----------
 def on_message_receive(data):
     """im.message.receive_v1 事件回调"""
@@ -185,32 +239,37 @@ def on_message_receive(data):
         sender = event.sender
         
         msg_type = message.message_type
-        # 调试：打印所有收到的消息类型
-        log(f"📨 WebSocket 收到消息: type={msg_type}, msg_id={message.message_id}")
-
         msg_id = message.message_id
         chat_id = message.chat_id
         sender_type = getattr(sender, 'sender_type', '') if sender else ''
 
-        # 只处理用户发的图片
+        # 过滤
         if sender_type == "app":
             return
-        if msg_type != "image":
+            
+        content_json = json.loads(message.content)
+
+        # 调试
+        log(f"📨 WebSocket 收到消息: type={msg_type}, msg_id={msg_id}")
+
+        # 图片消息
+        if msg_type == "image":
+            image_key = content_json.get("image_key", "")
+            if image_key:
+                t = threading.Thread(target=handle_image_message, args=(msg_id, chat_id, image_key))
+                t.daemon = True
+                t.start()
             return
 
-        content = json.loads(message.content)
-        image_key = content.get("image_key", "")
-        if not image_key:
-            log(f"⚠️ 图片消息但无 image_key: {message.content}")
+        # 文本消息
+        if msg_type == "text":
+            text = content_json.get("text", "")
+            if text:
+                t = threading.Thread(target=handle_text_message, args=(msg_id, chat_id, text))
+                t.daemon = True
+                t.start()
             return
 
-        log(f"📨 实时收到图片消息: msg_id={msg_id[:16]}..., image_key={image_key[:20]}...")
-
-        # 在新线程中处理，避免阻塞事件循环
-        t = threading.Thread(target=handle_image_message, args=(msg_id, chat_id, image_key))
-        t.daemon = True
-        t.start()
-
     except Exception as e:
         log(f"❌ on_message_receive 异常: {type(e).__name__}: {e}")
         import traceback

test_api.py

@@ -0,0 +1,21 @@
+import requests
+import json
+
+try:
+    # 尝试 OpenAI 格式
+    resp = requests.post("http://127.0.0.1:18789/v1/chat/completions",
+        json={
+            "model": "default",
+            "messages": [{"role": "user", "content": "hello"}]
+        }, timeout=5)
+    print(f"POST /v1/chat/completions: {resp.status_code}")
+    print(resp.text[:200])
+except Exception as e:
+    print(f"POST /v1/chat/completions error: {e}")
+
+try:
+    # 尝试 OpenClaw 原生格式 (如果有)
+    resp = requests.get("http://127.0.0.1:18789/health", timeout=5)
+    print(f"GET /health: {resp.status_code}")
+except Exception as e:
+    print(f"GET /health error: {e}")