feat: 添加 per-chat 对话历史（最近10轮）

image_daemon.py

@@ -20,6 +20,10 @@ OPENCLAW_GATEWAY = "http://127.0.0.1:18789/v1"
 _use_gateway = False  # 启动时探测决定
 _soul_prompt = ""     # SOUL.md 内容
 
+# 对话历史 (per chat_id)
+_chat_history = {}    # {chat_id: [{role, content}, ...]}
+MAX_HISTORY = 10      # 保留最近 N 轮
+
 # ---------- 日志 ----------
 def log(msg):
     ts = time.strftime("%H:%M:%S")
@@ -257,14 +261,14 @@ def check_openclaw_gateway():
         log(f"⚠️ Gateway 不可用 ({e})，使用外部 LLM + SOUL 人设")
 
 # ---------- LLM 对话 ----------
-def chat_with_llm(user_text):
+def chat_with_llm(user_text, history=None):
     """优先转发给本地 OpenClaw Gateway（带人设+插件），Fallback 到外部 LLM+SOUL"""
     try:
         if _use_gateway:
             # 走 OpenClaw Gateway：人设和工具由它处理
             resp = requests.post(
                 f"{OPENCLAW_GATEWAY}/chat/completions",
-                json={"model": "default", "messages": [{"role": "user", "content": user_text}], "stream": False},
+                json={"model": "default", "messages": (history or []) + [{"role": "user", "content": user_text}], "stream": False},
                 timeout=120
             )
             if resp.status_code == 200:
@@ -272,23 +276,20 @@ def chat_with_llm(user_text):
                 reply = data["choices"][0]["message"]["content"]
                 log(f"🤖 Gateway 回复: {reply[:60]}...")
                 return reply
-            log(f"⚠️ Gateway 失败 ({resp.status_code})，Fallback 到外部 LLM")
+            log(f"⚠️ Gateway 失败 ({resp.status_code})，Fallback到外部 LLM")
 
         # Fallback：外部 LLM + SOUL.md 人设
         if not API_KEY:
             return "抱歉，我的大脑连接中断了 (API_KEY missing)"
-        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": _soul_prompt or "You are a helpful assistant."},
-                {"role": "user", "content": user_text}
-            ],
-            "stream": False
-        }
+        soul = _soul_prompt or "You are a helpful assistant."
+        messages = [{"role": "system", "content": soul}] + (history or []) + [{"role": "user", "content": user_text}]
+        payload = {"model": MODEL_NAME, "messages": messages, "stream": False}
         log(f"🤖 外部 LLM ({MODEL_NAME}): {user_text[:50]}...")
-        resp = requests.post(url, headers=headers, json=payload, timeout=60)
+        resp = requests.post(
+            f"{API_BASE_URL}/chat/completions",
+            headers={"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"},
+            json=payload, timeout=60
+        )
         if resp.status_code == 200:
             reply = resp.json()["choices"][0]["message"]["content"]
             log(f"🤖 LLM 回复: {reply[:60]}...")
@@ -301,15 +302,23 @@ def chat_with_llm(user_text):
 
 # ---------- 处理文本消息 ----------
 def handle_text_message(message_id, chat_id, text):
-    """LLM -> 发送"""
+    """LLM (带历史) -> 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)
+    # 获取该用户的历史
+    history = _chat_history.get(chat_id, [])
+
+    reply = chat_with_llm(text, history)
     if reply:
+        # 更新历史
+        history = history + [
+            {"role": "user", "content": text},
+            {"role": "assistant", "content": reply}
+        ]
+        # 只保留最近 N 轮（每轮 2 条记录）
+        _chat_history[chat_id] = history[-(MAX_HISTORY * 2):]
         send_text(token, chat_id, reply)
 
 # ---------- 事件处理 ----------