feat: enhance AIPlanner payload structure for LM Studio compatibility by including 'input' field and improve response content extraction methods

ai_planner.py

@@ -196,17 +196,7 @@ class AIPlanner:
         spinner.start()
 
         try:
-            payload = {
-                "model": OLLAMA_MODEL,
-                "messages": [
-                    {"role": "system", "content": SYSTEM_PROMPT},
-                    {"role": "user",   "content": user_message},
-                ],
-                "format": "json",
-                "think": False,
-                "stream": False,
-                "options": {"temperature": 0.3, "num_ctx": 8192},
-            }
+            payload = self._build_chat_payload(user_message)
             resp = httpx.post(
                 f"{OLLAMA_HOST}/api/v1/chat",
                 json=payload,
@@ -219,12 +209,68 @@ class AIPlanner:
             spinner.join()
 
         dt = time.perf_counter() - t0
-        content = data["message"]["content"]
+        content = self._extract_response_content(data)
         print(f"[AI] 응답 수신 ({dt:.2f}s, {len(content)}자)")
         if _debug_enabled():
             print(f"[AI][디버그] raw={content[:300]}")
         return json.loads(content)
 
+    @staticmethod
+    def _build_chat_payload(user_message: str) -> dict:
+        messages = [
+            {"role": "system", "content": SYSTEM_PROMPT},
+            {"role": "user", "content": user_message},
+        ]
+        return {
+            "model": OLLAMA_MODEL,
+            "messages": messages,
+            # LM Studio(OpenAI Responses 호환)에서는 input 필드가 필수인 경우가 있어 함께 보낸다.
+            "input": user_message,
+            "format": "json",
+            "think": False,
+            "stream": False,
+            "options": {"temperature": 0.3, "num_ctx": 8192},
+        }
+
+    @staticmethod
+    def _extract_response_content(data: dict) -> str:
+        message = data.get("message")
+        if isinstance(message, dict):
+            content = message.get("content")
+            if isinstance(content, str) and content.strip():
+                return content
+
+        output_text = data.get("output_text")
+        if isinstance(output_text, str) and output_text.strip():
+            return output_text
+
+        choices = data.get("choices")
+        if isinstance(choices, list) and choices:
+            first = choices[0]
+            if isinstance(first, dict):
+                msg = first.get("message")
+                if isinstance(msg, dict):
+                    content = msg.get("content")
+                    if isinstance(content, str) and content.strip():
+                        return content
+
+        output = data.get("output")
+        if isinstance(output, list):
+            for item in output:
+                if not isinstance(item, dict):
+                    continue
+                content_items = item.get("content")
+                if not isinstance(content_items, list):
+                    continue
+                for c in content_items:
+                    if not isinstance(c, dict):
+                        continue
+                    text = c.get("text")
+                    if isinstance(text, str) and text.strip():
+                        return text
+
+        raise ValueError(f"응답에서 텍스트 콘텐츠를 찾지 못했습니다. keys={list(data.keys())}")
+
     @staticmethod
     def _ensure_move_before_build_actions(actions: list[dict]) -> list[dict]:
         """