fixed reasoning

backend/app/chat/service.py

@@ -103,6 +103,13 @@ class ChatService:
                 entry["tool_calls"] = json.loads(msg.tool_calls_json)
                 if not content:
                     entry["content"] = None
+            reasoning_data = LLMClient.deserialize_reasoning(msg.reasoning_json)
+            if reasoning_data:
+                LLMClient.attach_reasoning_to_message(
+                    entry,
+                    reasoning=reasoning_data.get("reasoning", ""),
+                    reasoning_details=reasoning_data.get("reasoning_details"),
+                )
             if msg.role == "tool" and msg.tool_call_id:
                 entry["tool_call_id"] = msg.tool_call_id
             messages.append(entry)
@@ -115,12 +122,14 @@ class ChatService:
         content: str = "",
         tool_calls: list[dict[str, Any]] | None = None,
         tool_call_id: str | None = None,
+        reasoning_json: str | None = None,
     ) -> Message:
         message = Message(
             session_id=session_id,
             role=role,
             content=content,
             tool_calls_json=json.dumps(tool_calls, ensure_ascii=False) if tool_calls else None,
+            reasoning_json=reasoning_json,
             tool_call_id=tool_call_id,
         )
         self.db.add(message)
@@ -143,11 +152,17 @@ class ChatService:
         for _ in range(MAX_TOOL_ROUNDS):
             content_parts: list[str] = []
             tool_calls: list[dict[str, Any]] = []
+            reasoning = ""
+            reasoning_details: list[Any] | None = None
 
             async for event in self.llm.stream_chat(messages, tools=TOOL_DEFINITIONS):
                 if event["type"] == "content":
                     content_parts.append(event["content"])
                     yield self._sse("token", {"content": event["content"]})
+                elif event["type"] == "reasoning":
+                    reasoning = event.get("reasoning", "") or reasoning
+                    if event.get("reasoning_details"):
+                        reasoning_details = event["reasoning_details"]
                 elif event["type"] == "error":
                     yield self._sse("error", {"message": event.get("content", "LLM error")})
                     return
@@ -160,12 +175,22 @@ class ChatService:
                     "content": "".join(content_parts) or None,
                     "tool_calls": tool_calls,
                 }
+                LLMClient.attach_reasoning_to_message(
+                    assistant_msg,
+                    reasoning=reasoning,
+                    reasoning_details=reasoning_details,
+                )
+                reasoning_json = LLMClient.serialize_reasoning(
+                    reasoning=reasoning,
+                    reasoning_details=reasoning_details,
+                )
                 messages.append(assistant_msg)
                 self._save_message(
                     session_id,
                     "assistant",
                     "".join(content_parts),
                     tool_calls=tool_calls,
+                    reasoning_json=reasoning_json,
                 )
 
                 for tool_call in tool_calls:
@@ -196,14 +221,16 @@ class ChatService:
                 continue
 
             final_content = "".join(content_parts)
+            if not final_content.strip() and reasoning:
+                final_content = reasoning
             if not final_content.strip():
                 yield self._sse(
                     "error",
                     {
                         "message": (
-                            "Модель не вернула текст. Проверь OPENROUTER_MODEL: "
-                            "нужна поддержка tool calling или отключи OPENROUTER_TOOLS_ENABLED=false. "
-                            "Для памяти можно задать MEMORY_EXTRACT_MODEL=deepseek/deepseek-chat."
+                            "Модель не вернула текст. Для deepseek-v4-pro: "
+                            "OPENROUTER_TOOLS_ENABLED=true и OPENROUTER_REASONING_EFFORT=none. "
+                            "Для памяти: MEMORY_EXTRACT_MODEL=deepseek/deepseek-chat."
                         ),
                     },
                 )