fixed reasoning

backend/app/api/schemas.py

@@ -20,6 +20,7 @@ class MessageOut(BaseModel):
     id: int
     role: str
     content: str
+    tool_calls_json: str | None = None
     created_at: datetime
 
     model_config = {"from_attributes": True}

backend/app/chat/service.py

@@ -1,4 +1,6 @@
+import asyncio
 import json
+import logging
 from collections.abc import AsyncIterator
 from typing import Any
 
@@ -6,6 +8,7 @@ from sqlalchemy import select
 from sqlalchemy.orm import Session
 
 from app.config import get_settings
+from app.db.base import SessionLocal
 from app.character.service import CharacterService
 from app.chat.notices import (
     POMODORO_TOOL_NAMES,
@@ -31,6 +34,22 @@ from app.tools.registry import TOOL_DEFINITIONS, execute_tool
 MAX_TOOL_ROUNDS = 5
 MAX_HISTORY_MESSAGES = 40
 
+logger = logging.getLogger(__name__)
+
+
+async def _extract_memory_background(
+    session_id: int,
+    user_text: str,
+    assistant_text: str,
+) -> None:
+    db = SessionLocal()
+    try:
+        await extract_after_turn(db, session_id, user_text, assistant_text)
+    except Exception as exc:
+        logger.warning("Background memory extraction failed: %s", exc)
+    finally:
+        db.close()
+
 
 class ChatService:
     def __init__(self, db: Session):
@@ -148,6 +167,7 @@ class ChatService:
 
         self._save_message(session_id, "user", user_text)
         messages = self._build_messages(session)
+        streamed_reply_parts: list[str] = []
 
         for _ in range(MAX_TOOL_ROUNDS):
             content_parts: list[str] = []
@@ -170,9 +190,13 @@ class ChatService:
                     tool_calls = event["tool_calls"]
 
             if tool_calls:
+                round_text = "".join(content_parts)
+                if round_text.strip():
+                    streamed_reply_parts.append(round_text)
+
                 assistant_msg: dict[str, Any] = {
                     "role": "assistant",
-                    "content": "".join(content_parts) or None,
+                    "content": round_text or None,
                     "tool_calls": tool_calls,
                 }
                 LLMClient.attach_reasoning_to_message(
@@ -188,7 +212,7 @@ class ChatService:
                 self._save_message(
                     session_id,
                     "assistant",
-                    "".join(content_parts),
+                    round_text,
                     tool_calls=tool_calls,
                     reasoning_json=reasoning_json,
                 )
@@ -220,10 +244,12 @@ class ChatService:
 
                 continue
 
-            final_content = "".join(content_parts)
-            if not final_content.strip() and reasoning:
-                final_content = reasoning
-            if not final_content.strip():
+            final_content = "".join(content_parts).strip()
+            if not final_content and streamed_reply_parts:
+                final_content = "".join(streamed_reply_parts).strip()
+            if not final_content and reasoning:
+                final_content = reasoning.strip()
+            if not final_content:
                 yield self._sse(
                     "error",
                     {
@@ -238,20 +264,11 @@ class ChatService:
 
             self._save_message(session_id, "assistant", final_content)
 
-            memory_meta: dict[str, Any] = {}
+            yield self._sse("done", {})
             if get_settings().memory_auto_extract:
-                extraction = await extract_after_turn(
-                    self.db,
-                    session_id,
-                    user_text,
-                    final_content,
+                asyncio.create_task(
+                    _extract_memory_background(session_id, user_text, final_content)
                 )
-                memory_meta = {
-                    "memory_extracted": extraction.get("count", 0),
-                    "memory_saved": extraction.get("saved", []),
-                }
-
-            yield self._sse("done", memory_meta)
             return
 
         yield self._sse("error", {"message": "Too many tool call rounds"})

backend/app/llm/client.py

@@ -147,8 +147,9 @@ class LLMClient:
         *,
         temperature: float = 0.7,
         model: str | None = None,
+        for_extraction: bool = False,
     ) -> dict[str, Any]:
-        use_tools = bool(tools) and self.tools_enabled
+        use_tools = bool(tools) and self.tools_enabled and not for_extraction
         kwargs: dict[str, Any] = {
             "model": model or self.model,
             "messages": messages,
@@ -156,9 +157,12 @@ class LLMClient:
         }
         if use_tools:
             kwargs["tools"] = tools
-        extra_body = self._reasoning_extra_body()
-        if extra_body:
-            kwargs["extra_body"] = extra_body
+        if for_extraction:
+            kwargs["extra_body"] = {"reasoning": {"effort": "none"}}
+        else:
+            extra_body = self._reasoning_extra_body()
+            if extra_body:
+                kwargs["extra_body"] = extra_body
 
         response = await self.client.chat.completions.create(**kwargs)
         message = response.choices[0].message

backend/app/memory/extract.py

@@ -82,6 +82,7 @@ async def _call_extractor(
         ],
         temperature=0.2,
         model=extract_model,
+        for_extraction=True,
     )
     raw = strip_markdown_json(result.get("content") or "")
     if not raw: