fix：工具调用存储问题

src/llm_models/model_client/openai_client.py

@@ -6,6 +6,7 @@ import json
 import re
 from dataclasses import dataclass, field
 from typing import Any, Callable, Coroutine, Dict, List, Tuple, cast
+from uuid import uuid4
 
 from json_repair import repair_json
 from openai import APIConnectionError, APIStatusError, AsyncOpenAI, AsyncStream
@@ -119,6 +120,13 @@ OpenAIResponseParser = Callable[[ChatCompletion], Tuple[APIResponse, UsageTuple
 """OpenAI 非流式响应解析函数类型。"""
 
 
+def _build_fallback_tool_call_id(prefix: str) -> str:
+    """为缺失原始调用 ID 的工具调用生成唯一兜底标识。"""
+
+    normalized_prefix = str(prefix).strip() or "tool_call"
+    return f"{normalized_prefix}_{uuid4().hex}"
+
+
 def _normalize_reasoning_parse_mode(parse_mode: str | ReasoningParseMode) -> ReasoningParseMode:
     """将配置中的推理解析模式收敛为枚举值。
 
@@ -609,7 +617,7 @@ def _extract_xml_tool_calls(
             arguments = _parse_tool_arguments(raw_arguments, parse_mode, response) if raw_arguments else {}
         tool_calls.append(
             ToolCall(
-                call_id=f"xml_tool_call_{len(tool_calls) + 1}",
+                call_id=_build_fallback_tool_call_id("xml_tool_call"),
                 func_name=function_name,
                 args=arguments,
             )
@@ -855,7 +863,7 @@ class _OpenAIStreamAccumulator:
                     if raw_arguments
                     else None
                 )
-                call_id = state.call_id or f"tool_call_{index}"
+                call_id = state.call_id or _build_fallback_tool_call_id(f"tool_call_{index}")
                 response.tool_calls.append(ToolCall(call_id=call_id, func_name=state.function_name, args=arguments))
 
         response.raw_data = {"model": self.model_name} if self.model_name else None

src/services/database_service.py

@@ -4,16 +4,18 @@ import json
 import time
 import traceback
 from datetime import datetime
-from typing import Any, Optional, cast
+from typing import TYPE_CHECKING, Any, Optional, cast
 
-from sqlalchemy import delete, func, select
-from sqlmodel import SQLModel
+from sqlalchemy import delete, func
+from sqlmodel import SQLModel, select
 
-from src.chat.message_receive.chat_manager import BotChatSession
 from src.common.database.database import get_db_session
 from src.common.database.database_model import ToolRecord
 from src.common.logger import get_logger
 
+if TYPE_CHECKING:
+    from src.chat.message_receive.chat_manager import BotChatSession
+
 logger = get_logger("database_service")
 
 
@@ -158,7 +160,7 @@ async def db_count(model_class: type[SQLModel], filters: Optional[dict[str, Any]
 
 
 async def store_tool_info(
-    chat_stream: BotChatSession,
+    chat_stream: "BotChatSession",
     builtin_prompt: Optional[str] = None,
     display_prompt: str = "",
     tool_id: str = "",
@@ -191,7 +193,7 @@ async def store_tool_info(
 
 
 async def store_action_info(
-    chat_stream: BotChatSession,
+    chat_stream: "BotChatSession",
     builtin_prompt: Optional[str] = None,
     display_prompt: str = "",
     thinking_id: str = "",

src/services/memory_flow_service.py

@@ -227,11 +227,6 @@ class MemoryAutomationService:
         await self.fact_writeback.shutdown()
         self._started = False
 
-    async def on_incoming_message(self, message: Any) -> None:
-        del message
-        if not self._started:
-            await self.start()
-
     async def on_message_sent(self, message: Any) -> None:
         if not self._started:
             await self.start()