update

backend/api/chat_router.py

@@ -119,8 +119,6 @@ async def chat_completion(
         f"knowledge_graph_id: {request.knowledge_graph_id}, "
         f"llm_provider: {request.llm_provider}, llm_model: {request.llm_model}, ip={client_ip}"
     )
-    request.llm_model = "qwen3.7-plus"
-    request.llm_provider = "tongyi"
     # ============ 内容审核前置处理 ============
     # 在 AI 处理前对用户消息进行内容审核
     try:
@@ -156,7 +154,6 @@ async def chat_completion(
                 f"labels: {[label.label for label in moderation_result.labels]}"
             )
             # 使用统一的错误响应格式（与图片审核一致）
-            from fastapi.responses import JSONResponse
             return JSONResponse(
                 status_code=status.HTTP_400_BAD_REQUEST,
                 content={
@@ -185,7 +182,6 @@ async def chat_completion(
                     f"request_id: {moderation_request_id}"
                 )
                 # 使用统一的错误响应格式（与图片审核一致）
-                from fastapi.responses import JSONResponse
                 return JSONResponse(
                     status_code=status.HTTP_400_BAD_REQUEST,
                     content={
@@ -260,7 +256,7 @@ async def chat_completion(
             enable_thinking=user_is_reasoner,
             logical_llm_id=llm_model_key,
         )
-        logger.debug(
+        logger.info(
             "chat_completion 模型: provider={} req_llm_model={} api_model={} user_is_reasoner={}",
             llm_provider,
             llm_model_key,

backend/core/config.py

@@ -162,6 +162,13 @@ class Settings(BaseSettings):
     embedding_model: str = "text-embedding-v4"  # 通义千问 Embedding 模型
     embedding_dimension: int = 1536  # Embedding 维度
 
+    # 辅助任务模型（意图判断、摘要生成等），默认与主聊天模型一致
+    # 可在 .env 中通过 UTILITY_LLM_MODEL 覆盖（如 qwen-plus-latest、qwen3-max）
+    utility_llm_model: str = Field(
+        default="qwen3-max",
+        validation_alias=AliasChoices("UTILITY_LLM_MODEL", "utility_llm_model"),
+    )
+
     # Neo4j 图数据库配置
     neo4j_uri: str = "bolt://127.0.0.1:7687"
     neo4j_user: str = "neo4j"

backend/core/llm_catalog.py

@@ -248,7 +248,6 @@ def build_chat_model(
     p = normalize_provider(provider)
     if p == "tongyi":
         api_key = (os.getenv("DASHSCOPE_API_KEY") or "").strip()
-        print("-----------------------------------api_key: ", api_key)
         if not api_key:
             raise ValueError("缺少 DASHSCOPE_API_KEY")
         base_url = llm_env.tongyi_openai_compatible_base_url().strip().rstrip("/")
@@ -284,7 +283,6 @@ def build_chat_model(
     else:
         raise ValueError(f"未知提供方: {provider}")
 
-    print("-----------------------------------走到这里了-----------------------------------",extra_kwargs)
     return ChatOpenAI(
         model=api_model,
         api_key=api_key,

backend/services/rag_intent_service.py

@@ -7,6 +7,7 @@ from typing import List, Dict, Optional
 from pydantic import BaseModel, Field
 from langchain_core.prompts import PromptTemplate
 
+from core.config import get_settings
 from core.llm_catalog import build_chat_model
 from logger.logging import get_logger
 
@@ -121,7 +122,7 @@ class RagIntentService:
     def __init__(self):
         self.model = build_chat_model(
             provider="tongyi",
-            api_model="qwen-plus-latest",
+            api_model=get_settings().utility_llm_model,
             streaming=False,
             temperature=0.1,  # 降低温度，让判断更稳定
         )

backend/services/summary_service.py

@@ -80,11 +80,12 @@ class SummaryService:
         
         async with cls._lock:
             if cls._llm_cache is None:
+                from core.llm_catalog import _DEFAULT_MODEL_BY_PROVIDER
                 cls._llm_cache = build_chat_model(
                     provider="tongyi",
-                    api_model="qwen-plus-latest",
+                    api_model=_DEFAULT_MODEL_BY_PROVIDER["tongyi"],
                     streaming=False,
-                    temperature=0.3,  # 适度提高灵活性，更好地总结全文
+                    temperature=0.3,
                 )
             return cls._llm_cache
     
@@ -194,9 +195,10 @@ sheet_summary： 对所有sheet表的描述进行总结，不超过20字；
         
         async with cls._lock:
             if cls._llm_cache is None:
+                from core.llm_catalog import _DEFAULT_MODEL_BY_PROVIDER
                 cls._llm_cache = build_chat_model(
                     provider="tongyi",
-                    api_model="qwen-plus-latest",
+                    api_model=_DEFAULT_MODEL_BY_PROVIDER["tongyi"],
                     streaming=False,
                     temperature=0.7,
                     model_kwargs={"response_format": {"type": "json_object"}},
@@ -283,9 +285,10 @@ csv_description: 对csv表格的内容进行描述，不超过20字；
         
         async with cls._lock:
             if cls._llm_cache is None:
+                from core.llm_catalog import _DEFAULT_MODEL_BY_PROVIDER
                 cls._llm_cache = build_chat_model(
                     provider="tongyi",
-                    api_model="qwen-plus-latest",
+                    api_model=_DEFAULT_MODEL_BY_PROVIDER["tongyi"],
                     streaming=False,
                     temperature=0.7,
                     model_kwargs={"response_format": {"type": "json_object"}},

backend/tools/tools.py

@@ -189,6 +189,7 @@ def text_to_image(
             f"开始生成图片（OpenAI 兼容 images/generations），model={model_image}, n={n_req}, size={size_norm}"
         )
 
+        try:
             response = client.images.generate(
                 model=model_image,
                 prompt=prompt,
@@ -196,6 +197,10 @@ def text_to_image(
                 n=n_req,
                 extra_body=extra_body,
             )
+        except Exception as api_err:
+            err_str = str(api_err)
+            logger.error(f"文生图 API 调用失败: {err_str}")
+            return f"图片生成失败：{err_str}"
 
         image_urls: list[str] = []
         for item in response.data or []:
@@ -475,6 +480,7 @@ def text_to_poster(
             "negative_prompt": negative,
         }
 
+        try:
             response = client.images.generate(
                 model=model_image,
                 prompt=prompt,
@@ -482,6 +488,10 @@ def text_to_poster(
                 n=1,
                 extra_body=extra_body,
             )
+        except Exception as api_err:
+            err_str = str(api_err)
+            logger.error(f"海报生成 API 调用失败: {err_str}")
+            return f"海报生成失败：{err_str}"
 
         image_urls: list[str] = []
         for item in response.data or []: