报告模型升级

src/server/deep_agent/agents/researcher.py

@@ -1,5 +1,6 @@
 from langchain.agents.middleware import dynamic_prompt, ModelRequest
 
+from src.server.deep_agent.init_llm import latest_llm, qwen_plus_llm
 from src.server.deep_agent.init_prompt import build_researcher_prompt
 from src.server.deep_agent.tools.crawl_tool import create_crawl4ai_batch_tool
 from src.server.deep_agent.tools.report_generator_tool import create_report_generator_tool
@@ -79,6 +80,7 @@ Do NOT use this sub-agent for:
 - User profile collection 
 - Image generation or editing tasks
         """,
+        "model": qwen_plus_llm,
         "system_prompt": build_researcher_prompt(),
         "middleware": [language_control],
         "tools": [

src/server/deep_agent/init_llm.py

@@ -3,7 +3,7 @@ from langchain_qwq import ChatQwen
 from src.core.config import settings
 
 llm = ChatQwen(
-    model="qwen3.5-flash",
+    model="qwen3.6-plus",
     max_tokens=3_000,
     timeout=None,
     max_retries=2,
@@ -21,6 +21,16 @@ qwen_plus_llm = ChatQwen(
     top_p=0.8,
     api_key=settings.QWEN_API_KEY
 )
+latest_llm = ChatQwen(
+    model="qwen3.6-plus",
+    max_tokens=3_000,
+    timeout=None,
+    max_retries=2,
+    streaming=False,
+    temperature=0.25,
+    top_p=0.8,
+    api_key=settings.QWEN_API_KEY
+)
 
 
 def build_main_llm(enable_thinking):

src/server/deep_agent/tools/research_tool.py

@@ -74,9 +74,16 @@ from typing import List, Set
 
 from ddgs import DDGS
 from langchain.tools import tool
+from pydantic import BaseModel, Field
 
 
-@tool
+class TopicResearchInput(BaseModel):
+    """Input for topic research tool."""
+    topic: List[str] = Field(description="List of separate research keyword strings. Example: ['modern sofa design', 'sustainable wood furniture']")
+    max_urls: int = Field(default=5, description="Maximum number of URLs to return")
+
+
+@tool(args_schema=TopicResearchInput)
 async def topic_research(topic: List[str], max_urls: int = 5) -> str:
     """
     In-depth research tool (DuckDuckGo version).
@@ -127,7 +134,6 @@ async def topic_research(topic: List[str], max_urls: int = 5) -> str:
     print(f" topic research !!!!!!!!!!!!!!!!!!!!! {selected_urls}")
     return json.dumps(selected_urls, ensure_ascii=False)
 
-
 # import asyncio
 #
 #