新增对话接口

src/server/deep_agent/tools/conversation_title_tool.py

@@ -1,6 +1,6 @@
 from langchain_core.prompts import PromptTemplate
 
-from src.server.deep_agent.agents.init_llm import title_llm
+from src.server.deep_agent.init_llm import title_llm
 
 
 def conversation_title(full_conversation):

src/server/deep_agent/tools/extract_suggested_questions.py

@@ -0,0 +1,75 @@
+import json
+from typing import List
+
+from langchain_core.messages import (
+    HumanMessage,
+    AIMessage,
+    ToolMessage,
+)
+
+from src.server.deep_agent.init_llm import suggested_llm
+
+
+def format_messages(messages, max_messages: int = 6) -> str:
+    """
+    将 LangGraph messages 转换为 LLM prompt 文本
+    """
+    messages = messages[-max_messages:]
+    lines: List[str] = []
+    for m in messages:
+        if isinstance(m, HumanMessage):
+            lines.append(f"User: {m.content}")
+        elif isinstance(m, AIMessage):
+            if m.content:
+                lines.append(f"Assistant: {m.content}")
+        elif isinstance(m, ToolMessage):
+            # Tool结果建议简单化
+            tool_output = str(m.content)
+            if len(tool_output) > 200:
+                tool_output = tool_output[:200] + "..."
+            lines.append(f"Tool Result: {tool_output}")
+    return "\n".join(lines)
+
+
+async def generate_suggested_questions(
+        agent,
+        thread_id: str,
+        max_messages: int = 6,
+) -> List[str]:
+    """
+    根据当前对话生成3条用户可能继续提问的问题
+    """
+    # 获取当前对话state
+    state = agent.get_state(
+        {"configurable": {"thread_id": thread_id}}
+    )
+    messages = state.values.get("messages", [])
+    if not messages:
+        return []
+    conversation = format_messages(messages, max_messages)
+
+    prompt = f"""
+    以下是用户与AI助手的对话：
+    {conversation}
+    请根据对话内容，生成3条用户可能继续提出的问题。
+    要求：
+    - 每条一句话
+    - 语言自然
+    - 不要解释
+    - 返回JSON数组
+    - 尽量与家具设计相关
+    示例：
+    ["问题1", "问题2", "问题3"]
+    """
+    result = await suggested_llm.ainvoke(prompt)
+
+    text = result.content.strip()
+
+    try:
+        questions = json.loads(text)
+
+        if isinstance(questions, list):
+            return questions[:3]
+    except Exception:
+        pass
+    return []

src/server/deep_agent/tools/report_generator_tool.py

@@ -2,27 +2,11 @@ import os
 import json
 import re
 from typing import Optional, List, Dict
-from langchain_qwq import ChatQwen
 from langgraph.config import get_stream_writer
 from pydantic import BaseModel, Field
 from langchain_core.tools import tool
 from langchain_core.messages import SystemMessage, HumanMessage
-
-from src.core.config import settings
-
-# =========================
-# LLM 初始化
-# =========================
-
-
-llm = ChatQwen(
-    enable_thinking=False,
-    model="qwen3.5-flash",
-    temperature=0.2,
-    max_tokens=3_000,
-    timeout=None,
-    max_retries=2,
-    api_key=settings.QWEN_API_KEY)
+from src.server.deep_agent.init_llm import repoer_llm
 
 
 # =========================
@@ -109,7 +93,7 @@ async def report_generator(
 
     full_report = ""
     try:
-        report_llm = llm.with_config(
+        report_llm = repoer_llm.with_config(
             callbacks=[]
         )
         async for chunk in report_llm.astream(