新增标题提取

src/routers/deep_agent_chat.py

@@ -6,18 +6,19 @@ from typing import AsyncGenerator
 
 from fastapi import APIRouter
 from fastapi.responses import StreamingResponse
-from src.schemas.chat import ChatRequest, HistoryResponse, HistoryItem
+from src.schemas.deep_agent_chat import DeepAgentChatRequest, HistoryResponse, HistoryItem
 from langchain_core.messages import HumanMessage, SystemMessage, AIMessageChunk, ToolMessage, AIMessage, ToolMessageChunk
 
 from src.server.deep_agent.agents.main_agent import build_main_agent
-from src.server.deep_agent.tools.extract_suggested_questions import format_messages, generate_suggested_questions
+from src.server.deep_agent.tools.conversation_title_tool import conversation_title
+from src.server.deep_agent.tools.extract_suggested_questions import generate_suggested_questions
 
 router = APIRouter(prefix="/chat", tags=["Furniture Design Chat"])
 logger = logging.getLogger(__name__)
 
 
 @router.post("/deep_agent_stream")
-async def chat_stream(request: ChatRequest):
+async def chat_stream(request: DeepAgentChatRequest):
     """
     ### 家具设计流式对话接口 (SSE)
 
@@ -34,6 +35,7 @@ async def chat_stream(request: ChatRequest):
     * `checkpoint_id`: (可选) 历史快照 ID。
     * `config_params`: (可选) 对话配置参数
     * `need_suggestion`: (可选) 是否需要建议按钮,需要建议的频率，0-1的浮点数
+    * `need_title`: (可选) 是否需要标题
     * `use_report`: (可选) 是否需要使用report功能 true/false
 
 
@@ -66,61 +68,6 @@ async def chat_stream(request: ChatRequest):
     ### 5. 响应流说明
     所有响应均以 data: 开头，JSON 字符串格式，末尾以 \n\n 结束
     响应流包含三种类型的事件：会话开始、节点消息、会话结束
-    会话开始:
-    ```
-        {
-          "thread_id": "str",
-          "is_branch": "boolean",
-          "status": "start"
-        }
-    ```
-    节点消息:
-    ```
-        {
-          "node": "节点名称（如Designer/Researcher/Main）",
-          "content": "消息内容",
-          "checkpoint_id": "快照ID",
-          "is_delta": "boolean",
-          "type": "消息类型",
-          "tool_name": "工具名称（可选）",
-          "tool_call_chunk": "工具调用片段（可选）",
-          "tool_call_id": "工具调用ID（可选）"
-        }
-
-    ```
-    报告增量消息：
-    ```
-    {
-      "node": "Researcher",
-      "type": "report_delta",
-      "content": "报告内容增量",
-      "is_delta": true,
-      "checkpoint_id": "xxx"
-    }
-    ```
-    AI 消息片段：
-    ```
-    {
-      "node": "Designer",
-      "content": "设计建议内容",
-      "checkpoint_id": "xxx",
-      "is_delta": true,
-      "type": "delta",
-      "tool_call_chunk": {...}
-    }
-    ```
-    工具执行结果：
-    ```
-    {
-      "node": "ToolExecutor",
-      "content": "工具执行结果",
-      "checkpoint_id": "xxx",
-      "is_delta": false,
-      "type": "tool_result",
-      "tool_name": "ImageGenerator",
-      "tool_call_id": "yyy"
-    }
-    ```
 
     """
     logger.info(f"chat request data: {request}")
@@ -306,9 +253,16 @@ async def chat_stream(request: ChatRequest):
                     })
                     yield f"data: {json.dumps(payload_out, ensure_ascii=False)}\n\n"
 
+        # 获取建议消息
         if request.need_suggestion > 0 and random.random() < request.need_suggestion:
             suggested_questions = await generate_suggested_questions(main_agent, target_thread_id)
             yield f"data: {json.dumps({'suggested_questions': suggested_questions}, ensure_ascii=False)}\n\n"
+
+        # 获取标题
+        if request.need_title:
+            title = await conversation_title(agent=main_agent, config=current_config)
+            yield f"data: {json.dumps({'title': title}, ensure_ascii=False)}\n\n"
+
         yield f"data: {json.dumps({'status': 'end'}, ensure_ascii=False)}\n\n"
 
     return StreamingResponse(event_generator(), media_type="text/event-stream")

src/schemas/deep_agent_chat.py

@@ -0,0 +1,37 @@
+from pydantic import BaseModel, Field, confloat
+from typing import Optional, List, Dict, Any
+
+
+class AgentConfig(BaseModel):
+    type: str = Field(..., description="家具类型，如：沙发、餐桌")
+    region: str = Field(..., description="地区/空间，如：客厅、卧室、户外")
+    style: str = Field(..., description="设计风格，如：极简、工业风、中式")
+    temperature: confloat(ge=0, le=2.0) = Field(default=0.7, description="模型温度")
+
+
+class DeepAgentChatRequest(BaseModel):
+    message: str = Field(..., description="用户的输入指令")
+    thread_id: Optional[str] = Field(None, description="会话线程ID，不传则开启新会话")
+    checkpoint_id: Optional[str] = Field(None, description="回溯点的ID，用于从历史点开启新对话")
+    config_params: Optional[AgentConfig] = None
+    need_suggestion: float = 0
+    need_title: bool = False
+    use_report: bool = False  # ← 新增：是否使用深度报告
+
+
+class HistoryItem(BaseModel):
+    checkpoint_id: str
+    last_message: str
+    node: Optional[str]
+    timestamp: Any
+
+
+class HistoryResponse(BaseModel):
+    thread_id: str
+    history: List[HistoryItem]
+
+
+class StreamChunk(BaseModel):
+    node: str
+    content: str
+    checkpoint_id: str

src/server/deep_agent/tools/conversation_title_tool.py

@@ -1,27 +1,44 @@
+from langchain_core.messages import HumanMessage, AIMessage
 from langchain_core.prompts import PromptTemplate
 
 from src.server.deep_agent.init_llm import title_llm
 
 
-def conversation_title(full_conversation):
-    title_prompt = PromptTemplate(
-        input_variables=["full_conversation"],
-        template="""
-        请严格按照以下要求生成对话标题：
-        1. 标题长度：8-15个字，纯中文，无标点、无特殊符号、无换行
-        2. 标题内容：基于完整对话，精准概括核心主题（兼顾用户需求和助手回复）
-        3. 标题风格：自然口语化，符合中文表达习惯，不冗余
-        
-        完整对话内容：
-        {full_conversation}
-        
-        仅输出标题，不要输出任何额外解释、说明或标点符号。
-        """
-    )
-    title_chain = title_prompt | title_llm
-    response = title_chain.invoke({"full_conversation": full_conversation})
-    return response
+async def conversation_title(agent, config):
+    state = agent.get_state(config)
+    messages = state.values.get("messages", [])
+    if len(messages) < 2:
+        return None
 
+    user_msg = None
+    ai_msg = None
 
-if __name__ == '__main__':
-    print(conversation_title("你好"))
+    for m in messages:
+        if isinstance(m, HumanMessage) and user_msg is None:
+            user_msg = m.content
+
+        if isinstance(m, AIMessage) and ai_msg is None:
+            ai_msg = m.content
+
+        if user_msg and ai_msg:
+            break
+    if not user_msg or not ai_msg:
+        return None
+
+    prompt = f"""
+    请根据以下首次对话内容，生成一个简洁、精准的标题（2-15个字）：
+
+    用户：{user_msg}
+    助手：{ai_msg}
+
+    要求：
+    1. 标题需概括对话核心内容
+    2. 语言简洁、符合中文表达习惯
+    3. 仅返回标题，不要额外解释
+    """
+    response = await title_llm.ainvoke(prompt)
+    title = response.content.strip()
+
+    # 去掉可能的符号
+    title = title.replace("《", "").replace("》", "")
+    return title