diff --git a/src/routers/deep_agent_chat.py b/src/routers/deep_agent_chat.py index 7c4177f..430b96d 100644 --- a/src/routers/deep_agent_chat.py +++ b/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") diff --git a/src/schemas/deep_agent_chat.py b/src/schemas/deep_agent_chat.py new file mode 100644 index 0000000..0294b82 --- /dev/null +++ b/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 diff --git a/src/server/deep_agent/tools/conversation_title_tool.py b/src/server/deep_agent/tools/conversation_title_tool.py index a0c1cb9..3c837f5 100644 --- a/src/server/deep_agent/tools/conversation_title_tool.py +++ b/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