382 lines
15 KiB
Python
382 lines
15 KiB
Python
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import logging
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import uuid
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import json
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from typing import AsyncGenerator
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from fastapi import APIRouter
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from fastapi.responses import StreamingResponse
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from src.schemas.chat import ChatRequest, HistoryResponse, HistoryItem
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from langchain_core.messages import HumanMessage, SystemMessage, AIMessageChunk, ToolMessage, AIMessage, ToolMessageChunk
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from src.server.deep_agent.agents.main_agent import build_main_agent
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router = APIRouter(prefix="/chat", tags=["Furniture Design Chat"])
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logger = logging.getLogger(__name__)
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@router.post("/deep_agent_stream")
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async def chat_stream(request: ChatRequest):
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"""
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### 家具设计流式对话接口 (SSE)
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通过此接口与 AI 家具设计专家团队进行实时沟通。支持 **记忆持久化** 和 **历史回溯分叉**。
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#### 1. 核心功能
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* **实时反馈**: 采用 Server-Sent Events (SSE) 技术，实时推送主管、设计师、视觉专家等节点的思考过程。
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* **上下文记忆**: 传入 `thread_id` 即可恢复之前的对话进度。
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* **版本分溯**: 传入 `checkpoint_id` 可准确定位到历史中的某一轮，并从该点开启新的设计分支。
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#### 2. 请求参数
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* `message`: 用户的设计意图（如：'我想设计一个极简风格的橡木办公桌'）。
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* `thread_id`: (可选) 现有项目的唯一标识。若不传，系统将自动分配并返回。
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* `checkpoint_id`: (可选) 历史快照 ID。
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* `config_params`: (可选) 对话配置参数
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* `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
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* `use_report`: (可选) 是否需要使用report功能 true/false
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#### 3. 响应流说明 (Data Format)
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响应以 `data: ` 开头的 JSON 字符串流形式发送：
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- **Session Start**: `{"thread_id": "...", "status": "start"}`
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- **Node Message**: `{"node": "Designer", "content": "...", "checkpoint_id": "..."}`
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- **Session End**: `{"status": "end"}`
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- **is_delta**: False/True，表示这个消息不是完整内容，只是 AI 正在生成的一小段内容（一个字、一个词、一句话），需要前端把这些片段拼接起来才能得到完整的回答。
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#### 4. 请求示例
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```
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{
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"message": "设计一款北欧风格的躺椅."
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}
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{
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"message": "就以上信息直接生成sketch.",
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"thread_id": "187e58af"
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}
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{
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"message": "不要躺椅,要桌子",
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"thread_id": "187e58af",
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"checkpoint_id": "1f101aa2-8f24-6e2a-8001-2952c3a7447a"
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}
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```
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### 5. 响应流说明
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所有响应均以 data: 开头，JSON 字符串格式，末尾以 \n\n 结束
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响应流包含三种类型的事件：会话开始、节点消息、会话结束
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会话开始:
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```
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{
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"thread_id": "str",
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"is_branch": "boolean",
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"status": "start"
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}
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```
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节点消息:
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```
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{
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"node": "节点名称（如Designer/Researcher/Main）",
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"content": "消息内容",
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"checkpoint_id": "快照ID",
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"is_delta": "boolean",
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"type": "消息类型",
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"suggestions": "建议列表（可选）",
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"tool_name": "工具名称（可选）",
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"tool_call_chunk": "工具调用片段（可选）",
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"tool_call_id": "工具调用ID（可选）"
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}
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```
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报告增量消息：
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```
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{
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"node": "Researcher",
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"type": "report_delta",
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"content": "报告内容增量",
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"is_delta": true,
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"checkpoint_id": "xxx"
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}
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```
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AI 消息片段：
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```
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{
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"node": "Designer",
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"content": "设计建议内容",
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"checkpoint_id": "xxx",
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"is_delta": true,
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"type": "delta",
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"tool_call_chunk": {...}
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}
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```
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工具执行结果：
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```
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{
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"node": "ToolExecutor",
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"content": "工具执行结果",
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"checkpoint_id": "xxx",
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"is_delta": false,
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"type": "tool_result",
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"tool_name": "ImageGenerator",
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"tool_call_id": "yyy"
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}
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```
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"""
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logger.info(f"chat request data: {request}")
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source_thread_id = request.thread_id
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checkpoint_id = request.checkpoint_id
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# 构建主agent
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main_agent = build_main_agent(request.use_report)
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# 1. 確定目標 thread_id
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is_branching = source_thread_id and checkpoint_id
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target_thread_id = str(uuid.uuid4())[:8] if is_branching else (source_thread_id or str(uuid.uuid4())[:8])
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# 2. 配置參數
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temp = request.config_params.temperature if request.config_params else 0.7
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need_suggestion = request.need_suggestion,
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current_config = {
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"recursion_limit": 120,
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"configurable": {
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"thread_id": target_thread_id,
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"llm_temperature": temp,
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"use_report": request.use_report,
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}
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}
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# 3. 初始化消息 + 系統提示 TODO 写入数据库
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initial_messages = []
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if not source_thread_id or is_branching:
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if request.config_params:
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cp = request.config_params
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system_prompt = (
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f"Current furniture design background settings：\n"
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f"- type: {cp.type}\n"
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f"- space/region: {cp.region}\n"
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f"- style tendency: {cp.style}\n"
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f"Please strictly follow the above settings in subsequent conversations。"
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)
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initial_messages.append(SystemMessage(content=system_prompt))
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# 4. 處理分支（從歷史 checkpoint 複製狀態）
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if is_branching:
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source_config = {
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"configurable": {
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"thread_id": source_thread_id,
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"checkpoint_id": checkpoint_id
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}
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}
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older_state = await main_agent.aget_state(source_config)
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combined_values = older_state.values.copy()
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if initial_messages:
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combined_values["messages"] = list(combined_values.get("messages", [])) + initial_messages
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await main_agent.aupdate_state(current_config, combined_values)
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async def event_generator() -> AsyncGenerator[str, None]:
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yield f"data: {json.dumps({'thread_id': target_thread_id, 'is_branch': is_branching, 'status': 'start'}, ensure_ascii=False)}\n\n"
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new_messages = initial_messages[:] if not source_thread_id else []
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new_messages.append(HumanMessage(content=request.message))
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input_data = {
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"messages": new_messages,
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}
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current_cp_id = None
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async for stream in main_agent.astream(
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input_data,
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config=current_config,
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stream_mode=["updates", "messages", "custom"], # 确保包含 "values"
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subgraphs=True
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):
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# logger.info(f"Received event: {event}")
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_, mode, chunks = stream
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if mode == "updates":
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# TODO 补充
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print(f"[updates] {chunks}")
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elif mode == "messages":
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token, metadata = chunks
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subagent_name = metadata.get('lc_agent_name', None)
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payload_out = {
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"node": subagent_name,
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# "checkpoint_id": current_cp_id or "unknown", TODO 替换为checkpoint_idns
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"is_delta": False,
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"content": "",
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"suggestions": [],
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"type": ""
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}
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if isinstance(token, AIMessageChunk): # 默认回复 思考内容
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reasoning = [b for b in token.content_blocks if b["type"] == "reasoning"]
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text = [b for b in token.content_blocks if b["type"] == "text"]
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if reasoning:
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payload_out.update({
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"type": "reasoning",
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"is_delta": True,
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"content": text,
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"tool_call_chunk": token.tool_call_chunks[0] if token.tool_call_chunks else None
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})
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elif text:
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payload_out.update({
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"type": "text",
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"is_delta": True,
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"content": text,
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"tool_call_chunk": token.tool_call_chunks[0] if token.tool_call_chunks else None
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})
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else:
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payload_out.update({
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"is_delta": True,
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"tool_call_chunk": token.tool_call_chunks[0] if token.tool_call_chunks else None
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})
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yield f"data: {json.dumps(payload_out, ensure_ascii=False)}\n\n"
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elif isinstance(token, ToolMessageChunk): # 工具返回
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text = [b for b in token.content_blocks if b["type"] == "text"]
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payload_out.update({
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"type": "tool_text",
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"is_delta": False,
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"content": text,
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"tool_name": token.name,
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})
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yield f"data: {json.dumps(payload_out, ensure_ascii=False)}\n\n"
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elif isinstance(token, ToolMessage): # 工具返回
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text = [b for b in token.content_blocks if b["type"] == "text"]
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payload_out.update({
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"type": "tool_text",
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"is_delta": False,
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"content": text,
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"tool_name": token.name,
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})
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yield f"data: {json.dumps(payload_out, ensure_ascii=False)}\n\n"
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else:
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continue
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elif mode == "custom":
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token, metadata = chunks
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subagent_name = metadata.get('lc_agent_name', None)
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payload_out = {
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"node": subagent_name,
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# "checkpoint_id": current_cp_id or "unknown", TODO 替换为checkpoint_idns
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"is_delta": False,
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"content": "",
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"suggestions": [],
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"type": ""
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}
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delta = chunks.get("delta", "").strip()
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if delta:
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payload_out.update({
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"type": chunks.get("type", ""),
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"is_delta": True,
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"content": delta,
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})
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yield f"data: {json.dumps(payload_out, ensure_ascii=False)}\n\n"
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# elif channel == "updates":
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# # 处理 updates（非 interrupt 的部分）
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# if isinstance(payload, dict):
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# for update_node, update_content in payload.items():
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# # 处理 reducer 包裹的值
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# if isinstance(update_content, dict):
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# for k, v in update_content.items():
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# if hasattr(v, "value"):
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# update_content[k] = v.value
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#
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# # 序列化 messages
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# if isinstance(update_content, dict) and "messages" in update_content:
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# msgs = []
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# for m in update_content["messages"]:
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# msgs.append({
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# "type": m.__class__.__name__,
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# "content": getattr(m, "content", ""),
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# "name": getattr(m, "name", None),
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# "tool_calls": getattr(m, "tool_calls", None),
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# })
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# update_content["messages"] = msgs
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#
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# yield f"data: {json.dumps({
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# "node": "Supervisor", # 或 update_node
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# "type": "updates",
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# "content": update_content,
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# "is_delta": False,
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# "checkpoint_id": current_cp_id,
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# }, ensure_ascii=False)}\n\n"
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#
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# elif channel == "custom":
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else:
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yield f"data: {json.dumps({'status': 'end'}, ensure_ascii=False)}\n\n"
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return StreamingResponse(event_generator(), media_type="text/event-stream")
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@router.get("/history/{thread_id}", response_model=HistoryResponse)
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async def get_chat_history(thread_id: str):
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"""
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### 获取项目设计历史记录
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|||
|
|
|
|||
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此接口用于拉取指定 `thread_id` 下的所有历史状态快照。它是实现 **“版本回溯”** 和 **“方案对比”** 的核心数据来源。
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#### 1. 功能说明
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* **快照列表**: 返回该项目从启动至今的所有关键节点（Checkpoints）。
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|||
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* **版本定位**: 每个历史点都包含一个唯一的 `checkpoint_id`。
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* **数据回溯**: 客户端获取此列表后，可以引导用户选择任意一个版本，并将其 `checkpoint_id` 传回 `/chat/stream` 接口以开启新的设计分支。
|
|||
|
|
|
|||
|
|
#### 2. 路径参数
|
|||
|
|
* `thread_id`: 设计项目的唯一标识符（由 `/chat/stream` 首次调用时生成或指定）。
|
|||
|
|
|
|||
|
|
#### 3. 返回字段定义
|
|||
|
|
* `thread_id`: 当前查询的项目ID。
|
|||
|
|
* `history`: 历史记录数组，包含：
|
|||
|
|
- `checkpoint_id`: 必填，回溯时使用的关键凭证。
|
|||
|
|
- `last_message`: 该阶段的最后一条消息摘要（方便前端预览）。
|
|||
|
|
- `node`: 产生该快照的节点名称（如 Designer, Visualizer）。
|
|||
|
|
- `timestamp`: 逻辑步骤序号。
|
|||
|
|
|
|||
|
|
#### 4. 响应示例
|
|||
|
|
```json
|
|||
|
|
{
|
|||
|
|
"thread_id": "proj_001",
|
|||
|
|
"history": [
|
|||
|
|
{
|
|||
|
|
"checkpoint_id": "d82f3a12",
|
|||
|
|
"last_message": "我想设计一款北欧风书架",
|
|||
|
|
"node": "Supervisor",
|
|||
|
|
"timestamp": 1
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"checkpoint_id": "f4k92m1a",
|
|||
|
|
"last_message": "建议使用浅色橡木材质，增加简约感...",
|
|||
|
|
"node": "Designer",
|
|||
|
|
"timestamp": 2
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
```
|
|||
|
|
"""
|
|||
|
|
config = {"configurable": {"thread_id": thread_id}, }
|
|||
|
|
history_data = []
|
|||
|
|
async for state in main_agent.aget_state_history(config):
|
|||
|
|
msg_content = "Initial"
|
|||
|
|
if state.values and "messages" in state.values:
|
|||
|
|
msgs = state.values["messages"]
|
|||
|
|
if msgs and len(msgs) > 0:
|
|||
|
|
last_msg = msgs[-1]
|
|||
|
|
# 获取内容并做摘要截断
|
|||
|
|
content = getattr(last_msg, "content", str(last_msg))
|
|||
|
|
msg_content = content
|
|||
|
|
|
|||
|
|
history_data.append(HistoryItem(
|
|||
|
|
checkpoint_id=state.config["configurable"]["checkpoint_id"],
|
|||
|
|
last_message=msg_content,
|
|||
|
|
node=state.metadata.get("source"),
|
|||
|
|
timestamp=state.metadata.get("step")
|
|||
|
|
))
|
|||
|
|
|
|||
|
|
return HistoryResponse(thread_id=thread_id, history=history_data)
|
|||
|
|
# try:
|
|||
|
|
|
|||
|
|
# except Exception as e:
|
|||
|
|
# raise HTTPException(status_code=404, detail=f"History not found: {str(e)}")
|