feat 接入report
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@@ -1,5 +1,8 @@
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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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@@ -7,6 +10,7 @@ from src.server.agent.graph import app # 导入已经 compile 好的 graph
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from langchain_core.messages import HumanMessage, SystemMessage
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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("/stream")
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@@ -52,29 +56,28 @@ async def chat_stream(request: ChatRequest):
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}
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```
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"""
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logger.debug(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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# 1. 确定目标 thread_id
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# 如果是回溯操作，我们生成一个新的 ID，或者由前端传入一个新的 target_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. 获取配置参数
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temp = request.config_params.temperature if request.config_params else 0.7
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# 构建基础 Config
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# 2. 配置参数
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temp = request.config_params.temperature if request.config_params else 0.7
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current_config = {
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"recursion_limit": 100,
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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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"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. 处理状态初始化与分支
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initial_messages = []
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# 如果是全新的对话（没有 source_thread_id），或者明确要求分叉
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# 3. 初始化消息 + 系统提示
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initial_messages = []
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if not source_thread_id or is_branching:
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# 如果用户传了标签，构造 SystemMessage 注入上下文
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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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@@ -86,7 +89,7 @@ async def chat_stream(request: ChatRequest):
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)
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initial_messages.append(SystemMessage(content=system_prompt))
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# 4. 执行分叉逻辑（搬运旧数据）
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# 4. 处理分支（从历史 checkpoint 复制状态）
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if is_branching:
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source_config = {
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"configurable": {
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@@ -95,80 +98,149 @@ async def chat_stream(request: ChatRequest):
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}
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}
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older_state = await app.aget_state(source_config)
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# 将旧消息和我们新定义的 SystemMessage 合并
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# update_state 会将这些消息推送到新 thread 的存储中
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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["messages"]) + initial_messages
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combined_values["messages"] = list(combined_values.get("messages", [])) + initial_messages
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await app.aupdate_state(current_config, combined_values)
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async def event_generator():
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# 初始推送状态信息
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async def event_generator() -> AsyncGenerator[str, None]:
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# 初始事件
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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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# 构造本次请求的输入
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# 如果是第一次开始，且有 initial_messages，则连同 user message 一起发送
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# --- 核心逻辑：构造本次请求的消息列表 ---
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new_messages = []
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if not source_thread_id and initial_messages:
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new_messages.extend(initial_messages)
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# 添加用户消息
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# 构造输入
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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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# --- 新增：强制绘图指令注入 ---
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# if request.force_sketch:
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# force_instruction = HumanMessage(
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# content="[SYSTEM_DIRECTIVE]: 用户点击了强制生成按钮。请立即根据当前上下文调用 generate_furniture_sketch 工具生成草图，无需确认。"
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# )
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# new_messages.append(force_instruction)
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input_data = {
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"messages": new_messages,
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"require_suggestion": request.need_suggestion # 初始由前端决定
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"require_suggestion": request.need_suggestion,
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"use_report": request.use_report,
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}
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async for event in app.astream(
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# 使用 astream_events v2 + stream_subgraphs=True 来捕获 DeepAgents 内部流式事件
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async for event in app.astream_events(
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input_data,
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current_config,
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stream_mode="updates"
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version="v2",
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config=current_config,
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stream_subgraphs=True,
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):
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for node_name, output in event.items():
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if "messages" in output:
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# 获取最新 state 以获取 checkpoint_id
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state = await app.aget_state(current_config)
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current_cp_id = state.config["configurable"].get("checkpoint_id")
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event_kind = event["event"]
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# 遍历本次 update 产生的所有消息
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for msg in output["messages"]:
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# 获取当前 checkpoint_id（安全方式，避免 KeyError）
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latest_state = await app.aget_state(current_config)
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configurable = latest_state.config.get("configurable", {})
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current_cp_id = configurable.get("checkpoint_id", "") # 如果没有，返回空字符串
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# ────────────────────────────────────────────────
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# 1. LLM token 流式输出（主图或子图的逐 token）
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# ────────────────────────────────────────────────
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if event_kind == "on_chat_model_stream":
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chunk = event["data"].get("chunk")
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if chunk and chunk.content:
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node_name = event.get("name", "Unknown")
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# 判断是否来自 Researcher 子图
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namespace = event.get("parent_ids", []) or event.get("namespace", [])
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if any("Researcher" in str(ns) for ns in namespace):
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node_name = "Researcher"
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payload = {
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"node": node_name,
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"content": chunk.content,
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"is_delta": True,
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"checkpoint_id": current_cp_id,
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"image_url": None,
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"suggestions": []
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}
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yield f"data: {json.dumps(payload, ensure_ascii=False)}\n\n"
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# ────────────────────────────────────────────────
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# 2. 自定义事件（report_delta 等）
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# ────────────────────────────────────────────────
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elif event_kind == "on_custom_event":
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custom_data = event["data"]
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if isinstance(custom_data, dict):
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if custom_data.get("type") == "report_delta":
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payload = {
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"node": node_name,
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"content": "",
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"node": "Researcher",
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"content": custom_data.get("delta", ""),
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"is_delta": True,
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"checkpoint_id": current_cp_id,
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"image_url": None,
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"suggestions": []
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}
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yield f"data: {json.dumps(payload, ensure_ascii=False)}\n\n"
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# 可选：报告开始/完成/错误等状态提示
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elif custom_data.get("type") in ("report_start", "report_complete", "report_error"):
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status_msg = {
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"report_start": "Start generating reports...",
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"report_complete": "Report generation completed",
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"report_error": f"Report generation failed: {custom_data.get('message', '')}"
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}.get(custom_data["type"], "")
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payload = {
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"node": "Researcher",
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"content": status_msg,
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"is_delta": False,
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"checkpoint_id": current_cp_id,
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"image_url": None,
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"suggestions": []
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}
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yield f"data: {json.dumps(payload, ensure_ascii=False)}\n\n"
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# ────────────────────────────────────────────────
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# 3. 节点启动 / 工具启动（进度提示）
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# ────────────────────────────────────────────────
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elif event_kind in {"on_tool_start", "on_tool_end"}:
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tool_name = event.get("name", "unknown_tool")
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tool_data = event.get("data", {})
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tool_input = tool_data.get("input", "")
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tool_output = tool_data.get("output", "")
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if event_kind == "on_tool_start":
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payload = {
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"node": tool_name,
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"content": tool_input,
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"is_delta": False,
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"checkpoint_id": current_cp_id,
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"image_url": None,
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"suggestions": []
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}
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yield f"data: {json.dumps(payload, ensure_ascii=False)}\n\n"
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else:
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if tool_name == "generate_furniture" and isinstance(tool_output, str):
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payload = {
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"node": tool_name,
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"content": "Design sketch has been generated for you.", # 给用户友好的文字提示
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"image_url": tool_output, # 直接传 URL 给前端显示
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"is_delta": False, # 这是一个完整事件，不是增量
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"checkpoint_id": current_cp_id,
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"suggestions": []
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}
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# --- 核心改动：提取建议按钮 ---
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# 无论是不是 Suggester 节点，只要消息里带了建议就提取
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if hasattr(msg, "additional_kwargs") and "suggestions" in msg.additional_kwargs:
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payload["suggestions"] = msg.additional_kwargs["suggestions"]
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content = msg.content
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# 逻辑判断：MinIO 图片处理
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if node_name == "Visualizer" and str(content).endswith("png") and "furniture/sketches" in str(content):
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payload["image_url"] = content
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payload["content"] = "已为您生成设计草图"
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else:
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payload["content"] = content
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# 如果消息既没有文本、也没有图片、也没有建议（比如中间的 ToolCall 消息），则跳过
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if not payload["content"] and not payload["image_url"] and not payload["suggestions"]:
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continue
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yield f"data: {json.dumps(payload, ensure_ascii=False)}\n\n"
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elif tool_name == "topic_research":
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payload = {
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"node": tool_name,
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"content": "Visiting...", # 给用户友好的文字提示
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"image_url": None, # 直接传 URL 给前端显示
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"search_list": tool_output.content,
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"is_delta": False, # 这是一个完整事件，不是增量
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"checkpoint_id": current_cp_id,
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"suggestions": []
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}
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yield f"data: {json.dumps(payload, ensure_ascii=False)}\n\n"
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else:
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# 可选：其他工具的通用处理（debug 或显示结果）
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if tool_output:
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payload = {
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"node": tool_name,
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"content": f"tool {tool_name} Execution completed：{str(tool_output)[:200]}...", # 截断避免过长
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"is_delta": False,
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"checkpoint_id": current_cp_id,
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"image_url": None,
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"suggestions": []
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}
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yield f"data: {json.dumps(payload, ensure_ascii=False)}\n\n"
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# 流结束
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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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@@ -218,7 +290,7 @@ async def get_chat_history(thread_id: str):
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}
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```
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"""
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config = {"configurable": {"thread_id": thread_id}}
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config = {"configurable": {"thread_id": thread_id}, }
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history_data = []
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async for state in app.aget_state_history(config):
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msg_content = "Initial"
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