401 lines
18 KiB
Python
401 lines
18 KiB
Python
import os
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import uuid
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import json
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import random
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import logging
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from minio import Minio
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from fastapi import APIRouter
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from typing import AsyncGenerator
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from fastapi.responses import StreamingResponse
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from langchain_core.messages import SystemMessage, AIMessageChunk, ToolMessage, AIMessage, ToolMessageChunk
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from src.core.config import PROJECT_ROOT, settings, MONGO_URI
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from src.server.deep_agent.agents.main_agent import build_main_agent
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from src.server.deep_agent.tools.conversation_title_tool import conversation_title
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from src.server.deep_agent.tools.generate_furniture_sketch import is_image_path_exist
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from src.schemas.deep_agent_chat import DeepAgentChatRequest, HistoryResponse, HistoryItem
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from src.server.deep_agent.tools.extract_suggested_questions import generate_suggested_questions
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from src.server.deep_agent.utils.mongodb_util import ThreadImageMinIOStore
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from src.server.utils.new_oss_client import is_minio_file_exist, oss_upload_image_file, oss_get_image, get_presigned_url
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router = APIRouter(prefix="/chat", tags=["Furniture Design Chat"])
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logger = logging.getLogger(__name__)
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image_store = ThreadImageMinIOStore(MONGO_URI, "agent_tool_generate_db")
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minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE)
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@router.post("/deep_agent_stream")
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async def chat_stream(request: DeepAgentChatRequest):
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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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* `enable_thinking`: 是否开启思考模式。
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* `quote_image_path`: 用户引用图片地址 如："fida-test/furniture/sketches/8a1804d1-5ac9-4d02-bf17-e65fa7272f65.png"。
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* `input_image_paths`: 用户上传图片地址集合如：["fida-test/furniture/sketches/8a1804d1-5ac9-4d02-bf17-e65fa7272f65.png"]。
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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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用户上传:
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{
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"message": "合并两张图一边一半,左右拼",
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"input_image_paths": [
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"fida-test/furniture/sketches/218adbd2-c312-4298-9a82-5a92601ac9e2.png",
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"fida-test/furniture/sketches/8a1804d1-5ac9-4d02-bf17-e65fa7272f65.png"
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]
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}
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用户引用:
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{
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"message": "描述这张图",
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"quote_image_path":"fida-test/furniture/sketches/218adbd2-c312-4298-9a82-5a92601ac9e2.png"
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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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"""
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if request.thread_id:
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need_title = False
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else:
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need_title = True
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source_thread_id = request.thread_id
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checkpoint_id = request.checkpoint_id
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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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# 构建主agent
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workspace_dir = os.path.join(PROJECT_ROOT, f"agent_workspace/{target_thread_id}")
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logger.info(f"chat request data: {request} | target_thread_id : workspace_dir: {workspace_dir}")
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main_agent = build_main_agent(request.use_report, workspace_dir, request.enable_thinking)
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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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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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is_first = True
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content = [{"type": "text", "text": request.message}]
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files = {
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"input_image": [],
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"quote_image": "",
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"current_image": ""
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}
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# 用户上传图片
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if request.input_image_paths:
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for path in request.input_image_paths:
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bucket, object_name = path.split('/', 1)
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image_url = get_presigned_url(oss_client=minio_client, bucket=bucket, object_name=object_name)
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content.append({"type": "image_url", "image_url": {"url": image_url}})
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files["input_image"].append(path)
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# 用户引用图片
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if request.quote_image_path:
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bucket, object_name = request.quote_image_path.split('/', 1)
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image_url = get_presigned_url(oss_client=minio_client, bucket=bucket, object_name=object_name)
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content.append({"type": "image_url", "image_url": {"url": image_url}})
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files["quote_image"] = request.quote_image_path
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# 用户最近生成图片
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if image_store.get_image_path(target_thread_id):
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current_image_path = image_store.get_image_path(target_thread_id).get("current_image_path", False)
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if current_image_path:
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bucket, object_name = current_image_path.split('/', 1)
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image_url = get_presigned_url(oss_client=minio_client, bucket=bucket, object_name=object_name)
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content.append({"type": "image_url", "image_url": {"url": image_url}})
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final_messages = {
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"messages": [
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{
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"role": "user",
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"content": content
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},
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],
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"files": files
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}
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async for stream in main_agent.astream(
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final_messages,
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config=current_config,
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stream_mode=["updates", "messages", "custom"],
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subgraphs=True
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):
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if is_first:
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checkpoint_id = main_agent.get_state(current_config).config.get("configurable").get("checkpoint_id")
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yield f"data: {json.dumps({'thread_id': target_thread_id, 'is_branch': is_branching, 'status': 'start', "checkpoint_id": checkpoint_id}, ensure_ascii=False)}\n\n"
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is_first = False
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_, mode, chunks = stream
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if mode == "updates": # 只做记录 不做事件返回
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logger.info(f"[updates] -- {chunks}")
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update_model_messages = chunks.get("model", None)
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update_tools_messages = chunks.get("tools", None)
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payload_out = {
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"node": "",
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"is_delta": False,
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"content": "",
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"type": "updates"
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}
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if update_model_messages:
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model_messages = update_model_messages.get("messages", [])
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for model_token in model_messages:
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if isinstance(model_token, AIMessage):
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model_content_blocks = model_token.content_blocks[0]
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model_name = model_token.name
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payload_out.update({
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"node": model_name if model_name else "main",
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"tool_calls": model_token.tool_calls
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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 update_tools_messages:
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tools_messages = update_tools_messages.get("messages", [])
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for tools_token in tools_messages:
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if isinstance(tools_token, ToolMessage):
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tool_content_blocks = tools_token.content_blocks[0]
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tool_name = tools_token.name
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logger.info(f"[updates] {tool_name} -- {tool_content_blocks}")
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else:
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logger.info(f"[updates] -- {chunks}")
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elif mode == "messages":
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# logger.info(f"[messages] -- {chunks}")
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token, metadata = chunks
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subagent_name = metadata.get('lc_agent_name', "main")
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payload_out = {
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"node": subagent_name,
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"is_delta": False,
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"content": "",
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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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if len(reasoning) == 1:
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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": reasoning[0].get("reasoning", ""),
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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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print(f"[reasoning] {reasoning}*************************************************************************************")
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elif text:
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if len(text) == 1:
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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[0].get("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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print(f"[text] {text}*************************************************************************************")
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else:
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payload_out.update({
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"type": "tool_call",
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"is_delta": True,
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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_result",
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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_result",
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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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logger.info(f"[custom] -- {chunks}")
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payload_out = {
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"node": "research-agent",
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"is_delta": False,
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"content": "",
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"type": ""
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}
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delta = chunks.get("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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# 获取建议消息
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if request.need_suggestion > 0 and random.random() < request.need_suggestion:
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suggested_questions = await generate_suggested_questions(main_agent, target_thread_id)
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yield f"data: {json.dumps({'suggested_questions': suggested_questions}, ensure_ascii=False)}\n\n"
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# 获取标题
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if need_title:
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title = await conversation_title(agent=main_agent, config=current_config)
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logger.info(f"[title] {title}")
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yield f"data: {json.dumps({'title': title}, ensure_ascii=False)}\n\n"
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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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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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* **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。
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#### 2. 路径参数
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* `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）。
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#### 3. 返回字段定义
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* `thread_id`: 当前查询的项目ID。
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* `history`: 历史记录数组，包含：
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- `checkpoint_id`: 必填，回溯时使用的关键凭证。
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- `last_message`: 该阶段的最后一条消息摘要（方便前端预览）。
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- `node`: 产生该快照的节点名称（如 Designer, Visualizer）。
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- `timestamp`: 逻辑步骤序号。
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#### 4. 响应示例
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```json
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{
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"thread_id": "proj_001",
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"history": [
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{
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"checkpoint_id": "d82f3a12",
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"last_message": "我想设计一款北欧风书架",
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"node": "Supervisor",
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"timestamp": 1
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},
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{
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"checkpoint_id": "f4k92m1a",
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"last_message": "建议使用浅色橡木材质，增加简约感...",
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"node": "Designer",
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"timestamp": 2
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}
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]
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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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history_data = []
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workspace_dir = os.path.join(PROJECT_ROOT, f"agent_workspace/{thread_id}")
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main_agent = build_main_agent(False, workspace_dir, enable_thinking=False)
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async for state in main_agent.aget_state_history(config):
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msg_content = "Initial"
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if state.values and "messages" in state.values:
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msgs = state.values["messages"]
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if msgs and len(msgs) > 0:
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last_msg = msgs[-1]
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# 获取内容并做摘要截断
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content = getattr(last_msg, "content", str(last_msg))
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msg_content = content
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history_data.append(HistoryItem(
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checkpoint_id=state.config["configurable"]["checkpoint_id"],
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last_message=msg_content,
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node=state.metadata.get("source"),
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timestamp=state.metadata.get("step")
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))
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return HistoryResponse(thread_id=thread_id, history=history_data)
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# try:
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# except Exception as e:
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# raise HTTPException(status_code=404, detail=f"History not found: {str(e)}")
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