1.优化隔离工作目录2.新增图像生成和编辑功能3.生成模型替换为本地flux2 klein

main.py

@@ -1,11 +1,11 @@
 import logging.config
+from pathlib import Path
 
 import uvicorn
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
-
 from logging_env import LOGGER_CONFIG_DICT
-from src.routers import chat, deep_agent_chat
+from src.routers import deep_agent_chat
 from src.routers import generate_3D
 from src.routers import flux2_gen_img
 
@@ -26,7 +26,6 @@ app_server.add_middleware(
 )
 
 # 包含路由
-app_server.include_router(chat.router)
 app_server.include_router(deep_agent_chat.router)
 app_server.include_router(generate_3D.router)
 app_server.include_router(flux2_gen_img.router)

pyproject.toml

@@ -48,4 +48,6 @@ dependencies = [
     "path>=17.1.1",
     "langgraph-checkpoint-postgres>=3.0.4",
     "langgraph-store-mongodb>=0.2.0",
+    "tool>=0.8.0",
+    "langchain-daytona>=0.0.3",
 ]

src/core/config.py

@@ -1,3 +1,5 @@
+from pathlib import Path
+
 from pydantic_settings import BaseSettings, SettingsConfigDict
 from pydantic import Field
 
@@ -44,3 +46,7 @@ class Settings(BaseSettings):
 
 settings = Settings()
 MONGO_URI = f"mongodb://{settings.MONGODB_USERNAME}:{settings.MONGODB_PASSWORD}@{settings.MONGODB_HOST}:{settings.MONGODB_PORT}"
+
+TOOL_DIR = Path(__file__).resolve().parent
+PROJECT_ROOT = TOOL_DIR.parent
+print(f"PROJECT_ROOT : {PROJECT_ROOT}")

src/routers/deep_agent_chat.py

@@ -1,4 +1,5 @@
 import logging
+import os
 import random
 import uuid
 import json
@@ -6,6 +7,8 @@ from typing import AsyncGenerator
 
 from fastapi import APIRouter
 from fastapi.responses import StreamingResponse
+
+from src.core.config import PROJECT_ROOT
 from src.schemas.deep_agent_chat import DeepAgentChatRequest, HistoryResponse, HistoryItem
 from langchain_core.messages import HumanMessage, SystemMessage, AIMessageChunk, ToolMessage, AIMessage, ToolMessageChunk
 
@@ -77,13 +80,15 @@ async def chat_stream(request: DeepAgentChatRequest):
     source_thread_id = request.thread_id
     checkpoint_id = request.checkpoint_id
 
-    # 构建主agent
-    main_agent = build_main_agent(request.use_report)
-
     # 1. 確定目標 thread_id
     is_branching = source_thread_id and checkpoint_id
     target_thread_id = str(uuid.uuid4())[:8] if is_branching else (source_thread_id or str(uuid.uuid4())[:8])
 
+    # 构建主agent
+    workspace_dir = os.path.join(PROJECT_ROOT, f"agent_workspace/{target_thread_id}")
+    print(f"target_thread_id : workspace_dir: {workspace_dir}")
+    main_agent = build_main_agent(request.use_report, workspace_dir)
+
     # 2. 配置參數
     temp = request.config_params.temperature if request.config_params else 0.7
 

src/schemas/deep_agent_chat.py

@@ -11,6 +11,7 @@ class AgentConfig(BaseModel):
 
 class DeepAgentChatRequest(BaseModel):
     message: str = Field(..., description="用户的输入指令")
+    # image_url: Optional[str] = Field(None, description="图片地址")  # ✅ 新增
     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

src/server/deep_agent/agents/main_agent.py

@@ -1,21 +1,22 @@
 from pathlib import Path
 
+from daytona import Daytona
 from deepagents import create_deep_agent
 from deepagents.backends import FilesystemBackend
 from langchain.agents.middleware import SummarizationMiddleware
 from langgraph.checkpoint.mongodb import MongoDBSaver
 from langgraph.checkpoint.serde.jsonplus import JsonPlusSerializer
 from pymongo import MongoClient
+from daytona import CreateSandboxFromSnapshotParams, Daytona
+from langchain_daytona import DaytonaSandbox
 
 from src.core.config import MONGO_URI
-from src.server.deep_agent.agents.painter import painter_subagent
-from src.server.deep_agent.agents.researcher import research_subagent
+from src.server.deep_agent.agents.painter import build_painter_subagent
+from src.server.deep_agent.agents.researcher import build_researcher_subagent
 from src.server.deep_agent.agents.user_profile import user_profile_subagent
 from src.server.deep_agent.init_llm import main_llm
 from src.server.deep_agent.init_prompt import build_system_prompt
 
-TOOL_DIR = Path(__file__).resolve().parent
-PROJECT_ROOT = TOOL_DIR.parent
 client = MongoClient(MONGO_URI)
 checkpointer = MongoDBSaver(
     client=client["furniture_agent_db"],
@@ -23,22 +24,42 @@ checkpointer = MongoDBSaver(
     collection_name="fida_agent_collection",
     serde=JsonPlusSerializer(pickle_fallback=True),  # ← 關鍵這一行
 )
-subagents = [
+
+
+class CanvasMiddleware:
+    def before_agent(self, state, agent_input, **kwargs):
+        canvas = state.get("canvas", {})
+
+        info = f"""
+        当前画布状态：
+        - image_path: {canvas.get("image_path")}
+        - 是否已有图片: {bool(canvas.get("image_path"))}
+        """
+
+        agent_input["messages"].append({
+            "role": "system",
+            "content": info
+        })
+
+        return state, agent_input
+
+
+def build_main_agent(use_report, workspace_dir):
+    research_subagent = build_researcher_subagent(workspace_dir)
+    painter_subagent = build_painter_subagent(workspace_dir)
+    subagents = [
         painter_subagent,
         research_subagent,
         user_profile_subagent
-]
-
-
-def build_main_agent(use_report):
+    ]
     main_agent = create_deep_agent(
         model=main_llm,
         system_prompt=build_system_prompt(use_report=use_report),
         subagents=subagents,
         checkpointer=checkpointer,
         backend=FilesystemBackend(
-            root_dir=str(PROJECT_ROOT / "agent_workspace"),
-            virtual_mode=False,  # 重要：關掉虛擬模式 → 真的寫硬碟
+            root_dir=workspace_dir,
+            virtual_mode=True,  # 重要：關掉虛擬模式 → 真的寫硬碟
         ),
         middleware=[
             SummarizationMiddleware(

src/server/deep_agent/agents/painter.py

@@ -2,7 +2,7 @@ from langchain.agents.middleware import wrap_tool_call
 
 from src.server.deep_agent.init_llm import llm
 from src.server.deep_agent.init_prompt import build_painter_prompt
-from src.server.deep_agent.tools.generate_furniture_sketch import generate_furniture
+from src.server.deep_agent.tools.generate_furniture_sketch import create_generate_furniture_tool, create_edit_furniture_tool
 
 
 @wrap_tool_call
@@ -12,11 +12,16 @@ async def log_tool_calls(request, handler):
     return handler(request)
 
 
-painter_subagent = {
+def build_painter_subagent(workspace_dir):
+    generate_furniture = create_generate_furniture_tool(workspace_dir)
+    edit_furniture = create_edit_furniture_tool(workspace_dir)
+
+    painter_subagent = {
         "name": "painter_subagent",
-    "description": "理解用户意图，使用prompt,调用generate_furniture工具生成家具sketch草图.",
+        "description": "理解用户意图,利用prompt编辑或生成家具sketch图像",
         "system_prompt": build_painter_prompt(),
-    "tools": [generate_furniture],
+        "tools": [generate_furniture, edit_furniture],
         "model": llm,
         # "middleware": [log_tool_calls],
-}
+    }
+    return painter_subagent

src/server/deep_agent/agents/researcher.py

@@ -1,12 +1,17 @@
 from src.server.deep_agent.init_llm import llm
 from src.server.deep_agent.init_prompt import build_researcher_prompt
-from src.server.deep_agent.tools.crawl_tool import crawl4ai_batch
-from src.server.deep_agent.tools.report_generator_tool import report_generator
+from src.server.deep_agent.tools.crawl_tool import create_crawl4ai_batch_tool
+from src.server.deep_agent.tools.report_generator_tool import create_report_generator_tool
 from src.server.deep_agent.tools.research_tool import topic_research
-from src.server.deep_agent.tools.structured_retrieval_tool import structured_retrieval
+from src.server.deep_agent.tools.structured_retrieval_tool import create_structured_retrieval_tool
 from src.server.deep_agent.tools.user_persona_tool import query_report_profile
 
-research_subagent = {
+
+def build_researcher_subagent(workspace_dir):
+    crawl4ai_batch = create_crawl4ai_batch_tool(workspace_dir)
+    structured_retrieval = create_structured_retrieval_tool(workspace_dir)
+    report_generator = create_report_generator_tool(workspace_dir)
+    research_subagent = {
         "name": "research_subagent",
         "description": "通过网络搜索对家具设计开展深度研究并整合结论",
         "system_prompt": build_researcher_prompt(),
@@ -18,4 +23,5 @@ research_subagent = {
             report_generator
         ],
         "model": llm
-}
+    }
+    return research_subagent

src/server/deep_agent/agents/vision_subagent.py

@@ -0,0 +1,77 @@
+import json
+
+from src.server.deep_agent.init_llm import vision_llm
+from src.server.deep_agent.tools.vision_analyze_tool import vision_analyze_tool
+
+vision_subagent = {
+    "name": "vision_subagent",
+    "description": "分析用户上传的图片，提取家具、风格、颜色、材质等信息",
+    "system_prompt": """
+        你是一个专业的视觉分析助手（家具设计方向）。
+        
+        你的任务：
+        1. 理解用户提供的图片（路径或URL）
+        2. 分析家具内容
+        3. 输出结构化JSON（不要解释）
+        
+        格式：
+        {
+          "objects": [],
+          "style": "",
+          "color": [],
+          "material": [],
+          "room_type": "",
+          "description": ""
+        }
+        """,
+    "tools": [],  # ❗这里不用tool，直接用多模态模型
+    "model": vision_llm,
+}
+
+
+def vision_execute(state):
+    image = state.get("image")
+
+    if image is None:
+        return {
+            "error": "NO_IMAGE"
+        }
+
+    prompt = """
+你是一个家具视觉分析模型。
+
+任务：分析图片并输出JSON：
+
+{
+  "objects": [],
+  "style": "",
+  "color": [],
+  "material": [],
+  "room_type": "",
+  "description": ""
+}
+
+规则：
+- 只基于图像内容
+- 不允许编造
+- objects 最多5个
+- color 最多3个
+- 只输出JSON
+"""
+
+    result = vision_llm.generate(
+        image=image,  # ⭐ 关键：真正喂图
+        prompt=prompt
+    )
+
+    return safe_parse_json(result)
+
+
+def safe_parse_json(text):
+    try:
+        return json.loads(text)
+    except:
+        return {
+            "error": "INVALID_JSON",
+            "raw": text
+        }

src/server/deep_agent/init_llm.py

@@ -49,3 +49,12 @@ repoer_llm = ChatQwen(
     timeout=None,
     max_retries=2,
     api_key=settings.QWEN_API_KEY)
+
+vision_llm = ChatQwen(
+    enable_thinking=False,
+    model="qwen3-vl-plus",
+    temperature=0.2,
+    max_tokens=3_000,
+    timeout=None,
+    max_retries=2,
+    api_key=settings.QWEN_API_KEY)

src/server/deep_agent/init_prompt.py

@@ -15,7 +15,10 @@ def build_system_prompt(use_report):
        负责生成完整报告、调研、总结、分析。
     
     3. painter_subagent
-        负责根据用户描述,构造适用于生成家具sketch的prompt,使用prompt用工具生成图片.
+        负责根据用户描述,构造适用于 生成家具sketch的prompt或编辑家具sketch的prompt
+        1.利用prompt用工具生成图片.
+        2.利用prompt和图片路径用工具编辑图片.
+
         
     ========================
     执行规则
@@ -51,31 +54,26 @@ def build_system_prompt(use_report):
     - research-subagent 只负责 **报告生成**
     不要混用职责。
     ========================
-    严格输出规则
-    ========================
-    - 当生成图片时，绝对不要输出图片路径、file:// 地址、URL、本地链接
-    - 只输出文字描述，不输出任何图片链接或路径
     """
     return system_prompt
 
 
 def build_painter_prompt():
     prompt = """
-    你是一名专业的prompt优化专家，专注于家具设计草图生成。你的任务是：
-        1. 分析用户查询，理解核心意图，包括家具类型、风格、尺寸、颜色、材料等关键元素
-        2. 基于意图，优化并生成一个详细、精确的prompt，适合用于AI图片生成工具创建家具sketch草图（例如，线条简洁、手绘风格、焦点在设计细节上）
-        3. 使用优化的prompt调用图片生成工具，生成并返回草图图片
-        4. 如果需要，建议额外变体或改进
-
-        输出格式：
-        - 用户意图总结（1–2段）
-        - 优化后的prompt（完整文本）
-        - 生成的图片描述（如果工具返回）
-        - 建议改进（项目符号，可选）
-   【严格输出规则】
-    - 当生成图片时，**绝对不要输出图片路径、file:// 地址、URL、本地链接**。
-    - 只输出文字描述，不输出任何图片链接或路径。
+    你是 painter_subagent，专门生成或编辑 sketch 图。
+    1. 每次开始决策前，先调用工具 read_file("/current_sketch_path.txt") 获取当前路径。
+       - 如果文件不存在或返回空 → 当前没有历史图，使用 generate_sketch。
+       - 如果有路径 → 检查用户意图是否为「修改/编辑/改成/调整/优化/把...变成」，如果是则必须使用 edit_sketch，并传入 image_path = 读取到的路径。
+    2. 生成或编辑完成后，**必须立即**调用 write_file("/current_sketch_path.txt", content=本次生成的图片完整路径) 来更新状态。
+    3. 【对用户隐藏路径】：
+       - 永远不要在最终回复给用户的任何消息中出现路径、/tmp/、/current_sketch_path.txt 等字符串！
+       - 回复格式只能是：
+         "图片已成功生成！"
+         或
+         "已按你的要求把狗改成猫，图片更新完成！"
+       - 如果前端支持图片展示，你可以直接返回图片（但不要带路径文字）。
 
+    现在开始严格遵守以上规则。
     """
     return prompt
 

src/server/deep_agent/tools/crawl_tool.py

@@ -1,3 +1,4 @@
+import os
 import time
 import asyncio
 from typing import List, Dict, Any
@@ -8,19 +9,6 @@ import uuid
 from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
 from langchain_core.tools import tool
 
-# ─────────────────────────────────────
-# 路径配置
-# ─────────────────────────────────────
-
-TOOL_DIR = Path(__file__).resolve().parent
-PROJECT_ROOT = TOOL_DIR.parent
-
-# DeepAgents 推荐目录
-SAVE_DIR = PROJECT_ROOT / "agent_workspace" / "raw_data"
-SAVE_DIR.mkdir(parents=True, exist_ok=True)
-
-print(f"tool save : {str(PROJECT_ROOT / "agent_workspace")}")
-
 # ─────────────────────────────────────
 # Browser 配置
 # ─────────────────────────────────────
@@ -65,7 +53,7 @@ def build_filename(url: str) -> str:
 # 单个 URL 抓取
 # ─────────────────────────────────────
 
-async def crawl_one(crawler, url: str, sem: asyncio.Semaphore) -> Dict[str, Any]:
+async def crawl_one(crawler, url: str, sem: asyncio.Semaphore, save_dir: str) -> Dict[str, Any]:
     async with sem:
         try:
             result = await crawler.arun(url=url, config=run_config)
@@ -87,7 +75,7 @@ async def crawl_one(crawler, url: str, sem: asyncio.Semaphore) -> Dict[str, Any]
                 }
 
             filename = build_filename(url)
-            filepath = SAVE_DIR / filename
+            filepath = os.path.join(save_dir, filename)
 
             header = (
                 f"<!-- Source: {url} -->\n"
@@ -115,7 +103,7 @@ async def crawl_one(crawler, url: str, sem: asyncio.Semaphore) -> Dict[str, Any]
 # Async 主逻辑
 # ─────────────────────────────────────
 
-async def _crawl4ai_batch(urls: List[str]) -> Dict[str, Any]:
+async def _crawl4ai_batch(urls: List[str], save_dir: str) -> Dict[str, Any]:
     urls = list(set(urls))  # 去重
 
     if not urls:
@@ -126,7 +114,7 @@ async def _crawl4ai_batch(urls: List[str]) -> Dict[str, Any]:
     async with AsyncWebCrawler(config=browser_config) as crawler:
 
         tasks = [
-            crawl_one(crawler, url, sem)
+            crawl_one(crawler, url, sem, save_dir)
             for url in urls
         ]
 
@@ -150,11 +138,9 @@ async def _crawl4ai_batch(urls: List[str]) -> Dict[str, Any]:
     }
 
 
-# ─────────────────────────────────────
-# Tool（同步）
-# ─────────────────────────────────────
-@tool
-def crawl4ai_batch(urls: List[str]) -> str:
+def create_crawl4ai_batch_tool(workspace_dir):
+    @tool
+    def crawl4ai_batch(urls: List[str]) -> str:
         """
         Batch crawl webpages and save their content as markdown files.
 
@@ -166,7 +152,11 @@ def crawl4ai_batch(urls: List[str]) -> str:
         """
 
         try:
-        result = asyncio.run(_crawl4ai_batch(urls))
+            save_dir = os.path.join(workspace_dir, "raw_data")
+            if not os.path.exists(save_dir):
+                os.makedirs(save_dir, exist_ok=True)
+
+            result = asyncio.run(_crawl4ai_batch(urls, save_dir))
 
             if "error" in result:
                 return f"❌ Error: {result['error']}"
@@ -174,7 +164,7 @@ def crawl4ai_batch(urls: List[str]) -> str:
             output = [
                 "### 批量抓取完成 ###",
                 f"成功保存文件: {result['count']}",
-            f"保存目录: {SAVE_DIR}",
+                f"保存目录: {workspace_dir}",
                 "",
                 "抓取详情:"
             ]
@@ -189,3 +179,5 @@ def crawl4ai_batch(urls: List[str]) -> str:
 
         except Exception as e:
             return f"🚨 爬虫系统异常: {str(e)}"
+
+    return crawl4ai_batch

src/server/deep_agent/tools/generate_furniture_sketch.py

@@ -1,15 +1,21 @@
 import json
 import logging
+import os
 import uuid
+from pathlib import Path
+from typing import Annotated
+
+import httpx
 from google.oauth2 import service_account
 from langchain_core.tools import tool
 from google import genai
 from google.genai.types import GenerateContentConfig, Modality
+from langgraph.prebuilt import ToolRuntime
 
 from minio import Minio
 
 from src.core.config import settings
-from src.server.utils.new_oss_client import oss_upload_image
+from src.server.utils.new_oss_client import oss_upload_image, oss_get_image, is_minio_file_exist, oss_upload_image_file
 
 logger = logging.getLogger(__name__)
 # 初始化全局凭证和客户端
@@ -27,47 +33,187 @@ client = genai.Client(
 )
 
 
-@tool
-async def generate_furniture(prompt: str) -> str:
+def is_image_path_exist(image_path):
+    try:
+        return Path(image_path).exists()
+    except:
+        return False
+
+
+def create_generate_furniture_tool(workspace_dir, width: int = 1024, height: int = 1024):
+    @tool
+    async def generate_furniture(prompt: str, runtime: ToolRuntime) -> str:
         """
         使用 Gemini 图像生成模型根据详细的英文提示词生成家具设计草图。
         """
-    print(f"\n[系统日志] 正在调用 Nano Banana (Gemini Image Gen) ...")
-
+        logger.info(f"\n[系统日志] 正在调用 generate_furniture ...")
         try:
-        response = client.models.generate_content(
-            model="gemini-2.5-flash-image",
-            contents=(f"Generate a professional furniture design sketch: {prompt}"),
-            config=GenerateContentConfig(
-                response_modalities=[Modality.TEXT, Modality.IMAGE],
-            ),
-        )
-
-        image_bytes = None
-        for part in response.candidates[0].content.parts:
-            if part.inline_data:
-                image_bytes = part.inline_data.data
-                break
-
-        if not image_bytes:
-            return "未能生成图像数据。"
+            # 1. 生成图像 - local flux2-klein
             object_name = f"furniture/sketches/{uuid.uuid4()}.png"
-        bucket = "fida-test"  # 替换为你的 bucket 名称
-        # 3. 调用你的上传函数
-        upload_res = oss_upload_image(
-            oss_client=minio_client,
-            bucket=bucket,
-            object_name=object_name,
-            image_bytes=image_bytes
+            bucket_name = "fida-test"  # 替换为你的 bucket 名称
+            request_data = {
+                "prompt": prompt,
+                "bucket_name": bucket_name,
+                "object_name": object_name,
+                "width": width,
+                "height": height
+            }
+            async with httpx.AsyncClient(timeout=120) as client:
+                resp = await client.post(
+                    f"http://{settings.FLUX2_GEN_IMG_MODEL_URL}/predict",
+                    json=request_data,
                 )
+            result = resp.json()
+            image_url = result.get("output_path", None)
+
+            if image_url:
+                filename = os.path.join(workspace_dir, image_url)
+                # 2. 创建本地目录（确保目录存在）
+                local_dir = os.path.dirname(filename)
+                if not os.path.exists(local_dir):
+                    os.makedirs(local_dir, exist_ok=True)
+
+                img = oss_get_image(oss_client=minio_client, bucket=image_url.split('/')[0], object_name=image_url[image_url.find('/') + 1:])
+                img.save(filename)
 
-        if upload_res:
-            # 4. 构造访问链接 (如果是私有 bucket，需使用 presigned_get_object)
-            # 这里简单示例为直接访问地址
-            image_url = f"{bucket}/{object_name}"
                 return image_url
             else:
-            return "图片生成成功，但上传至存储服务器失败。"
+                return f"Image generation failed."
+
         except Exception as e:
-        logger.warning(e)
+            logger.warning(f"绘图流程异常：{e}")
             return "绘图流程异常"
+
+    return generate_furniture
+
+
+def create_edit_furniture_tool(workspace_dir, width: int = 1024, height: int = 1024):
+    @tool
+    async def edit_furniture(prompt: str, input_image_path) -> str:
+        """
+        使用图像生成模型根据详细的英文提示词编辑家具设计草图。
+        """
+        logger.info(f"\n[系统日志] 正在调用 edit_furniture ...")
+
+        try:
+            # 0. 编辑前先检查工作环境和minio上是否存在该图像
+            input_image_path = input_image_path.lstrip('/')
+            filename = os.path.join(workspace_dir, input_image_path)
+            local_exist = is_image_path_exist(filename)
+            minio_exist = is_minio_file_exist(minio_client=minio_client, bucket_name=input_image_path.split('/')[0], object_name=input_image_path.split('/')[0])
+
+            if not local_exist and not minio_exist:
+                # 两个地方都不存在 直接报错
+                return f"Image generation failed."
+            elif local_exist and not minio_exist:
+                # 把本地的上传到minio
+                oss_upload_image_file(oss_client=minio_client, bucket=input_image_path.split('/')[0], object_name=input_image_path.split('/')[0], file_path=filename)
+            elif not local_exist and minio_exist:
+                # minio的下载到本地
+                img = oss_get_image(oss_client=minio_client, bucket=input_image_path.split('/')[0], object_name=input_image_path.split('/')[0], )
+                img.save(filename)
+            elif minio_exist and local_exist:
+                # 两个地方都存在 直接跳过
+                pass
+
+            # 1. 生成图像 - local flux2-klein
+            object_name = f"furniture/sketches/{uuid.uuid4()}.png"
+            bucket_name = "fida-test"  # 替换为你的 bucket 名称
+            request_data = {
+                "input_image_paths": [input_image_path],
+                "prompt": prompt,
+                "bucket_name": bucket_name,
+                "object_name": object_name,
+                "width": width,
+                "height": height
+            }
+            async with httpx.AsyncClient(timeout=120) as client:
+                resp = await client.post(
+                    f"http://{settings.FLUX2_GEN_IMG_MODEL_URL}/predict",
+                    json=request_data,
+                )
+            result = resp.json()
+            image_url = result.get("output_path", None)
+
+            if image_url:
+                filename = os.path.join(workspace_dir, image_url)
+                # 2. 创建本地目录（确保目录存在）
+                local_dir = os.path.dirname(filename)
+                if not os.path.exists(local_dir):
+                    os.makedirs(local_dir, exist_ok=True)
+
+                img = oss_get_image(oss_client=minio_client, bucket=image_url.split('/')[0], object_name=image_url[image_url.find('/') + 1:])
+                img.save(filename)
+                return image_url
+            else:
+                return f"Image generation failed."
+
+        except Exception as e:
+            logger.warning(f"edit_furniture error ：{e}")
+            return "edit_furniture error"
+
+    return edit_furniture
+
+# def create_generate_furniture_tool(workspace_dir):
+#     @tool
+#     async def generate_furniture(prompt: str) -> str:
+#         """
+#         使用 Gemini 图像生成模型根据详细的英文提示词生成家具设计草图。
+#         """
+#         print(f"\n[系统日志] 正在调用 Nano Banana (Gemini Image Gen) ...")
+#
+#         try:
+#             response = client.models.generate_content(
+#                 model="gemini-2.5-flash-image",
+#                 contents=(f"Generate a professional furniture design sketch: {prompt}"),
+#                 config=GenerateContentConfig(
+#                     response_modalities=[Modality.TEXT, Modality.IMAGE],
+#                 ),
+#             )
+#
+#             image_bytes = None
+#             for part in response.candidates[0].content.parts:
+#                 if part.inline_data:
+#                     image_bytes = part.inline_data.data
+#                     break
+#
+#             if not image_bytes:
+#                 return "未能生成图像数据。"
+#             # 1. 定义OSS存储路径和本地保存路径
+#             object_name = f"furniture/sketches/{uuid.uuid4()}.png"
+#             bucket = "fida-test"  # 替换为你的 bucket 名称
+#             filename = os.path.join(workspace_dir, f"{bucket}/{object_name}")
+#
+#             # 2. 创建本地目录（确保目录存在）
+#             local_dir = os.path.dirname(filename)
+#             if not os.path.exists(local_dir):
+#                 os.makedirs(local_dir, exist_ok=True)
+#
+#             # 3. 保存图片到本地文件（新增核心逻辑）
+#             try:
+#                 with open(filename, "wb") as f:
+#                     f.write(image_bytes)
+#                 print(f"[系统日志] 图片已保存到本地：{filename}")
+#             except Exception as save_e:
+#                 logger.warning(f"保存图片到本地失败：{save_e}")
+#                 # 本地保存失败不中断上传流程，仅记录日志
+#
+#             # 4. 上传图片到OSS（原有逻辑）
+#             upload_res = oss_upload_image(
+#                 oss_client=minio_client,
+#                 bucket=bucket,
+#                 object_name=object_name,
+#                 image_bytes=image_bytes
+#             )
+#
+#             if upload_res:
+#                 image_url = f"{bucket}/{object_name}"
+#                 return image_url
+#             else:
+#                 return f"图片生成成功（本地路径：{filename}），但上传至存储服务器失败。"
+#
+#         except Exception as e:
+#             logger.warning(f"绘图流程异常：{e}")
+#             return "绘图流程异常"
+#
+#     return generate_furniture

src/server/deep_agent/tools/report_generator_tool.py

@@ -32,12 +32,13 @@ class ReportInput(BaseModel):
 # LangGraph Tool
 # =========================
 
-@tool("report_generator", args_schema=ReportInput)
-async def report_generator(
+def create_report_generator_tool(workspace_dir):
+    @tool("report_generator", args_schema=ReportInput)
+    async def report_generator(
             report_topic: str,
             structured_data: List[Dict],
             language: str = "English"
-) -> dict:
+    ) -> dict:
         """
         Generate a professional design/market report
         directly from structured retrieval results.
@@ -118,7 +119,8 @@ async def report_generator(
         # =========================
         # 保存报告
         # =========================
-    output_dir = "workspace/reports"
+        output_dir = os.path.join(workspace_dir, "reports")
+        if not os.path.exists(output_dir):
             os.makedirs(output_dir, exist_ok=True)
 
         safe_topic = re.sub(r'[\\/*?:"<>|]', "", report_topic.replace(" ", "_"))
@@ -133,3 +135,5 @@ async def report_generator(
 
         # 返回完整内容（作为 tool result），同时正文已通过 delta 流式输出
         return report_content + f"\n\n✅ Report saved to: {filename}"
+
+    return report_generator

src/server/deep_agent/tools/structured_retrieval_tool.py

@@ -32,12 +32,91 @@ class StructuredRetrievalInput(BaseModel):
     source_url: Optional[str] = Field(None, description="Optional global source URL")
 
 
-@tool("structured_retrieval", args_schema=StructuredRetrievalInput)
-def structured_retrieval(
+def _extract_source_from_md(content: str) -> Optional[str]:
+    match = re.search(r"<!--\s*Source:\s*(.*?)\s*-->", content)
+    return match.group(1).strip() if match else None
+
+
+# =========================
+# Markdown Header Split
+# =========================
+
+def _split_markdown_by_headers(
+        content: str,
+        max_chars: int = 2000,
+        overlap: int = 150,
+):
+    header_re = re.compile(
+        r'^(#{1,6})\s+(.+?)\s*$',
+        re.MULTILINE
+    )
+
+    matches = list(header_re.finditer(content))
+
+    if not matches:
+        return _chunk_text(content, max_chars, overlap)
+
+    sections = []
+
+    for i, m in enumerate(matches):
+        start = m.start()
+        end = (
+            matches[i + 1].start()
+            if i + 1 < len(matches)
+            else len(content)
+        )
+
+        block = content[start:end].strip()
+        if block:
+            sections.append(block)
+
+    final_sections = []
+
+    for s in sections:
+        if len(s) > max_chars:
+            final_sections.extend(
+                _chunk_text(s, max_chars, overlap)
+            )
+        else:
+            final_sections.append(s)
+
+    return final_sections
+
+
+def _chunk_text(
+        text: str,
+        max_chars: int = 2000,
+        overlap: int = 150
+):
+    text = text.strip()
+    if len(text) <= max_chars:
+        return [text]
+
+    chunks = []
+    start = 0
+
+    while start < len(text):
+        end = min(len(text), start + max_chars)
+        chunk = text[start:end].strip()
+
+        if chunk:
+            chunks.append(chunk)
+
+        if end == len(text):
+            break
+
+        start = max(0, end - overlap)
+
+    return chunks
+
+
+def create_structured_retrieval_tool(workspace_dir):
+    @tool("structured_retrieval", args_schema=StructuredRetrievalInput)
+    def structured_retrieval(
             file_paths: List[str],
             query: str,
             source_url: Optional[str] = None
-) -> Dict:
+    ) -> Dict:
         """
         Batch structured extraction from markdown files.
         - Performs vector search + re-ranking
@@ -120,7 +199,12 @@ def structured_retrieval(
         json_filename = f"extracted_{safe_query}_{timestamp}.json"
 
         # 建議的儲存目錄（與 crawl4ai_batch 對齊）
-    output_dir = os.path.join(os.path.dirname(file_paths[0]), "..", "extracted")
+        output_dir = os.path.join(workspace_dir, "extracted")
+        if not os.path.exists(output_dir):
+            os.makedirs(output_dir, exist_ok=True)
+
+        if not os.path.exists(output_dir):
+            # 2. 不存在则创建（makedirs 支持创建多级目录，mkdir 只能创建单级）
             os.makedirs(output_dir, exist_ok=True)
 
         json_path = os.path.join(output_dir, json_filename)
@@ -146,80 +230,4 @@ def structured_retrieval(
             "summary": f"已提取 {len(top_items)} 個高相關片段，儲存於 {json_path}"
         }
 
-
-def _extract_source_from_md(content: str) -> Optional[str]:
-    match = re.search(r"<!--\s*Source:\s*(.*?)\s*-->", content)
-    return match.group(1).strip() if match else None
-
-
-# =========================
-# Markdown Header Split
-# =========================
-
-def _split_markdown_by_headers(
-        content: str,
-        max_chars: int = 2000,
-        overlap: int = 150,
-):
-    header_re = re.compile(
-        r'^(#{1,6})\s+(.+?)\s*$',
-        re.MULTILINE
-    )
-
-    matches = list(header_re.finditer(content))
-
-    if not matches:
-        return _chunk_text(content, max_chars, overlap)
-
-    sections = []
-
-    for i, m in enumerate(matches):
-        start = m.start()
-        end = (
-            matches[i + 1].start()
-            if i + 1 < len(matches)
-            else len(content)
-        )
-
-        block = content[start:end].strip()
-        if block:
-            sections.append(block)
-
-    final_sections = []
-
-    for s in sections:
-        if len(s) > max_chars:
-            final_sections.extend(
-                _chunk_text(s, max_chars, overlap)
-            )
-        else:
-            final_sections.append(s)
-
-    return final_sections
-
-
-def _chunk_text(
-        text: str,
-        max_chars: int = 2000,
-        overlap: int = 150
-):
-    text = text.strip()
-    if len(text) <= max_chars:
-        return [text]
-
-    chunks = []
-    start = 0
-
-    while start < len(text):
-        end = min(len(text), start + max_chars)
-        chunk = text[start:end].strip()
-
-        if chunk:
-            chunks.append(chunk)
-
-        if end == len(text):
-            break
-
-        start = max(0, end - overlap)
-
-    return chunks
+    return structured_retrieval

src/server/deep_agent/tools/vision_analyze_tool.py

@@ -0,0 +1,21 @@
+from langchain.tools import tool
+from langchain_core.messages import HumanMessage
+from PIL import Image
+import requests
+from io import BytesIO
+
+from src.server.deep_agent.init_llm import vision_llm
+
+
+@tool
+def analyze_image(image_url: str) -> str:
+    """分析给定URL的图像。输入图像URL，输出图像描述和关键观察。"""
+    response = requests.get(image_url)
+    image = Image.open(BytesIO(response.content))
+    # 这里使用模型直接分析图像（简化示例）
+    msg = HumanMessage(content=[
+        {"type": "text", "text": "详细描述这张图像，包括物体、颜色、场景和任何文本。"},
+        {"type": "image_url", "image_url": {"url": image_url}}
+    ])
+    result = vision_llm.invoke([msg])
+    return result.content

src/server/utils/new_oss_client.py

@@ -36,7 +36,7 @@ http_client = urllib3.PoolManager(
 
 
 #  获取图片
-def oss_get_image(oss_client, bucket, object_name, data_type):
+def oss_get_image(oss_client, bucket, object_name):
     # cv2 默认全通道读取
     image_object = None
     try:
@@ -57,9 +57,44 @@ def oss_upload_image(oss_client, bucket, object_name, image_bytes):
     return req
 
 
+def oss_upload_image_file(oss_client, bucket, object_name, file_path):
+    req = None
+    try:
+        req = oss_client.fput_object(
+            bucket_name=bucket,
+            object_name=object_name,
+            file_path=file_path
+        )
+    except Exception as e:
+        logger.warning(f" | 上传图片出现异常 ######: {e}")
+    return req
+
+
+def get_presigned_url(oss_client, bucket, object_name):
+    try:
+        presigned_url = oss_client.presigned_get_object(
+            bucket_name=bucket,
+            object_name=object_name,
+            expires=3600
+        )
+        return presigned_url
+    except Exception as e:
+        print(f"get_presigned_url exception :{e}")
+        return "object not found"
+
+
+def is_minio_file_exist(minio_client: Minio, bucket_name: str, object_name: str) -> bool:
+    try:
+        # 核心判断：检查MinIO中指定bucket+object是否存在
+        minio_client.stat_object(bucket_name, object_name)
+        return True
+    except Exception as e:
+        return False
+
+
 if __name__ == '__main__':
-    url = "fida-test/furniture/sketches/4449a66d-6267-43f7-86a2-1e42bd19ec61.png"
+    url = 'fida-test/furniture/sketches/9356e3d8-d56e-4478-adde-61b29119979b.png'
     read_type = "2"
-    img = oss_get_image(oss_client=minio_client, bucket=url.split('/')[0], object_name=url[url.find('/') + 1:], data_type=read_type)
+    img = oss_get_image(oss_client=minio_client, bucket=url.split('/')[0], object_name=url[url.find('/') + 1:])
     img.show()
     img.save("result.png")

uv.lock

@@ -21,11 +21,11 @@ wheels = [
 
 [[package]]
 name = "aiofiles"
-version = "25.1.0"
+version = "24.1.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/41/c3/534eac40372d8ee36ef40df62ec129bee4fdb5ad9706e58a29be53b2c970/aiofiles-25.1.0.tar.gz", hash = "sha256:a8d728f0a29de45dc521f18f07297428d56992a742f0cd2701ba86e44d23d5b2", size = 46354, upload-time = "2025-10-09T20:51:04.358Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/0b/03/a88171e277e8caa88a4c77808c20ebb04ba74cc4681bf1e9416c862de237/aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c", size = 30247, upload-time = "2024-06-24T11:02:03.584Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/bc/8a/340a1555ae33d7354dbca4faa54948d76d89a27ceef032c8c3bc661d003e/aiofiles-25.1.0-py3-none-any.whl", hash = "sha256:abe311e527c862958650f9438e859c1fa7568a141b22abcd015e120e86a85695", size = 14668, upload-time = "2025-10-09T20:51:03.174Z" },
+    { url = "https://files.pythonhosted.org/packages/a5/45/30bb92d442636f570cb5651bc661f52b610e2eec3f891a5dc3a4c3667db0/aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5", size = 15896, upload-time = "2024-06-24T11:02:01.529Z" },
 ]
 
 [[package]]
@@ -122,6 +122,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b4/63/278a98c715ae467624eafe375542d8ba9b4383a016df8fdefe0ae28382a7/aiohttp-3.13.3-cp314-cp314t-win_amd64.whl", hash = "sha256:44531a36aa2264a1860089ffd4dce7baf875ee5a6079d5fb42e261c704ef7344", size = 499694, upload-time = "2026-01-03T17:32:24.546Z" },
 ]
 
+[[package]]
+name = "aiohttp-retry"
+version = "2.9.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "aiohttp" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9d/61/ebda4d8e3d8cfa1fd3db0fb428db2dd7461d5742cea35178277ad180b033/aiohttp_retry-2.9.1.tar.gz", hash = "sha256:8eb75e904ed4ee5c2ec242fefe85bf04240f685391c4879d8f541d6028ff01f1", size = 13608, upload-time = "2024-11-06T10:44:54.574Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1a/99/84ba7273339d0f3dfa57901b846489d2e5c2cd731470167757f1935fffbd/aiohttp_retry-2.9.1-py3-none-any.whl", hash = "sha256:66d2759d1921838256a05a3f80ad7e724936f083e35be5abb5e16eed6be6dc54", size = 9981, upload-time = "2024-11-06T10:44:52.917Z" },
+]
+
 [[package]]
 name = "aiosignal"
 version = "1.4.0"
@@ -213,6 +225,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/38/0e/27be9fdef66e72d64c0cdc3cc2823101b80585f8119b5c112c2e8f5f7dab/anyio-4.12.1-py3-none-any.whl", hash = "sha256:d405828884fc140aa80a3c667b8beed277f1dfedec42ba031bd6ac3db606ab6c", size = 113592, upload-time = "2026-01-06T11:45:19.497Z" },
 ]
 
+[[package]]
+name = "argh"
+version = "0.31.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/4f/34/bc0b3577a818b4b70c6e318d23fe3c81fc3bb25f978ca8a3965cd8ee3af9/argh-0.31.3.tar.gz", hash = "sha256:f30023d8be14ca5ee6b1b3eeab829151d7bbda464ae07dc4dd5347919c5892f9", size = 57570, upload-time = "2024-07-13T17:54:59.729Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/d2/52/fcd83710b6f8786df80e5d335882d1b24d1f610f397703e94a6ffb0d6f66/argh-0.31.3-py3-none-any.whl", hash = "sha256:2edac856ff50126f6e47d884751328c9f466bacbbb6cbfdac322053d94705494", size = 44844, upload-time = "2024-07-13T17:54:57.706Z" },
+]
+
 [[package]]
 name = "argon2-cffi"
 version = "25.1.0"
@@ -678,6 +699,98 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/cf/7a/ea0f3e3ea74be36fc7cf54f966cde732a3de72697983cdb5646b0a4dacde/datetime-6.0-py3-none-any.whl", hash = "sha256:d19988f0657a4e72c9438344157254a8dcad6aea8cd5ae70a5d1b5a75e5dc930", size = 52637, upload-time = "2025-11-25T08:00:33.077Z" },
 ]
 
+[[package]]
+name = "daytona"
+version = "0.152.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "aiofiles" },
+    { name = "daytona-api-client" },
+    { name = "daytona-api-client-async" },
+    { name = "daytona-toolbox-api-client" },
+    { name = "daytona-toolbox-api-client-async" },
+    { name = "deprecated" },
+    { name = "environs" },
+    { name = "httpx" },
+    { name = "obstore" },
+    { name = "opentelemetry-api" },
+    { name = "opentelemetry-exporter-otlp-proto-http" },
+    { name = "opentelemetry-instrumentation-aiohttp-client" },
+    { name = "opentelemetry-sdk" },
+    { name = "pydantic" },
+    { name = "python-multipart" },
+    { name = "toml" },
+    { name = "websockets" },
+]
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