fix：取消品牌筛选逻辑

2.推荐outfit数量修改为1（快推荐模式）

.gitea/workflows/prod_build_manual.yaml

@@ -0,0 +1,40 @@
+name: 手动 LC python main 分支构建部署
+on:
+  workflow_dispatch:
+
+jobs:
+  scheduled_deploy:
+    runs-on: ubuntu-latest
+
+    env:
+      REMOTE_DEPLOY_PATH: /workspace/Trinity/Litserve_LC_Prod/lc_stylist_agent
+
+    steps:
+      - name: 1.检出代码
+        uses: actions/checkout@v4
+        with:
+          ref: 'main'
+
+      - name: 2.复制文件到服务器
+        uses: appleboy/scp-action@v0.1.7
+        with:
+          host: ${{ secrets.SERVER_HOST }}
+          username: ${{ secrets.SERVER_USER }}
+          password: ${{ secrets.SERVER_PASSWORD }}
+          source: "."
+          target: ${{ env.REMOTE_DEPLOY_PATH }}
+
+      - name: 3.重启docker-compose
+        uses: appleboy/ssh-action@v0.1.10
+        with:
+          host: ${{ secrets.SERVER_HOST }}
+          username: ${{ secrets.SERVER_USER }}
+          password: ${{ secrets.SERVER_PASSWORD }}
+          script: |
+            # 进入项目目录
+            cd ${{ env.REMOTE_DEPLOY_PATH }}
+            
+            docker-compose down 2>&1
+            docker-compose up -d --build --remove-orphans 2>&1
+
+#            docker image prune -f 2>&1

.gitignore

@@ -8,3 +8,5 @@ data/
 .prod_env
 google_application_credentials.json
 *.bash
+test
+app/google_application_credentials.json

Dockerfile

@@ -1,46 +1,37 @@
-# Change CUDA and cuDNN version here
+FROM ghcr.io/astral-sh/uv:latest AS uv_bin
 FROM nvidia/cuda:12.4.1-base-ubuntu22.04
-ARG PYTHON_VERSION=3.10
 
-ENV DEBIAN_FRONTEND=noninteractive
+# 1. 基础环境配置
+ENV UV_LINK_MODE=copy \
+    UV_COMPILE_BYTECODE=1 \
+    PYTHONUNBUFFERED=1 \
+    UV_PROJECT_ENVIRONMENT=/app/.venv
+
+COPY --from=uv_bin /uv /uvx /bin/
+
 RUN apt-get update && apt-get install -y --no-install-recommends \
-        software-properties-common \
         wget \
-    && add-apt-repository ppa:deadsnakes/ppa \
-    && apt-get update && apt-get install -y --no-install-recommends \
-        python$PYTHON_VERSION \
-        python$PYTHON_VERSION-dev \
-        python$PYTHON_VERSION-venv \
-    && wget https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
-    && python$PYTHON_VERSION get-pip.py \
-    && rm get-pip.py \
-    && ln -sf /usr/bin/python$PYTHON_VERSION /usr/bin/python \
-    && ln -sf /usr/local/bin/pip$PYTHON_VERSION /usr/local/bin/pip \
-    && python --version \
-    && pip --version \
-    && apt-get purge -y --auto-remove software-properties-common \
+        libcurl4-openssl-dev \
+        build-essential \
+        libgl1 \
+        libglib2.0-0 \
+        ca-certificates \
     && apt-get clean \
     && rm -rf /var/lib/apt/lists/*
 
-####### Add your own installation commands here #######
-# RUN pip install some-package
-# RUN wget https://path/to/some/data/or/weights
-# RUN apt-get update && apt-get install -y <package-name>
-RUN apt-get update && \
-    apt-get install -y libcurl4-openssl-dev build-essential
-
-RUN apt-get update
-RUN  apt-get -y install libgl1
-RUN  apt-get -y install libglib2.0-0
-
-
 WORKDIR /app
-COPY . /app
 
-# Install litserve and requirements
-RUN pip install --upgrade pip setuptools wheel
-RUN pip install --no-cache-dir litserve==0.2.16 -r requirements.txt
-RUN pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
+COPY pyproject.toml uv.lock ./
+
+ENV UV_COMPILE_BYTECODE=0
+
+RUN uv sync --frozen --no-dev --no-install-project --python 3.10
+
+# 4. 拷贝项目文件并安装项目本身
+COPY . .
+RUN uv sync --frozen --no-dev --python 3.10
+
+ENV PATH="/app/.venv/bin:$PATH"
+
 EXPOSE 8000
-CMD ["python", "-m","app.main"]
-#CMD ["tail", "-f","/dev/null"]
+CMD ["uv", "run","-m","app.main"]

app/config.py

@@ -16,6 +16,8 @@ class Settings(BaseSettings):
         env_file_encoding='utf-8',
         extra='ignore'  # 忽略环境变量中多余的键
     )
+    # 启动端口
+    SERVE_PROD: int = Field(default=8000, description='')
     # 调试配饰
     LOCAL: int = Field(default=0, description="是否在本地运行，1表示本地运行，0表示生产环境运行")
 

app/main.py

@@ -27,4 +27,4 @@ if __name__ == "__main__":
     agent_api = LCAgent(enable_async=True, api_path='/api/v1/agent')
     reface_api = ReFace(api_path='/api/v1/reface')
     server = ls.LitServer([chat_boot_api, agent_api, reface_api])
-    server.run(port=8000)
+    server.run(port=settings.SERVE_PROD)

app/server/ChatbotAgent/agent_server.py

@@ -1,8 +1,9 @@
 import asyncio
+import json
 import logging
 import uuid
 from enum import Enum
-from typing import List
+from typing import List, Optional
 from pydantic import Field
 import time
 
@@ -15,6 +16,7 @@ from app.server.ChatbotAgent.core.redis_manager import RedisManager
 from app.server.ChatbotAgent.core.stylist_agent_server import AsyncStylistAgent
 from app.server.ChatbotAgent.core.prompt import SUMMARY_PROMPT
 from app.server.ChatbotAgent.core.vector_database import VectorDatabase
+from app.server.utils.request_post import post_request
 
 logger = logging.getLogger(__name__)
 
@@ -56,6 +58,8 @@ class AgentRequestModel(BaseModel):
     batch_sources: List[str]
     callback_url: str
     gender: str
+    occasions: Optional[list] = None
+    request_summary: Optional[str] = None
 
 
 class LCAgent(ls.LitAPI):
@@ -105,8 +109,12 @@ class LCAgent(ls.LitAPI):
 
     async def background_run(self, request: AgentRequestModel, outfit_ids):
         # 1. 根据用户ID查询对话历史，总结对话内容
-        request_summary, occasions = await self.get_conversation_summary(request.session_id)
-        logger.info(f"request_summary: {request_summary}")
+        if request.request_summary and request.occasions:
+            request_summary = request.request_summary
+            occasions = request.occasions
+        else:
+            request_summary, occasions = await self.get_conversation_summary(request.session_id)
+        logger.info(f"request_summary: {request_summary},occasions : {occasions}")
 
         # 2.根据对话总结推荐搭配
         recommendation_results = await self.recommend_outfit(
@@ -138,7 +146,7 @@ class LCAgent(ls.LitAPI):
         history_messages = self.redis.get_history(session_id)
         if not history_messages:
             # 处理无历史记录的情况
-            return {"occasions": [], "summary": "User has no history provided."}
+            return "User has no history provided.", []
 
         input_message = "\n".join([f"{msg.role.value}: {msg.content}" for msg in history_messages])
         json_schema = StylistResponse.model_json_schema()
@@ -187,52 +195,45 @@ class LCAgent(ls.LitAPI):
         task_map = {}
 
         stylist_agent_kwages = self.stylist_agent_kwages.copy()
+        tasks_mapping = {}
         if num_outfits == 1:
+            tasks_mapping[outfit_ids[0]] = "fast"
+        else:
+            for i in range(num_outfits):
+                if i == 0:
+                    tasks_mapping[outfit_ids[i]] = "fast"
+                else:
+                    tasks_mapping[outfit_ids[i]] = "slow"
+
+        for k, v in tasks_mapping.items():
+            logger.info(f"fast request outfit_id is : {k}")
             # 通过请求数量判断 num == 1 单个outfit刷新
-            stylist_agent_kwages['outfit_id'] = outfit_ids[0]
+            stylist_agent_kwages['outfit_id'] = k
             stylist_agent_kwages['stylist_name'] = stylist_name
             stylist_agent_kwages['gender'] = gender
+            stylist_agent_kwages['callback_url'] = callback_url
+
             agent = AsyncStylistAgent(**stylist_agent_kwages)
-            task = agent.run_iterative_styling(
-                request_summary=request_summary,
-                occasions=occasions,
-                start_outfit=start_outfit,
-                batch_sources=batch_sources,
-                user_id=user_id,
-                callback_url=callback_url,
-            )
+            if v == "fast":
+                task = agent.run_quick_batch_styling(
+                    request_summary=request_summary,
+                    occasions=occasions,
+                    start_outfit=start_outfit,
+                    batch_sources=batch_sources,
+                    user_id=user_id,
+                    callback_url=callback_url,
+                )
+            else:
+                task = agent.run_iterative_styling(
+                    request_summary=request_summary,
+                    occasions=occasions,
+                    start_outfit=start_outfit,
+                    batch_sources=batch_sources,
+                    user_id=user_id,
+                    callback_url=callback_url,
+                )
             tasks.append(task)
-            task_map[task] = {"outfit_id": outfit_ids[0], "retries": 0}
-        elif num_outfits > 1:
-            # 通过请求数量判断 num > 1 四套搭配推荐 (1快 , num-1慢)
-            for i in range(num_outfits):
-                stylist_agent_kwages['outfit_id'] = outfit_ids[i]
-                stylist_agent_kwages['stylist_name'] = stylist_name
-                stylist_agent_kwages['gender'] = gender
-                agent = AsyncStylistAgent(**stylist_agent_kwages)
-                if i == 0:
-                    # 第一套搭配使用快速方法 一次跑出所有单品
-                    logger.info(f"fast request outfit_id is : {outfit_ids[i]}")
-                    task = agent.run_quick_batch_styling(
-                        request_summary=request_summary,
-                        occasions=occasions,
-                        start_outfit=start_outfit,
-                        batch_sources=batch_sources,
-                        user_id=user_id,
-                        callback_url=callback_url,
-                    )
-                else:
-                    # 后续
-                    task = agent.run_iterative_styling(
-                        request_summary=request_summary,
-                        occasions=occasions,
-                        start_outfit=start_outfit,
-                        batch_sources=batch_sources,
-                        user_id=user_id,
-                        callback_url=callback_url,
-                    )
-                tasks.append(task)
-                task_map[task] = {"outfit_id": outfit_ids[i], "retries": 0}
+            task_map[task] = {"outfit_id": k, "retries": 0}
         logger.info(f"--- Starting {num_outfits} concurrent outfit generation tasks. ---")
 
         # 2. 任务执行与重试循环
@@ -243,7 +244,8 @@ class LCAgent(ls.LitAPI):
         retry_limit = 1  # 允许重试一次
         while tasks_to_run:
             try:
-                results = await asyncio.gather(*tasks, return_exceptions=True)
+                results = await asyncio.gather(*tasks_to_run, return_exceptions=True)
+
                 next_tasks_to_run = []
                 for task, result in zip(tasks_to_run, results):
                     task_info = task_map[task]
@@ -255,7 +257,14 @@ class LCAgent(ls.LitAPI):
                         logger.error(f"Outfit {outfit_id} failed with error: {result}. Current retries: {current_retries}.")
 
                         if current_retries < retry_limit:
-                            # 尚未达到重试上限，准备重试
+                            # 尚未达到重试上限，准备重试 并通知前端
+                            object_data = {
+                                'outfit_id': outfit_id,
+                                "status": "retrying",
+                                "path": "",
+                            }
+                            post_request(url=f'{callback_url}/api/style/callback', data=json.dumps(object_data))
+
                             task_info["retries"] += 1
                             logger.info(f"--- Retrying outfit {outfit_id} (Attempt {current_retries + 1}/{retry_limit}). ---")
 
@@ -263,15 +272,27 @@ class LCAgent(ls.LitAPI):
                             stylist_agent_kwages['outfit_id'] = outfit_id
                             stylist_agent_kwages['stylist_name'] = stylist_name
                             stylist_agent_kwages['gender'] = gender
+                            stylist_agent_kwages['callback_url'] = callback_url
+
                             agent = AsyncStylistAgent(**stylist_agent_kwages)
-                            new_task = agent.run_quick_batch_styling(
-                                request_summary=request_summary,
-                                occasions=occasions,
-                                start_outfit=start_outfit,
-                                batch_sources=batch_sources,
-                                user_id=user_id,
-                                callback_url=callback_url
-                            )
+                            if tasks_mapping[outfit_id] == "fast":
+                                new_task = agent.run_quick_batch_styling(
+                                    request_summary=request_summary,
+                                    occasions=occasions,
+                                    start_outfit=start_outfit,
+                                    batch_sources=batch_sources,
+                                    user_id=user_id,
+                                    callback_url=callback_url,
+                                )
+                            else:
+                                new_task = agent.run_iterative_styling(
+                                    request_summary=request_summary,
+                                    occasions=occasions,
+                                    start_outfit=start_outfit,
+                                    batch_sources=batch_sources,
+                                    user_id=user_id,
+                                    callback_url=callback_url
+                                )
                             # 将新任务添加到下一轮运行列表，并更新任务映射
                             next_tasks_to_run.append(new_task)
                             task_map[new_task] = task_info  # 新任务继承旧任务的重试信息
@@ -279,8 +300,15 @@ class LCAgent(ls.LitAPI):
                             # 清理旧任务（可选，但推荐，以防内存泄漏或混淆）
                             del task_map[task]
                         else:
-                            # 达到重试上限，最终失败
+                            # 达到重试上限，最终失败 ， 并通知前端
+                            object_data = {
+                                'outfit_id': outfit_id,
+                                "status": "retry_failed",
+                                "path": "",
+                            }
+                            response = post_request(url=f'{callback_url}/api/style/callback', data=json.dumps(object_data))
                             failed_outfits.append(f"Outfit {outfit_id} ultimately failed after {retry_limit} retries: {result}")
+                            logger.info(f"request data ：{json.dumps(object_data, ensure_ascii=False, indent=2)} | JAVA callback info -> status:{response.status_code} | message:{response.text}")
                             del task_map[task]
 
                     else:
@@ -320,14 +348,19 @@ if __name__ == "__main__":
         # 2. 准备请求数据
         import json
         stylist_agent_kwages = agent_api.stylist_agent_kwages.copy()
-        with open("/mnt/data/workspace/Code/lc_stylist_agent/data/2025_q4/request_test.json", "r") as f:
+        if settings.LOCAL == 1:
+            request_file_path = "./data/2025_q4/request_test.json"
+        else:
+            request_file_path = "/mnt/data/workspace/Code/lc_stylist_agent/data/2025_q4/request_test.json"
+
+        with open(request_file_path, "r") as f:
             request_data = json.load(f)
 
         tasks_with_metadata = []
-        for test_content in request_data[20:25]:
+        for test_content in request_data[14:20]:
             occasions = test_content['occasions']
             request_summary = test_content['request_summary']
-            for stylist_name in ["edi", "vera"]:
+            for stylist_name in ["edi"]:
                 stylist_agent_kwages['outfit_id'] = test_content['test_case_id'] + "_" + "_".join(occasions) + f"_{stylist_name}"
                 stylist_agent_kwages['stylist_name'] = stylist_name
                 stylist_agent_kwages['gender'] = "female"

app/server/ChatbotAgent/chatbot_server.py

@@ -1,9 +1,13 @@
 import logging
+import sys
+
 import litserve as ls
 
 from typing import AsyncGenerator
 from google import genai
 from pydantic import BaseModel
+from sympy.core.evalf import rnd
+
 from app.config import settings
 from google.genai import types
 
@@ -95,42 +99,61 @@ class LCChatBot(ls.LitAPI):
 
 
 if __name__ == "__main__":
+    sys.stdout = open('permanent.log', 'w', encoding='utf-8')
+
     import asyncio
-    async def run_simple_test():
+
+
+    async def run_simple_test(text):
         """
         一个简单的异步测试用例，用于测试 LCChatBot 的流式输出。
         """
         print("\n" + "=" * 50)
-        print("--- 🔬 开始 LCChatBot 简单流式测试 ---")
+        # print("--- 🔬 开始 LCChatBot 简单流式测试 ---")
 
         # 1. 初始化 LitAPI 和其依赖
         chatbot_api = LCChatBot()
         chatbot_api.setup(device="cpu")
-        print("✅ Setup complete. Mock services initialized.")
+        # print("✅ Setup complete. Mock services initialized.")
 
         # 2. 构造请求数据
         request_data = PredictRequest(
             user_id="simple_user",
             session_id="simple_session",
-            user_message="I want an outfit. I am going to a evening party with friends. Suggest something stylish yet comfortable.",
+            user_message=text,
             gender="female"
         )
         chatbot_api.redis.clear_history(request_data.session_id)
 
-        print(f"-> 正在发送查询: {request_data.user_message}")
+        print(f"user: \n  {request_data.user_message}")
 
         # 3. 调用 predict 方法并处理流
         response_generator = chatbot_api.predict(request_data)
 
-        print("\n<- 接收流式响应:")
+        print("agent:")
 
         # 4. 异步迭代生成器，实时打印输出
         async for chunk in response_generator:
             print(chunk, end="", flush=True)
 
-    print("\n" + "=" * 50)
-    # 启动异步事件循环
-    try:
-        asyncio.run(run_simple_test())
-    except Exception as e:
-        print(f"\n发生致命错误: {e}")
+
+    text_list = [
+        '我要去参加好朋友的婚礼，你能帮我挑一套衣服吗？',
+        'I need something to wear for a big presentation at work tomorrow. I want to look powerful but still approachable.',
+        'Who do you think is the best world leader right now?',
+        'I’m going on a trip to Paris next week and need some outfits.',
+        'Help me find a cool outfit for a rock concert. I hate wearing dresses.',
+        'I want to look very cool, 或者是那种很有个性的风格 for a gallery opening.',
+        "I'm going to a gala. Please list 5 different dress styles for me and use bold text for the names.",
+        "I'm feeling really sad today and just want an outfit that matches my mood."
+    ]
+    for text in text_list:
+        asyncio.run(run_simple_test(text))
+    # print("\n" + "=" * 50)
+    # # 启动异步事件循环
+    # try:
+    #     asyncio.run(run_simple_test())
+    # except Exception as e:
+    #     print(f"\n发生致命错误: {e}")
+    #
+    sys.stdout.close()

app/server/ChatbotAgent/core/prompt.py

@@ -1,4 +1,4 @@
-BASIC_PROMPT = """You are a professional, friendly, and insightful AI {gender}'s styling assistant.
+BASIC_PROMPT_OLD = """You are a professional, friendly, and insightful AI {gender}'s styling assistant.
 
 Your primary mission is to engage in a multi-turn conversation with the user to fully understand their dressing intent. You must adopt a professional yet approachable tone.
 
@@ -21,6 +21,39 @@ Example Follow-up (mimicking a conversational flow):
 User: I want a chic outfit for dinner.
 Your Response: Hey there! A chic dinner outfit, I love that! To give you the perfect recommendations, tell me: is this a romantic date, business dinner, or celebration with friends? And what's your go-to style vibe: classic elegance or something with more edge?"""
 
+BASIC_PROMPT = """
+You are a professional, friendly, and insightful AI {gender}'s styling assistant. You are smart, young, and enthusiastic, turning styling into an exciting experience. Your tone is warm, confident, composed, and genuinely curious about the user's context.
+
+CONVERSATION GOALS:
+    1.  **Occasion:**  Determine the specific event (e.g., romantic dinner, summer wedding).
+    2.  **Style:** Pinpoint the desired aesthetic (e.g., minimalist, bohemian).
+    3.  **Vibe/Details:** Gather mood or constraints (e.g., comfort, specific colors).
+    4.  **Item Preference:** Identify preferences for silhouettes or specific items.
+
+PERSONALITY AND COMMUNICATION RULES:
+    -   Always reply in English only. If the user speaks another language, state in English that you cannot speak that language and steer back to styling.
+    -   Keep responses extremely SHORT (maximum 2 sentences).
+    -   Use clear, modern, and vivid language. Avoid clichés or corporate jargon.
+    -   Be positive and encouraging, never patronizing.
+    -   When uncertain, state assumptions and invite correction.
+    -   Gather sufficient intent before providing recommendations. Ask only 1 to 2 essential questions per turn.
+
+IMPORTANT GUARDRAILS:
+    -   ONLY help with styling ideas and outfit generation.
+    -   If asked about politics, controversial topics, or anything inappropriate, respond with a witty deflection and steer back to styling.
+
+OUTPUT FORMAT INSTRUCTION:
+-   **DO NOT** use any Markdown formatting whatsoever (no asterisks, bold text, lists, or code blocks).
+-   **ONLY** output the plain text response spoken by the AI Assistant.
+
+EXAMPLE DIALOGUES:
+    User: 我想找件衣服参加婚礼。 Response: I am so sorry, but I can only speak English! I would love to help you find the perfect wedding guest look if you can tell me the dress code or the venue vibe.
+
+    User: I need a chic outfit for dinner. Response: That sounds like a fabulous evening! Is this a romantic date or a casual night out with friends, and do you prefer a sleek dress or tailored separates?
+
+    User: What do you think about the current election? Response: I prefer to stick to the polls of fashion rather than politics! Tell me, what kind of style aesthetic are we aiming for today?
+"""
+
 SUMMARY_PROMPT = """
 You are an expert fashion request analyzer. Analyze the conversation history provided by the user.
 Your task is to:
@@ -31,24 +64,32 @@ Your task is to:
 Extract this information accurately from the chat history.
 """
 
-
 from app.taxonomy import FASHION_TAXONOMY, IGNORE_SUBCATEGORY, ALL_SUBCATEGORY_LIST
+
 core_outfit_template = f"""
 You are a professional fashion stylist Agent, specialized in creating complete, tailored outfits for {{gender}}. Your current task is to recommend items for the **{{current_category}}** stage, strictly **mimicking the style and preference** specified in the following Stylist Guide.
 
 Your task is to **create a cohesive and complete outfit**, strictly adhering to **BOTH** the user's explicit **Request Summary** and the **Outfit Style Guide**. You must decide the next logical item to add to the outfit based on the current stage and constraints. Descriptions of current outfit combination is listed in user's message.
 
+## RULE HIERARCHY (Priority Order):
+1. **STYLIST CORE RULES**: Adhere to the specific logic provided in the Stylist Guide.
+2. **GENERAL COORDINATION RULES**: 
+   - Limit outfit to **three main colors** within the **same color tone**.
+   - Preferred composition: **Two neutrals and one accent color**.
+   - **Silhouette Balance**: Loose top with fitted bottom, OR fitted top with loose bottom.
+   - **Pants Logic**: Strictly NO mid-calf length pants. Cropped pants MUST be paired with loafers or ankle boots.
+
 ---
 ## Request from the User:
 {{request_summary}}
 
-## Core Guidance Document: Outfit Style Guide
+## Core Guidance Document: Stylist Guide
 {{stylist_guide}}
 ---
 
 ## Your Workflow and Constraints
 
-1.  **Style Adherence**: You must strictly observe all rules in the Style Guide concerning **color palette, fit, layering principles, pattern restrictions , shoe coordination**.
+1.  **Style Adherence**: You must follow the Rule Hierarchy. If a rule in the Stylist Guide conflicts with the General Rules, the Stylist Guide takes precedence. You should strictly consider all rules in the Style Guide and general rules concerning **color palette, fit, layering principles, pattern restrictions , shoe coordination**.
 2.  **Uniqueness Mandate**: Every item must follow the **absolute no-repeat rule for subcategories** within its stage. Each subcategory from the allowed list can appear **exactly once** in the entire outfit. Furthermore, the categories 'dresses' and 'pants' and 'skirts' are mutually exclusive; they NORMALLY cannot be included in the same outfit.
 3.  **Step Planning**: The styling sequence must follow a logical approach (e.g., top-down, inside-out for clothing). Prioritize unused subcategories from the allowed list to avoid repetition.
 4.  **Structured Output**: Your output MUST be a valid JSON object. The strict JSON structure and field requirements are provided separately via the API schema.
@@ -88,17 +129,27 @@ You must strictly use the **JSON format** for your output, as follows:
 Now, please start building an outfit (with strictly unique categories for all items) and output the JSON for the first item.
 """
 
-
 accessories_template = f"""
 You are a professional fashion stylist Agent, specialized in creating complete, tailored outfits for {{gender}}. Your current task is to finalize the look by recommending accessories for the **{{current_category}}** stage, strictly **mimicking the style and preference** specified in the following Accessories Guide.
 
 Your final task is to **select the perfect set of accessories** to complete the given outfit. You must strictly adhere to **BOTH** the user's **Request Summary** and the **ACCESSORIES Style Guide**. The **full description of the existing outfit** is provided in the user's message.
 
+## RULE HIERARCHY (Priority Order):
+1. **ABSOLUTE PROHIBITION (Highest Priority)**: 
+   - **NO WATCHES**.
+   - **NO HATS**.
+   - **NO SOCKS**.
+   - Do not recommend these subcategories under any circumstances, even if requested or implied.
+2. **STYLIST CORE RULES**: Follow specific accessory preferences in the [Stylist's Accessories Guide].
+3. **GENERAL COORDINATION RULES**:
+   - Accessories must fit within the **three main colors** and **same color tone** established by the clothing.
+   - Maintain the **two neutrals and one accent color** balance.
+
 ---
 ## CONTEXT
 [User Request]: {{request_summary}}
 
-[Accessories Style Guide]:
+[Stylist's Accessories Guide]:
 {{stylist_guide}}
 
 ---
@@ -120,6 +171,15 @@ You are a professional fashion stylist Agent, specialized in creating complete,
 
 You must create a cohesive look that includes **Clothing, Shoes, Bags, and Accessories**. You must strictly adhere to **BOTH** the user's **Request Summary** and the **Combined Style Guide**.
 
+## RULE HIERARCHY (Priority Order):
+1. **ABSOLUTE PROHIBITION**: Strictly NO hats or watches or socks.
+2. **STYLIST CORE RULES**: Adhere to the specific logic provided in the Stylist Guide.
+3. **GENERAL COORDINATION RULES**: 
+   - Limit outfit to **three main colors** within the **same color tone**.
+   - Preferred composition: **Two neutrals and one accent color**.
+   - **Silhouette Balance**: Loose top with fitted bottom, OR fitted top with loose bottom.
+   - **Pants Logic**: Strictly NO mid-calf length pants. Cropped pants MUST be paired with loafers or ankle boots.
+
 ---
 ## Request from the User:
 {{request_summary}}
@@ -130,29 +190,28 @@ You must create a cohesive look that includes **Clothing, Shoes, Bags, and Acces
 
 ## GENERATION WORKFLOW & RULES
 
-1.  **Holistic Styling**: You are NOT recommending items step-by-step. You must visualize the final look and output **ALL** necessary items (Clothing, Shoes, Bags, Accessories) in a **single JSON response** using the `recommended_items` list.
+1. **Single Batch Output**: Visualize the final look and output **ALL** items in a single JSON response using the `recommended_items` list.
 
-2.  **Outfit Composition Rules (Mandatory)**:
-    * **CLOTHING**: Ensure **full body coverage**. You must include either [Top + Bottom] OR [One-piece (e.g., Dress/Jumpsuit)]. 'Dresses' and 'Skirts/Pants' are mutually exclusive.
-    * **SHOES**: **Exactly one (1) pair** of shoes is MANDATORY.
-    * **BAGS**: Recommend **0 or 1 bag**. Skip the bag only if the occasion or Style Guide explicitly suggests it (e.g., home wear, yoga).
-    * **ACCESSORIES**: Recommend a set of accessories (typically 1-3 items) that complement the clothing. Follow metal/material constraints in the guide.
-    Number of items in outfit must not exceed {{max_len}}.
+2. **Full Body Coverage & Composition (Mandatory)**:
+    * **Clothing**: You MUST ensure **full body coverage**. This requires including either [Top + Bottom] OR [a One-piece item like a Dress/Jumpsuit].
+    * **Mutual Exclusivity**: 'Dresses' and 'Skirts/Pants' are mutually exclusive; do not recommend both in one outfit.
+    * **Shoes**: Exactly **one (1) pair** of shoes is MANDATORY for every outfit.
+    * **Bags**: Recommend **0 or 1 bag**.
+    * **Accessories**: Recommend a set of accessories (e.g., jewelry, eyewear).
 
-3.  **Uniqueness Mandate**: 
-    * Each **subcategory** belonging to CLOTHING (e.g., 't-shirts', 'sweaters', 'jacket') can appear **EXACTLY ONCE** in the entire list. 
-    * But **subcategory** belonging to ACCESSORIES can repeat.
+3. **Strict Uniqueness Mandate (No Repeats)**: 
+    * Each **subcategory** (e.g., 'sunglasses', 'earrings', 't-shirts') can appear **EXACTLY ONCE** in the entire outfit. 
+    * DO NOT recommend two items from the same subcategory (e.g., do not recommend two different necklaces or two pairs of sunglasses).
 
-4.  **Exclusion List**: 
-    * The following subcategories are **STRICTLY FORBIDDEN**: ({IGNORE_SUBCATEGORY}). Do not include them in your recommendation.
+4. **Style Adherence**:
+    - **Proportions**: Explicitly describe the fit (Loose vs. Fitted) to show the required silhouette balance.
+    - **Color Harmony**: Ensure the combined colors of clothing, shoes, bags, and accessories stay within the 3-color tone limit.
 
-5.  **Style Adherence**: 
-    * Ensure all items coordinate in **color, fit, and material**.
-    * Strictly observe the layering principles and color palette defined in the Style Guide.
+5. **Exclusion List**:
+    - The following subcategories are **STRICTLY FORBIDDEN**: ({IGNORE_SUBCATEGORY}).
 
-6.  **Description Quality**: 
-    * The `description` field for every item must be **extremely detailed and precise** for high-accuracy vector search.
-    * It MUST include: **Color, Fit/Silhouette, Material/Detail, and Role in the Outfit.**
+6. **Description Quality**:
+    - Each `description` MUST include: **Color, Fit/Silhouette, Material/Detail, and Role in the Outfit.**
 
 ## OUTPUT FORMAT
 Output a valid JSON object matching the provided API schema. The `recommended_items` array must contain all the items for this outfit.
@@ -182,8 +241,8 @@ def build_iterative_schema(current_category):
     return schema
 
 
-def build_batch_schema(specified_category: str=""):
-    assert(specified_category in FASHION_TAXONOMY.keys() or specified_category == "")
+def build_batch_schema(specified_category: str = ""):
+    assert (specified_category in FASHION_TAXONOMY.keys() or specified_category == "")
     if not specified_category:
         category_range_desc = "the complete final outfit (including all categories)"
         subcategory_list = ALL_SUBCATEGORY_LIST

app/server/ChatbotAgent/core/stylist_agent_server.py

@@ -27,7 +27,7 @@ logger = logging.getLogger(__name__)
 
 
 class AsyncStylistAgent:
-    def __init__(self, local_db: str, gemini_model_name: str, outfit_id: str, stylist_name: str, gender: str):
+    def __init__(self, local_db: str, gemini_model_name: str, outfit_id: str, stylist_name: str, gender: str, callback_url: str):
         # self.outfit_items: List[Dict[str, str]] = []
         self.outfit_id = outfit_id
         self.stylist_name = stylist_name
@@ -38,13 +38,6 @@ class AsyncStylistAgent:
         self.local_db = local_db
         self.gemini_model_name = gemini_model_name
         self.stop_reason = ""
-        self.headers = {
-            'Accept': "*/*",
-            'Accept-Encoding': "gzip, deflate, br",
-            'User-Agent': "PostmanRuntime-ApipostRuntime/1.1.0",
-            'Connection': "keep-alive",
-            'Content-Type': "application/json"
-        }
 
         # 存储桶配置
         try:
@@ -59,6 +52,7 @@ class AsyncStylistAgent:
         )
         self.gcs_bucket = "lc_stylist_agent_outfit_items"
         self.minio_bucket = "lanecarford"
+        self.callback_url = f'{callback_url}/api/style/callback'
 
     def _load_style_guide(self, stylist_name: str):
         """加载 markdown 风格指南内容。"""
@@ -178,6 +172,7 @@ class AsyncStylistAgent:
             results = self.local_db.get_matched_item(
                 query_embedding,
                 category,
+                subcategory,
                 occasions=occasions,
                 batch_sources=batch_sources,
                 gender=gender,
@@ -188,8 +183,13 @@ class AsyncStylistAgent:
             results = []
 
         if not results:
-            print(f"数据库中未找到符合 '{category}' 和描述的单品。")
-            return None
+            self.post_operation(
+                status="failed",
+                message=f"数据库中未找到符合 '{category}' 和描述的单品。",
+                callback_url=self.callback_url,
+                img_path="",
+            )
+            raise Exception(f"数据库中未找到符合 '{category}' 和描述的单品。")
 
         # 3. 模拟 Agent 审核(实际应用中,你需要将图片发回给 Agent进行审核)
         best_meta = results[0]  # 第一个 batch 的第一个 metadata
@@ -200,6 +200,7 @@ class AsyncStylistAgent:
             "category": best_meta['category'],
             'description': best_meta['description'],
             "subcategory": best_meta['subcategory'],
+            "brand": best_meta['brand'],
             "gpt_description": item_description,
             "gpt_subcategory": subcategory,
             # 假设 'item_path' 存储在 metadata 中，或从 'item_id' 推导
@@ -239,7 +240,7 @@ class AsyncStylistAgent:
             context += "\nRecommend a **complete, full outfit**, including all items (clothing, shoes, bags, and accessories), strictly following the Request Summary and Style Guide. Output the **complete list** of items in a single JSON response."
         return context
 
-    def post_operation(self, status: str, message: str, callback_url: str, img_path: str):
+    def post_operation(self, status: str, message: str, callback_url: str, img_path: str, request_summary=None, occasions=None):
         """处理完成后的回调操作。"""
         if settings.LOCAL == 0:
             # 生产回调请求数据处理
@@ -256,10 +257,18 @@ class AsyncStylistAgent:
                 'status': status,
                 # 'message': message,
                 'path': img_path,
-                'outfit_id': self.outfit_id
+                'outfit_id': self.outfit_id,
+                "request_summary": request_summary,
+                "occasions": occasions
             }
-            response = post_request(url=callback_url, data=json.dumps(response_data), headers=self.headers)
-            logger.info(f"request data ：{json.dumps(response_data, ensure_ascii=False, indent=2)} | JAVA callback info -> status:{response.status_code} | message:{response.text}")
+
+            if status in ['failed']:
+                # 失败直接打印参数 不发送结果
+                response_data['message'] = message
+                logger.info(f"request data ：{json.dumps(response_data, ensure_ascii=False, indent=2)}")
+            else:
+                response = post_request(url=callback_url, data=json.dumps(response_data))
+                logger.info(f"request data ：{json.dumps(response_data, ensure_ascii=False, indent=2)} | JAVA callback info -> status:{response.status_code} | message:{response.text}")
             return response_data
         else:
             return {}
@@ -298,14 +307,13 @@ class AsyncStylistAgent:
             gemini_data = self._parse_gemini_response(gemini_response_text)
 
             if not gemini_data:
-                print("Agent 返回无效响应,终止流程。")
                 self.post_operation(
                     status="failed",
                     message="Agent returned invalid response, terminating process.",
                     callback_url=url,
                     img_path=merged_image_path,
                 )
-                break
+                raise Exception("Agent 返回无效响应,终止流程。")
 
             # 处理推荐单品
             if gemini_data.get('action') == 'recommend_item':
@@ -385,15 +393,17 @@ class AsyncStylistAgent:
         gemini_data = self._parse_gemini_response(gemini_response_text)
         recommended_items = gemini_data.get('recommended_items', [])
         reason = gemini_data.get('reason', '')
+        failed_found_item_count = 0
 
         if not recommended_items or not isinstance(recommended_items, List):
-            print("No recommended item from Gemini, terminating process.")
             self.post_operation(
                 status="failed",
                 message="Agent returned invalid response, terminating process.",
                 callback_url=url,
                 img_path=merged_image_path
             )
+            raise Exception("No recommended item from Gemini, terminating process.")
+
         else:
             for idx, rec_item in enumerate(recommended_items):
                 subcategory = rec_item.get('subcategory')
@@ -416,10 +426,21 @@ class AsyncStylistAgent:
 
                 new_item = self._get_next_item(description, category, subcategory, occasions, batch_sources, self.gender)
                 if not new_item or new_item['item_id'] in [x['item_id'] for x in self.outfit_items]:
+                    failed_found_item_count += 1
                     continue
                 else:
                     self.outfit_items.append(new_item)
                     print(f"Item {idx + 1}: ({subcategory}) {rec_item}, found item: {new_item}")
+
+            # 如果没有找到的item过于多，需要重试
+            if failed_found_item_count / len(recommended_items) > 0.5:
+                self.post_operation(
+                    status="failed",
+                    message=f"There are {failed_found_item_count} items (total {len(recommended_items)}) are not found in the database",
+                    callback_url=url,
+                    img_path=merged_image_path
+                )
+                raise Exception(f"There are {failed_found_item_count} items (total {len(recommended_items)}) are not found in the database")
         return reason
 
     async def run_iterative_styling(self, request_summary, occasions, start_outfit: Optional[List] = None, batch_sources: List = [], user_id="test", callback_url=""):
@@ -464,18 +485,20 @@ class AsyncStylistAgent:
         )
 
         final_image_path, _ = await self._merge_images(self.outfit_id, user_id, self.stylist_name)
+        # 推荐完成返回
         response_data = self.post_operation(
             status="stop",
             message=reason,
             callback_url=url,
-            img_path=final_image_path
+            img_path=final_image_path,
+            request_summary=request_summary,
+            occasions=occasions
         )
-        if settings.LOCAL == 1:
-            with open(os.path.join(settings.OUTFIT_OUTPUT_DIR, self.stylist_name, f'{self.outfit_id}.json'), 'w') as f:
-                json.dump({"request_summary": request_summary, "occasions": occasions, "items": self.outfit_items}, f, indent=2)
-
         end_time = time.monotonic()
         total_duration = end_time - start_time
+        if settings.LOCAL == 1:
+            with open(os.path.join(settings.OUTFIT_OUTPUT_DIR, self.stylist_name, f'{self.outfit_id}.json'), 'w') as f:
+                json.dump({"request_summary": request_summary, "occasions": occasions, "items": self.outfit_items, "total_duration": total_duration}, f, indent=2)
 
         return response_data, total_duration
 
@@ -502,20 +525,27 @@ class AsyncStylistAgent:
             user_id,
             url
         )
-
+        # 推荐即将完成 回调通知前端
+        self.post_operation(
+            status="almost_done",
+            message="Recommendation has been completed and the outfit is being assembled",
+            callback_url=url,
+            img_path="",
+        )
         final_image_path, _ = await self._merge_images(self.outfit_id, user_id, self.stylist_name)
         response_data = self.post_operation(
             status="stop",
             message=reason,
             callback_url=url,
-            img_path=final_image_path
+            img_path=final_image_path,
+            request_summary=request_summary,
+            occasions=occasions
         )
-        if settings.LOCAL == 1:
-            with open(os.path.join(settings.OUTFIT_OUTPUT_DIR, self.stylist_name, f'{self.outfit_id}.json'), 'w') as f:
-                json.dump({"request_summary": request_summary, "occasions": occasions, "items": self.outfit_items}, f, indent=2)
-
         end_time = time.monotonic()
         total_duration = end_time - start_time
+        if settings.LOCAL == 1:
+            with open(os.path.join(settings.OUTFIT_OUTPUT_DIR, self.stylist_name, f'{self.outfit_id}.json'), 'w') as f:
+                json.dump({"request_summary": request_summary, "occasions": occasions, "items": self.outfit_items, "total_duration": total_duration}, f, indent=2)
 
         return response_data, total_duration
 

app/server/ChatbotAgent/core/vector_database.py

@@ -5,7 +5,7 @@ from PIL import Image
 from typing import List, Dict, Any
 from transformers import CLIPProcessor, CLIPModel
 
-from app.taxonomy import OCCASION, CATEGORY_LIST, IGNORE_SUBCATEGORY
+from app.taxonomy import OCCASION, CATEGORY_LIST, IGNORE_SUBCATEGORY, BRAND_WHITELIST
 
 
 class VectorDatabase():
@@ -46,7 +46,7 @@ class VectorDatabase():
 
         return features.cpu().numpy().flatten().tolist()
         
-    def get_matched_item(self, embedding: List[float], category: str, occasions: List[str] = [], batch_sources: List[str] = [], gender: str = 'female', n_results: int = 1) -> List[Dict[str, Any]]:
+    def get_matched_item(self, embedding: List[float], category: str, subcategory: str, occasions: List[str] = [], batch_sources: List[str] = [], gender: str = 'female', n_results: int = 1) -> List[Dict[str, Any]]:
         if category not in CATEGORY_LIST:
             raise ValueError(f"Recommended {category} is not valid.")
         
@@ -59,6 +59,12 @@ class VectorDatabase():
             ]},
             {"subcategory": {"$nin": IGNORE_SUBCATEGORY}}
         ]
+
+        # 加了一条限制，但是部署到生产的时候把他设定为False
+        brand_strication = False
+        if brand_strication:
+            and_conditions.append({"brand": {"$in": BRAND_WHITELIST}})
+
         if batch_sources and len(batch_sources) > 0:
             if len(batch_sources) == 1:
                 and_conditions.append({"batch_source": batch_sources[0]})
@@ -80,10 +86,14 @@ class VectorDatabase():
             return []
         
         metadatas = results['metadatas'][0]  # List[Dict[str, Any]]
-        final_scores = []
+        candidate_pool = []
+        all_scores = []
         for idx, metadata in enumerate(metadatas):
             dist_img = results['distances'][0][idx]
             score_vec = 1 - dist_img  # cosine similarity range: [-1, 1]
+            score_subcategory = 0.0
+            if subcategory == metadata['subcategory']:
+                score_subcategory = 1
 
             score_occ = 0.0
             if occasions:
@@ -94,26 +104,42 @@ class VectorDatabase():
                     count += 1
                     status_val = metadata.get(occ, -1)
                     if status_val == 1:
-                        score_occ += 1.0
+                        score_occ += 5.0
                     elif status_val == 0:
                         score_occ += 0.0
                     else:
-                        score_occ -= 100.0
+                        score_occ -= 5.0
 
                 score_occ = score_occ / count if count else 0.0
 
-            final_score = 0.6 * score_vec + 0.4 * score_occ
-            final_scores.append(final_score)
+            final_score = 0.5 * score_vec + 0.1 * score_occ + 0.5 * score_subcategory
+            all_scores.append(final_score)
+            if final_score > 0:
+                candidate_pool.append({
+                    "score": final_score,
+                    "metadata": metadata
+                })
 
-        scores_arr = np.array(final_scores)
-        temperature = 0.5
-        scores_arr = scores_arr / temperature
+        if not candidate_pool:
+            print(f"All scores are negative: Ignore")
+            return []
+
+        # 采取topk截断
+        candidate_pool.sort(key=lambda x: x['score'], reverse=True)
+        current_k = min(10, len(candidate_pool))
+        top_candidates = candidate_pool[:current_k]
+        top_scores = np.array([x['score'] for x in top_candidates])
+
+        #降低温度，使得选择稳定
+        temperature = 0.2
+        scaled_scores = top_scores / temperature
         
         # Softmax: 将分数转换为概率
-        exp_scores = np.exp(scores_arr - np.max(scores_arr))
+        exp_scores = np.exp(scaled_scores - np.max(scaled_scores))
         probabilities = exp_scores / np.sum(exp_scores)
         
         # 采样 (或直接取 Top 1)
-        sampled_index = np.random.choice(a=len(results['ids'][0]), p=probabilities, size=n_results, replace=False)  # 不重复采样
-        sampled_items = [metadatas[i] for i in sampled_index]
+        sampled_index = np.random.choice(a=current_k, p=probabilities, size=min(n_results, current_k), replace=False)  # 不重复采样
+        sampled_items = [top_candidates[i]['metadata'] for i in sampled_index]
+
         return sampled_items

app/server/utils/request_post.py

@@ -4,18 +4,24 @@ import time
 import requests
 
 
-def post_request(url, data=None, json_data=None, headers=None, auth=None, timeout=5):
+def post_request(url, data=None, json_data=None, auth=None, timeout=5):
     """
     发送POST请求的封装函数
 
     :param url: 接口的URL地址
     :param data: 要发送的数据（字典形式，用于表单数据等，会自动编码）
     :param json_data: 要发送的JSON数据（字典形式，会自动转换为JSON字符串）
-    :param headers: 请求头字典
     :param auth: 认证信息（如 ('username', 'password') 形式用于基本认证）
     :param timeout: 超时时间，单位为秒
     :return: 返回接口的响应对象
     """
+    headers = {
+        'Accept': "*/*",
+        'Accept-Encoding': "gzip, deflate, br",
+        'User-Agent': "PostmanRuntime-ApipostRuntime/1.1.0",
+        'Connection': "keep-alive",
+        'Content-Type': "application/json"
+    }
     try:
         response = requests.post(
             url,
@@ -52,6 +58,6 @@ if __name__ == '__main__':
         'Content-Type': "application/json"
     }
     start_time = time.time()
-    X = post_request(url=url, data=json.dumps(object_data), headers=headers)
+    X = post_request(url=url, data=json.dumps(object_data))
     print(time.time() - start_time)
     print(X)

app/taxonomy.py

@@ -91,4 +91,43 @@ FASHION_TAXONOMY = {
 CATEGORY_LIST = list(FASHION_TAXONOMY.keys())
 ALL_SUBCATEGORY_LIST = sum(FASHION_TAXONOMY.values(), [])
 
-IGNORE_SUBCATEGORY = ['socks']
+IGNORE_SUBCATEGORY = ['socks', 'watches', 'hats']
+
+BRAND_WHITELIST = [
+    "ALAÏA",
+    "BALENCIAGA",
+    "BALMAIN",
+    "BARBOUR",
+    "BOTTEGA VENETA",
+    "BRUNELLO CUCINELLI",
+    "ALEXANDERWANG",
+    "CHLOÉ",
+    "DIOR",
+    "FEAR OF GOD",
+    "FENDI",
+    "GIA STUDIOS",
+    "GUCCI",
+    "HELMUT LANG",
+    "JACQUEMUS",
+    "JIMMY CHOO",
+    "JW ANDERSON",
+    "KHAITE",
+    "LEMAIRE",
+    "LOEWE",
+    "MAISON MARGIELA",
+    "MIU MIU",
+    "MM6 MAISON MARGIELA",
+    "MONCLER",
+    "PEDDER RED",
+    "PETER DO",
+    "PHOEBE PHILO",
+    "PRADA",
+    "RICK OWENS",
+    "SACAI",
+    "SKIMS",
+    "THE ROW",
+    "THEORY",
+    "THOM BROWNE",
+    "THE FRANKIE SHOP",
+    "TOTEME",
+]

pyproject.toml

@@ -0,0 +1,22 @@
+[project]
+name = "lc-stylist-agent"
+version = "0.1.0"
+description = "Add your description here"
+requires-python = "==3.10.*"
+dependencies = [
+    "chromadb==1.1.1",
+    "google-cloud-storage==2.19.0",
+    "google-genai==1.45.0",
+    "litserve>=0.2.16",
+    "minio==7.2.18",
+    "numpy==1.24.4",
+    "open-clip-torch==2.24.0",
+    "opencv-python==4.9.0.80",
+    "pydantic-settings==2.11.0",
+    "pytorch-fid==0.3.0",
+    "redis==6.4.0",
+    "setuptools==80.9.0",
+    "torch-fidelity==0.3.0",
+    "torchmetrics==1.4.0.post0",
+    "transformers==4.41.1",
+]