新增 取消agent配饰（保留鞋子）推荐，改为默认随机配饰搭配 使用json文件补充stylist删除掉的必要配饰

Dockerfile

@@ -40,8 +40,7 @@ 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
+RUN pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
-
 EXPOSE 8000
 CMD ["python", "-m","app.main"]
 #CMD ["tail", "-f","/dev/null"]

app/server/utils/minio_client.py

@@ -1,6 +1,7 @@
 import io
 import json
 import logging
+import time
 from io import BytesIO
 
 import cv2
@@ -115,10 +116,10 @@ if __name__ == '__main__':
     # url = "lanecarford/lc_stylist_agent_outfit_items/string/5de155d0-56a6-43e8-a2f1-7538fce86220.jpg"
     # url = "lanecarford/lc_stylist_agent_outfit_items/string/1cd1803c-5f51-4961-a4f2-2acd3e0d8294.jpg"
     url = [
-        'lanecarford/lc_stylist_agent_outfit_items/zhhtest20251120/d9df7c48-c7e5-47f9-be67-07f0d175d202.jpg',
+        'lanecarford/lc_stylist_agent_outfit_items/zhhtest20251121/4b595d3b-5d3d-4617-ae09-5fca92d935f7.jpg',
-        'lanecarford/lc_stylist_agent_outfit_items/zhhtest20251120/ddf39b9c-69f0-4b28-95ed-9d823fa82e35.jpg',
+        'lanecarford/lc_stylist_agent_outfit_items/zhhtest20251121/6d0d7540-5b61-45f2-a1fa-5cb1c7a3d0fa.jpg',
-        'lanecarford/lc_stylist_agent_outfit_items/zhhtest20251120/112194a0-dc1d-4151-8c58-82642142a553.jpg',
+        'lanecarford/lc_stylist_agent_outfit_items/zhhtest20251121/a4e51ccb-9b95-4718-8153-92ee0a39d0c8.jpg',
-        'lanecarford/lc_stylist_agent_outfit_items/zhhtest20251120/788007f1-e44b-4390-ad9e-a2d4ba406379.jpg'
+        'lanecarford/lc_stylist_agent_outfit_items/zhhtest20251121/cbebbcf6-cca2-4460-9f9f-d0b1000dc2cd.jpg'
     ]
     read_type = "1"
     for id, i in enumerate(url):

app/test/chromadb/embedding_query.py

@@ -0,0 +1,90 @@
+import random
+
+import chromadb
+from typing import Set, List, Dict, Union, Any
+
+import torch
+from PIL import Image
+from torch import no_grad
+from transformers import CLIPModel, CLIPProcessor
+
+from app.server.utils.minio_client import oss_get_image, minio_client
+from app.server.utils.minio_config import MINIO_LC_DATA_PATH
+
+# --- 你的配置 ---
+DB_PATH = "/workspace/lc_stylist_agent/db"
+COLLECTION_NAME = 'lc_clothing_embedding'
+# 设置一个足够大的限制来获取所有记录，或者使用分页（如果记录数非常庞大）
+MAX_LIMIT = 1000000
+
+client = chromadb.PersistentClient(path=DB_PATH)
+try:
+    collection = client.get_collection(name=COLLECTION_NAME)
+    print(f"✅ 连接到 Collection: {COLLECTION_NAME}")
+except ValueError:
+    print(f"⚠️ Collection '{COLLECTION_NAME}' 不存在。")
+    # 如果 collection 不存在，我们将跳过后续操作
+    collection = None
+
+from transformers import CLIPModel, CLIPProcessor
+
+
+def get_clip_embedding(data: str | Image.Image) -> List[float]:
+    """生成图像或文本的 CLIP 嵌入，并进行 L2 归一化。"""
+
+    embedding_model_name = "openai/clip-vit-base-patch32"
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model = CLIPModel.from_pretrained(embedding_model_name).to(device)
+    processor = CLIPProcessor.from_pretrained(embedding_model_name)
+
+    # 强制截断，解决序列长度问题
+    inputs = processor(
+        text=[data],
+        return_tensors="pt",
+        padding=True,
+        truncation=True
+    ).to(device)
+    with no_grad():
+        features = model.get_text_features(**inputs)
+
+    # L2 归一化
+    features = features / features.norm(p=2, dim=-1, keepdim=True)
+
+    return features.cpu().numpy().flatten().tolist()
+
+
+def query_local_db(embedding: List[float], category: str, n_results: int = 3) -> List[Dict[str, Any]]:
+    """
+    基于嵌入向量在本地数据库中查询相似单品。
+    实际应执行 ChromaDB 查询,并根据 category 进行过滤(metadatas)。
+    """
+    # 实际应执行向量查询
+    # 为了演示流程,返回一个模拟结果
+    results = collection.query(
+        query_embeddings=[embedding],
+        n_results=n_results,
+        where={
+            "$and": [
+                {"category": category},
+                {"modality": "image"},
+            ]
+        },
+        include=['documents', 'metadatas', 'distances']
+    )
+    return results
+
+
+if __name__ == '__main__':
+    embedding = get_clip_embedding("watch")
+    print(embedding)
+    result = query_local_db(embedding, "Watches", 20)
+    print(result)
+    ids = result['ids'][0]
+
+    random_single_id = random.choices(ids, k=2)
+    print(random_single_id)
+
+    # for id in ids:
+    #     path = id.replace("_img", ".jpg")
+    #     img = oss_get_image(oss_client=minio_client, path=f"{MINIO_LC_DATA_PATH}/{path}", data_type="PIL").convert('RGB')
+    #     img.save(path)

docker-compose.yml

@@ -13,4 +13,12 @@ services:
       - ./db:/db
       - /etc/localtime:/etc/localtime:ro
     ports:
-      - "10070:8000"
+      - "10070:8000"
+    deploy:
+      resources:
+        reservations:
+          devices:
+            # 告诉 Docker 使用所有可用的 NVIDIA GPU
+            - driver: nvidia
+              device_ids: ['0']
+              capabilities: [ gpu ]