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pangkaicheng
2025-12-10 17:27:56 +08:00
parent 0b1d948f77
commit 0e9546aa1a
12 changed files with 936 additions and 171 deletions
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12
app/server/ChatbotAgent/agent_server.py
View File

@@ -51,7 +51,7 @@ class AgentRequestModel(BaseModel):
session_id: str
num_outfits: int
stylist_path: str
batch_source: str
batch_sources: List[str]
callback_url: str
gender: str
max_len: int = 9
@@ -112,8 +112,8 @@ class LCAgent(ls.LitAPI):
request_summary=request_summary,
occasions=occasions,
stylist_name=request.stylist_path,
batch_source=request.batch_source,
start_outfit=[],
batch_sources=request.batch_sources,
num_outfits=request.num_outfits,
user_id=request.user_id,
gender=request.gender,
@@ -162,7 +162,7 @@ class LCAgent(ls.LitAPI):
return str(parsed_result.summary), [occ.value for occ in parsed_result.occasions]
async def recommend_outfit(
self, request_summary: str, occasions: List[str], batch_source: str, stylist_name: str, start_outfit=[],
self, request_summary: str, occasions: List[str], stylist_name: str, start_outfit: List = [], batch_sources: List[str] = [],
num_outfits: int = 1, user_id: str = "test", gender: str = "male",
callback_url: str = None, max_len: int = 9, outfit_ids=None
):
@@ -186,9 +186,9 @@ class LCAgent(ls.LitAPI):
task = agent.run_styling_process(
request_summary=request_summary,
occasions=occasions,
batch_source=batch_source,
stylist_name=stylist_name,
start_outfit=start_outfit,
batch_sources=batch_sources,
user_id=user_id,
callback_url=callback_url,
gender=gender,
@@ -227,9 +227,9 @@ class LCAgent(ls.LitAPI):
new_task = agent.run_styling_process(
request_summary=request_summary,
occasions=occasions,
batch_source=batch_source,
stylist_name=stylist_name,
start_outfit=start_outfit,
batch_sources=batch_sources,
user_id=user_id,
callback_url=callback_url
)
@@ -295,9 +295,9 @@ if __name__ == "__main__":
task = agent.run_styling_process(
request_summary=request_summary,
occasions=occasions,
batch_source="2025_q4",
stylist_name=stylist_name,
start_outfit=[],
batch_sources=["2025_q4"],
user_id=test_content['test_case_id'],
callback_url="http://mock-callback.com/result",
gender="female",

73
app/server/ChatbotAgent/core/stylist_agent_server.py
View File

@@ -16,11 +16,16 @@ from app.server.utils.img_operation import merge_images_to_square
from app.server.utils.minio_client import minio_client, oss_upload_image
from app.server.utils.request_post import post_request
from app.config import settings
from app.taxonomy import CLOTHING_CATEGORY, ACCESSORY_CATEGORY
from app.taxonomy import CATEGORY, ALL_CATEGORY, IGNORE_CATEGORY
logger = logging.getLogger(__name__)
IGNORE_CATEGORY = set(IGNORE_CATEGORY)
CLOTHING_CATEGORY = set(CATEGORY['clothing'] + CATEGORY['shoes'] + CATEGORY['bags']) - IGNORE_CATEGORY
ACCESSORY_CATEGORY = set(CATEGORY['accessories']) - IGNORE_CATEGORY
class AsyncStylistAgent:
def __init__(self, local_db, max_len: int, gemini_model_name: str, outfit_id=str):
# self.outfit_items: List[Dict[str, str]] = []
@@ -145,7 +150,7 @@ class AsyncStylistAgent:
```
* `action`: Must always be `"recommend_item"` until the outfit is complete.
* `category`: Must be an unused category from the following list: {CLOTHING_CATEGORY} (strictly no repeats, per the Category Uniqueness Mandate).
* `category`: Must be an unused category from the following list: {list(CLOTHING_CATEGORY)} (strictly no repeats, per the Category Uniqueness Mandate).
* `description`: This must be an **extremely detailed and precise** description of the item. This description is used for **high-accuracy vector search** in the database and must include:
* **Color** (e.g., milk tea, pure white, dark gray)
* **Fit/Silhouette** (e.g., Oversize, loose, slim-fit)
@@ -193,7 +198,7 @@ class AsyncStylistAgent:
---
## STRICT RULES
1. **Batch Recommendation**: Do NOT recommend items one by one. You must output the **COMPLETE LIST** of accessories (e.g., jewelry, bag, watch, hat) in a single response using the 'recommended_accessories' list.
2. **Allowed Categories**: Select only from: {ACCESSORY_CATEGORY}.
2. **Allowed Categories**: Select only from: {list(ACCESSORY_CATEGORY)}.
3. **Harmony & Constraints**:
- The accessories must complement the [Current Outfit Base].
- Strictly follow the [Accessories Style Guide] regarding metals (gold/silver), numbers, and prohibited items.
@@ -295,40 +300,46 @@ class AsyncStylistAgent:
print(f"Raw response: {response_text}")
return None
def _get_next_item(self, item_description: str, category: str, occasions: List[str], batch_source: str = "2025_q4", gender: str = "female") -> Optional[Dict[str, str]]:
def _get_next_item(self, item_description: str, category: str, occasions: List[str], batch_sources: List[str] = [], gender: str = "female") -> Optional[Dict[str, str]]:
"""
1. 根据描述生成嵌入。
2. 查询本地数据库以找到最佳匹配项。
3. 模拟 Agent 审核匹配项(这里简化为总是通过)。
"""
# 1. 生成查询嵌入
query_embedding = self.local_db.get_clip_embedding(item_description, is_image=False)
# 2. 执行查询,并过滤类别
try:
# 1. 生成查询嵌入
query_embedding = self.local_db.get_clip_embedding(item_description, is_image=False)
results = self.local_db.get_matched_item(
query_embedding,
category,
occasions=occasions,
batch_sources=batch_sources,
gender=gender,
n_results=1
)
except ValueError as e:
print(f"检测到无效参数错误：{e}")
results = []
# 2. 执行查询,并过滤类别
results = self.local_db.get_matched_item(query_embedding, category, occasions=occasions, batch_source=batch_source, gender=gender, n_results=1)
if not results:
print(f"❌ 数据库中未找到符合 '{category}' 和描述的单品。")
return None
# 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
item_id = best_meta['item_id'].replace("_img", "")
return {
"item_id": item_id, # 从 metadata 字典中安全获取
"category": best_meta['category'],
"gpt_description": item_description,
'description': best_meta['description'],
# 假设 'item_path' 存储在 metadata 中，或从 'item_id' 推导
# 这里假设 item_id 就是文件名的一部分
"image_path": os.path.join(f"{item_id}.jpg")
}
except Exception as e:
print(f"An error occurred during item retrieval: {e}")
if not results:
print(f"数据库中未找到符合 '{category}' 和描述的单品。")
return None
# 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
item_id = best_meta['item_id'].replace("_img", "")
return {
"item_id": item_id, # 从 metadata 字典中安全获取
"category": best_meta['category'],
"gpt_description": item_description,
'description': best_meta['description'],
# 假设 'item_path' 存储在 metadata 中，或从 'item_id' 推导
# 这里假设 item_id 就是文件名的一部分
"image_path": os.path.join(f"{item_id}.jpg")
}
def _build_user_input(self, recommend_acc=False) -> str:
"""构建发送给 Gemini 的用户输入,包含已选单品信息。"""
if not self.outfit_items:
@@ -353,7 +364,7 @@ class AsyncStylistAgent:
response = post_request(url=callback_url, data=json.dumps(response_data), headers=self.headers)
logger.info(f"request data ：{response_data} | JAVA callback info -> status:{response.status_code} | message:{response.text}")
async def run_styling_process(self, request_summary, occasions, stylist_name, batch_source="2025_q4", start_outfit=[], user_id="test", callback_url="", gender: str = "male"):
async def run_styling_process(self, request_summary, occasions, stylist_name, start_outfit=[], batch_sources=[], user_id="test", callback_url="", gender: str = "male"):
self.outfit_items = start_outfit
"""主流程控制循环。"""
print(f"--- Starting Agent (Outfit ID: {self.outfit_id}) ---")
@@ -412,7 +423,7 @@ class AsyncStylistAgent:
continue
# 4b. 在本地 DB 中查询单品
new_item = self._get_next_item(description, category, occasions, batch_source, gender)
new_item = self._get_next_item(description, category, occasions, batch_sources, gender)
if not new_item or new_item['item_id'] in [x['item_id'] for x in self.outfit_items]:
self.post_operation(
response_data,
@@ -460,7 +471,7 @@ class AsyncStylistAgent:
continue
# 4b. 在本地 DB 中查询单品
new_item = self._get_next_item(description, category, occasions, batch_source, gender)
new_item = self._get_next_item(description, category, occasions, batch_sources, gender)
if not new_item or new_item['item_id'] in [x['item_id'] for x in self.outfit_items]:
continue
else:

147
app/server/ChatbotAgent/core/vector_database.py
View File

@@ -8,13 +8,12 @@ from PIL import Image
from typing import List, Dict, Any
from transformers import CLIPProcessor, CLIPModel
from app.taxonomy import CATEGORY, OCCASION
from app.taxonomy import OCCASION, ALL_CATEGORY
class VectorDatabase():
def __init__(self, vector_db_dir: str, collection_name: str, embedding_model_name: str):
self.client = chromadb.PersistentClient(path=vector_db_dir)
self.collection = self.client.get_or_create_collection(
name=collection_name,
configuration={
@@ -23,17 +22,9 @@ class VectorDatabase():
}
}
)
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.model = CLIPModel.from_pretrained(embedding_model_name).to(self.device)
self.processor = CLIPProcessor.from_pretrained(embedding_model_name)
# self.cache_filtered_ids = self.load_filtered_ids([
# {"item_group_id": {"$ne": "Clothing"}},
# {"item_group_id": {"$ne": "Shoes"}},
# {"modality": "image"}
# ])
# self.total_count = len(self.cache_filtered_ids)
def get_clip_embedding(self, data: str | Image.Image, is_image: bool) -> List[float]:
"""生成图像或文本的 CLIP 嵌入，并进行 L2 归一化。"""
@@ -57,46 +48,32 @@ class VectorDatabase():
features = features / features.norm(p=2, dim=-1, keepdim=True)
return features.cpu().numpy().flatten().tolist()
def query_local_db(self, embedding: List[float], category: str, occasions: List[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)。
"""
for occasion in occasions:
where_clauses = {
"$and": [
{"category": category},
{"modality": "image"},
{"batch_source": '2025_q4'}
]
}
if occasion not in OCCASION:
continue
else:
where_clauses['$and'].append({occasion: 1})
results = self.collection.query(
query_embeddings=[embedding],
n_results=n_results,
where=where_clauses,
include=['metadatas', 'distances']
)
return results
def get_matched_item(self, embedding: List[float], category: str, occasions: List[str] = [], batch_source: str = "2025_q4", gender: str = 'female', n_results: int = 1) -> List[Dict[str, Any]]:
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]]:
if category not in ALL_CATEGORY:
raise ValueError(f"Recommended {category} is not valid.")
and_conditions = [
{"category": category},
{"modality": "image"},
{"$or": [
{"gender": gender},
{"gender": "unisex"},
]}
]
if batch_sources and len(batch_sources) > 0:
source_conditions = []
for source in batch_sources:
source_conditions.append({"batch_source": source})
# 将 Batch Source 的 OR 子句添加到主 AND 条件中
and_conditions.append({"$or": source_conditions})
results = self.collection.query(
query_embeddings=[embedding],
n_results=500,
where={
"$and": [
{"category": category},
{"modality": "image"},
{"gender": gender},
{"batch_source": batch_source}
]
},
include=['metadatas', 'distances']
where={"$and": and_conditions},
include=['metadatas', 'distances'],
)
if not results['ids'][0]:
return []
@@ -124,7 +101,7 @@ class VectorDatabase():
score_occ = score_occ / count if count else 0.0
final_score = 0.6 * score_vec + 0.3 * score_occ
final_score = 0.6 * score_vec + 0.4 * score_occ
final_scores.append(final_score)
scores_arr = np.array(final_scores)
@@ -139,79 +116,3 @@ class VectorDatabase():
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]
return sampled_items
def load_filtered_ids(self, filter_item):
# print("\n--- 初始化阶段：加载所有符合条件的 ID ---")
start_time = time.time()
FILTER_CRITERIA = {
"$and": filter_item
}
MAX_LIMIT = 100000
try:
# 获取所有符合条件的 ID
all_ids_results = self.collection.get(
where=FILTER_CRITERIA,
limit=MAX_LIMIT,
include=[]
)
all_matched_ids = all_ids_results['ids']
# print(f"🎉 成功加载 {len(all_matched_ids)} 个 ID 到缓存。")
print(time.time() - start_time)
return all_matched_ids
except Exception as e:
print(f"❌ 初始化失败：获取 ID 列表时发生错误: {e}")
return []
def random_get_accessories(self, ids):
# 2. 调用 ChromaDB：只查询这一个 ID 的详细信息
try:
final_results = self.collection.get(
ids=ids,
include=["metadatas"] # 你只需要元数据
)
# 提取结果
if final_results['ids']:
return final_results
else:
return None
except Exception as e:
print(f"❌ 获取最终记录时发生错误: {e}")
return None
if __name__ == '__main__':
stylist = {
'text': "gold necklace",
'count': 2,
'category': "Jewelry"
}
max_len = 5
local_db = VectorDatabase(vector_db_dir="/workspace/lc_stylist_agent/db", collection_name="lc_clothing_embedding", embedding_model_name="openai/clip-vit-base-patch32")
A = local_db.load_filtered_ids([
{"item_group_id": {"$ne": "Clothing"}},
{"item_group_id": {"$ne": "Shoes"}},
{"modality": "image"}
])
# print(db.random_get_accessories())
start_time = time.time()
X = local_db.random_get_accessories(['ELI699_img'])
print(X)
print(time.time() - start_time)
# query_embedding = local_db.get_clip_embedding(stylist['text'], is_image=False)
#
# results = local_db.query_local_db(query_embedding, stylist['category'], n_results=10)
# # 2. 从结果集中抽 stylist['count'] 个item
# stylist_item = random.choices(results['metadatas'][0], k=stylist['count'])
# stylist_item_ids = [item_id['item_id'] for item_id in stylist_item]
#
# # 3. 从随机库中抽取配饰，总数达到9件 ，需过滤掉已经抽中的item
# accessories_count = 9 - max_len - stylist['count']
#
# random_single_ids = random.choices(list(set(local_db.cache_filtered_ids) - set([f"{i}_img" for i in stylist_item_ids])), k=accessories_count)
# random_items = local_db.random_get_accessories(random_single_ids)['metadatas']
# all_items = stylist_item + random_items