TASK:lookbook上传

app/service/lookbooks/config/__init__.py

@@ -0,0 +1 @@
+from config import DOCUMENT_COLLECTION

app/service/lookbooks/config/config.py

@@ -0,0 +1,74 @@
+# config.py
+import os
+import platform
+if platform.system() == 'Linux':
+    __import__('pysqlite3')
+    import sys
+    sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
+import chromadb
+from langchain_openai import OpenAIEmbeddings
+from langchain_chroma import Chroma
+import tritonclient.grpc as grpcclient
+from minio import Minio
+
+# OpenAI settings
+OPENAI_API_KEY = "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"
+OPENAI_API_BASE = "https://pangkaichen-openai-prox-98.deno.dev/v1"
+
+# LangChain settings
+LANGCHAIN_TRACING_V2 = "true"
+LANGCHAIN_ENDPOINT = "https://api.smith.langchain.com"
+LANGCHAIN_API_KEY = "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"
+LANGCHAIN_PROJECT = "intelligent_fashion_agent"
+
+MINIO_URL = "18.167.251.121:8000"
+MINIO_SECURE = False
+MINIO_ACCESS = "e8zc55mzDOh4IzRrZ9Oa"
+MINIO_SECRET = "uHfqJ7UkwA1PTDGfnA44Hp9ux5YkZTkzZLjeOYhE"
+MINIO_BUCKET = "test"
+MINIO_CLIENT = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
+
+# Set environment variables
+os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
+os.environ["OPENAI_API_BASE"] = OPENAI_API_BASE
+os.environ["LANGCHAIN_TRACING_V2"] = LANGCHAIN_TRACING_V2
+os.environ["LANGCHAIN_ENDPOINT"] = LANGCHAIN_ENDPOINT
+os.environ["LANGCHAIN_API_KEY"] = LANGCHAIN_API_KEY
+os.environ["LANGCHAIN_PROJECT"] = LANGCHAIN_PROJECT
+
+os.environ["HTTPCLIENT_URL"] = "localhost:8000"
+os.environ["GRPCCLIENT_URL"] = "localhost:8001"
+
+chroma_client = chromadb.PersistentClient(path=r"./vector_database")
+embedding_fn = OpenAIEmbeddings(
+    openai_api_key=os.environ["OPENAI_API_KEY"],
+    openai_api_base=os.environ["OPENAI_API_BASE"],
+    model="text-embedding-3-small",
+)
+# item_collection = chroma_client.get_or_create_collection(name="fashion-product-embedding")
+ITEM_COLLECTION = Chroma(client=chroma_client,
+                         collection_name="fashion-product-description",
+                         embedding_function=embedding_fn)
+DOCUMENT_COLLECTION = Chroma(client=chroma_client, collection_name="lookbook", embedding_function=embedding_fn)
+ACCESSORY_COLLECTION = Chroma(client=chroma_client, collection_name="accessory", embedding_function=embedding_fn)
+
+# pinecone_client = Pinecone(api_key="a8341f5f-0078-4f1b-880a-612b036b6e70")
+# pinecore_index = pinecone_client.Index("fashion-product-embedding", namespace="Polyvore")
+
+
+OP_SERVER = os.environ.get('OP_SERVER', 'local')  # or "aws" or "A6000"
+if OP_SERVER == "local":
+    os.environ["HTTPCLIENT_URL"] = "localhost:8000"
+    os.environ["GRPCCLIENT_URL"] = "localhost:8001"
+    os.environ["OOTD_URL"] = "http://localhost:5000/ootd_dc"
+elif OP_SERVER == "A6000":
+    os.environ["HTTPCLIENT_URL"] = "host.docker.internal:20020"
+    os.environ["GRPCCLIENT_URL"] = "https://relaxing-unbiased-herring.ngrok-free.app"
+    os.environ["OOTD_URL"] = "http://localhost:5000/ootd_dc"
+elif OP_SERVER == "aws":
+    os.environ["HTTPCLIENT_URL"] = "host.docker.internal:8000"
+    os.environ["GRPCCLIENT_URL"] = "https://relaxing-unbiased-herring.ngrok-free.app"
+    # os.environ["OOTD_URL"] = "http://18.167.251.121:10001/ootd/ootd_dc"
+    os.environ["OOTD_URL"] = "https://muskox-many-bluegill.ngrok-free.app/ootd_dc"
+
+triton_client = grpcclient.InferenceServerClient(url=os.environ['GRPCCLIENT_URL'])

app/service/lookbooks/service.py

@@ -7,7 +7,7 @@ import aiofiles
 from openai import OpenAI
 from app.service.lookbooks.utils.image_utils import base64_encode_image, generate_text_id
 from app.service.lookbooks.utils.openai_utils import wait_for_job_completion
-
+from app.service.lookbooks.config import DOCUMENT_COLLECTION
 # 设置日志
 logger = logging.getLogger()
 
@@ -195,7 +195,7 @@ def save_to_vector_db(image_description_results_file, tag, year):
                     })
 
         # 将图像的描述和元数据添加到向量数据库中
-        collection.add_texts(texts=image_summaries, metadatas=image_metadatas, ids=list(image_ids))
+        DOCUMENT_COLLECTION.add_texts(texts=image_summaries, metadatas=image_metadatas, ids=list(image_ids))
         logger.info("Successfully saved data to vector database")
 
     except Exception as e: