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