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36 lines
1 KiB
Python
36 lines
1 KiB
Python
import os, dotenv
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from dotenv import load_dotenv
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load_dotenv()
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from llama_index.llms import AzureOpenAI
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from llama_index.embeddings import AzureOpenAIEmbedding
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from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext
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llm = AzureOpenAI(
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engine="azure-gpt-3.5",
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temperature=0.0,
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azure_endpoint="http://0.0.0.0:4000",
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api_key="sk-1234",
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api_version="2023-07-01-preview",
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)
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embed_model = AzureOpenAIEmbedding(
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deployment_name="azure-embedding-model",
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azure_endpoint="http://0.0.0.0:4000",
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api_key="sk-1234",
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api_version="2023-07-01-preview",
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)
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# response = llm.complete("The sky is a beautiful blue and")
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# print(response)
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documents = SimpleDirectoryReader("llama_index_data").load_data()
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service_context = ServiceContext.from_defaults(llm=llm, embed_model=embed_model)
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index = VectorStoreIndex.from_documents(documents, service_context=service_context)
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query_engine = index.as_query_engine()
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response = query_engine.query("What did the author do growing up?")
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print(response)
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