wehub-resource-sync
73 lines
2.6 KiB
Python
73 lines
2.6 KiB
Python
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""" This example shows how to use OpenAIConfigs to create a configured OpenAI client, most often used for
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Azure OpenAI access."""
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import os
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from llmware.models import ModelCatalog
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from llmware.configs import OpenAIConfig
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from openai import AzureOpenAI
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# Set the following environment variables:
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# - AZURE_OPENAI_ENDPOINT : found on your Azure OpenAI page
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# - AZURE_OPENAI_API_KEY : found on your Azure OpenAI page
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# - USER_MANAGED_OPENAI_API_KEY : found on you OpenAI API page
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#
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# Additionally, with this example, you will need an Azure OpenAI deployment
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# for gpt-4 and text-embedding-3-small, but feel free to replace these below.
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#
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# Make sure to replace the deployment names with your deployments in the
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# AzureOpenAI clients created below.
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# to start - OpenAI client is created in OpenAI Generative and Embedding models classes at the time of inference
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# the client will be created as a standard OpenAI client with the api_keys passed
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my_azure_client = OpenAIConfig().get_azure_client()
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print("my azure client to start: ", my_azure_client)
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# to configure an AzureOpenAI client, two steps:
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# first, create the client with openai >= 1.0 python SDK, (see above) e.g.:
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gpt4_client = AzureOpenAI(
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azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"),
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api_key=os.getenv("AZURE_OPENAI_API_KEY"),
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api_version="2024-02-01",
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azure_deployment="your-gpt-4-deployment-name"
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)
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# second, set the azure client in OpenAIConfigs as below:
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OpenAIConfig().set_azure_client(gpt4_client)
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print("my azure client - set: ", OpenAIConfig().get_azure_client())
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# now, run the inference like any other in llmware
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# OpenAI Generative call
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model = ModelCatalog().load_model("gpt-4")
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# the model will check the value of get_azure_client() in the configs -> if set, then will use
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response = model.inference("What is the future of AI")
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print("response: ", response)
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text_embedding_client = AzureOpenAI(
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azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"),
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api_key=os.getenv("AZURE_OPENAI_API_KEY"),
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api_version="2024-02-01",
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azure_deployment="your-text-embedding-3-small-deployment-name"
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)
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OpenAIConfig().set_azure_client(text_embedding_client)
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# OpenAI Embedding call
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model = ModelCatalog().load_model("text-embedding-3-small")
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embedding = model.embedding(["This is a sample sentence for an embedding test."])
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print("embedding: ", embedding)
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# reset so you can use the standard OpenAI client
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OpenAIConfig().set_azure_client(None)
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model = ModelCatalog().load_model("text-embedding-3-small", api_key=os.getenv("USER_MANAGED_OPENAI_API_KEY"))
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embedding = model.embedding(["This is a sample sentence for an embedding test."])
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print("embedding: ", embedding)
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