AzureOpenAILLM¶

Azure OpenAI LLM implementation running the async API client.

Attributes¶

model: the model name to use for the LLM i.e. the name of the Azure deployment.
base_url: the base URL to use for the Azure OpenAI API can be set with AZURE_OPENAI_ENDPOINT. Defaults to None which means that the value set for the environment variable AZURE_OPENAI_ENDPOINT will be used, or None if not set.
api_key: the API key to authenticate the requests to the Azure OpenAI API. Defaults to None which means that the value set for the environment variable AZURE_OPENAI_API_KEY will be used, or None if not set.
api_version: the API version to use for the Azure OpenAI API. Defaults to None which means that the value set for the environment variable OPENAI_API_VERSION will be used, or None if not set.

Examples¶

Generate text¶

from distilabel.llms import AzureOpenAILLM

llm = AzureOpenAILLM(model="gpt-4-turbo", api_key="api.key")

llm.load()

output = llm.generate_outputs(inputs=[[{"role": "user", "content": "Hello world!"}]])

Generate text from a custom endpoint following the OpenAI API¶

from distilabel.llms import AzureOpenAILLM

llm = AzureOpenAILLM(
    model="prometheus-eval/prometheus-7b-v2.0",
    base_url=r"http://localhost:8080/v1"
)

llm.load()

output = llm.generate_outputs(inputs=[[{"role": "user", "content": "Hello world!"}]])

Generate structured data¶

from pydantic import BaseModel
from distilabel.llms import AzureOpenAILLM

class User(BaseModel):
    name: str
    last_name: str
    id: int

llm = AzureOpenAILLM(
    model="gpt-4-turbo",
    api_key="api.key",
    structured_output={"schema": User}
)

llm.load()

output = llm.generate_outputs(inputs=[[{"role": "user", "content": "Create a user profile for the following marathon"}]])