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AnthropicLLM

Anthropic LLM implementation running the Async API client.

Attributes

  • model: the name of the model to use for the LLM e.g. "claude-3-opus-20240229", "claude-3-sonnet-20240229", etc. Available models can be checked here: Anthropic: Models overview.

  • api_key: the API key to authenticate the requests to the Anthropic API. If not provided, it will be read from ANTHROPIC_API_KEY environment variable.

  • base_url: the base URL to use for the Anthropic API. Defaults to None which means that https://api.anthropic.com will be used internally.

  • timeout: the maximum time in seconds to wait for a response. Defaults to 600.0.

  • max_retries: The maximum number of times to retry the request before failing. Defaults to 6.

  • http_client: if provided, an alternative HTTP client to use for calling Anthropic API. Defaults to None.

  • structured_output: a dictionary containing the structured output configuration configuration using instructor. You can take a look at the dictionary structure in InstructorStructuredOutputType from distilabel.steps.tasks.structured_outputs.instructor.

  • _api_key_env_var: the name of the environment variable to use for the API key. It is meant to be used internally.

  • _aclient: the AsyncAnthropic client to use for the Anthropic API. It is meant to be used internally. Set in the load method.

Runtime Parameters

  • api_key: the API key to authenticate the requests to the Anthropic API. If not provided, it will be read from ANTHROPIC_API_KEY environment variable.

  • base_url: the base URL to use for the Anthropic API. Defaults to "https://api.anthropic.com".

  • timeout: the maximum time in seconds to wait for a response. Defaults to 600.0.

  • max_retries: the maximum number of times to retry the request before failing. Defaults to 6.

Examples

Generate text

from distilabel.llms import AnthropicLLM

llm = AnthropicLLM(model="claude-3-opus-20240229", api_key="api.key")

llm.load()

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

Generate structured data

from pydantic import BaseModel
from distilabel.llms import AnthropicLLM

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

llm = AnthropicLLM(
    model="claude-3-opus-20240229",
    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"}]])