Source code for langchain.llms.anyscale
from typing import Any, Dict, List, Mapping, Optional
import requests
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.pydantic_v1 import Extra, root_validator
from langchain.utils import get_from_dict_or_env
[docs]class Anyscale(LLM):
    """Anyscale Service models.
    To use, you should have the environment variable ``ANYSCALE_SERVICE_URL``,
    ``ANYSCALE_SERVICE_ROUTE`` and ``ANYSCALE_SERVICE_TOKEN`` set with your Anyscale
    Service, or pass it as a named parameter to the constructor.
    Example:
        .. code-block:: python
            from langchain.llms import Anyscale
            anyscale = Anyscale(anyscale_service_url="SERVICE_URL",
                                anyscale_service_route="SERVICE_ROUTE",
                                anyscale_service_token="SERVICE_TOKEN")
            # Use Ray for distributed processing
            import ray
            prompt_list=[]
            @ray.remote
            def send_query(llm, prompt):
                resp = llm(prompt)
                return resp
            futures = [send_query.remote(anyscale, prompt) for prompt in prompt_list]
            results = ray.get(futures)
    """
    model_kwargs: Optional[dict] = None
    """Key word arguments to pass to the model. Reserved for future use"""
    anyscale_service_url: Optional[str] = None
    anyscale_service_route: Optional[str] = None
    anyscale_service_token: Optional[str] = None
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict: