Source code for langchain_community.llms.anyscale

"""Wrapper around Anyscale Endpoint"""
from typing import (
    Any,
    Dict,
    List,
    Mapping,
    Optional,
    Set,
)

from langchain_core.callbacks import (
    AsyncCallbackManagerForLLMRun,
    CallbackManagerForLLMRun,
)
from langchain_core.outputs import Generation, GenerationChunk, LLMResult
from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env

from langchain_community.llms.openai import (
    BaseOpenAI,
    acompletion_with_retry,
    completion_with_retry,
)
from langchain_community.utils.openai import is_openai_v1

DEFAULT_BASE_URL = "https://api.endpoints.anyscale.com/v1"
DEFAULT_MODEL = "mistralai/Mixtral-8x7B-Instruct-v0.1"


[docs]def update_token_usage( keys: Set[str], response: Dict[str, Any], token_usage: Dict[str, Any] ) -> None: """Update token usage.""" _keys_to_use = keys.intersection(response["usage"]) for _key in _keys_to_use: if _key not in token_usage: token_usage[_key] = response["usage"][_key] else: token_usage[_key] += response["usage"][_key]
[docs]def create_llm_result( choices: Any, prompts: List[str], token_usage: Dict[str, int], model_name: str ) -> LLMResult: """Create the LLMResult from the choices and prompts.""" generations = [] for i, _ in enumerate(prompts): choice = choices[i] generations.append( [ Generation( text=choice["message"]["content"], generation_info=dict( finish_reason=choice.get("finish_reason"), logprobs=choice.get("logprobs"), ), ) ] ) llm_output = {"token_usage": token_usage, "model_name": model_name} return LLMResult(generations=generations, llm_output=llm_output)
[docs]class Anyscale(BaseOpenAI): """Anyscale large language models. To use, you should have the environment variable ``ANYSCALE_API_KEY``set with your Anyscale Endpoint, or pass it as a named parameter to the constructor. To use with Anyscale Private Endpoint, please also set ``ANYSCALE_BASE_URL``. Example: .. code-block:: python from langchain.llms import Anyscale anyscalellm = Anyscale(anyscale_api_key="ANYSCALE_API_KEY") # To leverage Ray for parallel processing @ray.remote(num_cpus=1) def send_query(llm, text): resp = llm.invoke(text) return resp futures = [send_query.remote(anyscalellm, text) for text in texts] results = ray.get(futures) """ """Key word arguments to pass to the model.""" anyscale_api_base: str = Field(default=DEFAULT_BASE_URL) anyscale_api_key: SecretStr = Field(default=None) model_name: str = Field(default=DEFAULT_MODEL) prefix_messages: List = Field(default_factory=list)
[docs] @classmethod def is_lc_serializable(cls) -> bool: return False
@root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" values["anyscale_api_base"] = get_from_dict_or_env( values, "anyscale_api_base", "ANYSCALE_API_BASE", default=DEFAULT_BASE_URL, ) values["anyscale_api_key"] = convert_to_secret_str( get_from_dict_or_env(values, "anyscale_api_key", "ANYSCALE_API_KEY") ) values["model_name"] = get_from_dict_or_env( values, "model_name", "MODEL_NAME", default=DEFAULT_MODEL, ) try: import openai if is_openai_v1(): client_params = { "api_key": values["anyscale_api_key"].get_secret_value(), "base_url": values["anyscale_api_base"], # To do: future support # "organization": values["openai_organization"], # "timeout": values["request_timeout"], # "max_retries": values["max_retries"], # "default_headers": values["default_headers"], # "default_query": values["default_query"], # "http_client": values["http_client"], } if not values.get("client"): values["client"] = openai.OpenAI(**client_params).completions if not values.get("async_client"): values["async_client"] = openai.AsyncOpenAI( **client_params ).completions else: values["openai_api_base"] = values["anyscale_api_base"] values["openai_api_key"] = values["anyscale_api_key"].get_secret_value() values["client"] = openai.Completion except ImportError: raise ImportError( "Could not import openai python package. " "Please install it with `pip install openai`." ) if values["streaming"] and values["n"] > 1: raise ValueError("Cannot stream results when n > 1.") if values["streaming"] and values["best_of"] > 1: raise ValueError("Cannot stream results when best_of > 1.") return values @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" return { **{"model_name": self.model_name}, **super()._identifying_params, } @property def _invocation_params(self) -> Dict[str, Any]: """Get the parameters used to invoke the model.""" openai_creds: Dict[str, Any] = { "model": self.model_name, } if not is_openai_v1(): openai_creds.update( { "api_key": self.anyscale_api_key.get_secret_value(), "api_base": self.anyscale_api_base, } ) return {**openai_creds, **super()._invocation_params} @property def _llm_type(self) -> str: """Return type of llm.""" return "Anyscale LLM" def _generate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: """Call out to OpenAI's endpoint with k unique prompts. Args: prompts: The prompts to pass into the model. stop: Optional list of stop words to use when generating. Returns: The full LLM output. Example: .. code-block:: python response = openai.generate(["Tell me a joke."]) """ # TODO: write a unit test for this params = self._invocation_params params = {**params, **kwargs} sub_prompts = self.get_sub_prompts(params, prompts, stop) choices = [] token_usage: Dict[str, int] = {} # Get the token usage from the response. # Includes prompt, completion, and total tokens used. _keys = {"completion_tokens", "prompt_tokens", "total_tokens"} system_fingerprint: Optional[str] = None for _prompts in sub_prompts: if self.streaming: if len(_prompts) > 1: raise ValueError("Cannot stream results with multiple prompts.") generation: Optional[GenerationChunk] = None for chunk in self._stream(_prompts[0], stop, run_manager, **kwargs): if generation is None: generation = chunk else: generation += chunk assert generation is not None choices.append( { "text": generation.text, "finish_reason": generation.generation_info.get("finish_reason") if generation.generation_info else None, "logprobs": generation.generation_info.get("logprobs") if generation.generation_info else None, } ) else: response = completion_with_retry( ## THis is the ONLY change from BaseOpenAI()._generate() self, prompt=_prompts[0], run_manager=run_manager, **params, ) if not isinstance(response, dict): # V1 client returns the response in an PyDantic object instead of # dict. For the transition period, we deep convert it to dict. response = response.dict() choices.extend(response["choices"]) update_token_usage(_keys, response, token_usage) if not system_fingerprint: system_fingerprint = response.get("system_fingerprint") return self.create_llm_result( choices, prompts, params, token_usage, system_fingerprint=system_fingerprint, ) async def _agenerate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: """Call out to OpenAI's endpoint async with k unique prompts.""" params = self._invocation_params params = {**params, **kwargs} sub_prompts = self.get_sub_prompts(params, prompts, stop) choices = [] token_usage: Dict[str, int] = {} # Get the token usage from the response. # Includes prompt, completion, and total tokens used. _keys = {"completion_tokens", "prompt_tokens", "total_tokens"} system_fingerprint: Optional[str] = None for _prompts in sub_prompts: if self.streaming: if len(_prompts) > 1: raise ValueError("Cannot stream results with multiple prompts.") generation: Optional[GenerationChunk] = None async for chunk in self._astream( _prompts[0], stop, run_manager, **kwargs ): if generation is None: generation = chunk else: generation += chunk assert generation is not None choices.append( { "text": generation.text, "finish_reason": generation.generation_info.get("finish_reason") if generation.generation_info else None, "logprobs": generation.generation_info.get("logprobs") if generation.generation_info else None, } ) else: response = await acompletion_with_retry( ## THis is the ONLY change from BaseOpenAI()._agenerate() self, prompt=_prompts[0], run_manager=run_manager, **params, ) if not isinstance(response, dict): response = response.dict() choices.extend(response["choices"]) update_token_usage(_keys, response, token_usage) return self.create_llm_result( choices, prompts, params, token_usage, system_fingerprint=system_fingerprint, )