Source code for langchain_community.llms.nlpcloud

from typing import Any, Dict, List, Mapping, Optional

from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
from langchain_core.pydantic_v1 import Extra, SecretStr, root_validator
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env

[docs]class NLPCloud(LLM): """NLPCloud large language models. To use, you should have the ``nlpcloud`` python package installed, and the environment variable ``NLPCLOUD_API_KEY`` set with your API key. Example: .. code-block:: python from langchain_community.llms import NLPCloud nlpcloud = NLPCloud(model="finetuned-gpt-neox-20b") """ client: Any #: :meta private: model_name: str = "finetuned-gpt-neox-20b" """Model name to use.""" gpu: bool = True """Whether to use a GPU or not""" lang: str = "en" """Language to use (multilingual addon)""" temperature: float = 0.7 """What sampling temperature to use.""" max_length: int = 256 """The maximum number of tokens to generate in the completion.""" length_no_input: bool = True """Whether min_length and max_length should include the length of the input.""" remove_input: bool = True """Remove input text from API response""" remove_end_sequence: bool = True """Whether or not to remove the end sequence token.""" bad_words: List[str] = [] """List of tokens not allowed to be generated.""" top_p: float = 1.0 """Total probability mass of tokens to consider at each step.""" top_k: int = 50 """The number of highest probability tokens to keep for top-k filtering.""" repetition_penalty: float = 1.0 """Penalizes repeated tokens. 1.0 means no penalty.""" num_beams: int = 1 """Number of beams for beam search.""" num_return_sequences: int = 1 """How many completions to generate for each prompt.""" nlpcloud_api_key: Optional[SecretStr] = None class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" values["nlpcloud_api_key"] = convert_to_secret_str( get_from_dict_or_env(values, "nlpcloud_api_key", "NLPCLOUD_API_KEY") ) try: import nlpcloud values["client"] = nlpcloud.Client( values["model_name"], values["nlpcloud_api_key"].get_secret_value(), gpu=values["gpu"], lang=values["lang"], ) except ImportError: raise ImportError( "Could not import nlpcloud python package. " "Please install it with `pip install nlpcloud`." ) return values @property def _default_params(self) -> Mapping[str, Any]: """Get the default parameters for calling NLPCloud API.""" return { "temperature": self.temperature, "max_length": self.max_length, "length_no_input": self.length_no_input, "remove_input": self.remove_input, "remove_end_sequence": self.remove_end_sequence, "bad_words": self.bad_words, "top_p": self.top_p, "top_k": self.top_k, "repetition_penalty": self.repetition_penalty, "num_beams": self.num_beams, "num_return_sequences": self.num_return_sequences, } @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" return { **{"model_name": self.model_name}, **{"gpu": self.gpu}, **{"lang": self.lang}, **self._default_params, } @property def _llm_type(self) -> str: """Return type of llm.""" return "nlpcloud" def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Call out to NLPCloud's create endpoint. Args: prompt: The prompt to pass into the model. stop: Not supported by this interface (pass in init method) Returns: The string generated by the model. Example: .. code-block:: python response = nlpcloud("Tell me a joke.") """ if stop and len(stop) > 1: raise ValueError( "NLPCloud only supports a single stop sequence per generation." "Pass in a list of length 1." ) elif stop and len(stop) == 1: end_sequence = stop[0] else: end_sequence = None params = {**self._default_params, **kwargs} response = self.client.generation(prompt, end_sequence=end_sequence, **params) return response["generated_text"]