Source code for langchain_community.llms.writer

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

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

from langchain_community.llms.utils import enforce_stop_tokens

[docs]class Writer(LLM): """Writer large language models. To use, you should have the environment variable ``WRITER_API_KEY`` and ``WRITER_ORG_ID`` set with your API key and organization ID respectively. Example: .. code-block:: python from langchain_community.llms import Writer writer = Writer(model_id="palmyra-base") """ writer_org_id: Optional[str] = None """Writer organization ID.""" model_id: str = "palmyra-instruct" """Model name to use.""" min_tokens: Optional[int] = None """Minimum number of tokens to generate.""" max_tokens: Optional[int] = None """Maximum number of tokens to generate.""" temperature: Optional[float] = None """What sampling temperature to use.""" top_p: Optional[float] = None """Total probability mass of tokens to consider at each step.""" stop: Optional[List[str]] = None """Sequences when completion generation will stop.""" presence_penalty: Optional[float] = None """Penalizes repeated tokens regardless of frequency.""" repetition_penalty: Optional[float] = None """Penalizes repeated tokens according to frequency.""" best_of: Optional[int] = None """Generates this many completions server-side and returns the "best".""" logprobs: bool = False """Whether to return log probabilities.""" n: Optional[int] = None """How many completions to generate.""" writer_api_key: Optional[str] = None """Writer API key.""" base_url: Optional[str] = None """Base url to use, if None decides based on model name.""" class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and organization id exist in environment.""" writer_api_key = get_from_dict_or_env( values, "writer_api_key", "WRITER_API_KEY" ) values["writer_api_key"] = writer_api_key writer_org_id = get_from_dict_or_env(values, "writer_org_id", "WRITER_ORG_ID") values["writer_org_id"] = writer_org_id return values @property def _default_params(self) -> Mapping[str, Any]: """Get the default parameters for calling Writer API.""" return { "minTokens": self.min_tokens, "maxTokens": self.max_tokens, "temperature": self.temperature, "topP": self.top_p, "stop": self.stop, "presencePenalty": self.presence_penalty, "repetitionPenalty": self.repetition_penalty, "bestOf": self.best_of, "logprobs": self.logprobs, "n": self.n, } @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" return { **{"model_id": self.model_id, "writer_org_id": self.writer_org_id}, **self._default_params, } @property def _llm_type(self) -> str: """Return type of llm.""" return "writer" def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Call out to Writer's completions endpoint. Args: prompt: The prompt to pass into the model. stop: Optional list of stop words to use when generating. Returns: The string generated by the model. Example: .. code-block:: python response = Writer("Tell me a joke.") """ if self.base_url is not None: base_url = self.base_url else: base_url = ( "" f"/organization/{self.writer_org_id}" f"/model/{self.model_id}/completions" ) params = {**self._default_params, **kwargs} response = url=base_url, headers={ "Authorization": f"{self.writer_api_key}", "Content-Type": "application/json", "Accept": "application/json", }, json={"prompt": prompt, **params}, ) text = response.text if stop is not None: # I believe this is required since the stop tokens # are not enforced by the model parameters text = enforce_stop_tokens(text, stop) return text