Source code for langchain_community.llms.mosaicml

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

INSTRUCTION_KEY = "### Instruction:"
RESPONSE_KEY = "### Response:"
    "Below is an instruction that describes a task. "
    "Write a response that appropriately completes the request."

[docs]class MosaicML(LLM): """MosaicML LLM service. To use, you should have the environment variable ``MOSAICML_API_TOKEN`` set with your API token, or pass it as a named parameter to the constructor. Example: .. code-block:: python from langchain_community.llms import MosaicML endpoint_url = ( "" ) mosaic_llm = MosaicML( endpoint_url=endpoint_url, mosaicml_api_token="my-api-key" ) """ endpoint_url: str = ( "" ) """Endpoint URL to use.""" inject_instruction_format: bool = False """Whether to inject the instruction format into the prompt.""" model_kwargs: Optional[dict] = None """Keyword arguments to pass to the model.""" retry_sleep: float = 1.0 """How long to try sleeping for if a rate limit is encountered""" mosaicml_api_token: Optional[str] = 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.""" mosaicml_api_token = get_from_dict_or_env( values, "mosaicml_api_token", "MOSAICML_API_TOKEN" ) values["mosaicml_api_token"] = mosaicml_api_token return values @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" _model_kwargs = self.model_kwargs or {} return { **{"endpoint_url": self.endpoint_url}, **{"model_kwargs": _model_kwargs}, } @property def _llm_type(self) -> str: """Return type of llm.""" return "mosaic" def _transform_prompt(self, prompt: str) -> str: """Transform prompt.""" if self.inject_instruction_format: prompt = PROMPT_FOR_GENERATION_FORMAT.format( instruction=prompt, ) return prompt def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, is_retry: bool = False, **kwargs: Any, ) -> str: """Call out to a MosaicML LLM inference 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 = mosaic_llm.invoke("Tell me a joke.") """ _model_kwargs = self.model_kwargs or {} prompt = self._transform_prompt(prompt) payload = {"inputs": [prompt]} payload.update(_model_kwargs) payload.update(kwargs) # HTTP headers for authorization headers = { "Authorization": f"{self.mosaicml_api_token}", "Content-Type": "application/json", } # send request try: response =, headers=headers, json=payload) except requests.exceptions.RequestException as e: raise ValueError(f"Error raised by inference endpoint: {e}") try: if response.status_code == 429: if not is_retry: import time time.sleep(self.retry_sleep) return self._call(prompt, stop, run_manager, is_retry=True) raise ValueError( f"Error raised by inference API: rate limit exceeded.\nResponse: " f"{response.text}" ) parsed_response = response.json() # The inference API has changed a couple of times, so we add some handling # to be robust to multiple response formats. if isinstance(parsed_response, dict): output_keys = ["data", "output", "outputs"] for key in output_keys: if key in parsed_response: output_item = parsed_response[key] break else: raise ValueError( f"No valid key ({', '.join(output_keys)}) in response:" f" {parsed_response}" ) if isinstance(output_item, list): text = output_item[0] else: text = output_item else: raise ValueError(f"Unexpected response type: {parsed_response}") # Older versions of the API include the input in the output response if text.startswith(prompt): text = text[len(prompt) :] except requests.exceptions.JSONDecodeError as e: raise ValueError( f"Error raised by inference API: {e}.\nResponse: {response.text}" ) # TODO: replace when MosaicML supports custom stop tokens natively if stop is not None: text = enforce_stop_tokens(text, stop) return text