Source code for langchain_community.llms.baseten

import logging
import os
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 Field

logger = logging.getLogger(__name__)

[docs]class Baseten(LLM): """Baseten model This module allows using LLMs hosted on Baseten. The LLM deployed on Baseten must have the following properties: * Must accept input as a dictionary with the key "prompt" * May accept other input in the dictionary passed through with kwargs * Must return a string with the model output To use this module, you must: * Export your Baseten API key as the environment variable `BASETEN_API_KEY` * Get the model ID for your model from your Baseten dashboard * Identify the model deployment ("production" for all model library models) These code samples use [Mistral 7B Instruct]( from Baseten's model library. Examples: .. code-block:: python from langchain_community.llms import Baseten # Production deployment mistral = Baseten(model="MODEL_ID", deployment="production") mistral("What is the Mistral wind?") .. code-block:: python from langchain_community.llms import Baseten # Development deployment mistral = Baseten(model="MODEL_ID", deployment="development") mistral("What is the Mistral wind?") .. code-block:: python from langchain_community.llms import Baseten # Other published deployment mistral = Baseten(model="MODEL_ID", deployment="DEPLOYMENT_ID") mistral("What is the Mistral wind?") """ model: str deployment: str input: Dict[str, Any] = Field(default_factory=dict) model_kwargs: Dict[str, Any] = Field(default_factory=dict) @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" return { **{"model_kwargs": self.model_kwargs}, } @property def _llm_type(self) -> str: """Return type of model.""" return "baseten" def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: baseten_api_key = os.environ["BASETEN_API_KEY"] model_id = self.model if self.deployment == "production": model_url = f"https://model-{model_id}" elif self.deployment == "development": model_url = f"https://model-{model_id}" else: # try specific deployment ID model_url = f"https://model-{model_id}{self.deployment}/predict" response = model_url, headers={"Authorization": f"Api-Key {baseten_api_key}"}, json={"prompt": prompt, **kwargs}, ) return response.json()