Source code for langchain_community.llms.aviary

import dataclasses
import os
from typing import Any, Dict, List, Mapping, Optional, Union, cast

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]@dataclasses.dataclass class AviaryBackend: """Aviary backend. Attributes: backend_url: The URL for the Aviary backend. bearer: The bearer token for the Aviary backend. """ backend_url: str bearer: str def __post_init__(self) -> None: self.header = {"Authorization": self.bearer}
[docs] @classmethod def from_env(cls) -> "AviaryBackend": aviary_url = os.getenv("AVIARY_URL") assert aviary_url, "AVIARY_URL must be set" aviary_token = os.getenv("AVIARY_TOKEN", "") bearer = f"Bearer {aviary_token}" if aviary_token else "" aviary_url += "/" if not aviary_url.endswith("/") else "" return cls(aviary_url, bearer)
[docs]def get_models() -> List[str]: """List available models""" backend = AviaryBackend.from_env() request_url = backend.backend_url + "-/routes" response = requests.get(request_url, headers=backend.header, timeout=TIMEOUT) try: result = response.json() except requests.JSONDecodeError as e: raise RuntimeError( f"Error decoding JSON from {request_url}. Text response: {response.text}" ) from e result = sorted( [k.lstrip("/").replace("--", "/") for k in result.keys() if "--" in k] ) return result
[docs]def get_completions( model: str, prompt: str, use_prompt_format: bool = True, version: str = "", ) -> Dict[str, Union[str, float, int]]: """Get completions from Aviary models.""" backend = AviaryBackend.from_env() url = backend.backend_url + model.replace("/", "--") + "/" + version + "query" response = url, headers=backend.header, json={"prompt": prompt, "use_prompt_format": use_prompt_format}, timeout=TIMEOUT, ) try: return response.json() except requests.JSONDecodeError as e: raise RuntimeError( f"Error decoding JSON from {url}. Text response: {response.text}" ) from e
[docs]class Aviary(LLM): """Aviary hosted models. Aviary is a backend for hosted models. You can find out more about aviary at To get a list of the models supported on an aviary, follow the instructions on the website to install the aviary CLI and then use: `aviary models` AVIARY_URL and AVIARY_TOKEN environment variables must be set. Attributes: model: The name of the model to use. Defaults to "amazon/LightGPT". aviary_url: The URL for the Aviary backend. Defaults to None. aviary_token: The bearer token for the Aviary backend. Defaults to None. use_prompt_format: If True, the prompt template for the model will be ignored. Defaults to True. version: API version to use for Aviary. Defaults to None. Example: .. code-block:: python from langchain_community.llms import Aviary os.environ["AVIARY_URL"] = "<URL>" os.environ["AVIARY_TOKEN"] = "<TOKEN>" light = Aviary(model='amazon/LightGPT') output = light('How do you make fried rice?') """ model: str = "amazon/LightGPT" aviary_url: Optional[str] = None aviary_token: Optional[str] = None # If True the prompt template for the model will be ignored. use_prompt_format: bool = True # API version to use for Aviary version: Optional[str] = None class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @root_validator(pre=True) def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" aviary_url = get_from_dict_or_env(values, "aviary_url", "AVIARY_URL") aviary_token = get_from_dict_or_env(values, "aviary_token", "AVIARY_TOKEN") # Set env viarables for aviary sdk os.environ["AVIARY_URL"] = aviary_url os.environ["AVIARY_TOKEN"] = aviary_token try: aviary_models = get_models() except requests.exceptions.RequestException as e: raise ValueError(e) model = values.get("model") if model and model not in aviary_models: raise ValueError(f"{aviary_url} does not support model {values['model']}.") return values @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" return { "model_name": self.model, "aviary_url": self.aviary_url, } @property def _llm_type(self) -> str: """Return type of llm.""" return f"aviary-{self.model.replace('/', '-')}" def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Call out to Aviary Args: prompt: The prompt to pass into the model. Returns: The string generated by the model. Example: .. code-block:: python response = aviary("Tell me a joke.") """ kwargs = {"use_prompt_format": self.use_prompt_format} if self.version: kwargs["version"] = self.version output = get_completions( model=self.model, prompt=prompt, **kwargs, ) text = cast(str, output["generated_text"]) if stop: text = enforce_stop_tokens(text, stop) return text