Source code for langchain_community.llms.symblai_nebula

import json
import logging
from typing import Any, Callable, 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, SecretStr, root_validator
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
from requests import ConnectTimeout, ReadTimeout, RequestException
from tenacity import (

from langchain_community.llms.utils import enforce_stop_tokens

DEFAULT_NEBULA_SERVICE_PATH = "/v1/model/generate"

logger = logging.getLogger(__name__)

[docs]class Nebula(LLM): """Nebula Service models. To use, you should have the environment variable ``NEBULA_SERVICE_URL``, ``NEBULA_SERVICE_PATH`` and ``NEBULA_API_KEY`` set with your Nebula Service, or pass it as a named parameter to the constructor. Example: .. code-block:: python from langchain_community.llms import Nebula nebula = Nebula( nebula_service_url="NEBULA_SERVICE_URL", nebula_service_path="NEBULA_SERVICE_PATH", nebula_api_key="NEBULA_API_KEY", ) """ """Key/value arguments to pass to the model. Reserved for future use""" model_kwargs: Optional[dict] = None """Optional""" nebula_service_url: Optional[str] = None nebula_service_path: Optional[str] = None nebula_api_key: Optional[SecretStr] = None model: Optional[str] = None max_new_tokens: Optional[int] = 128 temperature: Optional[float] = 0.6 top_p: Optional[float] = 0.95 repetition_penalty: Optional[float] = 1.0 top_k: Optional[int] = 1 stop_sequences: Optional[List[str]] = None max_retries: Optional[int] = 10 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.""" nebula_service_url = get_from_dict_or_env( values, "nebula_service_url", "NEBULA_SERVICE_URL", DEFAULT_NEBULA_SERVICE_URL, ) nebula_service_path = get_from_dict_or_env( values, "nebula_service_path", "NEBULA_SERVICE_PATH", DEFAULT_NEBULA_SERVICE_PATH, ) nebula_api_key = convert_to_secret_str( get_from_dict_or_env(values, "nebula_api_key", "NEBULA_API_KEY", None) ) if nebula_service_url.endswith("/"): nebula_service_url = nebula_service_url[:-1] if not nebula_service_path.startswith("/"): nebula_service_path = "/" + nebula_service_path values["nebula_service_url"] = nebula_service_url values["nebula_service_path"] = nebula_service_path values["nebula_api_key"] = nebula_api_key return values @property def _default_params(self) -> Dict[str, Any]: """Get the default parameters for calling Cohere API.""" return { "max_new_tokens": self.max_new_tokens, "temperature": self.temperature, "top_k": self.top_k, "top_p": self.top_p, "repetition_penalty": self.repetition_penalty, } @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" _model_kwargs = self.model_kwargs or {} return { "nebula_service_url": self.nebula_service_url, "nebula_service_path": self.nebula_service_path, **{"model_kwargs": _model_kwargs}, } @property def _llm_type(self) -> str: """Return type of llm.""" return "nebula" def _invocation_params( self, stop_sequences: Optional[List[str]], **kwargs: Any ) -> dict: params = self._default_params if self.stop_sequences is not None and stop_sequences is not None: raise ValueError("`stop` found in both the input and default params.") elif self.stop_sequences is not None: params["stop_sequences"] = self.stop_sequences else: params["stop_sequences"] = stop_sequences return {**params, **kwargs} @staticmethod def _process_response(response: Any, stop: Optional[List[str]]) -> str: text = response["output"]["text"] if stop: text = enforce_stop_tokens(text, stop) return text def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Call out to Nebula Service 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 = nebula("Tell me a joke.") """ params = self._invocation_params(stop, **kwargs) prompt = prompt.strip() response = completion_with_retry( self, prompt=prompt, params=params, url=f"{self.nebula_service_url}{self.nebula_service_path}", ) _stop = params.get("stop_sequences") return self._process_response(response, _stop)
[docs]def make_request( self: Nebula, prompt: str, url: str = f"{DEFAULT_NEBULA_SERVICE_URL}{DEFAULT_NEBULA_SERVICE_PATH}", params: Optional[Dict] = None, ) -> Any: """Generate text from the model.""" params = params or {} api_key = None if self.nebula_api_key is not None: api_key = self.nebula_api_key.get_secret_value() headers = { "Content-Type": "application/json", "ApiKey": f"{api_key}", } body = {"prompt": prompt} # add params to body for key, value in params.items(): body[key] = value # make request response =, headers=headers, json=body) if response.status_code != 200: raise Exception( f"Request failed with status code {response.status_code}" f" and message {response.text}" ) return json.loads(response.text)
def _create_retry_decorator(llm: Nebula) -> Callable[[Any], Any]: min_seconds = 4 max_seconds = 10 # Wait 2^x * 1 second between each retry starting with # 4 seconds, then up to 10 seconds, then 10 seconds afterward max_retries = llm.max_retries if llm.max_retries is not None else 3 return retry( reraise=True, stop=stop_after_attempt(max_retries), wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds), retry=( retry_if_exception_type((RequestException, ConnectTimeout, ReadTimeout)) ), before_sleep=before_sleep_log(logger, logging.WARNING), )
[docs]def completion_with_retry(llm: Nebula, **kwargs: Any) -> Any: """Use tenacity to retry the completion call.""" retry_decorator = _create_retry_decorator(llm) @retry_decorator def _completion_with_retry(**_kwargs: Any) -> Any: return make_request(llm, **_kwargs) return _completion_with_retry(**kwargs)