Source code for langchain_community.llms.moonshot

from typing import Any, Dict, List, Optional

import requests
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models import LLM
from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env

from langchain_community.llms.utils import enforce_stop_tokens

MOONSHOT_SERVICE_URL_BASE = "https://api.moonshot.cn/v1"


class _MoonshotClient(BaseModel):
    """An API client that talks to the Moonshot server."""

    api_key: SecretStr
    """The API key to use for authentication."""
    base_url: str = MOONSHOT_SERVICE_URL_BASE

    def completion(self, request: Any) -> Any:
        headers = {"Authorization": f"Bearer {self.api_key.get_secret_value()}"}
        response = requests.post(
            f"{self.base_url}/chat/completions",
            headers=headers,
            json=request,
        )
        if not response.ok:
            raise ValueError(f"HTTP {response.status_code} error: {response.text}")
        return response.json()["choices"][0]["message"]["content"]


[docs]class MoonshotCommon(BaseModel): """Common parameters for Moonshot LLMs.""" _client: _MoonshotClient base_url: str = MOONSHOT_SERVICE_URL_BASE moonshot_api_key: Optional[SecretStr] = Field(default=None, alias="api_key") """Moonshot API key. Get it here: https://platform.moonshot.cn/console/api-keys""" model_name: str = Field(default="moonshot-v1-8k", alias="model") """Model name. Available models listed here: https://platform.moonshot.cn/pricing""" max_tokens = 1024 """Maximum number of tokens to generate.""" temperature = 0.3 """Temperature parameter (higher values make the model more creative).""" class Config: """Configuration for this pydantic object.""" allow_population_by_field_name = True @property def lc_secrets(self) -> dict: """A map of constructor argument names to secret ids. For example, {"moonshot_api_key": "MOONSHOT_API_KEY"} """ return {"moonshot_api_key": "MOONSHOT_API_KEY"} @property def _default_params(self) -> Dict[str, Any]: """Get the default parameters for calling OpenAI API.""" return { "model": self.model_name, "max_tokens": self.max_tokens, "temperature": self.temperature, } @property def _invocation_params(self) -> Dict[str, Any]: return {**{"model": self.model_name}, **self._default_params} @root_validator(pre=True) def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: """Build extra parameters. Override the superclass method, prevent the model parameter from being overridden. """ return values @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" values["moonshot_api_key"] = convert_to_secret_str( get_from_dict_or_env(values, "moonshot_api_key", "MOONSHOT_API_KEY") ) values["_client"] = _MoonshotClient( api_key=values["moonshot_api_key"], base_url=values["base_url"] if "base_url" in values else MOONSHOT_SERVICE_URL_BASE, ) return values @property def _llm_type(self) -> str: """Return type of llm.""" return "moonshot"
[docs]class Moonshot(MoonshotCommon, LLM): """Moonshot large language models. To use, you should have the environment variable ``MOONSHOT_API_KEY`` set with your API key. Referenced from https://platform.moonshot.cn/docs Example: .. code-block:: python from langchain_community.llms.moonshot import Moonshot moonshot = Moonshot(model="moonshot-v1-8k") """ class Config: """Configuration for this pydantic object.""" allow_population_by_field_name = True def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: request = self._invocation_params request["messages"] = [{"role": "user", "content": prompt}] request.update(kwargs) text = self._client.completion(request) if stop is not None: # This is required since the stop tokens # are not enforced by the model parameters text = enforce_stop_tokens(text, stop) return text