Source code for langchain_community.llms.minimax

"""Wrapper around Minimax APIs."""
from __future__ import annotations

import logging
from typing import (

import requests
from langchain_core.callbacks import (
from langchain_core.language_models.llms 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

logger = logging.getLogger(__name__)

class _MinimaxEndpointClient(BaseModel):
    """API client for the Minimax LLM endpoint."""

    host: str
    group_id: str
    api_key: SecretStr
    api_url: str

    @root_validator(pre=True, allow_reuse=True)
    def set_api_url(cls, values: Dict[str, Any]) -> Dict[str, Any]:
        if "api_url" not in values:
            host = values["host"]
            group_id = values["group_id"]
            api_url = f"{host}/v1/text/chatcompletion?GroupId={group_id}"
            values["api_url"] = api_url
        return values

    def post(self, request: Any) -> Any:
        headers = {"Authorization": f"Bearer {self.api_key.get_secret_value()}"}
        response =, headers=headers, json=request)
        # TODO: error handling and automatic retries
        if not response.ok:
            raise ValueError(f"HTTP {response.status_code} error: {response.text}")
        if response.json()["base_resp"]["status_code"] > 0:
            raise ValueError(
                f"API {response.json()['base_resp']['status_code']}"
                f" error: {response.json()['base_resp']['status_msg']}"
        return response.json()["reply"]

[docs]class MinimaxCommon(BaseModel): """Common parameters for Minimax large language models.""" _client: _MinimaxEndpointClient model: str = "abab5.5-chat" """Model name to use.""" max_tokens: int = 256 """Denotes the number of tokens to predict per generation.""" temperature: float = 0.7 """A non-negative float that tunes the degree of randomness in generation.""" top_p: float = 0.95 """Total probability mass of tokens to consider at each step.""" model_kwargs: Dict[str, Any] = Field(default_factory=dict) """Holds any model parameters valid for `create` call not explicitly specified.""" minimax_api_host: Optional[str] = None minimax_group_id: Optional[str] = None minimax_api_key: Optional[SecretStr] = None @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" values["minimax_api_key"] = convert_to_secret_str( get_from_dict_or_env(values, "minimax_api_key", "MINIMAX_API_KEY") ) values["minimax_group_id"] = get_from_dict_or_env( values, "minimax_group_id", "MINIMAX_GROUP_ID" ) # Get custom api url from environment. values["minimax_api_host"] = get_from_dict_or_env( values, "minimax_api_host", "MINIMAX_API_HOST", default="", ) values["_client"] = _MinimaxEndpointClient( # type: ignore[call-arg] host=values["minimax_api_host"], api_key=values["minimax_api_key"], group_id=values["minimax_group_id"], ) return values @property def _default_params(self) -> Dict[str, Any]: """Get the default parameters for calling OpenAI API.""" return { "model": self.model, "tokens_to_generate": self.max_tokens, "temperature": self.temperature, "top_p": self.top_p, **self.model_kwargs, } @property def _identifying_params(self) -> Dict[str, Any]: """Get the identifying parameters.""" return {**{"model": self.model}, **self._default_params} @property def _llm_type(self) -> str: """Return type of llm.""" return "minimax"
[docs]class Minimax(MinimaxCommon, LLM): """Minimax large language models. To use, you should have the environment variable ``MINIMAX_API_KEY`` and ``MINIMAX_GROUP_ID`` set with your API key, or pass them as a named parameter to the constructor. Example: . code-block:: python from langchain_community.llms.minimax import Minimax minimax = Minimax(model="<model_name>", minimax_api_key="my-api-key", minimax_group_id="my-group-id") """ def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: r"""Call out to Minimax's completion endpoint to chat Args: prompt: The prompt to pass into the model. Returns: The string generated by the model. Example: .. code-block:: python response = minimax("Tell me a joke.") """ request = self._default_params request["messages"] = [{"sender_type": "USER", "text": prompt}] request.update(kwargs) text = 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