"""Wrapper around Minimax APIs."""
from __future__ import annotations
import logging
from typing import (
Any,
Dict,
List,
Optional,
)
import requests
from langchain_core.callbacks import (
CallbackManagerForLLMRun,
)
from langchain_core.language_models.llms import LLM
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
from pydantic import BaseModel, ConfigDict, Field, SecretStr, model_validator
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
@model_validator(mode="before")
@classmethod
def set_api_url(cls, values: Dict[str, Any]) -> 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 = requests.post(self.api_url, 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."""
model_config = ConfigDict(protected_namespaces=())
_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
[docs]
@pre_init
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="https://api.minimax.chat",
)
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 = self._client.post(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
