Source code for langchain_community.llms.baichuan

from __future__ import annotations

import json
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.pydantic_v1 import 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__)


[docs]class BaichuanLLM(LLM): # TODO: Adding streaming support. """Baichuan large language models.""" model: str = "Baichuan2-Turbo-192k" """ Other models are available at https://platform.baichuan-ai.com/docs/api. """ temperature: float = 0.3 top_p: float = 0.95 timeout: int = 60 model_kwargs: Dict[str, Any] = Field(default_factory=dict) baichuan_api_host: Optional[str] = None baichuan_api_key: Optional[SecretStr] = None @root_validator() def validate_environment(cls, values: Dict) -> Dict: values["baichuan_api_key"] = convert_to_secret_str( get_from_dict_or_env(values, "baichuan_api_key", "BAICHUAN_API_KEY") ) values["baichuan_api_host"] = get_from_dict_or_env( values, "baichuan_api_host", "BAICHUAN_API_HOST", default="https://api.baichuan-ai.com/v1/chat/completions", ) return values @property def _default_params(self) -> Dict[str, Any]: return { "model": self.model, "temperature": self.temperature, "top_p": self.top_p, **self.model_kwargs, } def _post(self, request: Any) -> Any: headers = { "Content-Type": "application/json", "Authorization": f"Bearer {self.baichuan_api_key.get_secret_value()}", # type: ignore[union-attr] } try: response = requests.post( self.baichuan_api_host, # type: ignore[arg-type] headers=headers, json=request, timeout=self.timeout, ) if response.status_code == 200: parsed_json = json.loads(response.text) return parsed_json["choices"][0]["message"]["content"] else: response.raise_for_status() except Exception as e: raise ValueError(f"An error has occurred: {e}") def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: request = self._default_params request["messages"] = [{"role": "user", "content": prompt}] request.update(kwargs) text = self._post(request) if stop is not None: text = enforce_stop_tokens(text, stop) return text @property def _llm_type(self) -> str: """Return type of chat_model.""" return "baichuan-llm"