Source code for langchain_community.llms.baidu_qianfan_endpoint

from __future__ import annotations

import logging
from typing import (
    Any,
    AsyncIterator,
    Dict,
    Iterator,
    List,
    Optional,
)

from langchain_core.callbacks import (
    AsyncCallbackManagerForLLMRun,
    CallbackManagerForLLMRun,
)
from langchain_core.language_models.llms import LLM
from langchain_core.outputs import GenerationChunk
from langchain_core.pydantic_v1 import Field, root_validator
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env

logger = logging.getLogger(__name__)


[docs]class QianfanLLMEndpoint(LLM): """Baidu Qianfan hosted open source or customized models. To use, you should have the ``qianfan`` python package installed, and the environment variable ``qianfan_ak`` and ``qianfan_sk`` set with your API key and Secret Key. ak, sk are required parameters which you could get from https://cloud.baidu.com/product/wenxinworkshop Example: .. code-block:: python from langchain_community.llms import QianfanLLMEndpoint qianfan_model = QianfanLLMEndpoint(model="ERNIE-Bot", endpoint="your_endpoint", qianfan_ak="your_ak", qianfan_sk="your_sk") """ init_kwargs: Dict[str, Any] = Field(default_factory=dict) """init kwargs for qianfan client init, such as `query_per_second` which is associated with qianfan resource object to limit QPS""" model_kwargs: Dict[str, Any] = Field(default_factory=dict) """extra params for model invoke using with `do`.""" client: Any qianfan_ak: Optional[str] = None qianfan_sk: Optional[str] = None streaming: Optional[bool] = False """Whether to stream the results or not.""" model: str = "ERNIE-Bot-turbo" """Model name. you could get from https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu preset models are mapping to an endpoint. `model` will be ignored if `endpoint` is set """ endpoint: Optional[str] = None """Endpoint of the Qianfan LLM, required if custom model used.""" request_timeout: Optional[int] = 60 """request timeout for chat http requests""" top_p: Optional[float] = 0.8 temperature: Optional[float] = 0.95 penalty_score: Optional[float] = 1 """Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo. In the case of other model, passing these params will not affect the result. """ @root_validator() def validate_environment(cls, values: Dict) -> Dict: values["qianfan_ak"] = convert_to_secret_str( get_from_dict_or_env( values, "qianfan_ak", "QIANFAN_AK", default="", ) ) values["qianfan_sk"] = convert_to_secret_str( get_from_dict_or_env( values, "qianfan_sk", "QIANFAN_SK", default="", ) ) params = { **values.get("init_kwargs", {}), "model": values["model"], } if values["qianfan_ak"].get_secret_value() != "": params["ak"] = values["qianfan_ak"].get_secret_value() if values["qianfan_sk"].get_secret_value() != "": params["sk"] = values["qianfan_sk"].get_secret_value() if values["endpoint"] is not None and values["endpoint"] != "": params["endpoint"] = values["endpoint"] try: import qianfan values["client"] = qianfan.Completion(**params) except ImportError: raise ImportError( "qianfan package not found, please install it with " "`pip install qianfan`" ) return values @property def _identifying_params(self) -> Dict[str, Any]: return { **{"endpoint": self.endpoint, "model": self.model}, **super()._identifying_params, } @property def _llm_type(self) -> str: """Return type of llm.""" return "baidu-qianfan-endpoint" @property def _default_params(self) -> Dict[str, Any]: """Get the default parameters for calling Qianfan API.""" normal_params = { "model": self.model, "endpoint": self.endpoint, "stream": self.streaming, "request_timeout": self.request_timeout, "top_p": self.top_p, "temperature": self.temperature, "penalty_score": self.penalty_score, } return {**normal_params, **self.model_kwargs} def _convert_prompt_msg_params( self, prompt: str, **kwargs: Any, ) -> dict: if "streaming" in kwargs: kwargs["stream"] = kwargs.pop("streaming") return { **{"prompt": prompt, "model": self.model}, **self._default_params, **kwargs, } def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """Call out to an qianfan models endpoint for each generation with a prompt. 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 = qianfan_model.invoke("Tell me a joke.") """ if self.streaming: completion = "" for chunk in self._stream(prompt, stop, run_manager, **kwargs): completion += chunk.text return completion params = self._convert_prompt_msg_params(prompt, **kwargs) params["stop"] = stop response_payload = self.client.do(**params) return response_payload["result"] async def _acall( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: if self.streaming: completion = "" async for chunk in self._astream(prompt, stop, run_manager, **kwargs): completion += chunk.text return completion params = self._convert_prompt_msg_params(prompt, **kwargs) params["stop"] = stop response_payload = await self.client.ado(**params) return response_payload["result"] def _stream( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> Iterator[GenerationChunk]: params = self._convert_prompt_msg_params(prompt, **{**kwargs, "stream": True}) params["stop"] = stop for res in self.client.do(**params): if res: chunk = GenerationChunk(text=res["result"]) if run_manager: run_manager.on_llm_new_token(chunk.text) yield chunk async def _astream( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> AsyncIterator[GenerationChunk]: params = self._convert_prompt_msg_params(prompt, **{**kwargs, "stream": True}) params["stop"] = stop async for res in await self.client.ado(**params): if res: chunk = GenerationChunk(text=res["result"]) if run_manager: await run_manager.on_llm_new_token(chunk.text) yield chunk