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.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
from pydantic import Field, SecretStr

logger = logging.getLogger(__name__)


[docs] class QianfanLLMEndpoint(LLM): """Baidu Qianfan completion model integration. Setup: Install ``qianfan`` and set environment variables ``QIANFAN_AK``, ``QIANFAN_SK``. .. code-block:: bash pip install qianfan export QIANFAN_AK="your-api-key" export QIANFAN_SK="your-secret_key" Key init args — completion params: model: str Name of Qianfan model to use. temperature: Optional[float] Sampling temperature. endpoint: Optional[str] Endpoint of the Qianfan LLM top_p: Optional[float] What probability mass to use. Key init args — client params: timeout: Optional[int] Timeout for requests. api_key: Optional[str] Qianfan API KEY. If not passed in will be read from env var QIANFAN_AK. secret_key: Optional[str] Qianfan SECRET KEY. If not passed in will be read from env var QIANFAN_SK. See full list of supported init args and their descriptions in the params section. Instantiate: .. code-block:: python from langchain_community.llms import QianfanLLMEndpoint llm = QianfanLLMEndpoint( model="ERNIE-3.5-8K", # api_key="...", # secret_key="...", # other params... ) Invoke: .. code-block:: python input_text = "用50个字左右阐述,生命的意义在于" llm.invoke(input_text) .. code-block:: python '生命的意义在于体验、成长、爱与被爱、贡献与传承,以及对未知的勇敢探索与自我超越。' Stream: .. code-block:: python for chunk in llm.stream(input_text): print(chunk) .. code-block:: python 生命的意义 | 在于不断探索 | 与成长 | ,实现 | 自我价值,| 给予爱 | 并接受 | 爱, | 在经历 | 中感悟 | ,让 | 短暂的存在 | 绽放出无限 | 的光彩 | 与温暖 | 。 .. code-block:: python stream = llm.stream(input_text) full = next(stream) for chunk in stream: full += chunk full .. code-block:: '生命的意义在于探索、成长、爱与被爱、贡献价值、体验世界之美,以及在有限的时间里追求内心的平和与幸福。' Async: .. code-block:: python await llm.ainvoke(input_text) # stream: # async for chunk in llm.astream(input_text): # print(chunk) # batch: # await llm.abatch([input_text]) .. code-block:: python '生命的意义在于探索、成长、爱与被爱、贡献社会,在有限的时间里追寻无限的可能,实现自我价值,让生活充满色彩与意义。' """ # noqa: E501 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 = None qianfan_ak: Optional[SecretStr] = Field(default=None, alias="api_key") qianfan_sk: Optional[SecretStr] = Field(default=None, alias="secret_key") streaming: Optional[bool] = False """Whether to stream the results or not.""" model: Optional[str] = Field(default=None) """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 Default is set by `qianfan` SDK, not here """ endpoint: Optional[str] = None """Endpoint of the Qianfan LLM, required if custom model used.""" request_timeout: Optional[int] = Field(default=60, alias="timeout") """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. """
[docs] @pre_init def validate_environment(cls, values: Dict) -> Dict: values["qianfan_ak"] = convert_to_secret_str( get_from_dict_or_env( values, ["qianfan_ak", "api_key"], "QIANFAN_AK", default="", ) ) values["qianfan_sk"] = convert_to_secret_str( get_from_dict_or_env( values, ["qianfan_sk", "secret_key"], "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