Source code for langchain_community.llms.pipelineai

import logging
from typing import Any, Dict, List, Mapping, Optional

from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
from langchain_core.pydantic_v1 import (
    BaseModel,
    Extra,
    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 PipelineAI(LLM, BaseModel):
    """PipelineAI大型语言模型。

要使用，您应该已安装``pipeline-ai`` python包，并设置环境变量``PIPELINE_API_KEY``为您的API密钥。

可以传递给调用的任何有效参数都可以传递，即使在此类上没有明确保存。

示例：
    .. code-block:: python

        from langchain_community.llms import PipelineAI
        pipeline = PipelineAI(pipeline_key="")
"""

    pipeline_key: str = ""
    """目标管道的ID或标签"""

    pipeline_kwargs: Dict[str, Any] = Field(default_factory=dict)
    """保存任何管道参数，适用于“create”调用，未明确指定的参数。"""

    pipeline_api_key: Optional[SecretStr] = None

    class Config:
        """这是用于pydantic配置的设置。"""

        extra = Extra.forbid

    @root_validator(pre=True)
    def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
        """从传入的额外参数构建额外的kwargs。"""
        all_required_field_names = {field.alias for field in cls.__fields__.values()}

        extra = values.get("pipeline_kwargs", {})
        for field_name in list(values):
            if field_name not in all_required_field_names:
                if field_name in extra:
                    raise ValueError(f"Found {field_name} supplied twice.")
                logger.warning(
                    f"""{field_name} was transferred to pipeline_kwargs.
                    Please confirm that {field_name} is what you intended."""
                )
                extra[field_name] = values.pop(field_name)
        values["pipeline_kwargs"] = extra
        return values

    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """验证环境中是否存在API密钥和Python包。"""
        pipeline_api_key = convert_to_secret_str(
            get_from_dict_or_env(values, "pipeline_api_key", "PIPELINE_API_KEY")
        )
        values["pipeline_api_key"] = pipeline_api_key
        return values

    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        """获取识别参数。"""
        return {
            **{"pipeline_key": self.pipeline_key},
            **{"pipeline_kwargs": self.pipeline_kwargs},
        }

    @property
    def _llm_type(self) -> str:
        """llm的返回类型。"""
        return "pipeline_ai"

    def _call(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> str:
        """调用Pipeline Cloud端点。"""
        try:
            from pipeline import PipelineCloud
        except ImportError:
            raise ImportError(
                "Could not import pipeline-ai python package. "
                "Please install it with `pip install pipeline-ai`."
            )
        client = PipelineCloud(token=self.pipeline_api_key.get_secret_value())  # type: ignore[union-attr]
        params = self.pipeline_kwargs or {}
        params = {**params, **kwargs}

        run = client.run_pipeline(self.pipeline_key, [prompt, params])
        try:
            text = run.result_preview[0][0]
        except AttributeError:
            raise AttributeError(
                f"A pipeline run should have a `result_preview` attribute."
                f"Run was: {run}"
            )
        if stop is not None:
            # I believe this is required since the stop tokens
            # are not enforced by the pipeline parameters
            text = enforce_stop_tokens(text, stop)
        return text