langchain_community.llms.amazon_api_gateway

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

import requests
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
from langchain_core.pydantic_v1 import Extra

from langchain_community.llms.utils import enforce_stop_tokens


[docs]class ContentHandlerAmazonAPIGateway:
    """适配器，用于将来自Langchain的输入准备成LLM模型期望的格式。

它还提供了一个辅助函数，用于从模型响应中提取生成的文本。"""

[docs]    @classmethod
    def transform_input(
        cls, prompt: str, model_kwargs: Dict[str, Any]
    ) -> Dict[str, Any]:
        return {"inputs": prompt, "parameters": model_kwargs}

[docs]    @classmethod
    def transform_output(cls, response: Any) -> str:
        return response.json()[0]["generated_text"]


[docs]class AmazonAPIGateway(LLM):
    """使用Amazon API Gateway访问托管在AWS上的LLM模型。"""

    api_url: str
    """API网关URL"""

    headers: Optional[Dict] = None
    """API网关发送的HTTP标头，例如用于身份验证。"""

    model_kwargs: Optional[Dict] = None
    """传递给模型的关键字参数。"""

    content_handler: ContentHandlerAmazonAPIGateway = ContentHandlerAmazonAPIGateway()
    """提供输入和输出转换函数以处理LLM和端点之间的格式的内容处理程序类。"""

    class Config:
        """此pydantic对象的配置。"""

        extra = Extra.forbid

    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        """获取识别参数。"""
        _model_kwargs = self.model_kwargs or {}
        return {
            **{"api_url": self.api_url, "headers": self.headers},
            **{"model_kwargs": _model_kwargs},
        }

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

    def _call(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> str:
        """调用Amazon API Gateway模型。

参数：
    prompt: 传递给模型的提示。
    stop: 生成时可选的停用词列表。

返回：
    模型生成的字符串。

示例：
    .. code-block:: python

        response = se("Tell me a joke.")
"""
        _model_kwargs = self.model_kwargs or {}
        payload = self.content_handler.transform_input(prompt, _model_kwargs)

        try:
            response = requests.post(
                self.api_url,
                headers=self.headers,
                json=payload,
            )
            text = self.content_handler.transform_output(response)

        except Exception as error:
            raise ValueError(f"Error raised by the service: {error}")

        if stop is not None:
            text = enforce_stop_tokens(text, stop)

        return text
Source code for langchain_community.llms.amazon_api_gateway
