Source code for langchain_community.llms.opaqueprompts

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

from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models import BaseLanguageModel
from langchain_core.language_models.llms import LLM
from langchain_core.messages import AIMessage
from langchain_core.pydantic_v1 import Extra, root_validator
from langchain_core.utils import get_from_dict_or_env

logger = logging.getLogger(__name__)


class OpaquePrompts(LLM):
    """LLM使用OpaquePrompts来清理提示。

    在将提示传递给LLM之前，对另一个LLM进行包装并清理提示，然后对响应进行去除清理。

    要使用，应安装``opaqueprompts`` python包，并设置环境变量``OPAQUEPROMPTS_API_KEY``为您的API密钥，或将其作为构造函数的命名参数传递。

    示例:
        .. code-block:: python

            from langchain_community.llms import OpaquePrompts
            from langchain_community.chat_models import ChatOpenAI

            op_llm = OpaquePrompts(base_llm=ChatOpenAI())
"""

    base_llm: BaseLanguageModel
    """要使用的基本LLM。"""

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

        extra = Extra.forbid

    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """验证 OpaquePrompts API 密钥和 Python 包是否存在。"""
        try:
            import opaqueprompts as op
        except ImportError:
            raise ImportError(
                "Could not import the `opaqueprompts` Python package, "
                "please install it with `pip install opaqueprompts`."
            )
        if op.__package__ is None:
            raise ValueError(
                "Could not properly import `opaqueprompts`, "
                "opaqueprompts.__package__ is None."
            )

        api_key = get_from_dict_or_env(
            values, "opaqueprompts_api_key", "OPAQUEPROMPTS_API_KEY", default=""
        )
        if not api_key:
            raise ValueError(
                "Could not find OPAQUEPROMPTS_API_KEY in the environment. "
                "Please set it to your OpaquePrompts API key."
                "You can get it by creating an account on the OpaquePrompts website: "
                "https://opaqueprompts.opaque.co/ ."
            )
        return values

    def _call(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> str:
        """在调用基本LLM之前进行清理，并在之后进行去除清理。

参数：
    prompt：传递给模型的提示。

返回：
    模型生成的字符串。

示例：
    .. code-block:: python

        response = op_llm.invoke("Tell me a joke.")
"""
        import opaqueprompts as op

        _run_manager = run_manager or CallbackManagerForLLMRun.get_noop_manager()

        # sanitize the prompt by replacing the sensitive information with a placeholder
        sanitize_response: op.SanitizeResponse = op.sanitize([prompt])
        sanitized_prompt_value_str = sanitize_response.sanitized_texts[0]

        # TODO: Add in callbacks once child runs for LLMs are supported by LangSmith.
        # call the LLM with the sanitized prompt and get the response
        llm_response = self.base_llm.bind(stop=stop).invoke(
            sanitized_prompt_value_str,
        )
        if isinstance(llm_response, AIMessage):
            llm_response = llm_response.content

        # desanitize the response by restoring the original sensitive information
        desanitize_response: op.DesanitizeResponse = op.desanitize(
            llm_response,
            secure_context=sanitize_response.secure_context,
        )
        return desanitize_response.desanitized_text

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

这是基类方法的重写。
"""
        return "opaqueprompts"