langchain_community.llms.human — 🦜🔗 LangChain 0.2.0

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

from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
from langchain_core.pydantic_v1 import Field

from langchain_community.llms.utils import enforce_stop_tokens


def _display_prompt(prompt: str) -> None:
    """向用户显示给定的提示信息。"""
    print(f"\n{prompt}")  # noqa: T201


def _collect_user_input(
    separator: Optional[str] = None, stop: Optional[List[str]] = None
) -> str:
    """收集并返回用户输入的内容作为一个字符串。"""
    separator = separator or "\n"
    lines = []

    while True:
        line = input()
        if not line:
            break
        lines.append(line)

        if stop and any(seq in line for seq in stop):
            break
    # Combine all lines into a single string
    multi_line_input = separator.join(lines)
    return multi_line_input


[docs]class HumanInputLLM(LLM):
    """用户输入作为响应。"""

    input_func: Callable = Field(default_factory=lambda: _collect_user_input)
    prompt_func: Callable[[str], None] = Field(default_factory=lambda: _display_prompt)
    separator: str = "\n"
    input_kwargs: Mapping[str, Any] = {}
    prompt_kwargs: Mapping[str, Any] = {}

    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        """
        返回一个空字典，因为没有识别参数。
        """
        return {}

    @property
    def _llm_type(self) -> str:
        """返回LLM的类型。"""
        return "human-input"

    def _call(
        self,
        prompt: str,
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> str:
        """显示提示给用户，并将他们的输入作为响应返回。

参数:
    prompt (str): 要显示给用户的提示。
    stop (Optional[List[str]]): 一个停止字符串的列表。
    run_manager (Optional[CallbackManagerForLLMRun]): 目前未使用。

返回:
    str: 用户的输入作为响应。
"""
        self.prompt_func(prompt, **self.prompt_kwargs)
        user_input = self.input_func(
            separator=self.separator, stop=stop, **self.input_kwargs
        )

        if stop is not None:
            # I believe this is required since the stop tokens
            # are not enforced by the human themselves
            user_input = enforce_stop_tokens(user_input, stop)
        return user_input
Source code for langchain_community.llms.human
