Source code for langchain_community.llms.human

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:
    """Displays the given prompt to the user."""
    print(f"\n{prompt}")  # noqa: T201

def _collect_user_input(
    separator: Optional[str] = None, stop: Optional[List[str]] = None
) -> str:
    """Collects and returns user input as a single string."""
    separator = separator or "\n"
    lines = []

    while True:
        line = input()
        if not line:

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

[docs]class HumanInputLLM(LLM): """User input as the response.""" 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]: """ Returns an empty dictionary as there are no identifying parameters. """ return {} @property def _llm_type(self) -> str: """Returns the type of LLM.""" return "human-input" def _call( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: """ Displays the prompt to the user and returns their input as a response. Args: prompt (str): The prompt to be displayed to the user. stop (Optional[List[str]]): A list of stop strings. run_manager (Optional[CallbackManagerForLLMRun]): Currently not used. Returns: str: The user's input as a response. """ 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