mirror of
https://github.com/hwchase17/langchain
synced 2024-11-02 09:40:22 +00:00
86 lines
2.5 KiB
Python
86 lines
2.5 KiB
Python
from typing import Any, Callable, List, Mapping, Optional
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from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.language_models.llms import LLM
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from langchain_core.pydantic_v1 import Field
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from langchain_community.llms.utils import enforce_stop_tokens
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def _display_prompt(prompt: str) -> None:
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"""Displays the given prompt to the user."""
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print(f"\n{prompt}") # noqa: T201
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def _collect_user_input(
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separator: Optional[str] = None, stop: Optional[List[str]] = None
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) -> str:
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"""Collects and returns user input as a single string."""
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separator = separator or "\n"
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lines = []
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while True:
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line = input()
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if not line:
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break
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lines.append(line)
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if stop and any(seq in line for seq in stop):
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break
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# Combine all lines into a single string
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multi_line_input = separator.join(lines)
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return multi_line_input
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class HumanInputLLM(LLM):
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"""
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It returns user input as the response.
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"""
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input_func: Callable = Field(default_factory=lambda: _collect_user_input)
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prompt_func: Callable[[str], None] = Field(default_factory=lambda: _display_prompt)
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separator: str = "\n"
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input_kwargs: Mapping[str, Any] = {}
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prompt_kwargs: Mapping[str, Any] = {}
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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"""
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Returns an empty dictionary as there are no identifying parameters.
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"""
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return {}
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@property
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def _llm_type(self) -> str:
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"""Returns the type of LLM."""
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return "human-input"
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def _call(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> str:
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"""
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Displays the prompt to the user and returns their input as a response.
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Args:
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prompt (str): The prompt to be displayed to the user.
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stop (Optional[List[str]]): A list of stop strings.
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run_manager (Optional[CallbackManagerForLLMRun]): Currently not used.
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Returns:
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str: The user's input as a response.
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"""
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self.prompt_func(prompt, **self.prompt_kwargs)
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user_input = self.input_func(
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separator=self.separator, stop=stop, **self.input_kwargs
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)
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if stop is not None:
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# I believe this is required since the stop tokens
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# are not enforced by the human themselves
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user_input = enforce_stop_tokens(user_input, stop)
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return user_input
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