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127 lines
3.7 KiB
Python
127 lines
3.7 KiB
Python
"""BasePrompt schema definition."""
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import json
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from abc import ABC, abstractmethod
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Union
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import yaml
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from pydantic import BaseModel, Extra, root_validator
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from langchain.formatting import formatter
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DEFAULT_FORMATTER_MAPPING = {
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"f-string": formatter.format,
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}
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def check_valid_template(
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template: str, template_format: str, input_variables: List[str]
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) -> None:
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"""Check that template string is valid."""
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if template_format not in DEFAULT_FORMATTER_MAPPING:
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valid_formats = list(DEFAULT_FORMATTER_MAPPING)
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raise ValueError(
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f"Invalid template format. Got `{template_format}`;"
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f" should be one of {valid_formats}"
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)
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dummy_inputs = {input_variable: "foo" for input_variable in input_variables}
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try:
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formatter_func = DEFAULT_FORMATTER_MAPPING[template_format]
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formatter_func(template, **dummy_inputs)
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except KeyError:
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raise ValueError("Invalid prompt schema.")
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class BaseOutputParser(ABC):
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"""Class to parse the output of an LLM call."""
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@abstractmethod
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def parse(self, text: str) -> Union[str, List[str], Dict[str, str]]:
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"""Parse the output of an LLM call."""
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class ListOutputParser(ABC):
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"""Class to parse the output of an LLM call to a list."""
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@abstractmethod
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def parse(self, text: str) -> List[str]:
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"""Parse the output of an LLM call."""
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class BasePromptTemplate(BaseModel, ABC):
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"""Base prompt should expose the format method, returning a prompt."""
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input_variables: List[str]
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"""A list of the names of the variables the prompt template expects."""
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output_parser: Optional[BaseOutputParser] = None
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"""How to parse the output of calling an LLM on this formatted prompt."""
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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arbitrary_types_allowed = True
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@root_validator()
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def validate_variable_names(cls, values: Dict) -> Dict:
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"""Validate variable names do not restricted names."""
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if "stop" in values["input_variables"]:
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raise ValueError(
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"Cannot have an input variable named 'stop', as it is used internally,"
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" please rename."
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)
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return values
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@abstractmethod
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def format(self, **kwargs: Any) -> str:
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"""Format the prompt with the inputs.
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Args:
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kwargs: Any arguments to be passed to the prompt template.
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Returns:
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A formatted string.
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Example:
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.. code-block:: python
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prompt.format(variable1="foo")
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"""
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def _prompt_dict(self) -> Dict:
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"""Return a dictionary of the prompt."""
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return self.dict()
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def save(self, file_path: Union[Path, str]) -> None:
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"""Save the prompt.
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Args:
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file_path: Path to directory to save prompt to.
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Example:
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.. code-block:: python
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prompt.save(file_path="path/prompt.yaml")
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"""
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# Convert file to Path object.
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if isinstance(file_path, str):
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save_path = Path(file_path)
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else:
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save_path = file_path
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directory_path = save_path.parent
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directory_path.mkdir(parents=True, exist_ok=True)
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# Fetch dictionary to save
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prompt_dict = self._prompt_dict()
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if save_path.suffix == ".json":
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with open(file_path, "w") as f:
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json.dump(prompt_dict, f, indent=4)
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elif save_path.suffix == ".yaml":
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with open(file_path, "w") as f:
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yaml.dump(prompt_dict, f, default_flow_style=False)
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else:
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raise ValueError(f"{save_path} must be json or yaml")
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