langchain/langchain/prompts/base.py

"""BasePrompt schema definition."""
from __future__ import annotations

import json
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Callable, Dict, List, Mapping, Optional, Union

import yaml
from pydantic import BaseModel, Extra, Field, root_validator

from langchain.formatting import formatter
from langchain.output_parsers.base import BaseOutputParser
from langchain.output_parsers.list import (  # noqa: F401
    CommaSeparatedListOutputParser,
    ListOutputParser,
)
from langchain.output_parsers.regex import RegexParser  # noqa: F401
from langchain.schema import BaseMessage, HumanMessage, PromptValue


def jinja2_formatter(template: str, **kwargs: Any) -> str:
    """Format a template using jinja2."""
    try:
        from jinja2 import Template
    except ImportError:
        raise ValueError(
            "jinja2 not installed, which is needed to use the jinja2_formatter. "
            "Please install it with `pip install jinja2`."
        )

    return Template(template).render(**kwargs)


DEFAULT_FORMATTER_MAPPING: Dict[str, Callable] = {
    "f-string": formatter.format,
    "jinja2": jinja2_formatter,
}


def check_valid_template(
    template: str, template_format: str, input_variables: List[str]
) -> None:
    """Check that template string is valid."""
    if template_format not in DEFAULT_FORMATTER_MAPPING:
        valid_formats = list(DEFAULT_FORMATTER_MAPPING)
        raise ValueError(
            f"Invalid template format. Got `{template_format}`;"
            f" should be one of {valid_formats}"
        )
    dummy_inputs = {input_variable: "foo" for input_variable in input_variables}
    try:
        formatter_func = DEFAULT_FORMATTER_MAPPING[template_format]
        formatter_func(template, **dummy_inputs)
    except KeyError as e:
        raise ValueError(
            "Invalid prompt schema; check for mismatched or missing input parameters. "
            + str(e)
        )


class StringPromptValue(PromptValue):
    text: str

    def to_string(self) -> str:
        """Return prompt as string."""
        return self.text

    def to_messages(self) -> List[BaseMessage]:
        """Return prompt as messages."""
        return [HumanMessage(content=self.text)]


class BasePromptTemplate(BaseModel, ABC):
    """Base class for all prompt templates, returning a prompt."""

    input_variables: List[str]
    """A list of the names of the variables the prompt template expects."""
    output_parser: Optional[BaseOutputParser] = None
    """How to parse the output of calling an LLM on this formatted prompt."""
    partial_variables: Mapping[str, Union[str, Callable[[], str]]] = Field(
        default_factory=dict
    )

    class Config:
        """Configuration for this pydantic object."""

        extra = Extra.forbid
        arbitrary_types_allowed = True

    @abstractmethod
    def format_prompt(self, **kwargs: Any) -> PromptValue:
        """Create Chat Messages."""

    @root_validator()
    def validate_variable_names(cls, values: Dict) -> Dict:
        """Validate variable names do not include restricted names."""
        if "stop" in values["input_variables"]:
            raise ValueError(
                "Cannot have an input variable named 'stop', as it is used internally,"
                " please rename."
            )
        if "stop" in values["partial_variables"]:
            raise ValueError(
                "Cannot have an partial variable named 'stop', as it is used "
                "internally, please rename."
            )

        overall = set(values["input_variables"]).intersection(
            values["partial_variables"]
        )
        if overall:
            raise ValueError(
                f"Found overlapping input and partial variables: {overall}"
            )
        return values

    def partial(self, **kwargs: Union[str, Callable[[], str]]) -> BasePromptTemplate:
        """Return a partial of the prompt template."""
        prompt_dict = self.__dict__.copy()
        prompt_dict["input_variables"] = list(
            set(self.input_variables).difference(kwargs)
        )
        prompt_dict["partial_variables"] = {**self.partial_variables, **kwargs}
        return type(self)(**prompt_dict)

    def _merge_partial_and_user_variables(self, **kwargs: Any) -> Dict[str, Any]:
        # Get partial params:
        partial_kwargs = {
            k: v if isinstance(v, str) else v()
            for k, v in self.partial_variables.items()
        }
        return {**partial_kwargs, **kwargs}

    @abstractmethod
    def format(self, **kwargs: Any) -> str:
        """Format the prompt with the inputs.

        Args:
            kwargs: Any arguments to be passed to the prompt template.

        Returns:
            A formatted string.

        Example:

        .. code-block:: python

            prompt.format(variable1="foo")
        """

    @property
    @abstractmethod
    def _prompt_type(self) -> str:
        """Return the prompt type key."""

    def dict(self, **kwargs: Any) -> Dict:
        """Return dictionary representation of prompt."""
        prompt_dict = super().dict(**kwargs)
        prompt_dict["_type"] = self._prompt_type
        return prompt_dict

    def save(self, file_path: Union[Path, str]) -> None:
        """Save the prompt.

        Args:
            file_path: Path to directory to save prompt to.

        Example:
        .. code-block:: python

            prompt.save(file_path="path/prompt.yaml")
        """
        if self.partial_variables:
            raise ValueError("Cannot save prompt with partial variables.")
        # Convert file to Path object.
        if isinstance(file_path, str):
            save_path = Path(file_path)
        else:
            save_path = file_path

        directory_path = save_path.parent
        directory_path.mkdir(parents=True, exist_ok=True)

        # Fetch dictionary to save
        prompt_dict = self.dict()

        if save_path.suffix == ".json":
            with open(file_path, "w") as f:
                json.dump(prompt_dict, f, indent=4)
        elif save_path.suffix == ".yaml":
            with open(file_path, "w") as f:
                yaml.dump(prompt_dict, f, default_flow_style=False)
        else:
            raise ValueError(f"{save_path} must be json or yaml")


class StringPromptTemplate(BasePromptTemplate, ABC):
    """String prompt should expose the format method, returning a prompt."""

    def format_prompt(self, **kwargs: Any) -> PromptValue:
        """Create Chat Messages."""
        return StringPromptValue(text=self.format(**kwargs))