Source code for langchain.prompts.prompt

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

from pathlib import Path
from string import Formatter
from typing import Any, Dict, List, Union

from pydantic import Extra, root_validator

from langchain.prompts.base import (
    DEFAULT_FORMATTER_MAPPING,
    StringPromptTemplate,
    _get_jinja2_variables_from_template,
    check_valid_template,
)


[docs]class PromptTemplate(StringPromptTemplate): """Schema to represent a prompt for an LLM. Example: .. code-block:: python from langchain import PromptTemplate prompt = PromptTemplate(input_variables=["foo"], template="Say {foo}") """ input_variables: List[str] """A list of the names of the variables the prompt template expects.""" template: str """The prompt template.""" template_format: str = "f-string" """The format of the prompt template. Options are: 'f-string', 'jinja2'.""" validate_template: bool = True """Whether or not to try validating the template.""" @property def _prompt_type(self) -> str: """Return the prompt type key.""" return "prompt" class Config: """Configuration for this pydantic object.""" extra = Extra.forbid
[docs] 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") """ kwargs = self._merge_partial_and_user_variables(**kwargs) return DEFAULT_FORMATTER_MAPPING[self.template_format](self.template, **kwargs)
@root_validator() def template_is_valid(cls, values: Dict) -> Dict: """Check that template and input variables are consistent.""" if values["validate_template"]: all_inputs = values["input_variables"] + list(values["partial_variables"]) check_valid_template( values["template"], values["template_format"], all_inputs ) return values
[docs] @classmethod def from_examples( cls, examples: List[str], suffix: str, input_variables: List[str], example_separator: str = "\n\n", prefix: str = "", **kwargs: Any, ) -> PromptTemplate: """Take examples in list format with prefix and suffix to create a prompt. Intended to be used as a way to dynamically create a prompt from examples. Args: examples: List of examples to use in the prompt. suffix: String to go after the list of examples. Should generally set up the user's input. input_variables: A list of variable names the final prompt template will expect. example_separator: The separator to use in between examples. Defaults to two new line characters. prefix: String that should go before any examples. Generally includes examples. Default to an empty string. Returns: The final prompt generated. """ template = example_separator.join([prefix, *examples, suffix]) return cls(input_variables=input_variables, template=template, **kwargs)
[docs] @classmethod def from_file( cls, template_file: Union[str, Path], input_variables: List[str], **kwargs: Any ) -> PromptTemplate: """Load a prompt from a file. Args: template_file: The path to the file containing the prompt template. input_variables: A list of variable names the final prompt template will expect. Returns: The prompt loaded from the file. """ with open(str(template_file), "r") as f: template = f.read() return cls(input_variables=input_variables, template=template, **kwargs)
[docs] @classmethod def from_template(cls, template: str, **kwargs: Any) -> PromptTemplate: """Load a prompt template from a template.""" if "template_format" in kwargs and kwargs["template_format"] == "jinja2": # Get the variables for the template input_variables = _get_jinja2_variables_from_template(template) else: input_variables = { v for _, v, _, _ in Formatter().parse(template) if v is not None } return cls( input_variables=list(sorted(input_variables)), template=template, **kwargs )
# For backwards compatibility. Prompt = PromptTemplate