Source code for langchain_core.prompts.prompt

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

import warnings
from pathlib import Path
from typing import Any, Dict, List, Literal, Optional, Union

from langchain_core.prompts.string import (
    DEFAULT_FORMATTER_MAPPING,
    StringPromptTemplate,
    check_valid_template,
    get_template_variables,
    mustache_schema,
)
from langchain_core.pydantic_v1 import BaseModel, root_validator
from langchain_core.runnables.config import RunnableConfig


[docs]class PromptTemplate(StringPromptTemplate): """Prompt template for a language model. A prompt template consists of a string template. It accepts a set of parameters from the user that can be used to generate a prompt for a language model. The template can be formatted using either f-strings (default) or jinja2 syntax. *Security warning*: Prefer using `template_format="f-string"` instead of `template_format="jinja2"`, or make sure to NEVER accept jinja2 templates from untrusted sources as they may lead to arbitrary Python code execution. As of LangChain 0.0.329, Jinja2 templates will be rendered using Jinja2's SandboxedEnvironment by default. This sand-boxing should be treated as a best-effort approach rather than a guarantee of security, as it is an opt-out rather than opt-in approach. Despite the sand-boxing, we recommend to never use jinja2 templates from untrusted sources. Example: .. code-block:: python from langchain_core.prompts import PromptTemplate # Instantiation using from_template (recommended) prompt = PromptTemplate.from_template("Say {foo}") prompt.format(foo="bar") # Instantiation using initializer prompt = PromptTemplate(input_variables=["foo"], template="Say {foo}") """ @property def lc_attributes(self) -> Dict[str, Any]: return { "template_format": self.template_format, }
[docs] @classmethod def get_lc_namespace(cls) -> List[str]: """Get the namespace of the langchain object.""" return ["langchain", "prompts", "prompt"]
input_variables: List[str] """A list of the names of the variables the prompt template expects.""" template: str """The prompt template.""" template_format: Literal["f-string", "mustache", "jinja2"] = "f-string" """The format of the prompt template. Options are: 'f-string', 'mustache', 'jinja2'.""" validate_template: bool = False """Whether or not to try validating the template."""
[docs] def get_input_schema(self, config: RunnableConfig | None = None) -> type[BaseModel]: if self.template_format != "mustache": return super().get_input_schema(config) return mustache_schema(self.template)
def __add__(self, other: Any) -> PromptTemplate: """Override the + operator to allow for combining prompt templates.""" # Allow for easy combining if isinstance(other, PromptTemplate): if self.template_format != "f-string": raise ValueError( "Adding prompt templates only supported for f-strings." ) if other.template_format != "f-string": raise ValueError( "Adding prompt templates only supported for f-strings." ) input_variables = list( set(self.input_variables) | set(other.input_variables) ) template = self.template + other.template # If any do not want to validate, then don't validate_template = self.validate_template and other.validate_template partial_variables = {k: v for k, v in self.partial_variables.items()} for k, v in other.partial_variables.items(): if k in partial_variables: raise ValueError("Cannot have same variable partialed twice.") else: partial_variables[k] = v return PromptTemplate( template=template, input_variables=input_variables, partial_variables=partial_variables, template_format="f-string", validate_template=validate_template, ) elif isinstance(other, str): prompt = PromptTemplate.from_template(other) return self + prompt else: raise NotImplementedError(f"Unsupported operand type for +: {type(other)}") @property def _prompt_type(self) -> str: """Return the prompt type key.""" return "prompt"
[docs] def format(self, **kwargs: Any) -> str: 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"]: if values["template_format"] == "mustache": raise ValueError("Mustache templates cannot be validated.") all_inputs = values["input_variables"] + list(values["partial_variables"]) check_valid_template( values["template"], values["template_format"], all_inputs ) elif values.get("template_format"): values["input_variables"] = [ var for var in get_template_variables( values["template"], values["template_format"] ) if var not in values["partial_variables"] ] 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: Optional[List[str]] = None, **kwargs: Any, ) -> PromptTemplate: """Load a prompt from a file. Args: template_file: The path to the file containing the prompt template. input_variables: [DEPRECATED] A list of variable names the final prompt template will expect. input_variables is ignored as from_file now delegates to from_template(). Returns: The prompt loaded from the file. """ with open(str(template_file), "r") as f: template = f.read() if input_variables: warnings.warn( "`input_variables' is deprecated and ignored.", DeprecationWarning ) return cls.from_template(template=template, **kwargs)
[docs] @classmethod def from_template( cls, template: str, *, template_format: str = "f-string", partial_variables: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> PromptTemplate: """Load a prompt template from a template. *Security warning*: Prefer using `template_format="f-string"` instead of `template_format="jinja2"`, or make sure to NEVER accept jinja2 templates from untrusted sources as they may lead to arbitrary Python code execution. As of LangChain 0.0.329, Jinja2 templates will be rendered using Jinja2's SandboxedEnvironment by default. This sand-boxing should be treated as a best-effort approach rather than a guarantee of security, as it is an opt-out rather than opt-in approach. Despite the sand-boxing, we recommend to never use jinja2 templates from untrusted sources. Args: template: The template to load. template_format: The format of the template. Use `jinja2` for jinja2, and `f-string` or None for f-strings. partial_variables: A dictionary of variables that can be used to partially fill in the template. For example, if the template is `"{variable1} {variable2}"`, and `partial_variables` is `{"variable1": "foo"}`, then the final prompt will be `"foo {variable2}"`. Returns: The prompt template loaded from the template. """ input_variables = get_template_variables(template, template_format) _partial_variables = partial_variables or {} if _partial_variables: input_variables = [ var for var in input_variables if var not in _partial_variables ] return cls( input_variables=input_variables, template=template, template_format=template_format, # type: ignore[arg-type] partial_variables=_partial_variables, **kwargs, )