"""Prompt schema definition."""
from __future__ import annotations
import warnings
from pathlib import Path
from typing import Any, Optional, Union
from pydantic import BaseModel, model_validator
from langchain_core.prompts.string import (
DEFAULT_FORMATTER_MAPPING,
PromptTemplateFormat,
StringPromptTemplate,
check_valid_template,
get_template_variables,
mustache_schema,
)
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), jinja2,
or mustache 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(template="Say {foo}")
"""
@property
def lc_attributes(self) -> dict[str, Any]:
return {
"template_format": self.template_format,
}
@classmethod
def get_lc_namespace(cls) -> list[str]:
"""Get the namespace of the langchain object."""
return ["langchain", "prompts", "prompt"]
template: str
"""The prompt template."""
template_format: PromptTemplateFormat = "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."""
@model_validator(mode="before")
@classmethod
def pre_init_validation(cls, values: dict) -> Any:
"""Check that template and input variables are consistent."""
if values.get("template") is None:
# Will let pydantic fail with a ValidationError if template
# is not provided.
return values
# Set some default values based on the field defaults
values.setdefault("template_format", "f-string")
values.setdefault("partial_variables", {})
if values.get("validate_template"):
if values["template_format"] == "mustache":
msg = "Mustache templates cannot be validated."
raise ValueError(msg)
if "input_variables" not in values:
msg = "Input variables must be provided to validate the template."
raise ValueError(msg)
all_inputs = values["input_variables"] + list(values["partial_variables"])
check_valid_template(
values["template"], values["template_format"], all_inputs
)
if values["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
def get_input_schema(self, config: RunnableConfig | None = None) -> type[BaseModel]:
"""Get the input schema for the prompt.
Args:
config: The runnable configuration.
Returns:
The input schema for the prompt.
"""
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":
msg = "Adding prompt templates only supported for f-strings."
raise ValueError(msg)
if other.template_format != "f-string":
msg = "Adding prompt templates only supported for f-strings."
raise ValueError(msg)
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 = dict(self.partial_variables.items())
for k, v in other.partial_variables.items():
if k in partial_variables:
msg = "Cannot have same variable partialed twice."
raise ValueError(msg)
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:
msg = f"Unsupported operand type for +: {type(other)}"
raise NotImplementedError(msg)
@property
def _prompt_type(self) -> str:
"""Return the prompt type key."""
return "prompt"
[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,
encoding: Optional[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. Defaults to None.
encoding: The encoding system for opening the template file.
If not provided, will use the OS default.
input_variables is ignored as from_file now delegates to from_template().
Returns:
The prompt loaded from the file.
"""
with open(str(template_file), encoding=encoding) as f:
template = f.read()
if input_variables:
warnings.warn(
"`input_variables' is deprecated and ignored.",
DeprecationWarning,
stacklevel=2,
)
return cls.from_template(template=template, **kwargs)
[docs]
@classmethod
def from_template(
cls,
template: str,
*,
template_format: PromptTemplateFormat = "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 never using jinja2 templates
from untrusted sources.
Args:
template: The template to load.
template_format: The format of the template. Use `jinja2` for jinja2,
`mustache` for mustache, and `f-string` for f-strings.
Defaults to `f-string`.
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}"`. Defaults to None.
kwargs: Any other arguments to pass to the prompt template.
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,
)
