langchain.prompts.prompt.PromptTemplate

class langchain.prompts.prompt.PromptTemplate(*, input_variables: List[str], output_parser: Optional[BaseOutputParser] = None, partial_variables: Mapping[str, Union[str, Callable[[], str]]] = None, template: str, template_format: str = 'f-string', validate_template: bool = True)[source]

Bases: StringPromptTemplate

A 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.

Example

from langchain 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}")

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param input_variables: List[str] [Required]

A list of the names of the variables the prompt template expects.

param output_parser: Optional[BaseOutputParser] = None

How to parse the output of calling an LLM on this formatted prompt.

param partial_variables: Mapping[str, Union[str, Callable[[], str]]] [Optional]
param template: str [Required]

The prompt template.

param template_format: str = 'f-string'

The format of the prompt template. Options are: ‘f-string’, ‘jinja2’.

param validate_template: bool = True

Whether or not to try validating the template.

dict(**kwargs: Any) → Dict

Return dictionary representation of prompt.

format(**kwargs: Any) → str[source]

Format the prompt with the inputs.

Parameters

kwargs – Any arguments to be passed to the prompt template.

Returns

A formatted string.

Example

prompt.format(variable1="foo")
format_prompt(**kwargs: Any) → PromptValue

Create Chat Messages.

classmethod from_examples(examples: List[str], suffix: str, input_variables: List[str], example_separator: str = '\n\n', prefix: str = '', **kwargs: Any) → PromptTemplate[source]

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.

Parameters

  • 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.

classmethod from_file(template_file: Union[str, Path], input_variables: List[str], **kwargs: Any) → PromptTemplate[source]

Load a prompt from a file.

Parameters

  • 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.

classmethod from_template(template: str, *, template_format: str = 'f-string', partial_variables: Optional[Dict[str, Any]] = None, **kwargs: Any) → PromptTemplate[source]

Load a prompt template from a template.

Parameters

  • 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.

invoke(input: Dict, config: langchain.schema.runnable.RunnableConfig | None = None) → PromptValue
partial(**kwargs: Union[str, Callable[[], str]]) → BasePromptTemplate

Return a partial of the prompt template.

save(file_path: Union[Path, str]) → None

Save the prompt.

Parameters

file_path – Path to directory to save prompt to.

Example: .. code-block:: python

prompt.save(file_path=”path/prompt.yaml”)

validator template_is_valid  »  all fields[source]

Check that template and input variables are consistent.

to_json() → Union[SerializedConstructor, SerializedNotImplemented]
to_json_not_implemented() → SerializedNotImplemented
validator validate_variable_names  »  all fields

Validate variable names do not include restricted names.

property lc_attributes: Dict[str, Any]

Return a list of attribute names that should be included in the serialized kwargs. These attributes must be accepted by the constructor.

property lc_namespace: List[str]

Return the namespace of the langchain object. eg. [“langchain”, “llms”, “openai”]

property lc_secrets: Dict[str, str]

Return a map of constructor argument names to secret ids. eg. {“openai_api_key”: “OPENAI_API_KEY”}

property lc_serializable: bool

Return whether or not the class is serializable.

model Config

Bases: object

Configuration for this pydantic object.

arbitrary_types_allowed = True

Examples using PromptTemplate