PromptTemplates#

Prompt template classes.

pydantic model langchain.prompts.BaseChatPromptTemplate[source]#

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")

abstract format_messages(**kwargs: Any) → List[langchain.schema.BaseMessage][source]#: Format kwargs into a list of messages.

format_prompt(**kwargs: Any) → langchain.schema.PromptValue[source]#: Create Chat Messages.

pydantic model langchain.prompts.BasePromptTemplate[source]#

Base class for all prompt templates, returning a prompt.

field input_variables: List[str] [Required]#: A list of the names of the variables the prompt template expects.

field output_parser: Optional[langchain.schema.BaseOutputParser] = None#: How to parse the output of calling an LLM on this formatted prompt.

dict(**kwargs: Any) → Dict[source]#: Return dictionary representation of prompt.

abstract 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")

abstract format_prompt(**kwargs: Any) → langchain.schema.PromptValue[source]#: Create Chat Messages.

partial(**kwargs: Union[str, Callable[[], str]]) → langchain.prompts.base.BasePromptTemplate[source]#: Return a partial of the prompt template.

save(file_path: Union[pathlib.Path, str]) → None[source]#

Save the prompt.

Parameters: file_path – Path to directory to save prompt to.

Example: .. code-block:: python

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

pydantic model langchain.prompts.ChatPromptTemplate[source]#

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_messages(**kwargs: Any) → List[langchain.schema.BaseMessage][source]#: Format kwargs into a list of messages.

partial(**kwargs: Union[str, Callable[[], str]]) → langchain.prompts.base.BasePromptTemplate[source]#: Return a partial of the prompt template.

save(file_path: Union[pathlib.Path, str]) → None[source]#

Save the prompt.

Parameters: file_path – Path to directory to save prompt to.

Example: .. code-block:: python

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

pydantic model langchain.prompts.FewShotPromptTemplate[source]#

Prompt template that contains few shot examples.

field example_prompt: langchain.prompts.prompt.PromptTemplate [Required]#: PromptTemplate used to format an individual example.

field example_selector: Optional[langchain.prompts.example_selector.base.BaseExampleSelector] = None#: ExampleSelector to choose the examples to format into the prompt. Either this or examples should be provided.

field example_separator: str = '\n\n'#: String separator used to join the prefix, the examples, and suffix.

field examples: Optional[List[dict]] = None#: Examples to format into the prompt. Either this or example_selector should be provided.

field input_variables: List[str] [Required]#: A list of the names of the variables the prompt template expects.

field prefix: str = ''#: A prompt template string to put before the examples.

field suffix: str [Required]#: A prompt template string to put after the examples.

field template_format: str = 'f-string'#: The format of the prompt template. Options are: ‘f-string’, ‘jinja2’.

field validate_template: bool = True#: Whether or not to try validating the template.

dict(**kwargs: Any) → Dict[source]#: Return a dictionary of the 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")

pydantic model langchain.prompts.FewShotPromptWithTemplates[source]#

Prompt template that contains few shot examples.

field example_prompt: langchain.prompts.prompt.PromptTemplate [Required]#: PromptTemplate used to format an individual example.

field example_selector: Optional[langchain.prompts.example_selector.base.BaseExampleSelector] = None#: ExampleSelector to choose the examples to format into the prompt. Either this or examples should be provided.

field example_separator: str = '\n\n'#: String separator used to join the prefix, the examples, and suffix.

field examples: Optional[List[dict]] = None#: Examples to format into the prompt. Either this or example_selector should be provided.

field input_variables: List[str] [Required]#: A list of the names of the variables the prompt template expects.

field prefix: Optional[langchain.prompts.base.StringPromptTemplate] = None#: A PromptTemplate to put before the examples.

field suffix: langchain.prompts.base.StringPromptTemplate [Required]#: A PromptTemplate to put after the examples.

field template_format: str = 'f-string'#: The format of the prompt template. Options are: ‘f-string’, ‘jinja2’.

field validate_template: bool = True#: Whether or not to try validating the template.

dict(**kwargs: Any) → Dict[source]#: Return a dictionary of the 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")

pydantic model langchain.prompts.MessagesPlaceholder[source]#

Prompt template that assumes variable is already list of messages.

format_messages(**kwargs: Any) → List[langchain.schema.BaseMessage][source]#: To a BaseMessage.

property input_variables: List[str]#: Input variables for this prompt template.

langchain.prompts.Prompt#: alias of langchain.prompts.prompt.PromptTemplate

pydantic model langchain.prompts.PromptTemplate[source]#

Schema to represent a prompt for an LLM.

Example

from langchain import PromptTemplate
prompt = PromptTemplate(input_variables=["foo"], template="Say {foo}")

field input_variables: List[str] [Required]#: A list of the names of the variables the prompt template expects.

field template: str [Required]#: The prompt template.

field template_format: str = 'f-string'#: The format of the prompt template. Options are: ‘f-string’, ‘jinja2’.

field validate_template: bool = True#: Whether or not to try validating the template.

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")

classmethod from_examples(examples: List[str], suffix: str, input_variables: List[str], example_separator: str = '\n\n', prefix: str = '', **kwargs: Any) → langchain.prompts.prompt.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, pathlib.Path], input_variables: List[str], **kwargs: Any) → langchain.prompts.prompt.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, **kwargs: Any) → langchain.prompts.prompt.PromptTemplate[source]#: Load a prompt template from a template.

pydantic model langchain.prompts.StringPromptTemplate[source]#

String prompt should expose the format method, returning a prompt.

format_prompt(**kwargs: Any) → langchain.schema.PromptValue[source]#: Create Chat Messages.

langchain.prompts.load_prompt(path: Union[str, pathlib.Path]) → langchain.prompts.base.BasePromptTemplate[source]#: Unified method for loading a prompt from LangChainHub or local fs.