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PromptTemplates
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
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.
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]#
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
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.
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.

Prompts

Example Selector
By Harrison Chase