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    Pythonlangchain-corepromptsprompt
    Module●Since v0.1

    prompt

    Prompt schema definition.

    Used in Docs

    • Docusaurus integration

    Attributes

    attribute
    DEFAULT_FORMATTER_MAPPING: dict[str, Callable[..., str]]
    attribute
    PromptTemplateFormat: Literal['f-string', 'mustache', 'jinja2']

    Functions

    function
    check_valid_template

    Check that template string is valid.

    function
    get_template_variables

    Get the variables from the template.

    function
    mustache_schema

    Get the variables from a mustache template.

    Classes

    class
    StringPromptTemplate

    String prompt that exposes the format method, returning a prompt.

    class
    RunnableConfig

    Configuration for a Runnable.

    Note

    Custom values

    The TypedDict has total=False set intentionally to:

    • Allow partial configs to be created and merged together via merge_configs
    • Support config propagation from parent to child runnables via var_child_runnable_config (a ContextVar that automatically passes config down the call stack without explicit parameter passing), where configs are merged rather than replaced
    Example
    # Parent sets tags
    chain.invoke(input, config={"tags": ["parent"]})
    # Child automatically inherits and can add:
    # ensure_config({"tags": ["child"]}) -> {"tags": ["parent", "child"]}
    class
    PromptTemplate

    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

    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 sandboxing, we recommend to never use jinja2 templates from untrusted sources.

    View source on GitHub