convert_to_openai_function() — langchain Function Reference

Source Code

libs/core/langchain_core/utils/function_calling.py lines 361–480

def convert_to_openai_function(
    function: Mapping[str, Any] | type | Callable | BaseTool,
    *,
    strict: bool | None = None,
) -> dict[str, Any]:
    """Convert a raw function/class to an OpenAI function.

    Args:
        function: A dictionary, Pydantic `BaseModel` class, `TypedDict` class, a
            LangChain `Tool` object, or a Python function.

            If a dictionary is passed in, it is assumed to already be a valid OpenAI
            function, a JSON schema with top-level `title` key specified, an Anthropic
            format tool, or an Amazon Bedrock Converse format tool.
        strict: If `True`, model output is guaranteed to exactly match the JSON Schema
            provided in the function definition.

            If `None`, `strict` argument will not be included in function definition.

    Returns:
        A dict version of the passed in function which is compatible with the OpenAI
            function-calling API.

    Raises:
        ValueError: If function is not in a supported format.

    !!! warning "Behavior changed in `langchain-core` 0.3.16"

        `description` and `parameters` keys are now optional. Only `name` is
        required and guaranteed to be part of the output.
    """
    # an Anthropic format tool
    if isinstance(function, dict) and all(
        k in function for k in ("name", "input_schema")
    ):
        oai_function = {
            "name": function["name"],
            "parameters": function["input_schema"],
        }
        if "description" in function:
            oai_function["description"] = function["description"]
    # an Amazon Bedrock Converse format tool
    elif isinstance(function, dict) and "toolSpec" in function:
        oai_function = {
            "name": function["toolSpec"]["name"],
            "parameters": function["toolSpec"]["inputSchema"]["json"],
        }
        if "description" in function["toolSpec"]:
            oai_function["description"] = function["toolSpec"]["description"]
    # already in OpenAI function format
    elif isinstance(function, dict) and "name" in function:
        oai_function = {
            k: v
            for k, v in function.items()
            if k in {"name", "description", "parameters", "strict"}
        }
    # a JSON schema with title and description
    elif isinstance(function, dict) and "title" in function:
        function_copy = function.copy()
        oai_function = {"name": function_copy.pop("title")}
        if "description" in function_copy:
            oai_function["description"] = function_copy.pop("description")
        if function_copy and "properties" in function_copy:
            oai_function["parameters"] = function_copy
    elif isinstance(function, type) and is_basemodel_subclass(function):
        oai_function = cast("dict", _convert_pydantic_to_openai_function(function))
    elif is_typeddict(function):
        oai_function = cast(
            "dict", _convert_typed_dict_to_openai_function(cast("type", function))
        )
    elif isinstance(function, langchain_core.tools.base.BaseTool):
        oai_function = cast("dict", _format_tool_to_openai_function(function))
    elif callable(function):
        oai_function = cast(
            "dict", _convert_python_function_to_openai_function(function)
        )
    else:
        if isinstance(function, dict) and (
            "type" in function or "properties" in function
        ):
            msg = (