Source code for langchain_core.utils.function_calling

"""Methods for creating function specs in the style of OpenAI Functions"""

from __future__ import annotations

import collections
import inspect
import logging
import types
import typing
import uuid
from typing import (
    TYPE_CHECKING,
    Any,
    Callable,
    Dict,
    List,
    Literal,
    Optional,
    Set,
    Tuple,
    Type,
    Union,
    cast,
)

from typing_extensions import Annotated, TypedDict, get_args, get_origin, is_typeddict

from langchain_core._api import deprecated
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, ToolMessage
from langchain_core.pydantic_v1 import BaseModel, Field, create_model
from langchain_core.utils.json_schema import dereference_refs
from langchain_core.utils.pydantic import is_basemodel_subclass

if TYPE_CHECKING:
    from langchain_core.tools import BaseTool

logger = logging.getLogger(__name__)

PYTHON_TO_JSON_TYPES = {
    "str": "string",
    "int": "integer",
    "float": "number",
    "bool": "boolean",
}


[docs]class FunctionDescription(TypedDict): """Representation of a callable function to send to an LLM.""" name: str """The name of the function.""" description: str """A description of the function.""" parameters: dict """The parameters of the function."""
[docs]class ToolDescription(TypedDict): """Representation of a callable function to the OpenAI API.""" type: Literal["function"] """The type of the tool.""" function: FunctionDescription """The function description."""
def _rm_titles(kv: dict, prev_key: str = "") -> dict: new_kv = {} for k, v in kv.items(): if k == "title": if isinstance(v, dict) and prev_key == "properties" and "title" in v.keys(): new_kv[k] = _rm_titles(v, k) else: continue elif isinstance(v, dict): new_kv[k] = _rm_titles(v, k) else: new_kv[k] = v return new_kv
[docs]@deprecated( "0.1.16", alternative="langchain_core.utils.function_calling.convert_to_openai_function()", removal="1.0", ) def convert_pydantic_to_openai_function( model: Type[BaseModel], *, name: Optional[str] = None, description: Optional[str] = None, rm_titles: bool = True, ) -> FunctionDescription: """Converts a Pydantic model to a function description for the OpenAI API. Args: model: The Pydantic model to convert. name: The name of the function. If not provided, the title of the schema will be used. description: The description of the function. If not provided, the description of the schema will be used. rm_titles: Whether to remove titles from the schema. Defaults to True. Returns: The function description. """ if hasattr(model, "model_json_schema"): schema = model.model_json_schema() # Pydantic 2 else: schema = model.schema() # Pydantic 1 schema = dereference_refs(schema) schema.pop("definitions", None) title = schema.pop("title", "") default_description = schema.pop("description", "") return { "name": name or title, "description": description or default_description, "parameters": _rm_titles(schema) if rm_titles else schema, }
[docs]@deprecated( "0.1.16", alternative="langchain_core.utils.function_calling.convert_to_openai_tool()", removal="1.0", ) def convert_pydantic_to_openai_tool( model: Type[BaseModel], *, name: Optional[str] = None, description: Optional[str] = None, ) -> ToolDescription: """Converts a Pydantic model to a function description for the OpenAI API. Args: model: The Pydantic model to convert. name: The name of the function. If not provided, the title of the schema will be used. description: The description of the function. If not provided, the description of the schema will be used. Returns: The tool description. """ function = convert_pydantic_to_openai_function( model, name=name, description=description ) return {"type": "function", "function": function}
def _get_python_function_name(function: Callable) -> str: """Get the name of a Python function.""" return function.__name__
[docs]@deprecated( "0.1.16", alternative="langchain_core.utils.function_calling.convert_to_openai_function()", removal="1.0", ) def convert_python_function_to_openai_function( function: Callable, ) -> FunctionDescription: """Convert a Python function to an OpenAI function-calling API compatible dict. Assumes the Python function has type hints and a docstring with a description. If the docstring has Google Python style argument descriptions, these will be included as well. Args: function: The Python function to convert. Returns: The OpenAI function description. """ from langchain_core.tools.base import create_schema_from_function func_name = _get_python_function_name(function) model = create_schema_from_function( func_name, function, filter_args=(), parse_docstring=True, error_on_invalid_docstring=False, include_injected=False, ) return convert_pydantic_to_openai_function( model, name=func_name, description=model.__doc__, )
def _convert_typed_dict_to_openai_function(typed_dict: Type) -> FunctionDescription: visited: Dict = {} model = cast( Type[BaseModel], _convert_any_typed_dicts_to_pydantic(typed_dict, visited=visited), ) return convert_pydantic_to_openai_function(model) _MAX_TYPED_DICT_RECURSION = 25 def _convert_any_typed_dicts_to_pydantic( type_: Type, *, visited: Dict, depth: int = 0, ) -> Type: if type_ in visited: return visited[type_] elif depth >= _MAX_TYPED_DICT_RECURSION: return type_ elif is_typeddict(type_): typed_dict = type_ docstring = inspect.getdoc(typed_dict) annotations_ = typed_dict.__annotations__ description, arg_descriptions = _parse_google_docstring( docstring, list(annotations_) ) fields: dict = {} for arg, arg_type in annotations_.items(): if get_origin(arg_type) is Annotated: annotated_args = get_args(arg_type) new_arg_type = _convert_any_typed_dicts_to_pydantic( annotated_args[0], depth=depth + 1, visited=visited ) field_kwargs = { k: v for k, v in zip(("default", "description"), annotated_args[1:]) } if (field_desc := field_kwargs.get("description")) and not isinstance( field_desc, str ): raise ValueError( f"Invalid annotation for field {arg}. Third argument to " f"Annotated must be a string description, received value of " f"type {type(field_desc)}." ) elif arg_desc := arg_descriptions.get(arg): field_kwargs["description"] = arg_desc else: pass fields[arg] = (new_arg_type, Field(**field_kwargs)) else: new_arg_type = _convert_any_typed_dicts_to_pydantic( arg_type, depth=depth + 1, visited=visited ) field_kwargs = {"default": ...} if arg_desc := arg_descriptions.get(arg): field_kwargs["description"] = arg_desc fields[arg] = (new_arg_type, Field(**field_kwargs)) model = create_model(typed_dict.__name__, **fields) model.__doc__ = description visited[typed_dict] = model return model elif (origin := get_origin(type_)) and (type_args := get_args(type_)): subscriptable_origin = _py_38_safe_origin(origin) type_args = tuple( _convert_any_typed_dicts_to_pydantic(arg, depth=depth + 1, visited=visited) for arg in type_args ) return subscriptable_origin[type_args] else: return type_
[docs]@deprecated( "0.1.16", alternative="langchain_core.utils.function_calling.convert_to_openai_function()", removal="1.0", ) def format_tool_to_openai_function(tool: BaseTool) -> FunctionDescription: """Format tool into the OpenAI function API. Args: tool: The tool to format. Returns: The function description. """ from langchain_core.tools import simple is_simple_oai_tool = isinstance(tool, simple.Tool) and not tool.args_schema if tool.tool_call_schema and not is_simple_oai_tool: return convert_pydantic_to_openai_function( tool.tool_call_schema, name=tool.name, description=tool.description ) else: return { "name": tool.name, "description": tool.description, "parameters": { # This is a hack to get around the fact that some tools # do not expose an args_schema, and expect an argument # which is a string. # And Open AI does not support an array type for the # parameters. "properties": { "__arg1": {"title": "__arg1", "type": "string"}, }, "required": ["__arg1"], "type": "object", }, }
[docs]@deprecated( "0.1.16", alternative="langchain_core.utils.function_calling.convert_to_openai_tool()", removal="1.0", ) def format_tool_to_openai_tool(tool: BaseTool) -> ToolDescription: """Format tool into the OpenAI function API. Args: tool: The tool to format. Returns: The tool description. """ function = format_tool_to_openai_function(tool) return {"type": "function", "function": function}
[docs]def convert_to_openai_function( function: Union[Dict[str, Any], Type, Callable, BaseTool], *, strict: Optional[bool] = None, ) -> Dict[str, Any]: """Convert a raw function/class to an OpenAI function. .. versionchanged:: 0.2.29 ``strict`` arg added. 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 or a JSON schema with top-level 'title' and 'description' keys specified. 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. .. versionadded:: 0.2.29 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. """ from langchain_core.tools import BaseTool # already in OpenAI function format if isinstance(function, dict) and all( k in function for k in ("name", "description", "parameters") ): oai_function = function # a JSON schema with title and description elif isinstance(function, dict) and all( k in function for k in ("title", "description", "properties") ): function = function.copy() oai_function = { "name": function.pop("title"), "description": function.pop("description"), "parameters": function, } 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, 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: raise ValueError( f"Unsupported function\n\n{function}\n\nFunctions must be passed in" " as Dict, pydantic.BaseModel, or Callable. If they're a dict they must" " either be in OpenAI function format or valid JSON schema with top-level" " 'title' and 'description' keys." ) if strict is not None: oai_function["strict"] = strict if strict: # As of 08/06/24, OpenAI requires that additionalProperties be supplied and # set to False if strict is True. # All properties layer needs 'additionalProperties=False' oai_function["parameters"] = _recursive_set_additional_properties_false( oai_function["parameters"] ) return oai_function
[docs]def convert_to_openai_tool( tool: Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool], *, strict: Optional[bool] = None, ) -> Dict[str, Any]: """Convert a raw function/class to an OpenAI tool. .. versionchanged:: 0.2.29 ``strict`` arg added. Args: tool: Either a dictionary, a pydantic.BaseModel class, Python function, or BaseTool. If a dictionary is passed in, it is assumed to already be a valid OpenAI tool, OpenAI function, or a JSON schema with top-level 'title' and 'description' keys specified. 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 tool definition. .. versionadded:: 0.2.29 Returns: A dict version of the passed in tool which is compatible with the OpenAI tool-calling API. """ if isinstance(tool, dict) and tool.get("type") == "function" and "function" in tool: return tool oai_function = convert_to_openai_function(tool, strict=strict) oai_tool: Dict[str, Any] = {"type": "function", "function": oai_function} return oai_tool
[docs]def tool_example_to_messages( input: str, tool_calls: List[BaseModel], tool_outputs: Optional[List[str]] = None ) -> List[BaseMessage]: """Convert an example into a list of messages that can be fed into an LLM. This code is an adapter that converts a single example to a list of messages that can be fed into a chat model. The list of messages per example corresponds to: 1) HumanMessage: contains the content from which content should be extracted. 2) AIMessage: contains the extracted information from the model 3) ToolMessage: contains confirmation to the model that the model requested a tool correctly. The ToolMessage is required because some chat models are hyper-optimized for agents rather than for an extraction use case. Arguments: input: string, the user input tool_calls: List[BaseModel], a list of tool calls represented as Pydantic BaseModels tool_outputs: Optional[List[str]], a list of tool call outputs. Does not need to be provided. If not provided, a placeholder value will be inserted. Defaults to None. Returns: A list of messages Examples: .. code-block:: python from typing import List, Optional from langchain_core.pydantic_v1 import BaseModel, Field from langchain_openai import ChatOpenAI class Person(BaseModel): '''Information about a person.''' name: Optional[str] = Field(..., description="The name of the person") hair_color: Optional[str] = Field( ..., description="The color of the person's hair if known" ) height_in_meters: Optional[str] = Field( ..., description="Height in METERs" ) examples = [ ( "The ocean is vast and blue. It's more than 20,000 feet deep.", Person(name=None, height_in_meters=None, hair_color=None), ), ( "Fiona traveled far from France to Spain.", Person(name="Fiona", height_in_meters=None, hair_color=None), ), ] messages = [] for txt, tool_call in examples: messages.extend( tool_example_to_messages(txt, [tool_call]) ) """ messages: List[BaseMessage] = [HumanMessage(content=input)] openai_tool_calls = [] for tool_call in tool_calls: openai_tool_calls.append( { "id": str(uuid.uuid4()), "type": "function", "function": { # The name of the function right now corresponds to the name # of the pydantic model. This is implicit in the API right now, # and will be improved over time. "name": tool_call.__class__.__name__, "arguments": tool_call.json(), }, } ) messages.append( AIMessage(content="", additional_kwargs={"tool_calls": openai_tool_calls}) ) tool_outputs = tool_outputs or ["You have correctly called this tool."] * len( openai_tool_calls ) for output, tool_call_dict in zip(tool_outputs, openai_tool_calls): messages.append(ToolMessage(content=output, tool_call_id=tool_call_dict["id"])) # type: ignore return messages
def _parse_google_docstring( docstring: Optional[str], args: List[str], *, error_on_invalid_docstring: bool = False, ) -> Tuple[str, dict]: """Parse the function and argument descriptions from the docstring of a function. Assumes the function docstring follows Google Python style guide. """ if docstring: docstring_blocks = docstring.split("\n\n") if error_on_invalid_docstring: filtered_annotations = { arg for arg in args if arg not in ("run_manager", "callbacks", "return") } if filtered_annotations and ( len(docstring_blocks) < 2 or not docstring_blocks[1].startswith("Args:") ): raise ValueError("Found invalid Google-Style docstring.") descriptors = [] args_block = None past_descriptors = False for block in docstring_blocks: if block.startswith("Args:"): args_block = block break elif block.startswith("Returns:") or block.startswith("Example:"): # Don't break in case Args come after past_descriptors = True elif not past_descriptors: descriptors.append(block) else: continue description = " ".join(descriptors) else: if error_on_invalid_docstring: raise ValueError("Found invalid Google-Style docstring.") description = "" args_block = None arg_descriptions = {} if args_block: arg = None for line in args_block.split("\n")[1:]: if ":" in line: arg, desc = line.split(":", maxsplit=1) arg_descriptions[arg.strip()] = desc.strip() elif arg: arg_descriptions[arg.strip()] += " " + line.strip() return description, arg_descriptions def _py_38_safe_origin(origin: Type) -> Type: origin_union_type_map: Dict[Type, Any] = ( {types.UnionType: Union} if hasattr(types, "UnionType") else {} ) origin_map: Dict[Type, Any] = { dict: Dict, list: List, tuple: Tuple, set: Set, collections.abc.Iterable: typing.Iterable, collections.abc.Mapping: typing.Mapping, collections.abc.Sequence: typing.Sequence, collections.abc.MutableMapping: typing.MutableMapping, **origin_union_type_map, } return cast(Type, origin_map.get(origin, origin)) def _recursive_set_additional_properties_false( schema: Dict[str, Any], ) -> Dict[str, Any]: if isinstance(schema, dict): # Check if 'required' is a key at the current level or if the schema is empty, # in which case additionalProperties still needs to be specified. if "required" in schema or ( "properties" in schema and not schema["properties"] ): schema["additionalProperties"] = False # Recursively check 'properties' and 'items' if they exist if "properties" in schema: for value in schema["properties"].values(): _recursive_set_additional_properties_false(value) if "items" in schema: _recursive_set_additional_properties_false(schema["items"]) return schema