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230 | class FunctionTool(AsyncBaseTool):
"""Function Tool.
A tool that takes in a function and optionally handles workflow context.
"""
def __init__(
self,
fn: Optional[Callable[..., Any]] = None,
metadata: Optional[ToolMetadata] = None,
async_fn: Optional[AsyncCallable] = None,
) -> None:
if fn is None and async_fn is None:
raise ValueError("fn must be provided")
# If async_fn is provided explicitly, use it. Otherwise check if fn is async
if async_fn is not None:
self._async_fn = async_fn
self._fn = fn or async_to_sync(async_fn)
else:
assert fn is not None
if inspect.iscoroutinefunction(fn):
self._async_fn = fn
self._fn = async_to_sync(fn)
else:
self._fn = fn
self._async_fn = sync_to_async(fn)
# Determine if function requires context by inspecting signature
fn_to_inspect = fn or async_fn
assert fn_to_inspect is not None
sig = inspect.signature(fn_to_inspect)
self.requires_context = any(
param.annotation == Context for param in sig.parameters.values()
)
if metadata is None:
raise ValueError("metadata must be provided")
self._metadata = metadata
@classmethod
def from_defaults(
cls,
fn: Optional[Callable[..., Any]] = None,
name: Optional[str] = None,
description: Optional[str] = None,
return_direct: bool = False,
fn_schema: Optional[Type[BaseModel]] = None,
async_fn: Optional[AsyncCallable] = None,
tool_metadata: Optional[ToolMetadata] = None,
) -> "FunctionTool":
if tool_metadata is None:
fn_to_parse = fn or async_fn
assert fn_to_parse is not None, "fn must be provided"
name = name or fn_to_parse.__name__
docstring = fn_to_parse.__doc__
# Get function signature
fn_sig = inspect.signature(fn_to_parse)
# Remove ctx parameter from schema if present
has_ctx = any(
param.annotation == Context for param in fn_sig.parameters.values()
)
ctx_param_name = None
if has_ctx:
ctx_param_name = next(
param.name
for param in fn_sig.parameters.values()
if param.annotation == Context
)
fn_sig = fn_sig.replace(
parameters=[
param
for param in fn_sig.parameters.values()
if param.annotation != Context
]
)
# Handle FieldInfo defaults
fn_sig = fn_sig.replace(
parameters=[
param.replace(default=inspect.Parameter.empty)
if isinstance(param.default, FieldInfo)
else param
for param in fn_sig.parameters.values()
]
)
description = description or f"{name}{fn_sig}\n{docstring}"
if fn_schema is None:
fn_schema = create_schema_from_function(
f"{name}",
fn_to_parse,
additional_fields=None,
ignore_fields=[ctx_param_name]
if ctx_param_name is not None
else None,
)
tool_metadata = ToolMetadata(
name=name,
description=description,
fn_schema=fn_schema,
return_direct=return_direct,
)
return cls(
fn=fn,
metadata=tool_metadata,
async_fn=async_fn,
)
@property
def metadata(self) -> ToolMetadata:
"""Metadata."""
return self._metadata
@property
def fn(self) -> Callable[..., Any]:
"""Function."""
return self._fn
@property
def async_fn(self) -> AsyncCallable:
"""Async function."""
return self._async_fn
def call(
self, *args: Any, ctx: Optional[Context] = None, **kwargs: Any
) -> ToolOutput:
"""Call."""
if self.requires_context:
if ctx is None:
raise ValueError("Context is required for this tool")
tool_output = self._fn(ctx, *args, **kwargs)
else:
tool_output = self._fn(*args, **kwargs)
return ToolOutput(
content=str(tool_output),
tool_name=self.metadata.name,
raw_input={"args": args, "kwargs": kwargs},
raw_output=tool_output,
)
async def acall(
self, *args: Any, ctx: Optional[Context] = None, **kwargs: Any
) -> ToolOutput:
"""Call."""
if self.requires_context:
if ctx is None:
raise ValueError("Context is required for this tool")
tool_output = await self._async_fn(ctx, *args, **kwargs)
else:
tool_output = await self._async_fn(*args, **kwargs)
return ToolOutput(
content=str(tool_output),
tool_name=self.metadata.name,
raw_input={"args": args, "kwargs": kwargs},
raw_output=tool_output,
)
def to_langchain_tool(
self,
**langchain_tool_kwargs: Any,
) -> "Tool":
"""To langchain tool."""
from llama_index.core.bridge.langchain import Tool
langchain_tool_kwargs = self._process_langchain_tool_kwargs(
langchain_tool_kwargs
)
return Tool.from_function(
func=self.fn,
coroutine=self.async_fn,
**langchain_tool_kwargs,
)
def to_langchain_structured_tool(
self,
**langchain_tool_kwargs: Any,
) -> "StructuredTool":
"""To langchain structured tool."""
from llama_index.core.bridge.langchain import StructuredTool
langchain_tool_kwargs = self._process_langchain_tool_kwargs(
langchain_tool_kwargs
)
return StructuredTool.from_function(
func=self.fn,
coroutine=self.async_fn,
**langchain_tool_kwargs,
)
|
