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Tool spec

Base tool spec class.

BaseToolSpec #

Base tool spec class.

Source code in llama-index-core/llama_index/core/tools/tool_spec/base.py 
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class BaseToolSpec:
    """Base tool spec class."""

    # list of functions that you'd want to convert to spec
    spec_functions: List[SPEC_FUNCTION_TYPE]

    def get_fn_schema_from_fn_name(
        self, fn_name: str, spec_functions: Optional[List[SPEC_FUNCTION_TYPE]] = None
    ) -> Optional[Type[BaseModel]]:
        """Return map from function name.

        Return type is Optional, meaning that the schema can be None.
        In this case, it's up to the downstream tool implementation to infer the schema.

        """
        spec_functions = spec_functions or self.spec_functions
        for fn in spec_functions:
            if fn == fn_name:
                return create_schema_from_function(fn_name, getattr(self, fn_name))

        raise ValueError(f"Invalid function name: {fn_name}")

    def get_metadata_from_fn_name(
        self, fn_name: str, spec_functions: Optional[List[SPEC_FUNCTION_TYPE]] = None
    ) -> Optional[ToolMetadata]:
        """Return map from function name.

        Return type is Optional, meaning that the schema can be None.
        In this case, it's up to the downstream tool implementation to infer the schema.

        """
        try:
            func = getattr(self, fn_name)
        except AttributeError:
            return None
        name = fn_name
        docstring = func.__doc__ or ""
        description = f"{name}{signature(func)}\n{docstring}"
        fn_schema = self.get_fn_schema_from_fn_name(
            fn_name, spec_functions=spec_functions
        )
        return ToolMetadata(name=name, description=description, fn_schema=fn_schema)

    def to_tool_list(
        self,
        spec_functions: Optional[List[SPEC_FUNCTION_TYPE]] = None,
        func_to_metadata_mapping: Optional[Dict[str, ToolMetadata]] = None,
    ) -> List[FunctionTool]:
        """Convert tool spec to list of tools."""
        spec_functions = spec_functions or self.spec_functions
        func_to_metadata_mapping = func_to_metadata_mapping or {}
        tool_list = []
        for func_spec in spec_functions:
            func_sync = None
            func_async = None
            if isinstance(func_spec, str):
                func = getattr(self, func_spec)
                if asyncio.iscoroutinefunction(func):
                    func_async = func
                else:
                    func_sync = func
                metadata = func_to_metadata_mapping.get(func_spec, None)
                if metadata is None:
                    metadata = self.get_metadata_from_fn_name(func_spec)
            elif isinstance(func_spec, tuple) and len(func_spec) == 2:
                func_sync = getattr(self, func_spec[0])
                func_async = getattr(self, func_spec[1])
                metadata = func_to_metadata_mapping.get(func_spec[0], None)
                if metadata is None:
                    metadata = func_to_metadata_mapping.get(func_spec[1], None)
                    if metadata is None:
                        metadata = self.get_metadata_from_fn_name(func_spec[0])
            else:
                raise ValueError(
                    "spec_functions must be of type: List[Union[str, Tuple[str, str]]]"
                )

            if func_sync is None:
                if func_async is not None:
                    func_sync = patch_sync(func_async)
                else:
                    raise ValueError(
                        f"Could not retrieve a function for spec: {func_spec}"
                    )

            tool = FunctionTool.from_defaults(
                fn=func_sync,
                async_fn=func_async,
                tool_metadata=metadata,
            )
            tool_list.append(tool)
        return tool_list

get_fn_schema_from_fn_name #

get_fn_schema_from_fn_name(fn_name: str, spec_functions: Optional[List[SPEC_FUNCTION_TYPE]] = None) -> Optional[Type[BaseModel]]

Return map from function name.

Return type is Optional, meaning that the schema can be None. In this case, it's up to the downstream tool implementation to infer the schema.

Source code in llama-index-core/llama_index/core/tools/tool_spec/base.py 
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def get_fn_schema_from_fn_name(
    self, fn_name: str, spec_functions: Optional[List[SPEC_FUNCTION_TYPE]] = None
) -> Optional[Type[BaseModel]]:
    """Return map from function name.

    Return type is Optional, meaning that the schema can be None.
    In this case, it's up to the downstream tool implementation to infer the schema.

    """
    spec_functions = spec_functions or self.spec_functions
    for fn in spec_functions:
        if fn == fn_name:
            return create_schema_from_function(fn_name, getattr(self, fn_name))

    raise ValueError(f"Invalid function name: {fn_name}")

get_metadata_from_fn_name #

get_metadata_from_fn_name(fn_name: str, spec_functions: Optional[List[SPEC_FUNCTION_TYPE]] = None) -> Optional[ToolMetadata]

Return map from function name.

Return type is Optional, meaning that the schema can be None. In this case, it's up to the downstream tool implementation to infer the schema.

Source code in llama-index-core/llama_index/core/tools/tool_spec/base.py 
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def get_metadata_from_fn_name(
    self, fn_name: str, spec_functions: Optional[List[SPEC_FUNCTION_TYPE]] = None
) -> Optional[ToolMetadata]:
    """Return map from function name.

    Return type is Optional, meaning that the schema can be None.
    In this case, it's up to the downstream tool implementation to infer the schema.

    """
    try:
        func = getattr(self, fn_name)
    except AttributeError:
        return None
    name = fn_name
    docstring = func.__doc__ or ""
    description = f"{name}{signature(func)}\n{docstring}"
    fn_schema = self.get_fn_schema_from_fn_name(
        fn_name, spec_functions=spec_functions
    )
    return ToolMetadata(name=name, description=description, fn_schema=fn_schema)

to_tool_list #

to_tool_list(spec_functions: Optional[List[SPEC_FUNCTION_TYPE]] = None, func_to_metadata_mapping: Optional[Dict[str, ToolMetadata]] = None) -> List[FunctionTool]

Convert tool spec to list of tools.

Source code in llama-index-core/llama_index/core/tools/tool_spec/base.py 
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def to_tool_list(
    self,
    spec_functions: Optional[List[SPEC_FUNCTION_TYPE]] = None,
    func_to_metadata_mapping: Optional[Dict[str, ToolMetadata]] = None,
) -> List[FunctionTool]:
    """Convert tool spec to list of tools."""
    spec_functions = spec_functions or self.spec_functions
    func_to_metadata_mapping = func_to_metadata_mapping or {}
    tool_list = []
    for func_spec in spec_functions:
        func_sync = None
        func_async = None
        if isinstance(func_spec, str):
            func = getattr(self, func_spec)
            if asyncio.iscoroutinefunction(func):
                func_async = func
            else:
                func_sync = func
            metadata = func_to_metadata_mapping.get(func_spec, None)
            if metadata is None:
                metadata = self.get_metadata_from_fn_name(func_spec)
        elif isinstance(func_spec, tuple) and len(func_spec) == 2:
            func_sync = getattr(self, func_spec[0])
            func_async = getattr(self, func_spec[1])
            metadata = func_to_metadata_mapping.get(func_spec[0], None)
            if metadata is None:
                metadata = func_to_metadata_mapping.get(func_spec[1], None)
                if metadata is None:
                    metadata = self.get_metadata_from_fn_name(func_spec[0])
        else:
            raise ValueError(
                "spec_functions must be of type: List[Union[str, Tuple[str, str]]]"
            )

        if func_sync is None:
            if func_async is not None:
                func_sync = patch_sync(func_async)
            else:
                raise ValueError(
                    f"Could not retrieve a function for spec: {func_spec}"
                )

        tool = FunctionTool.from_defaults(
            fn=func_sync,
            async_fn=func_async,
            tool_metadata=metadata,
        )
        tool_list.append(tool)
    return tool_list