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langchain/agents/tools.py

@@ -1,10 +1,23 @@
 """Interface for tools."""
+import inspect
 from inspect import signature
-from typing import Any, Awaitable, Callable, Optional, Type, Union
+from typing import (
+    Any,
+    Awaitable,
+    Callable,
+    Dict,
+    Optional,
+    Sequence,
+    Tuple,
+    Type,
+    Union,
+)
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Field, create_model
 
-from langchain.tools.base import BaseTool, create_args_schema_model_from_signature
+from langchain.tools.base import (
+    BaseTool,
+)
 
 
 class Tool(BaseTool):
@@ -16,23 +29,22 @@ class Tool(BaseTool):
     coroutine: Optional[Callable[..., Awaitable[str]]] = None
     """The asynchronous version of the function."""
 
-    @property
+    def _run(self, tool_input: Union[str, BaseModel]) -> str:
-    def args(self) -> Type[BaseModel]:
-        """Generate an input pydantic model."""
-        if self.args_schema is not None:
-            return self.args_schema
-        # Infer the schema directly from the function to add more structured
-        # arguments.
-        return create_args_schema_model_from_signature(self.func)
-
-    def _run(self, *args: Any, **kwargs: Any) -> str:
         """Use the tool."""
-        return self.func(*args, **kwargs)
+        if isinstance(tool_input, str):
+            return self.func(tool_input)
+        else:
+            args, kwargs = _to_args_and_kwargs(tool_input)
+            return self.func(*args, **kwargs)
 
-    async def _arun(self, *args: Any, **kwargs: Any) -> str:
+    async def _arun(self, tool_input: Union[str, BaseModel]) -> str:
         """Use the tool asynchronously."""
         if self.coroutine:
-            return await self.coroutine(*args, **kwargs)
+            if isinstance(tool_input, str):
+                return await self.coroutine(tool_input)
+            else:
+                args, kwargs = _to_args_and_kwargs(tool_input)
+                return await self.coroutine(*args, **kwargs)
         raise NotImplementedError("Tool does not support async")
 
     # TODO: this is for backwards compatibility, remove in future
@@ -51,13 +63,89 @@ class InvalidTool(BaseTool):
     name = "invalid_tool"
     description = "Called when tool name is invalid."
 
-    def _run(self, tool_name: str) -> str:
+    def _run(self, tool_name: Union[str, BaseModel]) -> str:
         """Use the tool."""
-        return f"{tool_name} is not a valid tool, try another one."
+        return f"{str(tool_name)} is not a valid tool, try another one."
 
-    async def _arun(self, tool_name: str) -> str:
+    async def _arun(self, tool_name: Union[str, BaseModel]) -> str:
         """Use the tool asynchronously."""
-        return f"{tool_name} is not a valid tool, try another one."
+        return f"{str(tool_name)} is not a valid tool, try another one."
+
+
+def _to_args_and_kwargs(model: BaseModel) -> Tuple[Sequence, dict]:
+    """Convert pydantic model to args and kwargs."""
+    args = []
+    kwargs = {}
+    for name, field in model.__fields__.items():
+        value = getattr(model, name)
+        # Handle *args in the function signature
+        if field.field_info.extra.get("extra", {}).get("is_var_positional"):
+            if isinstance(value, str):
+                # Base case for backwards compatability
+                args.append(value)
+            elif value is not None:
+                args.extend(value)
+        # Handle **kwargs in the function signature
+        elif field.field_info.extra.get("extra", {}).get("is_var_keyword"):
+            if value is not None:
+                kwargs.update(value)
+        elif field.field_info.extra.get("extra", {}).get("is_keyword_only"):
+            kwargs[name] = value
+        else:
+            args.append(value)
+
+    return tuple(args), kwargs
+
+
+def _create_args_schema_model_from_signature(run_func: Callable) -> Type[BaseModel]:
+    """Create a pydantic model type from a function's signature."""
+    signature_ = inspect.signature(run_func)
+    field_definitions: Dict[str, Any] = {}
+
+    for name, param in signature_.parameters.items():
+        if name == "self":
+            continue
+        default_value = (
+            param.default if param.default != inspect.Parameter.empty else None
+        )
+        annotation = (
+            param.annotation if param.annotation != inspect.Parameter.empty else Any
+        )
+        # Handle functions with *args in the signature
+        if param.kind == inspect.Parameter.VAR_POSITIONAL:
+            field_definitions[name] = (
+                Any,
+                Field(default=None, extra={"is_var_positional": True}),
+            )
+        # handle functions with **kwargs in the signature
+        elif param.kind == inspect.Parameter.VAR_KEYWORD:
+            field_definitions[name] = (
+                Any,
+                Field(default=None, extra={"is_var_keyword": True}),
+            )
+        # Handle all other named parameters
+        else:
+            is_keyword_only = param.kind == inspect.Parameter.KEYWORD_ONLY
+            field_definitions[name] = (
+                annotation,
+                Field(
+                    default=default_value, extra={"is_keyword_only": is_keyword_only}
+                ),
+            )
+    return create_model("ArgsModel", **field_definitions)  # type: ignore
+
+
+def _create_schema_if_multiarg(
+    func: Callable,
+) -> Optional[Type[BaseModel]]:
+    signature_ = inspect.signature(func)
+    parameters = signature_.parameters
+    if len(parameters) == 1 and next(iter(parameters.values())).annotation == str:
+        # Default tools take a single string as input and don't need a dynamic
+        # schema validation
+        return None
+    else:
+        return _create_args_schema_model_from_signature(func)
 
 
 def tool(*args: Union[str, Callable], return_direct: bool = False) -> Callable:
@@ -87,7 +175,7 @@ def tool(*args: Union[str, Callable], return_direct: bool = False) -> Callable:
             # Description example:
             # search_api(query: str) - Searches the API for the query.
             description = f"{tool_name}{signature(func)} - {func.__doc__.strip()}"
-            args_schema = create_args_schema_model_from_signature(func)
+            args_schema = _create_schema_if_multiarg(func)
             tool_ = Tool(
                 name=tool_name,
                 func=func,

langchain/tools/base.py

@@ -1,77 +1,14 @@
 """Base implementation for tools or skills."""
 
-import inspect
 from abc import ABC, abstractmethod
-from typing import Any, Callable, Dict, Optional, Sequence, Tuple, Type, Union
+from typing import Any, Dict, Optional, Type, Union
 
-from pydantic import BaseModel, Extra, Field, create_model, validator
+from pydantic import BaseModel, Extra, Field, validator
 
 from langchain.callbacks import get_callback_manager
 from langchain.callbacks.base import BaseCallbackManager
 
 
-def create_args_schema_model_from_signature(run_func: Callable) -> Type[BaseModel]:
-    """Create a pydantic model type from a function's signature."""
-    signature_ = inspect.signature(run_func)
-    field_definitions: Dict[str, Any] = {}
-
-    for name, param in signature_.parameters.items():
-        if name == "self":
-            continue
-        default_value = (
-            param.default if param.default != inspect.Parameter.empty else None
-        )
-        annotation = (
-            param.annotation if param.annotation != inspect.Parameter.empty else Any
-        )
-        # Handle functions with *args in the signature
-        if param.kind == inspect.Parameter.VAR_POSITIONAL:
-            field_definitions[name] = (
-                Any,
-                Field(default=None, extra={"is_var_positional": True}),
-            )
-        # handle functions with **kwargs in the signature
-        elif param.kind == inspect.Parameter.VAR_KEYWORD:
-            field_definitions[name] = (
-                Any,
-                Field(default=None, extra={"is_var_keyword": True}),
-            )
-        # Handle all other named parameters
-        else:
-            is_keyword_only = param.kind == inspect.Parameter.KEYWORD_ONLY
-            field_definitions[name] = (
-                annotation,
-                Field(
-                    default=default_value, extra={"is_keyword_only": is_keyword_only}
-                ),
-            )
-    return create_model("ArgsModel", **field_definitions)  # type: ignore
-
-
-def _to_args_and_kwargs(model: BaseModel) -> Tuple[Sequence, dict]:
-    args = []
-    kwargs = {}
-    for name, field in model.__fields__.items():
-        value = getattr(model, name)
-        # Handle *args in the function signature
-        if field.field_info.extra.get("extra", {}).get("is_var_positional"):
-            if isinstance(value, str):
-                # Base case for backwards compatability
-                args.append(value)
-            elif value is not None:
-                args.extend(value)
-        # Handle **kwargs in the function signature
-        elif field.field_info.extra.get("extra", {}).get("is_var_keyword"):
-            if value is not None:
-                kwargs.update(value)
-        elif field.field_info.extra.get("extra", {}).get("is_keyword_only"):
-            kwargs[name] = value
-        else:
-            args.append(value)
-
-    return tuple(args), kwargs
-
-
 class BaseTool(ABC, BaseModel):
     """Interface LangChain tools must implement."""
 
@@ -89,27 +26,15 @@ class BaseTool(ABC, BaseModel):
         extra = Extra.forbid
         arbitrary_types_allowed = True
 
-    @property
-    def args(self) -> Type[BaseModel]:
-        """Generate an input pydantic model."""
-        if self.args_schema is not None:
-            return self.args_schema
-        return create_args_schema_model_from_signature(self._run)
-
     def _parse_input(
         self,
         tool_input: Union[str, Dict],
-    ) -> BaseModel:
+    ) -> Union[str, BaseModel]:
         """Convert tool input to pydantic model."""
-        pydantic_input_type = self.args
         if isinstance(tool_input, str):
-            # For backwards compatibility, a tool that only takes
+            return tool_input
-            # a single string input will be converted to a dict.
+        if self.args_schema is not None:
-            # to be validated.
+            return self.args_schema.parse_obj(tool_input)
-            field_name = next(iter(pydantic_input_type.__fields__))
-            tool_input = {field_name: tool_input}
-        if pydantic_input_type is not None:
-            return pydantic_input_type.parse_obj(tool_input)
         else:
             raise ValueError(
                 f"args_schema required for tool {self.name} in order to"
@@ -127,11 +52,11 @@ class BaseTool(ABC, BaseModel):
         return callback_manager or get_callback_manager()
 
     @abstractmethod
-    def _run(self, *args: Any, **kwargs: Any) -> str:
+    def _run(self, tool_input: Union[str, BaseModel]) -> str:
         """Use the tool."""
 
     @abstractmethod
-    async def _arun(self, *args: Any, **kwargs: Any) -> str:
+    async def _arun(self, tool_input: Union[str, BaseModel]) -> str:
         """Use the tool asynchronously."""
 
     def run(
@@ -156,8 +81,7 @@ class BaseTool(ABC, BaseModel):
             **kwargs,
         )
         try:
-            args, kwargs = _to_args_and_kwargs(run_input)
+            observation = self._run(run_input)
-            observation = self._run(*args, **kwargs)
         except (Exception, KeyboardInterrupt) as e:
             self.callback_manager.on_tool_error(e, verbose=verbose_)
             raise e
@@ -183,7 +107,7 @@ class BaseTool(ABC, BaseModel):
         if self.callback_manager.is_async:
             await self.callback_manager.on_tool_start(
                 {"name": self.name, "description": self.description},
-                str(run_input.dict()),
+                str(run_input),
                 verbose=verbose_,
                 color=start_color,
                 **kwargs,
@@ -191,15 +115,14 @@ class BaseTool(ABC, BaseModel):
         else:
             self.callback_manager.on_tool_start(
                 {"name": self.name, "description": self.description},
-                str(run_input.dict()),
+                str(run_input),
                 verbose=verbose_,
                 color=start_color,
                 **kwargs,
             )
         try:
             # We then call the tool on the tool input to get an observation
-            args, kwargs = _to_args_and_kwargs(run_input)
+            observation = await self._arun(run_input)
-            observation = await self._arun(*args, **kwargs)
         except (Exception, KeyboardInterrupt) as e:
             if self.callback_manager.is_async:
                 await self.callback_manager.on_tool_error(e, verbose=verbose_)
@@ -219,3 +142,7 @@ class BaseTool(ABC, BaseModel):
     def __call__(self, tool_input: str) -> str:
         """Make tool callable."""
         return self.run(tool_input)
+
+
+def foo(tool_input: str) -> str:
+    return tool_input

tests/unit_tests/agents/test_tools.py

@@ -34,8 +34,8 @@ class _MockStructuredTool(BaseTool):
     args_schema: Type[BaseModel] = _MockSchema
     description = "A Structured Tool"
 
-    def _run(self, arg1: int, arg2: bool, arg3: Optional[dict] = None) -> str:
+    def _run(self, schema: BaseModel) -> str:
-        return f"{arg1} {arg2} {arg3}"
+        return f"{schema.arg1} {schema.arg2} {schema.arg3}"
 
     async def _arun(self, arg1: int, arg2: bool, arg3: Optional[dict] = None) -> str:
         raise NotImplementedError