Force imports inside tool

src/agents/tools.py

@@ -22,6 +22,7 @@ import io
 import json
 import os
 import tempfile
+import textwrap
 from functools import lru_cache, wraps
 from pathlib import Path
 from typing import Any, Callable, Dict, List, Optional, Union
@@ -115,12 +116,77 @@ def validate_after_init(cls, do_validate_forward: bool = True):
     @wraps(original_init)
     def new_init(self, *args, **kwargs):
         original_init(self, *args, **kwargs)
-        if not isinstance(self, PipelineTool):
         self.validate_arguments(do_validate_forward=do_validate_forward)
 
     cls.__init__ = new_init
     return cls
 
+def validate_forward_method_args(cls):
+    """Validates that all names in forward method are properly defined.
+    In particular it will check that all imports are done within the function."""
+    if 'forward' not in cls.__dict__:
+        return
+
+    forward = cls.__dict__['forward']
+    source_code = textwrap.dedent(inspect.getsource(forward))
+    tree = ast.parse(source_code)
+    
+    # Get function arguments
+    func_node = tree.body[0]
+    arg_names = {arg.arg for arg in func_node.args.args}
+
+
+    import builtins
+    builtin_names = set(vars(builtins))
+
+    
+    # Find all used names that aren't arguments or self attributes
+    class NameChecker(ast.NodeVisitor):
+        def __init__(self):
+            self.undefined_names = set()
+            self.imports = {}
+            self.from_imports = {}
+
+        def visit_Import(self, node):
+            """Handle simple imports like 'import datetime'."""
+            for name in node.names:
+                actual_name = name.asname or name.name
+                self.imports[actual_name] = (name.name, actual_name)
+                
+        def visit_ImportFrom(self, node):
+            """Handle from imports like 'from datetime import datetime'."""
+            module = node.module or ''
+            for name in node.names:
+                actual_name = name.asname or name.name
+                self.from_imports[actual_name] = (module, name.name, actual_name)
+            
+        def visit_Name(self, node):
+            if (isinstance(node.ctx, ast.Load) and not (
+                node.id == "tool" or
+                node.id in builtin_names or
+                node.id in arg_names or 
+                node.id == 'self'
+            )):
+                if node.id not in self.from_imports and node.id not in self.imports:
+                    self.undefined_names.add(node.id)
+                
+        def visit_Attribute(self, node):
+            # Skip self.something
+            if not (isinstance(node.value, ast.Name) and node.value.id == 'self'):
+                self.generic_visit(node)
+    
+    checker = NameChecker()
+    checker.visit(tree)
+    
+    if checker.undefined_names:
+        raise ValueError(
+            f"""The following names in forward method are not defined: {', '.join(checker.undefined_names)}.
+            Make sure all imports and variables are defined within the method.
+            For instance:
+            
+            """
+        )
+
 AUTHORIZED_TYPES = [
     "string",
     "boolean",
@@ -136,7 +202,7 @@ CONVERSION_DICT = {"str": "string", "int": "integer", "float": "number"}
 
 class Tool:
     """
-    A base class for the functions used by the agent. Subclass this and implement the `__call__` method as well as the
+    A base class for the functions used by the agent. Subclass this and implement the `forward` method as well as the
     following class attributes:
 
     - **description** (`str`) -- A short description of what your tool does, the inputs it expects and the output(s) it
@@ -151,7 +217,7 @@ class Tool:
     - **output_type** (`type`) -- The type of the tool output. This is used by `launch_gradio_demo`
       or to make a nice space from your tool, and also can be used in the generated description for your tool.
 
-    You can also override the method [`~Tool.setup`] if your tool as an expensive operation to perform before being
+    You can also override the method [`~Tool.setup`] if your tool has an expensive operation to perform before being
     usable (such as loading a model). [`~Tool.setup`] will be called the first time you use your tool, but not at
     instantiation.
     """
@@ -166,8 +232,10 @@ class Tool:
 
     def __init_subclass__(cls, **kwargs):
         super().__init_subclass__(**kwargs)
+        validate_forward_method_args(cls)
         validate_after_init(cls, do_validate_forward=False)
 
+
     def validate_arguments(self, do_validate_forward: bool = True):
         required_attributes = {
             "description": str,
@@ -198,7 +266,6 @@ class Tool:
 
         assert getattr(self, "output_type", None) in AUTHORIZED_TYPES
         if do_validate_forward:
-            if not isinstance(self, PipelineTool):
             signature = inspect.signature(self.forward)
             if not set(signature.parameters.keys()) == set(self.inputs.keys()):
                 raise Exception(
@@ -206,9 +273,11 @@ class Tool:
                 )
 
     def forward(self, *args, **kwargs):
-        return NotImplemented("Write this method in your subclass of `Tool`.")
+        return NotImplementedError("Write this method in your subclass of `Tool`.")
 
     def __call__(self, *args, **kwargs):
+        if not self.is_initialized:
+            self.setup()
         args, kwargs = handle_agent_inputs(*args, **kwargs)
         outputs = self.forward(*args, **kwargs)
         return handle_agent_outputs(outputs, self.output_type)
@@ -225,7 +294,6 @@ class Tool:
         Saves the relevant code files for your tool so it can be pushed to the Hub. This will copy the code of your
         tool in `output_dir` as well as autogenerate:
 
-        - a config file named `tool_config.json`
         - an `app.py` file so that your tool can be converted to a space
         - a `requirements.txt` containing the names of the module used by your tool (as detected when inspecting its
           code)
@@ -677,166 +745,6 @@ def compile_jinja_template(template):
     return jinja_env.from_string(template)
 
 
-class PipelineTool(Tool):
-    """
-    A [`Tool`] tailored towards Transformer models. On top of the class attributes of the base class [`Tool`], you will
-    need to specify:
-
-    - **model_class** (`type`) -- The class to use to load the model in this tool.
-    - **default_checkpoint** (`str`) -- The default checkpoint that should be used when the user doesn't specify one.
-    - **pre_processor_class** (`type`, *optional*, defaults to [`AutoProcessor`]) -- The class to use to load the
-      pre-processor
-    - **post_processor_class** (`type`, *optional*, defaults to [`AutoProcessor`]) -- The class to use to load the
-      post-processor (when different from the pre-processor).
-
-    Args:
-        model (`str` or [`PreTrainedModel`], *optional*):
-            The name of the checkpoint to use for the model, or the instantiated model. If unset, will default to the
-            value of the class attribute `default_checkpoint`.
-        pre_processor (`str` or `Any`, *optional*):
-            The name of the checkpoint to use for the pre-processor, or the instantiated pre-processor (can be a
-            tokenizer, an image processor, a feature extractor or a processor). Will default to the value of `model` if
-            unset.
-        post_processor (`str` or `Any`, *optional*):
-            The name of the checkpoint to use for the post-processor, or the instantiated pre-processor (can be a
-            tokenizer, an image processor, a feature extractor or a processor). Will default to the `pre_processor` if
-            unset.
-        device (`int`, `str` or `torch.device`, *optional*):
-            The device on which to execute the model. Will default to any accelerator available (GPU, MPS etc...), the
-            CPU otherwise.
-        device_map (`str` or `dict`, *optional*):
-            If passed along, will be used to instantiate the model.
-        model_kwargs (`dict`, *optional*):
-            Any keyword argument to send to the model instantiation.
-        token (`str`, *optional*):
-            The token to use as HTTP bearer authorization for remote files. If unset, will use the token generated when
-            running `huggingface-cli login` (stored in `~/.huggingface`).
-        hub_kwargs (additional keyword arguments, *optional*):
-            Any additional keyword argument to send to the methods that will load the data from the Hub.
-    """
-
-    pre_processor_class = AutoProcessor
-    model_class = None
-    post_processor_class = AutoProcessor
-    default_checkpoint = None
-    description = "This is a pipeline tool"
-    name = "pipeline"
-    inputs = {"prompt": str}
-    output_type = str
-
-    def __init__(
-        self,
-        model=None,
-        pre_processor=None,
-        post_processor=None,
-        device=None,
-        device_map=None,
-        model_kwargs=None,
-        token=None,
-        **hub_kwargs,
-    ):
-        if not is_torch_available():
-            raise ImportError("Please install torch in order to use this tool.")
-
-        if not is_accelerate_available():
-            raise ImportError("Please install accelerate in order to use this tool.")
-
-        if model is None:
-            if self.default_checkpoint is None:
-                raise ValueError(
-                    "This tool does not implement a default checkpoint, you need to pass one."
-                )
-            model = self.default_checkpoint
-        if pre_processor is None:
-            pre_processor = model
-
-        self.model = model
-        self.pre_processor = pre_processor
-        self.post_processor = post_processor
-        self.device = device
-        self.device_map = device_map
-        self.model_kwargs = {} if model_kwargs is None else model_kwargs
-        if device_map is not None:
-            self.model_kwargs["device_map"] = device_map
-        self.hub_kwargs = hub_kwargs
-        self.hub_kwargs["token"] = token
-
-        super().__init__()
-
-    def setup(self):
-        """
-        Instantiates the `pre_processor`, `model` and `post_processor` if necessary.
-        """
-        if isinstance(self.pre_processor, str):
-            self.pre_processor = self.pre_processor_class.from_pretrained(
-                self.pre_processor, **self.hub_kwargs
-            )
-
-        if isinstance(self.model, str):
-            self.model = self.model_class.from_pretrained(
-                self.model, **self.model_kwargs, **self.hub_kwargs
-            )
-
-        if self.post_processor is None:
-            self.post_processor = self.pre_processor
-        elif isinstance(self.post_processor, str):
-            self.post_processor = self.post_processor_class.from_pretrained(
-                self.post_processor, **self.hub_kwargs
-            )
-
-        if self.device is None:
-            if self.device_map is not None:
-                self.device = list(self.model.hf_device_map.values())[0]
-            else:
-                self.device = PartialState().default_device
-
-        if self.device_map is None:
-            self.model.to(self.device)
-
-        super().setup()
-
-    def encode(self, raw_inputs):
-        """
-        Uses the `pre_processor` to prepare the inputs for the `model`.
-        """
-        return self.pre_processor(raw_inputs)
-
-    def forward(self, inputs):
-        """
-        Sends the inputs through the `model`.
-        """
-        with torch.no_grad():
-            return self.model(**inputs)
-
-    def decode(self, outputs):
-        """
-        Uses the `post_processor` to decode the model output.
-        """
-        return self.post_processor(outputs)
-
-    def __call__(self, *args, **kwargs):
-        args, kwargs = handle_agent_inputs(*args, **kwargs)
-
-        if not self.is_initialized:
-            self.setup()
-
-        encoded_inputs = self.encode(*args, **kwargs)
-
-        tensor_inputs = {
-            k: v for k, v in encoded_inputs.items() if isinstance(v, torch.Tensor)
-        }
-        non_tensor_inputs = {
-            k: v for k, v in encoded_inputs.items() if not isinstance(v, torch.Tensor)
-        }
-
-        encoded_inputs = send_to_device(tensor_inputs, self.device)
-        outputs = self.forward({**encoded_inputs, **non_tensor_inputs})
-        outputs = send_to_device(outputs, "cpu")
-        decoded_outputs = self.decode(outputs)
-
-        return handle_agent_outputs(decoded_outputs, self.output_type)
-
-
 def launch_gradio_demo(tool_class: Tool):
     """
     Launches a gradio demo for a tool. The corresponding tool class needs to properly implement the class attributes
@@ -1060,6 +968,8 @@ def tool(tool_function: Callable) -> Tool:
             "Tool return type not found: make sure your function has a return type hint!"
         )
     class_name = f"{parameters['name'].capitalize()}Tool"
+    if parameters["return"]["type"] == "object":
+        parameters["return"]["type"] = "any"
 
     class SpecificTool(Tool):
         name = parameters["name"]

tests/test_tools_common.py

@@ -162,3 +162,44 @@ class ToolTests(unittest.TestCase):
 
             assert coolfunc.output_type == "number"
         assert "docstring has no description for the argument" in str(e)
+
+    def test_tool_definition_needs_imports_in_function(self):
+        with pytest.raises(Exception) as e:
+            from datetime import datetime
+            @tool
+            def get_current_time() -> str:
+                """
+                Gets the current time.
+                """
+                return str(datetime.now())
+        assert "datetime" in str(e)
+
+        # Also test with classic definition
+        with pytest.raises(Exception) as e:
+            class GetCurrentTimeTool(Tool):
+                name="get_current_time_tool"
+                description="Gets the current time"
+                inputs = {}
+                output_type = "string"
+
+                def forward(self):
+                    return str(datetime.now())
+        assert "datetime" in str(e)
+
+        @tool
+        def get_current_time() -> str:
+            """
+            Gets the current time.
+            """
+            from datetime import datetime
+            return str(datetime.now())
+        
+        class GetCurrentTimeTool(Tool):
+            name="get_current_time_tool"
+            description="Gets the current time"
+            inputs = {}
+            output_type = "string"
+
+            def forward(self):
+                from datetime import datetime
+                return str(datetime.now())