refactor: auto extract tool properties from fn def (#24)

* auto extract tool properties from fn def

* update docstring

src/cleanlab_codex/codex_tool.py

@@ -2,23 +2,19 @@
 
 from __future__ import annotations
 
-from typing import Any, ClassVar, Optional
+from typing import Any, Optional
+
+from typing_extensions import Annotated
 
 from cleanlab_codex.project import Project
+from cleanlab_codex.utils.function import pydantic_model_from_function, required_properties_from_model
 
 
 class CodexTool:
     """A tool that connects to a Codex project to answer questions."""
 
     _tool_name = "ask_advisor"
-    _tool_description = "Asks an all-knowing advisor this query in cases where it cannot be answered from the provided Context. If the answer is available, this returns None."
-    _tool_properties: ClassVar[dict[str, Any]] = {
-        "question": {
-            "type": "string",
-            "description": "The question to ask the advisor. This should be the same as the original user question, except in cases where the user question is missing information that could be additionally clarified.",
-        }
-    }
-    _tool_requirements: ClassVar[list[str]] = ["question"]
+    _tool_description = "Asks an all-knowing advisor this query in cases where it cannot be answered from the provided Context. If the answer is unavailable, this returns None."
     DEFAULT_FALLBACK_ANSWER = "Based on the available information, I cannot provide a complete answer to this question."
 
     def __init__(
@@ -29,6 +25,9 @@ def __init__(
     ):
         self._project = project
         self._fallback_answer = fallback_answer
+        self._tool_function_schema = pydantic_model_from_function(self._tool_name, self.query)
+        self._tool_properties = self._tool_function_schema.model_json_schema()["properties"]
+        self._tool_requirements = required_properties_from_model(self._tool_function_schema)
 
     @classmethod
     def from_access_key(
@@ -86,11 +85,17 @@ def fallback_answer(self, value: Optional[str]) -> None:
         """Sets the fallback answer to use if the Codex project cannot answer the question."""
         self._fallback_answer = value
 
-    def query(self, question: str) -> Optional[str]:
-        """Asks an all-knowing advisor this question in cases where it cannot be answered from the provided Context.
+    def query(
+        self,
+        question: Annotated[
+            str,
+            "The question to ask the advisor. This should be the same as the original user question, except in cases where the user question is missing information that could be additionally clarified.",
+        ],
+    ) -> Optional[str]:
+        """Asks an all-knowing advisor this question in cases where it cannot be answered from the provided Context. If the answer is unavailable, this returns a fallback answer or None.
 
         Args:
-            question: The question to ask the advisor. This should be the same as the original user question, except in cases where the user question is missing information that could be additionally clarified.
+            question (str): The question to ask the advisor. This should be the same as the original user question, except in cases where the user question is missing information that could be additionally clarified.
 
         Returns:
             The answer to the question if available. If no answer is available, this returns a fallback answer or None.
@@ -130,17 +135,11 @@ def to_llamaindex_tool(self) -> Any:
         """
         from llama_index.core.tools import FunctionTool
 
-        from cleanlab_codex.utils.llamaindex import get_function_schema
-
         return FunctionTool.from_defaults(
             fn=self.query,
             name=self._tool_name,
             description=self._tool_description,
-            fn_schema=get_function_schema(
-                name=self._tool_name,
-                func=self.query,
-                tool_properties=self._tool_properties,
-            ),
+            fn_schema=self._tool_function_schema,
         )
 
     def to_langchain_tool(self) -> Any:
@@ -150,17 +149,11 @@ def to_langchain_tool(self) -> Any:
         """
         from langchain_core.tools.structured import StructuredTool
 
-        from cleanlab_codex.utils.langchain import create_args_schema
-
         return StructuredTool.from_function(
             func=self.query,
             name=self._tool_name,
             description=self._tool_description,
-            args_schema=create_args_schema(
-                name=self._tool_name,
-                func=self.query,
-                tool_properties=self._tool_properties,
-            ),
+            args_schema=self._tool_function_schema,
         )
 
     def to_aws_converse_tool(self) -> Any:

src/cleanlab_codex/utils/__init__.py

@@ -1,10 +1,10 @@
 from cleanlab_codex.utils.aws import Tool as AWSConverseTool
 from cleanlab_codex.utils.aws import ToolSpec as AWSToolSpec
 from cleanlab_codex.utils.aws import format_as_aws_converse_tool
+from cleanlab_codex.utils.function import FunctionParameters
 from cleanlab_codex.utils.openai import Function as OpenAIFunction
 from cleanlab_codex.utils.openai import Tool as OpenAITool
 from cleanlab_codex.utils.openai import format_as_openai_tool
-from cleanlab_codex.utils.types import FunctionParameters
 
 __all__ = [
     "FunctionParameters",

src/cleanlab_codex/utils/aws.py

@@ -4,7 +4,7 @@
 
 from pydantic import BaseModel, Field
 
-from cleanlab_codex.utils.types import FunctionParameters
+from cleanlab_codex.utils.function import FunctionParameters
 
 
 class ToolSpec(BaseModel):
@@ -27,6 +27,11 @@ def format_as_aws_converse_tool(
         toolSpec=ToolSpec(
             name=tool_name,
             description=tool_description,
-            inputSchema={"json": FunctionParameters(properties=tool_properties, required=required_properties)},
+            inputSchema={
+                "json": FunctionParameters(
+                    properties=tool_properties,
+                    required=required_properties,
+                )
+            },
         )
     ).model_dump(by_alias=True)

src/cleanlab_codex/utils/function.py

@@ -0,0 +1,79 @@
+from inspect import signature
+from typing import Any, Callable, Dict, List, Literal, Type
+
+from pydantic import BaseModel, Field, create_model
+from typing_extensions import Annotated, get_args, get_origin
+
+
+class Property(BaseModel):
+    type: Literal["string", "number", "integer", "boolean", "array", "object"]
+    description: str
+
+
+class FunctionParameters(BaseModel):
+    type: Literal["object"] = "object"
+    properties: Dict[str, Property]
+    required: List[str]
+
+
+def pydantic_model_from_function(name: str, func: Callable[..., Any]) -> Type[BaseModel]:
+    """
+    Create a pydantic model representing a function's schema.
+
+    For example, a function with the following signature:
+
+    ```python
+    def my_function(
+        a: Annotated[int, "This is an integer"], b: str = "default"
+    ) -> None: ...
+    ```
+
+    will be represented by the following pydantic model when `name="my_function"`:
+
+    ```python
+    class my_function(BaseModel):
+        a: int = Field(description="This is an integer")
+        b: str = "default"
+    ```
+
+    Args:
+        name: The name for the pydantic model.
+        func: The function to create a schema for.
+
+    Returns:
+        A pydantic model representing the function's schema.
+    """
+    fields = {}
+    params = signature(func).parameters
+
+    for param_name, param in params.items():
+        param_type = param.annotation
+        if isinstance(param_type, str):
+            param_type = eval(param_type)  # noqa: S307
+
+        param_default = param.default
+        description = None
+
+        if get_origin(param_type) is Annotated:
+            args = get_args(param_type)
+            param_type = args[0]
+            if isinstance(args[1], str):
+                description = args[1]
+
+        if param_type is param.empty:
+            param_type = Any
+
+        if param_default is param.empty:
+            fields[param_name] = (param_type, Field(description=description))
+        else:
+            fields[param_name] = (
+                param_type,
+                Field(default=param_default, description=description),
+            )
+
+    return create_model(name, **fields)  # type: ignore
+
+
+def required_properties_from_model(model: Type[BaseModel]) -> List[str]:
+    """Returns a list of required properties from a pydantic model."""
+    return [name for name, field in model.model_fields.items() if field.is_required()]

src/cleanlab_codex/utils/openai.py

@@ -4,7 +4,7 @@
 
 from pydantic import BaseModel
 
-from cleanlab_codex.utils.types import FunctionParameters
+from cleanlab_codex.utils.function import FunctionParameters
 
 
 class Tool(BaseModel):
@@ -28,6 +28,9 @@ def format_as_openai_tool(
         function=Function(
             name=tool_name,
             description=tool_description,
-            parameters=FunctionParameters(properties=tool_properties, required=required_properties),
+            parameters=FunctionParameters(
+                properties=tool_properties,
+                required=required_properties,
+            ),
         )
     ).model_dump()

tests/utils/test_function.py

@@ -0,0 +1,40 @@
+from typing import Any
+
+from typing_extensions import Annotated
+
+from cleanlab_codex.utils.function import pydantic_model_from_function
+
+
+def test_function_schema_with_annotated_params() -> None:
+    def function_with_annotated_params(
+        a: Annotated[str, "This is a string"],  # noqa: ARG001
+    ) -> None: ...
+
+    fn_schema = pydantic_model_from_function("test_function", function_with_annotated_params)
+    assert fn_schema.model_json_schema()["title"] == "test_function"
+    assert fn_schema.model_fields["a"].annotation is str
+    assert fn_schema.model_fields["a"].description == "This is a string"
+    assert fn_schema.model_fields["a"].is_required()
+
+
+def test_function_schema_without_annotations() -> None:
+    def function_without_annotations(a) -> None:  # type: ignore # noqa: ARG001
+        ...
+
+    fn_schema = pydantic_model_from_function("test_function", function_without_annotations)
+    assert fn_schema.model_json_schema()["title"] == "test_function"
+    assert fn_schema.model_fields["a"].annotation is Any  # type: ignore[comparison-overlap]
+    assert fn_schema.model_fields["a"].is_required()
+    assert fn_schema.model_fields["a"].description is None
+
+
+def test_function_schema_with_default_param() -> None:
+    def function_with_default_param(a: int = 1) -> None:  # noqa: ARG001
+        ...
+
+    fn_schema = pydantic_model_from_function("test_function", function_with_default_param)
+    assert fn_schema.model_json_schema()["title"] == "test_function"
+    assert fn_schema.model_fields["a"].annotation is int
+    assert fn_schema.model_fields["a"].default == 1
+    assert not fn_schema.model_fields["a"].is_required()
+    assert fn_schema.model_fields["a"].description is None