add doc code

docs/index.md

@@ -70,3 +70,7 @@ completion = openai.ChatCompletion.create(
 user_details = UserDetails.from_response(completion)
 print(user_details)  # name="John Doe", age=30
 ```
+
+# Code 
+
+::: openai_function_call

docs/openai_schema.md

@@ -36,7 +36,7 @@ user_details = UserDetails.from_response(completion)
 print(user_details)  # name="John Doe", age=30
 ```
 
-## Using the decorator 
+## Using the decorator
 
 You can also use a decorator but i recommend the class since you get nice autocompletes with VSCode
 
@@ -53,6 +53,6 @@ class UserDetails(BaseModel):
     age: int
 ```
 
-## OpenAISchema
+## Code Reference 
 
-::: openai_function_call.OpenAISchema
+::: openai_function_call

mkdocs.yml

@@ -11,6 +11,8 @@ plugins:
         python:
           options:
             members_order: alphabetical
+            allow_inspection: true
+            show_bases: true
 repo_url: https://github.com/jxnl/openai_function_call
 markdown_extensions:
   - pymdownx.critic

openai_function_call/function_calls.py

@@ -37,6 +37,31 @@ def _remove_a_key(d, remove_key) -> None:
 
 
 class openai_function:
+    """
+    Decorator to convert a function into an OpenAI function.
+
+    This decorator will convert a function into an OpenAI function. The
+    function will be validated using pydantic and the schema will be
+    generated from the function signature.
+
+    Example:
+        ```python
+        @openai_function
+        def sum(a: int, b: int) -> int:
+            return a + b
+
+        completion = openai.ChatCompletion.create(
+            ...
+            messages=[{
+                "content": "What is 1 + 1?",
+                "role": "user"
+            }]
+        )
+        sum.from_response(completion)
+        # 2
+        ```
+    """
+
     def __init__(self, func: Callable) -> None:
         self.func = func
         self.validate_func = validate_arguments(func)
@@ -66,7 +91,16 @@ def wrapper(*args, **kwargs):
         return wrapper(*args, **kwargs)
 
     def from_response(self, completion, throw_error=True):
-        """Execute the function from the response of an openai chat completion"""
+        """
+        Parse the response from OpenAI's API and return the function call
+
+        Parameters:
+            completion (openai.ChatCompletion): The response from OpenAI's API
+            throw_error (bool): Whether to throw an error if the response does not contain a function call
+
+        Returns:
+            result (any): result of the function call
+        """
         message = completion.choices[0].message
 
         if throw_error:
@@ -85,7 +119,13 @@ class OpenAISchema(BaseModel):
     @property
     def openai_schema(cls):
         """
-        Return the schema of the class in the format of OpenAI's schema
+        Return the schema in the format of OpenAI's schema as jsonschema
+
+        Note:
+            Its important to add a docstring to describe how to best use this class, it will be included in the description attribute and be part of the prompt.
+
+        Returns:
+            model_json_schema (dict): A dictionary in the format of OpenAI's schema as jsonschema
         """
         schema = cls.schema()
         parameters = {
@@ -101,7 +141,15 @@ def openai_schema(cls):
 
     @classmethod
     def from_response(cls, completion, throw_error=True):
-        """Execute the function from the response of an openai chat completion"""
+        """Execute the function from the response of an openai chat completion
+
+        Parameters:
+            completion (openai.ChatCompletion): The response from an openai chat completion
+            throw_error (bool): Whether to throw an error if the function call is not detected
+
+        Returns:
+            cls (OpenAISchema): An instance of the class
+        """
         message = completion.choices[0].message
 
         if throw_error: