Initial Work on Documentation

docs/source/usage.rst

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+Using Jambo
+===================
+
+Jambo is designed to be easy to use, it doesn't require any complex setup or configuration.
+Below a example of how to use Jambo to convert a JSON Schema into a Pydantic model.
+
+
+.. code-block:: python
+
+    from jambo import SchemaConverter
+
+    schema = {
+        "title": "Person",
+        "type": "object",
+        "properties": {
+            "name": {"type": "string"},
+            "age": {"type": "integer"},
+        },
+        "required": ["name"],
+    }
+
+    Person = SchemaConverter.build(schema)
+
+    obj = Person(name="Alice", age=30)
+    print(obj)
+    # Output: Person(name='Alice', age=30)
+
+
+The :py:meth:`SchemaConverter.build <jambo.SchemaConverter.build>` static method takes a JSON Schema dictionary and returns a Pydantic model class. You can then instantiate this class with the required fields, and it will automatically validate the data according to the schema.
+
+If passed a description inside the schema it will also add it to the Pydantic model using the `description` field. This is useful for AI Frameworks as: LangChain, CrewAI and others, as they use this description for passing context to LLMs.
+
+
+For more complex schemas and types see our documentation on
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+   usage.string