Initial Work on Documentation

docs/source/conf.py

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+# Configuration file for the Sphinx documentation builder.
+#
+# For the full list of built-in configuration values, see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Project information -----------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
+
+project = "jambo"
+copyright = "2025, Vitor Hideyoshi"
+author = "Vitor Hideyoshi"
+
+# -- General configuration ---------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
+
+extensions = [
+    "sphinx.ext.todo",
+    "sphinx.ext.viewcode",
+    "sphinx.ext.autodoc",
+    "sphinx.ext.napoleon",
+]
+
+
+templates_path = ["_templates"]
+exclude_patterns = []
+
+
+# -- Options for HTML output -------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
+
+html_theme = "sphinx_rtd_theme"
+html_static_path = ["_static"]
+
+
+# -- Options for autodoc -----------------------------------------------------
+add_module_names = False
+python_use_unqualified_type_names = True

docs/source/index.rst

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+.. jambo documentation master file, created by
+   sphinx-quickstart on Thu Jun 19 22:20:35 2025.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Jambo - JSON Schema to Pydantic Converter
+=========================================
+
+This is the documentation for Jambo, a tool that converts JSON Schema definitions into Pydantic models.
+Welcome to Jambo's documentation!
+
+
+Jambo is a Python package that automatically converts JSON Schema definitions into Pydantic models. It's designed to streamline schema validation and enforce type safety using Pydantic's powerful validation features.
+
+Created to simplifying the process of dynamically generating Pydantic models for AI frameworks like LangChain, CrewAI, and others.
+
+
+Installation
+------------------
+
+You can install Jambo using pip:
+
+.. code-block:: bash
+
+   pip install jambo
+
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+   usage
+   modules

docs/source/jambo.parser.rst

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+jambo.parser package
+====================
+
+Submodules
+----------
+
+jambo.parser.allof\_type\_parser module
+---------------------------------------
+
+.. automodule:: jambo.parser.allof_type_parser
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+jambo.parser.anyof\_type\_parser module
+---------------------------------------
+
+.. automodule:: jambo.parser.anyof_type_parser
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+jambo.parser.array\_type\_parser module
+---------------------------------------
+
+.. automodule:: jambo.parser.array_type_parser
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+jambo.parser.boolean\_type\_parser module
+-----------------------------------------
+
+.. automodule:: jambo.parser.boolean_type_parser
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+jambo.parser.float\_type\_parser module
+---------------------------------------
+
+.. automodule:: jambo.parser.float_type_parser
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+jambo.parser.int\_type\_parser module
+-------------------------------------
+
+.. automodule:: jambo.parser.int_type_parser
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+jambo.parser.object\_type\_parser module
+----------------------------------------
+
+.. automodule:: jambo.parser.object_type_parser
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+jambo.parser.ref\_type\_parser module
+-------------------------------------
+
+.. automodule:: jambo.parser.ref_type_parser
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+jambo.parser.string\_type\_parser module
+----------------------------------------
+
+.. automodule:: jambo.parser.string_type_parser
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: jambo.parser
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/source/jambo.rst

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+jambo package
+=============
+
+Subpackages
+-----------
+
+.. toctree::
+   :maxdepth: 4
+
+   jambo.parser
+   jambo.types
+
+Submodules
+----------
+
+jambo.schema\_converter module
+------------------------------
+
+.. automodule:: jambo.schema_converter
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: jambo
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/source/jambo.types.rst

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+jambo.types package
+===================
+
+Submodules
+----------
+
+jambo.types.json\_schema\_type module
+-------------------------------------
+
+.. automodule:: jambo.types.json_schema_type
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+jambo.types.type\_parser\_options module
+----------------------------------------
+
+.. automodule:: jambo.types.type_parser_options
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: jambo.types
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/source/modules.rst

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+jambo
+=====
+
+.. toctree::
+   :maxdepth: 4
+
+   jambo

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

docs/source/usage.string.rst

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+String Type
+=================
+
+The String type has the following supported properties:
+
+- maxLength: Maximum length of the string.
+- minLength: Minimum length of the string.
+- pattern: Regular expression pattern that the string must match.
+- format: A string format that can be used to validate the string (e.g., "email", "uri").
+
+And the additional generic properties:
+
+- default: Default value for the string.
+- description: Description of the string field.
+
+
+Examples
+-----------------
+
+1. Basic String with maxLength and minLength:
+
+.. code-block:: python
+
+    from jambo import SchemaConverter
+
+
+    schema = {
+        "title": "StringExample",
+        "type": "object",
+        "properties": {
+            "email": {
+                "type": "string",
+                "minLength": 5,
+                "maxLength": 50,
+            },
+        },
+        "required": ["email"],
+    }
+
+    Model = SchemaConverter.build(schema)
+
+    obj = Model(email="this_is_a_valid_string")
+    print(obj)
+    # Output: StringExample(email='this_is_a_valid_string')
+
+2. String with pattern and format: