koxudaxi/datamodel-code-generator

Features

• Schema-Driven Code Generators - Transforms structured data specifications into native, type-safe Python classes and data structures.
• JSON Schema to Python Generators - Generates validated Python data structures from schema definitions to keep code synchronized with data contracts.
• Avro Model Generation - Creates type-safe data models from diverse schema specifications including JSON Schema, XML, and Avro.
• Constraint-to-Code Mappers - Translates schema constraints like minimum, maximum, or pattern requirements into field validation definitions.
• Schema Normalizers - Normalizes diverse schema formats like JSON Schema, OpenAPI, and Avro into a consistent internal model for translation.
• Schema-to-Code CLI Generators - Provides a command-line interface to parse data formats and generate type-hinted Python code.
• API Client Generators - Generates type-safe client models from API specifications to ensure consistent data handling.
• Data Model Engineering - Transforms complex data formats into native Python data structures for robust application development.
• Data Model Generators - Transforms various API specifications into type-safe Pydantic models, dataclasses, and TypedDicts.
• API Schema Retrieval - Retrieves schema definitions from network URLs with support for custom headers and timeouts.
• Generated Field Exclusions - Adjusts field generation settings like optionality and default value stripping to meet specific data requirements.
• Literal Types - Converts enumeration fields into literal type annotations to simplify generated model structures.
• Dynamic Data Model Generation - Executes generated model code in temporary modules to provide live, usable classes at runtime.
• Identifier-Based Model References - Resolves internal and external schema references to prevent redundant class generation and promote code reuse.
• Schema Patching and Merging - Identifies and merges identical model or enum definitions across a schema to reduce redundancy and simplify the generated codebase.
• Type Mapping Overrides - Allows overriding default type generation to map schema formats to custom types.
• Strict Type Enforcement - Enforces strict type conversion rules during model generation to ensure data integrity.
• Field-Level Metadata Annotations - Uses annotated syntax for field definitions to preserve schema constraints and metadata in generated code.
• Automatizări de flux de lucru declanșate de build - Triggers automatic data model updates or verification whenever underlying schema files change.
• Custom Declarative Base Classes - Supports custom base class definitions and mixins to inject shared functionality into generated data models.
• Generated Code Organizers - Organizes generated models into modular file structures to maintain clean and manageable project codebases.
• Field Name Aliasing - Maps schema field and class names to safe Python identifiers using custom aliasing configurations.
• Project Configuration - Defines generation settings within project configuration files to ensure repeatable model creation.
• Template-Based Code Generators - Automates the creation of source code using dynamic templates and configurable rendering logic.
• Schema-Driven Inheritance Generators - Translates schema composition rules into explicit class inheritance hierarchies in generated Python models.
• Iterable Class Implementation - Implements sequence interfaces on root list-wrapping classes to allow direct indexing and iteration.
• Schema Reference Resolution - Fetches external schema definitions over network or filesystem with configurable resolution settings.
• Configuration-Driven Pipelines - Ensures reproducible generation workflows by defining parameters and execution sequences in static configuration files.
• Pydantic Schema Validation - Automates the creation of validated Pydantic data structures from schema definitions.
• Schema Default Values - Allows overriding schema-defined default values with custom mappings during the code generation process.
• Circular Dependency Resolution - Orders class definitions and resolves circular imports through forward references and modular splitting.
• Immutable Model Generation - Generates frozen, immutable models to enforce data integrity and prevent accidental modification.
• CI/CD Integrations - Automates data model generation and validation within continuous integration pipelines to ensure schema synchronization.
• Model Field Constraints - Configures model strictness to allow, ignore, or forbid extra fields not defined in the source schema.
• Model Naming Overrides - Overrides auto-generated class names using custom mappings to avoid collisions and improve readability.
• Package and Variable Name Collision Avoidances - Resolves naming conflicts between fields and types to maintain clarity in generated code.
• Parameter Name Normalization - Normalizes non-safe property names to snake case to ensure generated code follows standard naming conventions.
• Generated Container Types - Implements abstract container types in generated models to enhance type hinting flexibility and usability.
• Source Change Monitors - Monitors input schema files for modifications to trigger automatic code regeneration.
• Data Model Validation - Verifies that existing data models match current schema definitions to ensure consistency.
• Path Filters - Restricts model generation to a subset of API paths while maintaining necessary parameter and versioning context for the selected endpoints.
• OpenAPI to Python Converters - Maps OpenAPI schema definitions to native Python classes for type-safe API consumption.

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