df_generate_dart_models 0.17.1

A tool for generating data models and classes from annotations, offering greater flexibility than json_serializable or freezed.

---

Generates Dart data models from @GenerateDartModel annotations. Pairs with df_generate_dart_models_core, which supplies the annotations and base Model class.

Install

Add the runtime dependency to your project:

dependencies:
  df_generate_dart_models_core: ^0.10.0

Install the generator CLI globally:

dart pub global activate df_generate_dart_models

Use

Define a model template:

import 'package:df_generate_dart_models_core/df_generate_dart_models_core.dart';

part '_model_user.g.dart';

@GenerateDartModel(
  fields: {
    Field(fieldPath: ['id'], fieldType: String),
    Field(fieldPath: ['firstName'], fieldType: String, nullable: true),
    Field(fieldPath: ['lastName'], fieldType: String, nullable: true),
  },
  shouldInherit: true,
)
abstract class _ModelUser extends Model {
  const _ModelUser();
}

From the folder containing the template, run:

df_generate_dart_models --models-min

This writes the generated part file (e.g. _model_user.g.dart) next to it.

A VS Code extension is also available — right-click the folder and pick 🔹 Generate Dart Models (Minimal).

Generate models from an existing DBML schema

If you already have a DBML file, you can go the other direction — generate annotated Dart models from it. The reverse generator runs the forward codegen automatically, so a single command takes you from .dbml to ready-to-use Model* classes:

df_generate_dart_models_from_dbml -i schema.dbml -o lib/src/db_models --dialect postgres

• --dialect postgres|sqlite|generic controls the type-prefix vocabulary. Postgres-flavoured columns (jsonb, bytea, enum(name), timestamptz, …) become PG_*- field types so the existing PG mappers fire at runtime.
• Declared Enum {…} blocks become real Dart enums in a shared dbml_enums.dart, referenced by Type literal from each model.
• Ref: lines (and inline [ref: > table.col] notes) become references: + foreignKey: annotations.
• Right-click a .dbml file in VS Code and pick 🔹 Generate Dart Models from DBML to run the same pipeline.

Pass --no-codegen if you only want the annotation source files without the matching _*.g.dart.

Generate a DBML schema from your models

The inverse path also ships in the same package:

df_generate_dbml -i lib/src/db_models -o schema/

Walks every @GenerateDartModel-annotated file under the input directory, groups them by schema: value, and writes one <schema>.dbml per distinct schema. Enum-typed fields produce Enum "<name>" { … } blocks and enum(<name>) columns automatically — the variants are resolved via the analyzer, so no side-config is needed.

Generate SQL CREATE TABLE from your models

The DBML emitter is the bridge to SQL. dbml2sql (a Node CLI) converts the .dbml to dialect-specific CREATE TABLE statements — including CREATE TYPE … AS ENUM for declared enums, foreign-key constraints, composite primary keys, and NOT NULL. End-to-end:

# 1) Models → DBML (round-trips through this package).
df_generate_dbml -i lib/src/db_models -o schema/

# 2) DBML → SQL (one-time install of dbml2sql).
npm install -g @dbml/cli

dbml2sql --postgres schema/app.dbml > schema/app.postgres.sql
dbml2sql --mysql    schema/app.dbml > schema/app.mysql.sql
dbml2sql --mssql    schema/app.dbml > schema/app.mssql.sql

Because the PG_*- / SQLITE_*- prefixes on your fieldType strings drive the DBML column types directly (PG_varchar(120)-String → varchar(120), PG_jsonb-Map → jsonb, PG_bytea-Uint8List → bytea, an enum-typed field → enum(<name>)), the resulting SQL matches the Postgres / SQLite dialect you've already pinned in your Dart models — no separate migration spec needed.

---

🔍 For more information, refer to the API reference.

---

💬 Contributing and Discussions

This is an open-source project, and we warmly welcome contributions from everyone, regardless of experience level. Whether you're a seasoned developer or just starting out, contributing to this project is a fantastic way to learn, share your knowledge, and make a meaningful impact on the community.

☝️ Ways you can contribute

• Find us on Discord: Feel free to ask questions and engage with the community here: https://discord.gg/gEQ8y2nfyX.
• Share your ideas: Every perspective matters, and your ideas can spark innovation.
• Help others: Engage with other users by offering advice, solutions, or troubleshooting assistance.
• Report bugs: Help us identify and fix issues to make the project more robust.
• Suggest improvements or new features: Your ideas can help shape the future of the project.
• Help clarify documentation: Good documentation is key to accessibility. You can make it easier for others to get started by improving or expanding our documentation.
• Write articles: Share your knowledge by writing tutorials, guides, or blog posts about your experiences with the project. It's a great way to contribute and help others learn.

No matter how you choose to contribute, your involvement is greatly appreciated and valued!

☕ We drink a lot of coffee...

If you're enjoying this package and find it valuable, consider showing your appreciation with a small donation. Every bit helps in supporting future development. You can donate here: https://www.buymeacoffee.com/dev_cetera

LICENSE

This project is released under the MIT License. See LICENSE for more information.