There are at least three sets of LLM primitives: the AI Toolkit, GenUI and dartantic_interface. I've implemented two of these and dartantic_interface is far more complete. Do we want to just use dartantic_interface? Or if we'd like to settle on a subset, eg chat messages and parts, I'm happy to produce an "llm_primitives" package that we can all depend on and stop doing the conversions.

We have created a package that has adapters and full compatibility with zod and is currently in beta and does typed schema values using typed extensions with code generation and is working well. Maybe there is opportunity for collaboration on this also.

https://github.com/btwld/ack

We created an adapter to the json_schema_builder and is working well but has some features which are good on DX!

If json_schema_builder becomes the de facto standard, would love to use it as the main internals for ack

@leoafarias What does ack add on top of json_schema_builder? It looks pretty similar, just uses a different way to specify args (e.g. Ack.string().minLength(2), instead of S.string(minLength: 2)). Is it the "safeParse" functionality?

@gspencergoog we created it before the schema builder, so I think there is overlap now, but a quick sumary from your friend Gemini.

ack vs json_schema_builder

In short: ack parses data into typed Dart objects. json_schema_builder validates JSON against a spec.

What Makes ack Different

refine() — Custom Validation

final passwordSchema = Ack.string().minLength(8);

final schema = Ack.object({
  'password': passwordSchema,
  'confirmPassword': passwordSchema,
}).refine(
  (data) => data['password'] == data['confirmPassword'],
  message: 'Passwords must match',
);

transform() — Type Conversion

final dateSchema = Ack.string()
    .matches(r'^\d{4}-\d{2}-\d{2}$')
    .transform<DateTime>((s) => DateTime.parse(s!));

dateSchema.parse('2024-01-15'); // Returns DateTime, not String

Schema Composition

final adminSchema = userSchema.extend({'role': Ack.string()});
final updateSchema = userSchema.partial();  // All fields optional
final publicSchema = userSchema.pick(['name', 'email']);
final safeSchema = userSchema.omit(['password']);

Code Generation with Extension Types

@AckType()
final userSchema = Ack.object({
  'name': Ack.string(),
  'age': Ack.integer(),
});

// Generated:
extension type UserType(Map<String, Object?> _data)
    implements Map<String, Object?> {
  static UserType parse(Object? data) => UserType(userSchema.parse(data)!);
  
  String get name => _data['name'] as String;
  
  int get age => _data['age'] as int;
  UserType copyWith({String? name, int? age}) => UserType.parse({...});
}

Why extension types?

• Zero-cost — Compiles away, no runtime overhead
• Direct JSON — Map<String, Object?> is native JSON, no serialization needed
• Validation enforced — Must go through parse() to construct

Summary

Bottom line: ack composes schemas via code. json_schema_builder composes via spec keywords.

Oooh, that's pretty nice! I like the transform operation especially.

It seems pretty powerful. And if you did base the validation on json_schema_builder, you'd get the 2020-12 support too.

Thank you!

Published empty package to reserve the name: https://pub.dev/packages/ai_primitives
(It is unlisted now.)

Code: #606

As discussed in other threads, changed the name: https://pub.dev/packages/genai_primitives/versions

@csells , when you think you can issue the PR? (no rush here, I just want to understand the schedule)

Should be early next week. Sooner depending on how my Friday goes. I'm on a 27-hour train ride right now with no Wi-Fi or you'd have it sooner. : )

@csells , thank you for PR (#619)

I raised a lot of questions/suggestions. Let me know if you want to actively participate in the discussion or you just want me to merge the PR and follow up on other PRs.

We want to be thoughtful about details, because we will have a lot of dependents right away, and breaking changes will be impactful.

Let's iterate on this PR tomorrow when I'm off this train.

@csells we have an agent workflow outside of GenAI that Dartantic would work great for, so yes!

I don't think any is refactoring firebase_ai (or google_cloud_ai_generativelanguage_v1beta or any of the other packages that dartantic_ai depends on to talk to the various LLMs) but the rest looks right.