Implement structured output generation for both LlamaLanguageModel / MLXLanguageModel

[eastriverlee]

Related to #27

[Copilot]

Pull request overview

This PR implements structured output generation for LlamaLanguageModel and MLXLanguageModel by adding constrained token sampling to generate JSON that conforms to a schema. The implementation includes comprehensive tests covering various data types and structures.

Key changes:

• Added ConstrainedJSONGenerator that uses token-level sampling to generate schema-conformant JSON
• Implemented TokenBackend protocol with adapters for both Llama and MLX models
• Enhanced GenerationGuide to store constraint values for min/max on numbers and arrays
• Extended GenerationSchema with character validation and schema prompt generation

Reviewed changes

Copilot reviewed 9 out of 9 changed files in this pull request and generated 8 comments.

Show a summary per file

File	Description
Tests/AnyLanguageModelTests/StructuredGenerationTests.swift	Comprehensive test suite covering simple types, nested structs, enums, arrays, and optionals across all supported model types
Tests/AnyLanguageModelTests/GenerableMacroTests.swift	Added round-trip tests for enums, nested structs, and arrays
Sources/AnyLanguageModelMacros/GenerableMacro.swift	Refactored guide extraction to use a structured Constraints type and properly parse numeric ranges and array count constraints
Sources/AnyLanguageModel/StructuredGeneration.swift	New file implementing token-level constrained JSON generation with TokenBackend protocol and ConstrainedJSONGenerator
Sources/AnyLanguageModel/Models/SystemLanguageModel.swift	Updated to use schema-based generation for non-String types and added conversion to FoundationModels.DynamicGenerationSchema
Sources/AnyLanguageModel/Models/MLXLanguageModel.swift	Implemented MLXTokenBackend and structured JSON generation with proper token sampling and repetition penalty handling
Sources/AnyLanguageModel/Models/LlamaLanguageModel.swift	Implemented LlamaTokenBackend and structured JSON generation with batch-based decoding and sampler integration
Sources/AnyLanguageModel/GenerationSchema.swift	Added schemaPrompt() method, character validation for JSON strings, improved node equality checking, and support for constraint propagation
Sources/AnyLanguageModel/GenerationGuide.swift	Made GenerationGuide store actual constraint values (min/max, minCount/maxCount) for use during schema generation

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[mattt]

@eastriverlee Thank you for your contribution! And thank you for your patience. I'll have a chance to look a this soon.

[mattt]

@eastriverlee Thanks again for your patience. I just rebased, resolving the conflicts as best I could. I recently merged #59, which takes a slightly different approach for schema conversion. I'm working to harmonize these implementations now...