Package.swift

// swift-tools-version: 6.1
import PackageDescription

let package = Package(
    name: "coreai-kit",
    platforms: [.macOS("26.0"), .iOS("26.0")],
    products: [
        // LLM: ModelStore + ChatSession + FoundationModels provider (tool calling).
        .library(name: "CoreAIKit", targets: ["CoreAIKit"]),
        // CV: generic graph runner + typed pipelines (CLIP, depth, camera). Links only
        // the system CoreAI framework — no LLM runtime is pulled in.
        .library(name: "CoreAIKitVision", targets: ["CoreAIKitVision"]),
        // Text embeddings for on-device retrieval/RAG (EmbeddingGemma).
        .library(name: "CoreAIKitEmbeddings", targets: ["CoreAIKitEmbeddings"]),
        // SwiftUI components: model picker, chat transcript, stats bar.
        .library(name: "CoreAIKitUI", targets: ["CoreAIKitUI"]),
        // Anchored task ops (text, image, audio, video, search, forecast) over catalog
        // models — the stable task-level API. Re-exports the model layer, so this one
        // product + `import CoreAIOps` is the whole quick path.
        .library(name: "CoreAIOps", targets: ["CoreAIOps"]),
        // Answers "what will this app download" before someone has to answer it in a meeting.
        // An executable, so linking the libraries never drags it in.
        .executable(name: "coreai-doctor", targets: ["coreai-doctor"]),
        // System One on this machine without a Swift toolchain: `systemone serve` is the
        // /v1/systemone endpoint over a catalog model, `ask` one decision from the shell,
        // `models` what can decide. Built, signed and notarized by the release workflow and
        // installed with `brew install john-rocky/tap/systemone`.
        .executable(name: "systemone", targets: ["systemone"]),
    ],
    traits: [
        // Off by default. Enable it to download large files over Xet; swift-xet and its
        // dependencies are compiled only then. Forwards to swift-huggingface's trait.
        .trait(name: "Xet", description: "Enable Xet transport for model downloads.")
    ],
    dependencies: [
        // Community fork of apple/coreai-models (unaffiliated with Apple). 0.2.8-zoo is
        // 0.2.7-zoo with upstream's x86_64 guard on the Float16 code of three files: 0.2.6-zoo's
        // macOS 26 floor makes a Release (universal) app build compile the fork for x86_64,
        // which has no Float16, and those files failed there (the arm64 code is unchanged).
        // 0.2.7-zoo adds a
        // checkpoint of a hybrid model's recurrent state to the sequential engine
        // (InferenceEngine.checkpoint()): TypedDecisions checkpoints after a state's prefix
        // and each later question restores it instead of re-prefilling the whole prompt
        // (4.4-5.9x per decision on three hybrid decision models, macOS; iOS not measured).
        // The move from 0.2.4-zoo also brings 0.2.5-zoo (the engine stops at a stop sequence
        // instead of decoding to maxTokens) and 0.2.6-zoo (a macOS 26 / iOS 26 floor with
        // @available(macOS 27, iOS 27, *) on everything that touches Core AI; this package
        // does the same). 0.2.4-zoo is
        // upstream main through #207 (2026-08-28) plus the zoo patches to the pipelined
        // engine: hybrid/SSM extra states so Qwen3.5/3.6, LFM2.5 and Granite 4 load,
        // chunked prefill via a static-chunk "prefill" function, per-token/static inputs,
        // decode-only S=1 bundles (apple/coreai-models#212), the iOS dynamic-KV capacity
        // guard (apple/coreai-models#124), and EOS stop when the consumer breaks the
        // stream. Everything else is byte-for-byte upstream. See
        // https://github.com/john-rocky/coreai-models.
        // 0.2.4-zoo drops the fork's sampler-ordering drain, which upstream #121 made
        // redundant: macOS decode +43-52% (qwen3-0.6b, drain vs. none); iOS not measured.
        // 0.2.2-zoo and earlier predate upstream #121 (per-call sampler execution
        // descriptor; garbled text at temperature > 0 under pipelined decode).
        // For local engine work swap in .package(path: "../coreai-models") — branch
        // zoo-0.4 matches this tag.
        .package(url: "https://github.com/john-rocky/coreai-models", exact: "0.2.8-zoo"),
        .package(url: "https://github.com/huggingface/swift-transformers", from: "1.1.0"),
        .package(
            url: "https://github.com/huggingface/swift-huggingface.git",
            from: "0.11.0",
            traits: [
                .defaults,
                .trait(name: "Xet", condition: .when(traits: ["Xet"])),
            ]
        ),
    ],
    targets: [
        // Shared base: model identity + Hugging Face download/cache. No inference runtime.
        .target(
            name: "CoreAIKitCore",
            dependencies: [.product(name: "HuggingFace", package: "swift-huggingface")]
        ),
        // d1-3B's readout products in float64 through the BLAS calls NumPy makes for `E @ h` (Accelerate's new
        // interface, 64-bit integers), so the kit's logits equal the Python reference's bit for bit. A C target so the
        // two defines reach the Accelerate headers without unsafe flags, which a remote dependency may not carry.
        .target(
            name: "CoreAIKitD1BLAS",
            cSettings: [.define("ACCELERATE_NEW_LAPACK"), .define("ACCELERATE_LAPACK_ILP64")],
            linkerSettings: [.linkedFramework("Accelerate")]
        ),
        .target(
            name: "CoreAIKit",
            dependencies: [
                "CoreAIKitCore",
                "CoreAIKitD1BLAS",
                // The VL executor runs the paired vision tower through CoreAIKitVision's
                // GraphModel (stateless .aimodel runner); it also brings the CoreAI framework
                // link the vision path needs.
                "CoreAIKitVision",
                .product(name: "CoreAILM", package: "coreai-models"),
                .product(name: "Transformers", package: "swift-transformers"),
            ],
            // Mel filterbanks for the audio frontends, each bit-exact with its HF feature
            // extractor so the mel never recomputes librosa: Whisper-large-v3 ([201,128] fp32,
            // gated cos 1.0) and Parakeet ([128,257] fp32 librosa-slaney, gated token-exact e2e).
            resources: [
                .copy("Audio/Resources/mel_filters.f32"),
                .copy("Audio/Resources/parakeet_mel_filters_128x257.f32"),
                // Raw-Metal Gemma 4 kernels + oracle refs (compiled at load by
                // Gemma4MetalEngine — .txt so nothing tries to precompile them).
                .copy("Gemma4Metal/g4msl"),
            ]
        ),
        .target(
            name: "CoreAIKitVision",
            dependencies: ["CoreAIKitCore"],
            linkerSettings: [.linkedFramework("CoreAI")]
        ),
        .target(
            name: "CoreAIKitEmbeddings",
            dependencies: [
                "CoreAIKitCore",
                "CoreAIKitVision",
                .product(name: "Transformers", package: "swift-transformers"),
            ]
        ),
        .target(
            name: "CoreAIKitUI",
            dependencies: ["CoreAIKit"]
        ),
        .target(
            name: "CoreAIOps",
            dependencies: ["CoreAIKit", "CoreAIKitVision", "CoreAIKitEmbeddings"]
        ),
        .executableTarget(
            name: "coreai-doctor",
            dependencies: ["CoreAIOps"]
        ),
        .executableTarget(
            name: "systemone",
            dependencies: ["CoreAIOps"]
        ),
        .testTarget(
            name: "CoreAIKitTests",
            dependencies: ["CoreAIKit", "CoreAIKitVision", "CoreAIKitEmbeddings", "CoreAIOps"],
            // Read from the source tree (#filePath), not bundled: a test resource bundle would
            // make `Bundle.module` ambiguous beside CoreAIKit's under `@testable import`.
            exclude: ["Fixtures"],
            swiftSettings: [.define("COREAIKIT_XET", .when(traits: ["Xet"]))]
        ),
    ]
)