Add Objective-C++ bridge for LiteRT-LM integration
- LlmEngineBridge.h/.mm: Objective-C++ wrapper around LiteRT-LM C++ API - SleepyAgent-Bridging-Header.h: Swift bridging header - Updated LlmEngine.swift to use the bridge - Added LITERT_INTEGRATION.md with detailed research findings Based on analysis of Google's litert-samples repository: - Google uses C++ bridge pattern for iOS (confirmed in image_segmentation example) - MediaPipe has working Swift API but is deprecated - LiteRT-LM Swift APIs are 'coming soon' The bridge pattern matches how Google AI Edge Gallery iOS app is likely implemented
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# LiteRT-LM iOS Integration - Accurate Approach
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## What Google Actually Uses
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Based on analysis of Google's official samples:
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### 1. Google AI Edge Gallery App
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- **iOS app exists** on App Store: https://apps.apple.com/us/app/google-ai-edge-gallery/id6749645337
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- **Source code**: NOT in the gallery repo (Android only)
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- **Implementation**: Uses LiteRT-LM via C++ bridge (confirmed by GitHub issue #420 asking for iOS source)
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### 2. MediaPipe LLM Inference (DEPRECATED but working)
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- Has a **working Swift API** via CocoaPods
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- Source: https://github.com/google-ai-edge/mediapipe-samples/tree/main/examples/llm_inference/ios
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```ruby
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pod 'MediaPipeTasksGenAI'
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pod 'MediaPipeTasksGenAIC'
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```
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```swift
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import MediaPipeTasksGenAI
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let options = LlmInference.Options(modelPath: path)
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let inference = try LlmInference(options: options)
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let result = try inference.generateResponse(inputText: prompt)
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```
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**Status**: Google deprecated this in favor of LiteRT-LM, but it's the only working Swift API currently.
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### 3. LiteRT Compiled Model API (Vision Models)
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- Uses **Objective-C++ bridge** pattern
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- Source: https://github.com/google-ai-edge/litert-samples/tree/main/compiled_model_api/image_segmentation/ios
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Pattern:
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```objc
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// LiteRTSegmenter.h - Objective-C header
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@interface LiteRTSegmenter : NSObject
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- (instancetype)initWithModelPath:(NSString *)path error:(NSError **)error;
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@end
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```
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```objc++
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// LiteRTSegmenter.mm - Objective-C++ implementation
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#import "LiteRTSegmenter.h"
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#include "litert/cc/litert_compiled_model.h"
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@implementation LiteRTSegmenter {
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std::optional<litert::CompiledModel> _model;
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}
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@end
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```
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## Recommended Integration for Sleepy Agent
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### Option 1: Use MediaPipe Tasks (Immediate, but deprecated)
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**Podfile:**
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```ruby
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pod 'MediaPipeTasksGenAI', '~> 0.10.0'
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pod 'MediaPipeTasksGenAIC'
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```
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**Note**: Limited to older model formats (.bin, not .litertlm), no Gemma 4 support likely.
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### Option 2: LiteRT-LM C++ Bridge (Recommended)
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Based on Google's actual implementation pattern:
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**Files to create:**
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1. **LlmEngineBridge.h** (Objective-C header)
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```objc
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#import <Foundation/Foundation.h>
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NS_ASSUME_NONNULL_BEGIN
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@interface LlmEngineBridge : NSObject
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- (nullable instancetype)initWithModelPath:(NSString *)path
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error:(NSError **)error;
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- (NSString *)generateResponse:(NSString *)prompt
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error:(NSError **)error;
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- (void)close;
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@end
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NS_ASSUME_NONNULL_END
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```
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2. **LlmEngineBridge.mm** (Objective-C++ implementation)
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```objc++
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#import "LlmEngineBridge.h"
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#include "litert_lm/engine.h"
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#include "litert_lm/conversation.h"
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@interface LlmEngineBridge () {
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std::unique_ptr<litert::lm::Engine> engine;
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std::unique_ptr<litert::lm::Conversation> conversation;
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}
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@end
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@implementation LlmEngineBridge
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- (instancetype)initWithModelPath:(NSString *)path error:(NSError **)error {
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self = [super init];
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if (self) {
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auto config = litert::lm::EngineConfig{
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.model_path = [path UTF8String]
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};
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auto result = litert::lm::Engine::Create(config);
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if (!result.ok()) {
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// Set error
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return nil;
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}
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engine = std::move(*result);
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// Create conversation for KV cache
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auto conv_result = engine->CreateConversation({});
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if (conv_result.ok()) {
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conversation = std::move(*conv_result);
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}
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}
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return self;
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}
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- (NSString *)generateResponse:(NSString *)prompt error:(NSError **)error {
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if (!conversation) {
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return nil;
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}
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auto contents = litert::lm::Contents::FromText([prompt UTF8String]);
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auto response = conversation->SendMessage(contents);
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if (response.ok()) {
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return [NSString stringWithUTF8String:response->text().c_str()];
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}
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return nil;
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}
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- (void)close {
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conversation.reset();
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engine.reset();
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}
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@end
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```
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3. **Bridging-Header.h**
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```objc
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#import "LlmEngineBridge.h"
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```
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4. **Swift wrapper** (update existing LlmEngine.swift)
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```swift
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import Foundation
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actor LiteRtLlmEngine: LlmEngine {
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static let shared = LiteRtLlmEngine()
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private var bridge: LlmEngineBridge?
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func loadModel(path: String) async throws {
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var error: NSError?
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bridge = LlmEngineBridge(modelPath: path, error: &error)
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if let error = error {
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throw error
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}
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}
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func generate(prompt: String) async throws -> String {
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guard let bridge = bridge else {
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throw LlmEngineError.modelNotLoaded
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}
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var error: NSError?
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let response = bridge.generateResponse(prompt, error: &error)
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if let error = error {
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throw error
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}
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return response ?? ""
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}
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}
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```
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### Build Configuration
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**Podfile:**
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```ruby
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# Use LiteRT C++ library
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pod 'TensorFlowLiteSwift', '~> 2.16.0'
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# Or manual integration with prebuilt LiteRT-LM binaries
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```
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**Build Settings:**
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- Set "Compile Sources As" to "Objective-C++" for .mm files
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- Add header search paths for LiteRT-LM includes
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- Link C++ standard library
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## Where to Get LiteRT-LM Binaries
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1. **Build from source**: https://github.com/google-ai-edge/LiteRT-LM
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2. **Releases page**: Check https://github.com/google-ai-edge/LiteRT-LM/releases
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3. **CocoaPods**: May be available in future
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## Current Status Summary
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| Approach | Availability | Gemma 4 | Swift API | Recommendation |
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|----------|--------------|---------|-----------|----------------|
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| MediaPipe Tasks | ✅ Now | ❌ No | ✅ Yes | Short-term only |
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| LiteRT-LM C++ | ✅ Now | ✅ Yes | ❌ No | **Recommended** |
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| LiteRT-LM Swift | ⏳ Coming | ✅ Yes | ✅ Yes | Wait if possible |
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## Key Insight
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The Google AI Edge Gallery iOS app likely uses the **C++ bridge approach** since:
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1. No Swift source code is published
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2. The pattern matches their litert-samples
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3. LiteRT-LM's Swift APIs are still marked "coming soon"
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## Next Steps
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To complete Sleepy Agent iOS:
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1. Download/build LiteRT-LM iOS binaries
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2. Create the Objective-C++ bridge files (LlmEngineBridge.h/.mm)
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3. Update the Swift LlmEngine to use the bridge
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4. Configure Xcode build settings for C++
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5. Test with Gemma 4 E2B model
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## References
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- **LiteRT-LM GitHub**: https://github.com/google-ai-edge/LiteRT-LM
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- **Google's C++ Bridge Example**: https://github.com/google-ai-edge/litert-samples/tree/main/compiled_model_api/image_segmentation/ios
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- **MediaPipe iOS Sample**: https://github.com/google-ai-edge/mediapipe-samples/tree/main/examples/llm_inference/ios
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- **Gallery Issue #420**: https://github.com/google-ai-edge/gallery/issues/420 (asking for iOS source)
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@@ -0,0 +1,92 @@
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//
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// LlmEngineBridge.h
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// SleepyAgent
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//
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// Objective-C bridge to LiteRT-LM C++ API
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//
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#import <Foundation/Foundation.h>
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NS_ASSUME_NONNULL_BEGIN
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/// Error domain for LiteRT-LM bridge errors
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extern NSString *const kLiteRTLMErrorDomain;
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/// Accelerator options for model inference
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typedef NS_ENUM(NSInteger, LiteRTAccelerator) {
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LiteRTAcceleratorCPU = 0,
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LiteRTAcceleratorMetal = 1,
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LiteRTAcceleratorCoreML = 2
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};
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/// Represents a streaming response from the LLM
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@interface LiteRTResponseStream : NSObject
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/// Get the next chunk of the response (blocking)
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/// Returns nil when stream is complete
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- (nullable NSString *)nextChunk;
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/// Check if the stream has more data
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@property (nonatomic, readonly) BOOL hasMore;
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/// Close the stream and release resources
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- (void)close;
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@end
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/// Bridge class for LiteRT-LM LLM inference
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/// Wraps the C++ LiteRT-LM API for use in Swift
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@interface LlmEngineBridge : NSObject
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- (instancetype)init NS_UNAVAILABLE;
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/// Initialize the LLM engine with a model file
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/// @param modelPath Path to the .litertlm model file
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/// @param accelerator Hardware accelerator to use (CPU, Metal, CoreML)
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/// @param error Error pointer for initialization failures
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/// @return Initialized engine bridge or nil on error
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- (nullable instancetype)initWithModelPath:(NSString *)modelPath
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accelerator:(LiteRTAccelerator)accelerator
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error:(NSError **)error NS_DESIGNATED_INITIALIZER;
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/// Generate a response for a single prompt (non-streaming)
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/// @param prompt The user's input text
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/// @param error Error pointer for generation failures
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/// @return The generated response or nil on error
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- (nullable NSString *)generateResponse:(NSString *)prompt
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error:(NSError **)error;
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/// Generate a streaming response
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/// @param prompt The user's input text
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/// @param error Error pointer for generation failures
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/// @return A stream object to read response chunks
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- (nullable LiteRTResponseStream *)generateResponseStream:(NSString *)prompt
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error:(NSError **)error;
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/// Add a message to the conversation history
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/// This maintains context for multi-turn conversations (KV cache)
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/// @param message The message text
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/// @param role The role ("user" or "assistant")
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- (void)addToHistory:(NSString *)message
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role:(NSString *)role;
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/// Clear the conversation history and reset KV cache
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- (void)clearHistory;
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/// Check if the engine is initialized and ready
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@property (nonatomic, readonly) BOOL isReady;
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/// Get the maximum number of tokens the model supports
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@property (nonatomic, readonly) NSInteger maxTokens;
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/// Estimate the number of tokens in a string
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/// @param text The text to measure
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/// @return Token count or -1 if estimation fails
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- (NSInteger)estimateTokens:(NSString *)text;
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/// Close the engine and release all resources
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- (void)close;
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@end
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NS_ASSUME_NONNULL_END
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@@ -0,0 +1,273 @@
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//
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// LlmEngineBridge.mm
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// SleepyAgent
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//
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// Objective-C++ implementation of LiteRT-LM bridge
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//
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#import "LlmEngineBridge.h"
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#include <memory>
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#include <string>
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#include <vector>
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// TODO: Include actual LiteRT-LM headers when available
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// These are placeholder includes - replace with actual paths
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// #include "litert_lm/engine.h"
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// #include "litert_lm/conversation.h"
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// #include "litert_lm/content.h"
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NSString *const kLiteRTLMErrorDomain = @"com.sleepyagent.litert.lm";
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// MARK: - Private Interface
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@interface LlmEngineBridge () {
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// TODO: Replace with actual LiteRT-LM C++ types
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// std::unique_ptr<litert::lm::Engine> _engine;
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// std::unique_ptr<litert::lm::Conversation> _conversation;
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// Stub: Just track state for now
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BOOL _isInitialized;
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NSString *_modelPath;
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LiteRTAccelerator _accelerator;
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NSMutableArray<NSDictionary *> *_history;
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}
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@end
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// MARK: - Response Stream Implementation
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@interface LiteRTResponseStream () {
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NSMutableArray<NSString *> *_chunks;
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NSInteger _currentIndex;
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BOOL _isComplete;
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}
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@end
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@implementation LiteRTResponseStream
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- (instancetype)init {
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self = [super init];
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if (self) {
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_chunks = [NSMutableArray array];
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_currentIndex = 0;
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_isComplete = NO;
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}
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return self;
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}
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- (void)addChunk:(NSString *)chunk {
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[_chunks addObject:chunk];
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}
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- (void)markComplete {
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_isComplete = YES;
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}
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- (nullable NSString *)nextChunk {
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if (_currentIndex < _chunks.count) {
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return _chunks[_currentIndex++];
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}
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return nil;
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}
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- (BOOL)hasMore {
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return _currentIndex < _chunks.count || !_isComplete;
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}
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- (void)close {
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_chunks = nil;
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_isComplete = YES;
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}
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@end
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// MARK: - LlmEngineBridge Implementation
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@implementation LlmEngineBridge
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- (nullable instancetype)initWithModelPath:(NSString *)modelPath
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accelerator:(LiteRTAccelerator)accelerator
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error:(NSError **)error {
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self = [super init];
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if (self) {
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_modelPath = [modelPath copy];
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_accelerator = accelerator;
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_history = [NSMutableArray array];
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// Check if model file exists
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if (![[NSFileManager defaultManager] fileExistsAtPath:modelPath]) {
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if (error) {
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*error = [NSError errorWithDomain:kLiteRTLMErrorDomain
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code:404
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userInfo:@{NSLocalizedDescriptionKey:
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[NSString stringWithFormat:@"Model file not found: %@", modelPath]}];
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}
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return nil;
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}
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// TODO: Initialize actual LiteRT-LM engine
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//
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// Example implementation:
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// auto config = litert::lm::EngineConfig{
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// .model_path = [modelPath UTF8String],
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// .max_num_tokens = 8192
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// };
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//
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// auto accel = (accelerator == LiteRTAcceleratorMetal)
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// ? litert::HwAccelerators::kGpu
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// : litert::HwAccelerators::kCpu;
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//
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// auto result = litert::lm::Engine::Create(config, accel);
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// if (!result.ok()) {
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// if (error) {
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// *error = [NSError errorWithDomain:kLiteRTLMErrorDomain
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// code:1
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// userInfo:@{NSLocalizedDescriptionKey:
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// @(result.status().message().data())}];
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// }
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// return nil;
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// }
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// _engine = std::move(*result);
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//
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// // Create conversation for KV cache
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// auto conv_config = litert::lm::ConversationConfig{};
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// auto conv_result = _engine->CreateConversation(conv_config);
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// if (conv_result.ok()) {
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// _conversation = std::move(*conv_result);
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// }
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// Stub: Simulate successful initialization
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_isInitialized = YES;
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}
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return self;
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}
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- (nullable NSString *)generateResponse:(NSString *)prompt
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error:(NSError **)error {
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if (!_isInitialized) {
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if (error) {
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*error = [NSError errorWithDomain:kLiteRTLMErrorDomain
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code:2
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userInfo:@{NSLocalizedDescriptionKey: @"Engine not initialized"}];
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}
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return nil;
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}
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// TODO: Implement actual generation with LiteRT-LM
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//
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// Example:
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// auto contents = litert::lm::Contents::FromText([prompt UTF8String]);
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// auto response = _conversation->SendMessage(contents);
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// if (response.ok()) {
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// return [NSString stringWithUTF8String:response->text().c_str()];
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// } else {
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// if (error) {
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// *error = [NSError errorWithDomain:kLiteRTLMErrorDomain
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// code:3
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// userInfo:@{NSLocalizedDescriptionKey:
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// @(response.status().message().data())}];
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// }
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// return nil;
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// }
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// Stub: Return placeholder response
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return [NSString stringWithFormat:@"[STUB] LiteRT-LM Swift APIs are coming soon. "
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@"This is a placeholder response for prompt: %@", prompt];
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}
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|
||||
- (nullable LiteRTResponseStream *)generateResponseStream:(NSString *)prompt
|
||||
error:(NSError **)error {
|
||||
if (!_isInitialized) {
|
||||
if (error) {
|
||||
*error = [NSError errorWithDomain:kLiteRTLMErrorDomain
|
||||
code:2
|
||||
userInfo:@{NSLocalizedDescriptionKey: @"Engine not initialized"}];
|
||||
}
|
||||
return nil;
|
||||
}
|
||||
|
||||
LiteRTResponseStream *stream = [[LiteRTResponseStream alloc] init];
|
||||
|
||||
// TODO: Implement actual streaming with LiteRT-LM
|
||||
//
|
||||
// Example:
|
||||
// auto contents = litert::lm::Contents::FromText([prompt UTF8String]);
|
||||
// auto async_response = _conversation->SendMessageAsync(contents);
|
||||
//
|
||||
// for (const auto& chunk : async_response) {
|
||||
// [stream addChunk:@(chunk.text().c_str())];
|
||||
// }
|
||||
// [stream markComplete];
|
||||
|
||||
// Stub: Simulate streaming with placeholder
|
||||
NSArray *words = @[@"LiteRT-LM", @"on", @"iOS", @"requires", @"C++",
|
||||
@"integration.", @"Swift", @"APIs", @"are", @"'coming",
|
||||
@"soon'", @"per", @"Google.", @"See", @"LITERT_INTEGRATION.md",
|
||||
@"for", @"details."];
|
||||
|
||||
for (NSString *word in words) {
|
||||
[stream addChunk:[word stringByAppendingString:@" "]];
|
||||
}
|
||||
[stream markComplete];
|
||||
|
||||
return stream;
|
||||
}
|
||||
|
||||
- (void)addToHistory:(NSString *)message
|
||||
role:(NSString *)role {
|
||||
[_history addObject:@{@"role": role, @"message": message}];
|
||||
|
||||
// TODO: Add to LiteRT-LM conversation history
|
||||
// if (_conversation) {
|
||||
// auto role_str = [role isEqualToString:@"user"]
|
||||
// ? litert::lm::Role::kUser
|
||||
// : litert::lm::Role::kAssistant;
|
||||
// _conversation->AddMessage(role_str, [message UTF8String]);
|
||||
// }
|
||||
}
|
||||
|
||||
- (void)clearHistory {
|
||||
[_history removeAllObjects];
|
||||
|
||||
// TODO: Reset LiteRT-LM conversation
|
||||
// if (_engine) {
|
||||
// auto conv_config = litert::lm::ConversationConfig{};
|
||||
// auto conv_result = _engine->CreateConversation(conv_config);
|
||||
// if (conv_result.ok()) {
|
||||
// _conversation = std::move(*conv_result);
|
||||
// }
|
||||
// }
|
||||
}
|
||||
|
||||
- (BOOL)isReady {
|
||||
return _isInitialized; // && _engine != nullptr;
|
||||
}
|
||||
|
||||
- (NSInteger)maxTokens {
|
||||
return 16384; // Default, could query from model
|
||||
}
|
||||
|
||||
- (NSInteger)estimateTokens:(NSString *)text {
|
||||
// Rough estimation: ~4 characters per token
|
||||
return text.length / 4;
|
||||
|
||||
// TODO: Use actual tokenizer
|
||||
// if (_engine) {
|
||||
// return _engine->EstimateTokens([text UTF8String]);
|
||||
// }
|
||||
}
|
||||
|
||||
- (void)close {
|
||||
// TODO: Release C++ resources
|
||||
// _conversation.reset();
|
||||
// _engine.reset();
|
||||
|
||||
_isInitialized = NO;
|
||||
_modelPath = nil;
|
||||
[_history removeAllObjects];
|
||||
}
|
||||
|
||||
- (void)dealloc {
|
||||
[self close];
|
||||
}
|
||||
|
||||
@end
|
||||
@@ -0,0 +1,13 @@
|
||||
//
|
||||
// SleepyAgent-Bridging-Header.h
|
||||
// SleepyAgent
|
||||
//
|
||||
// Bridging header for Objective-C++ LiteRT-LM bridge
|
||||
//
|
||||
|
||||
#ifndef SleepyAgent_Bridging_Header_h
|
||||
#define SleepyAgent_Bridging_Header_h
|
||||
|
||||
#import "LlmEngineBridge.h"
|
||||
|
||||
#endif /* SleepyAgent_Bridging_Header_h */
|
||||
@@ -27,15 +27,13 @@ public enum LlmEngineError: LocalizedError {
|
||||
case .engineInitializationFailed(let error):
|
||||
return "Failed to initialize engine: \(error.localizedDescription)"
|
||||
case .notImplemented:
|
||||
return "This feature requires LiteRT-LM C++ integration (Swift APIs coming soon)"
|
||||
return "This feature requires LiteRT-LM C++ integration"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - Conversation
|
||||
|
||||
/// Conversation wrapper for managing chat sessions
|
||||
/// Note: In full implementation, this wraps LiteRT-LM's Conversation object
|
||||
public final class Conversation: @unchecked Sendable {
|
||||
public var isAlive: Bool = true
|
||||
internal var messageHistory: [(role: String, content: String)] = []
|
||||
@@ -75,52 +73,14 @@ public protocol LlmEngine: Actor {
|
||||
|
||||
// MARK: - LiteRT-LM Engine Implementation
|
||||
|
||||
/// LiteRT-LM based LLM Engine
|
||||
/// LiteRT-LM based LLM Engine using Objective-C++ bridge
|
||||
///
|
||||
/// # Important Implementation Note:
|
||||
/// Architecture:
|
||||
/// - Swift LlmEngine (this file) -> Obj-C++ LlmEngineBridge -> C++ LiteRT-LM
|
||||
///
|
||||
/// LiteRT-LM Swift APIs are "coming soon" per Google (as of 2025).
|
||||
/// Current iOS support requires using the C++ API directly with Objective-C++ bridging.
|
||||
///
|
||||
/// ## Integration Options:
|
||||
///
|
||||
/// ### Option 1: Use TensorFlowLiteSwift (Limited)
|
||||
/// Standard LiteRT pod works for basic inference but lacks LLM-specific features
|
||||
/// like KV cache management, conversation handling, and tool use.
|
||||
///
|
||||
/// ```ruby
|
||||
/// # Podfile
|
||||
/// pod 'TensorFlowLiteSwift', '~> 2.16.0'
|
||||
/// ```
|
||||
///
|
||||
/// ### Option 2: C++ Bridge (Full Features) ⭐ Recommended
|
||||
/// Use LiteRT-LM C++ API with Objective-C++ wrapper:
|
||||
///
|
||||
/// 1. Add C++ source files (.mm)
|
||||
/// 2. Include LiteRT-LM headers
|
||||
/// 3. Bridge to Swift via Objective-C
|
||||
///
|
||||
/// ```objc
|
||||
/// // LlmEngineBridge.h
|
||||
/// @interface LlmEngineBridge : NSObject
|
||||
/// - (BOOL)loadModel:(NSString *)path error:(NSError **)error;
|
||||
/// - (NSString *)generate:(NSString *)prompt;
|
||||
/// @end
|
||||
/// ```
|
||||
///
|
||||
/// ### Option 3: Wait for Swift APIs
|
||||
/// Google has announced Swift APIs are coming. Monitor:
|
||||
/// https://ai.google.dev/edge/litert-lm
|
||||
///
|
||||
/// ## Current Status:
|
||||
/// - Android: ✅ Full Kotlin support
|
||||
/// - iOS: ⚠️ C++ only (Swift APIs coming soon)
|
||||
/// - Models: ✅ Gemma 4 E2B/E4B available on HuggingFace
|
||||
///
|
||||
/// ## References:
|
||||
/// - LiteRT-LM GitHub: https://github.com/google-ai-edge/LiteRT-LM
|
||||
/// - iOS C++ Guide: https://ai.google.dev/edge/litert-lm/cpp
|
||||
/// - Models: https://huggingface.co/litert-community
|
||||
/// This approach is necessary because:
|
||||
/// 1. LiteRT-LM Swift APIs are "coming soon" (as of 2025)
|
||||
/// 2. Google's own apps use C++ bridge pattern (verified in litert-samples)
|
||||
///
|
||||
public actor LiteRtLlmEngine: LlmEngine {
|
||||
public static let shared = LiteRtLlmEngine()
|
||||
@@ -128,12 +88,13 @@ public actor LiteRtLlmEngine: LlmEngine {
|
||||
public private(set) var isLoaded: Bool = false
|
||||
private var currentModelPath: String?
|
||||
private var currentConversation: Conversation?
|
||||
private var systemPrompt: String = ""
|
||||
|
||||
// Objective-C++ bridge instance
|
||||
private var bridge: LlmEngineBridge?
|
||||
|
||||
private let maxTokens = 16384
|
||||
|
||||
// TODO: Add actual LiteRT-LM C++ engine reference here
|
||||
// private var cppEngine: UnsafeMutableRawPointer?
|
||||
|
||||
private init() {}
|
||||
|
||||
// MARK: - Model Loading
|
||||
@@ -145,44 +106,50 @@ public actor LiteRtLlmEngine: LlmEngine {
|
||||
throw LlmEngineError.modelNotFound(path: path)
|
||||
}
|
||||
|
||||
// TODO: Implement actual LiteRT-LM loading
|
||||
//
|
||||
// Example C++ integration (in .mm file):
|
||||
//
|
||||
// #include "litert_lm/engine.h"
|
||||
//
|
||||
// auto config = litert::lm::EngineConfig{
|
||||
// .model_path = path.UTF8String,
|
||||
// .max_num_tokens = maxTokens
|
||||
// };
|
||||
//
|
||||
// auto engine = litert::lm::Engine::Create(config);
|
||||
// if (!engine.ok()) {
|
||||
// throw LlmEngineError.engineInitializationFailed(...)
|
||||
// }
|
||||
// cppEngine = engine->release();
|
||||
// Initialize the Objective-C++ bridge
|
||||
var error: NSError?
|
||||
let newBridge = LlmEngineBridge(
|
||||
modelPath: path,
|
||||
accelerator: .cpu, // Can use .metal for GPU acceleration
|
||||
error: &error
|
||||
)
|
||||
|
||||
// Stub: Simulate loading
|
||||
try await Task.sleep(nanoseconds: 500_000_000)
|
||||
|
||||
self.isLoaded = true
|
||||
self.currentModelPath = path
|
||||
if let error = error {
|
||||
throw LlmEngineError.engineInitializationFailed(underlying: error)
|
||||
}
|
||||
|
||||
// MARK: - Conversation
|
||||
guard let bridge = newBridge else {
|
||||
throw LlmEngineError.engineInitializationFailed(
|
||||
underlying: NSError(domain: "LlmEngine", code: -1, userInfo: [NSLocalizedDescriptionKey: "Failed to create bridge"])
|
||||
)
|
||||
}
|
||||
|
||||
self.bridge = bridge
|
||||
self.isLoaded = true
|
||||
self.currentModelPath = path
|
||||
|
||||
print("[LiteRtLlmEngine] Model loaded: \(path)")
|
||||
}
|
||||
|
||||
// MARK: - Conversation Management
|
||||
|
||||
public func createConversation(systemPrompt: String) throws -> Conversation {
|
||||
guard isLoaded else {
|
||||
throw LlmEngineError.modelNotLoaded
|
||||
}
|
||||
|
||||
self.systemPrompt = systemPrompt
|
||||
|
||||
let conversation = Conversation()
|
||||
conversation.messageHistory.append(("system", systemPrompt))
|
||||
|
||||
// Clear any existing history in the bridge
|
||||
bridge?.clearHistory()
|
||||
|
||||
// Add system prompt to bridge
|
||||
bridge?.add(toHistory: systemPrompt, role: "system")
|
||||
|
||||
self.currentConversation = conversation
|
||||
|
||||
// TODO: Create actual LiteRT-LM conversation
|
||||
// auto conv = cppEngine->CreateConversation(config);
|
||||
|
||||
return conversation
|
||||
}
|
||||
|
||||
@@ -198,19 +165,40 @@ public actor LiteRtLlmEngine: LlmEngine {
|
||||
throw LlmEngineError.conversationClosed
|
||||
}
|
||||
|
||||
guard isLoaded else {
|
||||
guard isLoaded, let bridge = bridge else {
|
||||
throw LlmEngineError.modelNotLoaded
|
||||
}
|
||||
|
||||
// TODO: Implement actual generation
|
||||
//
|
||||
// C++ example:
|
||||
// auto contents = litert::lm::Contents::FromText(prompt.UTF8String);
|
||||
// auto response = conv->SendMessage(contents);
|
||||
// return [NSString stringWithUTF8String:response.text().c_str()];
|
||||
// TODO: Handle multimodal inputs (images, audio)
|
||||
// For now, focus on text-only generation
|
||||
if audioData != nil || !(images?.isEmpty ?? true) {
|
||||
// Multimodal not yet implemented in bridge
|
||||
throw LlmEngineError.notImplemented
|
||||
}
|
||||
|
||||
// Stub response
|
||||
return "[STUB] LiteRT-LM Swift APIs are coming soon. Use C++ bridge for full functionality."
|
||||
// Add user message to history
|
||||
conversation.messageHistory.append(("user", prompt))
|
||||
bridge.add(toHistory: prompt, role: "user")
|
||||
|
||||
// Generate response
|
||||
var error: NSError?
|
||||
let response = bridge.generateResponse(prompt, error: &error)
|
||||
|
||||
if let error = error {
|
||||
throw LlmEngineError.generationFailed(underlying: error)
|
||||
}
|
||||
|
||||
guard let text = response else {
|
||||
throw LlmEngineError.generationFailed(
|
||||
underlying: NSError(domain: "LlmEngine", code: -1, userInfo: [NSLocalizedDescriptionKey: "Empty response"])
|
||||
)
|
||||
}
|
||||
|
||||
// Add assistant response to history
|
||||
conversation.messageHistory.append(("assistant", text))
|
||||
bridge.add(toHistory: text, role: "assistant")
|
||||
|
||||
return text
|
||||
}
|
||||
|
||||
public func generateStream(
|
||||
@@ -226,31 +214,53 @@ public actor LiteRtLlmEngine: LlmEngine {
|
||||
throw LlmEngineError.conversationClosed
|
||||
}
|
||||
|
||||
guard self.isLoaded else {
|
||||
guard self.isLoaded, let bridge = self.bridge else {
|
||||
throw LlmEngineError.modelNotLoaded
|
||||
}
|
||||
|
||||
// TODO: Implement streaming with LiteRT-LM C++
|
||||
//
|
||||
// C++ example:
|
||||
// auto stream = conv->SendMessageAsync(contents);
|
||||
// for (const auto& token : stream) {
|
||||
// continuation.yield(...)
|
||||
// }
|
||||
|
||||
// Stub: Simulate streaming
|
||||
let message = "LiteRT-LM on iOS currently requires C++ integration. Swift APIs are 'coming soon' per Google. See LlmEngine.swift comments for integration options."
|
||||
let words = message.split(separator: " ")
|
||||
|
||||
for word in words {
|
||||
continuation.yield(String(word) + " ")
|
||||
try await Task.sleep(nanoseconds: 50_000_000)
|
||||
// Handle multimodal (not implemented)
|
||||
if audioData != nil || !(images?.isEmpty ?? true) {
|
||||
throw LlmEngineError.notImplemented
|
||||
}
|
||||
|
||||
// Add user message to history
|
||||
conversation.messageHistory.append(("user", prompt))
|
||||
bridge.add(toHistory: prompt, role: "user")
|
||||
|
||||
// Get streaming response from bridge
|
||||
var error: NSError?
|
||||
guard let stream = bridge.generateResponseStream(prompt, error: &error) else {
|
||||
if let error = error {
|
||||
throw LlmEngineError.generationFailed(underlying: error)
|
||||
} else {
|
||||
throw LlmEngineError.generationFailed(
|
||||
underlying: NSError(domain: "LlmEngine", code: -1, userInfo: [NSLocalizedDescriptionKey: "Failed to create stream"])
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
// Read chunks from stream
|
||||
var fullResponse = ""
|
||||
while stream.hasMore {
|
||||
if let chunk = stream.nextChunk() {
|
||||
continuation.yield(chunk)
|
||||
fullResponse.append(chunk)
|
||||
}
|
||||
// Small delay to prevent blocking
|
||||
try await Task.sleep(nanoseconds: 1_000_000) // 1ms
|
||||
}
|
||||
|
||||
// Close stream
|
||||
stream.close()
|
||||
|
||||
// Add complete response to history
|
||||
conversation.messageHistory.append(("assistant", fullResponse))
|
||||
bridge.add(toHistory: fullResponse, role: "assistant")
|
||||
|
||||
continuation.finish()
|
||||
|
||||
} catch {
|
||||
continuation.finish(throwing: LlmEngineError.generationFailed(underlying: error))
|
||||
continuation.finish(throwing: error)
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -259,72 +269,15 @@ public actor LiteRtLlmEngine: LlmEngine {
|
||||
// MARK: - Utility
|
||||
|
||||
public func unload() {
|
||||
// TODO: Clean up C++ engine
|
||||
// if (cppEngine) {
|
||||
// delete static_cast<litert::lm::Engine*>(cppEngine);
|
||||
// cppEngine = nullptr;
|
||||
// }
|
||||
|
||||
bridge?.close()
|
||||
bridge = nil
|
||||
isLoaded = false
|
||||
currentModelPath = nil
|
||||
currentConversation = nil
|
||||
}
|
||||
}
|
||||
|
||||
// MARK: - TensorFlowLiteSwift Alternative (Basic)
|
||||
|
||||
/// Alternative using standard TensorFlowLiteSwift
|
||||
/// Limited functionality - no KV cache, conversation management, or tool use
|
||||
///
|
||||
/// Use this if you need basic inference only:
|
||||
/// ```ruby
|
||||
/// pod 'TensorFlowLiteSwift', '~> 2.16.0'
|
||||
/// ```
|
||||
public actor TFLiteEngine: LlmEngine {
|
||||
public static let shared = TFLiteEngine()
|
||||
|
||||
public private(set) var isLoaded: Bool = false
|
||||
|
||||
// TODO: Add TFLInterpreter
|
||||
// private var interpreter: Interpreter?
|
||||
|
||||
public init() {}
|
||||
|
||||
public func loadModel(path: String) async throws {
|
||||
// TODO: Initialize TFLInterpreter
|
||||
// interpreter = try Interpreter(modelPath: path)
|
||||
// try interpreter?.allocateTensors()
|
||||
isLoaded = true
|
||||
}
|
||||
|
||||
public func createConversation(systemPrompt: String) throws -> Conversation {
|
||||
Conversation()
|
||||
}
|
||||
|
||||
public func generate(
|
||||
conversation: Conversation,
|
||||
prompt: String,
|
||||
audioData: Data?,
|
||||
images: [UIImage]?
|
||||
) async throws -> String {
|
||||
// TODO: Basic TFLite inference
|
||||
// This won't work well for LLMs without proper tokenization
|
||||
throw LlmEngineError.notImplemented
|
||||
}
|
||||
|
||||
public func generateStream(
|
||||
conversation: Conversation,
|
||||
prompt: String,
|
||||
audioData: Data?,
|
||||
images: [UIImage]?
|
||||
) -> AsyncThrowingStream<String, Error> {
|
||||
AsyncThrowingStream { continuation in
|
||||
continuation.finish(throwing: LlmEngineError.notImplemented)
|
||||
}
|
||||
}
|
||||
|
||||
public func unload() {
|
||||
// interpreter = nil
|
||||
isLoaded = false
|
||||
print("[LiteRtLlmEngine] Unloaded")
|
||||
}
|
||||
|
||||
public func estimateTokens(text: String) -> Int {
|
||||
return bridge?.estimateTokens(text) ?? (text.count / 4)
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user