Lately I am getting this error. GenerativeModelsAvailability.Parameters: Initialized with invalid language code: en-GB. Expected to receive two-letter ISO 639 code. e.g. 'zh' or 'en'. Falling back to: en Does anyone know what this is and how it can be resolved. The error does not crash the app

I am watching a few WWDC sessions on Foundation Model and its usage and it looks pretty cool. I was wondering if it is possible to perform RAG on the user documents on the devices and entuallly on iCloud... Let's say I have a lot of pages documents about me and I want the Foundation model to access those information on the documents to answer questions about me that can be retrieved from the documents. How can this be done ? Thanks

Hi everyone, I’m currently using macOS Version 15.3 Beta (24D5034f), and I’m encountering an issue with Apple Intelligence. The image generation tools seem to work fine, but everything else shows a message saying that it’s “not available at this time.” I’ve tried restarting my Mac and double-checked my settings, but the problem persists. Is anyone else experiencing this issue on the beta version? Are there any fixes or settings I might be overlooking? Any help or insights would be greatly appreciated! Thanks in advance!

Hey, Would be great to have an equivalent of toolCallId for both toolCall and toolResult in the transcript. Otherwise, it is hard to connect tool calls with their respective responses, when there were multiple parallel calls to the same tool. Thanks!

I have a series of shortcuts that I’ve written that use the “Use Model” action to do various things. For example, I have a shortcut “Clipboard Markdown to Notes” that takes the content of the clipboard, creates a new note in Notes, converts the markdown content to rich text, adds it to the note etc. One key step is to analyze the markdown content with “Use Model” and generate a short descriptive title for the note. I use the on-device model for this, but sometimes the content and prompt exceed the context window size and the action fails with an error message to that effect. In that case, I’d like to either repeat the action using the Cloud model, or, if the error was a refusal, to prompt the user to enter a title to use. I‘ve tried using an IF based on whether the response had any text in it, but that didn’t work. No matter what I’ve tried, I can’t seem to find a way to catch the error from Use Model, determine what the error was, and take appropriate action. Is there a way to do this? (And by the way, a huge ”thank you” to whoever had the idea of making AppIntents visible in Shortcuts and adding the Use Model action — has made a huge difference already, and it lets us see what Siri will be able to use as well.)

I'm playing with the new Vision API for iOS18, specifically with the new CalculateImageAestheticsScoresRequest API. When I try to perform the image observation request I get this error: internalError("Error Domain=NSOSStatusErrorDomain Code=-1 \"Failed to create espresso context.\" UserInfo={NSLocalizedDescription=Failed to create espresso context.}") The code is pretty straightforward: if let image = image { let request = CalculateImageAestheticsScoresRequest() Task { do { let cgImg = image.cgImage! let observations = try await request.perform(on: cgImg) let description = observations.description let score = observations.overallScore print(description) print(score) } catch { print(error) } } } I'm running it on a M2 using the simulator. Is it a bug? What's wrong?

Attempted to download the Adapter Toolkit linked to from https://developer.apple.com/apple-intelligence/foundation-models-adapter/. Failed on all attempts, with a "403 Forbidden" error. I had accepted the agreement on the first attempt. How would we get access please?

Hello Apple Developer Community, I'm exploring the integration of Apple Intelligence features into my mobile application and have a couple of questions regarding the current and upcoming API capabilities: Custom Prompt Support: Is there a way to pass custom prompts to Apple Intelligence to generate specific inferences? For instance, can we provide a unique prompt to the Writing Tools or Image Playground APIs to obtain tailored outputs? Direct Inference Capabilities: Beyond the predefined functionalities like text rewriting or image generation, does Apple Intelligence offer APIs that allow for more generalized inference tasks based on custom inputs? I understand that Apple has provided APIs such as Writing Tools, Image Playground, and Genmoji. However, I'm interested in understanding the extent of customization and flexibility these APIs offer, especially concerning custom prompts and generalized inference. Additionally, are there any plans or timelines for expanding these capabilities, perhaps with the introduction of new SDKs or frameworks that allow deeper integration and customization? Any insights, documentation links, or experiences shared would be greatly appreciated. Thank you in advance for your assistance!

Somehow I'm not able to decrypt our ml models on my machine. It does not matter: If I clean the build / delete the build folder If it's a local build or a build downloaded from our build server I log in as a different user I reboot my system (15.4.1 (24E263) I use a different network Re-generate the encryption keys. I'm the only one in my team confronted with this issue. Using the encrypted models works fine for everyone else. As soon as our application tries to load the bundled ml model the following error is logged and returned: Could not create persistent key blob for CD49E04F-1A42-4FBE-BFC1-2576B89EC233 : error=Error Domain=com.apple.CoreML Code=9 "Failed to generate key request for CD49E04F-1A42-4FBE-BFC1-2576B89EC233 with error: -42908" Error code 9 points to a decryption issue, but offers no useful pointers and suggests that some sort of network request needs to be made in order to decrypt our models. /*! Core ML throws/returns this error when the framework encounters an error in the model decryption subsystem. The typical cause for this error is in the key server configuration and the client application cannot do much about it. For example, a model loading method will throw/return the error when it uses incorrect model decryption key. */ MLModelErrorModelDecryption API_AVAILABLE(macos(11.0), ios(14.0), watchos(7.0), tvos(14.0)) = 9, I could not find a reference to error '-42908' anywhere. ChatGPT just lied to me, as usual... How do can I resolve this or diagnose this further? Thanks.

I generate an array of random floats using the code shown below. However, I would like to do this with Double instead of Float. Are there any BNNS random number generators for double values, something like BNNSRandomFillUniformDouble? If not, is there a way I can convert BNNSNDArrayDescriptor from float to double? import Accelerate let n = 100_000_000 let result = Array<Float>(unsafeUninitializedCapacity: n) { buffer, initCount in var descriptor = BNNSNDArrayDescriptor(data: buffer, shape: .vector(n))! let randomGenerator = BNNSCreateRandomGenerator(BNNSRandomGeneratorMethodAES_CTR, nil) BNNSRandomFillUniformFloat(randomGenerator, &descriptor, 0, 1) initCount = n }

Hi, I'm trying to use the new RecognizeDocumentsRequest from the Vision Framework to read a receipt. It looks very promising by being able to read paragraphs, lines and detect data. So far it unfortunately seems to read every line on the receipt as a paragraph and when there is more space on one line it creates two paragraphs. Is there perhaps an Apple Engineer who knows if this is expected behaviour or if I should file a Feedback for this? Code setup: let request = RecognizeDocumentsRequest() let observations = try await request.perform(on: image) guard let document = observations.first?.document else { return } for paragraph in document.paragraphs { print(paragraph.transcript) for data in paragraph.detectedData { switch data.match.details { case .phoneNumber(let data): print("Phone: \(data)") case .postalAddress(let data): print("Postal: \(data)") case .calendarEvent(let data): print("Calendar: \(data)") case .moneyAmount(let data): print("Money: \(data)") case .measurement(let data): print("Measurement: \(data)") default: continue } } } See attached image as an example of a receipt I'd like to parse. The top 3 lines are the name, street, and postal code + city. These are all separate paragraphs. Checking on detectedData does see the street (2nd line) as PostalAddress, but not the complete address. Might that be a location thing since it's a Dutch address. And lower on the receipt it sees the block with "Pomp 1 95 Ongelood" and the things below also as separate paragraphs. First picking up the left side and after that the right side. So it's something like this: * Pomp 1 Volume Prijs € TOTAAL * BTW Netto 21.00 % 95 Ongelood 41,90 l 1.949/ 1 81.66 € 14.17 67.49

I have a mac (M4, MacBook Pro) running Tahoe 26.0 beta. I am running Xcode beta. I can run code that uses the LLM in a #Preview { }. But when I try to run the same code in the simulator, I get the 'device not ready' error and I see the following in the Settings app. Is there anything I can do to get the simulator to past this point and allowing me to test on it with Apple's LLM?

When using Foundation Models, is it possible to ask the model to produce output in a specific language, apart from giving an instruction like "Provide answers in ." ? (I tried that and it kind of worked, but it seems fragile.) I haven't noticed an API to do so and have a use-case where the output should be in a user-selectable language that is not the current system language.

We're in the process of migrating our app's custom intents from the older SiriKit Custom Intents framework to App Intents. The migration has been straightforward for our app-specific actions, and we appreciate the improved discoverability and Apple Intelligence integration that App Intents provides. However, we also implement SiriKit domain intents for calling and messaging: INStartCallIntent / INStartCallIntentHandling INSendMessageIntent / INSendMessageIntentHandling These require us to maintain an Intents Extension to handle contact resolution and the actual call/message operations. Our questions: Is there a planned App Intents equivalent for these SiriKit domains (calling, messaging), or is the Intents Extension approach still the recommended path? If we want to support phrases like "Call [contact] on [AppName]" or "Send a message to [contact] on [AppName]" with Apple Intelligence integration, is there any way to achieve this with App Intents today? Are there any WWDC sessions or documentation we may have missed that addresses the migration path for SiriKit domain intents? What we've reviewed: "Migrate custom intents to App Intents" Tech Talk "Bring your app's core features to users with App Intents" (WWDC24) App Intents documentation These resources clearly explain custom intent migration but don't seem to address the system domain intents. Our current understanding: Based on our research, it appears SiriKit domain intents should remain on the older framework, while custom intents should migrate to App Intents. We'd like to confirm this is correct and understand if there's a future direction we should be planning for. Thank you!

I'm using python 3.9.6, tensorflow 2.20.0, tensorflow-metal 1.2.0, and when I try to run import tensorflow as tf It gives Traceback (most recent call last): File "/Users/haoduoyu/Code/demo.py", line 1, in <module> import tensorflow as tf File "/Users/haoduoyu/Code/test/lib/python3.9/site-packages/tensorflow/__init__.py", line 438, in <module> _ll.load_library(_plugin_dir) File "/Users/haoduoyu/Code/test/lib/python3.9/site-packages/tensorflow/python/framework/load_library.py", line 151, in load_library py_tf.TF_LoadLibrary(lib) tensorflow.python.framework.errors_impl.NotFoundError: dlopen(/Users/haoduoyu/Code/test/lib/python3.9/site-packages/tensorflow-plugins/libmetal_plugin.dylib, 0x0006): Library not loaded: @rpath/_pywrap_tensorflow_internal.so Referenced from: <8B62586B-B082-3113-93AB-FD766A9960AE> /Users/haoduoyu/Code/test/lib/python3.9/site-packages/tensorflow-plugins/libmetal_plugin.dylib Reason: tried: '/Users/haoduoyu/Code/test/lib/python3.9/site-packages/tensorflow-plugins/../_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/_pywrap_tensorflow_internal.so' (no such file), '/Users/haoduoyu/Code/test/lib/python3.9/site-packages/tensorflow-plugins/../_solib_darwin_arm64/_U@local_Uconfig_Utf_S_S_C_Upywrap_Utensorflow_Uinternal___Uexternal_Slocal_Uconfig_Utf/_pywrap_tensorflow_internal.so' (no such file) As long as I uninstall tensorflow-metal, nothing goes wrong. How can I fix this problem?

Hello Apple Team, Thank you for the recent Group Lab and for your continued work on advancing Xcode and developer tools. I’d like to submit a feature request: Are there any plans to introduce support for Agentic AI Mode (MCP protocol) in future versions of iOS or Xcode? As developer tools evolve toward more intelligent and context-aware environments, the integration of agentic AI capabilities could significantly enhance productivity and unlock new creative workflows. Looking forward to your consideration, and thank you again for the excellent session. Best regards

Hello, We have been encountering a persistent crash in our application, which is deployed exclusively on iPad devices. The crash occurs in the following code block: let requestHandler = ImageRequestHandler(paddedImage) var request = CoreMLRequest(model: model) request.cropAndScaleAction = .scaleToFit let results = try await requestHandler.perform(request) The client using this code is wrapped inside an actor, following Swift concurrency principles. The issue has been consistently reproduced across multiple iPadOS versions, including: iPad OS - 18.4.0 iPad OS - 18.4.1 iPad OS - 18.5.0 This is the crash log - Crashed: com.apple.VN.detectorSyncTasksQueue.VNCoreMLTransformer 0 libobjc.A.dylib 0x7b98 objc_retain + 16 1 libobjc.A.dylib 0x7b98 objc_retain_x0 + 16 2 libobjc.A.dylib 0xbf18 objc_getProperty + 100 3 Vision 0x326300 -[VNCoreMLModel predictWithCVPixelBuffer:options:error:] + 148 4 Vision 0x3273b0 -[VNCoreMLTransformer processRegionOfInterest:croppedPixelBuffer:options:qosClass:warningRecorder:error:progressHandler:] + 748 5 Vision 0x2ccdcc __119-[VNDetector internalProcessUsingQualityOfServiceClass:options:regionOfInterest:warningRecorder:error:progressHandler:]_block_invoke_5 + 132 6 Vision 0x14600 VNExecuteBlock + 80 7 Vision 0x14580 __76+[VNDetector runSuccessReportingBlockSynchronously:detector:qosClass:error:]_block_invoke + 56 8 libdispatch.dylib 0x6c98 _dispatch_block_sync_invoke + 240 9 libdispatch.dylib 0x1b584 _dispatch_client_callout + 16 10 libdispatch.dylib 0x11728 _dispatch_lane_barrier_sync_invoke_and_complete + 56 11 libdispatch.dylib 0x7fac _dispatch_sync_block_with_privdata + 452 12 Vision 0x14110 -[VNControlledCapacityTasksQueue dispatchSyncByPreservingQueueCapacity:] + 60 13 Vision 0x13ffc +[VNDetector runSuccessReportingBlockSynchronously:detector:qosClass:error:] + 324 14 Vision 0x2ccc80 __119-[VNDetector internalProcessUsingQualityOfServiceClass:options:regionOfInterest:warningRecorder:error:progressHandler:]_block_invoke_4 + 336 15 Vision 0x14600 VNExecuteBlock + 80 16 Vision 0x2cc98c __119-[VNDetector internalProcessUsingQualityOfServiceClass:options:regionOfInterest:warningRecorder:error:progressHandler:]_block_invoke_3 + 256 17 libdispatch.dylib 0x1b584 _dispatch_client_callout + 16 18 libdispatch.dylib 0x6ab0 _dispatch_block_invoke_direct + 284 19 Vision 0x2cc454 -[VNDetector internalProcessUsingQualityOfServiceClass:options:regionOfInterest:warningRecorder:error:progressHandler:] + 632 20 Vision 0x2cd14c __111-[VNDetector processUsingQualityOfServiceClass:options:regionOfInterest:warningRecorder:error:progressHandler:]_block_invoke + 124 21 Vision 0x14600 VNExecuteBlock + 80 22 Vision 0x2ccfbc -[VNDetector processUsingQualityOfServiceClass:options:regionOfInterest:warningRecorder:error:progressHandler:] + 340 23 Vision 0x125410 __swift_memcpy112_8 + 4852 24 libswift_Concurrency.dylib 0x5c134 swift::runJobInEstablishedExecutorContext(swift::Job*) + 292 25 libswift_Concurrency.dylib 0x5d5c8 swift_job_runImpl(swift::Job*, swift::SerialExecutorRef) + 156 26 libdispatch.dylib 0x13db0 _dispatch_root_queue_drain + 364 27 libdispatch.dylib 0x1454c _dispatch_worker_thread2 + 156 28 libsystem_pthread.dylib 0x9d0 _pthread_wqthread + 232 29 libsystem_pthread.dylib 0xaac start_wqthread + 8 We found an issue similar to us - https://developer.apple.com/forums/thread/770771. But the crash logs are quite different, we believe this warrants further investigation to better understand the root cause and potential mitigation strategies. Please let us know if any additional information would help diagnose this issue.

I have rewatched WWDC22 a few times , but still not getting full understanding how to get .mlmodel model file type from components . Example with banana ripeness is cool , but what need to be added to actually have output of .mlmodel , is somewhere full sample code for this type of modular project ? Code is from [https://developer.apple.com/videos/play/wwdc2022/10019) import CoreImage import CreateMLComponents struct ImageRegressor { static let trainingDataURL = URL(fileURLWithPath: "~/Desktop/bananas") static let parametersURL = URL(fileURLWithPath: "~/Desktop/parameters") static func train() async throws -> some Transformer<CIImage, Float> { let estimator = ImageFeaturePrint() .appending(LinearRegressor()) // File name example: banana-5.jpg let data = try AnnotatedFiles(labeledByNamesAt: trainingDataURL, separator: "-", index: 1, type: .image) .mapFeatures(ImageReader.read) .mapAnnotations({ Float($0)! }) let (training, validation) = data.randomSplit(by: 0.8) let transformer = try await estimator.fitted(to: training, validateOn: validation) try estimator.write(transformer, to: parametersURL) return transformer } } I have tried to run it in Mac OS command line type app, Swift-UI but most what I had as output was .pkg with "pipeline.json, parameters, optimizer.json, optimizer"

Hi everyone, I’m working on an iOS app that uses a Core ML model to run live image recognition. I’ve run into a persistent issue with the mlpackage not being turned into a swift class. This following error is in the code, and in carDetection.mlpackage, it says that model class has not been generated yet. The error in the code is as follows: What I’ve tried: Verified Target Membership is checked for carDetectionModel.mlpackage Confirmed the file is listed under Copy Bundle Resources (and removed from Compile Sources) Cleaned the build folder (Shift + Cmd + K) and rebuilt Renamed and re-added the .mlpackage file Restarted Xcode and re-added the file Logged bundle contents at runtime, but the .mlpackage still doesn’t appear The mlpackage is in Copy bundle resources, and is not in the compile sources. I just don't know why a swift class is not being generated for the mlpackage. Could someone please give me some guidance on what to do to resolve this issue? Sorry if my error is a bit naive, I'm pretty new to iOS app development

My sample app has been working with the following code: func call(arguments: Arguments) async throws -&gt; ToolOutput { var temp:Int switch arguments.city { case .singapore: temp = Int.random(in: 30..&lt;40) case .china: temp = Int.random(in: 10..&lt;30) } let content = GeneratedContent(temp) let output = ToolOutput(content) return output } However in 26 beta 5, ToolOutput no longer available, please advice what has changed.