build_module_unparty_demo

chat with me about the unparty-ios-demo in your project knowledgeEditI want to clarify - I don't have any prior project knowledge about "unparty-ios-demo" beyond the code documentation you've just shared with me. However, I can help analyze and discuss what I see in this iOS demo implementation. From the documentation, this appears to be an iOS app that does some interesting text processing and clustering work. Would you like to discuss any particular aspect of it? I can see it has several key components:

A data layer handling Entries and Clusters Natural Language Processing capabilities using CoreML A storage system with an abstract provider interface A REST API wrapper for something called "UNPARTY" SwiftUI views for displaying entries and clusters

What interests you most about this implementation? I'd be happy to dive deeper into any of these aspects. CopyRetryJMno need for any additional project knowledge beyond the code documentation. Let's start with adding our data models into swift. Correct my understanding here, I would create a folder called "models" and then create an EntryModel.swift and ClusterModel.swift?EditYes, that's a good approach! Creating separate files for each model in a dedicated "Models" folder follows Swift best practices for code organization. Let me help you set up these models. EntryModel.swiftClick to open code ClusterModel.swiftClick to open code I've enhanced the original models with:

Identifiable protocol conformance (useful for SwiftUI lists) Hashable protocol conformance (helpful for collections and diffing) Convenient initializers with default values Proper organization in separate files

Key points about the implementation:

Both models remain Codable as specified in the original code Added initializers make it easier to create instances with default values The Hashable implementation uses only the id field, which is sufficient for uniqueness

Would you like me to explain any part of these implementations in more detail? Or shall we move on to implementing the storage layer next? CopyRetryJMCreate a documentation page for the EntryModel.swift to explain how the code file worksEditEntryModel DocumentationClick to open document This documentation provides a comprehensive overview of the EntryModel implementation, including its structure, protocols, initialization, and usage patterns. Would you like me to explain any specific part in more detail or create similar documentation for the ClusterModel? CopyRetryJMthis is perfect- create similar documentation for the ClusterModelEditClusterModel DocumentationClick to open document This documentation provides a comprehensive overview of the ClusterModel implementation, following the same structure as the EntryModel documentation. Would you like me to explain any specific part in more detail or move on to implementing another component of the system? CopyRetryJMLet's move on to step 2 in our code documentation - NLP processing. Starting with CoreML for text embeddingsEditI'll help you implement the NLP processing using CoreML for text embeddings. Let's create a service to handle this functionality. TextEmbeddingsService.swiftClick to open code I've created a comprehensive TextEmbeddingsService that handles text embedding generation using CoreML. Here are the key features:

Model Management:

Loads a CoreML model for text embeddings Handles model compilation and initialization Includes error handling for model loading failures

Core Functionality:

Generates embeddings from input text Provides cosine similarity calculation Includes basic text preprocessing

Async Support:

Async methods for embedding generation Batch processing capabilities Task priority management

To use this service, you'll need to:

Add a CoreML model file to your project Initialize the service Call the appropriate methods to generate embeddings

Here's a quick example of how to use it: swiftCopydo { let embeddingsService = try TextEmbeddingsService()

// Synchronous usage
let embeddings = try embeddingsService.generateEmbeddings(for: "Sample text")

// Async usage
Task {
    let embeddings = try await embeddingsService.generateEmbeddingsAsync(for: "Sample text")
    // Use embeddings
}

} catch { print("Error: (error)") } Would you like me to explain any part in more detail? We can also move on to implementing the Natural Language framework tokenization next. CopyRetryClaude does not have the ability to run the code it generates yet.JMI would like for you not to assume. Sounds like the first step here should be adding a CoreML model file to the project? Correct me if I am wrong, but this means adding a CoreModel.swift within the Models directory?EditActually, I apologize for jumping ahead! Let me clarify - a CoreML model file (.mlmodel) is different from a Swift model file. A CoreML model is a trained machine learning model that needs to be added to your project. It's not a Swift file, but rather a specialized file format (.mlmodel) that contains the actual trained neural network or other machine learning model that can generate text embeddings. Before we implement any service code, we need to:

Obtain or train a CoreML model for text embeddings Add this .mlmodel file to the Xcode project

Do you already have a text embeddings model you plan to use? If not, we should discuss options like:

Converting an existing model (like BERT or other transformer models) to CoreML format Using Apple's built-in embedding models Training a custom model for your specific needs

Would you like to explore these options before we proceed with the implementation? CopyRetryJMI would like to train the custom model by using it within the app architecture.EditLet's create a training infrastructure for the custom model within the app. Since we'll be training our own model, we'll need a few components:

A training data manager to handle our training data A model trainer service A way to save and load our trained model

Let's start with the training data structure: MLTrainingService.swiftClick to open code To use this training service within your app:

First collect training data from user entries Train the model when appropriate (perhaps after a certain number of entries or on user request) Save the trained model for later use

Here's how you might use it: swiftCopylet trainingService = MLTrainingService()

// Add training data as users create entries trainingService.addTrainingData(.init( text: "Sample text", category: "category", metadata: nil ))

// Train the model Task { do { let model = try await trainingService.trainModel()

    // Save the model
    let documentsDirectory = FileManager.default.urls(for: .documentDirectory, in: .userDomainMask)[0]
    let modelURL = documentsDirectory.appendingPathComponent("TextEmbeddings.mlmodel")
    try trainingService.saveModel(model, to: modelURL)
    
} catch {
    print("Training error: \(error)")
}

} Would you like me to explain any part in more detail? We can also discuss:

When and how often to train the model What training data to collect How to handle model versioning How to evaluate model performance CopyRetryClaude does not have the ability to run the code it generates yet.JMLet's take a look at these 6 errors- that look largely related to functions not being defined within scope. I created a Services directory and added MLTrainingService.swift. Is the error for MLTextEmbedding not in scope-because we need to create a MLTextEmbeddingService.swift?Actually, the errors are occurring because we tried to use some CreateML and CoreML APIs that don't exist exactly as referenced in the code. Let me clarify:

MLTextEmbedding isn't a built-in class - we need to use NLEmbedding from the Natural Language framework instead The supervised training approach I suggested using CreateML isn't quite right for our use case

Let me provide a corrected version of the training service that uses the actual available APIs: MLTrainingService.swiftClick to open code Key changes from the previous version:

Removed CreateML dependency and uses NLEmbedding instead Uses Apple's pre-trained word embeddings rather than trying to train our own Focuses on generating embeddings rather than training a new model Properly uses the available Natural Language framework APIs

This approach is more practical because:

We leverage Apple's existing word embeddings We don't need to collect massive amounts of training data It will work immediately without needing a training phase The embeddings are production-ready and well-tested

Would you like me to explain any part of this implementation in more detail? CopyRetryJMCreate a documentation page for the MLTrainingService.swift to explain how the code file worksEditMLTrainingService DocumentationClick to open document This documentation provides a comprehensive overview of the MLTrainingService implementation. Would you like me to explain any specific part in more detail or help implement another component of the system? CopyRetryJMFor clarification, MLTrainingService.swift is handling the text embeddings in step 2- NLP Processing - CoreML?EditNot exactly. Let me clarify the components from step 2 "NLP Processing" in the original documentation: Copy2. NLP Processing

• CoreML for text embeddings

• Natural Language framework for tokenization

• Local clustering using k-means algorithm

• Threshold detection using cosine similarity The current MLTrainingService.swift is actually using Apple's Natural Language framework (NLEmbedding) rather than CoreML for embeddings. While it handles text embeddings, it's not using CoreML as specified in the requirements. To properly implement the CoreML requirement, we would need to:

First create/obtain a CoreML model (.mlmodel file) specifically for text embeddings Create a separate service that uses this CoreML model to generate embeddings

Would you like me to:

Rename the current service to NLEmbeddingService.swift since it uses NL framework Create a new CoreMLEmbeddingService.swift that properly implements the CoreML requirement Or discuss which approach (CoreML vs NL framework) would be better for your needs? CopyRetryClaude does not have the ability to run the code it generates yet.JMLet's keep the MLTrainingService.swift as it can work within the architecture. We need to discuss how we can create the following three components - A training data manager to handle our training data

• A model trainer service

• A way to save and load our trained model.EditLet's break down these three components and implement them one by one:

First, let's create a TrainingDataManager:

TrainingDataManager.swiftClick to open code

Now, let's create a ModelTrainerService:

ModelTrainerService.swiftClick to open code

Finally, let's create a ModelPersistenceManager:

ModelPersistenceManager.swiftClick to open code These three components work together:

TrainingDataManager:

Manages collection and storage of training data Handles data persistence Provides filtering and retrieval methods

ModelTrainerService:

Handles the actual model training process Works with batches of data Manages the current model instance

ModelPersistenceManager:

Handles saving and loading of trained models Manages model versions Stores metadata about each model version

To use these components together: swiftCopy// Initialize managers let dataManager = TrainingDataManager() let trainerService = ModelTrainerService(dataManager: dataManager) let persistenceManager = try ModelPersistenceManager()

// Training workflow Task { do { // Train model let model = try await trainerService.trainModel()

    // Save trained model
    try persistenceManager.saveModel(model, version: "1.0")
    
    // Load model when needed
    let loadedModel = try persistenceManager.loadLatestModel()
} catch {
    print("Error: \(error)")
}

} Would you like me to explain any specific part in more detail or create documentation for any of these components? CopyRetryClaude does not have the ability to run the code it generates yet.JMI'm going to slow you down a bit and go through each code file first. Starting with TrainingDataManager.swift. I receive an errorThe error is occurring because when making something Codable in Swift, we need to conform to both Encodable and Decodable protocols, and implement both encoding and decoding methods. Let me fix the Codable conformance in TrainingDataManager: TrainingDataManager.swift - Codable FixClick to open code Key changes made:

Implemented explicit encode(to:) and init(from:) methods Properly defined CodingKeys enum inside the TrainingData struct Handled the metadata property by explicitly excluding it from coding (since [String: Any] can't be automatically coded)

The error occurred because:

Swift needs to know how to encode/decode each property The [String: Any] type in metadata isn't automatically Codable We needed to provide custom encoding/decoding implementation

Would you like me to explain any part of this implementation in more detail? Or should we continue with implementing other parts of the TrainingDataManager? CopyRetryJMSince we have declared CodingKeys at the beginning- I now get a redeclaration error here. How can I modify this // MARK: - Codable Conformance extension TrainingDataManager.TrainingData: Codable { enum CodingKeys: String, CodingKey { case id, text, category, timestamp, isProcessed // Note: metadata is excluded from Codable as [String: Any] isn't Codable } }EditAh, I see the issue. Since we already have the CodingKeys enum declared in the struct, we don't need to redeclare it in the extension. Instead, we should either:

Remove the extension completely since we've moved the Codable implementation inside the struct, OR Keep the extension but only implement the encoding/decoding methods there

Let's go with option 2, which is often preferred for better code organization: swiftCopy// MARK: - Codable Conformance extension TrainingDataManager.TrainingData { // No need to redeclare CodingKeys here since it's already in the struct

func encode(to encoder: Encoder) throws {
    var container = encoder.container(keyedBy: CodingKeys.self)
    try container.encode(id, forKey: .id)
    try container.encode(text, forKey: .text)
    try container.encode(category, forKey: .category)
    try container.encode(timestamp, forKey: .timestamp)
    try container.encode(isProcessed, forKey: .isProcessed)
}

init(from decoder: Decoder) throws {
    let container = try decoder.container(keyedBy: CodingKeys.self)
    id = try container.decode(UUID.self, forKey: .id)
    text = try container.decode(String.self, forKey: .text)
    category = try container.decode(String?.self, forKey: .category)
    timestamp = try container.decode(Date.self, forKey: .timestamp)
    isProcessed = try container.decode(Bool.self, forKey: .isProcessed)
    metadata = nil // metadata is not decoded
}

} This keeps our code organized by:

Keeping the CodingKeys enum with the struct definition Moving the encoding/decoding implementation to an extension Avoiding the redeclaration error

Is there anything else you'd like me to clarify about this organization? CopyRetryClaude does not have the ability to run the code it generates yet.import Foundation

class TrainingDataManager { // Store training data with unique identifiers private var trainingDataSet: [UUID: TrainingData] = [:]

struct TrainingData {
    letpastedJMI ma not fully following - would you be able to rewrite the TrainingDataManager.swiftI'll help rewrite the TrainingDataManager.swift file in a cleaner way by removing duplications and organizing it better.

TrainingDataManager.swiftClick to open code Key changes made:

Removed the duplicate CodingKeys declaration Moved the Codable implementation to a separate extension Kept the main TrainingData struct clean with just properties and initializer Organized the code with clear MARK comments for different sections Maintained all the functionality while reducing redundancy

The code is now more organized and follows better Swift practices. Would you like me to explain any specific part in more detail? CopyRetryJMCreate a documentation page for the TrainingDataManager.swift to explain how the code file worksEditTrainingDataManager DocumentationClick to open document Would you like me to explain any specific part of the documentation in more detail? Or should we move on to documenting another component? CopyRetryJMCreate a documentation page for the ModelTrainerService.swift to explain how the code file works and exploration points to address the placeholder codesEditModelTrainerService DocumentationClick to open document CopyRetryJMCreate a documentation page for the ModelPersistenceManager.swift to explain how the code file worksEditModelPersistenceManager DocumentationClick to open document CopyRetryJMGreat, let's recap the code files we have created so far just so that we are aligned before going into our NLP Processing code filesEditLet's recap the code files we've created so far:

Models Directory:

EntryModel.swift: Defines the Entry struct for text content and its embeddings ClusterModel.swift: Defines the Cluster struct for grouping related entries

Services Directory:

MLTrainingService.swift: Handles model training and batch processing ModelPersistenceManager.swift: Manages saving and loading trained models TrainingDataManager.swift: Manages training data collection and storage

According to the original requirements in step 2 (NLP Processing), we still need to implement:

CoreML for text embeddings (though we've touched on this in MLTrainingService) Natural Language framework for tokenization Local clustering using k-means algorithm Threshold detection using cosine similarity

Would you like to start implementing any of these remaining NLP Processing components? We could begin with either:

A dedicated TextEmbeddingsService using CoreML A TokenizationService using the Natural Language framework A ClusteringService for the k-means implementation A SimilarityService for threshold detection

Which component would you prefer to tackle next? CopyRetryJMLet's start with a dedicated TextEmbeddingsService using CoreMLEditI'll help create the TextEmbeddingsService. Since this service will need to use CoreML, let's first outline what it needs to do and then implement it. TextEmbeddingsService.swiftClick to open code This service provides:

Model initialization from a CoreML model file Text preprocessing Embedding generation (both synchronous and async) Batch processing capabilities Cosine similarity calculation Error handling

To use this service, you'll need a CoreML model file. Would you like me to:

Create documentation for this service to explain its usage in detail? Explain how to obtain or create a suitable CoreML model? Show example usage with a sample model? Add additional preprocessing or post-processing features? CopyRetryJMLet's actually create a .mlmodel first-as this seems to be creating a bit of a bottleneck. Is there a simple/straightforward .mlmodel file we can create, that can be trained while the user is using the app (i know there is/just unsure what that initial file would/could look like)EditYes! For this situation, we can create a simple text classification model using Create ML (Apple's tool for creating ML models) as our starting point. This will give us a basic .mlmodel file that we can then update through training as users interact with the app. Let's create a simple starter model:

First, create a new .mlmodel file:

Open Xcode File > New > File Choose "Create ML model" under Core ML section Name it "TextClassifier.mlmodel"

Configure the model:

swiftCopyimport CreateML

let initialTrainingData = MLDataTable(dictionary: [ "text": ["initial sample text"], "label": ["default"] ])

let model = try MLTextClassifier(trainingData: initialTrainingData, textColumn: "text", labelColumn: "label")

// Save the initial model try model.write(to: URL(fileURLWithPath: "TextClassifier.mlmodel")) This creates a minimal model that we can include in the app and then update as we collect more data. The key advantage is that it's:

Small enough to bundle with the app Ready to be trained further Won't block the app's functionality while being updated

Would you like me to:

Show how to create this initial model step by step in Xcode? Explain how to integrate this starter model with our TextEmbeddingsService? Demonstrate how to update the model with new data as users interact with the app? CopyRetryClaude does not have the ability to run the code it generates yet.JMI'm not seeing the Create ML modelAh, I see the confusion. In newer versions of Xcode, the Create ML model option isn't directly in the New File menu. Instead, we have two options:

Use Create ML App (recommended for initial model creation):

Open Create ML app (it's a separate application from Xcode) Create > Text Classifier This gives us a UI to create and train our initial model

Or create programmatically in a Swift Playground:

File > New > Playground Choose macOS > Blank Use CreateML framework to create the model

Would you like me to walk you through either:

Creating the initial model using Create ML app (more visual, easier to start with) Creating it programmatically in a Playground (more flexible, but requires more code)

Both approaches will give us a .mlmodel file that we can add to our project. Which method would you prefer to try? CopyRetryJMLet's go with Create ML appEditI'll walk you through creating our initial text classifier model using Create ML app:

First, locate and open Create ML:

Open Spotlight (Command + Space) Type "Create ML" Open the Create ML app

Once Create ML is open:

Click "New Document" or File > New In the template chooser, select "Text" category Choose "Text Classifier" Click "Next" Name it "UnpartyTextClassifier"

For our initial training:

We need a minimal dataset to start with Create a simple CSV file with two columns:

"text" (the input text) "label" (the category)

Even just 2-3 entries per category will work for our starter model

Would you like me to show you:

How to set up this minimal training data? The specific settings we should use for training? How to export the model once it's trained?

Let me know which part you'd like to tackle first, and I'll provide detailed steps. CopyRetryJMLet's set up with minimal training data/settings and exportLet's create a minimal training dataset and configure the model. For a basic text classifier that we can build upon later, we'll start with a simple structure.

First, let's create a CSV file for our training data:

csvCopytext,label "Great app, love the interface",positive "The app keeps crashing",negative "Nice features but needs work",neutral "Cannot log in at all",negative "Perfect for my needs",positive "Works as expected",neutral To add this training data:

Click the "Choose" dropdown under "Training Data" Select "Choose Files..." Save the CSV data above to a file named "initial_training.csv" Select this file in the file picker

The current settings look good for our initial model:

Iterations: 10 (default) Algorithm: Maximum Entropy Validation: Auto (splits from training data)

After adding the data and confirming the settings, we can:

Click the "Train" button (top left play button) Once training completes, go to the Output tab Click "Export" to save the .mlmodel file Save it as "UnpartyTextClassifier.mlmodel"

Would you like me to:

Provide more example training data? Explain how to add the exported model to our Xcode project? Show how to test the model before exporting? CopyRetryClaude does not have the ability to run the code it generates yet.JMexplain how to add the exported model to our Xcode projectEditI'll show you how to add the exported .mlmodel file to our Xcode project:

In Xcode:

Select your project in the navigator Right-click on your project folder where you want to add the model Select "Add Files to ..." OR drag and drop the .mlmodel file directly into your project

When the file dialog appears:

Choose your exported "UnpartyTextClassifier.mlmodel" Make sure "Copy items if needed" is checked In "Add to targets," ensure your main app target is selected Click "Add"

After adding, select the .mlmodel file in Xcode and verify in the File Inspector (right panel):

Target Membership is checked for your app "Create ML Model Class" is set to YES "Swift Package" is set to NO

Xcode will automatically generate a Swift class for your model, making it accessible in your code like this: swiftCopyif let model = try? UnpartyTextClassifier(configuration: MLModelConfiguration()) { // Use model here } Would you like me to show you:

How to integrate this model with our existing TextEmbeddingsService? How to use the model in your code? How to handle model loading and errors? CopyRetryClaude does not have the ability to run the code it generates yet.JMCreate a documentation page for the UNPARTYTextClassifier.mlmodel to explain how the code file worksEditUNPARTYTextClassifier DocumentationClick to open document CopyRetryJMGreat, now seems like an appropriate time to create our NLP code files NLP Processing

• CoreML for text embeddings

• Natural Language framework for tokenization

• Local clustering using k-means algorithm

• Threshold detection using cosine similarityEditLet's organize these NLP components into separate service files. We'll create four distinct services to handle each aspect of NLP processing:

We already have TextEmbeddingsService.swift for CoreML embeddings Let's create TextTokenizationService.swift for Natural Language framework:

TextTokenizationService.swiftClick to open code

Let's create KMeansClusteringService.swift for clustering:

KMeansClusteringService.swiftClick to open code

Let's create SimilarityService.swift for threshold detection:

SimilarityService.swiftClick to open code These four services work together to provide complete NLP processing:

TextEmbeddingsService: Converts text to vector representations TextTokenizationService: Breaks text into meaningful tokens KMeansClusteringService: Groups similar vectors together SimilarityService: Determines how similar vectors are to each other

Would you like me to:

Create documentation for any of these services? Show how they work together? Add additional features to any service? Explain any specific implementation details? CopyRetryJMLet's address these files one-by-one starting with TextEmbeddingsService. I have an error of "Value of type 'NLModel' has no member 'predictedFeatures'"EditAh yes, I see the issue. The predictedFeatures method doesn't exist on NLModel. Let me correct the TextEmbeddingsService.swift to use the proper API methods: TextEmbeddingsService.swiftClick to open code The main changes are:

Added NLTagger for text processing Changed from using predictedFeatures to predictedLabel Added placeholder for converting predictions to embeddings

However, there's a larger architectural decision we need to make here. The current approach assumes our CoreML model outputs embeddings directly, but our UnpartyTextClassifier is actually a text classifier. We have a few options:

Use a different pre-trained model specifically for embeddings (like BERT or Word2Vec converted to CoreML) Use Apple's built-in word embeddings through NLEmbedding Create our own embedding layer in the classifier model

Which approach would you prefer to explore? CopyRetryJMLet me try to understand a bit more. Sounds like the TextEmbeddingService.swift is not needed because we have our text classifier, so instead we can use NLEmbedding? Am i understanding this correctly?EditYes, you're on the right track! Let me clarify:

Our current setup:

We have UnpartyTextClassifier.mlmodel which classifies text into categories This classifier doesn't generate embeddings (vector representations of text)

For embeddings, we have two options:

Use NLEmbedding (Apple's built-in word embeddings) - simpler, ready to use Create a separate CoreML model for embeddings - more complex, requires training

Since we already have a text classifier and just need embeddings for similarity matching and clustering, using NLEmbedding would be more straightforward. We could rename our service to better reflect what it does: swiftCopy// Instead of TextEmbeddingsService.swift class WordEmbeddingService { private let embedding: NLEmbedding?

init() {
    // Use Apple's built-in word embeddings
    self.embedding = NLEmbedding.wordEmbedding(for: .english)
}

// Rest of the implementation...

} Would you like me to:

Show how to implement this simpler approach using NLEmbedding? Explain how it would work alongside our existing text classifier? Or explore a different approach? CopyRetryClaude does not have the ability to run the code it generates yet.JMWe should keep it lightweight and just Apple's built-in word embeddings.EditYou're right. Let's create a streamlined WordEmbeddingService using Apple's built-in word embeddings: WordEmbeddingService.swiftClick to open code Key features:

Uses Apple's built-in word embeddings Can generate vectors for single words or text Can calculate word similarities Simple initialization and usage

Example usage: swiftCopylet service = WordEmbeddingService()

do { // Get vector for a word let vector = try service.generateVector(for: "hello")

// Get vector for text
let textVector = try service.generateVector(for: "hello world")

// Get similarity between words
let similarity = service.similarity(between: "happy", and: "joy")

} catch { print("Error: (error)") } Would you like me to:

Create documentation for this service? Add any additional functionality? Show how it integrates with our text classifier? CopyRetryClaude does not have the ability to run the code it generates yet.JMWe've got some redeclaration errors- could we not write the function as func generateVector(for word:String, for text:String) ?No, we can't combine them into one function with two for parameters - Swift doesn't allow multiple parameters with the same argument label (for in this case). Also, these functions serve different purposes - one generates a vector for a single word, while the other handles full text. Let's fix the redeclaration error by renaming one of the functions: swiftCopy// For single words func generateWordVector(for word: String) throws -> [Double] { guard let embedding = embedding else { throw EmbeddingError.embeddingNotInitialized }

guard let vector = embedding.vector(for: word) else {
    throw EmbeddingError.embeddingGenerationFailed
}

return vector

}

// For text passages func generateTextVector(for text: String) throws -> [Double] { guard !text.isEmpty else { throw EmbeddingError.invalidInput }

let words = text.split(separator: " ").map(String.init)
var vectors: [[Double]] = []

for word in words {
    if let vector = try? generateWordVector(for: word) {
        vectors.append(vector)
    }
}

guard !vectors.isEmpty else {
    throw EmbeddingError.embeddingGenerationFailed
}

return averageVectors(vectors)

} This approach:

Gives each function a distinct, descriptive name Makes it clear which function to use for what purpose Reuses the word vector function when generating text vectors Maintains the same functionality but with clearer naming

Would you like me to update the entire service with this new naming convention? CopyRetryClaude does not have the ability to run the code it generates yet.JMCreate a documentation page for the WordEmbeddingService.swift to explain how the code file worksEditWordEmbeddingService DocumentationClick to open document CopyRetryJMCreate a documentation page for the TextTokenizationService.swift to explain how the code file worksEditTextTokenizationService DocumentationClick to open document CopyRetryJMCreate a documentation page for the KMeansClusteringService.swift to explain how the code file worksEditKMeansClusteringService DocumentationClick to open document CopyRetryJMCreate a documentation page for the SimilarityService.swift to explain how the code file worksEditSimilarityService DocumentationClick to open document CopyRetryJMGreat, last task we will work on is the storage layer according to our initial code documentation - Storage Layer protocol StorageProvider { func saveEntry( entry: Entry) async throws func fetchClusters() async throws -> [Cluster] func updateCluster( cluster: Cluster) async throws }EditLet's create the storage layer implementation. First, let's create a protocol and then implement it with a concrete class that handles local storage. StorageProvider.swiftClick to open code Now, let's create a concrete implementation using FileManager for local storage: LocalStorageProvider.swiftClick to open code This implementation:

Creates a protocol defining the storage operations Implements local storage using FileManager Handles JSON encoding/decoding Provides async operations Includes error handling Adds helper methods for common operations

Would you like me to:

Create documentation for these files? Add more functionality (like batch operations)? Implement additional storage providers (like Core Data or SQLite)? Add data migration capabilities? CopyRetryJMCreate a documentation page for StorageProvider.swift to explain how the code file worksEditStorageProvider DocumentationClick to open document CopyRetryJMCreate a documentation page for LocalStorageProvider.swift to explain how the code file worksEditLocalStorageProvider DocumentationClick to open document