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Documentation Index

Fetch the complete documentation index at: https://docs.prism.byescaleira.com/llms.txt

Use this file to discover all available pages before exploring further.

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On-Device Training

PrismIntelligence wraps Apple’s CreateML framework so you can train classification and regression models entirely on-device. No data leaves the user’s machine.

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Text Classification

PrismTextIntelligence trains text classifiers from labeled samples:
Train a Text Classifier
import PrismIntelligence

// From dictionary rows
let classifier = PrismTextIntelligence(data: [
    ["text": "I love this app!", "label": "positive"],
    ["text": "Terrible experience", "label": "negative"],
    ["text": "It works fine", "label": "neutral"],
    ["text": "Best purchase ever", "label": "positive"],
    ["text": "Would not recommend", "label": "negative"]
])

let result = try await classifier.train()
You can also initialize from typed samples:
Typed Samples
let samples = [
    PrismTextTrainingSample(text: "Great product", label: "positive"),
    PrismTextTrainingSample(text: "Awful quality", label: "negative")
]

let classifier = PrismTextIntelligence(samples: samples)
let result = try await classifier.train()
Rows missing "text" or "label" keys are silently skipped. The trainer tracks invalid row count internally, and training fails if too few valid samples remain.

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Tabular Models

PrismTabularIntelligence handles structured data for classification and regression:
Tabular Classification
import PrismIntelligence

let tabular = PrismTabularIntelligence(
    data: trainingData,
    targetColumn: "category"
)

// Classification
let classifierResult = try await tabular.trainClassifier()

// Regression
let regressorResult = try await tabular.trainRegressor()

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Codable Training Data

Use PrismCodableTrainingData for serializable, type-safe training datasets:
Codable Training Data
let trainingData = PrismCodableTrainingData(
    features: ["text", "length"],
    labels: ["sentiment"],
    rows: [
        ["text": "Great app", "length": "9", "sentiment": "positive"],
        ["text": "Bad UX", "length": "6", "sentiment": "negative"]
    ]
)

// Serialize for storage
let encoded = try JSONEncoder().encode(trainingData)

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Model Manager

PrismModelManager is an actor that manages the lifecycle of trained models:
Managing Models
let manager = PrismModelManager()

// Register a model
let info = PrismModelInfo(
    id: "sentiment-v1",
    name: "Sentiment Classifier",
    type: .classifier,
    size: 1_200_000
)
await manager.register(info)

// List registered models
let models = await manager.list()

// Check if loaded
let sentimentModel = await manager.get(id: "sentiment-v1")
print(sentimentModel?.isLoaded ?? false)

// Unload when done
await manager.unload(id: "sentiment-v1")

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Model Types

TypeDescription
.classifierClassification model (text or tabular)
.regressorRegression model for numeric prediction
.nlpNatural language processing model
.embeddingEmbedding / vector model
.customUser-defined model type

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Local Trainer

PrismIntelligenceLocalTrainer is the low-level engine behind text and tabular training:
Direct Trainer Usage
let trainer = PrismIntelligenceLocalTrainer()

let result = try await trainer.trainTextClassifier(
    from: samples
)

switch result {
case .success(let model):
    print("Model trained: \(model)")
case .failure(let error):
    print("Training failed: \(error)")
}
For most use cases, prefer PrismTextIntelligence or PrismTabularIntelligence over using the trainer directly. They handle data validation and provide a simpler API.

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Complete Example

End-to-End Text Classifier
import PrismIntelligence

// 1. Prepare training data
let classifier = PrismTextIntelligence(data: [
    ["text": "The food was amazing", "label": "positive"],
    ["text": "Slow service, cold food", "label": "negative"],
    ["text": "Average meal, nothing special", "label": "neutral"],
    ["text": "Best restaurant in town!", "label": "positive"],
    ["text": "Never going back", "label": "negative"],
    ["text": "Decent portions", "label": "neutral"]
])

// 2. Train the model
let result = try await classifier.train()

// 3. Register with the model manager
let manager = PrismModelManager()
await manager.register(PrismModelInfo(
    id: "restaurant-sentiment",
    name: "Restaurant Review Sentiment",
    type: .classifier
))

// 4. Use for predictions
let prediction = try await classifier.predict("Absolutely delicious!")
print(prediction.label)      // "positive"
print(prediction.confidence) // 0.92