AI Features

AI Features in FlexonCLI 🤖

FlexonCLI provides comprehensive support for AI-related data structures and operations. This guide covers all AI-specific features and their usage.

Prompt Management

Structure

{
  "prompt": {
    "text": "Your prompt text",
    "metadata": {
      "model": "gpt-4",
      "temperature": 0.7,
      "maxTokens": 2000
    }
  }
}

Usage

# Store prompts with validation
flexon-cli serialize -i prompts.json -o ai_prompts.flexon -s prompt_schema.json

# Secure prompt storage
flexon-cli serialize -i sensitive_prompts.json -o secure_prompts.flexon -e myKey

Training Data Management

Data Structure

{
  "dataset": {
    "items": [
      {
        "input": "example input",
        "output": "expected output",
        "metadata": {
          "source": "training_set_1",
          "quality": 0.95
        }
      }
    ]
  }
}

Usage

# Package training data
flexon-cli serialize -i training.json -o dataset.flexon -s training_schema.json

# Include embeddings
flexon-cli serialize -i training.json -i embeddings.bin -o full_dataset.flexon

Embeddings Support

Features

• Vector embeddings storage
• Efficient binary format
• Compression support
• Direct numpy array compatibility

Usage

# Store embeddings
flexon-cli serialize -i vectors.bin -o embeddings.flexon

# Package with metadata
flexon-cli serialize -i vectors.bin -i metadata.json -o complete.flexon

Security Features

Fingerprinting

• Unique dataset identification
• Version tracking
• Integrity verification

Audit Trails

{
  "security": {
    "fingerprint": "sha256_hash",
    "auditTrail": [
      {
        "timestamp": "2025-01-21T01:54:15+02:00",
        "action": "access",
        "actor": "training_pipeline"
      }
    ]
  }
}

Usage

# Store with security features
flexon-cli serialize -i data.json -o secure.flexon -s security_schema.json

# Encrypt sensitive data
flexon-cli serialize -i sensitive.json -o protected.flexon -e myKey ChaCha20

Schema Validation

Prompt Schema

• Text validation
• Metadata requirements
• Model specifications
• Parameter ranges

Training Data Schema

• Input/output validation
• Quality metrics
• Source verification
• Metadata requirements

Usage

# Validate against schema
flexon-cli validate ai_data.flexon schema.json

# Serialize with validation
flexon-cli serialize -i data.json -o valid.flexon -s ai_schema.json

Performance Optimization

Compression

• Specialized for embedding data
• Efficient sparse matrix storage
• Optimized binary format

Benchmarking

# Benchmark AI data operations
flexon-cli benchmark -i large_embeddings.bin -o benchmark.flexon -b

# Compare formats
flexon-cli benchmark -i vectors.npy -o benchmark_results.json

Language Support

Python

import flexon

# Load embeddings
embeddings = flexon.load("vectors.flexon")

# Save with metadata
flexon.save({"vectors": embeddings, "metadata": info}, "data.flexon")

Other Languages

• Ruby: Native array support
• PHP: Vector operations
• Java: Matrix operations
• JavaScript: TypedArray support

Best Practices

1. Data Organization

   • Separate prompts and training data
   • Include comprehensive metadata
   • Use appropriate schemas

2. Security

   • Encrypt sensitive prompts
   • Maintain audit trails
   • Regular integrity checks

3. Performance

   • Use appropriate compression
   • Benchmark large operations
   • Monitor memory usage

4. Integration

   • Use language-specific bindings
   • Implement proper error handling
   • Regular validation checks