# Mq64 Prototype Implementation Plan

**Target**: Proof-of-concept implementation of Mq64 encoding specification  
**Timeline**: 4-6 weeks  
**Scope**: Core functionality validation and performance baseline

## Phase 1: Foundation (Week 1-2)

### 1.1 Python Prototype Development

**Goal**: Create minimal working Mq64 encoder/decoder in Python

**Deliverables**:
```python
# Core prototype modules
research/prototype/mq64_core.py       # Basic encoding/decoding logic
research/prototype/mq64_alphabets.py  # Hierarchical alphabet definitions
research/prototype/mq64_utils.py      # Helper functions and validation
research/prototype/test_mq64.py       # Basic test suite
```

**Key Functions**:
```python
def mq64_encode(embedding: np.ndarray, levels: List[int]) -> str:
    """Core encoding function"""

def mq64_decode(encoded: str, target_dims: Optional[int] = None) -> np.ndarray:
    """Progressive decoding function"""

def detect_matryoshka_levels(embedding: np.ndarray) -> List[int]:
    """Auto-detect optimal hierarchy levels"""

def validate_mq64(encoded: str) -> bool:
    """Validate encoding integrity"""
```

### 1.2 Alphabet System Implementation

**Unicode Character Sets**:
```python
# Simplified alphabet system for prototype
ALPHABETS = {
    0: {  # Level 1 (dims 1-64) - Standard ASCII
        0: "ABCDEFGHIJKLMNOP",    # pos 0,4,8,12...
        1: "QRSTUVWXYZabcdef",    # pos 1,5,9,13...
        2: "ghijklmnopqrstuv",    # pos 2,6,10,14...
        3: "wxyz0123456789-_"     # pos 3,7,11,15...
    },
    1: {  # Level 2 (dims 65-128) - Greek letters
        0: "ΑΒΓΔΕΖΗΘΙΚΛΜΝΞΟΠ",
        1: "ΡΣΤΥΦΧΨΩαβγδεζητ", 
        2: "θικλμνξοπρστυφχψ",
        3: "ωάέήίόύώΐΰ₀₁₂₃₄₅"
    },
    2: {  # Level 3 (dims 129-256) - Cyrillic
        0: "АБВГДЕЁЖЗИЙКЛМНОП",
        1: "РСТУФХЦЧШЩЪЫЬЭЮЯ",
        2: "абвгдеёжзийклмноп", 
        3: "рстуфхцчшщъыьэюя∇"
    }
}
```

### 1.3 Test Data Generation

**Matryoshka-like Test Data**:
```python
def generate_matryoshka_embedding(dims: int, decay_factor: float = 0.8) -> np.ndarray:
    """
    Generate synthetic embedding with Matryoshka-like properties.
    Early dimensions have higher variance, later dimensions decay.
    """
    embedding = np.random.randn(dims).astype(np.float32)
    
    # Apply exponential decay to simulate Matryoshka structure
    for i in range(dims):
        level = i // 64
        decay = decay_factor ** level
        embedding[i] *= decay
    
    # Quantize to uint8 for encoding
    normalized = (embedding + 1) * 127.5  # Scale to 0-255
    return np.clip(normalized, 0, 255).astype(np.uint8)
```

## Phase 2: Core Functionality (Week 2-3)

### 2.1 Progressive Decoding Implementation

**Progressive Decoding Algorithm**:
```python
def mq64_decode_progressive(encoded: str, target_dims: int) -> np.ndarray:
    """
    Decode only up to target_dims for progressive refinement.
    
    Key optimizations:
    - Skip parsing unnecessary levels
    - Early termination when target reached
    - Memory-efficient partial chunk decoding
    """
    levels = encoded.split(':')
    target_level = (target_dims - 1) // 64
    target_offset = target_dims % 64
    
    result = []
    
    # Decode complete levels
    for level_idx in range(target_level):
        if level_idx < len(levels):
            level_data = decode_level(levels[level_idx], level_idx)
            result.append(level_data)
    
    # Decode partial level
    if target_offset > 0 and target_level < len(levels):
        partial_data = decode_partial_level(
            levels[target_level], 
            level_idx=target_level,
            target_bytes=target_offset
        )
        result.append(partial_data)
    
    return np.concatenate(result)[:target_dims]
```

### 2.2 Error Detection System

**Level-wise Checksums**:
```python
def compute_level_checksum(chunks: List[str], level: int) -> str:
    """
    Compute position-safe checksum for level validation.
    Uses level-specific alphabet to ensure position safety.
    """
    # Simple prototype: XOR-based checksum with position weighting
    checksum_val = 0
    for i, chunk in enumerate(chunks):
        for j, char in enumerate(chunk):
            char_val = get_alphabet_value(char, level, j % 4)
            checksum_val ^= (char_val * (i + 1) * (j + 1)) % 16
    
    return encode_checksum(checksum_val, level)
```

### 2.3 Auto-Detection Logic

**Matryoshka Pattern Recognition**:
```python
def analyze_embedding_structure(embedding: np.ndarray) -> Dict[str, Any]:
    """
    Analyze embedding to detect Matryoshka-like structure.
    
    Returns:
        - suggested_levels: Optimal hierarchy breakpoints
        - information_density: Density score per 64-dim block
        - compression_ratio: Expected compression at each level
    """
    dims = len(embedding)
    block_size = 64
    num_blocks = (dims + block_size - 1) // block_size
    
    info_density = []
    for i in range(num_blocks):
        start_idx = i * block_size
        end_idx = min((i + 1) * block_size, dims)
        block = embedding[start_idx:end_idx]
        
        # Compute information metrics
        variance = np.var(block)
        entropy = compute_entropy(block)
        sparsity = np.sum(block == 0) / len(block)
        
        density_score = variance * entropy * (1 - sparsity)
        info_density.append(density_score)
    
    # Suggest levels based on density dropoff
    suggested_levels = []
    for i, density in enumerate(info_density):
        if i == 0 or density > 0.1 * info_density[0]:  # 10% threshold
            suggested_levels.append((i + 1) * block_size)
    
    return {
        'suggested_levels': suggested_levels,
        'information_density': info_density,
        'is_matryoshka_compatible': len(suggested_levels) > 1
    }
```

## Phase 3: Integration & Testing (Week 3-4)

### 3.1 Real Model Integration

**OpenAI Integration Test**:
```python
def test_openai_integration():
    """Test with real OpenAI text-embedding-3-large model."""
    import openai
    
    # Get embedding at multiple dimension levels
    test_text = "The quick brown fox jumps over the lazy dog."
    
    embeddings = {}
    for dims in [64, 128, 256, 512, 1024]:
        response = openai.embeddings.create(
            model="text-embedding-3-large",
            input=test_text,
            dimensions=dims
        )
        embeddings[dims] = np.array(response.data[0].embedding)
    
    # Test that smaller embeddings are prefixes of larger ones
    for smaller_dims in [64, 128, 256, 512]:
        smaller = embeddings[smaller_dims]
        larger = embeddings[min(d for d in embeddings.keys() if d > smaller_dims)]
        
        # Verify prefix property (key requirement for Matryoshka)
        assert np.allclose(smaller, larger[:smaller_dims], rtol=1e-5)
        print(f"✓ {smaller_dims}D is prefix of larger embedding")
    
    # Test Mq64 encoding
    full_embedding = embeddings[1024]
    encoded = mq64_encode(full_embedding, levels=[64, 128, 256, 512, 1024])
    
    # Test progressive decoding
    for target_dims in [64, 128, 256, 512, 1024]:
        decoded = mq64_decode(encoded, target_dims=target_dims)
        original = embeddings[target_dims]
        
        # Verify roundtrip accuracy
        assert np.allclose(decoded, original, rtol=1e-3)
        print(f"✓ Roundtrip accuracy for {target_dims}D: {np.mean(np.abs(decoded - original)):.6f}")
```

### 3.2 Performance Benchmarking

**Baseline Performance Tests**:
```python
def benchmark_mq64_performance():
    """Benchmark Mq64 vs existing QuadB64 schemes."""
    
    # Test data: various Matryoshka embedding sizes
    test_sizes = [64, 128, 256, 512, 1024, 2048]
    test_embeddings = {
        size: generate_matryoshka_embedding(size) 
        for size in test_sizes
    }
    
    results = []
    
    for size, embedding in test_embeddings.items():
        # Benchmark encoding
        start_time = time.time()
        encoded = mq64_encode(embedding, levels=[64, 128, 256, 512, 1024][:size//64])
        encode_time = time.time() - start_time
        
        # Benchmark progressive decoding
        decode_times = {}
        for target_dims in [64, 128, 256, 512, 1024]:
            if target_dims <= size:
                start_time = time.time()
                decoded = mq64_decode(encoded, target_dims=target_dims)
                decode_times[target_dims] = time.time() - start_time
        
        # Compare with standard Eq64
        start_time = time.time()
        eq64_encoded = eq64_encode(embedding)
        eq64_encode_time = time.time() - start_time
        
        results.append({
            'size': size,
            'mq64_encode_time': encode_time,
            'eq64_encode_time': eq64_encode_time,
            'mq64_decode_times': decode_times,
            'encoded_size': len(encoded),
            'eq64_size': len(eq64_encoded),
            'compression_ratio': len(eq64_encoded) / len(encoded)
        })
    
    return results
```

### 3.3 Position Safety Validation

**Substring Pollution Tests**:
```python
def test_position_safety():
    """Verify no substring matches across hierarchy levels."""
    
    # Generate diverse test embeddings
    test_embeddings = [
        generate_matryoshka_embedding(1024) for _ in range(100)
    ]
    
    encoded_strings = [
        mq64_encode(emb, levels=[64, 128, 256, 512, 1024])
        for emb in test_embeddings
    ]
    
    # Extract all possible substrings of various lengths
    all_substrings = set()
    for encoded in encoded_strings:
        for length in [3, 4, 5, 6, 7, 8]:  # Various substring lengths
            for i in range(len(encoded) - length + 1):
                substring = encoded[i:i+length]
                if ':' not in substring and '.' not in substring:  # Skip markers
                    all_substrings.add(substring)
    
    # Test for false positive matches
    false_positives = 0
    total_tests = 0
    
    for substring in list(all_substrings)[:1000]:  # Sample for performance
        matches = []
        for encoded in encoded_strings:
            # Count matches excluding the original occurrence
            encoded_clean = encoded.replace(':', '').replace('.', '')
            if substring in encoded_clean:
                matches.append(encoded)
        
        if len(matches) > 1:  # Found in multiple strings
            false_positives += 1
        total_tests += 1
    
    false_positive_rate = false_positives / total_tests
    print(f"False positive rate: {false_positive_rate:.4f}")
    assert false_positive_rate < 0.01, "Position safety violation detected"
```

## Phase 4: Validation & Documentation (Week 4-6)

### 4.1 Prototype Validation

**Validation Checklist**:
- [ ] Roundtrip accuracy: 100% bit-perfect reconstruction
- [ ] Progressive decoding: Correct subsets at each level
- [ ] Position safety: <1% false positive rate for substrings
- [ ] Performance baseline: Within 2x of Eq64 for equivalent operations
- [ ] Memory efficiency: Constant memory usage for streaming operations
- [ ] Error detection: >99% corruption detection rate

### 4.2 Integration Examples

**Vector Database Integration Prototype**:
```python
def create_mq64_pinecone_example():
    """Example integration with Pinecone using Mq64."""
    
    # Simulate vector database operations
    documents = [
        "Machine learning advances in 2024",
        "Quantum computing breakthrough",
        "Climate change solutions",
        # ... more documents
    ]
    
    # Generate embeddings (simulated OpenAI)
    embeddings = [get_openai_embedding(doc) for doc in documents]
    
    # Encode with Mq64
    encoded_embeddings = [
        mq64_encode(emb, levels=[64, 128, 256, 512, 1024])
        for emb in embeddings
    ]
    
    # Simulate Pinecone storage
    pinecone_data = []
    for i, (doc, encoded) in enumerate(zip(documents, encoded_embeddings)):
        pinecone_data.append({
            'id': f'doc_{i}',
            'vector': mq64_decode(encoded, target_dims=64),  # Store 64D for fast search
            'metadata': {
                'text': doc,
                'mq64_full': encoded,  # Store full encoding for refinement
                'mq64_levels': [64, 128, 256, 512, 1024]
            }
        })
    
    return pinecone_data
```

### 4.3 Documentation

**Prototype Documentation**:
```
research/prototype/README.md
├── Installation and Setup
├── Basic Usage Examples  
├── API Reference
├── Performance Benchmarks
├── Validation Results
└── Integration Examples
```

### 4.4 Next Steps Planning

**Transition to Production Implementation**:
1. **Rust Integration**: Port validated algorithms to uubed-rs
2. **Python Bindings**: Create PyO3 bindings for performance
3. **API Finalization**: Finalize public API based on prototype feedback
4. **Extended Testing**: Comprehensive test suite with real models
5. **Documentation**: Complete user documentation and tutorials

## Success Criteria

**Prototype Success Metrics**:
- [ ] **Functional**: All core operations working correctly
- [ ] **Performance**: Within 2x of existing QuadB64 performance  
- [ ] **Quality**: Position safety and roundtrip accuracy validated
- [ ] **Integration**: Working examples with real Matryoshka models
- [ ] **Documentation**: Clear specification and implementation guide

**Go/No-Go Decision Points**:
- **Week 2**: Basic encoding/decoding functional
- **Week 3**: Performance baseline acceptable
- **Week 4**: Position safety validation passed
- **Week 6**: Real model integration successful

This prototype implementation plan provides a structured approach to validating the Mq64 specification and establishing a foundation for production implementation in the uubed ecosystem.