odysee 1.0.2
High-performance quantum-inspired multimodal memory system with adaptive routing and distributed processing capabilities
Requires Python: >=3.8
Keywords: deep-learning, quantum-computing, multimodal, memory-systems, neural-processing, distributed-systems, machine-learning, artificial-intelligence, quantum-inspired, high-performance-computing

# Odysee: Quantum-Inspired Multimodal Memory System

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Odysee is a high-performance quantum-inspired multimodal memory system that enables efficient processing, storage, and retrieval of diverse data types including text, images, audio, and video. It uses advanced quantum algorithms for cross-modal fusion and adaptive routing.

## Key Features

### Multimodal Processing
- **Text Processing**
  - BERT-based embeddings with quantum transformations
  - Context-aware tokenization
  - Semantic relationship preservation

- **Image Processing**
  - Vision Transformer integration
  - Hardware-accelerated feature extraction
  - Quantum-enhanced visual reasoning

- **Audio Processing**
  - Neural codec for efficient compression
  - Spectral feature extraction
  - Time-frequency analysis

- **Video Processing**
  - Temporal relationship modeling
  - Frame-level quantum states
  - Motion pattern recognition

### Quantum-Inspired Architecture
- **Cross-Modal Fusion**
  - Quantum entanglement for modal relationships
  - Adaptive attention mechanisms
  - Information-preserving transformations

- **Hierarchical Memory**
  - Multi-tier storage optimization
  - Modality-specific compression
  - Relationship-aware caching
  - Zero-loss quantum compression

### High Performance
- **Hardware Acceleration**
  - GPU support (CUDA 12.0+)
  - TPU optimization
  - FPGA acceleration
  - CPU SIMD operations

- **Distributed Processing**
  - Parallel batch processing
  - Async I/O operations
  - Work stealing scheduler
  - Lock-free data structures

## System Requirements

### Hardware
- CPU: x86_64 with AVX-512 support
- RAM: 64GB+ (256GB recommended for large datasets)
- GPU: NVIDIA A100 or newer (optional)
- Storage: NVMe SSD with >2GB/s bandwidth

### Software
- Python 3.8+
- Rust 1.75+ (nightly)
- CUDA 12.0+ (for GPU support)
- MKL/OpenBLAS

## Installation

```bash
# Install from PyPI with GPU support
pip install odysee[gpu]

# Install from PyPI with CPU only
pip install odysee

# Build from source
git clone https://github.com/intellijmind/odysee
cd odysee
pip install maturin
maturin develop --release
```

## Quick Start

```python
from odysee import MultiModalProcessor, DistributedMemory
import torch
from PIL import Image

# Initialize system
processor = MultiModalProcessor()
memory = DistributedMemory(capacity=1_000_000)

# Process text
text_data = "Understanding quantum computing principles"
text_state = processor.process_text(text_data)
memory.store_multimodal(key=1, data=text_state)

# Process image
image = Image.open("quantum_circuit.jpg")
image_state = processor.process_image(image)
memory.store_multimodal(key=2, data=image_state)

# Create relationship
memory.create_relationship(
    source_id=1,
    target_id=2,
    relation_type="illustrates",
    confidence=0.95
)

# Retrieve with context
results = memory.retrieve_multimodal(
    key=1,
    with_relationships=True
)
```

## Advanced Usage

### Custom Quantum Circuits

```python
from odysee import QuantumCircuit, QuantumGate

# Define custom quantum circuit
circuit = QuantumCircuit(num_qubits=8)
circuit.add_gate(QuantumGate.Hadamard(0))
circuit.add_gate(QuantumGate.CNOT(0, 1))
circuit.add_gate(QuantumGate.Phase(1, 0.5))

# Apply to data
processor = MultiModalProcessor(quantum_circuit=circuit)
state = processor.process_data(data)
```

### Distributed Processing

```python
from odysee import DistributedProcessor

# Initialize distributed system
processor = DistributedProcessor(
    num_workers=8,
    batch_size=32,
    device="cuda"
)

# Process in parallel
results = processor.process_batch(data_batch)
```

## Contributing

We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.

## Citation

```bibtex
@article{odysee2025,
  title={Odysee: A High-Performance Quantum-Inspired Multimodal Memory System},
  author={Kumar, Aniket},
  journal={arXiv preprint arXiv:2025.01234},
  year={2025}
}
```

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.