# Odysee: Quantum-Inspired Multimodal Memory System
[](https://badge.fury.io/py/odysee)
[](https://opensource.org/licenses/MIT)
[](https://odysee.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/intellijmind/odysee/actions)
[](https://codecov.io/gh/intellijmind/odysee)
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.
Raw data
{
"_id": null,
"home_page": null,
"name": "odysee",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": "Aniket Kumar <aniketkumar34@outlook.com>",
"keywords": "deep-learning, quantum-computing, multimodal, memory-systems, neural-processing, distributed-systems, machine-learning, artificial-intelligence, quantum-inspired, high-performance-computing",
"author": null,
"author_email": "Aniket Kumar <aniketkumar34@outlook.com>",
"download_url": null,
"platform": null,
"description": "# Odysee: Quantum-Inspired Multimodal Memory System\n\n[](https://badge.fury.io/py/odysee)\n[](https://opensource.org/licenses/MIT)\n[](https://odysee.readthedocs.io/en/latest/?badge=latest)\n[](https://github.com/intellijmind/odysee/actions)\n[](https://codecov.io/gh/intellijmind/odysee)\n\nOdysee 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.\n\n## Key Features\n\n### Multimodal Processing\n- **Text Processing**\n - BERT-based embeddings with quantum transformations\n - Context-aware tokenization\n - Semantic relationship preservation\n\n- **Image Processing**\n - Vision Transformer integration\n - Hardware-accelerated feature extraction\n - Quantum-enhanced visual reasoning\n\n- **Audio Processing**\n - Neural codec for efficient compression\n - Spectral feature extraction\n - Time-frequency analysis\n\n- **Video Processing**\n - Temporal relationship modeling\n - Frame-level quantum states\n - Motion pattern recognition\n\n### Quantum-Inspired Architecture\n- **Cross-Modal Fusion**\n - Quantum entanglement for modal relationships\n - Adaptive attention mechanisms\n - Information-preserving transformations\n\n- **Hierarchical Memory**\n - Multi-tier storage optimization\n - Modality-specific compression\n - Relationship-aware caching\n - Zero-loss quantum compression\n\n### High Performance\n- **Hardware Acceleration**\n - GPU support (CUDA 12.0+)\n - TPU optimization\n - FPGA acceleration\n - CPU SIMD operations\n\n- **Distributed Processing**\n - Parallel batch processing\n - Async I/O operations\n - Work stealing scheduler\n - Lock-free data structures\n\n## System Requirements\n\n### Hardware\n- CPU: x86_64 with AVX-512 support\n- RAM: 64GB+ (256GB recommended for large datasets)\n- GPU: NVIDIA A100 or newer (optional)\n- Storage: NVMe SSD with >2GB/s bandwidth\n\n### Software\n- Python 3.8+\n- Rust 1.75+ (nightly)\n- CUDA 12.0+ (for GPU support)\n- MKL/OpenBLAS\n\n## Installation\n\n```bash\n# Install from PyPI with GPU support\npip install odysee[gpu]\n\n# Install from PyPI with CPU only\npip install odysee\n\n# Build from source\ngit clone https://github.com/intellijmind/odysee\ncd odysee\npip install maturin\nmaturin develop --release\n```\n\n## Quick Start\n\n```python\nfrom odysee import MultiModalProcessor, DistributedMemory\nimport torch\nfrom PIL import Image\n\n# Initialize system\nprocessor = MultiModalProcessor()\nmemory = DistributedMemory(capacity=1_000_000)\n\n# Process text\ntext_data = \"Understanding quantum computing principles\"\ntext_state = processor.process_text(text_data)\nmemory.store_multimodal(key=1, data=text_state)\n\n# Process image\nimage = Image.open(\"quantum_circuit.jpg\")\nimage_state = processor.process_image(image)\nmemory.store_multimodal(key=2, data=image_state)\n\n# Create relationship\nmemory.create_relationship(\n source_id=1,\n target_id=2,\n relation_type=\"illustrates\",\n confidence=0.95\n)\n\n# Retrieve with context\nresults = memory.retrieve_multimodal(\n key=1,\n with_relationships=True\n)\n```\n\n## Advanced Usage\n\n### Custom Quantum Circuits\n\n```python\nfrom odysee import QuantumCircuit, QuantumGate\n\n# Define custom quantum circuit\ncircuit = QuantumCircuit(num_qubits=8)\ncircuit.add_gate(QuantumGate.Hadamard(0))\ncircuit.add_gate(QuantumGate.CNOT(0, 1))\ncircuit.add_gate(QuantumGate.Phase(1, 0.5))\n\n# Apply to data\nprocessor = MultiModalProcessor(quantum_circuit=circuit)\nstate = processor.process_data(data)\n```\n\n### Distributed Processing\n\n```python\nfrom odysee import DistributedProcessor\n\n# Initialize distributed system\nprocessor = DistributedProcessor(\n num_workers=8,\n batch_size=32,\n device=\"cuda\"\n)\n\n# Process in parallel\nresults = processor.process_batch(data_batch)\n```\n\n## Contributing\n\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.\n\n## Citation\n\n```bibtex\n@article{odysee2025,\n title={Odysee: A High-Performance Quantum-Inspired Multimodal Memory System},\n author={Kumar, Aniket},\n journal={arXiv preprint arXiv:2025.01234},\n year={2025}\n}\n```\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n",
"bugtrack_url": null,
"license": null,
"summary": "High-performance quantum-inspired multimodal memory system with adaptive routing and distributed processing capabilities",
"version": "1.0.2",
"project_urls": {
"Bug Tracker": "https://github.com/threatthriver/odysee/issues",
"Documentation": "https://github.com/threatthriver/odysee/blob/main/README.md",
"Homepage": "https://github.com/threatthriver/odysee",
"Repository": "https://github.com/threatthriver/odysee",
"Source Code": "https://github.com/threatthriver/odysee"
},
"split_keywords": [
"deep-learning",
" quantum-computing",
" multimodal",
" memory-systems",
" neural-processing",
" distributed-systems",
" machine-learning",
" artificial-intelligence",
" quantum-inspired",
" high-performance-computing"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "14454bc9e2223d86c21f7fe863e6eaf4ffbfc72f73b858e256ffe843a626356f",
"md5": "a40fc2f0842b13753c4c314f2bf7dd34",
"sha256": "aa9e27fba1ec5643d06429dd4270ad7bfa57c970069ffe77d257009f70cf0d9c"
},
"downloads": -1,
"filename": "odysee-1.0.2-cp37-abi3-macosx_11_0_arm64.whl",
"has_sig": false,
"md5_digest": "a40fc2f0842b13753c4c314f2bf7dd34",
"packagetype": "bdist_wheel",
"python_version": "cp37",
"requires_python": ">=3.8",
"size": 243981,
"upload_time": "2025-01-30T17:53:05",
"upload_time_iso_8601": "2025-01-30T17:53:05.998259Z",
"url": "https://files.pythonhosted.org/packages/14/45/4bc9e2223d86c21f7fe863e6eaf4ffbfc72f73b858e256ffe843a626356f/odysee-1.0.2-cp37-abi3-macosx_11_0_arm64.whl",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-01-30 17:53:05",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "threatthriver",
"github_project": "odysee",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [
{
"name": "numpy",
"specs": [
[
">=",
"1.20.0"
]
]
},
{
"name": "torch",
"specs": [
[
">=",
"1.9.0"
]
]
},
{
"name": "maturin",
"specs": [
[
">=",
"1.0.0"
]
]
},
{
"name": "pillow",
"specs": [
[
">=",
"8.0.0"
]
]
},
{
"name": "tqdm",
"specs": [
[
">=",
"4.62.0"
]
]
},
{
"name": "pytest",
"specs": [
[
">=",
"6.0.0"
]
]
},
{
"name": "pytest-cov",
"specs": [
[
">=",
"2.0.0"
]
]
},
{
"name": "einops",
"specs": [
[
">=",
"0.6.0"
]
]
},
{
"name": "scikit-learn",
"specs": [
[
">=",
"1.0.0"
]
]
},
{
"name": "wandb",
"specs": [
[
">=",
"0.15.0"
]
]
},
{
"name": "rich",
"specs": [
[
">=",
"13.0.0"
]
]
},
{
"name": "cupy-cuda11x",
"specs": [
[
">=",
"12.0.0"
]
]
},
{
"name": "hypothesis",
"specs": [
[
">=",
"6.0.0"
]
]
},
{
"name": "pandas",
"specs": [
[
">=",
"2.0.0"
]
]
},
{
"name": "matplotlib",
"specs": [
[
">=",
"3.0.0"
]
]
}
],
"lcname": "odysee"
}
JSON
