aura-compression 2.0.2

AI-Optimized Hybrid Compression Protocol for Real-Time Communication

AURA Compression Toolkit

AURA is an experimental, Python-first playground for hybrid compression. It mixes template‑aware encoders, semantic heuristics, and audit-friendly metadata so you can explore how structured traffic (API chatter, AI↔AI messages, log streams) behaves under different strategies. The project is not production-ready, but it now ships with a lean test suite and CLI tooling that make local experiments straightforward.

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TL;DR

	Status
Vision	Efficient, auditable compression tuned for repetitive, structured text
Current maturity	Alpha — safe for prototyping only
Runtime support	CPython ≥ 3.10 (pure Python, no native deps)
Test coverage	~44 % (core pipelines + CLI smoke tests)
License	Apache 2.0 (see LICENSE for patent notice)

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Installation

git clone https://github.com/hendrixx-cnc/AURA.git
cd AURA
python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

The dev extra installs pytest, coverage tooling, and linters.

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Quick Start (Python API)

from aura_compression.compressor_refactored import ProductionHybridCompressor

compressor = ProductionHybridCompressor(
    enable_aura=False,          # disable background discovery worker
    enable_fast_path=True,
    enable_audit_logging=False,
    template_sync_interval_seconds=None,
)

message = "Order 42: status=ready"
payload, method, metadata = compressor.compress(message)
restored = compressor.decompress(payload)

assert restored == message
print(method.name, metadata["ratio"])

When does it shine?

• You control both ends of the link (AI ↔ AI, microservices, etc.)
• Payloads are verbose but structured (logs, JSON, templated replies)
• You’re comfortable tuning template libraries / cache policy

When to avoid it

• Need wire compatibility with gzip/zstd/brotli
• Response time budgets are tight (large-file compression is slow)
• You cannot ship persistent template state alongside payloads

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Large-File CLI

The tools/compress_large_file.py script provides a streaming container format. It records chunk metadata (including template usage) so decompression works on a fresh machine.

# Compress with a progress bar and write stats to JSON
python tools/compress_large_file.py compress \
  --input "/path/to/enwik8" \
  --output "/path/to/enwik8.aura" \
  --chunk-size 64K \
  --progress bar \
  --stats-format json \
  --stats-file stats/compress.json

# Round-trip integrity check without writing output
python tools/compress_large_file.py verify \
  --input "/path/to/enwik8.aura" \
  --progress percent

# Inspect container metadata (headers, sample chunks, template IDs)
python tools/compress_large_file.py info \
  --input "/path/to/enwik8.aura" \
  --max-chunks 5 \
  --stats-format table

Key switches:

Flag	Description
--chunk-size	Bytes or suffixed value (256K, 4M, …)
--progress	auto, bar, percent, none
--stats-format	table (default) or json
--stats-file	Path to persist stats output (useful in CI)

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Synthetic Network Smoke Test

To sanity-check the compressor against AI‑style traffic:

pytest tests/test_network_simulation_smoke.py -q

The generator streams ~120 messages (API calls, logs, chat replies, binary blobs) and asserts:

• Round-trip fidelity for every payload
• Multiple compression strategies selected
• Binary semantic templates triggered at least once
• Average compression ratio stays sensible (>0.5)

Use this as a starting point when tailoring the system to your own message mix.

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Testing & Coverage

pytest -q                # fast path (~40 s)
pytest --cov=src --cov=tools --cov-report=term-missing

Current suite highlights:

• tests/test_cli_utilities.py — input parsing, progress modes, container inspection
• tests/test_core_components.py — basic round-trip compressor + template matching
• tests/test_network_simulation_smoke.py — synthetic AI/network workload

Large areas of the codebase remain untested (BRIO internals, ML selector, legacy tools). Treat reported coverage as a proxy for explored functionality, not as a production safety net.

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Roadmap Snapshot

• ✅ Streamlined large-file CLI with inspect/verify subcommands
• ✅ Lean regression tests to keep core behavior honest
• 🔜 Refactor BRIO and ML pipelines into testable, modular units
• 🔜 Benchmark suite vs. gzip/zstd/brotli on realistic corpora
• 🔜 Documentation on template discovery + SQLite persistence internals

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Contributing

1. Open an issue describing your proposal.
2. Fork the repo and create a feature branch.
3. Keep changes focused; add tests when practical.
4. Run pytest -q before submitting your PR.

Helpful areas:

• Improving template discovery robustness (error handling, logging)
• Instrumentation and profiling of large-file compression
• Type hints / static analysis for critical modules
• Benchmarks and data-driven comparisons

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License & Patents

Licensed under Apache 2.0. The project references patent-pending techniques; the open-source distribution grants a royalty-free license for evaluation and non-commercial use. See LICENSE for full text and obligations.

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Contact

• Author: Todd Hendricks — [e-mail]
• Issues & discussions: GitHub Issues

If you do end up using AURA in research or prototyping, feedback on data sets, compression ratios, and pain points is greatly appreciated.