🍍 AULCE

Adaptive Universal Lossless Compression Engine (AULCE) is a research-grade, open-source lossless universal compression system inspired by the ideas behind the fictional Pied Piper in HBO’s Silicon Valley — but built entirely in the open, using modular pipelines, ML-based strategy selection, and explainable RAG reasoning.

It is designed to be universal, infra-aware, ML-driven, and explainable, not just another ZIP replacement.

This is not a fictional magic compressor. It is a full, end-to-end system with multi-stage compression pipelines, ML strategy selection, RAG explanations, evaluation, and live benchmarks.

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🚀 Why AULCE Exists

There is no open-source Pied Piper-like system:

• No public repo for universal, adaptive, lossless compression
• No ML-assisted strategy selection for all file types
• No explainable system showing why compression succeeds or fails

AULCE fills that gap with:

• ML-based strategy selector for heterogeneous file types
• Multi-stage hybrid compression pipelines
• Retrieval-Augmented Generation (RAG) to explain failures
• Tool-aware reasoning to prevent hallucination
• Transparent evaluation & benchmarking

All built in a reproducible, research-grade way.

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🧠 Core Design Goals

• 🧩 Universal – supports any file extension
• ⚡ Fast & adaptive – selects pipelines per file type
• 🔍 Explainable – RAG explains compression outcomes
• 🎯 Benchmark-first – compares against ZIP, TAR, 7z, Zstd
• 🛠️ Tool-aware – integrates analysis, logs, file context
• 🔓 Fully open – MIT/Apache 2.0 license

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📐 System Overview

AULCE treats compression as a decision problem, not a single algorithm.

Stage	Description
Ingestion	Reads files, extracts metadata
Feature Extraction	Entropy, size, symbol distribution, MIME type
ML Strategy Selector	Predicts best compression pipeline
Pipeline Engine	Applies hybrid compression strategy
Validator	Ensures lossless round-trip
Explainer (RAG)	Generates human-readable explanation when compression fails
Benchmarking	Evaluates against ZIP / 7z / Zstd

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📊 ML Model Overview

The ML model does not compress data directly, but predicts:

• Optimal pipeline for a file
• Expected compression ratio
• Execution time & memory
• Likelihood of improvement vs baseline

Attribute	Value
Model type	Random Forest / PyTorch hybrid
Inputs	Entropy, file size, MIME, symbol frequency, prior compression
Outputs	Pipeline selection, expected ratio, confidence
Library	scikit-learn, PyTorch
License	MIT

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🔍 Retrieval-Augmented Generation (RAG)

RAG explains why compression failed using:

• Embedded documentation
• Historical file comparisons
• Entropy & codec theory

RAG ensures anti-hallucination reasoning:

• Only cites retrieved documents
• References prior benchmarks
• Provides actionable explanations

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🏗️ Tech Stack

Layer	Choice
Backend	FastAPI, Python 3.11
ML	scikit-learn, PyTorch
Compression	Zstd, Brotli, LZMA, custom codecs
Embeddings	OpenAI / Hugging Face embeddings
RAG	LangChain + FAISS / Chroma
Frontend UI	React + Tailwind
PDF/Image Parsing	PyMuPDF, Pillow
Evaluation	Benchmark & hallucination metrics
Deployment	Docker, Docker Compose, AWS EC2

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🧱 Repository Structure

AULCE/
├── README.md
├── LICENSE
├── backend/
│   ├── api.py
│   ├── analyzer/
│   ├── compressors/
│   ├── selector/
│   ├── validator/
│   └── explainer/
├── ml/
│   ├── training/
│   ├── feature_engineering/
│   └── models/
├── rag/
│   ├── ingest.py
│   ├── retriever.py
│   └── explainer.py
├── benchmarks/
│   ├── datasets/
│   ├── runner.py
│   └── plot.py
├── frontend/
│   ├── src/
│   └── public/
├── scripts/
│   ├── run_benchmarks.py
│   └── prepare_data.py
└── docker-compose.yml

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🧱 ASCII Architecture Diagram

                     ┌─────────────────────┐
                     │   User / Client     │
                     │ (CLI, Web UI, API)  │
                     └─────────┬───────────┘
                               │
                               ▼
                     ┌─────────────────────┐
                     │    FastAPI Backend  │
                     └─────────┬───────────┘
                               │
           ┌───────────────────┼───────────────────┐
           │                   │                   │
           ▼                   ▼                   ▼
     ┌────────────┐      ┌───────────────┐   ┌────────────┐
     │   Analyzer │      │   ML Selector │   │  Pipeline  │
     └─────┬──────┘      └──────┬────────┘   └─────┬──────┘
           │                    │                  │
           ▼                    ▼                  ▼
     ┌────────────┐      ┌───────────────┐   ┌────────────┐
     │ Validator  │      │ RAG Explainer │   │ Benchmark  │
     └────────────┘      └───────────────┘   └────────────┘

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🧪 Training Pipeline

1. Collect diverse file corpus (PDF, images, audio, binaries, text)
2. Extract features (entropy, MIME type, symbol frequency)
3. Execute all pipelines → record compression ratios
4. Train ML strategy selector (Random Forest / PyTorch)
5. Validate on unseen file families
6. Persist model + feature schema

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🛠️ Tool-Aware Reasoning

• Checks system context (logs, OS, APIs)
• Requests missing information instead of guessing
• Grounded answers for explainability

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📊 Evaluation & Hallucination Metrics

• Compression ratio vs baseline (ZIP, 7z, Zstd)
• Execution time & memory
• ML strategy regret
• RAG faithfulness / hallucination score

Run batch evaluation:

python benchmarks/runner.py

Visualize:

python benchmarks/plot.py

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🌐 Quick Start (Docker)

docker build -t AULCE .
docker run -p 8000:8000 AULCE

Visit: http://localhost:8000

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⚖️ License

MIT

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⚠️ Disclaimer

• Inspired by fiction, implemented in reality
• No magic compression claims
• Fully transparent and reproducible

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