shaaha 2.0.0

Intelligent Meta-Dispatcher — AI-powered backend selection, agent execution, and code optimization.

Shaaha 🧠

One import. Best backend. Always.

Shaaha is a Python meta-dispatcher library. Instead of memorising which library is fastest, whether your system has a GPU, or whether a colleague has polars installed — you write import shaaha.dataframe and Shaaha figures it out for you, silently, at the moment you actually use it.

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The Problem Shaaha Solves

Problem	Without Shaaha	With Shaaha
Dependency hell	try: import cupy except: import numpy everywhere	import shaaha.math
GPU/CPU branching	Manual torch.cuda.is_available() checks	Automatic
Large vs small data	You pick polars or pandas	Shaaha picks for you
Team differences	Code breaks on colleagues' machines	Zero config
Library upgrades	Rewriting code when switching backends	Proxy API stays the same

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Install

pip install shaaha                      # zero dependencies
pip install "shaaha[default]"           # numpy, pandas, pillow, sklearn, matplotlib
pip install "shaaha[science]"           # full scientific stack
pip install "shaaha[gpu]"               # CUDA / GPU stack

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Quick Start

import shaaha

# ── DataFrames ─────────────────────────────────────────────────────────────
import shaaha.dataframe as df
# Shaaha picks polars (large data) or pandas (small) automatically
data = df.read_csv("data.csv")

# ── Math / Arrays ──────────────────────────────────────────────────────────
import shaaha.math as sm
# On a GPU machine → cupy / jax. On CPU → numpy.
arr = sm.array([1, 2, 3, 4, 5])
result = sm.sqrt(arr)

# ── Machine Learning ───────────────────────────────────────────────────────
import shaaha.ml as ml
# GPU available → torch. Otherwise → scikit-learn / xgboost.
model = ml.LinearRegression()

# ── Image Processing ───────────────────────────────────────────────────────
import shaaha.image as img
# OpenCV if installed, else Pillow — same API either way.
image = img.open("photo.jpg")

# ── NLP ────────────────────────────────────────────────────────────────────
import shaaha.nlp as nlp
# HuggingFace Transformers → spaCy → NLTK (whichever is installed)

# ── Check what's running ───────────────────────────────────────────────────
print(shaaha.status())
# {'version': '1.0.0', 'cuda_available': False, 'selected_backends': {...}}

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How It Works

Shaaha uses PEP 302 Import Hooks (sys.meta_path) to intercept Python's import machinery before it looks for physical files.

import shaaha.math
       │
       ▼
ShaahafFinder (sys.meta_path[0])
       │  domain = "math"
       ▼
   Router.resolve("math")
       │  checks: CUDA? → No. numpy installed? → Yes.
       ▼
  ProxyModule wrapping numpy
       │
       ▼
  shaaha.math.array  →  numpy.array

The ProxyModule is lazy — nothing is imported until you access an attribute. Initial import shaaha adds ~0ms to your startup time.

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Configuration

import shaaha

shaaha.configure(
    prefer_gpu=True,        # Use GPU backends when available (default: True)
    auto_install=False,     # Offer to pip-install missing best backends
    log_level="WARNING",    # 'DEBUG' to see routing decisions
    force_backend={         # Override routing for specific domains
        "math": "numpy",    # Always use numpy even if cupy is installed
    }
)

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Supported Domains

shaaha.<domain>	Best backend	Fallbacks
shaaha.math	cupy / jax	torch → numpy
shaaha.dataframe	polars	modin → pandas → dask
shaaha.ml	torch	xgboost → lightgbm → sklearn
shaaha.image	cv2	PIL / pillow
shaaha.nlp	transformers	spacy → nltk
shaaha.viz	plotly	altair → seaborn → matplotlib
shaaha.stats	statsmodels	scipy → sklearn
shaaha.http	httpx	requests → urllib3
shaaha.json	orjson	ujson → json (stdlib)

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Adding a New Backend (Contributing)

1. Open src/shaaha/registry.py
2. Add your backend to the relevant domain list:

"dataframe": [
    Backend("vaex", "vaex", weight=85, tags=["large_data", "out-of-core"]),
    # ... existing entries
]

3. Optionally add a wrapper in src/shaaha/wrappers/ if the API differs.
4. Open a Pull Request — that's it.

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Project Architecture

shaaha/
├── src/shaaha/
│   ├── __init__.py       # Registers sys.meta_path hook
│   ├── importer.py       # PEP 302 Finder + Loader
│   ├── router.py         # Decision engine (picks the winner)
│   ├── registry.py       # Library weights & domain mappings
│   ├── environment.py    # Hardware & dependency probe
│   ├── proxy.py          # Lazy proxy module system
│   ├── installer.py      # Optional auto-pip-install
│   └── wrappers/         # Unified API adapters per backend
├── tests/
│   └── test_shaaha.py    # 24 tests, 100% passing
├── pyproject.toml        # PEP 621 metadata
└── README.md

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For MSc Thesis

Shaaha implements three advanced CS concepts worth discussing in your thesis:

1. Python Import Protocol (PEP 302/451) — sys.meta_path, MetaPathFinder, Loader
2. Proxy Design Pattern — transparent delegation via __getattr__ on a ModuleType subclass
3. Context-Aware Dispatch — weighted scoring with hardware-aware overrides

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License

MIT © Shaaha