panini-nlp 0.1.0

Sanskrit NLP library grounded in Pāṇinian grammar. Deterministic, Graph-based, and Neuro-symbolic.

Panini-NLP

A Deterministic, Graph-based, Neuro-symbolic implementation of Pāṇini's Grammar.

panini-nlp is a Python library that decodes the Aṣṭādhyāyī not as a static text, but as a computable Cyclic Directed Graph. It provides the complete structural context of the grammar—every Sūtra and Dhātu—alongside deterministic engines for Sandhi, Prosody, and Phonetics.

📖 About

This project decodes the 2,500-year-old Aṣṭādhyāyī as the world's first Deterministic Generative AI.

Pāṇini's grammar is not a mere set of rules; it is a formal generative system that produces infinite valid Sanskrit words from finite roots using a highly compressed, algebraic source code. panini-nlp implements this architecture:

1. Universal Registry: A digital structural map of all 3,996 Sūtras and ~2,000 Dhātus.
2. Deterministic Engine: Mathematical implementation of Sandhi (phonetics) and Chandas (prosody).
3. Neuro-Symbolic Bridge: A GNN layer to handle ambiguity (Vipratisedha) where the deterministic path branches.

We are not inventing AI for Sanskrit; we are decoding the algorithm that was already written.

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🚀 Key Features

1. The Complete "Source Code"

Unlike other tools that implement only a few rules, panini-nlp contains the Full Registry:

• 3,996 Sūtras: Every rule from the Aṣṭādhyāyī is present as a Python function stub in panini_nlp/rules/.
• ~2,000 Dhātus: Every root from the Dhātupāṭha is registered in panini_nlp/roots/.
• Maheshvara Sutras: Implements the "Prime Number Architecture" (71.4% prime density) for phoneme compression.

2. Hybrid Neuro-Symbolic Architecture

• Symbolic Core: Deterministic engines for Sandhi (Euphonic Junction) and Chandas (Prosody).
• Neural Guidance: A Graph Neural Network (GNN) layer (panini_nlp/gnn) to resolve rule conflicts (Vipratisedha) by learning the graph topology.

3. "Seed Kernel" Efficiency

• The core logic fits in < 50KB.
• The entire structural knowledge base is generated from raw source texts.

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📦 Installation

# Clone the repository
git clone https://github.com/meru-os/panini-nlp.git
cd panini-nlp

# Install mostly-pure Python core
pip install .

# Install with Neural Network support (PyTorch)
pip install .[gnn]

🛠️ Usage Examples

1. Deterministic Sandhi

Apply formal rules like Akaḥ Savarṇe Dīrghaḥ (6.1.101).

from panini_nlp import SandhiEngine

sandhi = SandhiEngine()
result = sandhi.apply("dev", "alaya")
print(result) 
# Output: devālaya (Rule 6.1.101 applied)

2. Derivation Simulation (Prakriya)

Trace the path of a word through the grammar graph.

# Run the included demo script
# python3 examples/derive_brahman.py

from panini_nlp.rules import registry as rule_registry

# Access Rule 1.1.1 (Growth Definitions)
rule = rule_registry.get("1.1.1")
print(f"{rule.id}: {rule.text}") 
# Output: 1.1.1: vṛddhir ādaic

3. Prosody Analysis (Chandas)

Analyze the binary rhythm of a verse (Laghu/Guru).

from panini_nlp import ChandasAnalyzer

analyzer = ChandasAnalyzer()
meter = analyzer.analyze("dharmakṣetre kurukṣetre samavetā yuyutsavaḥ")
print(meter.pattern)
# Output: G L G G L G G G ... (Binary Stream)

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📂 Project Structure

panini-nlp/
├── panini_nlp/
│   ├── rules/          # 3996 Auto-generated Sutra stubs (Adhyaya 1-8)
│   ├── roots/          # ~2000 Auto-generated Root definitions (Gana 1-10)
│   ├── sandhi.py       # Deterministic Sandhi Engine
│   ├── chandas.py      # Pingala's Binary Prosody Algorithms
│   ├── maheshvara.py   # Prime Number Phonetic Analysis
│   ├── gnn/            # Graph Neural Network Models (PyTorch)
│   ├── data/           # Raw Source Texts (Ashtadhyayi.txt, Dhatupatha.txt)
│   └── validator.py    # Pipeline Orchestrator
├── examples/
│   ├── derive_brahman.py # Paper Example: Derivation of "Brahman"
│   └── derive_shuklam.py # Demo Example: "Shuklam Baradharam" analysis
├── requirements.txt
└── setup.py

License

MIT License. Free for research and education.