Metadata-Version: 2.4
Name: lnt-sovereign
Version: 1.0.2
Summary: High-performance neuro-symbolic verification layer for AI validation and logic-based guardrails.
Author-email: wassmi <algasim.w@gmail.com>
Project-URL: Homepage, https://github.com/wassmi/lnt-sovereign
Project-URL: Bug Tracker, https://github.com/wassmi/lnt-sovereign/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Security :: Cryptography
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: fastapi
Requires-Dist: uvicorn
Requires-Dist: pydantic>=2.0
Requires-Dist: numpy
Requires-Dist: numba
Requires-Dist: sqlalchemy
Requires-Dist: httpx
Requires-Dist: z3-solver
Requires-Dist: pytest-asyncio
Dynamic: license-file

# LNT: Logic Neutrality Tensor
**Deterministic Validation Layer for AI-Generated Intents**

## ⚖️ Overview (Proprietary Software)
LNT is a high-performance neuro-symbolic engine designed to enforce deterministic symbolic constraints on probabilistic AI outputs. It provides sub-millisecond scaling and formal Z3 consistency verification for mission-critical validation in FinTech, HealthTech, and automated infrastructure.

**License Notice**: LNT is proprietary software. This distribution contains the core engine and public example manifests. Industrial Rule Registries require a commercial license.

## 🚀 Key Technical Specifications
*   **Vectorized Kernel (BELM)**: SIMD-accelerated logic manifold ($O(n)$ complexity).
*   **Latency**: 2.54 ms for 10,000 concurrent constraints (Intel i7 benchmark).
*   **Formal Security**: SMT-based (Z3) manifest consistency verification.
*   **Audit Integrity**: SHA-256 signature-chained decision ledger for regulatory compliance.

## 🛠️ Quick Start

### Installation
```bash
pip install lnt-sovereign
```

### Minimal Working Example
```python
from lnt_sovereign.client import LNTClient

# Initialize the Validation Client (Toolbox Mode)
client = LNTClient()

# Define a structured proposal
proposal = {"funding": 15000, "context": {"age_days": 10}}

# Audit against a public logic manifest
result = client.audit(manifest_id="visa_application", proposal=proposal)

if result.status == "PASS":
    print(f"Validation Certified: Score {result.score}")
else:
    print(f"Policy Violations: {result.violations}")
```

## 📜 Technical Documentation
For detailed architecture, API reference, and the full whitepaper, visit the [LNT Documentation Portal](docs/index.md).

*   [**Technical Whitepaper**](docs/whitepaper.md): Mathematical foundations and benchmark distributions.
*   [**API Reference**](docs/api_reference.md): Complete parameter registry and error codes.
*   [**Integration Patterns**](docs/integration.md): Deployment guidelines for high-concurrency systems.

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*Maintained for high-reliability AI system development.*
