l0l1

SQL that learns. AI that validates. Privacy that protects.

Website | Documentation | API Reference | Examples | Skelf Research

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What is l0l1?

l0l1 is a developer toolkit for SQL analysis that combines AI-powered validation with continuous learning. It detects PII, suggests improvements, and gets smarter from your successful queries.

# Validate with AI insights
$ l0l1 validate "SELECT * FROM users WHERE email LIKE '%@%'"

# Detect PII automatically
$ l0l1 check-pii "SELECT ssn, email FROM customers"
  Found: SSN, EMAIL_ADDRESS
  Anonymized: SELECT <SSN>, <EMAIL> FROM customers

# Learn from your patterns
$ l0l1 complete "SELECT * FROM orders WHERE"
  Suggestions based on 47 learned patterns...

Installation

# Clone and install with uv (recommended)
git clone https://github.com/skelf-research/l0l1-api.git && cd l0l1-api
uv sync

# Or install from PyPI
pip install l0l1

Requirements: Python 3.11+, OpenAI or Anthropic API key

Quick Start

# Set your API key
export OPENAI_API_KEY="sk-..."

# Validate a query
uv run l0l1 validate "SELECT * FROM users WHERE id = 1"

# Start the API server
uv run l0l1-serve
# API at http://localhost:8000, docs at /docs

Core Features

Feature	Description
AI Validation	Multi-provider support (OpenAI, Anthropic) for query analysis
PII Detection	Presidio-powered detection and anonymization
Continuous Learning	Graph-based pattern learning from successful queries
Multi-Interface	CLI, REST API, Jupyter magic, VS Code extension
Schema Aware	Context-aware validation with schema introspection

Usage

CLI

l0l1 validate "SELECT * FROM users"           # Validate syntax and semantics
l0l1 explain "SELECT COUNT(*) FROM orders"    # Get AI explanation
l0l1 check-pii "SELECT email FROM users"      # Detect sensitive data
l0l1 complete "SELECT * FROM orders WHERE"    # AI-powered completion
l0l1 serve                                    # Start API server

REST API

# Start server
uv run l0l1-serve

# Validate query
curl -X POST http://localhost:8000/sql/validate \
  -H "Content-Type: application/json" \
  -d '{"query": "SELECT * FROM users", "schema_context": "users(id, name, email)"}'

# Check PII
curl -X POST http://localhost:8000/sql/check-pii \
  -H "Content-Type: application/json" \
  -d '{"query": "SELECT * FROM users WHERE email = '\''[email protected]'\''"}'

Python SDK

from l0l1.models.factory import ModelFactory
from l0l1.services.pii_detector import PIIDetector
from l0l1.services.learning_service import LearningService

# AI-powered validation
model = ModelFactory.get_default_model()
result = await model.validate_sql_query(
    "SELECT * FROM users WHERE id = 1",
    schema_context="users(id INT, name VARCHAR, email VARCHAR)"
)

# PII detection
detector = PIIDetector()
findings = detector.detect_pii("SELECT * FROM users WHERE ssn = '123-45-6789'")
anonymized, _ = detector.anonymize_sql(query)

# Learning from successful queries
learning = LearningService()
await learning.record_successful_query("workspace-1", query, execution_time=0.5)
suggestions = await learning.get_query_suggestions("SELECT * FROM", "workspace-1")

Jupyter Integration

# Load the magic extension
%load_ext l0l1.integrations.jupyter.magic

# Use magic commands
%%sql_validate
SELECT u.name, COUNT(o.id)
FROM users u JOIN orders o ON u.id = o.user_id
GROUP BY u.name

Configuration

Create a .env file or set environment variables:

# Required: AI Provider (at least one)
OPENAI_API_KEY=sk-...
# ANTHROPIC_API_KEY=sk-ant-...

# Optional: Provider selection
L0L1_AI_PROVIDER=openai          # or "anthropic"

# Optional: Features
L0L1_ENABLE_PII_DETECTION=true
L0L1_ENABLE_LEARNING=true

# Optional: Server
L0L1_API_PORT=8000
L0L1_CORS_ORIGINS=http://localhost:3000

See Configuration Guide for all options.

Architecture

l0l1/
├── api/           # FastAPI REST API
├── cli/           # Typer CLI
├── core/          # Configuration
├── models/        # AI providers (OpenAI, Anthropic)
├── services/      # Core services
│   ├── pii_detector.py      # PII detection (Presidio)
│   ├── learning_service.py  # Pattern learning
│   ├── database_service.py  # DB connections
│   └── schema_service.py    # Schema management
└── integrations/
    ├── jupyter/   # Notebook integration
    └── ide/       # LSP server

Development

# Setup dev environment
make setup

# Run tests
make test

# Lint and format
make lint
make format

# Start dev server with reload
make serve

# Build documentation
make docs

Docker

# Build and run
docker-compose up -d

# Check health
curl http://localhost:8000/health

Documentation

Full documentation at docs.skelfresearch.com/l0l1

• Getting Started
• Configuration
• CLI Reference
• REST API
• Jupyter Guide
• VS Code Extension

Contributing

Contributions welcome! See Development Guide.

# Fork, clone, then:
uv sync --all-extras
pre-commit install
make test

License

MIT License - see LICENSE

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Documentation | Issues | Discussions

Built by Skelf Research

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Part of Skelf Research

l0l1 is built by Skelf Research — an independent UK AI research lab publishing production-grade open-source projects.

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_{Released under MIT / Apache-2.0. © Skelf Research Limited.}