Advanced Text-to-SQL library with AI features
Project description
AI Prishtina - Text2SQL-LTM: The Most Advanced Text-to-SQL Library
โ Support This Project
If you find this project helpful, please consider supporting it:
AI PRISHTINA - Text2SQL-LTM is a comprehensive Text-to-SQL library, featuring cutting-edge AI capabilities. Built with production-ready architecture and to push the boundaries of what's possible in natural language to SQL conversion.
๐ Revolutionary Features
๐ง RAG-Enhanced Query Generation
- Vector-based knowledge retrieval with semantic search
- Schema-aware context augmentation for intelligent SQL generation
- Query pattern learning from successful executions
- Adaptive retrieval strategies that improve over time
- Knowledge fusion across different data sources
๐ AI-Powered SQL Validation & Auto-Correction
- Intelligent syntax validation with automatic error fixing
- Security vulnerability detection and prevention
- Performance optimization suggestions with impact analysis
- Cross-platform compatibility checking
- Best practice enforcement with educational feedback
๐ Intelligent Query Explanation & Teaching System
- Step-by-step query breakdown with visual execution flow
- Adaptive explanations based on user expertise level
- Interactive learning modes with guided practice
- Personalized learning paths with progress tracking
- Real-time teaching assistance for SQL education
๐ Automated Schema Discovery & Documentation
- AI-powered relationship inference between tables
- Column purpose detection using pattern recognition
- Data quality assessment with improvement suggestions
- Auto-generated documentation in multiple formats
- Business rule extraction from data patterns
๐ Advanced Security Analysis
- SQL injection detection with real-time prevention
- Privilege escalation monitoring and alerts
- Data exposure analysis with compliance checking (GDPR, PCI DSS, SOX)
- Vulnerability scanning with remediation guidance
- Security best practice validation
๐ Cross-Platform Query Translation
- Intelligent dialect conversion between 8+ database platforms
- Syntax optimization for target platforms
- Compatibility analysis with migration guidance
- Performance tuning for specific database engines
- Feature mapping across different SQL dialects
๐งช Automated Test Case Generation
- Comprehensive test suite creation for SQL queries
- Edge case detection and test generation
- Performance test automation with benchmarking
- Security test scenarios for vulnerability assessment
- Data validation testing with constraint checking
๐ Quick Start
Installation
pip install text2sql-ltm
30-Second Setup
import asyncio
from text2sql_ltm import create_simple_agent, Text2SQLSession
async def main():
# Just provide your API key - everything else uses smart defaults
agent = create_simple_agent(api_key="your_openai_key")
async with Text2SQLSession(agent) as session:
result = await session.query(
"Show me the top 10 customers by revenue this year",
user_id="user123"
)
print(f"Generated SQL: {result.sql}")
print(f"Confidence: {result.confidence}")
print(f"Explanation: {result.explanation}")
asyncio.run(main())
Feature-Rich Setup
# Enable advanced features with simple flags
agent = create_simple_agent(
api_key="your_openai_key",
enable_rag=True, # Vector-enhanced generation
enable_security_analysis=True, # Security scanning
enable_explanation=True, # AI teaching
enable_test_generation=True # Automated testing
)
Production Configuration
from text2sql_ltm import create_integrated_agent
# Load from configuration file
agent = create_integrated_agent(config_file="config/production.yaml")
# Or use configuration dictionary
agent = create_integrated_agent(config_dict={
"memory": {
"storage_backend": "postgresql",
"storage_url": "postgresql://user:pass@localhost/db"
},
"agent": {
"llm_provider": "openai",
"llm_model": "gpt-4",
"llm_api_key": "your_api_key"
},
"ai_features": {
"enable_rag": True,
"enable_validation": True,
"enable_multimodal": True,
"enable_security_analysis": True
}
})
๐ฏ Advanced Examples
Security Analysis
# Comprehensive security analysis
security_result = await agent.security_analyzer.analyze_security(
query="SELECT * FROM users WHERE id = ?",
user_id="user123",
context={"user_input": True}
)
print(f"Security Score: {security_result.risk_score}/10")
print(f"Vulnerabilities: {len(security_result.vulnerabilities)}")
print(f"Compliance: {security_result.compliance_status}")
Cross-Platform Translation
# Translate between database dialects
translation_result = await agent.query_translator.translate_query(
query="SELECT TOP 10 * FROM users",
source_dialect="sqlserver",
target_dialect="postgresql",
optimize_for_target=True
)
print(f"Original: {translation_result.original_query}")
print(f"Translated: {translation_result.translated_query}")
print(f"Compatibility: {translation_result.compatibility}")
Automated Testing
# Generate comprehensive test suite
test_suite = await agent.test_generator.generate_test_suite(
query="SELECT name, COUNT(*) FROM users GROUP BY name",
schema=schema_info,
test_types=["functional", "edge_case", "performance", "security"]
)
print(f"Generated {len(test_suite.test_cases)} test cases")
๐๏ธ Architecture
Text2SQL-LTM features a modular, production-ready architecture:
text2sql_ltm/
โโโ core/ # Core engine and interfaces
โโโ memory/ # Long-term memory system
โโโ rag/ # RAG components
โ โโโ retriever.py # Main RAG retriever
โ โโโ schema_rag.py # Schema-specific RAG
โ โโโ query_rag.py # Query pattern RAG
โ โโโ adaptive_rag.py # Self-improving RAG
โโโ ai_features/ # Advanced AI features
โ โโโ sql_validator.py # AI-powered validation
โ โโโ explainer.py # Intelligent explanation
โ โโโ schema_discovery.py # Schema analysis
โ โโโ query_translator.py # Cross-platform translation
โ โโโ security_analyzer.py # Security analysis
โ โโโ test_generator.py # Test automation
โโโ integrations/ # External integrations
๐ง Configuration
YAML Configuration
# config/production.yaml
memory:
storage_backend: "postgresql"
storage_url: "${DATABASE_URL}"
agent:
llm_provider: "openai"
llm_model: "gpt-4"
llm_api_key: "${OPENAI_API_KEY}"
ai_features:
enable_rag: true
enable_validation: true
enable_multimodal: true
enable_security_analysis: true
rag:
vector_store:
provider: "pinecone"
api_key: "${PINECONE_API_KEY}"
embedding:
provider: "openai"
api_key: "${OPENAI_API_KEY}"
security:
require_authentication: true
rate_limiting_enabled: true
Environment Variables
# Core API Keys
OPENAI_API_KEY=your_openai_key
DATABASE_URL=postgresql://user:pass@localhost/db
# Optional Services
PINECONE_API_KEY=your_pinecone_key
GOOGLE_VISION_API_KEY=your_google_key
REDIS_URL=redis://localhost:6379
๐งช Testing
Run the comprehensive test suite:
# Install with test dependencies
pip install text2sql-ltm[test]
# Run all tests
pytest tests/ -v
# Run with coverage
pytest tests/ --cov=text2sql_ltm --cov-report=html
# Run specific test categories
pytest tests/test_rag_system.py -v
pytest tests/test_multimodal.py -v
pytest tests/test_security.py -v
๐ Examples
Comprehensive examples are available in the examples/ directory:
-
Basic Usage - Getting started guide
-
Advanced Features - All AI features
-
Production Deployment - Enterprise setup
-
Security Analysis - Security features
๐ค Support & Licensing
Commercial License
Text2SQL-LTM is a commercial product with advanced enterprise features.
For licensing, pricing, and enterprise support, contact:
Alban Maxhuni, PhD
๐ง Email: info@albanmaxhuni.com
๐ Website: albanmaxhuni.com
License Options
- Individual License: For personal and small team use
- Enterprise License: For large organizations with advanced features
- Custom License: Tailored solutions for specific requirements
What's Included
- โ Full source code access
- โ Priority technical support
- โ Regular updates and new features
- โ Custom integration assistance
- โ Training and consultation
- โ SLA guarantees for enterprise
๐ Getting Started
- Install:
pip install text2sql-ltm - Contact: info@albanmaxhuni.com for licensing
- Configure: Set up your API keys and configuration
- Deploy: Use our production-ready templates
- Scale: Leverage enterprise features for your organization
Text2SQL-LTM: Revolutionizing database interaction through advanced AI. ๐
ยฉ 2024 AI Prishtina, Inc. All rights reserved.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file ai_prishtina_text2sql_ltm-1.0.3-py3-none-any.whl.
File metadata
- Download URL: ai_prishtina_text2sql_ltm-1.0.3-py3-none-any.whl
- Upload date:
- Size: 180.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.12.2
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
c1af0b52da88c249ad02304922b60ba4bff0ab0626cb1f98978465887811c640
|
|
| MD5 |
18eebbd729dab453de0c76eb8832c81d
|
|
| BLAKE2b-256 |
b505de4397a0d74e20c326d64c8f1d42c5061c5c804a39930bf6de3680fb0a2d
|