# AI Prishtina - Text2SQL-LTM: The Most Advanced Text-to-SQL Library
[](https://www.python.org/downloads/)
[](https://badge.fury.io/py/text2sql-ltm)
[](#license)
[](tests/)
[](tests/)
## ☕ Support This Project
If you find this project helpful, please consider supporting it:
[](https://coff.ee/albanmaxhuni)
**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.
## 🌟 Revolutionary Features
### 🧠 **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
- **Multi-modal knowledge fusion** across different data sources
### 🎤📷 **Multi-Modal Input Processing** *(Industry First)*
- **Voice-to-SQL**: Real-time speech recognition with SQL generation
- **Image-to-SQL**: OCR and table recognition from screenshots/charts
- **Handwriting recognition** for natural query input
- **Multi-modal fusion** combining voice, image, and text inputs
### 🔍 **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
### 🎓 **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
### 🔍 **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
### 🔒 **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**
### 🌐 **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
### 🧪 **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
## 🚀 Quick Start
### Installation
```bash
pip install text2sql-ltm
```
### 30-Second Setup
```python
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
```python
# Enable advanced features with simple flags
agent = create_simple_agent(
api_key="your_openai_key",
enable_rag=True, # Vector-enhanced generation
enable_multimodal=True, # Voice + Image processing
enable_security_analysis=True, # Security scanning
enable_explanation=True, # AI teaching
enable_test_generation=True # Automated testing
)
```
### Production Configuration
```python
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
}
})
```
## 🎯 Advanced Examples
### Multi-Modal Processing
```python
# Process voice input
voice_result = await agent.multimodal_processor.process_voice_input(
audio_data=voice_bytes,
language="en-US"
)
# Process table image
image_result = await agent.multimodal_processor.process_image_input(
image_data=image_bytes,
image_type="table_screenshot"
)
# Combined processing
combined_result = await agent.multimodal_processor.process_multi_modal_input([
voice_input, image_input, text_input
])
```
### Security Analysis
```python
# 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
```python
# 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
```python
# 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")
```
## 🏗️ Architecture
Text2SQL-LTM features a modular, production-ready architecture:
```
text2sql_ltm/
├── core/ # Core engine and interfaces
├── memory/ # Long-term memory system
├── rag/ # RAG components
│ ├── retriever.py # Main RAG retriever
│ ├── schema_rag.py # Schema-specific RAG
│ ├── query_rag.py # Query pattern RAG
│ └── adaptive_rag.py # Self-improving RAG
├── ai_features/ # Advanced AI features
│ ├── sql_validator.py # AI-powered validation
│ ├── multimodal.py # Multi-modal processing
│ ├── explainer.py # Intelligent explanation
│ ├── schema_discovery.py # Schema analysis
│ ├── query_translator.py # Cross-platform translation
│ ├── security_analyzer.py # Security analysis
│ └── test_generator.py # Test automation
└── integrations/ # External integrations
```
## 🔧 Configuration
### YAML Configuration
```yaml
# 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
```bash
# 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
```
## 🧪 Testing
Run the comprehensive test suite:
```bash
# 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
```
## 📚 Examples
Comprehensive examples are available in the `examples/` directory:
- **[Basic Usage](examples/basic_usage.py)** - Getting started guide
- **[Advanced Features](examples/advanced_features.py)** - All AI features
- **[Production Deployment](examples/production_deployment.py)** - Enterprise setup
- **[Multi-Modal Processing](examples/multimodal_examples.py)** - Voice and image
- **[Security Analysis](examples/security_examples.py)** - Security features
## 🤝 Support & Licensing
### Commercial License
Text2SQL-LTM is a **commercial product** with advanced enterprise features.
**For licensing, pricing, and enterprise support, contact:**
**Alban Maxhuni, PhD**
📧 **Email**: [info@albanmaxhuni.com](mailto:info@albanmaxhuni.com)
🌐 **Website**: [albanmaxhuni.com](https://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
- ✅ **Full source code access**
- ✅ **Priority technical support**
- ✅ **Regular updates and new features**
- ✅ **Custom integration assistance**
- ✅ **Training and consultation**
- ✅ **SLA guarantees for enterprise**
## 📞 Getting Started
1. **Install**: `pip install text2sql-ltm`
2. **Contact**: [info@albanmaxhuni.com](mailto:info@albanmaxhuni.com) for licensing
3. **Configure**: Set up your API keys and configuration
4. **Deploy**: Use our production-ready templates
5. **Scale**: Leverage enterprise features for your organization
---
**Text2SQL-LTM: Revolutionizing database interaction through advanced AI.** 🚀
*© 2024 AI Prishtina, Inc. All rights reserved.*
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"description": "# AI Prishtina - Text2SQL-LTM: The Most Advanced Text-to-SQL Library\n\n[](https://www.python.org/downloads/)\n[](https://badge.fury.io/py/text2sql-ltm)\n[](#license)\n[](tests/)\n[](tests/)\n\n## \u2615 Support This Project\n\nIf you find this project helpful, please consider supporting it:\n\n[](https://coff.ee/albanmaxhuni)\n\n**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.\n\n## \ud83c\udf1f Revolutionary Features\n\n### \ud83e\udde0 **RAG-Enhanced Query Generation**\n- **Vector-based knowledge retrieval** with semantic search\n- **Schema-aware context augmentation** for intelligent SQL generation \n- **Query pattern learning** from successful executions\n- **Adaptive retrieval strategies** that improve over time\n- **Multi-modal knowledge fusion** across different data sources\n\n### \ud83c\udfa4\ud83d\udcf7 **Multi-Modal Input Processing** *(Industry First)*\n- **Voice-to-SQL**: Real-time speech recognition with SQL generation\n- **Image-to-SQL**: OCR and table recognition from screenshots/charts\n- **Handwriting recognition** for natural query input\n- **Multi-modal fusion** combining voice, image, and text inputs\n\n### \ud83d\udd0d **AI-Powered SQL Validation & Auto-Correction**\n- **Intelligent syntax validation** with automatic error fixing\n- **Security vulnerability detection** and prevention\n- **Performance optimization suggestions** with impact analysis\n- **Cross-platform compatibility checking**\n- **Best practice enforcement** with educational feedback\n\n### \ud83c\udf93 **Intelligent Query Explanation & Teaching System**\n- **Step-by-step query breakdown** with visual execution flow\n- **Adaptive explanations** based on user expertise level\n- **Interactive learning modes** with guided practice\n- **Personalized learning paths** with progress tracking\n- **Real-time teaching assistance** for SQL education\n\n### \ud83d\udd0d **Automated Schema Discovery & Documentation**\n- **AI-powered relationship inference** between tables\n- **Column purpose detection** using pattern recognition\n- **Data quality assessment** with improvement suggestions\n- **Auto-generated documentation** in multiple formats\n- **Business rule extraction** from data patterns\n\n### \ud83d\udd12 **Advanced Security Analysis**\n- **SQL injection detection** with real-time prevention\n- **Privilege escalation monitoring** and alerts\n- **Data exposure analysis** with compliance checking (GDPR, PCI DSS, SOX)\n- **Vulnerability scanning** with remediation guidance\n- **Security best practice validation**\n\n### \ud83c\udf10 **Cross-Platform Query Translation**\n- **Intelligent dialect conversion** between 8+ database platforms\n- **Syntax optimization** for target platforms\n- **Compatibility analysis** with migration guidance\n- **Performance tuning** for specific database engines\n- **Feature mapping** across different SQL dialects\n\n### \ud83e\uddea **Automated Test Case Generation**\n- **Comprehensive test suite creation** for SQL queries\n- **Edge case detection** and test generation\n- **Performance test automation** with benchmarking\n- **Security test scenarios** for vulnerability assessment\n- **Data validation testing** with constraint checking\n\n## \ud83d\ude80 Quick Start\n\n### Installation\n\n```bash\npip install text2sql-ltm\n```\n\n### 30-Second Setup\n\n```python\nimport asyncio\nfrom text2sql_ltm import create_simple_agent, Text2SQLSession\n\nasync def main():\n # Just provide your API key - everything else uses smart defaults\n agent = create_simple_agent(api_key=\"your_openai_key\")\n \n async with Text2SQLSession(agent) as session:\n result = await session.query(\n \"Show me the top 10 customers by revenue this year\",\n user_id=\"user123\"\n )\n \n print(f\"Generated SQL: {result.sql}\")\n print(f\"Confidence: {result.confidence}\")\n print(f\"Explanation: {result.explanation}\")\n\nasyncio.run(main())\n```\n\n### Feature-Rich Setup\n\n```python\n# Enable advanced features with simple flags\nagent = create_simple_agent(\n api_key=\"your_openai_key\",\n enable_rag=True, # Vector-enhanced generation\n enable_multimodal=True, # Voice + Image processing \n enable_security_analysis=True, # Security scanning\n enable_explanation=True, # AI teaching\n enable_test_generation=True # Automated testing\n)\n```\n\n### Production Configuration\n\n```python\nfrom text2sql_ltm import create_integrated_agent\n\n# Load from configuration file\nagent = create_integrated_agent(config_file=\"config/production.yaml\")\n\n# Or use configuration dictionary\nagent = create_integrated_agent(config_dict={\n \"memory\": {\n \"storage_backend\": \"postgresql\",\n \"storage_url\": \"postgresql://user:pass@localhost/db\"\n },\n \"agent\": {\n \"llm_provider\": \"openai\",\n \"llm_model\": \"gpt-4\",\n \"llm_api_key\": \"your_api_key\"\n },\n \"ai_features\": {\n \"enable_rag\": True,\n \"enable_validation\": True,\n \"enable_multimodal\": True,\n \"enable_security_analysis\": True\n }\n})\n```\n\n## \ud83c\udfaf Advanced Examples\n\n### Multi-Modal Processing\n\n```python\n# Process voice input\nvoice_result = await agent.multimodal_processor.process_voice_input(\n audio_data=voice_bytes,\n language=\"en-US\"\n)\n\n# Process table image\nimage_result = await agent.multimodal_processor.process_image_input(\n image_data=image_bytes,\n image_type=\"table_screenshot\"\n)\n\n# Combined processing\ncombined_result = await agent.multimodal_processor.process_multi_modal_input([\n voice_input, image_input, text_input\n])\n```\n\n### Security Analysis\n\n```python\n# Comprehensive security analysis\nsecurity_result = await agent.security_analyzer.analyze_security(\n query=\"SELECT * FROM users WHERE id = ?\",\n user_id=\"user123\",\n context={\"user_input\": True}\n)\n\nprint(f\"Security Score: {security_result.risk_score}/10\")\nprint(f\"Vulnerabilities: {len(security_result.vulnerabilities)}\")\nprint(f\"Compliance: {security_result.compliance_status}\")\n```\n\n### Cross-Platform Translation\n\n```python\n# Translate between database dialects\ntranslation_result = await agent.query_translator.translate_query(\n query=\"SELECT TOP 10 * FROM users\",\n source_dialect=\"sqlserver\",\n target_dialect=\"postgresql\",\n optimize_for_target=True\n)\n\nprint(f\"Original: {translation_result.original_query}\")\nprint(f\"Translated: {translation_result.translated_query}\")\nprint(f\"Compatibility: {translation_result.compatibility}\")\n```\n\n### Automated Testing\n\n```python\n# Generate comprehensive test suite\ntest_suite = await agent.test_generator.generate_test_suite(\n query=\"SELECT name, COUNT(*) FROM users GROUP BY name\",\n schema=schema_info,\n test_types=[\"functional\", \"edge_case\", \"performance\", \"security\"]\n)\n\nprint(f\"Generated {len(test_suite.test_cases)} test cases\")\n```\n\n## \ud83c\udfd7\ufe0f Architecture\n\nText2SQL-LTM features a modular, production-ready architecture:\n\n```\ntext2sql_ltm/\n\u251c\u2500\u2500 core/ # Core engine and interfaces\n\u251c\u2500\u2500 memory/ # Long-term memory system\n\u251c\u2500\u2500 rag/ # RAG components\n\u2502 \u251c\u2500\u2500 retriever.py # Main RAG retriever\n\u2502 \u251c\u2500\u2500 schema_rag.py # Schema-specific RAG\n\u2502 \u251c\u2500\u2500 query_rag.py # Query pattern RAG\n\u2502 \u2514\u2500\u2500 adaptive_rag.py # Self-improving RAG\n\u251c\u2500\u2500 ai_features/ # Advanced AI features\n\u2502 \u251c\u2500\u2500 sql_validator.py # AI-powered validation\n\u2502 \u251c\u2500\u2500 multimodal.py # Multi-modal processing\n\u2502 \u251c\u2500\u2500 explainer.py # Intelligent explanation\n\u2502 \u251c\u2500\u2500 schema_discovery.py # Schema analysis\n\u2502 \u251c\u2500\u2500 query_translator.py # Cross-platform translation\n\u2502 \u251c\u2500\u2500 security_analyzer.py # Security analysis\n\u2502 \u2514\u2500\u2500 test_generator.py # Test automation\n\u2514\u2500\u2500 integrations/ # External integrations\n```\n\n## \ud83d\udd27 Configuration\n\n### YAML Configuration\n\n```yaml\n# config/production.yaml\nmemory:\n storage_backend: \"postgresql\"\n storage_url: \"${DATABASE_URL}\"\n\nagent:\n llm_provider: \"openai\"\n llm_model: \"gpt-4\"\n llm_api_key: \"${OPENAI_API_KEY}\"\n\nai_features:\n enable_rag: true\n enable_validation: true\n enable_multimodal: true\n enable_security_analysis: true\n \n rag:\n vector_store:\n provider: \"pinecone\"\n api_key: \"${PINECONE_API_KEY}\"\n embedding:\n provider: \"openai\"\n api_key: \"${OPENAI_API_KEY}\"\n\nsecurity:\n require_authentication: true\n rate_limiting_enabled: true\n```\n\n### Environment Variables\n\n```bash\n# Core API Keys\nOPENAI_API_KEY=your_openai_key\nDATABASE_URL=postgresql://user:pass@localhost/db\n\n# Optional Services\nPINECONE_API_KEY=your_pinecone_key\nGOOGLE_VISION_API_KEY=your_google_key\nREDIS_URL=redis://localhost:6379\n```\n\n## \ud83e\uddea Testing\n\nRun the comprehensive test suite:\n\n```bash\n# Install with test dependencies\npip install text2sql-ltm[test]\n\n# Run all tests\npytest tests/ -v\n\n# Run with coverage\npytest tests/ --cov=text2sql_ltm --cov-report=html\n\n# Run specific test categories\npytest tests/test_rag_system.py -v\npytest tests/test_multimodal.py -v\npytest tests/test_security.py -v\n```\n\n## \ud83d\udcda Examples\n\nComprehensive examples are available in the `examples/` directory:\n\n- **[Basic Usage](examples/basic_usage.py)** - Getting started guide\n- **[Advanced Features](examples/advanced_features.py)** - All AI features\n- **[Production Deployment](examples/production_deployment.py)** - Enterprise setup\n- **[Multi-Modal Processing](examples/multimodal_examples.py)** - Voice and image\n- **[Security Analysis](examples/security_examples.py)** - Security features\n\n## \ud83e\udd1d Support & Licensing\n\n### Commercial License\n\nText2SQL-LTM is a **commercial product** with advanced enterprise features. \n\n**For licensing, pricing, and enterprise support, contact:**\n\n**Alban Maxhuni, PhD**\n\ud83d\udce7 **Email**: [info@albanmaxhuni.com](mailto:info@albanmaxhuni.com) \n\ud83c\udf10 **Website**: [albanmaxhuni.com](https://albanmaxhuni.com)\n\n### License Options\n\n- **Individual License**: For personal and small team use\n- **Enterprise License**: For large organizations with advanced features\n- **Custom License**: Tailored solutions for specific requirements\n\n### What's Included\n\n- \u2705 **Full source code access**\n- \u2705 **Priority technical support**\n- \u2705 **Regular updates and new features**\n- \u2705 **Custom integration assistance**\n- \u2705 **Training and consultation**\n- \u2705 **SLA guarantees for enterprise**\n\n## \ud83d\udcde Getting Started\n\n1. **Install**: `pip install text2sql-ltm`\n2. **Contact**: [info@albanmaxhuni.com](mailto:info@albanmaxhuni.com) for licensing\n3. **Configure**: Set up your API keys and configuration\n4. **Deploy**: Use our production-ready templates\n5. **Scale**: Leverage enterprise features for your organization\n\n---\n\n**Text2SQL-LTM: Revolutionizing database interaction through advanced AI.** \ud83d\ude80\n\n*\u00a9 2024 AI Prishtina, Inc. All rights reserved.*\n",
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