An extensible, event-driven wrapper over PostgreSQL with integrated AI capabilities for enhanced database design, querying, and maintenance.

graph TD
    A[Client Application] -->|Standard Postgres Connection| D[PostgreSQL Database]
    A -->|API Requests| B[API Layer]
    B <--> C[AI Layer]
    C -->|SQL Queries| D
    C -->|Log Events| E[Event Store]
    F[LLM Core] <-->|Central Processing| C
    G[RAG System] <-->|Enhance Queries| C
    G -->|Query Metadata| E
    G -->|Query App Data| D
    H[Neural Query Optimizer] <-->|Optimize| C
    I[Anomaly Detection AI] <-->|Monitor| C
    J[AutoML for Indexing] <-->|Suggest Indexes| C
    K[Graph Neural Network] <-->|Analyze Relations| C
    L[Time Series Forecasting] <-->|Predict Trends| C
    M[Development Tools] -->|Integrate| B
    N[Monitoring/Analytics] -->|Track| E
    N -->|Feed Performance Data| G
    G -->|Provide Insights| C

• Dual Connectivity: Use as a standard PostgreSQL database or leverage AI-enhanced features through our API.
• LLM-Powered AI Layer: Natural language query understanding, intelligent schema recommendations, and more.
• RAG System: Enhances AI capabilities with context from both application data and metadata.
• Neural Query Optimizer: Deep learning-based query optimization that goes beyond traditional rule-based optimizers.
• Anomaly Detection: Real-time monitoring for security issues, data inconsistencies, and performance problems.
• AutoML for Indexing: Automated index recommendations and testing for optimal performance.
• Graph Neural Network: Analyzes data relationships to enhance schema design and query optimization.
• Time Series Forecasting: Predicts future trends in data and query patterns for proactive optimization.
• Comprehensive Monitoring: Analytics feed back into the AI layer for continuous improvement.

• PostgreSQL 13 or higher
• Python 3.8+
• Docker (optional, for containerized deployment)

1. Clone the repository:

   git clone https://github.com/your-username/ai-driven-database.git
   cd ai-driven-database
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Set up your PostgreSQL database and update the configuration in config.yaml.

4. Initialize the AI-driven wrapper:

   python init_ai_wrapper.py
   

5. Start the service:

   python run_server.py
   

Connect to the database using your preferred PostgreSQL client or ORM as usual.

Use our REST API to leverage AI-driven features:

import requests

response = requests.post('http://localhost:8000/api/v1/query', json={
    'natural_language_query': 'Find all high-value customers from the last month'
})

print(response.json())

We welcome contributions! Please see our Contributing Guide for more details.

This project is licensed under the MIT License - see the LICENSE file for details.

• PostgreSQL
• OpenAI for LLM technology
• All our amazing contributors!