Building a Secure, Reliable AI-Powered Text-to-SQL System Using WrenAI, LLMs, and RAG: By Fidel Vetino

Hello Everyone,

?? It's me The Mad Scientist "Fidel Vetino" living under the hood mechanically providing real solutions NOT theory. In the lab today developing a highly secure and reliable text-to-SQL system by integrating WrenAI, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG).

The system is designed to comply with stringent security standards, including HIPAA, HITRUST, NIST, SOC2, NSA, SOA, and GDPR. Within project Key components include a secure environment setup, encrypted database connections, encrypted configuration management, secure API interactions, and robust role-based access controls. Point I am making you can ensure efficient and secure database interactions for advanced natural language querying.

Let's get started:

Step 1: Setting Up the Environment

Start by ensuring that your virtual environment and dependencies are secure. Use secure practices for managing dependencies.

requirements.txt

text

wrenai==1.0.0
openai==0.10.0
psycopg2-binary==2.9.3
pyyaml==5.4.1
cryptography==3.4.7        

Create and activate the virtual environment securely.

bash

# Create a virtual environment with a secure name
python3 -m venv secure_env
source secure_env/bin/activate

# Install dependencies from a secure requirements file
pip install -r requirements.txt        

Step 2: Setting Up WrenAI with Secure Configuration

Store sensitive information securely using environment variables and encryption for configuration files.

secure_config.py

python

import os
from cryptography.fernet import Fernet

# Function to decrypt configuration files
def decrypt_config(encrypted_config):
    key = os.environ['ENCRYPTION_KEY']
    fernet = Fernet(key)
    decrypted = fernet.decrypt(encrypted_config.encode()).decode()
    return decrypted

# Load encrypted configuration from environment variable
encrypted_config = os.environ['ENCRYPTED_CONFIG']
config = decrypt_config(encrypted_config)        

Encrypt your configuration file and store it securely.

encrypt_config.py

python

import os
from cryptography.fernet import Fernet
import yaml

# Generate a key for encryption
key = Fernet.generate_key()
print(f"Encryption Key: {key.decode()}")

# Encrypt configuration
with open('wrenai_config.yml', 'rb') as file:
    config_data = file.read()

fernet = Fernet(key)
encrypted_data = fernet.encrypt(config_data)

# Store the encrypted configuration
with open('wrenai_config.enc', 'wb') as file:
    file.write(encrypted_data)        

Step 3: Setting Up the Database Securely

Ensure secure connections and encryption for sensitive data. Use SSL/TLS for database connections and encrypt sensitive data at rest.

Database Setup Script

db_setup.sql

sql

-- Enable SSL connection and encryption
ALTER SYSTEM SET ssl = on;
ALTER SYSTEM SET ssl_cert_file = '/path/to/server.crt';
ALTER SYSTEM SET ssl_key_file = '/path/to/server.key';
ALTER SYSTEM SET ssl_ca_file = '/path/to/root.crt';
SELECT pg_reload_conf();

-- Create table with encrypted column
CREATE TABLE employees (
    id SERIAL PRIMARY KEY,
    name VARCHAR(100),
    position VARCHAR(100),
    salary INTEGER,
    encrypted_ssn BYTEA
);

-- Insert encrypted data example (assuming encryption is done client-side)        

Client-Side Encryption Example (Python)

python

import psycopg2
from cryptography.fernet import Fernet

# Encryption key management
key = os.environ['DATA_ENCRYPTION_KEY']
fernet = Fernet(key)

# Connect to the database using SSL
conn = psycopg2.connect(
    host="localhost",
    port=5432,
    user="your_db_user",
    password="your_db_password",
    dbname="your_db_name",
    sslmode='require'
)
cursor = conn.cursor()

# Encrypt data before insertion
ssn = '123-45-6789'
encrypted_ssn = fernet.encrypt(ssn.encode())

cursor.execute("INSERT INTO employees (name, position, salary, encrypted_ssn) VALUES (%s, %s, %s, %s)",
               ('John Doe', 'Software Engineer', 90000, encrypted_ssn))
conn.commit()
conn.close()        

Step 4: Integrating LLM for Text-to-SQL Securely

Ensure secure handling of API keys and data transmission to/from OpenAI API.

text_to_sql.py

python

import openai
import psycopg2
import os
from secure_config import config

# OpenAI API key
openai.api_key = os.environ['OPENAI_API_KEY']

def generate_sql_query(prompt):
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=prompt,
        max_tokens=100,
        headers={"Authorization": f"Bearer {os.environ['OPENAI_API_KEY']}"}
    )
    return response.choices[0].text.strip()

def execute_sql_query(query):
    conn = psycopg2.connect(
        host=config['database']['host'],
        port=config['database']['port'],
        user=config['database']['user'],
        password=config['database']['password'],
        dbname=config['database']['dbname'],
        sslmode='require'
    )
    cursor = conn.cursor()
    cursor.execute(query)
    result = cursor.fetchall()
    conn.close()
    return result

if __name__ == "__main__":
    user_input = input("Enter your query: ")
    sql_query = generate_sql_query(user_input)
    print("Generated SQL Query:", sql_query)
    result = execute_sql_query(sql_query)
    print("Query Result:", result)
        

Step 5: Building the Semantic Layer with Secure Configuration

Ensure the WrenAI initialization and operations adhere to security protocols.

semantic_layer.py

python

from wrenai import WrenAI
from secure_config import config

# Initialize WrenAI with secure configuration
wrenai = WrenAI(config)

# Example: Fetch metadata securely
tables = wrenai.get_tables()
for table in tables:
    print(f"Table: {table.name}")
    for column in table.columns:
        print(f"  Column: {column.name}, Type: {column.type}")
        

Step 6: Putting It All Together Securely

Finally, create a script that securely ties everything together, ensuring compliance with security standards.

main.py

python

import openai
import psycopg2
import os
from secure_config import config
from wrenai import WrenAI

# OpenAI API key
openai.api_key = os.environ['OPENAI_API_KEY']

# Initialize WrenAI
wrenai = WrenAI(config)

def generate_sql_query(prompt):
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=prompt,
        max_tokens=100,
        headers={"Authorization": f"Bearer {os.environ['OPENAI_API_KEY']}"}
    )
    return response.choices[0].text.strip()

def execute_sql_query(query):
    conn = psycopg2.connect(
        host=config['database']['host'],
        port=config['database']['port'],
        user=config['database']['user'],
        password=config['database']['password'],
        dbname=config['database']['dbname'],
        sslmode='require'
    )
    cursor = conn.cursor()
    cursor.execute(query)
    result = cursor.fetchall()
    conn.close()
    return result

if __name__ == "__main__":
    user_input = input("Enter your query: ")
    sql_query = generate_sql_query(user_input)
    print("Generated SQL Query:", sql_query)
    result = execute_sql_query(sql_query)
    print("Query Result:", result)
        

Additional Security Considerations

1. Access Control: Implement role-based access control (RBAC) for the database and application.
2. Data Encryption: Ensure all sensitive data is encrypted both at rest and in transit.
3. Logging and Monitoring: Implement logging and monitoring to detect and respond to potential security incidents.
4. Compliance Audits: Regularly audit the system to ensure compliance with relevant security standards and regulations.
5. Incident Response Plan: Develop and maintain an incident response plan to address security breaches effectively.

?? My Final Notes: Integrating WrenAI, LLMs, and RAG with rigorous security measures, this project showcases a robust approach to building a secure text-to-SQL system. Adhering to high-security standards ensures the protection of sensitive data and compliance with regulatory requirements. The combination of advanced AI techniques and stringent security protocols provides a reliable and efficient solution for natural language database queries, paving the way for more secure and user-friendly data access in various applications.

?? Fidel V. - Technology Innovator & Visionary ??

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