private-db-llm 0.1.1

Generate and execute SQL queries against MySQL/PostgreSQL using an LLM

private-db-llm

Generate and execute SQL queries against MySQL or PostgreSQL using a Large Language Model (LLM)

Features

• Schema Extraction
  Automatically discovers tables, columns, and foreign-key relations.
• LLM-powered Query Generation
  Sends your prompt plus the database schema to an LLM and returns a syntactically valid SQL query.
• Multi-Provider Support
  Works with OpenAI (v1+), OpenRouter, and Ollama HTTP APIs.
• Multi-Database Support
  Out of the box, supports MySQL and PostgreSQL (via mysql-connector-python & psycopg2-binary).
• SOLID Architecture
  Clean separation of concerns, easy to extend to other databases or LLM providers.

Installation

pip install private-db-llm

Optional extras:

• If you need PostgreSQL support, also install

pip install psycopg2-binary

• To load credentials from a .env file, you can add

pip install python-dotenv 

Quick Start

1. Configure Your LLM

from private_db_llm.config import LLMConfig

llm_conf = LLMConfig(
    provider="openai",      # "openai", "openrouter", or "ollama"
    api_key="sk-…",
    base_url=None,          # for OpenRouter or Ollama HTTP endpoint
    model="gpt-4o-mini"
)

2. Create the Service

from private_db_llm.service import create_service

service = create_service(llm_conf)

3. Connect to Your Database

MySQL

import mysql.connector

mysql_conn = mysql.connector.connect(
    host="localhost",
    port=3306,
    user="root",
    password="your_mysql_password",
    database="your_mysql_db"
)

PostgreSQL

import psycopg2

postgres_conn = psycopg2.connect(
    host="localhost",
    port=5432,
    user="postgres_user",
    password="your_pg_password",
    dbname="your_pg_db"
)

4. Run a Prompt & Get JSON Results

# For MySQL
result_mysql = service.run(
    mysql_conn,
    "List all users who signed up in the last 7 days"
)
print(result_mysql)

# For PostgreSQL
result_pg = service.run(
    postgres_conn,
    "Show total sales per region for Q1"
)
print(result_pg)

Advanced

• Customizing the instruction template:

  • Pass your own instruction_template to DBLLMService if you want to tweak how the LLM is prompted.

• Adding a new database:

  1. Create <YourDB>SchemaExtractor & <YourDB>QueryExecutor classes.
  2. Hook them into service.run() based on the connection type.
  • Supporting another LLM provider:
  1. Subclass the LLMClient ABC.
  2. Add your implementation in llm_client.py.
  3. Register it in create_service().

Version Compatibility

• Python: >=3.12
• OpenAI Python SDK: >=1.0.0
• MySQL driver: mysql-connector-python>=8.0.0
• HTTP client: httpx>=0.23.0
• PostgreSQL driver: psycopg2-binary>=2.9.0

Contributing

1. Fork the repo
2. Create a feature branch
3. Write tests & update docs
4. Open a PR — all contributions welcome!

License

MIT © Matin Khosravi