🚀 Quickstart

Here's a quick start guide for using the Minds SQL Core project:

🛠️ Set Up Configuration:

• Ensure you have the necessary environment variables set up in a .env file or your environment configuration. These variables include:
  1. API_KEY: Your API key for authentication with LLMs like OpenAI, Gemini, LLAMA, etc.
  2. DB_URL: The URL or connection string for your database.
  3. EXAMPLE_PATH: The path to any example JSON files or data you may want to use for bulk indexing.

🎬 Initialize a Class:

• To harness the power of MindSQL, create a MindSQLCore instance by specifying the LLM, vector store, and database components. Here's an example using Google GenAi as the LLM, ChromaDB as the vector store, and Sqlite as the database:

  from mindsql.core import MindSQLCore
  from mindsql.databases import Sqlite
  from mindsql.llms import GoogleGenAi
  from mindsql.vectorstores import ChromaDB
  
  # Add Your Configurations
  config = {"api_key": "YOUR-API-KEY"}
  
  # Create MindSQLCore Instance
  minds = MindSQLCore(
      llm=GoogleGenAi(config=config),
      vectorstore=ChromaDB(),
      database=Sqlite()
  )

• Now, the minds object becomes the primary interface for interacting with the MindSQL package. Let's begin by establishing a connection with our database.

  # Create a Database Connection Using The Specified URL
  connection = minds.database.create_connection(url="YOUR_DATABASE_CONNECTION_URL")

  With this connection established, you are ready to utilize the functionalities of MindSQL for your project.

📑 Index DDL Statements:

• Use the index_all_ddls method to retrieve and index all Data Definition Language (DDL) statements from the specified database into the vector store.

  # Index All Data Definition Language (DDL) Statements in The Specified Database Into The Vectorstore
  minds.index_all_ddls(connection=connection, db_name='NAME_OF_THE_DB')

⚡ Execute Database Queries:

• Use the ask_db method to execute database queries and retrieve results.

• Provide the question, database connection, and optional parameters like table names or visualization preferences.

• The ask_db method can optionally generate charts based on the query results if the visualize parameter is set to True.

  response = minds.ask_db(question="YOUR-QUESTION", connection=conn, visualize=True, table_names=['TABLE_NAME'])

📊 Visualization:

• Visualize the data using the returned chart object.

  chart = response["chart"]
  chart.show()

• Close the DB connection once you are done:

  conn.close()