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BAML Demo

This repository contains demo code showcasing the BAML library. The demo demonstrates how to:

  • Select relevant tables from a SQL schema based on a user query.
  • Generate a SQL query based on the selected tables and columns.
  • Use chain-of-thought prompts to help the model decide which tables and columns are relevant.

Video demo: https://youtu.be/iBqTB_7Avt0

Overview

There are two main components in this demo:

  1. app/main.py
    This Python script performs the following:

    • Prompts the user for a SQL-related question (with an option to use a default).
    • Uses the BAML library to select relevant tables and columns based on the given question.
    • Filters the SQL schema to include only the relevant parts.
    • Requests the model to generate an SQL query.
    • Displays the selected tables, columns, and the generated SQL query with formatted colored output.

  2. app/baml_src/extract-tables.baml
    This BAML file defines:

    • A schema for table metadata.
    • A function to select relevant tables from the provided schema based on the user's question.
    • Testing blocks to verify the selection process for different queries.

Prerequisites

  • Python 3.7 or later.
  • The BAML client library installed and properly configured.
  • Dependencies such as colorama. You can install them via pip manually or through a requirements.txt if available.

Setup and Installation

  1. Clone the Repository

    git clone https://github.com/BoundaryML/baml-db-query.git
cd baml-db-query

  2. Install Dependencies

    consider using a virtual environment

    python -m venv venv

    source venv/bin/activate

    pip install -r requirements.txt

  3. Set Up the BAML

    Convert from *.baml to *.py using the following command:

    baml-cli generate

    You should now see a directory called baml_client.

    Alternatively, you can save any .baml file while you have the BAML VSCode/Cursor extension and that will run the command for you.

  4. Set Up the Environment

    • Set the OPENAI_API_KEY environment variable to your OpenAI API key.
    export OPENAI_API_KEY=<your-api-key>

Running the Demo

Run the demo using the following command:

BAML_LOG=warn python app/main.py

Example Output

Default question

BAML_LOG=warn python app/main.py
Enter your question (default: 'What are the top 10 most popular products in the database?'): What are the top 10 most popular products in the database?
--------------------------------------------------
Question: What are the top 10 most popular products in the database?
--------------------------------------------------
Selecting Relevant Tables...
Table Selected: cart
  - product_id
  - quantity
Table Selected: products
  - id
  - name
--------------------------------------------------
Generating SQL Query...
Query:
SELECT p.name, SUM(c.quantity) AS total_quantity FROM products p JOIN cart c ON p.id = c.product_id GROUP BY p.id, p.name ORDER BY total_quantity DESC LIMIT 10;
--------------------------------------------------

Unrelated question

BAML_LOG=warn python app/main.py
Enter your question (default: 'What are the top 10 most popular products in the database?'): how old is obama
--------------------------------------------------
Question: how old is obama
--------------------------------------------------
Selecting Relevant Tables...
Sorry, I can't help with that question.
Reason: The question is not relevant to the tables as it pertains to the age of a public figure, Barack Obama, which is not related to the database schema provided.

Feel free to explore and modify the demo to better understand and showcase the capabilities of BAML.