Vectorized Metadata for Intelligent SQL Generation

One of my recent technical explorations sits at the intersection of data architecture, natural language processing, and vector-based semantic modeling. The goal of this project was ambitious:

Enable machines to generate accurate SQL queries by understanding database schema context through embeddings of table and column metadata.

Instead of hard-coded query templates or rigid mapping rules, I worked on a system that understands database structure with semantic meaning — the same way a developer mentally maps real-world concepts to tables and fields.

The Core Principle

Traditionally, if you want an AI or system to generate SQL, you must manually teach it:

table names
column names
relationships
constraints
business meaning

But humans don’t just read labels — we infer meaning.
For example:

A column named total_amount is semantically related to:

payment
invoice
financial aggregation

A column named delivery_time relates to:

shipping
logistics
scheduling

My solution was to encode this semantic meaning using vector embeddings.

How It Works

1. Metadata Extraction

For each table, I collect:

table name
column names
column descriptions (if available)
comment annotations
inferred domain categories
relational links

2. Vectorization

All metadata strings are fed into an embedding model to generate vector representations.

This produces a semantic space where:

business_id is close to client_id
expires_at is close to valid_until
product vectors align near item, sku, inventory
quantity clusters with count, units, amount

This allows the system to understand that:

“number of orders today” maps to COUNT(*) over the Order table
“total revenue” refers to SUM(amount) in the financial context

3. Query Interpretation

User prompt:

Show me the top-selling products this week.

The system performs a vector search:

matches “top-selling” → quantity, sales, orderdetail
matches “products” → Product table
matches “this week” → time filters on datetime fields

4. SQL Synthesis

The final output may look like:


SELECT p.name, SUM(o.quantity) as total_sold
FROM OrderDetail o
JOIN Product p ON o.product_id = p.id
WHERE o.created_at >= NOW() - INTERVAL '7 days'
GROUP BY p.name
ORDER BY total_sold DESC;