Semantic knowledge base Innovation Release

The semantic knowledge base feature provides a vector-searchable representation of your database schema. It enables natural language discovery of tables, views, columns, and comments. Instead of manually searching through schema definitions, you can ask questions like "find columns related to customer payments."

How it works

When you create a semantic knowledge base:

1. Schema crawling: The system extracts metadata from PostgreSQL system catalogs, including table definitions, column types, constraints, defaults, and comments.

2. Vectorization: Each definition is converted into a vector embedding using your specified model. These vectors capture semantic meaning, so customer_email and user_email_address have similar vectors.

3. Schema monitoring: Event triggers or background jobs watch for schema changes. When someone adds a column, creates a table, or updates a comment, the knowledge base updates automatically.

4. Similarity search: When you query "payment methods", the system vectorizes your query and finds schema elements with similar vectors, returning results ranked by relevance.

Prerequisites

Before creating a semantic knowledge base, register an embedding model to convert schema definitions into vectors for similarity search.

SELECT aidb.create_model(
    'my_embedding_model',
    'openai_embeddings',
    aidb.embeddings_config(
        model => 'text-embedding-3-small',
        api_key => 'sk-...'
    )
);

Quick start

-- Create a knowledge base monitoring the public schema
SELECT aidb.create_semantic_kb(
    'my_kb',
    'my_embedding_model',
    ARRAY['public'],
    'Live'
);

-- Search for tables/columns semantically
SELECT schema_name, relation_name, column_name, definition, similarity
FROM aidb.get_metadata('my_kb', 'customer orders', 0.8, 10, 0);

Use cases

Text-to-SQL context retrieval

An AI agent translating natural language to SQL can retrieve relevant schema context:

SELECT aidb.create_semantic_kb(
    'sales_kb', 'my_embedding_model',
    ARRAY['sales', 'analytics'], 'Background', FALSE, NULL
);

-- Agent retrieves context for query generation
SELECT definition FROM aidb.get_metadata('sales_kb', 'sales by region', 0.75, 5, 0);

Development environment

Get immediate searchability after schema changes:

SELECT aidb.create_semantic_kb(
    'dev_kb', 'my_embedding_model',
    ARRAY['public'], 'Live', FALSE, NULL
);

Production environment

Use asynchronous processing to avoid DDL latency:

SELECT aidb.create_semantic_kb(
    'prod_kb', 'my_embedding_model',
    ARRAY['public', 'analytics'], 'Background', FALSE, NULL
);

Restricted trigger environments

When DDL triggers are prohibited:

SELECT aidb.create_semantic_kb(
    'restricted_kb', 'my_embedding_model',
    ARRAY['public'], 'Background', TRUE, NULL  -- bypass_triggers = TRUE
);

Next steps

• Usage — Learn how to create, manage, and search semantic knowledge bases
• Semantic aliases — Create reusable, parameterized SQL queries with semantic descriptions