Tools•generator

PostgreSQL for Databases for AI Apps

Generates optimized database schema snippets for AI workloads, focusing on embedding storage, conversation history, and metadata with explicit support for pgvector, Redis, or MongoDB

Try the tool

client runner

Generated SQL/DSL code

Run the tool to see output.

Examples

PostgreSQL with embeddings

{
  "database_type": "PostgreSQL",
  "embedding_dim": "768",
  "vector_search": true,
  "conversation_history": true,
  "metadata_fields": "user_id,timestamp"
}

Expected output

CREATE TABLE embeddings (id UUID PRIMARY KEY, vector VECTOR(768), metadata JSONB, created_at TIMESTAMP); CREATE INDEX idx_vector ON embeddings USING ivfflat (vector);

Redis with metadata

{
  "database_type": "Redis",
  "embedding_dim": "128",
  "vector_search": false,
  "conversation_history": false,
  "metadata_fields": "session_id,device_type"
}

Expected output

SET embeddings:123 {"metadata": {"session_id": "abc", "device_type": "mobile"}}

How it works

Analyzes database type, embedding dimensions, and storage requirements to generate tailored schema definitions with appropriate indexing strategies and data modeling patterns for AI workloads

Related tools

pgvectorSupabaseDrizzle