πΎ Module 2.3: Database Design - Memory for AI
Deep in the castle vaults: Module 2.3.
Designing for AI is different. You need Relational Metadata, Unstructured JSON, and High-dimensional Vectors all in one place. π Thread
π§ building https://chatvid.ai
https://bibigpt.co
https://pipigpt.co
https://airss.co
π£learning & earning while helping others
β€οΈmaking software, storytelling videos
β Summary: Postgres Maximalism
We use Postgres for Search, Auth, Vectors, and JSON.
Keep your stack simple and your architecture lean.
Next: 2.4 Task Queues.
Don't let heavy AI tasks block your database connections. π
4/ π‘οΈ Migration-Based Workflow
Never modify your schema manually.
BibiGPT follows a strict migration-based flow. Every change is an auditable .sql file in the repo.
Version control for your database is a non-negotiable.
3/ π Semantic Search
The core of RAG (Retrieval-Augmented Generation).
User Question -> Vector -> Cosine Distance -> Result.
We implement this logic inside Postgres using SQL functions. Computing happens right next to the data. Fast.
2/ π§ pgvector: The Brain of Postgres
Traditional SQL LIKE queries cannot find 'meaning'.
With pgvector, Postgres stores and retrieves Embeddings directly.
Ditch the complexity of a separate Vector DB like Pinecone. One database to rule them all.
1/ π¦ Hybrid Schema: Relational meets NoSQL
BibiGPT uses JSONB for AI outputs like subtitles and summaries.
Why? Because AI output structure changes fast.
JSONB gives us the flexibility of NoSQL without sacrificing SQL's strong consistency.
πΎ Module 2.3: Database Design - Memory for AI
Deep in the castle vaults: Module 2.3.
Designing for AI is different. You need Relational Metadata, Unstructured JSON, and High-dimensional Vectors all in one place. π Thread
β Summary: The Backend Gateway
From SSR clients to RLS deep defense, Supabase is more than a database β it is your gateway.
Next: 2.3 Database Design.
Modeling for AI Search and RAG. π
4/ π 2025 Standard: PKCE
For maximum security, we use the PKCE flow.
By exchanging codes on the backend, we eliminate the risk of leaking Access Tokens in the URL Hash.
Professional SaaS demands professional security.
3/ π§± SQL as the "Constitution"
Defining logic in SQL Policies is far more robust than scattered 'if' statements in code.
This is the lifeblood of multi-tenant security for any AI SaaS.
2/ π RLS: The Database Firewall
Row Level Security (RLS) is the killer feature.
Even if your API has a bug, the database layer enforces 'auth.uid() = user_id'.
Stop relying on fragile API checks; let the database verify identity.