Sign in
Skip to solution
easyAI Engineering

What is a vector database and when do you actually need one?

785 views
01

Understand the problem

Purpose-built ANN stores vs pgvector vs a numpy array — sizing the decision honestly.

vector-databasepgvectorinfrastructurerag
02

Attempt it yourself

Sketch your approach before reading the solution — that's what interviews test.

Nudge consolestandby

Stuck? Beam a request up — the console returns a conceptual nudge that guides your logic without spoiling the implementation.

03

Study the solution

A vector database stores embeddings with ANN indexes plus filtering, CRUD and scaling (Pinecone, Weaviate, Qdrant, Milvus). But you often do not need a dedicated one: under ~100k vectors a flat in-memory search is fine, and pgvector/SQLite extensions let you keep vectors next to your relational data with transactional

Solution ready — 2 min read

Classified // press E to declassify

04

Read the code

pgvector schema keeping vectors beside the data
CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE chunks (
  id         bigserial PRIMARY KEY,
  doc_id     bigint REFERENCES documents(id) ON DELETE CASCADE,
  tenant_id  bigint NOT NULL,
  content    text NOT NULL,
  embedding  vector(1536) NOT NULL
);
CREATE INDEX ON chunks USING hnsw (embedding vector_cosine_ops);
-- one transaction updates document + chunks: no cross-store drift
05

Join the discussion

Discussion (0)

Sign in to join the discussion.

No responses yet. Be the first to share what you think.

Transmission complete // awaiting log

KEEP THE
STREAK ALIVE.

Dossier 4 of 80 decoded in the AI Engineering track. One more won't hurt.

Back to track