Implementing Search: From Basic SQL LIKE to Full-Text to Vector
There's a search solution for every scale. Here's when to use each.
Level 1: SQL LIKE / ILIKE
Good enough for small datasets and prefix searches:
-- Prefix search (can use index)
SELECT * FROM products WHERE name ILIKE 'coffee%';
-- Substring search (sequential scan — slow at scale)
SELECT * FROM products WHERE name ILIKE '%coffee%';
Works well under 10k rows. Above that, full-text search is better.
Level 2: PostgreSQL Full-Text Search
-- Add a tsvector column for fast searching
ALTER TABLE products ADD COLUMN search_vector tsvector;
-- Populate it
UPDATE products SET search_vector =
to_tsvector('english', coalesce(name,'') || ' ' || coalesce(description,''));
-- Index it
CREATE INDEX idx_products_search ON products USING gin(search_vector);
-- Search with ranking
SELECT id, name,
ts_rank(search_vector, query) AS rank
FROM products, to_tsquery('english', 'coffee & beans') query
WHERE search_vector @@ query
ORDER BY rank DESC
LIMIT 20;
// Prisma raw query for full-text search
const results = await prisma.$queryRaw`
SELECT id, name, ts_rank(search_vector, query) as rank
FROM products, plainto_tsquery('english', ${searchTerm}) query
WHERE search_vector @@ query
ORDER BY rank DESC
LIMIT 20
`;
Level 3: Vector/Semantic Search
For 'find similar' or natural language search, embeddings beat keyword matching:
import Anthropic from '@anthropic-ai/sdk';
async function embedText(text: string): Promise<number[]> {
const response = await anthropic.embeddings.create({
model: 'voyage-3',
input: text,
});
return response.data[0].embedding;
}
// Store in pgvector
await prisma.$executeRaw`
INSERT INTO product_embeddings (product_id, embedding)
VALUES (${productId}, ${embedding}::vector)
`;
// Search by similarity
const queryEmbedding = await embedText(userQuery);
const results = await prisma.$queryRaw`
SELECT p.id, p.name,
1 - (e.embedding <=> ${queryEmbedding}::vector) AS similarity
FROM products p
JOIN product_embeddings e ON e.product_id = p.id
ORDER BY similarity DESC
LIMIT 10
`;
Which to Choose
| Scale / Need | Solution |
|---|---|
| < 10k rows, simple | SQL LIKE |
| > 10k rows, keyword | PostgreSQL FTS |
| Semantic / fuzzy | Vector search |
| Complex relevance tuning | Elasticsearch |
Vector search is central to RAG pipelines and AI-powered features. For a deep dive on embeddings, RAG architecture, and pgvector setup, the AI SaaS Starter Kit includes a working search implementation.
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