Table of Contents
Quick Answer
Embed your content with a modern embedding model, store in pgvector, and query with cosine similarity. For best results, combine with BM25 (keyword) in a hybrid score. Beats traditional keyword search on 80% of query types.
- Time to implement: 3-5 hours for a basic version
- Cost: ~$0.02 per 1M input tokens for embeddings
- Expected recall improvement: 20-60% over keyword-only
What You'll Need
- Supabase (self-hosted) with pgvector extension
- Embedding API (OpenAI-compatible)
- Node.js / Next.js app
- Content to search (articles, products, docs)
Steps
- Install pgvector. In Supabase SQL editor:
create extension if not exists vector;. - Add embedding column.
alter table articles add column embedding vector(1536);. Dimension matches your embedding model. - Create index. For <100K rows:
create index on articles using ivfflat (embedding vector_cosine_ops) with (lists = 100);. For larger: use HNSW. - Backfill embeddings. For each row, concat title + excerpt + body, call embedding API, store vector. Batch 100 rows per API call for speed.
- On insert/update trigger. Use Supabase Edge Function or app-level hook to re-embed when content changes.
- Query vector search. User types query → embed →
SELECT *, 1 - (embedding <=> $1) as similarity FROM articles ORDER BY embedding <=> $1 LIMIT 20. - Add hybrid BM25. Postgres has
tsvectorfor full-text. Combine:final_score = 0.5 * vector_score + 0.5 * bm25_score. Ask AI: "Generate a Postgres function that returns top-K hybrid-ranked results." - Optional: re-rank top 20 with cross-encoder. Cohere Rerank or a self-hosted bge-reranker slashes irrelevant results from top 3.
Common Mistakes
- Only indexing title: Embed full content for meaningful similarity.
- Wrong dimension: Mismatch between model and column dimension = error.
- No BM25 fallback: Pure vector misses exact-match queries like SKUs, names.
- No re-ranking: Top-20 vector results often have 3-5 off-topic hits. Re-rank fixes this.
- Not filtering by metadata: Always pre-filter by user/category/language, then vector search.
Top Tools
| Tool | Best For | Price |
|---|---|---|
| pgvector | Postgres vector store | Free |
| text-embedding-3-small | Cheap & good | $0.02/M tokens |
| bge-m3 | Self-hosted embed | Free |
| Cohere Rerank-compat | Re-ranking | $1/1K |
| tsvector (pg) | BM25 | Free |
Conclusion
Semantic search is a one-afternoon upgrade that dramatically improves product experience. Add pgvector, embed your content, layer hybrid scoring, and watch bounce rates drop. No new infrastructure needed.