How is semantic search different from keyword search?

Keyword search matches exact words, so it misses synonyms and paraphrases. Semantic search compares the meaning of the query and documents through embeddings, so a search for "cheap flights" can surface a passage about "low-cost airfare" even with no shared words.

Do I still need keyword search at all?

Often yes. Hybrid search combines semantic similarity with a keyword signal like BM25 so you get the recall of meaning-based matching plus the precision of exact terms, names, and codes that embeddings sometimes blur. Many production systems blend both scores.

What is re-ranking and is it worth it?

A re-ranker is a model that scores each retrieved candidate against the query more carefully than the initial vector search. Running it on the top 20 to 50 results usually improves the final ordering noticeably, at the cost of a little extra latency, so it is worth it for quality-sensitive apps.

How many results should I retrieve before re-ranking?

Retrieve more than you plan to show, commonly the top 20 to 50, then re-rank and display the best handful. Casting a wider net first gives the re-ranker good candidates to choose from without overwhelming it.

What infrastructure do I need to run this in production?

An embedding model, a vector store with an approximate nearest-neighbor index such as pgvector, Pinecone, or Qdrant, and optionally a re-ranker. Add caching for popular queries and a batch job to re-embed when documents change or you switch models.

How to Build a Semantic Search Engine with AI

What semantic search is

Semantic search finds documents by meaning rather than exact words. Instead of matching the literal tokens in a query, it embeds both the query and your documents into vectors and returns the documents whose vectors point in the most similar direction. A search for “cheap flights” then surfaces a passage about “low-cost airfare” even though they share no keywords — something traditional keyword search cannot do.

How the pipeline works

There are two phases. Indexing: each document is embedded into a vector and stored, with its text and metadata, in a vector database that builds an approximate nearest-neighbor index. Querying: the user’s natural-language query is embedded with the same model, the store returns the nearest vectors by cosine similarity, and an optional re-ranker reorders the top candidates for sharper results before you display them.

The demo below holds a small sample corpus. Type a query and watch it rank the documents by semantic relevance — notice how it favors meaning over exact word overlap, the core behavior of a real engine.

Tips for production semantic search

Consider hybrid search — blend the semantic score with a keyword signal like BM25 so exact names, codes, and rare terms are not lost. Retrieve more candidates than you show (top 20-50) and re-rank them with a cross-encoder for the best final ordering. Always store source metadata so you can filter by date, type, or permissions. Cache embeddings for popular queries, and re-embed your corpus whenever documents change or you switch embedding models, since vectors across models are not comparable.