Is semantic search better than keyword search?

For natural language queries, usually yes. Semantic search understands meaning while keyword search matches exact terms. However, keyword search is faster and better for exact matches. Many systems use hybrid search combining both.

What is hybrid search?

Combining semantic and keyword (BM25) search. Keywords catch exact matches; semantics catch conceptual relevance. Results are typically merged using reciprocal rank fusion or similar algorithms.

How accurate is semantic search?

Depends on: embedding model quality, domain relevance, chunking strategy, and query clarity. Well-implemented semantic search typically outperforms keyword search for natural language queries.

What embedding model should I use for semantic search?

Popular choices: OpenAI text-embedding-3, Cohere embed, open-source models like BGE or E5. Consider: accuracy, speed, cost, and domain relevance. Test with your actual queries and documents.

Book Free Assessment Calculator

Semantic Search

Search that understands meaning and intent rather than just matching keywords. Uses embeddings to find conceptually similar content.

In-Depth Explanation

Semantic search finds relevant content by understanding meaning and intent, rather than simply matching keywords. It uses embeddings to represent queries and documents as vectors, finding matches based on conceptual similarity.

How semantic search works:

Convert documents to embeddings (once)
Convert query to embedding (each search)
Find documents with similar embeddings
Rank by similarity score
Return top results

Advantages over keyword search:

Understands synonyms and related terms
Handles natural language queries
Finds conceptually relevant results
Works across languages (with multilingual models)
Doesn't require exact matches

Semantic search components:

Embedding model: Converts text to vectors
Vector database: Stores and searches embeddings
Similarity metric: Cosine, dot product, Euclidean
Ranking: Orders results by relevance

Business Context

Semantic search finds answers even when customers use different words than your documentation. It's far more effective than keyword search.

How Clever Ops Uses This

We implement semantic search for Australian businesses, enabling users to find information using natural language rather than guessing the right keywords.

Example Use Case

"Finding "refund policy" when user searches "how do I get my money back" - understanding the intent despite different words."

Frequently Asked Questions

Learn More

Understanding Vector Databases for Business

Discover how vector databases enable semantic search, power RAG systems, and revolutionize how AI accesses information. Complete guide to embeddings, similarity search, and choosing the right vector database.

Read article

Self-Service Portal Sentiment Analysis