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."