What is the difference between semantic search and similarity search?

Similarity search is the underlying operation (finding similar vectors). Semantic search is the application (finding semantically related text). Semantic search uses similarity search on text embeddings.

Which similarity metric should I use?

Cosine similarity is most common for text embeddings - it ignores magnitude and focuses on direction. If your embeddings are normalised, cosine and dot product are equivalent. Check your embedding model's recommendations.

What is approximate nearest neighbor (ANN)?

ANN algorithms trade perfect accuracy for much faster search. Instead of comparing against every vector, they use clever indexing to find approximately the nearest neighbors. Essential for large-scale vector databases.

How many results should similarity search return?

Depends on use case. RAG typically retrieves 3-10 chunks. Recommendations might show 5-20 items. Consider: downstream processing capacity, relevance drop-off, and user experience. Test to find optimal k.

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Similarity Search

Finding items in a database that are most similar to a query, typically using vector distance calculations on embeddings.

In-Depth Explanation

Similarity search (also called nearest neighbor search) finds items in a database that are most similar to a query vector. It's the core operation behind semantic search, recommendations, and RAG retrieval.

How similarity search works:

Query is converted to a vector
Algorithm searches the vector space
Items closest to query vector are returned
Distance/similarity scores indicate relevance

Similarity metrics:

Cosine similarity: Angle between vectors (most common)
Dot product: Magnitude-weighted similarity
Euclidean distance: Straight-line distance in space
Manhattan distance: Sum of absolute differences

Search algorithms:

Exact (brute force): Compare against all vectors
Approximate (ANN): Trade precision for speed
HNSW: Hierarchical navigable small world graphs
IVF: Inverted file indexing

Performance considerations:

Index size and memory requirements
Query latency vs accuracy trade-offs
Update frequency (real-time vs batch)

Business Context

Similarity search powers product recommendations, content discovery, and the retrieval component of RAG systems.

How Clever Ops Uses This

We optimise similarity search for Australian businesses, balancing accuracy, speed, and cost for each use case.

Example Use Case

"Finding products visually similar to an item a customer is browsing, or finding documents semantically related to a query."

Frequently Asked Questions

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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.

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