Similarity Search Patterns FAQs

Question 1

Does this skill include hybrid search patterns?

Accepted Answer

Yes, it includes patterns for combining semantic (dense) search with keyword (sparse) search to improve retrieval relevance and accuracy.

Question 2

How do I choose between Cosine and Euclidean distance?

Accepted Answer

Cosine similarity is generally best for normalized embeddings where orientation matters more than magnitude, while Euclidean (L2) is often preferred for raw embeddings where distance in space is the primary factor.

Question 3

When should I use HNSW vs. Flat indexing?

Accepted Answer

Use Flat indexing for small datasets where 100% recall is required. Use HNSW (Hierarchical Navigable Small World) for medium to large datasets where you need O(log n) search speed with high recall.

Question 4

What is the primary use of similarity search in AI?

Accepted Answer

It is primarily used for Retrieval-Augmented Generation (RAG), semantic search, and recommendation engines where you need to find pieces of information that are contextually related to a query vector.

Question 5

Which vector databases does this skill support?

Accepted Answer

The skill provides specific implementation patterns and code templates for Pinecone (managed), Qdrant (high-performance), and pgvector (PostgreSQL-based) vector stores.

Similarity Search Patterns

Key Features

Use Cases

Similarity Search Patterns

Key Features

Use Cases