similarity-search-patterns

You can implement similarity search by upserting vectors and querying nearest neighbors using the provided templates for Pinecone, Qdrant, pgvector, and Weaviate. The patterns include distance metrics, index types, and practical code examples.

The best way to build RAG retrieval is using vector search patterns to quickly find relevant document chunks. This approach uses scalable nearest neighbor queries with distance metrics like cosine similarity to match user queries.

Hybrid search combines vector similarity search with traditional keyword filtering. The provided templates include functionalities for upserting, searching, and executing hybrid queries across supported databases like pgvector and Weaviate.

HNSW and IVF+PQ index types are used to optimize nearest neighbor query performance based on scale and latency requirements. The Skill explains these index types alongside Flat indexing to help you choose the right approach.

Yes, you can use sentence-transformers and numpy to generate vector embeddings before upserting them into Qdrant. The dependencies support generating and manipulating vectors for your similarity search pipeline.

Supported distance metrics include cosine similarity and Euclidean distance. These metrics are essential for calculating vector similarities during nearest neighbor queries in semantic search and recommendation engines.