Question

A retail company is building a semantic product search system using Amazon Bedrock. Their engineering team generates embeddings for 2 million product descriptions using Amazon Titan Text Embeddings and stores them in Amazon OpenSearch Serverless. During testing, they observe that search relevance is inconsistent: some semantically similar products score poorly while unrelated products with longer descriptions rank higher. The team configured the OpenSearch k-NN index to use the L2 (Euclidean) distance metric. What is the MOST likely cause and appropriate fix?

Accepted Answer

The embeddings are not normalized, so L2 distance is biased by vector magnitude; switch the k-NN index to use the cosine similarity metric or normalize vectors before indexing.

Answer

The Titan embedding model dimension is too small; upgrade to a higher-dimensional embedding model to improve relevance.

Answer

OpenSearch Serverless does not support k-NN search reliably; migrate the vector store to Amazon Aurora with pgvector.

Answer

The product descriptions were not chunked; split each description into smaller chunks before generating embeddings.