Question

A developer builds a Bedrock Knowledge Base using Amazon Titan Text Embeddings V2 configured to output 1024-dimensional vectors, stored in an Amazon OpenSearch Serverless vector index. After several months, the team decides to switch the embedding model to Cohere Embed English, which produces 1024-dimensional vectors but uses a different embedding space and similarity distribution. What must the team do to ensure retrieval quality remains correct after the switch?

Accepted Answer

Re-ingest and re-embed the entire source corpus with the new model into a new (or recreated) vector index, since query and document embeddings must come from the same model to be comparable.

Answer

Simply update the embedding model in the Knowledge Base configuration; because both models output 1024 dimensions, the existing index and vectors remain fully compatible.

Answer

Keep the existing document vectors but only re-embed new queries with Cohere, relying on OpenSearch to normalize the vector spaces automatically.

Answer

Add a second vector field to the same index for Cohere embeddings and let Bedrock blend results from both embedding models during retrieval.