Your question is Embedding Models and Vector Search. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
Explain the tradeoffs between different embedding models and how you would optimize vector search performance at scale.
Discuss model quality, dimensionality, domain adaptation, index selection, compression, filtering, update strategy, and latency measurement. Include a practical Python design that builds an index, retrieves nearest neighbors, and evaluates recall and response time.