Your question is Key Considerations for Embeddings. 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).
What are the key considerations when selecting embeddings for a vector search application? Explain how you would compare candidate embedding models, prepare text, choose similarity and indexing settings, and validate retrieval quality. Include a practical Python evaluation approach covering relevance, latency, dimensionality, storage, domain fit, multilingual behavior, and robustness to out-of-vocabulary terms.