I jumped straight into the implementation and got the basic wiring done fine: embed the corpus, embed the query, run pairwise similarity, return the top match.
Clarify requirements (scale, latency, accuracy) and then propose a two-stage system: offline indexing of the corpus with the feature extractor, and online querying using approximate nearest neighbor search. Discuss trade-offs between exact and approximate methods, and how to handle updates to the corpus.
Pro tip: Emphasize the importance of normalizing embeddings and using cosine similarity for high-dimensional vectors, as this often improves retrieval performance. Also, mention that you would evaluate the system with recall@k and latency metrics to ensure it meets Apple's quality standards.
Ask about corpus size, query throughput, latency requirements, accuracy targets, and whether the corpus is static or dynamic. This informs the choice of indexing and search algorithms.
Use the feature extractor to compute embeddings for all corpus images. Store embeddings in an index structure (e.g., FAISS, Annoy, HNSW) optimized for the similarity function. Consider dimensionality reduction if needed.
For a query image, extract its embedding using the same feature extractor, then perform approximate nearest neighbor search in the index to retrieve the most similar corpus image. Return the image ID and similarity score.
Discuss trade-offs between exact and approximate search, memory vs. speed, and how to handle index updates (e.g., incremental indexing or periodic rebuilds). Mention distributed search for very large corpora.
Propose evaluation metrics (recall@k, latency, throughput) and monitoring for production. Discuss how to handle concept drift and re-indexing when the feature extractor is updated.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.