Your question is Embeddings and Vector Search at Scale. Take a moment with it on the right.
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How would you implement embeddings and vector search in a large-scale application?
Discuss the end-to-end practical design, including text preprocessing, embedding model selection, batch generation, vector indexing, metadata filtering, updates, query latency, and relevance evaluation. Explain how you would handle document chunking, duplicate content, model versioning, index refreshes, and monitoring. Include a concise Python implementation that demonstrates embedding creation, indexing, similarity search, and basic retrieval evaluation.