Your question is Embeddings for Search Recall and Precision. Take a moment with it on the right.
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How would you implement embeddings for a search application to optimize for recall and precision?
Explain a practical pipeline covering text preprocessing, embedding-model selection, vector indexing, similarity search, hybrid lexical retrieval, and reranking. Describe how you would tune retrieval parameters and evaluate recall and precision using labeled queries, including how you would handle semantic similarity, exact terms, synonyms, long documents, and out-of-vocabulary content. Include representative Python or pseudocode for indexing and querying.