Embedding and reranking models are typically used together in retrieval systems. The embedding model performs initial recall to retrieve a substantial set of candidates, while the reranking model sorts these candidates based on newly computed relevance scores to present the most accurate results for the user query. This two-stage retrieval process leverages the complementary strengths of both models: the efficiency and scalability of the embedding model for broad candidate retrieval, and the precision and fine-grained scoring capability of the reranking model for final ranking. This approach achieves superior retrieval performance compared to using either model alone. Below are basic usage examples. (View Highlight)