Cohere's Rerank API and Embed v3 enable high-accuracy semantic retrieval at scale. Understanding cross-encoders, retrieval pipelines, and domain-specific embeddings is essential for building production RAG systems.
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An engineer calls Cohere's Rerank API with a query and 50 candidate documents. What does the API return?
The Cohere Rerank API returns a ranked list of results, each containing a relevance_score and the index of the original document in the input array. This lets you re-sort a candidate set retrieved by a fast first-stage retriever (e.g., BM25 or vector search) using a more accurate cross-encoder model.
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What architectural difference makes a cross-encoder (used by Cohere Rerank) more accurate but slower than a bi-encoder?
A cross-encoder concatenates the query and candidate document into a single input and scores them together in one forward pass, capturing fine-grained interactions. A bi-encoder encodes query and documents independently, enabling pre-computation but missing direct query-document interactions. Rerankers trade speed for accuracy.
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A developer uses co.embed(texts=chunks, model='embed-english-v3.0', input_type='search_document'). Why must input_type be set differently for query embeddings?
Cohere Embed v3 was trained with separate input_type modes (search_query vs search_document) to optimize the embedding space for asymmetric retrieval. Using search_document for both query and documents degrades retrieval quality because the model expects different representations for each role.
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Which Cohere model is optimized for retrieval-augmented generation (RAG) with long context and grounded responses?
Command R+ is Cohere's flagship model designed specifically for RAG workflows, supporting very long context windows and a documents parameter that grounds responses in retrieved content. It also supports tool use and citation generation, making it well-suited for enterprise search applications.
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An engineer retrieves 1000 documents with vector search then calls Rerank to get the top 10. What is this two-stage pattern called?
The retrieve-and-rerank pattern uses a fast first-stage retriever (ANN vector search or BM25) to get a broad candidate set, then applies an expensive but accurate cross-encoder reranker to select the final top-k. This balances recall from broad retrieval with precision from accurate reranking.