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QuickBuild

Cohere/Jina-style rerank: POST {query, documents: string[] | {text}[], top_n, return_documents} and get {results: [{index, relevance_score, document?}]} sorted by relevance. Models (Cloudflare Workers AI): bge-reranker-base (cross-encoder, English/Chinese, scores 0..1); bge-m3 (multilingual similarity scoring) (default bge-reranker-base; other names map to it). Up to 100 documents, each scored on its first 4000 chars; one fixed price per call. Use after vector search to pick the best passages for an LLM. Errors are never charged. All params optional: with none it ranks a small sample.