context-ranking

Rank retrieved context chunks by applying a multi-stage pipeline with first-stage scoring, cross-encoder reranking, and MMR diversity filtering to surface the best information for your query.

Reduce redundant context in retrieval pipelines by applying Maximal Marginal Relevance (MMR) diversity filtering, which selects chunks that balance query relevance with information diversity.

Cross-encoder reranking improves context selection by deeply analyzing query-chunk pairs after first-stage BM25 and cosine similarity scoring, yielding highly precise relevance scores for chatbot responses.

Yes, you can get per-chunk metadata after ranking, as the pipeline generates final scores complete with source paths and confidence levels to support reliable prompt construction.

No external dependencies are required to apply MMR and cross-encoder reranking, as the Skill operates independently without needing additional components or libraries to function.

Avoid a multi-stage context ranking pipeline when your application requires minimal latency or when processing extremely simple queries where basic single-stage retrieval already surfaces sufficient context.