context-ranking

# Context Ranking Context ranking is the process of ordering retrieved text chunks so the most relevant, diverse, and useful information rises to the top. In any retrieval pipeline, the initial search returns a broad set of candidates -- many of which are only tangentially related to the query. Ranking transforms this unordered candidate set into a prioritized list, enabling downstream steps (context assembly, prompt construction) to select the best material and discard the rest. Effective ranking is the difference between a grounded, precise answer and a vague, off-topic one. ## Workflow 1. **Collect Candidate Chunks**: Gather the initial set of retrieved chunks from the search layer. This is typically the top-k results (k = 15-30) from a vector search, keyword search, or hybrid search. Each chunk arrives with a preliminary score (e.g., cosine similarity or BM25 score) and source metadata. 2. **Apply First-Stage Scoring**: Score each candidate with a fast, lightweight algorithm. BM25 is the standard choice for keyword relevance; cosine similarity between the query embedding and chunk embedding is the standard for semantic relevance. In hybrid pipelines, compute both scores and combine them using Reciprocal Rank Fusion (RRF) or a weighted linear combination. This stage is meant to be fast and run over all candidates. 3. **Rerank with a Cross-Encoder**: Pass the top candidates (typically 15-25) from the first stage through a cross-encoder reranker. Unlike bi-encoder embed...