Cross-Encoder Reranking
Reranking is a two-stage retrieval process that scores an initial batch of retrieved documents using a cross-encoder model to surface the most relevant passage to the top.
Cross-encoder reranking is a secondary relevance scoring pass that jointly evaluates the query and retrieved candidate chunks to reorder them with high semantic precision before LLM synthesis.
How Reranking works in practice
Initial vector retrieval is fast (bi-encoder), computing similarities independently. However, bi-encoders can miss subtle semantic relationships.
A reranker takes the top-20 retrieved candidates and scores each candidate against the query simultaneously, pushing the true answer to position #1.
How SiteMind implements Reranking
SiteMind applies Reciprocal Rank Fusion (RRF) and relevance scoring across dense vector, full-text tsvector, and trigram candidates to ensure optimal chunk ranking before generation.
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Related Technical Concepts
Hybrid Search (Dense + Sparse Retrieval)
Hybrid Search merges dense vector search with sparse keyword matching to deliver superior accuracy across both conceptual and exact-term queries.
Reciprocal Rank Fusion (RRF)
Reciprocal Rank Fusion (RRF) is an algorithmic scoring method that merges and ranks search results from multiple independent retrieval pipelines.
Vector Database & pgvector
A vector database is specialized storage optimized to index high-dimensional embeddings and execute sub-second approximate nearest neighbor (ANN) searches.
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