Reranker
Take a list of candidate documents (typically fetched by a prior vector search) and a search query, ask the configured reranker model to score each document's relevance to the query, and emit only the documents above the configured score threshold — in the order returned by the reranker provider.
This is the standard "rerank" step in a retrieval pipeline: vector search returns a broad recall set, and the reranker tightens precision before passing to the LLM.
Pre-requisite: Install an AI provider application that exposes a reranker
model from Profile > {Organization} > Applications (e.g. Cohere, Mistral, Voyage).
Parameters
Configured AI application supporting reranking.
Reranker model identifier from the selected provider. Defaults to the provider's recommended reranker model when left empty.
Field inside each candidate's payload holding the rerankable text (the
snippet the model is shown to compute relevance). Defaults to content
when left empty.
Minimum relevance score (0–1) a document must reach to be emitted. Documents below the threshold are dropped.
Input
Candidate documents as an embedding-search response object (Json or a
File containing one) of the shape
{"data": [{"identifier": ..., "score": ..., "payload": {...}}, ...]} —
the format emitted by the vector/embeddings search functions. The
rerankable text is looked up under the configured document key inside
each element's payload. If no candidate exposes the document key, the
step fails rather than emitting an empty result.
The search query (plain text) used to score relevance.
Output
The same response object with data filtered to the documents that passed
the score threshold, in the order returned by the reranker provider. Each
kept element's score is replaced by the reranker's relevance score;
identifier and payload are passed through unchanged.