Reciprocal Rank Fusion Reranker
This is the default reranker used by LanceDB hybrid search. Reciprocal Rank Fusion (RRF) is an algorithm that evaluates the search scores by leveraging the positions/rank of the documents. The implementation follows thispaper.
Note
Supported Query Types: Hybrid
importnumpyimportlancedbfromlancedb.embeddingsimportget_registryfromlancedb.pydanticimportLanceModel,Vectorfromlancedb.rerankersimportRRFRerankerembedder=get_registry().get("sentence-transformers").create()db=lancedb.connect("~/.lancedb")classSchema(LanceModel):text:str=embedder.SourceField()vector:Vector(embedder.ndims())=embedder.VectorField()data=[{"text":"hello world"},{"text":"goodbye world"}]tbl=db.create_table("test",schema=Schema,mode="overwrite")tbl.add(data)reranker=RRFReranker()# Run hybrid search with a rerankertbl.create_fts_index("text",replace=True)result=tbl.search("hello",query_type="hybrid").rerank(reranker=reranker).to_list()Accepted Arguments
| Argument | Type | Default | Description |
|---|---|---|---|
K | int | 60 | A constant used in the RRF formula (default is 60). Experiments indicate that k = 60 was near-optimal, but that the choice is not critical. |
return_score | str | "relevance" | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the_relevance_score. If "all", will return all scores from the vector and FTS search along with the relevance score. |
Supported Scores for each query type
You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:
Hybrid Search
return_score | Status | Description |
|---|---|---|
relevance | ✅ Supported | Returned rows only have the_relevance_score column. |
all | ✅ Supported | Returned rows have vector(_distance) and FTS(score) along with Hybrid Search score(_relevance_score). |