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Reciprocal Rank Fusion Reranker

Reciprocal Rank Fusion (RRF) is an algorithm that evaluates the search scores by leveraging the positions/rank of the documents. The implementation follows this paper.

Note

Supported Query Types: Hybrid

import numpy
import lancedb
from lancedb.embeddings import get_registry
from lancedb.pydantic import LanceModel, Vector
from lancedb.rerankers import RRFReranker

embedder = get_registry().get("sentence-transformers").create()
db = lancedb.connect("~/.lancedb")

class Schema(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 reranker
tbl.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:

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)