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Linear Combination Reranker

Note

This is depricated. It is recommended to use the RRFReranker instead, if you want to use a score based reranker.

It combines the results of semantic and full-text search using a linear combination of the scores. The weights for the linear combination can be specified. It defaults to 0.7, i.e, 70% weight for semantic search and 30% weight for full-text search.

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 LinearCombinationReranker

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 = LinearCombinationReranker()

# 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
weight float 0.7 The weight to use for the semantic search score. The weight for the full-text search score is 1 - weights.
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 Returns only have the _relevance_score column
all ✅ Supported Returns have vector(_distance) and FTS(score) along with Hybrid Search score(_distance)