zvec_db.rerankers.cross_encoder.base

Base class for cross-encoder reranking.

Classes

BaseCrossEncoderReranker(query[, topn, ...])	Abstract base class for cross-encoder reranking.
CrossEncoderPropertyMixin()	Mixin to auto-generate properties from private attributes.

class zvec_db.rerankers.cross_encoder.base.CrossEncoderPropertyMixin

Mixin to auto-generate properties from private attributes.

Subclasses should define _public_names tuple with attribute names (without underscore prefix) to expose as properties.

class zvec_db.rerankers.cross_encoder.base.BaseCrossEncoderReranker(query, topn=10, rerank_field=None, fusion_score_weight=1.0)

Abstract base class for cross-encoder reranking.

This class provides the common infrastructure for cross-encoder scoring. Subclasses must implement the _compute_scores_batch() method to define their scoring strategy.

Parameters:

• query (str) – Query for reranking. Required.

• topn (int, optional) – Number of top documents to return after reranking. Defaults to 10.

• rerank_field (Optional[str], optional) – Document field to use for reranking. If None, uses the entire document content. Defaults to None.

• fusion_score_weight (float, optional) –

  Weight for blending cross-encoder scores with fusion scores.

  Formula: final_score = cross_encoder_score × weight + fusion_score × (1 - weight)

  • weight = 1.0 → 100% cross-encoder, 0% fusion (pure cross-encoder, default)

  • weight = 0.8 → 80% cross-encoder, 20% fusion

  • weight = 0.5 → 50% cross-encoder, 50% fusion

  • weight = 0.0 → 0% cross-encoder, 100% fusion (pure fusion)

  Defaults to 1.0 (pure cross-encoder score).

Note

• Subclasses must implement _compute_scores_batch() or _compute_score()

• Cross-encoder reranking is more accurate but slower than score fusion

• For large document sets, consider using max_batch_size to limit API calls

__init__(query, topn=10, rerank_field=None, fusion_score_weight=1.0)

Parameters:

• query (str)

• topn (int)

• rerank_field (str | None)

• fusion_score_weight (float)

property query: str

Default query for reranking.

Type:

str

property fusion_score_weight: float

Weight for blending cross-encoder scores with fusion scores.

Type:

float

rerank(query_results, query=None)

Rerank documents using cross-encoder scoring.

Parameters:

• query_results (dict[str, list[Doc]]) – Results from one or more vector queries.

• query (Optional[str], optional) – Query for reranking. Overrides constructor value if provided.

Returns:

Reranked documents with cross-encoder scores.

Return type:

list[Doc]