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Implement semantic search reranking in LanceDB using Cross Encoder models. Features configurable model selection, device optimization, and comprehensive scoring options for all search types.
CrossEncoderReranker() to the rerank() method.
Note: Supported query types – Hybrid, Vector, and FTS.
| Argument | Type | Default | Description |
|---|---|---|---|
model_name | str | "cross-encoder/ms-marco-TinyBERT-L-6" | The name of the reranker model to use. |
column | str | "text" | The name of the column to use as input to the cross encoder model. |
device | str | None | The device to use for the cross encoder model. If None, will use “cuda” if available, otherwise “cpu”. |
trust_remote_code | bool | True | Passed to Sentence Transformers model loading. Set this to False when you only want to load models that do not require custom repository code. |
return_score | str | "relevance" | Options are “relevance” or “all”. The type of score to return. If “relevance”, returns only _relevance_score. If “all” is supported, returns relevance score along with the vector and/or FTS scores depending on query type. |
sentence-transformers, so install the local model
runtime dependencies you need, such as PyTorch and any device-specific acceleration packages.
return_score | Status | Description |
|---|---|---|
relevance | ✅ Supported | Results only have the _relevance_score column. |
all | ❌ Not Supported | Results have vector(_distance) and FTS(score) along with Hybrid Search score(_relevance_score). |
return_score | Status | Description |
|---|---|---|
relevance | ✅ Supported | Results only have the _relevance_score column. |
all | ✅ Supported | Results have vector(_distance) along with Hybrid Search score(_relevance_score). |
return_score | Status | Description |
|---|---|---|
relevance | ✅ Supported | Results only have the _relevance_score column. |
all | ✅ Supported | Results have FTS(score) along with Hybrid Search score(_relevance_score). |
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