> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lancedb.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Reranking Search Results

> Use a reranker to improve search relevance.

Reranking is the process of re-ordering search results to improve relevance, often using a
different model than the one used for the initial search. LanceDB has built-in support for reranking
with models from Cohere, Sentence-Transformers, and more.

### Quickstart

To use a reranker, you perform a search and then pass the results to the `rerank()` method.

<CodeGroup>
  ```python Python icon="python" theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
  import lancedb
  from lancedb.rerank import CohereReranker

  db = lancedb.connect("/tmp/lancedb")
  table = db.open_table("my_table")

  query = "what is the capital of france"

  # Search with reranking
  reranker = CohereReranker()
  reranked_results = table.search(query).limit(10).rerank(reranker=reranker).to_df()
  ```
</CodeGroup>

### Supported Rerankers

LanceDB supports several rerankers out of the box. Here are a few examples:

| Reranker               | Default Model                          |
| ---------------------- | -------------------------------------- |
| `CohereReranker`       | `rerank-english-v2.0`                  |
| `CrossEncoderReranker` | `cross-encoder/ms-marco-MiniLM-L-6-v2` |
| `ColbertReranker`      | `colbert-ir/colbertv2.0`               |

You can find more details about these and other rerankers in the [integrations](/integrations/reranking) section.

<Note>
  **SDK coverage differs across languages**

  The provider-specific rerankers in the table above
  (`CohereReranker`, `CrossEncoderReranker`, `ColbertReranker`, and others under `lancedb.rerankers`)
  are currently **Python-only**. The TypeScript and Rust SDKs currently expose the generic `Reranker`
  interface (`rerankHybrid` / `rerank_hybrid`) and the built-in `RRFReranker`. To use a
  model-based reranker from TypeScript or Rust, you must implement the `Reranker` interface yourself.
</Note>

### Multi-vector reranking

Most rerankers support reranking based on multiple vectors. To rerank based on multiple vectors, you can pass a list of vectors to the `rerank` method. Here's an example of how to rerank based on multiple vector columns using the `CrossEncoderReranker`:

<CodeGroup>
  ```python Python icon="python" theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
  from lancedb.rerankers import CrossEncoderReranker

  reranker = CrossEncoderReranker()

  query = "hello"

  # `deduplicate=True` requires `_rowid` on every input result set,
  # so call `.with_row_id(True)` on each search before passing it in.
  res1 = table.search(query, vector_column_name="vector").limit(3).with_row_id(True)
  res2 = table.search(query, vector_column_name="text_vector").limit(3).with_row_id(True)
  res3 = table.search(query, vector_column_name="meta_vector").limit(3).with_row_id(True)

  reranked = reranker.rerank_multivector([res1, res2, res3], deduplicate=True)
  ```
</CodeGroup>

* Passing `deduplicate=True` to `rerank_multivector(...)` raises a `ValueError` if any of the
  input result sets is missing the `_rowid` column. Therefore, it's recommended to add `.with_row_id(True)` to every
  `table.search(...)` call before reranking, or omit `deduplicate=True` if you don't need it.
* `RRFReranker.rerank_multivector(...)` always requires `_rowid` on its inputs, regardless of
  the `deduplicate` flag.

## Creating Custom Rerankers

LanceDB also allows you to create custom rerankers by extending the base `Reranker` class. The custom reranker
should implement the `rerank` method that takes a list of search results and returns a reranked list of
search results. This is covered in more detail in the [creating custom rerankers](/reranking/custom-reranker/) section.