> ## 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.

# VoyageAI Reranker

> Integrate VoyageAI's cutting-edge reranking models with LanceDB. Features model selection, API key management, and comprehensive scoring options for all search types.

export const PyRerankingVoyageaiUsage = "import os\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import VoyageAIReranker\n\nembedder = get_registry().get(\"sentence-transformers\").create()\ndb = lancedb.connect(\"~/.lancedb\")\n\nclass Schema(LanceModel):\n    text: str = embedder.SourceField()\n    vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n    {\"text\": \"hello world\"},\n    {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema, mode=\"overwrite\")\ntbl.add(data)\nreranker = VoyageAIReranker(model_name=\"rerank-2\")\n\n# Run vector search with a reranker\nresult = tbl.search(\"hello\").rerank(reranker=reranker).to_list()\n\n# Run FTS search with a reranker\nresult = tbl.search(\"hello\", query_type=\"fts\").rerank(reranker=reranker).to_list()\n\n# Run hybrid search with a reranker\ntbl.create_fts_index(\"text\", replace=True)\nresult = (\n    tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\n";

# VoyageAI Reranker

Voyage AI provides cutting-edge embedding and rerankers.

This reranker uses the [VoyageAI](https://docs.voyageai.com/docs/) API to rerank the search results. You can use this reranker by passing `VoyageAIReranker()` to the `rerank()` method. Note that you'll either need to set the `VOYAGE_API_KEY` environment variable or pass the `api_key` argument to use this reranker.

> **Note:** Supported query types – Hybrid, Vector, and FTS.

<CodeGroup>
  <CodeBlock filename="Python" language="Python" icon="python">
    {PyRerankingVoyageaiUsage}
  </CodeBlock>
</CodeGroup>

## Accepted Arguments

| Argument       | Type   | Default       | Description                                                                                                                                                                                                                                  |
| -------------- | ------ | ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `model_name`   | `str`  | `None`        | The name of the reranker model to use. Available models are: rerank-2, rerank-2-lite                                                                                                                                                         |
| `column`       | `str`  | `"text"`      | The name of the column to use as input to the cross encoder model.                                                                                                                                                                           |
| `top_n`        | `str`  | `None`        | The number of results to return. If None, will return all results.                                                                                                                                                                           |
| `api_key`      | `str`  | `None`        | The API key for the Voyage AI API. If not provided, the `VOYAGE_API_KEY` environment variable is used.                                                                                                                                       |
| `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" is supported, will return relevance score along with the vector and/or fts scores depending on query type |
| `truncation`   | `bool` | `None`        | Whether to truncate the input to satisfy the "context length limit" on the query and the documents.                                                                                                                                          |

## 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:

### Hybrid Search

| `return_score` | Status          | Description                                                                                          |
| -------------- | --------------- | ---------------------------------------------------------------------------------------------------- |
| `relevance`    | ✅ Supported     | Returns only have the `_relevance_score` column                                                      |
| `all`          | ❌ Not Supported | Returns have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`) |

### Vector Search

| `return_score` | Status      | Description                                                                         |
| -------------- | ----------- | ----------------------------------------------------------------------------------- |
| `relevance`    | ✅ Supported | Returns only have the `_relevance_score` column                                     |
| `all`          | ✅ Supported | Returns have vector(`_distance`) along with Hybrid Search score(`_relevance_score`) |

### FTS Search

| `return_score` | Status      | Description                                                                  |
| -------------- | ----------- | ---------------------------------------------------------------------------- |
| `relevance`    | ✅ Supported | Returns only have the `_relevance_score` column                              |
| `all`          | ✅ Supported | Returns have FTS(`score`) along with Hybrid Search score(`_relevance_score`) |