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60 lines
3.1 KiB
Plaintext
60 lines
3.1 KiB
Plaintext
---
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title: Voyage AI Reranker
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sidebarTitle: "Voyage AI"
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description: Integrate Voyage AI's cutting-edge reranking models with LanceDB. Features model selection, API key management, and comprehensive scoring options for all search types.
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---
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import { PyRerankingVoyageaiUsage } from '/snippets/provider_examples.mdx';
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# Voyage AI Reranker {#voyageai-reranker}
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Voyage AI provides cutting-edge embedding and rerankers.
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This reranker uses the [Voyage AI](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.
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> **Note:** Supported query types: Hybrid, Vector, and FTS.
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Replace `your-api-key` with your provider API key. In application code, load
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credentials from environment variables or a secrets manager.
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<CodeGroup>
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyRerankingVoyageaiUsage}
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</CodeBlock>
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</CodeGroup>
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## Accepted Arguments {#accepted-arguments}
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| Argument | Type | Default | Description |
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| --- | --- | --- | --- |
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| `model_name` | `str` | `None` | The name of the reranker model to use. Available models are: rerank-2, rerank-2-lite |
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| `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. |
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| `top_n` | `str` | `None` | The number of results to return. If None, will return all results. |
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| `api_key` | `str` | `None` | The API key for the Voyage AI API. If not provided, the `VOYAGE_API_KEY` environment variable is used. |
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| `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 |
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| `truncation` | `bool` | `None` | Whether to truncate the input to satisfy the "context length limit" on the query and the documents. |
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## Supported Scores for each query type {#supported-scores-for-each-query-type}
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You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:
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### Hybrid Search {#hybrid-search}
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|`return_score`| Status | Description |
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| --- | --- | --- |
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| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
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| `all` | ❌ Not Supported | Returns have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`) |
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### Vector Search {#vector-search}
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|`return_score`| Status | Description |
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| --- | --- | --- |
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| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
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| `all` | ✅ Supported | Returns have vector(`_distance`) along with Hybrid Search score(`_relevance_score`) |
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### FTS Search {#fts-search}
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|`return_score`| Status | Description |
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| --- | --- | --- |
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| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
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| `all` | ✅ Supported | Returns have FTS(`score`) along with Hybrid Search score(`_relevance_score`) |
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