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Jack Ye
b21fb2a4dc
The pages describing this code lived in lancedb/docs, a repository with ten stars that neither contributing guide mentioned. A user who hit a wrong page while reading the source had to find a separate, near-invisible repository to fix it. These 102 pages now sit beside the code, and a documentation fix is a pull request in the repository the reader already had open. `docs/web` is a complete Mintlify site: `cd docs/web && mint dev` serves it with no other checkout. Enterprise pages stay private and are merged in when the published site is assembled, so links into them do not resolve here — every open-source page does. `docs/web-tests` holds the tests the code examples are extracted from. They ran against a published wheel before; here they can run against this repository's own SDK, so an example that stops compiling fails the pull request that broke it rather than being noticed later. Both directories sit beside `docs/src` and are invisible to it: mkdocs reads `docs_dir: src`, so the SDK reference builds and deploys exactly as before.
49 lines
2.5 KiB
Plaintext
49 lines
2.5 KiB
Plaintext
---
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title: MRR Reranker
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sidebarTitle: "MRR Algorithm"
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description: Combine and rerank search results using Mean Reciprocal Rank (MRR) algorithm in LanceDB. Supports weighted scoring for hybrid and multivector search.
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---
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import { PyRerankingMrrUsage } from '/snippets/integrations.mdx';
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# MRR Reranker
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This reranker uses the Mean Reciprocal Rank (MRR) algorithm to combine and rerank search results from vector and full-text search. You can use this reranker by passing `MRRReranker()` to the `rerank()` method. The MRR algorithm calculates the average of reciprocal ranks across different search results, providing a balanced way to merge results from multiple ranking systems.
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> **Note:** Supported query types – Hybrid and Multivector search.
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<CodeGroup>
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyRerankingMrrUsage}
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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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| `weight_vector` | `float` | `0.5` | Weight for vector search results (0.0 to 1.0). |
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| `weight_fts` | `float` | `0.5` | Weight for FTS search results (0.0 to 1.0). |
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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", will return all scores from the vector and FTS search along with the relevance score. |
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**Note:** `weight_vector` + `weight_fts` must equal 1.0.
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For multivector reranking, input result sets need `_rowid` so LanceDB can identify the same row
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across the ranked lists. Add `.with_row_id(True)` to each vector search before passing the results
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to the reranker.
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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 | Results only have the `_relevance_score` column. |
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| `all` | ✅ Supported | Results have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`). |
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### Multivector Search {#multivector-search}
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|`return_score`| Status | Description |
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| --- | --- | --- |
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| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
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| `all` | ✅ Supported | Results have vector distances from all searches along with `_relevance_score`. |
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