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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.
44 lines
2.2 KiB
Plaintext
44 lines
2.2 KiB
Plaintext
---
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title: Linear Combination Reranker
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sidebarTitle: "Linear Combination"
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description: Learn about LanceDB's deprecated Linear Combination Reranker for combining semantic and full-text search scores.
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---
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import { PyRerankingLinearCombinationUsage } from '/snippets/integrations.mdx';
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# Linear Combination Reranker
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> **Note:** This reranker is deprecated. Use the `RRFReranker` if you need a score-based reranker.
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The Linear Combination Reranker combines the results of semantic and full-text search using a linear combination of the scores. The weights for the linear combination can be specified, and defaults to 0.7, i.e, 70% weight for semantic search and 30% weight for full-text search.
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> **Note:** Supported query type – Hybrid search only.
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<CodeGroup>
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyRerankingLinearCombinationUsage}
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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` | `float` | `0.7` | The weight to use for the semantic search score. The weight for the full-text search score is `1 - weight`. |
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| `fill` | `float` | `1.0` | Score used when a result is missing from one side of the hybrid query. The default strongly penalizes missing vector or FTS matches. |
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| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", returns only `_relevance_score`. If "all", returns all scores from the vector and FTS search along with the relevance score. |
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`weight` must be between `0` and `1`. If either the vector or FTS side returns no rows, the reranker
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returns the non-empty side with `_relevance_score` attached rather than failing.
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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(`_distance`) |
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