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Jack Ye
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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.
57 lines
3.2 KiB
Plaintext
57 lines
3.2 KiB
Plaintext
---
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title: Reciprocal Rank Fusion Reranker
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sidebarTitle: "RRF Algorithm"
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description: Learn about LanceDB's default Reciprocal Rank Fusion (RRF) reranker for hybrid search. Implements the Cormack et al. algorithm for optimal search result ranking.
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---
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import { PyRerankingRrfUsage } from '/snippets/integrations.mdx';
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# Reciprocal Rank Fusion Reranker
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**Reciprocal Rank Fusion (RRF)** is a model-free way to merge several ranked result lists into a
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single ordering. Rather than comparing raw similarity scores (which aren't directly comparable
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across, say, a vector search and a full-text search), RRF looks only at each document's *rank
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position* in each list. It scores every document with the formula `1 / (rank + K)`, sums those
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contributions across the lists, and re-sorts by the total. Documents that rank highly in more than
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one search rise to the top. Because there's no model to load or call, it's fast and cheap, which is
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why it's the default reranker for LanceDB hybrid search. The implementation follows the
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[Cormack et al. paper](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf).
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> **Supported query types:** hybrid search and [multi-vector reranking](/reranking#multi-vector-reranking).
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> Because RRF fuses two or more ranked lists, it can't rerank a single vector or full-text result set
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> on its own. Calling `rerank_vector` or `rerank_fts` on an `RRFReranker` raises `NotImplementedError`.
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<CodeGroup>
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyRerankingRrfUsage}
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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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| `K` | `int` | `60` | A constant used in the RRF formula (default is 60). Experiments indicate that k = 60 was near-optimal, but that the choice is not critical. |
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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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`K` must be greater than `0`. In TypeScript, construct the built-in reranker with
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`await RRFReranker.create(k)` before passing it to `.rerank(...)`.
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## Multi-vector reranking {#multi-vector-reranking}
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`RRFReranker` can also fuse the results of several vector searches with `rerank_multivector`, applying
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the same rank-fusion algorithm across more than two lists. Every input result set must include the
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`_rowid` column, so add `.with_row_id(True)` to each `table.search(...)` call before reranking,
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otherwise the call raises a `ValueError`. See [multi-vector reranking](/reranking#multi-vector-reranking)
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for a full example.
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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 | Returned rows only have the `_relevance_score` column. |
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| `all` | ✅ Supported | Returned rows have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`). |
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