mirror of
https://github.com/lancedb/lancedb.git
synced 2026-10-12 23:02:28 +00:00
Jack YeandBrendan Clement
47b7babc08
Move the public website pages into `docs/web` beside the SDK, with
shared navigation and executable example sources in `docs/web-tests`.
The combined website is assembled by
[lancedb/docs#355](https://github.com/lancedb/docs/pull/355), with six
Enterprise replacements from
[lancedb/sophon#7681](https://github.com/lancedb/sophon/pull/7681).
This includes Function guidance, runnable Quickstarts, regenerated
snippets, production contact/demo fixes, the legacy SQL route, and the
LeRobot redirect. LeRobot remains attributed to Geneva; all 34 Geneva
pages remain in the docs repository until Function officially launches.
Materialized-view guidance follows the full rebuild refresh introduced
by #4373: local refresh writes the complete result, has no `full`
argument, supports `source_version`, and no longer requires stable row
IDs. Deployment SQL refresh options and stable-ID setup are qualified by
version/configuration. Function input guidance distinguishes ordinary
stored columns from computed inputs supported by newer deployments.
**Validation:** independently accepted local checks include 95 Python
tests (real Enterprise excluded), eight TypeScript tests, Rust
Quickstart, snippet parity, typos, deterministic assembly, Mint
validation and links/anchors/redirects. The combined baseline retains
all 181 production routes and 17 legacy redirects. Examples ran locally
against source-built Python SDK 0.40.0b13; existing GitHub workflows do
not run `docs/web-tests`. New main at `069b2f7d` is 0.41.0b0 and adds
computed Function inputs; that SDK was not exercised by the accepted
tests. Latest prose corrections receive focused site validation.
**Merge/rollout:** merge this and #7681 before #355's combined CI, which
reads producer `main`. This is migration infrastructure: section
overlays, source-owned skills and automatic versioned paired releases
remain follow-ups. The current manual publisher is transitional. Hosted
validation, a supported SDK/Enterprise release pair and a real
Enterprise walkthrough remain pending.
Production Mintlify watches `lancedb/docs:deploy-freeze`, directory
`docs`; preserve that source through review. The separate SDK GitHub
Pages reference workflow rebuilds on main merges.
**Updated-head CI (9a50f8db):**
[Typos](https://github.com/lancedb/lancedb/actions/runs/37654185443/job/112904990558),
[PR
title](https://github.com/lancedb/lancedb/actions/runs/37654179507/job/112904974595)
and
[Aikido](https://app.aikido.dev/featurebranch/scan/215386145?groupId=74229)
pass. Critical findings in the example lockfiles were patched: all Sharp
copies use 0.35.5, protobufjs 7.6.6, tar 7.5.22, quinn-proto 0.11.19,
zerovec 0.11.8 and zerovec-derive 0.11.6. The SDK's product dependencies
are unchanged. The revised lockfiles pass eight TypeScript examples,
HuggingFace decode/resize/crop/rescale/normalize plus AVIF roundtrip,
and Rust Quickstart. Aikido passing does not mean every advisory is
cleared; npm still reports 29 high and five moderate advisories in the
example dependency graph. No scanner bypass or suppression was added.
The Function prose correction passes combined/standalone Mint,
deterministic assembly, routes and assets. The revised 325-file site
checksum is
`175bec06864abb400dd292e667ba93448c0479b8359fd7bf6146afabf9040647`. This
PR remains open for independent review; further merges are on hold.
---------
Co-authored-by: Brendan Clement <brendan.clement@lancedb.com>
52 lines
2.6 KiB
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52 lines
2.6 KiB
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---
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title: OpenAI Reranker (Experimental)
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sidebarTitle: "OpenAI"
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description: Explore experimental search reranking using OpenAI's GPT models in LanceDB. Features configurable model selection, API key management, and comprehensive scoring options for all search types.
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---
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import { PyRerankingOpenaiUsage } from '/snippets/integrations.mdx';
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# OpenAI Reranker (Experimental)
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This reranker uses OpenAI chat model to rerank the search results. You can use this reranker by passing `OpenAI()` to the `rerank()` method.
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> **Note:** Supported query types – Hybrid, Vector, and FTS.
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> **Warning:** This reranker is experimental. OpenAI does not have a dedicated reranking model, so it uses a chat model under the hood.
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<CodeGroup>
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<CodeBlock filename="Python" language="Python" icon="python">
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{PyRerankingOpenaiUsage}
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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` | `"gpt-4-turbo-preview"` | The name of the reranker model to use.|
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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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| `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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| `api_key` | `str` | `None` | The API key to use. If None, will use the OPENAI_API_KEY environment variable.
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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` | ❌ Not Supported | Results 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 | Results only have the `_relevance_score` column. |
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| `all` | ✅ Supported | Results 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 | Results only have the `_relevance_score` column. |
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| `all` | ✅ Supported | Results have FTS(`score`) along with Hybrid Search score(`_relevance_score`). |
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