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alexcastgonzandXuanwo
8cf115b081
OpenAI shuts down `gpt-4-turbo` and its snapshots on 2026-10-23 (https://developers.openai.com/api/docs/deprecations), so `OpenaiReranker` fails for every user who relies on the default `gpt-4-turbo-preview` after that date. This changes the default to `gpt-5.6-sol`, the replacement OpenAI names for `gpt-4-turbo` on that page, in the class, its docstring and the integration docs page. `_rerank` also stops sending `temperature=0`: the GPT-5 models only accept the default temperature, so keeping it would fail the new default on its first call. The reranker still asks for `json_object` output and the rest of the request is unchanged. The new test calls `_rerank` with a mocked client and checks that no `temperature` is sent. It fails on main and passes with the change. No request was made to OpenAI. This change was made with Claude Code. Co-authored-by: Xuanwo <github@xuanwo.io>
55 lines
2.7 KiB
Plaintext
55 lines
2.7 KiB
Plaintext
---
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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/provider_examples.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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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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{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-5.6-sol"` | 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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