mirror of
https://github.com/lancedb/lancedb.git
synced 2026-10-20 18:52:28 +00:00
Brendan Clement
198812fd53
The existing docs have 254 findings from our shared Vale rules. Contributors would otherwise inherit these when editing the same lines. Clean up the prose across 80 pages: correct product names and a repeated word, simplify a few phrases, and replace dashes with punctuation that fits each sentence. The pinned Vale scan now reports zero findings, with no suppressions.
55 lines
2.7 KiB
Plaintext
55 lines
2.7 KiB
Plaintext
---
|
|
title: OpenAI Reranker (Experimental)
|
|
sidebarTitle: "OpenAI"
|
|
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.
|
|
|
|
---
|
|
|
|
import { PyRerankingOpenaiUsage } from '/snippets/provider_examples.mdx';
|
|
|
|
# OpenAI Reranker (Experimental)
|
|
|
|
This reranker uses OpenAI chat model to rerank the search results. You can use this reranker by passing `OpenAI()` to the `rerank()` method.
|
|
> **Note:** Supported query types: Hybrid, Vector, and FTS.
|
|
|
|
> **Warning:** This reranker is experimental. OpenAI does not have a dedicated reranking model, so it uses a chat model under the hood.
|
|
|
|
Replace `your-api-key` with your provider API key. In application code, load
|
|
credentials from environment variables or a secrets manager.
|
|
|
|
<CodeGroup>
|
|
<CodeBlock filename="Python" language="Python" icon="python">
|
|
{PyRerankingOpenaiUsage}
|
|
</CodeBlock>
|
|
</CodeGroup>
|
|
|
|
## Accepted Arguments {#accepted-arguments}
|
|
| Argument | Type | Default | Description |
|
|
| --- | --- | --- | --- |
|
|
| `model_name` | `str` | `"gpt-4-turbo-preview"` | The name of the reranker model to use.|
|
|
| `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. |
|
|
| `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. |
|
|
| `api_key` | `str` | `None` | The API key to use. If None, will use the OPENAI_API_KEY environment variable.
|
|
|
|
|
|
## Supported Scores for each query type {#supported-scores-for-each-query-type}
|
|
You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:
|
|
|
|
### Hybrid Search {#hybrid-search}
|
|
|`return_score`| Status | Description |
|
|
| --- | --- | --- |
|
|
| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
|
|
| `all` | ❌ Not Supported | Results have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`). |
|
|
|
|
### Vector Search {#vector-search}
|
|
|`return_score`| Status | Description |
|
|
| --- | --- | --- |
|
|
| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
|
|
| `all` | ✅ Supported | Results have vector(`_distance`) along with Hybrid Search score(`_relevance_score`). |
|
|
|
|
### FTS Search {#fts-search}
|
|
|`return_score`| Status | Description |
|
|
| --- | --- | --- |
|
|
| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
|
|
| `all` | ✅ Supported | Results have FTS(`score`) along with Hybrid Search score(`_relevance_score`). |
|