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docs: assorted copyedits (#1998)

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This includes a handful of minor edits I made while reading the docs. In
addition to a few spelling fixes,
* standardize on "rerank" over "re-rank" in prose
* terminate sentences with periods or colons as appropriate
* replace some usage of dashes with colons, such as in "Try it yourself
- <link>"

All changes are surface-level. No changes to semantics or structure.

---------

Co-authored-by: Will Jones <willjones127@gmail.com>
This commit is contained in:
Wyatt Alt
2025-01-06 15:04:48 -08:00
committed by GitHub
parent b474f98049
commit 0b45ef93c0
31 changed files with 161 additions and 164 deletions
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19
docs/src/reranking/answerdotai.md
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@@ -1,7 +1,6 @@
# AnswersDotAI Rerankers
This integration allows using answersdotai's rerankers to rerank the search results. [Rerankers](https://github.com/AnswerDotAI/rerankers)
A lightweight, low-dependency, unified API to use all common reranking and cross-encoder models.
This integration uses [AnswersDotAI's rerankers](https://github.com/AnswerDotAI/rerankers) to rerank the search results, providing a lightweight, low-dependency, unified API to use all common reranking and cross-encoder models.
!!! note
Supported Query Types: Hybrid, Vector, FTS
@@ -45,10 +44,10 @@ Accepted Arguments
----------------
| Argument | Type | Default | Description |
| --- | --- | --- | --- |
| `model_type` | `str` | `"colbert"` | The type of model to use. Supported model types can be found here - https://github.com/AnswerDotAI/rerankers |
| `model_type` | `str` | `"colbert"` | The type of model to use. Supported model types can be found here: https://github.com/AnswerDotAI/rerankers. |
| `model_name` | `str` | `"answerdotai/answerai-colbert-small-v1"` | 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 |
| `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. |
@@ -58,17 +57,17 @@ You can specify the type of scores you want the reranker to return. The followin
### Hybrid Search
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
| `all` | ❌ Not Supported | Returns have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`) |
| `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
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
| `all` | ✅ Supported | Returns have vector(`_distance`) along with Hybrid Search score(`_relevance_score`) |
| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
| `all` | ✅ Supported | Results have vector(`_distance`) along with Hybrid Search score(`_relevance_score`). |
### FTS Search
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
| `all` | ✅ Supported | Returns have FTS(`score`) along with Hybrid Search score(`_relevance_score`) |
| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
| `all` | ✅ Supported | Results have FTS(`score`) along with Hybrid Search score(`_relevance_score`). |

14
docs/src/reranking/cohere.md
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@@ -1,6 +1,6 @@
# Cohere Reranker
This re-ranker uses the [Cohere](https://cohere.ai/) API to rerank the search results. You can use this re-ranker by passing `CohereReranker()` to the `rerank()` method. Note that you'll either need to set the `COHERE_API_KEY` environment variable or pass the `api_key` argument to use this re-ranker.
This reranker uses the [Cohere](https://cohere.ai/) API to rerank the search results. You can use this reranker by passing `CohereReranker()` to the `rerank()` method. Note that you'll either need to set the `COHERE_API_KEY` environment variable or pass the `api_key` argument to use this reranker.
!!! note
@@ -62,17 +62,17 @@ You can specify the type of scores you want the reranker to return. The followin
### Hybrid Search
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
| `all` | ❌ Not Supported | Returns have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`) |
| `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
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
| `all` | ✅ Supported | Returns have vector(`_distance`) along with Hybrid Search score(`_relevance_score`) |
| `relevance` | ✅ Supported | Results only have the `_relevance_score` column |
| `all` | ✅ Supported | Results have vector(`_distance`) along with Hybrid Search score(`_relevance_score`) |
### FTS Search
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
| `all` | ✅ Supported | Returns have FTS(`score`) along with Hybrid Search score(`_relevance_score`) |
| `relevance` | ✅ Supported | Results only have the `_relevance_score` column |
| `all` | ✅ Supported | Results have FTS(`score`) along with Hybrid Search score(`_relevance_score`) |

16
docs/src/reranking/colbert.md
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@@ -1,6 +1,6 @@
# ColBERT Reranker
This re-ranker uses ColBERT model to rerank the search results. You can use this re-ranker by passing `ColbertReranker()` to the `rerank()` method.
This reranker uses ColBERT model to rerank the search results. You can use this reranker by passing `ColbertReranker()` to the `rerank()` method.
!!! note
Supported Query Types: Hybrid, Vector, FTS
@@ -46,7 +46,7 @@ Accepted Arguments
| `model_name` | `str` | `"colbert-ir/colbertv2.0"` | 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. |
| `device` | `str` | `None` | The device to use for the cross encoder model. If None, will use "cuda" if available, otherwise "cpu". |
| `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 |
| `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. |
## Supported Scores for each query type
@@ -55,17 +55,17 @@ You can specify the type of scores you want the reranker to return. The followin
### Hybrid Search
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
| `all` | ❌ Not Supported | Returns have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`) |
| `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
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
| `all` | ✅ Supported | Returns have vector(`_distance`) along with Hybrid Search score(`_relevance_score`) |
| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
| `all` | ✅ Supported | Results have vector(`_distance`) along with Hybrid Search score(`_relevance_score`). |
### FTS Search
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
| `all` | ✅ Supported | Returns have FTS(`score`) along with Hybrid Search score(`_relevance_score`) |
| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
| `all` | ✅ Supported | Results have FTS(`score`) along with Hybrid Search score(`_relevance_score`). |

16
docs/src/reranking/cross_encoder.md
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@@ -1,6 +1,6 @@
# Cross Encoder Reranker
This re-ranker uses Cross Encoder models from sentence-transformers to rerank the search results. You can use this re-ranker by passing `CrossEncoderReranker()` to the `rerank()` method.
This reranker uses Cross Encoder models from sentence-transformers to rerank the search results. You can use this reranker by passing `CrossEncoderReranker()` to the `rerank()` method.
!!! note
Supported Query Types: Hybrid, Vector, FTS
@@ -46,7 +46,7 @@ Accepted Arguments
| `model_name` | `str` | `""cross-encoder/ms-marco-TinyBERT-L-6"` | 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. |
| `device` | `str` | `None` | The device to use for the cross encoder model. If None, will use "cuda" if available, otherwise "cpu". |
| `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 |
| `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. |
## 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:
@@ -54,17 +54,17 @@ You can specify the type of scores you want the reranker to return. The followin
### Hybrid Search
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
| `all` | ❌ Not Supported | Returns have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`) |
| `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
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
| `all` | ✅ Supported | Returns have vector(`_distance`) along with Hybrid Search score(`_relevance_score`) |
| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
| `all` | ✅ Supported | Results have vector(`_distance`) along with Hybrid Search score(`_relevance_score`). |
### FTS Search
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
| `all` | ✅ Supported | Returns have FTS(`score`) along with Hybrid Search score(`_relevance_score`) |
| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
| `all` | ✅ Supported | Results have FTS(`score`) along with Hybrid Search score(`_relevance_score`). |

9
docs/src/reranking/custom_reranker.md
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@@ -1,9 +1,10 @@
## Building Custom Rerankers
You can build your own custom reranker by subclassing the `Reranker` class and implementing the `rerank_hybrid()` method. Optionally, you can also implement the `rerank_vector()` and `rerank_fts()` methods if you want to support reranking for vector and FTS search separately.
Here's an example of a custom reranker that combines the results of semantic and full-text search using a linear combination of the scores.
The `Reranker` base interface comes with a `merge_results()` method that can be used to combine the results of semantic and full-text search. This is a vanilla merging algorithm that simply concatenates the results and removes the duplicates without taking the scores into consideration. It only keeps the first copy of the row encountered. This works well in cases that don't require the scores of semantic and full-text search to combine the results. If you want to use the scores or want to support `return_score="all"`, you'll need to implement your own merging algorithm.
Here's an example of a custom reranker that combines the results of semantic and full-text search using a linear combination of the scores:
```python
from lancedb.rerankers import Reranker
@@ -42,7 +43,7 @@ class MyReranker(Reranker):
```
### Example of a Custom Reranker
For the sake of simplicity let's build custom reranker that just enchances the Cohere Reranker by accepting a filter query, and accept other CohereReranker params as kwags.
For the sake of simplicity let's build custom reranker that enhances the Cohere Reranker by accepting a filter query, and accepts other CohereReranker params as kwargs.
```python
@@ -83,6 +84,6 @@ class ModifiedCohereReranker(CohereReranker):
```
!!! tip
The `vector_results` and `fts_results` are pyarrow tables. Lean more about pyarrow tables [here](https://arrow.apache.org/docs/python). It can be convered to other data types like pandas dataframe, pydict, pylist etc.
The `vector_results` and `fts_results` are pyarrow tables. Lean more about pyarrow tables [here](https://arrow.apache.org/docs/python). It can be converted to other data types like pandas dataframe, pydict, pylist etc.
For example, You can convert them to pandas dataframes using `to_pandas()` method and perform any operations you want. After you are done, you can convert the dataframe back to pyarrow table using `pa.Table.from_pandas()` method and return it.
For example, You can convert them to pandas dataframes using `to_pandas()` method and perform any operations you want. After you are done, you can convert the dataframe back to pyarrow table using `pa.Table.from_pandas()` method and return it.

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docs/src/reranking/index.md
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@@ -13,7 +13,7 @@ LanceDB comes with some built-in rerankers. Some of the rerankers that are avail
## Using a Reranker
Using rerankers is optional for vector and FTS. However, for hybrid search, rerankers are required. To use a reranker, you need to create an instance of the reranker and pass it to the `rerank` method of the query builder.
Using rerankers is optional for vector and FTS. However, for hybrid search, rerankers are required. To use a reranker, you need to create an instance of the reranker and pass it to the `rerank` method of the query builder:
```python
import lancedb
@@ -64,7 +64,7 @@ reranked = reranker.rerank_multivector([res1, res2, res3], deduplicate=True)
```
## Available Rerankers
LanceDB comes with some built-in rerankers. Here are some of the rerankers that are available in LanceDB:
LanceDB comes with the following built-in rerankers:
- [Cohere Reranker](./cohere.md)
- [Cross Encoder Reranker](./cross_encoder.md)
@@ -78,4 +78,4 @@ LanceDB comes with some built-in rerankers. Here are some of the rerankers that
## Creating Custom Rerankers
LanceDB also you to create custom rerankers by extending the base `Reranker` class. The custom reranker should implement the `rerank` method that takes a list of search results and returns a reranked list of search results. This is covered in more detail in the [Creating Custom Rerankers](./custom_reranker.md) section.
LanceDB also you to create custom rerankers by extending the base `Reranker` class. The custom reranker should implement the `rerank` method that takes a list of search results and returns a reranked list of search results. This is covered in more detail in the [Creating Custom Rerankers](./custom_reranker.md) section.

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docs/src/reranking/jina.md
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@@ -1,6 +1,6 @@
# Jina Reranker
This re-ranker uses the [Jina](https://jina.ai/reranker/) API to rerank the search results. You can use this re-ranker by passing `JinaReranker()` to the `rerank()` method. Note that you'll either need to set the `JINA_API_KEY` environment variable or pass the `api_key` argument to use this re-ranker.
This reranker uses the [Jina](https://jina.ai/reranker/) API to rerank the search results. You can use this reranker by passing `JinaReranker()` to the `rerank()` method. Note that you'll either need to set the `JINA_API_KEY` environment variable or pass the `api_key` argument to use this reranker.
!!! note
@@ -48,11 +48,11 @@ Accepted Arguments
----------------
| Argument | Type | Default | Description |
| --- | --- | --- | --- |
| `model_name` | `str` | `"jina-reranker-v2-base-multilingual"` | The name of the reranker model to use. You can find the list of available models in https://jina.ai/reranker/|
| `model_name` | `str` | `"jina-reranker-v2-base-multilingual"` | The name of the reranker model to use. You can find the list of available models in https://jina.ai/reranker. |
| `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. |
| `top_n` | `str` | `None` | The number of results to return. If None, will return all results. |
| `api_key` | `str` | `None` | The API key for the Jina API. If not provided, the `JINA_API_KEY` environment variable is used. |
| `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 |
| `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. |
@@ -62,17 +62,17 @@ You can specify the type of scores you want the reranker to return. The followin
### Hybrid Search
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
| `all` | ❌ Not Supported | Returns have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`) |
| `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
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
| `all` | ✅ Supported | Returns have vector(`_distance`) along with Hybrid Search score(`_relevance_score`) |
| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
| `all` | ✅ Supported | Results have vector(`_distance`) along with Hybrid Search score(`_relevance_score`). |
### FTS Search
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
| `all` | ✅ Supported | Returns have FTS(`score`) along with Hybrid Search score(`_relevance_score`) |
| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
| `all` | ✅ Supported | Results have FTS(`score`) along with Hybrid Search score(`_relevance_score`). |

8
docs/src/reranking/linear_combination.md
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@@ -1,9 +1,9 @@
# Linear Combination Reranker
!!! note
This is depricated. It is recommended to use the `RRFReranker` instead, if you want to use a score based reranker.
This is deprecated. It is recommended to use the `RRFReranker` instead, if you want to use a score-based reranker.
It 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. It defaults to 0.7, i.e, 70% weight for semantic search and 30% weight for full-text search.
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.
!!! note
Supported Query Types: Hybrid
@@ -51,5 +51,5 @@ You can specify the type of scores you want the reranker to return. The followin
### Hybrid Search
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
| `all` | ✅ Supported | Returns have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_distance`) |
| `relevance` | ✅ Supported | Results only have the `_relevance_score` column |
| `all` | ✅ Supported | Results have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_distance`) |

18
docs/src/reranking/openai.md
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@@ -1,11 +1,11 @@
# OpenAI Reranker (Experimental)
This re-ranker uses OpenAI chat model to rerank the search results. You can use this re-ranker by passing `OpenAI()` to the `rerank()` method.
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, FTS
!!! warning
This re-ranker is experimental. OpenAI doesn't have a dedicated reranking model, so we are using the chat model for reranking.
This reranker is experimental. OpenAI doesn't have a dedicated reranking model, so we are using the chat model for reranking.
```python
import numpy
@@ -47,7 +47,7 @@ Accepted Arguments
| --- | --- | --- | --- |
| `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 |
| `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.
@@ -57,17 +57,17 @@ You can specify the type of scores you want the reranker to return. The followin
### Hybrid Search
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
| `all` | ❌ Not Supported | Returns have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`) |
| `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
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
| `all` | ✅ Supported | Returns have vector(`_distance`) along with Hybrid Search score(`_relevance_score`) |
| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
| `all` | ✅ Supported | Results have vector(`_distance`) along with Hybrid Search score(`_relevance_score`). |
### FTS Search
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column |
| `all` | ✅ Supported | Returns have FTS(`score`) along with Hybrid Search score(`_relevance_score`) |
| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. |
| `all` | ✅ Supported | Results have FTS(`score`) along with Hybrid Search score(`_relevance_score`). |

8
docs/src/reranking/rrf.md
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@@ -1,6 +1,6 @@
# Reciprocal Rank Fusion Reranker
This is the default re-ranker used by LanceDB hybrid search. Reciprocal Rank Fusion (RRF) is an algorithm that evaluates the search scores by leveraging the positions/rank of the documents. The implementation follows this [paper](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf).
This is the default reranker used by LanceDB hybrid search. Reciprocal Rank Fusion (RRF) is an algorithm that evaluates the search scores by leveraging the positions/rank of the documents. The implementation follows this [paper](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf).
!!! note
@@ -39,7 +39,7 @@ Accepted Arguments
----------------
| Argument | Type | Default | Description |
| --- | --- | --- | --- |
| `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 |
| `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. |
| `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. |
@@ -49,5 +49,5 @@ You can specify the type of scores you want the reranker to return. The followin
### Hybrid Search
|`return_score`| Status | Description |
| --- | --- | --- |
| `relevance` | ✅ Supported | Returned rows only have the `_relevance_score` column |
| `all` | ✅ Supported | Returned rows have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`) |
| `relevance` | ✅ Supported | Returned rows only have the `_relevance_score` column. |
| `all` | ✅ Supported | Returned rows have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`). |

2
docs/src/reranking/voyageai.md
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@@ -2,7 +2,7 @@
Voyage AI provides cutting-edge embedding and rerankers.
This re-ranker uses the [VoyageAI](https://docs.voyageai.com/docs/) API to rerank the search results. You can use this re-ranker by passing `VoyageAIReranker()` to the `rerank()` method. Note that you'll either need to set the `VOYAGE_API_KEY` environment variable or pass the `api_key` argument to use this re-ranker.
This reranker uses the [VoyageAI](https://docs.voyageai.com/docs/) API to rerank the search results. You can use this reranker by passing `VoyageAIReranker()` to the `rerank()` method. Note that you'll either need to set the `VOYAGE_API_KEY` environment variable or pass the `api_key` argument to use this reranker.
!!! note