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docs: reference updates for Weaviate 1.39 #474

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3 changes: 1 addition & 2 deletions _includes/feature-notes/boost.mdx
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@@ -1,3 +1,2 @@
:::caution Preview — added in `v1.38`
This is a preview feature. The API may change in future releases.
:::info Added in `v1.39`
:::
8 changes: 0 additions & 8 deletions docs/deploy/configuration/monitoring.md
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Expand Up @@ -282,14 +282,6 @@ These metrics track Write-Ahead Log (WAL) recovery operations during startup.
| `schema_reads_leader_seconds` | Duration of schema reads that are passed to the leader | `type` | `Summary` |
| `schema_wait_for_version_seconds` | Duration of waiting for a schema version to be reached | `type` | `Summary` |

#### Schema transactions (deprecated)

| Metric | Description | Labels | Type |
| ---------------------------- | --------------------------------------------------------------------------------------- | --------------------- | --------- |
| `schema_tx_opened_total` | Total number of opened schema transactions | `ownership` | `Counter` |
| `schema_tx_closed_total` | Total number of closed schema transactions. A close must be either successful or failed | `ownership`, `status` | `Counter` |
| `schema_tx_duration_seconds` | Mean duration of a tx by status | `ownership`, `status` | `Summary` |

#### RAFT metrics (internal)

| Metric | Description | Labels | Type |
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4 changes: 2 additions & 2 deletions docs/weaviate/search/bm25.md
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Expand Up @@ -16,7 +16,7 @@ import GoCode from '!!raw-loader!/\_includes/code/howto/go/docs/mainpkg/search-b
import JavaV6Code from "!!raw-loader!/\_includes/code/java-v6/src/test/java/SearchKeywordTest.java";
import CSharpCode from "!!raw-loader!/\_includes/code/csharp/SearchKeywordTest.cs";
import GQLCode from '!!raw-loader!/\_includes/code/howto/search.bm25.gql.py';
import BoostPreview from '/_includes/feature-notes/boost.mdx';
import BoostNote from '/_includes/feature-notes/boost.mdx';

`Keyword` search, also called "BM25 (Best match 25)" or "sparse vector" search, returns objects that have the highest BM25F scores.

Expand Down Expand Up @@ -767,7 +767,7 @@ Set the tokenization method to `trigram` at the property level when creating you

## Soft-rank with Boost

<BoostPreview/>
<BoostNote/>

Keyword (BM25) queries accept an optional `boost` argument that promotes or demotes matching documents without removing them — useful for biasing results by recency, popularity, a soft filter, or another property. Matching documents move up. Everything else stays in the results but ranks lower.

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4 changes: 2 additions & 2 deletions docs/weaviate/search/boost.md
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Expand Up @@ -10,9 +10,9 @@ import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import FilteredTextBlock from '@site/src/components/Documentation/FilteredTextBlock';
import PyCode from '!!raw-loader!/_includes/code/howto/search.boost.py';
import BoostPreview from '/_includes/feature-notes/boost.mdx';
import BoostNote from '/_includes/feature-notes/boost.mdx';

<BoostPreview/>
<BoostNote/>

**Boost** soft-ranks search results — promote or demote matching documents without removing them from the result set. Matching documents move up. Non-matching documents stay in the results but rank lower.

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4 changes: 2 additions & 2 deletions docs/weaviate/search/hybrid.md
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Expand Up @@ -16,7 +16,7 @@ import GoCode from '!!raw-loader!/\_includes/code/howto/go/docs/mainpkg/search-h
import JavaV6Code from "!!raw-loader!/\_includes/code/java-v6/src/test/java/SearchHybridTest.java";
import CSharpCode from "!!raw-loader!/\_includes/code/csharp/SearchHybridTest.cs";
import GQLCode from '!!raw-loader!/\_includes/code/howto/search.hybrid.gql.py';
import BoostPreview from '/_includes/feature-notes/boost.mdx';
import BoostNote from '/_includes/feature-notes/boost.mdx';

`Hybrid` search combines the results of a vector search and a keyword (BM25F) search by fusing the two result sets.

Expand Down Expand Up @@ -1041,7 +1041,7 @@ import TokenizationNote from '/\_includes/tokenization.mdx'

## Soft-rank with Boost

<BoostPreview/>
<BoostNote/>

Hybrid queries accept an optional `boost` argument that promotes or demotes matching documents without removing them — useful for biasing results by recency, popularity, a soft filter, or another property.

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4 changes: 2 additions & 2 deletions docs/weaviate/search/rerank.md
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Expand Up @@ -16,7 +16,7 @@ import SimilarityPyCode from '!!raw-loader!/_includes/code/howto/search.similari
import SimilarityPyCodeV3 from '!!raw-loader!/_includes/code/howto/search.similarity-v3.py';
import SimilarityTSCode from '!!raw-loader!/_includes/code/howto/search.similarity.ts';
import GoCode from '!!raw-loader!/_includes/code/howto/go/docs/mainpkg/search-rerank_test.go';
import BoostPreview from '/_includes/feature-notes/boost.mdx';
import BoostNote from '/_includes/feature-notes/boost.mdx';

Reranking modules reorder the search result set according to a different set of criteria or a different (e.g. more expensive) algorithm.

Expand Down Expand Up @@ -199,7 +199,7 @@ The response should look like this:

## Soft-rank with Boost

<BoostPreview/>
<BoostNote/>

For lightweight result reordering based on filters, property values, or time / numeric decay — without calling an external rerank model — use [Boost](./boost.md). Rerank and Boost can be used independently. Pick rerank when you need a smarter model to re-rank the top-N, and Boost when you want to bias by simple signals already on the objects.

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4 changes: 2 additions & 2 deletions docs/weaviate/search/similarity.md
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Expand Up @@ -15,7 +15,7 @@ import TSCode from '!!raw-loader!/\_includes/code/howto/search.similarity.ts';
import GoCode from '!!raw-loader!/\_includes/code/howto/go/docs/mainpkg/search-similarity_test.go';
import JavaV6Code from "!!raw-loader!/\_includes/code/java-v6/src/test/java/SearchSimilarityTest.java";
import CSharpCode from "!!raw-loader!/\_includes/code/csharp/SearchSimilarityTest.cs";
import BoostPreview from '/_includes/feature-notes/boost.mdx';
import BoostNote from '/_includes/feature-notes/boost.mdx';

Vector search returns the objects with most similar vectors to that of the query.

Expand Down Expand Up @@ -719,7 +719,7 @@ A larger candidate set (higher top-level `limit`) gives MMR more results to choo

## Soft-rank with Boost

<BoostPreview/>
<BoostNote/>

Vector search queries accept an optional `boost` argument that promotes or demotes matching documents without removing them — useful for biasing results by recency, popularity, a soft filter, or another property. Matching documents move up. Everything else stays in the results but ranks lower.

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