Merge branch 'main' into lucene_snapshot

docs/changelog/112258.yaml

@@ -0,0 +1,6 @@
+pr: 112258
+summary: Updated Date Range to Follow Documentation When Assuming Missing Values
+area: Search
+type: bug
+issues: 
+  - 111484

docs/changelog/113588.yaml

@@ -0,0 +1,5 @@
+pr: 113588
+summary: Add asset criticality indices for `kibana_system_user`
+area: Security
+type: enhancement
+issues: []

docs/reference/how-to/knn-search.asciidoc

@@ -45,6 +45,12 @@ results contains the full document `_source`. When the documents contain
 high-dimensional `dense_vector` fields, the `_source` can be quite large and
 expensive to load. This could significantly slow down the speed of kNN search.
 
+NOTE: <<docs-reindex, reindex>>, <<docs-update, update>>,
+and <<docs-update-by-query, update by query>> operations generally
+require the `_source` field. Disabling `_source` for a field might result in
+expected behavior for these operations. For example, reindex might not actually
+contain the `dense_vector` field in the new index.
+
 You can disable storing `dense_vector` fields in the `_source` through the
 <<include-exclude, `excludes`>> mapping parameter. This prevents loading and
 returning large vectors during search, and also cuts down on the index size.
@@ -102,14 +108,14 @@ merges smaller segments into larger ones through a background
 explicit steps to reduce the number of index segments.
 
 [discrete]
-==== Force merge to one segment
-
-The <<indices-forcemerge,force merge>> operation forces an index merge. If you
-force merge to one segment, the kNN search only need to check a single,
-all-inclusive HNSW graph. Force merging `dense_vector` fields is an expensive
-operation that can take significant time to complete.
-
-include::{es-ref-dir}/indices/forcemerge.asciidoc[tag=force-merge-read-only-warn]
+==== Increase maximum segment size
+
+{es} provides many tunable settings for controlling the merge process. One
+important setting is `index.merge.policy.max_merged_segment`. This controls
+the maximum size of the segments that are created during the merge process.
+By increasing the value, you can reduce the number of segments in the index.
+The default value is `5GB`, but that might be too small for larger dimensional vectors.
+Consider increasing this value to `10GB` or `20GB` can help reduce the number of segments.
 
 [discrete]
 ==== Create large segments during bulk indexing

docs/reference/inference/put-inference.asciidoc

@@ -58,7 +58,7 @@ The following services are available through the {infer} API.
 You can find the available task types next to the service name. 
 Click the links to review the configuration details of the services:
 
-* <<infer-service-alibabacloud-ai-search,AlibabaCloud AI Search>> (`rerank`, `sparse_embedding`, `text_embedding`)
+* <<infer-service-alibabacloud-ai-search,AlibabaCloud AI Search>> (`completion`, `rerank`, `sparse_embedding`, `text_embedding`)
 * <<infer-service-amazon-bedrock,Amazon Bedrock>> (`completion`, `text_embedding`)
 * <<infer-service-anthropic,Anthropic>> (`completion`)
 * <<infer-service-azure-ai-studio,Azure AI Studio>> (`completion`, `text_embedding`)

docs/reference/inference/service-alibabacloud-ai-search.asciidoc

@@ -24,9 +24,10 @@ include::inference-shared.asciidoc[tag=task-type]
 --
 Available task types:
 
-* `text_embedding`,
+* `completion`,
+* `rerank`
 * `sparse_embedding`,
-* `rerank`.
+* `text_embedding`.
 --
 
 [discrete]
@@ -55,31 +56,43 @@ A valid API key for the AlibabaCloud AI Search API.
 The name of the model service to use for the {infer} task.
 +
 --
-Available service_ids for the `text_embedding` task:
+Available service_ids for the `completion` task:
 
-* `ops-text-embedding-001`
-* `ops-text-embedding-zh-001`
-* `ops-text-embedding-en-001`
-* `ops-text-embedding-002`
+* `ops-qwen-turbo`
+* `qwen-turbo`
+* `qwen-plus`
+* `qwen-max`
+÷ `qwen-max-longcontext`
 
-For the supported `text_embedding` service_ids, refer to the https://help.aliyun.com/zh/open-search/search-platform/developer-reference/text-embedding-api-details[documentation].
+For the supported `completion` service_ids, refer to the https://help.aliyun.com/zh/open-search/search-platform/developer-reference/text-generation-api-details[documentation].
+
+Available service_id for the `rerank` task is:
+
+* `ops-bge-reranker-larger`
+
+For the supported `rerank` service_id, refer to the https://help.aliyun.com/zh/open-search/search-platform/developer-reference/ranker-api-details[documentation].
 
 Available service_id for the `sparse_embedding` task:
 
 * `ops-text-sparse-embedding-001`
 
 For the supported `sparse_embedding` service_id, refer to the https://help.aliyun.com/zh/open-search/search-platform/developer-reference/text-sparse-embedding-api-details[documentation].
 
-Available service_id for the `rerank` task is:
+Available service_ids for the `text_embedding` task:
 
-* `ops-bge-reranker-larger`
+* `ops-text-embedding-001`
+* `ops-text-embedding-zh-001`
+* `ops-text-embedding-en-001`
+* `ops-text-embedding-002`
+
+For the supported `text_embedding` service_ids, refer to the https://help.aliyun.com/zh/open-search/search-platform/developer-reference/text-embedding-api-details[documentation].
 
-For the supported `rerank` service_id, refer to the https://help.aliyun.com/zh/open-search/search-platform/developer-reference/ranker-api-details[documentation].
 --
 
 `host`:::
 (Required, string)
-The name of the host address used for the {infer} task. You can find the host address at https://opensearch.console.aliyun.com/cn-shanghai/rag/api-key[ the API keys section] of the documentation.
+The name of the host address used for the {infer} task.
+You can find the host address at https://opensearch.console.aliyun.com/cn-shanghai/rag/api-key[the API keys section] of the documentation.
 
 `workspace`:::
 (Required, string)
@@ -130,25 +143,43 @@ If `true`, the token name will be returned in the response. Defaults to `false`
 [[inference-example-alibabacloud-ai-search]]
 ==== AlibabaCloud AI Search service examples
 
-The following example shows how to create an {infer} endpoint called `alibabacloud_ai_search_embeddings` to perform a `text_embedding` task type.
+The following example shows how to create an {infer} endpoint called `alibabacloud_ai_search_completion` to perform a `completion` task type.
 
 [source,console]
 ------------------------------------------------------------
-PUT _inference/text_embedding/alibabacloud_ai_search_embeddings
+PUT _inference/completion/alibabacloud_ai_search_completion
+{
+    "service": "alibabacloud-ai-search",
+    "service_settings": {
+        "host" : "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
+        "api_key": "{{API_KEY}}",
+        "service_id": "ops-qwen-turbo",
+        "workspace" : "default"
+    }
+}
+------------------------------------------------------------
+// TEST[skip:TBD]
+
+
+The next example shows how to create an {infer} endpoint called `alibabacloud_ai_search_rerank` to perform a `rerank` task type.
+
+[source,console]
+------------------------------------------------------------
+PUT _inference/rerank/alibabacloud_ai_search_rerank
 {
     "service": "alibabacloud-ai-search",
     "service_settings": {
         "api_key": "<api_key>",
-        "service_id": "ops-text-embedding-001",
+        "service_id": "ops-bge-reranker-larger",
         "host": "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
         "workspace": "default"
     }
 }
 ------------------------------------------------------------
 // TEST[skip:TBD]
 
-The following example shows how to create an {infer} endpoint called
-`alibabacloud_ai_search_sparse` to perform a `sparse_embedding` task type.
+
+The following example shows how to create an {infer} endpoint called `alibabacloud_ai_search_sparse` to perform a `sparse_embedding` task type.
 
 [source,console]
 ------------------------------------------------------------
@@ -165,20 +196,23 @@ PUT _inference/sparse_embedding/alibabacloud_ai_search_sparse
 ------------------------------------------------------------
 // TEST[skip:TBD]
 
-The next example shows how to create an {infer} endpoint called
-`alibabacloud_ai_search_rerank` to perform a `rerank` task type.
+
+The following example shows how to create an {infer} endpoint called `alibabacloud_ai_search_embeddings` to perform a `text_embedding` task type.
 
 [source,console]
 ------------------------------------------------------------
-PUT _inference/rerank/alibabacloud_ai_search_rerank
+PUT _inference/text_embedding/alibabacloud_ai_search_embeddings
 {
     "service": "alibabacloud-ai-search",
     "service_settings": {
         "api_key": "<api_key>",
-        "service_id": "ops-bge-reranker-larger",
+        "service_id": "ops-text-embedding-001",
         "host": "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
         "workspace": "default"
     }
 }
 ------------------------------------------------------------
 // TEST[skip:TBD]
+
+
+