Release notes fo 8.18.1 (#2763) (#2778)

Co-authored-by: Josh Mock <joshua.mock@elastic.co>

.dockerignore

@@ -5,3 +5,4 @@ elasticsearch
 .git
 lib
 junit-output
+.tap

.github/make.sh

@@ -65,7 +65,7 @@ codegen)
   if [ -v "$VERSION" ] || [[ -z "$VERSION" ]]; then
     # fall back to branch name or `main` if no VERSION is set
     branch_name=$(git rev-parse --abbrev-ref HEAD)
-    if [[ "$branch_name" =~ ^[0-9]+\.[0-9]+ ]]; then
+    if [[ "$branch_name" =~ ^[0-9]+\.([0-9]+|x) ]]; then
       echo -e "\033[36;1mTARGET: codegen -> No VERSION argument found, using branch name: \`$branch_name\`\033[0m"
       VERSION="$branch_name"
     else
@@ -150,7 +150,7 @@ if [[ -z "${BUILDKITE+x}" ]] && [[ -z "${CI+x}" ]] && [[ -z "${GITHUB_ACTIONS+x}
     -u "$(id -u):$(id -g)" \
     --volume "$repo:/usr/src/elasticsearch-js" \
     --volume /usr/src/elasticsearch-js/node_modules \
-    --volume "$(realpath $repo/../elastic-client-generator-js):/usr/src/elastic-client-generator-js" \
+    --volume "$(realpath "$repo/../elastic-client-generator-js"):/usr/src/elastic-client-generator-js" \
     --env "WORKFLOW=$WORKFLOW" \
     --name make-elasticsearch-js \
     --rm \
@@ -159,6 +159,14 @@ if [[ -z "${BUILDKITE+x}" ]] && [[ -z "${CI+x}" ]] && [[ -z "${GITHUB_ACTIONS+x}
       node .buildkite/make.mjs --task $TASK ${TASK_ARGS[*]}"
 else
   echo -e "\033[34;1mINFO: Running in CI mode"
+
+  # determine branch to clone
+  GENERATOR_BRANCH="main"
+  if [[ "$VERSION" == 8.* ]]; then
+    GENERATOR_BRANCH="8.x"
+  fi
+  echo -e "\033[34;1mINFO: Generator branch: $GENERATOR_BRANCH"
+
   docker run \
     --volume "$repo:/usr/src/elasticsearch-js" \
     --volume /usr/src/elasticsearch-js/node_modules \
@@ -168,7 +176,7 @@ else
     --rm \
     $product \
     /bin/bash -c "cd /usr/src && \
-      git clone https://$CLIENTS_GITHUB_TOKEN@github.com/elastic/elastic-client-generator-js.git && \
+      git clone --branch $GENERATOR_BRANCH https://$CLIENTS_GITHUB_TOKEN@github.com/elastic/elastic-client-generator-js.git && \
       mkdir -p /usr/src/elastic-client-generator-js/output && \
       cd /usr/src/elasticsearch-js && \
       node .buildkite/make.mjs --task $TASK ${TASK_ARGS[*]}"

.gitignore

@@ -67,3 +67,4 @@ junit-output
 bun.lockb
 test-results
 processinfo
+.tap

.npmignore

@@ -73,3 +73,4 @@ CONTRIBUTING.md
 
 src
 bun.lockb
+.tap

docs/basic-config.asciidoc

@@ -167,16 +167,19 @@ const client = new Client({
 ----
 
 |`nodeFilter`
-a|`function` - Filters which node not to use for a request. +
+a|`function` - Takes a `Connection` and returns `true` if it can be sent a request, otherwise `false`. +
 _Default:_
 [source,js]
 ----
-function defaultNodeFilter (node) {
-  // avoid master only nodes
-  if (node.roles.master === true &&
-      node.roles.data === false &&
-      node.roles.ingest === false) {
-    return false
+function defaultNodeFilter (conn) {
+  if (conn.roles != null) {
+    if (
+      // avoid master-only nodes
+      conn.roles.master &&
+      !conn.roles.data &&
+      !conn.roles.ingest &&
+      !conn.roles.ml
+    ) return false
   }
   return true
 }

docs/changelog.asciidoc

@@ -1,6 +1,76 @@
 [[changelog-client]]
 == Release notes
 
+[discrete]
+=== 8.18.1
+
+[discrete]
+==== Fixes
+
+[discrete]
+===== Fix broken node roles and node filter
+
+The docs note a `nodeFilter` option on the client that will, by default, filter the nodes based on any `roles` values that are set at instantition. At some point, this functionality was partially disabled. This brings the feature back, ensuring that it matches what the documentation has said it does all along.
+
+[discrete]
+=== 8.18.0
+
+[discrete]
+==== Features
+
+[discrete]
+===== Support for Elasticsearch `v8.18`
+
+You can find all the API changes
+https://www.elastic.co/guide/en/elasticsearch/reference/8.18/release-notes-8.18.0.html[here].
+
+[discrete]
+==== Fixes
+
+[discrete]
+===== Improved Cloud ID parsing
+
+When using a Cloud ID as the `cloud` parameter to instantiate the client, that ID was assumed to be in the correct format. New assertions have been added to verify that format and throw a `ConfigurationError` if it is invalid. See https://github.com/elastic/elasticsearch-js/issues/2694[#2694].
+
+[discrete]
+=== 8.17.0
+
+[discrete]
+==== Features
+
+[discrete]
+===== Support for Elasticsearch `v8.17`
+
+You can find all the API changes
+https://www.elastic.co/guide/en/elasticsearch/reference/8.17/release-notes-8.17.0.html[here].
+
+[discrete]
+=== 8.16.3
+
+[discrete]
+==== Fixes
+
+[discrete]
+===== Improved support for Elasticsearch `v8.16`
+
+Updated TypeScript types based on fixes and improvements to the Elasticsearch specification.
+
+[discrete]
+=== 8.16.2
+
+[discrete]
+==== Fixes
+
+[discrete]
+===== Improved support for Elasticsearch `v8.16`
+
+Updated TypeScript types based on fixes and improvements to the Elasticsearch specification.
+
+[discrete]
+===== Drop testing artifacts from npm package
+
+Tap, the unit testing tool used by this project, was recently upgraded and started writing to a `.tap` directory. Since tests are run prior to an `npm publish` in CI, this directory was being included in the published package and bloating its size.
+
 [discrete]
 === 8.16.1
 
@@ -37,11 +107,27 @@ The ES|QL helper can now return results as an Apache Arrow `Table` or `RecordBat
 
 The client's `disablePrototypePoisoningProtection` option was set to `true` by default, but when it was set to any other value it was ignored, making it impossible to enable prototype poisoning protection without providing a custom serializer implementation.
 
+[discrete]
+=== 8.15.3
+
+[discrete]
+==== Fixes
+
+[discrete]
+===== Improved support for Elasticsearch `v8.15`
+
+Updated TypeScript types based on fixes and improvements to the Elasticsearch specification.
+
+[discrete]
+===== Drop testing artifacts from npm package
+
+Tap, the unit testing tool, was recently upgraded and started writing to a `.tap` directory. Since tests are run prior to an `npm publish` in CI, this directory was being included in the published package and bloating its size.
+
 [discrete]
 === 8.15.2
 
 [discrete]
-==== Features
+==== Fixes
 
 [discrete]
 ===== Improved support for Elasticsearch `v8.15`
@@ -52,7 +138,7 @@ Updated TypeScript types based on fixes and improvements to the Elasticsearch sp
 === 8.15.1
 
 [discrete]
-==== Features
+==== Fixes
 
 [discrete]
 ===== Improved support for Elasticsearch `v8.15`

docs/doc_examples/00ad41bde67beac991534ae0e04b1296.asciidoc

@@ -0,0 +1,11 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.indices.getDataStream({
+  name: "my-data-stream",
+  filter_path: "data_streams.indices.index_name",
+});
+console.log(response);
+----

docs/doc_examples/015e6e6132b6d6d44bddb06bc3b316ed.asciidoc

@@ -0,0 +1,46 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.search({
+  index: "retrievers_example",
+  retriever: {
+    rrf: {
+      retrievers: [
+        {
+          standard: {
+            query: {
+              range: {
+                year: {
+                  gt: 2023,
+                },
+              },
+            },
+          },
+        },
+        {
+          standard: {
+            query: {
+              term: {
+                topic: "elastic",
+              },
+            },
+          },
+        },
+      ],
+      rank_window_size: 10,
+      rank_constant: 1,
+    },
+  },
+  _source: false,
+  aggs: {
+    topics: {
+      terms: {
+        field: "topic",
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/0165d22da5f2fc7678392b31d8eb5566.asciidoc

@@ -0,0 +1,18 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.inference.put({
+  task_type: "rerank",
+  inference_id: "my-rerank-model",
+  inference_config: {
+    service: "cohere",
+    service_settings: {
+      model_id: "rerank-english-v3.0",
+      api_key: "{{COHERE_API_KEY}}",
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/0722b302b2b3275a988d858044f99d5d.asciidoc

@@ -3,9 +3,8 @@
 
 [source, js]
 ----
-const response = await client.cluster.getSettings({
-  flat_settings: "true",
-  filter_path: "transient",
+const response = await client.indices.getMapping({
+  index: "kibana_sample_data_ecommerce",
 });
 console.log(response);
 ----

docs/doc_examples/074e4602d1ca54412380a40867d078bc.asciidoc

@@ -11,6 +11,8 @@ const response = await client.indices.putSettings({
     "index.indexing.slowlog.threshold.index.debug": "2s",
     "index.indexing.slowlog.threshold.index.trace": "500ms",
     "index.indexing.slowlog.source": "1000",
+    "index.indexing.slowlog.reformat": true,
+    "index.indexing.slowlog.include.user": true,
   },
 });
 console.log(response);

docs/doc_examples/082e78c7a2061a7c4a52b494e5ede0e8.asciidoc

@@ -0,0 +1,42 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.indices.create({
+  index: "my-rank-vectors-bit",
+  mappings: {
+    properties: {
+      my_vector: {
+        type: "rank_vectors",
+        element_type: "bit",
+      },
+    },
+  },
+});
+console.log(response);
+
+const response1 = await client.bulk({
+  index: "my-rank-vectors-bit",
+  refresh: "true",
+  operations: [
+    {
+      index: {
+        _id: "1",
+      },
+    },
+    {
+      my_vector: [127, -127, 0, 1, 42],
+    },
+    {
+      index: {
+        _id: "2",
+      },
+    },
+    {
+      my_vector: "8100012a7f",
+    },
+  ],
+});
+console.log(response1);
+----

docs/doc_examples/0bc6155e0c88062a4d8490da49db3aa8.asciidoc

@@ -0,0 +1,49 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.search({
+  index: "retrievers_example_nested",
+  retriever: {
+    rrf: {
+      retrievers: [
+        {
+          standard: {
+            query: {
+              nested: {
+                path: "nested_field",
+                inner_hits: {
+                  name: "nested_vector",
+                  _source: false,
+                  fields: ["nested_field.paragraph_id"],
+                },
+                query: {
+                  knn: {
+                    field: "nested_field.nested_vector",
+                    query_vector: [1, 0, 0.5],
+                    k: 10,
+                  },
+                },
+              },
+            },
+          },
+        },
+        {
+          standard: {
+            query: {
+              term: {
+                topic: "ai",
+              },
+            },
+          },
+        },
+      ],
+      rank_window_size: 10,
+      rank_constant: 1,
+    },
+  },
+  _source: ["topic"],
+});
+console.log(response);
+----

docs/doc_examples/0bee07a581c5776e068f6f4efad5a399.asciidoc

@@ -3,8 +3,12 @@
 
 [source, js]
 ----
-const response = await client.esql.asyncQuery({
-  format: "json",
+const response = await client.transport.request({
+  method: "POST",
+  path: "/_query/async",
+  querystring: {
+    format: "json",
+  },
   body: {
     query:
       "\n    FROM my-index-000001,cluster_one:my-index-000001,cluster_two:my-index*\n    | STATS COUNT(http.response.status_code) BY user.id\n    | LIMIT 2\n  ",

docs/doc_examples/0c52af573c9401a2a687e86a4beb182b.asciidoc

@@ -10,7 +10,7 @@ const response = await client.ingest.putPipeline({
     {
       attachment: {
         field: "data",
-        remove_binary: false,
+        remove_binary: true,
       },
     },
   ],

docs/doc_examples/0d689ac6e78be5d438f9b5d441be2b44.asciidoc

@@ -0,0 +1,57 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.search({
+  index: "retrievers_example",
+  retriever: {
+    rrf: {
+      retrievers: [
+        {
+          standard: {
+            query: {
+              term: {
+                topic: "elastic",
+              },
+            },
+          },
+        },
+        {
+          rrf: {
+            retrievers: [
+              {
+                standard: {
+                  query: {
+                    query_string: {
+                      query:
+                        "(information retrieval) OR (artificial intelligence)",
+                      default_field: "text",
+                    },
+                  },
+                },
+              },
+              {
+                knn: {
+                  field: "vector",
+                  query_vector: [0.23, 0.67, 0.89],
+                  k: 3,
+                  num_candidates: 5,
+                },
+              },
+            ],
+            rank_window_size: 10,
+            rank_constant: 1,
+          },
+        },
+      ],
+      rank_window_size: 10,
+      rank_constant: 1,
+    },
+  },
+  _source: false,
+  size: 1,
+  explain: true,
+});
+console.log(response);
+----

docs/doc_examples/0dfde6a9d953822fd4b3aa0121ddd8fb.asciidoc

@@ -3,8 +3,9 @@
 
 [source, js]
 ----
-const response = await client.searchApplication.renderQuery({
-  name: "my-app",
+const response = await client.transport.request({
+  method: "POST",
+  path: "/_application/search_application/my-app/_render_query",
   body: {
     params: {
       query_string: "my first query",

docs/doc_examples/0e83f140237d75469a428ff403564bb5.asciidoc

@@ -1,15 +0,0 @@
-// This file is autogenerated, DO NOT EDIT
-// Use `node scripts/generate-docs-examples.js` to generate the docs examples
-
-[source, js]
-----
-const response = await client.cluster.putSettings({
-  persistent: {
-    "cluster.routing.allocation.disk.watermark.low": "100gb",
-    "cluster.routing.allocation.disk.watermark.high": "50gb",
-    "cluster.routing.allocation.disk.watermark.flood_stage": "10gb",
-    "cluster.info.update.interval": "1m",
-  },
-});
-console.log(response);
-----

docs/doc_examples/0f028f71f04c1d569fab402869565a84.asciidoc

@@ -0,0 +1,15 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.indices.putSettings({
+  index: ".reindexed-v9-ml-anomalies-custom-example",
+  settings: {
+    index: {
+      number_of_replicas: "<original_number_of_replicas>",
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/120fcf9f55128d6a81d5e87a9c235bbd.asciidoc

@@ -0,0 +1,20 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.transport.request({
+  method: "POST",
+  path: "/_inference/chat_completion/openai-completion/_stream",
+  body: {
+    model: "gpt-4o",
+    messages: [
+      {
+        role: "user",
+        content: "What is Elastic?",
+      },
+    ],
+  },
+});
+console.log(response);
+----

docs/doc_examples/12adea5d76f73d94d80d42f53f67563f.asciidoc

@@ -0,0 +1,11 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.indices.addBlock({
+  index: ".ml-anomalies-custom-example",
+  block: "read_only",
+});
+console.log(response);
+----

docs/doc_examples/141ef0ebaa3b0772892b79b9bb85efb0.asciidoc

@@ -3,11 +3,13 @@
 
 [source, js]
 ----
-const response = await client.inference.inference({
+const response = await client.inference.put({
   task_type: "my-inference-endpoint",
   inference_id: "_update",
-  service_settings: {
-    api_key: "<API_KEY>",
+  inference_config: {
+    service_settings: {
+      api_key: "<API_KEY>",
+    },
   },
 });
 console.log(response);

docs/doc_examples/16634cfa7916cf4e8048a1d70e6240f2.asciidoc

@@ -11,7 +11,7 @@ const response = await client.searchApplication.put({
       script: {
         lang: "mustache",
         source:
-          '\n        {\n          "query": {\n            "bool": {\n              "must": [\n              {{#query}}\n                \n              {{/query}}\n            ],\n            "filter": {{#toJson}}_es_filters{{/toJson}}\n            }\n          },\n          "_source": {\n            "includes": ["title", "plot"]\n            },\n            "highlight": {\n              "fields": {\n                "title": { "fragment_size": 0 },\n                "plot": { "fragment_size": 200 }\n                }\n                },\n                "aggs": {{#toJson}}_es_aggs{{/toJson}},\n                "from": {{from}},\n                "size": {{size}},\n                "sort": {{#toJson}}_es_sort_fields{{/toJson}}\n                }\n                ',
+          '\n        {\n          "query": {\n            "bool": {\n              "must": [\n              {{#query}}\n              {{/query}}\n            ],\n            "filter": {{#toJson}}_es_filters{{/toJson}}\n            }\n          },\n          "_source": {\n            "includes": ["title", "plot"]\n            },\n            "highlight": {\n              "fields": {\n                "title": { "fragment_size": 0 },\n                "plot": { "fragment_size": 200 }\n                }\n                },\n                "aggs": {{#toJson}}_es_aggs{{/toJson}},\n                "from": {{from}},\n                "size": {{size}},\n                "sort": {{#toJson}}_es_sort_fields{{/toJson}}\n                }\n                ',
         params: {
           query: "",
           _es_filters: {},

docs/doc_examples/17b1647c8509543f2388c886f2584a20.asciidoc

@@ -16,7 +16,7 @@ const response = await client.search({
         },
       },
       field: "text",
-      inference_id: "my-cohere-rerank-model",
+      inference_id: "elastic-rerank",
       inference_text: "How often does the moon hide the sun?",
       rank_window_size: 100,
       min_score: 0.5,

docs/doc_examples/19c00c6b29bc7dbc5e92b3668da2da93.asciidoc

@@ -3,7 +3,9 @@
 
 [source, js]
 ----
-const response = await client.simulate.ingest({
+const response = await client.transport.request({
+  method: "POST",
+  path: "/_ingest/_simulate",
   body: {
     docs: [
       {

docs/doc_examples/1ead35c954963e83f89872048dabdbe9.asciidoc

@@ -0,0 +1,19 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.security.queryRole({
+  query: {
+    bool: {
+      must_not: {
+        term: {
+          "metadata._reserved": true,
+        },
+      },
+    },
+  },
+  sort: ["name"],
+});
+console.log(response);
+----

docs/doc_examples/22b176a184517cf1b5801f5eb4f17f97.asciidoc

@@ -3,8 +3,8 @@
 
 [source, js]
 ----
-const response = await client.security.queryRole({
-  sort: ["name"],
+const response = await client.indices.rollover({
+  alias: "datastream",
 });
 console.log(response);
 ----

docs/doc_examples/246763219ec06172f7aa57bba28d344a.asciidoc

@@ -0,0 +1,67 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.indices.create({
+  index: "my-rank-vectors-bit",
+  mappings: {
+    properties: {
+      my_vector: {
+        type: "rank_vectors",
+        element_type: "bit",
+      },
+    },
+  },
+});
+console.log(response);
+
+const response1 = await client.bulk({
+  index: "my-rank-vectors-bit",
+  refresh: "true",
+  operations: [
+    {
+      index: {
+        _id: "1",
+      },
+    },
+    {
+      my_vector: [127, -127, 0, 1, 42],
+    },
+    {
+      index: {
+        _id: "2",
+      },
+    },
+    {
+      my_vector: "8100012a7f",
+    },
+  ],
+});
+console.log(response1);
+
+const response2 = await client.search({
+  index: "my-rank-vectors-bit",
+  query: {
+    script_score: {
+      query: {
+        match_all: {},
+      },
+      script: {
+        source: "maxSimDotProduct(params.query_vector, 'my_vector')",
+        params: {
+          query_vector: [
+            [
+              0.35, 0.77, 0.95, 0.15, 0.11, 0.08, 0.58, 0.06, 0.44, 0.52, 0.21,
+              0.62, 0.65, 0.16, 0.64, 0.39, 0.93, 0.06, 0.93, 0.31, 0.92, 0,
+              0.66, 0.86, 0.92, 0.03, 0.81, 0.31, 0.2, 0.92, 0.95, 0.64, 0.19,
+              0.26, 0.77, 0.64, 0.78, 0.32, 0.97, 0.84,
+            ],
+          ],
+        },
+      },
+    },
+  },
+});
+console.log(response2);
+----

docs/doc_examples/2577acb462b95bd4394523cf2f8a661f.asciidoc

@@ -1,28 +0,0 @@
-// This file is autogenerated, DO NOT EDIT
-// Use `node scripts/generate-docs-examples.js` to generate the docs examples
-
-[source, js]
-----
-const response = await client.esql.query({
-  format: "txt",
-  query:
-    "\n      FROM library\n    | SORT page_count DESC\n    | KEEP name, author\n    | LOOKUP era ON author\n    | LIMIT 5\n  ",
-  tables: {
-    era: {
-      author: {
-        keyword: [
-          "Frank Herbert",
-          "Peter F. Hamilton",
-          "Vernor Vinge",
-          "Alastair Reynolds",
-          "James S.A. Corey",
-        ],
-      },
-      era: {
-        keyword: ["The New Wave", "Diamond", "Diamond", "Diamond", "Hadron"],
-      },
-    },
-  },
-});
-console.log(response);
-----

docs/doc_examples/272e27bf1fcc4fe5dbd4092679dd0342.asciidoc

@@ -0,0 +1,11 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.indices.addBlock({
+  index: ".ml-anomalies-custom-example",
+  block: "write",
+});
+console.log(response);
+----

docs/doc_examples/2a1eece9a59ac1773edcf0a932c26de0.asciidoc

@@ -3,7 +3,9 @@
 
 [source, js]
 ----
-const response = await client.security.oidcLogout({
+const response = await client.transport.request({
+  method: "POST",
+  path: "/_security/oidc/logout",
   body: {
     token:
       "dGhpcyBpcyBub3QgYSByZWFsIHRva2VuIGJ1dCBpdCBpcyBvbmx5IHRlc3QgZGF0YS4gZG8gbm90IHRyeSB0byByZWFkIHRva2VuIQ==",

docs/doc_examples/2a21674c40f9b182a8944769d20b2357.asciidoc

@@ -0,0 +1,26 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.search({
+  index: "my-rank-vectors-float",
+  query: {
+    script_score: {
+      query: {
+        match_all: {},
+      },
+      script: {
+        source: "maxSimDotProduct(params.query_vector, 'my_vector')",
+        params: {
+          query_vector: [
+            [0.5, 10, 6],
+            [-0.5, 10, 10],
+          ],
+        },
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/2a67608dadbf220a2f040f3a79d3677d.asciidoc

@@ -0,0 +1,35 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.ingest.putPipeline({
+  id: "attachment",
+  description: "Extract attachment information including original binary",
+  processors: [
+    {
+      attachment: {
+        field: "data",
+        remove_binary: false,
+      },
+    },
+  ],
+});
+console.log(response);
+
+const response1 = await client.index({
+  index: "my-index-000001",
+  id: "my_id",
+  pipeline: "attachment",
+  document: {
+    data: "e1xydGYxXGFuc2kNCkxvcmVtIGlwc3VtIGRvbG9yIHNpdCBhbWV0DQpccGFyIH0=",
+  },
+});
+console.log(response1);
+
+const response2 = await client.get({
+  index: "my-index-000001",
+  id: "my_id",
+});
+console.log(response2);
+----

docs/doc_examples/2afd49985950cbcccf727fa858d00067.asciidoc

@@ -0,0 +1,24 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.indices.create({
+  index: "test-index",
+  query: {
+    match: {
+      my_field: "Which country is Paris in?",
+    },
+  },
+  highlight: {
+    fields: {
+      my_field: {
+        type: "semantic",
+        number_of_fragments: 2,
+        order: "score",
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/2afdf0d83724953aa2875b5fb37d60cc.asciidoc

@@ -3,10 +3,12 @@
 
 [source, js]
 ----
-const response = await client.esql.asyncQueryGet({
-  id: "FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=",
-  wait_for_completion_timeout: "30s",
-  body: null,
+const response = await client.transport.request({
+  method: "GET",
+  path: "/_query/async/FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=",
+  querystring: {
+    wait_for_completion_timeout: "30s",
+  },
 });
 console.log(response);
 ----

docs/doc_examples/2f72a63c73dd672ac2dc3997ad15dd41.asciidoc

@@ -0,0 +1,23 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.indices.create({
+  index: "test-index",
+  mappings: {
+    properties: {
+      source_field: {
+        type: "text",
+        fields: {
+          infer_field: {
+            type: "semantic_text",
+            inference_id: ".elser-2-elasticsearch",
+          },
+        },
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/30d051f534aeb884176eedb2c11dac85.asciidoc

@@ -0,0 +1,23 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.inference.put({
+  task_type: "rerank",
+  inference_id: "my-elastic-rerank",
+  inference_config: {
+    service: "elasticsearch",
+    service_settings: {
+      model_id: ".rerank-v1",
+      num_threads: 1,
+      adaptive_allocations: {
+        enabled: true,
+        min_number_of_allocations: 1,
+        max_number_of_allocations: 4,
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/31832bd71c31c46a1ccf8d1c210d89d4.asciidoc

@@ -0,0 +1,28 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.search({
+  index: "my-index-*",
+  query: {
+    bool: {
+      must: [
+        {
+          match: {
+            "user.id": "kimchy",
+          },
+        },
+      ],
+      must_not: [
+        {
+          terms: {
+            _index: ["my-index-01"],
+          },
+        },
+      ],
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/32c8c86702ccd68eb70f1573409c2a1f.asciidoc

@@ -0,0 +1,31 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.ilm.putLifecycle({
+  name: "my_policy",
+  policy: {
+    phases: {
+      hot: {
+        actions: {
+          rollover: {
+            max_primary_shard_size: "50gb",
+          },
+          searchable_snapshot: {
+            snapshot_repository: "backing_repo",
+            replicate_for: "14d",
+          },
+        },
+      },
+      delete: {
+        min_age: "28d",
+        actions: {
+          delete: {},
+        },
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/3312c82f81816bf76629db9582991812.asciidoc

@@ -14,6 +14,7 @@ const response = await client.indices.putSettings({
     "index.search.slowlog.threshold.fetch.info": "800ms",
     "index.search.slowlog.threshold.fetch.debug": "500ms",
     "index.search.slowlog.threshold.fetch.trace": "200ms",
+    "index.search.slowlog.include.user": true,
   },
 });
 console.log(response);

docs/doc_examples/36792c81c053e0555407d1e83e7e054f.asciidoc

@@ -0,0 +1,70 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.search({
+  index: "movies",
+  size: 10,
+  retriever: {
+    rescorer: {
+      rescore: {
+        window_size: 50,
+        query: {
+          rescore_query: {
+            script_score: {
+              query: {
+                match_all: {},
+              },
+              script: {
+                source:
+                  "cosineSimilarity(params.queryVector, 'product-vector_final_stage') + 1.0",
+                params: {
+                  queryVector: [-0.5, 90, -10, 14.8, -156],
+                },
+              },
+            },
+          },
+        },
+      },
+      retriever: {
+        rrf: {
+          rank_window_size: 100,
+          retrievers: [
+            {
+              standard: {
+                query: {
+                  sparse_vector: {
+                    field: "plot_embedding",
+                    inference_id: "my-elser-model",
+                    query: "films that explore psychological depths",
+                  },
+                },
+              },
+            },
+            {
+              standard: {
+                query: {
+                  multi_match: {
+                    query: "crime",
+                    fields: ["plot", "title"],
+                  },
+                },
+              },
+            },
+            {
+              knn: {
+                field: "vector",
+                query_vector: [10, 22, 77],
+                k: 10,
+                num_candidates: 10,
+              },
+            },
+          ],
+        },
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/3722dad876023e0757138dd5a6d3240e.asciidoc

@@ -0,0 +1,23 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.indices.create({
+  index: "my-index",
+  settings: {
+    index: {
+      number_of_shards: 3,
+      "blocks.write": true,
+    },
+  },
+  mappings: {
+    properties: {
+      field1: {
+        type: "text",
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/38ba93890494bfa7beece58dffa44f98.asciidoc

@@ -1,23 +0,0 @@
-// This file is autogenerated, DO NOT EDIT
-// Use `node scripts/generate-docs-examples.js` to generate the docs examples
-
-[source, js]
-----
-const response = await client.bulk({
-  index: "test-index",
-  operations: [
-    {
-      update: {
-        _id: "1",
-      },
-    },
-    {
-      doc: {
-        infer_field: "updated inference field",
-        source_field: "updated source field",
-      },
-    },
-  ],
-});
-console.log(response);
-----

docs/doc_examples/3a204b57072a104d9b50f3a9e064a8f6.asciidoc

@@ -0,0 +1,19 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.search({
+  index: ".ml-anomalies-custom-example",
+  size: 0,
+  aggs: {
+    job_ids: {
+      terms: {
+        field: "job_id",
+        size: 100,
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/3bc4a3681e3ea9cb3de49f72085807d8.asciidoc

@@ -0,0 +1,61 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.search({
+  index: "retrievers_example",
+  retriever: {
+    linear: {
+      retrievers: [
+        {
+          retriever: {
+            standard: {
+              query: {
+                function_score: {
+                  query: {
+                    term: {
+                      topic: "ai",
+                    },
+                  },
+                  functions: [
+                    {
+                      script_score: {
+                        script: {
+                          source: "doc['timestamp'].value.millis",
+                        },
+                      },
+                    },
+                  ],
+                  boost_mode: "replace",
+                },
+              },
+              sort: {
+                timestamp: {
+                  order: "asc",
+                },
+              },
+            },
+          },
+          weight: 2,
+          normalizer: "minmax",
+        },
+        {
+          retriever: {
+            knn: {
+              field: "vector",
+              query_vector: [0.23, 0.67, 0.89],
+              k: 3,
+              num_candidates: 5,
+            },
+          },
+          weight: 1.5,
+        },
+      ],
+      rank_window_size: 10,
+    },
+  },
+  _source: false,
+});
+console.log(response);
+----

docs/doc_examples/3ea4c971b3f47735dcc207ee2645fa03.asciidoc

@@ -0,0 +1,16 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.indices.updateAliases({
+  actions: [
+    {
+      remove_index: {
+        index: "my-index-2099.05.06-000001",
+      },
+    },
+  ],
+});
+console.log(response);
+----

docs/doc_examples/3f1fe5f5f99b98d0891f38003e10b636.asciidoc

@@ -3,7 +3,9 @@
 
 [source, js]
 ----
-const response = await client.esql.asyncQuery({
+const response = await client.transport.request({
+  method: "POST",
+  path: "/_query/async",
   body: {
     query:
       "\n    FROM library\n    | EVAL year = DATE_TRUNC(1 YEARS, release_date)\n    | STATS MAX(page_count) BY year\n    | SORT year\n    | LIMIT 5\n  ",

docs/doc_examples/3f9dcf2aa42f3ecfb5ebfe48c1774103.asciidoc

@@ -0,0 +1,18 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.search({
+  index: "kibana_sample_data_ecommerce",
+  size: 0,
+  aggs: {
+    order_stats: {
+      stats: {
+        field: "taxful_total_price",
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/405511f7c1f12cc0a227b4563fe7b2e2.asciidoc

@@ -3,9 +3,9 @@
 
 [source, js]
 ----
-const response = await client.esql.asyncQueryGet({
-  id: "FkpMRkJGS1gzVDRlM3g4ZzMyRGlLbkEaTXlJZHdNT09TU2VTZVBoNDM3cFZMUToxMDM=",
-  body: null,
+const response = await client.transport.request({
+  method: "GET",
+  path: "/_query/async/FkpMRkJGS1gzVDRlM3g4ZzMyRGlLbkEaTXlJZHdNT09TU2VTZVBoNDM3cFZMUToxMDM=",
 });
 console.log(response);
 ----

docs/doc_examples/41d24383d29b2808a65258a0a3256e96.asciidoc

@@ -4,16 +4,12 @@
 [source, js]
 ----
 const response = await client.indices.create({
-  index: "semantic-embeddings",
+  index: "jinaai-index",
   mappings: {
     properties: {
-      semantic_text: {
-        type: "semantic_text",
-        inference_id: "my-elser-endpoint",
-      },
       content: {
-        type: "text",
-        copy_to: "semantic_text",
+        type: "semantic_text",
+        inference_id: "jinaai-embeddings",
       },
     },
   },

docs/doc_examples/45954b8aaedfed57012be8b6538b0a24.asciidoc

@@ -0,0 +1,47 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.transport.request({
+  method: "POST",
+  path: "/_inference/chat_completion/openai-completion/_stream",
+  body: {
+    messages: [
+      {
+        role: "user",
+        content: [
+          {
+            type: "text",
+            text: "What's the price of a scarf?",
+          },
+        ],
+      },
+    ],
+    tools: [
+      {
+        type: "function",
+        function: {
+          name: "get_current_price",
+          description: "Get the current price of a item",
+          parameters: {
+            type: "object",
+            properties: {
+              item: {
+                id: "123",
+              },
+            },
+          },
+        },
+      },
+    ],
+    tool_choice: {
+      type: "function",
+      function: {
+        name: "get_current_price",
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/48e142e6c69014e0509d4c9251749d77.asciidoc

@@ -10,7 +10,8 @@ const response = await client.inference.put({
     service: "openai",
     service_settings: {
       api_key: "<api_key>",
-      model_id: "text-embedding-ada-002",
+      model_id: "text-embedding-3-small",
+      dimensions: 128,
     },
   },
 });

docs/doc_examples/4b91ad7c9b44e07db4a4e81390f19ad3.asciidoc

@@ -3,9 +3,9 @@
 
 [source, js]
 ----
-const response = await client.inference.streamInference({
-  task_type: "completion",
-  inference_id: "openai-completion",
+const response = await client.transport.request({
+  method: "POST",
+  path: "/_inference/completion/openai-completion/_stream",
   body: {
     input: "What is Elastic?",
   },

docs/doc_examples/4de4bb55bbc0a76c75d256f245a3ee3f.asciidoc

@@ -0,0 +1,17 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.inference.put({
+  task_type: "sparse_embedding",
+  inference_id: "elser-model-eis",
+  inference_config: {
+    service: "elastic",
+    service_settings: {
+      model_name: "elser",
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/4edfb5934d14ad7655bd7e19a112b5c0.asciidoc

@@ -8,11 +8,6 @@ const response = await client.search({
   query: {
     bool: {
       must: [
-        {
-          term: {
-            "category.keyword": "Main Course",
-          },
-        },
         {
           term: {
             tags: "vegetarian",
@@ -27,6 +22,11 @@ const response = await client.search({
         },
       ],
       should: [
+        {
+          term: {
+            category: "Main Course",
+          },
+        },
         {
           multi_match: {
             query: "curry spicy",

docs/doc_examples/519e46350316a33162740e5d7968aa2c.asciidoc

@@ -3,15 +3,18 @@
 
 [source, js]
 ----
-const response = await client.knnSearch({
-  index: "my-index",
+const response = await client.search({
+  index: "image-index",
   knn: {
-    field: "image_vector",
-    query_vector: [0.3, 0.1, 1.2],
+    field: "image-vector",
+    query_vector: [-5, 9, -12],
     k: 10,
     num_candidates: 100,
+    rescore_vector: {
+      oversample: 2,
+    },
   },
-  _source: ["name", "file_type"],
+  fields: ["title", "file-type"],
 });
 console.log(response);
 ----

docs/doc_examples/537bce129338d9227bccb6a0283dab45.asciidoc

@@ -5,7 +5,7 @@
 ----
 const response = await client.cluster.putSettings({
   persistent: {
-    "cluster.routing.allocation.disk.watermark.low": "30gb",
+    "migrate.data_stream_reindex_max_request_per_second": 10000,
   },
 });
 console.log(response);

docs/doc_examples/53d9d2ec9cb8d211772d764e76fe6890.asciidoc

@@ -0,0 +1,18 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.ingest.simulate({
+  id: "query_helper_pipeline",
+  docs: [
+    {
+      _source: {
+        content:
+          "artificial intelligence in medicine articles published in the last 12 months",
+      },
+    },
+  ],
+});
+console.log(response);
+----

docs/doc_examples/57dc15e5ad663c342fd5c1d86fcd1b29.asciidoc

@@ -3,7 +3,9 @@
 
 [source, js]
 ----
-const response = await client.security.oidcPrepareAuthentication({
+const response = await client.transport.request({
+  method: "POST",
+  path: "/_security/oidc/prepare",
   body: {
     realm: "oidc1",
     state: "lGYK0EcSLjqH6pkT5EVZjC6eIW5YCGgywj2sxROO",

docs/doc_examples/5836b09198feb1269ed12839b416123d.asciidoc

@@ -0,0 +1,16 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.search({
+  index: "jinaai-index",
+  query: {
+    semantic: {
+      field: "content",
+      query: "who inspired taking care of the sea?",
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/59aa5216630f80c5dc298fc5bba4a819.asciidoc

@@ -0,0 +1,10 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.indices.getSettings({
+  index: ".reindexed-v9-ml-anomalies-custom-example",
+});
+console.log(response);
+----

docs/doc_examples/5bba213a7f543190139d1a69ab2ed076.asciidoc

@@ -3,8 +3,12 @@
 
 [source, js]
 ----
-const response = await client.esql.asyncQuery({
-  format: "json",
+const response = await client.transport.request({
+  method: "POST",
+  path: "/_query/async",
+  querystring: {
+    format: "json",
+  },
   body: {
     query:
       "\n    FROM cluster_one:my-index*,cluster_two:logs*\n    | STATS COUNT(http.response.status_code) BY user.id\n    | LIMIT 2\n  ",

docs/doc_examples/5f16358ebb5d14b86f57612d5f92d923.asciidoc

@@ -9,7 +9,6 @@ const response = await client.indices.create({
     properties: {
       inference_field: {
         type: "semantic_text",
-        inference_id: "my-elser-endpoint",
       },
     },
   },

docs/doc_examples/68d7f7d4d268ee98caead5aef19933d6.asciidoc

@@ -45,7 +45,7 @@ console.log(response);
 
 const response1 = await client.indices.putIndexTemplate({
   name: 2,
-  index_patterns: ["k8s*"],
+  index_patterns: ["k9s*"],
   composed_of: ["destination_template"],
   data_stream: {},
 });

docs/doc_examples/6b67c6121efb86ee100d40c2646f77b5.asciidoc

@@ -4,9 +4,11 @@
 [source, js]
 ----
 const response = await client.indices.putSettings({
-  index: "my-index-000001",
+  index: "*",
   settings: {
     "index.search.slowlog.include.user": true,
+    "index.search.slowlog.threshold.fetch.warn": "30s",
+    "index.search.slowlog.threshold.query.warn": "30s",
   },
 });
 console.log(response);

docs/doc_examples/6b6fd0a5942dfb9762ad2790cf421a80.asciidoc

@@ -11,7 +11,7 @@ const response = await client.searchApplication.put({
       script: {
         lang: "mustache",
         source:
-          '\n        {\n          "query": {\n            "bool": {\n              "must": [\n              {{#query}}\n                \n              {{/query}}\n            ],\n            "filter": {{#toJson}}_es_filters{{/toJson}}\n            }\n          },\n          "_source": {\n            "includes": ["title", "plot"]\n          },\n          "aggs": {{#toJson}}_es_aggs{{/toJson}},\n          "from": {{from}},\n          "size": {{size}},\n          "sort": {{#toJson}}_es_sort_fields{{/toJson}}\n        }\n      ',
+          '\n        {\n          "query": {\n            "bool": {\n              "must": [\n              {{#query}}\n              {{/query}}\n            ],\n            "filter": {{#toJson}}_es_filters{{/toJson}}\n            }\n          },\n          "_source": {\n            "includes": ["title", "plot"]\n          },\n          "aggs": {{#toJson}}_es_aggs{{/toJson}},\n          "from": {{from}},\n          "size": {{size}},\n          "sort": {{#toJson}}_es_sort_fields{{/toJson}}\n        }\n      ',
         params: {
           query: "",
           _es_filters: {},

docs/doc_examples/6baf72c04d48cb04c2f8be609ff3b3b5.asciidoc

@@ -6,14 +6,15 @@
 const response = await client.search({
   index: "test-index",
   query: {
-    nested: {
-      path: "inference_field.inference.chunks",
-      query: {
-        sparse_vector: {
-          field: "inference_field.inference.chunks.embeddings",
-          inference_id: "my-inference-id",
-          query: "mountain lake",
-        },
+    match: {
+      my_semantic_field: "Which country is Paris in?",
+    },
+  },
+  highlight: {
+    fields: {
+      my_semantic_field: {
+        number_of_fragments: 2,
+        order: "score",
       },
     },
   },

docs/doc_examples/6e498b9dc753b94abf2618c407fa5cd8.asciidoc

@@ -0,0 +1,16 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.reindex({
+  wait_for_completion: "false",
+  source: {
+    index: ".ml-anomalies-custom-example",
+  },
+  dest: {
+    index: ".reindexed-v9-ml-anomalies-custom-example",
+  },
+});
+console.log(response);
+----

docs/doc_examples/6f3b723bf6179b96c3413597ed7f49e1.asciidoc

@@ -3,7 +3,9 @@
 
 [source, js]
 ----
-const response = await client.security.bulkUpdateApiKeys({
+const response = await client.transport.request({
+  method: "POST",
+  path: "/_security/api_key/_bulk_update",
   body: {
     ids: ["VuaCfGcBCdbkQm-e5aOx", "H3_AhoIBA9hmeQJdg7ij"],
   },

docs/doc_examples/6fa02c2ad485bbe91f44b321158250f3.asciidoc

@@ -12,6 +12,13 @@ const response = await client.search({
       fields: ["my_field", "my_field._2gram", "my_field._3gram"],
     },
   },
+  highlight: {
+    fields: {
+      my_field: {
+        matched_fields: ["my_field._index_prefix"],
+      },
+    },
+  },
 });
 console.log(response);
 ----

docs/doc_examples/730045fae3743c39b612813a42c330c3.asciidoc

@@ -0,0 +1,24 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.search({
+  index: "my-index-000001",
+  query: {
+    prefix: {
+      full_name: {
+        value: "ki",
+      },
+    },
+  },
+  highlight: {
+    fields: {
+      full_name: {
+        matched_fields: ["full_name._index_prefix"],
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/7478ff69113fb53f41ea07cdf911fa67.asciidoc

@@ -0,0 +1,33 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.search({
+  index: "kibana_sample_data_ecommerce",
+  size: 0,
+  aggs: {
+    daily_sales: {
+      date_histogram: {
+        field: "order_date",
+        calendar_interval: "day",
+      },
+      aggs: {
+        daily_revenue: {
+          sum: {
+            field: "taxful_total_price",
+          },
+        },
+        smoothed_revenue: {
+          moving_fn: {
+            buckets_path: "daily_revenue",
+            window: 3,
+            script: "MovingFunctions.unweightedAvg(values)",
+          },
+        },
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/74b229a6e020113e5749099451979c89.asciidoc

@@ -1,26 +0,0 @@
-// This file is autogenerated, DO NOT EDIT
-// Use `node scripts/generate-docs-examples.js` to generate the docs examples
-
-[source, js]
-----
-const response = await client.search({
-  index: "test-index",
-  query: {
-    nested: {
-      path: "inference_field.inference.chunks",
-      query: {
-        knn: {
-          field: "inference_field.inference.chunks.embeddings",
-          query_vector_builder: {
-            text_embedding: {
-              model_id: "my_inference_id",
-              model_text: "mountain lake",
-            },
-          },
-        },
-      },
-    },
-  },
-});
-console.log(response);
-----

docs/doc_examples/76e02434835630cb830724beb92df354.asciidoc

@@ -0,0 +1,44 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.search({
+  index: "retrievers_example",
+  retriever: {
+    rrf: {
+      retrievers: [
+        {
+          knn: {
+            field: "vector",
+            query_vector: [0.23, 0.67, 0.89],
+            k: 3,
+            num_candidates: 5,
+          },
+        },
+        {
+          text_similarity_reranker: {
+            retriever: {
+              standard: {
+                query: {
+                  term: {
+                    topic: "ai",
+                  },
+                },
+              },
+            },
+            field: "text",
+            inference_id: "my-rerank-model",
+            inference_text:
+              "Can I use generative AI to identify user intent and improve search relevance?",
+          },
+        },
+      ],
+      rank_window_size: 10,
+      rank_constant: 1,
+    },
+  },
+  _source: false,
+});
+console.log(response);
+----

docs/doc_examples/77518e8c6198acfe77c0934fd2fe65cb.asciidoc

@@ -3,7 +3,9 @@
 
 [source, js]
 ----
-const response = await client.textStructure.findMessageStructure({
+const response = await client.transport.request({
+  method: "POST",
+  path: "/_text_structure/find_message_structure",
   body: {
     messages: [
       "[2024-03-05T10:52:36,256][INFO ][o.a.l.u.VectorUtilPanamaProvider] [laptop] Java vector incubator API enabled; uses preferredBitSize=128",

docs/doc_examples/77cebba946fe648873a1e7375c13df41.asciidoc

@@ -5,11 +5,8 @@
 ----
 const response = await client.cluster.putSettings({
   persistent: {
-    "cluster.indices.close.enable": false,
-    "indices.recovery.max_bytes_per_sec": "50mb",
-  },
-  transient: {
-    "*": null,
+    "cluster.routing.allocation.disk.watermark.low": "90%",
+    "cluster.routing.allocation.disk.watermark.high": "95%",
   },
 });
 console.log(response);

docs/doc_examples/78043831fd32004a82930c8ac8a1d809.asciidoc

@@ -0,0 +1,46 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.search({
+  index: "retrievers_example",
+  retriever: {
+    text_similarity_reranker: {
+      retriever: {
+        rrf: {
+          retrievers: [
+            {
+              standard: {
+                query: {
+                  query_string: {
+                    query:
+                      "(information retrieval) OR (artificial intelligence)",
+                    default_field: "text",
+                  },
+                },
+              },
+            },
+            {
+              knn: {
+                field: "vector",
+                query_vector: [0.23, 0.67, 0.89],
+                k: 3,
+                num_candidates: 5,
+              },
+            },
+          ],
+          rank_window_size: 10,
+          rank_constant: 1,
+        },
+      },
+      field: "text",
+      inference_id: "my-rerank-model",
+      inference_text:
+        "What are the state of the art applications of AI in information retrieval?",
+    },
+  },
+  _source: false,
+});
+console.log(response);
+----

docs/doc_examples/790684b45bef2bb848ea932f0fd0cfbd.asciidoc

@@ -0,0 +1,35 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.search({
+  query: {
+    intervals: {
+      my_text: {
+        all_of: {
+          ordered: false,
+          max_gaps: 1,
+          intervals: [
+            {
+              match: {
+                query: "my favorite food",
+                max_gaps: 0,
+                ordered: true,
+              },
+            },
+            {
+              match: {
+                query: "cold porridge",
+                max_gaps: 4,
+                ordered: true,
+              },
+            },
+          ],
+        },
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/79d206a528be704050a437adce2496dd.asciidoc

@@ -4,17 +4,18 @@
 [source, js]
 ----
 const response = await client.inference.put({
-  task_type: "sparse_embedding",
-  inference_id: "my-elser-endpoint",
+  task_type: "rerank",
+  inference_id: "my-elastic-rerank",
   inference_config: {
-    service: "elser",
+    service: "elasticsearch",
     service_settings: {
+      model_id: ".rerank-v1",
+      num_threads: 1,
       adaptive_allocations: {
         enabled: true,
-        min_number_of_allocations: 3,
+        min_number_of_allocations: 1,
         max_number_of_allocations: 10,
       },
-      num_threads: 1,
     },
   },
 });

docs/doc_examples/7a2fdfd7b0553d63440af7598f9ad867.asciidoc

@@ -4,7 +4,7 @@
 [source, js]
 ----
 const response = await client.indices.create({
-  index: "my-index-000002",
+  index: "my-index-000003",
   mappings: {
     properties: {
       inference_field: {

docs/doc_examples/7ba29f0be2297b54a640b0a17d7ef5ca.asciidoc

@@ -0,0 +1,11 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.transport.request({
+  method: "DELETE",
+  path: "/_ingest/ip_location/database/my-database-id",
+});
+console.log(response);
+----

docs/doc_examples/7bdc283b96c7a965fae23013647b8578.asciidoc

@@ -7,14 +7,14 @@ const response = await client.indices.create({
   index: "test-index",
   mappings: {
     properties: {
-      infer_field: {
-        type: "semantic_text",
-        inference_id: "my-elser-endpoint",
-      },
       source_field: {
         type: "text",
         copy_to: "infer_field",
       },
+      infer_field: {
+        type: "semantic_text",
+        inference_id: ".elser-2-elasticsearch",
+      },
     },
   },
 });

docs/doc_examples/7dd0d9cc6c5982a2c003d301e90feeba.asciidoc

@@ -0,0 +1,37 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.search({
+  index: "kibana_sample_data_ecommerce",
+  size: 0,
+  aggs: {
+    daily_sales: {
+      date_histogram: {
+        field: "order_date",
+        calendar_interval: "day",
+        format: "yyyy-MM-dd",
+      },
+      aggs: {
+        revenue: {
+          sum: {
+            field: "taxful_total_price",
+          },
+        },
+        unique_customers: {
+          cardinality: {
+            field: "customer_id",
+          },
+        },
+        avg_basket_size: {
+          avg: {
+            field: "total_quantity",
+          },
+        },
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/80135e8c644e34cc70ce8a4e7915d1a2.asciidoc

@@ -12,7 +12,7 @@ const response = await client.ingest.putPipeline({
         field: "data",
         indexed_chars: 11,
         indexed_chars_field: "max_size",
-        remove_binary: false,
+        remove_binary: true,
       },
     },
   ],

docs/doc_examples/80dd7f5882c59b9c1c90e8351937441f.asciidoc

@@ -3,7 +3,9 @@
 
 [source, js]
 ----
-const response = await client.security.bulkUpdateApiKeys({
+const response = await client.transport.request({
+  method: "POST",
+  path: "/_security/api_key/_bulk_update",
   body: {
     ids: ["VuaCfGcBCdbkQm-e5aOx", "H3_AhoIBA9hmeQJdg7ij"],
     role_descriptors: {

docs/doc_examples/82bb6c61dab959f4446dc5ecab7ecbdf.asciidoc

@@ -0,0 +1,34 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.transport.request({
+  method: "POST",
+  path: "/_inference/chat_completion/openai-completion/_stream",
+  body: {
+    messages: [
+      {
+        role: "assistant",
+        content: "Let's find out what the weather is",
+        tool_calls: [
+          {
+            id: "call_KcAjWtAww20AihPHphUh46Gd",
+            type: "function",
+            function: {
+              name: "get_current_weather",
+              arguments: '{"location":"Boston, MA"}',
+            },
+          },
+        ],
+      },
+      {
+        role: "tool",
+        content: "The weather is cold",
+        tool_call_id: "call_KcAjWtAww20AihPHphUh46Gd",
+      },
+    ],
+  },
+});
+console.log(response);
+----

docs/doc_examples/8621c05cc7cf3880bde751f6670a0c3a.asciidoc

@@ -4,9 +4,11 @@
 [source, js]
 ----
 const response = await client.indices.putSettings({
-  index: "my-index-000001",
+  index: ".reindexed-v9-ml-anomalies-custom-example",
   settings: {
-    "index.blocks.read_only_allow_delete": null,
+    index: {
+      number_of_replicas: 0,
+    },
   },
 });
 console.log(response);

docs/doc_examples/89f547649895176c246bb8c41313ff21.asciidoc

@@ -0,0 +1,12 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.esql.query({
+  query:
+    '\nFROM library\n| EVAL year = DATE_EXTRACT("year", release_date)\n| WHERE page_count > ? AND match(author, ?, {"minimum_should_match": ?})\n| LIMIT 5\n',
+  params: [300, "Frank Herbert", 2],
+});
+console.log(response);
+----

docs/doc_examples/8a0b5f759de3f27f0801c1176e616117.asciidoc

@@ -9,7 +9,6 @@ const response = await client.indices.create({
     properties: {
       content: {
         type: "semantic_text",
-        inference_id: "my-elser-endpoint",
       },
     },
   },

docs/doc_examples/8c47c80139f40f25db44f5781ca2dfbe.asciidoc

@@ -3,8 +3,8 @@
 
 [source, js]
 ----
-const response = await client.indices.unfreeze({
-  index: "my-index-000001",
+const response = await client.indices.getAlias({
+  index: ".ml-anomalies-custom-example",
 });
 console.log(response);
 ----

docs/doc_examples/8c639d3eef5c2de29e12bd9c6a42d3d4.asciidoc

@@ -0,0 +1,39 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.search({
+  index: "kibana_sample_data_ecommerce",
+  size: 0,
+  aggs: {
+    categories: {
+      terms: {
+        field: "category.keyword",
+        size: 5,
+        order: {
+          total_revenue: "desc",
+        },
+      },
+      aggs: {
+        total_revenue: {
+          sum: {
+            field: "taxful_total_price",
+          },
+        },
+        avg_order_value: {
+          avg: {
+            field: "taxful_total_price",
+          },
+        },
+        total_items: {
+          sum: {
+            field: "total_quantity",
+          },
+        },
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/91e106a2affbc8df32cd940684a779ed.asciidoc

@@ -0,0 +1,17 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.transport.request({
+  method: "PUT",
+  path: "/_ingest/ip_location/database/my-database-1",
+  body: {
+    name: "GeoIP2-Domain",
+    maxmind: {
+      account_id: "1234567",
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/9250ac57ec81d5192e8ad4c462438489.asciidoc

@@ -0,0 +1,42 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.bulk({
+  index: "jinaai-index",
+  operations: [
+    {
+      index: {
+        _index: "jinaai-index",
+        _id: "1",
+      },
+    },
+    {
+      content:
+        "Sarah Johnson is a talented marine biologist working at the Oceanographic Institute. Her groundbreaking research on coral reef ecosystems has garnered international attention and numerous accolades.",
+    },
+    {
+      index: {
+        _index: "jinaai-index",
+        _id: "2",
+      },
+    },
+    {
+      content:
+        "She spends months at a time diving in remote locations, meticulously documenting the intricate relationships between various marine species. ",
+    },
+    {
+      index: {
+        _index: "jinaai-index",
+        _id: "3",
+      },
+    },
+    {
+      content:
+        "Her dedication to preserving these delicate underwater environments has inspired a new generation of conservationists.",
+    },
+  ],
+});
+console.log(response);
+----

docs/doc_examples/9313f534e1aa266cde7d4af74665497f.asciidoc

@@ -0,0 +1,13 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.connector.put({
+  connector_id: "my-{service-name-stub}-connector",
+  index_name: "my-elasticsearch-index",
+  name: "Content synced from {service-name}",
+  service_type: "{service-name-stub}",
+});
+console.log(response);
+----

docs/doc_examples/931817b168e055ecf738785c721125dd.asciidoc

@@ -0,0 +1,32 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.ingest.putPipeline({
+  id: "query_helper_pipeline",
+  processors: [
+    {
+      script: {
+        source:
+          "ctx.prompt = 'Please generate an elasticsearch search query on index `articles_index` for the following natural language query. Dates are in the field `@timestamp`, document types are in the field `type` (options are `news`, `publication`), categories in the field `category` and can be multiple (options are `medicine`, `pharmaceuticals`, `technology`), and document names are in the field `title` which should use a fuzzy match. Ignore fields which cannot be determined from the natural language query context: ' + ctx.content",
+      },
+    },
+    {
+      inference: {
+        model_id: "openai_chat_completions",
+        input_output: {
+          input_field: "prompt",
+          output_field: "query",
+        },
+      },
+    },
+    {
+      remove: {
+        field: "prompt",
+      },
+    },
+  ],
+});
+console.log(response);
+----

docs/doc_examples/948418e0ef1b7e7cfee2f11be715d7d2.asciidoc

@@ -0,0 +1,89 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.indices.create({
+  index: "retrievers_example_nested",
+  settings: {
+    number_of_shards: 1,
+  },
+  mappings: {
+    properties: {
+      nested_field: {
+        type: "nested",
+        properties: {
+          paragraph_id: {
+            type: "keyword",
+          },
+          nested_vector: {
+            type: "dense_vector",
+            dims: 3,
+            similarity: "l2_norm",
+            index: true,
+            index_options: {
+              type: "flat",
+            },
+          },
+        },
+      },
+      topic: {
+        type: "keyword",
+      },
+    },
+  },
+});
+console.log(response);
+
+const response1 = await client.index({
+  index: "retrievers_example_nested",
+  id: 1,
+  document: {
+    nested_field: [
+      {
+        paragraph_id: "1a",
+        nested_vector: [-1.12, -0.59, 0.78],
+      },
+      {
+        paragraph_id: "1b",
+        nested_vector: [-0.12, 1.56, 0.42],
+      },
+      {
+        paragraph_id: "1c",
+        nested_vector: [1, -1, 0],
+      },
+    ],
+    topic: ["ai"],
+  },
+});
+console.log(response1);
+
+const response2 = await client.index({
+  index: "retrievers_example_nested",
+  id: 2,
+  document: {
+    nested_field: [
+      {
+        paragraph_id: "2a",
+        nested_vector: [0.23, 1.24, 0.65],
+      },
+    ],
+    topic: ["information_retrieval"],
+  },
+});
+console.log(response2);
+
+const response3 = await client.index({
+  index: "retrievers_example_nested",
+  id: 3,
+  document: {
+    topic: ["ai"],
+  },
+});
+console.log(response3);
+
+const response4 = await client.indices.refresh({
+  index: "retrievers_example_nested",
+});
+console.log(response4);
+----

docs/doc_examples/96e88611f99e6834bd64b58dc8a282c1.asciidoc

@@ -0,0 +1,18 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.indices.create({
+  index: "my-index-000002",
+  mappings: {
+    properties: {
+      inference_field: {
+        type: "semantic_text",
+        inference_id: "my-openai-endpoint",
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/97c6c07f46f4177f0565a04bc50924a3.asciidoc

@@ -0,0 +1,37 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.search({
+  index: "retrievers_example",
+  retriever: {
+    rrf: {
+      retrievers: [
+        {
+          standard: {
+            query: {
+              query_string: {
+                query: "(information retrieval) OR (artificial intelligence)",
+                default_field: "text",
+              },
+            },
+          },
+        },
+        {
+          knn: {
+            field: "vector",
+            query_vector: [0.23, 0.67, 0.89],
+            k: 3,
+            num_candidates: 5,
+          },
+        },
+      ],
+      rank_window_size: 10,
+      rank_constant: 1,
+    },
+  },
+  _source: false,
+});
+console.log(response);
+----

docs/doc_examples/99fb82d49ac477e6a9dfdd71f9465374.asciidoc

@@ -0,0 +1,11 @@
+// This file is autogenerated, DO NOT EDIT
+// Use `node scripts/generate-docs-examples.js` to generate the docs examples
+
+[source, js]
+----
+const response = await client.transport.request({
+  method: "DELETE",
+  path: "/_ingest/ip_location/database/example-database-id",
+});
+console.log(response);
+----

docs/doc_examples/9afa0844883b7471883aa378a8dd10b4.asciidoc

@@ -3,9 +3,9 @@
 
 [source, js]
 ----
-const response = await client.searchApplication.postBehavioralAnalyticsEvent({
-  collection_name: "my_analytics_collection",
-  event_type: "search_click",
+const response = await client.transport.request({
+  method: "POST",
+  path: "/_application/analytics/my_analytics_collection/event/search_click",
   body: {
     session: {
       id: "1797ca95-91c9-4e2e-b1bd-9c38e6f386a9",