Auto-generated API code (#2578)

(cherry picked from commit ed3cace127)

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

.buildkite/make.mjs

@@ -125,6 +125,13 @@ async function codegen (args) {
   await $`cp -R ${join(import.meta.url, '..', '..', 'elastic-client-generator-js', 'output')}/* ${join(import.meta.url, '..', 'src', 'api')}`
   await $`mv ${join(import.meta.url, '..', 'src', 'api', 'reference.asciidoc')} ${join(import.meta.url, '..', 'docs', 'reference.asciidoc')}`
   await $`npm run build`
+
+  // run docs example generation
+  if (version === 'main') {
+    await $`node ./scripts/generate-docs-examples.js`
+  } else {
+    await $`node ./scripts/generate-docs-examples.js ${version.split('.').slice(0, 2).join('.')}`
+  }
 }
 
 function onError (err) {

.dockerignore

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

.github/make.sh

@@ -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[*]}"

.github/workflows/auto-merge.yml

@@ -0,0 +1,18 @@
+name: Automerge
+
+on:
+  pull_request_review:
+    types:
+      - submitted
+
+jobs:
+  automerge:
+    runs-on: ubuntu-latest
+    if: github.event.review.state == 'approved'
+    steps:
+      - uses: reitermarkus/automerge@v2
+        with:
+          token: ${{ secrets.GH_TOKEN }}
+          merge-method: squash
+          pull-request-author-associations: OWNER
+          review-author-associations: OWNER,CONTRIBUTOR

.github/workflows/nodejs.yml

@@ -2,7 +2,7 @@
 name: Node CI
 
 on:
-  pull_request_target: {}
+  pull_request: {}
 
 jobs:
   paths-filter:

.gitignore

@@ -64,3 +64,7 @@ test/bundlers/parcel-test/.parcel-cache
 
 lib
 junit-output
+bun.lockb
+test-results
+processinfo
+.tap

.npmignore

@@ -72,3 +72,5 @@ CODE_OF_CONDUCT.md
 CONTRIBUTING.md
 
 src
+bun.lockb
+.tap

docs/changelog.asciidoc

@@ -1,6 +1,44 @@
 [[changelog-client]]
 == Release notes
 
+[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]
+==== Fixes
+
+[discrete]
+===== Improved support for Elasticsearch `v8.15`
+
+Updated TypeScript types based on fixes and improvements to the Elasticsearch specification.
+
+[discrete]
+=== 8.15.1
+
+[discrete]
+==== Fixes
+
+[discrete]
+===== Improved support for Elasticsearch `v8.15`
+
+Updated TypeScript types based on fixes and improvements to the Elasticsearch specification.
+
 [discrete]
 === 8.15.0
 

docs/configuration.asciidoc

@@ -2,10 +2,11 @@
 == Configuration
 
 
-The client is designed to be easily configured for your needs. In the following 
+The client is designed to be easily configured for your needs. In the following
 section, you can see the possible options that you can use to configure it.
 
 * <<basic-config>>
 * <<advanced-config>>
+* <<timeout-best-practices>>
 * <<child>>
 * <<client-testing>>

docs/doc_examples/00fea15cbca83be9d5f1a024ff2ec708.asciidoc

@@ -3,10 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_inference/text_embedding/my-e5-model",
-  body: {
+const response = await client.inference.put({
+  task_type: "text_embedding",
+  inference_id: "my-e5-model",
+  inference_config: {
     service: "elasticsearch",
     service_settings: {
       num_allocations: 1,

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/01ae196538fac197eedbbf458a4ef31b.asciidoc

@@ -5,10 +5,16 @@
 ----
 const response = await client.indices.create({
   index: "idx",
-  mappings: {
-    _source: {
-      mode: "synthetic",
+  settings: {
+    index: {
+      mapping: {
+        source: {
+          mode: "synthetic",
+        },
+      },
     },
+  },
+  mappings: {
     properties: {
       kwd: {
         type: "keyword",

docs/doc_examples/01b23f09d2b7f140faf649eadbbf3ac3.asciidoc

@@ -26,7 +26,7 @@ const response1 = await client.cluster.putComponentTemplate({
           type: "keyword",
           script: {
             source:
-              "emit(doc['@timestamp'].value.dayOfWeekEnum.getDisplayName(TextStyle.FULL, Locale.ROOT))",
+              "emit(doc['@timestamp'].value.dayOfWeekEnum.getDisplayName(TextStyle.FULL, Locale.ENGLISH))",
           },
         },
       },

docs/doc_examples/01cd0ea360282a2c591a366679d7187d.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.tasks.list({
+  human: "true",
+  detailed: "true",
+  actions: "indices:data/write/bulk",
+});
+console.log(response);
+----

docs/doc_examples/04412d11783dac25b5fd2ec5407078a3.asciidoc

@@ -3,13 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_connector/my-connector/_api_key_id",
-  body: {
-    api_key_id: "my-api-key-id",
-    api_key_secret_id: "my-connector-secret-id",
-  },
+const response = await client.connector.updateApiKeyId({
+  connector_id: "my-connector",
+  api_key_id: "my-api-key-id",
+  api_key_secret_id: "my-connector-secret-id",
 });
 console.log(response);
 ----

docs/doc_examples/04de2e3a9c00c2056b07bf9cf9e63a99.asciidoc

@@ -3,10 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_inference/text_embedding/google_vertex_ai_embeddings",
-  body: {
+const response = await client.inference.put({
+  task_type: "text_embedding",
+  inference_id: "google_vertex_ai_embeddings",
+  inference_config: {
     service: "googlevertexai",
     service_settings: {
       service_account_json: "<service_account_json>",

docs/doc_examples/05e637284bc3bedd46e0b7c26ad983c4.asciidoc

@@ -0,0 +1,21 @@
+// 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: "alibabacloud_ai_search_embeddings_pipeline",
+  processors: [
+    {
+      inference: {
+        model_id: "alibabacloud_ai_search_embeddings",
+        input_output: {
+          input_field: "content",
+          output_field: "content_embedding",
+        },
+      },
+    },
+  ],
+});
+console.log(response);
+----

docs/doc_examples/0709a38613d2de90d418ce12b36af30e.asciidoc

@@ -3,6 +3,6 @@
 
 [source, js]
 ----
-const response = await client.simulate.ingest({});
+const response = await client.cluster.reroute();
 console.log(response);
 ----

docs/doc_examples/0722b302b2b3275a988d858044f99d5d.asciidoc

@@ -3,8 +3,8 @@
 
 [source, js]
 ----
-const response = await client.nodes.hotThreads({
-  node_id: "my-node,my-other-node",
+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/083b92e8ea264e49bf9fd40fc6a3094b.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: "text_embedding",
+  inference_id: "my-e5-model",
+  inference_config: {
+    service: "elasticsearch",
+    service_settings: {
+      adaptive_allocations: {
+        enabled: true,
+        min_number_of_allocations: 3,
+        max_number_of_allocations: 10,
+      },
+      num_threads: 1,
+      model_id: ".multilingual-e5-small",
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/0ad8edd10542ec2c4d5d8700d7e2ba97.asciidoc

@@ -3,10 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_inference/text_embedding/amazon_bedrock_embeddings",
-  body: {
+const response = await client.inference.put({
+  task_type: "text_embedding",
+  inference_id: "amazon_bedrock_embeddings",
+  inference_config: {
     service: "amazonbedrock",
     service_settings: {
       access_key: "<aws_access_key>",

docs/doc_examples/0ade87c8cb0e3c188d2e3dce279d5cc2.asciidoc

@@ -3,29 +3,26 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_connector/my-g-drive-connector/_filtering",
-  body: {
-    rules: [
-      {
-        field: "file_extension",
-        id: "exclude-txt-files",
-        order: 0,
-        policy: "exclude",
-        rule: "equals",
-        value: "txt",
-      },
-      {
-        field: "_",
-        id: "DEFAULT",
-        order: 1,
-        policy: "include",
-        rule: "regex",
-        value: ".*",
-      },
-    ],
-  },
+const response = await client.connector.updateFiltering({
+  connector_id: "my-g-drive-connector",
+  rules: [
+    {
+      field: "file_extension",
+      id: "exclude-txt-files",
+      order: 0,
+      policy: "exclude",
+      rule: "equals",
+      value: "txt",
+    },
+    {
+      field: "_",
+      id: "DEFAULT",
+      order: 1,
+      policy: "include",
+      rule: "regex",
+      value: ".*",
+    },
+  ],
 });
 console.log(response);
 ----

docs/doc_examples/0b4e50f1b5a0537cbb1a41276bb51c54.asciidoc

@@ -10,7 +10,7 @@ const response = await client.search({
       type: "keyword",
       script: {
         source:
-          "emit(doc['@timestamp'].value.dayOfWeekEnum\n        .getDisplayName(TextStyle.FULL, Locale.ROOT))",
+          "emit(doc['@timestamp'].value.dayOfWeekEnum\n        .getDisplayName(TextStyle.FULL, Locale.ENGLISH))",
       },
     },
   },

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

@@ -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.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  ",
+    include_ccs_metadata: true,
+  },
+});
+console.log(response);
+----

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/0c8be7aec84ea86b243904f5d4162f5a.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: "cooking_blog",
+  query: {
+    match: {
+      title: {
+        query: "fluffy pancakes breakfast",
+        minimum_should_match: 2,
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/0d30077cd34e93377a3a86f2ebd69415.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.connector.put({
+  connector_id: "my-connector",
+  index_name: "search-google-drive",
+  name: "My Connector",
+  description: "My Connector to sync data to Elastic index from Google Drive",
+  service_type: "google_drive",
+  language: "en",
+});
+console.log(response);
+----

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 +3,24 @@
 
 [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",
+      text_fields: [
+        {
+          name: "title",
+          boost: 5,
+        },
+        {
+          name: "description",
+          boost: 1,
+        },
+      ],
+    },
+  },
 });
 console.log(response);
 ----

docs/doc_examples/0e31b8ad176b31028becf9500989bcbd.asciidoc

@@ -0,0 +1,21 @@
+// 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: "text_embedding",
+  inference_id: "watsonx-embeddings",
+  inference_config: {
+    service: "watsonxai",
+    service_settings: {
+      api_key: "<api_key>",
+      url: "<url>",
+      model_id: "ibm/slate-30m-english-rtrvr",
+      project_id: "<project_id>",
+      api_version: "2024-03-14",
+    },
+  },
+});
+console.log(response);
+----

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/0fbca60a487f5f22a4d51d73b2434cc4.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.indices.create({
+  index: "elser-embeddings",
+  mappings: {
+    properties: {
+      content_embedding: {
+        type: "sparse_vector",
+      },
+      content: {
+        type: "text",
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/10a16abe990288253ea25a1b1712fe3d.asciidoc

@@ -5,6 +5,11 @@
 ----
 const response = await client.security.queryUser({
   with_profile_uid: "true",
+  query: {
+    prefix: {
+      roles: "other",
+    },
+  },
 });
 console.log(response);
 ----

docs/doc_examples/11be807bdeaeecc8174dec88e0851ea7.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.transport.request({
+  method: "GET",
+  path: "/_connector/_sync_job",
+  querystring: {
+    connector_id: "my-connector-id",
+    size: "1",
+  },
+});
+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/12e9e758f7f18a6cbf27e9d0aea57a19.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.update({
+  index: ".elastic-connectors",
+  id: "connector_id",
+  doc: {
+    features: {
+      native_connector_api_keys: {
+        enabled: true,
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/13ecdf99114098c76b050397d9c3d4e6.asciidoc

@@ -3,13 +3,11 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "POST",
-  path: "/_inference/sparse_embedding/my-elser-model",
-  body: {
-    input:
-      "The sky above the port was the color of television tuned to a dead channel.",
-  },
+const response = await client.inference.inference({
+  task_type: "sparse_embedding",
+  inference_id: "my-elser-model",
+  input:
+    "The sky above the port was the color of television tuned to a dead channel.",
 });
 console.log(response);
 ----

docs/doc_examples/13fe12cdb73bc89f07a83f1e6b127511.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: "google-vertex-ai-embeddings",
+  mappings: {
+    properties: {
+      content_embedding: {
+        type: "dense_vector",
+        dims: 768,
+        element_type: "float",
+        similarity: "dot_product",
+      },
+      content: {
+        type: "text",
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/141ef0ebaa3b0772892b79b9bb85efb0.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.inference.put({
+  task_type: "my-inference-endpoint",
+  inference_id: "_update",
+  inference_config: {
+    service_settings: {
+      api_key: "<API_KEY>",
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/14a33c364873c2f930ca83d0a3005389.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.cluster.allocationExplain({
+  index: "my-index",
+  shard: 0,
+  primary: false,
+});
+console.log(response);
+----

docs/doc_examples/1522a9297151d7046e6345b9b27539ca.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.connector.updateConfiguration({
+  connector_id: "my-connector-id",
+  values: {
+    host: "127.0.0.1",
+    port: 5432,
+    username: "myuser",
+    password: "mypassword",
+    database: "chinook",
+    schema: "public",
+    tables: "album,artist",
+  },
+});
+console.log(response);
+----

docs/doc_examples/15f769bbd7b5fddeb3353ae726b71b14.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-bit-vectors",
+  query: {
+    script_score: {
+      query: {
+        match_all: {},
+      },
+      script: {
+        source: "dotProduct(params.query_vector, 'my_dense_vector')",
+        params: {
+          query_vector: [
+            0.23, 1.45, 3.67, 4.89, -0.56, 2.34, 3.21, 1.78, -2.45, 0.98, -0.12,
+            3.45, 4.56, 2.78, 1.23, 0.67, 3.89, 4.12, -2.34, 1.56, 0.78, 3.21,
+            4.12, 2.45, -1.67, 0.34, -3.45, 4.56, -2.78, 1.23, -0.67, 3.89,
+            -4.34, 2.12, -1.56, 0.78, -3.21, 4.45, 2.12, 1.67,
+          ],
+        },
+      },
+    },
+  },
+});
+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/16a7ce08b4a6b3af269f27eecc71d664.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.delete({
+  index: "books",
+});
+console.log(response);
+
+const response1 = await client.indices.delete({
+  index: "my-explicit-mappings-books",
+});
+console.log(response1);
+----

docs/doc_examples/17316a81c9dbdd120b7754116bf0461c.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.security.createApiKey({
+  name: "my-connector-api-key",
+  role_descriptors: {
+    "my-connector-connector-role": {
+      cluster: ["monitor", "manage_connector"],
+      indices: [
+        {
+          names: [
+            "my-index_name",
+            ".search-acl-filter-my-index_name",
+            ".elastic-connectors*",
+          ],
+          privileges: ["all"],
+          allow_restricted_indices: false,
+        },
+      ],
+    },
+  },
+});
+console.log(response);
+----

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/193704020a19714dec390452a4e75e8d.asciidoc

@@ -3,8 +3,8 @@
 
 [source, js]
 ----
-const response = await client.cluster.reroute({
-  metric: "none",
+const response = await client.indices.create({
+  index: "books",
 });
 console.log(response);
 ----

docs/doc_examples/19c00c6b29bc7dbc5e92b3668da2da93.asciidoc

@@ -3,6 +3,27 @@
 
 [source, js]
 ----
-const response = await client.simulate.ingest({});
+const response = await client.transport.request({
+  method: "POST",
+  path: "/_ingest/_simulate",
+  body: {
+    docs: [
+      {
+        _index: "my-index",
+        _id: "123",
+        _source: {
+          foo: "bar",
+        },
+      },
+      {
+        _index: "my-index",
+        _id: "456",
+        _source: {
+          foo: "rab",
+        },
+      },
+    ],
+  },
+});
 console.log(response);
 ----

docs/doc_examples/1a56df055b94466ca76818e0858752c6.asciidoc

@@ -3,10 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_inference/text_embedding/openai_embeddings",
-  body: {
+const response = await client.inference.put({
+  task_type: "text_embedding",
+  inference_id: "openai_embeddings",
+  inference_config: {
     service: "openai",
     service_settings: {
       api_key: "<api_key>",

docs/doc_examples/1a7483796087053ba55029d0dc2ab356.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.index({
+  index: "mv",
+  refresh: "true",
+  document: {
+    a: [2, null, 1],
+  },
+});
+console.log(response);
+
+const response1 = await client.esql.query({
+  query: "FROM mv | LIMIT 1",
+});
+console.log(response1);
+----

docs/doc_examples/1a9e03ce0355872a7db27fedc783fbec.asciidoc

@@ -3,10 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_inference/rerank/google_vertex_ai_rerank",
-  body: {
+const response = await client.inference.put({
+  task_type: "rerank",
+  inference_id: "google_vertex_ai_rerank",
+  inference_config: {
     service: "googlevertexai",
     service_settings: {
       service_account_json: "<service_account_json>",

docs/doc_examples/1c330f0fc9eac19d0edeb8c4017b9b93.asciidoc

@@ -4,7 +4,7 @@
 [source, js]
 ----
 const response = await client.ingest.putPipeline({
-  id: "hugging_face_embeddings",
+  id: "hugging_face_embeddings_pipeline",
   processors: [
     {
       inference: {

docs/doc_examples/1c9dac4183a3532c91dbd1a46907729b.asciidoc

@@ -4,7 +4,7 @@
 [source, js]
 ----
 const response = await client.indices.delete({
-  index: "my-index",
+  index: "music",
 });
 console.log(response);
 ----

docs/doc_examples/1cbecd19be22979aefb45b4f160e77ea.asciidoc

@@ -0,0 +1,21 @@
+// 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: "google_vertex_ai_embeddings_pipeline",
+  processors: [
+    {
+      inference: {
+        model_id: "google_vertex_ai_embeddings",
+        input_output: {
+          input_field: "content",
+          output_field: "content_embedding",
+        },
+      },
+    },
+  ],
+});
+console.log(response);
+----

docs/doc_examples/1dadb7efe27b6c0c231eb6535e413bd9.asciidoc

@@ -3,10 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_inference/text_embedding/azure_ai_studio_embeddings",
-  body: {
+const response = await client.inference.put({
+  task_type: "text_embedding",
+  inference_id: "azure_ai_studio_embeddings",
+  inference_config: {
     service: "azureaistudio",
     service_settings: {
       api_key: "<api_key>",

docs/doc_examples/1e26353d546d733634187b8c3a7837a7.asciidoc

@@ -3,12 +3,8 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "GET",
-  path: "/_connector",
-  querystring: {
-    service_type: "sharepoint_online",
-  },
+const response = await client.connector.list({
+  service_type: "sharepoint_online",
 });
 console.log(response);
 ----

docs/doc_examples/1fb2c77c0988bc6545040b20e3afa7e9.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.security.createApiKey({
+  name: "john-api-key",
+  expiration: "1d",
+  role_descriptors: {
+    "sharepoint-online-role": {
+      index: [
+        {
+          names: ["sharepoint-search-application"],
+          privileges: ["read"],
+          query: {
+            template: {
+              params: {
+                access_control: ["john@example.co", "Engineering Members"],
+              },
+              source:
+                '\n              {\n                "bool": {\n                  "should": [\n                    {\n                      "bool": {\n                        "must_not": {\n                          "exists": {\n                            "field": "_allow_access_control"\n                          }\n                        }\n                      }\n                    },\n                    {\n                      "terms": {\n                        "_allow_access_control.enum": {{#toJson}}access_control{{/toJson}}\n                      }\n                    }\n                  ]\n                }\n              }\n              ',
+            },
+          },
+        },
+      ],
+      restriction: {
+        workflows: ["search_application_query"],
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/20179a8889e949d6a8ee5fbf2ba35c96.asciidoc

@@ -0,0 +1,22 @@
+// 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: "google-vertex-ai-embeddings",
+  knn: {
+    field: "content_embedding",
+    query_vector_builder: {
+      text_embedding: {
+        model_id: "google_vertex_ai_embeddings",
+        model_text: "Calculate fuel cost",
+      },
+    },
+    k: 10,
+    num_candidates: 100,
+  },
+  _source: ["id", "content"],
+});
+console.log(response);
+----

docs/doc_examples/216e24f05cbb82c1718713fbab8623d2.asciidoc

@@ -5,7 +5,7 @@
 ----
 const response = await client.ingest.putPipeline({
   id: "geoip",
-  description: "Add geoip info",
+  description: "Add ip geolocation info",
   processors: [
     {
       geoip: {

docs/doc_examples/21cd01cb90d3ea1acd0ab22d7edd2c88.asciidoc

@@ -3,10 +3,10 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_inference/text_embedding/azure_ai_studio_embeddings",
-  body: {
+const response = await client.inference.put({
+  task_type: "text_embedding",
+  inference_id: "azure_ai_studio_embeddings",
+  inference_config: {
     service: "azureaistudio",
     service_settings: {
       api_key: "<api_key>",

docs/doc_examples/21d41e8cbd107fbdf0901f885834dafc.asciidoc

@@ -5,10 +5,16 @@
 ----
 const response = await client.indices.create({
   index: "idx",
-  mappings: {
-    _source: {
-      mode: "synthetic",
+  settings: {
+    index: {
+      mapping: {
+        source: {
+          mode: "synthetic",
+        },
+      },
     },
+  },
+  mappings: {
     properties: {
       card: {
         type: "wildcard",

docs/doc_examples/22b176a184517cf1b5801f5eb4f17f97.asciidoc

@@ -3,8 +3,8 @@
 
 [source, js]
 ----
-const response = await client.cluster.getSettings({
-  flat_settings: "true",
+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/24a037008e0fc2550ecb6a5d36c04a93.asciidoc

@@ -10,7 +10,7 @@ const response = await client.search({
     "date.day_of_week": {
       type: "keyword",
       script:
-        "emit(doc['date'].value.dayOfWeekEnum.getDisplayName(TextStyle.FULL, Locale.ROOT))",
+        "emit(doc['date'].value.dayOfWeekEnum.getDisplayName(TextStyle.FULL, Locale.ENGLISH))",
     },
   },
   aggs: {

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/2968ffb8135f77ba3a9b876dd4918119.asciidoc

@@ -11,7 +11,7 @@ const response = await client.reindex({
   },
   dest: {
     index: "azure-ai-studio-embeddings",
-    pipeline: "azure_ai_studio_embeddings",
+    pipeline: "azure_ai_studio_embeddings_pipeline",
   },
 });
 console.log(response);

docs/doc_examples/2a1eece9a59ac1773edcf0a932c26de0.asciidoc

@@ -3,6 +3,14 @@
 
 [source, js]
 ----
-const response = await client.security.oidcLogout({});
+const response = await client.transport.request({
+  method: "POST",
+  path: "/_security/oidc/logout",
+  body: {
+    token:
+      "dGhpcyBpcyBub3QgYSByZWFsIHRva2VuIGJ1dCBpdCBpcyBvbmx5IHRlc3QgZGF0YS4gZG8gbm90IHRyeSB0byByZWFkIHRva2VuIQ==",
+    refresh_token: "vLBPvmAB6KvwvJZr27cS",
+  },
+});
 console.log(response);
 ----

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/2acf75803494fef29f9ca70671aa6be1.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.security.bulkDeleteRole({
+  names: ["my_admin_role", "superuser"],
+});
+console.log(response);
+----

docs/doc_examples/2afdf0d83724953aa2875b5fb37d60cc.asciidoc

@@ -3,9 +3,12 @@
 
 [source, js]
 ----
-const response = await client.esql.asyncQueryGet({
-  id: "FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=",
-  wait_for_completion_timeout: "30s",
+const response = await client.transport.request({
+  method: "GET",
+  path: "/_query/async/FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=",
+  querystring: {
+    wait_for_completion_timeout: "30s",
+  },
 });
 console.log(response);
 ----

docs/doc_examples/2c079d1ae4819a0c206b9e1aa5623523.asciidoc

@@ -0,0 +1,77 @@
+// 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-000001",
+  mappings: {
+    properties: {
+      attributes: {
+        type: "passthrough",
+        priority: 10,
+        properties: {
+          id: {
+            type: "keyword",
+          },
+        },
+      },
+    },
+  },
+});
+console.log(response);
+
+const response1 = await client.index({
+  index: "my-index-000001",
+  id: 1,
+  document: {
+    attributes: {
+      id: "foo",
+      zone: 10,
+    },
+  },
+});
+console.log(response1);
+
+const response2 = await client.search({
+  index: "my-index-000001",
+  query: {
+    bool: {
+      must: [
+        {
+          match: {
+            id: "foo",
+          },
+        },
+        {
+          match: {
+            zone: 10,
+          },
+        },
+      ],
+    },
+  },
+});
+console.log(response2);
+
+const response3 = await client.search({
+  index: "my-index-000001",
+  query: {
+    bool: {
+      must: [
+        {
+          match: {
+            "attributes.id": "foo",
+          },
+        },
+        {
+          match: {
+            "attributes.zone": 10,
+          },
+        },
+      ],
+    },
+  },
+});
+console.log(response3);
+----

docs/doc_examples/2c86840a46242a38cf82024a9321be46.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.indices.create({
+  index: "my-explicit-mappings-books",
+  mappings: {
+    dynamic: false,
+    properties: {
+      name: {
+        type: "text",
+      },
+      author: {
+        type: "text",
+      },
+      release_date: {
+        type: "date",
+        format: "yyyy-MM-dd",
+      },
+      page_count: {
+        type: "integer",
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/2d0244c020075595acb625aa5ba8f455.asciidoc

@@ -0,0 +1,25 @@
+// 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.index({
+  index: "idx_keep",
+  id: 1,
+  document: {
+    path: {
+      to: [
+        {
+          foo: [3, 2, 1],
+        },
+        {
+          foo: [30, 20, 10],
+        },
+      ],
+      bar: "baz",
+    },
+    ids: [200, 100, 300, 100],
+  },
+});
+console.log(response);
+----

docs/doc_examples/2e09666d3ad5ad9afc22763ee6e97a2b.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.slm.putLifecycle({
+  policy_id: "hourly-snapshots",
+  schedule: "1h",
+  name: "<hourly-snap-{now/d}>",
+  repository: "my_repository",
+  config: {
+    indices: ["data-*", "important"],
+  },
+});
+console.log(response);
+----

docs/doc_examples/2e7844477b41fcfa9efefee4ec0e7101.asciidoc

@@ -0,0 +1,43 @@
+// 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",
+  retriever: {
+    rule: {
+      match_criteria: {
+        query_string: "puggles",
+        user_country: "us",
+      },
+      ruleset_ids: ["my-ruleset"],
+      retriever: {
+        rrf: {
+          retrievers: [
+            {
+              standard: {
+                query: {
+                  query_string: {
+                    query: "pugs",
+                  },
+                },
+              },
+            },
+            {
+              standard: {
+                query: {
+                  query_string: {
+                    query: "puggles",
+                  },
+                },
+              },
+            },
+          ],
+        },
+      },
+    },
+  },
+});
+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/30fa37c9575fe81a0ea7c12cfc08e277.asciidoc

@@ -0,0 +1,25 @@
+// 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: "bad_example_index",
+  mappings: {
+    properties: {
+      field_1: {
+        type: "text",
+        copy_to: "field_2",
+      },
+      field_2: {
+        type: "text",
+        copy_to: "field_3",
+      },
+      field_3: {
+        type: "text",
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/310bdfb0d0d75bac7bff036a3fe51d4d.asciidoc

@@ -4,7 +4,7 @@
 [source, js]
 ----
 const response = await client.ingest.putPipeline({
-  id: "azure_ai_studio_embeddings",
+  id: "azure_ai_studio_embeddings_pipeline",
   processors: [
     {
       inference: {

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/334811cfceb6858aeec5b3461717dd63.asciidoc

@@ -5,7 +5,7 @@
 ----
 const response = await client.ingest.putPipeline({
   id: "geoip",
-  description: "Add geoip info",
+  description: "Add ip geolocation info",
   processors: [
     {
       geoip: {

docs/doc_examples/339c4e5af9f9069ad9912aa574488b59.asciidoc

@@ -0,0 +1,52 @@
+// 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-bit-vectors",
+  mappings: {
+    properties: {
+      my_dense_vector: {
+        type: "dense_vector",
+        index: false,
+        element_type: "bit",
+        dims: 40,
+      },
+    },
+  },
+});
+console.log(response);
+
+const response1 = await client.index({
+  index: "my-index-bit-vectors",
+  id: 1,
+  document: {
+    my_dense_vector: [8, 5, -15, 1, -7],
+  },
+});
+console.log(response1);
+
+const response2 = await client.index({
+  index: "my-index-bit-vectors",
+  id: 2,
+  document: {
+    my_dense_vector: [-1, 115, -3, 4, -128],
+  },
+});
+console.log(response2);
+
+const response3 = await client.index({
+  index: "my-index-bit-vectors",
+  id: 3,
+  document: {
+    my_dense_vector: [2, 18, -5, 0, -124],
+  },
+});
+console.log(response3);
+
+const response4 = await client.indices.refresh({
+  index: "my-index-bit-vectors",
+});
+console.log(response4);
+----

docs/doc_examples/342ddf9121aeddd82fea2464665e25da.asciidoc

@@ -3,14 +3,11 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_connector/my-connector",
-  body: {
-    index_name: "search-google-drive",
-    name: "My Connector",
-    service_type: "google_drive",
-  },
+const response = await client.connector.put({
+  connector_id: "my-connector",
+  index_name: "search-google-drive",
+  name: "My Connector",
+  service_type: "google_drive",
 });
 console.log(response);
 ----

docs/doc_examples/36ac0ef9ea63efc431580f7ade8ad53c.asciidoc

@@ -11,7 +11,7 @@ const response = await client.reindex({
   },
   dest: {
     index: "openai-embeddings",
-    pipeline: "openai_embeddings",
+    pipeline: "openai_embeddings_pipeline",
   },
 });
 console.log(response);

docs/doc_examples/37f367ca81a16d3aef4ef7126ec33a2e.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.search({
+  index: "movies",
+  size: 10,
+  retriever: {
+    rescorer: {
+      rescore: {
+        query: {
+          window_size: 50,
+          rescore_query: {
+            script_score: {
+              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/398389933901b572a06a752bc780af7c.asciidoc

@@ -0,0 +1,21 @@
+// 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: "completion",
+  inference_id: "anthropic_completion",
+  inference_config: {
+    service: "anthropic",
+    service_settings: {
+      api_key: "<api_key>",
+      model_id: "<model_id>",
+    },
+    task_settings: {
+      max_tokens: 1024,
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/3a489743e49902df38e3368cae00717a.asciidoc

@@ -0,0 +1,8 @@
+// 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.nodes.hotThreads();
+console.log(response);
+----

docs/doc_examples/3a4953663a5a3809b692c27446e16b7f.asciidoc

@@ -11,7 +11,7 @@ const response = await client.reindex({
   },
   dest: {
     index: "amazon-bedrock-embeddings",
-    pipeline: "amazon_bedrock_embeddings",
+    pipeline: "amazon_bedrock_embeddings_pipeline",
   },
 });
 console.log(response);

docs/doc_examples/3ab8f65fcb55a0e3664c55749ec41efd.asciidoc

@@ -29,6 +29,7 @@ const response = await client.indices.create({
             "arabic_normalization",
             "persian_normalization",
             "persian_stop",
+            "persian_stem",
           ],
         },
       },

docs/doc_examples/3c0d0c38e1c819a35a68cdba5ae8ccc4.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.inference.put({
+  task_type: "text_embedding",
+  inference_id: "alibabacloud_ai_search_embeddings",
+  inference_config: {
+    service: "alibabacloud-ai-search",
+    service_settings: {
+      api_key: "<api_key>",
+      service_id: "<service_id>",
+      host: "<host>",
+      workspace: "<workspace>",
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/3ea4c971b3f47735dcc207ee2645fa03.asciidoc

@@ -6,15 +6,8 @@
 const response = await client.indices.updateAliases({
   actions: [
     {
-      remove: {
-        index: "my-index-000001",
-        alias: "my-index",
-      },
-    },
-    {
-      add: {
-        index: "my-index-000002",
-        alias: "my-index",
+      remove_index: {
+        index: "my-index-2099.05.06-000001",
       },
     },
   ],

docs/doc_examples/3f1fe5f5f99b98d0891f38003e10b636.asciidoc

@@ -3,6 +3,14 @@
 
 [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  ",
+    wait_for_completion_timeout: "2s",
+  },
+});
 console.log(response);
 ----

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

@@ -3,8 +3,9 @@
 
 [source, js]
 ----
-const response = await client.esql.asyncQueryGet({
-  id: "FkpMRkJGS1gzVDRlM3g4ZzMyRGlLbkEaTXlJZHdNT09TU2VTZVBoNDM3cFZMUToxMDM=",
+const response = await client.transport.request({
+  method: "GET",
+  path: "/_query/async/FkpMRkJGS1gzVDRlM3g4ZzMyRGlLbkEaTXlJZHdNT09TU2VTZVBoNDM3cFZMUToxMDM=",
 });
 console.log(response);
 ----

docs/doc_examples/40f287bf733420bbab134b74c7d0ea5d.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.index({
+  index: "amazon-reviews",
+  id: 1,
+  document: {
+    review_text:
+      "This product is lifechanging! I'm telling all my friends about it.",
+    review_vector: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8],
+  },
+});
+console.log(response);
+----

docs/doc_examples/41175d304e660da2931764f9a4418fd3.asciidoc

@@ -3,16 +3,13 @@
 
 [source, js]
 ----
-const response = await client.transport.request({
-  method: "PUT",
-  path: "/_connector/my-connector/_pipeline",
-  body: {
-    pipeline: {
-      extract_binary_content: true,
-      name: "my-connector-pipeline",
-      reduce_whitespace: true,
-      run_ml_inference: true,
-    },
+const response = await client.connector.updatePipeline({
+  connector_id: "my-connector",
+  pipeline: {
+    extract_binary_content: true,
+    name: "my-connector-pipeline",
+    reduce_whitespace: true,
+    run_ml_inference: true,
   },
 });
 console.log(response);