Auto-generated API code

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/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/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/c02c2916b97b6fa7db82dbc7f0378310.asciidoc → 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/4dab4c5168047ba596af1beb0e55b845.asciidoc → 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/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/191074b2eebd5f74e628c2ada4b6d2e4.asciidoc → 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/19f1f9f25933f8e7aba59a10881c648b.asciidoc → 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/2fd458d37aab509fe2d970c0b6e2a10f.asciidoc → 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/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/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);
+----