Auto-generated code for 8.15

docs/doc_examples/1aa96eeaf63fc967e166d1a2fcdccccc.asciidoc → docs/doc_examples/0393ca5a2942e1f00ed87546d0d50732.asciidoc

@@ -4,7 +4,7 @@
 [source, js]
 ----
 const response = await client.indices.create({
-  index: "my-index-000002",
+  index: "my-index-000003",
   mappings: {
     properties: {
       metrics: {
@@ -29,7 +29,7 @@ const response = await client.indices.create({
 console.log(response);
 
 const response1 = await client.indices.getMapping({
-  index: "my-index-000002",
+  index: "my-index-000003",
 });
 console.log(response1);
 ----

docs/doc_examples/840f8c863c30b04abcf2dd66b846f157.asciidoc → docs/doc_examples/083b92e8ea264e49bf9fd40fc6a3094b.asciidoc

@@ -14,6 +14,7 @@ const response = await client.inference.put({
         min_number_of_allocations: 3,
         max_number_of_allocations: 10,
       },
+      num_threads: 1,
       model_id: ".multilingual-e5-small",
     },
   },

docs/doc_examples/43d9e314431336a6f084cea76dfd6489.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: "restaurants",
+  retriever: {
+    knn: {
+      field: "vector",
+      query_vector: [10, 22, 77],
+      k: 10,
+      num_candidates: 10,
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/1b60ad542abb511cbd926ac8c55b609c.asciidoc → docs/doc_examples/565386eee0951865a684e41fab53b40c.asciidoc

@@ -14,6 +14,7 @@ const response = await client.inference.put({
         min_number_of_allocations: 3,
         max_number_of_allocations: 10,
       },
+      num_threads: 1,
     },
   },
 });

docs/doc_examples/58f6b72009512851843c7b7a20e9504a.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.indices.create({
+  index: "my-index-000002",
+  mappings: {
+    properties: {
+      inference_field: {
+        type: "semantic_text",
+        inference_id: "my-elser-endpoint-for-ingest",
+        search_inference_id: "my-elser-endpoint-for-search",
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/69541f0bb81ab3797926bb2a00607cda.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.inference.put({
+  task_type: "rerank",
+  inference_id: "my-msmarco-minilm-model",
+  inference_config: {
+    service: "elasticsearch",
+    service_settings: {
+      num_allocations: 1,
+      num_threads: 1,
+      model_id: "cross-encoder__ms-marco-minilm-l-6-v2",
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/6e6b78e6b689a5d6aa637271b6d084e2.asciidoc

@@ -0,0 +1,45 @@
+// 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",
+  retriever: {
+    rrf: {
+      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/a4ec42130f3c75fc9d1d5f7cb6222cd5.asciidoc → docs/doc_examples/745864ef2427188241a4702b94ea57be.asciidoc

@@ -11,7 +11,7 @@ const response = await client.search({
       filter: {
         range: {
           price: {
-            to: "500",
+            lte: "500",
           },
         },
       },

docs/doc_examples/7b1b947bddd7e78f77da265f7e645a61.asciidoc

@@ -0,0 +1,51 @@
+// 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-000004",
+  mappings: {
+    properties: {
+      metrics: {
+        subobjects: "auto",
+        properties: {
+          time: {
+            type: "object",
+            properties: {
+              min: {
+                type: "long",
+              },
+            },
+          },
+          to: {
+            type: "object",
+            properties: {
+              inner_metrics: {
+                type: "object",
+                subobjects: "auto",
+                properties: {
+                  time: {
+                    type: "object",
+                    properties: {
+                      max: {
+                        type: "long",
+                      },
+                    },
+                  },
+                },
+              },
+            },
+          },
+        },
+      },
+    },
+  },
+});
+console.log(response);
+
+const response1 = await client.indices.getMapping({
+  index: "my-index-000004",
+});
+console.log(response1);
+----

docs/doc_examples/9aa2327ae315c39f2bce2bd22e0deb1b.asciidoc → docs/doc_examples/8417d8d35ec5fc5665dfb2f95d6d1101.asciidoc

@@ -17,7 +17,7 @@ const response = await client.search({
         {
           range: {
             "result.execution_time": {
-              from: "now-10s",
+              gte: "now-10s",
             },
           },
         },

docs/doc_examples/8477e77e4fad19af66f03f81b8f2592b.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.search({
+  index: "my-index",
+  query: {
+    semantic: {
+      field: "inference_field",
+      query: "mountain lake",
+      inner_hits: {
+        from: 1,
+        size: 1,
+      },
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/bdb671866e2f0195f8dfbdb7f20bf591.asciidoc → docs/doc_examples/8cad5d95a0e7c103f08be53d0b172558.asciidoc

@@ -9,7 +9,11 @@ const response = await client.inference.put({
   inference_config: {
     service: "elser",
     service_settings: {
-      num_allocations: 1,
+      adaptive_allocations: {
+        enabled: true,
+        min_number_of_allocations: 3,
+        max_number_of_allocations: 10,
+      },
       num_threads: 1,
     },
   },

docs/doc_examples/8d750dfc067b1184c32a2423c60e4d06.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.search({
+  index: "my-index",
+  query: {
+    semantic: {
+      field: "inference_field",
+      query: "mountain lake",
+      inner_hits: {},
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/9169d19a80175ec94f80865d0f9bef4c.asciidoc

@@ -0,0 +1,36 @@
+// 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: "restaurants",
+  retriever: {
+    rrf: {
+      retrievers: [
+        {
+          standard: {
+            query: {
+              multi_match: {
+                query: "Austria",
+                fields: ["city", "region"],
+              },
+            },
+          },
+        },
+        {
+          knn: {
+            field: "vector",
+            query_vector: [10, 22, 77],
+            k: 10,
+            num_candidates: 10,
+          },
+        },
+      ],
+      rank_constant: 1,
+      rank_window_size: 50,
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/971fd23adb81bb5842c7750e0379336a.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: "movies",
+  retriever: {
+    text_similarity_reranker: {
+      retriever: {
+        standard: {
+          query: {
+            match: {
+              genre: "drama",
+            },
+          },
+        },
+      },
+      field: "plot",
+      inference_id: "my-msmarco-minilm-model",
+      inference_text: "films that explore psychological depths",
+    },
+  },
+});
+console.log(response);
+----

docs/doc_examples/9dfe3b02bd15409b4b8b36e9756e8f94.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.index({
+  index: "my-index",
+  id: "lake_tahoe",
+  document: {
+    inference_field: [
+      "Lake Tahoe is the largest alpine lake in North America",
+      "When hiking in the area, please be on alert for bears",
+    ],
+  },
+});
+console.log(response);
+----

docs/doc_examples/a1dda7e7c01be96a4acf7b725d70385f.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: "index",
+  retriever: {
+    text_similarity_reranker: {
+      retriever: {
+        standard: {
+          query: {
+            match_phrase: {
+              text: "landmark in Paris",
+            },
+          },
+        },
+      },
+      field: "text",
+      inference_id: "my-cohere-rerank-model",
+      inference_text: "Most famous landmark in Paris",
+      rank_window_size: 100,
+      min_score: 0.5,
+    },
+  },
+});
+console.log(response);
+----