From 539f4894961c7fc1e626682fb238dee880309be1 Mon Sep 17 00:00:00 2001
From: Florian Bernd <git@flobernd.de>
Date: Tue, 18 Mar 2025 08:11:21 +0100
Subject: [PATCH] Emit `codegen_name` for response value bodies (#3953)

* Emit `codegen_name` for response value bodies

* Run make contrib

---------

Co-authored-by: Quentin Pradet <quentin.pradet@elastic.co>
(cherry picked from commit 331583c54ed24eb2a33f853eec97776e4fdb1590)

# Conflicts:
#	output/schema/schema-serverless.json
---
 compiler/src/model/build-model.ts             | 13 ++++++-
 output/openapi/elasticsearch-openapi.json     |  4 +--
 .../elasticsearch-serverless-openapi.json     |  4 +--
 output/schema/schema-serverless.json          | 15 ++++++--
 output/schema/schema.json                     | 35 +++++++++++++++++--
 5 files changed, 62 insertions(+), 9 deletions(-)

diff --git a/compiler/src/model/build-model.ts b/compiler/src/model/build-model.ts
index 96bb7f511b..400a0f008b 100644
--- a/compiler/src/model/build-model.ts
+++ b/compiler/src/model/build-model.ts
@@ -383,7 +383,18 @@ function compileClassOrInterfaceDeclaration (declaration: ClassDeclaration | Int
             if (property.valueOf.kind === 'instance_of' && property.valueOf.type.name === 'Void') {
               type.body = { kind: 'no_body' }
             } else {
-              type.body = { kind: 'value', value: property.valueOf }
+              const tags = parseJsDocTags((member as PropertySignature).getJsDocs())
+              // TODO: Enable this after adding the missing codegen names.
+              // assert(
+              //     member as Node,
+              //     tags.codegen_name != null,
+              //     'You should configure a body @codegen_name'
+              // )
+              type.body = {
+                kind: 'value',
+                value: property.valueOf,
+                codegenName: tags.codegen_name
+              }
             }
           } else {
             type.body = { kind: 'properties', properties: property.properties }
diff --git a/output/openapi/elasticsearch-openapi.json b/output/openapi/elasticsearch-openapi.json
index 7fa23b4615..86e25d8963 100644
--- a/output/openapi/elasticsearch-openapi.json
+++ b/output/openapi/elasticsearch-openapi.json
@@ -22270,7 +22270,7 @@
                 "MlEvaluateDataFrameRequestExample4": {
                   "summary": "Regression example 1",
                   "description": "Run `POST _ml/data_frame/_evaluate` to evaluate the testing error of a regression job for an annotated index. The term query in the body limits evaluation to be performed on the test split only. The `actual_field` contains the ground truth for house prices. The `predicted_field` contains the house price calculated by the regression analysis.\n",
-                  "value": "{\n  \"index\": \"house_price_predictions\",\n  \"query\": {\n    \"bool\": {\n      \"filter\": [\n        {\n          \"term\": {\n            \"ml.is_training\": false\n          }\n        }\n      ]\n    },\n    \"evaluation\": {\n      \"regression\": {\n        \"actual_field\": \"price\",\n        \"predicted_field\": \"ml.price_prediction\",\n        \"metrics\": {\n          \"r_squared\": {},\n          \"mse\": {},\n          \"msle\": {\n            \"offset\": 10\n          },\n          \"huber\": {\n            \"delta\": 1.5\n          }\n        }\n      }\n    }\n  }\n}"
+                  "value": "{\n  \"index\": \"house_price_predictions\",\n  \"query\": {\n    \"bool\": {\n      \"filter\": [\n        {\n          \"term\": {\n            \"ml.is_training\": false\n          }\n        }\n      ]\n    }\n  },\n  \"evaluation\": {\n    \"regression\": {\n      \"actual_field\": \"price\",\n      \"predicted_field\": \"ml.price_prediction\",\n      \"metrics\": {\n        \"r_squared\": {},\n        \"mse\": {},\n        \"msle\": {\n          \"offset\": 10\n        },\n        \"huber\": {\n          \"delta\": 1.5\n        }\n      }\n    }\n  }\n}"
                 },
                 "MlEvaluateDataFrameRequestExample5": {
                   "summary": "Regression example 2",
@@ -113780,7 +113780,7 @@
               "MultiTermVectorsRequestExample2": {
                 "summary": "Simplified syntax",
                 "description": "Run `POST /my-index-000001/_mtermvectors`. If all requested documents are in same index and the parameters are the same, you can use a simplified syntax.\n",
-                "value": "{\n  \"ids\": [ \"1\", \"2\" ],\n  \"parameters\": {\n    \"fields\": [\n      \"message\"\n    ],\n    \"term_statistics\": true\n  }\n}"
+                "value": "{\n  \"ids\": [ \"1\", \"2\" ],\n  \"fields\": [\n    \"message\"\n  ],\n  \"term_statistics\": true\n}"
               },
               "MultiTermVectorsRequestExample3": {
                 "summary": "Artificial documents",
diff --git a/output/openapi/elasticsearch-serverless-openapi.json b/output/openapi/elasticsearch-serverless-openapi.json
index 4d2498dd2a..c99c282e5a 100644
--- a/output/openapi/elasticsearch-serverless-openapi.json
+++ b/output/openapi/elasticsearch-serverless-openapi.json
@@ -12807,7 +12807,7 @@
                 "MlEvaluateDataFrameRequestExample4": {
                   "summary": "Regression example 1",
                   "description": "Run `POST _ml/data_frame/_evaluate` to evaluate the testing error of a regression job for an annotated index. The term query in the body limits evaluation to be performed on the test split only. The `actual_field` contains the ground truth for house prices. The `predicted_field` contains the house price calculated by the regression analysis.\n",
-                  "value": "{\n  \"index\": \"house_price_predictions\",\n  \"query\": {\n    \"bool\": {\n      \"filter\": [\n        {\n          \"term\": {\n            \"ml.is_training\": false\n          }\n        }\n      ]\n    },\n    \"evaluation\": {\n      \"regression\": {\n        \"actual_field\": \"price\",\n        \"predicted_field\": \"ml.price_prediction\",\n        \"metrics\": {\n          \"r_squared\": {},\n          \"mse\": {},\n          \"msle\": {\n            \"offset\": 10\n          },\n          \"huber\": {\n            \"delta\": 1.5\n          }\n        }\n      }\n    }\n  }\n}"
+                  "value": "{\n  \"index\": \"house_price_predictions\",\n  \"query\": {\n    \"bool\": {\n      \"filter\": [\n        {\n          \"term\": {\n            \"ml.is_training\": false\n          }\n        }\n      ]\n    }\n  },\n  \"evaluation\": {\n    \"regression\": {\n      \"actual_field\": \"price\",\n      \"predicted_field\": \"ml.price_prediction\",\n      \"metrics\": {\n        \"r_squared\": {},\n        \"mse\": {},\n        \"msle\": {\n          \"offset\": 10\n        },\n        \"huber\": {\n          \"delta\": 1.5\n        }\n      }\n    }\n  }\n}"
                 },
                 "MlEvaluateDataFrameRequestExample5": {
                   "summary": "Regression example 2",
@@ -66588,7 +66588,7 @@
               "MultiTermVectorsRequestExample2": {
                 "summary": "Simplified syntax",
                 "description": "Run `POST /my-index-000001/_mtermvectors`. If all requested documents are in same index and the parameters are the same, you can use a simplified syntax.\n",
-                "value": "{\n  \"ids\": [ \"1\", \"2\" ],\n  \"parameters\": {\n    \"fields\": [\n      \"message\"\n    ],\n    \"term_statistics\": true\n  }\n}"
+                "value": "{\n  \"ids\": [ \"1\", \"2\" ],\n  \"fields\": [\n    \"message\"\n  ],\n  \"term_statistics\": true\n}"
               },
               "MultiTermVectorsRequestExample3": {
                 "summary": "Artificial documents",
diff --git a/output/schema/schema-serverless.json b/output/schema/schema-serverless.json
index b351cae3bf..88f98b5b21 100644
--- a/output/schema/schema-serverless.json
+++ b/output/schema/schema-serverless.json
@@ -19451,6 +19451,7 @@
     },
     {
       "body": {
+        "codegenName": "data",
         "kind": "value",
         "value": {
           "kind": "instance_of",
@@ -20954,6 +20955,7 @@
     },
     {
       "body": {
+        "codegenName": "source",
         "kind": "value",
         "value": {
           "kind": "instance_of",
@@ -23296,6 +23298,7 @@
     },
     {
       "body": {
+        "codegenName": "indices",
         "kind": "value",
         "value": {
           "key": {
@@ -23430,6 +23433,7 @@
     },
     {
       "body": {
+        "codegenName": "aliases",
         "kind": "value",
         "value": {
           "key": {
@@ -23959,6 +23963,7 @@
     },
     {
       "body": {
+        "codegenName": "mappings",
         "kind": "value",
         "value": {
           "key": {
@@ -24128,6 +24133,7 @@
     },
     {
       "body": {
+        "codegenName": "settings",
         "kind": "value",
         "value": {
           "key": {
@@ -28112,6 +28118,7 @@
     },
     {
       "body": {
+        "codegenName": "pipelines",
         "kind": "value",
         "value": {
           "key": {
@@ -28675,6 +28682,7 @@
     },
     {
       "body": {
+        "codegenName": "pipelines",
         "kind": "value",
         "value": {
           "key": {
@@ -30057,7 +30065,7 @@
         "MlEvaluateDataFrameRequestExample4": {
           "description": "Run `POST _ml/data_frame/_evaluate` to evaluate the testing error of a regression job for an annotated index. The term query in the body limits evaluation to be performed on the test split only. The `actual_field` contains the ground truth for house prices. The `predicted_field` contains the house price calculated by the regression analysis.\n",
           "summary": "Regression example 1",
-          "value": "{\n  \"index\": \"house_price_predictions\",\n  \"query\": {\n    \"bool\": {\n      \"filter\": [\n        {\n          \"term\": {\n            \"ml.is_training\": false\n          }\n        }\n      ]\n    },\n    \"evaluation\": {\n      \"regression\": {\n        \"actual_field\": \"price\",\n        \"predicted_field\": \"ml.price_prediction\",\n        \"metrics\": {\n          \"r_squared\": {},\n          \"mse\": {},\n          \"msle\": {\n            \"offset\": 10\n          },\n          \"huber\": {\n            \"delta\": 1.5\n          }\n        }\n      }\n    }\n  }\n}"
+          "value": "{\n  \"index\": \"house_price_predictions\",\n  \"query\": {\n    \"bool\": {\n      \"filter\": [\n        {\n          \"term\": {\n            \"ml.is_training\": false\n          }\n        }\n      ]\n    }\n  },\n  \"evaluation\": {\n    \"regression\": {\n      \"actual_field\": \"price\",\n      \"predicted_field\": \"ml.price_prediction\",\n      \"metrics\": {\n        \"r_squared\": {},\n        \"mse\": {},\n        \"msle\": {\n          \"offset\": 10\n        },\n        \"huber\": {\n          \"delta\": 1.5\n        }\n      }\n    }\n  }\n}"
         },
         "MlEvaluateDataFrameRequestExample5": {
           "description": "Run `POST _ml/data_frame/_evaluate` to evaluate the training error of a regression job for an annotated index. The term query in the body limits evaluation to be performed on the training split only. The `actual_field` contains the ground truth for house prices. The `predicted_field` contains the house price calculated by the regression analysis.\n",
@@ -32400,6 +32408,7 @@
     },
     {
       "body": {
+        "codegenName": "documents",
         "kind": "value",
         "value": {
           "kind": "array_of",
@@ -37390,7 +37399,7 @@
         "MultiTermVectorsRequestExample2": {
           "description": "Run `POST /my-index-000001/_mtermvectors`. If all requested documents are in same index and the parameters are the same, you can use a simplified syntax.\n",
           "summary": "Simplified syntax",
-          "value": "{\n  \"ids\": [ \"1\", \"2\" ],\n  \"parameters\": {\n    \"fields\": [\n      \"message\"\n    ],\n    \"term_statistics\": true\n  }\n}"
+          "value": "{\n  \"ids\": [ \"1\", \"2\" ],\n  \"fields\": [\n    \"message\"\n  ],\n  \"term_statistics\": true\n}"
         },
         "MultiTermVectorsRequestExample3": {
           "description": "Run `POST /_mtermvectors` to generate term vectors for artificial documents provided in the body of the request. The mapping used is determined by the specified `_index`.\n",
@@ -41138,6 +41147,7 @@
     },
     {
       "body": {
+        "codegenName": "analytics",
         "kind": "value",
         "value": {
           "key": {
@@ -41904,6 +41914,7 @@
     },
     {
       "body": {
+        "codegenName": "vector_tiles",
         "kind": "value",
         "value": {
           "kind": "instance_of",
diff --git a/output/schema/schema.json b/output/schema/schema.json
index e83d339b7c..92ed582749 100644
--- a/output/schema/schema.json
+++ b/output/schema/schema.json
@@ -27770,6 +27770,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "source",
         "value": {
           "kind": "instance_of",
           "type": {
@@ -31865,7 +31866,7 @@
         "MultiTermVectorsRequestExample2": {
           "description": "Run `POST /my-index-000001/_mtermvectors`. If all requested documents are in same index and the parameters are the same, you can use a simplified syntax.\n",
           "summary": "Simplified syntax",
-          "value": "{\n  \"ids\": [ \"1\", \"2\" ],\n  \"parameters\": {\n    \"fields\": [\n      \"message\"\n    ],\n    \"term_statistics\": true\n  }\n}"
+          "value": "{\n  \"ids\": [ \"1\", \"2\" ],\n  \"fields\": [\n    \"message\"\n  ],\n  \"term_statistics\": true\n}"
         },
         "MultiTermVectorsRequestExample3": {
           "description": "Run `POST /_mtermvectors` to generate term vectors for artificial documents provided in the body of the request. The mapping used is determined by the specified `_index`.\n",
@@ -42479,6 +42480,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "vector_tiles",
         "value": {
           "kind": "instance_of",
           "type": {
@@ -113659,6 +113661,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "infos",
         "value": {
           "kind": "dictionary_of",
           "key": {
@@ -124768,6 +124771,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "data",
         "value": {
           "kind": "instance_of",
           "type": {
@@ -128715,6 +128719,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "lifecycles",
         "value": {
           "kind": "dictionary_of",
           "key": {
@@ -136879,6 +136884,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "disk_usage",
         "value": {
           "kind": "user_defined_value"
         }
@@ -136954,6 +136960,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "result",
         "value": {
           "kind": "user_defined_value"
         }
@@ -138566,6 +138573,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "indices",
         "value": {
           "kind": "dictionary_of",
           "key": {
@@ -138732,6 +138740,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "aliases",
         "value": {
           "kind": "dictionary_of",
           "key": {
@@ -139333,6 +139342,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "field_mappings",
         "value": {
           "kind": "dictionary_of",
           "key": {
@@ -139706,6 +139716,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "mappings",
         "value": {
           "kind": "dictionary_of",
           "key": {
@@ -140124,6 +140135,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "settings",
         "value": {
           "kind": "dictionary_of",
           "key": {
@@ -140229,6 +140241,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "templates",
         "value": {
           "kind": "dictionary_of",
           "key": {
@@ -140793,6 +140806,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "result",
         "value": {
           "kind": "user_defined_value"
         }
@@ -142775,6 +142789,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "statuses",
         "value": {
           "kind": "dictionary_of",
           "key": {
@@ -143636,6 +143651,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "infos",
         "value": {
           "kind": "dictionary_of",
           "key": {
@@ -156968,6 +156984,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "pipelines",
         "value": {
           "kind": "dictionary_of",
           "key": {
@@ -158997,6 +159014,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "pipelines",
         "value": {
           "kind": "dictionary_of",
           "key": {
@@ -174234,7 +174252,7 @@
         "MlEvaluateDataFrameRequestExample4": {
           "description": "Run `POST _ml/data_frame/_evaluate` to evaluate the testing error of a regression job for an annotated index. The term query in the body limits evaluation to be performed on the test split only. The `actual_field` contains the ground truth for house prices. The `predicted_field` contains the house price calculated by the regression analysis.\n",
           "summary": "Regression example 1",
-          "value": "{\n  \"index\": \"house_price_predictions\",\n  \"query\": {\n    \"bool\": {\n      \"filter\": [\n        {\n          \"term\": {\n            \"ml.is_training\": false\n          }\n        }\n      ]\n    },\n    \"evaluation\": {\n      \"regression\": {\n        \"actual_field\": \"price\",\n        \"predicted_field\": \"ml.price_prediction\",\n        \"metrics\": {\n          \"r_squared\": {},\n          \"mse\": {},\n          \"msle\": {\n            \"offset\": 10\n          },\n          \"huber\": {\n            \"delta\": 1.5\n          }\n        }\n      }\n    }\n  }\n}"
+          "value": "{\n  \"index\": \"house_price_predictions\",\n  \"query\": {\n    \"bool\": {\n      \"filter\": [\n        {\n          \"term\": {\n            \"ml.is_training\": false\n          }\n        }\n      ]\n    }\n  },\n  \"evaluation\": {\n    \"regression\": {\n      \"actual_field\": \"price\",\n      \"predicted_field\": \"ml.price_prediction\",\n      \"metrics\": {\n        \"r_squared\": {},\n        \"mse\": {},\n        \"msle\": {\n          \"offset\": 10\n        },\n        \"huber\": {\n          \"delta\": 1.5\n        }\n      }\n    }\n  }\n}"
         },
         "MlEvaluateDataFrameRequestExample5": {
           "description": "Run `POST _ml/data_frame/_evaluate` to evaluate the training error of a regression job for an annotated index. The term query in the body limits evaluation to be performed on the training split only. The `actual_field` contains the ground truth for house prices. The `predicted_field` contains the house price calculated by the regression analysis.\n",
@@ -179420,6 +179438,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "documents",
         "value": {
           "kind": "array_of",
           "value": {
@@ -196059,6 +196078,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "capabilities",
         "value": {
           "kind": "dictionary_of",
           "key": {
@@ -196294,6 +196314,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "capabilities",
         "value": {
           "kind": "dictionary_of",
           "key": {
@@ -197401,6 +197422,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "analytics",
         "value": {
           "kind": "dictionary_of",
           "key": {
@@ -198338,6 +198360,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "result",
         "value": {
           "kind": "user_defined_value"
         }
@@ -203864,6 +203887,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "result",
         "value": {
           "kind": "dictionary_of",
           "key": {
@@ -205049,6 +205073,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "privileges",
         "value": {
           "kind": "dictionary_of",
           "key": {
@@ -205131,6 +205156,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "roles",
         "value": {
           "kind": "dictionary_of",
           "key": {
@@ -205413,6 +205439,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "role_mappings",
         "value": {
           "kind": "dictionary_of",
           "key": {
@@ -205496,6 +205523,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "service_accoutns",
         "value": {
           "kind": "dictionary_of",
           "key": {
@@ -206261,6 +206289,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "users",
         "value": {
           "kind": "dictionary_of",
           "key": {
@@ -208431,6 +208460,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "result",
         "value": {
           "kind": "dictionary_of",
           "key": {
@@ -214136,6 +214166,7 @@
       "kind": "response",
       "body": {
         "kind": "value",
+        "codegenName": "lifecycles",
         "value": {
           "kind": "dictionary_of",
           "key": {