Merge pull request #1784 from vespa-engine/laura/fix-sample-app-json

Fix deployment of sample apps to not use dry run on master

Author: lperry25

.github/workflows/update-cloud-samples.yml

@@ -142,7 +142,7 @@ jobs:
       - name: Push Zip and JSON to S3
         env:
           # Add `--dryrun` if not on "push" event on default branch.
-          AWS_S3_OPTIONS: --color=on --no-progress ${{ (github.event_name == 'push' && github.ref_name == 'master' ) &&  '' || ' --dryrun' }}
+          AWS_S3_OPTIONS: --color=on --no-progress ${{ github.ref_name == 'master' &&  '' || ' --dryrun' }}
         run: |
           # Not an app, but artifact from the setup job.
           mv ./apps/console-json ./console-json

console-sample-apps.json

@@ -1,60 +1,60 @@
 [
-    {
-      "name": "album-recommendation",
-      "shortname": "album-recommendation",
-      "title": "Album Recommendation",
-      "description": "This application ranks music albums using a user profile: Albums with scores for a set of categories are matched with a user's preference.",
-      "features": [
-        "Search",
-        "Recommendation"
-      ],
-      "category": "Getting Started",
-      "repository": "https://github.com/vespa-engine/sample-apps/tree/master/album-recommendation#readme",
-      "exampleQuery": {
-        "yql": "select * from music where true",
-        "ranking": {
-            "profile": "rank_albums",
-            "features": {
-                "query(user_profile)": "{{cat:pop}:0.8,{cat:rock}:0.2,{cat:jazz}:0.1}"
-            }
+  {
+    "name": "album-recommendation",
+    "shortname": "album-recommendation",
+    "title": "Album Recommendation",
+    "description": "This application ranks music albums using a user profile: Albums with scores for a set of categories are matched with a user's preference.",
+    "features": [
+      "Search",
+      "Recommendation"
+    ],
+    "category": "Getting Started",
+    "repository": "https://github.com/vespa-engine/sample-apps/tree/master/album-recommendation#readme",
+    "exampleQuery": {
+      "yql": "select * from music where true",
+      "ranking": {
+        "profile": "rank_albums",
+        "features": {
+          "query(user_profile)": "{{cat:pop}:0.8,{cat:rock}:0.2,{cat:jazz}:0.1}"
         }
       }
-    },
-    {
-      "name": "text-search",
-      "shortname": "text-search",
-      "title": "Text Search",
-      "description": "The Text Search Tutorial demonstrates traditional text search using BM25/Vespa nativeRank, and is a good start to using the MS Marco dataset.",
-      "features": [
-        "BM25",
-        "nativeRank",
-        "MS Marco"
-      ],
-      "category": "Getting Started",
-      "repository": "https://github.com/vespa-engine/sample-apps/tree/master/text-search#readme",
-      "exampleQuery": {
-        "yql": "select title,url,id from msmarco where userQuery()",
-        "query": "what is dad bod"
-      }
-    },
-    {
-      "name": "colbert",
-      "shortname": "colbert",
-      "title": "Simple hybrid search with ColBERT",
-      "description": "This application uses a single vector embedding model for retrieval and ColBERT (multi-token vector representation) for re-ranking. This semantic search application demonstrates the colbert-embedder and the tensor expressions for ColBERT MaxSim.",
-      "features": [
-        "ColBERT",
-        "Re-ranking"
-      ],
-      "category": "Ranking",
-      "repository": "https://github.com/vespa-engine/sample-apps/tree/master/colbert#readme",
-      "exampleQuery": {
-        "query": "shipping stuff over the sea",
-        "yql": "select * from doc where userQuery() or ({targetHits: 100}nearestNeighbor(embedding, q))",
-        "input": {
-          "query(q)": "embed(e5, @query)",
-          "query(qt)": "embed(colbert, @query)"
-        }
+    }
+  },
+  {
+    "name": "text-search",
+    "shortname": "text-search",
+    "title": "Text Search",
+    "description": "The Text Search Tutorial demonstrates traditional text search using BM25/Vespa nativeRank, and is a good start to using the MS Marco dataset.",
+    "features": [
+      "BM25",
+      "nativeRank",
+      "MS Marco"
+    ],
+    "category": "Getting Started",
+    "repository": "https://github.com/vespa-engine/sample-apps/tree/master/text-search#readme",
+    "exampleQuery": {
+      "yql": "select title,url,id from msmarco where userQuery()",
+      "query": "what is dad bod"
+    }
+  },
+  {
+    "name": "colbert",
+    "shortname": "colbert",
+    "title": "Simple hybrid search with ColBERT",
+    "description": "This application uses a single vector embedding model for retrieval and ColBERT (multi-token vector representation) for re-ranking. This semantic search application demonstrates the colbert-embedder and the tensor expressions for ColBERT MaxSim.",
+    "features": [
+      "ColBERT",
+      "Re-ranking"
+    ],
+    "category": "Ranking",
+    "repository": "https://github.com/vespa-engine/sample-apps/tree/master/colbert#readme",
+    "exampleQuery": {
+      "query": "shipping stuff over the sea",
+      "yql": "select * from doc where userQuery() or ({targetHits: 100}nearestNeighbor(embedding, q))",
+      "input": {
+        "query(q)": "embed(e5, @query)",
+        "query(qt)": "embed(colbert, @query)"
       }
     }
-  ]
+  }
+]