Updating local pretrained model version to fix integration test (opensearch-project#1163)

* Updating local pretrained model version to fix end to end integration tests

Signed-off-by: Joshua Palis <jpalis@amazon.com>

* updating pretrained model version for use cases

Signed-off-by: Joshua Palis <jpalis@amazon.com>

---------

Signed-off-by: Joshua Palis <jpalis@amazon.com>

Author: joshpalis

src/main/resources/defaults/hybrid-search-with-local-model-defaults.json

@@ -5,7 +5,7 @@
   "register_local_pretrained_model.description": "This is a sentence transformer model",
   "register_local_pretrained_model.model_format": "TORCH_SCRIPT",
   "register_local_pretrained_model.deploy": "true",
-  "register_local_pretrained_model.version": "1.0.1",
+  "register_local_pretrained_model.version": "1.0.2",
   "create_ingest_pipeline.pipeline_id": "nlp-ingest-pipeline",
   "create_ingest_pipeline.description": "A text embedding pipeline",
   "create_ingest_pipeline.model_id": "123",

src/main/resources/defaults/semantic-search-with-local-model-defaults.json

@@ -5,7 +5,7 @@
   "register_local_pretrained_model.description": "This is a sentence transformer model",
   "register_local_pretrained_model.model_format": "TORCH_SCRIPT",
   "register_local_pretrained_model.deploy": "true",
-  "register_local_pretrained_model.version": "1.0.1",
+  "register_local_pretrained_model.version": "1.0.2",
   "create_ingest_pipeline.pipeline_id": "nlp-ingest-pipeline",
   "create_ingest_pipeline.description": "A text embedding pipeline",
   "text_embedding.field_map.input": "passage_text",

src/test/java/org/opensearch/flowframework/rest/FlowFrameworkRestApiIT.java

@@ -820,7 +820,7 @@ public void testSemanticSearchWithLocalModelEndToEnd() throws Exception {
         }
         Map<String, Object> defaults = new HashMap<>();
         defaults.put("register_local_pretrained_model.name", "huggingface/sentence-transformers/all-MiniLM-L6-v2");
-        defaults.put("register_local_pretrained_model.version", "1.0.1");
+        defaults.put("register_local_pretrained_model.version", "1.0.2");
         defaults.put("text_embedding.field_map.output.dimension", 384);
 
         Response response = createAndProvisionWorkflowWithUseCaseWithContent(client(), "semantic_search_with_local_model", defaults);