Update default queries; update quick config fields; misc updates (#660) (#661)

* Change default to match query



* Only show embedding field in quick config if unknown



* add gear in header for newhomepage



---------


(cherry picked from commit c729bec)

Signed-off-by: Tyler Ohlsen <ohltyler@amazon.com>
Signed-off-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

Author: opensearch-trigger-bot[bot]

public/pages/workflow_detail/components/header.tsx

@@ -297,7 +297,7 @@ export function WorkflowDetailHeader(props: WorkflowDetailHeaderProps) {
               } as TopNavMenuIconData,
               {
                 iconType: 'exit',
-                tooltip: 'Return to projects',
+                tooltip: 'Return to workflows',
                 ariaLabel: 'Exit',
                 href: constructHrefWithDataSourceId(
                   APP_PATH.WORKFLOWS,
@@ -312,6 +312,13 @@ export function WorkflowDetailHeader(props: WorkflowDetailHeaderProps) {
                 run: () => setIntroFlyoutOpened(true),
                 controlType: 'icon',
               } as TopNavMenuIconData,
+              {
+                iconType: 'gear',
+                tooltip: 'Edit workflow settings',
+                ariaLabel: 'Edit workflow settings',
+                run: () => setIsEditWorkflowModalOpen(true),
+                controlType: 'icon',
+              } as TopNavMenuIconData,
             ]}
             screenTitle={workflowName}
             showDataSourceMenu={dataSourceEnabled}

public/pages/workflows/new_workflow/quick_configure_modal.tsx

@@ -223,13 +223,11 @@ export function QuickConfigureModal(props: QuickConfigureModalProps) {
       const connector = connectors[selectedModel.connectorId];
       if (connector !== undefined) {
         const dimensions = getEmbeddingModelDimensions(connector);
-        if (dimensions === undefined) {
-          setUnknownEmbeddingLength(true);
-        }
+        // dimensions may be undefined. set state vars accordingly
         setUnknownEmbeddingLength(dimensions === undefined);
         setQuickConfigureFields({
           ...quickConfigureFields,
-          embeddingLength: getEmbeddingModelDimensions(connector),
+          embeddingLength: dimensions,
         });
       }
     }

public/pages/workflows/new_workflow/quick_configure_optional_fields.tsx

@@ -28,7 +28,7 @@ import {
   WORKFLOW_TYPE,
 } from '../../../../common';
 import { AppState } from '../../../store';
-import { parseModelInputs } from '../../../utils';
+import { getEmbeddingModelDimensions, parseModelInputs } from '../../../utils';
 
 interface QuickConfigureOptionalFieldsProps {
   workflowType?: WORKFLOW_TYPE;
@@ -127,6 +127,24 @@ export function QuickConfigureOptionalFields(
     props.setFields({ ...props.fields, ...optionalFieldValues });
   }, [optionalFieldValues]);
 
+  // Keep track of if an embedding model is selected with an unknown embedding length.
+  // Only expose the form field if it is unknown, else hide from the user.
+  const [unknownEmbeddingLength, setUnknownEmbeddingLength] = useState<boolean>(
+    false
+  );
+  useEffect(() => {
+    const selectedModel = deployedModels.find(
+      (model) => model.id === props.fields?.embeddingModelId
+    );
+    if (selectedModel?.connectorId !== undefined) {
+      const connector = connectors[selectedModel.connectorId];
+      if (connector !== undefined) {
+        const dimensions = getEmbeddingModelDimensions(connector);
+        setUnknownEmbeddingLength(dimensions === undefined);
+      }
+    }
+  }, [props.fields?.embeddingModelId, deployedModels, connectors]);
+
   return (
     <EuiAccordion
       id="optionalConfiguration"
@@ -202,24 +220,28 @@ export function QuickConfigureOptionalFields(
                 }}
               />
             </EuiCompressedFormRow>
-            <EuiSpacer size="s" />
-            <EuiCompressedFormRow
-              fullWidth={true}
-              label={'Embedding length'}
-              isInvalid={false}
-              helpText="The length / dimension of the generated vector embeddings. Autofilled values may be inaccurate."
-            >
-              <EuiCompressedFieldNumber
-                fullWidth={true}
-                value={props.fields?.embeddingLength || ''}
-                onChange={(e) => {
-                  setOptionalFieldValues({
-                    ...optionalFieldValues,
-                    embeddingLength: Number(e.target.value),
-                  });
-                }}
-              />
-            </EuiCompressedFormRow>
+            {unknownEmbeddingLength && (
+              <>
+                <EuiSpacer size="s" />
+                <EuiCompressedFormRow
+                  fullWidth={true}
+                  label={'Embedding length'}
+                  isInvalid={false}
+                  helpText="The length / dimension of the generated vector embeddings."
+                >
+                  <EuiCompressedFieldNumber
+                    fullWidth={true}
+                    value={props.fields?.embeddingLength || ''}
+                    onChange={(e) => {
+                      setOptionalFieldValues({
+                        ...optionalFieldValues,
+                        embeddingLength: Number(e.target.value),
+                      });
+                    }}
+                  />
+                </EuiCompressedFormRow>
+              </>
+            )}
           </>
         )}
         {(props.workflowType === WORKFLOW_TYPE.RAG ||

public/pages/workflows/new_workflow/utils.ts

@@ -28,7 +28,7 @@ import {
   SEMANTIC_SEARCH_QUERY_NEURAL,
   MULTIMODAL_SEARCH_QUERY_NEURAL,
   HYBRID_SEARCH_QUERY_MATCH_NEURAL,
-  TERM_QUERY_TEXT,
+  MATCH_QUERY_TEXT,
 } from '../../../../common';
 import { generateId } from '../../../utils';
 import semver from 'semver';
@@ -164,7 +164,7 @@ export function fetchSemanticSearchMetadata(version: string): UIState {
   });
 
   baseState.config.search.request.value = customStringify(
-    isPreV219 ? SEMANTIC_SEARCH_QUERY_NEURAL : TERM_QUERY_TEXT
+    isPreV219 ? SEMANTIC_SEARCH_QUERY_NEURAL : MATCH_QUERY_TEXT
   );
 
   baseState.config.search.enrichRequest.processors = isPreV219
@@ -224,7 +224,7 @@ export function fetchHybridSearchMetadata(version: string): UIState {
   });
 
   baseState.config.search.request.value = customStringify(
-    isPreV219 ? HYBRID_SEARCH_QUERY_MATCH_NEURAL : TERM_QUERY_TEXT
+    isPreV219 ? HYBRID_SEARCH_QUERY_MATCH_NEURAL : MATCH_QUERY_TEXT
   );
 
   baseState.config.search.enrichResponse.processors = [
@@ -263,9 +263,9 @@ export function fetchVectorSearchWithRAGMetadata(version: string): UIState {
   baseState.config.ingest.index.settings.value = customStringify({
     [`index.knn`]: true,
   });
-  // Search config: term query => ML inference processor for generating embeddings =>
+  // Search config: match query => ML inference processor for generating embeddings =>
   // ML inference processor for returning LLM-generated response of results
-  baseState.config.search.request.value = customStringify(TERM_QUERY_TEXT);
+  baseState.config.search.request.value = customStringify(MATCH_QUERY_TEXT);
   baseState.config.search.enrichRequest.processors = [
     injectQueryTemplateInProcessor(
       new MLSearchRequestProcessor().toObj(),