data sync

2 files changed, 5 insertions, 5 deletions

diff --git a/src/client/cognitive_services/CognitiveServices.ts b/src/client/cognitive_services/CognitiveServices.ts
index b23441552..7c660c347 100644
--- a/src/client/cognitive_services/CognitiveServices.ts
+++ b/src/client/cognitive_services/CognitiveServices.ts
@@ -269,12 +269,12 @@ export namespace CognitiveServices {
                 //keyterms = ["father", "king"];
                 let args = { method: 'POST', uri: Utils.prepend("/recommender"), body: { keyphrases: keyterms }, json: true };
                 await requestPromise.post(args).then(async (wordvecs) => {
-                    if (wordvecs.shape[0] > 0) {
+                    if (wordvecs.length > 0) {
                         console.log("successful vectorization!");
                         var vectorValues = new Set<number[]>();
                         wordvecs.forEach((wordvec: any) => {
                             //console.log(wordvec.word);
-                            vectorValues.add(wordvec.values as number[]);
+                            vectorValues.add(wordvec as number[]);
                         });
                         ClientRecommender.Instance.mean(vectorValues, dataDoc, mainDoc);
                     } // adds document to internal doc set
diff --git a/src/server/index.ts b/src/server/index.ts
index ac803a253..49957775d 100644
--- a/src/server/index.ts
+++ b/src/server/index.ts
@@ -692,9 +692,9 @@ recommender.testModel();
 app.post("/recommender", async (req, res) => {
     let keyphrases = req.body.keyphrases;
     let wordvecs = await recommender.vectorize(keyphrases);
-    let embedding: number[][] = [];
-    if (wordvecs && wordvecs.array()) {
-        wordvecs.array().then(array => embedding = array as number[][]);
+    let embedding: Float32Array = new Float32Array();
+    if (wordvecs && wordvecs.dataSync()) {
+        embedding = wordvecs.dataSync() as Float32Array;
     }
     res.send(embedding);
 });