write data

personas_vector/to_vector.py

@@ -58,7 +58,7 @@ if __name__ == "__main__":
             tags_vector_dict[i] = vec
         except Exception as e:
             pass
-    # redis_client_db.hmset("personas_tags_embedding", tags_vector_dict)
+    redis_client_db.hmset("personas_tags_embedding", tags_vector_dict)
 
     print(len(tags_vector_dict.items()))
     # print(random.choice(list(tags_vector_dict.items())))
@@ -91,21 +91,21 @@ if __name__ == "__main__":
     base_dir = os.getcwd()
     model_dir = os.path.join(base_dir, "_models")
     index_path = os.path.join(model_dir, "faiss_personas_vector.index")
-    # faiss.write_index(index2, index_path)
+    faiss.write_index(index2, index_path)
     print(index_path)
 
-    device vector
-    for _, row in device_tags_df.iterrows():
-        vecs = []
-        for i in row["business_tags"]:
-            # vec = tags_vector_dict.get(i, np.array([]))
-            vec = tags_vector_dict.get(i)
-            if vec:
-                vecs.append(np.array(json.loads(vec)).astype("float32"))
-        if vecs:
-            t = np.array([np.average(vecs, axis=0)]).astype("float32")
-            D, I = index2.search(t, 10)
-            print(row["cl_id"], row["business_tags"])
-            print(I)
+    # device vector
+    # for _, row in device_tags_df.iterrows():
+    #     vecs = []
+    #     for i in row["business_tags"]:
+    #         # vec = tags_vector_dict.get(i, np.array([]))
+    #         vec = tags_vector_dict.get(i)
+    #         if vec:
+    #             vecs.append(np.array(json.loads(vec)).astype("float32"))
+    #     if vecs:
+    #         t = np.array([np.average(vecs, axis=0)]).astype("float32")
+    #         D, I = index2.search(t, 10)
+    #         print(row["cl_id"], row["business_tags"])
+    #         print(I)
 
 # curl "http://172.16.31.17:9000/gm-dbmw-tractate-read/_search?pretty" -d '{"query": {"term": {"id": "10269"}}, "_source": {"include": ["content", "portrait_tag_name"]}}'