diff --git a/qdrant-landing/content/documentation/tutorials/search-beginners.md b/qdrant-landing/content/documentation/tutorials/search-beginners.md
index 8c10f7e52..ffd768cc4 100644
--- a/qdrant-landing/content/documentation/tutorials/search-beginners.md
+++ b/qdrant-landing/content/documentation/tutorials/search-beginners.md
@@ -33,9 +33,13 @@ pip install -U sentence-transformers
 Once encoded, this data needs to be kept somewhere. Qdrant lets you store data as embeddings. You can also use Qdrant to run search queries against this data. This means that you can ask the engine to give you relevant answers that go way beyond keyword matching.
 
 ```bash
-pip install qdrant-client
+pip install -U qdrant-client
 ```
 
+<aside role="status">
+This tutorial requires qdrant-client version 1.7.1 or higher.
+</aside>
+
 ### Import the models 
 
 Once the two main frameworks are defined, you need to specify the exact models this engine will use. Before you do, activate the Python prompt (`>>>`) with the `python` command.
@@ -172,10 +176,10 @@ qdrant.recreate_collection(
 Tell the database to upload `documents` to the `my_books` collection. This will give each record an id and a payload. The payload is just the metadata from the dataset.
 
 ```python
-qdrant.upload_records(
+qdrant.upload_points(
     collection_name="my_books",
-    records=[
-        models.Record(
+    points=[
+        models.PointStruct(
             id=idx, vector=encoder.encode(doc["description"]).tolist(), payload=doc
         )
         for idx, doc in enumerate(documents)