From c118d5432afcb8a455c2ae34b51972758cd2d2cb Mon Sep 17 00:00:00 2001
From: midegdugarova <59020389+midegdugarova@users.noreply.github.com>
Date: Thu, 25 Jun 2026 21:15:49 +0200
Subject: [PATCH] docs(embeddings): use current query_points API in OpenAI
 example

---
 qdrant-landing/content/documentation/embeddings/openai.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/qdrant-landing/content/documentation/embeddings/openai.md b/qdrant-landing/content/documentation/embeddings/openai.md
index 4ffe4c888b..fcf1e72224 100644
--- a/qdrant-landing/content/documentation/embeddings/openai.md
+++ b/qdrant-landing/content/documentation/embeddings/openai.md
@@ -80,9 +80,9 @@ client.upsert(collection_name, points)
 Once the documents are indexed, you can search for the most relevant documents using the same model.
 
 ```python
-client.search(
+client.query_points(
     collection_name=collection_name,
-    query_vector=openai_client.embeddings.create(
+    query=openai_client.embeddings.create(
         input=["What is the best to use for vector search scaling?"],
         model=embedding_model,
     )