diff --git a/examples/image_search/README.md b/examples/image_search/README.md
index f19da798..4f982552 100644
--- a/examples/image_search/README.md
+++ b/examples/image_search/README.md
@@ -14,7 +14,6 @@ We appreciate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/c
 - Qdrant for Vector Storage
 - FastApi for backend
 
-
 ## Setup
 - Make sure Postgres and Qdrant are running
   ```
@@ -22,20 +21,6 @@ We appreciate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/c
   export COCOINDEX_DATABASE_URL="postgres://cocoindex:cocoindex@localhost/cocoindex"
   ```
 
-- Create Qdrant Collection
-  ```
-  curl -X PUT 'http://localhost:6333/collections/image_search' \
-    -H 'Content-Type: application/json' \
-    -d '{
-      "vectors": {
-        "embedding": {
-          "size": 768,
-          "distance": "Cosine"
-        }
-      }
-    }'
-  ```
-
 ## Run
 - Install dependencies:
   ```
diff --git a/examples/text_embedding_qdrant/README.md b/examples/text_embedding_qdrant/README.md
index 42bce2f3..3727e99f 100644
--- a/examples/text_embedding_qdrant/README.md
+++ b/examples/text_embedding_qdrant/README.md
@@ -19,7 +19,6 @@ We appreciate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/c
 ### Query
 We use Qdrant client to query the index, and reuse the embedding operation in the indexing flow.
 
-
 ## Pre-requisites
 
 - [Install Postgres](https://cocoindex.io/docs/getting_started/installation#-install-postgres) if you don't have one. Although the target store is Qdrant, CocoIndex uses Postgress to track the data lineage for incremental processing.
@@ -30,24 +29,6 @@ We use Qdrant client to query the index, and reuse the embedding operation in th
    docker run -d -p 6334:6334 -p 6333:6333 qdrant/qdrant
    ```
 
-- [Create a collection](https://qdrant.tech/documentation/concepts/vectors/#named-vectors) to export the embeddings to.
-
-   ```bash
-   curl  -X PUT \
-     'http://localhost:6333/collections/cocoindex' \
-     --header 'Content-Type: application/json' \
-     --data-raw '{
-     "vectors": {
-       "text_embedding": {
-         "size": 384,
-         "distance": "Cosine"
-       }
-     }
-   }'
-   ```
-
-   You can view the collections and data with the Qdrant dashboard at <http://localhost:6333/dashboard>.
-
 ## Run
 
 - Install dependencies:
@@ -62,6 +43,9 @@ We use Qdrant client to query the index, and reuse the embedding operation in th
    cocoindex setup main.py
    ```
 
+   It will automatically create a collection in Qdrant.
+   You can view the collections and data with the Qdrant dashboard at <http://localhost:6333/dashboard>.
+
 - Update index:
 
    ```bash