diff --git a/localization/v2.4.x/site/en/getstarted/run-milvus-k8s/install_cluster-helm.md b/localization/v2.4.x/site/en/getstarted/run-milvus-k8s/install_cluster-helm.md
index 1c8d57555..c6b3c803d 100644
--- a/localization/v2.4.x/site/en/getstarted/run-milvus-k8s/install_cluster-helm.md
+++ b/localization/v2.4.x/site/en/getstarted/run-milvus-k8s/install_cluster-helm.md
@@ -277,7 +277,7 @@ $ helm upgrade my-release zilliztech/milvus
 </ul></li>
 <li><p><a href="/docs/upgrade_milvus_cluster-helm.md">Upgrade Milvus Using Helm Chart</a>.</p></li>
 <li><p><a href="/docs/scaleout.md">Scale your Milvus cluster</a>.</p></li>
-<li><p>Deploy your Milvu cluster on clouds:</p>
+<li><p>Deploy your Milvus cluster on clouds:</p>
 <ul>
 <li><a href="/docs/eks.md">Amazon EKS</a></li>
 <li><a href="/docs/gcp.md">Google Cloud</a></li>
diff --git a/localization/v2.4.x/site/en/reference/schema.json b/localization/v2.4.x/site/en/reference/schema.json
index 6d07edbc9..b2390ab59 100644
--- a/localization/v2.4.x/site/en/reference/schema.json
+++ b/localization/v2.4.x/site/en/reference/schema.json
@@ -1 +1 @@
-{"codeList":["from pymilvus import FieldSchema\nid_field = FieldSchema(name=\"id\", dtype=DataType.INT64, is_primary=True, description=\"primary id\")\nage_field = FieldSchema(name=\"age\", dtype=DataType.INT64, description=\"age\")\nembedding_field = FieldSchema(name=\"embedding\", dtype=DataType.FLOAT_VECTOR, dim=128, description=\"vector\")\n\n# The following creates a field and use it as the partition key\nposition_field = FieldSchema(name=\"position\", dtype=DataType.VARCHAR, max_length=256, is_partition_key=True)\n","from pymilvus import FieldSchema\n\nfields = [\n  FieldSchema(name=\"id\", dtype=DataType.INT64, is_primary=True),\n  # configure default value `25` for field `age`\n  FieldSchema(name=\"age\", dtype=DataType.INT64, default_value=25, description=\"age\"),\n  embedding_field = FieldSchema(name=\"embedding\", dtype=DataType.FLOAT_VECTOR, dim=128, description=\"vector\")\n]\n","from pymilvus import FieldSchema, CollectionSchema\nid_field = FieldSchema(name=\"id\", dtype=DataType.INT64, is_primary=True, description=\"primary id\")\nage_field = FieldSchema(name=\"age\", dtype=DataType.INT64, description=\"age\")\nembedding_field = FieldSchema(name=\"embedding\", dtype=DataType.FLOAT_VECTOR, dim=128, description=\"vector\")\n\n# Enable partition key on a field if you need to implement multi-tenancy based on the partition-key field\nposition_field = FieldSchema(name=\"position\", dtype=DataType.VARCHAR, max_length=256, is_partition_key=True)\n\n# Set enable_dynamic_field to True if you need to use dynamic fields. \nschema = CollectionSchema(fields=[id_field, age_field, embedding_field], auto_id=False, enable_dynamic_field=True, description=\"desc of a collection\")\n","from pymilvus import Collection\ncollection_name1 = \"tutorial_1\"\ncollection1 = Collection(name=collection_name1, schema=schema, using='default', shards_num=2)\n","import pandas as pd\ndf = pd.DataFrame({\n    \"id\": [i for i in range(nb)],\n    \"age\": [random.randint(20, 40) for i in range(nb)],\n    \"embedding\": [[random.random() for _ in range(dim)] for _ in range(nb)],\n    \"position\": \"test_pos\"\n})\n\ncollection, ins_res = Collection.construct_from_dataframe(\n    'my_collection',\n    df,\n    primary_field='id',\n    auto_id=False\n    )\n"],"headingContent":"Manage Schema","anchorList":[{"label":"Manage Schema","href":"Manage-Schema","type":1,"isActive":false},{"label":"Field schema","href":"Field-schema","type":2,"isActive":false},{"label":"Collection schema","href":"Collection-schema","type":2,"isActive":false},{"label":"What's next","href":"Whats-next","type":2,"isActive":false}]}
\ No newline at end of file
+{"codeList":["from pymilvus import FieldSchema\nid_field = FieldSchema(name=\"id\", dtype=DataType.INT64, is_primary=True, description=\"primary id\")\nage_field = FieldSchema(name=\"age\", dtype=DataType.INT64, description=\"age\")\nembedding_field = FieldSchema(name=\"embedding\", dtype=DataType.FLOAT_VECTOR, dim=128, description=\"vector\")\n\n# The following creates a field and use it as the partition key\nposition_field = FieldSchema(name=\"position\", dtype=DataType.VARCHAR, max_length=256, is_partition_key=True)\n","from pymilvus import FieldSchema\n\nfields = [\n  FieldSchema(name=\"id\", dtype=DataType.INT64, is_primary=True),\n  # configure default value `25` for field `age`\n  FieldSchema(name=\"age\", dtype=DataType.INT64, default_value=25, description=\"age\"),\n  embedding_field = FieldSchema(name=\"embedding\", dtype=DataType.FLOAT_VECTOR, dim=128, description=\"vector\")\n]\n","from pymilvus import FieldSchema, CollectionSchema\nid_field = FieldSchema(name=\"id\", dtype=DataType.INT64, is_primary=True, description=\"primary id\")\nage_field = FieldSchema(name=\"age\", dtype=DataType.INT64, description=\"age\")\nembedding_field = FieldSchema(name=\"embedding\", dtype=DataType.FLOAT_VECTOR, dim=128, description=\"vector\")\n\n# Enable partition key on a field if you need to implement multi-tenancy based on the partition-key field\nposition_field = FieldSchema(name=\"position\", dtype=DataType.VARCHAR, max_length=256, is_partition_key=True)\n\n# Set enable_dynamic_field to True if you need to use dynamic fields. \nschema = CollectionSchema(fields=[id_field, age_field, embedding_field], auto_id=False, enable_dynamic_field=True, description=\"desc of a collection\")\n","from pymilvus import Collection,connections\nconn = connections.connect(host=\"127.0.0.1\", port=19530)\ncollection_name1 = \"tutorial_1\"\ncollection1 = Collection(name=collection_name1, schema=schema, using='default', shards_num=2)\n","import pandas as pd\ndf = pd.DataFrame({\n    \"id\": [i for i in range(nb)],\n    \"age\": [random.randint(20, 40) for i in range(nb)],\n    \"embedding\": [[random.random() for _ in range(dim)] for _ in range(nb)],\n    \"position\": \"test_pos\"\n})\n\ncollection, ins_res = Collection.construct_from_dataframe(\n    'my_collection',\n    df,\n    primary_field='id',\n    auto_id=False\n    )\n"],"headingContent":"Manage Schema","anchorList":[{"label":"Manage Schema","href":"Manage-Schema","type":1,"isActive":false},{"label":"Field schema","href":"Field-schema","type":2,"isActive":false},{"label":"Collection schema","href":"Collection-schema","type":2,"isActive":false},{"label":"What's next","href":"Whats-next","type":2,"isActive":false}]}
\ No newline at end of file
diff --git a/localization/v2.4.x/site/en/reference/schema.md b/localization/v2.4.x/site/en/reference/schema.md
index c72790d0b..0e1fcc8fe 100644
--- a/localization/v2.4.x/site/en/reference/schema.md
+++ b/localization/v2.4.x/site/en/reference/schema.md
@@ -39,7 +39,7 @@ title: Manage Schema
 <h3 id="Field-schema-properties" class="common-anchor-header">Field schema properties</h3><table class="properties">
     <thead>
     <tr>
-        <th>Properties</td>
+        <th>Properties</th>
         <th>Description</th>
         <th>Note</th>
     </tr>
@@ -157,7 +157,7 @@ fields = [
 <h3 id="Collection-schema-properties" class="common-anchor-header">Collection schema properties</h3><table class="properties">
     <thead>
     <tr>
-        <th>Properties</td>
+        <th>Properties</th>
         <th>Description</th>
         <th>Note</th>
     </tr>
@@ -200,7 +200,8 @@ position_field = FieldSchema(name=<span class="hljs-string">&quot;position&quot;
 schema = CollectionSchema(fields=[id_field, age_field, embedding_field], auto_id=<span class="hljs-literal">False</span>, enable_dynamic_field=<span class="hljs-literal">True</span>, description=<span class="hljs-string">&quot;desc of a collection&quot;</span>)
 <button class="copy-code-btn"></button></code></pre>
 <p>Create a collection with the schema specified:</p>
-<pre><code translate="no" class="language-python"><span class="hljs-keyword">from</span> pymilvus <span class="hljs-keyword">import</span> <span class="hljs-title class_">Collection</span>
+<pre><code translate="no" class="language-python"><span class="hljs-keyword">from</span> pymilvus <span class="hljs-keyword">import</span> <span class="hljs-title class_">Collection</span>,connections
+conn = connections.<span class="hljs-title function_">connect</span>(host=<span class="hljs-string">&quot;127.0.0.1&quot;</span>, port=<span class="hljs-number">19530</span>)
 collection_name1 = <span class="hljs-string">&quot;tutorial_1&quot;</span>
 collection1 = <span class="hljs-title class_">Collection</span>(name=collection_name1, schema=schema, using=<span class="hljs-string">&#x27;default&#x27;</span>, shards_num=<span class="hljs-number">2</span>)
 <button class="copy-code-btn"></button></code></pre>
diff --git a/localization/v2.4.x/site/en/tutorials/tutorials-overview.md b/localization/v2.4.x/site/en/tutorials/tutorials-overview.md
index 7a6f870cd..7bfa735e0 100644
--- a/localization/v2.4.x/site/en/tutorials/tutorials-overview.md
+++ b/localization/v2.4.x/site/en/tutorials/tutorials-overview.md
@@ -28,7 +28,7 @@ title: Tutorials Overview
 <tr><td><a href="/docs/multimodal_rag_with_milvus.md">Multimodal RAG with Milvus</a></td><td>RAG</td><td>vector search, dynamic field</td></tr>
 <tr><td><a href="/docs/image_similarity_search.md">Image Search with Milvus</a></td><td>Semantic Search</td><td>vector search, dynamic field</td></tr>
 <tr><td><a href="/docs/hybrid_search_with_milvus.md">Hybrid Search with Milvus</a></td><td>Hybrid Search</td><td>hybrid search, multi vector, dense embedding, sparse embedding</td></tr>
-<tr><td><a href="/docs/multimodal_rag_with_milvus.md">Multimodal Search using multi vectors</a></td><td>Semantic Search</td><td>multi vector, hybrid search</td></tr>
+<tr><td><a href="/docs/multimodal_rag_with_milvus.md">Multimodal Search using Multi Vectors</a></td><td>Semantic Search</td><td>multi vector, hybrid search</td></tr>
 <tr><td><a href="/docs/question_answering_system.md">Question Answering System</a></td><td>Question Answering</td><td>vector search</td></tr>
 <tr><td><a href="/docs/recommendation_system.md">Recommender System</a></td><td>Recommendation System</td><td>vector search</td></tr>
 <tr><td><a href="/docs/video_similarity_search.md">Video Similarity Search</a></td><td>Semantic Search</td><td>vector search</td></tr>