Preview PR pingcap/docs#18779 and this preview is triggered from commit

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markdown-pages/en/tidbcloud/master/TOC.md

@@ -252,6 +252,10 @@
       - [Langchain](/vector-search-integrate-with-langchain.md)
     - Embedding Models/Services
       - [Jina AI](/vector-search-integrate-with-jinaai-embedding.md)
+    - ORM Libraries
+      - [SQLAlchemy](/vector-search-integrate-with-sqlalchemy.md)
+      - [peewee](/vector-search-integrate-with-peewee.md)
+      - [Django ORM](/vector-search-integrate-with-django-orm.md)
   - Reference
     - [Vector Data Types](/vector-search-data-types.md)
     - [Vector Functions and Operators](/vector-search-functions-and-operators.md)

markdown-pages/en/tidbcloud/master/tidb-cloud/tidb-cloud-release-notes.md

@@ -124,6 +124,7 @@ This page lists the release notes of [TiDB Cloud](https://www.pingcap.com/tidb-c
 
     - [Vector data types](/vector-search-data-types.md), [vector index](/vector-search-index.md), and [vector functions and operators](/vector-search-functions-and-operators.md).
     - Ecosystem integrations with [LangChain](/vector-search-integrate-with-langchain.md), [LlamaIndex](/vector-search-integrate-with-llamaindex.md), and [JinaAI](/vector-search-integrate-with-jinaai-embedding.md).
+    - Programming language support for Python: [SQLAlchemy](/vector-search-integrate-with-sqlalchemy.md), [Peewee](/vector-search-integrate-with-peewee.md), and [Django ORM](/vector-search-integrate-with-django-orm.md).
     - Sample applications and tutorials: perform semantic searches for documents using [Python](/vector-search-get-started-using-python.md) or [SQL](/vector-search-get-started-using-sql.md).
 
   For more information, see [Vector search (beta) overview](/vector-search-overview.md).

markdown-pages/en/tidbcloud/master/vector-search-get-started-using-python.md

@@ -42,7 +42,7 @@ To complete this tutorial, you need:
 **If you don't have a TiDB cluster, you can create one as follows:**
 
 - (Recommended) Follow [Creating a TiDB Cloud Serverless cluster](/develop/dev-guide-build-cluster-in-cloud.md) to create your own TiDB Cloud cluster.
-- Follow [Deploy a local test TiDB cluster](https://docs.pingcap.com/tidb/stable/quick-start-with-tidb#deploy-a-local-test-cluster) or [Deploy a production TiDB cluster](https://docs.pingcap.com/tidb/stable/production-deployment-using-tiup) to create a local cluster.
+- Follow [Deploy a local test TiDB cluster](https://docs.pingcap.com/tidb/stable/quick-start-with-tidb#deploy-a-local-test-cluster) or [Deploy a production TiDB cluster](https://docs.pingcap.com/tidb/stable/production-deployment-using-tiup) to create a local cluster of v8.4.0 or a later version.
 
 </CustomContent>
 

markdown-pages/en/tidbcloud/master/vector-search-get-started-using-sql.md

@@ -46,7 +46,7 @@ To complete this tutorial, you need:
 **If you don't have a TiDB cluster, you can create one as follows:**
 
 - (Recommended) Follow [Creating a TiDB Cloud Serverless cluster](/develop/dev-guide-build-cluster-in-cloud.md) to create your own TiDB Cloud cluster.
-- Follow [Deploy a local test TiDB cluster](https://docs.pingcap.com/tidb/stable/quick-start-with-tidb#deploy-a-local-test-cluster) or [Deploy a production TiDB cluster](https://docs.pingcap.com/tidb/stable/production-deployment-using-tiup) to create a local cluster.
+- Follow [Deploy a local test TiDB cluster](https://docs.pingcap.com/tidb/stable/quick-start-with-tidb#deploy-a-local-test-cluster) or [Deploy a production TiDB cluster](https://docs.pingcap.com/tidb/stable/production-deployment-using-tiup) to create a local cluster of v8.4.0 or a later version.
 
 </CustomContent>
 

markdown-pages/en/tidbcloud/master/vector-search-integrate-with-django-orm.md

@@ -40,7 +40,7 @@ To complete this tutorial, you need:
 **If you don't have a TiDB cluster, you can create one as follows:**
 
 - (Recommended) Follow [Creating a TiDB Cloud Serverless cluster](/develop/dev-guide-build-cluster-in-cloud.md) to create your own TiDB Cloud cluster.
-- Follow [Deploy a local test TiDB cluster](https://docs.pingcap.com/tidb/stable/quick-start-with-tidb#deploy-a-local-test-cluster) or [Deploy a production TiDB cluster](https://docs.pingcap.com/tidb/stable/production-deployment-using-tiup) to create a local cluster.
+- Follow [Deploy a local test TiDB cluster](https://docs.pingcap.com/tidb/stable/quick-start-with-tidb#deploy-a-local-test-cluster) or [Deploy a production TiDB cluster](https://docs.pingcap.com/tidb/stable/production-deployment-using-tiup) to create a local cluster of v8.4.0 or a later version.
 
 </CustomContent>
 
@@ -239,34 +239,6 @@ class Document(models.Model):
    embedding = VectorField(dimensions=3)
 ```
 
-#### Define a vector column optimized with index
-
-> **Note**
->
-> This section is only applicable to [TiDB Serverless](https://docs.pingcap.com/tidbcloud/select-cluster-tier#tidb-serverless) clusters.
-
-Define a 3-dimensional vector column and optimize it with a [vector search index](/vector-search-index.md) (HNSW index).
-
-```python
-class DocumentWithIndex(models.Model):
-   content = models.TextField()
-   # Note:
-   #   - Using comment to add hnsw index is a temporary solution. In the future it will use `CREATE INDEX` syntax.
-   #   - Currently the HNSW index cannot be changed after the table has been created.
-   #   - Only Django >= 4.2 supports `db_comment`.
-   embedding = VectorField(dimensions=3, db_comment="hnsw(distance=cosine)")
-```
-
-TiDB will use this index to speed up vector search queries based on the cosine distance function.
-
-### Store documents with embeddings
-
-```python
-Document.objects.create(content="dog", embedding=[1, 2, 1])
-Document.objects.create(content="fish", embedding=[1, 2, 4])
-Document.objects.create(content="tree", embedding=[1, 0, 0])
-```
-
 ### Search the nearest neighbor documents
 
 TiDB Vector support the following distance functions:

markdown-pages/en/tidbcloud/master/vector-search-integrate-with-jinaai-embedding.md

@@ -40,7 +40,7 @@ To complete this tutorial, you need:
 **If you don't have a TiDB cluster, you can create one as follows:**
 
 - (Recommended) Follow [Creating a TiDB Cloud Serverless cluster](/develop/dev-guide-build-cluster-in-cloud.md) to create your own TiDB Cloud cluster.
-- Follow [Deploy a local test TiDB cluster](https://docs.pingcap.com/tidb/stable/quick-start-with-tidb#deploy-a-local-test-cluster) or [Deploy a production TiDB cluster](https://docs.pingcap.com/tidb/stable/production-deployment-using-tiup) to create a local cluster.
+- Follow [Deploy a local test TiDB cluster](https://docs.pingcap.com/tidb/stable/quick-start-with-tidb#deploy-a-local-test-cluster) or [Deploy a production TiDB cluster](https://docs.pingcap.com/tidb/stable/production-deployment-using-tiup) to create a local cluster of v8.4.0 or a later version.
 
 </CustomContent>
 

markdown-pages/en/tidbcloud/master/vector-search-integrate-with-langchain.md

@@ -45,7 +45,7 @@ To complete this tutorial, you need:
 **If you don't have a TiDB cluster, you can create one as follows:**
 
 - (Recommended) Follow [Creating a TiDB Cloud Serverless cluster](/develop/dev-guide-build-cluster-in-cloud.md) to create your own TiDB Cloud cluster.
-- Follow [Deploy a local test TiDB cluster](https://docs.pingcap.com/tidb/stable/quick-start-with-tidb#deploy-a-local-test-cluster) or [Deploy a production TiDB cluster](https://docs.pingcap.com/tidb/stable/production-deployment-using-tiup) to create a local cluster.
+- Follow [Deploy a local test TiDB cluster](https://docs.pingcap.com/tidb/stable/quick-start-with-tidb#deploy-a-local-test-cluster) or [Deploy a production TiDB cluster](https://docs.pingcap.com/tidb/stable/production-deployment-using-tiup) to create a local cluster of v8.4.0 or a later version.
 
 </CustomContent>
 

markdown-pages/en/tidbcloud/master/vector-search-integrate-with-llamaindex.md

@@ -45,7 +45,7 @@ To complete this tutorial, you need:
 **If you don't have a TiDB cluster, you can create one as follows:**
 
 - (Recommended) Follow [Creating a TiDB Cloud Serverless cluster](/develop/dev-guide-build-cluster-in-cloud.md) to create your own TiDB Cloud cluster.
-- Follow [Deploy a local test TiDB cluster](https://docs.pingcap.com/tidb/stable/quick-start-with-tidb#deploy-a-local-test-cluster) or [Deploy a production TiDB cluster](https://docs.pingcap.com/tidb/stable/production-deployment-using-tiup) to create a local cluster.
+- Follow [Deploy a local test TiDB cluster](https://docs.pingcap.com/tidb/stable/quick-start-with-tidb#deploy-a-local-test-cluster) or [Deploy a production TiDB cluster](https://docs.pingcap.com/tidb/stable/production-deployment-using-tiup) to create a local cluster of v8.4.0 or a later version.
 
 </CustomContent>
 

markdown-pages/en/tidbcloud/master/vector-search-integrate-with-peewee.md

@@ -40,7 +40,7 @@ To complete this tutorial, you need:
 **If you don't have a TiDB cluster, you can create one as follows:**
 
 - (Recommended) Follow [Creating a TiDB Cloud Serverless cluster](/develop/dev-guide-build-cluster-in-cloud.md) to create your own TiDB Cloud cluster.
-- Follow [Deploy a local test TiDB cluster](https://docs.pingcap.com/tidb/stable/quick-start-with-tidb#deploy-a-local-test-cluster) or [Deploy a production TiDB cluster](https://docs.pingcap.com/tidb/stable/production-deployment-using-tiup) to create a local cluster.
+- Follow [Deploy a local test TiDB cluster](https://docs.pingcap.com/tidb/stable/quick-start-with-tidb#deploy-a-local-test-cluster) or [Deploy a production TiDB cluster](https://docs.pingcap.com/tidb/stable/production-deployment-using-tiup) to create a local cluster of v8.4.0 or a later version.
 
 </CustomContent>
 
@@ -237,26 +237,6 @@ class Document(Model):
     embedding = VectorField(3)
 ```
 
-#### Define a vector column optimized with index
-
-> **Note**
->
-> This section is only applicable to [TiDB Serverless](https://docs.pingcap.com/tidbcloud/select-cluster-tier#tidb-serverless) clusters.
-
-Define a 3-dimensional vector column and optimize it with a [vector search index](/vector-search-index.md) (HNSW index).
-
-```python
-class DocumentWithIndex(Model):
-    class Meta:
-        database = db
-        table_name = 'peewee_demo_documents_with_index'
-
-    content = TextField()
-    embedding = VectorField(3, constraints=[SQL("COMMENT 'hnsw(distance=cosine)'")])
-```
-
-TiDB will use this index to accelerate vector search queries based on the cosine distance function.
-
 ### Store documents with embeddings
 
 ```python

markdown-pages/en/tidbcloud/master/vector-search-integrate-with-sqlalchemy.md

@@ -40,7 +40,7 @@ To complete this tutorial, you need:
 **If you don't have a TiDB cluster, you can create one as follows:**
 
 - (Recommended) Follow [Creating a TiDB Cloud Serverless cluster](/develop/dev-guide-build-cluster-in-cloud.md) to create your own TiDB Cloud cluster.
-- Follow [Deploy a local test TiDB cluster](https://docs.pingcap.com/tidb/stable/quick-start-with-tidb#deploy-a-local-test-cluster) or [Deploy a production TiDB cluster](https://docs.pingcap.com/tidb/stable/production-deployment-using-tiup) to create a local cluster.
+- Follow [Deploy a local test TiDB cluster](https://docs.pingcap.com/tidb/stable/quick-start-with-tidb#deploy-a-local-test-cluster) or [Deploy a production TiDB cluster](https://docs.pingcap.com/tidb/stable/production-deployment-using-tiup) to create a local cluster of v8.4.0 or a later version.
 
 </CustomContent>
 
@@ -201,34 +201,6 @@ class Document(Base):
     embedding = Column(VectorType(3))
 ```
 
-#### Define a vector column optimized with index
-
-> **Note**
->
-> This section is only applicable to [TiDB Serverless](https://docs.pingcap.com/tidbcloud/select-cluster-tier#tidb-serverless) clusters.
-
-Define a 3-dimensional vector column and optimize it with a [vector search index](/vector-search-index.md) (HNSW index).
-
-```python
-class DocumentWithIndex(Base):
-    __tablename__ = 'sqlalchemy_demo_documents_with_index'
-    id = Column(Integer, primary_key=True)
-    content = Column(Text)
-    embedding = Column(VectorType(3), comment="hnsw(distance=cosine)")
-```
-
-TiDB will use this index to accelerate vector search queries based on the cosine distance function.
-
-### Store documents with embeddings
-
-```python
-with Session(engine) as session:
-   session.add(Document(content="dog", embedding=[1, 2, 1]))
-   session.add(Document(content="fish", embedding=[1, 2, 4]))
-   session.add(Document(content="tree", embedding=[1, 0, 0]))
-   session.commit()
-```
-
 ### Search the nearest neighbor documents
 
 Search for the top-3 documents that are semantically closest to the query vector `[1, 2, 3]` based on the cosine distance function.

markdown-pages/en/tidbcloud/master/vector-search-integration-overview.md

@@ -1,11 +1,11 @@
 ---
 title: Vector Search Integration Overview
-summary: An overview of TiDB vector search integration, including supported AI frameworks and embedding models.
+summary: An overview of TiDB vector search integration, including supported AI frameworks, embedding models, and ORM libraries.
 ---
 
 # Vector Search Integration Overview
 
-This document provides an overview of TiDB vector search integration, including supported AI frameworks and embedding models.
+This document provides an overview of TiDB vector search integration, including supported AI frameworks, embedding models, and Object Relational Mapping (ORM) libraries.
 
 <CustomContent platform="tidb">
 
@@ -40,4 +40,40 @@ The following table lists some mainstream embedding service providers and the co
 
 | Embedding service providers | Tutorial                                                                                                            |
 |-----------------------------|---------------------------------------------------------------------------------------------------------------------|
-| Jina AI                     | [Integrate Vector Search with Jina AI Embeddings API](/vector-search-integrate-with-jinaai-embedding.md) |
+| Jina AI                     | [Integrate Vector Search with Jina AI Embeddings API](/vector-search-integrate-with-jinaai-embedding.md) |
+
+## Object Relational Mapping (ORM) libraries
+
+You can integrate TiDB Vector Search with your ORM library to interact with the TiDB database.
+
+The following table lists the supported ORM libraries and the corresponding integration tutorials:
+
+<table>
+  <tr>
+    <th>Language</th>
+    <th>ORM/Client</th>
+    <th>How to install</th>
+    <th>Tutorial</th>
+  </tr>
+  <tr>
+    <td rowspan="4">Python</td>
+    <td>TiDB Vector Client</td>
+    <td><code>pip install tidb-vector[client]</code></td>
+    <td><a href="/tidbcloud/vector-search-get-started-using-python">Get Started with Vector Search Using Python</a></td>
+  </tr>
+  <tr>
+    <td>SQLAlchemy</td>
+    <td><code>pip install tidb-vector</code></td>
+    <td><a href="/tidbcloud/vector-search-integrate-with-sqlalchemy">Integrate TiDB Vector Search with SQLAlchemy</a></td>
+  </tr>
+  <tr>
+    <td>peewee</td>
+    <td><code>pip install tidb-vector</code></td>
+    <td><a href="/tidbcloud/vector-search-integrate-with-peewee">Integrate TiDB Vector Search with peewee</a></td>
+  </tr>
+  <tr>
+    <td>Django</td>
+    <td><code>pip install django-tidb[vector]</code></td>
+    <td><a href="/tidbcloud/vector-search-integrate-with-django-orm">Integrate TiDB Vector Search with Django</a></td>
+  </tr>
+</table>