update vector search docs

TOC-tidb-cloud.md

@@ -10,7 +10,7 @@
   - [Roadmap](/tidb-cloud/tidb-cloud-roadmap.md)
 - Get Started
   - [Try Out TiDB Cloud](/tidb-cloud/tidb-cloud-quickstart.md)
-  - [Try Out TiDB + AI](/tidb-cloud/vector-search-get-started-using-python.md)
+  - [Try Out TiDB + AI](/vector-search-get-started-using-python.md)
   - [Try Out HTAP](/tidb-cloud/tidb-cloud-htap-quickstart.md)
   - [Try Out TiDB Cloud CLI](/tidb-cloud/get-started-with-cli.md)
   - [Perform a PoC](/tidb-cloud/tidb-cloud-poc.md)
@@ -241,27 +241,27 @@
 - Explore Data
   - [Chat2Query (Beta) in SQL Editor](/tidb-cloud/explore-data-with-chat2query.md)
 - Vector Search (Beta)
-  - [Overview](/tidb-cloud/vector-search-overview.md)
+  - [Overview](/vector-search-overview.md)
   - Get Started
-    - [Get Started with SQL](/tidb-cloud/vector-search-get-started-using-sql.md)
-    - [Get Started with Python](/tidb-cloud/vector-search-get-started-using-python.md)
+    - [Get Started with SQL](/vector-search-get-started-using-sql.md)
+    - [Get Started with Python](/vector-search-get-started-using-python.md)
   - Integrations
-    - [Overview](/tidb-cloud/vector-search-integration-overview.md)
+    - [Overview](/vector-search-integration-overview.md)
     - AI Frameworks
-      - [LlamaIndex](/tidb-cloud/vector-search-integrate-with-llamaindex.md)
-      - [Langchain](/tidb-cloud/vector-search-integrate-with-langchain.md)
+      - [LlamaIndex](/vector-search-integrate-with-llamaindex.md)
+      - [Langchain](/vector-search-integrate-with-langchain.md)
     - Embedding Models/Services
-      - [Jina AI](/tidb-cloud/vector-search-integrate-with-jinaai-embedding.md)
+      - [Jina AI](/vector-search-integrate-with-jinaai-embedding.md)
     - ORM Libraries
-      - [SQLAlchemy](/tidb-cloud/vector-search-integrate-with-sqlalchemy.md)
-      - [peewee](/tidb-cloud/vector-search-integrate-with-peewee.md)
-      - [Django ORM](/tidb-cloud/vector-search-integrate-with-django-orm.md)
+      - [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](/tidb-cloud/vector-search-data-types.md)
-    - [Vector Functions and Operators](/tidb-cloud/vector-search-functions-and-operators.md)
-    - [Vector Index](/tidb-cloud/vector-search-index.md)
-  - [Improve Performance](/tidb-cloud/vector-search-improve-performance.md)
-  - [Limitations](/tidb-cloud/vector-search-limitations.md)
+    - [Vector Data Types](/vector-search-data-types.md)
+    - [Vector Functions and Operators](/vector-search-functions-and-operators.md)
+    - [Vector Index](/vector-search-index.md)
+  - [Improve Performance](/vector-search-improve-performance.md)
+  - [Limitations](/vector-search-limitations.md)
   - [Changelogs](/tidb-cloud/vector-search-changelogs.md)
 - Data Service (Beta)
   - [Overview](/tidb-cloud/data-service-overview.md)

tidb-cloud/data-service-manage-endpoint.md

@@ -44,7 +44,7 @@ In TiDB Cloud Data Service, you can generate one or multiple endpoints automatic
 
         For each operation you select, TiDB Cloud Data Service will generate a corresponding endpoint. If you select a batch operation (such as `POST (Batch Create)`), the generated endpoint lets you operate on multiple rows in a single request.
 
-        If the table you selected contains [vector data types](/tidb-cloud/vector-search-data-types.md), you can enable the **Vector Search Operations** option and select a vector distance function to generate a vector search endpoint that automatically calculates vector distances based on your selected distance function. The supported [vector distance functions](/tidb-cloud/vector-search-functions-and-operators.md) include the following:
+        If the table you selected contains [vector data types](/vector-search-data-types.md), you can enable the **Vector Search Operations** option and select a vector distance function to generate a vector search endpoint that automatically calculates vector distances based on your selected distance function. The supported [vector distance functions](/vector-search-functions-and-operators.md) include the following:
 
         - `VEC_L2_DISTANCE` (default): calculates the L2 distance (Euclidean distance) between two vectors.
         - `VEC_COSINE_DISTANCE`: calculates the cosine distance between two vectors.

tidb-cloud/tidb-cloud-release-notes.md

@@ -76,7 +76,7 @@ This page lists the release notes of [TiDB Cloud](https://www.pingcap.com/tidb-c
 
 - [Data Service (beta)](https://tidbcloud.com/console/data-service) supports automatically generating vector search endpoints.
 
-    If your table contains [vector data types](/tidb-cloud/vector-search-data-types.md), you can automatically generate a vector search endpoint that calculates vector distances based on your selected distance function.
+    If your table contains [vector data types](/vector-search-data-types.md), you can automatically generate a vector search endpoint that calculates vector distances based on your selected distance function.
 
     This feature enables seamless integration with AI platforms such as [Dify](https://docs.dify.ai/guides/tools) and [GPTs](https://openai.com/blog/introducing-gpts), enhancing your applications with advanced natural language processing and AI capabilities for more complex tasks and intelligent solutions.
 
@@ -122,12 +122,12 @@ This page lists the release notes of [TiDB Cloud](https://www.pingcap.com/tidb-c
 
     The vector search (beta) feature provides an advanced search solution for performing semantic similarity searches across various data types, including documents, images, audio, and video. This feature enables developers to easily build scalable applications with generative artificial intelligence (AI) capabilities using familiar MySQL skills. Key features include:
 
-    - [Vector data types](/tidb-cloud/vector-search-data-types.md), [vector index](/tidb-cloud/vector-search-index.md), and [vector functions and operators](/tidb-cloud/vector-search-functions-and-operators.md).
-    - Ecosystem integrations with [LangChain](/tidb-cloud/vector-search-integrate-with-langchain.md), [LlamaIndex](/tidb-cloud/vector-search-integrate-with-llamaindex.md), and [JinaAI](/tidb-cloud/vector-search-integrate-with-jinaai-embedding.md).
-    - Programming language support for Python: [SQLAlchemy](/tidb-cloud/vector-search-integrate-with-sqlalchemy.md), [Peewee](/tidb-cloud/vector-search-integrate-with-peewee.md), and [Django ORM](/tidb-cloud/vector-search-integrate-with-django-orm.md).
-    - Sample applications and tutorials: perform semantic searches for documents using [Python](/tidb-cloud/vector-search-get-started-using-python.md) or [SQL](/tidb-cloud/vector-search-get-started-using-sql.md).
+    - [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](/tidb-cloud/vector-search-overview.md).
+  For more information, see [Vector search (beta) overview](/vector-search-overview.md).
 
 - [TiDB Cloud Serverless](/tidb-cloud/select-cluster-tier.md#tidb-cloud-serverless) now offers weekly email reports for organization owners.