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 Data Types](/vector-search-data-types.md)
+    - [Vector Functions and Operators](/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)
+  - [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

@@ -40,7 +40,7 @@ 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.
 
@@ -86,12 +86,12 @@ 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](/tidb-cloud/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 Serverless](/tidb-cloud/select-cluster-tier.md#tidb-serverless) now offers weekly email reports for organization owners.
 

tidb-cloud/vector-search-index.md

@@ -7,7 +7,7 @@ summary: Learn how to build and use the vector search index to accelerate K-Near
 
 K-nearest neighbors (KNN) search is the problem of finding the K closest points for a given point in a vector space. The most straightforward approach to solving this problem is a brute force search, where the distance between all points in the vector space and the reference point is computed. This method guarantees perfect accuracy, but it is usually too slow for practical applications. Thus, nearest neighbors search problems are often solved with approximate algorithms.
 
-In TiDB, you can create and utilize vector search indexes for such approximate nearest neighbor (ANN) searches over columns with [vector data types](/tidb-cloud/vector-search-data-types.md). By using vector search indexes, vector search queries could be finished in milliseconds.
+In TiDB, you can create and utilize vector search indexes for such approximate nearest neighbor (ANN) searches over columns with [vector data types](/vector-search-data-types.md). By using vector search indexes, vector search queries could be finished in milliseconds.
 
 TiDB currently supports the following vector search index algorithms:
 
@@ -21,7 +21,7 @@ TiDB currently supports the following vector search index algorithms:
 
 [HNSW](https://en.wikipedia.org/wiki/Hierarchical_navigable_small_world) is one of the most popular vector indexing algorithms. The HNSW index provides good performance with relatively high accuracy (> 98% in typical cases).
 
-To create an HNSW vector index, specify the index definition in the comment of a column with a [vector data type](/tidb-cloud/vector-search-data-types.md) when creating the table:
+To create an HNSW vector index, specify the index definition in the comment of a column with a [vector data type](/vector-search-data-types.md) when creating the table:
 
 ```sql
 CREATE TABLE vector_table_with_index (
@@ -44,9 +44,9 @@ The vector index can only be created for fixed-dimensional vector columns like `
 If you are using programming language SDKs or ORMs, refer to the following documentation for creating vector indexes:
 
 - Python: [TiDB Vector SDK for Python](https://github.com/pingcap/tidb-vector-python)
-- Python: [SQLAlchemy](/tidb-cloud/vector-search-integrate-with-sqlalchemy.md)
-- Python: [Peewee](/tidb-cloud/vector-search-integrate-with-peewee.md)
-- Python: [Django](/tidb-cloud/vector-search-integrate-with-django-orm.md)
+- Python: [SQLAlchemy](/vector-search-integrate-with-sqlalchemy.md)
+- Python: [Peewee](/vector-search-integrate-with-peewee.md)
+- Python: [Django](/vector-search-integrate-with-django-orm.md)
 
 Be aware of the following limitations when creating the vector index. These limitations might be removed in future releases:
 
@@ -270,5 +270,5 @@ See [`EXPLAIN`](/sql-statements/sql-statement-explain.md), [`EXPLAIN ANALYZE`](/
 
 ## See also
 
-- [Improve Vector Search Performance](/tidb-cloud/vector-search-improve-performance.md)
-- [Vector Data Types](/tidb-cloud/vector-search-data-types.md)
+- [Improve Vector Search Performance](/vector-search-improve-performance.md)
+- [Vector Data Types](/vector-search-data-types.md)

tidb-cloud/vector-search-data-types.md → vector-search-data-types.md

@@ -57,7 +57,7 @@ As dimension 3 is enforced for the `embedding` column in the preceding example,
 ERROR 1105 (HY000): vector has 2 dimensions, does not fit VECTOR(3)
 ```
 
-See [Vector Functions and Operators](/tidb-cloud/vector-search-functions-and-operators.md) for available functions and operators over the Vector data type.
+See [Vector Functions and Operators](/vector-search-functions-and-operators.md) for available functions and operators over the Vector data type.
 
 See [Vector Search Index](/tidb-cloud/vector-search-index.md) for building and using a vector search index.
 
@@ -79,7 +79,7 @@ However you cannot build a [Vector Search Index](/tidb-cloud/vector-search-index
 
 ## Comparison
 
-You can compare vector data types using [comparison operators](/functions-and-operators/operators.md) such as `=`, `!=`, `<`, `>`, `<=`, and `>=`. For a complete list of comparison operators and functions for vector data types, see [Vector Functions and Operators](/tidb-cloud/vector-search-functions-and-operators.md).
+You can compare vector data types using [comparison operators](/functions-and-operators/operators.md) such as `=`, `!=`, `<`, `>`, `<=`, and `>=`. For a complete list of comparison operators and functions for vector data types, see [Vector Functions and Operators](/vector-search-functions-and-operators.md).
 
 Vector data types are compared element-wise numerically. Examples:
 
@@ -228,7 +228,7 @@ To cast vector into its string representation explicitly, use the `VEC_AS_TEXT()
 1 row in set (0.01 sec)
 ```
 
-For additional cast functions, see [Vector Functions and Operators](/tidb-cloud/vector-search-functions-and-operators.md).
+For additional cast functions, see [Vector Functions and Operators](/vector-search-functions-and-operators.md).
 
 ### Cast between Vector ⇔ other data types
 
@@ -240,14 +240,14 @@ It is currently not possible to cast between Vector and other data types (like `
 - You cannot store `NaN`, `Infinity`, or `-Infinity` values in the vector data type.
 - Currently Vector data types cannot store double-precision floating numbers. This will be supported in future release.
 
-For other limitations, see [Vector Search Limitations](/tidb-cloud/vector-search-limitations.md).
+For other limitations, see [Vector Search Limitations](/vector-search-limitations.md).
 
 ## MySQL compatibility
 
 Vector data types are TiDB specific, and are not supported in MySQL.
 
 ## See also
 
-- [Vector Functions and Operators](/tidb-cloud/vector-search-functions-and-operators.md)
+- [Vector Functions and Operators](/vector-search-functions-and-operators.md)
 - [Vector Search Index](/tidb-cloud/vector-search-index.md)
-- [Improve Vector Search Performance](/tidb-cloud/vector-search-improve-performance.md)
+- [Improve Vector Search Performance](/vector-search-improve-performance.md)

tidb-cloud/vector-search-functions-and-operators.md → vector-search-functions-and-operators.md

@@ -11,7 +11,7 @@ summary: Learn about functions and operators available for Vector Data Types.
 
 ## Vector functions
 
-The following functions are designed specifically for [Vector Data Types](/tidb-cloud/vector-search-data-types.md).
+The following functions are designed specifically for [Vector Data Types](/vector-search-data-types.md).
 
 **Vector Distance Functions:**
 
@@ -33,7 +33,7 @@ The following functions are designed specifically for [Vector Data Types](/tidb-
 
 ## Extended built-in functions and operators
 
-The following built-in functions and operators are extended, supporting operating on [Vector Data Types](/tidb-cloud/vector-search-data-types.md).
+The following built-in functions and operators are extended, supporting operating on [Vector Data Types](/vector-search-data-types.md).
 
 **Arithmetic operators:**
 
@@ -42,7 +42,7 @@ The following built-in functions and operators are extended, supporting operatin
 | [`+`](https://dev.mysql.com/doc/refman/8.0/en/arithmetic-functions.html#operator_plus)  | Vector element-wise addition operator    |
 | [`-`](https://dev.mysql.com/doc/refman/8.0/en/arithmetic-functions.html#operator_minus) | Vector element-wise subtraction operator |
 
-For more information about how vector arithmetic works, see [Vector Data Type | Arithmetic](/tidb-cloud/vector-search-data-types.md#arithmetic).
+For more information about how vector arithmetic works, see [Vector Data Type | Arithmetic](/vector-search-data-types.md#arithmetic).
 
 **Aggregate (GROUP BY) functions:**
 
@@ -74,7 +74,7 @@ For more information about how vector arithmetic works, see [Vector Data Type |
 | [`!=`, `<>`](https://dev.mysql.com/doc/refman/8.0/en/comparison-operators.html#operator_not-equal)                  | Not equal operator                                    |
 | [`NOT IN()`](https://dev.mysql.com/doc/refman/8.0/en/comparison-operators.html#operator_not-in)                     | Check whether a value is not within a set of values   |
 
-For more information about how vectors are compared, see [Vector Data Type | Comparison](/tidb-cloud/vector-search-data-types.md#comparison).
+For more information about how vectors are compared, see [Vector Data Type | Comparison](/vector-search-data-types.md#comparison).
 
 **Control flow functions:**
 
@@ -92,7 +92,7 @@ For more information about how vectors are compared, see [Vector Data Type | Com
 | [`CAST()`](https://dev.mysql.com/doc/refman/8.0/en/cast-functions.html#function_cast)       | Cast a value as a certain type |
 | [`CONVERT()`](https://dev.mysql.com/doc/refman/8.0/en/cast-functions.html#function_convert) | Cast a value as a certain type |
 
-For more information about how to use `CAST()`, see [Vector Data Type | Cast](/tidb-cloud/vector-search-data-types.md#cast).
+For more information about how to use `CAST()`, see [Vector Data Type | Cast](/vector-search-data-types.md#cast).
 
 ## Full references
 
@@ -279,4 +279,4 @@ The vector functions and the extended usage of built-in functions and operators
 
 ## See also
 
-- [Vector Data Types](/tidb-cloud/vector-search-data-types.md)
+- [Vector Data Types](/vector-search-data-types.md)

tidb-cloud/vector-search-get-started-using-python.md → vector-search-get-started-using-python.md

@@ -7,7 +7,7 @@ summary: Learn how to quickly develop an AI application that performs semantic s
 
 This tutorial demonstrates how to develop a simple AI application that provides **semantic search** features. Unlike traditional keyword search, semantic search intelligently understands the meaning behind your query. For example, if you have documents titled "dog", "fish", and "tree", and you search for "a swimming animal", the application would identify "fish" as the most relevant result.
 
-Throughout this tutorial, you will develop this AI application using [TiDB Vector Search](/tidb-cloud/vector-search-overview.md), Python, [TiDB Vector SDK for Python](https://github.com/pingcap/tidb-vector-python), and AI models.
+Throughout this tutorial, you will develop this AI application using [TiDB Vector Search](/vector-search-overview.md), Python, [TiDB Vector SDK for Python](https://github.com/pingcap/tidb-vector-python), and AI models.
 
 > **Note**
 >
@@ -44,7 +44,7 @@ pip install sqlalchemy pymysql sentence-transformers tidb-vector python-dotenv
 ```
 
 - `tidb-vector`: the Python client for interacting with Vector Search in TiDB Cloud.
-- [`sentence-transformers`](https://sbert.net): a Python library that provides pre-trained models for generating [vector embeddings](/tidb-cloud/vector-search-overview.md#vector-embedding) from text.
+- [`sentence-transformers`](https://sbert.net): a Python library that provides pre-trained models for generating [vector embeddings](/vector-search-overview.md#vector-embedding) from text.
 
 ### Step 3. Configure the connection string to the TiDB cluster
 
@@ -79,7 +79,7 @@ pip install sqlalchemy pymysql sentence-transformers tidb-vector python-dotenv
 
 ### Step 4. Initialize the embedding model
 
-An [embedding model](/tidb-cloud/vector-search-overview.md#embedding-model) transforms data into [vector embeddings](/tidb-cloud/vector-search-overview.md#vector-embedding). This example uses the pre-trained model [**msmarco-MiniLM-L12-cos-v5**](https://huggingface.co/sentence-transformers/msmarco-MiniLM-L12-cos-v5) for text embedding. This lightweight model, provided by the `sentence-transformers` library, transforms text data into 384-dimensional vector embeddings.
+An [embedding model](/vector-search-overview.md#embedding-model) transforms data into [vector embeddings](/vector-search-overview.md#vector-embedding). This example uses the pre-trained model [**msmarco-MiniLM-L12-cos-v5**](https://huggingface.co/sentence-transformers/msmarco-MiniLM-L12-cos-v5) for text embedding. This lightweight model, provided by the `sentence-transformers` library, transforms text data into 384-dimensional vector embeddings.
 
 To set up the model, copy the following code into the `example.py` file. This code initializes a `SentenceTransformer` instance and defines a `text_to_embedding()` function for later use.
 
@@ -191,5 +191,5 @@ This demonstration shows how vector search can efficiently locate the most relev
 
 ## See also
 
-- [Vector Data Types](/tidb-cloud/vector-search-data-types.md)
+- [Vector Data Types](/vector-search-data-types.md)
 - [Vector Search Index](/tidb-cloud/vector-search-index.md)