Update links in more files

TOC.md

@@ -891,7 +891,7 @@
       - [Date and Time Types](/data-type-date-and-time.md)
       - [String Types](/data-type-string.md)
       - [JSON Type](/data-type-json.md)
-      - [Vector Types](/vector-search-data-types.md)
+      - [Vector Types](/vector-search/vector-search-data-types.md)
     - Functions and Operators
       - [Overview](/functions-and-operators/functions-and-operators-overview.md)
       - [Type Conversion in Expression Evaluation](/functions-and-operators/type-conversion-in-expression-evaluation.md)
@@ -905,7 +905,7 @@
       - [Encryption and Compression Functions](/functions-and-operators/encryption-and-compression-functions.md)
       - [Locking Functions](/functions-and-operators/locking-functions.md)
       - [Information Functions](/functions-and-operators/information-functions.md)
-      - [Vector Functions and Operators](/vector-search-functions-and-operators.md)
+      - [Vector Functions and Operators](/vector-search/vector-search-functions-and-operators.md)
       - JSON Functions
         - [Overview](/functions-and-operators/json-functions.md)
         - [Functions That Create JSON](/functions-and-operators/json-functions/json-functions-create.md)

vector-search/vector-search-data-types.md

@@ -26,7 +26,7 @@ The following Vector data types are currently available:
 
 Using vector data types provides the following advantages over using the [`JSON`](/data-type-json.md) type:
 
-- Vector index support: You can build a [vector search index](/vector-search-index.md) to speed up vector searching.
+- Vector index support: You can build a [vector search index](/vector-search/vector-search-index.md) to speed up vector searching.
 - Dimension enforcement: You can specify a dimension to forbid inserting vectors with different dimensions.
 - Optimized storage format: Vector data types are optimized for handling vector data, offering better space efficiency and performance compared to `JSON` types.
 
@@ -65,9 +65,9 @@ In the following example, because dimension `3` is enforced for the `embedding`
 ERROR 1105 (HY000): vector has 2 dimensions, does not fit VECTOR(3)
 ```
 
-For available functions and operators over the vector data types, see [Vector Functions and Operators](/vector-search-functions-and-operators.md).
+For available functions and operators over the vector data types, see [Vector Functions and Operators](/vector-search/vector-search-functions-and-operators.md).
 
-For more information about building and using a vector search index, see [Vector Search Index](/vector-search-index.md).
+For more information about building and using a vector search index, see [Vector Search Index](/vector-search/vector-search-index.md).
 
 ## Store vectors with different dimensions
 
@@ -83,11 +83,11 @@ INSERT INTO vector_table VALUES (1, '[0.3, 0.5, -0.1]'); -- 3 dimensions vector,
 INSERT INTO vector_table VALUES (2, '[0.3, 0.5]');       -- 2 dimensions vector, OK
 ```
 
-However, note that you cannot build a [vector search index](/vector-search-index.md) for this column, as vector distances can be only calculated between vectors with the same dimensions.
+However, note that you cannot build a [vector search index](/vector-search/vector-search-index.md) for this column, as vector distances can be only calculated between vectors with the same dimensions.
 
 ## 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](/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/vector-search-functions-and-operators.md).
 
 Vector data types are compared element-wise numerically. For example:
 
@@ -231,7 +231,7 @@ You can also explicitly cast a vector to its string representation. Take using t
 1 row in set (0.01 sec)
 ```
 
-For additional cast functions, see [Vector Functions and Operators](/vector-search-functions-and-operators.md).
+For additional cast functions, see [Vector Functions and Operators](/vector-search/vector-search-functions-and-operators.md).
 
 ### Cast between Vector ⇔ other data types
 
@@ -241,14 +241,14 @@ Note that vector data type columns stored in a table cannot be converted to othe
 
 ## Restrictions
 
-For restrictions on vector data types, see [Vector search limitations](/vector-search-limitations.md) and [Vector index restrictions](/vector-search-index.md#restrictions).
+For restrictions on vector data types, see [Vector search limitations](/vector-search/vector-search-limitations.md) and [Vector index restrictions](/vector-search/vector-search-index.md#restrictions).
 
 ## MySQL compatibility
 
 Vector data types are TiDB specific, and are not supported in MySQL.
 
 ## See also
 
-- [Vector Functions and Operators](/vector-search-functions-and-operators.md)
-- [Vector Search Index](/vector-search-index.md)
-- [Improve Vector Search Performance](/vector-search-improve-performance.md)
+- [Vector Functions and Operators](/vector-search/vector-search-functions-and-operators.md)
+- [Vector Search Index](/vector-search/vector-search-index.md)
+- [Improve Vector Search Performance](/vector-search/vector-search-improve-performance.md)

vector-search/vector-search-functions-and-operators.md

@@ -21,7 +21,7 @@ This document lists the functions and operators available for Vector data types.
 
 ## Vector functions
 
-The following functions are designed specifically for [Vector data types](/vector-search-data-types.md).
+The following functions are designed specifically for [Vector data types](/vector-search/vector-search-data-types.md).
 
 **Vector distance functions:**
 
@@ -43,7 +43,7 @@ The following functions are designed specifically for [Vector data types](/vecto
 
 ## Extended built-in functions and operators
 
-The following built-in functions and operators are extended to support operations on [Vector data types](/vector-search-data-types.md).
+The following built-in functions and operators are extended to support operations on [Vector data types](/vector-search/vector-search-data-types.md).
 
 **Arithmetic operators:**
 
@@ -52,7 +52,7 @@ The following built-in functions and operators are extended to support operation
 | [`+`](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](/vector-search-data-types.md#arithmetic).
+For more information about how vector arithmetic works, see [Vector Data Type | Arithmetic](/vector-search/vector-search-data-types.md#arithmetic).
 
 **Aggregate (GROUP BY) functions:**
 
@@ -84,7 +84,7 @@ 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](/vector-search-data-types.md#comparison).
+For more information about how vectors are compared, see [Vector Data Type | Comparison](/vector-search/vector-search-data-types.md#comparison).
 
 **Control flow functions:**
 
@@ -102,7 +102,7 @@ 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 string or vector |
 | [`CONVERT()`](https://dev.mysql.com/doc/refman/8.0/en/cast-functions.html#function_convert) | Cast a value as a string |
 
-For more information about how to use `CAST()`, see [Vector Data Type | Cast](/vector-search-data-types.md#cast).
+For more information about how to use `CAST()`, see [Vector Data Type | Cast](/vector-search/vector-search-data-types.md#cast).
 
 ## Full references
 
@@ -289,4 +289,4 @@ The vector functions and the extended usage of built-in functions and operators
 
 ## See also
 
-- [Vector Data Types](/vector-search-data-types.md)
+- [Vector Data Types](/vector-search/vector-search-data-types.md)

vector-search/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 and returns the most relevant result. 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](/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/vector-search-overview.md), Python, [TiDB Vector SDK for Python](https://github.com/pingcap/tidb-vector-python), and AI models.
 
 <CustomContent platform="tidb">
 
@@ -69,7 +69,7 @@ pip install sqlalchemy pymysql sentence-transformers tidb-vector python-dotenv
 ```
 
 - `tidb-vector`: the Python client for interacting with TiDB vector search.
-- [`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.
+- [`sentence-transformers`](https://sbert.net): a Python library that provides pre-trained models for generating [vector embeddings](/vector-search/vector-search-overview.md#vector-embedding) from text.
 
 ### Step 3. Configure the connection string to the TiDB cluster
 
@@ -135,7 +135,7 @@ The following are descriptions for each parameter:
 
 ### Step 4. Initialize the embedding model
 
-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.
+An [embedding model](/vector-search/vector-search-overview.md#embedding-model) transforms data into [vector embeddings](/vector-search/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.
 
@@ -247,5 +247,5 @@ Therefore, according to the output, the swimming animal is most likely a fish, o
 
 ## See also
 
-- [Vector Data Types](/vector-search-data-types.md)
-- [Vector Search Index](/vector-search-index.md)
+- [Vector Data Types](/vector-search/vector-search-data-types.md)
+- [Vector Search Index](/vector-search/vector-search-index.md)

vector-search/vector-search-get-started-using-sql.md

@@ -5,7 +5,7 @@ summary: Learn how to quickly get started with Vector Search in TiDB using SQL s
 
 # Get Started with Vector Search via SQL
 
-TiDB extends MySQL syntax to support [Vector Search](/vector-search-overview.md) and introduce new [Vector data types](/vector-search-data-types.md) and several [vector functions](/vector-search-functions-and-operators.md).
+TiDB extends MySQL syntax to support [Vector Search](/vector-search/vector-search-overview.md) and introduce new [Vector data types](/vector-search/vector-search-data-types.md) and several [vector functions](/vector-search/vector-search-functions-and-operators.md).
 
 This tutorial demonstrates how to get started with TiDB Vector Search just using SQL statements. You will learn how to use the [MySQL command-line client](https://dev.mysql.com/doc/refman/8.4/en/mysql.html) to complete the following operations:
 
@@ -90,7 +90,7 @@ mysql --comments --host 127.0.0.1 --port 4000 -u root
 
 ### Step 2. Create a vector table
 
-When creating a table, you can define a column as a [vector](/vector-search-overview.md#vector-embedding) column by specifying the `VECTOR` data type.
+When creating a table, you can define a column as a [vector](/vector-search/vector-search-overview.md#vector-embedding) column by specifying the `VECTOR` data type.
 
 For example, to create a table `embedded_documents` with a three-dimensional `VECTOR` column, execute the following SQL statements using your MySQL CLI:
 
@@ -113,7 +113,7 @@ Query OK, 0 rows affected (0.27 sec)
 
 ### Step 3. Insert vector embeddings to the table
 
-Insert three documents with their [vector embeddings](/vector-search-overview.md#vector-embedding) into the `embedded_documents` table:
+Insert three documents with their [vector embeddings](/vector-search/vector-search-overview.md#vector-embedding) into the `embedded_documents` table:
 
 ```sql
 INSERT INTO embedded_documents
@@ -134,7 +134,7 @@ Records: 3  Duplicates: 0  Warnings: 0
 >
 > This example simplifies the dimensions of the vector embeddings and uses only 3-dimensional vectors for demonstration purposes.
 >
-> In real-world applications, [embedding models](/vector-search-overview.md#embedding-model) often produce vector embeddings with hundreds or thousands of dimensions.
+> In real-world applications, [embedding models](/vector-search/vector-search-overview.md#embedding-model) often produce vector embeddings with hundreds or thousands of dimensions.
 
 ### Step 4. Query the vector table
 
@@ -191,5 +191,5 @@ Therefore, according to the output, the swimming animal is most likely a fish, o
 
 ## See also
 
-- [Vector Data Types](/vector-search-data-types.md)
-- [Vector Search Index](/vector-search-index.md)
+- [Vector Data Types](/vector-search/vector-search-data-types.md)
+- [Vector Search Index](/vector-search/vector-search-index.md)

vector-search/vector-search-improve-performance.md

@@ -21,7 +21,7 @@ TiDB Vector Search enables you to perform Approximate Nearest Neighbor (ANN) que
 
 ## Add vector search index for vector columns
 
-The [vector search index](/vector-search-index.md) dramatically improves the performance of vector search queries, usually by 10x or more, with a trade-off of only a small decrease of recall rate.
+The [vector search index](/vector-search/vector-search-index.md) dramatically improves the performance of vector search queries, usually by 10x or more, with a trade-off of only a small decrease of recall rate.
 
 ## Ensure vector indexes are fully built
 

vector-search/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 method for finding the K closest points to a given point in a vector space. The most straightforward approach to perform KNN search is a brute force search, which calculates the distance between the given vector and all other vectors in the space. This approach guarantees perfect accuracy, but it is usually too slow for real-world use. Therefore, approximate algorithms are commonly used in KNN search to enhance speed and efficiency.
 
-In TiDB, you can create and use 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.
+In TiDB, you can create and use vector search indexes for such approximate nearest neighbor (ANN) searches over columns with [vector data types](/vector-search/vector-search-data-types.md). By using vector search indexes, vector search queries could be finished in milliseconds.
 
 <CustomContent platform="tidb">
 
@@ -39,7 +39,7 @@ Currently, TiDB supports the [HNSW (Hierarchical Navigable Small World)](https:/
 
 [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, up to 98% in specific cases.
 
-In TiDB, you can create an HNSW index for a column with a [vector data type](/vector-search-data-types.md) in either of the following ways:
+In TiDB, you can create an HNSW index for a column with a [vector data type](/vector-search/vector-search-data-types.md) in either of the following ways:
 
 - When creating a table, use the following syntax to specify the vector column for the HNSW index:
 
@@ -259,5 +259,5 @@ See [`EXPLAIN`](/sql-statements/sql-statement-explain.md), [`EXPLAIN ANALYZE`](/
 
 ## See also
 
-- [Improve Vector Search Performance](/vector-search-improve-performance.md)
-- [Vector Data Types](/vector-search-data-types.md)
+- [Improve Vector Search Performance](/vector-search/vector-search-improve-performance.md)
+- [Vector Data Types](/vector-search/vector-search-data-types.md)

vector-search/vector-search-integrate-with-django-orm.md

@@ -5,7 +5,7 @@ summary: Learn how to integrate TiDB Vector Search with Django ORM to store embe
 
 # Integrate TiDB Vector Search with Django ORM
 
-This tutorial walks you through how to use [Django](https://www.djangoproject.com/) ORM to interact with the [TiDB Vector Search](/vector-search-overview.md), store embeddings, and perform vector search queries.
+This tutorial walks you through how to use [Django](https://www.djangoproject.com/) ORM to interact with the [TiDB Vector Search](/vector-search/vector-search-overview.md), store embeddings, and perform vector search queries.
 
 <CustomContent platform="tidb">
 
@@ -276,5 +276,5 @@ results = Document.objects.annotate(
 
 ## See also
 
-- [Vector Data Types](/vector-search-data-types.md)
-- [Vector Search Index](/vector-search-index.md)
+- [Vector Data Types](/vector-search/vector-search-data-types.md)
+- [Vector Search Index](/vector-search/vector-search-index.md)

vector-search/vector-search-integrate-with-jinaai-embedding.md

@@ -291,5 +291,5 @@ with Session(engine) as session:
 
 ## See also
 
-- [Vector Data Types](/vector-search-data-types.md)
-- [Vector Search Index](/vector-search-index.md)
+- [Vector Data Types](/vector-search/vector-search-data-types.md)
+- [Vector Search Index](/vector-search/vector-search-index.md)

vector-search/vector-search-integrate-with-langchain.md

@@ -5,7 +5,7 @@ summary: Learn how to integrate TiDB Vector Search with LangChain.
 
 # Integrate Vector Search with LangChain
 
-This tutorial demonstrates how to integrate the [vector search](/vector-search-overview.md) feature of TiDB with [LangChain](https://python.langchain.com/).
+This tutorial demonstrates how to integrate the [vector search](/vector-search/vector-search-overview.md) feature of TiDB with [LangChain](https://python.langchain.com/).
 
 <CustomContent platform="tidb">
 
@@ -650,5 +650,5 @@ The expected output is as follows:
 
 ## See also
 
-- [Vector Data Types](/vector-search-data-types.md)
-- [Vector Search Index](/vector-search-index.md)
+- [Vector Data Types](/vector-search/vector-search-data-types.md)
+- [Vector Search Index](/vector-search/vector-search-index.md)