diff --git a/TOC.md b/TOC.md
index 71c80a7100174..909bb7e1cc9c1 100644
--- a/TOC.md
+++ b/TOC.md
@@ -926,7 +926,7 @@
       - [TiDB Specific Functions](/functions-and-operators/tidb-functions.md)
       - [Comparisons between Functions and Syntax of Oracle and TiDB](/oracle-functions-to-tidb.md)
     - [Clustered Indexes](/clustered-indexes.md)
-    - [Vector Index](/vector-search-index.md)
+    - [Vector Index](/vector-search/vector-search-index.md)
     - [Constraints](/constraints.md)
     - [Generated Columns](/generated-columns.md)
     - [SQL Mode](/sql-mode.md)
diff --git a/tidb-cloud/data-service-manage-endpoint.md b/tidb-cloud/data-service-manage-endpoint.md
index a96a92e1d0c60..e948161811d54 100644
--- a/tidb-cloud/data-service-manage-endpoint.md
+++ b/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](/vector-search/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:
+        If the table you selected contains [vector data types](/vector-search/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/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.
diff --git a/vector-search/vector-search-improve-performance.md b/vector-search/vector-search-improve-performance.md
index 6dd649f7fa530..09e13adda85ba 100644
--- a/vector-search/vector-search-improve-performance.md
+++ b/vector-search/vector-search-improve-performance.md
@@ -25,7 +25,7 @@ The [vector search index](/vector-search/vector-search-index.md) dramatically im
 
 ## Ensure vector indexes are fully built
 
-After you insert a large volume of vector data, some of it might be in the Delta layer waiting for persistence. The vector index for such data will be built after the data is persisted. Until all vector data is indexed, vector search performance is suboptimal. To check the index build progress, see [View index build progress](/vector-search-index.md#view-index-build-progress).
+After you insert a large volume of vector data, some of it might be in the Delta layer waiting for persistence. The vector index for such data will be built after the data is persisted. Until all vector data is indexed, vector search performance is suboptimal. To check the index build progress, see [View index build progress](/vector-search/vector-search-index.md#view-index-build-progress).
 
 ## Reduce vector dimensions or shorten embeddings
 
diff --git a/vector-search/vector-search-integration-overview.md b/vector-search/vector-search-integration-overview.md
index d856ce4977c7d..01edddbed0b57 100644
--- a/vector-search/vector-search-integration-overview.md
+++ b/vector-search/vector-search-integration-overview.md
@@ -25,8 +25,8 @@ TiDB provides official support for the following AI frameworks, enabling you to
 
 | AI frameworks | Tutorial                                                                                          |
 |---------------|---------------------------------------------------------------------------------------------------|
-| Langchain     | [Integrate Vector Search with LangChain](/vector-search-integrate-with-langchain.md)   |
-| LlamaIndex    | [Integrate Vector Search with LlamaIndex](/vector-search-integrate-with-llamaindex.md) |
+| Langchain     | [Integrate Vector Search with LangChain](/vector-search/vector-search-integrate-with-langchain.md)   |
+| LlamaIndex    | [Integrate Vector Search with LlamaIndex](/vector-search/vector-search-integrate-with-llamaindex.md) |
 
 Moreover, you can also use TiDB for various purposes, such as document storage and knowledge graph storage for AI applications.
 
@@ -40,7 +40,7 @@ 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/vector-search-integrate-with-jinaai-embedding.md) |
 
 ## Object Relational Mapping (ORM) libraries
 
diff --git a/vector-search/vector-search-limitations.md b/vector-search/vector-search-limitations.md
index 325dadb6f21c1..6d3ede16bb1a8 100644
--- a/vector-search/vector-search-limitations.md
+++ b/vector-search/vector-search-limitations.md
@@ -31,7 +31,7 @@ This document describes the known limitations of TiDB vector search.
 
 ## Vector index limitations
 
-See [Vector search restrictions](/vector-search-index.md#restrictions).
+See [Vector search restrictions](/vector-search/vector-search-index.md#restrictions).
 
 ## Compatibility with TiDB tools