Update more files

pingcap · Jan 3, 2025 · 6337e02 · 6337e02
1 parent eb5141a
commit 6337e02
Show file tree

Hide file tree

Showing 5 changed files with 7 additions and 7 deletions.
diff --git 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
@@ -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
@@ -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
@@ -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
@@ -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