Release new docs to master

Author: Milvus-doc-bot

v2.5.x/site/en/release_notes.md

@@ -24,9 +24,9 @@ Milvus 2.5.0-beta brings significant advancements to enhance usability, scalabil
 
 Milvus 2.5 supports full text search implemented with Sparse-BM25! This feature is an important complement to Milvus's strong semantic search capabilities, especially in scenarios involving rare words or technical terms. In previous versions, Milvus supported sparse vectors to assist with keyword search scenarios. These sparse vectors were generated outside of Milvus by neural models like SPLADEv2/BGE-M3 or statistical models such as the BM25 algorithm.
 
-Milvus 2.5 has built-in tokenization and sparse vector extraction, extending the API from only receiving vectors as input to directly accepting text. BM25 statistical information is updated in real time as data is inserted, enhancing usability and accuracy. Additionally, sparse vectors based on approximate nearest neighbor (ANN) algorithms offer more powerful performance than standard keyword search systems.
+Powered by [Tantivy](https://github.com/quickwit-oss/tantivy), Milvus 2.5 has built-in analyzers and sparse vector extraction, extending the API from only receiving vectors as input to directly accepting text. BM25 statistical information is updated in real time as data is inserted, enhancing usability and accuracy. Additionally, sparse vectors based on approximate nearest neighbor (ANN) algorithms offer more powerful performance than standard keyword search systems.
 
-For details, refer to [Full Text Search](full-text-search.md).
+For details, refer to [Analyzer Overview](analyzer-overview.md) and [Full Text Search](full-text-search.md).
 
 #### Cluster Management WebUI (Beta)
 
@@ -36,9 +36,9 @@ Milvus 2.5 introduces a built-in Cluster Management WebUI, reducing system maint
 
 #### Text Match
 
-Milvus 2.5 leverages analyzers and indexing from Tantivy for text preprocessing and index building, supporting precise natural language matching of text data based on specific terms. This feature is primarily used for filtered search to satisfy specific conditions and can incorporate scalar filtering to refine query results, allowing similarity searches within vectors that meet scalar criteria.
+Milvus 2.5 leverages analyzers and indexing from [Tantivy](https://github.com/quickwit-oss/tantivy) for text preprocessing and index building, supporting precise natural language matching of text data based on specific terms. This feature is primarily used for filtered search to satisfy specific conditions and can incorporate scalar filtering to refine query results, allowing similarity searches within vectors that meet scalar criteria.
 
-For details, refer to [Text Match](keyword-match.md).
+For details, refer to [Analyzer Overview](analyzer-overview.md) and [Text Match](keyword-match.md).
 
 #### Bitmap Index
 

v2.5.x/site/en/userGuide/schema/analyzer/analyzer-overview.md

@@ -8,7 +8,7 @@ summary: "In text processing, an analyzer is a crucial component that converts r
 
 In text processing, an **analyzer** is a crucial component that converts raw text into a structured, searchable format. Each analyzer typically consists of two core elements: **tokenizer** and **filter**. Together, they transform input text into tokens, refine these tokens, and prepare them for efficient indexing and retrieval.​
 
-In Milvus, analyzers are configured during collection creation when you add `VARCHAR` fields to the collection schema. Tokens produced by an analyzer can be used to build an index for text matching or converted into sparse embeddings for full text search. For more information, refer to [Text Match](keyword-match.md) or [​Full Text Search](full-text-search.md).​
+Powered by [Tantivy](https://github.com/quickwit-oss/tantivy), analyzers in Milvus are configured during collection creation when you add `VARCHAR` fields to the collection schema. Tokens produced by an analyzer can be used to build an index for text matching or converted into sparse embeddings for full text search. For more information, refer to [Text Match](keyword-match.md) or [​Full Text Search](full-text-search.md).​
 
 <div class="alert note">
 

v2.5.x/site/en/userGuide/search-query-get/full-text-search.md

@@ -23,7 +23,7 @@ Full text search simplifies the process of text-based searching by eliminating t
 
 1. **Text input**: You insert raw text documents or provide query text without any need for manual embedding.​
 
-2. **Text analysis**: Milvus uses an analyzer to tokenize input text into individual, searchable terms.​
+2. **Text analysis**: Milvus uses an analyzer to tokenize input text into individual, searchable terms.​ For more information on analyzers, refer to [Analyzer Overview](analyzer-overview.md).
 
 3. **Function processing**: The built-in function receives tokenized terms and converts them into sparse vector representations.​
 

v2.5.x/site/en/userGuide/search-query-get/metric.md

@@ -212,7 +212,7 @@ Parameter description:​
 
 - `​D`: The document being evaluated.​
 
-- `​TF(qi​,D)`: Term frequency, representing how often term ​qi​appears in document ​D.​
+- `​TF(qi​,D)`: Term frequency, representing how often term `​qi` ​appears in document `​D`.​
 
 - `​IDF(qi​)`: Inverse document frequency, calculated as:​