Update version info to v0.14.1 (#3720)

### What problem does this PR solve?

Update version info to v0.14.1

### Type of change

- [x] Documentation Update

---------

Signed-off-by: jinhai <[email protected]>

README.md

@@ -20,7 +20,7 @@
         <img alt="Static Badge" src="https://img.shields.io/badge/Online-Demo-4e6b99">
     </a>
     <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
-        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.14.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.14.0">
+        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.14.1-brightgreen" alt="docker pull infiniflow/ragflow:v0.14.1">
     </a>
     <a href="https://github.com/infiniflow/ragflow/releases/latest">
         <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Latest%20Release" alt="Latest Release">
@@ -176,14 +176,14 @@ releases! 🌟
    ```
 
    > - To download a RAGFlow slim Docker image of a specific version, update the `RAGFLOW_IMAGE` variable in *
-       *docker/.env** to your desired version. For example, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0-slim`. After
+       *docker/.env** to your desired version. For example, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1-slim`. After
        making this change, rerun the command above to initiate the download.
    > - To download the dev version of RAGFlow Docker image *including* embedding models and Python libraries, update the
        `RAGFLOW_IMAGE` variable in **docker/.env** to `RAGFLOW_IMAGE=infiniflow/ragflow:dev`. After making this change,
        rerun the command above to initiate the download.
    > - To download a specific version of RAGFlow Docker image *including* embedding models and Python libraries, update
        the `RAGFLOW_IMAGE` variable in **docker/.env** to your desired version. For example,
-       `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0`. After making this change, rerun the command above to initiate the
+       `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1`. After making this change, rerun the command above to initiate the
        download.
 
    > **NOTE:** A RAGFlow Docker image that includes embedding models and Python libraries is approximately 9GB in size

README_id.md

@@ -20,7 +20,7 @@
         <img alt="Lencana Daring" src="https://img.shields.io/badge/Online-Demo-4e6b99">
     </a>
     <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
-        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.14.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.14.0">
+        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.14.1-brightgreen" alt="docker pull infiniflow/ragflow:v0.14.1">
     </a>
     <a href="https://github.com/infiniflow/ragflow/releases/latest">
         <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Rilis%20Terbaru" alt="Rilis Terbaru">
@@ -169,14 +169,14 @@ Coba demo kami di [https://demo.ragflow.io](https://demo.ragflow.io).
    ```
 
    > - Untuk mengunduh versi tertentu dari image Docker RAGFlow slim, perbarui variabel `RAGFlow_IMAGE` di *
-       *docker/.env** sesuai dengan versi yang diinginkan. Misalnya, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0-slim`.
+       *docker/.env** sesuai dengan versi yang diinginkan. Misalnya, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1-slim`.
        Setelah mengubah ini, jalankan ulang perintah di atas untuk memulai unduhan.
    > - Untuk mengunduh versi dev dari image Docker RAGFlow *termasuk* model embedding dan library Python, perbarui
        variabel `RAGFlow_IMAGE` di **docker/.env** menjadi `RAGFLOW_IMAGE=infiniflow/ragflow:dev`. Setelah mengubah ini,
        jalankan ulang perintah di atas untuk memulai unduhan.
    > - Untuk mengunduh versi tertentu dari image Docker RAGFlow *termasuk* model embedding dan library Python, perbarui
        variabel `RAGFlow_IMAGE` di **docker/.env** sesuai dengan versi yang diinginkan. Misalnya,
-       `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0`. Setelah mengubah ini, jalankan ulang perintah di atas untuk memulai unduhan.
+       `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1`. Setelah mengubah ini, jalankan ulang perintah di atas untuk memulai unduhan.
 
    > **CATATAN:** Image Docker RAGFlow yang mencakup model embedding dan library Python berukuran sekitar 9GB
    dan mungkin memerlukan waktu lebih lama untuk dimuat.

README_ja.md

@@ -20,7 +20,7 @@
         <img alt="Static Badge" src="https://img.shields.io/badge/Online-Demo-4e6b99">
     </a>
     <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
-        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.14.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.14.0">
+        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.14.1-brightgreen" alt="docker pull infiniflow/ragflow:v0.14.1">
     </a>
     <a href="https://github.com/infiniflow/ragflow/releases/latest">
         <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Latest%20Release" alt="Latest Release">
@@ -148,9 +148,9 @@
    $ docker compose -f docker-compose.yml up -d
    ```
 
-   > - 特定のバージョンのRAGFlow slim Dockerイメージをダウンロードするには、**docker/.env**内の`RAGFlow_IMAGE`変数を希望のバージョンに更新します。例えば、`RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0`とします。この変更を行った後、上記のコマンドを再実行してダウンロードを開始してください。
+   > - 特定のバージョンのRAGFlow slim Dockerイメージをダウンロードするには、**docker/.env**内の`RAGFlow_IMAGE`変数を希望のバージョンに更新します。例えば、`RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1`とします。この変更を行った後、上記のコマンドを再実行してダウンロードを開始してください。
    > - RAGFlowの埋め込みモデルとPythonライブラリを含む開発版Dockerイメージをダウンロードするには、**docker/.env**内の`RAGFlow_IMAGE`変数を`RAGFLOW_IMAGE=infiniflow/ragflow:dev`に更新します。この変更を行った後、上記のコマンドを再実行してダウンロードを開始してください。
-   > - 特定のバージョンのRAGFlow Dockerイメージ（埋め込みモデルとPythonライブラリを含む）をダウンロードするには、**docker/.env**内の`RAGFlow_IMAGE`変数を希望のバージョンに更新します。例えば、`RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0`とします。この変更を行った後、上記のコマンドを再実行してダウンロードを開始してください。  
+   > - 特定のバージョンのRAGFlow Dockerイメージ（埋め込みモデルとPythonライブラリを含む）をダウンロードするには、**docker/.env**内の`RAGFlow_IMAGE`変数を希望のバージョンに更新します。例えば、`RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1`とします。この変更を行った後、上記のコマンドを再実行してダウンロードを開始してください。  
    
    > **NOTE:** 埋め込みモデルとPythonライブラリを含むRAGFlow Dockerイメージのサイズは約9GBであり、読み込みにかなりの時間がかかる場合があります。
 

README_ko.md

@@ -20,7 +20,7 @@
         <img alt="Static Badge" src="https://img.shields.io/badge/Online-Demo-4e6b99">
     </a>
     <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
-        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.14.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.14.0">
+        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.14.1-brightgreen" alt="docker pull infiniflow/ragflow:v0.14.1">
     </a>
     <a href="https://github.com/infiniflow/ragflow/releases/latest">
         <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Latest%20Release" alt="Latest Release">
@@ -152,9 +152,9 @@
    $ docker compose -f docker-compose.yml up -d
    ```
    
-   > - 특정 버전의 RAGFlow slim Docker 이미지를 다운로드하려면, **docker/.env**에서 `RAGFlow_IMAGE` 변수를 원하는 버전으로 업데이트하세요. 예를 들어, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0-slim`으로 설정합니다. 이 변경을 완료한 후, 위의 명령을 다시 실행하여 다운로드를 시작하세요.
+   > - 특정 버전의 RAGFlow slim Docker 이미지를 다운로드하려면, **docker/.env**에서 `RAGFlow_IMAGE` 변수를 원하는 버전으로 업데이트하세요. 예를 들어, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1-slim`으로 설정합니다. 이 변경을 완료한 후, 위의 명령을 다시 실행하여 다운로드를 시작하세요.
    > - RAGFlow의 임베딩 모델과 Python 라이브러리를 포함한 개발 버전 Docker 이미지를 다운로드하려면, **docker/.env**에서 `RAGFlow_IMAGE` 변수를 `RAGFLOW_IMAGE=infiniflow/ragflow:dev`로 업데이트하세요. 이 변경을 완료한 후, 위의 명령을 다시 실행하여 다운로드를 시작하세요.
-   > - 특정 버전의 RAGFlow Docker 이미지를 임베딩 모델과 Python 라이브러리를 포함하여 다운로드하려면, **docker/.env**에서 `RAGFlow_IMAGE` 변수를 원하는 버전으로 업데이트하세요. 예를 들어, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0` 로 설정합니다. 이 변경을 완료한 후, 위의 명령을 다시 실행하여 다운로드를 시작하세요.  
+   > - 특정 버전의 RAGFlow Docker 이미지를 임베딩 모델과 Python 라이브러리를 포함하여 다운로드하려면, **docker/.env**에서 `RAGFlow_IMAGE` 변수를 원하는 버전으로 업데이트하세요. 예를 들어, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1` 로 설정합니다. 이 변경을 완료한 후, 위의 명령을 다시 실행하여 다운로드를 시작하세요.  
    
    > **NOTE:** 임베딩 모델과 Python 라이브러리를 포함한 RAGFlow Docker 이미지의 크기는 약 9GB이며, 로드하는 데 상당히 오랜 시간이 걸릴 수 있습니다.
 

README_zh.md

@@ -20,7 +20,7 @@
         <img alt="Static Badge" src="https://img.shields.io/badge/Online-Demo-4e6b99">
     </a>
     <a href="https://hub.docker.com/r/infiniflow/ragflow" target="_blank">
-        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.14.0-brightgreen" alt="docker pull infiniflow/ragflow:v0.14.0">
+        <img src="https://img.shields.io/badge/docker_pull-ragflow:v0.14.1-brightgreen" alt="docker pull infiniflow/ragflow:v0.14.1">
     </a>
     <a href="https://github.com/infiniflow/ragflow/releases/latest">
         <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Latest%20Release" alt="Latest Release">
@@ -149,9 +149,9 @@
    $ docker compose -f docker-compose.yml up -d
    ```
 
-   > - 如果你想下载并运行特定版本的 RAGFlow slim Docker 镜像，请在 **docker/.env** 文件中找到 `RAGFLOW_IMAGE` 变量，将其改为对应版本。例如 `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0-slim`，然后再运行上述命令。
+   > - 如果你想下载并运行特定版本的 RAGFlow slim Docker 镜像，请在 **docker/.env** 文件中找到 `RAGFLOW_IMAGE` 变量，将其改为对应版本。例如 `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1-slim`，然后再运行上述命令。
    > - 如果您想安装内置 embedding 模型和 Python 库的 dev 版本的 Docker 镜像，需要将 **docker/.env** 文件中的 `RAGFLOW_IMAGE` 变量修改为： `RAGFLOW_IMAGE=infiniflow/ragflow:dev`。
-   > - 如果您想安装内置 embedding 模型和 Python 库的指定版本的 RAGFlow Docker 镜像，需要将 **docker/.env** 文件中的 `RAGFLOW_IMAGE` 变量修改为： `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0`。修改后，再运行上面的命令。
+   > - 如果您想安装内置 embedding 模型和 Python 库的指定版本的 RAGFlow Docker 镜像，需要将 **docker/.env** 文件中的 `RAGFLOW_IMAGE` 变量修改为： `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1`。修改后，再运行上面的命令。
    > **注意：** 安装内置 embedding 模型和 Python 库的指定版本的 RAGFlow Docker 镜像大小约 9 GB，可能需要更长时间下载，请耐心等待。
    
 4. 服务器启动成功后再次确认服务器状态：

docs/guides/configure_knowledge_base.md

@@ -103,7 +103,7 @@ RAGFlow features visibility and explainability, allowing you to view the chunkin
 
 2. Hover over each snapshot for a quick view of each chunk.
 
-3. Double click the chunked texts to add keywords or make *manual* changes where necessary:
+3. Double-click the chunked texts to add keywords or make *manual* changes where necessary:
 
    ![update chunk](https://github.com/infiniflow/ragflow/assets/93570324/1d84b408-4e9f-46fd-9413-8c1059bf9c76)
 
@@ -111,7 +111,7 @@ RAGFlow features visibility and explainability, allowing you to view the chunkin
 You can add keywords to a file chunk to increase its ranking for queries containing those keywords. This action increases its keyword weight and can improve its position in search list.  
 :::
 
-4. In Retrieval testing, ask a quick question in **Test text** to double check if your configurations work:
+4. In Retrieval testing, ask a quick question in **Test text** to double-check if your configurations work:
 
    _As you can tell from the following, RAGFlow responds with truthful citations._
 
@@ -128,7 +128,7 @@ RAGFlow uses multiple recall of both full-text search and vector search in its c
 
 ## Search for knowledge base
 
-As of RAGFlow v0.14.0, the search feature is still in a rudimentary form, supporting only knowledge base search by name.
+As of RAGFlow v0.14.1, the search feature is still in a rudimentary form, supporting only knowledge base search by name.
 
 ![search knowledge base](https://github.com/infiniflow/ragflow/assets/93570324/836ae94c-2438-42be-879e-c7ad2a59693e)
 

docs/guides/deploy_local_llm.mdx

@@ -108,7 +108,7 @@ Click on your logo **>** **Model Providers** **>** **System Model Settings** to
 
 Update your chat model accordingly in **Chat Configuration**:
 
-> If your local model is an embedding model, update it on the configruation page of your knowledge base.
+> If your local model is an embedding model, update it on the configuration page of your knowledge base.
 
 ## Deploy a local model using Xinference
 
@@ -161,7 +161,7 @@ Click on your logo **>** **Model Providers** **>** **System Model Settings** to
 
 Update your chat model accordingly in **Chat Configuration**:
 
-> If your local model is an embedding model, update it on the configruation page of your knowledge base.
+> If your local model is an embedding model, update it on the configuration page of your knowledge base.
 
 ## Deploy a local model using IPEX-LLM
 

docs/guides/develop/acquire_ragflow_api_key.md

@@ -7,7 +7,7 @@ slug: /acquire_ragflow_api_key
 
 A key is required for the RAGFlow server to authenticate your requests via HTTP or a Python API. This documents provides instructions on obtaining a RAGFlow API key.
 
-1. Click your avatar on the top right corner of the RAGFlow UI to access the configuration page.
+1. Click your avatar in the top right corner of the RAGFlow UI to access the configuration page.
 2. Click **API** to switch to the **API** page.
 3. Obtain a RAGFlow API key:
 

docs/guides/manage_files.md

@@ -81,4 +81,4 @@ RAGFlow's file management allows you to download an uploaded file:
 
 ![download_file](https://github.com/infiniflow/ragflow/assets/93570324/cf3b297f-7d9b-4522-bf5f-4f45743e4ed5)
 
-> As of RAGFlow v0.14.0, bulk download is not supported, nor can you download an entire folder. 
+> As of RAGFlow v0.14.1, bulk download is not supported, nor can you download an entire folder. 

docs/guides/manage_team_members.md

@@ -17,7 +17,7 @@ By default, each RAGFlow user is assigned a single team named after their name.
 Team members are currently *not* allowed to invite users to your team, and only you, the team owner, is permitted to do so.
 :::
 
-To enter the **Team** page, click on your avatar on the top right corner of the page **>** Team:
+To enter the **Team** page, click on your avatar in the top right corner of the page **>** Team:
 
 ![team](https://github.com/user-attachments/assets/0eac2503-26bc-4568-b3f2-bcd84069a07a)
 

docs/guides/run_health_check.md

@@ -5,7 +5,7 @@ slug: /run_health_check
 
 # Run health check on RAGFlow's dependencies
 
-Double check the health status of RAGFlow's dependencies.
+Double-check the health status of RAGFlow's dependencies.
 
 The operation of RAGFlow depends on four services:
 
@@ -16,7 +16,7 @@ The operation of RAGFlow depends on four services:
 
 If an exception or error occurs related to any of the above services, such as `Exception: Can't connect to ES cluster`, refer to this document to check their health status.
 
-You can also click you avatar on the top right corner of the page **>** System to view the visualized health status of RAGFlow's core services. The following screenshot shows that all services are 'green' (running healthily). The task executor displays the *cumulative* number of completed and failed document parsing tasks from the past 30 minutes:
+You can also click you avatar in the top right corner of the page **>** System to view the visualized health status of RAGFlow's core services. The following screenshot shows that all services are 'green' (running healthily). The task executor displays the *cumulative* number of completed and failed document parsing tasks from the past 30 minutes:
 
 ![system_status_page](https://github.com/user-attachments/assets/b0c1a11e-93e3-4947-b17a-1bfb4cdab6e4)
 

docs/guides/start_chat.md

@@ -19,7 +19,7 @@ You start an AI conversation by creating an assistant.
 
    - **Assistant name** is the name of your chat assistant. Each assistant corresponds to a dialogue with a unique combination of knowledge bases, prompts, hybrid search configurations, and large model settings.
    - **Empty response**:
-     - If you wish to *confine* RAGFlow's answers to your knowledge bases, leave a response here. Then when it doesn't retrieve an answer, it *uniformly* responds with what you set here.
+     - If you wish to *confine* RAGFlow's answers to your knowledge bases, leave a response here. Then, when it doesn't retrieve an answer, it *uniformly* responds with what you set here.
      - If you wish RAGFlow to *improvise* when it doesn't retrieve an answer from your knowledge bases, leave it blank, which may give rise to hallucinations.
    - **Show Quote**: This is a key feature of RAGFlow and enabled by default. RAGFlow does not work like a black box. instead, it clearly shows the sources of information that its responses are based on.
    - Select the corresponding knowledge bases. You can select one or multiple knowledge bases, but ensure that they use the same embedding model, otherwise an error would occur.

docs/guides/upgrade_ragflow.mdx

@@ -62,16 +62,16 @@ To upgrade RAGFlow, you must upgrade **both** your code **and** your Docker imag
    git clone https://github.com/infiniflow/ragflow.git
    ```
 
-2. Switch to the latest, officially published release, e.g., `v0.14.0`:
+2. Switch to the latest, officially published release, e.g., `v0.14.1`:
 
    ```bash
-   git checkout v0.14.0
+   git checkout v0.14.1
    ```
 
 3. Update **ragflow/docker/.env** as follows:
 
    ```bash
-   RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0
+   RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1
    ```
 
 4. Update the RAGFlow image and restart RAGFlow:

docs/quickstart.mdx

@@ -32,9 +32,9 @@ This section provides instructions on setting up the RAGFlow server on Linux. If
 <details>
   <summary>1. Ensure <code>vm.max_map_count</code> &ge; 262144:</summary>
 
-   `vm.max_map_count`. This value sets the maximum number of memory map areas a process may have. Its default value is 65530. While most applications require fewer than a thousand maps, reducing this value can result in abmornal behaviors, and the system will throw out-of-memory errors when a process reaches the limitation. 
+   `vm.max_map_count`. This value sets the maximum number of memory map areas a process may have. Its default value is 65530. While most applications require fewer than a thousand maps, reducing this value can result in abnormal behaviors, and the system will throw out-of-memory errors when a process reaches the limitation.
 
-   RAGFlow v0.14.0 uses Elasticsearch for multiple recall. Setting the value of `vm.max_map_count` correctly is crucial to the proper functioning of the Elasticsearch component.
+   RAGFlow v0.14.1 uses Elasticsearch or [Infinity](https://github.com/infiniflow/infinity) for multiple recall. Setting the value of `vm.max_map_count` correctly is crucial to the proper functioning of the Elasticsearch component.
 
 <Tabs
   defaultValue="linux"
@@ -184,9 +184,9 @@ This section provides instructions on setting up the RAGFlow server on Linux. If
    $ docker compose -f docker-compose.yml up -d
    ```
 
-   > - To download a RAGFlow slim Docker image of a specific version, update the `RAGFlOW_IMAGE` variable in **docker/.env** to your desired version. For example, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0-slim`. After making this change, rerun the command above to initiate the download.
+   > - To download a RAGFlow slim Docker image of a specific version, update the `RAGFlOW_IMAGE` variable in **docker/.env** to your desired version. For example, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1-slim`. After making this change, rerun the command above to initiate the download.
    > - To download the dev version of RAGFlow Docker image *including* embedding models and Python libraries, update the `RAGFlOW_IMAGE` variable in **docker/.env** to `RAGFLOW_IMAGE=infiniflow/ragflow:dev`. After making this change, rerun the command above to initiate the download.
-   > - To download a specific version of RAGFlow Docker image *including* embedding models and Python libraries, update the `RAGFlOW_IMAGE` variable in **docker/.env** to your desired version. For example, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.0`. After making this change, rerun the command above to initiate the download.
+   > - To download a specific version of RAGFlow Docker image *including* embedding models and Python libraries, update the `RAGFlOW_IMAGE` variable in **docker/.env** to your desired version. For example, `RAGFLOW_IMAGE=infiniflow/ragflow:v0.14.1`. After making this change, rerun the command above to initiate the download.
 
 :::tip NOTE
 A RAGFlow Docker image that includes embedding models and Python libraries is approximately 9GB in size and may take significantly longer time to load.