Refa: code format. (#9342)

### What problem does this PR solve?


### Type of change

- [x] Refactoring

README.md

@@ -22,7 +22,7 @@
         <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/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.20.0">
+        <img src="https://img.shields.io/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.20.1">
     </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">
@@ -190,7 +190,7 @@ releases! 🌟
 > All Docker images are built for x86 platforms. We don't currently offer Docker images for ARM64.
 > If you are on an ARM64 platform, follow [this guide](https://ragflow.io/docs/dev/build_docker_image) to build a Docker image compatible with your system.
 
-   > The command below downloads the `v0.20.0-slim` edition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from `v0.20.0-slim`, update the `RAGFLOW_IMAGE` variable accordingly in **docker/.env** before using `docker compose` to start the server. For example: set `RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.0` for the full edition `v0.20.0`.
+   > The command below downloads the `v0.20.1-slim` edition of the RAGFlow Docker image. See the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from `v0.20.1-slim`, update the `RAGFLOW_IMAGE` variable accordingly in **docker/.env** before using `docker compose` to start the server. For example: set `RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.1` for the full edition `v0.20.1`.
 
    ```bash
    $ cd ragflow/docker
@@ -203,8 +203,8 @@ releases! 🌟
 
    | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
    |-------------------|-----------------|-----------------------|--------------------------|
-   | v0.20.0           | &approx;9       | :heavy_check_mark:    | Stable release           |
-   | v0.20.0-slim      | &approx;2       | ❌                   | Stable release            |
+   | v0.20.1           | &approx;9       | :heavy_check_mark:    | Stable release           |
+   | v0.20.1-slim      | &approx;2       | ❌                   | Stable release            |
    | nightly           | &approx;9       | :heavy_check_mark:    | _Unstable_ nightly build |
    | nightly-slim      | &approx;2       | ❌                   | _Unstable_ nightly build  |
 

README_id.md

@@ -22,7 +22,7 @@
         <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/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.20.0">
+        <img src="https://img.shields.io/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.20.1">
     </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">
@@ -181,7 +181,7 @@ Coba demo kami di [https://demo.ragflow.io](https://demo.ragflow.io).
 > Semua gambar Docker dibangun untuk platform x86. Saat ini, kami tidak menawarkan gambar Docker untuk ARM64.
 > Jika Anda menggunakan platform ARM64, [silakan gunakan panduan ini untuk membangun gambar Docker yang kompatibel dengan sistem Anda](https://ragflow.io/docs/dev/build_docker_image).
 
-> Perintah di bawah ini mengunduh edisi v0.20.0-slim dari gambar Docker RAGFlow. Silakan merujuk ke tabel berikut untuk deskripsi berbagai edisi RAGFlow. Untuk mengunduh edisi RAGFlow yang berbeda dari v0.20.0-slim, perbarui variabel RAGFLOW_IMAGE di docker/.env sebelum menggunakan docker compose untuk memulai server. Misalnya, atur RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.0 untuk edisi lengkap v0.20.0.
+> Perintah di bawah ini mengunduh edisi v0.20.1-slim dari gambar Docker RAGFlow. Silakan merujuk ke tabel berikut untuk deskripsi berbagai edisi RAGFlow. Untuk mengunduh edisi RAGFlow yang berbeda dari v0.20.1-slim, perbarui variabel RAGFLOW_IMAGE di docker/.env sebelum menggunakan docker compose untuk memulai server. Misalnya, atur RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.1 untuk edisi lengkap v0.20.1.
 
 ```bash
 $ cd ragflow/docker
@@ -194,8 +194,8 @@ $ docker compose -f docker-compose.yml up -d
 
 | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
 | ----------------- | --------------- | --------------------- | ------------------------ |
-| v0.20.0           | &approx;9       | :heavy_check_mark:    | Stable release           |
-| v0.20.0-slim      | &approx;2       | ❌                    | Stable release           |
+| v0.20.1           | &approx;9       | :heavy_check_mark:    | Stable release           |
+| v0.20.1-slim      | &approx;2       | ❌                    | Stable release           |
 | nightly           | &approx;9       | :heavy_check_mark:    | _Unstable_ nightly build |
 | nightly-slim      | &approx;2       | ❌                    | _Unstable_ nightly build |
 

README_ja.md

@@ -22,7 +22,7 @@
         <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/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.20.0">
+        <img src="https://img.shields.io/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.20.1">
     </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">
@@ -160,7 +160,7 @@
 > 現在、公式に提供されているすべての Docker イメージは x86 アーキテクチャ向けにビルドされており、ARM64 用の Docker イメージは提供されていません。
 > ARM64 アーキテクチャのオペレーティングシステムを使用している場合は、[このドキュメント](https://ragflow.io/docs/dev/build_docker_image)を参照して Docker イメージを自分でビルドしてください。
 
-   > 以下のコマンドは、RAGFlow Docker イメージの v0.20.0-slim エディションをダウンロードします。異なる RAGFlow エディションの説明については、以下の表を参照してください。v0.20.0-slim とは異なるエディションをダウンロードするには、docker/.env ファイルの RAGFLOW_IMAGE 変数を適宜更新し、docker compose を使用してサーバーを起動してください。例えば、完全版 v0.20.0 をダウンロードするには、RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.0 と設定します。
+   > 以下のコマンドは、RAGFlow Docker イメージの v0.20.1-slim エディションをダウンロードします。異なる RAGFlow エディションの説明については、以下の表を参照してください。v0.20.1-slim とは異なるエディションをダウンロードするには、docker/.env ファイルの RAGFLOW_IMAGE 変数を適宜更新し、docker compose を使用してサーバーを起動してください。例えば、完全版 v0.20.1 をダウンロードするには、RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.1 と設定します。
 
    ```bash
    $ cd ragflow/docker
@@ -173,8 +173,8 @@
 
    | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
    | ----------------- | --------------- | --------------------- | ------------------------ |
-   | v0.20.0           | &approx;9       | :heavy_check_mark:    | Stable release           |
-   | v0.20.0-slim      | &approx;2       | ❌                    | Stable release           |
+   | v0.20.1           | &approx;9       | :heavy_check_mark:    | Stable release           |
+   | v0.20.1-slim      | &approx;2       | ❌                    | Stable release           |
    | nightly           | &approx;9       | :heavy_check_mark:    | _Unstable_ nightly build |
    | nightly-slim      | &approx;2       | ❌                     | _Unstable_ nightly build |
 

README_ko.md

@@ -22,7 +22,7 @@
         <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/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.20.0">
+        <img src="https://img.shields.io/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.20.1">
     </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">
@@ -160,7 +160,7 @@
 > 모든 Docker 이미지는 x86 플랫폼을 위해 빌드되었습니다. 우리는 현재 ARM64 플랫폼을 위한 Docker 이미지를 제공하지 않습니다.
 > ARM64 플랫폼을 사용 중이라면, [시스템과 호환되는 Docker 이미지를 빌드하려면 이 가이드를 사용해 주세요](https://ragflow.io/docs/dev/build_docker_image).
 
-   > 아래 명령어는 RAGFlow Docker 이미지의 v0.20.0-slim 버전을 다운로드합니다. 다양한 RAGFlow 버전에 대한 설명은 다음 표를 참조하십시오. v0.20.0-slim과 다른 RAGFlow 버전을 다운로드하려면, docker/.env 파일에서 RAGFLOW_IMAGE 변수를 적절히 업데이트한 후 docker compose를 사용하여 서버를 시작하십시오. 예를 들어, 전체 버전인 v0.20.0을 다운로드하려면 RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.0로 설정합니다.
+   > 아래 명령어는 RAGFlow Docker 이미지의 v0.20.1-slim 버전을 다운로드합니다. 다양한 RAGFlow 버전에 대한 설명은 다음 표를 참조하십시오. v0.20.1-slim과 다른 RAGFlow 버전을 다운로드하려면, docker/.env 파일에서 RAGFLOW_IMAGE 변수를 적절히 업데이트한 후 docker compose를 사용하여 서버를 시작하십시오. 예를 들어, 전체 버전인 v0.20.1을 다운로드하려면 RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.1로 설정합니다.
 
    ```bash
    $ cd ragflow/docker
@@ -173,8 +173,8 @@
 
    | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
    | ----------------- | --------------- | --------------------- | ------------------------ |
-   | v0.20.0           | &approx;9       | :heavy_check_mark:    | Stable release           |
-   | v0.20.0-slim      | &approx;2       | ❌                    | Stable release           |
+   | v0.20.1           | &approx;9       | :heavy_check_mark:    | Stable release           |
+   | v0.20.1-slim      | &approx;2       | ❌                    | Stable release           |
    | nightly           | &approx;9       | :heavy_check_mark:    | _Unstable_ nightly build |
    | nightly-slim      | &approx;2       | ❌                     | _Unstable_ nightly build |
 

README_pt_br.md

@@ -22,7 +22,7 @@
         <img alt="Badge Estático" 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/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.20.0">
+        <img src="https://img.shields.io/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.20.1">
     </a>
     <a href="https://github.com/infiniflow/ragflow/releases/latest">
         <img src="https://img.shields.io/github/v/release/infiniflow/ragflow?color=blue&label=Última%20Relese" alt="Última Versão">
@@ -180,7 +180,7 @@ Experimente nossa demo em [https://demo.ragflow.io](https://demo.ragflow.io).
 > Todas as imagens Docker são construídas para plataformas x86. Atualmente, não oferecemos imagens Docker para ARM64.
 > Se você estiver usando uma plataforma ARM64, por favor, utilize [este guia](https://ragflow.io/docs/dev/build_docker_image) para construir uma imagem Docker compatível com o seu sistema.
 
-    > O comando abaixo baixa a edição `v0.20.0-slim` da imagem Docker do RAGFlow. Consulte a tabela a seguir para descrições de diferentes edições do RAGFlow. Para baixar uma edição do RAGFlow diferente da `v0.20.0-slim`, atualize a variável `RAGFLOW_IMAGE` conforme necessário no **docker/.env** antes de usar `docker compose` para iniciar o servidor. Por exemplo: defina `RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.0` para a edição completa `v0.20.0`.
+    > O comando abaixo baixa a edição `v0.20.1-slim` da imagem Docker do RAGFlow. Consulte a tabela a seguir para descrições de diferentes edições do RAGFlow. Para baixar uma edição do RAGFlow diferente da `v0.20.1-slim`, atualize a variável `RAGFLOW_IMAGE` conforme necessário no **docker/.env** antes de usar `docker compose` para iniciar o servidor. Por exemplo: defina `RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.1` para a edição completa `v0.20.1`.
 
     ```bash
     $ cd ragflow/docker
@@ -193,8 +193,8 @@ Experimente nossa demo em [https://demo.ragflow.io](https://demo.ragflow.io).
 
     | Tag da imagem RAGFlow | Tamanho da imagem (GB) | Possui modelos de incorporação? | Estável?                 |
     | --------------------- | ---------------------- | ------------------------------- | ------------------------ |
-    | v0.20.0               | ~9                     | :heavy_check_mark:              | Lançamento estável       |
-    | v0.20.0-slim          | ~2                     | ❌                              | Lançamento estável       |
+    | v0.20.1               | ~9                     | :heavy_check_mark:              | Lançamento estável       |
+    | v0.20.1-slim          | ~2                     | ❌                              | Lançamento estável       |
     | nightly               | ~9                     | :heavy_check_mark:              | _Instável_ build noturno |
     | nightly-slim          | ~2                     | ❌                               | _Instável_ build noturno |
 

README_tzh.md

@@ -22,7 +22,7 @@
         <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/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.20.0">
+        <img src="https://img.shields.io/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.20.1">
     </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">
@@ -183,7 +183,7 @@
 > 所有 Docker 映像檔都是為 x86 平台建置的。目前，我們不提供 ARM64 平台的 Docker 映像檔。
 > 如果您使用的是 ARM64 平台，請使用 [這份指南](https://ragflow.io/docs/dev/build_docker_image) 來建置適合您系統的 Docker 映像檔。
 
-   > 執行以下指令會自動下載 RAGFlow slim Docker 映像 `v0.20.0-slim`。請參考下表查看不同 Docker 發行版的說明。如需下載不同於 `v0.20.0-slim` 的 Docker 映像，請在執行 `docker compose` 啟動服務之前先更新 **docker/.env** 檔案內的 `RAGFLOW_IMAGE` 變數。例如，你可以透過設定 `RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.0` 來下載 RAGFlow 鏡像的 `v0.20.0` 完整發行版。
+   > 執行以下指令會自動下載 RAGFlow slim Docker 映像 `v0.20.1-slim`。請參考下表查看不同 Docker 發行版的說明。如需下載不同於 `v0.20.1-slim` 的 Docker 映像，請在執行 `docker compose` 啟動服務之前先更新 **docker/.env** 檔案內的 `RAGFLOW_IMAGE` 變數。例如，你可以透過設定 `RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.1` 來下載 RAGFlow 鏡像的 `v0.20.1` 完整發行版。
 
    ```bash
    $ cd ragflow/docker
@@ -196,8 +196,8 @@
 
    | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
    | ----------------- | --------------- | --------------------- | ------------------------ |
-   | v0.20.0           | &approx;9       | :heavy_check_mark:    | Stable release           |
-   | v0.20.0-slim      | &approx;2       | ❌                    | Stable release           |
+   | v0.20.1           | &approx;9       | :heavy_check_mark:    | Stable release           |
+   | v0.20.1-slim      | &approx;2       | ❌                    | Stable release           |
    | nightly           | &approx;9       | :heavy_check_mark:    | _Unstable_ nightly build |
    | nightly-slim      | &approx;2       | ❌                     | _Unstable_ nightly build |
 

README_zh.md

@@ -22,7 +22,7 @@
         <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/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.20.0">
+        <img src="https://img.shields.io/docker/pulls/infiniflow/ragflow?label=Docker%20Pulls&color=0db7ed&logo=docker&logoColor=white&style=flat-square" alt="docker pull infiniflow/ragflow:v0.20.1">
     </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">
@@ -183,7 +183,7 @@
 > 请注意，目前官方提供的所有 Docker 镜像均基于 x86 架构构建，并不提供基于 ARM64 的 Docker 镜像。
 > 如果你的操作系统是 ARM64 架构，请参考[这篇文档](https://ragflow.io/docs/dev/build_docker_image)自行构建 Docker 镜像。
 
-   > 运行以下命令会自动下载 RAGFlow slim Docker 镜像 `v0.20.0-slim`。请参考下表查看不同 Docker 发行版的描述。如需下载不同于 `v0.20.0-slim` 的 Docker 镜像，请在运行 `docker compose` 启动服务之前先更新 **docker/.env** 文件内的 `RAGFLOW_IMAGE` 变量。比如，你可以通过设置 `RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.0` 来下载 RAGFlow 镜像的 `v0.20.0` 完整发行版。
+   > 运行以下命令会自动下载 RAGFlow slim Docker 镜像 `v0.20.1-slim`。请参考下表查看不同 Docker 发行版的描述。如需下载不同于 `v0.20.1-slim` 的 Docker 镜像，请在运行 `docker compose` 启动服务之前先更新 **docker/.env** 文件内的 `RAGFLOW_IMAGE` 变量。比如，你可以通过设置 `RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.1` 来下载 RAGFlow 镜像的 `v0.20.1` 完整发行版。
 
    ```bash
    $ cd ragflow/docker
@@ -196,8 +196,8 @@
 
    | RAGFlow image tag | Image size (GB) | Has embedding models? | Stable?                  |
    | ----------------- | --------------- | --------------------- | ------------------------ |
-   | v0.20.0           | &approx;9       | :heavy_check_mark:    | Stable release           |
-   | v0.20.0-slim      | &approx;2       | ❌                    | Stable release           |
+   | v0.20.1           | &approx;9       | :heavy_check_mark:    | Stable release           |
+   | v0.20.1-slim      | &approx;2       | ❌                    | Stable release           |
    | nightly           | &approx;9       | :heavy_check_mark:    | _Unstable_ nightly build |
    | nightly-slim      | &approx;2       | ❌                     | _Unstable_ nightly build |
 

agent/templates/sql_assistant.json

@@ -0,0 +1,724 @@
+{
+    "id": 17,
+    "title": "SQL Assistant",
+    "description": "SQL Assistant is an AI-powered tool that lets business users turn plain-English questions into fully formed SQL queries. Simply type your question (e.g., “Show me last quarter’s top 10 products by revenue”) and SQL Assistant generates the exact SQL, runs it against your database, and returns the results in seconds. ",
+    "canvas_type": "Marketing",
+    "dsl": {
+            "components": {
+                "Agent:WickedGoatsDivide": {
+                    "downstream": [
+                        "ExeSQL:TiredShirtsPull"
+                    ],
+                    "obj": {
+                        "component_name": "Agent",
+                        "params": {
+                            "delay_after_error": 1,
+                            "description": "",
+                            "exception_default_value": "",
+                            "exception_goto": [],
+                            "exception_method": "",
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+                                        "role": "user"
+                                    }
+                                ],
+                                "sys_prompt": "### ROLE\nYou are a Text-to-SQL assistant.  \nGiven a relational database schema and a natural-language request, you must produce a **single, syntactically-correct MySQL query** that answers the request.  \nReturn **nothing except the SQL statement itself**\u2014no code fences, no commentary, no explanations, no comments, no trailing semicolon if not required.\n\n\n###  EXAMPLES  \n-- Example 1  \nUser: List every product name and its unit price.  \nSQL:\nSELECT name, unit_price FROM Products;\n\n-- Example 2  \nUser: Show the names and emails of customers who placed orders in January 2025.  \nSQL:\nSELECT DISTINCT c.name, c.email\nFROM Customers c\nJOIN Orders o ON o.customer_id = c.id\nWHERE o.order_date BETWEEN '2025-01-01' AND '2025-01-31';\n\n-- Example 3  \nUser: How many orders have a status of \"Completed\" for each month in 2024?  \nSQL:\nSELECT DATE_FORMAT(order_date, '%Y-%m') AS month,\n       COUNT(*) AS completed_orders\nFROM Orders\nWHERE status = 'Completed'\n  AND YEAR(order_date) = 2024\nGROUP BY month\nORDER BY month;\n\n-- Example 4  \nUser: Which products generated at least \\$10 000 in total revenue?  \nSQL:\nSELECT p.id, p.name, SUM(oi.quantity * oi.unit_price) AS revenue\nFROM Products p\nJOIN OrderItems oi ON oi.product_id = p.id\nGROUP BY p.id, p.name\nHAVING revenue >= 10000\nORDER BY revenue DESC;\n\n\n### OUTPUT GUIDELINES\n1. Think through the schema and the request.  \n2. Write **only** the final MySQL query.  \n3. Do **not** wrap the query in back-ticks or markdown fences.  \n4. Do **not** add explanations, comments, or additional text\u2014just the SQL.",
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+                                "top_p": 0.3,
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+                                "visual_files_var": ""
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+                            "label": "Agent",
+                            "name": "SQL Generator "
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+                        "dragging": false,
+                        "id": "Agent:WickedGoatsDivide",
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+                                "database": "",
+                                "db_type": "mysql",
+                                "host": "",
+                                "max_records": 1024,
+                                "outputs": {
+                                    "formalized_content": {
+                                        "type": "string",
+                                        "value": ""
+                                    },
+                                    "json": {
+                                        "type": "Array<Object>",
+                                        "value": []
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+                                "password": "20010812Yy!",
+                                "port": 3306,
+                                "sql": "Agent:WickedGoatsDivide@content",
+                                "username": "13637682833@163.com"
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+                            "label": "ExeSQL",
+                            "name": "ExeSQL"
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+                        "dragging": false,
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+                            "form": {
+                                "text": "Searches for relevant database creation statements.\n\nIt should label with a knowledgebase to which the schema is dumped in. You could use \" General \" as parsing method, \" 2 \" as chunk size and \" ; \"  as delimiter."
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+                            "label": "Note",
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+                    },
+                    {
+                        "data": {
+                            "form": {
+                                "text": "Searches for samples about question to SQL. \n\nYou could use \" Q&A \" as parsing method.\n\nPlease check this dataset:\nhttps://huggingface.co/datasets/InfiniFlow/text2sql"
+                            },
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+                            "name": "Note: Question to SQL"
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+                        "dragHandle": ".note-drag-handle",
+                        "dragging": false,
+                        "height": 154,
+                        "id": "Note:ElevenLionsJoke",
+                        "measured": {
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+                        "selected": false,
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+                        "targetPosition": "left",
+                        "type": "noteNode",
+                        "width": 345
+                    },
+                    {
+                        "data": {
+                            "form": {
+                                "text": "Searches for description about meanings of tables and fields.\n\nYou could use \" General \" as parsing method, \" 2 \" as chunk size and \" ### \"  as delimiter."
+                            },
+                            "label": "Note",
+                            "name": "Note: Database Description"
+                        },
+                        "dragHandle": ".note-drag-handle",
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+                        "height": 158,
+                        "id": "Note:ManyRosesTrade",
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+                    },
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+                                "text": "The Agent learns which tables may be available based on the responses from three knowledge bases and converts the user's input into SQL statements."
+                            },
+                            "label": "Note",
+                            "name": "Note: SQL Generator"
+                        },
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+                        "id": "Note:RudeHousesInvite",
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+                        },
+                        "position": {
+                            "x": 1106.9254833678003,
+                            "y": 290.5891036507015
+                        },
+                        "resizing": false,
+                        "selected": false,
+                        "sourcePosition": "right",
+                        "targetPosition": "left",
+                        "type": "noteNode",
+                        "width": 383
+                    },
+                    {
+                        "data": {
+                            "form": {
+                                "text": "Connect to your database to execute SQL statements."
+                            },
+                            "label": "Note",
+                            "name": "Note: SQL Executor"
+                        },
+                        "dragHandle": ".note-drag-handle",
+                        "dragging": false,
+                        "id": "Note:HungryBatsLay",
+                        "measured": {
+                            "height": 136,
+                            "width": 255
+                        },
+                        "position": {
+                            "x": 1185,
+                            "y": -30
+                        },
+                        "selected": false,
+                        "sourcePosition": "right",
+                        "targetPosition": "left",
+                        "type": "noteNode"
+                    }
+                ]
+            },
+            "history": [],
+            "messages": [],
+            "path": [],
+            "retrieval": []
+        },
+    "avatar": "data:image/jpeg;base64,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"
+}

api/apps/canvas_app.py

@@ -32,8 +32,7 @@ from api.db.services.user_service import TenantService
 from api.db.services.user_canvas_version import UserCanvasVersionService
 from api.settings import RetCode
 from api.utils import get_uuid
-from api.utils.api_utils import get_json_result, server_error_response, validate_request, get_data_error_result, \
-    get_error_data_result
+from api.utils.api_utils import get_json_result, server_error_response, validate_request, get_data_error_result
 from agent.canvas import Canvas
 from peewee import MySQLDatabase, PostgresqlDatabase
 from api.db.db_models import APIToken
@@ -62,7 +61,7 @@ def canvas_list():
 @login_required
 def rm():
     for i in request.json["canvas_ids"]:
-        if not UserCanvasService.query(user_id=current_user.id,id=i):
+        if not UserCanvasService.accessible(i, current_user.id):
             return get_json_result(
                 data=False, message='Only owner of canvas authorized for this operation.',
                 code=RetCode.OPERATING_ERROR)
@@ -86,7 +85,7 @@ def save():
         if not UserCanvasService.save(**req):
             return get_data_error_result(message="Fail to save canvas.")
     else:
-        if not UserCanvasService.query(user_id=current_user.id, id=req["id"]):
+        if not UserCanvasService.accessible(req["id"], current_user.id):
             return get_json_result(
                 data=False, message='Only owner of canvas authorized for this operation.',
                 code=RetCode.OPERATING_ERROR)
@@ -100,9 +99,9 @@ def save():
 @manager.route('/get/<canvas_id>', methods=['GET'])  # noqa: F821
 @login_required
 def get(canvas_id):
-    e, c = UserCanvasService.get_by_tenant_id(canvas_id)
-    if not e or c["user_id"] != current_user.id:
+    if not UserCanvasService.accessible(canvas_id, current_user.id):
         return get_data_error_result(message="canvas not found.")
+    e, c = UserCanvasService.get_by_tenant_id(canvas_id)
     return get_json_result(data=c)
 
 
@@ -131,14 +130,15 @@ def run():
     files = req.get("files", [])
     inputs = req.get("inputs", {})
     user_id = req.get("user_id", current_user.id)
-    e, cvs = UserCanvasService.get_by_id(req["id"])
-    if not e:
-        return get_data_error_result(message="canvas not found.")
-    if not UserCanvasService.query(user_id=current_user.id, id=req["id"]):
+    if not UserCanvasService.accessible(req["id"], current_user.id):
         return get_json_result(
             data=False, message='Only owner of canvas authorized for this operation.',
             code=RetCode.OPERATING_ERROR)
 
+    e, cvs = UserCanvasService.get_by_id(req["id"])
+    if not e:
+        return get_data_error_result(message="canvas not found.")
+
     if not isinstance(cvs.dsl, str):
         cvs.dsl = json.dumps(cvs.dsl, ensure_ascii=False)
 
@@ -172,14 +172,14 @@ def run():
 @login_required
 def reset():
     req = request.json
+    if not UserCanvasService.accessible(req["id"], current_user.id):
+        return get_json_result(
+            data=False, message='Only owner of canvas authorized for this operation.',
+            code=RetCode.OPERATING_ERROR)
     try:
         e, user_canvas = UserCanvasService.get_by_id(req["id"])
         if not e:
             return get_data_error_result(message="canvas not found.")
-        if not UserCanvasService.query(user_id=current_user.id, id=req["id"]):
-            return get_json_result(
-                data=False, message='Only owner of canvas authorized for this operation.',
-                code=RetCode.OPERATING_ERROR)
 
         canvas = Canvas(json.dumps(user_canvas.dsl), current_user.id)
         canvas.reset()
@@ -290,15 +290,12 @@ def input_form():
 @login_required
 def debug():
     req = request.json
+    if not UserCanvasService.accessible(req["id"], current_user.id):
+        return get_json_result(
+            data=False, message='Only owner of canvas authorized for this operation.',
+            code=RetCode.OPERATING_ERROR)
     try:
         e, user_canvas = UserCanvasService.get_by_id(req["id"])
-        if not e:
-            return get_data_error_result(message="canvas not found.")
-        if not UserCanvasService.query(user_id=current_user.id, id=req["id"]):
-            return get_json_result(
-                data=False, message='Only owner of canvas authorized for this operation.',
-                code=RetCode.OPERATING_ERROR)
-
         canvas = Canvas(json.dumps(user_canvas.dsl), current_user.id)
         canvas.reset()
         canvas.message_id = get_uuid()
@@ -404,6 +401,12 @@ def list_kbs():
 def setting():
     req = request.json
     req["user_id"] = current_user.id
+
+    if not UserCanvasService.accessible(req["id"], current_user.id):
+        return get_json_result(
+            data=False, message='Only owner of canvas authorized for this operation.',
+            code=RetCode.OPERATING_ERROR)
+
     e,flow = UserCanvasService.get_by_id(req["id"])
     if not e:
         return get_data_error_result(message="canvas not found.")
@@ -415,10 +418,7 @@ def setting():
         flow["permission"] = req["permission"]
     if req["avatar"]:
         flow["avatar"] = req["avatar"]
-    if not UserCanvasService.query(user_id=current_user.id, id=req["id"]):
-        return get_json_result(
-            data=False, message='Only owner of canvas authorized for this operation.',
-            code=RetCode.OPERATING_ERROR)
+
     num= UserCanvasService.update_by_id(req["id"], flow)
     return get_json_result(data=num)
 
@@ -441,8 +441,10 @@ def trace():
 @login_required
 def sessions(canvas_id):
     tenant_id = current_user.id
-    if not UserCanvasService.query(user_id=tenant_id, id=canvas_id):
-        return get_error_data_result(message=f"You don't own the agent {canvas_id}.")
+    if not UserCanvasService.accessible(canvas_id, tenant_id):
+        return get_json_result(
+            data=False, message='Only owner of canvas authorized for this operation.',
+            code=RetCode.OPERATING_ERROR)
 
     user_id = request.args.get("user_id")
     page_number = int(request.args.get("page", 1))

api/apps/sdk/session.py

@@ -833,34 +833,49 @@ def chatbot_completions(dialog_id):
 
 
 @manager.route("/agentbots/<agent_id>/completions", methods=["POST"])  # noqa: F821
-@token_required
-def agent_bot_completions(tenant_id, agent_id):
+def agent_bot_completions(agent_id):
     req = request.json
 
+    token = request.headers.get("Authorization").split()
+    if len(token) != 2:
+        return get_error_data_result(message='Authorization is not valid!"')
+    token = token[1]
+    objs = APIToken.query(beta=token)
+    if not objs:
+        return get_error_data_result(message='Authentication error: API key is invalid!"')
+
     if req.get("stream", True):
-        resp = Response(agent_completion(tenant_id, agent_id, **req), mimetype="text/event-stream")
+        resp = Response(agent_completion(objs[0].tenant_id, agent_id, **req), mimetype="text/event-stream")
         resp.headers.add_header("Cache-control", "no-cache")
         resp.headers.add_header("Connection", "keep-alive")
         resp.headers.add_header("X-Accel-Buffering", "no")
         resp.headers.add_header("Content-Type", "text/event-stream; charset=utf-8")
         return resp
 
-    for answer in agent_completion(tenant_id, agent_id, **req):
+    for answer in agent_completion(objs[0].tenant_id, agent_id, **req):
         return get_result(data=answer)
 
 
 @manager.route("/agentbots/<agent_id>/inputs", methods=["GET"])  # noqa: F821
-@token_required
-def begin_inputs(tenant_id, agent_id):
+def begin_inputs(agent_id):
+    token = request.headers.get("Authorization").split()
+    if len(token) != 2:
+        return get_error_data_result(message='Authorization is not valid!"')
+    token = token[1]
+    objs = APIToken.query(beta=token)
+    if not objs:
+        return get_error_data_result(message='Authentication error: API key is invalid!"')
+
     e, cvs = UserCanvasService.get_by_id(agent_id)
     if not e:
         return get_error_data_result(f"Can't find agent by ID: {agent_id}")
 
-    canvas = Canvas(json.dumps(cvs.dsl), tenant_id)
+    canvas = Canvas(json.dumps(cvs.dsl), objs[0].tenant_id)
     return get_result(
         data={
             "title": cvs.title,
             "avatar": cvs.avatar,
             "inputs": canvas.get_component_input_form("begin"),
+            "prologue": canvas.get_prologue()
         }
     )

api/db/services/canvas_service.py

@@ -121,6 +121,18 @@ class UserCanvasService(CommonService):
         agents = agents.paginate(page_number, items_per_page)
         return list(agents.dicts()), count
 
+    @classmethod
+    @DB.connection_context()
+    def accessible(cls, canvas_id, tenant_id):
+        from api.db.services.user_service import UserTenantService
+        e, c = UserCanvasService.get_by_tenant_id(canvas_id)
+        if not e:
+            return False
+
+        tids = [t.tenant_id for t in UserTenantService.query(user_id=tenant_id)]
+        if c["user_id"] != canvas_id and c["user_id"]  not in tids:
+            return False
+        return True
 
 def completion(tenant_id, agent_id, session_id=None, **kwargs):
     query = kwargs.get("query", "") or kwargs.get("question", "")
@@ -172,6 +184,7 @@ def completion(tenant_id, agent_id, session_id=None, **kwargs):
     conv.message.append({"role": "assistant", "content": txt, "created_at": time.time(), "id": message_id})
     conv.reference = canvas.get_reference()
     conv.errors = canvas.error
+    conv.dsl = str(canvas)
     conv = conv.to_dict()
     API4ConversationService.append_message(conv["id"], conv)
 

api/db/services/llm_service.py

@@ -225,10 +225,11 @@ class TenantLLMService(CommonService):
                 if llm_id == llm["llm_name"]:
                     return llm["model_type"].split(",")[-1]
 
-        for llm in TenantLLMService.query(llm_name=llm_id):
+        for llm in LLMService.query(llm_name=llm_id):
             return llm.model_type
 
-        for llm in LLMService.query(llm_name=llm_id):
+        llm = TenantLLMService.get_or_none(llm_name=llm_id)
+        if llm:
             return llm.model_type
         for llm in TenantLLMService.query(llm_name=llm_id):
             return llm.model_type

docker/.env

@@ -93,13 +93,13 @@ REDIS_PASSWORD=infini_rag_flow
 SVR_HTTP_PORT=9380
 
 # The RAGFlow Docker image to download.
-# Defaults to the v0.20.0-slim edition, which is the RAGFlow Docker image without embedding models.
-RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.0-slim
+# Defaults to the v0.20.1-slim edition, which is the RAGFlow Docker image without embedding models.
+RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.1-slim
 #
 # To download the RAGFlow Docker image with embedding models, uncomment the following line instead:
-# RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.0
+# RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.1
 #
-# The Docker image of the v0.20.0 edition includes built-in embedding models:
+# The Docker image of the v0.20.1 edition includes built-in embedding models:
 #   - BAAI/bge-large-zh-v1.5
 #   - maidalun1020/bce-embedding-base_v1
 #

docker/README.md

@@ -79,8 +79,8 @@ The [.env](./.env) file contains important environment variables for Docker.
 - `RAGFLOW-IMAGE`  
   The Docker image edition. Available editions:  
   
-  - `infiniflow/ragflow:v0.20.0-slim` (default): The RAGFlow Docker image without embedding models.  
-  - `infiniflow/ragflow:v0.20.0`: The RAGFlow Docker image with embedding models including:
+  - `infiniflow/ragflow:v0.20.1-slim` (default): The RAGFlow Docker image without embedding models.  
+  - `infiniflow/ragflow:v0.20.1`: The RAGFlow Docker image with embedding models including:
     - Built-in embedding models:
       - `BAAI/bge-large-zh-v1.5` 
       - `maidalun1020/bce-embedding-base_v1`

docs/configurations.md

@@ -99,8 +99,8 @@ RAGFlow utilizes MinIO as its object storage solution, leveraging its scalabilit
 - `RAGFLOW-IMAGE`  
   The Docker image edition. Available editions:  
   
-  - `infiniflow/ragflow:v0.20.0-slim` (default): The RAGFlow Docker image without embedding models.  
-  - `infiniflow/ragflow:v0.20.0`: The RAGFlow Docker image with embedding models including:
+  - `infiniflow/ragflow:v0.20.1-slim` (default): The RAGFlow Docker image without embedding models.  
+  - `infiniflow/ragflow:v0.20.1`: The RAGFlow Docker image with embedding models including:
     - Built-in embedding models:
       - `BAAI/bge-large-zh-v1.5` 
       - `maidalun1020/bce-embedding-base_v1`

docs/develop/build_docker_image.mdx

@@ -77,7 +77,7 @@ After building the infiniflow/ragflow:nightly-slim image, you are ready to launc
 
 1. Edit Docker Compose Configuration
 
-Open the `docker/.env` file. Find the `RAGFLOW_IMAGE` setting and change the image reference from `infiniflow/ragflow:v0.20.0-slim` to `infiniflow/ragflow:nightly-slim` to use the pre-built image.
+Open the `docker/.env` file. Find the `RAGFLOW_IMAGE` setting and change the image reference from `infiniflow/ragflow:v0.20.1-slim` to `infiniflow/ragflow:nightly-slim` to use the pre-built image.
 
 
 2. Launch the Service

docs/faq.mdx

@@ -30,17 +30,17 @@ The "garbage in garbage out" status quo remains unchanged despite the fact that
 
 Each RAGFlow release is available in two editions:
 
-- **Slim edition**: excludes built-in embedding models and is identified by a **-slim** suffix added to the version name. Example: `infiniflow/ragflow:v0.20.0-slim`
-- **Full edition**: includes built-in embedding models and has no suffix added to the version name. Example: `infiniflow/ragflow:v0.20.0`
+- **Slim edition**: excludes built-in embedding models and is identified by a **-slim** suffix added to the version name. Example: `infiniflow/ragflow:v0.20.1-slim`
+- **Full edition**: includes built-in embedding models and has no suffix added to the version name. Example: `infiniflow/ragflow:v0.20.1`
 
 ---
 
 ### Which embedding models can be deployed locally?
 
-RAGFlow offers two Docker image editions, `v0.20.0-slim` and `v0.20.0`:  
+RAGFlow offers two Docker image editions, `v0.20.1-slim` and `v0.20.1`:  
   
-- `infiniflow/ragflow:v0.20.0-slim` (default): The RAGFlow Docker image without embedding models.  
-- `infiniflow/ragflow:v0.20.0`: The RAGFlow Docker image with embedding models including:
+- `infiniflow/ragflow:v0.20.1-slim` (default): The RAGFlow Docker image without embedding models.  
+- `infiniflow/ragflow:v0.20.1`: The RAGFlow Docker image with embedding models including:
   - Built-in embedding models:
     - `BAAI/bge-large-zh-v1.5`
     - `maidalun1020/bce-embedding-base_v1`

docs/guides/agent/agent_component_reference/agent.mdx

@@ -9,7 +9,7 @@ The component equipped with reasoning, tool usage, and multi-agent collaboration
 
 ---
 
-An **Agent** component fine-tunes the LLM and sets its prompt. From v0.20.0 onwards, an **Agent** component is able to work independently and with the following capabilities:
+An **Agent** component fine-tunes the LLM and sets its prompt. From v0.20.1 onwards, an **Agent** component is able to work independently and with the following capabilities:
 
 - Autonomous reasoning with reflection and adjustment based on environmental feedback.
 - Use of tools or subagents to complete tasks.
@@ -92,10 +92,10 @@ The waiting period in seconds that the agent observes before retrying a failed t
 
 ### Max rounds
 
-Defines the maximum number reflection rounds of the selected chat model. Defaults to 5 rounds.
+Defines the maximum number reflection rounds of the selected chat model. Defaults to 1 round.
 
 :::tip NOTE
-You can set the value to 1 to shorten your agent's response time.
+Increasing this value will significantly extend your agent's response time.
 :::
 
 ### Output

docs/guides/agent/agent_component_reference/retrieval.mdx

@@ -9,7 +9,7 @@ A component that retrieves information from specified datasets.
 
 ## Scenarios
 
-A **Retrieval** component is essential in most RAG scenarios, where information is extracted from designated knowledge bases before being sent to the LLM for content generation. As of v0.20.0, a **Retrieval** component can operate either as a workflow component or as a tool of an **Agent**, enabling the Agent to control its invocation and search queries.
+A **Retrieval** component is essential in most RAG scenarios, where information is extracted from designated knowledge bases before being sent to the LLM for content generation. As of v0.20.1, a **Retrieval** component can operate either as a workflow component or as a tool of an **Agent**, enabling the Agent to control its invocation and search queries.
 
 ## Configurations
 

docs/guides/chat/start_chat.md

@@ -48,7 +48,7 @@ You start an AI conversation by creating an assistant.
      - If no target language is selected, the system will search only in the language of your query, which may cause relevant information in other languages to be missed.
    - **Variable** refers to the variables (keys) to be used in the system prompt. `{knowledge}` is a reserved variable. Click **Add** to add more variables for the system prompt.
       - If you are uncertain about the logic behind **Variable**, leave it *as-is*.
-      - As of v0.20.0, if you add custom variables here, the only way you can pass in their values is to call:
+      - As of v0.20.1, if you add custom variables here, the only way you can pass in their values is to call:
          - HTTP method [Converse with chat assistant](../../references/http_api_reference.md#converse-with-chat-assistant), or
          - Python method [Converse with chat assistant](../../references/python_api_reference.md#converse-with-chat-assistant).
 

docs/guides/dataset/configure_knowledge_base.md

@@ -128,7 +128,7 @@ See [Run retrieval test](./run_retrieval_test.md) for details.
 
 ## Search for knowledge base
 
-As of RAGFlow v0.20.0, the search feature is still in a rudimentary form, supporting only knowledge base search by name.
+As of RAGFlow v0.20.1, 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/manage_files.md

@@ -87,4 +87,4 @@ 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 v0.20.0, bulk download is not supported, nor can you download an entire folder. 
+> As of RAGFlow v0.20.1, bulk download is not supported, nor can you download an entire folder. 

docs/guides/tracing.mdx

@@ -18,7 +18,7 @@ RAGFlow ships with a built-in [Langfuse](https://langfuse.com) integration so th
 Langfuse stores traces, spans and prompt payloads in a purpose-built observability backend and offers filtering and visualisations on top.  
 
 :::info NOTE
-• RAGFlow **≥ 0.20.0** (contains the Langfuse connector)  
+• RAGFlow **≥ 0.20.1** (contains the Langfuse connector)  
 • A Langfuse workspace (cloud or self-hosted) with a _Project Public Key_ and _Secret Key_
 :::
 

docs/guides/upgrade_ragflow.mdx

@@ -66,10 +66,10 @@ 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., `v0.20.0`:
+2. Switch to the latest, officially published release, e.g., `v0.20.1`:
 
    ```bash
-   git checkout -f v0.20.0
+   git checkout -f v0.20.1
    ```
 
 3. Update **ragflow/docker/.env**:
@@ -83,14 +83,14 @@ To upgrade RAGFlow, you must upgrade **both** your code **and** your Docker imag
   <TabItem value="slim">
 
 ```bash
-RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.0-slim
+RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.1-slim
 ```
 
   </TabItem>
   <TabItem value="full">
 
 ```bash
-RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.0
+RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.1
 ```
 
   </TabItem>
@@ -114,10 +114,10 @@ No, you do not need to. Upgrading RAGFlow in itself will *not* remove your uploa
 1. From an environment with Internet access, pull the required Docker image.
 2. Save the Docker image to a **.tar** file.
    ```bash
-   docker save -o ragflow.v0.20.0.tar infiniflow/ragflow:v0.20.0
+   docker save -o ragflow.v0.20.1.tar infiniflow/ragflow:v0.20.1
    ```
 3. Copy the **.tar** file to the target server.
 4. Load the **.tar** file into Docker:
    ```bash
-   docker load -i ragflow.v0.20.0.tar
+   docker load -i ragflow.v0.20.1.tar
    ```

docs/quickstart.mdx

@@ -44,7 +44,7 @@ This section provides instructions on setting up the RAGFlow server on Linux. If
 
    `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 v0.20.0 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.
+   RAGFlow v0.20.1 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"
@@ -184,13 +184,13 @@ This section provides instructions on setting up the RAGFlow server on Linux. If
    ```bash
    $ git clone https://github.com/infiniflow/ragflow.git
    $ cd ragflow/docker
-   $ git checkout -f v0.20.0
+   $ git checkout -f v0.20.1
    ```
 
 3. Use the pre-built Docker images and start up the server:
 
    :::tip NOTE
-   The command below downloads the `v0.20.0-slim` edition of the RAGFlow Docker image. Refer to the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from `v0.20.0-slim`, update the `RAGFLOW_IMAGE` variable accordingly in **docker/.env** before using `docker compose` to start the server. For example: set `RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.0` for the full edition `v0.20.0`.
+   The command below downloads the `v0.20.1-slim` edition of the RAGFlow Docker image. Refer to the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from `v0.20.1-slim`, update the `RAGFLOW_IMAGE` variable accordingly in **docker/.env** before using `docker compose` to start the server. For example: set `RAGFLOW_IMAGE=infiniflow/ragflow:v0.20.1` for the full edition `v0.20.1`.
    :::
 
    ```bash
@@ -207,8 +207,8 @@ This section provides instructions on setting up the RAGFlow server on Linux. If
 
 | RAGFlow image tag   | Image size (GB) | Has embedding models and Python packages? | Stable?                  |
 | ------------------- | --------------- | ----------------------------------------- | ------------------------ |
-| `v0.20.0`           | &approx;9       | :heavy_check_mark:                        | Stable release           |
-| `v0.20.0-slim`      | &approx;2       | ❌                                        | Stable release           |
+| `v0.20.1`           | &approx;9       | :heavy_check_mark:                        | Stable release           |
+| `v0.20.1-slim`      | &approx;2       | ❌                                        | Stable release           |
 | `nightly`           | &approx;9       | :heavy_check_mark:                        | *Unstable* nightly build |
 | `nightly-slim`      | &approx;2       | ❌                                        | *Unstable* nightly build |
 
@@ -217,7 +217,7 @@ This section provides instructions on setting up the RAGFlow server on Linux. If
 ```
 
 :::danger IMPORTANT
-The embedding models included in `v0.20.0` and `nightly` are:
+The embedding models included in `v0.20.1` and `nightly` are:
 
 - BAAI/bge-large-zh-v1.5
 - maidalun1020/bce-embedding-base_v1

docs/references/glossary.mdx

@@ -19,7 +19,7 @@ import TOCInline from '@theme/TOCInline';
 
 ### Cross-language search
 
-Cross-language search (also known as cross-lingual retrieval) is a feature introduced in version 0.20.0. It enables users to submit queries in one language (for example, English) and retrieve relevant documents written in other languages such as Chinese or Spanish. This feature is enabled by the system’s default chat model, which translates queries to ensure accurate matching of semantic meaning across languages.
+Cross-language search (also known as cross-lingual retrieval) is a feature introduced in version 0.20.1. It enables users to submit queries in one language (for example, English) and retrieve relevant documents written in other languages such as Chinese or Spanish. This feature is enabled by the system’s default chat model, which translates queries to ensure accurate matching of semantic meaning across languages.
 
 By enabling cross-language search, users can effortlessly access a broader range of information regardless of language barriers, significantly enhancing the system’s usability and inclusiveness.
 

docs/release_notes.md

@@ -22,6 +22,32 @@ The embedding models included in a full edition are:
 These two embedding models are optimized specifically for English and Chinese, so performance may be compromised if you use them to embed documents in other languages.
 :::
 
+## v0.20.1
+
+Released on August 8, 2025.
+
+### New Features
+
+- The **Retrieval** component now supports the dynamic specification of knowledge base names using variables.
+- The user interface now includes a French language option.
+
+### Added Models
+
+- ChatGPT 5
+- Claude 4.1
+
+### New agent Templates (both workflow and agentic)
+
+- SQL Assistant Workflow: Empowers non-technical teams (e.g., operations, product) to independently query business data.
+- Choose Your Knowledge Base Workflow: Lets users select a knowledge base to query during conversations. [#9325](https://github.com/infiniflow/ragflow/pull/9325)
+- Choose Your Knowledge Base Agent: Delivers higher-quality responses with extended reasoning time, suited for complex queries. [#9325](https://github.com/infiniflow/ragflow/pull/9325)
+
+### Fixed Issues
+
+- The **Agent** component was unable to invoke models installed via vLLM.
+- Agents could not be shared with the team.
+- Embedding an Agent into a webpage was not functioning properly.
+
 ## v0.20.0
 
 Released on August 4, 2025.

helm/values.yaml

@@ -33,13 +33,13 @@ env:
   REDIS_PASSWORD: infini_rag_flow_helm
 
   # The RAGFlow Docker image to download.
-  # Defaults to the v0.20.0-slim edition, which is the RAGFlow Docker image without embedding models.
-  RAGFLOW_IMAGE: infiniflow/ragflow:v0.20.0-slim
+  # Defaults to the v0.20.1-slim edition, which is the RAGFlow Docker image without embedding models.
+  RAGFLOW_IMAGE: infiniflow/ragflow:v0.20.1-slim
   #
   # To download the RAGFlow Docker image with embedding models, uncomment the following line instead:
-  # RAGFLOW_IMAGE: infiniflow/ragflow:v0.20.0
+  # RAGFLOW_IMAGE: infiniflow/ragflow:v0.20.1
   #
-  # The Docker image of the v0.20.0 edition includes:
+  # The Docker image of the v0.20.1 edition includes:
   # - Built-in embedding models:
   #   - BAAI/bge-large-zh-v1.5
   #   - BAAI/bge-reranker-v2-m3

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ragflow"
-version = "0.20.0"
+version = "0.20.1"
 description = "[RAGFlow](https://ragflow.io/) is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. It offers a streamlined RAG workflow for businesses of any scale, combining LLM (Large Language Models) to provide truthful question-answering capabilities, backed by well-founded citations from various complex formatted data."
 authors = [{ name = "Zhichang Yu", email = "yuzhichang@gmail.com" }]
 license-files = ["LICENSE"]

sdk/python/pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ragflow-sdk"
-version = "0.20.0"
+version = "0.20.1"
 description = "Python client sdk of [RAGFlow](https://github.com/infiniflow/ragflow). RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding."
 authors = [{ name = "Zhichang Yu", email = "yuzhichang@gmail.com" }]
 license = { text = "Apache License, Version 2.0" }

sdk/python/uv.lock

@@ -342,7 +342,7 @@ wheels = [
 
 [[package]]
 name = "ragflow-sdk"
-version = "0.20.0"
+version = "0.20.1"
 source = { virtual = "." }
 dependencies = [
     { name = "beartype" },

uv.lock

@@ -5188,7 +5188,7 @@ wheels = [
 
 [[package]]
 name = "ragflow"
-version = "0.20.0"
+version = "0.20.1"
 source = { virtual = "." }
 dependencies = [
     { name = "akshare" },

web/src/pages/agent/chat/use-send-agent-message.ts

@@ -361,5 +361,6 @@ export const useSendAgentMessage = (
     resetSession,
     findReferenceByMessageId,
     appendUploadResponseList,
+    addNewestOneAnswer,
   };
 };

web/src/pages/agent/interface.ts

@@ -38,4 +38,5 @@ export type IInputs = {
   avatar: string;
   title: string;
   inputs: Record<string, BeginQuery>;
+  prologue: string;
 };

web/src/pages/agent/setting-dialog/setting-form.tsx

@@ -26,8 +26,8 @@ import { useForm } from 'react-hook-form';
 
 const formSchema = z.object({
   title: z.string().min(1, {}),
-  avatar: z.array(z.custom<File>()),
-  description: z.string(),
+  avatar: z.array(z.custom<File>()).optional().nullable(),
+  description: z.string().optional().nullable(),
   permission: z.string(),
 });
 

web/src/pages/next-chats/share/index.tsx

@@ -60,6 +60,7 @@ const ChatContainer = () => {
     parameterDialogVisible,
     showParameterDialog,
     sendFormMessage,
+    addNewestOneAnswer,
     ok,
     resetSession,
   } = useSendNextSharedMessage(addEventList);
@@ -75,6 +76,7 @@ const ChatContainer = () => {
     avatar: '',
     title: '',
     inputs: {},
+    prologue: '',
   });
   const handleUploadFile: NonNullable<FileUploadProps['onUpload']> =
     useCallback(
@@ -97,9 +99,18 @@ const ChatContainer = () => {
       avatar,
       title,
       inputs: inputs,
+      prologue: '',
     });
   }, [inputsData, setAgentInfo]);
 
+  React.useEffect(() => {
+    if (inputsData.prologue) {
+      addNewestOneAnswer({
+        answer: inputsData.prologue,
+      });
+    }
+  }, [inputsData.prologue, addNewestOneAnswer]);
+
   React.useEffect(() => {
     if (inputsData && inputsData.inputs && !isEmpty(inputsData.inputs)) {
       showParameterDialog();