chore: auto-dify init commit

Signed-off-by: -LAN- <laipz8200@outlook.com>
Co-authored-by: -LAN- <laipz8200@outlook.com>

.gitattributes

@@ -1,5 +1,5 @@
 # Ensure that .sh scripts use LF as line separator, even if they are checked out
-# to Windows(NTFS) file-system, by a user of Docker for Window. 
+# to Windows(NTFS) file-system, by a user of Docker for Windows. 
 # These .sh scripts will be run from the Container after `docker compose up -d`.
 # If they appear to be CRLF style, Dash from the Container will fail to execute
 # them.

.github/workflows/style.yml

@@ -92,7 +92,7 @@ jobs:
 
       - name: Web style check
         if: steps.changed-files.outputs.any_changed == 'true'
-        run: yarn run lint
+        run: pnpm run lint
 
   docker-compose-template:
     name: Docker Compose Template

.github/workflows/tool-test-sdks.yaml

@@ -38,7 +38,7 @@ jobs:
           cache-dependency-path: 'pnpm-lock.yaml'
 
       - name: Install Dependencies
-        run: pnpm install
+        run: pnpm install --frozen-lockfile
 
       - name: Test
         run: pnpm test

.github/workflows/web-tests.yml

@@ -31,19 +31,25 @@ jobs:
         uses: tj-actions/changed-files@v45
         with:
           files: web/**
+      # to run pnpm, should install package canvas, but it always install failed on amd64 under ubuntu-latest
+      # - name: Install pnpm
+      #   uses: pnpm/action-setup@v4
+      #   with:
+      #     version: 10
+      #     run_install: false
 
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        if: steps.changed-files.outputs.any_changed == 'true'
-        with:
-          node-version: 20
-          cache: pnpm
-          cache-dependency-path: ./web/package.json
+      # - name: Setup Node.js
+      #   uses: actions/setup-node@v4
+      #   if: steps.changed-files.outputs.any_changed == 'true'
+      #   with:
+      #     node-version: 20
+      #     cache: pnpm
+      #     cache-dependency-path: ./web/package.json
 
-      - name: Install dependencies
-        if: steps.changed-files.outputs.any_changed == 'true'
-        run: pnpm install --frozen-lockfile
+      # - name: Install dependencies
+      #   if: steps.changed-files.outputs.any_changed == 'true'
+      #   run: pnpm install --frozen-lockfile
 
-      - name: Run tests
-        if: steps.changed-files.outputs.any_changed == 'true'
-        run: pnpm test
+      # - name: Run tests
+      #   if: steps.changed-files.outputs.any_changed == 'true'
+      #   run: pnpm test

.gitignore

@@ -183,6 +183,7 @@ docker/nginx/conf.d/default.conf
 docker/nginx/ssl/*
 !docker/nginx/ssl/.gitkeep
 docker/middleware.env
+docker/docker-compose.override.yaml
 
 sdks/python-client/build
 sdks/python-client/dist

CONTRIBUTING.md

@@ -73,7 +73,7 @@ Dify requires the following dependencies to build, make sure they're installed o
 * [Docker](https://www.docker.com/)
 * [Docker Compose](https://docs.docker.com/compose/install/)
 * [Node.js v18.x (LTS)](http://nodejs.org)
-* [npm](https://www.npmjs.com/) version 8.x.x or [Yarn](https://yarnpkg.com/)
+* [pnpm](https://pnpm.io/)
 * [Python](https://www.python.org/) version 3.11.x or 3.12.x
 
 ### 4. Installations

CONTRIBUTING_CN.md

@@ -70,7 +70,7 @@ Dify 依赖以下工具和库：
 - [Docker](https://www.docker.com/)
 - [Docker Compose](https://docs.docker.com/compose/install/)
 - [Node.js v18.x (LTS)](http://nodejs.org)
-- [npm](https://www.npmjs.com/) version 8.x.x or [Yarn](https://yarnpkg.com/)
+- [pnpm](https://pnpm.io/)
 - [Python](https://www.python.org/) version 3.11.x or 3.12.x
 
 ### 4. 安装

CONTRIBUTING_DE.md

@@ -0,0 +1,155 @@
+# MITWIRKEN
+
+So, du möchtest zu Dify beitragen – das ist großartig, wir können es kaum erwarten, zu sehen, was du beisteuern wirst. Als ein Startup mit begrenzter Mitarbeiterzahl und Finanzierung haben wir große Ambitionen, den intuitivsten Workflow zum Aufbau und zur Verwaltung von LLM-Anwendungen zu entwickeln. Jede Unterstützung aus der Community zählt wirklich.
+
+Dieser Leitfaden, ebenso wie Dify selbst, ist ein ständig in Entwicklung befindliches Projekt. Wir schätzen Ihr Verständnis, falls er zeitweise hinter dem tatsächlichen Projekt zurückbleibt, und freuen uns über jegliches Feedback, das uns hilft, ihn zu verbessern.
+
+Bezüglich der Lizenzierung nehmen Sie sich bitte einen Moment Zeit, um unser kurzes [License and Contributor Agreement](./LICENSE) zu lesen. Die Community hält sich außerdem an den [Code of Conduct](https://github.com/langgenius/.github/blob/main/CODE_OF_CONDUCT.md).
+
+## Bevor Sie loslegen
+
+[Finde](https://github.com/langgenius/dify/issues?q=is:issue+is:open) ein bestehendes Issue, oder [öffne](https://github.com/langgenius/dify/issues/new/choose) ein neues. Wir kategorisieren Issues in zwei Typen:
+
+### Feature-Anfragen
+
+* Wenn Sie eine neue Feature-Anfrage stellen, bitten wir Sie zu erklären, was das vorgeschlagene Feature bewirken soll und so viel Kontext wie möglich bereitzustellen. [@perzeusss](https://github.com/perzeuss) hat einen soliden [Feature Request Copilot](https://udify.app/chat/MK2kVSnw1gakVwMX) entwickelt, der Ihnen dabei hilft, Ihre Anforderungen zu formulieren. Probieren Sie ihn gerne aus.
+
+* Wenn Sie eines der bestehenden Issues übernehmen möchten, hinterlassen Sie einfach einen Kommentar darunter, in dem Sie uns dies mitteilen.
+
+  Ein Teammitglied, das in der entsprechenden Richtung arbeitet, wird hinzugezogen. Wenn alles in Ordnung ist, gibt es das Okay, mit der Codierung zu beginnen. Wir bitten Sie, mit der Umsetzung des Features zu warten, damit keine Ihrer Arbeiten verloren gehen sollte unsererseits Änderungen vorgeschlagen werden.
+
+  Je nachdem, in welchen Bereich das vorgeschlagene Feature fällt, können Sie mit verschiedenen Teammitgliedern sprechen. Hier ist eine Übersicht der Bereiche, an denen unsere Teammitglieder derzeit arbeiten:
+
+  | Member                                                       | Scope                                                |
+  | ------------------------------------------------------------ | ---------------------------------------------------- |
+  | [@yeuoly](https://github.com/Yeuoly)                         | Architecting Agents                                  |
+  | [@jyong](https://github.com/JohnJyong)                       | RAG pipeline design                                  |
+  | [@GarfieldDai](https://github.com/GarfieldDai)               | Building workflow orchestrations                     |
+  | [@iamjoel](https://github.com/iamjoel) & [@zxhlyh](https://github.com/zxhlyh) | Making our frontend a breeze to use                  |
+  | [@guchenhe](https://github.com/guchenhe) & [@crazywoola](https://github.com/crazywoola) | Developer experience, points of contact for anything |
+  | [@takatost](https://github.com/takatost)                     | Overall product direction and architecture           |
+
+  Wie wir Prioritäten setzen:
+
+  | Feature Type                                                 | Priority        |
+  | ------------------------------------------------------------ | --------------- |
+  | Funktionen mit hoher Priorität, wie sie von einem Teammitglied gekennzeichnet wurden     | High Priority   |
+  | Beliebte Funktionsanfragen von unserem [Community-Feedback-Board](https://github.com/langgenius/dify/discussions/categories/feedbacks) | Medium Priority |
+  | Nicht-Kernfunktionen und kleinere Verbesserungen                    | Low Priority    |
+  | Wertvoll, aber nicht unmittelbar                                  | Future-Feature  |
+
+### Sonstiges (e.g. bug report, performance optimization, typo correction)
+
+* Fangen Sie sofort an zu programmieren..
+
+  Wie wir Prioritäten setzen:
+
+  | Issue Type                                                   | Priority        |
+  | ------------------------------------------------------------ | --------------- |
+  | Fehler in Kernfunktionen (Anmeldung nicht möglich, Anwendungen funktionieren nicht, Sicherheitslücken) | Critical        |
+  | Nicht-kritische Fehler, Leistungsverbesserungen                     | Medium Priority |
+  | Kleinere Fehlerkorrekturen (Schreibfehler, verwirrende, aber funktionierende Benutzeroberfläche)                | Low Priority    |
+
+## Installieren
+
+Hier sind die Schritte, um Dify für die Entwicklung einzurichten:
+
+### 1. Fork dieses Repository
+
+### 2. Clone das Repo
+
+ Klonen Sie das geforkte Repository von Ihrem Terminal aus:
+
+```shell
+git clone git@github.com:<github_username>/dify.git
+```
+
+### 3. Abhängigkeiten prüfen
+
+Dify benötigt die folgenden Abhängigkeiten zum Bauen – stellen Sie sicher, dass sie auf Ihrem System installiert sind:
+
+* [Docker](https://www.docker.com/)
+* [Docker Compose](https://docs.docker.com/compose/install/)
+* [Node.js v18.x (LTS)](http://nodejs.org)
+* [pnpm](https://pnpm.io/)
+* [Python](https://www.python.org/) version 3.11.x or 3.12.x
+
+### 4. Installationen
+
+Dify setzt sich aus einem Backend und einem Frontend zusammen. Wechseln Sie in das Backend-Verzeichnis mit `cd api/` und folgen Sie der [Backend README](api/README.md) zur Installation. Öffnen Sie in einem separaten Terminal das Frontend-Verzeichnis mit `cd web/` und folgen Sie der [Frontend README](web/README.md) zur Installation.
+
+Überprüfen Sie die [Installation FAQ](https://docs.dify.ai/learn-more/faq/install-faq) für eine Liste bekannter Probleme und Schritte zur Fehlerbehebung.
+
+### 5. Besuchen Sie dify in Ihrem Browser
+
+Um Ihre Einrichtung zu validieren, öffnen Sie Ihren Browser und navigieren Sie zu [http://localhost:3000](http://localhost:3000) (Standardwert oder Ihre selbst konfigurierte URL und Port). Sie sollten nun Dify im laufenden Betrieb sehen.
+
+## Entwickeln
+
+Wenn Sie einen Modellanbieter hinzufügen, ist [dieser Leitfaden](https://github.com/langgenius/dify/blob/main/api/core/model_runtime/README.md) für Sie.
+
+Wenn Sie einen Tool-Anbieter für Agent oder Workflow hinzufügen möchten, ist [dieser Leitfaden](./api/core/tools/README.md) für Sie.
+
+Um Ihnen eine schnelle Orientierung zu bieten, wo Ihr Beitrag passt, folgt eine kurze, kommentierte Übersicht des Backends und Frontends von Dify:
+
+### Backend
+
+Dify’s Backend ist in Python geschrieben und nutzt [Flask](https://flask.palletsprojects.com/en/3.0.x/) als Web-Framework. Es verwendet [SQLAlchemy](https://www.sqlalchemy.org/) für ORM und [Celery](https://docs.celeryq.dev/en/stable/getting-started/introduction.html) für Task-Queueing. Die Autorisierungslogik erfolgt über Flask-login.
+
+```text
+[api/]
+├── constants             // Konstante Einstellungen, die in der gesamten Codebasis verwendet werden.
+├── controllers           // API-Routendefinitionen und Logik zur Bearbeitung von Anfragen.
+├── core                  // Orchestrierung von Kernanwendungen, Modellintegrationen und Tools.
+├── docker                // Konfigurationen im Zusammenhang mit Docker und Containerisierung.
+├── events                // Ereignisbehandlung und -verarbeitung
+├── extensions            // Erweiterungen mit Frameworks/Plattformen von Drittanbietern.
+├── fields                // Felddefinitionen für die Serialisierung/Marshalling.
+├── libs                  // Wiederverwendbare Bibliotheken und Hilfsprogramme
+├── migrations            // Skripte für die Datenbankmigration.
+├── models                // Datenbankmodelle und Schemadefinitionen.
+├── services              // Gibt die Geschäftslogik an.
+├── storage               // Speicherung privater Schlüssel.   
+├── tasks                 // Handhabung von asynchronen Aufgaben und Hintergrundaufträgen.
+└── tests
+```
+
+### Frontend
+
+Die Website basiert auf einem [Next.js](https://nextjs.org/)-Boilerplate in TypeScript und verwendet [Tailwind CSS](https://tailwindcss.com/) für das Styling. [React-i18next](https://react.i18next.com/) wird für die Internationalisierung genutzt.
+
+```text
+[web/]
+├── app                   // Layouts, Seiten und Komponenten
+│   ├── (commonLayout)    // gemeinsames Layout für die gesamte Anwendung
+│   ├── (shareLayout)     // Layouts, die speziell für tokenspezifische Sitzungen gemeinsam genutzt werden 
+│   ├── activate          // Seite aufrufen
+│   ├── components        // gemeinsam genutzt von Seiten und Layouts
+│   ├── install           // Seite installieren
+│   ├── signin            // Anmeldeseite
+│   └── styles            // global geteilte Stile
+├── assets                // Statische Vermögenswerte
+├── bin                   // Skripte, die beim Build-Schritt ausgeführt werden
+├── config                // einstellbare Einstellungen und Optionen 
+├── context               // gemeinsame Kontexte, die von verschiedenen Teilen der Anwendung verwendet werden
+├── dictionaries          // Sprachspezifische Übersetzungsdateien 
+├── docker                // Container-Konfigurationen
+├── hooks                 // Wiederverwendbare Haken
+├── i18n                  // Konfiguration der Internationalisierung
+├── models                // beschreibt Datenmodelle und Formen von API-Antworten
+├── public                // Meta-Assets wie Favicon
+├── service               // legt Formen von API-Aktionen fest
+├── test                  
+├── types                 // Beschreibungen von Funktionsparametern und Rückgabewerten
+└── utils                 // Gemeinsame Nutzenfunktionen
+```
+
+## Einreichung Ihrer PR
+
+Am Ende ist es Zeit, einen Pull Request (PR) in unserem Repository zu eröffnen. Für wesentliche Features mergen wir diese zunächst in den `deploy/dev`-Branch zum Testen, bevor sie in den `main`-Branch übernommen werden. Falls Sie auf Probleme wie Merge-Konflikte stoßen oder nicht wissen, wie man einen Pull Request erstellt, schauen Sie sich [GitHub's Pull Request Tutorial](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests) an.
+
+Und das war's! Sobald Ihr PR gemerged wurde, werden Sie als Mitwirkender in unserem [README](https://github.com/langgenius/dify/blob/main/README.md) aufgeführt.
+
+## Hilfe bekommen
+
+Wenn Sie beim Beitragen jemals nicht weiter wissen oder eine brennende Frage haben, richten Sie Ihre Anfrage einfach über das entsprechende GitHub-Issue an uns oder besuchen Sie unseren [Discord](https://discord.gg/8Tpq4AcN9c) für ein kurzes Gespräch.

CONTRIBUTING_JA.md

@@ -73,7 +73,7 @@ Dify を構築するには次の依存関係が必要です。それらがシス
 - [Docker](https://www.docker.com/)
 - [Docker Compose](https://docs.docker.com/compose/install/)
 - [Node.js v18.x (LTS)](http://nodejs.org)
-- [npm](https://www.npmjs.com/) version 8.x.x or [Yarn](https://yarnpkg.com/)
+- [pnpm](https://pnpm.io/)
 - [Python](https://www.python.org/) version 3.11.x or 3.12.x
 
 ### 4. インストール

CONTRIBUTING_TW.md

@@ -0,0 +1,153 @@
+# 貢獻指南
+
+您想為 Dify 做出貢獻 - 這太棒了，我們迫不及待地想看看您的成果。作為一家人力和資金有限的初創公司，我們有宏大的抱負，希望設計出最直觀的工作流程來構建和管理 LLM 應用程式。來自社群的任何幫助都非常珍貴，真的。
+
+鑑於我們的現狀，我們需要靈活且快速地發展，但同時也希望確保像您這樣的貢獻者能夠獲得盡可能順暢的貢獻體驗。我們編寫了這份貢獻指南，目的是幫助您熟悉代碼庫以及我們如何與貢獻者合作，讓您可以更快地進入有趣的部分。
+
+這份指南，就像 Dify 本身一樣，是不斷發展的。如果有時它落後於實際項目，我們非常感謝您的理解，也歡迎任何改進的反饋。
+
+關於授權，請花一分鐘閱讀我們簡短的[授權和貢獻者協議](./LICENSE)。社群也遵守[行為準則](https://github.com/langgenius/.github/blob/main/CODE_OF_CONDUCT.md)。
+
+## 在開始之前
+
+[尋找](https://github.com/langgenius/dify/issues?q=is:issue+is:open)現有的 issue，或[創建](https://github.com/langgenius/dify/issues/new/choose)一個新的。我們將 issues 分為 2 種類型：
+
+### 功能請求
+
+- 如果您要開啟新的功能請求，我們希望您能解釋所提議的功能要達成什麼目標，並且盡可能包含更多的相關背景資訊。[@perzeusss](https://github.com/perzeuss) 已經製作了一個實用的[功能請求輔助工具](https://udify.app/chat/MK2kVSnw1gakVwMX)，能幫助您草擬您的需求。歡迎試用。
+
+- 如果您想從現有問題中選擇一個來處理，只需在其下方留言表示即可。
+
+  相關方向的團隊成員會加入討論。如果一切順利，他們會同意您開始編寫代碼。我們要求您在得到許可前先不要開始處理該功能，以免我們提出變更時您的工作成果被浪費。
+
+  根據所提議功能的領域不同，您可能會與不同的團隊成員討論。以下是目前每位團隊成員所負責的領域概述：
+
+  | 成員                                                                                    | 負責領域                       |
+  | --------------------------------------------------------------------------------------- | ------------------------------ |
+  | [@yeuoly](https://github.com/Yeuoly)                                                    | 設計 Agents 架構               |
+  | [@jyong](https://github.com/JohnJyong)                                                  | RAG 管道設計                   |
+  | [@GarfieldDai](https://github.com/GarfieldDai)                                          | 建構工作流程編排               |
+  | [@iamjoel](https://github.com/iamjoel) & [@zxhlyh](https://github.com/zxhlyh)           | 打造易用的前端界面             |
+  | [@guchenhe](https://github.com/guchenhe) & [@crazywoola](https://github.com/crazywoola) | 開發者體驗，各類問題的聯絡窗口 |
+  | [@takatost](https://github.com/takatost)                                                | 整體產品方向與架構             |
+
+  我們如何排定優先順序：
+
+  | 功能類型                                                                                                | 優先級   |
+  | ------------------------------------------------------------------------------------------------------- | -------- |
+  | 被團隊成員標記為高優先級的功能                                                                          | 高優先級 |
+  | 來自我們[社群回饋版](https://github.com/langgenius/dify/discussions/categories/feedbacks)的熱門功能請求 | 中優先級 |
+  | 非核心功能和次要增強                                                                                    | 低優先級 |
+  | 有價值但非急迫的功能                                                                                    | 未來功能 |
+
+### 其他事項 (例如錯誤回報、效能優化、錯字更正)
+
+- 可以直接開始編寫程式碼。
+
+  我們如何排定優先順序：
+
+  | 問題類型                                              | 優先級   |
+  | ----------------------------------------------------- | -------- |
+  | 核心功能的錯誤 (無法登入、應用程式無法運行、安全漏洞) | 重要     |
+  | 非關鍵性錯誤、效能提升                                | 中優先級 |
+  | 小修正 (錯字、令人困惑但仍可運作的使用者界面)         | 低優先級 |
+
+## 安裝
+
+以下是設置 Dify 開發環境的步驟：
+
+### 1. 分叉此存儲庫
+
+### 2. 複製代碼庫
+
+從您的終端機複製分叉的代碼庫：
+
+```shell
+git clone git@github.com:<github_username>/dify.git
+```
+
+- [Docker](https://www.docker.com/)
+- [Docker Compose](https://docs.docker.com/compose/install/)
+- [Node.js v18.x (LTS)](http://nodejs.org)
+- [pnpm](https://pnpm.io/)
+- [Python](https://www.python.org/) version 3.11.x or 3.12.x
+
+### 4. 安裝
+
+Dify 由後端和前端組成。透過 `cd api/` 導航至後端目錄，然後按照[後端 README](api/README.md)進行安裝。在另一個終端機視窗中，透過 `cd web/` 導航至前端目錄，然後按照[前端 README](web/README.md)進行安裝。
+
+查閱[安裝常見問題](https://docs.dify.ai/learn-more/faq/install-faq)了解常見問題和故障排除步驟的列表。
+
+### 5. 在瀏覽器中訪問 Dify
+
+要驗證您的設置，請在瀏覽器中訪問 [http://localhost:3000](http://localhost:3000)（預設值，或您自行設定的 URL 和埠號）。現在您應該能看到 Dify 已啟動並運行。
+
+## 開發
+
+如果您要添加模型提供者，請參考[此指南](https://github.com/langgenius/dify/blob/main/api/core/model_runtime/README.md)。
+
+如果您要為 Agent 或工作流程添加工具提供者，請參考[此指南](./api/core/tools/README.md)。
+
+為了幫助您快速找到您的貢獻適合的位置，以下是 Dify 後端和前端的簡要註解大綱：
+
+### 後端
+
+Dify 的後端使用 Python 的 [Flask](https://flask.palletsprojects.com/en/3.0.x/) 框架編寫。它使用 [SQLAlchemy](https://www.sqlalchemy.org/) 作為 ORM 工具，使用 [Celery](https://docs.celeryq.dev/en/stable/getting-started/introduction.html) 進行任務佇列處理。授權邏輯則透過 Flask-login 實現。
+
+```text
+[api/]
+├── constants             // 整個專案中使用的常數與設定值
+├── controllers           // API 路由定義與請求處理邏輯
+├── core                  // 核心應用服務、模型整合與工具實現
+├── docker                // Docker 容器化相關設定檔案
+├── events                // 事件處理與流程管理機制
+├── extensions            // 與第三方框架或平台的整合擴充功能
+├── fields                // 資料序列化與結構定義欄位
+├── libs                  // 可重複使用的共用程式庫與輔助工具
+├── migrations            // 資料庫結構變更與遷移腳本
+├── models                // 資料庫模型與資料結構定義
+├── services              // 核心業務邏輯與功能實現
+├── storage               // 私鑰與敏感資訊儲存機制
+├── tasks                 // 非同步任務與背景作業處理器
+└── tests
+```
+
+### 前端
+
+網站基於 [Next.js](https://nextjs.org/) 的 Typescript 樣板，並使用 [Tailwind CSS](https://tailwindcss.com/) 進行樣式設計。[React-i18next](https://react.i18next.com/) 用於國際化。
+
+```text
+[web/]
+├── app                   // 頁面佈局與介面元件
+│   ├── (commonLayout)    // 應用程式共用佈局結構
+│   ├── (shareLayout)     // Token 會話專用共享佈局
+│   ├── activate          // 帳號啟用頁面
+│   ├── components        // 頁面與佈局共用元件
+│   ├── install           // 系統安裝頁面
+│   ├── signin            // 使用者登入頁面
+│   └── styles            // 全域共用樣式定義
+├── assets                // 靜態資源檔案庫
+├── bin                   // 建構流程執行腳本
+├── config                // 系統可調整設定與選項
+├── context               // 應用程式狀態共享上下文
+├── dictionaries          // 多語系翻譯詞彙庫
+├── docker                // Docker 容器設定檔
+├── hooks                 // 可重複使用的 React Hooks
+├── i18n                  // 國際化與本地化設定
+├── models                // 資料結構與 API 回應模型
+├── public                // 靜態資源與網站圖標
+├── service               // API 操作介面定義
+├── test                  // 測試用例與測試框架
+├── types                 // TypeScript 型別定義
+└── utils                 // 共用輔助功能函式庫
+```
+
+## 提交您的 PR
+
+最後，是時候向我們的存儲庫開啟拉取請求（PR）了。對於主要功能，我們會先將它們合併到 `deploy/dev` 分支進行測試，然後才會進入 `main` 分支。如果您遇到合併衝突或不知道如何開啟拉取請求等問題，請查看 [GitHub 的拉取請求教學](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests)。
+
+就是這樣！一旦您的 PR 被合併，您將作為貢獻者出現在我們的 [README](https://github.com/langgenius/dify/blob/main/README.md) 中。
+
+## 獲取幫助
+
+如果您在貢獻過程中遇到困難或有迫切的問題，只需通過相關的 GitHub issue 向我們提問，或加入我們的 [Discord](https://discord.gg/8Tpq4AcN9c) 進行快速交流。

CONTRIBUTING_VI.md

@@ -72,7 +72,7 @@ Dify yêu cầu các phụ thuộc sau để build, hãy đảm bảo chúng đ
 - [Docker](https://www.docker.com/)
 - [Docker Compose](https://docs.docker.com/compose/install/)
 - [Node.js v18.x (LTS)](http://nodejs.org)
-- [npm](https://www.npmjs.com/) phiên bản 8.x.x hoặc [Yarn](https://yarnpkg.com/)
+- [pnpm](https://pnpm.io/)
 - [Python](https://www.python.org/) phiên bản 3.11.x hoặc 3.12.x
 
 ### 4. Cài đặt

LICENSE

@@ -1,12 +1,12 @@
 # Open Source License
 
-Dify is licensed under the Apache License 2.0, with the following additional conditions:
+Dify is licensed under a modified version of the Apache License 2.0, with the following additional conditions:
 
 1. Dify may be utilized commercially, including as a backend service for other applications or as an application development platform for enterprises. Should the conditions below be met, a commercial license must be obtained from the producer:
 
-a. Multi-tenant service: Unless explicitly authorized by Dify in writing, you may not use the Dify source code to operate a multi-tenant environment. 
+a. Multi-tenant service: Unless explicitly authorized by Dify in writing, you may not use the Dify source code to operate a multi-tenant environment.
     - Tenant Definition: Within the context of Dify, one tenant corresponds to one workspace. The workspace provides a separated area for each tenant's data and configurations.
-    
+
 b. LOGO and copyright information: In the process of using Dify's frontend, you may not remove or modify the LOGO or copyright information in the Dify console or applications. This restriction is inapplicable to uses of Dify that do not involve its frontend.
     - Frontend Definition: For the purposes of this license, the "frontend" of Dify includes all components located in the `web/` directory when running Dify from the raw source code, or the "web" image when running Dify with Docker.
 
@@ -21,19 +21,4 @@ Apart from the specific conditions mentioned above, all other rights and restric
 
 The interactive design of this product is protected by appearance patent.
 
-© 2024 LangGenius, Inc.
-
-
-----------
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
+© 2025 LangGenius, Inc.

README.md

@@ -40,6 +40,7 @@
 
 <p align="center">
   <a href="./README.md"><img alt="README in English" src="https://img.shields.io/badge/English-d9d9d9"></a>
+  <a href="./README_TW.md"><img alt="繁體中文文件" src="https://img.shields.io/badge/繁體中文-d9d9d9"></a>
   <a href="./README_CN.md"><img alt="简体中文版自述文件" src="https://img.shields.io/badge/简体中文-d9d9d9"></a>
   <a href="./README_JA.md"><img alt="日本語のREADME" src="https://img.shields.io/badge/日本語-d9d9d9"></a>
   <a href="./README_ES.md"><img alt="README en Español" src="https://img.shields.io/badge/Español-d9d9d9"></a>
@@ -49,16 +50,18 @@
   <a href="./README_AR.md"><img alt="README بالعربية" src="https://img.shields.io/badge/العربية-d9d9d9"></a>
   <a href="./README_TR.md"><img alt="Türkçe README" src="https://img.shields.io/badge/Türkçe-d9d9d9"></a>
   <a href="./README_VI.md"><img alt="README Tiếng Việt" src="https://img.shields.io/badge/Ti%E1%BA%BFng%20Vi%E1%BB%87t-d9d9d9"></a>
+  <a href="./README_DE.md"><img alt="README in Deutsch" src="https://img.shields.io/badge/German-d9d9d9"></a>
+  <a href="./README_BN.md"><img alt="README in বাংলা" src="https://img.shields.io/badge/বাংলা-d9d9d9"></a>
 </p>
 
-
-Dify is an open-source LLM app development platform. Its intuitive interface combines agentic AI workflow, RAG pipeline, agent capabilities, model management, observability features and more, letting you quickly go from prototype to production. 
+Dify is an open-source LLM app development platform. Its intuitive interface combines agentic AI workflow, RAG pipeline, agent capabilities, model management, observability features and more, letting you quickly go from prototype to production.
 
 ## Quick start
+
 > Before installing Dify, make sure your machine meets the following minimum system requirements:
-> 
->- CPU >= 2 Core
->- RAM >= 4 GiB
+>
+> - CPU >= 2 Core
+> - RAM >= 4 GiB
 
 </br>
 
@@ -74,41 +77,40 @@ docker compose up -d
 After running, you can access the Dify dashboard in your browser at [http://localhost/install](http://localhost/install) and start the initialization process.
 
 #### Seeking help
+
 Please refer to our [FAQ](https://docs.dify.ai/getting-started/install-self-hosted/faqs) if you encounter problems setting up Dify. Reach out to [the community and us](#community--contact) if you are still having issues.
 
 > If you'd like to contribute to Dify or do additional development, refer to our [guide to deploying from source code](https://docs.dify.ai/getting-started/install-self-hosted/local-source-code)
 
 ## Key features
-**1. Workflow**: 
-  Build and test powerful AI workflows on a visual canvas, leveraging all the following features and beyond.
 
+**1. Workflow**:
+Build and test powerful AI workflows on a visual canvas, leveraging all the following features and beyond.
 
-  https://github.com/langgenius/dify/assets/13230914/356df23e-1604-483d-80a6-9517ece318aa
+https://github.com/langgenius/dify/assets/13230914/356df23e-1604-483d-80a6-9517ece318aa
 
-
-
-**2. Comprehensive model support**: 
-  Seamless integration with hundreds of proprietary / open-source LLMs from dozens of inference providers and self-hosted solutions, covering GPT, Mistral, Llama3, and any OpenAI API-compatible models. A full list of supported model providers can be found [here](https://docs.dify.ai/getting-started/readme/model-providers).
+**2. Comprehensive model support**:
+Seamless integration with hundreds of proprietary / open-source LLMs from dozens of inference providers and self-hosted solutions, covering GPT, Mistral, Llama3, and any OpenAI API-compatible models. A full list of supported model providers can be found [here](https://docs.dify.ai/getting-started/readme/model-providers).
 
 ![providers-v5](https://github.com/langgenius/dify/assets/13230914/5a17bdbe-097a-4100-8363-40255b70f6e3)
 
+**3. Prompt IDE**:
+Intuitive interface for crafting prompts, comparing model performance, and adding additional features such as text-to-speech to a chat-based app.
 
-**3. Prompt IDE**: 
-  Intuitive interface for crafting prompts, comparing model performance, and adding additional features such as text-to-speech to a chat-based app. 
+**4. RAG Pipeline**:
+Extensive RAG capabilities that cover everything from document ingestion to retrieval, with out-of-box support for text extraction from PDFs, PPTs, and other common document formats.
 
-**4. RAG Pipeline**: 
-  Extensive RAG capabilities that cover everything from document ingestion to retrieval, with out-of-box support for text extraction from PDFs, PPTs, and other common document formats.
+**5. Agent capabilities**:
+You can define agents based on LLM Function Calling or ReAct, and add pre-built or custom tools for the agent. Dify provides 50+ built-in tools for AI agents, such as Google Search, DALL·E, Stable Diffusion and WolframAlpha.
 
-**5. Agent capabilities**: 
-  You can define agents based on LLM Function Calling or ReAct, and add pre-built or custom tools for the agent. Dify provides 50+ built-in tools for AI agents, such as Google Search, DALL·E, Stable Diffusion and WolframAlpha.
+**6. LLMOps**:
+Monitor and analyze application logs and performance over time. You could continuously improve prompts, datasets, and models based on production data and annotations.
 
-**6. LLMOps**: 
-  Monitor and analyze application logs and performance over time. You could continuously improve prompts, datasets, and models based on production data and annotations.
-
-**7. Backend-as-a-Service**: 
-  All of Dify's offerings come with corresponding APIs, so you could effortlessly integrate Dify into your own business logic.
+**7. Backend-as-a-Service**:
+All of Dify's offerings come with corresponding APIs, so you could effortlessly integrate Dify into your own business logic.
 
 ## Feature Comparison
+
 <table style="width: 100%;">
   <tr>
     <th align="center">Feature</th>
@@ -178,24 +180,22 @@ Please refer to our [FAQ](https://docs.dify.ai/getting-started/install-self-host
 ## Using Dify
 
 - **Cloud </br>**
-We host a [Dify Cloud](https://dify.ai) service for anyone to try with zero setup. It provides all the capabilities of the self-deployed version, and includes 200 free GPT-4 calls in the sandbox plan.
+  We host a [Dify Cloud](https://dify.ai) service for anyone to try with zero setup. It provides all the capabilities of the self-deployed version, and includes 200 free GPT-4 calls in the sandbox plan.
 
 - **Self-hosting Dify Community Edition</br>**
-Quickly get Dify running in your environment with this [starter guide](#quick-start).
-Use our [documentation](https://docs.dify.ai) for further references and more in-depth instructions.
+  Quickly get Dify running in your environment with this [starter guide](#quick-start).
+  Use our [documentation](https://docs.dify.ai) for further references and more in-depth instructions.
 
 - **Dify for enterprise / organizations</br>**
-We provide additional enterprise-centric features. [Log your questions for us through this chatbot](https://udify.app/chat/22L1zSxg6yW1cWQg) or [send us an email](mailto:business@dify.ai?subject=[GitHub]Business%20License%20Inquiry) to discuss enterprise needs. </br>
+  We provide additional enterprise-centric features. [Log your questions for us through this chatbot](https://udify.app/chat/22L1zSxg6yW1cWQg) or [send us an email](mailto:business@dify.ai?subject=[GitHub]Business%20License%20Inquiry) to discuss enterprise needs. </br>
   > For startups and small businesses using AWS, check out [Dify Premium on AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6) and deploy it to your own AWS VPC with one-click. It's an affordable AMI offering with the option to create apps with custom logo and branding.
 
-
 ## Staying ahead
 
 Star Dify on GitHub and be instantly notified of new releases.
 
 ![star-us](https://github.com/langgenius/dify/assets/13230914/b823edc1-6388-4e25-ad45-2f6b187adbb4)
 
-
 ## Advanced Setup
 
 If you need to customize the configuration, please refer to the comments in our [.env.example](docker/.env.example) file and update the corresponding values in your `.env` file. Additionally, you might need to make adjustments to the `docker-compose.yaml` file itself, such as changing image versions, port mappings, or volume mounts, based on your specific deployment environment and requirements. After making any changes, please re-run `docker-compose up -d`. You can find the full list of available environment variables [here](https://docs.dify.ai/getting-started/install-self-hosted/environments).
@@ -211,32 +211,34 @@ If you'd like to configure a highly-available setup, there are community-contrib
 Deploy Dify to Cloud Platform with a single click using [terraform](https://www.terraform.io/)
 
 ##### Azure Global
+
 - [Azure Terraform by @nikawang](https://github.com/nikawang/dify-azure-terraform)
 
 ##### Google Cloud
+
 - [Google Cloud Terraform by @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)
 
 #### Using AWS CDK for Deployment
 
 Deploy Dify to AWS with [CDK](https://aws.amazon.com/cdk/)
 
-##### AWS 
+##### AWS
+
 - [AWS CDK by @KevinZhao](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
 
 ## Contributing
 
-For those who'd like to contribute code, see our [Contribution Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md). 
+For those who'd like to contribute code, see our [Contribution Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
 At the same time, please consider supporting Dify by sharing it on social media and at events and conferences.
 
-
 > We are looking for contributors to help with translating Dify to languages other than Mandarin or English. If you are interested in helping, please see the [i18n README](https://github.com/langgenius/dify/blob/main/web/i18n/README.md) for more information, and leave us a comment in the `global-users` channel of our [Discord Community Server](https://discord.gg/8Tpq4AcN9c).
 
 ## Community & contact
 
-* [Github Discussion](https://github.com/langgenius/dify/discussions). Best for: sharing feedback and asking questions.
-* [GitHub Issues](https://github.com/langgenius/dify/issues). Best for: bugs you encounter using Dify.AI, and feature proposals. See our [Contribution Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
-* [Discord](https://discord.gg/FngNHpbcY7). Best for: sharing your applications and hanging out with the community.
-* [X(Twitter)](https://twitter.com/dify_ai). Best for: sharing your applications and hanging out with the community.
+- [Github Discussion](https://github.com/langgenius/dify/discussions). Best for: sharing feedback and asking questions.
+- [GitHub Issues](https://github.com/langgenius/dify/issues). Best for: bugs you encounter using Dify.AI, and feature proposals. See our [Contribution Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
+- [Discord](https://discord.gg/FngNHpbcY7). Best for: sharing your applications and hanging out with the community.
+- [X(Twitter)](https://twitter.com/dify_ai). Best for: sharing your applications and hanging out with the community.
 
 **Contributors**
 
@@ -248,7 +250,6 @@ At the same time, please consider supporting Dify by sharing it on social media
 
 [![Star History Chart](https://api.star-history.com/svg?repos=langgenius/dify&type=Date)](https://star-history.com/#langgenius/dify&Date)
 
-
 ## Security disclosure
 
 To protect your privacy, please avoid posting security issues on GitHub. Instead, send your questions to security@dify.ai and we will provide you with a more detailed answer.
@@ -256,4 +257,3 @@ To protect your privacy, please avoid posting security issues on GitHub. Instead
 ## License
 
 This repository is available under the [Dify Open Source License](LICENSE), which is essentially Apache 2.0 with a few additional restrictions.
-

README_AR.md

@@ -45,6 +45,7 @@
   <a href="./README_AR.md"><img alt="README بالعربية" src="https://img.shields.io/badge/العربية-d9d9d9"></a>
   <a href="./README_TR.md"><img alt="Türkçe README" src="https://img.shields.io/badge/Türkçe-d9d9d9"></a>
   <a href="./README_VI.md"><img alt="README Tiếng Việt" src="https://img.shields.io/badge/Ti%E1%BA%BFng%20Vi%E1%BB%87t-d9d9d9"></a>
+  <a href="./README_BN.md"><img alt="README in বাংলা" src="https://img.shields.io/badge/বাংলা-d9d9d9"></a>
 </p>
 
 <div style="text-align: right;">
@@ -53,8 +54,7 @@
 
 **1. سير العمل**:  قم ببناء واختبار سير عمل الذكاء الاصطناعي القوي على قماش بصري، مستفيدًا من جميع الميزات التالية وأكثر.
 
-  https://github.com/langgenius/dify/assets/13230914/356df23e-1604-483d-80a6-9517ece318aa
-
+  <https://github.com/langgenius/dify/assets/13230914/356df23e-1604-483d-80a6-9517ece318aa>
 
 **2. الدعم الشامل للنماذج**: تكامل سلس مع مئات من LLMs الخاصة / مفتوحة المصدر من عشرات من موفري التحليل والحلول المستضافة ذاتيًا، مما يغطي GPT و Mistral و Llama3 وأي نماذج متوافقة مع واجهة OpenAI API. يمكن العثور على قائمة كاملة بمزودي النموذج المدعومين [هنا](https://docs.dify.ai/getting-started/readme/model-providers).
 
@@ -69,7 +69,9 @@
 **6. الـ LLMOps**: راقب وتحلل سجلات التطبيق والأداء على مر الزمن. يمكنك تحسين الأوامر والبيانات والنماذج باستمرار استنادًا إلى البيانات الإنتاجية والتعليقات.
 
 **7.الواجهة الخلفية (Backend) كخدمة**: تأتي جميع عروض Dify مع APIs مطابقة، حتى يمكنك دمج Dify بسهولة في منطق أعمالك الخاص.
+
 ## مقارنة الميزات
+
 <table style="width: 100%;">
   <tr>
     <th align="center">الميزة</th>
@@ -136,8 +138,8 @@
   </tr>
 </table>
 
-
 ## استخدام Dify
+
 - **سحابة </br>**
 نحن نستضيف [خدمة Dify Cloud](https://dify.ai) لأي شخص لتجربتها بدون أي إعدادات. توفر كل قدرات النسخة التي تمت استضافتها ذاتيًا، وتتضمن 200 أمر GPT-4 مجانًا في خطة الصندوق الرملي.
 
@@ -147,15 +149,19 @@
 
 - **مشروع Dify للشركات / المؤسسات</br>**
 نحن نوفر ميزات إضافية مركزة على الشركات. [جدول اجتماع معنا](https://cal.com/guchenhe/30min) أو [أرسل لنا بريدًا إلكترونيًا](mailto:business@dify.ai?subject=[GitHub]Business%20License%20Inquiry) لمناقشة احتياجات الشركات. </br>
+
 > بالنسبة للشركات الناشئة والشركات الصغيرة التي تستخدم خدمات AWS، تحقق من [Dify Premium على AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6) ونشرها في شبكتك الخاصة على AWS VPC بنقرة واحدة. إنها عرض AMI بأسعار معقولة مع خيار إنشاء تطبيقات بشعار وعلامة تجارية مخصصة.
+>
 ## البقاء قدمًا
 
 قم بإضافة نجمة إلى Dify على GitHub وتلق تنبيهًا فوريًا بالإصدارات الجديدة.
 
 ![نجمنا](https://github.com/langgenius/dify/assets/13230914/b823edc1-6388-4e25-ad45-2f6b187adbb4)
+
 ## البداية السريعة
+>
 > قبل تثبيت Dify، تأكد من أن جهازك يلبي الحد الأدنى من متطلبات النظام التالية:
-> 
+>
 >- معالج >= 2 نواة
 >- ذاكرة وصول عشوائي (RAM) >= 4 جيجابايت
 
@@ -188,24 +194,26 @@ docker compose up -d
 انشر Dify إلى منصة السحابة بنقرة واحدة باستخدام [terraform](https://www.terraform.io/)
 
 ##### Azure Global
+
 - [Azure Terraform بواسطة @nikawang](https://github.com/nikawang/dify-azure-terraform)
 
 ##### Google Cloud
+
 - [Google Cloud Terraform بواسطة @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)
 
 #### استخدام AWS CDK للنشر
 
 انشر Dify على AWS باستخدام [CDK](https://aws.amazon.com/cdk/)
 
-##### AWS 
+##### AWS
+
 - [AWS CDK بواسطة @KevinZhao](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
 
 ## المساهمة
 
-لأولئك الذين يرغبون في المساهمة، انظر إلى [دليل المساهمة](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md) لدينا. 
+لأولئك الذين يرغبون في المساهمة، انظر إلى [دليل المساهمة](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md) لدينا.
 في الوقت نفسه، يرجى النظر في دعم Dify عن طريق مشاركته على وسائل التواصل الاجتماعي وفي الفعاليات والمؤتمرات.
 
-
 > نحن نبحث عن مساهمين لمساعدة في ترجمة Dify إلى لغات أخرى غير اللغة الصينية المندرين أو الإنجليزية. إذا كنت مهتمًا بالمساعدة، يرجى الاطلاع على [README للترجمة](https://github.com/langgenius/dify/blob/main/web/i18n/README.md) لمزيد من المعلومات، واترك لنا تعليقًا في قناة `global-users` على [خادم المجتمع على Discord](https://discord.gg/8Tpq4AcN9c).
 
 **المساهمون**
@@ -215,26 +223,26 @@ docker compose up -d
 </a>
 
 ## المجتمع والاتصال
-* [مناقشة Github](https://github.com/langgenius/dify/discussions). الأفضل لـ: مشاركة التعليقات وطرح الأسئلة.
-* [المشكلات على GitHub](https://github.com/langgenius/dify/issues). الأفضل لـ: الأخطاء التي تواجهها في استخدام Dify.AI، واقتراحات الميزات. انظر [دليل المساهمة](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
-* [Discord](https://discord.gg/FngNHpbcY7). الأفضل لـ: مشاركة تطبيقاتك والترفيه مع المجتمع.
-* [تويتر](https://twitter.com/dify_ai). الأفضل لـ: مشاركة تطبيقاتك والترفيه مع المجتمع.
+- [مناقشة Github](https://github.com/langgenius/dify/discussions). الأفضل لـ: مشاركة التعليقات وطرح الأسئلة.
+- [المشكلات على GitHub](https://github.com/langgenius/dify/issues). الأفضل لـ: الأخطاء التي تواجهها في استخدام Dify.AI، واقتراحات الميزات. انظر [دليل المساهمة](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
+- [Discord](https://discord.gg/FngNHpbcY7). الأفضل لـ: مشاركة تطبيقاتك والترفيه مع المجتمع.
+- [تويتر](https://twitter.com/dify_ai). الأفضل لـ: مشاركة تطبيقاتك والترفيه مع المجتمع.
 
 ## تاريخ النجمة
 
 [![Star History Chart](https://api.star-history.com/svg?repos=langgenius/dify&type=Date)](https://star-history.com/#langgenius/dify&Date)
 
-
 ## الكشف عن الأمان
 
-لحماية خصوصيتك، يرجى تجنب نشر مشكلات الأمان على GitHub. بدلاً من ذلك، أرسل أسئلتك إلى security@dify.ai وسنقدم لك إجابة أكثر تفصيلاً.
+لحماية خصوصيتك، يرجى تجنب نشر مشكلات الأمان على GitHub. بدلاً من ذلك، أرسل أسئلتك إلى <security@dify.ai> وسنقدم لك إجابة أكثر تفصيلاً.
 
 ## الرخصة
 
 هذا المستودع متاح تحت [رخصة البرنامج الحر Dify](LICENSE)، والتي تعتبر بشكل أساسي Apache 2.0 مع بعض القيود الإضافية.
+
 ## الكشف عن الأمان
 
-لحماية خصوصيتك، يرجى تجنب نشر مشكلات الأمان على GitHub. بدلاً من ذلك، أرسل أسئلتك إلى security@dify.ai وسنقدم لك إجابة أكثر تفصيلاً.
+لحماية خصوصيتك، يرجى تجنب نشر مشكلات الأمان على GitHub. بدلاً من ذلك، أرسل أسئلتك إلى <security@dify.ai> وسنقدم لك إجابة أكثر تفصيلاً.
 
 ## الرخصة
 

README_BN.md

@@ -0,0 +1,258 @@
+![cover-v5-optimized](https://github.com/langgenius/dify/assets/13230914/f9e19af5-61ba-4119-b926-d10c4c06ebab)
+
+<p align="center">
+  📌 <a href="https://dify.ai/blog/introducing-dify-workflow-file-upload-a-demo-on-ai-podcast">ডিফাই ওয়ার্কফ্লো ফাইল আপলোড পরিচিতি: গুগল নোটবুক-এলএম পডকাস্ট পুনর্নির্মাণ</a>
+</p>
+
+<p align="center">
+  <a href="https://cloud.dify.ai">ডিফাই ক্লাউড</a> ·
+  <a href="https://docs.dify.ai/getting-started/install-self-hosted">সেল্ফ-হোস্টিং</a> ·
+  <a href="https://docs.dify.ai">ডকুমেন্টেশন</a> ·
+  <a href="https://udify.app/chat/22L1zSxg6yW1cWQg">ব্যাবসায়িক অনুসন্ধান</a>
+</p>
+
+<p align="center">
+    <a href="https://dify.ai" target="_blank">
+        <img alt="Static Badge" src="https://img.shields.io/badge/Product-F04438"></a>
+    <a href="https://dify.ai/pricing" target="_blank">
+        <img alt="Static Badge" src="https://img.shields.io/badge/free-pricing?logo=free&color=%20%23155EEF&label=pricing&labelColor=%20%23528bff"></a>
+    <a href="https://discord.gg/FngNHpbcY7" target="_blank">
+        <img src="https://img.shields.io/discord/1082486657678311454?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb"
+            alt="chat on Discord"></a>
+    <a href="https://reddit.com/r/difyai" target="_blank">  
+        <img src="https://img.shields.io/reddit/subreddit-subscribers/difyai?style=plastic&logo=reddit&label=r%2Fdifyai&labelColor=white" 
+            alt="join Reddit"></a>
+    <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
+        <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
+            alt="follow on X(Twitter)"></a>
+    <a href="https://www.linkedin.com/company/langgenius/" target="_blank">
+        <img src="https://custom-icon-badges.demolab.com/badge/LinkedIn-0A66C2?logo=linkedin-white&logoColor=fff"
+            alt="follow on LinkedIn"></a>
+    <a href="https://hub.docker.com/u/langgenius" target="_blank">
+        <img alt="Docker Pulls" src="https://img.shields.io/docker/pulls/langgenius/dify-web?labelColor=%20%23FDB062&color=%20%23f79009"></a>
+    <a href="https://github.com/langgenius/dify/graphs/commit-activity" target="_blank">
+        <img alt="Commits last month" src="https://img.shields.io/github/commit-activity/m/langgenius/dify?labelColor=%20%2332b583&color=%20%2312b76a"></a>
+    <a href="https://github.com/langgenius/dify/" target="_blank">
+        <img alt="Issues closed" src="https://img.shields.io/github/issues-search?query=repo%3Alanggenius%2Fdify%20is%3Aclosed&label=issues%20closed&labelColor=%20%237d89b0&color=%20%235d6b98"></a>
+    <a href="https://github.com/langgenius/dify/discussions/" target="_blank">
+        <img alt="Discussion posts" src="https://img.shields.io/github/discussions/langgenius/dify?labelColor=%20%239b8afb&color=%20%237a5af8"></a>
+</p>
+
+<p align="center">
+  <a href="./README.md"><img alt="README in English" src="https://img.shields.io/badge/English-d9d9d9"></a>
+  <a href="./README_CN.md"><img alt="简体中文版自述文件" src="https://img.shields.io/badge/简体中文-d9d9d9"></a>
+  <a href="./README_JA.md"><img alt="日本語のREADME" src="https://img.shields.io/badge/日本語-d9d9d9"></a>
+  <a href="./README_ES.md"><img alt="README en Español" src="https://img.shields.io/badge/Español-d9d9d9"></a>
+  <a href="./README_FR.md"><img alt="README en Français" src="https://img.shields.io/badge/Français-d9d9d9"></a>
+  <a href="./README_KL.md"><img alt="README tlhIngan Hol" src="https://img.shields.io/badge/Klingon-d9d9d9"></a>
+  <a href="./README_KR.md"><img alt="README in Korean" src="https://img.shields.io/badge/한국어-d9d9d9"></a>
+  <a href="./README_AR.md"><img alt="README بالعربية" src="https://img.shields.io/badge/العربية-d9d9d9"></a>
+  <a href="./README_TR.md"><img alt="Türkçe README" src="https://img.shields.io/badge/Türkçe-d9d9d9"></a>
+  <a href="./README_VI.md"><img alt="README Tiếng Việt" src="https://img.shields.io/badge/Ti%E1%BA%BFng%20Vi%E1%BB%87t-d9d9d9"></a>
+  <a href="./README_DE.md"><img alt="README in Deutsch" src="https://img.shields.io/badge/German-d9d9d9"></a>
+  <a href="./README_BN.md"><img alt="README in বাংলা" src="https://img.shields.io/badge/বাংলা-d9d9d9"></a>
+</p>
+
+ডিফাই একটি ওপেন-সোর্স LLM অ্যাপ ডেভেলপমেন্ট প্ল্যাটফর্ম। এটি ইন্টুইটিভ ইন্টারফেস, এজেন্টিক AI ওয়ার্কফ্লো, RAG পাইপলাইন, এজেন্ট ক্যাপাবিলিটি, মডেল ম্যানেজমেন্ট, মনিটরিং সুবিধা এবং আরও অনেক কিছু একত্রিত করে, যা দ্রুত প্রোটোটাইপ থেকে প্রোডাকশন পর্যন্ত নিয়ে যেতে সহায়তা করে।
+
+## কুইক স্টার্ট
+>
+>  ডিফাই ইনস্টল করার আগে, নিশ্চিত করুন যে আপনার মেশিন নিম্নলিখিত ন্যূনতম কনফিগারেশনের প্রয়োজনীয়তা পূরন করে :
+>
+>- সিপিউ >= 2 কোর
+>- র‍্যাম >= 4 জিবি
+
+</br>
+
+ডিফাই সার্ভার চালু করার সবচেয়ে সহজ উপায় [docker compose](docker/docker-compose.yaml) মাধ্যমে। নিম্নলিখিত কমান্ডগুলো ব্যবহার করে ডিফাই চালানোর আগে, নিশ্চিত করুন যে আপনার মেশিনে [Docker](https://docs.docker.com/get-docker/) এবং [Docker Compose](https://docs.docker.com/compose/install/) ইনস্টল করা আছে :
+```bash
+cd dify
+cd docker
+cp .env.example .env
+docker compose up -d
+```
+চালানোর পর, আপনি আপনার ব্রাউজারে [http://localhost/install](http://localhost/install)-এ ডিফাই ড্যাশবোর্ডে অ্যাক্সেস করতে পারেন এবং ইনিশিয়ালাইজেশন প্রক্রিয়া শুরু করতে পারেন।
+
+#### সাহায্যের খোঁজে
+
+ডিফাই সেট আপ করতে সমস্যা হলে দয়া করে আমাদের [FAQ](https://docs.dify.ai/getting-started/install-self-hosted/faqs) দেখুন। যদি তবুও সমস্যা থেকে থাকে, তাহলে [কমিউনিটি এবং আমাদের](#community--contact)  সাথে যোগাযোগ করুন।
+
+> যদি আপনি ডিফাইতে অবদান রাখতে বা অতিরিক্ত উন্নয়ন করতে চান, আমাদের [সোর্স কোড থেকে ডিপ্লয়মেন্টের গাইড](https://docs.dify.ai/getting-started/install-self-hosted/local-source-code) দেখুন।
+
+## প্রধান ফিচারসমূহ
+
+**১. ওয়ার্কফ্লো**:
+    ভিজ্যুয়াল ক্যানভাসে  AI ওয়ার্কফ্লো তৈরি এবং পরীক্ষা করুন, নিম্নলিখিত সব ফিচার এবং  তার বাইরেও আরও অনেক কিছু ব্যবহার করে।
+
+  <https://github.com/langgenius/dify/assets/13230914/356df23e-1604-483d-80a6-9517ece318aa>
+
+**২.  মডেল সাপোর্ট**:
+  GPT, Mistral, Llama3, এবং যেকোনো OpenAI API-সামঞ্জস্যপূর্ণ মডেলসহ, কয়েক ডজন ইনফারেন্স প্রদানকারী এবং সেল্ফ-হোস্টেড সমাধান থেকে শুরু করে প্রোপ্রাইটরি/ওপেন-সোর্স LLM-এর সাথে সহজে ইন্টিগ্রেশন। সমর্থিত মডেল প্রদানকারীদের একটি সম্পূর্ণ তালিকা পাওয়া যাবে [এখানে](https://docs.dify.ai/getting-started/readme/model-providers)।
+
+![providers-v5](https://github.com/langgenius/dify/assets/13230914/5a17bdbe-097a-4100-8363-40255b70f6e3)
+
+**3.  প্রম্পট IDE**:
+      প্রম্পট তৈরি, মডেলের পারফরম্যান্স তুলনা এবং চ্যাট-বেজড অ্যাপে টেক্সট-টু-স্পিচের মতো  বৈশিষ্ট্য যুক্ত করার জন্য ইন্টুইটিভ ইন্টারফেস। 
+
+**4. RAG পাইপলাইন**:
+      ডকুমেন্ট ইনজেশন থেকে শুরু করে রিট্রিভ পর্যন্ত সবকিছুই বিস্তৃত RAG ক্যাপাবিলিটির আওতাভুক্ত।  PDF, PPT এবং অন্যান্য সাধারণ ডকুমেন্ট ফর্ম্যাট থেকে টেক্সট এক্সট্রাকশনের জন্য আউট-অফ-বক্স সাপোর্ট। 
+
+**5.  এজেন্ট ক্যাপাবিলিটি**:
+  LLM ফাংশন কলিং বা ReAct উপর ভিত্তি করে এজেন্ট ডিফাইন করতে পারেন এবং এজেন্টের জন্য পূর্ব-নির্মিত বা কাস্টম টুলস যুক্ত করতে পারেন। Dify AI এজেন্টদের জন্য 50+ বিল্ট-ইন টুলস সরবরাহ করে, যেমন Google Search, DALL·E, Stable Diffusion এবং WolframAlpha।
+
+**6.  এলএলএম-অপ্স**:
+      সময়ের সাথে সাথে অ্যাপ্লিকেশন লগ এবং পারফরম্যান্স মনিটর এবং বিশ্লেষণ করুন।  প্রডাকশন ডেটা এবং annotation এর উপর ভিত্তি করে প্রম্পট, ডেটাসেট এবং মডেলগুলিকে ক্রমাগত উন্নত করতে পারেন।
+
+**7. ব্যাকএন্ড-অ্যাজ-এ-সার্ভিস**:
+    ডিফাই-এর সমস্ত অফার সংশ্লিষ্ট API-সহ আছে, যাতে আপনি অনায়াসে ডিফাইকে আপনার নিজস্ব বিজনেস লজিকে ইন্টেগ্রেট করতে পারেন।
+
+## বৈশিষ্ট্য তুলনা
+
+<table style="width: 100%;">
+  <tr>
+    <th align="center">বৈশিষ্ট্য</th>
+    <th align="center">Dify.AI</th>
+    <th align="center">LangChain</th>
+    <th align="center">Flowise</th>
+    <th align="center">OpenAI Assistants API</th>
+  </tr>
+  <tr>
+    <td align="center">প্রোগ্রামিং পদ্ধতি</td>
+    <td align="center">API + App-oriented</td>
+    <td align="center">Python Code</td>
+    <td align="center">App-oriented</td>
+    <td align="center">API-oriented</td>
+  </tr>
+  <tr>
+    <td align="center">সাপোর্টেড LLMs</td>
+    <td align="center">Rich Variety</td>
+    <td align="center">Rich Variety</td>
+    <td align="center">Rich Variety</td>
+    <td align="center">OpenAI-only</td>
+  </tr>
+  <tr>
+    <td align="center">RAG ইঞ্জিন</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+  </tr>
+  <tr>
+    <td align="center">এজেন্ট</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+    <td align="center">✅</td>
+  </tr>
+  <tr>
+    <td align="center">ওয়ার্কফ্লো</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+  </tr>
+  <tr>
+    <td align="center">অবজার্ভেবল</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+    <td align="center">❌</td>
+  </tr>
+  <tr>
+    <td align="center">এন্টারপ্রাইজ ফিচার (SSO/Access control)</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+    <td align="center">❌</td>
+    <td align="center">❌</td>
+  </tr>
+  <tr>
+    <td align="center">লোকাল ডেপ্লয়মেন্ট</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+  </tr>
+</table>
+
+## ডিফাই-এর ব্যবহার 
+
+- **ক্লাউড </br>**
+জিরো সেটাপে ব্যবহার করতে আমাদের [Dify Cloud](https://dify.ai) সার্ভিসটি ব্যবহার করতে পারেন। এখানে সেল্ফহোস্টিং-এর সকল ফিচার ও ক্যাপাবিলিটিসহ স্যান্ডবক্সে ২০০ জিপিটি-৪ কল ফ্রি পাবেন।
+
+- **সেল্ফহোস্টিং ডিফাই কমিউনিটি সংস্করণ</br>**
+সেল্ফহোস্ট করতে এই [স্টার্টার গাইড](#quick-start) ব্যবহার করে দ্রুত আপনার এনভায়রনমেন্টে ডিফাই চালান।
+আরো ইন-ডেপথ রেফারেন্সের জন্য [ডকুমেন্টেশন](https://docs.dify.ai) দেখেন।
+
+- **এন্টারপ্রাইজ / প্রতিষ্ঠানের জন্য Dify</br>**
+আমরা এন্টারপ্রাইজ/প্রতিষ্ঠান-কেন্দ্রিক সেবা প্রদান করে থাকি  । [এই চ্যাটবটের মাধ্যমে আপনার প্রশ্নগুলি আমাদের জন্য লগ করুন।](https://udify.app/chat/22L1zSxg6yW1cWQg) অথবা [আমাদের ইমেল পাঠান](mailto:business@dify.ai?subject=[GitHub]Business%20License%20Inquiry) আপনার চাহিদা সম্পর্কে আলোচনা করার জন্য। </br>
+
+  > AWS ব্যবহারকারী স্টার্টআপ এবং ছোট ব্যবসার জন্য, [AWS মার্কেটপ্লেসে Dify Premium](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6) দেখুন এবং এক-ক্লিকের মাধ্যমে এটি আপনার নিজস্ব AWS VPC-তে ডিপ্লয় করুন। এটি একটি সাশ্রয়ী মূল্যের AMI অফার, যাতে কাস্টম লোগো এবং ব্র্যান্ডিং সহ অ্যাপ তৈরির সুবিধা আছে।
+
+## এগিয়ে থাকুন
+
+GitHub-এ ডিফাইকে স্টার দিয়ে রাখুন এবং নতুন রিলিজের খবর তাৎক্ষণিকভাবে পান।
+
+![star-us](https://github.com/langgenius/dify/assets/13230914/b823edc1-6388-4e25-ad45-2f6b187adbb4)
+
+## Advanced Setup
+
+যদি আপনার কনফিগারেশনটি কাস্টমাইজ করার প্রয়োজন হয়, তাহলে অনুগ্রহ করে আমাদের [.env.example](docker/.env.example) ফাইল দেখুন এবং আপনার `.env` ফাইলে সংশ্লিষ্ট মানগুলি আপডেট করুন। এছাড়াও, আপনার নির্দিষ্ট এনভায়রনমেন্ট এবং প্রয়োজনীয়তার উপর ভিত্তি করে আপনাকে `docker-compose.yaml` ফাইলে সমন্বয় করতে হতে পারে, যেমন ইমেজ ভার্সন পরিবর্তন করা, পোর্ট ম্যাপিং করা, অথবা ভলিউম মাউন্ট করা। 
+যেকোনো পরিবর্তন করার পর, অনুগ্রহ করে `docker-compose up -d` পুনরায় চালান। ভেরিয়েবলের সম্পূর্ণ তালিকা [এখানে] (https://docs.dify.ai/getting-started/install-self-hosted/environments) খুঁজে পেতে পারেন।
+
+যদি আপনি একটি হাইলি এভেইলেবল সেটআপ কনফিগার করতে চান, তাহলে কমিউনিটি  [Helm Charts](https://helm.sh/) এবং YAML ফাইল রয়েছে যা Dify কে Kubernetes-এ ডিপ্লয় করার প্রক্রিয়া বর্ণনা করে।
+
+- [Helm Chart by @LeoQuote](https://github.com/douban/charts/tree/master/charts/dify)
+- [Helm Chart by @BorisPolonsky](https://github.com/BorisPolonsky/dify-helm)
+- [YAML file by @Winson-030](https://github.com/Winson-030/dify-kubernetes)
+
+#### টেরাফর্ম ব্যবহার করে ডিপ্লয়
+
+[terraform](https://www.terraform.io/) ব্যবহার করে এক ক্লিকেই ক্লাউড প্ল্যাটফর্মে Dify ডিপ্লয় করুন।
+
+##### অ্যাজুর গ্লোবাল
+
+- [Azure Terraform by @nikawang](https://github.com/nikawang/dify-azure-terraform)
+
+##### গুগল ক্লাউড
+
+- [Google Cloud Terraform by @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)
+
+#### AWS CDK ব্যবহার করে ডিপ্লয়
+
+[CDK](https://aws.amazon.com/cdk/) দিয়ে AWS-এ Dify ডিপ্লয় করুন
+
+##### AWS
+
+- [AWS CDK by @KevinZhao](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
+
+## Contributing
+
+যারা কোড অবদান রাখতে চান, তাদের জন্য আমাদের [অবদান নির্দেশিকা] দেখুন (https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md)।
+একই সাথে, সোশ্যাল মিডিয়া এবং ইভেন্ট এবং কনফারেন্সে এটি শেয়ার করে Dify কে সমর্থন করুন।
+
+> আমরা ম্যান্ডারিন বা ইংরেজি ছাড়া অন্য ভাষায় Dify অনুবাদ করতে সাহায্য করার জন্য অবদানকারীদের খুঁজছি। আপনি যদি সাহায্য করতে আগ্রহী হন, তাহলে আরও তথ্যের জন্য [i18n README](https://github.com/langgenius/dify/blob/main/web/i18n/README.md) দেখুন এবং আমাদের [ডিসকর্ড কমিউনিটি সার্ভার](https://discord.gg/8Tpq4AcN9c) এর `গ্লোবাল-ইউজারস` চ্যানেলে আমাদের একটি মন্তব্য করুন।
+
+## কমিউনিটি এবং যোগাযোগ
+
+- [Github Discussion](https://github.com/langgenius/dify/discussions) ফিডব্যাক এবং প্রতিক্রিয়া জানানোর মাধ্যম।
+- [GitHub Issues](https://github.com/langgenius/dify/issues). Dify.AI ব্যবহার করে আপনি যেসব বাগের সম্মুখীন হন এবং ফিচার প্রস্তাবনা।  আমাদের [অবদান নির্দেশিকা](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md) দেখুন।
+- [Discord](https://discord.gg/FngNHpbcY7) আপনার এপ্লিকেশন শেয়ার এবং কমিউনিটি আড্ডার মাধ্যম। 
+- [X(Twitter)](https://twitter.com/dify_ai) আপনার এপ্লিকেশন শেয়ার এবং কমিউনিটি আড্ডার মাধ্যম। 
+
+**অবদানকারীদের তালিকা**
+
+<a href="https://github.com/langgenius/dify/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=langgenius/dify" />
+</a>
+
+## স্টার হিস্ট্রি
+
+[![Star History Chart](https://api.star-history.com/svg?repos=langgenius/dify&type=Date)](https://star-history.com/#langgenius/dify&Date)
+
+## নিরাপত্তা  বিষয়ক
+
+আপনার গোপনীয়তা রক্ষা করতে, অনুগ্রহ করে GitHub-এ নিরাপত্তা সংক্রান্ত সমস্যা পোস্ট করা এড়িয়ে চলুন। পরিবর্তে, আপনার প্রশ্নগুলি <security@dify.ai> ঠিকানায় পাঠান এবং আমরা আপনাকে আরও বিস্তারিত উত্তর প্রদান করব।
+
+## লাইসেন্স
+
+এই রিপোজিটরিটি [ডিফাই ওপেন সোর্স লাইসেন্স](LICENSE) এর অধিনে , যা মূলত অ্যাপাচি ২.০, তবে কিছু অতিরিক্ত বিধিনিষেধ রয়েছে।

README_CN.md

@@ -45,6 +45,7 @@
   <a href="./README_AR.md"><img alt="README بالعربية" src="https://img.shields.io/badge/العربية-d9d9d9"></a>
   <a href="./README_TR.md"><img alt="Türkçe README" src="https://img.shields.io/badge/Türkçe-d9d9d9"></a>
   <a href="./README_VI.md"><img alt="README Tiếng Việt" src="https://img.shields.io/badge/Ti%E1%BA%BFng%20Vi%E1%BB%87t-d9d9d9"></a>
+  <a href="./README_BN.md"><img alt="README in বাংলা" src="https://img.shields.io/badge/বাংলা-d9d9d9"></a>
 </div>
 
 

README_DE.md

@@ -0,0 +1,259 @@
+![cover-v5-optimized](https://github.com/langgenius/dify/assets/13230914/f9e19af5-61ba-4119-b926-d10c4c06ebab)
+
+<p align="center">
+  📌 <a href="https://dify.ai/blog/introducing-dify-workflow-file-upload-a-demo-on-ai-podcast">Einführung in Dify Workflow File Upload: Google NotebookLM Podcast nachbilden</a>
+</p>
+
+<p align="center">
+  <a href="https://cloud.dify.ai">Dify Cloud</a> ·
+  <a href="https://docs.dify.ai/getting-started/install-self-hosted">Selbstgehostetes</a> ·
+  <a href="https://docs.dify.ai">Dokumentation</a> ·
+  <a href="https://udify.app/chat/22L1zSxg6yW1cWQg">Anfrage an Unternehmen</a>
+</p>
+
+<p align="center">
+    <a href="https://dify.ai" target="_blank">
+        <img alt="Static Badge" src="https://img.shields.io/badge/Product-F04438"></a>
+    <a href="https://dify.ai/pricing" target="_blank">
+        <img alt="Static Badge" src="https://img.shields.io/badge/free-pricing?logo=free&color=%20%23155EEF&label=pricing&labelColor=%20%23528bff"></a>
+    <a href="https://discord.gg/FngNHpbcY7" target="_blank">
+        <img src="https://img.shields.io/discord/1082486657678311454?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb"
+            alt="chat on Discord"></a>
+    <a href="https://reddit.com/r/difyai" target="_blank">  
+        <img src="https://img.shields.io/reddit/subreddit-subscribers/difyai?style=plastic&logo=reddit&label=r%2Fdifyai&labelColor=white"
+            alt="join Reddit"></a>
+    <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
+        <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
+            alt="follow on X(Twitter)"></a>
+    <a href="https://www.linkedin.com/company/langgenius/" target="_blank">
+        <img src="https://custom-icon-badges.demolab.com/badge/LinkedIn-0A66C2?logo=linkedin-white&logoColor=fff"
+            alt="follow on LinkedIn"></a>
+    <a href="https://hub.docker.com/u/langgenius" target="_blank">
+        <img alt="Docker Pulls" src="https://img.shields.io/docker/pulls/langgenius/dify-web?labelColor=%20%23FDB062&color=%20%23f79009"></a>
+    <a href="https://github.com/langgenius/dify/graphs/commit-activity" target="_blank">
+        <img alt="Commits last month" src="https://img.shields.io/github/commit-activity/m/langgenius/dify?labelColor=%20%2332b583&color=%20%2312b76a"></a>
+    <a href="https://github.com/langgenius/dify/" target="_blank">
+        <img alt="Issues closed" src="https://img.shields.io/github/issues-search?query=repo%3Alanggenius%2Fdify%20is%3Aclosed&label=issues%20closed&labelColor=%20%237d89b0&color=%20%235d6b98"></a>
+    <a href="https://github.com/langgenius/dify/discussions/" target="_blank">
+        <img alt="Discussion posts" src="https://img.shields.io/github/discussions/langgenius/dify?labelColor=%20%239b8afb&color=%20%237a5af8"></a>
+</p>
+
+<p align="center">
+  <a href="./README.md"><img alt="README in English" src="https://img.shields.io/badge/English-d9d9d9"></a>
+  <a href="./README_CN.md"><img alt="简体中文版自述文件" src="https://img.shields.io/badge/简体中文-d9d9d9"></a>
+  <a href="./README_JA.md"><img alt="日本語のREADME" src="https://img.shields.io/badge/日本語-d9d9d9"></a>
+  <a href="./README_ES.md"><img alt="README en Español" src="https://img.shields.io/badge/Español-d9d9d9"></a>
+  <a href="./README_FR.md"><img alt="README en Français" src="https://img.shields.io/badge/Français-d9d9d9"></a>
+  <a href="./README_KL.md"><img alt="README tlhIngan Hol" src="https://img.shields.io/badge/Klingon-d9d9d9"></a>
+  <a href="./README_KR.md"><img alt="README in Korean" src="https://img.shields.io/badge/한국어-d9d9d9"></a>
+  <a href="./README_AR.md"><img alt="README بالعربية" src="https://img.shields.io/badge/العربية-d9d9d9"></a>
+  <a href="./README_TR.md"><img alt="Türkçe README" src="https://img.shields.io/badge/Türkçe-d9d9d9"></a>
+  <a href="./README_VI.md"><img alt="README Tiếng Việt" src="https://img.shields.io/badge/Ti%E1%BA%BFng%20Vi%E1%BB%87t-d9d9d9"></a>
+  <a href="./README_DE.md"><img alt="README in Deutsch" src="https://img.shields.io/badge/German-d9d9d9"></a>
+  <a href="./README_BN.md"><img alt="README in বাংলা" src="https://img.shields.io/badge/বাংলা-d9d9d9"></a>
+</p>
+
+Dify ist eine Open-Source-Plattform zur Entwicklung von LLM-Anwendungen. Ihre intuitive Benutzeroberfläche vereint agentenbasierte KI-Workflows, RAG-Pipelines, Agentenfunktionen, Modellverwaltung, Überwachungsfunktionen und mehr, sodass Sie schnell von einem Prototyp in die Produktion übergehen können.
+
+## Schnellstart
+> Bevor Sie Dify installieren, stellen Sie sicher, dass Ihr System die folgenden Mindestanforderungen erfüllt:
+> 
+>- CPU >= 2 Core
+>- RAM >= 4 GiB
+
+</br>
+
+Der einfachste Weg, den Dify-Server zu starten, ist über [docker compose](docker/docker-compose.yaml). Stellen Sie vor dem Ausführen von Dify mit den folgenden Befehlen sicher, dass [Docker](https://docs.docker.com/get-docker/) und [Docker Compose](https://docs.docker.com/compose/install/) auf Ihrem System installiert sind:
+
+```bash
+cd dify
+cd docker
+cp .env.example .env
+docker compose up -d
+```
+
+Nachdem Sie den Server gestartet haben, können Sie über Ihren Browser auf das Dify Dashboard unter [http://localhost/install](http://localhost/install) zugreifen und den Initialisierungsprozess starten.
+
+#### Hilfe suchen
+Bitte beachten Sie unsere [FAQ](https://docs.dify.ai/getting-started/install-self-hosted/faqs), wenn Sie Probleme bei der Einrichtung von Dify haben. Wenden Sie sich an [die Community und uns](#community--contact), falls weiterhin Schwierigkeiten auftreten.
+
+> Wenn Sie zu Dify beitragen oder zusätzliche Entwicklungen durchführen möchten, lesen Sie bitte unseren [Leitfaden zur Bereitstellung aus dem Quellcode](https://docs.dify.ai/getting-started/install-self-hosted/local-source-code).
+
+## Wesentliche Merkmale
+**1. Workflow**: 
+  Erstellen und testen Sie leistungsstarke KI-Workflows auf einer visuellen Oberfläche, wobei Sie alle der folgenden Funktionen und darüber hinaus nutzen können.
+
+
+  https://github.com/langgenius/dify/assets/13230914/356df23e-1604-483d-80a6-9517ece318aa
+
+
+
+**2. Umfassende Modellunterstützung**: 
+  Nahtlose Integration mit Hunderten von proprietären und Open-Source-LLMs von Dutzenden Inferenzanbietern und selbstgehosteten Lösungen, die GPT, Mistral, Llama3 und alle mit der OpenAI API kompatiblen Modelle abdecken. Eine vollständige Liste der unterstützten Modellanbieter finden Sie [hier](https://docs.dify.ai/getting-started/readme/model-providers).
+
+
+![providers-v5](https://github.com/langgenius/dify/assets/13230914/5a17bdbe-097a-4100-8363-40255b70f6e3)
+
+
+**3. Prompt IDE**: 
+  Intuitive Benutzeroberfläche zum Erstellen von Prompts, zum Vergleichen der Modellleistung und zum Hinzufügen zusätzlicher Funktionen wie Text-to-Speech in einer chatbasierten Anwendung.
+
+**4. RAG Pipeline**: 
+  Umfassende RAG-Funktionalitäten, die alles von der Dokumenteneinlesung bis zur -abfrage abdecken, mit sofort einsatzbereiter Unterstützung für die Textextraktion aus PDFs, PPTs und anderen gängigen Dokumentformaten.
+
+**5. Fähigkeiten des Agenten**: 
+  Sie können Agenten basierend auf LLM Function Calling oder ReAct definieren und vorgefertigte oder benutzerdefinierte Tools für den Agenten hinzufügen. Dify stellt über 50 integrierte Tools für KI-Agenten bereit, wie zum Beispiel Google Search, DALL·E, Stable Diffusion und WolframAlpha.
+
+**6. LLMOps**: 
+  Überwachen und analysieren Sie Anwendungsprotokolle und die Leistung im Laufe der Zeit. Sie können kontinuierlich Prompts, Datensätze und Modelle basierend auf Produktionsdaten und Annotationen verbessern.
+
+**7. Backend-as-a-Service**: 
+  Alle Dify-Angebote kommen mit entsprechenden APIs, sodass Sie Dify mühelos in Ihre eigene Geschäftslogik integrieren können.
+
+## Vergleich der Merkmale
+<table style="width: 100%;">
+  <tr>
+    <th align="center">Feature</th>
+    <th align="center">Dify.AI</th>
+    <th align="center">LangChain</th>
+    <th align="center">Flowise</th>
+    <th align="center">OpenAI Assistants API</th>
+  </tr>
+  <tr>
+    <td align="center">Programming Approach</td>
+    <td align="center">API + App-oriented</td>
+    <td align="center">Python Code</td>
+    <td align="center">App-oriented</td>
+    <td align="center">API-oriented</td>
+  </tr>
+  <tr>
+    <td align="center">Supported LLMs</td>
+    <td align="center">Rich Variety</td>
+    <td align="center">Rich Variety</td>
+    <td align="center">Rich Variety</td>
+    <td align="center">OpenAI-only</td>
+  </tr>
+  <tr>
+    <td align="center">RAG Engine</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+  </tr>
+  <tr>
+    <td align="center">Agent</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+    <td align="center">✅</td>
+  </tr>
+  <tr>
+    <td align="center">Workflow</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+  </tr>
+  <tr>
+    <td align="center">Observability</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+    <td align="center">❌</td>
+  </tr>
+  <tr>
+    <td align="center">Enterprise Feature (SSO/Access control)</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+    <td align="center">❌</td>
+    <td align="center">❌</td>
+  </tr>
+  <tr>
+    <td align="center">Local Deployment</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+  </tr>
+</table>
+
+## Dify verwenden
+
+- **Cloud </br>**
+Wir hosten einen [Dify Cloud](https://dify.ai)-Service, den jeder ohne Einrichtung ausprobieren kann. Er bietet alle Funktionen der selbstgehosteten Version und beinhaltet 200 kostenlose GPT-4-Aufrufe im Sandbox-Plan.
+
+- **Selbstgehostete Dify Community Edition</br>**
+Starten Sie Dify schnell in Ihrer Umgebung mit diesem [Schnellstart-Leitfaden](#quick-start). Nutzen Sie unsere [Dokumentation](https://docs.dify.ai) für weiterführende Informationen und detaillierte Anweisungen.
+
+- **Dify für Unternehmen / Organisationen</br>**
+Wir bieten zusätzliche, unternehmensspezifische Funktionen. [Über diesen Chatbot können Sie uns Ihre Fragen mitteilen](https://udify.app/chat/22L1zSxg6yW1cWQg) oder [senden Sie uns eine E-Mail](mailto:business@dify.ai?subject=[GitHub]Business%20License%20Inquiry), um Ihre unternehmerischen Bedürfnisse zu besprechen. </br>
+  > Für Startups und kleine Unternehmen, die AWS nutzen, schauen Sie sich [Dify Premium on AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6) an und stellen Sie es mit nur einem Klick in Ihrer eigenen AWS VPC bereit. Es handelt sich um ein erschwingliches AMI-Angebot mit der Option, Apps mit individuellem Logo und Branding zu erstellen.
+
+
+## Immer einen Schritt voraus
+
+Star Dify auf GitHub und lassen Sie sich sofort über neue Releases benachrichtigen.
+
+![star-us](https://github.com/langgenius/dify/assets/13230914/b823edc1-6388-4e25-ad45-2f6b187adbb4)
+
+
+## Erweiterte Einstellungen
+
+Falls Sie die Konfiguration anpassen müssen, lesen Sie bitte die Kommentare in unserer [.env.example](docker/.env.example)-Datei und aktualisieren Sie die entsprechenden Werte in Ihrer `.env`-Datei. Zusätzlich müssen Sie eventuell Anpassungen an der `docker-compose.yaml`-Datei vornehmen, wie zum Beispiel das Ändern von Image-Versionen, Portzuordnungen oder Volumen-Mounts, je nach Ihrer spezifischen Einsatzumgebung und Ihren Anforderungen. Nachdem Sie Änderungen vorgenommen haben, starten Sie `docker-compose up -d` erneut. Eine vollständige Liste der verfügbaren Umgebungsvariablen finden Sie [hier](https://docs.dify.ai/getting-started/install-self-hosted/environments).
+
+Falls Sie eine hochverfügbare Konfiguration einrichten möchten, gibt es von der Community bereitgestellte [Helm Charts](https://helm.sh/) und YAML-Dateien, die es ermöglichen, Dify auf Kubernetes bereitzustellen.
+
+- [Helm Chart by @LeoQuote](https://github.com/douban/charts/tree/master/charts/dify)
+- [Helm Chart by @BorisPolonsky](https://github.com/BorisPolonsky/dify-helm)
+- [YAML file by @Winson-030](https://github.com/Winson-030/dify-kubernetes)
+
+#### Terraform für die Bereitstellung verwenden
+
+Stellen Sie Dify mit nur einem Klick mithilfe von [terraform](https://www.terraform.io/) auf einer Cloud-Plattform bereit.
+
+##### Azure Global
+- [Azure Terraform by @nikawang](https://github.com/nikawang/dify-azure-terraform)
+
+##### Google Cloud
+- [Google Cloud Terraform by @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)
+
+#### Verwendung von AWS CDK für die Bereitstellung
+
+Bereitstellung von Dify auf AWS mit [CDK](https://aws.amazon.com/cdk/)
+
+##### AWS 
+- [AWS CDK by @KevinZhao](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
+
+## Contributing
+
+Falls Sie Code beitragen möchten, lesen Sie bitte unseren [Contribution Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md). Gleichzeitig bitten wir Sie, Dify zu unterstützen, indem Sie es in den sozialen Medien teilen und auf Veranstaltungen und Konferenzen präsentieren.
+
+
+> Wir suchen Mitwirkende, die dabei helfen, Dify in weitere Sprachen zu übersetzen – außer Mandarin oder Englisch. Wenn Sie Interesse an einer Mitarbeit haben, lesen Sie bitte die [i18n README](https://github.com/langgenius/dify/blob/main/web/i18n/README.md) für weitere Informationen und hinterlassen Sie einen Kommentar im `global-users`-Kanal unseres [Discord Community Servers](https://discord.gg/8Tpq4AcN9c).
+
+## Gemeinschaft & Kontakt
+
+* [Github Discussion](https://github.com/langgenius/dify/discussions). Am besten geeignet für: den Austausch von Feedback und das Stellen von Fragen.
+* [GitHub Issues](https://github.com/langgenius/dify/issues). Am besten für: Fehler, auf die Sie bei der Verwendung von Dify.AI stoßen, und Funktionsvorschläge. Siehe unseren [Contribution Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
+* [Discord](https://discord.gg/FngNHpbcY7).  Am besten geeignet für: den Austausch von Bewerbungen und den Austausch mit der Community.
+* [X(Twitter)](https://twitter.com/dify_ai). Am besten geeignet für: den Austausch von Bewerbungen und den Austausch mit der Community.
+
+**Mitwirkende**
+
+<a href="https://github.com/langgenius/dify/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=langgenius/dify" />
+</a>
+
+## Star-Geschichte
+
+[![Star History Chart](https://api.star-history.com/svg?repos=langgenius/dify&type=Date)](https://star-history.com/#langgenius/dify&Date)
+
+
+## Offenlegung der Sicherheit
+
+Um Ihre Privatsphäre zu schützen, vermeiden Sie es bitte, Sicherheitsprobleme auf GitHub zu posten. Schicken Sie Ihre Fragen stattdessen an security@dify.ai und wir werden Ihnen eine ausführlichere Antwort geben.
+
+## Lizenz
+
+Dieses Repository steht unter der [Dify Open Source License](LICENSE), die im Wesentlichen Apache 2.0 mit einigen zusätzlichen Einschränkungen ist.
+

README_ES.md

@@ -45,6 +45,7 @@
   <a href="./README_AR.md"><img alt="README بالعربية" src="https://img.shields.io/badge/العربية-d9d9d9"></a>
   <a href="./README_TR.md"><img alt="Türkçe README" src="https://img.shields.io/badge/Türkçe-d9d9d9"></a>
   <a href="./README_VI.md"><img alt="README Tiếng Việt" src="https://img.shields.io/badge/Ti%E1%BA%BFng%20Vi%E1%BB%87t-d9d9d9"></a>
+  <a href="./README_BN.md"><img alt="README in বাংলা" src="https://img.shields.io/badge/বাংলা-d9d9d9"></a>
 </p>
 
 #

README_FR.md

@@ -45,6 +45,7 @@
   <a href="./README_AR.md"><img alt="README بالعربية" src="https://img.shields.io/badge/العربية-d9d9d9"></a>
   <a href="./README_TR.md"><img alt="Türkçe README" src="https://img.shields.io/badge/Türkçe-d9d9d9"></a>
   <a href="./README_VI.md"><img alt="README Tiếng Việt" src="https://img.shields.io/badge/Ti%E1%BA%BFng%20Vi%E1%BB%87t-d9d9d9"></a>
+  <a href="./README_BN.md"><img alt="README in বাংলা" src="https://img.shields.io/badge/বাংলা-d9d9d9"></a>
 </p>
 
 #
@@ -55,7 +56,7 @@
 Dify est une plateforme de développement d'applications LLM open source. Son interface intuitive combine un flux de travail d'IA, un pipeline RAG, des capacités d'agent, une gestion de modèles, des fonctionnalités d'observabilité, et plus encore, vous permettant de passer rapidement du prototype à la production. Voici une liste des fonctionnalités principales:
 </br> </br>
 
-**1. Flux de travail**: 
+**1. Flux de travail** : 
   Construisez et testez des flux de travail d'IA puissants sur un canevas visuel, en utilisant toutes les fonctionnalités suivantes et plus encore.
 
 
@@ -63,27 +64,25 @@ Dify est une plateforme de développement d'applications LLM open source. Son in
 
 
 
-**2. Prise en charge complète des modèles**: 
+**2. Prise en charge complète des modèles** : 
   Intégration transparente avec des centaines de LLM propriétaires / open source provenant de dizaines de fournisseurs d'inférence et de solutions auto-hébergées, couvrant GPT, Mistral, Llama3, et tous les modèles compatibles avec l'API OpenAI. Une liste complète des fournisseurs de modèles pris en charge se trouve [ici](https://docs.dify.ai/getting-started/readme/model-providers).
 
 ![providers-v5](https://github.com/langgenius/dify/assets/13230914/5a17bdbe-097a-4100-8363-40255b70f6e3)
 
 
-**3. IDE de prompt**: 
+**3. IDE de prompt** : 
   Interface intuitive pour créer des prompts, comparer les performances des modèles et ajouter des fonctionnalités supplémentaires telles que la synthèse vocale à une application basée sur des chats. 
 
-**4. Pipeline RAG**: 
+**4. Pipeline RAG** : 
   Des capacités RAG étendues qui couvrent tout, de l'ingestion de documents à la récupération, avec un support prêt à l'emploi pour l'extraction de texte à partir de PDF, PPT et autres formats de document courants.
 
-**5. Capac
-
-ités d'agent**: 
+**5. Capacités d'agent** : 
   Vous pouvez définir des agents basés sur l'appel de fonction LLM ou ReAct, et ajouter des outils pré-construits ou personnalisés pour l'agent. Dify fournit plus de 50 outils intégrés pour les agents d'IA, tels que la recherche Google, DALL·E, Stable Diffusion et WolframAlpha.
 
-**6. LLMOps**: 
+**6. LLMOps** : 
   Surveillez et analysez les journaux d'application et les performances au fil du temps. Vous pouvez continuellement améliorer les prompts, les ensembles de données et les modèles en fonction des données de production et des annotations.
 
-**7. Backend-as-a-Service**: 
+**7. Backend-as-a-Service** : 
   Toutes les offres de Dify sont accompagnées d'API correspondantes, vous permettant d'intégrer facilement Dify dans votre propre logique métier.
 
 

README_JA.md

@@ -15,7 +15,7 @@
     <a href="https://discord.gg/FngNHpbcY7" target="_blank">
         <img src="https://img.shields.io/discord/1082486657678311454?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb"
             alt="Discordでチャット"></a>
-    <a href="https://reddit.com/r/difyai" target="_blank">  
+    <a href="https://reddit.com/r/difyai" target="_blank">
         <img src="https://img.shields.io/reddit/subreddit-subscribers/difyai?style=plastic&logo=reddit&label=r%2Fdifyai&labelColor=white"
             alt="Reddit"></a>
     <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
@@ -45,6 +45,7 @@
   <a href="./README_AR.md"><img alt="README بالعربية" src="https://img.shields.io/badge/العربية-d9d9d9"></a>
   <a href="./README_TR.md"><img alt="Türkçe README" src="https://img.shields.io/badge/Türkçe-d9d9d9"></a>
   <a href="./README_VI.md"><img alt="README Tiếng Việt" src="https://img.shields.io/badge/Ti%E1%BA%BFng%20Vi%E1%BB%87t-d9d9d9"></a>
+  <a href="./README_BN.md"><img alt="README in বাংলা" src="https://img.shields.io/badge/বাংলা-d9d9d9"></a>
 </p>
 
 #
@@ -56,7 +57,7 @@
 DifyはオープンソースのLLMアプリケーション開発プラットフォームです。直感的なインターフェイスには、AIワークフロー、RAGパイプライン、エージェント機能、モデル管理、観測機能などが組み合わさっており、プロトタイプから生産まで迅速に進めることができます。以下の機能が含まれます：
 </br> </br>
 
-**1. ワークフロー**: 
+**1. ワークフロー**:
   強力なAIワークフローをビジュアルキャンバス上で構築し、テストできます。すべての機能、および以下の機能を使用できます。
 
 
@@ -64,25 +65,25 @@ DifyはオープンソースのLLMアプリケーション開発プラットフ
 
 
 
-**2. 総合的なモデルサポート**: 
+**2. 総合的なモデルサポート**:
   数百ものプロプライエタリ/オープンソースのLLMと、数十もの推論プロバイダーおよびセルフホスティングソリューションとのシームレスな統合を提供します。GPT、Mistral、Llama3、OpenAI APIと互換性のあるすべてのモデルを統合されています。サポートされているモデルプロバイダーの完全なリストは[こちら](https://docs.dify.ai/getting-started/readme/model-providers)をご覧ください。
 
 ![providers-v5](https://github.com/langgenius/dify/assets/13230914/5a17bdbe-097a-4100-8363-40255b70f6e3)
 
 
-**3. プロンプトIDE**: 
+**3. プロンプトIDE**:
   プロンプトの作成、モデルパフォーマンスの比較が行え、チャットベースのアプリに音声合成などの機能も追加できます。
 
-**4. RAGパイプライン**: 
+**4. RAGパイプライン**:
   ドキュメントの取り込みから検索までをカバーする広範なRAG機能ができます。ほかにもPDF、PPT、その他の一般的なドキュメントフォーマットからのテキスト抽出のサポートも提供します。
 
-**5. エージェント機能**: 
+**5. エージェント機能**:
   LLM Function CallingやReActに基づくエージェントの定義が可能で、AIエージェント用のプリビルトまたはカスタムツールを追加できます。Difyには、Google検索、DALL·E、Stable Diffusion、WolframAlphaなどのAIエージェント用の50以上の組み込みツールが提供します。
 
-**6. LLMOps**: 
+**6. LLMOps**:
   アプリケーションのログやパフォーマンスを監視と分析し、生産のデータと注釈に基づいて、プロンプト、データセット、モデルを継続的に改善できます。
 
-**7. Backend-as-a-Service**: 
+**7. Backend-as-a-Service**:
   すべての機能はAPIを提供されており、Difyを自分のビジネスロジックに簡単に統合できます。
 
 
@@ -164,7 +165,7 @@ DifyはオープンソースのLLMアプリケーション開発プラットフ
 
 - **企業/組織向けのDify</br>**
 企業中心の機能を提供しています。[メールを送信](mailto:business@dify.ai?subject=[GitHub]Business%20License%20Inquiry)して企業のニーズについて相談してください。 </br>
-  > AWSを使用しているスタートアップ企業や中小企業の場合は、[AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6)のDify Premiumをチェックして、ワンクリックで自分のAWS VPCにデプロイできます。さらに、手頃な価格のAMIオファリングどして、ロゴやブランディングをカスタマイズしてアプリケーションを作成するオプションがあります。
+  > AWSを使用しているスタートアップ企業や中小企業の場合は、[AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6)のDify Premiumをチェックして、ワンクリックで自分のAWS VPCにデプロイできます。さらに、手頃な価格のAMIオファリングとして、ロゴやブランディングをカスタマイズしてアプリケーションを作成するオプションがあります。
 
 
 ## 最新の情報を入手
@@ -177,7 +178,7 @@ GitHub上でDifyにスターを付けることで、Difyに関する新しいニ
 
 ## クイックスタート
 > Difyをインストールする前に、お使いのマシンが以下の最小システム要件を満たしていることを確認してください：
-> 
+>
 >- CPU >= 2コア
 >- RAM >= 4GB
 
@@ -219,7 +220,7 @@ docker compose up -d
 
 [CDK](https://aws.amazon.com/cdk/) を使用して、DifyをAWSにデプロイします
 
-##### AWS 
+##### AWS
 - [@KevinZhaoによるAWS CDK](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
 
 ## 貢献

README_KL.md

@@ -45,6 +45,7 @@
   <a href="./README_AR.md"><img alt="README بالعربية" src="https://img.shields.io/badge/العربية-d9d9d9"></a>
   <a href="./README_TR.md"><img alt="Türkçe README" src="https://img.shields.io/badge/Türkçe-d9d9d9"></a>
   <a href="./README_VI.md"><img alt="README Tiếng Việt" src="https://img.shields.io/badge/Ti%E1%BA%BFng%20Vi%E1%BB%87t-d9d9d9"></a>
+  <a href="./README_BN.md"><img alt="README in বাংলা" src="https://img.shields.io/badge/বাংলা-d9d9d9"></a>
 </p>
 
 #

README_KR.md

@@ -45,6 +45,7 @@
   <a href="./README_AR.md"><img alt="README بالعربية" src="https://img.shields.io/badge/العربية-d9d9d9"></a>
   <a href="./README_TR.md"><img alt="Türkçe README" src="https://img.shields.io/badge/Türkçe-d9d9d9"></a>
   <a href="./README_VI.md"><img alt="README Tiếng Việt" src="https://img.shields.io/badge/Ti%E1%BA%BFng%20Vi%E1%BB%87t-d9d9d9"></a>
+    <a href="./README_BN.md"><img alt="README in বাংলা" src="https://img.shields.io/badge/বাংলা-d9d9d9"></a>
 </p>
 
 

README_PT.md

@@ -50,6 +50,7 @@
   <a href="./README_TR.md"><img alt="README em Turco" src="https://img.shields.io/badge/Türkçe-d9d9d9"></a>
   <a href="./README_VI.md"><img alt="README em Vietnamita" src="https://img.shields.io/badge/Ti%E1%BA%BFng%20Vi%E1%BB%87t-d9d9d9"></a>
   <a href="./README_PT.md"><img alt="README em Português - BR" src="https://img.shields.io/badge/Portugu%C3%AAs-BR?style=flat&label=BR&color=d9d9d9"></a>
+  <a href="./README_BN.md"><img alt="README in বাংলা" src="https://img.shields.io/badge/বাংলা-d9d9d9"></a>
 </p>
 
 Dify é uma plataforma de desenvolvimento de aplicativos LLM de código aberto. Sua interface intuitiva combina workflow de IA, pipeline RAG, capacidades de agente, gerenciamento de modelos, recursos de observabilidade e muito mais, permitindo que você vá rapidamente do protótipo à produção. Aqui está uma lista das principais funcionalidades:

README_SI.md

@@ -47,6 +47,7 @@
   <a href="./README_TR.md"><img alt="Türkçe README" src="https://img.shields.io/badge/Türkçe-d9d9d9"></a>
   <a href="./README_VI.md"><img alt="README Tiếng Việt" src="https://img.shields.io/badge/Ti%E1%BA%BFng%20Vi%E1%BB%87t-d9d9d9"></a>
   <a href="./README_SI.md"><img alt="README Slovenščina" src="https://img.shields.io/badge/Sloven%C5%A1%C4%8Dina-d9d9d9"></a>
+  <a href="./README_BN.md"><img alt="README in বাংলা" src="https://img.shields.io/badge/বাংলা-d9d9d9"></a>
 </p>
 
 

README_TR.md

@@ -45,6 +45,7 @@
   <a href="./README_AR.md"><img alt="README بالعربية" src="https://img.shields.io/badge/العربية-d9d9d9"></a>
   <a href="./README_TR.md"><img alt="Türkçe README" src="https://img.shields.io/badge/Türkçe-d9d9d9"></a>
   <a href="./README_VI.md"><img alt="README Tiếng Việt" src="https://img.shields.io/badge/Ti%E1%BA%BFng%20Vi%E1%BB%87t-d9d9d9"></a>
+  <a href="./README_BN.md"><img alt="README in বাংলা" src="https://img.shields.io/badge/বাংলা-d9d9d9"></a>
 </p>
 
 

README_TW.md

@@ -0,0 +1,258 @@
+![cover-v5-optimized](https://github.com/langgenius/dify/assets/13230914/f9e19af5-61ba-4119-b926-d10c4c06ebab)
+
+<p align="center">
+  📌 <a href="https://dify.ai/blog/introducing-dify-workflow-file-upload-a-demo-on-ai-podcast">介紹 Dify 工作流程檔案上傳功能：重現 Google NotebookLM Podcast</a>
+</p>
+
+<p align="center">
+  <a href="https://cloud.dify.ai">Dify 雲端服務</a> ·
+  <a href="https://docs.dify.ai/getting-started/install-self-hosted">自行託管</a> ·
+  <a href="https://docs.dify.ai">說明文件</a> ·
+  <a href="https://udify.app/chat/22L1zSxg6yW1cWQg">企業諮詢</a>
+</p>
+
+<p align="center">
+    <a href="https://dify.ai" target="_blank">
+        <img alt="Static Badge" src="https://img.shields.io/badge/Product-F04438"></a>
+    <a href="https://dify.ai/pricing" target="_blank">
+        <img alt="Static Badge" src="https://img.shields.io/badge/free-pricing?logo=free&color=%20%23155EEF&label=pricing&labelColor=%20%23528bff"></a>
+    <a href="https://discord.gg/FngNHpbcY7" target="_blank">
+        <img src="https://img.shields.io/discord/1082486657678311454?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb"
+            alt="chat on Discord"></a>
+    <a href="https://reddit.com/r/difyai" target="_blank">  
+        <img src="https://img.shields.io/reddit/subreddit-subscribers/difyai?style=plastic&logo=reddit&label=r%2Fdifyai&labelColor=white"
+            alt="join Reddit"></a>
+    <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
+        <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
+            alt="follow on X(Twitter)"></a>
+    <a href="https://www.linkedin.com/company/langgenius/" target="_blank">
+        <img src="https://custom-icon-badges.demolab.com/badge/LinkedIn-0A66C2?logo=linkedin-white&logoColor=fff"
+            alt="follow on LinkedIn"></a>
+    <a href="https://hub.docker.com/u/langgenius" target="_blank">
+        <img alt="Docker Pulls" src="https://img.shields.io/docker/pulls/langgenius/dify-web?labelColor=%20%23FDB062&color=%20%23f79009"></a>
+    <a href="https://github.com/langgenius/dify/graphs/commit-activity" target="_blank">
+        <img alt="Commits last month" src="https://img.shields.io/github/commit-activity/m/langgenius/dify?labelColor=%20%2332b583&color=%20%2312b76a"></a>
+    <a href="https://github.com/langgenius/dify/" target="_blank">
+        <img alt="Issues closed" src="https://img.shields.io/github/issues-search?query=repo%3Alanggenius%2Fdify%20is%3Aclosed&label=issues%20closed&labelColor=%20%237d89b0&color=%20%235d6b98"></a>
+    <a href="https://github.com/langgenius/dify/discussions/" target="_blank">
+        <img alt="Discussion posts" src="https://img.shields.io/github/discussions/langgenius/dify?labelColor=%20%239b8afb&color=%20%237a5af8"></a>
+</p>
+
+<p align="center">
+  <a href="./README.md"><img alt="README in English" src="https://img.shields.io/badge/English-d9d9d9"></a>
+  <a href="./README_TW.md"><img alt="繁體中文文件" src="https://img.shields.io/badge/繁體中文-d9d9d9"></a>
+  <a href="./README_CN.md"><img alt="简体中文版自述文件" src="https://img.shields.io/badge/简体中文-d9d9d9"></a>
+  <a href="./README_JA.md"><img alt="日本語のREADME" src="https://img.shields.io/badge/日本語-d9d9d9"></a>
+  <a href="./README_ES.md"><img alt="README en Español" src="https://img.shields.io/badge/Español-d9d9d9"></a>
+  <a href="./README_FR.md"><img alt="README en Français" src="https://img.shields.io/badge/Français-d9d9d9"></a>
+  <a href="./README_KL.md"><img alt="README tlhIngan Hol" src="https://img.shields.io/badge/Klingon-d9d9d9"></a>
+  <a href="./README_KR.md"><img alt="README in Korean" src="https://img.shields.io/badge/한국어-d9d9d9"></a>
+  <a href="./README_AR.md"><img alt="README بالعربية" src="https://img.shields.io/badge/العربية-d9d9d9"></a>
+  <a href="./README_TR.md"><img alt="Türkçe README" src="https://img.shields.io/badge/Türkçe-d9d9d9"></a>
+  <a href="./README_VI.md"><img alt="README Tiếng Việt" src="https://img.shields.io/badge/Ti%E1%BA%BFng%20Vi%E1%BB%87t-d9d9d9"></a>
+  <a href="./README_DE.md"><img alt="README in Deutsch" src="https://img.shields.io/badge/German-d9d9d9"></a>
+</p>
+
+Dify 是一個開源的 LLM 應用程式開發平台。其直觀的界面結合了智能代理工作流程、RAG 管道、代理功能、模型管理、可觀察性功能等，讓您能夠快速從原型進展到生產環境。
+
+## 快速開始
+
+> 安裝 Dify 之前，請確保您的機器符合以下最低系統要求：
+>
+> - CPU >= 2 核心
+> - 記憶體 >= 4 GiB
+
+</br>
+
+啟動 Dify 伺服器最簡單的方式是透過 [docker compose](docker/docker-compose.yaml)。在使用以下命令運行 Dify 之前，請確保您的機器已安裝 [Docker](https://docs.docker.com/get-docker/) 和 [Docker Compose](https://docs.docker.com/compose/install/)：
+
+```bash
+cd dify
+cd docker
+cp .env.example .env
+docker compose up -d
+```
+
+運行後，您可以在瀏覽器中通過 [http://localhost/install](http://localhost/install) 訪問 Dify 儀表板並開始初始化過程。
+
+### 尋求幫助
+
+如果您在設置 Dify 時遇到問題，請參考我們的 [常見問題](https://docs.dify.ai/getting-started/install-self-hosted/faqs)。如果仍有疑問，請聯絡 [社區和我們](#community--contact)。
+
+> 如果您想為 Dify 做出貢獻或進行額外開發，請參考我們的 [從原始碼部署指南](https://docs.dify.ai/getting-started/install-self-hosted/local-source-code)
+
+## 核心功能
+
+**1. 工作流程**：
+在視覺化畫布上建立和測試強大的 AI 工作流程，利用以下所有功能及更多。
+
+https://github.com/langgenius/dify/assets/13230914/356df23e-1604-483d-80a6-9517ece318aa
+
+**2. 全面的模型支援**：
+無縫整合來自數十個推理提供商和自託管解決方案的數百個專有/開源 LLM，涵蓋 GPT、Mistral、Llama3 和任何與 OpenAI API 兼容的模型。您可以在[此處](https://docs.dify.ai/getting-started/readme/model-providers)找到支援的模型提供商完整列表。
+
+![providers-v5](https://github.com/langgenius/dify/assets/13230914/5a17bdbe-097a-4100-8363-40255b70f6e3)
+
+**3. 提示詞 IDE**：
+直觀的界面，用於編寫提示詞、比較模型性能，以及為聊天型應用程式添加文字轉語音等額外功能。
+
+**4. RAG 管道**：
+廣泛的 RAG 功能，涵蓋從文件擷取到檢索的全部流程，內建支援從 PDF、PPT 和其他常見文件格式提取文本。
+
+**5. 代理功能**：
+您可以基於 LLM 函數調用或 ReAct 定義代理，並為代理添加預構建或自定義工具。Dify 為 AI 代理提供 50 多種內建工具，如 Google 搜尋、DALL·E、Stable Diffusion 和 WolframAlpha。
+
+**6. LLMOps**：
+監控並分析應用程式日誌和長期效能。您可以根據生產數據和標註持續改進提示詞、數據集和模型。
+
+**7. 後端即服務**：
+Dify 的所有功能都提供相應的 API，因此您可以輕鬆地將 Dify 整合到您自己的業務邏輯中。
+
+## 功能比較
+
+<table style="width: 100%;">
+  <tr>
+    <th align="center">功能</th>
+    <th align="center">Dify.AI</th>
+    <th align="center">LangChain</th>
+    <th align="center">Flowise</th>
+    <th align="center">OpenAI Assistants API</th>
+  </tr>
+  <tr>
+    <td align="center">程式設計方法</td>
+    <td align="center">API + 應用導向</td>
+    <td align="center">Python 代碼</td>
+    <td align="center">應用導向</td>
+    <td align="center">API 導向</td>
+  </tr>
+  <tr>
+    <td align="center">支援的 LLM 模型</td>
+    <td align="center">豐富多樣</td>
+    <td align="center">豐富多樣</td>
+    <td align="center">豐富多樣</td>
+    <td align="center">僅限 OpenAI</td>
+  </tr>
+  <tr>
+    <td align="center">RAG 引擎</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+  </tr>
+  <tr>
+    <td align="center">代理功能</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+    <td align="center">✅</td>
+  </tr>
+  <tr>
+    <td align="center">工作流程</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+  </tr>
+  <tr>
+    <td align="center">可觀察性</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+    <td align="center">❌</td>
+  </tr>
+  <tr>
+    <td align="center">企業級功能 (SSO/存取控制)</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+    <td align="center">❌</td>
+    <td align="center">❌</td>
+  </tr>
+  <tr>
+    <td align="center">本地部署</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+  </tr>
+</table>
+
+## 使用 Dify
+
+- **雲端服務 </br>**
+  我們提供 [Dify Cloud](https://dify.ai) 服務，任何人都可以零配置嘗試。它提供與自部署版本相同的所有功能，並在沙盒計劃中包含 200 次免費 GPT-4 調用。
+
+- **自託管 Dify 社區版</br>**
+  使用這份[快速指南](#快速開始)在您的環境中快速運行 Dify。
+  使用我們的[文檔](https://docs.dify.ai)獲取更多參考和深入指導。
+
+- **企業/組織版 Dify</br>**
+  我們提供額外的企業中心功能。[通過這個聊天機器人記錄您的問題](https://udify.app/chat/22L1zSxg6yW1cWQg)或[發送電子郵件給我們](mailto:business@dify.ai?subject=[GitHub]Business%20License%20Inquiry)討論企業需求。</br>
+  > 對於使用 AWS 的初創企業和小型企業，請查看 [AWS Marketplace 上的 Dify Premium](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6)，並一鍵部署到您自己的 AWS VPC。這是一個經濟實惠的 AMI 產品，可選擇使用自定義徽標和品牌創建應用。
+
+## 保持領先
+
+在 GitHub 上為 Dify 加星，即時獲取新版本通知。
+
+![star-us](https://github.com/langgenius/dify/assets/13230914/b823edc1-6388-4e25-ad45-2f6b187adbb4)
+
+## 進階設定
+
+如果您需要自定義配置，請參考我們的 [.env.example](docker/.env.example) 文件中的註釋，並在您的 `.env` 文件中更新相應的值。此外，根據您特定的部署環境和需求，您可能需要調整 `docker-compose.yaml` 文件本身，例如更改映像版本、端口映射或卷掛載。進行任何更改後，請重新運行 `docker-compose up -d`。您可以在[這裡](https://docs.dify.ai/getting-started/install-self-hosted/environments)找到可用環境變數的完整列表。
+
+如果您想配置高可用性設置，社區貢獻的 [Helm Charts](https://helm.sh/) 和 YAML 文件允許在 Kubernetes 上部署 Dify。
+
+- [由 @LeoQuote 提供的 Helm Chart](https://github.com/douban/charts/tree/master/charts/dify)
+- [由 @BorisPolonsky 提供的 Helm Chart](https://github.com/BorisPolonsky/dify-helm)
+- [由 @Winson-030 提供的 YAML 文件](https://github.com/Winson-030/dify-kubernetes)
+
+### 使用 Terraform 進行部署
+
+使用 [terraform](https://www.terraform.io/) 一鍵部署 Dify 到雲端平台
+
+### Azure 全球
+
+- [由 @nikawang 提供的 Azure Terraform](https://github.com/nikawang/dify-azure-terraform)
+
+### Google Cloud
+
+- [由 @sotazum 提供的 Google Cloud Terraform](https://github.com/DeNA/dify-google-cloud-terraform)
+
+### 使用 AWS CDK 進行部署
+
+使用 [CDK](https://aws.amazon.com/cdk/) 部署 Dify 到 AWS
+
+### AWS
+
+- [由 @KevinZhao 提供的 AWS CDK](https://github.com/aws-samples/solution-for-deploying-dify-on-aws)
+
+## 貢獻
+
+對於想要貢獻程式碼的開發者，請參閱我們的[貢獻指南](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md)。
+同時，也請考慮透過在社群媒體和各種活動與會議上分享 Dify 來支持我們。
+
+> 我們正在尋找貢獻者協助將 Dify 翻譯成中文和英文以外的語言。如果您有興趣幫忙，請查看 [i18n README](https://github.com/langgenius/dify/blob/main/web/i18n/README.md) 獲取更多資訊，並在我們的 [Discord 社群伺服器](https://discord.gg/8Tpq4AcN9c) 的 `global-users` 頻道留言給我們。
+
+## 社群與聯絡方式
+
+- [Github Discussion](https://github.com/langgenius/dify/discussions)：最適合分享反饋和提問。
+- [GitHub Issues](https://github.com/langgenius/dify/issues)：最適合報告使用 Dify.AI 時遇到的問題和提出功能建議。請參閱我們的[貢獻指南](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md)。
+- [Discord](https://discord.gg/FngNHpbcY7)：最適合分享您的應用程式並與社群互動。
+- [X(Twitter)](https://twitter.com/dify_ai)：最適合分享您的應用程式並與社群互動。
+
+**貢獻者**
+
+<a href="https://github.com/langgenius/dify/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=langgenius/dify" />
+</a>
+
+## 星星歷史
+
+[![Star History Chart](https://api.star-history.com/svg?repos=langgenius/dify&type=Date)](https://star-history.com/#langgenius/dify&Date)
+
+## 安全揭露
+
+為保護您的隱私，請避免在 GitHub 上發布安全性問題。請將您的問題發送至 security@dify.ai，我們將為您提供更詳細的答覆。
+
+## 授權條款
+
+本代碼庫採用 [Dify 開源授權](LICENSE)，這基本上是 Apache 2.0 授權加上一些額外限制條款。

README_VI.md

@@ -45,6 +45,7 @@
   <a href="./README_AR.md"><img alt="README بالعربية" src="https://img.shields.io/badge/العربية-d9d9d9"></a>
   <a href="./README_TR.md"><img alt="Türkçe README" src="https://img.shields.io/badge/Türkçe-d9d9d9"></a>
   <a href="./README_VI.md"><img alt="README Tiếng Việt" src="https://img.shields.io/badge/Ti%E1%BA%BFng%20Vi%E1%BB%87t-d9d9d9"></a>
+  <a href="./README_BN.md"><img alt="README in বাংলা" src="https://img.shields.io/badge/বাংলা-d9d9d9"></a>
 </p>
 
 

api/.env.example

@@ -427,7 +427,6 @@ PLUGIN_DAEMON_URL=http://127.0.0.1:5002
 PLUGIN_REMOTE_INSTALL_PORT=5003
 PLUGIN_REMOTE_INSTALL_HOST=localhost
 PLUGIN_MAX_PACKAGE_SIZE=15728640
-INNER_API_KEY=QaHbTe77CtuXmsfyhR7+vRjI/+XbV1AaFy691iy+kGDv2Jvy0/eAh8Y1
 INNER_API_KEY_FOR_PLUGIN=QaHbTe77CtuXmsfyhR7+vRjI/+XbV1AaFy691iy+kGDv2Jvy0/eAh8Y1
 
 # Marketplace configuration

api/.ruff.toml

@@ -7,7 +7,7 @@ line-length = 120
 quote-style = "double"
 
 [lint]
-preview = true
+preview = false
 select = [
     "B", # flake8-bugbear rules
     "C4", # flake8-comprehensions
@@ -18,7 +18,6 @@ select = [
     "N", # pep8-naming
     "PT", # flake8-pytest-style rules
     "PLC0208", # iteration-over-set
-    "PLC2801", # unnecessary-dunder-call
     "PLC0414", # useless-import-alias
     "PLE0604", # invalid-all-object
     "PLE0605", # invalid-all-format
@@ -46,7 +45,6 @@ ignore = [
     "E712", # true-false-comparison
     "E721", # type-comparison
     "E722", # bare-except
-    "E731", # lambda-assignment
     "F821", # undefined-name
     "F841", # unused-variable
     "FURB113", # repeated-append

api/app_factory.py

@@ -2,6 +2,7 @@ import logging
 import time
 
 from configs import dify_config
+from contexts.wrapper import RecyclableContextVar
 from dify_app import DifyApp
 
 
@@ -16,6 +17,12 @@ def create_flask_app_with_configs() -> DifyApp:
     dify_app = DifyApp(__name__)
     dify_app.config.from_mapping(dify_config.model_dump())
 
+    # add before request hook
+    @dify_app.before_request
+    def before_request():
+        # add an unique identifier to each request
+        RecyclableContextVar.increment_thread_recycles()
+
     return dify_app
 
 

api/commands.py

@@ -707,12 +707,13 @@ def extract_unique_plugins(output_file: str, input_file: str):
 @click.option(
     "--output_file", prompt=True, help="The file to store the installed plugins.", default="installed_plugins.jsonl"
 )
-def install_plugins(input_file: str, output_file: str):
+@click.option("--workers", prompt=True, help="The number of workers to install plugins.", default=100)
+def install_plugins(input_file: str, output_file: str, workers: int):
     """
     Install plugins.
     """
     click.echo(click.style("Starting install plugins.", fg="white"))
 
-    PluginMigration.install_plugins(input_file, output_file)
+    PluginMigration.install_plugins(input_file, output_file, workers)
 
     click.echo(click.style("Install plugins completed.", fg="green"))

api/configs/feature/__init__.py

@@ -389,11 +389,6 @@ class InnerAPIConfig(BaseSettings):
         default=False,
     )
 
-    INNER_API_KEY: Optional[str] = Field(
-        description="API key for accessing the internal API",
-        default=None,
-    )
-
 
 class LoggingConfig(BaseSettings):
     """

api/configs/middleware/__init__.py

@@ -167,8 +167,8 @@ class DatabaseConfig(BaseSettings):
         default=False,
     )
 
-    RETRIEVAL_SERVICE_WORKER: NonNegativeInt = Field(
-        description="If True, enables the retrieval service worker.",
+    RETRIEVAL_SERVICE_EXECUTORS: NonNegativeInt = Field(
+        description="Number of processes for the retrieval service, default to CPU cores.",
         default=os.cpu_count(),
     )
 

api/configs/middleware/vdb/oracle_config.py

@@ -1,6 +1,6 @@
 from typing import Optional
 
-from pydantic import Field, PositiveInt
+from pydantic import Field
 from pydantic_settings import BaseSettings
 
 
@@ -9,16 +9,6 @@ class OracleConfig(BaseSettings):
     Configuration settings for Oracle database
     """
 
-    ORACLE_HOST: Optional[str] = Field(
-        description="Hostname or IP address of the Oracle database server (e.g., 'localhost' or 'oracle.example.com')",
-        default=None,
-    )
-
-    ORACLE_PORT: PositiveInt = Field(
-        description="Port number on which the Oracle database server is listening (default is 1521)",
-        default=1521,
-    )
-
     ORACLE_USER: Optional[str] = Field(
         description="Username for authenticating with the Oracle database",
         default=None,
@@ -29,7 +19,28 @@ class OracleConfig(BaseSettings):
         default=None,
     )
 
-    ORACLE_DATABASE: Optional[str] = Field(
-        description="Name of the Oracle database or service to connect to (e.g., 'ORCL' or 'pdborcl')",
+    ORACLE_DSN: Optional[str] = Field(
+        description="Oracle database connection string. For traditional database, use format 'host:port/service_name'. "
+        "For autonomous database, use the service name from tnsnames.ora in the wallet",
         default=None,
     )
+
+    ORACLE_CONFIG_DIR: Optional[str] = Field(
+        description="Directory containing the tnsnames.ora configuration file. Only used in thin mode connection",
+        default=None,
+    )
+
+    ORACLE_WALLET_LOCATION: Optional[str] = Field(
+        description="Oracle wallet directory path containing the wallet files for secure connection",
+        default=None,
+    )
+
+    ORACLE_WALLET_PASSWORD: Optional[str] = Field(
+        description="Password to decrypt the Oracle wallet, if it is encrypted",
+        default=None,
+    )
+
+    ORACLE_IS_AUTONOMOUS: bool = Field(
+        description="Flag indicating whether connecting to Oracle Autonomous Database",
+        default=False,
+    )

api/contexts/__init__.py

@@ -2,7 +2,10 @@ from contextvars import ContextVar
 from threading import Lock
 from typing import TYPE_CHECKING
 
+from contexts.wrapper import RecyclableContextVar
+
 if TYPE_CHECKING:
+    from core.model_runtime.entities.model_entities import AIModelEntity
     from core.plugin.entities.plugin_daemon import PluginModelProviderEntity
     from core.tools.plugin_tool.provider import PluginToolProviderController
     from core.workflow.entities.variable_pool import VariablePool
@@ -12,8 +15,25 @@ tenant_id: ContextVar[str] = ContextVar("tenant_id")
 
 workflow_variable_pool: ContextVar["VariablePool"] = ContextVar("workflow_variable_pool")
 
-plugin_tool_providers: ContextVar[dict[str, "PluginToolProviderController"]] = ContextVar("plugin_tool_providers")
-plugin_tool_providers_lock: ContextVar[Lock] = ContextVar("plugin_tool_providers_lock")
+"""
+To avoid race-conditions caused by gunicorn thread recycling, using RecyclableContextVar to replace with
+"""
+plugin_tool_providers: RecyclableContextVar[dict[str, "PluginToolProviderController"]] = RecyclableContextVar(
+    ContextVar("plugin_tool_providers")
+)
 
-plugin_model_providers: ContextVar[list["PluginModelProviderEntity"] | None] = ContextVar("plugin_model_providers")
-plugin_model_providers_lock: ContextVar[Lock] = ContextVar("plugin_model_providers_lock")
+plugin_tool_providers_lock: RecyclableContextVar[Lock] = RecyclableContextVar(ContextVar("plugin_tool_providers_lock"))
+
+plugin_model_providers: RecyclableContextVar[list["PluginModelProviderEntity"] | None] = RecyclableContextVar(
+    ContextVar("plugin_model_providers")
+)
+
+plugin_model_providers_lock: RecyclableContextVar[Lock] = RecyclableContextVar(
+    ContextVar("plugin_model_providers_lock")
+)
+
+plugin_model_schema_lock: RecyclableContextVar[Lock] = RecyclableContextVar(ContextVar("plugin_model_schema_lock"))
+
+plugin_model_schemas: RecyclableContextVar[dict[str, "AIModelEntity"]] = RecyclableContextVar(
+    ContextVar("plugin_model_schemas")
+)

api/contexts/wrapper.py

@@ -0,0 +1,65 @@
+from contextvars import ContextVar
+from typing import Generic, TypeVar
+
+T = TypeVar("T")
+
+
+class HiddenValue:
+    pass
+
+
+_default = HiddenValue()
+
+
+class RecyclableContextVar(Generic[T]):
+    """
+    RecyclableContextVar is a wrapper around ContextVar
+    It's safe to use in gunicorn with thread recycling, but features like `reset` are not available for now
+
+    NOTE: you need to call `increment_thread_recycles` before requests
+    """
+
+    _thread_recycles: ContextVar[int] = ContextVar("thread_recycles")
+
+    @classmethod
+    def increment_thread_recycles(cls):
+        try:
+            recycles = cls._thread_recycles.get()
+            cls._thread_recycles.set(recycles + 1)
+        except LookupError:
+            cls._thread_recycles.set(0)
+
+    def __init__(self, context_var: ContextVar[T]):
+        self._context_var = context_var
+        self._updates = ContextVar[int](context_var.name + "_updates", default=0)
+
+    def get(self, default: T | HiddenValue = _default) -> T:
+        thread_recycles = self._thread_recycles.get(0)
+        self_updates = self._updates.get()
+        if thread_recycles > self_updates:
+            self._updates.set(thread_recycles)
+
+        # check if thread is recycled and should be updated
+        if thread_recycles < self_updates:
+            return self._context_var.get()
+        else:
+            # thread_recycles >= self_updates, means current context is invalid
+            if isinstance(default, HiddenValue) or default is _default:
+                raise LookupError
+            else:
+                return default
+
+    def set(self, value: T):
+        # it leads to a situation that self.updates is less than cls.thread_recycles if `set` was never called before
+        # increase it manually
+        thread_recycles = self._thread_recycles.get(0)
+        self_updates = self._updates.get()
+        if thread_recycles > self_updates:
+            self._updates.set(thread_recycles)
+
+        if self._updates.get() == self._thread_recycles.get(0):
+            # after increment,
+            self._updates.set(self._updates.get() + 1)
+
+        # set the context
+        self._context_var.set(value)

api/controllers/console/__init__.py

@@ -71,7 +71,7 @@ from .app import (
 from .auth import activate, data_source_bearer_auth, data_source_oauth, forgot_password, login, oauth
 
 # Import billing controllers
-from .billing import billing
+from .billing import billing, compliance
 
 # Import datasets controllers
 from .datasets import (

api/controllers/console/app/generator.py

@@ -1,4 +1,5 @@
 import os
+from typing import cast
 
 from flask_login import current_user  # type: ignore
 from flask_restful import Resource, reqparse  # type: ignore
@@ -11,8 +12,11 @@ from controllers.console.app.error import (
     ProviderQuotaExceededError,
 )
 from controllers.console.wraps import account_initialization_required, setup_required
+from core.auto.workflow_generator.workflow_generator import WorkflowGenerator
 from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
 from core.llm_generator.llm_generator import LLMGenerator
+from core.model_manager import ModelManager
+from core.model_runtime.entities.model_entities import ModelType
 from core.model_runtime.errors.invoke import InvokeError
 from libs.login import login_required
 
@@ -85,5 +89,45 @@ class RuleCodeGenerateApi(Resource):
         return code_result
 
 
+class AutoGenerateWorkflowApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self):
+        """
+        Auto generate workflow
+        """
+
+        parser = reqparse.RequestParser()
+        parser.add_argument("instruction", type=str, required=True, location="json")
+        parser.add_argument("model_config", type=dict, required=True, location="json")
+        tenant_id = cast(str, current_user.current_tenant_id)
+        args = parser.parse_args()
+        instruction = args.get("instruction")
+        if not instruction:
+            raise ValueError("Instruction is required")
+        if not args.get("model_config"):
+            raise ValueError("Model config is required")
+        model_config = cast(dict, args.get("model_config"))
+        model_manager = ModelManager()
+        model_instance = model_manager.get_model_instance(
+            tenant_id=tenant_id,
+            model_type=ModelType.LLM,
+            provider=model_config.get("provider", ""),
+            model=model_config.get("name", ""),
+        )
+        workflow_generator = WorkflowGenerator(
+            model_instance=model_instance,
+        )
+        workflow_yaml = workflow_generator.generate_workflow(
+            user_requirement=instruction,
+        )
+        return workflow_yaml
+
+
 api.add_resource(RuleGenerateApi, "/rule-generate")
 api.add_resource(RuleCodeGenerateApi, "/rule-code-generate")
+api.add_resource(
+    AutoGenerateWorkflowApi,
+    "/auto-generate",
+)

api/controllers/console/app/site.py

@@ -2,7 +2,6 @@ from datetime import UTC, datetime
 
 from flask_login import current_user  # type: ignore
 from flask_restful import Resource, marshal_with, reqparse  # type: ignore
-from sqlalchemy.orm import Session
 from werkzeug.exceptions import Forbidden, NotFound
 
 from constants.languages import supported_language
@@ -51,37 +50,35 @@ class AppSite(Resource):
         if not current_user.is_editor:
             raise Forbidden()
 
-        with Session(db.engine) as session:
-            site = session.query(Site).filter(Site.app_id == app_model.id).first()
+        site = db.session.query(Site).filter(Site.app_id == app_model.id).first()
+        if not site:
+            raise NotFound
 
-            if not site:
-                raise NotFound
+        for attr_name in [
+            "title",
+            "icon_type",
+            "icon",
+            "icon_background",
+            "description",
+            "default_language",
+            "chat_color_theme",
+            "chat_color_theme_inverted",
+            "customize_domain",
+            "copyright",
+            "privacy_policy",
+            "custom_disclaimer",
+            "customize_token_strategy",
+            "prompt_public",
+            "show_workflow_steps",
+            "use_icon_as_answer_icon",
+        ]:
+            value = args.get(attr_name)
+            if value is not None:
+                setattr(site, attr_name, value)
 
-            for attr_name in [
-                "title",
-                "icon_type",
-                "icon",
-                "icon_background",
-                "description",
-                "default_language",
-                "chat_color_theme",
-                "chat_color_theme_inverted",
-                "customize_domain",
-                "copyright",
-                "privacy_policy",
-                "custom_disclaimer",
-                "customize_token_strategy",
-                "prompt_public",
-                "show_workflow_steps",
-                "use_icon_as_answer_icon",
-            ]:
-                value = args.get(attr_name)
-                if value is not None:
-                    setattr(site, attr_name, value)
-
-            site.updated_by = current_user.id
-            site.updated_at = datetime.now(UTC).replace(tzinfo=None)
-            session.commit()
+        site.updated_by = current_user.id
+        site.updated_at = datetime.now(UTC).replace(tzinfo=None)
+        db.session.commit()
 
         return site
 

api/controllers/console/app/workflow.py

@@ -1,8 +1,10 @@
 import json
 import logging
+from typing import cast
 
 from flask import abort, request
 from flask_restful import Resource, inputs, marshal_with, reqparse  # type: ignore
+from sqlalchemy.orm import Session
 from werkzeug.exceptions import Forbidden, InternalServerError, NotFound
 
 import services
@@ -13,6 +15,7 @@ from controllers.console.app.wraps import get_app_model
 from controllers.console.wraps import account_initialization_required, setup_required
 from core.app.apps.base_app_queue_manager import AppQueueManager
 from core.app.entities.app_invoke_entities import InvokeFrom
+from extensions.ext_database import db
 from factories import variable_factory
 from fields.workflow_fields import workflow_fields, workflow_pagination_fields
 from fields.workflow_run_fields import workflow_run_node_execution_fields
@@ -24,7 +27,7 @@ from models.account import Account
 from models.model import AppMode
 from services.app_generate_service import AppGenerateService
 from services.errors.app import WorkflowHashNotEqualError
-from services.workflow_service import WorkflowService
+from services.workflow_service import DraftWorkflowDeletionError, WorkflowInUseError, WorkflowService
 
 logger = logging.getLogger(__name__)
 
@@ -246,6 +249,80 @@ class WorkflowDraftRunIterationNodeApi(Resource):
             raise InternalServerError()
 
 
+class AdvancedChatDraftRunLoopNodeApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT])
+    def post(self, app_model: App, node_id: str):
+        """
+        Run draft workflow loop node
+        """
+        # The role of the current user in the ta table must be admin, owner, or editor
+        if not current_user.is_editor:
+            raise Forbidden()
+
+        if not isinstance(current_user, Account):
+            raise Forbidden()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument("inputs", type=dict, location="json")
+        args = parser.parse_args()
+
+        try:
+            response = AppGenerateService.generate_single_loop(
+                app_model=app_model, user=current_user, node_id=node_id, args=args, streaming=True
+            )
+
+            return helper.compact_generate_response(response)
+        except services.errors.conversation.ConversationNotExistsError:
+            raise NotFound("Conversation Not Exists.")
+        except services.errors.conversation.ConversationCompletedError:
+            raise ConversationCompletedError()
+        except ValueError as e:
+            raise e
+        except Exception:
+            logging.exception("internal server error.")
+            raise InternalServerError()
+
+
+class WorkflowDraftRunLoopNodeApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.WORKFLOW])
+    def post(self, app_model: App, node_id: str):
+        """
+        Run draft workflow loop node
+        """
+        # The role of the current user in the ta table must be admin, owner, or editor
+        if not current_user.is_editor:
+            raise Forbidden()
+
+        if not isinstance(current_user, Account):
+            raise Forbidden()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument("inputs", type=dict, location="json")
+        args = parser.parse_args()
+
+        try:
+            response = AppGenerateService.generate_single_loop(
+                app_model=app_model, user=current_user, node_id=node_id, args=args, streaming=True
+            )
+
+            return helper.compact_generate_response(response)
+        except services.errors.conversation.ConversationNotExistsError:
+            raise NotFound("Conversation Not Exists.")
+        except services.errors.conversation.ConversationCompletedError:
+            raise ConversationCompletedError()
+        except ValueError as e:
+            raise e
+        except Exception:
+            logging.exception("internal server error.")
+            raise InternalServerError()
+
+
 class DraftWorkflowRunApi(Resource):
     @setup_required
     @login_required
@@ -365,10 +442,38 @@ class PublishedWorkflowApi(Resource):
         if not isinstance(current_user, Account):
             raise Forbidden()
 
-        workflow_service = WorkflowService()
-        workflow = workflow_service.publish_workflow(app_model=app_model, account=current_user)
+        parser = reqparse.RequestParser()
+        parser.add_argument("marked_name", type=str, required=False, default="", location="json")
+        parser.add_argument("marked_comment", type=str, required=False, default="", location="json")
+        args = parser.parse_args()
 
-        return {"result": "success", "created_at": TimestampField().format(workflow.created_at)}
+        # Validate name and comment length
+        if args.marked_name and len(args.marked_name) > 20:
+            raise ValueError("Marked name cannot exceed 20 characters")
+        if args.marked_comment and len(args.marked_comment) > 100:
+            raise ValueError("Marked comment cannot exceed 100 characters")
+
+        workflow_service = WorkflowService()
+        with Session(db.engine) as session:
+            workflow = workflow_service.publish_workflow(
+                session=session,
+                app_model=app_model,
+                account=current_user,
+                marked_name=args.marked_name or "",
+                marked_comment=args.marked_comment or "",
+            )
+
+            app_model.workflow_id = workflow.id
+            db.session.commit()
+
+            workflow_created_at = TimestampField().format(workflow.created_at)
+
+            session.commit()
+
+        return {
+            "result": "success",
+            "created_at": workflow_created_at,
+        }
 
 
 class DefaultBlockConfigsApi(Resource):
@@ -490,32 +595,193 @@ class PublishedAllWorkflowApi(Resource):
         parser = reqparse.RequestParser()
         parser.add_argument("page", type=inputs.int_range(1, 99999), required=False, default=1, location="args")
         parser.add_argument("limit", type=inputs.int_range(1, 100), required=False, default=20, location="args")
+        parser.add_argument("user_id", type=str, required=False, location="args")
+        parser.add_argument("named_only", type=inputs.boolean, required=False, default=False, location="args")
         args = parser.parse_args()
-        page = args.get("page")
-        limit = args.get("limit")
+        page = int(args.get("page", 1))
+        limit = int(args.get("limit", 10))
+        user_id = args.get("user_id")
+        named_only = args.get("named_only", False)
+
+        if user_id:
+            if user_id != current_user.id:
+                raise Forbidden()
+            user_id = cast(str, user_id)
+
         workflow_service = WorkflowService()
-        workflows, has_more = workflow_service.get_all_published_workflow(app_model=app_model, page=page, limit=limit)
+        with Session(db.engine) as session:
+            workflows, has_more = workflow_service.get_all_published_workflow(
+                session=session,
+                app_model=app_model,
+                page=page,
+                limit=limit,
+                user_id=user_id,
+                named_only=named_only,
+            )
 
-        return {"items": workflows, "page": page, "limit": limit, "has_more": has_more}
+            return {
+                "items": workflows,
+                "page": page,
+                "limit": limit,
+                "has_more": has_more,
+            }
 
 
-api.add_resource(DraftWorkflowApi, "/apps/<uuid:app_id>/workflows/draft")
-api.add_resource(WorkflowConfigApi, "/apps/<uuid:app_id>/workflows/draft/config")
-api.add_resource(AdvancedChatDraftWorkflowRunApi, "/apps/<uuid:app_id>/advanced-chat/workflows/draft/run")
-api.add_resource(DraftWorkflowRunApi, "/apps/<uuid:app_id>/workflows/draft/run")
-api.add_resource(WorkflowTaskStopApi, "/apps/<uuid:app_id>/workflow-runs/tasks/<string:task_id>/stop")
-api.add_resource(DraftWorkflowNodeRunApi, "/apps/<uuid:app_id>/workflows/draft/nodes/<string:node_id>/run")
+class WorkflowByIdApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    @marshal_with(workflow_fields)
+    def patch(self, app_model: App, workflow_id: str):
+        """
+        Update workflow attributes
+        """
+        # Check permission
+        if not current_user.is_editor:
+            raise Forbidden()
+
+        if not isinstance(current_user, Account):
+            raise Forbidden()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument("marked_name", type=str, required=False, location="json")
+        parser.add_argument("marked_comment", type=str, required=False, location="json")
+        args = parser.parse_args()
+
+        # Validate name and comment length
+        if args.marked_name and len(args.marked_name) > 20:
+            raise ValueError("Marked name cannot exceed 20 characters")
+        if args.marked_comment and len(args.marked_comment) > 100:
+            raise ValueError("Marked comment cannot exceed 100 characters")
+        args = parser.parse_args()
+
+        # Prepare update data
+        update_data = {}
+        if args.get("marked_name") is not None:
+            update_data["marked_name"] = args["marked_name"]
+        if args.get("marked_comment") is not None:
+            update_data["marked_comment"] = args["marked_comment"]
+
+        if not update_data:
+            return {"message": "No valid fields to update"}, 400
+
+        workflow_service = WorkflowService()
+
+        # Create a session and manage the transaction
+        with Session(db.engine, expire_on_commit=False) as session:
+            workflow = workflow_service.update_workflow(
+                session=session,
+                workflow_id=workflow_id,
+                tenant_id=app_model.tenant_id,
+                account_id=current_user.id,
+                data=update_data,
+            )
+
+            if not workflow:
+                raise NotFound("Workflow not found")
+
+            # Commit the transaction in the controller
+            session.commit()
+
+        return workflow
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def delete(self, app_model: App, workflow_id: str):
+        """
+        Delete workflow
+        """
+        # Check permission
+        if not current_user.is_editor:
+            raise Forbidden()
+
+        if not isinstance(current_user, Account):
+            raise Forbidden()
+
+        workflow_service = WorkflowService()
+
+        # Create a session and manage the transaction
+        with Session(db.engine) as session:
+            try:
+                workflow_service.delete_workflow(
+                    session=session, workflow_id=workflow_id, tenant_id=app_model.tenant_id
+                )
+                # Commit the transaction in the controller
+                session.commit()
+            except WorkflowInUseError as e:
+                abort(400, description=str(e))
+            except DraftWorkflowDeletionError as e:
+                abort(400, description=str(e))
+            except ValueError as e:
+                raise NotFound(str(e))
+
+        return None, 204
+
+
+api.add_resource(
+    DraftWorkflowApi,
+    "/apps/<uuid:app_id>/workflows/draft",
+)
+api.add_resource(
+    WorkflowConfigApi,
+    "/apps/<uuid:app_id>/workflows/draft/config",
+)
+api.add_resource(
+    AdvancedChatDraftWorkflowRunApi,
+    "/apps/<uuid:app_id>/advanced-chat/workflows/draft/run",
+)
+api.add_resource(
+    DraftWorkflowRunApi,
+    "/apps/<uuid:app_id>/workflows/draft/run",
+)
+api.add_resource(
+    WorkflowTaskStopApi,
+    "/apps/<uuid:app_id>/workflow-runs/tasks/<string:task_id>/stop",
+)
+api.add_resource(
+    DraftWorkflowNodeRunApi,
+    "/apps/<uuid:app_id>/workflows/draft/nodes/<string:node_id>/run",
+)
 api.add_resource(
     AdvancedChatDraftRunIterationNodeApi,
     "/apps/<uuid:app_id>/advanced-chat/workflows/draft/iteration/nodes/<string:node_id>/run",
 )
 api.add_resource(
-    WorkflowDraftRunIterationNodeApi, "/apps/<uuid:app_id>/workflows/draft/iteration/nodes/<string:node_id>/run"
+    WorkflowDraftRunIterationNodeApi,
+    "/apps/<uuid:app_id>/workflows/draft/iteration/nodes/<string:node_id>/run",
 )
-api.add_resource(PublishedWorkflowApi, "/apps/<uuid:app_id>/workflows/publish")
-api.add_resource(PublishedAllWorkflowApi, "/apps/<uuid:app_id>/workflows")
-api.add_resource(DefaultBlockConfigsApi, "/apps/<uuid:app_id>/workflows/default-workflow-block-configs")
 api.add_resource(
-    DefaultBlockConfigApi, "/apps/<uuid:app_id>/workflows/default-workflow-block-configs/<string:block_type>"
+    AdvancedChatDraftRunLoopNodeApi,
+    "/apps/<uuid:app_id>/advanced-chat/workflows/draft/loop/nodes/<string:node_id>/run",
+)
+api.add_resource(
+    WorkflowDraftRunLoopNodeApi,
+    "/apps/<uuid:app_id>/workflows/draft/loop/nodes/<string:node_id>/run",
+)
+api.add_resource(
+    PublishedWorkflowApi,
+    "/apps/<uuid:app_id>/workflows/publish",
+)
+api.add_resource(
+    PublishedAllWorkflowApi,
+    "/apps/<uuid:app_id>/workflows",
+)
+api.add_resource(
+    DefaultBlockConfigsApi,
+    "/apps/<uuid:app_id>/workflows/default-workflow-block-configs",
+)
+api.add_resource(
+    DefaultBlockConfigApi,
+    "/apps/<uuid:app_id>/workflows/default-workflow-block-configs/<string:block_type>",
+)
+api.add_resource(
+    ConvertToWorkflowApi,
+    "/apps/<uuid:app_id>/convert-to-workflow",
+)
+api.add_resource(
+    WorkflowByIdApi,
+    "/apps/<uuid:app_id>/workflows/<string:workflow_id>",
 )
-api.add_resource(ConvertToWorkflowApi, "/apps/<uuid:app_id>/convert-to-workflow")

api/controllers/console/app/workflow_app_log.py

@@ -1,13 +1,18 @@
+from datetime import datetime
+
 from flask_restful import Resource, marshal_with, reqparse  # type: ignore
 from flask_restful.inputs import int_range  # type: ignore
+from sqlalchemy.orm import Session
 
 from controllers.console import api
 from controllers.console.app.wraps import get_app_model
 from controllers.console.wraps import account_initialization_required, setup_required
+from extensions.ext_database import db
 from fields.workflow_app_log_fields import workflow_app_log_pagination_fields
 from libs.login import login_required
 from models import App
 from models.model import AppMode
+from models.workflow import WorkflowRunStatus
 from services.workflow_app_service import WorkflowAppService
 
 
@@ -24,17 +29,38 @@ class WorkflowAppLogApi(Resource):
         parser = reqparse.RequestParser()
         parser.add_argument("keyword", type=str, location="args")
         parser.add_argument("status", type=str, choices=["succeeded", "failed", "stopped"], location="args")
+        parser.add_argument(
+            "created_at__before", type=str, location="args", help="Filter logs created before this timestamp"
+        )
+        parser.add_argument(
+            "created_at__after", type=str, location="args", help="Filter logs created after this timestamp"
+        )
         parser.add_argument("page", type=int_range(1, 99999), default=1, location="args")
         parser.add_argument("limit", type=int_range(1, 100), default=20, location="args")
         args = parser.parse_args()
 
+        args.status = WorkflowRunStatus(args.status) if args.status else None
+        if args.created_at__before:
+            args.created_at__before = datetime.fromisoformat(args.created_at__before.replace("Z", "+00:00"))
+
+        if args.created_at__after:
+            args.created_at__after = datetime.fromisoformat(args.created_at__after.replace("Z", "+00:00"))
+
         # get paginate workflow app logs
         workflow_app_service = WorkflowAppService()
-        workflow_app_log_pagination = workflow_app_service.get_paginate_workflow_app_logs(
-            app_model=app_model, args=args
-        )
+        with Session(db.engine) as session:
+            workflow_app_log_pagination = workflow_app_service.get_paginate_workflow_app_logs(
+                session=session,
+                app_model=app_model,
+                keyword=args.keyword,
+                status=args.status,
+                created_at_before=args.created_at__before,
+                created_at_after=args.created_at__after,
+                page=args.page,
+                limit=args.limit,
+            )
 
-        return workflow_app_log_pagination
+            return workflow_app_log_pagination
 
 
 api.add_resource(WorkflowAppLogApi, "/apps/<uuid:app_id>/workflow-app-logs")

api/controllers/console/billing/compliance.py

@@ -0,0 +1,35 @@
+from flask import request
+from flask_login import current_user  # type: ignore
+from flask_restful import Resource, reqparse  # type: ignore
+
+from libs.helper import extract_remote_ip
+from libs.login import login_required
+from services.billing_service import BillingService
+
+from .. import api
+from ..wraps import account_initialization_required, only_edition_cloud, setup_required
+
+
+class ComplianceApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @only_edition_cloud
+    def get(self):
+        parser = reqparse.RequestParser()
+        parser.add_argument("doc_name", type=str, required=True, location="args")
+        args = parser.parse_args()
+
+        ip_address = extract_remote_ip(request)
+        device_info = request.headers.get("User-Agent", "Unknown device")
+
+        return BillingService.get_compliance_download_link(
+            doc_name=args.doc_name,
+            account_id=current_user.id,
+            tenant_id=current_user.current_tenant_id,
+            ip=ip_address,
+            device_info=device_info,
+        )
+
+
+api.add_resource(ComplianceApi, "/compliance/download")

api/controllers/console/error.py

@@ -101,3 +101,9 @@ class AccountInFreezeError(BaseHTTPException):
         "This email account has been deleted within the past 30 days"
         "and is temporarily unavailable for new account registration."
     )
+
+
+class CompilanceRateLimitError(BaseHTTPException):
+    error_code = "compilance_rate_limit"
+    description = "Rate limit exceeded for downloading compliance report."
+    code = 429

api/controllers/files/image_preview.py

@@ -1,3 +1,5 @@
+from urllib.parse import quote
+
 from flask import Response, request
 from flask_restful import Resource, reqparse  # type: ignore
 from werkzeug.exceptions import NotFound
@@ -71,7 +73,8 @@ class FilePreviewApi(Resource):
         if upload_file.size > 0:
             response.headers["Content-Length"] = str(upload_file.size)
         if args["as_attachment"]:
-            response.headers["Content-Disposition"] = f"attachment; filename={upload_file.name}"
+            encoded_filename = quote(upload_file.name)
+            response.headers["Content-Disposition"] = f"attachment; filename*=UTF-8''{encoded_filename}"
 
         return response
 

api/controllers/service_api/app/message.py

@@ -70,7 +70,7 @@ class MessageListApi(Resource):
 
         try:
             return MessageService.pagination_by_first_id(
-                app_model, end_user, args["conversation_id"], args["first_id"], args["limit"], "desc"
+                app_model, end_user, args["conversation_id"], args["first_id"], args["limit"]
             )
         except services.errors.conversation.ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")

api/controllers/service_api/app/workflow.py

@@ -1,7 +1,9 @@
 import logging
+from datetime import datetime
 
 from flask_restful import Resource, fields, marshal_with, reqparse  # type: ignore
 from flask_restful.inputs import int_range  # type: ignore
+from sqlalchemy.orm import Session
 from werkzeug.exceptions import InternalServerError
 
 from controllers.service_api import api
@@ -25,7 +27,7 @@ from extensions.ext_database import db
 from fields.workflow_app_log_fields import workflow_app_log_pagination_fields
 from libs import helper
 from models.model import App, AppMode, EndUser
-from models.workflow import WorkflowRun
+from models.workflow import WorkflowRun, WorkflowRunStatus
 from services.app_generate_service import AppGenerateService
 from services.workflow_app_service import WorkflowAppService
 
@@ -125,17 +127,34 @@ class WorkflowAppLogApi(Resource):
         parser = reqparse.RequestParser()
         parser.add_argument("keyword", type=str, location="args")
         parser.add_argument("status", type=str, choices=["succeeded", "failed", "stopped"], location="args")
+        parser.add_argument("created_at__before", type=str, location="args")
+        parser.add_argument("created_at__after", type=str, location="args")
         parser.add_argument("page", type=int_range(1, 99999), default=1, location="args")
         parser.add_argument("limit", type=int_range(1, 100), default=20, location="args")
         args = parser.parse_args()
 
+        args.status = WorkflowRunStatus(args.status) if args.status else None
+        if args.created_at__before:
+            args.created_at__before = datetime.fromisoformat(args.created_at__before.replace("Z", "+00:00"))
+
+        if args.created_at__after:
+            args.created_at__after = datetime.fromisoformat(args.created_at__after.replace("Z", "+00:00"))
+
         # get paginate workflow app logs
         workflow_app_service = WorkflowAppService()
-        workflow_app_log_pagination = workflow_app_service.get_paginate_workflow_app_logs(
-            app_model=app_model, args=args
-        )
+        with Session(db.engine) as session:
+            workflow_app_log_pagination = workflow_app_service.get_paginate_workflow_app_logs(
+                session=session,
+                app_model=app_model,
+                keyword=args.keyword,
+                status=args.status,
+                created_at_before=args.created_at__before,
+                created_at_after=args.created_at__after,
+                page=args.page,
+                limit=args.limit,
+            )
 
-        return workflow_app_log_pagination
+            return workflow_app_log_pagination
 
 
 api.add_resource(WorkflowRunApi, "/workflows/run")

api/controllers/service_api/dataset/document.py

@@ -336,6 +336,10 @@ class DocumentUpdateByFileApi(DatasetApiResource):
 
         if not dataset:
             raise ValueError("Dataset is not exist.")
+
+        # indexing_technique is already set in dataset since this is an update
+        args["indexing_technique"] = dataset.indexing_technique
+
         if "file" in request.files:
             # save file info
             file = request.files["file"]

api/controllers/service_api/wraps.py

@@ -154,7 +154,7 @@ def validate_dataset_token(view=None):
             )  # TODO: only owner information is required, so only one is returned.
             if tenant_account_join:
                 tenant, ta = tenant_account_join
-                account = Account.query.filter_by(id=ta.account_id).first()
+                account = db.session.query(Account).filter(Account.id == ta.account_id).first()
                 # Login admin
                 if account:
                     account.current_tenant = tenant

api/core/agent/entities.py

@@ -1,7 +1,7 @@
 from enum import StrEnum
 from typing import Any, Optional, Union
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 
 from core.tools.entities.tool_entities import ToolInvokeMessage, ToolProviderType
 
@@ -14,7 +14,7 @@ class AgentToolEntity(BaseModel):
     provider_type: ToolProviderType
     provider_id: str
     tool_name: str
-    tool_parameters: dict[str, Any] = {}
+    tool_parameters: dict[str, Any] = Field(default_factory=dict)
     plugin_unique_identifier: str | None = None
 
 

api/core/app/app_config/easy_ui_based_app/model_config/manager.py

@@ -2,9 +2,9 @@ from collections.abc import Mapping
 from typing import Any
 
 from core.app.app_config.entities import ModelConfigEntity
-from core.entities import DEFAULT_PLUGIN_ID
 from core.model_runtime.entities.model_entities import ModelPropertyKey, ModelType
 from core.model_runtime.model_providers.model_provider_factory import ModelProviderFactory
+from core.plugin.entities.plugin import ModelProviderID
 from core.provider_manager import ProviderManager
 
 
@@ -61,9 +61,7 @@ class ModelConfigManager:
             raise ValueError(f"model.provider is required and must be in {str(model_provider_names)}")
 
         if "/" not in config["model"]["provider"]:
-            config["model"]["provider"] = (
-                f"{DEFAULT_PLUGIN_ID}/{config['model']['provider']}/{config['model']['provider']}"
-            )
+            config["model"]["provider"] = str(ModelProviderID(config["model"]["provider"]))
 
         if config["model"]["provider"] not in model_provider_names:
             raise ValueError(f"model.provider is required and must be in {str(model_provider_names)}")

api/core/app/app_config/entities.py

@@ -17,8 +17,8 @@ class ModelConfigEntity(BaseModel):
     provider: str
     model: str
     mode: Optional[str] = None
-    parameters: dict[str, Any] = {}
-    stop: list[str] = []
+    parameters: dict[str, Any] = Field(default_factory=dict)
+    stop: list[str] = Field(default_factory=list)
 
 
 class AdvancedChatMessageEntity(BaseModel):
@@ -132,7 +132,7 @@ class ExternalDataVariableEntity(BaseModel):
 
     variable: str
     type: str
-    config: dict[str, Any] = {}
+    config: dict[str, Any] = Field(default_factory=dict)
 
 
 class DatasetRetrieveConfigEntity(BaseModel):
@@ -188,7 +188,7 @@ class SensitiveWordAvoidanceEntity(BaseModel):
     """
 
     type: str
-    config: dict[str, Any] = {}
+    config: dict[str, Any] = Field(default_factory=dict)
 
 
 class TextToSpeechEntity(BaseModel):

api/core/app/apps/advanced_chat/app_generator.py

@@ -223,6 +223,61 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             stream=streaming,
         )
 
+    def single_loop_generate(
+        self,
+        app_model: App,
+        workflow: Workflow,
+        node_id: str,
+        user: Account | EndUser,
+        args: Mapping,
+        streaming: bool = True,
+    ) -> Mapping[str, Any] | Generator[str | Mapping[str, Any], Any, None]:
+        """
+        Generate App response.
+
+        :param app_model: App
+        :param workflow: Workflow
+        :param user: account or end user
+        :param args: request args
+        :param invoke_from: invoke from source
+        :param stream: is stream
+        """
+        if not node_id:
+            raise ValueError("node_id is required")
+
+        if args.get("inputs") is None:
+            raise ValueError("inputs is required")
+
+        # convert to app config
+        app_config = AdvancedChatAppConfigManager.get_app_config(app_model=app_model, workflow=workflow)
+
+        # init application generate entity
+        application_generate_entity = AdvancedChatAppGenerateEntity(
+            task_id=str(uuid.uuid4()),
+            app_config=app_config,
+            conversation_id=None,
+            inputs={},
+            query="",
+            files=[],
+            user_id=user.id,
+            stream=streaming,
+            invoke_from=InvokeFrom.DEBUGGER,
+            extras={"auto_generate_conversation_name": False},
+            single_loop_run=AdvancedChatAppGenerateEntity.SingleLoopRunEntity(node_id=node_id, inputs=args["inputs"]),
+        )
+        contexts.tenant_id.set(application_generate_entity.app_config.tenant_id)
+        contexts.plugin_tool_providers.set({})
+        contexts.plugin_tool_providers_lock.set(threading.Lock())
+
+        return self._generate(
+            workflow=workflow,
+            user=user,
+            invoke_from=InvokeFrom.DEBUGGER,
+            application_generate_entity=application_generate_entity,
+            conversation=None,
+            stream=streaming,
+        )
+
     def _generate(
         self,
         *,

api/core/app/apps/advanced_chat/app_runner.py

@@ -79,6 +79,13 @@ class AdvancedChatAppRunner(WorkflowBasedAppRunner):
                 node_id=self.application_generate_entity.single_iteration_run.node_id,
                 user_inputs=dict(self.application_generate_entity.single_iteration_run.inputs),
             )
+        elif self.application_generate_entity.single_loop_run:
+            # if only single loop run is requested
+            graph, variable_pool = self._get_graph_and_variable_pool_of_single_loop(
+                workflow=workflow,
+                node_id=self.application_generate_entity.single_loop_run.node_id,
+                user_inputs=dict(self.application_generate_entity.single_loop_run.inputs),
+            )
         else:
             inputs = self.application_generate_entity.inputs
             query = self.application_generate_entity.query

api/core/app/apps/advanced_chat/generate_task_pipeline.py

@@ -23,10 +23,14 @@ from core.app.entities.queue_entities import (
     QueueIterationCompletedEvent,
     QueueIterationNextEvent,
     QueueIterationStartEvent,
+    QueueLoopCompletedEvent,
+    QueueLoopNextEvent,
+    QueueLoopStartEvent,
     QueueMessageReplaceEvent,
     QueueNodeExceptionEvent,
     QueueNodeFailedEvent,
     QueueNodeInIterationFailedEvent,
+    QueueNodeInLoopFailedEvent,
     QueueNodeRetryEvent,
     QueueNodeStartedEvent,
     QueueNodeSucceededEvent,
@@ -372,7 +376,13 @@ class AdvancedChatAppGenerateTaskPipeline:
 
                 if node_finish_resp:
                     yield node_finish_resp
-            elif isinstance(event, QueueNodeFailedEvent | QueueNodeInIterationFailedEvent | QueueNodeExceptionEvent):
+            elif isinstance(
+                event,
+                QueueNodeFailedEvent
+                | QueueNodeInIterationFailedEvent
+                | QueueNodeInLoopFailedEvent
+                | QueueNodeExceptionEvent,
+            ):
                 with Session(db.engine, expire_on_commit=False) as session:
                     workflow_node_execution = self._workflow_cycle_manager._handle_workflow_node_execution_failed(
                         session=session, event=event
@@ -472,6 +482,54 @@ class AdvancedChatAppGenerateTaskPipeline:
                     )
 
                 yield iter_finish_resp
+            elif isinstance(event, QueueLoopStartEvent):
+                if not self._workflow_run_id:
+                    raise ValueError("workflow run not initialized.")
+
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_run = self._workflow_cycle_manager._get_workflow_run(
+                        session=session, workflow_run_id=self._workflow_run_id
+                    )
+                    loop_start_resp = self._workflow_cycle_manager._workflow_loop_start_to_stream_response(
+                        session=session,
+                        task_id=self._application_generate_entity.task_id,
+                        workflow_run=workflow_run,
+                        event=event,
+                    )
+
+                yield loop_start_resp
+            elif isinstance(event, QueueLoopNextEvent):
+                if not self._workflow_run_id:
+                    raise ValueError("workflow run not initialized.")
+
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_run = self._workflow_cycle_manager._get_workflow_run(
+                        session=session, workflow_run_id=self._workflow_run_id
+                    )
+                    loop_next_resp = self._workflow_cycle_manager._workflow_loop_next_to_stream_response(
+                        session=session,
+                        task_id=self._application_generate_entity.task_id,
+                        workflow_run=workflow_run,
+                        event=event,
+                    )
+
+                yield loop_next_resp
+            elif isinstance(event, QueueLoopCompletedEvent):
+                if not self._workflow_run_id:
+                    raise ValueError("workflow run not initialized.")
+
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_run = self._workflow_cycle_manager._get_workflow_run(
+                        session=session, workflow_run_id=self._workflow_run_id
+                    )
+                    loop_finish_resp = self._workflow_cycle_manager._workflow_loop_completed_to_stream_response(
+                        session=session,
+                        task_id=self._application_generate_entity.task_id,
+                        workflow_run=workflow_run,
+                        event=event,
+                    )
+
+                yield loop_finish_resp
             elif isinstance(event, QueueWorkflowSucceededEvent):
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")
@@ -582,6 +640,15 @@ class AdvancedChatAppGenerateTaskPipeline:
                         session.commit()
 
                     yield workflow_finish_resp
+                elif event.stopped_by in (
+                    QueueStopEvent.StopBy.INPUT_MODERATION,
+                    QueueStopEvent.StopBy.ANNOTATION_REPLY,
+                ):
+                    # When hitting input-moderation or annotation-reply, the workflow will not start
+                    with Session(db.engine, expire_on_commit=False) as session:
+                        # Save message
+                        self._save_message(session=session)
+                        session.commit()
 
                 yield self._message_end_to_stream_response()
                 break

api/core/app/apps/message_based_app_generator.py

@@ -42,7 +42,6 @@ class MessageBasedAppGenerator(BaseAppGenerator):
             ChatAppGenerateEntity,
             CompletionAppGenerateEntity,
             AgentChatAppGenerateEntity,
-            AgentChatAppGenerateEntity,
         ],
         queue_manager: AppQueueManager,
         conversation: Conversation,

api/core/app/apps/workflow/app_generator.py

@@ -250,6 +250,60 @@ class WorkflowAppGenerator(BaseAppGenerator):
             streaming=streaming,
         )
 
+    def single_loop_generate(
+        self,
+        app_model: App,
+        workflow: Workflow,
+        node_id: str,
+        user: Account | EndUser,
+        args: Mapping[str, Any],
+        streaming: bool = True,
+    ) -> Mapping[str, Any] | Generator[str | Mapping[str, Any], None, None]:
+        """
+        Generate App response.
+
+        :param app_model: App
+        :param workflow: Workflow
+        :param user: account or end user
+        :param args: request args
+        :param invoke_from: invoke from source
+        :param stream: is stream
+        """
+        if not node_id:
+            raise ValueError("node_id is required")
+
+        if args.get("inputs") is None:
+            raise ValueError("inputs is required")
+
+        # convert to app config
+        app_config = WorkflowAppConfigManager.get_app_config(app_model=app_model, workflow=workflow)
+
+        # init application generate entity
+        application_generate_entity = WorkflowAppGenerateEntity(
+            task_id=str(uuid.uuid4()),
+            app_config=app_config,
+            inputs={},
+            files=[],
+            user_id=user.id,
+            stream=streaming,
+            invoke_from=InvokeFrom.DEBUGGER,
+            extras={"auto_generate_conversation_name": False},
+            single_loop_run=WorkflowAppGenerateEntity.SingleLoopRunEntity(node_id=node_id, inputs=args["inputs"]),
+            workflow_run_id=str(uuid.uuid4()),
+        )
+        contexts.tenant_id.set(application_generate_entity.app_config.tenant_id)
+        contexts.plugin_tool_providers.set({})
+        contexts.plugin_tool_providers_lock.set(threading.Lock())
+
+        return self._generate(
+            app_model=app_model,
+            workflow=workflow,
+            user=user,
+            invoke_from=InvokeFrom.DEBUGGER,
+            application_generate_entity=application_generate_entity,
+            streaming=streaming,
+        )
+
     def _generate_worker(
         self,
         flask_app: Flask,

api/core/app/apps/workflow/app_runner.py

@@ -81,6 +81,13 @@ class WorkflowAppRunner(WorkflowBasedAppRunner):
                 node_id=self.application_generate_entity.single_iteration_run.node_id,
                 user_inputs=self.application_generate_entity.single_iteration_run.inputs,
             )
+        elif self.application_generate_entity.single_loop_run:
+            # if only single loop run is requested
+            graph, variable_pool = self._get_graph_and_variable_pool_of_single_loop(
+                workflow=workflow,
+                node_id=self.application_generate_entity.single_loop_run.node_id,
+                user_inputs=self.application_generate_entity.single_loop_run.inputs,
+            )
         else:
             inputs = self.application_generate_entity.inputs
             files = self.application_generate_entity.files

api/core/app/apps/workflow/generate_task_pipeline.py

@@ -18,9 +18,13 @@ from core.app.entities.queue_entities import (
     QueueIterationCompletedEvent,
     QueueIterationNextEvent,
     QueueIterationStartEvent,
+    QueueLoopCompletedEvent,
+    QueueLoopNextEvent,
+    QueueLoopStartEvent,
     QueueNodeExceptionEvent,
     QueueNodeFailedEvent,
     QueueNodeInIterationFailedEvent,
+    QueueNodeInLoopFailedEvent,
     QueueNodeRetryEvent,
     QueueNodeStartedEvent,
     QueueNodeSucceededEvent,
@@ -323,7 +327,13 @@ class WorkflowAppGenerateTaskPipeline:
 
                 if node_success_response:
                     yield node_success_response
-            elif isinstance(event, QueueNodeFailedEvent | QueueNodeInIterationFailedEvent | QueueNodeExceptionEvent):
+            elif isinstance(
+                event,
+                QueueNodeFailedEvent
+                | QueueNodeInIterationFailedEvent
+                | QueueNodeInLoopFailedEvent
+                | QueueNodeExceptionEvent,
+            ):
                 with Session(db.engine, expire_on_commit=False) as session:
                     workflow_node_execution = self._workflow_cycle_manager._handle_workflow_node_execution_failed(
                         session=session,
@@ -429,6 +439,57 @@ class WorkflowAppGenerateTaskPipeline:
 
                 yield iter_finish_resp
 
+            elif isinstance(event, QueueLoopStartEvent):
+                if not self._workflow_run_id:
+                    raise ValueError("workflow run not initialized.")
+
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_run = self._workflow_cycle_manager._get_workflow_run(
+                        session=session, workflow_run_id=self._workflow_run_id
+                    )
+                    loop_start_resp = self._workflow_cycle_manager._workflow_loop_start_to_stream_response(
+                        session=session,
+                        task_id=self._application_generate_entity.task_id,
+                        workflow_run=workflow_run,
+                        event=event,
+                    )
+
+                yield loop_start_resp
+
+            elif isinstance(event, QueueLoopNextEvent):
+                if not self._workflow_run_id:
+                    raise ValueError("workflow run not initialized.")
+
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_run = self._workflow_cycle_manager._get_workflow_run(
+                        session=session, workflow_run_id=self._workflow_run_id
+                    )
+                    loop_next_resp = self._workflow_cycle_manager._workflow_loop_next_to_stream_response(
+                        session=session,
+                        task_id=self._application_generate_entity.task_id,
+                        workflow_run=workflow_run,
+                        event=event,
+                    )
+
+                yield loop_next_resp
+
+            elif isinstance(event, QueueLoopCompletedEvent):
+                if not self._workflow_run_id:
+                    raise ValueError("workflow run not initialized.")
+
+                with Session(db.engine, expire_on_commit=False) as session:
+                    workflow_run = self._workflow_cycle_manager._get_workflow_run(
+                        session=session, workflow_run_id=self._workflow_run_id
+                    )
+                    loop_finish_resp = self._workflow_cycle_manager._workflow_loop_completed_to_stream_response(
+                        session=session,
+                        task_id=self._application_generate_entity.task_id,
+                        workflow_run=workflow_run,
+                        event=event,
+                    )
+
+                yield loop_finish_resp
+
             elif isinstance(event, QueueWorkflowSucceededEvent):
                 if not self._workflow_run_id:
                     raise ValueError("workflow run not initialized.")

api/core/app/apps/workflow_app_runner.py

@@ -9,9 +9,13 @@ from core.app.entities.queue_entities import (
     QueueIterationCompletedEvent,
     QueueIterationNextEvent,
     QueueIterationStartEvent,
+    QueueLoopCompletedEvent,
+    QueueLoopNextEvent,
+    QueueLoopStartEvent,
     QueueNodeExceptionEvent,
     QueueNodeFailedEvent,
     QueueNodeInIterationFailedEvent,
+    QueueNodeInLoopFailedEvent,
     QueueNodeRetryEvent,
     QueueNodeStartedEvent,
     QueueNodeSucceededEvent,
@@ -38,7 +42,12 @@ from core.workflow.graph_engine.entities.event import (
     IterationRunNextEvent,
     IterationRunStartedEvent,
     IterationRunSucceededEvent,
+    LoopRunFailedEvent,
+    LoopRunNextEvent,
+    LoopRunStartedEvent,
+    LoopRunSucceededEvent,
     NodeInIterationFailedEvent,
+    NodeInLoopFailedEvent,
     NodeRunExceptionEvent,
     NodeRunFailedEvent,
     NodeRunRetrieverResourceEvent,
@@ -173,6 +182,96 @@ class WorkflowBasedAppRunner(AppRunner):
 
         return graph, variable_pool
 
+    def _get_graph_and_variable_pool_of_single_loop(
+        self,
+        workflow: Workflow,
+        node_id: str,
+        user_inputs: dict,
+    ) -> tuple[Graph, VariablePool]:
+        """
+        Get variable pool of single loop
+        """
+        # fetch workflow graph
+        graph_config = workflow.graph_dict
+        if not graph_config:
+            raise ValueError("workflow graph not found")
+
+        graph_config = cast(dict[str, Any], graph_config)
+
+        if "nodes" not in graph_config or "edges" not in graph_config:
+            raise ValueError("nodes or edges not found in workflow graph")
+
+        if not isinstance(graph_config.get("nodes"), list):
+            raise ValueError("nodes in workflow graph must be a list")
+
+        if not isinstance(graph_config.get("edges"), list):
+            raise ValueError("edges in workflow graph must be a list")
+
+        # filter nodes only in loop
+        node_configs = [
+            node
+            for node in graph_config.get("nodes", [])
+            if node.get("id") == node_id or node.get("data", {}).get("loop_id", "") == node_id
+        ]
+
+        graph_config["nodes"] = node_configs
+
+        node_ids = [node.get("id") for node in node_configs]
+
+        # filter edges only in loop
+        edge_configs = [
+            edge
+            for edge in graph_config.get("edges", [])
+            if (edge.get("source") is None or edge.get("source") in node_ids)
+            and (edge.get("target") is None or edge.get("target") in node_ids)
+        ]
+
+        graph_config["edges"] = edge_configs
+
+        # init graph
+        graph = Graph.init(graph_config=graph_config, root_node_id=node_id)
+
+        if not graph:
+            raise ValueError("graph not found in workflow")
+
+        # fetch node config from node id
+        loop_node_config = None
+        for node in node_configs:
+            if node.get("id") == node_id:
+                loop_node_config = node
+                break
+
+        if not loop_node_config:
+            raise ValueError("loop node id not found in workflow graph")
+
+        # Get node class
+        node_type = NodeType(loop_node_config.get("data", {}).get("type"))
+        node_version = loop_node_config.get("data", {}).get("version", "1")
+        node_cls = NODE_TYPE_CLASSES_MAPPING[node_type][node_version]
+
+        # init variable pool
+        variable_pool = VariablePool(
+            system_variables={},
+            user_inputs={},
+            environment_variables=workflow.environment_variables,
+        )
+
+        try:
+            variable_mapping = node_cls.extract_variable_selector_to_variable_mapping(
+                graph_config=workflow.graph_dict, config=loop_node_config
+            )
+        except NotImplementedError:
+            variable_mapping = {}
+
+        WorkflowEntry.mapping_user_inputs_to_variable_pool(
+            variable_mapping=variable_mapping,
+            user_inputs=user_inputs,
+            variable_pool=variable_pool,
+            tenant_id=workflow.tenant_id,
+        )
+
+        return graph, variable_pool
+
     def _handle_event(self, workflow_entry: WorkflowEntry, event: GraphEngineEvent) -> None:
         """
         Handle event
@@ -216,6 +315,7 @@ class WorkflowBasedAppRunner(AppRunner):
                     node_run_index=event.route_node_state.index,
                     predecessor_node_id=event.predecessor_node_id,
                     in_iteration_id=event.in_iteration_id,
+                    in_loop_id=event.in_loop_id,
                     parallel_mode_run_id=event.parallel_mode_run_id,
                     inputs=inputs,
                     process_data=process_data,
@@ -240,6 +340,7 @@ class WorkflowBasedAppRunner(AppRunner):
                     node_run_index=event.route_node_state.index,
                     predecessor_node_id=event.predecessor_node_id,
                     in_iteration_id=event.in_iteration_id,
+                    in_loop_id=event.in_loop_id,
                     parallel_mode_run_id=event.parallel_mode_run_id,
                     agent_strategy=event.agent_strategy,
                 )
@@ -272,6 +373,7 @@ class WorkflowBasedAppRunner(AppRunner):
                     outputs=outputs,
                     execution_metadata=execution_metadata,
                     in_iteration_id=event.in_iteration_id,
+                    in_loop_id=event.in_loop_id,
                 )
             )
         elif isinstance(event, NodeRunFailedEvent):
@@ -302,6 +404,7 @@ class WorkflowBasedAppRunner(AppRunner):
                     if event.route_node_state.node_run_result
                     else {},
                     in_iteration_id=event.in_iteration_id,
+                    in_loop_id=event.in_loop_id,
                 )
             )
         elif isinstance(event, NodeRunExceptionEvent):
@@ -332,6 +435,7 @@ class WorkflowBasedAppRunner(AppRunner):
                     if event.route_node_state.node_run_result
                     else {},
                     in_iteration_id=event.in_iteration_id,
+                    in_loop_id=event.in_loop_id,
                 )
             )
         elif isinstance(event, NodeInIterationFailedEvent):
@@ -362,18 +466,49 @@ class WorkflowBasedAppRunner(AppRunner):
                     error=event.error,
                 )
             )
+        elif isinstance(event, NodeInLoopFailedEvent):
+            self._publish_event(
+                QueueNodeInLoopFailedEvent(
+                    node_execution_id=event.id,
+                    node_id=event.node_id,
+                    node_type=event.node_type,
+                    node_data=event.node_data,
+                    parallel_id=event.parallel_id,
+                    parallel_start_node_id=event.parallel_start_node_id,
+                    parent_parallel_id=event.parent_parallel_id,
+                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
+                    start_at=event.route_node_state.start_at,
+                    inputs=event.route_node_state.node_run_result.inputs
+                    if event.route_node_state.node_run_result
+                    else {},
+                    process_data=event.route_node_state.node_run_result.process_data
+                    if event.route_node_state.node_run_result
+                    else {},
+                    outputs=event.route_node_state.node_run_result.outputs or {}
+                    if event.route_node_state.node_run_result
+                    else {},
+                    execution_metadata=event.route_node_state.node_run_result.metadata
+                    if event.route_node_state.node_run_result
+                    else {},
+                    in_loop_id=event.in_loop_id,
+                    error=event.error,
+                )
+            )
         elif isinstance(event, NodeRunStreamChunkEvent):
             self._publish_event(
                 QueueTextChunkEvent(
                     text=event.chunk_content,
                     from_variable_selector=event.from_variable_selector,
                     in_iteration_id=event.in_iteration_id,
+                    in_loop_id=event.in_loop_id,
                 )
             )
         elif isinstance(event, NodeRunRetrieverResourceEvent):
             self._publish_event(
                 QueueRetrieverResourcesEvent(
-                    retriever_resources=event.retriever_resources, in_iteration_id=event.in_iteration_id
+                    retriever_resources=event.retriever_resources,
+                    in_iteration_id=event.in_iteration_id,
+                    in_loop_id=event.in_loop_id,
                 )
             )
         elif isinstance(event, AgentLogEvent):
@@ -387,6 +522,7 @@ class WorkflowBasedAppRunner(AppRunner):
                     status=event.status,
                     data=event.data,
                     metadata=event.metadata,
+                    node_id=event.node_id,
                 )
             )
         elif isinstance(event, ParallelBranchRunStartedEvent):
@@ -397,6 +533,7 @@ class WorkflowBasedAppRunner(AppRunner):
                     parent_parallel_id=event.parent_parallel_id,
                     parent_parallel_start_node_id=event.parent_parallel_start_node_id,
                     in_iteration_id=event.in_iteration_id,
+                    in_loop_id=event.in_loop_id,
                 )
             )
         elif isinstance(event, ParallelBranchRunSucceededEvent):
@@ -407,6 +544,7 @@ class WorkflowBasedAppRunner(AppRunner):
                     parent_parallel_id=event.parent_parallel_id,
                     parent_parallel_start_node_id=event.parent_parallel_start_node_id,
                     in_iteration_id=event.in_iteration_id,
+                    in_loop_id=event.in_loop_id,
                 )
             )
         elif isinstance(event, ParallelBranchRunFailedEvent):
@@ -417,6 +555,7 @@ class WorkflowBasedAppRunner(AppRunner):
                     parent_parallel_id=event.parent_parallel_id,
                     parent_parallel_start_node_id=event.parent_parallel_start_node_id,
                     in_iteration_id=event.in_iteration_id,
+                    in_loop_id=event.in_loop_id,
                     error=event.error,
                 )
             )
@@ -476,6 +615,62 @@ class WorkflowBasedAppRunner(AppRunner):
                     error=event.error if isinstance(event, IterationRunFailedEvent) else None,
                 )
             )
+        elif isinstance(event, LoopRunStartedEvent):
+            self._publish_event(
+                QueueLoopStartEvent(
+                    node_execution_id=event.loop_id,
+                    node_id=event.loop_node_id,
+                    node_type=event.loop_node_type,
+                    node_data=event.loop_node_data,
+                    parallel_id=event.parallel_id,
+                    parallel_start_node_id=event.parallel_start_node_id,
+                    parent_parallel_id=event.parent_parallel_id,
+                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
+                    start_at=event.start_at,
+                    node_run_index=workflow_entry.graph_engine.graph_runtime_state.node_run_steps,
+                    inputs=event.inputs,
+                    predecessor_node_id=event.predecessor_node_id,
+                    metadata=event.metadata,
+                )
+            )
+        elif isinstance(event, LoopRunNextEvent):
+            self._publish_event(
+                QueueLoopNextEvent(
+                    node_execution_id=event.loop_id,
+                    node_id=event.loop_node_id,
+                    node_type=event.loop_node_type,
+                    node_data=event.loop_node_data,
+                    parallel_id=event.parallel_id,
+                    parallel_start_node_id=event.parallel_start_node_id,
+                    parent_parallel_id=event.parent_parallel_id,
+                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
+                    index=event.index,
+                    node_run_index=workflow_entry.graph_engine.graph_runtime_state.node_run_steps,
+                    output=event.pre_loop_output,
+                    parallel_mode_run_id=event.parallel_mode_run_id,
+                    duration=event.duration,
+                )
+            )
+        elif isinstance(event, (LoopRunSucceededEvent | LoopRunFailedEvent)):
+            self._publish_event(
+                QueueLoopCompletedEvent(
+                    node_execution_id=event.loop_id,
+                    node_id=event.loop_node_id,
+                    node_type=event.loop_node_type,
+                    node_data=event.loop_node_data,
+                    parallel_id=event.parallel_id,
+                    parallel_start_node_id=event.parallel_start_node_id,
+                    parent_parallel_id=event.parent_parallel_id,
+                    parent_parallel_start_node_id=event.parent_parallel_start_node_id,
+                    start_at=event.start_at,
+                    node_run_index=workflow_entry.graph_engine.graph_runtime_state.node_run_steps,
+                    inputs=event.inputs,
+                    outputs=event.outputs,
+                    metadata=event.metadata,
+                    steps=event.steps,
+                    error=event.error if isinstance(event, LoopRunFailedEvent) else None,
+                )
+            )
 
     def get_workflow(self, app_model: App, workflow_id: str) -> Optional[Workflow]:
         """

api/core/app/entities/app_invoke_entities.py

@@ -63,9 +63,9 @@ class ModelConfigWithCredentialsEntity(BaseModel):
     model_schema: AIModelEntity
     mode: str
     provider_model_bundle: ProviderModelBundle
-    credentials: dict[str, Any] = {}
-    parameters: dict[str, Any] = {}
-    stop: list[str] = []
+    credentials: dict[str, Any] = Field(default_factory=dict)
+    parameters: dict[str, Any] = Field(default_factory=dict)
+    stop: list[str] = Field(default_factory=list)
 
     # pydantic configs
     model_config = ConfigDict(protected_namespaces=())
@@ -94,7 +94,7 @@ class AppGenerateEntity(BaseModel):
     call_depth: int = 0
 
     # extra parameters, like: auto_generate_conversation_name
-    extras: dict[str, Any] = {}
+    extras: dict[str, Any] = Field(default_factory=dict)
 
     # tracing instance
     trace_manager: Optional[TraceQueueManager] = None
@@ -187,6 +187,16 @@ class AdvancedChatAppGenerateEntity(ConversationAppGenerateEntity):
 
     single_iteration_run: Optional[SingleIterationRunEntity] = None
 
+    class SingleLoopRunEntity(BaseModel):
+        """
+        Single Loop Run Entity.
+        """
+
+        node_id: str
+        inputs: Mapping
+
+    single_loop_run: Optional[SingleLoopRunEntity] = None
+
 
 class WorkflowAppGenerateEntity(AppGenerateEntity):
     """
@@ -206,3 +216,13 @@ class WorkflowAppGenerateEntity(AppGenerateEntity):
         inputs: dict
 
     single_iteration_run: Optional[SingleIterationRunEntity] = None
+
+    class SingleLoopRunEntity(BaseModel):
+        """
+        Single Loop Run Entity.
+        """
+
+        node_id: str
+        inputs: dict
+
+    single_loop_run: Optional[SingleLoopRunEntity] = None

api/core/app/entities/queue_entities.py

@@ -30,6 +30,9 @@ class QueueEvent(StrEnum):
     ITERATION_START = "iteration_start"
     ITERATION_NEXT = "iteration_next"
     ITERATION_COMPLETED = "iteration_completed"
+    LOOP_START = "loop_start"
+    LOOP_NEXT = "loop_next"
+    LOOP_COMPLETED = "loop_completed"
     NODE_STARTED = "node_started"
     NODE_SUCCEEDED = "node_succeeded"
     NODE_FAILED = "node_failed"
@@ -149,6 +152,89 @@ class QueueIterationCompletedEvent(AppQueueEvent):
     error: Optional[str] = None
 
 
+class QueueLoopStartEvent(AppQueueEvent):
+    """
+    QueueLoopStartEvent entity
+    """
+
+    event: QueueEvent = QueueEvent.LOOP_START
+    node_execution_id: str
+    node_id: str
+    node_type: NodeType
+    node_data: BaseNodeData
+    parallel_id: Optional[str] = None
+    """parallel id if node is in parallel"""
+    parallel_start_node_id: Optional[str] = None
+    """parallel start node id if node is in parallel"""
+    parent_parallel_id: Optional[str] = None
+    """parent parallel id if node is in parallel"""
+    parent_parallel_start_node_id: Optional[str] = None
+    """parent parallel start node id if node is in parallel"""
+    start_at: datetime
+
+    node_run_index: int
+    inputs: Optional[Mapping[str, Any]] = None
+    predecessor_node_id: Optional[str] = None
+    metadata: Optional[Mapping[str, Any]] = None
+
+
+class QueueLoopNextEvent(AppQueueEvent):
+    """
+    QueueLoopNextEvent entity
+    """
+
+    event: QueueEvent = QueueEvent.LOOP_NEXT
+
+    index: int
+    node_execution_id: str
+    node_id: str
+    node_type: NodeType
+    node_data: BaseNodeData
+    parallel_id: Optional[str] = None
+    """parallel id if node is in parallel"""
+    parallel_start_node_id: Optional[str] = None
+    """parallel start node id if node is in parallel"""
+    parent_parallel_id: Optional[str] = None
+    """parent parallel id if node is in parallel"""
+    parent_parallel_start_node_id: Optional[str] = None
+    """parent parallel start node id if node is in parallel"""
+    parallel_mode_run_id: Optional[str] = None
+    """iteratoin run in parallel mode run id"""
+    node_run_index: int
+    output: Optional[Any] = None  # output for the current loop
+    duration: Optional[float] = None
+
+
+class QueueLoopCompletedEvent(AppQueueEvent):
+    """
+    QueueLoopCompletedEvent entity
+    """
+
+    event: QueueEvent = QueueEvent.LOOP_COMPLETED
+
+    node_execution_id: str
+    node_id: str
+    node_type: NodeType
+    node_data: BaseNodeData
+    parallel_id: Optional[str] = None
+    """parallel id if node is in parallel"""
+    parallel_start_node_id: Optional[str] = None
+    """parallel start node id if node is in parallel"""
+    parent_parallel_id: Optional[str] = None
+    """parent parallel id if node is in parallel"""
+    parent_parallel_start_node_id: Optional[str] = None
+    """parent parallel start node id if node is in parallel"""
+    start_at: datetime
+
+    node_run_index: int
+    inputs: Optional[Mapping[str, Any]] = None
+    outputs: Optional[Mapping[str, Any]] = None
+    metadata: Optional[Mapping[str, Any]] = None
+    steps: int = 0
+
+    error: Optional[str] = None
+
+
 class QueueTextChunkEvent(AppQueueEvent):
     """
     QueueTextChunkEvent entity
@@ -160,6 +246,8 @@ class QueueTextChunkEvent(AppQueueEvent):
     """from variable selector"""
     in_iteration_id: Optional[str] = None
     """iteration id if node is in iteration"""
+    in_loop_id: Optional[str] = None
+    """loop id if node is in loop"""
 
 
 class QueueAgentMessageEvent(AppQueueEvent):
@@ -189,6 +277,8 @@ class QueueRetrieverResourcesEvent(AppQueueEvent):
     retriever_resources: list[dict]
     in_iteration_id: Optional[str] = None
     """iteration id if node is in iteration"""
+    in_loop_id: Optional[str] = None
+    """loop id if node is in loop"""
 
 
 class QueueAnnotationReplyEvent(AppQueueEvent):
@@ -278,6 +368,8 @@ class QueueNodeStartedEvent(AppQueueEvent):
     """parent parallel start node id if node is in parallel"""
     in_iteration_id: Optional[str] = None
     """iteration id if node is in iteration"""
+    in_loop_id: Optional[str] = None
+    """loop id if node is in loop"""
     start_at: datetime
     parallel_mode_run_id: Optional[str] = None
     """iteratoin run in parallel mode run id"""
@@ -305,6 +397,8 @@ class QueueNodeSucceededEvent(AppQueueEvent):
     """parent parallel start node id if node is in parallel"""
     in_iteration_id: Optional[str] = None
     """iteration id if node is in iteration"""
+    in_loop_id: Optional[str] = None
+    """loop id if node is in loop"""
     start_at: datetime
 
     inputs: Optional[Mapping[str, Any]] = None
@@ -315,6 +409,8 @@ class QueueNodeSucceededEvent(AppQueueEvent):
     error: Optional[str] = None
     """single iteration duration map"""
     iteration_duration_map: Optional[dict[str, float]] = None
+    """single loop duration map"""
+    loop_duration_map: Optional[dict[str, float]] = None
 
 
 class QueueAgentLogEvent(AppQueueEvent):
@@ -331,6 +427,7 @@ class QueueAgentLogEvent(AppQueueEvent):
     status: str
     data: Mapping[str, Any]
     metadata: Optional[Mapping[str, Any]] = None
+    node_id: str
 
 
 class QueueNodeRetryEvent(QueueNodeStartedEvent):
@@ -368,6 +465,41 @@ class QueueNodeInIterationFailedEvent(AppQueueEvent):
     """parent parallel start node id if node is in parallel"""
     in_iteration_id: Optional[str] = None
     """iteration id if node is in iteration"""
+    in_loop_id: Optional[str] = None
+    """loop id if node is in loop"""
+    start_at: datetime
+
+    inputs: Optional[Mapping[str, Any]] = None
+    process_data: Optional[Mapping[str, Any]] = None
+    outputs: Optional[Mapping[str, Any]] = None
+    execution_metadata: Optional[Mapping[NodeRunMetadataKey, Any]] = None
+
+    error: str
+
+
+class QueueNodeInLoopFailedEvent(AppQueueEvent):
+    """
+    QueueNodeInLoopFailedEvent entity
+    """
+
+    event: QueueEvent = QueueEvent.NODE_FAILED
+
+    node_execution_id: str
+    node_id: str
+    node_type: NodeType
+    node_data: BaseNodeData
+    parallel_id: Optional[str] = None
+    """parallel id if node is in parallel"""
+    parallel_start_node_id: Optional[str] = None
+    """parallel start node id if node is in parallel"""
+    parent_parallel_id: Optional[str] = None
+    """parent parallel id if node is in parallel"""
+    parent_parallel_start_node_id: Optional[str] = None
+    """parent parallel start node id if node is in parallel"""
+    in_iteration_id: Optional[str] = None
+    """iteration id if node is in iteration"""
+    in_loop_id: Optional[str] = None
+    """loop id if node is in loop"""
     start_at: datetime
 
     inputs: Optional[Mapping[str, Any]] = None
@@ -399,6 +531,8 @@ class QueueNodeExceptionEvent(AppQueueEvent):
     """parent parallel start node id if node is in parallel"""
     in_iteration_id: Optional[str] = None
     """iteration id if node is in iteration"""
+    in_loop_id: Optional[str] = None
+    """loop id if node is in loop"""
     start_at: datetime
 
     inputs: Optional[Mapping[str, Any]] = None
@@ -430,6 +564,8 @@ class QueueNodeFailedEvent(AppQueueEvent):
     """parent parallel start node id if node is in parallel"""
     in_iteration_id: Optional[str] = None
     """iteration id if node is in iteration"""
+    in_loop_id: Optional[str] = None
+    """loop id if node is in loop"""
     start_at: datetime
 
     inputs: Optional[Mapping[str, Any]] = None
@@ -549,6 +685,8 @@ class QueueParallelBranchRunStartedEvent(AppQueueEvent):
     """parent parallel start node id if node is in parallel"""
     in_iteration_id: Optional[str] = None
     """iteration id if node is in iteration"""
+    in_loop_id: Optional[str] = None
+    """loop id if node is in loop"""
 
 
 class QueueParallelBranchRunSucceededEvent(AppQueueEvent):
@@ -566,6 +704,8 @@ class QueueParallelBranchRunSucceededEvent(AppQueueEvent):
     """parent parallel start node id if node is in parallel"""
     in_iteration_id: Optional[str] = None
     """iteration id if node is in iteration"""
+    in_loop_id: Optional[str] = None
+    """loop id if node is in loop"""
 
 
 class QueueParallelBranchRunFailedEvent(AppQueueEvent):
@@ -583,4 +723,6 @@ class QueueParallelBranchRunFailedEvent(AppQueueEvent):
     """parent parallel start node id if node is in parallel"""
     in_iteration_id: Optional[str] = None
     """iteration id if node is in iteration"""
+    in_loop_id: Optional[str] = None
+    """loop id if node is in loop"""
     error: str

api/core/app/entities/task_entities.py

@@ -59,6 +59,9 @@ class StreamEvent(Enum):
     ITERATION_STARTED = "iteration_started"
     ITERATION_NEXT = "iteration_next"
     ITERATION_COMPLETED = "iteration_completed"
+    LOOP_STARTED = "loop_started"
+    LOOP_NEXT = "loop_next"
+    LOOP_COMPLETED = "loop_completed"
     TEXT_CHUNK = "text_chunk"
     TEXT_REPLACE = "text_replace"
     AGENT_LOG = "agent_log"
@@ -248,6 +251,7 @@ class NodeStartStreamResponse(StreamResponse):
         parent_parallel_id: Optional[str] = None
         parent_parallel_start_node_id: Optional[str] = None
         iteration_id: Optional[str] = None
+        loop_id: Optional[str] = None
         parallel_run_id: Optional[str] = None
         agent_strategy: Optional[AgentNodeStrategyInit] = None
 
@@ -275,6 +279,7 @@ class NodeStartStreamResponse(StreamResponse):
                 "parent_parallel_id": self.data.parent_parallel_id,
                 "parent_parallel_start_node_id": self.data.parent_parallel_start_node_id,
                 "iteration_id": self.data.iteration_id,
+                "loop_id": self.data.loop_id,
             },
         }
 
@@ -310,6 +315,7 @@ class NodeFinishStreamResponse(StreamResponse):
         parent_parallel_id: Optional[str] = None
         parent_parallel_start_node_id: Optional[str] = None
         iteration_id: Optional[str] = None
+        loop_id: Optional[str] = None
 
     event: StreamEvent = StreamEvent.NODE_FINISHED
     workflow_run_id: str
@@ -342,6 +348,7 @@ class NodeFinishStreamResponse(StreamResponse):
                 "parent_parallel_id": self.data.parent_parallel_id,
                 "parent_parallel_start_node_id": self.data.parent_parallel_start_node_id,
                 "iteration_id": self.data.iteration_id,
+                "loop_id": self.data.loop_id,
             },
         }
 
@@ -377,6 +384,7 @@ class NodeRetryStreamResponse(StreamResponse):
         parent_parallel_id: Optional[str] = None
         parent_parallel_start_node_id: Optional[str] = None
         iteration_id: Optional[str] = None
+        loop_id: Optional[str] = None
         retry_index: int = 0
 
     event: StreamEvent = StreamEvent.NODE_RETRY
@@ -410,6 +418,7 @@ class NodeRetryStreamResponse(StreamResponse):
                 "parent_parallel_id": self.data.parent_parallel_id,
                 "parent_parallel_start_node_id": self.data.parent_parallel_start_node_id,
                 "iteration_id": self.data.iteration_id,
+                "loop_id": self.data.loop_id,
                 "retry_index": self.data.retry_index,
             },
         }
@@ -430,6 +439,7 @@ class ParallelBranchStartStreamResponse(StreamResponse):
         parent_parallel_id: Optional[str] = None
         parent_parallel_start_node_id: Optional[str] = None
         iteration_id: Optional[str] = None
+        loop_id: Optional[str] = None
         created_at: int
 
     event: StreamEvent = StreamEvent.PARALLEL_BRANCH_STARTED
@@ -452,6 +462,7 @@ class ParallelBranchFinishedStreamResponse(StreamResponse):
         parent_parallel_id: Optional[str] = None
         parent_parallel_start_node_id: Optional[str] = None
         iteration_id: Optional[str] = None
+        loop_id: Optional[str] = None
         status: str
         error: Optional[str] = None
         created_at: int
@@ -548,6 +559,93 @@ class IterationNodeCompletedStreamResponse(StreamResponse):
     data: Data
 
 
+class LoopNodeStartStreamResponse(StreamResponse):
+    """
+    NodeStartStreamResponse entity
+    """
+
+    class Data(BaseModel):
+        """
+        Data entity
+        """
+
+        id: str
+        node_id: str
+        node_type: str
+        title: str
+        created_at: int
+        extras: dict = {}
+        metadata: Mapping = {}
+        inputs: Mapping = {}
+        parallel_id: Optional[str] = None
+        parallel_start_node_id: Optional[str] = None
+
+    event: StreamEvent = StreamEvent.LOOP_STARTED
+    workflow_run_id: str
+    data: Data
+
+
+class LoopNodeNextStreamResponse(StreamResponse):
+    """
+    NodeStartStreamResponse entity
+    """
+
+    class Data(BaseModel):
+        """
+        Data entity
+        """
+
+        id: str
+        node_id: str
+        node_type: str
+        title: str
+        index: int
+        created_at: int
+        pre_loop_output: Optional[Any] = None
+        extras: dict = {}
+        parallel_id: Optional[str] = None
+        parallel_start_node_id: Optional[str] = None
+        parallel_mode_run_id: Optional[str] = None
+        duration: Optional[float] = None
+
+    event: StreamEvent = StreamEvent.LOOP_NEXT
+    workflow_run_id: str
+    data: Data
+
+
+class LoopNodeCompletedStreamResponse(StreamResponse):
+    """
+    NodeCompletedStreamResponse entity
+    """
+
+    class Data(BaseModel):
+        """
+        Data entity
+        """
+
+        id: str
+        node_id: str
+        node_type: str
+        title: str
+        outputs: Optional[Mapping] = None
+        created_at: int
+        extras: Optional[dict] = None
+        inputs: Optional[Mapping] = None
+        status: WorkflowNodeExecutionStatus
+        error: Optional[str] = None
+        elapsed_time: float
+        total_tokens: int
+        execution_metadata: Optional[Mapping] = None
+        finished_at: int
+        steps: int
+        parallel_id: Optional[str] = None
+        parallel_start_node_id: Optional[str] = None
+
+    event: StreamEvent = StreamEvent.LOOP_COMPLETED
+    workflow_run_id: str
+    data: Data
+
+
 class TextChunkStreamResponse(StreamResponse):
     """
     TextChunkStreamResponse entity
@@ -719,6 +817,7 @@ class AgentLogStreamResponse(StreamResponse):
         status: str
         data: Mapping[str, Any]
         metadata: Optional[Mapping[str, Any]] = None
+        node_id: str
 
     event: StreamEvent = StreamEvent.AGENT_LOG
     data: Data

api/core/app/task_pipeline/workflow_cycle_manage.py

@@ -14,9 +14,13 @@ from core.app.entities.queue_entities import (
     QueueIterationCompletedEvent,
     QueueIterationNextEvent,
     QueueIterationStartEvent,
+    QueueLoopCompletedEvent,
+    QueueLoopNextEvent,
+    QueueLoopStartEvent,
     QueueNodeExceptionEvent,
     QueueNodeFailedEvent,
     QueueNodeInIterationFailedEvent,
+    QueueNodeInLoopFailedEvent,
     QueueNodeRetryEvent,
     QueueNodeStartedEvent,
     QueueNodeSucceededEvent,
@@ -29,6 +33,9 @@ from core.app.entities.task_entities import (
     IterationNodeCompletedStreamResponse,
     IterationNodeNextStreamResponse,
     IterationNodeStartStreamResponse,
+    LoopNodeCompletedStreamResponse,
+    LoopNodeNextStreamResponse,
+    LoopNodeStartStreamResponse,
     NodeFinishStreamResponse,
     NodeRetryStreamResponse,
     NodeStartStreamResponse,
@@ -304,6 +311,7 @@ class WorkflowCycleManage:
             {
                 NodeRunMetadataKey.PARALLEL_MODE_RUN_ID: event.parallel_mode_run_id,
                 NodeRunMetadataKey.ITERATION_ID: event.in_iteration_id,
+                NodeRunMetadataKey.LOOP_ID: event.in_loop_id,
             }
         )
         workflow_node_execution.created_at = datetime.now(UTC).replace(tzinfo=None)
@@ -344,7 +352,10 @@ class WorkflowCycleManage:
         self,
         *,
         session: Session,
-        event: QueueNodeFailedEvent | QueueNodeInIterationFailedEvent | QueueNodeExceptionEvent,
+        event: QueueNodeFailedEvent
+        | QueueNodeInIterationFailedEvent
+        | QueueNodeInLoopFailedEvent
+        | QueueNodeExceptionEvent,
     ) -> WorkflowNodeExecution:
         """
         Workflow node execution failed
@@ -396,6 +407,7 @@ class WorkflowCycleManage:
         origin_metadata = {
             NodeRunMetadataKey.ITERATION_ID: event.in_iteration_id,
             NodeRunMetadataKey.PARALLEL_MODE_RUN_ID: event.parallel_mode_run_id,
+            NodeRunMetadataKey.LOOP_ID: event.in_loop_id,
         }
         merged_metadata = (
             {**jsonable_encoder(event.execution_metadata), **origin_metadata}
@@ -540,6 +552,7 @@ class WorkflowCycleManage:
                 parent_parallel_id=event.parent_parallel_id,
                 parent_parallel_start_node_id=event.parent_parallel_start_node_id,
                 iteration_id=event.in_iteration_id,
+                loop_id=event.in_loop_id,
                 parallel_run_id=event.parallel_mode_run_id,
                 agent_strategy=event.agent_strategy,
             ),
@@ -563,6 +576,7 @@ class WorkflowCycleManage:
         event: QueueNodeSucceededEvent
         | QueueNodeFailedEvent
         | QueueNodeInIterationFailedEvent
+        | QueueNodeInLoopFailedEvent
         | QueueNodeExceptionEvent,
         task_id: str,
         workflow_node_execution: WorkflowNodeExecution,
@@ -601,6 +615,7 @@ class WorkflowCycleManage:
                 parent_parallel_id=event.parent_parallel_id,
                 parent_parallel_start_node_id=event.parent_parallel_start_node_id,
                 iteration_id=event.in_iteration_id,
+                loop_id=event.in_loop_id,
             ),
         )
 
@@ -646,6 +661,7 @@ class WorkflowCycleManage:
                 parent_parallel_id=event.parent_parallel_id,
                 parent_parallel_start_node_id=event.parent_parallel_start_node_id,
                 iteration_id=event.in_iteration_id,
+                loop_id=event.in_loop_id,
                 retry_index=event.retry_index,
             ),
         )
@@ -664,6 +680,7 @@ class WorkflowCycleManage:
                 parent_parallel_id=event.parent_parallel_id,
                 parent_parallel_start_node_id=event.parent_parallel_start_node_id,
                 iteration_id=event.in_iteration_id,
+                loop_id=event.in_loop_id,
                 created_at=int(time.time()),
             ),
         )
@@ -687,6 +704,7 @@ class WorkflowCycleManage:
                 parent_parallel_id=event.parent_parallel_id,
                 parent_parallel_start_node_id=event.parent_parallel_start_node_id,
                 iteration_id=event.in_iteration_id,
+                loop_id=event.in_loop_id,
                 status="succeeded" if isinstance(event, QueueParallelBranchRunSucceededEvent) else "failed",
                 error=event.error if isinstance(event, QueueParallelBranchRunFailedEvent) else None,
                 created_at=int(time.time()),
@@ -770,6 +788,83 @@ class WorkflowCycleManage:
             ),
         )
 
+    def _workflow_loop_start_to_stream_response(
+        self, *, session: Session, task_id: str, workflow_run: WorkflowRun, event: QueueLoopStartEvent
+    ) -> LoopNodeStartStreamResponse:
+        # receive session to make sure the workflow_run won't be expired, need a more elegant way to handle this
+        _ = session
+        return LoopNodeStartStreamResponse(
+            task_id=task_id,
+            workflow_run_id=workflow_run.id,
+            data=LoopNodeStartStreamResponse.Data(
+                id=event.node_id,
+                node_id=event.node_id,
+                node_type=event.node_type.value,
+                title=event.node_data.title,
+                created_at=int(time.time()),
+                extras={},
+                inputs=event.inputs or {},
+                metadata=event.metadata or {},
+                parallel_id=event.parallel_id,
+                parallel_start_node_id=event.parallel_start_node_id,
+            ),
+        )
+
+    def _workflow_loop_next_to_stream_response(
+        self, *, session: Session, task_id: str, workflow_run: WorkflowRun, event: QueueLoopNextEvent
+    ) -> LoopNodeNextStreamResponse:
+        # receive session to make sure the workflow_run won't be expired, need a more elegant way to handle this
+        _ = session
+        return LoopNodeNextStreamResponse(
+            task_id=task_id,
+            workflow_run_id=workflow_run.id,
+            data=LoopNodeNextStreamResponse.Data(
+                id=event.node_id,
+                node_id=event.node_id,
+                node_type=event.node_type.value,
+                title=event.node_data.title,
+                index=event.index,
+                pre_loop_output=event.output,
+                created_at=int(time.time()),
+                extras={},
+                parallel_id=event.parallel_id,
+                parallel_start_node_id=event.parallel_start_node_id,
+                parallel_mode_run_id=event.parallel_mode_run_id,
+                duration=event.duration,
+            ),
+        )
+
+    def _workflow_loop_completed_to_stream_response(
+        self, *, session: Session, task_id: str, workflow_run: WorkflowRun, event: QueueLoopCompletedEvent
+    ) -> LoopNodeCompletedStreamResponse:
+        # receive session to make sure the workflow_run won't be expired, need a more elegant way to handle this
+        _ = session
+        return LoopNodeCompletedStreamResponse(
+            task_id=task_id,
+            workflow_run_id=workflow_run.id,
+            data=LoopNodeCompletedStreamResponse.Data(
+                id=event.node_id,
+                node_id=event.node_id,
+                node_type=event.node_type.value,
+                title=event.node_data.title,
+                outputs=event.outputs,
+                created_at=int(time.time()),
+                extras={},
+                inputs=event.inputs or {},
+                status=WorkflowNodeExecutionStatus.SUCCEEDED
+                if event.error is None
+                else WorkflowNodeExecutionStatus.FAILED,
+                error=None,
+                elapsed_time=(datetime.now(UTC).replace(tzinfo=None) - event.start_at).total_seconds(),
+                total_tokens=event.metadata.get("total_tokens", 0) if event.metadata else 0,
+                execution_metadata=event.metadata,
+                finished_at=int(time.time()),
+                steps=event.steps,
+                parallel_id=event.parallel_id,
+                parallel_start_node_id=event.parallel_start_node_id,
+            ),
+        )
+
     def _fetch_files_from_node_outputs(self, outputs_dict: Mapping[str, Any]) -> Sequence[Mapping[str, Any]]:
         """
         Fetch files from node outputs
@@ -864,5 +959,6 @@ class WorkflowCycleManage:
                 status=event.status,
                 data=event.data,
                 metadata=event.metadata,
+                node_id=event.node_id,
             ),
         )

api/core/auto/config/custom.yaml

@@ -0,0 +1,27 @@
+# 自定义配置文件
+workflow_generator:
+  # 用于生成工作流的模型配置
+  models:
+    default: my-gpt-4o-mini  # 默认使用的模型
+    available:  # 可用的模型列表
+      my-gpt-4o-mini:
+        model_name: gpt-4o-mini
+        base_url: https://api.pandalla.ai/v1
+        key_path: ./openai_key
+        max_tokens: 4096
+      my-gpt-4o:
+        model_name: gpt-4o
+        base_url: https://api.pandalla.ai/v1
+        key_path: ./openai_key
+        max_tokens: 4096
+
+# 调试配置
+debug:
+  enabled: false  # 默认不启用调试模式，可通过命令行参数 --debug 启用
+  dir: debug/     # 调试信息保存目录
+  save_options:   # 调试信息保存选项
+    prompt: true  # 保存提示词
+    response: true  # 保存大模型响应
+    json: true  # 保存JSON解析过程
+    workflow: true  # 保存工作流生成过程
+  case_id_format: "%Y%m%d_%H%M%S_%f"  # 运行ID格式，使用datetime.strftime格式

api/core/auto/config/default.yaml

@@ -0,0 +1,33 @@
+# 默认配置文件
+
+# 工作流生成器配置
+workflow_generator:
+  # 用于生成工作流的模型配置
+  models:
+    default: gpt-4  # 默认使用的模型
+    available:  # 可用的模型列表
+      gpt-4:
+        model_name: gpt-4
+        base_url: https://api.openai.com/v1
+        key_path: ./openai_key
+        max_tokens: 8192
+      gpt-4-turbo:
+        model_name: gpt-4-1106-preview
+        base_url: https://api.openai.com/v1
+        key_path: ./openai_key
+        max_tokens: 4096
+
+# 工作流节点配置
+workflow_nodes:
+  # LLM节点默认配置（使用 Dify 平台配置的模型）
+  llm:
+    provider: zhipuai
+    model: glm-4-flash
+    max_tokens: 16384
+    temperature: 0.7
+    mode: chat
+
+# 输出配置
+output:
+  dir: output/
+  filename: generated_workflow.yml 

api/core/auto/node_types/__init__.py

@@ -0,0 +1,78 @@
+from .agent import AgentNodeType
+from .answer import AnswerNodeType
+from .assigner import AssignerNodeType
+from .code import CodeLanguage, CodeNodeType, OutputVar
+from .common import (
+    BlockEnum,
+    CommonEdgeType,
+    CommonNodeType,
+    CompleteEdge,
+    CompleteNode,
+    Context,
+    InputVar,
+    InputVarType,
+    Memory,
+    ModelConfig,
+    PromptItem,
+    PromptRole,
+    ValueSelector,
+    Variable,
+    VarType,
+    VisionSetting,
+)
+from .end import EndNodeType
+from .http import HttpNodeType
+from .if_else import IfElseNodeType
+from .iteration import IterationNodeType
+from .iteration_start import IterationStartNodeType
+from .knowledge_retrieval import KnowledgeRetrievalNodeType
+from .list_operator import ListFilterNodeType
+from .llm import LLMNodeType, VisionConfig
+from .note_node import NoteNodeType
+from .parameter_extractor import ParameterExtractorNodeType
+from .question_classifier import QuestionClassifierNodeType
+from .start import StartNodeType
+from .template_transform import TemplateTransformNodeType
+from .tool import ToolNodeType
+from .variable_assigner import VariableAssignerNodeType
+
+__all__ = [
+    "AgentNodeType",
+    "AnswerNodeType",
+    "AssignerNodeType",
+    "BlockEnum",
+    "CodeLanguage",
+    "CodeNodeType",
+    "CommonEdgeType",
+    "CommonNodeType",
+    "CompleteEdge",
+    "CompleteNode",
+    "Context",
+    "EndNodeType",
+    "HttpNodeType",
+    "IfElseNodeType",
+    "InputVar",
+    "InputVarType",
+    "IterationNodeType",
+    "IterationStartNodeType",
+    "KnowledgeRetrievalNodeType",
+    "LLMNodeType",
+    "ListFilterNodeType",
+    "Memory",
+    "ModelConfig",
+    "NoteNodeType",
+    "OutputVar",
+    "ParameterExtractorNodeType",
+    "PromptItem",
+    "PromptRole",
+    "QuestionClassifierNodeType",
+    "StartNodeType",
+    "TemplateTransformNodeType",
+    "ToolNodeType",
+    "ValueSelector",
+    "VarType",
+    "Variable",
+    "VariableAssignerNodeType",
+    "VisionConfig",
+    "VisionSetting",
+]

api/core/auto/node_types/agent.py

@@ -0,0 +1,34 @@
+from typing import Any, Optional
+
+from pydantic import BaseModel
+
+from .common import BlockEnum, CommonNodeType
+
+# Introduce previously defined CommonNodeType and ToolVarInputs
+# Assume they are defined in the same module
+
+
+class ToolVarInputs(BaseModel):
+    variable_name: Optional[str] = None
+    default_value: Optional[Any] = None
+
+
+class AgentNodeType(CommonNodeType):
+    agent_strategy_provider_name: Optional[str] = None
+    agent_strategy_name: Optional[str] = None
+    agent_strategy_label: Optional[str] = None
+    agent_parameters: Optional[ToolVarInputs] = None
+    output_schema: dict[str, Any]
+    plugin_unique_identifier: Optional[str] = None
+
+
+# 示例用法
+if __name__ == "__main__":
+    example_node = AgentNodeType(
+        title="Example Agent",
+        desc="An agent node example",
+        type=BlockEnum.agent,
+        output_schema={"key": "value"},
+        agent_parameters=ToolVarInputs(variable_name="example_var", default_value="default"),
+    )
+    print(example_node)

api/core/auto/node_types/answer.py

@@ -0,0 +1,21 @@
+from .common import BlockEnum, CommonNodeType, Variable
+
+
+class AnswerNodeType(CommonNodeType):
+    variables: list[Variable]
+    answer: str
+
+
+# Example usage
+if __name__ == "__main__":
+    example_node = AnswerNodeType(
+        title="Example Answer Node",
+        desc="An answer node example",
+        type=BlockEnum.answer,
+        answer="This is the answer",
+        variables=[
+            Variable(variable="var1", value_selector=["node1", "key1"]),
+            Variable(variable="var2", value_selector=["node2", "key2"]),
+        ],
+    )
+    print(example_node)

api/core/auto/node_types/assigner.py

@@ -0,0 +1,62 @@
+from enum import Enum
+from typing import Any, Optional
+
+from pydantic import BaseModel, Field
+
+from .common import BlockEnum, CommonNodeType
+
+# Import previously defined CommonNodeType and ValueSelector
+# Assume they are defined in the same module
+
+
+class WriteMode(str, Enum):
+    overwrite = "over-write"
+    clear = "clear"
+    append = "append"
+    extend = "extend"
+    set = "set"
+    increment = "+="
+    decrement = "-="
+    multiply = "*="
+    divide = "/="
+
+
+class AssignerNodeInputType(str, Enum):
+    variable = "variable"
+    constant = "constant"
+
+
+class AssignerNodeOperation(BaseModel):
+    variable_selector: Any  # Placeholder for ValueSelector type
+    input_type: AssignerNodeInputType
+    operation: WriteMode
+    value: Any
+
+
+class AssignerNodeType(CommonNodeType):
+    version: Optional[str] = Field(None, pattern="^[12]$")  # Version is '1' or '2'
+    items: list[AssignerNodeOperation]
+
+
+# Example usage
+if __name__ == "__main__":
+    example_node = AssignerNodeType(
+        title="Example Assigner Node",
+        desc="An assigner node example",
+        type=BlockEnum.variable_assigner,
+        items=[
+            AssignerNodeOperation(
+                variable_selector={"nodeId": "node1", "key": "value"},  # Example ValueSelector
+                input_type=AssignerNodeInputType.variable,
+                operation=WriteMode.set,
+                value="newValue",
+            ),
+            AssignerNodeOperation(
+                variable_selector={"nodeId": "node2", "key": "value"},
+                input_type=AssignerNodeInputType.constant,
+                operation=WriteMode.increment,
+                value=1,
+            ),
+        ],
+    )
+    print(example_node)

api/core/auto/node_types/code.py

@@ -0,0 +1,56 @@
+from enum import Enum
+from typing import Optional
+
+from pydantic import BaseModel
+
+from core.auto.node_types.common import BlockEnum, CommonNodeType, Variable, VarType
+
+# 引入之前定义的 CommonNodeType、VarType 和 Variable
+# 假设它们在同一模块中定义
+
+
+class CodeLanguage(str, Enum):
+    python3 = "python3"
+    javascript = "javascript"
+    json = "json"
+
+
+class OutputVar(BaseModel):
+    type: VarType
+    children: Optional[None] = None  # 未来支持嵌套
+
+    def dict(self, *args, **kwargs):
+        """自定义序列化方法，确保正确序列化"""
+        result = {"type": self.type.value if isinstance(self.type, Enum) else self.type}
+
+        if self.children is not None:
+            result["children"] = self.children
+
+        return result
+
+
+class CodeNodeType(CommonNodeType):
+    variables: list[Variable]
+    code_language: CodeLanguage
+    code: str
+    outputs: dict[str, OutputVar]
+
+
+# 示例用法
+if __name__ == "__main__":
+    # 创建示例节点
+    example_node = CodeNodeType(
+        title="Example Code Node",
+        desc="A code node example",
+        type=BlockEnum.code,
+        code_language=CodeLanguage.python3,
+        code="print('Hello, World!')",
+        outputs={
+            "output1": OutputVar(type=VarType.string),
+            "output2": OutputVar(type=VarType.number),
+        },
+        variables=[
+            Variable(variable="var1", value_selector=["node1", "key1"]),
+        ],
+    )
+    print(example_node.get_all_required_fields())

api/core/auto/node_types/common.py

@@ -0,0 +1,690 @@
+from enum import Enum
+from typing import Any, Optional, Union
+
+import yaml
+from pydantic import BaseModel, Field
+
+
+# BlockEnum 枚举
+class BlockEnum(str, Enum):
+    start = "start"
+    end = "end"
+    answer = "answer"
+    llm = "llm"
+    knowledge_retrieval = "knowledge-retrieval"
+    question_classifier = "question-classifier"
+    if_else = "if-else"
+    code = "code"
+    template_transform = "template-transform"
+    http_request = "http-request"
+    variable_assigner = "variable-assigner"
+    variable_aggregator = "variable-aggregator"
+    tool = "tool"
+    parameter_extractor = "parameter-extractor"
+    iteration = "iteration"
+    document_extractor = "document-extractor"
+    list_operator = "list-operator"
+    iteration_start = "iteration-start"
+    assigner = "assigner"  # is now named as VariableAssigner
+    agent = "agent"
+
+
+# Error枚举
+class ErrorHandleMode(str, Enum):
+    terminated = "terminated"
+    continue_on_error = "continue-on-error"
+    remove_abnormal_output = "remove-abnormal-output"
+
+
+class ErrorHandleTypeEnum(str, Enum):
+    none = ("none",)
+    failBranch = ("fail-branch",)
+    defaultValue = ("default-value",)
+
+
+# Branch 类型
+class Branch(BaseModel):
+    id: str
+    name: str
+
+
+# NodeRunningStatus 枚举
+class NodeRunningStatus(str, Enum):
+    not_start = "not-start"
+    waiting = "waiting"
+    running = "running"
+    succeeded = "succeeded"
+    failed = "failed"
+    exception = "exception"
+    retry = "retry"
+
+
+# 创建一个基类来统一CommonNodeType和CommonEdgeType的序列化逻辑
+class BaseType(BaseModel):
+    """基类，用于统一CommonNodeType和CommonEdgeType的序列化逻辑"""
+
+    def to_json(self) -> dict[str, Any]:
+        """
+        将对象转换为JSON格式的字典,通过循环模型字段来构建JSON数据
+        """
+        json_data = {}
+
+        # 获取模型的所有字段
+        for field_name, field_value in self.__dict__.items():
+            if field_value is not None:
+                # 特殊处理Branch类型的列表
+                if field_name == "_targetBranches" and field_value is not None:
+                    json_data[field_name] = [branch.dict(exclude_none=True) for branch in field_value]
+                # 处理枚举类型
+                elif isinstance(field_value, Enum):
+                    json_data[field_name] = field_value.value
+                # 处理嵌套的Pydantic模型
+                elif hasattr(field_value, "dict") and callable(field_value.dict):
+                    json_data[field_name] = field_value.dict(exclude_none=True)
+                # 处理列表中的Pydantic模型
+                elif isinstance(field_value, list):
+                    processed_list = []
+                    for item in field_value:
+                        if hasattr(item, "dict") and callable(item.dict):
+                            processed_list.append(item.dict(exclude_none=True))
+                        else:
+                            processed_list.append(item)
+                    json_data[field_name] = processed_list
+                # 处理字典中的Pydantic模型
+                elif isinstance(field_value, dict):
+                    processed_dict = {}
+                    for key, value in field_value.items():
+                        if hasattr(value, "dict") and callable(value.dict):
+                            processed_dict[key] = value.dict(exclude_none=True)
+                        else:
+                            processed_dict[key] = value
+                    json_data[field_name] = processed_dict
+                # 其他字段直接添加
+                else:
+                    json_data[field_name] = field_value
+
+        return json_data
+
+
+# CommonNodeType 类型
+class CommonNodeType(BaseType):
+    _connectedSourceHandleIds: Optional[list[str]] = None
+    _connectedTargetHandleIds: Optional[list[str]] = None
+    _targetBranches: Optional[list[Branch]] = None
+    _isSingleRun: Optional[bool] = None
+    _runningStatus: Optional[NodeRunningStatus] = None
+    _singleRunningStatus: Optional[NodeRunningStatus] = None
+    _isCandidate: Optional[bool] = None
+    _isBundled: Optional[bool] = None
+    _children: Optional[list[str]] = None
+    _isEntering: Optional[bool] = None
+    _showAddVariablePopup: Optional[bool] = None
+    _holdAddVariablePopup: Optional[bool] = None
+    _iterationLength: Optional[int] = None
+    _iterationIndex: Optional[int] = None
+    _inParallelHovering: Optional[bool] = None
+    isInIteration: Optional[bool] = None
+    iteration_id: Optional[str] = None
+    selected: Optional[bool] = None
+    title: str
+    desc: str
+    type: BlockEnum
+    width: Optional[float] = None
+    height: Optional[float] = None
+
+    @classmethod
+    def get_all_required_fields(cls) -> dict[str, str]:
+        """
+        获取所有必选字段，包括从父类继承的字段
+        这是一个类方法，可以通过类直接调用
+        """
+        all_required_fields = {}
+
+        # 获取所有父类（除了 object 和 BaseModel）
+        mro = [c for c in cls.__mro__ if c not in (object, BaseModel, BaseType)]
+
+        # 从父类到子类的顺序处理，这样子类的字段会覆盖父类的同名字段
+        for class_type in reversed(mro):
+            if hasattr(class_type, "__annotations__"):
+                for field_name, field_info in class_type.__annotations__.items():
+                    # 检查字段是否有默认值
+                    has_default = hasattr(class_type, field_name)
+                    # 检查字段是否为可选类型
+                    is_optional = "Optional" in str(field_info)
+
+                    # 如果字段没有默认值且不是Optional类型，则为必选字段
+                    if not has_default and not is_optional:
+                        all_required_fields[field_name] = str(field_info)
+
+        return all_required_fields
+
+
+# CommonEdgeType 类型
+class CommonEdgeType(BaseType):
+    _hovering: Optional[bool] = None
+    _connectedNodeIsHovering: Optional[bool] = None
+    _connectedNodeIsSelected: Optional[bool] = None
+    _run: Optional[bool] = None
+    _isBundled: Optional[bool] = None
+    isInIteration: Optional[bool] = None
+    iteration_id: Optional[str] = None
+    sourceType: BlockEnum
+    targetType: BlockEnum
+
+
+class ValueSelector(BaseModel):
+    """Value selector for selecting values from other nodes."""
+
+    value: list[str] = Field(default_factory=list)
+
+    def dict(self, *args, **kwargs):
+        """自定义序列化方法，直接返回 value 列表"""
+        return self.value
+
+
+# Add Context class for LLM node
+class Context(BaseModel):
+    """Context configuration for LLM node."""
+
+    enabled: bool = False
+    variable_selector: Optional[ValueSelector] = None
+
+    def dict(self, *args, **kwargs):
+        """自定义序列化方法，确保 variable_selector 字段正确序列化"""
+        result = {"enabled": self.enabled}
+
+        if self.variable_selector:
+            result["variable_selector"] = self.variable_selector.dict()
+        else:
+            result["variable_selector"] = []
+
+        return result
+
+
+# Variable 类型
+class Variable(BaseModel):
+    """
+    变量类型，用于定义节点的输入/输出变量
+    与Dify中的Variable类型保持一致
+    """
+
+    variable: str  # 变量名
+    label: Optional[Union[str, dict[str, str]]] = None  # 变量标签，可以是字符串或对象
+    value_selector: list[str]  # 变量值选择器，格式为[nodeId, key]
+    variable_type: Optional[str] = None  # 变量类型，对应Dify中的VarType枚举
+    value: Optional[str] = None  # 变量值（常量值）
+    options: Optional[list[str]] = None  # 选项列表（用于select类型）
+    required: Optional[bool] = None  # 是否必填
+    isParagraph: Optional[bool] = None  # 是否为段落
+    max_length: Optional[int] = None  # 最大长度
+
+    def dict(self, *args, **kwargs):
+        """自定义序列化方法，确保正确序列化"""
+        result = {"variable": self.variable}
+
+        if self.label is not None:
+            result["label"] = self.label
+
+        if self.value_selector:
+            result["value_selector"] = self.value_selector
+
+        if self.variable_type is not None:
+            result["type"] = self.variable_type  # 使用type而不是variable_type，与Dify保持一致
+
+        if self.value is not None:
+            result["value"] = self.value
+
+        if self.options is not None:
+            result["options"] = self.options
+
+        if self.required is not None:
+            result["required"] = self.required
+
+        if self.isParagraph is not None:
+            result["isParagraph"] = self.isParagraph
+
+        if self.max_length is not None:
+            result["max_length"] = self.max_length
+
+        return result
+
+
+# EnvironmentVariable 类型
+class EnvironmentVariable(BaseModel):
+    id: str
+    name: str
+    value: Any
+    value_type: str  # Expecting to be either 'string', 'number', or 'secret'
+
+
+# ConversationVariable 类型
+class ConversationVariable(BaseModel):
+    id: str
+    name: str
+    value_type: str
+    value: Any
+    description: str
+
+
+# GlobalVariable 类型
+class GlobalVariable(BaseModel):
+    name: str
+    value_type: str  # Expecting to be either 'string' or 'number'
+    description: str
+
+
+# VariableWithValue 类型
+class VariableWithValue(BaseModel):
+    key: str
+    value: str
+
+
+# InputVarType 枚举
+class InputVarType(str, Enum):
+    text_input = "text-input"
+    paragraph = "paragraph"
+    select = "select"
+    number = "number"
+    url = "url"
+    files = "files"
+    json = "json"
+    contexts = "contexts"
+    iterator = "iterator"
+    file = "file"
+    file_list = "file-list"
+
+
+# InputVar 类型
+class InputVar(BaseModel):
+    type: InputVarType
+    label: Union[str, dict[str, Any]]  # 可以是字符串或对象
+    variable: str
+    max_length: Optional[int] = None
+    default: Optional[str] = None
+    required: bool
+    hint: Optional[str] = None
+    options: Optional[list[str]] = None
+    value_selector: Optional[list[str]] = None
+
+    def dict(self, *args, **kwargs):
+        """自定义序列化方法，确保正确序列化"""
+        result = {
+            "type": self.type.value if isinstance(self.type, Enum) else self.type,
+            "label": self.label,
+            "variable": self.variable,
+            "required": self.required,
+        }
+
+        if self.max_length is not None:
+            result["max_length"] = self.max_length
+
+        if self.default is not None:
+            result["default"] = self.default
+
+        if self.hint is not None:
+            result["hint"] = self.hint
+
+        if self.options is not None:
+            result["options"] = self.options
+
+        if self.value_selector is not None:
+            result["value_selector"] = self.value_selector
+
+        return result
+
+
+# ModelConfig 类型
+class ModelConfig(BaseModel):
+    provider: str
+    name: str
+    mode: str
+    completion_params: dict[str, Any]
+
+
+# PromptRole 枚举
+class PromptRole(str, Enum):
+    system = "system"
+    user = "user"
+    assistant = "assistant"
+
+
+# EditionType 枚举
+class EditionType(str, Enum):
+    basic = "basic"
+    jinja2 = "jinja2"
+
+
+# PromptItem 类型
+class PromptItem(BaseModel):
+    id: Optional[str] = None
+    role: Optional[PromptRole] = None
+    text: str
+    edition_type: Optional[EditionType] = None
+    jinja2_text: Optional[str] = None
+
+    def dict(self, *args, **kwargs):
+        """自定义序列化方法，确保 role 字段正确序列化"""
+        result = {"id": self.id, "text": self.text}
+
+        if self.role:
+            result["role"] = self.role.value
+
+        if self.edition_type:
+            result["edition_type"] = self.edition_type.value
+
+        if self.jinja2_text:
+            result["jinja2_text"] = self.jinja2_text
+
+        return result
+
+
+# MemoryRole 枚举
+class MemoryRole(str, Enum):
+    user = "user"
+    assistant = "assistant"
+
+
+# RolePrefix 类型
+class RolePrefix(BaseModel):
+    user: str
+    assistant: str
+
+
+# Memory 类型
+class Memory(BaseModel):
+    role_prefix: Optional[RolePrefix] = None
+    window: dict[str, Any]  # Expecting to have 'enabled' and 'size'
+    query_prompt_template: str
+
+
+# VarType 枚举
+class VarType(str, Enum):
+    string = "string"
+    number = "number"
+    secret = "secret"
+    boolean = "boolean"
+    object = "object"
+    file = "file"
+    array = "array"
+    arrayString = "array[string]"
+    arrayNumber = "array[number]"
+    arrayObject = "array[object]"
+    arrayFile = "array[file]"
+    any = "any"
+
+
+# Var 类型
+class Var(BaseModel):
+    variable: str
+    type: VarType
+    children: Optional[list["Var"]] = None  # Self-reference
+    isParagraph: Optional[bool] = None
+    isSelect: Optional[bool] = None
+    options: Optional[list[str]] = None
+    required: Optional[bool] = None
+    des: Optional[str] = None
+    isException: Optional[bool] = None
+
+    def dict(self, *args, **kwargs):
+        """自定义序列化方法，确保type字段正确序列化"""
+        result = {"variable": self.variable, "type": self.type.value if isinstance(self.type, Enum) else self.type}
+
+        if self.children is not None:
+            result["children"] = [child.dict() for child in self.children]
+
+        if self.isParagraph is not None:
+            result["isParagraph"] = self.isParagraph
+
+        if self.isSelect is not None:
+            result["isSelect"] = self.isSelect
+
+        if self.options is not None:
+            result["options"] = self.options
+
+        if self.required is not None:
+            result["required"] = self.required
+
+        if self.des is not None:
+            result["des"] = self.des
+
+        if self.isException is not None:
+            result["isException"] = self.isException
+
+        return result
+
+
+# NodeOutPutVar 类型
+class NodeOutPutVar(BaseModel):
+    nodeId: str
+    title: str
+    vars: list[Var]
+    isStartNode: Optional[bool] = None
+
+
+# Block 类型
+class Block(BaseModel):
+    classification: Optional[str] = None
+    type: BlockEnum
+    title: str
+    description: Optional[str] = None
+
+
+# NodeDefault 类型
+class NodeDefault(BaseModel):
+    defaultValue: dict[str, Any]
+    getAvailablePrevNodes: Any  # Placeholder for function reference
+    getAvailableNextNodes: Any  # Placeholder for function reference
+    checkValid: Any  # Placeholder for function reference
+
+
+# OnSelectBlock 类型
+class OnSelectBlock(BaseModel):
+    nodeType: BlockEnum
+    additional_data: Optional[dict[str, Any]] = None
+
+
+# WorkflowRunningStatus 枚举
+class WorkflowRunningStatus(str, Enum):
+    waiting = "waiting"
+    running = "running"
+    succeeded = "succeeded"
+    failed = "failed"
+    stopped = "stopped"
+
+
+# WorkflowVersion 枚举
+class WorkflowVersion(str, Enum):
+    draft = "draft"
+    latest = "latest"
+
+
+# OnNodeAdd 类型
+class OnNodeAdd(BaseModel):
+    nodeType: BlockEnum
+    sourceHandle: Optional[str] = None
+    targetHandle: Optional[str] = None
+    toolDefaultValue: Optional[dict[str, Any]] = None
+
+
+# CheckValidRes 类型
+class CheckValidRes(BaseModel):
+    isValid: bool
+    errorMessage: Optional[str] = None
+
+
+# RunFile 类型
+class RunFile(BaseModel):
+    type: str
+    transfer_method: list[str]
+    url: Optional[str] = None
+    upload_file_id: Optional[str] = None
+
+
+# WorkflowRunningData 类型
+class WorkflowRunningData(BaseModel):
+    task_id: Optional[str] = None
+    message_id: Optional[str] = None
+    conversation_id: Optional[str] = None
+    result: dict[str, Any]  # Expecting a structured object
+    tracing: Optional[list[dict[str, Any]]] = None  # Placeholder for NodeTracing
+
+
+# HistoryWorkflowData 类型
+class HistoryWorkflowData(BaseModel):
+    id: str
+    sequence_number: int
+    status: str
+    conversation_id: Optional[str] = None
+
+
+# ChangeType 枚举
+class ChangeType(str, Enum):
+    changeVarName = "changeVarName"
+    remove = "remove"
+
+
+# MoreInfo 类型
+class MoreInfo(BaseModel):
+    type: ChangeType
+    payload: Optional[dict[str, Any]] = None
+
+
+# ToolWithProvider 类型
+class ToolWithProvider(BaseModel):
+    tools: list[dict[str, Any]]  # Placeholder for Tool type
+
+
+# SupportUploadFileTypes 枚举
+class SupportUploadFileTypes(str, Enum):
+    image = "image"
+    document = "document"
+    audio = "audio"
+    video = "video"
+    custom = "custom"
+
+
+# UploadFileSetting 类型
+class UploadFileSetting(BaseModel):
+    allowed_file_upload_methods: list[str]
+    allowed_file_types: list[SupportUploadFileTypes]
+    allowed_file_extensions: Optional[list[str]] = None
+    max_length: int
+    number_limits: Optional[int] = None
+
+
+# VisionSetting 类型
+class VisionSetting(BaseModel):
+    variable_selector: list[str]
+    detail: dict[str, Any]  # Placeholder for Resolution type
+
+
+# 创建一个基类来统一序列化逻辑
+class CompleteBase(BaseModel):
+    """基类，用于统一CompleteNode和CompleteEdge的序列化逻辑"""
+
+    def to_json(self):
+        """将对象转换为JSON格式的字典"""
+        json_data = {}
+
+        # 获取模型的所有字段
+        for field_name, field_value in self.__dict__.items():
+            if field_value is not None:
+                # 处理嵌套的数据对象
+                if field_name == "data" and hasattr(field_value, "to_json"):
+                    json_data[field_name] = field_value.to_json()
+                # 处理枚举类型
+                elif isinstance(field_value, Enum):
+                    json_data[field_name] = field_value.value
+                # 处理嵌套的Pydantic模型
+                elif hasattr(field_value, "dict") and callable(field_value.dict):
+                    json_data[field_name] = field_value.dict(exclude_none=True)
+                # 处理列表中的Pydantic模型
+                elif isinstance(field_value, list):
+                    processed_list = []
+                    for item in field_value:
+                        if hasattr(item, "dict") and callable(item.dict):
+                            processed_list.append(item.dict(exclude_none=True))
+                        else:
+                            processed_list.append(item)
+                    json_data[field_name] = processed_list
+                # 处理字典中的Pydantic模型
+                elif isinstance(field_value, dict):
+                    processed_dict = {}
+                    for key, value in field_value.items():
+                        if hasattr(value, "dict") and callable(value.dict):
+                            processed_dict[key] = value.dict(exclude_none=True)
+                        else:
+                            processed_dict[key] = value
+                    json_data[field_name] = processed_dict
+                # 其他字段直接添加
+                else:
+                    json_data[field_name] = field_value
+
+        return json_data
+
+    def to_yaml(self):
+        """将对象转换为YAML格式的字符串"""
+        return yaml.dump(self.to_json(), allow_unicode=True)
+
+
+class CompleteNode(CompleteBase):
+    id: str
+    position: dict
+    height: int
+    width: float
+    positionAbsolute: dict
+    selected: bool
+    sourcePosition: Union[dict, str]
+    targetPosition: Union[dict, str]
+    type: str
+    data: Optional[Union[CommonNodeType, None]] = None  # Flexible field to store CommonNodeType or None
+
+    def add_data(self, data: Union[CommonNodeType, None]):
+        self.data = data
+
+    def to_json(self):
+        json_data = super().to_json()
+
+        # 特殊处理sourcePosition和targetPosition
+        json_data["sourcePosition"] = "right"  # 直接输出为字符串"right"
+        json_data["targetPosition"] = "left"  # 直接输出为字符串"left"
+
+        # 确保 width 是整数而不是浮点数
+        if isinstance(json_data["width"], float):
+            json_data["width"] = int(json_data["width"])
+
+        return json_data
+
+
+class CompleteEdge(CompleteBase):
+    id: str
+    source: str
+    sourceHandle: str
+    target: str
+    targetHandle: str
+    type: str
+    zIndex: int
+    data: Optional[Union[CommonEdgeType, None]] = None  # Flexible field to store CommonEdgeType or None
+
+    def add_data(self, data: Union[CommonEdgeType, None]):
+        self.data = data
+
+
+# 示例用法
+if __name__ == "__main__":
+    # 这里可以添加示例数据进行验证
+    common_node = CompleteNode(
+        id="1740019130520",
+        position={"x": 80, "y": 282},
+        height=100,
+        width=100,
+        positionAbsolute={"x": 80, "y": 282},
+        selected=True,
+        sourcePosition={"x": 80, "y": 282},
+        targetPosition={"x": 80, "y": 282},
+        type="custom",
+    )
+    common_data = CommonNodeType(title="示例节点", desc="这是一个示例节点", type="")
+    print(CommonNodeType.get_all_required_fields())
+    common_node.add_data(common_data)
+    # print(common_node)

api/core/auto/node_types/end.py

@@ -0,0 +1,22 @@
+from .common import BlockEnum, CommonNodeType, Variable
+
+# Import previously defined CommonNodeType and Variable
+# Assume they are defined in the same module
+
+
+class EndNodeType(CommonNodeType):
+    outputs: list[Variable]
+
+
+# Example usage
+if __name__ == "__main__":
+    example_node = EndNodeType(
+        title="Example End Node",
+        desc="An end node example",
+        type=BlockEnum.end,
+        outputs=[
+            Variable(variable="outputVar1", value_selector=["node1", "key1"]),
+            Variable(variable="outputVar2", value_selector=["node2", "key2"]),
+        ],
+    )
+    print(example_node)

api/core/auto/node_types/http.py

@@ -0,0 +1,127 @@
+from enum import Enum
+from typing import Optional, Union
+
+from pydantic import BaseModel
+
+from .common import BlockEnum, CommonNodeType, ValueSelector, Variable
+
+# Import previously defined CommonNodeType, ValueSelector, and Variable
+# Assume they are defined in the same module
+
+
+class Method(str, Enum):
+    """HTTP request methods."""
+
+    get = "get"
+    post = "post"
+    head = "head"
+    patch = "patch"
+    put = "put"
+    delete = "delete"
+
+
+class BodyType(str, Enum):
+    """HTTP request body types."""
+
+    none = "none"
+    formData = "form-data"
+    xWwwFormUrlencoded = "x-www-form-urlencoded"
+    rawText = "raw-text"
+    json = "json"
+    binary = "binary"
+
+
+class BodyPayloadValueType(str, Enum):
+    """Types of values in body payload."""
+
+    text = "text"
+    file = "file"
+
+
+class BodyPayload(BaseModel):
+    """Body payload item for HTTP requests."""
+
+    id: Optional[str] = None
+    key: Optional[str] = None
+    type: BodyPayloadValueType
+    file: Optional[ValueSelector] = None  # Used when type is file
+    value: Optional[str] = None  # Used when type is text
+
+
+class Body(BaseModel):
+    """HTTP request body configuration."""
+
+    type: BodyType
+    data: Union[str, list[BodyPayload]]  # string is deprecated, will convert to BodyPayload
+
+
+class AuthorizationType(str, Enum):
+    """HTTP authorization types."""
+
+    none = "no-auth"
+    apiKey = "api-key"
+
+
+class APIType(str, Enum):
+    """API key types."""
+
+    basic = "basic"
+    bearer = "bearer"
+    custom = "custom"
+
+
+class AuthConfig(BaseModel):
+    """Authorization configuration."""
+
+    type: APIType
+    api_key: str
+    header: Optional[str] = None
+
+
+class Authorization(BaseModel):
+    """HTTP authorization settings."""
+
+    type: AuthorizationType
+    config: Optional[AuthConfig] = None
+
+
+class Timeout(BaseModel):
+    """HTTP request timeout settings."""
+
+    connect: Optional[int] = None
+    read: Optional[int] = None
+    write: Optional[int] = None
+    max_connect_timeout: Optional[int] = None
+    max_read_timeout: Optional[int] = None
+    max_write_timeout: Optional[int] = None
+
+
+class HttpNodeType(CommonNodeType):
+    """HTTP request node type implementation."""
+
+    variables: list[Variable]
+    method: Method
+    url: str
+    headers: str
+    params: str
+    body: Body
+    authorization: Authorization
+    timeout: Timeout
+
+
+# Example usage
+if __name__ == "__main__":
+    example_node = HttpNodeType(
+        title="Example HTTP Node",
+        desc="An HTTP request node example",
+        type=BlockEnum.http_request,
+        variables=[Variable(variable="var1", value_selector=["node1", "key1"])],
+        method=Method.get,
+        url="https://api.example.com/data",
+        headers="{}",
+        params="{}",
+        body=Body(type=BodyType.none, data=[]),
+        authorization=Authorization(type=AuthorizationType.none),
+        timeout=Timeout(connect=30, read=30, write=30),
+    )
+    print(example_node)

api/core/auto/node_types/if_else.py

@@ -0,0 +1,99 @@
+from enum import Enum
+from typing import Optional, Union
+
+from pydantic import BaseModel
+
+from .common import BlockEnum, CommonNodeType, ValueSelector, VarType
+from .tool import VarType as NumberVarType
+
+# Import previously defined CommonNodeType, ValueSelector, Var, and VarType
+# Assume they are defined in the same module
+
+
+class LogicalOperator(str, Enum):
+    and_ = "and"
+    or_ = "or"
+
+
+class ComparisonOperator(str, Enum):
+    contains = "contains"
+    notContains = "not contains"
+    startWith = "start with"
+    endWith = "end with"
+    is_ = "is"
+    isNot = "is not"
+    empty = "empty"
+    notEmpty = "not empty"
+    equal = "="
+    notEqual = "≠"
+    largerThan = ">"
+    lessThan = "<"
+    largerThanOrEqual = "≥"
+    lessThanOrEqual = "≤"
+    isNull = "is null"
+    isNotNull = "is not null"
+    in_ = "in"
+    notIn = "not in"
+    allOf = "all of"
+    exists = "exists"
+    notExists = "not exists"
+    equals = "="  # Alias for equal for compatibility
+
+
+class Condition(BaseModel):
+    id: str
+    varType: VarType
+    variable_selector: Optional[ValueSelector]
+    key: Optional[str] = None  # Sub variable key
+    comparison_operator: Optional[ComparisonOperator] = None
+    value: Union[str, list[str]]
+    numberVarType: Optional[NumberVarType]
+    sub_variable_condition: Optional["CaseItem"] = None  # Recursive reference
+
+
+class CaseItem(BaseModel):
+    case_id: str
+    logical_operator: LogicalOperator
+    conditions: list[Condition]
+
+
+class IfElseNodeType(CommonNodeType):
+    logical_operator: Optional[LogicalOperator] = None
+    conditions: Optional[list[Condition]] = None
+    cases: list[CaseItem]
+    isInIteration: bool
+
+
+# Example usage
+if __name__ == "__main__":
+    example_node = IfElseNodeType(
+        title="Example IfElse Node",
+        desc="An if-else node example",
+        type=BlockEnum.if_else,
+        logical_operator=LogicalOperator.and_,
+        conditions=[
+            Condition(
+                id="condition1",
+                varType=VarType.string,
+                variable_selector={"nodeId": "varNode", "key": "value"},
+                comparison_operator=ComparisonOperator.is_,
+                value="exampleValue",
+            )
+        ],
+        cases=[
+            CaseItem(
+                case_id="case1",
+                logical_operator=LogicalOperator.or_,
+                conditions=[
+                    Condition(
+                        id="condition2",
+                        varType=VarType.number,
+                        value="10",
+                        comparison_operator=ComparisonOperator.largerThan,
+                    )
+                ],
+            )
+        ],
+        isInIteration=True,
+    )
+    print(example_node)

api/core/auto/node_types/iteration.py

@@ -0,0 +1,45 @@
+from enum import Enum
+from typing import Optional
+
+from .common import BlockEnum, CommonNodeType, ValueSelector, VarType
+
+
+class ErrorHandleMode(str, Enum):
+    """Error handling modes for iteration."""
+
+    terminated = "terminated"
+    continue_on_error = "continue-on-error"
+    remove_abnormal_output = "remove-abnormal-output"
+
+
+class IterationNodeType(CommonNodeType):
+    """Iteration node type implementation."""
+
+    startNodeType: Optional[BlockEnum] = None
+    start_node_id: str  # Start node ID in the iteration
+    iteration_id: Optional[str] = None
+    iterator_selector: ValueSelector
+    output_selector: ValueSelector
+    output_type: VarType  # Output type
+    is_parallel: bool  # Open the parallel mode or not
+    parallel_nums: int  # The numbers of parallel
+    error_handle_mode: ErrorHandleMode  # How to handle error in the iteration
+    _isShowTips: bool  # Show tips when answer node in parallel mode iteration
+
+
+# 示例用法
+if __name__ == "__main__":
+    example_node = IterationNodeType(
+        title="Example Iteration Node",
+        desc="An iteration node example",
+        type=BlockEnum.iteration,
+        start_node_id="startNode1",
+        iterator_selector=ValueSelector(value=["iteratorNode", "value"]),
+        output_selector=ValueSelector(value=["outputNode", "value"]),
+        output_type=VarType.string,
+        is_parallel=True,
+        parallel_nums=5,
+        error_handle_mode=ErrorHandleMode.continue_on_error,
+        _isShowTips=True,
+    )
+    print(example_node)

api/core/auto/node_types/iteration_start.py

@@ -0,0 +1,25 @@
+from .common import BlockEnum, CommonNodeType
+
+# 引入之前定义的 CommonNodeType
+# 假设它们在同一模块中定义
+
+
+class IterationStartNodeType(CommonNodeType):
+    """
+    Iteration Start node type implementation.
+
+    This node type is used as the starting point within an iteration block.
+    It inherits all properties from CommonNodeType without adding any additional fields.
+    """
+
+    pass  # 仅仅继承 CommonNodeType，无其他字段
+
+
+# 示例用法
+if __name__ == "__main__":
+    example_node = IterationStartNodeType(
+        title="Example Iteration Start Node",
+        desc="An iteration start node example",
+        type=BlockEnum.iteration_start,
+    )
+    print(example_node)

api/core/auto/node_types/knowledge_retrieval.py

@@ -0,0 +1,115 @@
+from enum import Enum
+from typing import Optional
+
+from pydantic import BaseModel
+
+from .common import BlockEnum, CommonNodeType, ModelConfig, ValueSelector
+
+
+class RetrieveType(str, Enum):
+    """Retrieval mode types."""
+
+    single = "single"
+    multiple = "multiple"
+
+
+class RerankingModeEnum(str, Enum):
+    """Reranking mode types."""
+
+    simple = "simple"
+    advanced = "advanced"
+
+
+class VectorSetting(BaseModel):
+    """Vector weight settings."""
+
+    vector_weight: float
+    embedding_provider_name: str
+    embedding_model_name: str
+
+
+class KeywordSetting(BaseModel):
+    """Keyword weight settings."""
+
+    keyword_weight: float
+
+
+class Weights(BaseModel):
+    """Weight configuration for retrieval."""
+
+    vector_setting: VectorSetting
+    keyword_setting: KeywordSetting
+
+
+class RerankingModel(BaseModel):
+    """Reranking model configuration."""
+
+    provider: str
+    model: str
+
+
+class MultipleRetrievalConfig(BaseModel):
+    """Configuration for multiple retrieval mode."""
+
+    top_k: int
+    score_threshold: Optional[float] = None
+    reranking_model: Optional[RerankingModel] = None
+    reranking_mode: Optional[RerankingModeEnum] = None
+    weights: Optional[Weights] = None
+    reranking_enable: Optional[bool] = None
+
+
+class SingleRetrievalConfig(BaseModel):
+    """Configuration for single retrieval mode."""
+
+    model: ModelConfig
+
+
+class DataSet(BaseModel):
+    """Dataset information."""
+
+    id: str
+    name: str
+    description: Optional[str] = None
+
+
+class KnowledgeRetrievalNodeType(CommonNodeType):
+    """Knowledge retrieval node type implementation."""
+
+    query_variable_selector: ValueSelector
+    dataset_ids: list[str]
+    retrieval_mode: RetrieveType
+    multiple_retrieval_config: Optional[MultipleRetrievalConfig] = None
+    single_retrieval_config: Optional[SingleRetrievalConfig] = None
+    _datasets: Optional[list[DataSet]] = None
+
+
+# Example usage
+if __name__ == "__main__":
+    example_node = KnowledgeRetrievalNodeType(
+        title="Example Knowledge Retrieval Node",
+        desc="A knowledge retrieval node example",
+        type=BlockEnum.knowledge_retrieval,
+        query_variable_selector=ValueSelector(value=["queryNode", "query"]),
+        dataset_ids=["dataset1", "dataset2"],
+        retrieval_mode=RetrieveType.multiple,
+        multiple_retrieval_config=MultipleRetrievalConfig(
+            top_k=10,
+            score_threshold=0.5,
+            reranking_model=RerankingModel(provider="example_provider", model="example_model"),
+            reranking_mode=RerankingModeEnum.simple,
+            weights=Weights(
+                vector_setting=VectorSetting(
+                    vector_weight=0.7, embedding_provider_name="provider1", embedding_model_name="model1"
+                ),
+                keyword_setting=KeywordSetting(keyword_weight=0.3),
+            ),
+            reranking_enable=True,
+        ),
+        single_retrieval_config=SingleRetrievalConfig(
+            model=ModelConfig(
+                provider="example_provider", name="example_model", mode="chat", completion_params={"temperature": 0.7}
+            )
+        ),
+    )
+    print(example_node)

api/core/auto/node_types/list_operator.py

@@ -0,0 +1,73 @@
+from enum import Enum
+from typing import Optional, Union
+
+from pydantic import BaseModel
+
+from .common import BlockEnum, CommonNodeType, ValueSelector, VarType
+
+# Import ComparisonOperator from if_else.py
+from .if_else import ComparisonOperator
+
+
+class OrderBy(str, Enum):
+    ASC = "asc"
+    DESC = "desc"
+
+
+class Limit(BaseModel):
+    enabled: bool
+    size: Optional[int] = None
+
+
+class Condition(BaseModel):
+    key: str
+    comparison_operator: ComparisonOperator
+    value: Union[str, int, list[str]]
+
+
+class FilterBy(BaseModel):
+    enabled: bool
+    conditions: list[Condition]
+
+
+class ExtractBy(BaseModel):
+    enabled: bool
+    serial: Optional[str] = None
+
+
+class OrderByConfig(BaseModel):
+    enabled: bool
+    key: Union[ValueSelector, str]
+    value: OrderBy
+
+
+class ListFilterNodeType(CommonNodeType):
+    """List filter/operator node type implementation."""
+
+    variable: ValueSelector
+    var_type: VarType
+    item_var_type: VarType
+    filter_by: FilterBy
+    extract_by: ExtractBy
+    order_by: OrderByConfig
+    limit: Limit
+
+
+# 示例用法
+if __name__ == "__main__":
+    example_node = ListFilterNodeType(
+        title="Example List Filter Node",
+        desc="A list filter node example",
+        type=BlockEnum.list_operator,  # Fixed: use list_operator instead of list_filter
+        variable=ValueSelector(value=["varNode", "value"]),
+        var_type=VarType.string,
+        item_var_type=VarType.number,
+        filter_by=FilterBy(
+            enabled=True,
+            conditions=[Condition(key="status", comparison_operator=ComparisonOperator.equals, value="active")],
+        ),
+        extract_by=ExtractBy(enabled=True, serial="serial_1"),
+        order_by=OrderByConfig(enabled=True, key="created_at", value=OrderBy.DESC),
+        limit=Limit(enabled=True, size=100),
+    )
+    print(example_node)

api/core/auto/node_types/llm.py

@@ -0,0 +1,66 @@
+from typing import Optional, Union
+
+from pydantic import BaseModel
+
+from .common import (
+    BlockEnum,
+    CommonNodeType,
+    Context,
+    Memory,
+    ModelConfig,
+    PromptItem,
+    Variable,
+    VisionSetting,
+)
+
+
+class PromptConfig(BaseModel):
+    """Configuration for prompt template variables."""
+
+    jinja2_variables: Optional[list[Variable]] = None
+
+
+class VisionConfig(BaseModel):
+    """Configuration for vision settings."""
+
+    enabled: bool = False
+    configs: Optional[VisionSetting] = None
+
+    def dict(self, *args, **kwargs):
+        """自定义序列化方法，确保正确序列化"""
+        result = {"enabled": self.enabled}
+
+        if self.configs:
+            result["configs"] = self.configs.dict()
+
+        return result
+
+
+class LLMNodeType(CommonNodeType):
+    """LLM node type implementation."""
+
+    model: ModelConfig
+    prompt_template: Union[list[PromptItem], PromptItem]
+    prompt_config: Optional[PromptConfig] = None
+    memory: Optional[Memory] = None
+    context: Optional[Context] = Context(enabled=False, variable_selector=None)
+    vision: Optional[VisionConfig] = VisionConfig(enabled=False)
+
+
+# 示例用法
+if __name__ == "__main__":
+    example_node = LLMNodeType(
+        title="Example LLM Node",
+        desc="A LLM node example",
+        type=BlockEnum.llm,
+        model=ModelConfig(provider="zhipuai", name="glm-4-flash", mode="chat", completion_params={"temperature": 0.7}),
+        prompt_template=[
+            PromptItem(
+                id="system-id", role="system", text="你是一个代码工程师，你会根据用户的需求给出用户所需要的函数"
+            ),
+            PromptItem(id="user-id", role="user", text="给出两数相加的python 函数代码，函数名 func 不要添加其他内容"),
+        ],
+        context=Context(enabled=False, variable_selector=None),
+        vision=VisionConfig(enabled=False),
+    )
+    print(example_node)

api/core/auto/node_types/note_node.py

@@ -0,0 +1,38 @@
+from enum import Enum
+
+from .common import BlockEnum, CommonNodeType
+
+# Import previously defined CommonNodeType
+# Assume it is defined in the same module
+
+
+class NoteTheme(str, Enum):
+    blue = "blue"
+    cyan = "cyan"
+    green = "green"
+    yellow = "yellow"
+    pink = "pink"
+    violet = "violet"
+
+
+class NoteNodeType(CommonNodeType):
+    """Custom note node type implementation."""
+
+    text: str
+    theme: NoteTheme
+    author: str
+    showAuthor: bool
+
+
+# Example usage
+if __name__ == "__main__":
+    example_node = NoteNodeType(
+        title="Example Note Node",
+        desc="A note node example",
+        type=BlockEnum.custom_note,
+        text="This is a note.",
+        theme=NoteTheme.green,
+        author="John Doe",
+        showAuthor=True,
+    )
+    print(example_node)

api/core/auto/node_types/parameter_extractor.py

@@ -0,0 +1,85 @@
+from enum import Enum
+from typing import Optional
+
+from pydantic import BaseModel
+
+from .common import BlockEnum, CommonNodeType, Memory, ModelConfig, ValueSelector, VisionSetting
+
+# Import previously defined CommonNodeType, Memory, ModelConfig, ValueSelector, and VisionSetting
+# Assume they are defined in the same module
+
+
+class ParamType(str, Enum):
+    """Parameter types for extraction."""
+
+    string = "string"
+    number = "number"
+    bool = "bool"
+    select = "select"
+    arrayString = "array[string]"
+    arrayNumber = "array[number]"
+    arrayObject = "array[object]"
+
+
+class Param(BaseModel):
+    """Parameter definition for extraction."""
+
+    name: str
+    type: ParamType
+    options: Optional[list[str]] = None
+    description: str
+    required: Optional[bool] = None
+
+
+class ReasoningModeType(str, Enum):
+    """Reasoning mode types for parameter extraction."""
+
+    prompt = "prompt"
+    functionCall = "function_call"
+
+
+class VisionConfig(BaseModel):
+    """Vision configuration."""
+
+    enabled: bool
+    configs: Optional[VisionSetting] = None
+
+
+class ParameterExtractorNodeType(CommonNodeType):
+    """Parameter extractor node type implementation."""
+
+    model: ModelConfig
+    query: ValueSelector
+    reasoning_mode: ReasoningModeType
+    parameters: List[Param]
+    instruction: str
+    memory: Optional[Memory] = None
+    vision: VisionConfig
+
+
+# Example usage
+if __name__ == "__main__":
+    example_node = ParameterExtractorNodeType(
+        title="Example Parameter Extractor Node",
+        desc="A parameter extractor node example",
+        type=BlockEnum.parameter_extractor,
+        model=ModelConfig(
+            provider="example_provider", name="example_model", mode="chat", completion_params={"temperature": 0.7}
+        ),
+        query=ValueSelector(value=["queryNode", "value"]),
+        reasoning_mode=ReasoningModeType.prompt,
+        parameters=[
+            Param(name="param1", type=ParamType.string, description="This is a string parameter", required=True),
+            Param(
+                name="param2",
+                type=ParamType.number,
+                options=["1", "2", "3"],
+                description="This is a number parameter",
+                required=False,
+            ),
+        ],
+        instruction="Please extract the parameters from the input.",
+        memory=Memory(window={"enabled": True, "size": 10}, query_prompt_template="Extract parameters from: {{query}}"),
+        vision=VisionConfig(enabled=True, configs={"setting": "example_setting"}),
+    )
+    print(example_node)

api/core/auto/node_types/question_classifier.py

@@ -0,0 +1,51 @@
+from typing import Optional
+
+from pydantic import BaseModel
+
+from .common import BlockEnum, CommonNodeType, Memory, ModelConfig, ValueSelector, VisionSetting
+
+# Import previously defined CommonNodeType, Memory, ModelConfig, ValueSelector, and VisionSetting
+# Assume they are defined in the same module
+
+
+class Topic(BaseModel):
+    """Topic for classification."""
+
+    id: str
+    name: str
+
+
+class VisionConfig(BaseModel):
+    """Vision configuration."""
+
+    enabled: bool
+    configs: Optional[VisionSetting] = None
+
+
+class QuestionClassifierNodeType(CommonNodeType):
+    """Question classifier node type implementation."""
+
+    query_variable_selector: ValueSelector
+    model: ModelConfig
+    classes: list[Topic]
+    instruction: str
+    memory: Optional[Memory] = None
+    vision: VisionConfig
+
+
+# Example usage
+if __name__ == "__main__":
+    example_node = QuestionClassifierNodeType(
+        title="Example Question Classifier Node",
+        desc="A question classifier node example",
+        type=BlockEnum.question_classifier,
+        query_variable_selector=ValueSelector(value=["queryNode", "value"]),
+        model=ModelConfig(
+            provider="example_provider", name="example_model", mode="chat", completion_params={"temperature": 0.7}
+        ),
+        classes=[Topic(id="1", name="Science"), Topic(id="2", name="Mathematics"), Topic(id="3", name="Literature")],
+        instruction="Classify the given question into the appropriate topic.",
+        memory=Memory(window={"enabled": True, "size": 10}, query_prompt_template="Classify this question: {{query}}"),
+        vision=VisionConfig(enabled=True, configs={"setting": "example_setting"}),
+    )
+    print(example_node)

api/core/auto/node_types/start.py

@@ -0,0 +1,22 @@
+from .common import BlockEnum, CommonNodeType, InputVar
+
+# Import previously defined CommonNodeType and InputVar
+# Assume they are defined in the same module
+
+
+class StartNodeType(CommonNodeType):
+    variables: list[InputVar]
+
+
+# Example usage
+if __name__ == "__main__":
+    example_node = StartNodeType(
+        title="Example Start Node",
+        desc="A start node example",
+        type=BlockEnum.start,
+        variables=[
+            InputVar(type="text-input", label="Input 1", variable="input1", required=True),
+            InputVar(type="number", label="Input 2", variable="input2", required=True),
+        ],
+    )
+    print(example_node)

api/core/auto/node_types/template_transform.py

@@ -0,0 +1,26 @@
+from .common import BlockEnum, CommonNodeType, Variable
+
+# 引入之前定义的 CommonNodeType 和 Variable
+# 假设它们在同一模块中定义
+
+
+class TemplateTransformNodeType(CommonNodeType):
+    """Template transform node type implementation."""
+
+    variables: list[Variable]
+    template: str
+
+
+# 示例用法
+if __name__ == "__main__":
+    example_node = TemplateTransformNodeType(
+        title="Example Template Transform Node",
+        desc="A template transform node example",
+        type=BlockEnum.template_transform,
+        variables=[
+            Variable(variable="var1", value_selector=["node1", "key1"]),
+            Variable(variable="var2", value_selector=["node2", "key2"]),
+        ],
+        template="Hello, {{ var1 }}! You have {{ var2 }} new messages.",
+    )
+    print(example_node)

api/core/auto/node_types/tool.py

@@ -0,0 +1,54 @@
+from enum import Enum
+from typing import Any, Optional, Union
+
+from pydantic import BaseModel
+
+from .common import BlockEnum, CommonNodeType, ValueSelector
+
+# Import previously defined CommonNodeType and ValueSelector
+# Assume they are defined in the same module
+
+
+class VarType(str, Enum):
+    variable = "variable"
+    constant = "constant"
+    mixed = "mixed"
+
+
+class ToolVarInputs(BaseModel):
+    type: VarType
+    value: Optional[Union[str, ValueSelector, Any]] = None
+
+
+class ToolNodeType(CommonNodeType):
+    """Tool node type implementation."""
+
+    provider_id: str
+    provider_type: Any  # Placeholder for CollectionType
+    provider_name: str
+    tool_name: str
+    tool_label: str
+    tool_parameters: dict[str, ToolVarInputs]
+    tool_configurations: dict[str, Any]
+    output_schema: dict[str, Any]
+
+
+# Example usage
+if __name__ == "__main__":
+    example_node = ToolNodeType(
+        title="Example Tool Node",
+        desc="A tool node example",
+        type=BlockEnum.tool,
+        provider_id="12345",
+        provider_type="some_collection_type",  # Placeholder for CollectionType
+        provider_name="Example Provider",
+        tool_name="Example Tool",
+        tool_label="Example Tool Label",
+        tool_parameters={
+            "input1": ToolVarInputs(type=VarType.variable, value="some_value"),
+            "input2": ToolVarInputs(type=VarType.constant, value="constant_value"),
+        },
+        tool_configurations={"config1": "value1", "config2": {"nested": "value2"}},
+        output_schema={"output1": "string", "output2": "number"},
+    )
+    print(example_node.json(indent=2))  # Print as JSON format for viewing

api/core/auto/node_types/variable_assigner.py

@@ -0,0 +1,56 @@
+from pydantic import BaseModel
+
+from .common import BlockEnum, CommonNodeType, ValueSelector, VarType
+
+
+class VarGroupItem(BaseModel):
+    """Variable group item configuration."""
+
+    output_type: VarType
+    variables: list[ValueSelector]
+
+
+class GroupConfig(VarGroupItem):
+    """Group configuration for advanced settings."""
+
+    group_name: str
+    groupId: str
+
+
+class AdvancedSettings(BaseModel):
+    """Advanced settings for variable assigner."""
+
+    group_enabled: bool
+    groups: list[GroupConfig]
+
+
+class VariableAssignerNodeType(CommonNodeType, VarGroupItem):
+    """Variable assigner node type implementation."""
+
+    advanced_settings: AdvancedSettings
+
+    class Config:
+        arbitrary_types_allowed = True
+
+
+# Example usage
+if __name__ == "__main__":
+    example_node = VariableAssignerNodeType(
+        title="Example Variable Assigner Node",
+        desc="A variable assigner node example",
+        type=BlockEnum.variable_assigner,
+        output_type=VarType.string,
+        variables=[ValueSelector(value=["varNode1", "value1"]), ValueSelector(value=["varNode2", "value2"])],
+        advanced_settings=AdvancedSettings(
+            group_enabled=True,
+            groups=[
+                GroupConfig(
+                    group_name="Group 1",
+                    groupId="group1",
+                    output_type=VarType.number,
+                    variables=[ValueSelector(value=["varNode3", "value3"])],
+                )
+            ],
+        ),
+    )
+    print(example_node.json(indent=2))  # Print as JSON format for viewing

api/core/auto/output/emotion_analysis_workflow.yml

@@ -0,0 +1,239 @@
+app:
+  description: ''
+  icon: 🤖
+  icon_background: '#FFEAD5'
+  mode: workflow
+  name: 情绪分析工作流
+  use_icon_as_answer_icon: false
+kind: app
+version: 0.1.2
+workflow:
+  conversation_variables: []
+  environment_variables: []
+  features:
+    file_upload:
+      allowed_file_extensions:
+      - .JPG
+      - .JPEG
+      - .PNG
+      - .GIF
+      - .WEBP
+      - .SVG
+      allowed_file_types:
+      - image
+      allowed_file_upload_methods:
+      - local_file
+      - remote_url
+      enabled: false
+      fileUploadConfig:
+        audio_file_size_limit: 50
+        batch_count_limit: 5
+        file_size_limit: 15
+        image_file_size_limit: 10
+        video_file_size_limit: 100
+      image:
+        enabled: false
+        number_limits: 3
+        transfer_methods:
+        - local_file
+        - remote_url
+      number_limits: 3
+    opening_statement: ''
+    retriever_resource:
+      enabled: true
+    sensitive_word_avoidance:
+      enabled: false
+    speech_to_text:
+      enabled: false
+    suggested_questions: []
+    suggested_questions_after_answer:
+      enabled: false
+    text_to_speech:
+      enabled: false
+      language: ''
+      voice: ''
+  graph:
+    edges:
+    - id: 1740019130520-source-1740019130521-target
+      source: '1740019130520'
+      sourceHandle: source
+      target: '1740019130521'
+      targetHandle: target
+      type: custom
+      zIndex: 0
+      data:
+        isInIteration: false
+        sourceType: start
+        targetType: llm
+    - id: 1740019130521-source-1740019130522-target
+      source: '1740019130521'
+      sourceHandle: source
+      target: '1740019130522'
+      targetHandle: target
+      type: custom
+      zIndex: 0
+      data:
+        isInIteration: false
+        sourceType: llm
+        targetType: code
+    - id: 1740019130522-source-1740019130523-target
+      source: '1740019130522'
+      sourceHandle: source
+      target: '1740019130523'
+      targetHandle: target
+      type: custom
+      zIndex: 0
+      data:
+        isInIteration: false
+        sourceType: code
+        targetType: template-transform
+    - id: 1740019130523-source-1740019130524-target
+      source: '1740019130523'
+      sourceHandle: source
+      target: '1740019130524'
+      targetHandle: target
+      type: custom
+      zIndex: 0
+      data:
+        isInIteration: false
+        sourceType: template-transform
+        targetType: end
+    nodes:
+    - id: '1740019130520'
+      position:
+        x: 80
+        y: 282
+      height: 116
+      width: 244
+      positionAbsolute:
+        x: 80
+        y: 282
+      selected: false
+      sourcePosition: right
+      targetPosition: left
+      type: custom
+      data:
+        title: 开始节点
+        desc: 开始节点，接收用户输入的文本。
+        type: start
+        variables:
+        - type: text-input
+          label: input_text
+          variable: input_text
+          required: true
+          max_length: 48
+          options: []
+    - id: '1740019130521'
+      position:
+        x: 380
+        y: 282
+      height: 98
+      width: 244
+      positionAbsolute:
+        x: 380
+        y: 282
+      selected: false
+      sourcePosition: right
+      targetPosition: left
+      type: custom
+      data:
+        title: LLM节点
+        desc: LLM节点分析文本情绪，识别出积极、消极或中性情绪。
+        type: llm
+        model:
+          provider: zhipuai
+          name: glm-4-flash
+          mode: chat
+          completion_params:
+            temperature: 0.7
+        prompt_template:
+        - id: 1740019130521-system
+          text: 请分析以下文本的情绪，并返回情绪类型（积极、消极或中性）。
+          role: system
+        - id: 1740019130521-user
+          text: 分析此文本的情绪：{{input_text}}
+          role: user
+        context:
+          enabled: false
+          variable_selector: []
+        vision:
+          enabled: false
+    - id: '1740019130522'
+      position:
+        x: 680
+        y: 282
+      height: 54
+      width: 244
+      positionAbsolute:
+        x: 680
+        y: 282
+      selected: false
+      sourcePosition: right
+      targetPosition: left
+      type: custom
+      data:
+        title: 代码节点
+        desc: 代码节点将根据LLM分析的结果处理情绪类型。
+        type: code
+        variables:
+        - variable: emotion
+          value_selector:
+          - '1740019130521'
+          - emotion
+        code_language: python3
+        code: "def analyze_sentiment(emotion):\n    if emotion == 'positive':\n  \
+          \      return '积极'\n    elif emotion == 'negative':\n        return '消极'\n\
+          \    else:\n        return '中性'\n\nemotion = '{{emotion}}'\nresult = analyze_sentiment(emotion)\n\
+          return {'result': result}"
+        outputs:
+          sentiment_result:
+            type: string
+    - id: '1740019130523'
+      position:
+        x: 980
+        y: 282
+      height: 54
+      width: 244
+      positionAbsolute:
+        x: 980
+        y: 282
+      selected: false
+      sourcePosition: right
+      targetPosition: left
+      type: custom
+      data:
+        title: 模板节点
+        desc: 模板节点将情绪分析结果格式化输出。
+        type: template-transform
+        variables:
+        - variable: sentiment_result
+          value_selector:
+          - '1740019130522'
+          - sentiment_result
+        template: 文本的情绪分析结果为：{{sentiment_result}}
+    - id: '1740019130524'
+      position:
+        x: 1280
+        y: 282
+      height: 90
+      width: 244
+      positionAbsolute:
+        x: 1280
+        y: 282
+      selected: false
+      sourcePosition: right
+      targetPosition: left
+      type: custom
+      data:
+        title: 结束节点
+        desc: 结束节点，返回格式化后的情绪分析结果。
+        type: end
+        outputs:
+        - variable: output
+          value_selector:
+          - '1740019130523'
+          - output
+    viewport:
+      x: 92.96659905656679
+      y: 79.13437154762897
+      zoom: 0.9002006986311041

api/core/auto/output/test_workflow.yml

@@ -0,0 +1,247 @@
+app:
+  description: ''
+  icon: 🤖
+  icon_background: '#FFEAD5'
+  mode: workflow
+  name: 计算两个数字之和
+  use_icon_as_answer_icon: false
+kind: app
+version: 0.1.2
+workflow:
+  conversation_variables: []
+  environment_variables: []
+  features:
+    file_upload:
+      allowed_file_extensions:
+      - .JPG
+      - .JPEG
+      - .PNG
+      - .GIF
+      - .WEBP
+      - .SVG
+      allowed_file_types:
+      - image
+      allowed_file_upload_methods:
+      - local_file
+      - remote_url
+      enabled: false
+      fileUploadConfig:
+        audio_file_size_limit: 50
+        batch_count_limit: 5
+        file_size_limit: 15
+        image_file_size_limit: 10
+        video_file_size_limit: 100
+      image:
+        enabled: false
+        number_limits: 3
+        transfer_methods:
+        - local_file
+        - remote_url
+      number_limits: 3
+    opening_statement: ''
+    retriever_resource:
+      enabled: true
+    sensitive_word_avoidance:
+      enabled: false
+    speech_to_text:
+      enabled: false
+    suggested_questions: []
+    suggested_questions_after_answer:
+      enabled: false
+    text_to_speech:
+      enabled: false
+      language: ''
+      voice: ''
+  graph:
+    edges:
+    - id: 1740019130520-source-1740019130521-target
+      source: '1740019130520'
+      sourceHandle: source
+      target: '1740019130521'
+      targetHandle: target
+      type: custom
+      zIndex: 0
+      data:
+        isInIteration: false
+        sourceType: start
+        targetType: llm
+    - id: 1740019130521-source-1740019130522-target
+      source: '1740019130521'
+      sourceHandle: source
+      target: '1740019130522'
+      targetHandle: target
+      type: custom
+      zIndex: 0
+      data:
+        isInIteration: false
+        sourceType: llm
+        targetType: code
+    - id: 1740019130522-source-1740019130523-target
+      source: '1740019130522'
+      sourceHandle: source
+      target: '1740019130523'
+      targetHandle: target
+      type: custom
+      zIndex: 0
+      data:
+        isInIteration: false
+        sourceType: code
+        targetType: template-transform
+    - id: 1740019130523-source-1740019130524-target
+      source: '1740019130523'
+      sourceHandle: source
+      target: '1740019130524'
+      targetHandle: target
+      type: custom
+      zIndex: 0
+      data:
+        isInIteration: false
+        sourceType: template-transform
+        targetType: end
+    nodes:
+    - id: '1740019130520'
+      position:
+        x: 80
+        y: 282
+      height: 116
+      width: 244
+      positionAbsolute:
+        x: 80
+        y: 282
+      selected: false
+      sourcePosition: right
+      targetPosition: left
+      type: custom
+      data:
+        title: 开始节点
+        desc: 开始节点，接收两个数字输入参数。
+        type: start
+        variables:
+        - type: number
+          label: num1
+          variable: num1
+          required: true
+          max_length: 48
+          options: []
+        - type: number
+          label: num2
+          variable: num2
+          required: true
+          max_length: 48
+          options: []
+    - id: '1740019130521'
+      position:
+        x: 380
+        y: 282
+      height: 98
+      width: 244
+      positionAbsolute:
+        x: 380
+        y: 282
+      selected: false
+      sourcePosition: right
+      targetPosition: left
+      type: custom
+      data:
+        title: LLM节点
+        desc: LLM节点，根据输入的两个数字生成计算它们之和的Python函数。
+        type: llm
+        model:
+          provider: openai
+          name: gpt-4
+          mode: chat
+          completion_params:
+            temperature: 0.7
+        prompt_template:
+        - id: 1740019130521-system
+          text: 你是一个Python开发助手，请根据以下输入生成一个计算两数之和的Python函数。
+          role: system
+        - id: 1740019130521-user
+          text: 请为两个数字{{num1}}和{{num2}}生成一个Python函数，计算它们的和。
+          role: user
+        context:
+          enabled: false
+          variable_selector: []
+        vision:
+          enabled: false
+    - id: '1740019130522'
+      position:
+        x: 680
+        y: 282
+      height: 54
+      width: 244
+      positionAbsolute:
+        x: 680
+        y: 282
+      selected: false
+      sourcePosition: right
+      targetPosition: left
+      type: custom
+      data:
+        title: 代码节点
+        desc: 代码节点，执行LLM生成的Python函数，并计算结果。
+        type: code
+        variables:
+        - variable: num1
+          value_selector:
+          - '1740019130520'
+          - num1
+        - variable: num2
+          value_selector:
+          - '1740019130520'
+          - num2
+        code_language: python3
+        code: "def calculate_sum(num1, num2):\n    return num1 + num2\n\nresult =\
+          \ calculate_sum({{num1}}, {{num2}})\nreturn result"
+        outputs:
+          result:
+            type: number
+    - id: '1740019130523'
+      position:
+        x: 980
+        y: 282
+      height: 54
+      width: 244
+      positionAbsolute:
+        x: 980
+        y: 282
+      selected: false
+      sourcePosition: right
+      targetPosition: left
+      type: custom
+      data:
+        title: 模板节点
+        desc: 模板节点，将计算结果格式化为输出字符串。
+        type: template-transform
+        variables:
+        - variable: result
+          value_selector:
+          - '1740019130522'
+          - result
+        template: '计算结果为: {{result}}'
+    - id: '1740019130524'
+      position:
+        x: 1280
+        y: 282
+      height: 90
+      width: 244
+      positionAbsolute:
+        x: 1280
+        y: 282
+      selected: false
+      sourcePosition: right
+      targetPosition: left
+      type: custom
+      data:
+        title: 结束节点
+        desc: 结束节点，输出格式化后的结果。
+        type: end
+        outputs:
+        - variable: output
+          value_selector:
+          - '1740019130523'
+          - output
+    viewport:
+      x: 92.96659905656679
+      y: 79.13437154762897
+      zoom: 0.9002006986311041

api/core/auto/output/text_analysis_workflow.yml

@@ -0,0 +1,262 @@
+app:
+  description: ''
+  icon: 🤖
+  icon_background: '#FFEAD5'
+  mode: workflow
+  name: 文本分析工作流
+  use_icon_as_answer_icon: false
+kind: app
+version: 0.1.2
+workflow:
+  conversation_variables: []
+  environment_variables: []
+  features:
+    file_upload:
+      allowed_file_extensions:
+      - .JPG
+      - .JPEG
+      - .PNG
+      - .GIF
+      - .WEBP
+      - .SVG
+      allowed_file_types:
+      - image
+      allowed_file_upload_methods:
+      - local_file
+      - remote_url
+      enabled: false
+      fileUploadConfig:
+        audio_file_size_limit: 50
+        batch_count_limit: 5
+        file_size_limit: 15
+        image_file_size_limit: 10
+        video_file_size_limit: 100
+      image:
+        enabled: false
+        number_limits: 3
+        transfer_methods:
+        - local_file
+        - remote_url
+      number_limits: 3
+    opening_statement: ''
+    retriever_resource:
+      enabled: true
+    sensitive_word_avoidance:
+      enabled: false
+    speech_to_text:
+      enabled: false
+    suggested_questions: []
+    suggested_questions_after_answer:
+      enabled: false
+    text_to_speech:
+      enabled: false
+      language: ''
+      voice: ''
+  graph:
+    edges:
+    - id: 1740019130520-source-1740019130521-target
+      source: '1740019130520'
+      sourceHandle: source
+      target: '1740019130521'
+      targetHandle: target
+      type: custom
+      zIndex: 0
+      data:
+        isInIteration: false
+        sourceType: start
+        targetType: llm
+    - id: 1740019130520-source-1740019130522-target
+      source: '1740019130520'
+      sourceHandle: source
+      target: '1740019130522'
+      targetHandle: target
+      type: custom
+      zIndex: 0
+      data:
+        isInIteration: false
+        sourceType: start
+        targetType: code
+    - id: 1740019130521-source-1740019130523-target
+      source: '1740019130521'
+      sourceHandle: source
+      target: '1740019130523'
+      targetHandle: target
+      type: custom
+      zIndex: 0
+      data:
+        isInIteration: false
+        sourceType: llm
+        targetType: template-transform
+    - id: 1740019130522-source-1740019130523-target
+      source: '1740019130522'
+      sourceHandle: source
+      target: '1740019130523'
+      targetHandle: target
+      type: custom
+      zIndex: 0
+      data:
+        isInIteration: false
+        sourceType: code
+        targetType: template-transform
+    - id: 1740019130523-source-1740019130524-target
+      source: '1740019130523'
+      sourceHandle: source
+      target: '1740019130524'
+      targetHandle: target
+      type: custom
+      zIndex: 0
+      data:
+        isInIteration: false
+        sourceType: template-transform
+        targetType: end
+    nodes:
+    - id: '1740019130520'
+      position:
+        x: 80
+        y: 282
+      height: 116
+      width: 244
+      positionAbsolute:
+        x: 80
+        y: 282
+      selected: false
+      sourcePosition: right
+      targetPosition: left
+      type: custom
+      data:
+        title: 开始节点
+        desc: 接收用户输入的文本参数
+        type: start
+        variables:
+        - type: text-input
+          label: user_text
+          variable: user_text
+          required: true
+          max_length: 48
+          options: []
+    - id: '1740019130521'
+      position:
+        x: 380
+        y: 282
+      height: 98
+      width: 244
+      positionAbsolute:
+        x: 380
+        y: 282
+      selected: false
+      sourcePosition: right
+      targetPosition: left
+      type: custom
+      data:
+        title: LLM节点
+        desc: 使用大语言模型进行情感分析，返回文本的情感结果
+        type: llm
+        model:
+          provider: zhipuai
+          name: glm-4-flash
+          mode: chat
+          completion_params:
+            temperature: 0.7
+        prompt_template:
+        - id: 1740019130521-system
+          text: 请分析以下文本的情感，返回积极、消极或中性
+          role: system
+        - id: 1740019130521-user
+          text: '{{user_text}}'
+          role: user
+        context:
+          enabled: false
+          variable_selector: []
+        vision:
+          enabled: false
+    - id: '1740019130522'
+      position:
+        x: 680
+        y: 282
+      height: 54
+      width: 244
+      positionAbsolute:
+        x: 680
+        y: 282
+      selected: false
+      sourcePosition: right
+      targetPosition: left
+      type: custom
+      data:
+        title: 代码节点
+        desc: 计算文本的统计信息，包括字符数、单词数和句子数
+        type: code
+        variables:
+        - variable: text_for_analysis
+          value_selector:
+          - '1740019130520'
+          - user_text
+        code_language: python3
+        code: "import re\n\ndef main(text):\n    char_count = len(text)\n    word_count\
+          \ = len(text.split())\n    sentence_count = len(re.findall(r'[.!?]', text))\n\
+          \    return {'char_count': char_count, 'word_count': word_count, 'sentence_count':\
+          \ sentence_count}"
+        outputs:
+          text_statistics:
+            type: object
+    - id: '1740019130523'
+      position:
+        x: 980
+        y: 282
+      height: 54
+      width: 244
+      positionAbsolute:
+        x: 980
+        y: 282
+      selected: false
+      sourcePosition: right
+      targetPosition: left
+      type: custom
+      data:
+        title: 模板节点
+        desc: 将情感分析结果和统计信息组合成格式化报告
+        type: template-transform
+        variables:
+        - variable: sentiment_result
+          value_selector:
+          - '1740019130521'
+          - sentiment_result
+        - variable: text_statistics
+          value_selector:
+          - '1740019130522'
+          - text_statistics
+        template: '情感分析结果：{{sentiment_result}}
+
+          文本统计信息：
+
+          字符数：{{text_statistics.char_count}}
+
+          单词数：{{text_statistics.word_count}}
+
+          句子数：{{text_statistics.sentence_count}}'
+    - id: '1740019130524'
+      position:
+        x: 1280
+        y: 282
+      height: 90
+      width: 244
+      positionAbsolute:
+        x: 1280
+        y: 282
+      selected: false
+      sourcePosition: right
+      targetPosition: left
+      type: custom
+      data:
+        title: 结束节点
+        desc: 返回最终的分析报告
+        type: end
+        outputs:
+        - variable: final_report
+          value_selector:
+          - '1740019130523'
+          - output
+    viewport:
+      x: 92.96659905656679
+      y: 79.13437154762897
+      zoom: 0.9002006986311041

api/core/auto/workflow_generator/__init__.py

@@ -0,0 +1,8 @@
+"""
+工作流生成器包
+用于根据用户需求生成Dify工作流
+"""
+
+from .workflow_generator import WorkflowGenerator
+
+__all__ = ["WorkflowGenerator"]

api/core/auto/workflow_generator/generators/__init__.py

@@ -0,0 +1,9 @@
+"""
+节点和边生成器包
+"""
+
+from .edge_generator import EdgeGenerator
+from .layout_engine import LayoutEngine
+from .node_generator import NodeGenerator
+
+__all__ = ["EdgeGenerator", "LayoutEngine", "NodeGenerator"]

api/core/auto/workflow_generator/generators/edge_generator.py

@@ -0,0 +1,101 @@
+"""
+Edge Generator
+Used to generate edges in the workflow
+"""
+
+from core.auto.node_types.common import CommonEdgeType, CompleteEdge, CompleteNode
+from core.auto.workflow_generator.models.workflow_description import ConnectionDescription
+
+
+class EdgeGenerator:
+    """Edge generator for creating workflow edges"""
+
+    @staticmethod
+    def create_edges(nodes: list[CompleteNode], connections: list[ConnectionDescription]) -> list[CompleteEdge]:
+        """
+        Create edges based on nodes and connection information
+
+        Args:
+            nodes: list of nodes
+            connections: list of connection descriptions
+
+        Returns:
+            list of edges
+        """
+        edges = []
+
+        # If connection information is provided, create edges based on it
+        if connections:
+            for connection in connections:
+                source_id = connection.source
+                target_id = connection.target
+
+                if not source_id or not target_id:
+                    continue
+
+                # Find source and target nodes
+                source_node = next((node for node in nodes if node.id == source_id), None)
+                target_node = next((node for node in nodes if node.id == target_id), None)
+
+                if not source_node or not target_node:
+                    continue
+
+                # Get node types
+                source_type = source_node.data.type
+                target_type = target_node.data.type
+
+                # Create edge
+                edge_id = f"{source_id}-source-{target_id}-target"
+
+                # Create edge data
+                edge_data = CommonEdgeType(isInIteration=False, sourceType=source_type, targetType=target_type)
+
+                # Create complete edge
+                edge = CompleteEdge(
+                    id=edge_id,
+                    source=source_id,
+                    sourceHandle="source",
+                    target=target_id,
+                    targetHandle="target",
+                    type="custom",
+                    zIndex=0,
+                )
+
+                # Add edge data
+                edge.add_data(edge_data)
+
+                edges.append(edge)
+        # If no connection information is provided, automatically create edges
+        else:
+            # Create edges based on node order
+            for i in range(len(nodes) - 1):
+                source_node = nodes[i]
+                target_node = nodes[i + 1]
+
+                # Get node types
+                source_type = source_node.data.type
+                target_type = target_node.data.type
+
+                # Create edge
+                edge_id = f"{source_node.id}-source-{target_node.id}-target"
+
+                # Create edge data
+                edge_data = CommonEdgeType(isInIteration=False, sourceType=source_type, targetType=target_type)
+
+                # Create complete edge
+                edge = CompleteEdge(
+                    id=edge_id,
+                    source=source_node.id,
+                    sourceHandle="source",
+                    target=target_node.id,
+                    targetHandle="target",
+                    type="custom",
+                    zIndex=0,
+                )
+
+                # Add edge data
+                edge.add_data(edge_data)
+
+                edges.append(edge)
+
+        return edges

api/core/auto/workflow_generator/generators/layout_engine.py

@@ -0,0 +1,77 @@
+"""
+Layout Engine
+Used to arrange the positions of nodes and edges
+"""
+
+from core.auto.node_types.common import CompleteEdge, CompleteNode
+
+
+class LayoutEngine:
+    """Layout engine"""
+
+    @staticmethod
+    def apply_layout(nodes: list[CompleteNode]) -> None:
+        """
+        Apply layout, arranging nodes in a row
+
+        Args:
+            nodes: list of nodes
+        """
+        # Simple linear layout, arranging nodes from left to right
+        x_position = 80
+        y_position = 282
+
+        for node in nodes:
+            node.position = {"x": x_position, "y": y_position}
+            node.positionAbsolute = {"x": x_position, "y": y_position}
+
+            # Update position for the next node
+            x_position += 300  # Horizontal spacing between nodes
+
+    @staticmethod
+    def apply_topological_layout(nodes: list[CompleteNode], edges: list[CompleteEdge]) -> None:
+        """
+        Apply topological sort layout, arranging nodes based on their dependencies
+
+        Args:
+            nodes: list of nodes
+            edges: list of edges
+        """
+        # Create mapping from node ID to node
+        node_map = {node.id: node for node in nodes}
+
+        # Create adjacency list
+        adjacency_list = {node.id: [] for node in nodes}
+        for edge in edges:
+            adjacency_list[edge.source].append(edge.target)
+
+        # Create in-degree table
+        in_degree = {node.id: 0 for node in nodes}
+        for source, targets in adjacency_list.items():
+            for target in targets:
+                in_degree[target] += 1
+
+        # Topological sort
+        queue = [node_id for node_id, degree in in_degree.items() if degree == 0]
+        sorted_nodes = []
+
+        while queue:
+            current = queue.pop(0)
+            sorted_nodes.append(current)
+
+            for neighbor in adjacency_list[current]:
+                in_degree[neighbor] -= 1
+                if in_degree[neighbor] == 0:
+                    queue.append(neighbor)
+
+        # Apply layout
+        x_position = 80
+        y_position = 282
+
+        for node_id in sorted_nodes:
+            node = node_map[node_id]
+            node.position = {"x": x_position, "y": y_position}
+            node.positionAbsolute = {"x": x_position, "y": y_position}
+
+            # Update position for the next node
+            x_position += 300  # Horizontal spacing between nodes

api/core/auto/workflow_generator/generators/node_generator.py

@@ -0,0 +1,446 @@
+"""
+Node Generator
+Generate nodes based on workflow description
+"""
+
+from core.auto.node_types.code import CodeLanguage, CodeNodeType, OutputVar
+from core.auto.node_types.common import (
+    BlockEnum,
+    CompleteNode,
+    Context,
+    InputVar,
+    ModelConfig,
+    PromptItem,
+    PromptRole,
+    ValueSelector,
+    Variable,
+)
+from core.auto.node_types.end import EndNodeType
+from core.auto.node_types.llm import LLMNodeType, VisionConfig
+from core.auto.node_types.start import StartNodeType
+from core.auto.node_types.template_transform import TemplateTransformNodeType
+from core.auto.workflow_generator.models.workflow_description import NodeDescription
+from core.auto.workflow_generator.utils.prompts import DEFAULT_MODEL_CONFIG, DEFAULT_SYSTEM_PROMPT
+from core.auto.workflow_generator.utils.type_mapper import map_string_to_var_type, map_var_type_to_input_type
+
+
+class NodeGenerator:
+    """Node generator for creating workflow nodes"""
+
+    @staticmethod
+    def create_nodes(node_descriptions: list[NodeDescription]) -> list[CompleteNode]:
+        """
+        Create nodes based on node descriptions
+
+        Args:
+            node_descriptions: list of node descriptions
+
+        Returns:
+            list of nodes
+        """
+        nodes = []
+
+        for node_desc in node_descriptions:
+            node_type = node_desc.type
+
+            if node_type == "start":
+                node = NodeGenerator._create_start_node(node_desc)
+            elif node_type == "llm":
+                node = NodeGenerator._create_llm_node(node_desc)
+            elif node_type == "code":
+                node = NodeGenerator._create_code_node(node_desc)
+            elif node_type == "template":
+                node = NodeGenerator._create_template_node(node_desc)
+            elif node_type == "end":
+                node = NodeGenerator._create_end_node(node_desc)
+            else:
+                raise ValueError(f"Unsupported node type: {node_type}")
+
+            nodes.append(node)
+
+        return nodes
+
+    @staticmethod
+    def _create_start_node(node_desc: NodeDescription) -> CompleteNode:
+        """Create start node"""
+        variables = []
+
+        for var in node_desc.variables or []:
+            input_var = InputVar(
+                type=map_var_type_to_input_type(var.type),
+                label=var.name,
+                variable=var.name,
+                required=var.required,
+                max_length=48,
+                options=[],
+            )
+            variables.append(input_var)
+
+        start_node = StartNodeType(
+            title=node_desc.title, desc=node_desc.description or "", type=BlockEnum.start, variables=variables
+        )
+
+        return CompleteNode(
+            id=node_desc.id,
+            type="custom",
+            position={"x": 0, "y": 0},  # Temporary position, will be updated later
+            height=118,  # Increase height to match reference file
+            width=244,
+            positionAbsolute={"x": 0, "y": 0},
+            selected=False,
+            sourcePosition="right",
+            targetPosition="left",
+            data=start_node,
+        )
+
+    @staticmethod
+    def _create_llm_node(node_desc: NodeDescription) -> CompleteNode:
+        """Create LLM node"""
+        # Build prompt template
+        prompt_template = []
+
+        # Add system prompt
+        system_prompt = node_desc.system_prompt or DEFAULT_SYSTEM_PROMPT
+        prompt_template.append(PromptItem(id=f"{node_desc.id}-system", role=PromptRole.system, text=system_prompt))
+
+        # Add user prompt
+        user_prompt = node_desc.user_prompt or "Please answer these questions:"
+
+        # Build variable list
+        variables = []
+        for var in node_desc.variables or []:
+            source_node = var.source_node or ""
+            source_variable = var.source_variable or ""
+
+            print(
+                f"DEBUG: Processing variable {var.name}, source_node={source_node}, source_variable={source_variable}"
+            )
+
+            # If source node is an LLM node, ensure source_variable is 'text'
+            if source_node:
+                # Check if the source node is an LLM node by checking connections
+                # This is a simple heuristic - if the source node is connected to a node with 'llm' in its ID
+                # or if the source node has 'llm' in its ID, assume it's an LLM node
+                if "llm" in source_node.lower():
+                    print(f"DEBUG: Found LLM node {source_node}")
+                    if source_variable != "text":
+                        old_var = source_variable
+                        source_variable = "text"  # LLM nodes output variable is always 'text'
+                        print(
+                            f"Auto-fixing: Changed source variable from '{old_var}' to 'text' for LLM node {source_node}"  # noqa: E501
+                        )
+
+            # Check if the user prompt already contains correctly formatted variable references
+            # Variable references in LLM nodes should be in the format {{#nodeID.variableName#}}
+            correct_format = f"{{{{#{source_node}.{source_variable}#}}}}"
+            simple_format = f"{{{{{var.name}}}}}"
+
+            # If simple format is used in the prompt, replace it with the correct format
+            if simple_format in user_prompt and source_node and source_variable:
+                user_prompt = user_prompt.replace(simple_format, correct_format)
+
+            variable = Variable(variable=var.name, value_selector=[source_node, source_variable])
+            variables.append(variable)
+
+        # Update user prompt
+        prompt_template.append(PromptItem(id=f"{node_desc.id}-user", role=PromptRole.user, text=user_prompt))
+
+        # Use default model configuration, prioritize configuration in node description
+        provider = node_desc.provider or DEFAULT_MODEL_CONFIG["provider"]
+        model = node_desc.model or DEFAULT_MODEL_CONFIG["model"]
+
+        llm_node = LLMNodeType(
+            title=node_desc.title,
+            desc=node_desc.description or "",
+            type=BlockEnum.llm,
+            model=ModelConfig(
+                provider=provider,
+                name=model,
+                mode=DEFAULT_MODEL_CONFIG["mode"],
+                completion_params=DEFAULT_MODEL_CONFIG["completion_params"],
+            ),
+            prompt_template=prompt_template,
+            variables=variables,
+            context=Context(enabled=False, variable_selector=ValueSelector(value=[])),
+            vision=VisionConfig(enabled=False),
+        )
+
+        return CompleteNode(
+            id=node_desc.id,
+            type="custom",
+            position={"x": 0, "y": 0},  # Temporary position, will be updated later
+            height=126,  # Increase height to match reference file
+            width=244,
+            positionAbsolute={"x": 0, "y": 0},
+            selected=False,
+            sourcePosition="right",
+            targetPosition="left",
+            data=llm_node,
+        )
+
+    @staticmethod
+    def _create_code_node(node_desc: NodeDescription) -> CompleteNode:
+        """Create code node"""
+        # Build variable list and function parameter names
+        variables = []
+        var_names = []
+        var_mapping = {}  # Used to store mapping from variable names to function parameter names
+
+        # First, identify all LLM nodes in the workflow
+        llm_nodes = set()
+        for connection in node_desc.workflow_description.connections:
+            for node in node_desc.workflow_description.nodes:
+                if node.id == connection.source and node.type.lower() == "llm":
+                    llm_nodes.add(node.id)
+
+        for var in node_desc.variables or []:
+            source_node = var.source_node or ""
+            source_variable = var.source_variable or ""
+
+            # Check if source node is an LLM node and warn if source_variable is not 'text'
+            if source_node in llm_nodes and source_variable != "text":
+                print(
+                    f"WARNING: LLM node {source_node} output variable should be 'text', but got '{source_variable}'. This may cause issues in Dify."  # noqa: E501
+                )
+                print("         Consider changing the source_variable to 'text' in your workflow description.")
+                # Auto-fix: Always use 'text' as the source variable for LLM nodes
+                old_var = source_variable
+                source_variable = "text"
+                print(f"Auto-fixing: Changed source variable from '{old_var}' to 'text' for LLM node {source_node}")
+            elif source_node and "llm" in source_node.lower() and source_variable != "text":
+                # Fallback heuristic check based on node ID
+                print(
+                    f"WARNING: Node {source_node} appears to be an LLM node based on its ID, but source_variable is not 'text'."  # noqa: E501
+                )
+                print("         Consider changing the source_variable to 'text' in your workflow description.")
+                # Auto-fix: Always use 'text' as the source variable for LLM nodes
+                old_var = source_variable
+                source_variable = "text"
+                print(f"Auto-fixing: Changed source variable from '{old_var}' to 'text' for LLM node {source_node}")
+
+            # Use variable name as function parameter name
+            variable_name = var.name  # Variable name defined in this node
+            param_name = variable_name  # Function parameter name must match variable name
+
+            # Validate variable name format
+            if not variable_name.replace("_", "").isalnum():
+                raise ValueError(
+                    f"Invalid variable name: {variable_name}. Variable names must only contain letters, numbers, and underscores."  # noqa: E501
+                )
+            if not variable_name[0].isalpha() and variable_name[0] != "_":
+                raise ValueError(
+                    f"Invalid variable name: {variable_name}. Variable names must start with a letter or underscore."
+                )
+
+            var_names.append(param_name)
+            var_mapping[variable_name] = param_name
+
+            variable = Variable(variable=variable_name, value_selector=[source_node, source_variable])
+            variables.append(variable)
+
+        # Build output
+        outputs = {}
+        for output in node_desc.outputs or []:
+            # Validate output variable name format
+            if not output.name.replace("_", "").isalnum():
+                raise ValueError(
+                    f"Invalid output variable name: {output.name}. Output names must only contain letters, numbers, and underscores."  # noqa: E501
+                )
+            if not output.name[0].isalpha() and output.name[0] != "_":
+                raise ValueError(
+                    f"Invalid output variable name: {output.name}. Output names must start with a letter or underscore."
+                )
+
+            outputs[output.name] = OutputVar(type=map_string_to_var_type(output.type))
+
+        # Generate code, ensure function parameters match variable names, return values match output names
+        output_names = [output.name for output in node_desc.outputs or []]
+
+        # Build function parameter list
+        params_str = ", ".join(var_names) if var_names else ""
+
+        # Build return value dictionary
+        return_dict = {}
+        for output_name in output_names:
+            # Use the first variable as the return value by default
+            return_dict[output_name] = var_names[0] if var_names else f'"{output_name}"'
+
+        return_dict_str = ", ".join([f'"{k}": {v}' for k, v in return_dict.items()])
+
+        # Default code template, ensure return dictionary type matches output variable
+        default_code = f"""def main({params_str}):
+    # Write your code here
+    # Process input variables
+    
+    # Return a dictionary, key names must match variable names defined in outputs
+    return {{{return_dict_str}}}"""
+
+        # If custom code is provided, ensure it meets the specifications
+        if node_desc.code:
+            custom_code = node_desc.code
+            # Check if it contains main function definition
+            if not custom_code.strip().startswith("def main("):
+                # Try to fix the code by adding main function with correct parameters
+                custom_code = f"def main({params_str}):\n" + custom_code.strip()
+            else:
+                # Extract function parameters from the existing main function
+                import re
+
+                func_params = re.search(r"def\s+main\s*\((.*?)\)", custom_code)
+                if func_params:
+                    existing_params = [p.strip() for p in func_params.group(1).split(",") if p.strip()]
+                    # Verify that all required parameters are present
+                    missing_params = set(var_names) - set(existing_params)
+                    if missing_params:
+                        # 尝试修复代码，将函数参数替换为正确的参数名
+                        old_params = func_params.group(1)
+                        new_params = params_str
+                        custom_code = custom_code.replace(f"def main({old_params})", f"def main({new_params})")
+                        print(
+                            f"Warning: Fixed missing parameters in code node: {', '.join(missing_params)}. Function parameters must match variable names defined in this node."  # noqa: E501
+                        )
+
+            # Check if the return value is a dictionary and keys match output variables
+            for output_name in output_names:
+                if f'"{output_name}"' not in custom_code and f"'{output_name}'" not in custom_code:
+                    # Code may not meet specifications, use default code
+                    custom_code = default_code
+                    break
+
+            # Use fixed code
+            code = custom_code
+        else:
+            code = default_code
+
+        code_node = CodeNodeType(
+            title=node_desc.title,
+            desc=node_desc.description or "",
+            type=BlockEnum.code,
+            code_language=CodeLanguage.python3,
+            code=code,
+            variables=variables,
+            outputs=outputs,
+        )
+
+        return CompleteNode(
+            id=node_desc.id,
+            type="custom",
+            position={"x": 0, "y": 0},  # Temporary position, will be updated later
+            height=82,  # Increase height to match reference file
+            width=244,
+            positionAbsolute={"x": 0, "y": 0},
+            selected=False,
+            sourcePosition="right",
+            targetPosition="left",
+            data=code_node,
+        )
+
+    @staticmethod
+    def _create_template_node(node_desc: NodeDescription) -> CompleteNode:
+        """Create template node"""
+        # Build variable list
+        variables = []
+        template_text = node_desc.template or ""
+
+        # Collect all node IDs referenced in the template
+        referenced_nodes = set()
+        for var in node_desc.variables or []:
+            source_node = var.source_node or ""
+            source_variable = var.source_variable or ""
+
+            variable = Variable(variable=var.name, value_selector=[source_node, source_variable])
+            variables.append(variable)
+
+            if source_node:
+                referenced_nodes.add(source_node)
+
+            # Modify variable reference format in the template
+            # Replace {{#node_id.variable#}} with {{ variable }}
+            if source_node and source_variable:
+                template_text = template_text.replace(f"{{{{#{source_node}.{source_variable}#}}}}", f"{{ {var.name} }}")
+
+        # Check if a reference to the start node needs to be added
+        # If the template contains a reference to the start node but the variable list does not have a corresponding variable  # noqa: E501
+        import re
+
+        start_node_refs = re.findall(r"{{#(\d+)\.([^#]+)#}}", template_text)
+
+        for node_id, var_name in start_node_refs:
+            # Check if there is already a reference to this variable
+            if not any(v.variable == var_name for v in variables):
+                # Add reference to start node variable
+                variable = Variable(variable=var_name, value_selector=[node_id, var_name])
+                variables.append(variable)
+
+                # Modify variable reference format in the template
+                template_text = template_text.replace(f"{{{{#{node_id}.{var_name}#}}}}", f"{{ {var_name} }}")
+
+        # Get all variable names
+        var_names = [var.variable for var in variables]
+
+        # Simple and crude method: directly replace all possible variable reference formats
+        for var_name in var_names:
+            # Replace {var_name} with {{ var_name }}
+            template_text = template_text.replace("{" + var_name + "}", "{{ " + var_name + " }}")
+            # Replace { var_name } with {{ var_name }}
+            template_text = template_text.replace("{ " + var_name + " }", "{{ " + var_name + " }}")
+            # Replace {var_name } with {{ var_name }}
+            template_text = template_text.replace("{" + var_name + " }", "{{ " + var_name + " }}")
+            # Replace { var_name} with {{ var_name }}
+            template_text = template_text.replace("{ " + var_name + "}", "{{ " + var_name + " }}")
+            # Replace {{{ var_name }}} with {{ var_name }}
+            template_text = template_text.replace("{{{ " + var_name + " }}}", "{{ " + var_name + " }}")
+            # Replace {{{var_name}}} with {{ var_name }}
+            template_text = template_text.replace("{{{" + var_name + "}}}", "{{ " + var_name + " }}")
+
+        # Use regular expression to replace all triple curly braces with double curly braces
+        template_text = re.sub(r"{{{([^}]+)}}}", r"{{ \1 }}", template_text)
+
+        template_node = TemplateTransformNodeType(
+            title=node_desc.title,
+            desc=node_desc.description or "",
+            type=BlockEnum.template_transform,
+            template=template_text,
+            variables=variables,
+        )
+
+        return CompleteNode(
+            id=node_desc.id,
+            type="custom",
+            position={"x": 0, "y": 0},  # Temporary position, will be updated later
+            height=82,  # Increase height to match reference file
+            width=244,
+            positionAbsolute={"x": 0, "y": 0},
+            selected=False,
+            sourcePosition="right",
+            targetPosition="left",
+            data=template_node,
+        )
+
+    @staticmethod
+    def _create_end_node(node_desc: NodeDescription) -> CompleteNode:
+        """Create end node"""
+        # Build output variable list
+        outputs = []
+        for output in node_desc.outputs or []:
+            variable = Variable(
+                variable=output.name, value_selector=[output.source_node or "", output.source_variable or ""]
+            )
+            outputs.append(variable)
+
+        end_node = EndNodeType(
+            title=node_desc.title, desc=node_desc.description or "", type=BlockEnum.end, outputs=outputs
+        )
+
+        return CompleteNode(
+            id=node_desc.id,
+            type="custom",
+            position={"x": 0, "y": 0},  # Temporary position, will be updated later
+            height=90,
+            width=244,
+            positionAbsolute={"x": 0, "y": 0},
+            selected=False,
+            sourcePosition="right",
+            targetPosition="left",
+            data=end_node,
+        )

api/core/auto/workflow_generator/models/__init__.py

@@ -0,0 +1,7 @@
+"""
+模型包
+"""
+
+from .workflow_description import ConnectionDescription, NodeDescription, WorkflowDescription
+
+__all__ = ["ConnectionDescription", "NodeDescription", "WorkflowDescription"]

api/core/auto/workflow_generator/models/workflow_description.py

@@ -0,0 +1,80 @@
+"""
+Workflow Description Model
+Used to represent the simplified workflow description generated by large language models
+"""
+
+from typing import Optional
+
+from pydantic import BaseModel, Field
+
+
+class VariableDescription(BaseModel):
+    """Variable description"""
+
+    name: str
+    type: str
+    description: Optional[str] = None
+    required: bool = True
+    source_node: Optional[str] = None
+    source_variable: Optional[str] = None
+
+
+class OutputDescription(BaseModel):
+    """Output description"""
+
+    name: str
+    type: str = "string"
+    description: Optional[str] = None
+    source_node: Optional[str] = None
+    source_variable: Optional[str] = None
+
+
+class NodeDescription(BaseModel):
+    """Node description"""
+
+    id: str
+    type: str
+    title: str
+    description: Optional[str] = ""
+    variables: Optional[list[VariableDescription]] = Field(default_factory=list)
+    outputs: Optional[list[OutputDescription]] = Field(default_factory=list)
+
+    # LLM node specific fields
+    system_prompt: Optional[str] = None
+    user_prompt: Optional[str] = None
+    provider: Optional[str] = "zhipuai"
+    model: Optional[str] = "glm-4-flash"
+
+    # Code node specific fields
+    code: Optional[str] = None
+
+    # Template node specific fields
+    template: Optional[str] = None
+
+    # Reference to workflow description, used for node relationship analysis
+    workflow_description: Optional["WorkflowDescription"] = Field(default=None, exclude=True)
+
+    class Config:
+        exclude = {"workflow_description"}
+
+
+class ConnectionDescription(BaseModel):
+    """Connection description"""
+
+    source: str
+    target: str
+
+
+class WorkflowDescription(BaseModel):
+    """Workflow description"""
+
+    name: str
+    description: Optional[str] = ""
+    nodes: list[NodeDescription]
+    connections: list[ConnectionDescription]
+
+    def __init__(self, **data):
+        super().__init__(**data)
+        # Add workflow description reference to each node
+        for node in self.nodes:
+            node.workflow_description = self

api/core/auto/workflow_generator/utils/__init__.py

@@ -0,0 +1,16 @@
+"""
+工具函数包
+"""
+
+from .llm_client import LLMClient
+from .prompts import DEFAULT_MODEL_CONFIG, DEFAULT_SYSTEM_PROMPT, build_workflow_prompt
+from .type_mapper import map_string_to_var_type, map_var_type_to_input_type
+
+__all__ = [
+    "DEFAULT_MODEL_CONFIG",
+    "DEFAULT_SYSTEM_PROMPT",
+    "LLMClient",
+    "build_workflow_prompt",
+    "map_string_to_var_type",
+    "map_var_type_to_input_type",
+]

api/core/auto/workflow_generator/utils/config_manager.py

@@ -0,0 +1,142 @@
+"""
+Configuration Manager
+Used to manage configurations and prompts
+"""
+
+import os
+import time
+from pathlib import Path
+from typing import Any, Optional
+
+import yaml
+
+
+class ConfigManager:
+    """Configuration manager for managing configurations"""
+
+    def __init__(self, config_dir: str = "config"):
+        """
+        Initialize configuration manager
+
+        Args:
+            config_dir: Configuration directory path
+        """
+        self.config_dir = Path(config_dir)
+        self.config: dict[str, Any] = {}
+        self.last_load_time: float = 0
+        self.reload_interval: float = 60  # Check every 60 seconds
+        self._load_config()
+
+    def _should_reload(self) -> bool:
+        """Check if configuration needs to be reloaded"""
+        return time.time() - self.last_load_time > self.reload_interval
+
+    def _load_config(self) -> dict[str, Any]:
+        """Load configuration files"""
+        default_config = self._load_yaml(self.config_dir / "default.yaml")
+        custom_config = self._load_yaml(self.config_dir / "custom.yaml")
+        self.config = self._deep_merge(default_config, custom_config)
+        self.last_load_time = time.time()
+        return self.config
+
+    @staticmethod
+    def _load_yaml(path: Path) -> dict[str, Any]:
+        """Load YAML file"""
+        try:
+            with open(path, encoding="utf-8") as f:
+                return yaml.safe_load(f) or {}
+        except FileNotFoundError:
+            print(f"Warning: Config file not found at {path}")
+            return {}
+        except Exception as e:
+            print(f"Error loading config file {path}: {e}")
+            return {}
+
+    @staticmethod
+    def _deep_merge(dict1: dict, dict2: dict) -> dict:
+        """Recursively merge two dictionaries"""
+        result = dict1.copy()
+        for key, value in dict2.items():
+            if key in result and isinstance(result[key], dict) and isinstance(value, dict):
+                result[key] = ConfigManager._deep_merge(result[key], value)
+            else:
+                result[key] = value
+        return result
+
+    def get(self, *keys: str, default: Any = None) -> Any:
+        """
+        Get configuration value
+
+        Args:
+            *keys: Configuration key path
+            default: Default value
+
+        Returns:
+            Configuration value or default value
+        """
+        if self._should_reload():
+            self._load_config()
+
+        current = self.config
+        for key in keys:
+            if isinstance(current, dict) and key in current:
+                current = current[key]
+            else:
+                return default
+        return current
+
+    @property
+    def workflow_generator(self) -> dict[str, Any]:
+        """Get workflow generator configuration"""
+        return self.get("workflow_generator", default={})
+
+    @property
+    def workflow_nodes(self) -> dict[str, Any]:
+        """Get workflow nodes configuration"""
+        return self.get("workflow_nodes", default={})
+
+    @property
+    def output(self) -> dict[str, Any]:
+        """Get output configuration"""
+        return self.get("output", default={})
+
+    def get_output_path(self, filename: Optional[str] = None) -> str:
+        """
+        Get output file path
+
+        Args:
+            filename: Optional filename, uses default filename from config if not specified
+
+        Returns:
+            Complete output file path
+        """
+        output_config = self.output
+        output_dir = output_config.get("dir", "output/")
+        output_filename = filename or output_config.get("filename", "generated_workflow.yml")
+        return os.path.join(output_dir, output_filename)
+
+    def get_workflow_model(self, model_name: Optional[str] = None) -> dict[str, Any]:
+        """
+        Get workflow generation model configuration
+
+        Args:
+            model_name: Model name, uses default model if not specified
+
+        Returns:
+            Model configuration
+        """
+        models = self.workflow_generator.get("models", {})
+
+        if not model_name:
+            model_name = models.get("default")
+
+        return models.get("available", {}).get(model_name, {})
+
+    def get_llm_node_config(self) -> dict[str, Any]:
+        """
+        Get LLM node configuration
+
+        Returns:
+            LLM node configuration
+        """
+        return self.workflow_nodes.get("llm", {})

api/core/auto/workflow_generator/utils/debug_manager.py

@@ -0,0 +1,151 @@
+"""
+Debug Manager
+Used to manage debug information saving
+"""
+
+import datetime
+import json
+import os
+import uuid
+from typing import Any, Optional, Union
+
+
+class DebugManager:
+    """Debug manager for managing debug information saving"""
+
+    _instance = None
+
+    def __new__(cls, *args, **kwargs):
+        """Singleton pattern"""
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+            cls._instance._initialized = False
+        return cls._instance
+
+    def __init__(self, config: dict[str, Any] = {}, debug_enabled: bool = False):
+        """
+        Initialize debug manager
+
+        Args:
+            config: Debug configuration
+            debug_enabled: Whether to enable debug mode
+        """
+        # Avoid repeated initialization
+        if self._initialized:
+            return
+
+        self._initialized = True
+        self.config = config or {}
+        self.debug_enabled = debug_enabled or self.config.get("enabled", False)
+        self.debug_dir = self.config.get("dir", "debug/")
+        self.save_options = self.config.get(
+            "save_options", {"prompt": True, "response": True, "json": True, "workflow": True}
+        )
+
+        # Generate run ID
+        self.case_id = self._generate_case_id()
+        self.case_dir = os.path.join(self.debug_dir, self.case_id)
+
+        # If debug is enabled, create debug directory
+        if self.debug_enabled:
+            os.makedirs(self.case_dir, exist_ok=True)
+            print(f"Debug mode enabled, debug information will be saved to: {self.case_dir}")
+
+    def _generate_case_id(self) -> str:
+        """
+        Generate run ID
+
+        Returns:
+            Run ID
+        """
+        # Use format from configuration to generate run ID
+        format_str = self.config.get("case_id_format", "%Y%m%d_%H%M%S_%f")
+        timestamp = datetime.datetime.now().strftime(format_str)
+
+        # Add random string
+        random_str = str(uuid.uuid4())[:8]
+
+        return f"{timestamp}_{random_str}"
+
+    def save_text(self, content: str, filename: str, subdir: Optional[str] = None) -> Optional[str]:
+        """
+        Save text content to file
+
+        Args:
+            content: Text content
+            filename: File name
+            subdir: Subdirectory name
+
+        Returns:
+            Saved file path, returns None if debug is not enabled
+        """
+        if not self.debug_enabled:
+            return None
+
+        try:
+            # Determine save path
+            save_dir = self.case_dir
+            if subdir:
+                save_dir = os.path.join(save_dir, subdir)
+                os.makedirs(save_dir, exist_ok=True)
+
+            file_path = os.path.join(save_dir, filename)
+
+            # Save content
+            with open(file_path, "w", encoding="utf-8") as f:
+                f.write(content)
+
+            print(f"Debug information saved to: {file_path}")
+            return file_path
+        except Exception as e:
+            print(f"Failed to save debug information: {e}")
+            return None
+
+    def save_json(self, data: Union[dict, list], filename: str, subdir: Optional[str] = None) -> Optional[str]:
+        """
+        Save JSON data to file
+
+        Args:
+            data: JSON data
+            filename: File name
+            subdir: Subdirectory name
+
+        Returns:
+            Saved file path, returns None if debug is not enabled
+        """
+        if not self.debug_enabled:
+            return None
+
+        try:
+            # Determine save path
+            save_dir = self.case_dir
+            if subdir:
+                save_dir = os.path.join(save_dir, subdir)
+                os.makedirs(save_dir, exist_ok=True)
+
+            file_path = os.path.join(save_dir, filename)
+
+            # Save content
+            with open(file_path, "w", encoding="utf-8") as f:
+                json.dump(data, f, ensure_ascii=False, indent=2)
+
+            print(f"Debug information saved to: {file_path}")
+            return file_path
+        except Exception as e:
+            print(f"Failed to save debug information: {e}")
+            return None
+
+    def should_save(self, option: str) -> bool:
+        """
+        Check if specified type of debug information should be saved
+
+        Args:
+            option: Debug information type
+
+        Returns:
+            Whether it should be saved
+        """
+        if not self.debug_enabled:
+            return False
+
+        return self.save_options.get(option, False)

api/core/auto/workflow_generator/utils/llm_client.py

@@ -0,0 +1,438 @@
+"""
+LLM Client
+Used to call LLM API
+"""
+
+import json
+import re
+from typing import Any
+
+from core.auto.workflow_generator.utils.debug_manager import DebugManager
+from core.auto.workflow_generator.utils.prompts import DEFAULT_SYSTEM_PROMPT
+from core.model_manager import ModelInstance
+from core.model_runtime.entities.message_entities import SystemPromptMessage, UserPromptMessage
+
+
+class LLMClient:
+    """LLM Client"""
+
+    def __init__(self, model_instance: ModelInstance, debug_manager: DebugManager):
+        """
+        Initialize LLM client
+
+        Args:
+            api_key: API key
+            model: Model name
+            api_base: API base URL
+            max_tokens: Maximum number of tokens to generate
+            debug_manager: Debug manager
+        """
+
+        self.debug_manager = debug_manager or DebugManager()
+        self.model_instance = model_instance
+
+    def generate(self, prompt: str) -> str:
+        """
+        Generate text
+
+        Args:
+            prompt: Prompt text
+
+        Returns:
+            Generated text
+        """
+
+        # Save prompt
+        if self.debug_manager.should_save("prompt"):
+            self.debug_manager.save_text(prompt, "prompt.txt", "llm")
+
+        try:
+            response = self.model_instance.invoke_llm(
+                prompt_messages=[
+                    SystemPromptMessage(content=DEFAULT_SYSTEM_PROMPT),
+                    UserPromptMessage(content=prompt),
+                ],
+                model_parameters={"temperature": 0.7, "max_tokens": 4900},
+            )
+            content = ""
+            for chunk in response:
+                content += chunk.delta.message.content
+            print(f"Generation complete, text length: {len(content)} characters")
+
+            # Save response
+            if self.debug_manager.should_save("response"):
+                self.debug_manager.save_text(content, "response.txt", "llm")
+
+            return content
+        except Exception as e:
+            print(f"Error generating text: {e}")
+            raise e
+
+    def extract_json(self, text: str) -> dict[str, Any]:
+        """
+        Extract JSON from text
+
+        Args:
+            text: Text containing JSON
+
+        Returns:
+            Extracted JSON object
+        """
+        print("Starting JSON extraction from text...")
+
+        # Save original text
+        if self.debug_manager.should_save("json"):
+            self.debug_manager.save_text(text, "original_text.txt", "json")
+
+        # Use regex to extract JSON part
+        json_match = re.search(r"```json\n(.*?)\n```", text, re.DOTALL)
+        if json_match:
+            json_str = json_match.group(1)
+            print("Successfully extracted JSON from code block")
+        else:
+            # Try to match code block without language identifier
+            json_match = re.search(r"```\n(.*?)\n```", text, re.DOTALL)
+            if json_match:
+                json_str = json_match.group(1)
+                print("Successfully extracted JSON from code block without language identifier")
+            else:
+                # Try to match JSON surrounded by curly braces
+                json_match = re.search(r"(\{.*\})", text, re.DOTALL)
+                if json_match:
+                    json_str = json_match.group(1)
+                    print("Successfully extracted JSON from curly braces")
+                else:
+                    # Try to parse entire text
+                    json_str = text
+                    print("No JSON code block found, attempting to parse entire text")
+
+        # Save extracted JSON string
+        if self.debug_manager.should_save("json"):
+            self.debug_manager.save_text(json_str, "extracted_json.txt", "json")
+
+        # Try multiple methods to parse JSON
+        try:
+            # Try direct parsing
+            result = json.loads(json_str)
+        except json.JSONDecodeError as e:
+            print(f"Direct JSON parsing failed: {e}, attempting basic cleaning")
+            try:
+                # Try basic cleaning
+                cleaned_text = self._clean_text(json_str)
+                if self.debug_manager.should_save("json"):
+                    self.debug_manager.save_text(cleaned_text, "cleaned_json_1.txt", "json")
+                result = json.loads(cleaned_text)
+            except json.JSONDecodeError as e:
+                print(f"JSON parsing after basic cleaning failed: {e}, attempting to fix common errors")
+                try:
+                    # Try fixing common errors
+                    fixed_text = self._fix_json_errors(json_str)
+                    if self.debug_manager.should_save("json"):
+                        self.debug_manager.save_text(fixed_text, "cleaned_json_2.txt", "json")
+                    result = json.loads(fixed_text)
+                except json.JSONDecodeError as e:
+                    print(f"JSON parsing after fixing common errors failed: {e}, attempting aggressive cleaning")
+                    try:
+                        # Try aggressive cleaning
+                        aggressive_cleaned = self._aggressive_clean(json_str)
+                        if self.debug_manager.should_save("json"):
+                            self.debug_manager.save_text(aggressive_cleaned, "cleaned_json_3.txt", "json")
+                        result = json.loads(aggressive_cleaned)
+                    except json.JSONDecodeError as e:
+                        print(f"JSON parsing after aggressive cleaning failed: {e}, attempting manual JSON extraction")
+                        # Try manual JSON structure extraction
+                        result = self._manual_json_extraction(json_str)
+                        if self.debug_manager.should_save("json"):
+                            self.debug_manager.save_json(result, "manual_json.json", "json")
+
+        # Check for nested workflow structure
+        if "workflow" in result and isinstance(result["workflow"], dict):
+            print("Detected nested workflow structure, extracting top-level data")
+            # Extract workflow name and description
+            name = result.get("name", "Text Analysis Workflow")
+            description = result.get("description", "")
+
+            # Extract nodes and connections
+            nodes = result["workflow"].get("nodes", [])
+            connections = []
+
+            # If there are connections, extract them
+            if "connections" in result["workflow"]:
+                connections = result["workflow"]["connections"]
+
+            # Build standard format workflow description
+            result = {"name": name, "description": description, "nodes": nodes, "connections": connections}
+
+        # Save final parsed JSON
+        if self.debug_manager.should_save("json"):
+            self.debug_manager.save_json(result, "final_json.json", "json")
+
+        print(
+            f"JSON parsing successful, contains {len(result.get('nodes', []))} nodes and {len(result.get('connections', []))} connections"  # noqa: E501
+        )
+        return result
+
+    def _clean_text(self, text: str) -> str:
+        """
+        Clean text by removing non-JSON characters
+
+        Args:
+            text: Text to clean
+
+        Returns:
+            Cleaned text
+        """
+        print("Starting text cleaning...")
+        # Remove characters that might cause JSON parsing to fail
+        lines = text.split("\n")
+        cleaned_lines = []
+
+        in_json = False
+        for line in lines:
+            if line.strip().startswith("{") or line.strip().startswith("["):
+                in_json = True
+
+            if in_json:
+                cleaned_lines.append(line)
+
+            if line.strip().endswith("}") or line.strip().endswith("]"):
+                in_json = False
+
+        cleaned_text = "\n".join(cleaned_lines)
+        print(f"Text cleaning complete, length before: {len(text)}, length after: {len(cleaned_text)}")
+        return cleaned_text
+
+    def _fix_json_errors(self, text: str) -> str:
+        """
+        Fix common JSON errors
+
+        Args:
+            text: Text to fix
+
+        Returns:
+            Fixed text
+        """
+        print("Attempting to fix JSON errors...")
+
+        # Replace single quotes with double quotes
+        text = re.sub(r"'([^']*)'", r'"\1"', text)
+
+        # Fix missing commas
+        text = re.sub(r"}\s*{", "},{", text)
+        text = re.sub(r"]\s*{", "],{", text)
+        text = re.sub(r"}\s*\[", r"},\[", text)
+        text = re.sub(r"]\s*\[", r"],\[", text)
+
+        # Fix extra commas
+        text = re.sub(r",\s*}", "}", text)
+        text = re.sub(r",\s*]", "]", text)
+
+        # Ensure property names have double quotes
+        text = re.sub(r"([{,]\s*)(\w+)(\s*:)", r'\1"\2"\3', text)
+
+        return text
+
+    def _aggressive_clean(self, text: str) -> str:
+        """
+        More aggressive text cleaning
+
+        Args:
+            text: Text to clean
+
+        Returns:
+            Cleaned text
+        """
+        print("Using aggressive cleaning method...")
+
+        # Try to find outermost curly braces
+        start_idx = text.find("{")
+        end_idx = text.rfind("}")
+
+        if start_idx != -1 and end_idx != -1 and start_idx < end_idx:
+            text = text[start_idx : end_idx + 1]
+
+        # Remove comments
+        text = re.sub(r"//.*?\n", "\n", text)
+        text = re.sub(r"/\*.*?\*/", "", text, flags=re.DOTALL)
+
+        # Fix JSON format
+        text = self._fix_json_errors(text)
+
+        # Remove escape characters
+        text = text.replace("\\n", "\n").replace("\\t", "\t").replace('\\"', '"')
+
+        # Fix potential Unicode escape issues
+        text = re.sub(r"\\u([0-9a-fA-F]{4})", lambda m: chr(int(m.group(1), 16)), text)
+
+        return text
+
+    def _manual_json_extraction(self, text: str) -> dict[str, Any]:
+        """
+        Manual JSON structure extraction
+
+        Args:
+            text: Text to extract from
+
+        Returns:
+            Extracted JSON object
+        """
+        print("Attempting manual JSON structure extraction...")
+
+        # Extract workflow name
+        name_match = re.search(r'"name"\s*:\s*"([^"]*)"', text)
+        name = name_match.group(1) if name_match else "Simple Workflow"
+
+        # Extract workflow description
+        desc_match = re.search(r'"description"\s*:\s*"([^"]*)"', text)
+        description = desc_match.group(1) if desc_match else "Automatically generated workflow"
+
+        # Extract nodes
+        nodes = []
+        node_matches = re.finditer(r'\{\s*"id"\s*:\s*"([^"]*)"\s*,\s*"type"\s*:\s*"([^"]*)"', text)
+
+        for match in node_matches:
+            node_id = match.group(1)
+            node_type = match.group(2)
+
+            # Extract node title
+            title_match = re.search(rf'"id"\s*:\s*"{node_id}".*?"title"\s*:\s*"([^"]*)"', text, re.DOTALL)
+            title = title_match.group(1) if title_match else f"{node_type.capitalize()} Node"
+
+            # Extract node description
+            desc_match = re.search(rf'"id"\s*:\s*"{node_id}".*?"description"\s*:\s*"([^"]*)"', text, re.DOTALL)
+            desc = desc_match.group(1) if desc_match else ""
+
+            # Create basic node based on node type
+            if node_type == "start":
+                # Extract variables
+                variables = []
+                var_section_match = re.search(rf'"id"\s*:\s*"{node_id}".*?"variables"\s*:\s*\[(.*?)\]', text, re.DOTALL)
+
+                if var_section_match:
+                    var_section = var_section_match.group(1)
+                    var_matches = re.finditer(r'\{\s*"name"\s*:\s*"([^"]*)"\s*,\s*"type"\s*:\s*"([^"]*)"', var_section)
+
+                    for var_match in var_matches:
+                        var_name = var_match.group(1)
+                        var_type = var_match.group(2)
+
+                        # Extract variable description
+                        var_desc_match = re.search(
+                            rf'"name"\s*:\s*"{var_name}".*?"description"\s*:\s*"([^"]*)"', var_section, re.DOTALL
+                        )
+                        var_desc = var_desc_match.group(1) if var_desc_match else ""
+
+                        # Extract required status
+                        var_required_match = re.search(
+                            rf'"name"\s*:\s*"{var_name}".*?"required"\s*:\s*(true|false)', var_section, re.DOTALL
+                        )
+                        var_required = var_required_match.group(1).lower() == "true" if var_required_match else True
+
+                        variables.append(
+                            {"name": var_name, "type": var_type, "description": var_desc, "required": var_required}
+                        )
+
+                # If no variables found but this is a greeting workflow, add default user_name variable
+                if not variables and ("greeting" in name.lower()):
+                    variables.append(
+                        {"name": "user_name", "type": "string", "description": "User's name", "required": True}
+                    )
+
+                nodes.append({"id": node_id, "type": "start", "title": title, "desc": desc, "variables": variables})
+            elif node_type == "llm":
+                # Extract system prompt
+                system_prompt_match = re.search(
+                    rf'"id"\s*:\s*"{node_id}".*?"system_prompt"\s*:\s*"([^"]*)"', text, re.DOTALL
+                )
+                system_prompt = system_prompt_match.group(1) if system_prompt_match else "You are a helpful assistant"
+
+                # Extract user prompt
+                user_prompt_match = re.search(
+                    rf'"id"\s*:\s*"{node_id}".*?"user_prompt"\s*:\s*"([^"]*)"', text, re.DOTALL
+                )
+                user_prompt = user_prompt_match.group(1) if user_prompt_match else "Please answer the user's question"
+
+                nodes.append(
+                    {
+                        "id": node_id,
+                        "type": "llm",
+                        "title": title,
+                        "desc": desc,
+                        "provider": "zhipuai",
+                        "model": "glm-4-flash",
+                        "system_prompt": system_prompt,
+                        "user_prompt": user_prompt,
+                        "variables": [],
+                    }
+                )
+            elif node_type in ("template", "template-transform"):
+                # Extract template content
+                template_match = re.search(rf'"id"\s*:\s*"{node_id}".*?"template"\s*:\s*"([^"]*)"', text, re.DOTALL)
+                template = template_match.group(1) if template_match else ""
+
+                # Fix triple curly brace issue in template, replace {{{ with {{ and }}} with }}
+                template = template.replace("{{{", "{{").replace("}}}", "}}")
+
+                nodes.append(
+                    {
+                        "id": node_id,
+                        "type": "template-transform",
+                        "title": title,
+                        "desc": desc,
+                        "template": template,
+                        "variables": [],
+                    }
+                )
+            elif node_type == "end":
+                # Extract outputs
+                outputs = []
+                output_section_match = re.search(
+                    rf'"id"\s*:\s*"{node_id}".*?"outputs"\s*:\s*\[(.*?)\]', text, re.DOTALL
+                )
+
+                if output_section_match:
+                    output_section = output_section_match.group(1)
+                    output_matches = re.finditer(
+                        r'\{\s*"name"\s*:\s*"([^"]*)"\s*,\s*"type"\s*:\s*"([^"]*)"', output_section
+                    )
+
+                    for output_match in output_matches:
+                        output_name = output_match.group(1)
+                        output_type = output_match.group(2)
+
+                        # Extract source node
+                        source_node_match = re.search(
+                            rf'"name"\s*:\s*"{output_name}".*?"source_node"\s*:\s*"([^"]*)"', output_section, re.DOTALL
+                        )
+                        source_node = source_node_match.group(1) if source_node_match else ""
+
+                        # Extract source variable
+                        source_var_match = re.search(
+                            rf'"name"\s*:\s*"{output_name}".*?"source_variable"\s*:\s*"([^"]*)"',
+                            output_section,
+                            re.DOTALL,
+                        )
+                        source_var = source_var_match.group(1) if source_var_match else ""
+
+                        outputs.append(
+                            {
+                                "name": output_name,
+                                "type": output_type,
+                                "source_node": source_node,
+                                "source_variable": source_var,
+                            }
+                        )
+
+                nodes.append({"id": node_id, "type": "end", "title": title, "desc": desc, "outputs": outputs})
+            else:
+                # Other node types
+                nodes.append({"id": node_id, "type": node_type, "title": title, "desc": desc})
+
+        # Extract connections
+        connections = []
+        conn_matches = re.finditer(r'\{\s*"source"\s*:\s*"([^"]*)"\s*,\s*"target"\s*:\s*"([^"]*)"', text)
+
+        for match in conn_matches:
+            connections.append({"source": match.group(1), "target": match.group(2)})
+
+        return {"name": name, "description": description, "nodes": nodes, "connections": connections}