release(update): bump version to 0.10.0-beta2

- Update CURRENT_VERSION in configuration settings - Update Docker images to use version 0.10.0-beta2 - Change web package version in package.json
feat(ci): add release branch to build-push workflow
2024-10-01 13:58:37 +08:00 · 2024-10-01 13:30:36 +08:00 · 2024-10-01 13:21:58 +08:00 · 2024-10-01 13:08:06 +08:00 · 2024-09-30 23:34:54 +08:00 · 2024-09-30 23:34:04 +08:00
520 changed files with 10350 additions and 11238 deletions
@@ -39,7 +39,7 @@ jobs:
            api/pyproject.toml
            api/poetry.lock

-      - name: Check Poetry lockfile
+      - name: Poetry check
        run: |
          poetry check -C api --lock
          poetry show -C api
@@ -47,9 +47,6 @@ jobs:
      - name: Install dependencies
        run: poetry install -C api --with dev

-      - name: Check dependencies in pyproject.toml
-        run: poetry run -C api bash dev/pytest/pytest_artifacts.sh
-
      - name: Run Unit tests
        run: poetry run -C api bash dev/pytest/pytest_unit_tests.sh

@@ -17,7 +17,7 @@
            alt="chat on Discord"></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>
+            alt="follow on Twitter"></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">
@@ -196,14 +196,10 @@ If you'd like to configure a highly-available setup, there are community-contrib

 #### Using Terraform for Deployment

-Deploy Dify to Cloud Platform with a single click using [terraform](https://www.terraform.io/)
-
 ##### Azure Global
+Deploy Dify to Azure with a single click using [terraform](https://www.terraform.io/).
 - [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)
-
 ## Contributing

 For those who'd like to contribute code, see our [Contribution Guide](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md). 
@@ -223,7 +219,7 @@ At the same time, please consider supporting Dify by sharing it on social media
 * [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.
+* [Twitter](https://twitter.com/dify_ai). Best for: sharing your applications and hanging out with the community.

 ## Star history

@@ -17,7 +17,7 @@
            alt="chat on Discord"></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>
+            alt="follow on Twitter"></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">
@@ -179,13 +179,10 @@ docker compose up -d

 #### استخدام Terraform للتوزيع

-انشر Dify إلى منصة السحابة بنقرة واحدة باستخدام [terraform](https://www.terraform.io/)
-
 ##### Azure Global
+استخدم [terraform](https://www.terraform.io/) لنشر Dify على Azure بنقرة واحدة.
 - [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)

 ## المساهمة

@@ -17,7 +17,7 @@
            alt="chat on Discord"></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>
+            alt="follow on Twitter"></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">
@@ -202,14 +202,10 @@ docker compose up -d

 #### 使用 Terraform 部署

-使用 [terraform](https://www.terraform.io/) 一键将 Dify 部署到云平台
-
 ##### Azure Global
+使用 [terraform](https://www.terraform.io/) 一键部署 Dify 到 Azure。
 - [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)
-
 ## Star History

 [![Star History Chart](https://api.star-history.com/svg?repos=langgenius/dify&type=Date)](https://star-history.com/#langgenius/dify&Date)
@@ -236,7 +232,7 @@ docker compose up -d
 - [GitHub Issues](https://github.com/langgenius/dify/issues)。👉：使用 Dify.AI 时遇到的错误和问题，请参阅[贡献指南](CONTRIBUTING.md)。
 - [电子邮件支持](mailto:[email protected]?subject=[GitHub]Questions%20About%20Dify)。👉：关于使用 Dify.AI 的问题。
 - [Discord](https://discord.gg/FngNHpbcY7)。👉：分享您的应用程序并与社区交流。
- [X(Twitter)](https://twitter.com/dify_ai)。👉：分享您的应用程序并与社区交流。
+- [Twitter](https://twitter.com/dify_ai)。👉：分享您的应用程序并与社区交流。
 - [商业许可](mailto:[email protected]?subject=[GitHub]Business%20License%20Inquiry)。👉：有关商业用途许可 Dify.AI 的商业咨询。
 - [微信]() 👉：扫描下方二维码，添加微信好友，备注 Dify，我们将邀请您加入 Dify 社区。  
 <img src="./images/wechat.png" alt="wechat" width="100"/>
@@ -17,7 +17,7 @@
            alt="chat en Discord"></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="seguir en X(Twitter)"></a>
+            alt="seguir en Twitter"></a>
    <a href="https://hub.docker.com/u/langgenius" target="_blank">
        <img alt="Descargas de Docker" 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">
@@ -204,13 +204,10 @@ Si desea configurar una configuración de alta disponibilidad, la comunidad prop

 #### Uso de Terraform para el despliegue

-Despliega Dify en una plataforma en la nube con un solo clic utilizando [terraform](https://www.terraform.io/)
-
 ##### Azure Global
+Utiliza [terraform](https://www.terraform.io/) para desplegar Dify en Azure con un solo clic.
 - [Azure Terraform por @nikawang](https://github.com/nikawang/dify-azure-terraform)

-##### Google Cloud
- [Google Cloud Terraform por @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)

 ## Contribuir

@@ -231,7 +228,7 @@ Al mismo tiempo, considera apoyar a Dify compartiéndolo en redes sociales y en
 * [Discusión en GitHub](https://github.com/langgenius/dify/discussions). Lo mejor para: compartir comentarios y hacer preguntas.
 * [Reporte de problemas en GitHub](https://github.com/langgenius/dify/issues). Lo mejor para: errores que encuentres usando Dify.AI y propuestas de características. Consulta nuestra [Guía de contribución](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
 * [Discord](https://discord.gg/FngNHpbcY7). Lo mejor para: compartir tus aplicaciones y pasar el rato con la comunidad.
-* [X(Twitter)](https://twitter.com/dify_ai). Lo mejor para: compartir tus aplicaciones y pasar el rato con la comunidad.
+* [Twitter](https://twitter.com/dify_ai). Lo mejor para: compartir tus aplicaciones y pasar el rato con la comunidad.

 ## Historial de Estrellas

@@ -17,7 +17,7 @@
            alt="chat sur Discord"></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="suivre sur X(Twitter)"></a>
+            alt="suivre sur Twitter"></a>
    <a href="https://hub.docker.com/u/langgenius" target="_blank">
        <img alt="Tirages Docker" 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">
@@ -202,13 +202,10 @@ Si vous souhaitez configurer une configuration haute disponibilité, la communau

 #### Utilisation de Terraform pour le déploiement

-Déployez Dify sur une plateforme cloud en un clic en utilisant [terraform](https://www.terraform.io/)
-
 ##### Azure Global
+Utilisez [terraform](https://www.terraform.io/) pour déployer Dify sur Azure en un clic.
 - [Azure Terraform par @nikawang](https://github.com/nikawang/dify-azure-terraform)

-##### Google Cloud
- [Google Cloud Terraform par @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)

 ## Contribuer

@@ -229,7 +226,7 @@ Dans le même temps, veuillez envisager de soutenir Dify en le partageant sur le
 * [Discussion GitHub](https://github.com/langgenius/dify/discussions). Meilleur pour: partager des commentaires et poser des questions.
 * [Problèmes GitHub](https://github.com/langgenius/dify/issues). Meilleur pour: les bogues que vous rencontrez en utilisant Dify.AI et les propositions de fonctionnalités. Consultez notre [Guide de contribution](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
 * [Discord](https://discord.gg/FngNHpbcY7). Meilleur pour: partager vos applications et passer du temps avec la communauté.
-* [X(Twitter)](https://twitter.com/dify_ai). Meilleur pour: partager vos applications et passer du temps avec la communauté.
+* [Twitter](https://twitter.com/dify_ai). Meilleur pour: partager vos applications et passer du temps avec la communauté.

 ## Historique des étoiles

@@ -17,7 +17,7 @@
            alt="Discordでチャット"></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="X(Twitter)でフォロー"></a>
+            alt="Twitterでフォロー"></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">
@@ -68,7 +68,7 @@ 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
  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**: 
-  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。
+  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ーポイントも提供します。

 **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。
@@ -201,13 +201,10 @@ docker compose up -d

 #### Terraformを使用したデプロイ

-[terraform](https://www.terraform.io/) を使用して、ワンクリックでDifyをクラウドプラットフォームにデプロイします
-
 ##### Azure Global
- [@nikawangによるAzure Terraform](https://github.com/nikawang/dify-azure-terraform)
+[terraform](https://www.terraform.io/) を使用して、AzureにDifyをワンクリックでデプロイします。
+- [nikawangのAzure Terraform](https://github.com/nikawang/dify-azure-terraform)

-##### Google Cloud
- [@sotazumによるGoogle Cloud Terraform](https://github.com/DeNA/dify-google-cloud-terraform)

 ## 貢献

@@ -228,7 +225,7 @@ docker compose up -d
 * [Github Discussion](https://github.com/langgenius/dify/discussions). 主に: フィードバックの共有や質問。
 * [GitHub Issues](https://github.com/langgenius/dify/issues). 主に: Dify.AIを使用する際に発生するエラーや問題については、[貢献ガイド](CONTRIBUTING_JA.md)を参照してください
 * [Discord](https://discord.gg/FngNHpbcY7). 主に: アプリケーションの共有やコミュニティとの交流。
-* [X(Twitter)](https://twitter.com/dify_ai). 主に: アプリケーションの共有やコミュニティとの交流。
+* [Twitter](https://twitter.com/dify_ai). 主に: アプリケーションの共有やコミュニティとの交流。



@@ -17,7 +17,7 @@
            alt="chat on Discord"></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>
+            alt="follow on Twitter"></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">
@@ -202,13 +202,10 @@ If you'd like to configure a highly-available setup, there are community-contrib

 #### Terraform atorlugu pilersitsineq

-wa'logh nIqHom neH ghun deployment toy'wI' [terraform](https://www.terraform.io/) lo'laH.
-
 ##### Azure Global
- [Azure Terraform mung @nikawang](https://github.com/nikawang/dify-azure-terraform)
+Atoruk [terraform](https://www.terraform.io/) Dify-mik Azure-mut ataatsikkut ikkussuilluarlugu.
+- [Azure Terraform atorlugu @nikawang](https://github.com/nikawang/dify-azure-terraform)

-##### Google Cloud
- [Google Cloud Terraform qachlot @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)

 ## Contributing

@@ -231,7 +228,7 @@ At the same time, please consider supporting Dify by sharing it on social media
 ). 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.
+* [Twitter](https://twitter.com/dify_ai). Best for: sharing your applications and hanging out with the community.

 ## Star History

@@ -17,7 +17,7 @@
            alt="chat on Discord"></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>
+            alt="follow on Twitter"></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">
@@ -39,6 +39,7 @@
  <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>
+
 </p>


@@ -194,14 +195,10 @@ Dify를 Kubernetes에 배포하고 프리미엄 스케일링 설정을 구성했

 #### Terraform을 사용한 배포

-[terraform](https://www.terraform.io/)을 사용하여 단 한 번의 클릭으로 Dify를 클라우드 플랫폼에 배포하십시오
-
 ##### Azure Global
+[terraform](https://www.terraform.io/)을 사용하여 Azure에 Dify를 원클릭으로 배포하세요.
 - [nikawang의 Azure Terraform](https://github.com/nikawang/dify-azure-terraform)

-##### Google Cloud
- [sotazum의 Google Cloud Terraform](https://github.com/DeNA/dify-google-cloud-terraform)
-
 ## 기여

 코드에 기여하고 싶은 분들은 [기여 가이드](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md)를 참조하세요.
@@ -17,7 +17,7 @@
            alt="Discord'da sohbet et"></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="X(Twitter)'da takip et"></a>
+            alt="Twitter'da takip et"></a>
    <a href="https://hub.docker.com/u/langgenius" target="_blank">
        <img alt="Docker Çekmeleri" 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">
@@ -200,13 +200,9 @@ Yüksek kullanılabilirliğe sahip bir kurulum yapılandırmak isterseniz, Dify'

 #### Dağıtım için Terraform Kullanımı

-Dify'ı bulut platformuna tek tıklamayla dağıtın [terraform](https://www.terraform.io/) kullanarak
-
 ##### Azure Global
- [Azure Terraform tarafından @nikawang](https://github.com/nikawang/dify-azure-terraform)
-
-##### Google Cloud
- [Google Cloud Terraform tarafından @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)
+[Terraform](https://www.terraform.io/) kullanarak Dify'ı Azure'a tek tıklamayla dağıtın.
+- [@nikawang tarafından Azure Terraform](https://github.com/nikawang/dify-azure-terraform)

 ## Katkıda Bulunma

@@ -226,7 +222,7 @@ Aynı zamanda, lütfen Dify'ı sosyal medyada, etkinliklerde ve konferanslarda p
 * [Github Tartışmaları](https://github.com/langgenius/dify/discussions). En uygun: geri bildirim paylaşmak ve soru sormak için.
 * [GitHub Sorunları](https://github.com/langgenius/dify/issues). En uygun: Dify.AI kullanırken karşılaştığınız hatalar ve özellik önerileri için. [Katkı Kılavuzumuza](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md) bakın.
 * [Discord](https://discord.gg/FngNHpbcY7). En uygun: uygulamalarınızı paylaşmak ve toplulukla vakit geçirmek için.
-* [X(Twitter)](https://twitter.com/dify_ai). En uygun: uygulamalarınızı paylaşmak ve toplulukla vakit geçirmek için.
+* [Twitter](https://twitter.com/dify_ai). En uygun: uygulamalarınızı paylaşmak ve toplulukla vakit geçirmek için.

 ## Star history

@@ -17,7 +17,7 @@
            alt="chat trên Discord"></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="theo dõi trên X(Twitter)"></a>
+            alt="theo dõi trên Twitter"></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">
@@ -196,14 +196,10 @@ Nếu bạn muốn cấu hình một cài đặt có độ sẵn sàng cao, có

 #### Sử dụng Terraform để Triển khai

-Triển khai Dify lên nền tảng đám mây với một cú nhấp chuột bằng cách sử dụng [terraform](https://www.terraform.io/)
-
 ##### Azure Global
+Triển khai Dify lên Azure chỉ với một cú nhấp chuột bằng cách sử dụng [terraform](https://www.terraform.io/).
 - [Azure Terraform bởi @nikawang](https://github.com/nikawang/dify-azure-terraform)

-##### Google Cloud
- [Google Cloud Terraform bởi @sotazum](https://github.com/DeNA/dify-google-cloud-terraform)
-
 ## Đóng góp

 Đối với những người muốn đóng góp mã, xem [Hướng dẫn Đóng góp](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md) của chúng tôi. 
@@ -223,7 +219,7 @@ Triển khai Dify lên nền tảng đám mây với một cú nhấp chuột b
 * [Thảo luận GitHub](https://github.com/langgenius/dify/discussions). Tốt nhất cho: chia sẻ phản hồi và đặt câu hỏi.
 * [Vấn đề GitHub](https://github.com/langgenius/dify/issues). Tốt nhất cho: lỗi bạn gặp phải khi sử dụng Dify.AI và đề xuất tính năng. Xem [Hướng dẫn Đóng góp](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md) của chúng tôi.
 * [Discord](https://discord.gg/FngNHpbcY7). Tốt nhất cho: chia sẻ ứng dụng của bạn và giao lưu với cộng đồng.
-* [X(Twitter)](https://twitter.com/dify_ai). Tốt nhất cho: chia sẻ ứng dụng của bạn và giao lưu với cộng đồng.
+* [Twitter](https://twitter.com/dify_ai). Tốt nhất cho: chia sẻ ứng dụng của bạn và giao lưu với cộng đồng.

 ## Lịch sử Yêu thích

@@ -20,9 +20,6 @@ FILES_URL=http://127.0.0.1:5001
 # The time in seconds after the signature is rejected
 FILES_ACCESS_TIMEOUT=300

-# Access token expiration time in minutes
-ACCESS_TOKEN_EXPIRE_MINUTES=60
-
 # celery configuration
 CELERY_BROKER_URL=redis://:difyai123456@localhost:6379/1

@@ -42,7 +39,7 @@ DB_DATABASE=dify

 # Storage configuration
 # use for store upload files, private keys...
-# storage type: local, s3, azure-blob, google-storage, tencent-cos, huawei-obs, volcengine-tos, baidu-obs, supabase
+# storage type: local, s3, azure-blob, google-storage, tencent-cos, huawei-obs, volcengine-tos
 STORAGE_TYPE=local
 STORAGE_LOCAL_PATH=storage
 S3_USE_AWS_MANAGED_IAM=false
@@ -82,12 +79,6 @@ HUAWEI_OBS_SECRET_KEY=your-secret-key
 HUAWEI_OBS_ACCESS_KEY=your-access-key
 HUAWEI_OBS_SERVER=your-server-url

-# Baidu OBS Storage Configuration
-BAIDU_OBS_BUCKET_NAME=your-bucket-name
-BAIDU_OBS_SECRET_KEY=your-secret-key
-BAIDU_OBS_ACCESS_KEY=your-access-key
-BAIDU_OBS_ENDPOINT=your-server-url
-
 # OCI Storage configuration
 OCI_ENDPOINT=your-endpoint
 OCI_BUCKET_NAME=your-bucket-name
@@ -102,16 +93,11 @@ VOLCENGINE_TOS_ACCESS_KEY=your-access-key
 VOLCENGINE_TOS_SECRET_KEY=your-secret-key
 VOLCENGINE_TOS_REGION=your-region

-# Supabase Storage Configuration
-SUPABASE_BUCKET_NAME=your-bucket-name
-SUPABASE_API_KEY=your-access-key
-SUPABASE_URL=your-server-url
-
 # CORS configuration
 WEB_API_CORS_ALLOW_ORIGINS=http://127.0.0.1:3000,*
 CONSOLE_CORS_ALLOW_ORIGINS=http://127.0.0.1:3000,*

-# Vector database configuration, support: weaviate, qdrant, milvus, myscale, relyt, pgvecto_rs, pgvector, pgvector, chroma, opensearch, tidb_vector, vikingdb
+# Vector database configuration, support: weaviate, qdrant, milvus, myscale, relyt, pgvecto_rs, pgvector, pgvector, chroma, opensearch, tidb_vector
 VECTOR_STORE=weaviate

 # Weaviate configuration
@@ -211,24 +197,6 @@ OPENSEARCH_USER=admin
 OPENSEARCH_PASSWORD=admin
 OPENSEARCH_SECURE=true

-# Baidu configuration
-BAIDU_VECTOR_DB_ENDPOINT=http://127.0.0.1:5287
-BAIDU_VECTOR_DB_CONNECTION_TIMEOUT_MS=30000
-BAIDU_VECTOR_DB_ACCOUNT=root
-BAIDU_VECTOR_DB_API_KEY=dify
-BAIDU_VECTOR_DB_DATABASE=dify
-BAIDU_VECTOR_DB_SHARD=1
-BAIDU_VECTOR_DB_REPLICAS=3
-
-# ViKingDB configuration
-VIKINGDB_ACCESS_KEY=your-ak
-VIKINGDB_SECRET_KEY=your-sk
-VIKINGDB_REGION=cn-shanghai
-VIKINGDB_HOST=api-vikingdb.xxx.volces.com
-VIKINGDB_SCHEMA=http
-VIKINGDB_CONNECTION_TIMEOUT=30
-VIKINGDB_SOCKET_TIMEOUT=30
-
 # Upload configuration
 UPLOAD_FILE_SIZE_LIMIT=15
 UPLOAD_FILE_BATCH_LIMIT=5
@@ -299,9 +267,6 @@ HTTP_REQUEST_MAX_WRITE_TIMEOUT=600
 HTTP_REQUEST_NODE_MAX_BINARY_SIZE=10485760
 HTTP_REQUEST_NODE_MAX_TEXT_SIZE=1048576

-# Respect X-* headers to redirect clients
-RESPECT_XFORWARD_HEADERS_ENABLED=false
-
 # Log file path
 LOG_FILE=

@@ -26,7 +26,7 @@ from commands import register_commands
 from configs import dify_config

 # DO NOT REMOVE BELOW
-from events import event_handlers  # noqa: F401
+from events import event_handlers
 from extensions import (
    ext_celery,
    ext_code_based_extension,
@@ -36,7 +36,6 @@ from extensions import (
    ext_login,
    ext_mail,
    ext_migrate,
-    ext_proxy_fix,
    ext_redis,
    ext_sentry,
    ext_storage,
@@ -46,7 +45,7 @@ from extensions.ext_login import login_manager
 from libs.passport import PassportService

 # TODO: Find a way to avoid importing models here
-from models import account, dataset, model, source, task, tool, tools, web  # noqa: F401
+from models import account, dataset, model, source, task, tool, tools, web
 from services.account_service import AccountService

 # DO NOT REMOVE ABOVE
@@ -158,7 +157,6 @@ def initialize_extensions(app):
    ext_mail.init_app(app)
    ext_hosting_provider.init_app(app)
    ext_sentry.init_app(app)
-    ext_proxy_fix.init_app(app)


 # Flask-Login configuration
@@ -184,10 +182,10 @@ def load_user_from_request(request_from_flask_login):
    decoded = PassportService().verify(auth_token)
    user_id = decoded.get("user_id")

-    logged_in_account = AccountService.load_logged_in_account(account_id=user_id)
-    if logged_in_account:
-        contexts.tenant_id.set(logged_in_account.current_tenant_id)
-    return logged_in_account
+    account = AccountService.load_logged_in_account(account_id=user_id, token=auth_token)
+    if account:
+        contexts.tenant_id.set(account.current_tenant_id)
+    return account


@login_manager.unauthorized_handler
@@ -347,14 +347,6 @@ def migrate_knowledge_vector_database():
                    index_name = Dataset.gen_collection_name_by_id(dataset_id)
                    index_struct_dict = {"type": "elasticsearch", "vector_store": {"class_prefix": index_name}}
                    dataset.index_struct = json.dumps(index_struct_dict)
-                elif vector_type == VectorType.BAIDU:
-                    dataset_id = dataset.id
-                    collection_name = Dataset.gen_collection_name_by_id(dataset_id)
-                    index_struct_dict = {
-                        "type": VectorType.BAIDU,
-                        "vector_store": {"class_prefix": collection_name},
-                    }
-                    dataset.index_struct = json.dumps(index_struct_dict)
                else:
                    raise ValueError(f"Vector store {vector_type} is not supported.")

@@ -257,12 +257,6 @@ class HttpConfig(BaseSettings):
        default=None,
    )

-    RESPECT_XFORWARD_HEADERS_ENABLED: bool = Field(
-        description="Enable or disable the X-Forwarded-For Proxy Fix middleware from Werkzeug"
-        " to respect X-* headers to redirect clients",
-        default=False,
-    )
-

 class InnerAPIConfig(BaseSettings):
    """
@@ -370,9 +364,9 @@ class WorkflowConfig(BaseSettings):
    )


-class AuthConfig(BaseSettings):
+class OAuthConfig(BaseSettings):
    """
-    Configuration for authentication and OAuth
+    Configuration for OAuth authentication
    """

    OAUTH_REDIRECT_PATH: str = Field(
@@ -381,7 +375,7 @@ class AuthConfig(BaseSettings):
    )

    GITHUB_CLIENT_ID: Optional[str] = Field(
-        description="GitHub OAuth client ID",
+        description="GitHub OAuth client secret",
        default=None,
    )

@@ -400,11 +394,6 @@ class AuthConfig(BaseSettings):
        default=None,
    )

-    ACCESS_TOKEN_EXPIRE_MINUTES: PositiveInt = Field(
-        description="Expiration time for access tokens in minutes",
-        default=60,
-    )
-

 class ModerationConfig(BaseSettings):
    """
@@ -623,7 +612,6 @@ class PositionConfig(BaseSettings):
 class FeatureConfig(
    # place the configs in alphabet order
    AppExecutionConfig,
-    AuthConfig,  # Changed from OAuthConfig to AuthConfig
    BillingConfig,
    CodeExecutionSandboxConfig,
    DataSetConfig,
@@ -638,13 +626,14 @@ class FeatureConfig(
    MailConfig,
    ModelLoadBalanceConfig,
    ModerationConfig,
-    PositionConfig,
+    OAuthConfig,
    RagEtlConfig,
    SecurityConfig,
    ToolConfig,
    UpdateConfig,
    WorkflowConfig,
    WorkspaceConfig,
+    PositionConfig,
    # hosted services config
    HostedServiceConfig,
    CeleryBeatConfig,
@@ -8,11 +8,9 @@ from configs.middleware.cache.redis_config import RedisConfig
 from configs.middleware.storage.aliyun_oss_storage_config import AliyunOSSStorageConfig
 from configs.middleware.storage.amazon_s3_storage_config import S3StorageConfig
 from configs.middleware.storage.azure_blob_storage_config import AzureBlobStorageConfig
-from configs.middleware.storage.baidu_obs_storage_config import BaiduOBSStorageConfig
 from configs.middleware.storage.google_cloud_storage_config import GoogleCloudStorageConfig
 from configs.middleware.storage.huawei_obs_storage_config import HuaweiCloudOBSStorageConfig
 from configs.middleware.storage.oci_storage_config import OCIStorageConfig
-from configs.middleware.storage.supabase_storage_config import SupabaseStorageConfig
 from configs.middleware.storage.tencent_cos_storage_config import TencentCloudCOSStorageConfig
 from configs.middleware.storage.volcengine_tos_storage_config import VolcengineTOSStorageConfig
 from configs.middleware.vdb.analyticdb_config import AnalyticdbConfig
@@ -28,7 +26,6 @@ from configs.middleware.vdb.qdrant_config import QdrantConfig
 from configs.middleware.vdb.relyt_config import RelytConfig
 from configs.middleware.vdb.tencent_vector_config import TencentVectorDBConfig
 from configs.middleware.vdb.tidb_vector_config import TiDBVectorConfig
-from configs.middleware.vdb.vikingdb_config import VikingDBConfig
 from configs.middleware.vdb.weaviate_config import WeaviateConfig


@@ -193,22 +190,6 @@ class CeleryConfig(DatabaseConfig):
        return self.CELERY_BROKER_URL.startswith("rediss://") if self.CELERY_BROKER_URL else False


-class InternalTestConfig(BaseSettings):
-    """
-    Configuration settings for Internal Test
-    """
-
-    AWS_SECRET_ACCESS_KEY: Optional[str] = Field(
-        description="Internal test AWS secret access key",
-        default=None,
-    )
-
-    AWS_ACCESS_KEY_ID: Optional[str] = Field(
-        description="Internal test AWS access key ID",
-        default=None,
-    )
-
-
 class MiddlewareConfig(
    # place the configs in alphabet order
    CeleryConfig,
@@ -219,14 +200,12 @@ class MiddlewareConfig(
    StorageConfig,
    AliyunOSSStorageConfig,
    AzureBlobStorageConfig,
-    BaiduOBSStorageConfig,
    GoogleCloudStorageConfig,
-    HuaweiCloudOBSStorageConfig,
-    OCIStorageConfig,
-    S3StorageConfig,
-    SupabaseStorageConfig,
    TencentCloudCOSStorageConfig,
+    HuaweiCloudOBSStorageConfig,
    VolcengineTOSStorageConfig,
+    S3StorageConfig,
+    OCIStorageConfig,
    # configs of vdb and vdb providers
    VectorStoreConfig,
    AnalyticdbConfig,
@@ -243,7 +222,5 @@ class MiddlewareConfig(
    TiDBVectorConfig,
    WeaviateConfig,
    ElasticsearchConfig,
-    InternalTestConfig,
-    VikingDBConfig,
 ):
    pass
@@ -1,29 +0,0 @@
-from typing import Optional
-
-from pydantic import BaseModel, Field
-
-
-class BaiduOBSStorageConfig(BaseModel):
-    """
-    Configuration settings for Baidu Object Storage Service (OBS)
-    """
-
-    BAIDU_OBS_BUCKET_NAME: Optional[str] = Field(
-        description="Name of the Baidu OBS bucket to store and retrieve objects (e.g., 'my-obs-bucket')",
-        default=None,
-    )
-
-    BAIDU_OBS_ACCESS_KEY: Optional[str] = Field(
-        description="Access Key ID for authenticating with Baidu OBS",
-        default=None,
-    )
-
-    BAIDU_OBS_SECRET_KEY: Optional[str] = Field(
-        description="Secret Access Key for authenticating with Baidu OBS",
-        default=None,
-    )
-
-    BAIDU_OBS_ENDPOINT: Optional[str] = Field(
-        description="URL of the Baidu OSS endpoint for your chosen region (e.g., 'https://.bj.bcebos.com')",
-        default=None,
-    )
@@ -1,24 +0,0 @@
-from typing import Optional
-
-from pydantic import BaseModel, Field
-
-
-class SupabaseStorageConfig(BaseModel):
-    """
-    Configuration settings for Supabase Object Storage Service
-    """
-
-    SUPABASE_BUCKET_NAME: Optional[str] = Field(
-        description="Name of the Supabase bucket to store and retrieve objects (e.g., 'dify-bucket')",
-        default=None,
-    )
-
-    SUPABASE_API_KEY: Optional[str] = Field(
-        description="API KEY for authenticating with Supabase",
-        default=None,
-    )
-
-    SUPABASE_URL: Optional[str] = Field(
-        description="URL of the Supabase",
-        default=None,
-    )
@@ -1,45 +0,0 @@
-from typing import Optional
-
-from pydantic import Field, NonNegativeInt, PositiveInt
-from pydantic_settings import BaseSettings
-
-
-class BaiduVectorDBConfig(BaseSettings):
-    """
-    Configuration settings for Baidu Vector Database
-    """
-
-    BAIDU_VECTOR_DB_ENDPOINT: Optional[str] = Field(
-        description="URL of the Baidu Vector Database service (e.g., 'http://vdb.bj.baidubce.com')",
-        default=None,
-    )
-
-    BAIDU_VECTOR_DB_CONNECTION_TIMEOUT_MS: PositiveInt = Field(
-        description="Timeout in milliseconds for Baidu Vector Database operations (default is 30000 milliseconds)",
-        default=30000,
-    )
-
-    BAIDU_VECTOR_DB_ACCOUNT: Optional[str] = Field(
-        description="Account for authenticating with the Baidu Vector Database",
-        default=None,
-    )
-
-    BAIDU_VECTOR_DB_API_KEY: Optional[str] = Field(
-        description="API key for authenticating with the Baidu Vector Database service",
-        default=None,
-    )
-
-    BAIDU_VECTOR_DB_DATABASE: Optional[str] = Field(
-        description="Name of the specific Baidu Vector Database to connect to",
-        default=None,
-    )
-
-    BAIDU_VECTOR_DB_SHARD: PositiveInt = Field(
-        description="Number of shards for the Baidu Vector Database (default is 1)",
-        default=1,
-    )
-
-    BAIDU_VECTOR_DB_REPLICAS: NonNegativeInt = Field(
-        description="Number of replicas for the Baidu Vector Database (default is 3)",
-        default=3,
-    )
@@ -1,37 +0,0 @@
-from typing import Optional
-
-from pydantic import BaseModel, Field
-
-
-class VikingDBConfig(BaseModel):
-    """
-    Configuration for connecting to Volcengine VikingDB.
-    Refer to the following documentation for details on obtaining credentials:
-    https://www.volcengine.com/docs/6291/65568
-    """
-
-    VIKINGDB_ACCESS_KEY: Optional[str] = Field(
-        default=None, description="The Access Key provided by Volcengine VikingDB for API authentication."
-    )
-    VIKINGDB_SECRET_KEY: Optional[str] = Field(
-        default=None, description="The Secret Key provided by Volcengine VikingDB for API authentication."
-    )
-    VIKINGDB_REGION: Optional[str] = Field(
-        default="cn-shanghai",
-        description="The region of the Volcengine VikingDB service.(e.g., 'cn-shanghai', 'cn-beijing').",
-    )
-    VIKINGDB_HOST: Optional[str] = Field(
-        default="api-vikingdb.mlp.cn-shanghai.volces.com",
-        description="The host of the Volcengine VikingDB service.(e.g., 'api-vikingdb.volces.com', \
-            'api-vikingdb.mlp.cn-shanghai.volces.com')",
-    )
-    VIKINGDB_SCHEME: Optional[str] = Field(
-        default="http",
-        description="The scheme of the Volcengine VikingDB service.(e.g., 'http', 'https').",
-    )
-    VIKINGDB_CONNECTION_TIMEOUT: Optional[int] = Field(
-        default=30, description="The connection timeout of the Volcengine VikingDB service."
-    )
-    VIKINGDB_SOCKET_TIMEOUT: Optional[int] = Field(
-        default=30, description="The socket timeout of the Volcengine VikingDB service."
-    )
@@ -9,7 +9,7 @@ class PackagingInfo(BaseSettings):

    CURRENT_VERSION: str = Field(
        description="Dify version",
-        default="0.10.0-beta3",
+        default="0.10.0-beta2",
    )

    COMMIT_SHA: str = Field(
@@ -189,7 +189,6 @@ class ChatConversationApi(Resource):
                        subquery.c.from_end_user_session_id.ilike(keyword_filter),
                    ),
                )
-                .group_by(Conversation.id)
            )

        account = current_user
@@ -7,7 +7,7 @@ from flask_restful import Resource, reqparse
 import services
 from controllers.console import api
 from controllers.console.setup import setup_required
-from libs.helper import email, extract_remote_ip
+from libs.helper import email, get_remote_ip
 from libs.password import valid_password
 from models import Account
 from services.account_service import AccountService, TenantService
@@ -40,16 +40,17 @@ class LoginApi(Resource):
                "data": "workspace not found, please contact system admin to invite you to join in a workspace",
            }

-        token_pair = AccountService.login(account=account, ip_address=extract_remote_ip(request))
+        token = AccountService.login(account, ip_address=get_remote_ip(request))

-        return {"result": "success", "data": token_pair.model_dump()}
+        return {"result": "success", "data": token}


 class LogoutApi(Resource):
    @setup_required
    def get(self):
        account = cast(Account, flask_login.current_user)
-        AccountService.logout(account=account)
+        token = request.headers.get("Authorization", "").split(" ")[1]
+        AccountService.logout(account=account, token=token)
        flask_login.logout_user()
        return {"result": "success"}

@@ -105,19 +106,5 @@ class ResetPasswordApi(Resource):
        return {"result": "success"}


-class RefreshTokenApi(Resource):
-    def post(self):
-        parser = reqparse.RequestParser()
-        parser.add_argument("refresh_token", type=str, required=True, location="json")
-        args = parser.parse_args()
-
-        try:
-            new_token_pair = AccountService.refresh_token(args["refresh_token"])
-            return {"result": "success", "data": new_token_pair.model_dump()}
-        except Exception as e:
-            return {"result": "fail", "data": str(e)}, 401
-
-
 api.add_resource(LoginApi, "/login")
 api.add_resource(LogoutApi, "/logout")
-api.add_resource(RefreshTokenApi, "/refresh-token")
@@ -9,7 +9,7 @@ from flask_restful import Resource
 from configs import dify_config
 from constants.languages import languages
 from extensions.ext_database import db
-from libs.helper import extract_remote_ip
+from libs.helper import get_remote_ip
 from libs.oauth import GitHubOAuth, GoogleOAuth, OAuthUserInfo
 from models import Account
 from models.account import AccountStatus
@@ -82,14 +82,9 @@ class OAuthCallback(Resource):

        TenantService.create_owner_tenant_if_not_exist(account)

-        token_pair = AccountService.login(
-            account=account,
-            ip_address=extract_remote_ip(request),
-        )
+        token = AccountService.login(account, ip_address=get_remote_ip(request))

-        return redirect(
-            f"{dify_config.CONSOLE_WEB_URL}?access_token={token_pair.access_token}&refresh_token={token_pair.refresh_token}"
-        )
+        return redirect(f"{dify_config.CONSOLE_WEB_URL}?console_token={token}")


 def _get_account_by_openid_or_email(provider: str, user_info: OAuthUserInfo) -> Optional[Account]:
@@ -617,8 +617,6 @@ class DatasetRetrievalSettingApi(Resource):
                | VectorType.CHROMA
                | VectorType.TENCENT
                | VectorType.PGVECTO_RS
-                | VectorType.BAIDU
-                | VectorType.VIKINGDB
            ):
                return {"retrieval_method": [RetrievalMethod.SEMANTIC_SEARCH.value]}
            case (
@@ -655,8 +653,6 @@ class DatasetRetrievalSettingMockApi(Resource):
                | VectorType.CHROMA
                | VectorType.TENCENT
                | VectorType.PGVECTO_RS
-                | VectorType.BAIDU
-                | VectorType.VIKINGDB
            ):
                return {"retrieval_method": [RetrievalMethod.SEMANTIC_SEARCH.value]}
            case (
@@ -13,7 +13,6 @@ from libs.login import login_required
 from services.dataset_service import DatasetService
 from services.external_knowledge_service import ExternalDatasetService
 from services.hit_testing_service import HitTestingService
-from services.knowledge_service import ExternalDatasetTestService


 def _validate_name(name):
@@ -233,31 +232,8 @@ class ExternalKnowledgeHitTestingApi(Resource):
            raise InternalServerError(str(e))


-class BedrockRetrievalApi(Resource):
-    # this api is only for internal testing
-    def post(self):
-        parser = reqparse.RequestParser()
-        parser.add_argument("retrieval_setting", nullable=False, required=True, type=dict, location="json")
-        parser.add_argument(
-            "query",
-            nullable=False,
-            required=True,
-            type=str,
-        )
-        parser.add_argument("knowledge_id", nullable=False, required=True, type=str)
-        args = parser.parse_args()
-
-        # Call the knowledge retrieval service
-        result = ExternalDatasetTestService.knowledge_retrieval(
-            args["retrieval_setting"], args["query"], args["knowledge_id"]
-        )
-        return result, 200
-
-
 api.add_resource(ExternalKnowledgeHitTestingApi, "/datasets/<uuid:dataset_id>/external-hit-testing")
 api.add_resource(ExternalDatasetCreateApi, "/datasets/external")
 api.add_resource(ExternalApiTemplateListApi, "/datasets/external-knowledge-api")
 api.add_resource(ExternalApiTemplateApi, "/datasets/external-knowledge-api/<uuid:external_knowledge_api_id>")
 api.add_resource(ExternalApiUseCheckApi, "/datasets/external-knowledge-api/<uuid:external_knowledge_api_id>/use-check")
-# this api is only for internal test
-api.add_resource(BedrockRetrievalApi, "/test/retrieval")
@@ -18,7 +18,7 @@ message_fields = {
    "inputs": fields.Raw,
    "query": fields.String,
    "answer": fields.String,
-    "message_files": fields.List(fields.Nested(message_file_fields)),
+    "message_files": fields.List(fields.Nested(message_file_fields), attribute="files"),
    "feedback": fields.Nested(feedback_fields, attribute="user_feedback", allow_null=True),
    "created_at": TimestampField,
 }
@@ -4,7 +4,7 @@ from flask import request
 from flask_restful import Resource, reqparse

 from configs import dify_config
-from libs.helper import StrLen, email, extract_remote_ip
+from libs.helper import StrLen, email, get_remote_ip
 from libs.password import valid_password
 from models.model import DifySetup
 from services.account_service import RegisterService, TenantService
@@ -46,7 +46,7 @@ class SetupApi(Resource):

        # setup
        RegisterService.setup(
-            email=args["email"], name=args["name"], password=args["password"], ip_address=extract_remote_ip(request)
+            email=args["email"], name=args["name"], password=args["password"], ip_address=get_remote_ip(request)
        )

        return {"result": "success"}, 201
@@ -126,12 +126,13 @@ class ModelProviderIconApi(Resource):
    Get model provider icon
    """

+    @setup_required
+    @login_required
+    @account_initialization_required
    def get(self, provider: str, icon_type: str, lang: str):
        model_provider_service = ModelProviderService()
        icon, mimetype = model_provider_service.get_model_provider_icon(
-            provider=provider,
-            icon_type=icon_type,
-            lang=lang,
+            provider=provider, icon_type=icon_type, lang=lang
        )

        return send_file(io.BytesIO(icon), mimetype=mimetype)
@@ -1,7 +0,0 @@
-from libs.exception import BaseHTTPException
-
-
-class UnsupportedFileTypeError(BaseHTTPException):
-    error_code = "unsupported_file_type"
-    description = "File type not allowed."
-    code = 415
@@ -4,7 +4,7 @@ from werkzeug.exceptions import NotFound

 import services
 from controllers.files import api
-from controllers.files.error import UnsupportedFileTypeError
+from libs.exception import BaseHTTPException
 from services.account_service import TenantService
 from services.file_service import FileService

@@ -80,3 +80,9 @@ class WorkspaceWebappLogoApi(Resource):
 api.add_resource(ImagePreviewApi, "/files/<uuid:file_id>/image-preview")
 api.add_resource(FilePreviewApi, "/files/<uuid:file_id>/file-preview")
 api.add_resource(WorkspaceWebappLogoApi, "/files/workspaces/<uuid:workspace_id>/webapp-logo")
+
+
+class UnsupportedFileTypeError(BaseHTTPException):
+    error_code = "unsupported_file_type"
+    description = "File type not allowed."
+    code = 415
@@ -3,8 +3,8 @@ from flask_restful import Resource, reqparse
 from werkzeug.exceptions import Forbidden, NotFound

 from controllers.files import api
-from controllers.files.error import UnsupportedFileTypeError
 from core.tools.tool_file_manager import ToolFileManager
+from libs.exception import BaseHTTPException


 class ToolFilePreviewApi(Resource):
@@ -43,3 +43,9 @@ class ToolFilePreviewApi(Resource):


 api.add_resource(ToolFilePreviewApi, "/files/tools/<uuid:file_id>.<string:extension>")
+
+
+class UnsupportedFileTypeError(BaseHTTPException):
+    error_code = "unsupported_file_type"
+    description = "File type not allowed."
+    code = 415
@@ -4,7 +4,6 @@ from flask_restful import Resource, reqparse
 from werkzeug.exceptions import InternalServerError, NotFound

 import services
-from constants import UUID_NIL
 from controllers.service_api import api
 from controllers.service_api.app.error import (
    AppUnavailableError,
@@ -108,7 +107,6 @@ class ChatApi(Resource):
        parser.add_argument("conversation_id", type=uuid_value, location="json")
        parser.add_argument("retriever_from", type=str, required=False, default="dev", location="json")
        parser.add_argument("auto_generate_name", type=bool, required=False, default=True, location="json")
-        parser.add_argument("parent_message_id", type=uuid_value, required=False, default=UUID_NIL, location="json")

        args = parser.parse_args()

@@ -48,7 +48,7 @@ class MessageListApi(Resource):
        "tool_input": fields.String,
        "created_at": TimestampField,
        "observation": fields.String,
-        "message_files": fields.List(fields.String),
+        "message_files": fields.List(fields.String, attribute="files"),
    }

    message_fields = {
@@ -58,7 +58,7 @@ class MessageListApi(Resource):
        "inputs": fields.Raw,
        "query": fields.String,
        "answer": fields.String(attribute="re_sign_file_url_answer"),
-        "message_files": fields.List(fields.Nested(message_file_fields)),
+        "message_files": fields.List(fields.Nested(message_file_fields), attribute="files"),
        "feedback": fields.Nested(feedback_fields, attribute="user_feedback", allow_null=True),
        "retriever_resources": fields.List(fields.Nested(retriever_resource_fields)),
        "created_at": TimestampField,
@@ -62,7 +62,7 @@ class MessageListApi(WebApiResource):
        "inputs": FilesContainedField,
        "query": fields.String,
        "answer": fields.String(attribute="re_sign_file_url_answer"),
-        "message_files": fields.List(fields.Nested(message_file_fields)),
+        "message_files": fields.List(fields.Nested(message_file_fields), attribute="files"),
        "feedback": fields.Nested(feedback_fields, attribute="user_feedback", allow_null=True),
        "retriever_resources": fields.List(fields.Nested(retriever_resource_fields)),
        "created_at": TimestampField,
@@ -17,7 +17,7 @@ message_fields = {
    "inputs": fields.Raw,
    "query": fields.String,
    "answer": fields.String,
-    "message_files": fields.List(fields.Nested(message_file_fields)),
+    "message_files": fields.List(fields.Nested(message_file_fields), attribute="files"),
    "feedback": fields.Nested(feedback_fields, attribute="user_feedback", allow_null=True),
    "created_at": TimestampField,
 }
@@ -43,7 +43,7 @@ from core.tools.tool.tool import Tool
 from core.tools.tool_manager import ToolManager
 from extensions.ext_database import db
 from factories import file_factory
-from models.model import Conversation, Message, MessageAgentThought, MessageFile
+from models.model import Conversation, Message, MessageAgentThought
 from models.tools import ToolConversationVariables

 logger = logging.getLogger(__name__)
@@ -495,7 +495,7 @@ class BaseAgentRunner(AppRunner):
        return result

    def organize_agent_user_prompt(self, message: Message) -> UserPromptMessage:
-        files = db.session.query(MessageFile).filter(MessageFile.message_id == message.id).all()
+        files = message.message_files
        if files:
            file_extra_config = FileUploadConfigManager.convert(message.app_model_config.to_dict())

@@ -369,7 +369,7 @@ class CotAgentRunner(BaseAgentRunner, ABC):
        return message

    def _organize_historic_prompt_messages(
-        self, current_session_messages: Optional[list[PromptMessage]] = None
+        self, current_session_messages: list[PromptMessage] = None
    ) -> list[PromptMessage]:
        """
        organize historic prompt messages
@@ -29,7 +29,7 @@ class CotChatAgentRunner(CotAgentRunner):

        return SystemPromptMessage(content=system_prompt)

-    def _organize_user_query(self, query, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
+    def _organize_user_query(self, query, prompt_messages: list[PromptMessage] = None) -> list[PromptMessage]:
        """
        Organize user query
        """
@@ -1,5 +1,4 @@
 import json
-from typing import Optional

 from core.agent.cot_agent_runner import CotAgentRunner
 from core.model_runtime.entities.message_entities import AssistantPromptMessage, PromptMessage, UserPromptMessage
@@ -22,7 +21,7 @@ class CotCompletionAgentRunner(CotAgentRunner):

        return system_prompt

-    def _organize_historic_prompt(self, current_session_messages: Optional[list[PromptMessage]] = None) -> str:
+    def _organize_historic_prompt(self, current_session_messages: list[PromptMessage] = None) -> str:
        """
        Organize historic prompt
        """
@@ -2,7 +2,7 @@ import json
 import logging
 from collections.abc import Generator
 from copy import deepcopy
-from typing import Any, Optional, Union
+from typing import Any, Union

 from core.agent.base_agent_runner import BaseAgentRunner
 from core.app.apps.base_app_queue_manager import PublishFrom
@@ -375,7 +375,7 @@ class FunctionCallAgentRunner(BaseAgentRunner):
        return tool_calls

    def _init_system_message(
-        self, prompt_template: str, prompt_messages: Optional[list[PromptMessage]] = None
+        self, prompt_template: str, prompt_messages: list[PromptMessage] = None
    ) -> list[PromptMessage]:
        """
        Initialize system message
@@ -390,7 +390,7 @@ class FunctionCallAgentRunner(BaseAgentRunner):

        return prompt_messages

-    def _organize_user_query(self, query, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
+    def _organize_user_query(self, query, prompt_messages: list[PromptMessage] = None) -> list[PromptMessage]:
        """
        Organize user query
        """
@@ -14,7 +14,7 @@ class CotAgentOutputParser:
    ) -> Generator[Union[str, AgentScratchpadUnit.Action], None, None]:
        def parse_action(json_str):
            try:
-                action = json.loads(json_str, strict=False)
+                action = json.loads(json_str)
                action_name = None
                action_input = None

@@ -121,7 +121,6 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
            # always enable retriever resource in debugger mode
            app_config.additional_features.show_retrieve_source = True

-        workflow_run_id = str(uuid.uuid4())
        # init application generate entity
        application_generate_entity = AdvancedChatAppGenerateEntity(
            task_id=str(uuid.uuid4()),
@@ -138,7 +137,6 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
            invoke_from=invoke_from,
            extras=extras,
            trace_manager=trace_manager,
-            workflow_run_id=workflow_run_id,
        )
        contexts.tenant_id.set(application_generate_entity.app_config.tenant_id)

@@ -135,9 +135,6 @@ class AdvancedChatAppRunner(WorkflowBasedAppRunner):
                SystemVariableKey.CONVERSATION_ID: self.conversation.id,
                SystemVariableKey.USER_ID: user_id,
                SystemVariableKey.DIALOGUE_COUNT: conversation_dialogue_count,
-                SystemVariableKey.APP_ID: app_config.app_id,
-                SystemVariableKey.WORKFLOW_ID: app_config.workflow_id,
-                SystemVariableKey.WORKFLOW_RUN_ID: self.application_generate_entity.workflow_run_id,
            }

            # init variable pool
@@ -9,7 +9,6 @@ from core.app.apps.advanced_chat.app_generator_tts_publisher import AppGenerator
 from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
 from core.app.entities.app_invoke_entities import (
    AdvancedChatAppGenerateEntity,
-    InvokeFrom,
 )
 from core.app.entities.queue_entities import (
    QueueAdvancedChatMessageEndEvent,
@@ -46,20 +45,17 @@ from core.app.entities.task_entities import (
 from core.app.task_pipeline.based_generate_task_pipeline import BasedGenerateTaskPipeline
 from core.app.task_pipeline.message_cycle_manage import MessageCycleManage
 from core.app.task_pipeline.workflow_cycle_manage import WorkflowCycleManage
-from core.model_runtime.entities.llm_entities import LLMUsage
 from core.model_runtime.utils.encoders import jsonable_encoder
 from core.ops.ops_trace_manager import TraceQueueManager
 from core.workflow.enums import SystemVariableKey
 from core.workflow.graph_engine.entities.graph_runtime_state import GraphRuntimeState
-from enums import CreatedByRole
 from enums.workflow_nodes import NodeType
 from events.message_event import message_was_created
 from extensions.ext_database import db
-from models import Conversation, EndUser, Message, MessageFile
 from models.account import Account
+from models.model import Conversation, EndUser, Message
 from models.workflow import (
    Workflow,
-    WorkflowNodeExecution,
    WorkflowRunStatus,
 )

@@ -76,7 +72,6 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
    _workflow: Workflow
    _user: Union[Account, EndUser]
    _workflow_system_variables: dict[SystemVariableKey, Any]
-    _wip_workflow_node_executions: dict[str, WorkflowNodeExecution]

    def __init__(
        self,
@@ -113,14 +108,9 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
            SystemVariableKey.FILES: application_generate_entity.files,
            SystemVariableKey.CONVERSATION_ID: conversation.id,
            SystemVariableKey.USER_ID: user_id,
-            SystemVariableKey.DIALOGUE_COUNT: conversation.dialogue_count,
-            SystemVariableKey.APP_ID: application_generate_entity.app_config.app_id,
-            SystemVariableKey.WORKFLOW_ID: workflow.id,
-            SystemVariableKey.WORKFLOW_RUN_ID: application_generate_entity.workflow_run_id,
        }

        self._task_state = WorkflowTaskState()
-        self._wip_workflow_node_executions = {}

        self._conversation_name_generate_thread = None
        self._recorded_files: list[Mapping[str, Any]] = []
@@ -498,6 +488,10 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
            self._conversation_name_generate_thread.join()

    def _save_message(self, graph_runtime_state: Optional[GraphRuntimeState] = None) -> None:
+        """
+        Save message.
+        :return:
+        """
        self._refetch_message()

        self._message.answer = self._task_state.answer
@@ -505,22 +499,6 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
        self._message.message_metadata = (
            json.dumps(jsonable_encoder(self._task_state.metadata)) if self._task_state.metadata else None
        )
-        message_files = [
-            MessageFile(
-                message_id=self._message.id,
-                type=file["type"],
-                transfer_method=file["transfer_method"],
-                url=file["remote_url"],
-                belongs_to="assistant",
-                upload_file_id=file["related_id"],
-                created_by_role=CreatedByRole.ACCOUNT
-                if self._message.invoke_from in {InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER}
-                else CreatedByRole.END_USER,
-                created_by=self._message.from_account_id or self._message.from_end_user_id or "",
-            )
-            for file in self._recorded_files
-        ]
-        db.session.add_all(message_files)

        if graph_runtime_state and graph_runtime_state.llm_usage:
            usage = graph_runtime_state.llm_usage
@@ -533,10 +511,6 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
            self._message.total_price = usage.total_price
            self._message.currency = usage.currency

-            self._task_state.metadata["usage"] = jsonable_encoder(usage)
-        else:
-            self._task_state.metadata["usage"] = jsonable_encoder(LLMUsage.empty_usage())
-
        db.session.commit()

        message_was_created.send(
@@ -263,7 +263,7 @@ class CompletionAppGenerator(MessageBasedAppGenerator):
        file_extra_config = FileUploadConfigManager.convert(override_model_config_dict)
        if file_extra_config:
            file_objs = file_factory.build_from_mappings(
-                mappings=message.message_files,
+                mappings=message.files,
                tenant_id=app_model.tenant_id,
                user_id=user.id,
                role=role,
@@ -235,13 +235,13 @@ class MessageBasedAppGenerator(BaseAppGenerator):
        for file in application_generate_entity.files:
            message_file = MessageFile(
                message_id=message.id,
-                type=file.type,
-                transfer_method=file.transfer_method,
+                type=file.type.value,
+                transfer_method=file.transfer_method.value,
                belongs_to="user",
                url=file.remote_url,
                upload_file_id=file.related_id,
                created_by_role=("account" if account_id else "end_user"),
-                created_by=account_id or end_user_id or "",
+                created_by=account_id or end_user_id,
            )
            db.session.add(message_file)
            db.session.commit()
@@ -95,7 +95,6 @@ class WorkflowAppGenerator(BaseAppGenerator):
        )

        inputs: Mapping[str, Any] = args["inputs"]
-        workflow_run_id = str(uuid.uuid4())
        # init application generate entity
        application_generate_entity = WorkflowAppGenerateEntity(
            task_id=str(uuid.uuid4()),
@@ -107,7 +106,6 @@ class WorkflowAppGenerator(BaseAppGenerator):
            invoke_from=invoke_from,
            call_depth=call_depth,
            trace_manager=trace_manager,
-            workflow_run_id=workflow_run_id,
        )
        contexts.tenant_id.set(application_generate_entity.app_config.tenant_id)

@@ -89,9 +89,6 @@ class WorkflowAppRunner(WorkflowBasedAppRunner):
            system_inputs = {
                SystemVariableKey.FILES: files,
                SystemVariableKey.USER_ID: user_id,
-                SystemVariableKey.APP_ID: app_config.app_id,
-                SystemVariableKey.WORKFLOW_ID: app_config.workflow_id,
-                SystemVariableKey.WORKFLOW_RUN_ID: self.application_generate_entity.workflow_run_id,
            }

            variable_pool = VariablePool(
@@ -51,7 +51,6 @@ from models.workflow import (
    Workflow,
    WorkflowAppLog,
    WorkflowAppLogCreatedFrom,
-    WorkflowNodeExecution,
    WorkflowRun,
    WorkflowRunStatus,
 )
@@ -69,7 +68,6 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
    _task_state: WorkflowTaskState
    _application_generate_entity: WorkflowAppGenerateEntity
    _workflow_system_variables: dict[SystemVariableKey, Any]
-    _wip_workflow_node_executions: dict[str, WorkflowNodeExecution]

    def __init__(
        self,
@@ -98,13 +96,9 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
        self._workflow_system_variables = {
            SystemVariableKey.FILES: application_generate_entity.files,
            SystemVariableKey.USER_ID: user_id,
-            SystemVariableKey.APP_ID: application_generate_entity.app_config.app_id,
-            SystemVariableKey.WORKFLOW_ID: workflow.id,
-            SystemVariableKey.WORKFLOW_RUN_ID: application_generate_entity.workflow_run_id,
        }

        self._task_state = WorkflowTaskState()
-        self._wip_workflow_node_executions = {}

    def process(self) -> Union[WorkflowAppBlockingResponse, Generator[WorkflowAppStreamResponse, None, None]]:
        """
@@ -152,7 +152,6 @@ class AdvancedChatAppGenerateEntity(AppGenerateEntity):

    conversation_id: Optional[str] = None
    parent_message_id: Optional[str] = None
-    workflow_run_id: Optional[str] = None
    query: str

    class SingleIterationRunEntity(BaseModel):
@@ -173,7 +172,6 @@ class WorkflowAppGenerateEntity(AppGenerateEntity):

    # app config
    app_config: WorkflowUIBasedAppConfig
-    workflow_run_id: Optional[str] = None

    class SingleIterationRunEntity(BaseModel):
        """
@@ -1,4 +1,4 @@
-from collections.abc import Mapping, Sequence
+from collections.abc import Mapping
 from enum import Enum
 from typing import Any, Optional

@@ -120,7 +120,7 @@ class MessageEndStreamResponse(StreamResponse):
    event: StreamEvent = StreamEvent.MESSAGE_END
    id: str
    metadata: dict = {}
-    files: Optional[Sequence[Mapping[str, Any]]] = None
+    files: Optional[list[Mapping[str, Any]]] = None


 class MessageFileStreamResponse(StreamResponse):
@@ -213,7 +213,7 @@ class WorkflowFinishStreamResponse(StreamResponse):
        created_by: Optional[dict] = None
        created_at: int
        finished_at: int
-        files: Optional[Sequence[Mapping[str, Any]]] = []
+        files: Optional[list[dict]] = []

    event: StreamEvent = StreamEvent.WORKFLOW_FINISHED
    workflow_run_id: str
@@ -298,7 +298,7 @@ class NodeFinishStreamResponse(StreamResponse):
        execution_metadata: Optional[dict] = None
        created_at: int
        finished_at: int
-        files: Optional[Sequence[Mapping[str, Any]]] = []
+        files: Optional[list[dict]] = []
        parallel_id: Optional[str] = None
        parallel_start_node_id: Optional[str] = None
        parent_parallel_id: Optional[str] = None
@@ -1,10 +1,8 @@
-import logging
 from threading import Thread
 from typing import Optional, Union

 from flask import Flask, current_app

-from configs import dify_config
 from core.app.entities.app_invoke_entities import (
    AdvancedChatAppGenerateEntity,
    AgentChatAppGenerateEntity,
@@ -84,9 +82,7 @@ class MessageCycleManage:
                try:
                    name = LLMGenerator.generate_conversation_name(app_model.tenant_id, query)
                    conversation.name = name
-                except Exception as e:
-                    if dify_config.DEBUG:
-                        logging.exception(f"generate conversation name failed: {e}")
+                except:
                    pass

                db.session.merge(conversation)
@@ -1,6 +1,6 @@
 import json
 import time
-from collections.abc import Mapping, Sequence
+from collections.abc import Mapping
 from datetime import datetime, timezone
 from typing import Any, Optional, Union, cast

@@ -36,11 +36,12 @@ from core.tools.tool_manager import ToolManager
 from core.workflow.enums import SystemVariableKey
 from core.workflow.nodes.tool.entities import ToolNodeData
 from core.workflow.workflow_entry import WorkflowEntry
-from enums import CreatedByRole, NodeType, WorkflowRunTriggeredFrom
+from enums import NodeType, WorkflowRunTriggeredFrom
 from extensions.ext_database import db
 from models.account import Account
 from models.model import EndUser
 from models.workflow import (
+    CreatedByRole,
    Workflow,
    WorkflowNodeExecution,
    WorkflowNodeExecutionStatus,
@@ -56,7 +57,6 @@ class WorkflowCycleManage:
    _user: Union[Account, EndUser]
    _task_state: WorkflowTaskState
    _workflow_system_variables: dict[SystemVariableKey, Any]
-    _wip_workflow_node_executions: dict[str, WorkflowNodeExecution]

    def _handle_workflow_run_start(self) -> WorkflowRun:
        max_sequence = (
@@ -85,9 +85,6 @@ class WorkflowCycleManage:

        # init workflow run
        workflow_run = WorkflowRun()
-        workflow_run_id = self._workflow_system_variables[SystemVariableKey.WORKFLOW_RUN_ID]
-        if workflow_run_id:
-            workflow_run.id = workflow_run_id
        workflow_run.tenant_id = self._workflow.tenant_id
        workflow_run.app_id = self._workflow.app_id
        workflow_run.sequence_number = new_sequence_number
@@ -253,8 +250,6 @@ class WorkflowCycleManage:
        db.session.refresh(workflow_node_execution)
        db.session.close()

-        self._wip_workflow_node_executions[workflow_node_execution.node_execution_id] = workflow_node_execution
-
        return workflow_node_execution

    def _handle_workflow_node_execution_success(self, event: QueueNodeSucceededEvent) -> WorkflowNodeExecution:
@@ -266,39 +261,22 @@ class WorkflowCycleManage:
        workflow_node_execution = self._refetch_workflow_node_execution(event.node_execution_id)

        inputs = WorkflowEntry.handle_special_values(event.inputs)
-        process_data = WorkflowEntry.handle_special_values(event.process_data)
        outputs = WorkflowEntry.handle_special_values(event.outputs)
-        execution_metadata = (
-            json.dumps(jsonable_encoder(event.execution_metadata)) if event.execution_metadata else None
-        )
-        finished_at = datetime.now(timezone.utc).replace(tzinfo=None)
-        elapsed_time = (finished_at - event.start_at).total_seconds()
-
-        db.session.query(WorkflowNodeExecution).filter(WorkflowNodeExecution.id == workflow_node_execution.id).update(
-            {
-                WorkflowNodeExecution.status: WorkflowNodeExecutionStatus.SUCCEEDED.value,
-                WorkflowNodeExecution.inputs: json.dumps(inputs) if inputs else None,
-                WorkflowNodeExecution.process_data: json.dumps(process_data) if event.process_data else None,
-                WorkflowNodeExecution.outputs: json.dumps(outputs) if outputs else None,
-                WorkflowNodeExecution.execution_metadata: execution_metadata,
-                WorkflowNodeExecution.finished_at: finished_at,
-                WorkflowNodeExecution.elapsed_time: elapsed_time,
-            }
-        )
-
-        db.session.commit()
-        db.session.close()
        process_data = WorkflowEntry.handle_special_values(event.process_data)

        workflow_node_execution.status = WorkflowNodeExecutionStatus.SUCCEEDED.value
        workflow_node_execution.inputs = json.dumps(inputs) if inputs else None
        workflow_node_execution.process_data = json.dumps(process_data) if process_data else None
        workflow_node_execution.outputs = json.dumps(outputs) if outputs else None
-        workflow_node_execution.execution_metadata = execution_metadata
-        workflow_node_execution.finished_at = finished_at
-        workflow_node_execution.elapsed_time = elapsed_time
+        workflow_node_execution.execution_metadata = (
+            json.dumps(jsonable_encoder(event.execution_metadata)) if event.execution_metadata else None
+        )
+        workflow_node_execution.finished_at = datetime.now(timezone.utc).replace(tzinfo=None)
+        workflow_node_execution.elapsed_time = (workflow_node_execution.finished_at - event.start_at).total_seconds()

-        self._wip_workflow_node_executions.pop(workflow_node_execution.node_execution_id)
+        db.session.commit()
+        db.session.refresh(workflow_node_execution)
+        db.session.close()

        return workflow_node_execution

@@ -311,36 +289,20 @@ class WorkflowCycleManage:
        workflow_node_execution = self._refetch_workflow_node_execution(event.node_execution_id)

        inputs = WorkflowEntry.handle_special_values(event.inputs)
-        process_data = WorkflowEntry.handle_special_values(event.process_data)
        outputs = WorkflowEntry.handle_special_values(event.outputs)
-        finished_at = datetime.now(timezone.utc).replace(tzinfo=None)
-        elapsed_time = (finished_at - event.start_at).total_seconds()
-
-        db.session.query(WorkflowNodeExecution).filter(WorkflowNodeExecution.id == workflow_node_execution.id).update(
-            {
-                WorkflowNodeExecution.status: WorkflowNodeExecutionStatus.FAILED.value,
-                WorkflowNodeExecution.error: event.error,
-                WorkflowNodeExecution.inputs: json.dumps(inputs) if inputs else None,
-                WorkflowNodeExecution.process_data: json.dumps(process_data) if event.process_data else None,
-                WorkflowNodeExecution.outputs: json.dumps(outputs) if outputs else None,
-                WorkflowNodeExecution.finished_at: finished_at,
-                WorkflowNodeExecution.elapsed_time: elapsed_time,
-            }
-        )
-
-        db.session.commit()
-        db.session.close()
        process_data = WorkflowEntry.handle_special_values(event.process_data)

        workflow_node_execution.status = WorkflowNodeExecutionStatus.FAILED.value
        workflow_node_execution.error = event.error
+        workflow_node_execution.finished_at = datetime.now(timezone.utc).replace(tzinfo=None)
        workflow_node_execution.inputs = json.dumps(inputs) if inputs else None
        workflow_node_execution.process_data = json.dumps(process_data) if process_data else None
        workflow_node_execution.outputs = json.dumps(outputs) if outputs else None
-        workflow_node_execution.finished_at = finished_at
-        workflow_node_execution.elapsed_time = elapsed_time
+        workflow_node_execution.elapsed_time = (workflow_node_execution.finished_at - event.start_at).total_seconds()

-        self._wip_workflow_node_executions.pop(workflow_node_execution.node_execution_id)
+        db.session.commit()
+        db.session.refresh(workflow_node_execution)
+        db.session.close()

        return workflow_node_execution

@@ -642,7 +604,7 @@ class WorkflowCycleManage:
            ),
        )

-    def _fetch_files_from_node_outputs(self, outputs_dict: dict) -> Sequence[Mapping[str, Any]]:
+    def _fetch_files_from_node_outputs(self, outputs_dict: dict) -> list[dict]:
        """
        Fetch files from node outputs
        :param outputs_dict: node outputs dict
@@ -659,7 +621,7 @@ class WorkflowCycleManage:

        return files

-    def _fetch_files_from_variable_value(self, value: Union[dict, list]) -> Sequence[Mapping[str, Any]]:
+    def _fetch_files_from_variable_value(self, value: Union[dict, list]) -> list[dict]:
        """
        Fetch files from variable value
        :param value: variable value
@@ -671,17 +633,17 @@ class WorkflowCycleManage:
        files = []
        if isinstance(value, list):
            for item in value:
-                file = self._get_file_var_from_value(item)
-                if file:
-                    files.append(file)
+                file_var = self._get_file_var_from_value(item)
+                if file_var:
+                    files.append(file_var)
        elif isinstance(value, dict):
-            file = self._get_file_var_from_value(value)
-            if file:
-                files.append(file)
+            file_var = self._get_file_var_from_value(value)
+            if file_var:
+                files.append(file_var)

        return files

-    def _get_file_var_from_value(self, value: Union[dict, list]) -> Mapping[str, Any] | None:
+    def _get_file_var_from_value(self, value: Union[dict, list]) -> Mapping[str, str | int | None] | None:
        """
        Get file var from value
        :param value: variable value
@@ -714,7 +676,17 @@ class WorkflowCycleManage:
        :param node_execution_id: workflow node execution id
        :return:
        """
-        workflow_node_execution = self._wip_workflow_node_executions.get(node_execution_id)
+        workflow_node_execution = (
+            db.session.query(WorkflowNodeExecution)
+            .filter(
+                WorkflowNodeExecution.tenant_id == self._application_generate_entity.app_config.tenant_id,
+                WorkflowNodeExecution.app_id == self._application_generate_entity.app_config.app_id,
+                WorkflowNodeExecution.workflow_id == self._workflow.id,
+                WorkflowNodeExecution.triggered_from == WorkflowNodeExecutionTriggeredFrom.WORKFLOW_RUN.value,
+                WorkflowNodeExecution.node_execution_id == node_execution_id,
+            )
+            .first()
+        )

        if not workflow_node_execution:
            raise Exception(f"Workflow node execution not found: {node_execution_id}")
@@ -1,9 +1,9 @@
+import os
 from collections.abc import Mapping, Sequence
 from typing import Any, Optional, TextIO, Union

 from pydantic import BaseModel

-from configs import dify_config
 from core.ops.entities.trace_entity import TraceTaskName
 from core.ops.ops_trace_manager import TraceQueueManager, TraceTask
 from core.tools.entities.tool_entities import ToolInvokeMessage
@@ -50,8 +50,7 @@ class DifyAgentCallbackHandler(BaseModel):
        tool_inputs: Mapping[str, Any],
    ) -> None:
        """Do nothing."""
-        if dify_config.DEBUG:
-            print_text("\n[on_tool_start] ToolCall:" + tool_name + "\n" + str(tool_inputs) + "\n", color=self.color)
+        print_text("\n[on_tool_start] ToolCall:" + tool_name + "\n" + str(tool_inputs) + "\n", color=self.color)

    def on_tool_end(
        self,
@@ -63,12 +62,11 @@ class DifyAgentCallbackHandler(BaseModel):
        trace_manager: Optional[TraceQueueManager] = None,
    ) -> None:
        """If not the final action, print out observation."""
-        if dify_config.DEBUG:
-            print_text("\n[on_tool_end]\n", color=self.color)
-            print_text("Tool: " + tool_name + "\n", color=self.color)
-            print_text("Inputs: " + str(tool_inputs) + "\n", color=self.color)
-            print_text("Outputs: " + str(tool_outputs)[:1000] + "\n", color=self.color)
-            print_text("\n")
+        print_text("\n[on_tool_end]\n", color=self.color)
+        print_text("Tool: " + tool_name + "\n", color=self.color)
+        print_text("Inputs: " + str(tool_inputs) + "\n", color=self.color)
+        print_text("Outputs: " + str(tool_outputs)[:1000] + "\n", color=self.color)
+        print_text("\n")

        if trace_manager:
            trace_manager.add_trace_task(
@@ -84,33 +82,30 @@ class DifyAgentCallbackHandler(BaseModel):

    def on_tool_error(self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any) -> None:
        """Do nothing."""
-        if dify_config.DEBUG:
-            print_text("\n[on_tool_error] Error: " + str(error) + "\n", color="red")
+        print_text("\n[on_tool_error] Error: " + str(error) + "\n", color="red")

    def on_agent_start(self, thought: str) -> None:
        """Run on agent start."""
-        if dify_config.DEBUG:
-            if thought:
-                print_text(
-                    "\n[on_agent_start] \nCurrent Loop: " + str(self.current_loop) + "\nThought: " + thought + "\n",
-                    color=self.color,
-                )
-            else:
-                print_text("\n[on_agent_start] \nCurrent Loop: " + str(self.current_loop) + "\n", color=self.color)
+        if thought:
+            print_text(
+                "\n[on_agent_start] \nCurrent Loop: " + str(self.current_loop) + "\nThought: " + thought + "\n",
+                color=self.color,
+            )
+        else:
+            print_text("\n[on_agent_start] \nCurrent Loop: " + str(self.current_loop) + "\n", color=self.color)

    def on_agent_finish(self, color: Optional[str] = None, **kwargs: Any) -> None:
        """Run on agent end."""
-        if dify_config.DEBUG:
-            print_text("\n[on_agent_finish]\n Loop: " + str(self.current_loop) + "\n", color=self.color)
+        print_text("\n[on_agent_finish]\n Loop: " + str(self.current_loop) + "\n", color=self.color)

        self.current_loop += 1

    @property
    def ignore_agent(self) -> bool:
        """Whether to ignore agent callbacks."""
-        return not dify_config.DEBUG
+        return not os.environ.get("DEBUG") or os.environ.get("DEBUG").lower() != "true"

    @property
    def ignore_chat_model(self) -> bool:
        """Whether to ignore chat model callbacks."""
-        return not dify_config.DEBUG
+        return not os.environ.get("DEBUG") or os.environ.get("DEBUG").lower() != "true"
@@ -44,6 +44,7 @@ class DatasetIndexToolCallbackHandler:
                DocumentSegment.index_node_id == document.metadata["doc_id"]
            )

+            # if 'dataset_id' in document.metadata:
            if "dataset_id" in document.metadata:
                query = query.filter(DocumentSegment.dataset_id == document.metadata["dataset_id"])

@@ -5,7 +5,6 @@ from typing import Optional, cast
 import numpy as np
 from sqlalchemy.exc import IntegrityError

-from configs import dify_config
 from core.embedding.embedding_constant import EmbeddingInputType
 from core.model_manager import ModelInstance
 from core.model_runtime.entities.model_entities import ModelPropertyKey
@@ -111,8 +110,6 @@ class CacheEmbedding(Embeddings):
            embedding_results = embedding_result.embeddings[0]
            embedding_results = (embedding_results / np.linalg.norm(embedding_results)).tolist()
        except Exception as ex:
-            if dify_config.DEBUG:
-                logging.exception(f"Failed to embed query text: {ex}")
            raise ex

        try:
@@ -125,8 +122,6 @@ class CacheEmbedding(Embeddings):
            encoded_str = encoded_vector.decode("utf-8")
            redis_client.setex(embedding_cache_key, 600, encoded_str)
        except Exception as ex:
-            if dify_config.DEBUG:
-                logging.exception("Failed to add embedding to redis %s", ex)
-            raise ex
+            logging.exception("Failed to add embedding to redis %s", ex)

        return embedding_results
@@ -43,13 +43,13 @@ class File(BaseModel):
    remote_url: Optional[str] = None  # remote url
    related_id: Optional[str] = None
    filename: Optional[str] = None
-    extension: Optional[str] = Field(default=None, description="File extension, should contains dot")
+    extension: Optional[str] = None
    mime_type: Optional[str] = None
-    size: int = -1
+    size: int = 0
    _extra_config: FileExtraConfig | None = None

    def to_dict(self) -> Mapping[str, str | int | None]:
-        data = self.model_dump(mode="json")
+        data = self.model_dump()
        return {
            **data,
            "url": self.generate_url(),
@@ -211,9 +211,9 @@ class IndexingRunner:
        tenant_id: str,
        extract_settings: list[ExtractSetting],
        tmp_processing_rule: dict,
-        doc_form: Optional[str] = None,
+        doc_form: str = None,
        doc_language: str = "English",
-        dataset_id: Optional[str] = None,
+        dataset_id: str = None,
        indexing_technique: str = "economy",
    ) -> dict:
        """
@@ -1,7 +1,7 @@
 from typing import Optional

 from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
-from core.file import FileType, file_manager
+from core.file import file_manager
 from core.model_manager import ModelInstance
 from core.model_runtime.entities import (
    AssistantPromptMessage,
@@ -60,11 +60,7 @@ class TokenBufferMemory:
        # instead of all messages from the conversation, we only need to extract messages
        # that belong to the thread of last message
        thread_messages = extract_thread_messages(messages)
-
-        # for newly created message, its answer is temporarily empty, we don't need to add it to memory
-        if thread_messages and not thread_messages[0].answer:
-            thread_messages.pop(0)
-
+        thread_messages.pop(0)
        messages = list(reversed(thread_messages))

        prompt_messages = []
@@ -72,7 +68,7 @@ class TokenBufferMemory:
            files = db.session.query(MessageFile).filter(MessageFile.message_id == message.id).all()
            if files:
                file_extra_config = None
-                if self.conversation.mode not in {AppMode.ADVANCED_CHAT, AppMode.WORKFLOW}:
+                if self.conversation.mode not in {AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value}:
                    file_extra_config = FileUploadConfigManager.convert(self.conversation.model_config)
                else:
                    if message.workflow_run_id:
@@ -98,10 +94,7 @@ class TokenBufferMemory:
                    prompt_message_contents: list[PromptMessageContent] = []
                    prompt_message_contents.append(TextPromptMessageContent(data=message.query))
                    for file_obj in file_objs:
-                        if file_obj.type != FileType.IMAGE:
-                            continue
-                        prompt_message = file_manager.to_prompt_message_content(file_obj)
-                        prompt_message_contents.append(prompt_message)
+                        prompt_message_contents.append(file_manager.to_prompt_message_content(file_obj))

                    prompt_messages.append(UserPromptMessage(content=prompt_message_contents))
            else:
@@ -1,7 +1,7 @@
 import logging
 import os
-from collections.abc import Callable, Generator, Iterable, Sequence
-from typing import IO, Any, Optional, Union, cast
+from collections.abc import Callable, Generator, Sequence
+from typing import IO, Optional, Union, cast

 from core.embedding.embedding_constant import EmbeddingInputType
 from core.entities.provider_configuration import ProviderConfiguration, ProviderModelBundle
@@ -274,7 +274,7 @@ class ModelInstance:
            user=user,
        )

-    def invoke_tts(self, content_text: str, tenant_id: str, voice: str, user: Optional[str] = None) -> Iterable[bytes]:
+    def invoke_tts(self, content_text: str, tenant_id: str, voice: str, user: Optional[str] = None) -> str:
        """
        Invoke large language tts model

@@ -298,7 +298,7 @@ class ModelInstance:
            voice=voice,
        )

-    def _round_robin_invoke(self, function: Callable[..., Any], *args, **kwargs):
+    def _round_robin_invoke(self, function: Callable, *args, **kwargs):
        """
        Round-robin invoke
        :param function: function to invoke
@@ -94,7 +94,7 @@ class LargeLanguageModel(AIModel):
        )

        try:
-            if "response_format" in model_parameters and model_parameters["response_format"] in {"JSON", "XML"}:
+            if "response_format" in model_parameters:
                result = self._code_block_mode_wrapper(
                    model=model,
                    credentials=credentials,
@@ -1,8 +1,7 @@
 import logging
 import re
 from abc import abstractmethod
-from collections.abc import Iterable
-from typing import Any, Optional
+from typing import Optional

 from pydantic import ConfigDict

@@ -23,14 +22,8 @@ class TTSModel(AIModel):
    model_config = ConfigDict(protected_namespaces=())

    def invoke(
-        self,
-        model: str,
-        tenant_id: str,
-        credentials: dict,
-        content_text: str,
-        voice: str,
-        user: Optional[str] = None,
-    ) -> Iterable[bytes]:
+        self, model: str, tenant_id: str, credentials: dict, content_text: str, voice: str, user: Optional[str] = None
+    ):
        """
        Invoke large language model

@@ -57,14 +50,8 @@ class TTSModel(AIModel):

    @abstractmethod
    def _invoke(
-        self,
-        model: str,
-        tenant_id: str,
-        credentials: dict,
-        content_text: str,
-        voice: str,
-        user: Optional[str] = None,
-    ) -> Iterable[bytes]:
+        self, model: str, tenant_id: str, credentials: dict, content_text: str, voice: str, user: Optional[str] = None
+    ):
        """
        Invoke large language model

@@ -81,27 +68,27 @@ class TTSModel(AIModel):

    def get_tts_model_voices(self, model: str, credentials: dict, language: Optional[str] = None) -> list:
        """
-        Retrieves the list of voices supported by a given text-to-speech (TTS) model.
+        Get voice for given tts model voices

-        :param language: The language for which the voices are requested.
-        :param model: The name of the TTS model.
-        :param credentials: The credentials required to access the TTS model.
-        :return: A list of voices supported by the TTS model.
+        :param language: tts language
+        :param model: model name
+        :param credentials: model credentials
+        :return: voices lists
        """
        model_schema = self.get_model_schema(model, credentials)

-        if not model_schema or ModelPropertyKey.VOICES not in model_schema.model_properties:
-            raise ValueError("this model does not support voice")
+        if model_schema and ModelPropertyKey.VOICES in model_schema.model_properties:
+            voices = model_schema.model_properties[ModelPropertyKey.VOICES]
+            if language:
+                return [
+                    {"name": d["name"], "value": d["mode"]}
+                    for d in voices
+                    if language and language in d.get("language")
+                ]
+            else:
+                return [{"name": d["name"], "value": d["mode"]} for d in voices]

-        voices = model_schema.model_properties[ModelPropertyKey.VOICES]
-        if language:
-            return [
-                {"name": d["name"], "value": d["mode"]} for d in voices if language and language in d.get("language")
-            ]
-        else:
-            return [{"name": d["name"], "value": d["mode"]} for d in voices]
-
-    def _get_model_default_voice(self, model: str, credentials: dict) -> Any:
+    def _get_model_default_voice(self, model: str, credentials: dict) -> any:
        """
        Get voice for given tts model

@@ -124,10 +111,8 @@ class TTSModel(AIModel):
        """
        model_schema = self.get_model_schema(model, credentials)

-        if not model_schema or ModelPropertyKey.AUDIO_TYPE not in model_schema.model_properties:
-            raise ValueError("this model does not support audio type")
-
-        return model_schema.model_properties[ModelPropertyKey.AUDIO_TYPE]
+        if model_schema and ModelPropertyKey.AUDIO_TYPE in model_schema.model_properties:
+            return model_schema.model_properties[ModelPropertyKey.AUDIO_TYPE]

    def _get_model_word_limit(self, model: str, credentials: dict) -> int:
        """
@@ -136,10 +121,8 @@ class TTSModel(AIModel):
        """
        model_schema = self.get_model_schema(model, credentials)

-        if not model_schema or ModelPropertyKey.WORD_LIMIT not in model_schema.model_properties:
-            raise ValueError("this model does not support word limit")
-
-        return model_schema.model_properties[ModelPropertyKey.WORD_LIMIT]
+        if model_schema and ModelPropertyKey.WORD_LIMIT in model_schema.model_properties:
+            return model_schema.model_properties[ModelPropertyKey.WORD_LIMIT]

    def _get_model_workers_limit(self, model: str, credentials: dict) -> int:
        """
@@ -148,10 +131,8 @@ class TTSModel(AIModel):
        """
        model_schema = self.get_model_schema(model, credentials)

-        if not model_schema or ModelPropertyKey.MAX_WORKERS not in model_schema.model_properties:
-            raise ValueError("this model does not support max workers")
-
-        return model_schema.model_properties[ModelPropertyKey.MAX_WORKERS]
+        if model_schema and ModelPropertyKey.MAX_WORKERS in model_schema.model_properties:
+            return model_schema.model_properties[ModelPropertyKey.MAX_WORKERS]

    @staticmethod
    def _split_text_into_sentences(org_text, max_length=2000, pattern=r"[。.!?]"):
@@ -169,7 +169,7 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
        stop: Optional[list[str]] = None,
        stream: bool = True,
        user: Optional[str] = None,
-        callbacks: Optional[list[Callback]] = None,
+        callbacks: list[Callback] = None,
    ) -> Union[LLMResult, Generator]:
        """
        Code block mode wrapper for invoking large language model
@@ -1081,81 +1081,8 @@ LLM_BASE_MODELS = [
            ),
        ),
    ),
-    AzureBaseModel(
-        base_model_name="o1-preview",
-        entity=AIModelEntity(
-            model="fake-deployment-name",
-            label=I18nObject(
-                en_US="fake-deployment-name-label",
-            ),
-            model_type=ModelType.LLM,
-            features=[
-                ModelFeature.AGENT_THOUGHT,
-            ],
-            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
-            model_properties={
-                ModelPropertyKey.MODE: LLMMode.CHAT.value,
-                ModelPropertyKey.CONTEXT_SIZE: 128000,
-            },
-            parameter_rules=[
-                ParameterRule(
-                    name="response_format",
-                    label=I18nObject(zh_Hans="回复格式", en_US="response_format"),
-                    type="string",
-                    help=I18nObject(
-                        zh_Hans="指定模型必须输出的格式", en_US="specifying the format that the model must output"
-                    ),
-                    required=False,
-                    options=["text", "json_object"],
-                ),
-                _get_max_tokens(default=512, min_val=1, max_val=32768),
-            ],
-            pricing=PriceConfig(
-                input=15.00,
-                output=60.00,
-                unit=0.000001,
-                currency="USD",
-            ),
-        ),
-    ),
-    AzureBaseModel(
-        base_model_name="o1-mini",
-        entity=AIModelEntity(
-            model="fake-deployment-name",
-            label=I18nObject(
-                en_US="fake-deployment-name-label",
-            ),
-            model_type=ModelType.LLM,
-            features=[
-                ModelFeature.AGENT_THOUGHT,
-            ],
-            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
-            model_properties={
-                ModelPropertyKey.MODE: LLMMode.CHAT.value,
-                ModelPropertyKey.CONTEXT_SIZE: 128000,
-            },
-            parameter_rules=[
-                ParameterRule(
-                    name="response_format",
-                    label=I18nObject(zh_Hans="回复格式", en_US="response_format"),
-                    type="string",
-                    help=I18nObject(
-                        zh_Hans="指定模型必须输出的格式", en_US="specifying the format that the model must output"
-                    ),
-                    required=False,
-                    options=["text", "json_object"],
-                ),
-                _get_max_tokens(default=512, min_val=1, max_val=65536),
-            ],
-            pricing=PriceConfig(
-                input=3.00,
-                output=12.00,
-                unit=0.000001,
-                currency="USD",
-            ),
-        ),
-    ),
 ]
+
 EMBEDDING_BASE_MODELS = [
    AzureBaseModel(
        base_model_name="text-embedding-ada-002",
@@ -53,9 +53,6 @@ model_credential_schema:
      type: select
      required: true
      options:
-        - label:
-            en_US: 2024-09-01-preview
-          value: 2024-09-01-preview
        - label:
            en_US: 2024-08-01-preview
          value: 2024-08-01-preview
@@ -123,18 +120,6 @@ model_credential_schema:
          show_on:
            - variable: __model_type
              value: llm
-        - label:
-            en_US: o1-mini
-          value: o1-mini
-          show_on:
-            - variable: __model_type
-              value: llm
-        - label:
-            en_US: o1-preview
-          value: o1-preview
-          show_on:
-            - variable: __model_type
-              value: llm
        - label:
            en_US: gpt-4o-mini
          value: gpt-4o-mini
@@ -312,24 +312,10 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
        if user:
            extra_model_kwargs["user"] = user

-        # clear illegal prompt messages
-        prompt_messages = self._clear_illegal_prompt_messages(model, prompt_messages)
-
-        block_as_stream = False
-        if model.startswith("o1"):
-            if stream:
-                block_as_stream = True
-                stream = False
-
-                if "stream_options" in extra_model_kwargs:
-                    del extra_model_kwargs["stream_options"]
-
-            if "stop" in extra_model_kwargs:
-                del extra_model_kwargs["stop"]
-
        # chat model
+        messages = [self._convert_prompt_message_to_dict(m) for m in prompt_messages]
        response = client.chat.completions.create(
-            messages=[self._convert_prompt_message_to_dict(m) for m in prompt_messages],
+            messages=messages,
            model=model,
            stream=stream,
            **model_parameters,
@@ -339,91 +325,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
        if stream:
            return self._handle_chat_generate_stream_response(model, credentials, response, prompt_messages, tools)

-        block_result = self._handle_chat_generate_response(model, credentials, response, prompt_messages, tools)
-
-        if block_as_stream:
-            return self._handle_chat_block_as_stream_response(block_result, prompt_messages, stop)
-
-        return block_result
-
-    def _handle_chat_block_as_stream_response(
-        self,
-        block_result: LLMResult,
-        prompt_messages: list[PromptMessage],
-        stop: Optional[list[str]] = None,
-    ) -> Generator[LLMResultChunk, None, None]:
-        """
-        Handle llm chat response
-
-        :param model: model name
-        :param credentials: credentials
-        :param response: response
-        :param prompt_messages: prompt messages
-        :param tools: tools for tool calling
-        :param stop: stop words
-        :return: llm response chunk generator
-        """
-        text = block_result.message.content
-        text = cast(str, text)
-
-        if stop:
-            text = self.enforce_stop_tokens(text, stop)
-
-        yield LLMResultChunk(
-            model=block_result.model,
-            prompt_messages=prompt_messages,
-            system_fingerprint=block_result.system_fingerprint,
-            delta=LLMResultChunkDelta(
-                index=0,
-                message=AssistantPromptMessage(content=text),
-                finish_reason="stop",
-                usage=block_result.usage,
-            ),
-        )
-
-    def _clear_illegal_prompt_messages(self, model: str, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
-        """
-        Clear illegal prompt messages for OpenAI API
-
-        :param model: model name
-        :param prompt_messages: prompt messages
-        :return: cleaned prompt messages
-        """
-        checklist = ["gpt-4-turbo", "gpt-4-turbo-2024-04-09"]
-
-        if model in checklist:
-            # count how many user messages are there
-            user_message_count = len([m for m in prompt_messages if isinstance(m, UserPromptMessage)])
-            if user_message_count > 1:
-                for prompt_message in prompt_messages:
-                    if isinstance(prompt_message, UserPromptMessage):
-                        if isinstance(prompt_message.content, list):
-                            prompt_message.content = "\n".join(
-                                [
-                                    item.data
-                                    if item.type == PromptMessageContentType.TEXT
-                                    else "[IMAGE]"
-                                    if item.type == PromptMessageContentType.IMAGE
-                                    else ""
-                                    for item in prompt_message.content
-                                ]
-                            )
-
-        if model.startswith("o1"):
-            system_message_count = len([m for m in prompt_messages if isinstance(m, SystemPromptMessage)])
-            if system_message_count > 0:
-                new_prompt_messages = []
-                for prompt_message in prompt_messages:
-                    if isinstance(prompt_message, SystemPromptMessage):
-                        prompt_message = UserPromptMessage(
-                            content=prompt_message.content,
-                            name=prompt_message.name,
-                        )
-
-                    new_prompt_messages.append(prompt_message)
-                prompt_messages = new_prompt_messages
-
-        return prompt_messages
+        return self._handle_chat_generate_response(model, credentials, response, prompt_messages, tools)

    def _handle_chat_generate_response(
        self,
@@ -658,7 +560,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
            tokens_per_message = 4
            # if there's a name, the role is omitted
            tokens_per_name = -1
-        elif model.startswith("gpt-35-turbo") or model.startswith("gpt-4") or model.startswith("o1"):
+        elif model.startswith("gpt-35-turbo") or model.startswith("gpt-4"):
            tokens_per_message = 3
            tokens_per_name = 1
        else:
@@ -1,6 +1,6 @@
 import concurrent.futures
 import copy
-from typing import Any, Optional
+from typing import Optional

 from openai import AzureOpenAI

@@ -19,7 +19,7 @@ class AzureOpenAIText2SpeechModel(_CommonAzureOpenAI, TTSModel):

    def _invoke(
        self, model: str, tenant_id: str, credentials: dict, content_text: str, voice: str, user: Optional[str] = None
-    ) -> Any:
+    ) -> any:
        """
        _invoke text2speech model

@@ -56,7 +56,7 @@ class AzureOpenAIText2SpeechModel(_CommonAzureOpenAI, TTSModel):
        except Exception as ex:
            raise CredentialsValidateFailedError(str(ex))

-    def _tts_invoke_streaming(self, model: str, credentials: dict, content_text: str, voice: str) -> Any:
+    def _tts_invoke_streaming(self, model: str, credentials: dict, content_text: str, voice: str) -> any:
        """
        _tts_invoke_streaming text2speech model
        :param model: model name
@@ -50,62 +50,34 @@ provider_credential_schema:
          label:
            en_US: US East (N. Virginia)
            zh_Hans: 美国东部 (弗吉尼亚北部)
-        - value: us-east-2
-          label:
-            en_US: US East (Ohio)
-            zh_Hans: 美国东部 (弗吉尼亚北部)
        - value: us-west-2
          label:
            en_US: US West (Oregon)
            zh_Hans: 美国西部 (俄勒冈州)
-        - value: ap-south-1
-          label:
-            en_US: Asia Pacific (Mumbai)
-            zh_Hans: 亚太地区（孟买）
        - value: ap-southeast-1
          label:
            en_US: Asia Pacific (Singapore)
            zh_Hans: 亚太地区 (新加坡)
-        - value: ap-southeast-2
-          label:
-            en_US: Asia Pacific (Sydney)
-            zh_Hans: 亚太地区 (悉尼)
        - value: ap-northeast-1
          label:
            en_US: Asia Pacific (Tokyo)
            zh_Hans: 亚太地区 (东京)
-        - value: ap-northeast-2
-          label:
-            en_US: Asia Pacific (Seoul)
-            zh_Hans: 亚太地区（首尔）
-        - value: ca-central-1
-          label:
-            en_US: Canada (Central)
-            zh_Hans: 加拿大（中部）
        - value: eu-central-1
          label:
            en_US: Europe (Frankfurt)
            zh_Hans: 欧洲 (法兰克福)
-        - value: eu-west-1
-          label:
-            en_US: Europe (Ireland)
-            zh_Hans: 欧洲（爱尔兰）
        - value: eu-west-2
          label:
-            en_US: Europe (London)
+            en_US: Eu west London (London)
            zh_Hans: 欧洲西部 (伦敦)
-        - value: eu-west-3
-          label:
-            en_US: Europe (Paris)
-            zh_Hans: 欧洲（巴黎）
-        - value: sa-east-1
-          label:
-            en_US: South America (São Paulo)
-            zh_Hans: 南美洲（圣保罗）
        - value: us-gov-west-1
          label:
            en_US: AWS GovCloud (US-West)
            zh_Hans: AWS GovCloud (US-West)
+        - value: ap-southeast-2
+          label:
+            en_US: Asia Pacific (Sydney)
+            zh_Hans: 亚太地区 (悉尼)
    - variable: model_for_validation
      required: false
      label:
@@ -92,7 +92,7 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
        stop: Optional[list[str]] = None,
        stream: bool = True,
        user: Optional[str] = None,
-        callbacks: Optional[list[Callback]] = None,
+        callbacks: list[Callback] = None,
    ) -> Union[LLMResult, Generator]:
        """
        Code block mode wrapper for invoking large language model
@@ -511,7 +511,7 @@ class FireworksLargeLanguageModel(_CommonFireworks, LargeLanguageModel):
        model: str,
        messages: list[PromptMessage],
        tools: Optional[list[PromptMessageTool]] = None,
-        credentials: Optional[dict] = None,
+        credentials: dict = None,
    ) -> int:
        """
        Approximate num tokens with GPT2 tokenizer.
@@ -1,4 +1,4 @@
-from typing import Any, Optional
+from typing import Optional

 import httpx

@@ -46,7 +46,7 @@ class FishAudioText2SpeechModel(TTSModel):
        content_text: str,
        voice: str,
        user: Optional[str] = None,
-    ) -> Any:
+    ) -> any:
        """
        Invoke text2speech model

@@ -87,7 +87,7 @@ class FishAudioText2SpeechModel(TTSModel):
        except Exception as ex:
            raise CredentialsValidateFailedError(str(ex))

-    def _tts_invoke_streaming(self, model: str, credentials: dict, content_text: str, voice: str) -> Any:
+    def _tts_invoke_streaming(self, model: str, credentials: dict, content_text: str, voice: str) -> any:
        """
        Invoke streaming text2speech model
        :param model: model name
@@ -112,7 +112,7 @@ class FishAudioText2SpeechModel(TTSModel):
        except Exception as ex:
            raise InvokeBadRequestError(str(ex))

-    def _tts_invoke_streaming_sentence(self, credentials: dict, content_text: str, voice: Optional[str] = None) -> Any:
+    def _tts_invoke_streaming_sentence(self, credentials: dict, content_text: str, voice: Optional[str] = None) -> any:
        """
        Invoke streaming text2speech model

@@ -1,15 +0,0 @@
- gemini-1.5-pro
- gemini-1.5-pro-latest
- gemini-1.5-pro-001
- gemini-1.5-pro-002
- gemini-1.5-pro-exp-0801
- gemini-1.5-pro-exp-0827
- gemini-1.5-flash
- gemini-1.5-flash-latest
- gemini-1.5-flash-001
- gemini-1.5-flash-002
- gemini-1.5-flash-exp-0827
- gemini-1.5-flash-8b-exp-0827
- gemini-1.5-flash-8b-exp-0924
- gemini-pro
- gemini-pro-vision
@@ -5,4 +5,3 @@
 - llama3-8b-8192
 - mixtral-8x7b-32768
 - llama2-70b-4096
- llama-guard-3-8b
@@ -1,25 +0,0 @@
-model: llama-guard-3-8b
-label:
-  zh_Hans: Llama-Guard-3-8B
-  en_US: Llama-Guard-3-8B
-model_type: llm
-features:
-  - agent-thought
-model_properties:
-  mode: chat
-  context_size: 8192
-parameter_rules:
-  - name: temperature
-    use_template: temperature
-  - name: top_p
-    use_template: top_p
-  - name: max_tokens
-    use_template: max_tokens
-    default: 512
-    min: 1
-    max: 8192
-pricing:
-  input: '0.20'
-  output: '0.20'
-  unit: '0.000001'
-  currency: USD
@@ -61,19 +61,11 @@ class JinaRerankModel(RerankModel):

            rerank_documents = []
            for result in results["results"]:
-                index = result["index"]
-                if "document" in result:
-                    text = result["document"]["text"]
-                else:
-                    # llama.cpp rerank maynot return original documents
-                    text = docs[index]
-
                rerank_document = RerankDocument(
-                    index=index,
-                    text=text,
+                    index=result["index"],
+                    text=result["document"]["text"],
                    score=result["relevance_score"],
                )
-
                if score_threshold is None or result["relevance_score"] >= score_threshold:
                    rerank_documents.append(rerank_document)

@@ -70,19 +70,11 @@ class LocalaiRerankModel(RerankModel):

            rerank_documents = []
            for result in results["results"]:
-                index = result["index"]
-                if "document" in result:
-                    text = result["document"]["text"]
-                else:
-                    # llama.cpp rerank maynot return original documents
-                    text = docs[index]
-
                rerank_document = RerankDocument(
-                    index=index,
-                    text=text,
+                    index=result["index"],
+                    text=result["document"]["text"],
                    score=result["relevance_score"],
                )
-
                if score_threshold is None or result["relevance_score"] >= score_threshold:
                    rerank_documents.append(rerank_document)

@@ -111,7 +111,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
        stop: Optional[list[str]] = None,
        stream: bool = True,
        user: Optional[str] = None,
-        callbacks: Optional[list[Callback]] = None,
+        callbacks: list[Callback] = None,
    ) -> Union[LLMResult, Generator]:
        """
        Code block mode wrapper for invoking large language model
@@ -2,8 +2,6 @@ from typing import IO, Optional

 from openai import OpenAI

-from core.model_runtime.entities.common_entities import I18nObject
-from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelType
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.speech2text_model import Speech2TextModel
 from core.model_runtime.model_providers.openai._common import _CommonOpenAI
@@ -60,18 +58,3 @@ class OpenAISpeech2TextModel(_CommonOpenAI, Speech2TextModel):
        response = client.audio.transcriptions.create(model=model, file=file)

        return response.text
-
-    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
-        """
-        used to define customizable model schema
-        """
-        entity = AIModelEntity(
-            model=model,
-            label=I18nObject(en_US=model),
-            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
-            model_type=ModelType.SPEECH2TEXT,
-            model_properties={},
-            parameter_rules=[],
-        )
-
-        return entity
@@ -1,5 +1,5 @@
 import concurrent.futures
-from typing import Any, Optional
+from typing import Optional

 from openai import OpenAI

@@ -16,7 +16,7 @@ class OpenAIText2SpeechModel(_CommonOpenAI, TTSModel):

    def _invoke(
        self, model: str, tenant_id: str, credentials: dict, content_text: str, voice: str, user: Optional[str] = None
-    ) -> Any:
+    ) -> any:
        """
        _invoke text2speech model

@@ -55,7 +55,7 @@ class OpenAIText2SpeechModel(_CommonOpenAI, TTSModel):
        except Exception as ex:
            raise CredentialsValidateFailedError(str(ex))

-    def _tts_invoke_streaming(self, model: str, credentials: dict, content_text: str, voice: str) -> Any:
+    def _tts_invoke_streaming(self, model: str, credentials: dict, content_text: str, voice: str) -> any:
        """
        _tts_invoke_streaming text2speech model

@@ -688,7 +688,7 @@ class OAIAPICompatLargeLanguageModel(_CommonOaiApiCompat, LargeLanguageModel):
        model: str,
        messages: list[PromptMessage],
        tools: Optional[list[PromptMessageTool]] = None,
-        credentials: Optional[dict] = None,
+        credentials: dict = None,
    ) -> int:
        """
        Approximate num tokens with GPT2 tokenizer.
@@ -3,8 +3,6 @@ from urllib.parse import urljoin

 import requests

-from core.model_runtime.entities.common_entities import I18nObject
-from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelType
 from core.model_runtime.errors.invoke import InvokeBadRequestError
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.speech2text_model import Speech2TextModel
@@ -61,18 +59,3 @@ class OAICompatSpeech2TextModel(_CommonOaiApiCompat, Speech2TextModel):
                self._invoke(model, credentials, audio_file)
        except Exception as ex:
            raise CredentialsValidateFailedError(str(ex))
-
-    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
-        """
-        used to define customizable model schema
-        """
-        entity = AIModelEntity(
-            model=model,
-            label=I18nObject(en_US=model),
-            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
-            model_type=ModelType.SPEECH2TEXT,
-            model_properties={},
-            parameter_rules=[],
-        )
-
-        return entity
@@ -14,10 +14,6 @@
 - google/gemini-pro
 - cohere/command-r-plus
 - cohere/command-r
- meta-llama/llama-3.2-1b-instruct
- meta-llama/llama-3.2-3b-instruct
- meta-llama/llama-3.2-11b-vision-instruct
- meta-llama/llama-3.2-90b-vision-instruct
 - meta-llama/llama-3.1-405b-instruct
 - meta-llama/llama-3.1-70b-instruct
 - meta-llama/llama-3.1-8b-instruct
@@ -26,7 +22,6 @@
 - mistralai/mixtral-8x22b-instruct
 - mistralai/mixtral-8x7b-instruct
 - mistralai/mistral-7b-instruct
- qwen/qwen-2.5-72b-instruct
 - qwen/qwen-2-72b-instruct
 - deepseek/deepseek-chat
 - deepseek/deepseek-coder
@@ -27,9 +27,9 @@ parameter_rules:
  - name: max_tokens
    use_template: max_tokens
    required: true
-    default: 8192
+    default: 4096
    min: 1
-    max: 8192
+    max: 4096
  - name: response_format
    use_template: response_format
 pricing:
@@ -1,45 +0,0 @@
-model: meta-llama/llama-3.2-11b-vision-instruct
-label:
-  zh_Hans: llama-3.2-11b-vision-instruct
-  en_US: llama-3.2-11b-vision-instruct
-model_type: llm
-features:
-  - agent-thought
-model_properties:
-  mode: chat
-  context_size: 131072
-parameter_rules:
-  - name: temperature
-    use_template: temperature
-  - name: top_p
-    use_template: top_p
-  - name: top_k
-    label:
-      zh_Hans: 取样数量
-      en_US: Top k
-    type: int
-    help:
-      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
-      en_US: Only sample from the top K options for each subsequent token.
-  - name: max_tokens
-    use_template: max_tokens
-  - name: context_length_exceeded_behavior
-    default: None
-    label:
-      zh_Hans: 上下文长度超出行为
-      en_US: Context Length Exceeded Behavior
-    help:
-      zh_Hans: 上下文长度超出行为
-      en_US: Context Length Exceeded Behavior
-    type: string
-    options:
-      - None
-      - truncate
-      - error
-  - name: response_format
-    use_template: response_format
-pricing:
-  input: '0.055'
-  output: '0.055'
-  unit: '0.000001'
-  currency: USD
@@ -1,45 +0,0 @@
-model: meta-llama/llama-3.2-1b-instruct
-label:
-  zh_Hans: llama-3.2-1b-instruct
-  en_US: llama-3.2-1b-instruct
-model_type: llm
-features:
-  - agent-thought
-model_properties:
-  mode: chat
-  context_size: 131072
-parameter_rules:
-  - name: temperature
-    use_template: temperature
-  - name: top_p
-    use_template: top_p
-  - name: top_k
-    label:
-      zh_Hans: 取样数量
-      en_US: Top k
-    type: int
-    help:
-      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
-      en_US: Only sample from the top K options for each subsequent token.
-  - name: max_tokens
-    use_template: max_tokens
-  - name: context_length_exceeded_behavior
-    default: None
-    label:
-      zh_Hans: 上下文长度超出行为
-      en_US: Context Length Exceeded Behavior
-    help:
-      zh_Hans: 上下文长度超出行为
-      en_US: Context Length Exceeded Behavior
-    type: string
-    options:
-      - None
-      - truncate
-      - error
-  - name: response_format
-    use_template: response_format
-pricing:
-  input: '0.01'
-  output: '0.02'
-  unit: '0.000001'
-  currency: USD
@@ -1,45 +0,0 @@
-model: meta-llama/llama-3.2-3b-instruct
-label:
-  zh_Hans: llama-3.2-3b-instruct
-  en_US: llama-3.2-3b-instruct
-model_type: llm
-features:
-  - agent-thought
-model_properties:
-  mode: chat
-  context_size: 131072
-parameter_rules:
-  - name: temperature
-    use_template: temperature
-  - name: top_p
-    use_template: top_p
-  - name: top_k
-    label:
-      zh_Hans: 取样数量
-      en_US: Top k
-    type: int
-    help:
-      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
-      en_US: Only sample from the top K options for each subsequent token.
-  - name: max_tokens
-    use_template: max_tokens
-  - name: context_length_exceeded_behavior
-    default: None
-    label:
-      zh_Hans: 上下文长度超出行为
-      en_US: Context Length Exceeded Behavior
-    help:
-      zh_Hans: 上下文长度超出行为
-      en_US: Context Length Exceeded Behavior
-    type: string
-    options:
-      - None
-      - truncate
-      - error
-  - name: response_format
-    use_template: response_format
-pricing:
-  input: '0.03'
-  output: '0.05'
-  unit: '0.000001'
-  currency: USD
@@ -1,45 +0,0 @@
-model: meta-llama/llama-3.2-90b-vision-instruct
-label:
-  zh_Hans: llama-3.2-90b-vision-instruct
-  en_US: llama-3.2-90b-vision-instruct
-model_type: llm
-features:
-  - agent-thought
-model_properties:
-  mode: chat
-  context_size: 131072
-parameter_rules:
-  - name: temperature
-    use_template: temperature
-  - name: top_p
-    use_template: top_p
-  - name: top_k
-    label:
-      zh_Hans: 取样数量
-      en_US: Top k
-    type: int
-    help:
-      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
-      en_US: Only sample from the top K options for each subsequent token.
-  - name: max_tokens
-    use_template: max_tokens
-  - name: context_length_exceeded_behavior
-    default: None
-    label:
-      zh_Hans: 上下文长度超出行为
-      en_US: Context Length Exceeded Behavior
-    help:
-      zh_Hans: 上下文长度超出行为
-      en_US: Context Length Exceeded Behavior
-    type: string
-    options:
-      - None
-      - truncate
-      - error
-  - name: response_format
-    use_template: response_format
-pricing:
-  input: '0.35'
-  output: '0.4'
-  unit: '0.000001'
-  currency: USD
@@ -1,30 +0,0 @@
-model: qwen/qwen-2.5-72b-instruct
-label:
-  en_US: qwen-2.5-72b-instruct
-model_type: llm
-features:
-  - agent-thought
-model_properties:
-  mode: chat
-  context_size: 131072
-parameter_rules:
-  - name: temperature
-    use_template: temperature
-  - name: max_tokens
-    use_template: max_tokens
-    type: int
-    default: 512
-    min: 1
-    max: 8192
-    help:
-      zh_Hans: 指定生成结果长度的上限。如果生成结果截断，可以调大该参数。
-      en_US: Specifies the upper limit on the length of generated results. If the generated results are truncated, you can increase this parameter.
-  - name: top_p
-    use_template: top_p
-  - name: frequency_penalty
-    use_template: frequency_penalty
-pricing:
-  input: "0.35"
-  output: "0.4"
-  unit: "0.000001"
-  currency: USD
@@ -77,7 +77,7 @@ class SageMakerText2SpeechModel(TTSModel):
        """
        pass

-    def _detect_lang_code(self, content: str, map_dict: Optional[dict] = None):
+    def _detect_lang_code(self, content: str, map_dict: dict = None):
        map_dict = {"zh": "<|zh|>", "en": "<|en|>", "ja": "<|jp|>", "zh-TW": "<|yue|>", "ko": "<|ko|>"}

        response = self.comprehend_client.detect_dominant_language(Text=content)
@@ -192,7 +192,7 @@ class SageMakerText2SpeechModel(TTSModel):
            InvokeBadRequestError: [InvokeBadRequestError, KeyError, ValueError],
        }

-    def _get_model_default_voice(self, model: str, credentials: dict) -> Any:
+    def _get_model_default_voice(self, model: str, credentials: dict) -> any:
        return ""

    def _get_model_word_limit(self, model: str, credentials: dict) -> int:
@@ -225,7 +225,7 @@ class SageMakerText2SpeechModel(TTSModel):
        json_obj = json.loads(json_str)
        return json_obj

-    def _tts_invoke_streaming(self, model_type: str, payload: dict, sagemaker_endpoint: str) -> Any:
+    def _tts_invoke_streaming(self, model_type: str, payload: dict, sagemaker_endpoint: str) -> any:
        """
        _tts_invoke_streaming text2speech model

@@ -1,18 +1,8 @@
 from collections.abc import Generator
 from typing import Optional, Union

-from core.model_runtime.entities.common_entities import I18nObject
-from core.model_runtime.entities.llm_entities import LLMMode, LLMResult
+from core.model_runtime.entities.llm_entities import LLMResult
 from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool
-from core.model_runtime.entities.model_entities import (
-    AIModelEntity,
-    FetchFrom,
-    ModelFeature,
-    ModelPropertyKey,
-    ModelType,
-    ParameterRule,
-    ParameterType,
-)
 from core.model_runtime.model_providers.openai_api_compatible.llm.llm import OAIAPICompatLargeLanguageModel


@@ -39,53 +29,3 @@ class SiliconflowLargeLanguageModel(OAIAPICompatLargeLanguageModel):
    def _add_custom_parameters(cls, credentials: dict) -> None:
        credentials["mode"] = "chat"
        credentials["endpoint_url"] = "https://api.siliconflow.cn/v1"
-
-    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
-        return AIModelEntity(
-            model=model,
-            label=I18nObject(en_US=model, zh_Hans=model),
-            model_type=ModelType.LLM,
-            features=[ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL, ModelFeature.STREAM_TOOL_CALL]
-            if credentials.get("function_calling_type") == "tool_call"
-            else [],
-            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
-            model_properties={
-                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 8000)),
-                ModelPropertyKey.MODE: LLMMode.CHAT.value,
-            },
-            parameter_rules=[
-                ParameterRule(
-                    name="temperature",
-                    use_template="temperature",
-                    label=I18nObject(en_US="Temperature", zh_Hans="温度"),
-                    type=ParameterType.FLOAT,
-                ),
-                ParameterRule(
-                    name="max_tokens",
-                    use_template="max_tokens",
-                    default=512,
-                    min=1,
-                    max=int(credentials.get("max_tokens", 1024)),
-                    label=I18nObject(en_US="Max Tokens", zh_Hans="最大标记"),
-                    type=ParameterType.INT,
-                ),
-                ParameterRule(
-                    name="top_p",
-                    use_template="top_p",
-                    label=I18nObject(en_US="Top P", zh_Hans="Top P"),
-                    type=ParameterType.FLOAT,
-                ),
-                ParameterRule(
-                    name="top_k",
-                    use_template="top_k",
-                    label=I18nObject(en_US="Top K", zh_Hans="Top K"),
-                    type=ParameterType.FLOAT,
-                ),
-                ParameterRule(
-                    name="frequency_penalty",
-                    use_template="frequency_penalty",
-                    label=I18nObject(en_US="Frequency Penalty", zh_Hans="重复惩罚"),
-                    type=ParameterType.FLOAT,
-                ),
-            ],
-        )
@@ -20,7 +20,6 @@ supported_model_types:
  - speech2text
 configurate_methods:
  - predefined-model
-  - customizable-model
 provider_credential_schema:
  credential_form_schemas:
    - variable: api_key
@@ -31,57 +30,3 @@ provider_credential_schema:
      placeholder:
        zh_Hans: 在此输入您的 API Key
        en_US: Enter your API Key
-model_credential_schema:
-  model:
-    label:
-      en_US: Model Name
-      zh_Hans: 模型名称
-    placeholder:
-      en_US: Enter your model name
-      zh_Hans: 输入模型名称
-  credential_form_schemas:
-    - variable: api_key
-      label:
-        en_US: API Key
-      type: secret-input
-      required: true
-      placeholder:
-        zh_Hans: 在此输入您的 API Key
-        en_US: Enter your API Key
-    - variable: context_size
-      label:
-        zh_Hans: 模型上下文长度
-        en_US: Model context size
-      required: true
-      type: text-input
-      default: '4096'
-      placeholder:
-        zh_Hans: 在此输入您的模型上下文长度
-        en_US: Enter your Model context size
-    - variable: max_tokens
-      label:
-        zh_Hans: 最大 token 上限
-        en_US: Upper bound for max tokens
-      default: '4096'
-      type: text-input
-      show_on:
-        - variable: __model_type
-          value: llm
-    - variable: function_calling_type
-      label:
-        en_US: Function calling
-      type: select
-      required: false
-      default: no_call
-      options:
-        - value: no_call
-          label:
-            en_US: Not Support
-            zh_Hans: 不支持
-        - value: function_call
-          label:
-            en_US: Support
-            zh_Hans: 支持
-      show_on:
-        - variable: __model_type
-          value: llm
@@ -1 +0,0 @@
- gte-rerank
@@ -1,4 +0,0 @@
-model: gte-rerank
-model_type: rerank
-model_properties:
-  context_size: 4000
@@ -1,136 +0,0 @@
-from typing import Optional
-
-import dashscope
-from dashscope.common.error import (
-    AuthenticationError,
-    InvalidParameter,
-    RequestFailure,
-    ServiceUnavailableError,
-    UnsupportedHTTPMethod,
-    UnsupportedModel,
-)
-
-from core.model_runtime.entities.rerank_entities import RerankDocument, RerankResult
-from core.model_runtime.errors.invoke import (
-    InvokeAuthorizationError,
-    InvokeBadRequestError,
-    InvokeConnectionError,
-    InvokeError,
-    InvokeRateLimitError,
-    InvokeServerUnavailableError,
-)
-from core.model_runtime.errors.validate import CredentialsValidateFailedError
-from core.model_runtime.model_providers.__base.rerank_model import RerankModel
-
-
-class GTERerankModel(RerankModel):
-    """
-    Model class for GTE rerank model.
-    """
-
-    def _invoke(
-        self,
-        model: str,
-        credentials: dict,
-        query: str,
-        docs: list[str],
-        score_threshold: Optional[float] = None,
-        top_n: Optional[int] = None,
-        user: Optional[str] = None,
-    ) -> RerankResult:
-        """
-        Invoke rerank model
-
-        :param model: model name
-        :param credentials: model credentials
-        :param query: search query
-        :param docs: docs for reranking
-        :param score_threshold: score threshold
-        :param top_n: top n
-        :param user: unique user id
-        :return: rerank result
-        """
-        if len(docs) == 0:
-            return RerankResult(model=model, docs=docs)
-
-        # initialize client
-        dashscope.api_key = credentials["dashscope_api_key"]
-
-        response = dashscope.TextReRank.call(
-            query=query,
-            documents=docs,
-            model=model,
-            top_n=top_n,
-            return_documents=True,
-        )
-
-        rerank_documents = []
-        for _, result in enumerate(response.output.results):
-            # format document
-            rerank_document = RerankDocument(
-                index=result.index,
-                score=result.relevance_score,
-                text=result["document"]["text"],
-            )
-
-            # score threshold check
-            if score_threshold is not None:
-                if result.relevance_score >= score_threshold:
-                    rerank_documents.append(rerank_document)
-            else:
-                rerank_documents.append(rerank_document)
-
-        return RerankResult(model=model, docs=rerank_documents)
-
-    def validate_credentials(self, model: str, credentials: dict) -> None:
-        """
-        Validate model credentials
-
-        :param model: model name
-        :param credentials: model credentials
-        :return:
-        """
-        try:
-            self.invoke(
-                model=model,
-                credentials=credentials,
-                query="What is the capital of the United States?",
-                docs=[
-                    "Carson City is the capital city of the American state of Nevada. At the 2010 United States "
-                    "Census, Carson City had a population of 55,274.",
-                    "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that "
-                    "are a political division controlled by the United States. Its capital is Saipan.",
-                ],
-                score_threshold=0.8,
-            )
-        except Exception as ex:
-            print(ex)
-            raise CredentialsValidateFailedError(str(ex))
-
-    @property
-    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
-        """
-        Map model invoke error to unified error
-        The key is the error type thrown to the caller
-        The value is the error type thrown by the model,
-        which needs to be converted into a unified error type for the caller.
-
-        :return: Invoke error mapping
-        """
-        return {
-            InvokeConnectionError: [
-                RequestFailure,
-            ],
-            InvokeServerUnavailableError: [
-                ServiceUnavailableError,
-            ],
-            InvokeRateLimitError: [],
-            InvokeAuthorizationError: [
-                AuthenticationError,
-            ],
-            InvokeBadRequestError: [
-                InvalidParameter,
-                UnsupportedModel,
-                UnsupportedHTTPMethod,
-            ],
-        }
@@ -18,7 +18,6 @@ supported_model_types:
  - llm
  - tts
  - text-embedding
-  - rerank
 configurate_methods:
  - predefined-model
  - customizable-model
@@ -1,6 +1,6 @@
 import threading
 from queue import Queue
-from typing import Any, Optional
+from typing import Optional

 import dashscope
 from dashscope import SpeechSynthesizer
@@ -20,7 +20,7 @@ class TongyiText2SpeechModel(_CommonTongyi, TTSModel):

    def _invoke(
        self, model: str, tenant_id: str, credentials: dict, content_text: str, voice: str, user: Optional[str] = None
-    ) -> Any:
+    ) -> any:
        """
        _invoke text2speech model

@@ -58,7 +58,7 @@ class TongyiText2SpeechModel(_CommonTongyi, TTSModel):
        except Exception as ex:
            raise CredentialsValidateFailedError(str(ex))

-    def _tts_invoke_streaming(self, model: str, credentials: dict, content_text: str, voice: str) -> Any:
+    def _tts_invoke_streaming(self, model: str, credentials: dict, content_text: str, voice: str) -> any:
        """
        _tts_invoke_streaming text2speech model

@@ -7,7 +7,6 @@ from collections.abc import Generator
 from typing import Optional, Union, cast

 import google.auth.transport.requests
-import requests
 import vertexai.generative_models as glm
 from anthropic import AnthropicVertex, Stream
 from anthropic.types import (
@@ -654,15 +653,9 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
                    if c.type == PromptMessageContentType.TEXT:
                        parts.append(glm.Part.from_text(c.data))
                    else:
-                        message_content = cast(ImagePromptMessageContent, c)
-                        if not message_content.data.startswith("data:"):
-                            url_arr = message_content.data.split(".")
-                            mime_type = f"image/{url_arr[-1]}"
-                            parts.append(glm.Part.from_uri(mime_type=mime_type, uri=message_content.data))
-                        else:
-                            metadata, data = c.data.split(",", 1)
-                            mime_type = metadata.split(";", 1)[0].split(":")[1]
-                            parts.append(glm.Part.from_data(mime_type=mime_type, data=data))
+                        metadata, data = c.data.split(",", 1)
+                        mime_type = metadata.split(";", 1)[0].split(":")[1]
+                        parts.append(glm.Part.from_data(mime_type=mime_type, data=data))
                glm_content = glm.Content(role="user", parts=parts)
            return glm_content
        elif isinstance(message, AssistantPromptMessage):
@@ -1,4 +0,0 @@
- rerank-2
- rerank-lite-2
- rerank-1
- rerank-lite-1
@@ -1,4 +0,0 @@
-model: rerank-2
-model_type: rerank
-model_properties:
-  context_size: 16000
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
-LAN-	ce774c6d15	release(update): bump version to 0.10.0-beta2 - Update CURRENT_VERSION in configuration settings - Update Docker images to use version 0.10.0-beta2 - Change web package version in package.json	2024-10-01 13:58:37 +08:00
-LAN-	6bb06dda90	feat(ci): add release branch to build-push workflow - Include `release/0.10.0-beta` in branches triggering build and push jobs - Ensure automatic builds for the new beta release branch	2024-10-01 13:30:36 +08:00
-LAN-	26c8beb804	refactor(identity): update model identity key - Change "model_identity" to "dify_model_identity" for consistency - Adjust file validation logic to use updated key in various components and tests	2024-10-01 13:21:58 +08:00
-LAN-	5c64792793	refactor(models, helper): update identifiers and proxy transport code - Renamed `model_identity` to `dify_model_identity` for clearer specificity - Added `proxy` parameter to `httpx.HTTPTransport` for SSRF protection adjustments	2024-10-01 13:08:06 +08:00
-LAN-	afb3e317d3	Merge branch 'feat/attachments' into release/0.10.0-beta1	2024-09-30 23:34:54 +08:00
-LAN-	1e62ad23e6	chore(version): bump to 0.10.0-beta1 - Update version in packaging configuration - Change docker images to new version in docker-compose files - Update web package.json with new version	2024-09-30 23:34:04 +08:00
-LAN-	f9baabc9f9	fix(tests): handle invalid variable type for document_extractor_node - Imported StringVariable to properly mock an invalid variable type. - Enhanced test case `test_run_invalid_variable_type` to use StringVariable instead of a plain string.	2024-09-30 23:32:51 +08:00
-LAN-	94b946c715	fix(document_extractor): handle empty variable values properly - Added check for variable.value existence before type validation. - Prevents erroneous type checks on empty variables, improving robustness.	2024-09-30 23:32:51 +08:00
-LAN-	3ff88f4f3c	refactor(api): consolidate allowed extensions handling - Unified allowed extensions into a single `DOCUMENT_EXTENSIONS` reference - Adjusted checks and imports in controllers and services to use the new constant - Enhanced text extraction to support additional file types (EPUB, EML, MSG)	2024-09-30 23:32:51 +08:00
-LAN-	e7f425be91	fix(node): correct conditional checks in list filter node - Added check for variable value before type checking in ListFilterNode to prevent unnecessary errors. - Fixed incorrect workflow feature path in AppDslService to ensure correct data fetching.	2024-09-30 23:32:51 +08:00
-LAN-	584053bc01	feat(api): Enhance multi modal support.	2024-09-30 23:32:51 +08:00