version to 0.6.8 (#4347)

Co-authored-by: 陈力坤 <likunchen@caixin.com>

.devcontainer/README.md

@@ -1,4 +1,4 @@
-# Devlopment with devcontainer
+# Development with devcontainer
 This project includes a devcontainer configuration that allows you to open the project in a container with a fully configured development environment.
 Both frontend and backend environments are initialized when the container is started.
 ## GitHub Codespaces
@@ -33,5 +33,5 @@ Performance Impact: While usually minimal, programs running inside a devcontaine
 if you see such error message when you open this project in codespaces:
 ![Alt text](troubleshooting.png)
 
-a simple workaround is change `/signin` endpoint into another one, then login with github account and close the tab, then change it back to `/signin` endpoint. Then all things will be fine.
+a simple workaround is change `/signin` endpoint into another one, then login with GitHub account and close the tab, then change it back to `/signin` endpoint. Then all things will be fine.
 The reason is `signin` endpoint is not allowed in codespaces, details can be found [here](https://github.com/orgs/community/discussions/5204)

.devcontainer/devcontainer.json

@@ -32,8 +32,8 @@
 			]
 		}
 	},
-	"postStartCommand": "cd api && pip install -r requirements.txt",
-	"postCreateCommand": "cd web && npm install"
+	"postStartCommand": "./.devcontainer/post_start_command.sh",
+	"postCreateCommand": "./.devcontainer/post_create_command.sh"
 
 	// Features to add to the dev container. More info: https://containers.dev/features.
 	// "features": {},

.devcontainer/post_create_command.sh

@@ -0,0 +1,10 @@
+#!/bin/bash
+
+cd web && npm install
+
+echo 'alias start-api="cd /workspaces/dify/api && flask run --host 0.0.0.0 --port=5001 --debug"' >> ~/.bashrc
+echo 'alias start-worker="cd /workspaces/dify/api && celery -A app.celery worker -P gevent -c 1 --loglevel INFO -Q dataset,generation,mail"' >> ~/.bashrc
+echo 'alias start-web="cd /workspaces/dify/web && npm run dev"' >> ~/.bashrc
+echo 'alias start-containers="cd /workspaces/dify/docker && docker-compose -f docker-compose.middleware.yaml -p dify up -d"' >> ~/.bashrc
+
+source /home/vscode/.bashrc

.devcontainer/post_start_command.sh

@@ -0,0 +1,3 @@
+#!/bin/bash
+
+cd api && pip install -r requirements.txt

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -8,13 +8,13 @@ body:
       label: Self Checks
       description: "To make sure we get to you in time, please check the following :)"
       options:
-        - label: This is only for bug report, if you would like to ask a quesion, please head to [Discussions](https://github.com/langgenius/dify/discussions/categories/general).
+        - label: This is only for bug report, if you would like to ask a question, please head to [Discussions](https://github.com/langgenius/dify/discussions/categories/general).
           required: true
         - label: I have searched for existing issues [search for existing issues](https://github.com/langgenius/dify/issues), including closed ones.
           required: true
         - label: I confirm that I am using English to submit this report (我已阅读并同意 [Language Policy](https://github.com/langgenius/dify/issues/1542)).
           required: true
-        - label: "Pleas do not modify this template :) and fill in all the required fields."
+        - label: "Please do not modify this template :) and fill in all the required fields."
           required: true
 
   - type: input

.github/ISSUE_TEMPLATE/document_issue.yml

@@ -1,7 +1,7 @@
 name: "📚 Documentation Issue"
 description: Report issues in our documentation
 labels:
-  - ducumentation
+  - documentation
 body:
   - type: checkboxes
     attributes:
@@ -12,7 +12,7 @@ body:
           required: true
         - label: I confirm that I am using English to submit report (我已阅读并同意 [Language Policy](https://github.com/langgenius/dify/issues/1542)).
           required: true
-        - label: "Pleas do not modify this template :) and fill in all the required fields."
+        - label: "Please do not modify this template :) and fill in all the required fields."
           required: true
   - type: textarea
     attributes:

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -12,7 +12,7 @@ body:
           required: true
         - label: I confirm that I am using English to submit this report (我已阅读并同意 [Language Policy](https://github.com/langgenius/dify/issues/1542)).
           required: true
-        - label: "Pleas do not modify this template :) and fill in all the required fields."
+        - label: "Please do not modify this template :) and fill in all the required fields."
           required: true
   - type: textarea
     attributes:

.github/ISSUE_TEMPLATE/translation_issue.yml

@@ -12,7 +12,7 @@ body:
           required: true
         - label: I confirm that I am using English to submit this report (我已阅读并同意 [Language Policy](https://github.com/langgenius/dify/issues/1542)).
           required: true
-        - label: "Pleas do not modify this template :) and fill in all the required fields."
+        - label: "Please do not modify this template :) and fill in all the required fields."
           required: true
   - type: input
     attributes:

.github/workflows/api-tests.yml

@@ -10,36 +10,14 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.10", "3.11", "3.12"]
-
-    env:
-      OPENAI_API_KEY: sk-IamNotARealKeyJustForMockTestKawaiiiiiiiiii
-      AZURE_OPENAI_API_BASE: https://difyai-openai.openai.azure.com
-      AZURE_OPENAI_API_KEY: xxxxb1707exxxxxxxxxxaaxxxxxf94
-      ANTHROPIC_API_KEY: sk-ant-api11-IamNotARealKeyJustForMockTestKawaiiiiiiiiii-NotBaka-ASkksz
-      CHATGLM_API_BASE: http://a.abc.com:11451
-      XINFERENCE_SERVER_URL: http://a.abc.com:11451
-      XINFERENCE_GENERATION_MODEL_UID: generate
-      XINFERENCE_CHAT_MODEL_UID: chat
-      XINFERENCE_EMBEDDINGS_MODEL_UID: embedding
-      XINFERENCE_RERANK_MODEL_UID: rerank
-      GOOGLE_API_KEY: abcdefghijklmnopqrstuvwxyz
-      HUGGINGFACE_API_KEY: hf-awuwuwuwuwuwuwuwuwuwuwuwuwuwuwuwuwu
-      HUGGINGFACE_TEXT_GEN_ENDPOINT_URL: a
-      HUGGINGFACE_TEXT2TEXT_GEN_ENDPOINT_URL: b
-      HUGGINGFACE_EMBEDDINGS_ENDPOINT_URL: c
-      MOCK_SWITCH: true
-      CODE_MAX_STRING_LENGTH: 80000
+        python-version:
+          - "3.10"
+          - "3.11"
 
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
 
-      - name: Install APT packages
-        uses: awalsh128/cache-apt-pkgs-action@v1
-        with:
-          packages: ffmpeg
-
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v5
         with:
@@ -52,11 +30,44 @@ jobs:
       - name: Install dependencies
         run: pip install -r ./api/requirements.txt -r ./api/requirements-dev.txt
 
+      - name: Run Unit tests
+        run: dev/pytest/pytest_unit_tests.sh
+
       - name: Run ModelRuntime
         run: dev/pytest/pytest_model_runtime.sh
 
       - name: Run Tool
         run: dev/pytest/pytest_tools.sh
 
+      - name: Set up Sandbox
+        uses: hoverkraft-tech/compose-action@v2.0.0
+        with:
+          compose-file: |
+            docker/docker-compose.middleware.yaml
+          services: |
+            sandbox
+            ssrf_proxy
+
       - name: Run Workflow
         run: dev/pytest/pytest_workflow.sh
+
+      - name: Set up Vector Stores (Weaviate, Qdrant, PGVector, Milvus, PgVecto-RS)
+        uses: hoverkraft-tech/compose-action@v2.0.0
+        with:
+          compose-file: |
+            docker/docker-compose.middleware.yaml
+            docker/docker-compose.qdrant.yaml
+            docker/docker-compose.milvus.yaml
+            docker/docker-compose.pgvecto-rs.yaml
+            docker/docker-compose.pgvector.yaml
+          services: |
+            weaviate
+            qdrant
+            etcd
+            minio
+            milvus-standalone
+            pgvecto-rs
+            pgvector
+
+      - name: Test Vector Stores
+        run: dev/pytest/pytest_vdb.sh

CONTRIBUTING.md

@@ -4,7 +4,7 @@ We need to be nimble and ship fast given where we are, but we also want to make
 
 This guide, like Dify itself, is a constant work in progress. We highly appreciate your understanding if at times it lags behind the actual project, and welcome any feedback for us to improve.
 
-In terms of licensing, please take a minute to read our short [License and Contributor Agreement](./license). The community also adheres to the [code of conduct](https://github.com/langgenius/.github/blob/main/CODE_OF_CONDUCT.md).
+In terms of licensing, please take a minute to read our short [License and Contributor Agreement](./LICENSE). The community also adheres to the [code of conduct](https://github.com/langgenius/.github/blob/main/CODE_OF_CONDUCT.md).
 
 ## Before you jump in
 

CONTRIBUTING_CN.md

@@ -4,7 +4,7 @@
 
 这份指南，就像 Dify 本身一样，是一个不断改进的工作。如果有时它落后于实际项目，我们非常感谢你的理解，并欢迎任何反馈以供我们改进。
 
-在许可方面，请花一分钟阅读我们简短的[许可证和贡献者协议](./license)。社区还遵守[行为准则](https://github.com/langgenius/.github/blob/main/CODE_OF_CONDUCT.md)。
+在许可方面，请花一分钟阅读我们简短的[许可证和贡献者协议](./LICENSE)。社区还遵守[行为准则](https://github.com/langgenius/.github/blob/main/CODE_OF_CONDUCT.md)。
 
 ## 在开始之前
 

README.md

@@ -29,19 +29,15 @@
 </p>
 
 <p align="center">
-  <a href="./README.md"><img alt="Commits last month" src="https://img.shields.io/badge/English-d9d9d9"></a>
-  <a href="./README_CN.md"><img alt="Commits last month" src="https://img.shields.io/badge/简体中文-d9d9d9"></a>
-  <a href="./README_JA.md"><img alt="Commits last month" src="https://img.shields.io/badge/日本語-d9d9d9"></a>
-  <a href="./README_ES.md"><img alt="Commits last month" src="https://img.shields.io/badge/Español-d9d9d9"></a>
-  <a href="./README_FR.md"><img alt="Commits last month" src="https://img.shields.io/badge/Français-d9d9d9"></a>
-  <a href="./README_KL.md"><img alt="Commits last month" src="https://img.shields.io/badge/Klingon-d9d9d9"></a>
+  <a href="./README.md"><img alt="README in English" src="https://img.shields.io/badge/English-d9d9d9"></a>
+  <a href="./README_CN.md"><img alt="简体中文版自述文件" src="https://img.shields.io/badge/简体中文-d9d9d9"></a>
+  <a href="./README_JA.md"><img alt="日本語のREADME" src="https://img.shields.io/badge/日本語-d9d9d9"></a>
+  <a href="./README_ES.md"><img alt="README en Español" src="https://img.shields.io/badge/Español-d9d9d9"></a>
+  <a href="./README_FR.md"><img alt="README en Français" src="https://img.shields.io/badge/Français-d9d9d9"></a>
+  <a href="./README_KL.md"><img alt="README tlhIngan Hol" src="https://img.shields.io/badge/Klingon-d9d9d9"></a>
 </p>
 
-#
 
-<p align="center">
-  <a href="https://trendshift.io/repositories/2152" target="_blank"><img src="https://trendshift.io/api/badge/repositories/2152" alt="langgenius%2Fdify | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
-</p>
 Dify is an open-source LLM app development platform. Its intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features and more, letting you quickly go from prototype to production. Here's a list of the core features:
 </br> </br>
 
@@ -54,7 +50,7 @@ Dify is an open-source LLM app development platform. Its intuitive interface com
 
 
 **2. Comprehensive model support**: 
-  Seamless integration with hundreds of proprietary / open-source LLMs from dozens of inference providers and self-hosted solutions, covering GPT, Mistral, Llama2, and any OpenAI API-compatible models. A full list of supported model providers can be found [here](https://docs.dify.ai/getting-started/readme/model-providers).
+  Seamless integration with hundreds of proprietary / open-source LLMs from dozens of inference providers and self-hosted solutions, covering GPT, Mistral, Llama3, and any OpenAI API-compatible models. A full list of supported model providers can be found [here](https://docs.dify.ai/getting-started/readme/model-providers).
 
 ![providers-v5](https://github.com/langgenius/dify/assets/13230914/5a17bdbe-097a-4100-8363-40255b70f6e3)
 
@@ -109,7 +105,7 @@ Dify is an open-source LLM app development platform. Its intuitive interface com
     <td align="center">Agent</td>
     <td align="center">✅</td>
     <td align="center">✅</td>
-    <td align="center">✅</td>
+    <td align="center">❌</td>
     <td align="center">✅</td>
   </tr>
   <tr>
@@ -127,7 +123,7 @@ Dify is an open-source LLM app development platform. Its intuitive interface com
     <td align="center">❌</td>
   </tr>
   <tr>
-    <td align="center">Enterprise Feature (SSO/Access control)</td>
+    <td align="center">Enterprise Features (SSO/Access control)</td>
     <td align="center">✅</td>
     <td align="center">❌</td>
     <td align="center">❌</td>

README_CN.md

@@ -111,7 +111,7 @@ Dify 是一个开源的 LLM 应用开发平台。其直观的界面结合了 AI
     <td align="center">Agent</td>
     <td align="center">✅</td>
     <td align="center">✅</td>
-    <td align="center">✅</td>
+    <td align="center">❌</td>
     <td align="center">✅</td>
   </tr>
   <tr>

README_ES.md

@@ -54,7 +54,7 @@ Dify es una plataforma de desarrollo de aplicaciones de LLM de código abierto.
 
 
 **2. Soporte de modelos completo**: 
-  Integración perfecta con cientos de LLMs propietarios / de código abierto de docenas de proveedores de inferencia y soluciones auto-alojadas, que cubren GPT, Mistral, Llama2 y cualquier modelo compatible con la API de OpenAI. Se puede encontrar una lista completa de proveedores de modelos admitidos [aquí](https://docs.dify.ai/getting-started/readme/model-providers).
+  Integración perfecta con cientos de LLMs propietarios / de código abierto de docenas de proveedores de inferencia y soluciones auto-alojadas, que cubren GPT, Mistral, Llama3 y cualquier modelo compatible con la API de OpenAI. Se puede encontrar una lista completa de proveedores de modelos admitidos [aquí](https://docs.dify.ai/getting-started/readme/model-providers).
 
 ![proveedores-v5](https://github.com/langgenius/dify/assets/13230914/5a17bdbe-097a-4100-8363-40255b70f6e3)
 
@@ -111,7 +111,7 @@ es basados en LLM Function Calling o ReAct, y agregar herramientas preconstruida
     <td align="center">Agente</td>
     <td align="center">✅</td>
     <td align="center">✅</td>
-    <td align="center">✅</td>
+    <td align="center">❌</td>
     <td align="center">✅</td>
   </tr>
   <tr>

README_FR.md

@@ -54,7 +54,7 @@ Dify est une plateforme de développement d'applications LLM open source. Son in
 
 
 **2. Prise en charge complète des modèles**: 
-  Intégration transparente avec des centaines de LLM propriétaires / open source provenant de dizaines de fournisseurs d'inférence et de solutions auto-hébergées, couvrant GPT, Mistral, Llama2, et tous les modèles compatibles avec l'API OpenAI. Une liste complète des fournisseurs de modèles pris en charge se trouve [ici](https://docs.dify.ai/getting-started/readme/model-providers).
+  Intégration transparente avec des centaines de LLM propriétaires / open source provenant de dizaines de fournisseurs d'inférence et de solutions auto-hébergées, couvrant GPT, Mistral, Llama3, et tous les modèles compatibles avec l'API OpenAI. Une liste complète des fournisseurs de modèles pris en charge se trouve [ici](https://docs.dify.ai/getting-started/readme/model-providers).
 
 ![providers-v5](https://github.com/langgenius/dify/assets/13230914/5a17bdbe-097a-4100-8363-40255b70f6e3)
 
@@ -111,7 +111,7 @@ ités d'agent**:
     <td align="center">Agent</td>
     <td align="center">✅</td>
     <td align="center">✅</td>
-    <td align="center">✅</td>
+    <td align="center">❌</td>
     <td align="center">✅</td>
   </tr>
   <tr>

README_JA.md

@@ -2,7 +2,7 @@
 
 <p align="center">
   <a href="https://cloud.dify.ai">Dify Cloud</a> ·
-  <a href="https://docs.dify.ai/getting-started/install-self-hosted">自己ホスティング</a> ·
+  <a href="https://docs.dify.ai/getting-started/install-self-hosted">セルフホスト</a> ·
   <a href="https://docs.dify.ai">ドキュメント</a> ·
   <a href="https://cal.com/guchenhe/dify-demo">デモのスケジュール</a>
 </p>
@@ -54,10 +54,8 @@ DifyはオープンソースのLLMアプリケーション開発プラットフ
 
 
 
-**2. 網羅的なモデルサポート**: 
-  数百のプロプライエタリ/オープンソースのLLMと、数十の推論プロバイダーおよびセルフホスティングソリューションとのシームレスな統合を提供します。GPT、Mistral、Llama2、およびOpenAI API互換のモデルをカバーします。サポートされているモデルプロバイダーの完全なリストは[こちら](https://docs
-
-.dify.ai/getting-started/readme/model-providers)をご覧ください。
+**2. 包括的なモデルサポート**: 
+  数百のプロプライエタリ/オープンソースのLLMと、数十の推論プロバイダーおよびセルフホスティングソリューションとのシームレスな統合を提供します。GPT、Mistral、Llama3、およびOpenAI API互換のモデルをカバーします。サポートされているモデルプロバイダーの完全なリストは[こちら](https://docs.dify.ai/getting-started/readme/model-providers)をご覧ください。
 
 ![providers-v5](https://github.com/langgenius/dify/assets/13230914/5a17bdbe-097a-4100-8363-40255b70f6e3)
 
@@ -96,9 +94,9 @@ 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
   </tr>
   <tr>
     <td align="center">サポートされているLLM</td>
-    <td align="center">豊富なバリエーション</td>
-    <td align="center">豊富なバリエーション</td>
-    <td align="center">豊富なバリエーション</td>
+    <td align="center">バリエーション豊富</td>
+    <td align="center">バリエーション豊富</td>
+    <td align="center">バリエーション豊富</td>
     <td align="center">OpenAIのみ</td>
   </tr>
   <tr>
@@ -112,7 +110,7 @@ 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
     <td align="center">エージェント</td>
     <td align="center">✅</td>
     <td align="center">✅</td>
-    <td align="center">✅</td>
+    <td align="center">❌</td>
     <td align="center">✅</td>
   </tr>
   <tr>
@@ -148,36 +146,34 @@ 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
 ## Difyの使用方法
 
 - **クラウド </br>**
-[こちら](https://dify.ai)のDify Cloudサービスを利用して、セットアップが不要で誰でも試すことができます。サンドボックスプランでは、200回の無料のGPT-4呼び出しが含まれています。
+[こちら](https://dify.ai)のDify Cloudサービスを利用して、セットアップ不要で試すことができます。サンドボックスプランには、200回の無料のGPT-4呼び出しが含まれています。
 
 - **Dify Community Editionのセルフホスティング</br>**
-この[スターターガイド](#quick-start)を使用して、環境でDifyをすばやく実行できます。
-さらなる参照や詳細な手順については、[ドキュメント](https://docs.dify.ai)をご覧ください。
+この[スターターガイド](#quick-start)を使用して、ローカル環境でDifyを簡単に実行できます。
+さらなる参考資料や詳細な手順については、[ドキュメント](https://docs.dify.ai)をご覧ください。
 
 - **エンタープライズ/組織向けのDify</br>**
-追加のエンタープライズ向け機能を提供しています。[こちらからミーティ
-
-ングを予約](https://cal.com/guchenhe/30min)したり、[メールを送信](mailto:business@dify.ai?subject=[GitHub]Business%20License%20Inquiry)してエンタープライズのニーズについて相談してください。 </br>
+追加のエンタープライズ向け機能を提供しています。[こちらからミーティングを予約](https://cal.com/guchenhe/30min)したり、[メールを送信](mailto:business@dify.ai?subject=[GitHub]Business%20License%20Inquiry)してエンタープライズのニーズについて相談してください。 </br>
   > AWSを使用しているスタートアップや中小企業の場合は、[AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6)のDify Premiumをチェックして、ワンクリックで独自のAWS VPCにデプロイできます。カスタムロゴとブランディングでアプリを作成するオプションを備えた手頃な価格のAMIオファリングです。
 
 
-## 先を見る
+## 最新の情報を入手
 
-GitHubでDifyにスターを付け、新しいリリースをすぐに通知されます。
+GitHub上でDifyにスターを付けることで、Difyに関する新しいニュースを受け取れます。
 
 ![star-us](https://github.com/langgenius/dify/assets/13230914/b823edc1-6388-4e25-ad45-2f6b187adbb4)
 
 
 
 ## クイックスタート
-> Difyをインストールする前に、マシンが以下の最小システム要件を満たしていることを確認してください：
+> Difyをインストールする前に、お使いのマシンが以下の最小システム要件を満たしていることを確認してください：
 > 
 >- CPU >= 2コア
 >- RAM >= 4GB
 
 </br>
 
-Difyサーバーを起動する最も簡単な方法は、当社の[docker-compose.yml](docker/docker-compose.yaml)ファイルを実行することです。インストールコマンドを実行する前に、マシンに[Docker](https://docs.docker.com/get-docker/)と[Docker Compose](https://docs.docker.com/compose/install/)がインストールされていることを確認してください。
+Difyサーバーを起動する最も簡単な方法は、[docker-compose.yml](docker/docker-compose.yaml)ファイルを実行することです。インストールコマンドを実行する前に、マシンに[Docker](https://docs.docker.com/get-docker/)と[Docker Compose](https://docs.docker.com/compose/install/)がインストールされていることを確認してください。
 
 ```bash
 cd docker
@@ -220,7 +216,7 @@ docker compose up -d
 * [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。
 * [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。
 
-または、直接チームメンバーとミーティングをスケジュールします：
+または、直接チームメンバーとミーティングをスケジュール：
 
 <table>
   <tr>
@@ -231,7 +227,7 @@ docker compose up -d
     <td><a href='https://cal.com
 
 /guchenhe/30min'>ミーティング</a></td>
-    <td>無料の30分間のミーティングをスケジュールしてください。</td>
+    <td>無料の30分間のミーティングをスケジュール</td>
   </tr>
   <tr>
     <td><a href='mailto:support@dify.ai?subject=[GitHub]Technical%20Support'>技術サポート</a></td>
@@ -246,4 +242,4 @@ docker compose up -d
 
 ## ライセンス
 
-プロジェクトはMITライセンスの下で利用可能です。[LICENSE](LICENSE)をご参照ください。
+このリポジトリは、Dify Open Source License にいくつかの追加制限を加えた[Difyオープンソースライセンス](LICENSE)の下で利用可能です。

README_KL.md

@@ -54,7 +54,7 @@ Dify is an open-source LLM app development platform. Its intuitive interface com
 
 
 **2. Comprehensive model support**: 
-  Seamless integration with hundreds of proprietary / open-source LLMs from dozens of inference providers and self-hosted solutions, covering GPT, Mistral, Llama2, and any OpenAI API-compatible models. A full list of supported model providers can be found [here](https://docs.dify.ai/getting-started/readme/model-providers).
+  Seamless integration with hundreds of proprietary / open-source LLMs from dozens of inference providers and self-hosted solutions, covering GPT, Mistral, Llama3, and any OpenAI API-compatible models. A full list of supported model providers can be found [here](https://docs.dify.ai/getting-started/readme/model-providers).
 
 ![providers-v5](https://github.com/langgenius/dify/assets/13230914/5a17bdbe-097a-4100-8363-40255b70f6e3)
 
@@ -111,7 +111,7 @@ Dify is an open-source LLM app development platform. Its intuitive interface com
     <td align="center">Agent</td>
     <td align="center">✅</td>
     <td align="center">✅</td>
-    <td align="center">✅</td>
+    <td align="center">❌</td>
     <td align="center">✅</td>
   </tr>
   <tr>

api/.env.example

@@ -1,6 +1,3 @@
-# Server Edition
-EDITION=SELF_HOSTED
-
 # Your App secret key will be used for securely signing the session cookie
 # Make sure you are changing this key for your deployment with a strong key.
 # You can generate a strong key using `openssl rand -base64 42`.
@@ -52,12 +49,23 @@ AZURE_BLOB_ACCOUNT_NAME=your-account-name
 AZURE_BLOB_ACCOUNT_KEY=your-account-key
 AZURE_BLOB_CONTAINER_NAME=yout-container-name
 AZURE_BLOB_ACCOUNT_URL=https://<your_account_name>.blob.core.windows.net
+# Aliyun oss Storage configuration
+ALIYUN_OSS_BUCKET_NAME=your-bucket-name
+ALIYUN_OSS_ACCESS_KEY=your-access-key
+ALIYUN_OSS_SECRET_KEY=your-secret-key
+ALIYUN_OSS_ENDPOINT=your-endpoint
+ALIYUN_OSS_AUTH_VERSION=v1
+ALIYUN_OSS_REGION=your-region
+
+# Google Storage configuration
+GOOGLE_STORAGE_BUCKET_NAME=yout-bucket-name
+GOOGLE_STORAGE_SERVICE_ACCOUNT_JSON=your-google-service-account-json-base64-string
 
 # 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, relyt
+# Vector database configuration, support: weaviate, qdrant, milvus, relyt, pgvecto_rs, pgvector
 VECTOR_STORE=weaviate
 
 # Weaviate configuration
@@ -70,6 +78,8 @@ WEAVIATE_BATCH_SIZE=100
 QDRANT_URL=http://localhost:6333
 QDRANT_API_KEY=difyai123456
 QDRANT_CLIENT_TIMEOUT=20
+QDRANT_GRPC_ENABLED=false
+QDRANT_GRPC_PORT=6334
 
 # Milvus configuration
 MILVUS_HOST=127.0.0.1
@@ -85,6 +95,20 @@ RELYT_USER=postgres
 RELYT_PASSWORD=postgres
 RELYT_DATABASE=postgres
 
+# PGVECTO_RS configuration
+PGVECTO_RS_HOST=localhost
+PGVECTO_RS_PORT=5431
+PGVECTO_RS_USER=postgres
+PGVECTO_RS_PASSWORD=difyai123456
+PGVECTO_RS_DATABASE=postgres
+
+# PGVector configuration
+PGVECTOR_HOST=127.0.0.1
+PGVECTOR_PORT=5433
+PGVECTOR_USER=postgres
+PGVECTOR_PASSWORD=postgres
+PGVECTOR_DATABASE=postgres
+
 # Upload configuration
 UPLOAD_FILE_SIZE_LIMIT=15
 UPLOAD_FILE_BATCH_LIMIT=5
@@ -118,25 +142,6 @@ NOTION_CLIENT_SECRET=you-client-secret
 NOTION_CLIENT_ID=you-client-id
 NOTION_INTERNAL_SECRET=you-internal-secret
 
-# Hosted Model Credentials
-HOSTED_OPENAI_API_KEY=
-HOSTED_OPENAI_API_BASE=
-HOSTED_OPENAI_API_ORGANIZATION=
-HOSTED_OPENAI_TRIAL_ENABLED=false
-HOSTED_OPENAI_QUOTA_LIMIT=200
-HOSTED_OPENAI_PAID_ENABLED=false
-
-HOSTED_AZURE_OPENAI_ENABLED=false
-HOSTED_AZURE_OPENAI_API_KEY=
-HOSTED_AZURE_OPENAI_API_BASE=
-HOSTED_AZURE_OPENAI_QUOTA_LIMIT=200
-
-HOSTED_ANTHROPIC_API_BASE=
-HOSTED_ANTHROPIC_API_KEY=
-HOSTED_ANTHROPIC_TRIAL_ENABLED=false
-HOSTED_ANTHROPIC_QUOTA_LIMIT=600000
-HOSTED_ANTHROPIC_PAID_ENABLED=false
-
 ETL_TYPE=dify
 UNSTRUCTURED_API_URL=
 
@@ -160,3 +165,13 @@ CODE_MAX_NUMBER_ARRAY_LENGTH=1000
 # API Tool configuration
 API_TOOL_DEFAULT_CONNECT_TIMEOUT=10
 API_TOOL_DEFAULT_READ_TIMEOUT=60
+
+# HTTP Node configuration
+HTTP_REQUEST_MAX_CONNECT_TIMEOUT=300
+HTTP_REQUEST_MAX_READ_TIMEOUT=600
+HTTP_REQUEST_MAX_WRITE_TIMEOUT=600
+HTTP_REQUEST_NODE_MAX_BINARY_SIZE=10485760 # 10MB
+HTTP_REQUEST_NODE_MAX_TEXT_SIZE=1048576 # 1MB
+
+# Log file path
+LOG_FILE=

api/app.py

@@ -1,28 +1,28 @@
 import os
-import sys
-from logging.handlers import RotatingFileHandler
 
 if not os.environ.get("DEBUG") or os.environ.get("DEBUG").lower() != 'true':
     from gevent import monkey
 
     monkey.patch_all()
-    # if os.environ.get("VECTOR_STORE") == 'milvus':
+
     import grpc.experimental.gevent
 
     grpc.experimental.gevent.init_gevent()
 
 import json
 import logging
+import sys
 import threading
 import time
 import warnings
+from logging.handlers import RotatingFileHandler
 
 from flask import Flask, Response, request
 from flask_cors import CORS
 from werkzeug.exceptions import Unauthorized
 
 from commands import register_commands
-from config import CloudEditionConfig, Config
+from config import Config
 
 # DO NOT REMOVE BELOW
 from events import event_handlers
@@ -75,16 +75,9 @@ config_type = os.getenv('EDITION', default='SELF_HOSTED')  # ce edition first
 # ----------------------------
 
 
-def create_app(test_config=None) -> Flask:
+def create_app() -> Flask:
     app = DifyApp(__name__)
-
-    if test_config:
-        app.config.from_object(test_config)
-    else:
-        if config_type == "CLOUD":
-            app.config.from_object(CloudEditionConfig())
-        else:
-            app.config.from_object(Config())
+    app.config.from_object(Config())
 
     app.secret_key = app.config['SECRET_KEY']
 
@@ -101,6 +94,7 @@ def create_app(test_config=None) -> Flask:
             ),
             logging.StreamHandler(sys.stdout)
         ]
+
     logging.basicConfig(
         level=app.config.get('LOG_LEVEL'),
         format=app.config.get('LOG_FORMAT'),

api/commands.py

@@ -305,6 +305,14 @@ def migrate_knowledge_vector_database():
                         "vector_store": {"class_prefix": collection_name}
                     }
                     dataset.index_struct = json.dumps(index_struct_dict)
+                elif vector_type == "pgvector":
+                    dataset_id = dataset.id
+                    collection_name = Dataset.gen_collection_name_by_id(dataset_id)
+                    index_struct_dict = {
+                        "type": 'pgvector',
+                        "vector_store": {"class_prefix": collection_name}
+                    }
+                    dataset.index_struct = json.dumps(index_struct_dict)
                 else:
                     raise ValueError(f"Vector store {config.get('VECTOR_STORE')} is not supported.")
 

api/config.py

@@ -5,6 +5,7 @@ import dotenv
 dotenv.load_dotenv()
 
 DEFAULTS = {
+    'EDITION': 'SELF_HOSTED',
     'DB_USERNAME': 'postgres',
     'DB_PASSWORD': '',
     'DB_HOST': 'localhost',
@@ -36,6 +37,8 @@ DEFAULTS = {
     'WEAVIATE_GRPC_ENABLED': 'True',
     'WEAVIATE_BATCH_SIZE': 100,
     'QDRANT_CLIENT_TIMEOUT': 20,
+    'QDRANT_GRPC_ENABLED': 'False',
+    'QDRANT_GRPC_PORT': '6334',
     'CELERY_BACKEND': 'database',
     'LOG_LEVEL': 'INFO',
     'LOG_FILE': '',
@@ -104,9 +107,9 @@ class Config:
         # ------------------------
         # General Configurations.
         # ------------------------
-        self.CURRENT_VERSION = "0.6.4"
+        self.CURRENT_VERSION = "0.6.8"
         self.COMMIT_SHA = get_env('COMMIT_SHA')
-        self.EDITION = "SELF_HOSTED"
+        self.EDITION = get_env('EDITION')
         self.DEPLOY_ENV = get_env('DEPLOY_ENV')
         self.TESTING = False
         self.LOG_LEVEL = get_env('LOG_LEVEL')
@@ -208,10 +211,18 @@ class Config:
         self.AZURE_BLOB_ACCOUNT_KEY = get_env('AZURE_BLOB_ACCOUNT_KEY')
         self.AZURE_BLOB_CONTAINER_NAME = get_env('AZURE_BLOB_CONTAINER_NAME')
         self.AZURE_BLOB_ACCOUNT_URL = get_env('AZURE_BLOB_ACCOUNT_URL')
+        self.ALIYUN_OSS_BUCKET_NAME=get_env('ALIYUN_OSS_BUCKET_NAME')
+        self.ALIYUN_OSS_ACCESS_KEY=get_env('ALIYUN_OSS_ACCESS_KEY')
+        self.ALIYUN_OSS_SECRET_KEY=get_env('ALIYUN_OSS_SECRET_KEY')
+        self.ALIYUN_OSS_ENDPOINT=get_env('ALIYUN_OSS_ENDPOINT')
+        self.ALIYUN_OSS_REGION=get_env('ALIYUN_OSS_REGION')
+        self.ALIYUN_OSS_AUTH_VERSION=get_env('ALIYUN_OSS_AUTH_VERSION')
+        self.GOOGLE_STORAGE_BUCKET_NAME = get_env('GOOGLE_STORAGE_BUCKET_NAME')
+        self.GOOGLE_STORAGE_SERVICE_ACCOUNT_JSON_BASE64 = get_env('GOOGLE_STORAGE_SERVICE_ACCOUNT_JSON_BASE64')
 
         # ------------------------
         # Vector Store Configurations.
-        # Currently, only support: qdrant, milvus, zilliz, weaviate, relyt
+        # Currently, only support: qdrant, milvus, zilliz, weaviate, relyt, pgvector
         # ------------------------
         self.VECTOR_STORE = get_env('VECTOR_STORE')
         self.KEYWORD_STORE = get_env('KEYWORD_STORE')
@@ -219,6 +230,8 @@ class Config:
         self.QDRANT_URL = get_env('QDRANT_URL')
         self.QDRANT_API_KEY = get_env('QDRANT_API_KEY')
         self.QDRANT_CLIENT_TIMEOUT = get_env('QDRANT_CLIENT_TIMEOUT')
+        self.QDRANT_GRPC_ENABLED = get_env('QDRANT_GRPC_ENABLED')
+        self.QDRANT_GRPC_PORT = get_env('QDRANT_GRPC_PORT')
 
         # milvus / zilliz setting
         self.MILVUS_HOST = get_env('MILVUS_HOST')
@@ -241,6 +254,20 @@ class Config:
         self.RELYT_PASSWORD = get_env('RELYT_PASSWORD')
         self.RELYT_DATABASE = get_env('RELYT_DATABASE')
 
+        # pgvecto rs settings
+        self.PGVECTO_RS_HOST = get_env('PGVECTO_RS_HOST')
+        self.PGVECTO_RS_PORT = get_env('PGVECTO_RS_PORT')
+        self.PGVECTO_RS_USER = get_env('PGVECTO_RS_USER')
+        self.PGVECTO_RS_PASSWORD = get_env('PGVECTO_RS_PASSWORD')
+        self.PGVECTO_RS_DATABASE = get_env('PGVECTO_RS_DATABASE')
+
+        # pgvector settings
+        self.PGVECTOR_HOST = get_env('PGVECTOR_HOST')
+        self.PGVECTOR_PORT = get_env('PGVECTOR_PORT')
+        self.PGVECTOR_USER = get_env('PGVECTOR_USER')
+        self.PGVECTOR_PASSWORD = get_env('PGVECTOR_PASSWORD')
+        self.PGVECTOR_DATABASE = get_env('PGVECTOR_DATABASE')
+
         # ------------------------
         # Mail Configurations.
         # ------------------------
@@ -256,7 +283,7 @@ class Config:
         self.SMTP_USE_TLS = get_bool_env('SMTP_USE_TLS')
         
         # ------------------------
-        # Workpace Configurations.
+        # Workspace Configurations.
         # ------------------------
         self.INVITE_EXPIRY_HOURS = int(get_env('INVITE_EXPIRY_HOURS'))
 
@@ -295,6 +322,12 @@ class Config:
         # ------------------------
         # Platform Configurations.
         # ------------------------
+        self.GITHUB_CLIENT_ID = get_env('GITHUB_CLIENT_ID')
+        self.GITHUB_CLIENT_SECRET = get_env('GITHUB_CLIENT_SECRET')
+        self.GOOGLE_CLIENT_ID = get_env('GOOGLE_CLIENT_ID')
+        self.GOOGLE_CLIENT_SECRET = get_env('GOOGLE_CLIENT_SECRET')
+        self.OAUTH_REDIRECT_PATH = get_env('OAUTH_REDIRECT_PATH')
+
         self.HOSTED_OPENAI_API_KEY = get_env('HOSTED_OPENAI_API_KEY')
         self.HOSTED_OPENAI_API_BASE = get_env('HOSTED_OPENAI_API_BASE')
         self.HOSTED_OPENAI_API_ORGANIZATION = get_env('HOSTED_OPENAI_API_ORGANIZATION')
@@ -341,17 +374,3 @@ class Config:
 
         self.KEYWORD_DATA_SOURCE_TYPE = get_env('KEYWORD_DATA_SOURCE_TYPE')
         self.ENTERPRISE_ENABLED = get_bool_env('ENTERPRISE_ENABLED')
-
-
-class CloudEditionConfig(Config):
-
-    def __init__(self):
-        super().__init__()
-
-        self.EDITION = "CLOUD"
-
-        self.GITHUB_CLIENT_ID = get_env('GITHUB_CLIENT_ID')
-        self.GITHUB_CLIENT_SECRET = get_env('GITHUB_CLIENT_SECRET')
-        self.GOOGLE_CLIENT_ID = get_env('GOOGLE_CLIENT_ID')
-        self.GOOGLE_CLIENT_SECRET = get_env('GOOGLE_CLIENT_SECRET')
-        self.OAUTH_REDIRECT_PATH = get_env('OAUTH_REDIRECT_PATH')

api/constants/languages.py

@@ -1,10 +1,11 @@
 
 
-languages = ['en-US', 'zh-Hans', 'pt-BR', 'es-ES', 'fr-FR', 'de-DE', 'ja-JP', 'ko-KR', 'ru-RU', 'it-IT', 'uk-UA', 'vi-VN']
+languages = ['en-US', 'zh-Hans', 'zh-Hant', 'pt-BR', 'es-ES', 'fr-FR', 'de-DE', 'ja-JP', 'ko-KR', 'ru-RU', 'it-IT', 'uk-UA', 'vi-VN', 'pl-PL']
 
 language_timezone_mapping = {
     'en-US': 'America/New_York',
     'zh-Hans': 'Asia/Shanghai',
+    'zh-Hant': 'Asia/Taipei',
     'pt-BR': 'America/Sao_Paulo',
     'es-ES': 'Europe/Madrid',
     'fr-FR': 'Europe/Paris',
@@ -15,6 +16,7 @@ language_timezone_mapping = {
     'it-IT': 'Europe/Rome',
     'uk-UA': 'Europe/Kyiv',
     'vi-VN': 'Asia/Ho_Chi_Minh',
+    'pl-PL': 'Europe/Warsaw',
 }
 
 

api/controllers/console/__init__.py

@@ -53,5 +53,8 @@ from .explore import (
     workflow,
 )
 
+# Import tag controllers
+from .tag import tags
+
 # Import workspace controllers
 from .workspace import account, members, model_providers, models, tool_providers, workspace

api/controllers/console/app/app.py

@@ -1,17 +1,16 @@
 import json
+import uuid
 
 from flask_login import current_user
-from flask_restful import Resource, inputs, marshal_with, reqparse
-from werkzeug.exceptions import BadRequest, Forbidden
+from flask_restful import Resource, inputs, marshal, marshal_with, reqparse
+from werkzeug.exceptions import BadRequest, Forbidden, abort
 
 from controllers.console import api
 from controllers.console.app.wraps import get_app_model
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required, cloud_edition_billing_resource_check
-from core.agent.entities import AgentToolEntity
 from core.tools.tool_manager import ToolManager
 from core.tools.utils.configuration import ToolParameterConfigurationManager
-from extensions.ext_database import db
 from fields.app_fields import (
     app_detail_fields,
     app_detail_fields_with_site,
@@ -20,6 +19,7 @@ from fields.app_fields import (
 from libs.login import login_required
 from models.model import App, AppMode, AppModelConfig
 from services.app_service import AppService
+from services.tag_service import TagService
 
 ALLOW_CREATE_APP_MODES = ['chat', 'agent-chat', 'advanced-chat', 'workflow', 'completion']
 
@@ -29,21 +29,29 @@ class AppListApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    @marshal_with(app_pagination_fields)
     def get(self):
         """Get app list"""
+        def uuid_list(value):
+            try:
+                return [str(uuid.UUID(v)) for v in value.split(',')]
+            except ValueError:
+                abort(400, message="Invalid UUID format in tag_ids.")
         parser = reqparse.RequestParser()
         parser.add_argument('page', type=inputs.int_range(1, 99999), required=False, default=1, location='args')
         parser.add_argument('limit', type=inputs.int_range(1, 100), required=False, default=20, location='args')
         parser.add_argument('mode', type=str, choices=['chat', 'workflow', 'agent-chat', 'channel', 'all'], default='all', location='args', required=False)
         parser.add_argument('name', type=str, location='args', required=False)
+        parser.add_argument('tag_ids', type=uuid_list, location='args', required=False)
+
         args = parser.parse_args()
 
         # get app list
         app_service = AppService()
         app_pagination = app_service.get_paginate_apps(current_user.current_tenant_id, args)
+        if not app_pagination:
+            return {'data': [], 'total': 0, 'page': 1, 'limit': 20, 'has_more': False}
 
-        return app_pagination
+        return marshal(app_pagination, app_pagination_fields)
 
     @setup_required
     @login_required
@@ -108,43 +116,9 @@ class AppApi(Resource):
     @marshal_with(app_detail_fields_with_site)
     def get(self, app_model):
         """Get app detail"""
-        # get original app model config
-        if app_model.mode == AppMode.AGENT_CHAT.value or app_model.is_agent:
-            model_config: AppModelConfig = app_model.app_model_config
-            agent_mode = model_config.agent_mode_dict
-            # decrypt agent tool parameters if it's secret-input
-            for tool in agent_mode.get('tools') or []:
-                if not isinstance(tool, dict) or len(tool.keys()) <= 3:
-                    continue
-                agent_tool_entity = AgentToolEntity(**tool)
-                # get tool
-                try:
-                    tool_runtime = ToolManager.get_agent_tool_runtime(
-                        tenant_id=current_user.current_tenant_id,
-                        agent_tool=agent_tool_entity,
-                    )
-                    manager = ToolParameterConfigurationManager(
-                        tenant_id=current_user.current_tenant_id,
-                        tool_runtime=tool_runtime,
-                        provider_name=agent_tool_entity.provider_id,
-                        provider_type=agent_tool_entity.provider_type,
-                    )
+        app_service = AppService()
 
-                    # get decrypted parameters
-                    if agent_tool_entity.tool_parameters:
-                        parameters = manager.decrypt_tool_parameters(agent_tool_entity.tool_parameters or {})
-                        masked_parameter = manager.mask_tool_parameters(parameters or {})
-                    else:
-                        masked_parameter = {}
-
-                    # override tool parameters
-                    tool['tool_parameters'] = masked_parameter
-                except Exception as e:
-                    pass
-
-            # override agent mode
-            model_config.agent_mode = json.dumps(agent_mode)
-            db.session.commit()
+        app_model = app_service.get_app(app_model)
 
         return app_model
 

api/controllers/console/app/error.py

@@ -91,3 +91,9 @@ class DraftWorkflowNotExist(BaseHTTPException):
     error_code = 'draft_workflow_not_exist'
     description = "Draft workflow need to be initialized."
     code = 400
+
+
+class DraftWorkflowNotSync(BaseHTTPException):
+    error_code = 'draft_workflow_not_sync'
+    description = "Workflow graph might have been modified, please refresh and resubmit."
+    code = 400

api/controllers/console/app/model_config.py

@@ -57,6 +57,7 @@ class ModelConfigResource(Resource):
                 try:
                     tool_runtime = ToolManager.get_agent_tool_runtime(
                         tenant_id=current_user.current_tenant_id,
+                        app_id=app_model.id,
                         agent_tool=agent_tool_entity,
                     )
                     manager = ToolParameterConfigurationManager(
@@ -64,6 +65,7 @@ class ModelConfigResource(Resource):
                         tool_runtime=tool_runtime,
                         provider_name=agent_tool_entity.provider_id,
                         provider_type=agent_tool_entity.provider_type,
+                        identity_id=f'AGENT.{app_model.id}'
                     )
                 except Exception as e:
                     continue
@@ -94,6 +96,7 @@ class ModelConfigResource(Resource):
                     try:
                         tool_runtime = ToolManager.get_agent_tool_runtime(
                             tenant_id=current_user.current_tenant_id,
+                            app_id=app_model.id,
                             agent_tool=agent_tool_entity,
                         )
                     except Exception as e:
@@ -104,6 +107,7 @@ class ModelConfigResource(Resource):
                     tool_runtime=tool_runtime,
                     provider_name=agent_tool_entity.provider_id,
                     provider_type=agent_tool_entity.provider_type,
+                    identity_id=f'AGENT.{app_model.id}'
                 )
                 manager.delete_tool_parameters_cache()
 
@@ -111,9 +115,11 @@ class ModelConfigResource(Resource):
                 if agent_tool_entity.tool_parameters:
                     if key not in masked_parameter_map:
                         continue
-
-                    if agent_tool_entity.tool_parameters == masked_parameter_map[key]:
-                        agent_tool_entity.tool_parameters = parameter_map[key]
+                    
+                    for masked_key, masked_value in masked_parameter_map[key].items():
+                        if masked_key in agent_tool_entity.tool_parameters and \
+                                agent_tool_entity.tool_parameters[masked_key] == masked_value:
+                            agent_tool_entity.tool_parameters[masked_key] = parameter_map[key].get(masked_key)
 
                 # encrypt parameters
                 if agent_tool_entity.tool_parameters:

api/controllers/console/app/workflow.py

@@ -7,7 +7,7 @@ from werkzeug.exceptions import InternalServerError, NotFound
 
 import services
 from controllers.console import api
-from controllers.console.app.error import ConversationCompletedError, DraftWorkflowNotExist
+from controllers.console.app.error import ConversationCompletedError, DraftWorkflowNotExist, DraftWorkflowNotSync
 from controllers.console.app.wraps import get_app_model
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required
@@ -20,6 +20,7 @@ from libs.helper import TimestampField, uuid_value
 from libs.login import current_user, login_required
 from models.model import App, AppMode
 from services.app_generate_service import AppGenerateService
+from services.errors.app import WorkflowHashNotEqualError
 from services.workflow_service import WorkflowService
 
 logger = logging.getLogger(__name__)
@@ -59,6 +60,7 @@ class DraftWorkflowApi(Resource):
             parser = reqparse.RequestParser()
             parser.add_argument('graph', type=dict, required=True, nullable=False, location='json')
             parser.add_argument('features', type=dict, required=True, nullable=False, location='json')
+            parser.add_argument('hash', type=str, required=False, location='json')
             args = parser.parse_args()
         elif 'text/plain' in content_type:
             try:
@@ -71,7 +73,8 @@ class DraftWorkflowApi(Resource):
 
                 args = {
                     'graph': data.get('graph'),
-                    'features': data.get('features')
+                    'features': data.get('features'),
+                    'hash': data.get('hash')
                 }
             except json.JSONDecodeError:
                 return {'message': 'Invalid JSON data'}, 400
@@ -79,15 +82,21 @@ class DraftWorkflowApi(Resource):
             abort(415)
 
         workflow_service = WorkflowService()
-        workflow = workflow_service.sync_draft_workflow(
-            app_model=app_model,
-            graph=args.get('graph'),
-            features=args.get('features'),
-            account=current_user
-        )
+
+        try:
+            workflow = workflow_service.sync_draft_workflow(
+                app_model=app_model,
+                graph=args.get('graph'),
+                features=args.get('features'),
+                unique_hash=args.get('hash'),
+                account=current_user
+            )
+        except WorkflowHashNotEqualError:
+            raise DraftWorkflowNotSync()
 
         return {
             "result": "success",
+            "hash": workflow.unique_hash,
             "updated_at": TimestampField().format(workflow.updated_at or workflow.created_at)
         }
 

api/controllers/console/app/workflow_statistic.py

@@ -227,7 +227,7 @@ class WorkflowAverageAppInteractionStatistic(Resource):
                     {{start}}
                     {{end}}
                 GROUP BY date, c.created_by) sub
-            GROUP BY sub.created_by, sub.date
+            GROUP BY sub.date
             """
         arg_dict = {'tz': account.timezone, 'app_id': app_model.id, 'triggered_from': WorkflowRunTriggeredFrom.APP_RUN.value}
 

api/controllers/console/datasets/datasets.py

@@ -48,11 +48,14 @@ class DatasetListApi(Resource):
         limit = request.args.get('limit', default=20, type=int)
         ids = request.args.getlist('ids')
         provider = request.args.get('provider', default="vendor")
+        search = request.args.get('keyword', default=None, type=str)
+        tag_ids = request.args.getlist('tag_ids')
+
         if ids:
             datasets, total = DatasetService.get_datasets_by_ids(ids, current_user.current_tenant_id)
         else:
             datasets, total = DatasetService.get_datasets(page, limit, provider,
-                                                          current_user.current_tenant_id, current_user)
+                                                          current_user.current_tenant_id, current_user, search, tag_ids)
 
         # check embedding setting
         provider_manager = ProviderManager()
@@ -184,6 +187,10 @@ class DatasetApi(Resource):
                             help='Invalid indexing technique.')
         parser.add_argument('permission', type=str, location='json', choices=(
             'only_me', 'all_team_members'), help='Invalid permission.')
+        parser.add_argument('embedding_model', type=str,
+                            location='json', help='Invalid embedding model.')
+        parser.add_argument('embedding_model_provider', type=str,
+                            location='json', help='Invalid embedding model provider.')
         parser.add_argument('retrieval_model', type=dict, location='json', help='Invalid retrieval model.')
         args = parser.parse_args()
 
@@ -469,13 +476,13 @@ class DatasetRetrievalSettingApi(Resource):
     @account_initialization_required
     def get(self):
         vector_type = current_app.config['VECTOR_STORE']
-        if vector_type == 'milvus':
+        if vector_type in {"milvus", "relyt", "pgvector", "pgvecto_rs"}:
             return {
                 'retrieval_method': [
                     'semantic_search'
                 ]
             }
-        elif vector_type == 'qdrant' or vector_type == 'weaviate':
+        elif vector_type in {"qdrant", "weaviate"}:
             return {
                 'retrieval_method': [
                     'semantic_search', 'full_text_search', 'hybrid_search'
@@ -490,14 +497,13 @@ class DatasetRetrievalSettingMockApi(Resource):
     @login_required
     @account_initialization_required
     def get(self, vector_type):
-
-        if vector_type == 'milvus':
+        if vector_type in {'milvus', 'relyt', 'pgvector'}:
             return {
                 'retrieval_method': [
                     'semantic_search'
                 ]
             }
-        elif vector_type == 'qdrant' or vector_type == 'weaviate':
+        elif vector_type in {'qdrant', 'weaviate'}:
             return {
                 'retrieval_method': [
                     'semantic_search', 'full_text_search', 'hybrid_search'
@@ -506,10 +512,27 @@ class DatasetRetrievalSettingMockApi(Resource):
         else:
             raise ValueError("Unsupported vector db type.")
 
+class DatasetErrorDocs(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self, dataset_id):
+        dataset_id_str = str(dataset_id)
+        dataset = DatasetService.get_dataset(dataset_id_str)
+        if dataset is None:
+            raise NotFound("Dataset not found.")
+        results = DocumentService.get_error_documents_by_dataset_id(dataset_id_str)
+
+        return {
+            'data': [marshal(item, document_status_fields) for item in results],
+            'total': len(results)
+        }, 200
+
 
 api.add_resource(DatasetListApi, '/datasets')
 api.add_resource(DatasetApi, '/datasets/<uuid:dataset_id>')
 api.add_resource(DatasetQueryApi, '/datasets/<uuid:dataset_id>/queries')
+api.add_resource(DatasetErrorDocs, '/datasets/<uuid:dataset_id>/error-docs')
 api.add_resource(DatasetIndexingEstimateApi, '/datasets/indexing-estimate')
 api.add_resource(DatasetRelatedAppListApi, '/datasets/<uuid:dataset_id>/related-apps')
 api.add_resource(DatasetIndexingStatusApi, '/datasets/<uuid:dataset_id>/indexing-status')

api/controllers/console/datasets/datasets_document.py

@@ -1,3 +1,4 @@
+import logging
 from datetime import datetime, timezone
 
 from flask import request
@@ -233,7 +234,7 @@ class DatasetDocumentListApi(Resource):
                             location='json')
         parser.add_argument('data_source', type=dict, required=False, location='json')
         parser.add_argument('process_rule', type=dict, required=False, location='json')
-        parser.add_argument('duplicate', type=bool, nullable=False, location='json')
+        parser.add_argument('duplicate', type=bool, default=True, nullable=False, location='json')
         parser.add_argument('original_document_id', type=str, required=False, location='json')
         parser.add_argument('doc_form', type=str, default='text_model', required=False, nullable=False, location='json')
         parser.add_argument('doc_language', type=str, default='English', required=False, nullable=False,
@@ -393,9 +394,6 @@ class DocumentBatchIndexingEstimateApi(DocumentResource):
     def get(self, dataset_id, batch):
         dataset_id = str(dataset_id)
         batch = str(batch)
-        dataset = DatasetService.get_dataset(dataset_id)
-        if dataset is None:
-            raise NotFound("Dataset not found.")
         documents = self.get_batch_documents(dataset_id, batch)
         response = {
             "tokens": 0,
@@ -883,6 +881,49 @@ class DocumentRecoverApi(DocumentResource):
         return {'result': 'success'}, 204
 
 
+class DocumentRetryApi(DocumentResource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self, dataset_id):
+        """retry document."""
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('document_ids', type=list, required=True, nullable=False,
+                            location='json')
+        args = parser.parse_args()
+        dataset_id = str(dataset_id)
+        dataset = DatasetService.get_dataset(dataset_id)
+        retry_documents = []
+        if not dataset:
+            raise NotFound('Dataset not found.')
+        for document_id in args['document_ids']:
+            try:
+                document_id = str(document_id)
+
+                document = DocumentService.get_document(dataset.id, document_id)
+
+                # 404 if document not found
+                if document is None:
+                    raise NotFound("Document Not Exists.")
+
+                # 403 if document is archived
+                if DocumentService.check_archived(document):
+                    raise ArchivedDocumentImmutableError()
+
+                # 400 if document is completed
+                if document.indexing_status == 'completed':
+                    raise DocumentAlreadyFinishedError()
+                retry_documents.append(document)
+            except Exception as e:
+                logging.error(f"Document {document_id} retry failed: {str(e)}")
+                continue
+        # retry document
+        DocumentService.retry_document(dataset_id, retry_documents)
+
+        return {'result': 'success'}, 204
+
+
 api.add_resource(GetProcessRuleApi, '/datasets/process-rule')
 api.add_resource(DatasetDocumentListApi,
                  '/datasets/<uuid:dataset_id>/documents')
@@ -908,3 +949,4 @@ api.add_resource(DocumentStatusApi,
                  '/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/status/<string:action>')
 api.add_resource(DocumentPauseApi, '/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/processing/pause')
 api.add_resource(DocumentRecoverApi, '/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/processing/resume')
+api.add_resource(DocumentRetryApi, '/datasets/<uuid:dataset_id>/retry')

api/controllers/console/tag/tags.py

@@ -0,0 +1,159 @@
+from flask import request
+from flask_login import current_user
+from flask_restful import Resource, marshal_with, reqparse
+from werkzeug.exceptions import Forbidden
+
+from controllers.console import api
+from controllers.console.setup import setup_required
+from controllers.console.wraps import account_initialization_required
+from fields.tag_fields import tag_fields
+from libs.login import login_required
+from models.model import Tag
+from services.tag_service import TagService
+
+
+def _validate_name(name):
+    if not name or len(name) < 1 or len(name) > 40:
+        raise ValueError('Name must be between 1 to 50 characters.')
+    return name
+
+
+class TagListApi(Resource):
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @marshal_with(tag_fields)
+    def get(self):
+        tag_type = request.args.get('type', type=str)
+        keyword = request.args.get('keyword', default=None, type=str)
+        tags = TagService.get_tags(tag_type, current_user.current_tenant_id, keyword)
+
+        return tags, 200
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self):
+        # The role of the current user in the ta table must be admin or owner
+        if not current_user.is_admin_or_owner:
+            raise Forbidden()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('name', nullable=False, required=True,
+                            help='Name must be between 1 to 50 characters.',
+                            type=_validate_name)
+        parser.add_argument('type', type=str, location='json',
+                            choices=Tag.TAG_TYPE_LIST,
+                            nullable=True,
+                            help='Invalid tag type.')
+        args = parser.parse_args()
+        tag = TagService.save_tags(args)
+
+        response = {
+            'id': tag.id,
+            'name': tag.name,
+            'type': tag.type,
+            'binding_count': 0
+        }
+
+        return response, 200
+
+
+class TagUpdateDeleteApi(Resource):
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def patch(self, tag_id):
+        tag_id = str(tag_id)
+        # The role of the current user in the ta table must be admin or owner
+        if not current_user.is_admin_or_owner:
+            raise Forbidden()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('name', nullable=False, required=True,
+                            help='Name must be between 1 to 50 characters.',
+                            type=_validate_name)
+        args = parser.parse_args()
+        tag = TagService.update_tags(args, tag_id)
+
+        binding_count = TagService.get_tag_binding_count(tag_id)
+
+        response = {
+            'id': tag.id,
+            'name': tag.name,
+            'type': tag.type,
+            'binding_count': binding_count
+        }
+
+        return response, 200
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def delete(self, tag_id):
+        tag_id = str(tag_id)
+        # The role of the current user in the ta table must be admin or owner
+        if not current_user.is_admin_or_owner:
+            raise Forbidden()
+
+        TagService.delete_tag(tag_id)
+
+        return 200
+
+
+class TagBindingCreateApi(Resource):
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self):
+        # The role of the current user in the ta table must be admin or owner
+        if not current_user.is_admin_or_owner:
+            raise Forbidden()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('tag_ids', type=list, nullable=False, required=True, location='json',
+                            help='Tag IDs is required.')
+        parser.add_argument('target_id', type=str, nullable=False, required=True, location='json',
+                            help='Target ID is required.')
+        parser.add_argument('type', type=str, location='json',
+                            choices=Tag.TAG_TYPE_LIST,
+                            nullable=True,
+                            help='Invalid tag type.')
+        args = parser.parse_args()
+        TagService.save_tag_binding(args)
+
+        return 200
+
+
+class TagBindingDeleteApi(Resource):
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self):
+        # The role of the current user in the ta table must be admin or owner
+        if not current_user.is_admin_or_owner:
+            raise Forbidden()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('tag_id', type=str, nullable=False, required=True,
+                            help='Tag ID is required.')
+        parser.add_argument('target_id', type=str, nullable=False, required=True,
+                            help='Target ID is required.')
+        parser.add_argument('type', type=str, location='json',
+                            choices=Tag.TAG_TYPE_LIST,
+                            nullable=True,
+                            help='Invalid tag type.')
+        args = parser.parse_args()
+        TagService.delete_tag_binding(args)
+
+        return 200
+
+
+api.add_resource(TagListApi, '/tags')
+api.add_resource(TagUpdateDeleteApi, '/tags/<uuid:tag_id>')
+api.add_resource(TagBindingCreateApi, '/tag-bindings/create')
+api.add_resource(TagBindingDeleteApi, '/tag-bindings/remove')

api/controllers/console/workspace/members.py

@@ -9,7 +9,7 @@ from controllers.console.wraps import account_initialization_required, cloud_edi
 from extensions.ext_database import db
 from fields.member_fields import account_with_role_list_fields
 from libs.login import login_required
-from models.account import Account
+from models.account import Account, TenantAccountRole
 from services.account_service import RegisterService, TenantService
 from services.errors.account import AccountAlreadyInTenantError
 
@@ -43,7 +43,7 @@ class MemberInviteEmailApi(Resource):
         invitee_emails = args['emails']
         invitee_role = args['role']
         interface_language = args['language']
-        if invitee_role not in ['admin', 'normal']:
+        if invitee_role not in [TenantAccountRole.ADMIN, TenantAccountRole.NORMAL]:
             return {'code': 'invalid-role', 'message': 'Invalid role'}, 400
 
         inviter = current_user

api/controllers/console/workspace/models.py

@@ -11,6 +11,7 @@ from core.model_runtime.entities.model_entities import ModelType
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.utils.encoders import jsonable_encoder
 from libs.login import login_required
+from models.account import TenantAccountRole
 from services.model_provider_service import ModelProviderService
 
 
@@ -94,7 +95,7 @@ class ModelProviderModelApi(Resource):
     @login_required
     @account_initialization_required
     def post(self, provider: str):
-        if current_user.current_tenant.current_role not in ['admin', 'owner']:
+        if not TenantAccountRole.is_privileged_role(current_user.current_tenant.current_role):
             raise Forbidden()
 
         tenant_id = current_user.current_tenant_id
@@ -125,7 +126,7 @@ class ModelProviderModelApi(Resource):
     @login_required
     @account_initialization_required
     def delete(self, provider: str):
-        if current_user.current_tenant.current_role not in ['admin', 'owner']:
+        if not TenantAccountRole.is_privileged_role(current_user.current_tenant.current_role):
             raise Forbidden()
 
         tenant_id = current_user.current_tenant_id

api/controllers/service_api/dataset/dataset.py

@@ -26,8 +26,11 @@ class DatasetApi(DatasetApiResource):
         page = request.args.get('page', default=1, type=int)
         limit = request.args.get('limit', default=20, type=int)
         provider = request.args.get('provider', default="vendor")
+        search = request.args.get('keyword', default=None, type=str)
+        tag_ids = request.args.getlist('tag_ids')
+
         datasets, total = DatasetService.get_datasets(page, limit, provider,
-                                                      tenant_id, current_user)
+                                                      tenant_id, current_user, search, tag_ids)
         # check embedding setting
         provider_manager = ProviderManager()
         configurations = provider_manager.get_configurations(

api/core/agent/base_agent_runner.py

@@ -163,6 +163,7 @@ class BaseAgentRunner(AppRunner):
         """
         tool_entity = ToolManager.get_agent_tool_runtime(
             tenant_id=self.tenant_id,
+            app_id=self.app_config.app_id,
             agent_tool=tool,
         )
         tool_entity.load_variables(self.variables_pool)

api/core/agent/cot_agent_runner.py

@@ -219,7 +219,7 @@ class CotAgentRunner(BaseAgentRunner, ABC):
                         tool_input={scratchpad.action.action_name: scratchpad.action.action_input},
                         thought=scratchpad.thought,
                         observation={scratchpad.action.action_name: tool_invoke_response},
-                        tool_invoke_meta=tool_invoke_meta.to_dict(),
+                        tool_invoke_meta={scratchpad.action.action_name: tool_invoke_meta.to_dict()},
                         answer=scratchpad.agent_response,
                         messages_ids=message_file_ids,
                         llm_usage=usage_dict['usage']

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

@@ -18,7 +18,7 @@ from core.workflow.entities.node_entities import SystemVariable
 from core.workflow.nodes.base_node import UserFrom
 from core.workflow.workflow_engine_manager import WorkflowEngineManager
 from extensions.ext_database import db
-from models.model import App, Conversation, Message
+from models.model import App, Conversation, EndUser, Message
 from models.workflow import Workflow
 
 logger = logging.getLogger(__name__)
@@ -56,6 +56,14 @@ class AdvancedChatAppRunner(AppRunner):
         query = application_generate_entity.query
         files = application_generate_entity.files
 
+        user_id = None
+        if application_generate_entity.invoke_from in [InvokeFrom.WEB_APP, InvokeFrom.SERVICE_API]:
+            end_user = db.session.query(EndUser).filter(EndUser.id == application_generate_entity.user_id).first()
+            if end_user:
+                user_id = end_user.session_id
+        else:
+            user_id = application_generate_entity.user_id
+
         # moderation
         if self.handle_input_moderation(
                 queue_manager=queue_manager,
@@ -98,7 +106,8 @@ class AdvancedChatAppRunner(AppRunner):
             system_inputs={
                 SystemVariable.QUERY: query,
                 SystemVariable.FILES: files,
-                SystemVariable.CONVERSATION: conversation.id,
+                SystemVariable.CONVERSATION_ID: conversation.id,
+                SystemVariable.USER_ID: user_id
             },
             callbacks=workflow_callbacks
         )

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

@@ -8,6 +8,8 @@ from core.app.entities.task_entities import (
     ChatbotAppStreamResponse,
     ErrorStreamResponse,
     MessageEndStreamResponse,
+    NodeFinishStreamResponse,
+    NodeStartStreamResponse,
     PingStreamResponse,
 )
 
@@ -111,6 +113,8 @@ class AdvancedChatAppGenerateResponseConverter(AppGenerateResponseConverter):
             if isinstance(sub_stream_response, ErrorStreamResponse):
                 data = cls._error_to_stream_response(sub_stream_response.err)
                 response_chunk.update(data)
+            elif isinstance(sub_stream_response, NodeStartStreamResponse | NodeFinishStreamResponse):
+                response_chunk.update(sub_stream_response.to_ignore_detail_dict())
             else:
                 response_chunk.update(sub_stream_response.to_dict())
 

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

@@ -28,9 +28,9 @@ from core.app.entities.task_entities import (
     AdvancedChatTaskState,
     ChatbotAppBlockingResponse,
     ChatbotAppStreamResponse,
+    ChatflowStreamGenerateRoute,
     ErrorStreamResponse,
     MessageEndStreamResponse,
-    StreamGenerateRoute,
     StreamResponse,
 )
 from core.app.task_pipeline.based_generate_task_pipeline import BasedGenerateTaskPipeline
@@ -84,13 +84,19 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
         """
         super().__init__(application_generate_entity, queue_manager, user, stream)
 
+        if isinstance(self._user, EndUser):
+            user_id = self._user.session_id
+        else:
+            user_id = self._user.id
+
         self._workflow = workflow
         self._conversation = conversation
         self._message = message
         self._workflow_system_variables = {
             SystemVariable.QUERY: message.query,
             SystemVariable.FILES: application_generate_entity.files,
-            SystemVariable.CONVERSATION: conversation.id,
+            SystemVariable.CONVERSATION_ID: conversation.id,
+            SystemVariable.USER_ID: user_id
         }
 
         self._task_state = AdvancedChatTaskState(
@@ -337,7 +343,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
             **extras
         )
 
-    def _get_stream_generate_routes(self) -> dict[str, StreamGenerateRoute]:
+    def _get_stream_generate_routes(self) -> dict[str, ChatflowStreamGenerateRoute]:
         """
         Get stream generate routes.
         :return:
@@ -360,7 +366,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                 continue
 
             for start_node_id in start_node_ids:
-                stream_generate_routes[start_node_id] = StreamGenerateRoute(
+                stream_generate_routes[start_node_id] = ChatflowStreamGenerateRoute(
                     answer_node_id=answer_node_id,
                     generate_route=generate_route
                 )
@@ -424,15 +430,14 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
             for route_chunk in route_chunks:
                 if route_chunk.type == 'text':
                     route_chunk = cast(TextGenerateRouteChunk, route_chunk)
-                    for token in route_chunk.text:
-                        # handle output moderation chunk
-                        should_direct_answer = self._handle_output_moderation_chunk(token)
-                        if should_direct_answer:
-                            continue
 
-                        self._task_state.answer += token
-                        yield self._message_to_stream_response(token, self._message.id)
-                        time.sleep(0.01)
+                    # handle output moderation chunk
+                    should_direct_answer = self._handle_output_moderation_chunk(route_chunk.text)
+                    if should_direct_answer:
+                        continue
+
+                    self._task_state.answer += route_chunk.text
+                    yield self._message_to_stream_response(route_chunk.text, self._message.id)
                 else:
                     break
 
@@ -457,10 +462,8 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
         for route_chunk in route_chunks:
             if route_chunk.type == 'text':
                 route_chunk = cast(TextGenerateRouteChunk, route_chunk)
-                for token in route_chunk.text:
-                    self._task_state.answer += token
-                    yield self._message_to_stream_response(token, self._message.id)
-                    time.sleep(0.01)
+                self._task_state.answer += route_chunk.text
+                yield self._message_to_stream_response(route_chunk.text, self._message.id)
             else:
                 route_chunk = cast(VarGenerateRouteChunk, route_chunk)
                 value_selector = route_chunk.value_selector

api/core/app/apps/base_app_generator.py

@@ -13,7 +13,9 @@ class BaseAppGenerator:
         for variable_config in variables:
             variable = variable_config.variable
 
-            if variable not in user_inputs or not user_inputs[variable]:
+            if (variable not in user_inputs
+                    or user_inputs[variable] is None
+                    or (isinstance(user_inputs[variable], str) and user_inputs[variable] == '')):
                 if variable_config.required:
                     raise ValueError(f"{variable} is required in input form")
                 else:
@@ -22,21 +24,29 @@ class BaseAppGenerator:
 
             value = user_inputs[variable]
 
-            if value:
-                if not isinstance(value, str):
+            if value is not None:
+                if variable_config.type != VariableEntity.Type.NUMBER and not isinstance(value, str):
                     raise ValueError(f"{variable} in input form must be a string")
+                elif variable_config.type == VariableEntity.Type.NUMBER and isinstance(value, str):
+                    if '.' in value:
+                        value = float(value)
+                    else:
+                        value = int(value)
 
             if variable_config.type == VariableEntity.Type.SELECT:
                 options = variable_config.options if variable_config.options is not None else []
                 if value not in options:
                     raise ValueError(f"{variable} in input form must be one of the following: {options}")
-            else:
+            elif variable_config.type in [VariableEntity.Type.TEXT_INPUT, VariableEntity.Type.PARAGRAPH]:
                 if variable_config.max_length is not None:
                     max_length = variable_config.max_length
                     if len(value) > max_length:
                         raise ValueError(f'{variable} in input form must be less than {max_length} characters')
 
-            filtered_inputs[variable] = value.replace('\x00', '') if value else None
+            if value and isinstance(value, str):
+                filtered_inputs[variable] = value.replace('\x00', '')
+            else:
+                filtered_inputs[variable] = value if value is not None else None
 
         return filtered_inputs
 

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

@@ -14,7 +14,7 @@ from core.workflow.entities.node_entities import SystemVariable
 from core.workflow.nodes.base_node import UserFrom
 from core.workflow.workflow_engine_manager import WorkflowEngineManager
 from extensions.ext_database import db
-from models.model import App
+from models.model import App, EndUser
 from models.workflow import Workflow
 
 logger = logging.getLogger(__name__)
@@ -36,6 +36,14 @@ class WorkflowAppRunner:
         app_config = application_generate_entity.app_config
         app_config = cast(WorkflowAppConfig, app_config)
 
+        user_id = None
+        if application_generate_entity.invoke_from in [InvokeFrom.WEB_APP, InvokeFrom.SERVICE_API]:
+            end_user = db.session.query(EndUser).filter(EndUser.id == application_generate_entity.user_id).first()
+            if end_user:
+                user_id = end_user.session_id
+        else:
+            user_id = application_generate_entity.user_id
+
         app_record = db.session.query(App).filter(App.id == app_config.app_id).first()
         if not app_record:
             raise ValueError("App not found")
@@ -67,7 +75,8 @@ class WorkflowAppRunner:
             else UserFrom.END_USER,
             user_inputs=inputs,
             system_inputs={
-                SystemVariable.FILES: files
+                SystemVariable.FILES: files,
+                SystemVariable.USER_ID: user_id
             },
             callbacks=workflow_callbacks
         )

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

@@ -5,6 +5,8 @@ from typing import cast
 from core.app.apps.base_app_generate_response_converter import AppGenerateResponseConverter
 from core.app.entities.task_entities import (
     ErrorStreamResponse,
+    NodeFinishStreamResponse,
+    NodeStartStreamResponse,
     PingStreamResponse,
     WorkflowAppBlockingResponse,
     WorkflowAppStreamResponse,
@@ -68,4 +70,24 @@ class WorkflowAppGenerateResponseConverter(AppGenerateResponseConverter):
         :param stream_response: stream response
         :return:
         """
-        return cls.convert_stream_full_response(stream_response)
+        for chunk in stream_response:
+            chunk = cast(WorkflowAppStreamResponse, chunk)
+            sub_stream_response = chunk.stream_response
+
+            if isinstance(sub_stream_response, PingStreamResponse):
+                yield 'ping'
+                continue
+
+            response_chunk = {
+                'event': sub_stream_response.event.value,
+                'workflow_run_id': chunk.workflow_run_id,
+            }
+
+            if isinstance(sub_stream_response, ErrorStreamResponse):
+                data = cls._error_to_stream_response(sub_stream_response.err)
+                response_chunk.update(data)
+            elif isinstance(sub_stream_response, NodeStartStreamResponse | NodeFinishStreamResponse):
+                response_chunk.update(sub_stream_response.to_ignore_detail_dict())
+            else:
+                response_chunk.update(sub_stream_response.to_dict())
+            yield json.dumps(response_chunk)

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

@@ -28,11 +28,13 @@ from core.app.entities.task_entities import (
     WorkflowAppBlockingResponse,
     WorkflowAppStreamResponse,
     WorkflowFinishStreamResponse,
+    WorkflowStreamGenerateNodes,
     WorkflowTaskState,
 )
 from core.app.task_pipeline.based_generate_task_pipeline import BasedGenerateTaskPipeline
 from core.app.task_pipeline.workflow_cycle_manage import WorkflowCycleManage
-from core.workflow.entities.node_entities import SystemVariable
+from core.workflow.entities.node_entities import NodeType, SystemVariable
+from core.workflow.nodes.end.end_node import EndNode
 from extensions.ext_database import db
 from models.account import Account
 from models.model import EndUser
@@ -40,6 +42,7 @@ from models.workflow import (
     Workflow,
     WorkflowAppLog,
     WorkflowAppLogCreatedFrom,
+    WorkflowNodeExecution,
     WorkflowRun,
 )
 
@@ -71,12 +74,19 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
         """
         super().__init__(application_generate_entity, queue_manager, user, stream)
 
+        if isinstance(self._user, EndUser):
+            user_id = self._user.session_id
+        else:
+            user_id = self._user.id
+
         self._workflow = workflow
         self._workflow_system_variables = {
             SystemVariable.FILES: application_generate_entity.files,
+            SystemVariable.USER_ID: user_id
         }
 
         self._task_state = WorkflowTaskState()
+        self._stream_generate_nodes = self._get_stream_generate_nodes()
 
     def process(self) -> Union[WorkflowAppBlockingResponse, Generator[WorkflowAppStreamResponse, None, None]]:
         """
@@ -161,6 +171,14 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
                 )
             elif isinstance(event, QueueNodeStartedEvent):
                 workflow_node_execution = self._handle_node_start(event)
+
+                # search stream_generate_routes if node id is answer start at node
+                if not self._task_state.current_stream_generate_state and event.node_id in self._stream_generate_nodes:
+                    self._task_state.current_stream_generate_state = self._stream_generate_nodes[event.node_id]
+
+                    # generate stream outputs when node started
+                    yield from self._generate_stream_outputs_when_node_started()
+
                 yield self._workflow_node_start_to_stream_response(
                     event=event,
                     task_id=self._application_generate_entity.task_id,
@@ -168,6 +186,7 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
                 )
             elif isinstance(event, QueueNodeSucceededEvent | QueueNodeFailedEvent):
                 workflow_node_execution = self._handle_node_finished(event)
+
                 yield self._workflow_node_finish_to_stream_response(
                     task_id=self._application_generate_entity.task_id,
                     workflow_node_execution=workflow_node_execution
@@ -187,6 +206,11 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
                 if delta_text is None:
                     continue
 
+                if not self._is_stream_out_support(
+                        event=event
+                ):
+                    continue
+
                 self._task_state.answer += delta_text
                 yield self._text_chunk_to_stream_response(delta_text)
             elif isinstance(event, QueueMessageReplaceEvent):
@@ -248,3 +272,142 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
             task_id=self._application_generate_entity.task_id,
             text=TextReplaceStreamResponse.Data(text=text)
         )
+
+    def _get_stream_generate_nodes(self) -> dict[str, WorkflowStreamGenerateNodes]:
+        """
+        Get stream generate nodes.
+        :return:
+        """
+        # find all answer nodes
+        graph = self._workflow.graph_dict
+        end_node_configs = [
+            node for node in graph['nodes']
+            if node.get('data', {}).get('type') == NodeType.END.value
+        ]
+
+        # parse stream output node value selectors of end nodes
+        stream_generate_routes = {}
+        for node_config in end_node_configs:
+            # get generate route for stream output
+            end_node_id = node_config['id']
+            generate_nodes = EndNode.extract_generate_nodes(graph, node_config)
+            start_node_ids = self._get_end_start_at_node_ids(graph, end_node_id)
+            if not start_node_ids:
+                continue
+
+            for start_node_id in start_node_ids:
+                stream_generate_routes[start_node_id] = WorkflowStreamGenerateNodes(
+                    end_node_id=end_node_id,
+                    stream_node_ids=generate_nodes
+                )
+
+        return stream_generate_routes
+
+    def _get_end_start_at_node_ids(self, graph: dict, target_node_id: str) \
+            -> list[str]:
+        """
+        Get end start at node id.
+        :param graph: graph
+        :param target_node_id: target node ID
+        :return:
+        """
+        nodes = graph.get('nodes')
+        edges = graph.get('edges')
+
+        # fetch all ingoing edges from source node
+        ingoing_edges = []
+        for edge in edges:
+            if edge.get('target') == target_node_id:
+                ingoing_edges.append(edge)
+
+        if not ingoing_edges:
+            return []
+
+        start_node_ids = []
+        for ingoing_edge in ingoing_edges:
+            source_node_id = ingoing_edge.get('source')
+            source_node = next((node for node in nodes if node.get('id') == source_node_id), None)
+            if not source_node:
+                continue
+
+            node_type = source_node.get('data', {}).get('type')
+            if node_type in [
+                NodeType.IF_ELSE.value,
+                NodeType.QUESTION_CLASSIFIER.value
+            ]:
+                start_node_id = target_node_id
+                start_node_ids.append(start_node_id)
+            elif node_type == NodeType.START.value:
+                start_node_id = source_node_id
+                start_node_ids.append(start_node_id)
+            else:
+                sub_start_node_ids = self._get_end_start_at_node_ids(graph, source_node_id)
+                if sub_start_node_ids:
+                    start_node_ids.extend(sub_start_node_ids)
+
+        return start_node_ids
+
+    def _generate_stream_outputs_when_node_started(self) -> Generator:
+        """
+        Generate stream outputs.
+        :return:
+        """
+        if self._task_state.current_stream_generate_state:
+            stream_node_ids = self._task_state.current_stream_generate_state.stream_node_ids
+
+            for node_id, node_execution_info in self._task_state.ran_node_execution_infos.items():
+                if node_id not in stream_node_ids:
+                    continue
+
+                node_execution_info = self._task_state.ran_node_execution_infos[node_id]
+
+                # get chunk node execution
+                route_chunk_node_execution = db.session.query(WorkflowNodeExecution).filter(
+                    WorkflowNodeExecution.id == node_execution_info.workflow_node_execution_id).first()
+
+                if not route_chunk_node_execution:
+                    continue
+
+                outputs = route_chunk_node_execution.outputs_dict
+
+                if not outputs:
+                    continue
+
+                # get value from outputs
+                text = outputs.get('text')
+
+                if text:
+                    self._task_state.answer += text
+                    yield self._text_chunk_to_stream_response(text)
+
+            db.session.close()
+
+    def _is_stream_out_support(self, event: QueueTextChunkEvent) -> bool:
+        """
+        Is stream out support
+        :param event: queue text chunk event
+        :return:
+        """
+        if not event.metadata:
+            return False
+
+        if 'node_id' not in event.metadata:
+            return False
+
+        node_id = event.metadata.get('node_id')
+        node_type = event.metadata.get('node_type')
+        stream_output_value_selector = event.metadata.get('value_selector')
+        if not stream_output_value_selector:
+            return False
+
+        if not self._task_state.current_stream_generate_state:
+            return False
+
+        if node_id not in self._task_state.current_stream_generate_state.stream_node_ids:
+            return False
+
+        if node_type != NodeType.LLM:
+            # only LLM support chunk stream output
+            return False
+
+        return True

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

@@ -6,6 +6,7 @@ from core.app.entities.queue_entities import (
     QueueNodeFailedEvent,
     QueueNodeStartedEvent,
     QueueNodeSucceededEvent,
+    QueueTextChunkEvent,
     QueueWorkflowFailedEvent,
     QueueWorkflowStartedEvent,
     QueueWorkflowSucceededEvent,
@@ -119,7 +120,15 @@ class WorkflowEventTriggerCallback(BaseWorkflowCallback):
         """
         Publish text chunk
         """
-        pass
+        self._queue_manager.publish(
+            QueueTextChunkEvent(
+                text=text,
+                metadata={
+                    "node_id": node_id,
+                    **metadata
+                }
+            ), PublishFrom.APPLICATION_MANAGER
+        )
 
     def on_event(self, event: AppQueueEvent) -> None:
         """

api/core/app/entities/app_invoke_entities.py

@@ -72,7 +72,7 @@ class AppGenerateEntity(BaseModel):
     # app config
     app_config: AppConfig
 
-    inputs: dict[str, str]
+    inputs: dict[str, Any]
     files: list[FileVar] = []
     user_id: str
 

api/core/app/entities/task_entities.py

@@ -9,9 +9,17 @@ from core.workflow.entities.node_entities import NodeType
 from core.workflow.nodes.answer.entities import GenerateRouteChunk
 
 
-class StreamGenerateRoute(BaseModel):
+class WorkflowStreamGenerateNodes(BaseModel):
     """
-    StreamGenerateRoute entity
+    WorkflowStreamGenerateNodes entity
+    """
+    end_node_id: str
+    stream_node_ids: list[str]
+
+
+class ChatflowStreamGenerateRoute(BaseModel):
+    """
+    ChatflowStreamGenerateRoute entity
     """
     answer_node_id: str
     generate_route: list[GenerateRouteChunk]
@@ -55,6 +63,8 @@ class WorkflowTaskState(TaskState):
     ran_node_execution_infos: dict[str, NodeExecutionInfo] = {}
     latest_node_execution_info: Optional[NodeExecutionInfo] = None
 
+    current_stream_generate_state: Optional[WorkflowStreamGenerateNodes] = None
+
 
 class AdvancedChatTaskState(WorkflowTaskState):
     """
@@ -62,7 +72,7 @@ class AdvancedChatTaskState(WorkflowTaskState):
     """
     usage: LLMUsage
 
-    current_stream_generate_state: Optional[StreamGenerateRoute] = None
+    current_stream_generate_state: Optional[ChatflowStreamGenerateRoute] = None
 
 
 class StreamEvent(Enum):
@@ -236,6 +246,24 @@ class NodeStartStreamResponse(StreamResponse):
     workflow_run_id: str
     data: Data
 
+    def to_ignore_detail_dict(self):
+        return {
+            "event": self.event.value,
+            "task_id": self.task_id,
+            "workflow_run_id": self.workflow_run_id,
+            "data": {
+                "id": self.data.id,
+                "node_id": self.data.node_id,
+                "node_type": self.data.node_type,
+                "title": self.data.title,
+                "index": self.data.index,
+                "predecessor_node_id": self.data.predecessor_node_id,
+                "inputs": None,
+                "created_at": self.data.created_at,
+                "extras": {}
+            }
+        }
+
 
 class NodeFinishStreamResponse(StreamResponse):
     """
@@ -266,6 +294,31 @@ class NodeFinishStreamResponse(StreamResponse):
     workflow_run_id: str
     data: Data
 
+    def to_ignore_detail_dict(self):
+        return {
+            "event": self.event.value,
+            "task_id": self.task_id,
+            "workflow_run_id": self.workflow_run_id,
+            "data": {
+                "id": self.data.id,
+                "node_id": self.data.node_id,
+                "node_type": self.data.node_type,
+                "title": self.data.title,
+                "index": self.data.index,
+                "predecessor_node_id": self.data.predecessor_node_id,
+                "inputs": None,
+                "process_data": None,
+                "outputs": None,
+                "status": self.data.status,
+                "error": None,
+                "elapsed_time": self.data.elapsed_time,
+                "execution_metadata": None,
+                "created_at": self.data.created_at,
+                "finished_at": self.data.finished_at,
+                "files": []
+            }
+        }
+
 
 class TextChunkStreamResponse(StreamResponse):
     """

api/core/app/task_pipeline/message_cycle_manage.py

@@ -118,7 +118,8 @@ class MessageCycleManage:
         :param event: event
         :return:
         """
-        self._task_state.metadata['retriever_resources'] = event.retriever_resources
+        if self._application_generate_entity.app_config.additional_features.show_retrieve_source:
+            self._task_state.metadata['retriever_resources'] = event.retriever_resources
 
     def _get_response_metadata(self) -> dict:
         """

api/core/helper/code_executor/code_executor.py

@@ -1,13 +1,20 @@
+import logging
+import time
+from enum import Enum
+from threading import Lock
 from typing import Literal, Optional
 
-from httpx import post
+from httpx import get, post
 from pydantic import BaseModel
 from yarl import URL
 
 from config import get_env
+from core.helper.code_executor.entities import CodeDependency
 from core.helper.code_executor.javascript_transformer import NodeJsTemplateTransformer
-from core.helper.code_executor.jina2_transformer import Jinja2TemplateTransformer
-from core.helper.code_executor.python_transformer import PythonTemplateTransformer
+from core.helper.code_executor.jinja2_transformer import Jinja2TemplateTransformer
+from core.helper.code_executor.python_transformer import PYTHON_STANDARD_PACKAGES, PythonTemplateTransformer
+
+logger = logging.getLogger(__name__)
 
 # Code Executor
 CODE_EXECUTION_ENDPOINT = get_env('CODE_EXECUTION_ENDPOINT')
@@ -27,10 +34,34 @@ class CodeExecutionResponse(BaseModel):
     message: str
     data: Data
 
+class CodeLanguage(str, Enum):
+    PYTHON3 = 'python3'
+    JINJA2 = 'jinja2'
+    JAVASCRIPT = 'javascript'
+
 
 class CodeExecutor:
+    dependencies_cache = {}
+    dependencies_cache_lock = Lock()
+
+    code_template_transformers = {
+        CodeLanguage.PYTHON3: PythonTemplateTransformer,
+        CodeLanguage.JINJA2: Jinja2TemplateTransformer,
+        CodeLanguage.JAVASCRIPT: NodeJsTemplateTransformer,
+    }
+
+    code_language_to_running_language = {
+        CodeLanguage.JAVASCRIPT: 'nodejs',
+        CodeLanguage.JINJA2: CodeLanguage.PYTHON3,
+        CodeLanguage.PYTHON3: CodeLanguage.PYTHON3,
+    }
+
     @classmethod
-    def execute_code(cls, language: Literal['python3', 'javascript', 'jinja2'], preload: str, code: str) -> str:
+    def execute_code(cls, 
+                     language: Literal['python3', 'javascript', 'jinja2'], 
+                     preload: str, 
+                     code: str, 
+                     dependencies: Optional[list[CodeDependency]] = None) -> str:
         """
         Execute code
         :param language: code language
@@ -44,13 +75,15 @@ class CodeExecutor:
         }
 
         data = {
-            'language': 'python3' if language == 'jinja2' else
-                        'nodejs' if language == 'javascript' else
-                        'python3' if language == 'python3' else None,
+            'language': cls.code_language_to_running_language.get(language),
             'code': code,
-            'preload': preload
+            'preload': preload,
+            'enable_network': True
         }
 
+        if dependencies:
+            data['dependencies'] = [dependency.dict() for dependency in dependencies]
+
         try:
             response = post(str(url), json=data, headers=headers, timeout=CODE_EXECUTION_TIMEOUT)
             if response.status_code == 503:
@@ -78,7 +111,7 @@ class CodeExecutor:
         return response.data.stdout
 
     @classmethod
-    def execute_workflow_code_template(cls, language: Literal['python3', 'javascript', 'jinja2'], code: str, inputs: dict) -> dict:
+    def execute_workflow_code_template(cls, language: Literal['python3', 'javascript', 'jinja2'], code: str, inputs: dict, dependencies: Optional[list[CodeDependency]] = None) -> dict:
         """
         Execute code
         :param language: code language
@@ -86,21 +119,67 @@ class CodeExecutor:
         :param inputs: inputs
         :return:
         """
-        template_transformer = None
-        if language == 'python3':
-            template_transformer = PythonTemplateTransformer
-        elif language == 'jinja2':
-            template_transformer = Jinja2TemplateTransformer
-        elif language == 'javascript':
-            template_transformer = NodeJsTemplateTransformer
-        else:
-            raise CodeExecutionException('Unsupported language')
+        template_transformer = cls.code_template_transformers.get(language)
+        if not template_transformer:
+            raise CodeExecutionException(f'Unsupported language {language}')
 
-        runner, preload = template_transformer.transform_caller(code, inputs)
+        runner, preload, dependencies = template_transformer.transform_caller(code, inputs, dependencies)
 
         try:
-            response = cls.execute_code(language, preload, runner)
+            response = cls.execute_code(language, preload, runner, dependencies)
         except CodeExecutionException as e:
             raise e
 
-        return template_transformer.transform_response(response)
+        return template_transformer.transform_response(response)
+    
+    @classmethod
+    def list_dependencies(cls, language: Literal['python3']) -> list[CodeDependency]:
+        with cls.dependencies_cache_lock:
+            if language in cls.dependencies_cache:
+                # check expiration
+                dependencies = cls.dependencies_cache[language]
+                if dependencies['expiration'] > time.time():
+                    return dependencies['data']
+                # remove expired cache
+                del cls.dependencies_cache[language]
+        
+        dependencies = cls._get_dependencies(language)
+        with cls.dependencies_cache_lock:
+            cls.dependencies_cache[language] = {
+                'data': dependencies,
+                'expiration': time.time() + 60
+            }
+        
+        return dependencies
+        
+    @classmethod
+    def _get_dependencies(cls, language: Literal['python3']) -> list[CodeDependency]:
+        """
+        List dependencies
+        """
+        url = URL(CODE_EXECUTION_ENDPOINT) / 'v1' / 'sandbox' / 'dependencies'
+
+        headers = {
+            'X-Api-Key': CODE_EXECUTION_API_KEY
+        }
+
+        running_language = cls.code_language_to_running_language.get(language)
+        if isinstance(running_language, Enum):
+            running_language = running_language.value
+
+        data = {
+            'language': running_language,
+        }
+
+        try:
+            response = get(str(url), params=data, headers=headers, timeout=CODE_EXECUTION_TIMEOUT)
+            if response.status_code != 200:
+                raise Exception(f'Failed to list dependencies, got status code {response.status_code}, please check if the sandbox service is running')
+            response = response.json()
+            dependencies = response.get('data', {}).get('dependencies', [])
+            return [
+                CodeDependency(**dependency) for dependency in dependencies if dependency.get('name') not in PYTHON_STANDARD_PACKAGES
+            ]
+        except Exception as e:
+            logger.exception(f'Failed to list dependencies: {e}')
+            return []

api/core/helper/code_executor/entities.py

@@ -0,0 +1,6 @@
+from pydantic import BaseModel
+
+
+class CodeDependency(BaseModel):
+    name: str
+    version: str

api/core/helper/code_executor/javascript_transformer.py

@@ -1,6 +1,8 @@
 import json
 import re
+from typing import Optional
 
+from core.helper.code_executor.entities import CodeDependency
 from core.helper.code_executor.template_transformer import TemplateTransformer
 
 NODEJS_RUNNER = """// declare main function here
@@ -22,7 +24,8 @@ NODEJS_PRELOAD = """"""
 
 class NodeJsTemplateTransformer(TemplateTransformer):
     @classmethod
-    def transform_caller(cls, code: str, inputs: dict) -> tuple[str, str]:
+    def transform_caller(cls, code: str, inputs: dict, 
+                         dependencies: Optional[list[CodeDependency]] = None) -> tuple[str, str, list[CodeDependency]]:
         """
         Transform code to python runner
         :param code: code
@@ -37,7 +40,7 @@ class NodeJsTemplateTransformer(TemplateTransformer):
         runner = NODEJS_RUNNER.replace('{{code}}', code)
         runner = runner.replace('{{inputs}}', inputs_str)
 
-        return runner, NODEJS_PRELOAD
+        return runner, NODEJS_PRELOAD, []
 
     @classmethod
     def transform_response(cls, response: str) -> dict:

api/core/helper/code_executor/jinja2_formatter.py

@@ -0,0 +1,17 @@
+from core.helper.code_executor.code_executor import CodeExecutor
+
+
+class Jinja2Formatter:
+    @classmethod
+    def format(cls, template: str, inputs: str) -> str:
+        """
+        Format template
+        :param template: template
+        :param inputs: inputs
+        :return:
+        """
+        result = CodeExecutor.execute_workflow_code_template(
+            language='jinja2', code=template, inputs=inputs
+        )
+
+        return result['result']

api/core/helper/code_executor/jinja2_transformer.py

@@ -1,10 +1,16 @@
 import json
 import re
+from base64 import b64encode
+from typing import Optional
 
+from core.helper.code_executor.entities import CodeDependency
+from core.helper.code_executor.python_transformer import PYTHON_STANDARD_PACKAGES
 from core.helper.code_executor.template_transformer import TemplateTransformer
 
 PYTHON_RUNNER = """
 import jinja2
+from json import loads
+from base64 import b64decode
 
 template = jinja2.Template('''{{code}}''')
 
@@ -12,7 +18,8 @@ def main(**inputs):
     return template.render(**inputs)
 
 # execute main function, and return the result
-output = main(**{{inputs}})
+inputs = b64decode('{{inputs}}').decode('utf-8')
+output = main(**loads(inputs))
 
 result = f'''<<RESULT>>{output}<<RESULT>>'''
 
@@ -39,6 +46,7 @@ JINJA2_PRELOAD_TEMPLATE = """{% set fruits = ['Apple'] %}
 
 JINJA2_PRELOAD = f"""
 import jinja2
+from base64 import b64decode
 
 def _jinja2_preload_():
     # prepare jinja2 environment, load template and render before to avoid sandbox issue
@@ -50,9 +58,11 @@ if __name__ == '__main__':
 
 """
 
+
 class Jinja2TemplateTransformer(TemplateTransformer):
     @classmethod
-    def transform_caller(cls, code: str, inputs: dict) -> tuple[str, str]:
+    def transform_caller(cls, code: str, inputs: dict, 
+                         dependencies: Optional[list[CodeDependency]] = None) -> tuple[str, str, list[CodeDependency]]:
         """
         Transform code to python runner
         :param code: code
@@ -60,11 +70,25 @@ class Jinja2TemplateTransformer(TemplateTransformer):
         :return:
         """
 
+        inputs_str = b64encode(json.dumps(inputs, ensure_ascii=False).encode()).decode('utf-8')
+
         # transform jinja2 template to python code
         runner = PYTHON_RUNNER.replace('{{code}}', code)
-        runner = runner.replace('{{inputs}}', json.dumps(inputs, indent=4, ensure_ascii=False))
+        runner = runner.replace('{{inputs}}', inputs_str)
 
-        return runner, JINJA2_PRELOAD
+        if not dependencies:
+            dependencies = []
+
+        # add native packages and jinja2
+        for package in PYTHON_STANDARD_PACKAGES.union(['jinja2']):
+            dependencies.append(CodeDependency(name=package, version=''))
+
+        # deduplicate
+        dependencies = list({
+            dep.name: dep for dep in dependencies if dep.name
+        }.values())
+
+        return runner, JINJA2_PRELOAD, dependencies
 
     @classmethod
     def transform_response(cls, response: str) -> dict:

api/core/helper/code_executor/python_transformer.py

@@ -1,17 +1,24 @@
 import json
 import re
+from base64 import b64encode
+from typing import Optional
 
+from core.helper.code_executor.entities import CodeDependency
 from core.helper.code_executor.template_transformer import TemplateTransformer
 
 PYTHON_RUNNER = """# declare main function here
 {{code}}
 
+from json import loads, dumps
+from base64 import b64decode
+
 # execute main function, and return the result
 # inputs is a dict, and it
-output = main(**{{inputs}})
+inputs = b64decode('{{inputs}}').decode('utf-8')
+output = main(**json.loads(inputs))
 
 # convert output to json and print
-output = json.dumps(output, indent=4)
+output = dumps(output, indent=4)
 
 result = f'''<<RESULT>>
 {output}
@@ -20,32 +27,17 @@ result = f'''<<RESULT>>
 print(result)
 """
 
-PYTHON_PRELOAD = """
-# prepare general imports
-import json
-import datetime
-import math
-import random
-import re
-import string
-import sys
-import time
-import traceback
-import uuid
-import os
-import base64
-import hashlib
-import hmac
-import binascii
-import collections
-import functools
-import operator
-import itertools
-"""
+PYTHON_PRELOAD = """"""
+
+PYTHON_STANDARD_PACKAGES = set([
+    'json', 'datetime', 'math', 'random', 're', 'string', 'sys', 'time', 'traceback', 'uuid', 'os', 'base64',
+    'hashlib', 'hmac', 'binascii', 'collections', 'functools', 'operator', 'itertools', 'uuid', 
+])
 
 class PythonTemplateTransformer(TemplateTransformer):
     @classmethod
-    def transform_caller(cls, code: str, inputs: dict) -> tuple[str, str]:
+    def transform_caller(cls, code: str, inputs: dict, 
+                         dependencies: Optional[list[CodeDependency]] = None) -> tuple[str, str, list[CodeDependency]]:
         """
         Transform code to python runner
         :param code: code
@@ -54,13 +46,24 @@ class PythonTemplateTransformer(TemplateTransformer):
         """
         
         # transform inputs to json string
-        inputs_str = json.dumps(inputs, indent=4, ensure_ascii=False)
+        inputs_str = b64encode(json.dumps(inputs, ensure_ascii=False).encode()).decode('utf-8')
 
         # replace code and inputs
         runner = PYTHON_RUNNER.replace('{{code}}', code)
         runner = runner.replace('{{inputs}}', inputs_str)
 
-        return runner, PYTHON_PRELOAD
+        # add standard packages
+        if dependencies is None:
+            dependencies = []
+
+        for package in PYTHON_STANDARD_PACKAGES:
+            if package not in dependencies:
+                dependencies.append(CodeDependency(name=package, version=''))
+
+        # deduplicate
+        dependencies = list({dep.name: dep for dep in dependencies if dep.name}.values())
+
+        return runner, PYTHON_PRELOAD, dependencies
     
     @classmethod
     def transform_response(cls, response: str) -> dict:

api/core/helper/code_executor/template_transformer.py

@@ -1,10 +1,14 @@
 from abc import ABC, abstractmethod
+from typing import Optional
+
+from core.helper.code_executor.entities import CodeDependency
 
 
 class TemplateTransformer(ABC):
     @classmethod
     @abstractmethod
-    def transform_caller(cls, code: str, inputs: dict) -> tuple[str, str]:
+    def transform_caller(cls, code: str, inputs: dict, 
+                         dependencies: Optional[list[CodeDependency]] = None) -> tuple[str, str, list[CodeDependency]]:
         """
         Transform code to python runner
         :param code: code

api/core/helper/tool_parameter_cache.py

@@ -11,12 +11,13 @@ class ToolParameterCacheType(Enum):
 
 class ToolParameterCache:
     def __init__(self, 
-                 tenant_id: str, 
-                 provider: str, 
-                 tool_name: str, 
-                 cache_type: ToolParameterCacheType
+            tenant_id: str, 
+            provider: str, 
+            tool_name: str, 
+            cache_type: ToolParameterCacheType,
+            identity_id: str
         ):
-        self.cache_key = f"{cache_type.value}_secret:tenant_id:{tenant_id}:provider:{provider}:tool_name:{tool_name}"
+        self.cache_key = f"{cache_type.value}_secret:tenant_id:{tenant_id}:provider:{provider}:tool_name:{tool_name}:identity_id:{identity_id}"
 
     def get(self) -> Optional[dict]:
         """

api/core/model_runtime/model_providers/_position.yaml

@@ -26,4 +26,6 @@
 - yi
 - openllm
 - localai
+- volcengine_maas
 - openai_api_compatible
+- deepseek

api/core/model_runtime/model_providers/azure_openai/_constant.py

@@ -482,6 +482,158 @@ LLM_BASE_MODELS = [
             )
         )
     ),
+    AzureBaseModel(
+        base_model_name='gpt-4-turbo',
+        entity=AIModelEntity(
+            model='fake-deployment-name',
+            label=I18nObject(
+                en_US='fake-deployment-name-label',
+            ),
+            model_type=ModelType.LLM,
+            features=[
+                ModelFeature.AGENT_THOUGHT,
+                ModelFeature.VISION,
+                ModelFeature.MULTI_TOOL_CALL,
+                ModelFeature.STREAM_TOOL_CALL,
+            ],
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_properties={
+                ModelPropertyKey.MODE: LLMMode.CHAT.value,
+                ModelPropertyKey.CONTEXT_SIZE: 128000,
+            },
+            parameter_rules=[
+                ParameterRule(
+                    name='temperature',
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
+                ),
+                ParameterRule(
+                    name='top_p',
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
+                ),
+                ParameterRule(
+                    name='presence_penalty',
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.PRESENCE_PENALTY],
+                ),
+                ParameterRule(
+                    name='frequency_penalty',
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
+                ),
+                _get_max_tokens(default=512, min_val=1, max_val=4096),
+                ParameterRule(
+                    name='seed',
+                    label=I18nObject(
+                        zh_Hans='种子',
+                        en_US='Seed'
+                    ),
+                    type='int',
+                    help=I18nObject(
+                        zh_Hans='如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，您应该参考 system_fingerprint 响应参数来监视变化。',
+                        en_US='If specified, model will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.'
+                    ),
+                    required=False,
+                    precision=2,
+                    min=0,
+                    max=1,
+                ),
+                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']
+                ),
+            ],
+            pricing=PriceConfig(
+                input=0.001,
+                output=0.003,
+                unit=0.001,
+                currency='USD',
+            )
+        )
+    ),
+    AzureBaseModel(
+        base_model_name='gpt-4-turbo-2024-04-09',
+        entity=AIModelEntity(
+            model='fake-deployment-name',
+            label=I18nObject(
+                en_US='fake-deployment-name-label',
+            ),
+            model_type=ModelType.LLM,
+            features=[
+                ModelFeature.AGENT_THOUGHT,
+                ModelFeature.VISION,
+                ModelFeature.MULTI_TOOL_CALL,
+                ModelFeature.STREAM_TOOL_CALL,
+            ],
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_properties={
+                ModelPropertyKey.MODE: LLMMode.CHAT.value,
+                ModelPropertyKey.CONTEXT_SIZE: 128000,
+            },
+            parameter_rules=[
+                ParameterRule(
+                    name='temperature',
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
+                ),
+                ParameterRule(
+                    name='top_p',
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
+                ),
+                ParameterRule(
+                    name='presence_penalty',
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.PRESENCE_PENALTY],
+                ),
+                ParameterRule(
+                    name='frequency_penalty',
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
+                ),
+                _get_max_tokens(default=512, min_val=1, max_val=4096),
+                ParameterRule(
+                    name='seed',
+                    label=I18nObject(
+                        zh_Hans='种子',
+                        en_US='Seed'
+                    ),
+                    type='int',
+                    help=I18nObject(
+                        zh_Hans='如果指定，模型将尽最大努力进行确定性采样，使得重复的具有相同种子和参数的请求应该返回相同的结果。不能保证确定性，您应该参考 system_fingerprint 响应参数来监视变化。',
+                        en_US='If specified, model will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.'
+                    ),
+                    required=False,
+                    precision=2,
+                    min=0,
+                    max=1,
+                ),
+                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']
+                ),
+            ],
+            pricing=PriceConfig(
+                input=0.001,
+                output=0.003,
+                unit=0.001,
+                currency='USD',
+            )
+        )
+    ),
     AzureBaseModel(
         base_model_name='gpt-4-vision-preview',
         entity=AIModelEntity(

api/core/model_runtime/model_providers/azure_openai/azure_openai.yaml

@@ -99,6 +99,12 @@ model_credential_schema:
           show_on:
             - variable: __model_type
               value: llm
+        - label:
+            en_US: gpt-4-turbo
+          value: gpt-4-turbo
+          show_on:
+            - variable: __model_type
+              value: llm
         - label:
             en_US: gpt-4-turbo-2024-04-09
           value: gpt-4-turbo-2024-04-09

api/core/model_runtime/model_providers/bedrock/bedrock.yaml

@@ -70,6 +70,10 @@ provider_credential_schema:
           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:

api/core/model_runtime/model_providers/bedrock/llm/_position.yaml

@@ -8,5 +8,10 @@
 - anthropic.claude-3-haiku-v1:0
 - cohere.command-light-text-v14
 - cohere.command-text-v14
+- meta.llama3-8b-instruct-v1:0
+- meta.llama3-70b-instruct-v1:0
 - meta.llama2-13b-chat-v1
 - meta.llama2-70b-chat-v1
+- mistral.mistral-large-2402-v1:0
+- mistral.mixtral-8x7b-instruct-v0:1
+- mistral.mistral-7b-instruct-v0:2

api/core/model_runtime/model_providers/bedrock/llm/llm.py

@@ -370,29 +370,14 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
         :return:md = genai.GenerativeModel(model)
         """
         prefix = model.split('.')[0]
-
+        model_name = model.split('.')[1]
         if isinstance(messages, str):
             prompt = messages
         else:
-            prompt = self._convert_messages_to_prompt(messages, prefix)
+            prompt = self._convert_messages_to_prompt(messages, prefix, model_name)
 
         return self._get_num_tokens_by_gpt2(prompt)
     
-    def _convert_messages_to_prompt(self, model_prefix: str, messages: list[PromptMessage]) -> str:
-        """
-        Format a list of messages into a full prompt for the Google model
-
-        :param messages: List of PromptMessage to combine.
-        :return: Combined string with necessary human_prompt and ai_prompt tags.
-        """
-        messages = messages.copy()  # don't mutate the original list
-        
-        text = "".join(
-            self._convert_one_message_to_text(message, model_prefix)
-            for message in messages
-        )
-
-        return text.rstrip()
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
         """
@@ -432,7 +417,7 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 
-    def _convert_one_message_to_text(self, message: PromptMessage, model_prefix: str) -> str:
+    def _convert_one_message_to_text(self, message: PromptMessage, model_prefix: str, model_name: Optional[str] = None) -> str:
         """
         Convert a single message to a string.
 
@@ -446,9 +431,21 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
             ai_prompt = "\n\nAssistant:"
 
         elif model_prefix == "meta":
-            human_prompt_prefix = "\n[INST]"
+            # LLAMA3
+            if model_name.startswith("llama3"):
+                human_prompt_prefix = "<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n"
+                human_prompt_postfix = "<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+                ai_prompt = "\n\nAssistant:"
+            else:
+                # LLAMA2
+                human_prompt_prefix = "\n[INST]"
+                human_prompt_postfix = "[\\INST]\n"
+                ai_prompt = ""
+
+        elif model_prefix == "mistral":
+            human_prompt_prefix = "<s>[INST]"
             human_prompt_postfix = "[\\INST]\n"
-            ai_prompt = ""
+            ai_prompt = "\n\nAssistant:"
 
         elif model_prefix == "amazon":
             human_prompt_prefix = "\n\nUser:"
@@ -473,11 +470,12 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
 
         return message_text
 
-    def _convert_messages_to_prompt(self, messages: list[PromptMessage], model_prefix: str) -> str:
+    def _convert_messages_to_prompt(self, messages: list[PromptMessage], model_prefix: str, model_name: Optional[str] = None) -> str:
         """
         Format a list of messages into a full prompt for the Anthropic, Amazon and Llama models
 
         :param messages: List of PromptMessage to combine.
+        :param model_name: specific model name.Optional,just to distinguish llama2 and llama3
         :return: Combined string with necessary human_prompt and ai_prompt tags.
         """
         if not messages:
@@ -488,18 +486,20 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
             messages.append(AssistantPromptMessage(content=""))
 
         text = "".join(
-            self._convert_one_message_to_text(message, model_prefix)
+            self._convert_one_message_to_text(message, model_prefix, model_name)
             for message in messages
         )
 
         # trim off the trailing ' ' that might come from the "Assistant: "
         return text.rstrip()
 
-    def _create_payload(self, model_prefix: str, prompt_messages: list[PromptMessage], model_parameters: dict, stop: Optional[list[str]] = None, stream: bool = True):
+    def _create_payload(self, model: str, prompt_messages: list[PromptMessage], model_parameters: dict, stop: Optional[list[str]] = None, stream: bool = True):
         """
         Create payload for bedrock api call depending on model provider
         """
         payload = dict()
+        model_prefix = model.split('.')[0]
+        model_name = model.split('.')[1]
 
         if model_prefix == "amazon":
             payload["textGenerationConfig"] = { **model_parameters }
@@ -519,6 +519,13 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
                 payload["frequencyPenalty"] = {model_parameters.get("frequencyPenalty")}
             if model_parameters.get("countPenalty"):
                 payload["countPenalty"] = {model_parameters.get("countPenalty")}
+        
+        elif model_prefix == "mistral":
+            payload["temperature"] = model_parameters.get("temperature")
+            payload["top_p"] = model_parameters.get("top_p")
+            payload["max_tokens"] = model_parameters.get("max_tokens")
+            payload["prompt"] = self._convert_messages_to_prompt(prompt_messages, model_prefix)
+            payload["stop"] = stop[:10] if stop else []
 
         elif model_prefix == "anthropic":
             payload = { **model_parameters }
@@ -532,7 +539,7 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
         
         elif model_prefix == "meta":
             payload = { **model_parameters }
-            payload["prompt"] = self._convert_messages_to_prompt(prompt_messages, model_prefix)
+            payload["prompt"] = self._convert_messages_to_prompt(prompt_messages, model_prefix, model_name)
 
         else:
             raise ValueError(f"Got unknown model prefix {model_prefix}")
@@ -567,7 +574,7 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
         )
 
         model_prefix = model.split('.')[0]
-        payload = self._create_payload(model_prefix, prompt_messages, model_parameters, stop, stream)
+        payload = self._create_payload(model, prompt_messages, model_parameters, stop, stream)
 
         # need workaround for ai21 models which doesn't support streaming
         if stream and model_prefix != "ai21":
@@ -648,6 +655,11 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
             output = response_body.get("generation").strip('\n')
             prompt_tokens = response_body.get("prompt_token_count")
             completion_tokens = response_body.get("generation_token_count")
+        
+        elif model_prefix == "mistral":
+            output = response_body.get("outputs")[0].get("text")
+            prompt_tokens = response.get('ResponseMetadata').get('HTTPHeaders').get('x-amzn-bedrock-input-token-count')
+            completion_tokens = response.get('ResponseMetadata').get('HTTPHeaders').get('x-amzn-bedrock-output-token-count')
 
         else:
             raise ValueError(f"Got unknown model prefix {model_prefix} when handling block response")
@@ -731,6 +743,10 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
                 content_delta = payload.get("text")
                 finish_reason = payload.get("finish_reason")
             
+            elif model_prefix == "mistral":
+                content_delta = payload.get('outputs')[0].get("text")
+                finish_reason = payload.get('outputs')[0].get("stop_reason")
+
             elif model_prefix == "meta":
                 content_delta = payload.get("generation").strip('\n')
                 finish_reason = payload.get("stop_reason")

api/core/model_runtime/model_providers/bedrock/llm/meta.llama3-70b-instruct-v1.yaml

@@ -0,0 +1,23 @@
+model: meta.llama3-70b-instruct-v1:0
+label:
+  en_US: Llama 3 Instruct 70B
+model_type: llm
+model_properties:
+  mode: completion
+  context_size: 8192
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_gen_len
+    use_template: max_tokens
+    required: true
+    default: 512
+    min: 1
+    max: 2048
+pricing:
+  input: '0.00265'
+  output: '0.0035'
+  unit: '0.00001'
+  currency: USD

api/core/model_runtime/model_providers/bedrock/llm/meta.llama3-8b-instruct-v1.yaml

@@ -0,0 +1,23 @@
+model: meta.llama3-8b-instruct-v1:0
+label:
+  en_US: Llama 3 Instruct 8B
+model_type: llm
+model_properties:
+  mode: completion
+  context_size: 8192
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_gen_len
+    use_template: max_tokens
+    required: true
+    default: 512
+    min: 1
+    max: 2048
+pricing:
+  input: '0.0004'
+  output: '0.0006'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/bedrock/llm/mistral.mistral-7b-instruct-v0.2.yaml

@@ -0,0 +1,39 @@
+model: mistral.mistral-7b-instruct-v0:2
+label:
+  en_US: Mistral 7B Instruct
+model_type: llm
+model_properties:
+  mode: completion
+  context_size: 32000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    required: false
+    default: 0.5
+  - name: top_p
+    use_template: top_p
+    required: false
+    default: 0.9
+  - name: top_k
+    use_template: 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.
+    required: false
+    default: 50
+    max: 200
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    default: 512
+    min: 1
+    max: 8192
+pricing:
+  input: '0.00015'
+  output: '0.0002'
+  unit: '0.00001'
+  currency: USD

api/core/model_runtime/model_providers/bedrock/llm/mistral.mistral-large-2402-v1.0.yaml

@@ -0,0 +1,27 @@
+model: mistral.mistral-large-2402-v1:0
+label:
+  en_US: Mistral Large
+model_type: llm
+model_properties:
+  mode: completion
+  context_size: 32000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    required: false
+    default: 0.7
+  - name: top_p
+    use_template: top_p
+    required: false
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    default: 512
+    min: 1
+    max: 4096
+pricing:
+  input: '0.008'
+  output: '0.024'
+  unit: '0.001'
+  currency: USD

api/core/model_runtime/model_providers/bedrock/llm/mistral.mixtral-8x7b-instruct-v0.1.yaml

@@ -0,0 +1,39 @@
+model: mistral.mixtral-8x7b-instruct-v0:1
+label:
+  en_US: Mixtral 8X7B Instruct
+model_type: llm
+model_properties:
+  mode: completion
+  context_size: 32000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    required: false
+    default: 0.5
+  - name: top_p
+    use_template: top_p
+    required: false
+    default: 0.9
+  - name: top_k
+    use_template: 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.
+    required: false
+    default: 50
+    max: 200
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    default: 512
+    min: 1
+    max: 8192
+pricing:
+  input: '0.00045'
+  output: '0.0007'
+  unit: '0.00001'
+  currency: USD

api/core/model_runtime/model_providers/chatglm/llm/llm.py

@@ -1,6 +1,5 @@
 import logging
 from collections.abc import Generator
-from os.path import join
 from typing import Optional, cast
 
 from httpx import Timeout
@@ -19,6 +18,7 @@ from openai import (
 )
 from openai.types.chat import ChatCompletion, ChatCompletionChunk
 from openai.types.chat.chat_completion_message import FunctionCall
+from yarl import URL
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
 from core.model_runtime.entities.message_entities import (
@@ -265,7 +265,7 @@ class ChatGLMLargeLanguageModel(LargeLanguageModel):
         client_kwargs = {
             "timeout": Timeout(315.0, read=300.0, write=10.0, connect=5.0),
             "api_key": "1",
-            "base_url": join(credentials['api_base'], 'v1')
+            "base_url": str(URL(credentials['api_base']) / 'v1')
         }
 
         return client_kwargs

api/core/model_runtime/model_providers/cohere/cohere.yaml

@@ -32,6 +32,15 @@ provider_credential_schema:
         zh_Hans: 在此输入您的 API Key
         en_US: Enter your API Key
       show_on: [ ]
+    - variable: base_url
+      label:
+        zh_Hans: API Base
+        en_US: API Base
+      type: text-input
+      required: false
+      placeholder:
+        zh_Hans: 在此输入您的 API Base，如 https://api.cohere.ai/v1
+        en_US: Enter your API Base, e.g. https://api.cohere.ai/v1
 model_credential_schema:
   model:
     label:
@@ -70,3 +79,12 @@ model_credential_schema:
       placeholder:
         zh_Hans: 在此输入您的 API Key
         en_US: Enter your API Key
+    - variable: base_url
+      label:
+        zh_Hans: API Base
+        en_US: API Base
+      type: text-input
+      required: false
+      placeholder:
+        zh_Hans: 在此输入您的 API Base，如 https://api.cohere.ai/v1
+        en_US: Enter your API Base, e.g. https://api.cohere.ai/v1

api/core/model_runtime/model_providers/cohere/llm/llm.py

@@ -173,7 +173,7 @@ class CohereLargeLanguageModel(LargeLanguageModel):
         :return: full response or stream response chunk generator result
         """
         # initialize client
-        client = cohere.Client(credentials.get('api_key'))
+        client = cohere.Client(credentials.get('api_key'), base_url=credentials.get('base_url'))
 
         if stop:
             model_parameters['end_sequences'] = stop
@@ -233,7 +233,8 @@ class CohereLargeLanguageModel(LargeLanguageModel):
 
         return response
 
-    def _handle_generate_stream_response(self, model: str, credentials: dict, response: Iterator[GenerateStreamedResponse],
+    def _handle_generate_stream_response(self, model: str, credentials: dict,
+                                         response: Iterator[GenerateStreamedResponse],
                                          prompt_messages: list[PromptMessage]) -> Generator:
         """
         Handle llm stream response
@@ -317,7 +318,7 @@ class CohereLargeLanguageModel(LargeLanguageModel):
         :return: full response or stream response chunk generator result
         """
         # initialize client
-        client = cohere.Client(credentials.get('api_key'))
+        client = cohere.Client(credentials.get('api_key'), base_url=credentials.get('base_url'))
 
         if stop:
             model_parameters['stop_sequences'] = stop
@@ -636,7 +637,7 @@ class CohereLargeLanguageModel(LargeLanguageModel):
         :return: number of tokens
         """
         # initialize client
-        client = cohere.Client(credentials.get('api_key'))
+        client = cohere.Client(credentials.get('api_key'), base_url=credentials.get('base_url'))
 
         response = client.tokenize(
             text=text,

api/core/model_runtime/model_providers/cohere/rerank/rerank.py

@@ -44,7 +44,7 @@ class CohereRerankModel(RerankModel):
             )
 
         # initialize client
-        client = cohere.Client(credentials.get('api_key'))
+        client = cohere.Client(credentials.get('api_key'), base_url=credentials.get('base_url'))
         response = client.rerank(
             query=query,
             documents=docs,

api/core/model_runtime/model_providers/cohere/text_embedding/text_embedding.py

@@ -141,7 +141,7 @@ class CohereTextEmbeddingModel(TextEmbeddingModel):
             return []
 
         # initialize client
-        client = cohere.Client(credentials.get('api_key'))
+        client = cohere.Client(credentials.get('api_key'), base_url=credentials.get('base_url'))
 
         response = client.tokenize(
             text=text,
@@ -180,7 +180,7 @@ class CohereTextEmbeddingModel(TextEmbeddingModel):
         :return: embeddings and used tokens
         """
         # initialize client
-        client = cohere.Client(credentials.get('api_key'))
+        client = cohere.Client(credentials.get('api_key'), base_url=credentials.get('base_url'))
 
         # call embedding model
         response = client.embed(

api/core/model_runtime/model_providers/deepseek/_assets/icon_l_en.svg

@@ -0,0 +1,22 @@
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+	<desc>
+			Created with Pixso.
+	</desc>
+	<defs>
+		<clipPath id="clip30_2029">
+			<rect id="_图层_1" width="134.577469" height="25.511124" transform="translate(60.422485 10.022217)" fill="white"/>
+		</clipPath>
+	</defs>
+	<g clip-path="url(#clip30_2029)">
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@@ -0,0 +1,3 @@
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+    <path id="path" d="M55.613,3.471C55.018,3.179 54.761,3.735 54.413,4.018C54.294,4.109 54.193,4.227 54.092,4.337C53.222,5.266 52.206,5.876 50.878,5.803C48.936,5.694 47.278,6.305 45.813,7.79C45.501,5.959 44.466,4.865 42.891,4.164C42.067,3.799 41.233,3.435 40.656,2.642C40.253,2.077 40.143,1.448 39.942,0.829C39.814,0.455 39.685,0.073 39.255,0.009C38.788,-0.064 38.605,0.328 38.421,0.656C37.689,1.995 37.405,3.471 37.432,4.965C37.496,8.327 38.916,11.006 41.737,12.91C42.058,13.129 42.14,13.347 42.039,13.666C41.847,14.323 41.618,14.96 41.416,15.616C41.288,16.035 41.096,16.127 40.647,15.944C39.099,15.297 37.762,14.341 36.58,13.184C34.575,11.243 32.761,9.102 30.499,7.425C29.968,7.033 29.436,6.669 28.887,6.323C26.579,4.082 29.189,2.241 29.794,2.022C30.425,1.795 30.013,1.011 27.971,1.02C25.928,1.029 24.06,1.713 21.679,2.624C21.331,2.761 20.964,2.861 20.589,2.943C18.427,2.533 16.183,2.441 13.838,2.706C9.424,3.198 5.898,5.284 3.306,8.847C0.191,13.129 -0.541,17.994 0.356,23.069C1.3,28.417 4.029,32.845 8.224,36.308C12.575,39.897 17.584,41.656 23.3,41.319C26.771,41.118 30.636,40.654 34.996,36.964C36.095,37.51 37.249,37.729 39.163,37.893C40.638,38.03 42.058,37.82 43.157,37.592C44.878,37.228 44.759,35.633 44.137,35.342C39.09,32.991 40.198,33.948 39.191,33.173C41.755,30.139 45.62,26.987 47.132,16.773C47.251,15.962 47.15,15.452 47.132,14.796C47.122,14.395 47.214,14.241 47.672,14.195C48.936,14.049 50.163,13.703 51.29,13.083C54.56,11.298 55.878,8.364 56.19,4.847C56.236,4.309 56.181,3.753 55.613,3.471ZM27.119,35.123C22.228,31.278 19.856,30.012 18.876,30.066C17.96,30.121 18.125,31.169 18.326,31.852C18.537,32.526 18.812,32.991 19.196,33.583C19.462,33.975 19.645,34.558 18.931,34.996C17.356,35.971 14.617,34.667 14.489,34.604C11.301,32.727 8.636,30.249 6.758,26.859C4.945,23.597 3.892,20.099 3.718,16.363C3.672,15.461 3.938,15.142 4.835,14.979C6.017,14.76 7.235,14.714 8.416,14.887C13.408,15.616 17.658,17.848 21.221,21.384C23.254,23.397 24.793,25.802 26.377,28.153C28.062,30.649 29.876,33.028 32.184,34.977C32.999,35.661 33.649,36.18 34.272,36.563C32.395,36.772 29.262,36.818 27.119,35.123ZM29.464,20.044C29.464,19.643 29.784,19.325 30.187,19.325C30.279,19.325 30.361,19.343 30.435,19.37C30.535,19.407 30.627,19.461 30.7,19.543C30.828,19.671 30.902,19.853 30.902,20.044C30.902,20.445 30.581,20.764 30.178,20.764C29.775,20.764 29.464,20.445 29.464,20.044ZM36.745,23.78C36.278,23.971 35.811,24.135 35.362,24.153C34.666,24.19 33.906,23.907 33.494,23.561C32.853,23.023 32.395,22.723 32.202,21.784C32.12,21.384 32.166,20.764 32.239,20.409C32.404,19.643 32.221,19.151 31.68,18.705C31.241,18.34 30.682,18.24 30.068,18.24C29.839,18.24 29.629,18.14 29.473,18.058C29.216,17.93 29.006,17.612 29.207,17.22C29.271,17.092 29.583,16.783 29.656,16.728C30.49,16.254 31.451,16.409 32.34,16.764C33.164,17.101 33.787,17.721 34.684,18.596C35.6,19.652 35.765,19.944 36.287,20.737C36.7,21.356 37.075,21.994 37.331,22.723C37.487,23.179 37.286,23.552 36.745,23.78Z" style="fill:rgb(77,107,254);fill-rule:nonzero;"/>
+</svg>

api/core/model_runtime/model_providers/deepseek/deepseek.py

@@ -0,0 +1,33 @@
+import logging
+
+from core.model_runtime.entities.model_entities import ModelType
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.model_provider import ModelProvider
+
+logger = logging.getLogger(__name__)
+
+
+
+class DeepSeekProvider(ModelProvider):
+
+    def validate_provider_credentials(self, credentials: dict) -> None:
+        """
+        Validate provider credentials
+        if validate failed, raise exception
+
+        :param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
+        """
+        try:
+            model_instance = self.get_model_instance(ModelType.LLM)
+
+            # Use `deepseek-chat` model for validate,
+            # no matter what model you pass in, text completion model or chat model
+            model_instance.validate_credentials(
+                model='deepseek-chat',
+                credentials=credentials
+            )
+        except CredentialsValidateFailedError as ex:
+            raise ex
+        except Exception as ex:
+            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            raise ex

api/core/model_runtime/model_providers/deepseek/deepseek.yaml

@@ -0,0 +1,41 @@
+provider: deepseek
+label:
+  en_US: deepseek
+  zh_Hans: 深度求索
+description:
+  en_US: Models provided by deepseek, such as deepseek-chat、deepseek-coder.
+  zh_Hans: 深度求索提供的模型，例如 deepseek-chat、deepseek-coder 。
+icon_small:
+  en_US: icon_s_en.svg
+icon_large:
+  en_US: icon_l_en.svg
+background: "#c0cdff"
+help:
+  title:
+    en_US: Get your API Key from deepseek
+    zh_Hans: 从深度求索获取 API Key
+  url:
+    en_US: https://platform.deepseek.com/api_keys
+supported_model_types:
+  - llm
+configurate_methods:
+  - predefined-model
+provider_credential_schema:
+  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: endpoint_url
+      label:
+        zh_Hans: 自定义 API endpoint 地址
+        en_US: Custom API endpoint URL
+      type: text-input
+      required: false
+      placeholder:
+        zh_Hans: Base URL, e.g. https://api.deepseek.com/v1 or https://api.deepseek.com
+        en_US: Base URL, e.g. https://api.deepseek.com/v1 or https://api.deepseek.com

api/core/model_runtime/model_providers/deepseek/llm/_position.yaml

@@ -0,0 +1,2 @@
+- deepseek-chat
+- deepseek-coder

api/core/model_runtime/model_providers/deepseek/llm/deepseek-chat.yaml

@@ -0,0 +1,64 @@
+model: deepseek-chat
+label:
+  zh_Hans: deepseek-chat
+  en_US: deepseek-chat
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 32000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    type: float
+    default: 1
+    min: 0.0
+    max: 2.0
+    help:
+      zh_Hans: 控制生成结果的多样性和随机性。数值越小，越严谨；数值越大，越发散。
+      en_US: Control the diversity and randomness of generated results. The smaller the value, the more rigorous it is; the larger the value, the more divergent it is.
+  - name: max_tokens
+    use_template: max_tokens
+    type: int
+    default: 4096
+    min: 1
+    max: 32000
+    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
+    type: float
+    default: 1
+    min: 0.01
+    max: 1.00
+    help:
+      zh_Hans: 控制生成结果的随机性。数值越小，随机性越弱；数值越大，随机性越强。一般而言，top_p 和 temperature 两个参数选择一个进行调整即可。
+      en_US: Control the randomness of generated results. The smaller the value, the weaker the randomness; the larger the value, the stronger the randomness. Generally speaking, you can adjust one of the two parameters top_p and temperature.
+  - name: logprobs
+    help:
+      zh_Hans: 是否返回所输出 token 的对数概率。如果为 true，则在 message 的 content 中返回每个输出 token 的对数概率。
+      en_US: Whether to return the log probability of the output token. If true, returns the log probability of each output token in the content of message .
+    type: boolean
+  - name: top_logprobs
+    type: int
+    default: 0
+    min: 0
+    max: 20
+    help:
+      zh_Hans: 一个介于 0 到 20 之间的整数 N，指定每个输出位置返回输出概率 top N 的 token，且返回这些 token 的对数概率。指定此参数时，logprobs 必须为 true。
+      en_US: An integer N between 0 and 20, specifying that each output position returns the top N tokens with output probability, and returns the logarithmic probability of these tokens. When specifying this parameter, logprobs must be true.
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    default: 0
+    min: -2.0
+    max: 2.0
+    help:
+      zh_Hans: 介于 -2.0 和 2.0 之间的数字。如果该值为正，那么新 token 会根据其在已有文本中的出现频率受到相应的惩罚，降低模型重复相同内容的可能性。
+      en_US: A number between -2.0 and 2.0. If the value is positive, new tokens are penalized based on their frequency of occurrence in existing text, reducing the likelihood that the model will repeat the same content.
+pricing:
+  input: '1'
+  output: '2'
+  unit: '0.000001'
+  currency: RMB

api/core/model_runtime/model_providers/deepseek/llm/deepseek-coder.yaml

@@ -0,0 +1,26 @@
+model: deepseek-coder
+label:
+  zh_Hans: deepseek-coder
+  en_US: deepseek-coder
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 16000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 1
+    default: 0.5
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 32000
+    default: 1024

api/core/model_runtime/model_providers/deepseek/llm/llm.py

@@ -0,0 +1,113 @@
+from collections.abc import Generator
+from typing import Optional, Union
+from urllib.parse import urlparse
+
+import tiktoken
+
+from core.model_runtime.entities.llm_entities import LLMResult
+from core.model_runtime.entities.message_entities import (
+    PromptMessage,
+    PromptMessageTool,
+)
+from core.model_runtime.model_providers.openai.llm.llm import OpenAILargeLanguageModel
+
+
+class DeepSeekLargeLanguageModel(OpenAILargeLanguageModel):
+
+    def _invoke(self, model: str, credentials: dict,
+                prompt_messages: list[PromptMessage], model_parameters: dict,
+                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
+                stream: bool = True, user: Optional[str] = None) \
+            -> Union[LLMResult, Generator]:
+        self._add_custom_parameters(credentials)
+
+        return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        self._add_custom_parameters(credentials)
+        super().validate_credentials(model, credentials)
+
+
+    # refactored from openai model runtime, use cl100k_base for calculate token number
+    def _num_tokens_from_string(self, model: str, text: str,
+                                tools: Optional[list[PromptMessageTool]] = None) -> int:
+        """
+        Calculate num tokens for text completion model with tiktoken package.
+
+        :param model: model name
+        :param text: prompt text
+        :param tools: tools for tool calling
+        :return: number of tokens
+        """
+        encoding = tiktoken.get_encoding("cl100k_base")
+        num_tokens = len(encoding.encode(text))
+
+        if tools:
+            num_tokens += self._num_tokens_for_tools(encoding, tools)
+
+        return num_tokens
+
+    # refactored from openai model runtime, use cl100k_base for calculate token number
+    def _num_tokens_from_messages(self, model: str, messages: list[PromptMessage],
+                                  tools: Optional[list[PromptMessageTool]] = None) -> int:
+        """Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
+
+        Official documentation: https://github.com/openai/openai-cookbook/blob/
+        main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
+        encoding = tiktoken.get_encoding("cl100k_base")
+        tokens_per_message = 3
+        tokens_per_name = 1
+
+        num_tokens = 0
+        messages_dict = [self._convert_prompt_message_to_dict(m) for m in messages]
+        for message in messages_dict:
+            num_tokens += tokens_per_message
+            for key, value in message.items():
+                # Cast str(value) in case the message value is not a string
+                # This occurs with function messages
+                # TODO: The current token calculation method for the image type is not implemented,
+                #  which need to download the image and then get the resolution for calculation,
+                #  and will increase the request delay
+                if isinstance(value, list):
+                    text = ''
+                    for item in value:
+                        if isinstance(item, dict) and item['type'] == 'text':
+                            text += item['text']
+
+                    value = text
+
+                if key == "tool_calls":
+                    for tool_call in value:
+                        for t_key, t_value in tool_call.items():
+                            num_tokens += len(encoding.encode(t_key))
+                            if t_key == "function":
+                                for f_key, f_value in t_value.items():
+                                    num_tokens += len(encoding.encode(f_key))
+                                    num_tokens += len(encoding.encode(f_value))
+                            else:
+                                num_tokens += len(encoding.encode(t_key))
+                                num_tokens += len(encoding.encode(t_value))
+                else:
+                    num_tokens += len(encoding.encode(str(value)))
+
+                if key == "name":
+                    num_tokens += tokens_per_name
+
+        # every reply is primed with <im_start>assistant
+        num_tokens += 3
+
+        if tools:
+            num_tokens += self._num_tokens_for_tools(encoding, tools)
+
+        return num_tokens
+
+    @staticmethod
+    def _add_custom_parameters(credentials: dict) -> None:
+        credentials['mode'] = 'chat'
+        credentials['openai_api_key']=credentials['api_key']
+        if 'endpoint_url' not in credentials or credentials['endpoint_url'] == "":
+            credentials['openai_api_base']='https://api.deepseek.com'
+        else:
+            parsed_url = urlparse(credentials['endpoint_url'])
+            credentials['openai_api_base']=f"{parsed_url.scheme}://{parsed_url.netloc}"
+

api/core/model_runtime/model_providers/groq/groq.py

@@ -19,7 +19,7 @@ class GroqProvider(ModelProvider):
             model_instance = self.get_model_instance(ModelType.LLM)
 
             model_instance.validate_credentials(
-                model='llama2-70b-4096',
+                model='llama3-8b-8192',
                 credentials=credentials
             )
         except CredentialsValidateFailedError as ex:

api/core/model_runtime/model_providers/groq/llm/llama3-70b-8192.yaml

@@ -0,0 +1,25 @@
+model: llama3-70b-8192
+label:
+  zh_Hans: Llama-3-70B-8192
+  en_US: Llama-3-70B-8192
+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: '0.05'
+  output: '0.1'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/groq/llm/llama3-8b-8192.yaml

@@ -0,0 +1,25 @@
+model: llama3-8b-8192
+label:
+  zh_Hans: Llama-3-8B-8192
+  en_US: Llama-3-8B-8192
+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: '0.59'
+  output: '0.79'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/jina/jina.yaml

@@ -19,6 +19,7 @@ supported_model_types:
   - rerank
 configurate_methods:
   - predefined-model
+  - customizable-model
 provider_credential_schema:
   credential_form_schemas:
     - variable: api_key
@@ -29,3 +30,40 @@ 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: base_url
+      label:
+        zh_Hans: 服务器 URL
+        en_US: Base URL
+      type: text-input
+      required: true
+      placeholder:
+        zh_Hans: Base URL, e.g. https://api.jina.ai/v1
+        en_US: Base URL, e.g. https://api.jina.ai/v1
+      default: 'https://api.jina.ai/v1'
+    - variable: context_size
+      label:
+        zh_Hans: 上下文大小
+        en_US: Context size
+      placeholder:
+        zh_Hans: 输入上下文大小
+        en_US: Enter context size
+      required: false
+      type: text-input
+      default: '8192'

api/core/model_runtime/model_providers/jina/rerank/rerank.py

@@ -2,6 +2,8 @@ from typing import Optional
 
 import httpx
 
+from core.model_runtime.entities.common_entities import I18nObject
+from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType
 from core.model_runtime.entities.rerank_entities import RerankDocument, RerankResult
 from core.model_runtime.errors.invoke import (
     InvokeAuthorizationError,
@@ -38,9 +40,13 @@ class JinaRerankModel(RerankModel):
         if len(docs) == 0:
             return RerankResult(model=model, docs=[])
 
+        base_url = credentials.get('base_url', 'https://api.jina.ai/v1')
+        if base_url.endswith('/'):
+            base_url = base_url[:-1]
+
         try:
             response = httpx.post(
-                "https://api.jina.ai/v1/rerank",
+                base_url + '/rerank',
                 json={
                     "model": model,
                     "query": query,
@@ -103,3 +109,19 @@ class JinaRerankModel(RerankModel):
             InvokeAuthorizationError: [httpx.HTTPStatusError],  
             InvokeBadRequestError: [httpx.RequestError]
         }
+
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
+        """
+            generate custom model entities from credentials
+        """
+        entity = AIModelEntity(
+            model=model,
+            label=I18nObject(en_US=model),
+            model_type=ModelType.RERANK,
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_properties={
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get('context_size'))
+            }
+        )
+
+        return entity

api/core/model_runtime/model_providers/jina/text_embedding/text_embedding.py

@@ -4,7 +4,8 @@ from typing import Optional
 
 from requests import post
 
-from core.model_runtime.entities.model_entities import PriceType
+from core.model_runtime.entities.common_entities import I18nObject
+from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType, PriceType
 from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
 from core.model_runtime.errors.invoke import (
     InvokeAuthorizationError,
@@ -23,8 +24,7 @@ class JinaTextEmbeddingModel(TextEmbeddingModel):
     """
     Model class for Jina text embedding model.
     """
-    api_base: str = 'https://api.jina.ai/v1/embeddings'
-    models: list[str] = ['jina-embeddings-v2-base-en', 'jina-embeddings-v2-small-en', 'jina-embeddings-v2-base-zh', 'jina-embeddings-v2-base-de']
+    api_base: str = 'https://api.jina.ai/v1'
 
     def _invoke(self, model: str, credentials: dict,
                 texts: list[str], user: Optional[str] = None) \
@@ -39,11 +39,14 @@ class JinaTextEmbeddingModel(TextEmbeddingModel):
         :return: embeddings result
         """
         api_key = credentials['api_key']
-        if model not in self.models:
-            raise InvokeBadRequestError('Invalid model name')
         if not api_key:
             raise CredentialsValidateFailedError('api_key is required')
-        url = self.api_base
+
+        base_url = credentials.get('base_url', self.api_base)
+        if base_url.endswith('/'):
+            base_url = base_url[:-1]
+
+        url = base_url + '/embeddings'
         headers = {
             'Authorization': 'Bearer ' + api_key,
             'Content-Type': 'application/json'
@@ -70,7 +73,7 @@ class JinaTextEmbeddingModel(TextEmbeddingModel):
                 elif response.status_code == 500:
                     raise InvokeServerUnavailableError(msg)
                 else:
-                    raise InvokeError(msg)
+                    raise InvokeBadRequestError(msg)
             except JSONDecodeError as e:
                 raise InvokeServerUnavailableError(f"Failed to convert response to json: {e} with text: {response.text}")
 
@@ -118,8 +121,8 @@ class JinaTextEmbeddingModel(TextEmbeddingModel):
         """
         try:
             self._invoke(model=model, credentials=credentials, texts=['ping'])
-        except InvokeAuthorizationError:
-            raise CredentialsValidateFailedError('Invalid api key')
+        except Exception as e:
+            raise CredentialsValidateFailedError(f'Credentials validation failed: {e}')
 
     @property
     def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
@@ -137,7 +140,8 @@ class JinaTextEmbeddingModel(TextEmbeddingModel):
                 InvokeAuthorizationError
             ],
             InvokeBadRequestError: [
-                KeyError
+                KeyError,
+                InvokeBadRequestError
             ]
         }
     
@@ -170,3 +174,19 @@ class JinaTextEmbeddingModel(TextEmbeddingModel):
         )
 
         return usage
+
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
+        """
+            generate custom model entities from credentials
+        """
+        entity = AIModelEntity(
+            model=model,
+            label=I18nObject(en_US=model),
+            model_type=ModelType.TEXT_EMBEDDING,
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_properties={
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get('context_size'))
+            }
+        )
+
+        return entity

api/core/model_runtime/model_providers/leptonai/leptonai.py

@@ -0,0 +1,29 @@
+import logging
+
+from core.model_runtime.entities.model_entities import ModelType
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.model_provider import ModelProvider
+
+logger = logging.getLogger(__name__)
+
+class LeptonAIProvider(ModelProvider):
+
+    def validate_provider_credentials(self, credentials: dict) -> None:
+        """
+        Validate provider credentials
+        if validate failed, raise exception
+
+        :param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
+        """
+        try:
+            model_instance = self.get_model_instance(ModelType.LLM)
+
+            model_instance.validate_credentials(
+                model='llama2-7b',
+                credentials=credentials
+            )
+        except CredentialsValidateFailedError as ex:
+            raise ex
+        except Exception as ex:
+            logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+            raise ex

api/core/model_runtime/model_providers/leptonai/leptonai.yaml

@@ -0,0 +1,29 @@
+provider: leptonai
+label:
+  zh_Hans: Lepton AI
+  en_US: Lepton AI
+icon_small:
+  en_US: icon_s_en.png
+icon_large:
+  en_US: icon_l_en.png
+background: "#F5F5F4"
+help:
+  title:
+    en_US: Get your API Key from Lepton AI
+    zh_Hans: 从 Lepton AI 获取 API Key
+  url:
+    en_US: https://dashboard.lepton.ai
+supported_model_types:
+  - llm
+configurate_methods:
+  - predefined-model
+provider_credential_schema:
+  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

api/core/model_runtime/model_providers/leptonai/llm/_position.yaml

@@ -0,0 +1,6 @@
+- gemma-7b
+- mistral-7b
+- mixtral-8x7b
+- llama2-7b
+- llama2-13b
+- llama3-70b

api/core/model_runtime/model_providers/leptonai/llm/gemma-7b.yaml

@@ -0,0 +1,20 @@
+model: gemma-7b
+label:
+  zh_Hans: gemma-7b
+  en_US: gemma-7b
+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: 1024
+    min: 1
+    max: 1024

api/core/model_runtime/model_providers/leptonai/llm/llama2-13b.yaml

@@ -0,0 +1,20 @@
+model: llama2-13b
+label:
+  zh_Hans: llama2-13b
+  en_US: llama2-13b
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 4096
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 1024
+    min: 1
+    max: 1024

api/core/model_runtime/model_providers/leptonai/llm/llama2-7b.yaml

@@ -0,0 +1,20 @@
+model: llama2-7b
+label:
+  zh_Hans: llama2-7b
+  en_US: llama2-7b
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 4096
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 1024
+    min: 1
+    max: 1024

api/core/model_runtime/model_providers/leptonai/llm/llama3-70b.yaml

@@ -0,0 +1,20 @@
+model: llama3-70b
+label:
+  zh_Hans: llama3-70b
+  en_US: llama3-70b
+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: 1024
+    min: 1
+    max: 1024

api/core/model_runtime/model_providers/leptonai/llm/llm.py

@@ -0,0 +1,34 @@
+from collections.abc import Generator
+from typing import Optional, Union
+
+from core.model_runtime.entities.llm_entities import LLMResult
+from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool
+from core.model_runtime.model_providers.openai_api_compatible.llm.llm import OAIAPICompatLargeLanguageModel
+
+
+class LeptonAILargeLanguageModel(OAIAPICompatLargeLanguageModel):
+    MODEL_PREFIX_MAP = {
+        'llama2-7b': 'llama2-7b',
+        'gemma-7b': 'gemma-7b',
+        'mistral-7b': 'mistral-7b',
+        'mixtral-8x7b': 'mixtral-8x7b',
+        'llama3-70b': 'llama3-70b',
+        'llama2-13b': 'llama2-13b',
+    } 
+    def _invoke(self, model: str, credentials: dict,
+                prompt_messages: list[PromptMessage], model_parameters: dict,
+                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
+                stream: bool = True, user: Optional[str] = None) \
+            -> Union[LLMResult, Generator]:
+        self._add_custom_parameters(credentials, model)
+        return super()._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream)
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        self._add_custom_parameters(credentials, model)
+        super().validate_credentials(model, credentials)
+
+    @classmethod
+    def _add_custom_parameters(cls, credentials: dict, model: str) -> None:
+        credentials['mode'] = 'chat'
+        credentials['endpoint_url'] = f'https://{cls.MODEL_PREFIX_MAP[model]}.lepton.run/api/v1'
+        

api/core/model_runtime/model_providers/leptonai/llm/mistral-7b.yaml

@@ -0,0 +1,20 @@
+model: mistral-7b
+label:
+  zh_Hans: mistral-7b
+  en_US: mistral-7b
+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: 1024
+    min: 1
+    max: 1024

api/core/model_runtime/model_providers/leptonai/llm/mixtral-8x7b.yaml

@@ -0,0 +1,20 @@
+model: mixtral-8x7b
+label:
+  zh_Hans: mixtral-8x7b
+  en_US: mixtral-8x7b
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 32000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 1024
+    min: 1
+    max: 1024

api/core/model_runtime/model_providers/localai/localai.yaml

@@ -15,6 +15,8 @@ help:
 supported_model_types:
   - llm
   - text-embedding
+  - rerank
+  - speech2text
 configurate_methods:
   - customizable-model
 model_credential_schema:
@@ -57,6 +59,9 @@ model_credential_schema:
         zh_Hans: 在此输入LocalAI的服务器地址，如 http://192.168.1.100:8080
         en_US: Enter the url of your LocalAI, e.g. http://192.168.1.100:8080
     - variable: context_size
+      show_on:
+        - variable: __model_type
+          value: llm
       label:
         zh_Hans: 上下文大小
         en_US: Context size