redis iam check

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

.github/workflows/api-tests.yml

@@ -4,6 +4,13 @@ on:
   pull_request:
     branches:
       - main
+    paths:
+      - api/**
+      - docker/**
+
+concurrency:
+  group: api-tests-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
 
 jobs:
   test:
@@ -51,7 +58,7 @@ jobs:
       - name: Run Workflow
         run: dev/pytest/pytest_workflow.sh
 
-      - name: Set up Vector Stores (Weaviate, Qdrant, PGVector, Milvus, PgVecto-RS)
+      - name: Set up Vector Stores (Weaviate, Qdrant, PGVector, Milvus, PgVecto-RS, Chroma)
         uses: hoverkraft-tech/compose-action@v2.0.0
         with:
           compose-file: |
@@ -60,6 +67,7 @@ jobs:
             docker/docker-compose.milvus.yaml
             docker/docker-compose.pgvecto-rs.yaml
             docker/docker-compose.pgvector.yaml
+            docker/docker-compose.chroma.yaml
           services: |
             weaviate
             qdrant
@@ -68,6 +76,82 @@ jobs:
             milvus-standalone
             pgvecto-rs
             pgvector
+            chroma
 
       - name: Test Vector Stores
         run: dev/pytest/pytest_vdb.sh
+
+  test-in-poetry:
+    name: API Tests
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version:
+          - "3.10"
+          - "3.11"
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Install Poetry
+        uses: abatilo/actions-poetry@v3
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: 'poetry'
+          cache-dependency-path: |
+            api/pyproject.toml
+            api/poetry.lock
+
+      - name: Poetry check
+        run: poetry check -C api
+
+      - name: Install dependencies
+        run: poetry install -C api --with dev
+
+      - name: Run Unit tests
+        run: poetry run -C api bash dev/pytest/pytest_unit_tests.sh
+
+      - name: Run ModelRuntime
+        run: poetry run -C api bash dev/pytest/pytest_model_runtime.sh
+
+      - name: Run Tool
+        run: poetry run -C api bash 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: poetry run -C api bash dev/pytest/pytest_workflow.sh
+
+      - name: Set up Vector Stores (Weaviate, Qdrant, PGVector, Milvus, PgVecto-RS, Chroma)
+        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
+            docker/docker-compose.chroma.yaml
+          services: |
+            weaviate
+            qdrant
+            etcd
+            minio
+            milvus-standalone
+            pgvecto-rs
+            pgvector
+            chroma
+
+      - name: Test Vector Stores
+        run: poetry run -C api bash dev/pytest/pytest_vdb.sh

.github/workflows/db-migration-test.yml

@@ -0,0 +1,57 @@
+name: DB Migration Test
+
+on:
+  pull_request:
+    branches:
+      - main
+    paths:
+      - api/migrations/**
+
+concurrency:
+  group: db-migration-test-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  db-migration-test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version:
+          - "3.10"
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Install Poetry
+        uses: abatilo/actions-poetry@v3
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: 'poetry'
+          cache-dependency-path: |
+            api/pyproject.toml
+            api/poetry.lock
+
+      - name: Install dependencies
+        run: poetry install -C api
+
+      - name: Set up Middleware
+        uses: hoverkraft-tech/compose-action@v2.0.0
+        with:
+          compose-file: |
+            docker/docker-compose.middleware.yaml
+          services: |
+            db
+
+      - name: Prepare configs
+        run: |
+          cd api
+          cp .env.example .env
+
+      - name: Run DB Migration
+        run: |
+          cd api
+          poetry run python -m flask db upgrade

.github/workflows/style.yml

@@ -6,7 +6,7 @@ on:
       - main
 
 concurrency:
-  group: dep-${{ github.head_ref || github.run_id }}
+  group: style-${{ github.head_ref || github.run_id }}
   cancel-in-progress: true
 
 jobs:
@@ -18,54 +18,92 @@ jobs:
       - name: Checkout code
         uses: actions/checkout@v4
 
+      - name: Check changed files
+        id: changed-files
+        uses: tj-actions/changed-files@v44
+        with:
+          files: api/**
+
+      - name: Install Poetry
+        uses: abatilo/actions-poetry@v3
+
       - name: Set up Python
         uses: actions/setup-python@v5
+        if: steps.changed-files.outputs.any_changed == 'true'
         with:
           python-version: '3.10'
 
       - name: Python dependencies
-        run: pip install ruff dotenv-linter
+        if: steps.changed-files.outputs.any_changed == 'true'
+        run: poetry install -C api --only lint
 
       - name: Ruff check
-        run: ruff check ./api
+        if: steps.changed-files.outputs.any_changed == 'true'
+        run: poetry run -C api ruff check --preview ./api
 
       - name: Dotenv check
-        run: dotenv-linter ./api/.env.example ./web/.env.example
+        if: steps.changed-files.outputs.any_changed == 'true'
+        run: poetry run -C api dotenv-linter ./api/.env.example ./web/.env.example
 
       - name: Lint hints
         if: failure()
         run: echo "Please run 'dev/reformat' to fix the fixable linting errors."
 
-  test:
-    name: ESLint and SuperLinter
+  web-style:
+    name: Web Style
     runs-on: ubuntu-latest
-    needs: python-style
+    defaults:
+      run:
+        working-directory: ./web
 
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
+
+      - name: Check changed files
+        id: changed-files
+        uses: tj-actions/changed-files@v44
         with:
-          fetch-depth: 0
+          files: web/**
 
       - name: Setup NodeJS
         uses: actions/setup-node@v4
+        if: steps.changed-files.outputs.any_changed == 'true'
         with:
           node-version: 20
           cache: yarn
           cache-dependency-path: ./web/package.json
 
       - name: Web dependencies
-        run: |
-          cd ./web
-          yarn install --frozen-lockfile
+        if: steps.changed-files.outputs.any_changed == 'true'
+        run: yarn install --frozen-lockfile
 
       - name: Web style check
-        run: |
-          cd ./web
-          yarn run lint
+        if: steps.changed-files.outputs.any_changed == 'true'
+        run: yarn run lint
+
+
+  superlinter:
+    name: SuperLinter
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Check changed files
+        id: changed-files
+        uses: tj-actions/changed-files@v44
+        with:
+          files: |
+            **.sh
+            **.yaml
+            **.yml
+            Dockerfile
 
       - name: Super-linter
         uses: super-linter/super-linter/slim@v6
+        if: steps.changed-files.outputs.any_changed == 'true'
         env:
           BASH_SEVERITY: warning
           DEFAULT_BRANCH: main
@@ -76,4 +114,5 @@ jobs:
           VALIDATE_BASH_EXEC: true
           VALIDATE_GITHUB_ACTIONS: true
           VALIDATE_DOCKERFILE_HADOLINT: true
+          VALIDATE_XML: true
           VALIDATE_YAML: true

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

@@ -4,6 +4,13 @@ on:
   pull_request:
     branches:
       - main
+    paths:
+      - sdks/**
+
+concurrency:
+  group: sdk-tests-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
 jobs:
   build:
     name: unit test for Node.js SDK

.gitignore

@@ -134,7 +134,8 @@ dmypy.json
 web/.vscode/settings.json
 
 # Intellij IDEA Files
-.idea/
+.idea/*
+!.idea/vcs.xml
 .ideaDataSources/
 
 api/.env
@@ -148,6 +149,7 @@ docker/volumes/qdrant/*
 docker/volumes/etcd/*
 docker/volumes/minio/*
 docker/volumes/milvus/*
+docker/volumes/chroma/*
 
 sdks/python-client/build
 sdks/python-client/dist

.idea/vcs.xml

@@ -0,0 +1,16 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="IssueNavigationConfiguration">
+    <option name="links">
+      <list>
+        <IssueNavigationLink>
+          <option name="issueRegexp" value="#(\d+)" />
+          <option name="linkRegexp" value="https://github.com/langgenius/dify/issues/$1" />
+        </IssueNavigationLink>
+      </list>
+    </option>
+  </component>
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

README.md

@@ -36,6 +36,7 @@
   <a href="./README_FR.md"><img alt="README en Français" src="https://img.shields.io/badge/Français-d9d9d9"></a>
   <a href="./README_KL.md"><img alt="README tlhIngan Hol" src="https://img.shields.io/badge/Klingon-d9d9d9"></a>
   <a href="./README_KR.md"><img alt="README in Korean" src="https://img.shields.io/badge/한국어-d9d9d9"></a>
+  <a href="./README_AR.md"><img alt="README بالعربية" src="https://img.shields.io/badge/العربية-d9d9d9"></a>
 </p>
 
 

README_AR.md

@@ -0,0 +1,225 @@
+![cover-v5-optimized](https://github.com/langgenius/dify/assets/13230914/f9e19af5-61ba-4119-b926-d10c4c06ebab)
+
+<p align="center">
+  <a href="https://cloud.dify.ai">Dify Cloud</a> ·
+  <a href="https://docs.dify.ai/getting-started/install-self-hosted">الاستضافة الذاتية</a> ·
+  <a href="https://docs.dify.ai">التوثيق</a> ·
+  <a href="https://cal.com/guchenhe/60-min-meeting">استفسارات الشركات</a>
+</p>
+
+<p align="center">
+    <a href="https://dify.ai" target="_blank">
+        <img alt="Static Badge" src="https://img.shields.io/badge/Product-F04438"></a>
+    <a href="https://dify.ai/pricing" target="_blank">
+        <img alt="Static Badge" src="https://img.shields.io/badge/free-pricing?logo=free&color=%20%23155EEF&label=pricing&labelColor=%20%23528bff"></a>
+    <a href="https://discord.gg/FngNHpbcY7" target="_blank">
+        <img src="https://img.shields.io/discord/1082486657678311454?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb"
+            alt="chat on Discord"></a>
+    <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
+        <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
+            alt="follow on Twitter"></a>
+    <a href="https://hub.docker.com/u/langgenius" target="_blank">
+        <img alt="Docker Pulls" src="https://img.shields.io/docker/pulls/langgenius/dify-web?labelColor=%20%23FDB062&color=%20%23f79009"></a>
+    <a href="https://github.com/langgenius/dify/graphs/commit-activity" target="_blank">
+        <img alt="Commits last month" src="https://img.shields.io/github/commit-activity/m/langgenius/dify?labelColor=%20%2332b583&color=%20%2312b76a"></a>
+    <a href="https://github.com/langgenius/dify/" target="_blank">
+        <img alt="Issues closed" src="https://img.shields.io/github/issues-search?query=repo%3Alanggenius%2Fdify%20is%3Aclosed&label=issues%20closed&labelColor=%20%237d89b0&color=%20%235d6b98"></a>
+    <a href="https://github.com/langgenius/dify/discussions/" target="_blank">
+        <img alt="Discussion posts" src="https://img.shields.io/github/discussions/langgenius/dify?labelColor=%20%239b8afb&color=%20%237a5af8"></a>
+</p>
+
+<p align="center">
+  <a href="./README.md"><img alt="README in English" src="https://img.shields.io/badge/English-d9d9d9"></a>
+  <a href="./README_CN.md"><img alt="简体中文版自述文件" src="https://img.shields.io/badge/简体中文-d9d9d9"></a>
+  <a href="./README_JA.md"><img alt="日本語のREADME" src="https://img.shields.io/badge/日本語-d9d9d9"></a>
+  <a href="./README_ES.md"><img alt="README en Español" src="https://img.shields.io/badge/Español-d9d9d9"></a>
+  <a href="./README_FR.md"><img alt="README en Français" src="https://img.shields.io/badge/Français-d9d9d9"></a>
+  <a href="./README_KL.md"><img alt="README tlhIngan Hol" src="https://img.shields.io/badge/Klingon-d9d9d9"></a>
+  <a href="./README_KR.md"><img alt="README in Korean" src="https://img.shields.io/badge/한국어-d9d9d9"></a>
+  <a href="./README_AR.md"><img alt="README بالعربية" src="https://img.shields.io/badge/العربية-d9d9d9"></a>
+</p>
+
+<div style="text-align: right;">
+مشروع Dify هو منصة تطوير تطبيقات الذكاء الصناعي مفتوحة المصدر. تجمع واجهته البديهية بين سير العمل الذكي بالذكاء الاصطناعي وخط أنابيب RAG وقدرات الوكيل وإدارة النماذج وميزات الملاحظة وأكثر من ذلك، مما يتيح لك الانتقال بسرعة من المرحلة التجريبية إلى الإنتاج. إليك قائمة بالميزات الأساسية:
+</br> </br>
+
+**1. سير العمل**:  قم ببناء واختبار سير عمل الذكاء الاصطناعي القوي على قماش بصري، مستفيدًا من جميع الميزات التالية وأكثر.
+
+  https://github.com/langgenius/dify/assets/13230914/356df23e-1604-483d-80a6-9517ece318aa
+
+
+**2. الدعم الشامل للنماذج**: تكامل سلس مع مئات من LLMs الخاصة / مفتوحة المصدر من عشرات من موفري التحليل والحلول المستضافة ذاتيًا، مما يغطي GPT و Mistral و Llama3 وأي نماذج متوافقة مع واجهة OpenAI API. يمكن العثور على قائمة كاملة بمزودي النموذج المدعومين [هنا](https://docs.dify.ai/getting-started/readme/model-providers).
+
+![providers-v5](https://github.com/langgenius/dify/assets/13230914/5a17bdbe-097a-4100-8363-40255b70f6e3)
+
+**3. بيئة التطوير للأوامر**: واجهة بيئة التطوير المبتكرة لصياغة الأمر ومقارنة أداء النموذج، وإضافة ميزات إضافية مثل تحويل النص إلى كلام إلى تطبيق قائم على الدردشة.
+
+**4. خط أنابيب RAG**: قدرات RAG الواسعة التي تغطي كل شيء من استيعاب الوثائق إلى الاسترجاع، مع الدعم الفوري لاستخراج النص من ملفات PDF و PPT وتنسيقات الوثائق الشائعة الأخرى.
+
+**5. قدرات الوكيل**: يمكنك تعريف الوكلاء بناءً على أمر وظيفة LLM أو ReAct، وإضافة أدوات مدمجة أو مخصصة للوكيل. توفر Dify أكثر من 50 أداة مدمجة لوكلاء الذكاء الاصطناعي، مثل البحث في Google و DELL·E وStable Diffusion و WolframAlpha.
+
+**6. الـ LLMOps**: راقب وتحلل سجلات التطبيق والأداء على مر الزمن. يمكنك تحسين الأوامر والبيانات والنماذج باستمرار استنادًا إلى البيانات الإنتاجية والتعليقات.
+
+**7.الواجهة الخلفية (Backend) كخدمة**: تأتي جميع عروض Dify مع APIs مطابقة، حتى يمكنك دمج Dify بسهولة في منطق أعمالك الخاص.
+## مقارنة الميزات
+<table style="width: 100%;">
+  <tr>
+    <th align="center">الميزة</th>
+    <th align="center">Dify.AI</th>
+    <th align="center">LangChain</th>
+    <th align="center">Flowise</th>
+    <th align="center">OpenAI Assistants API</th>
+  </tr>
+  <tr>
+    <td align="center">نهج البرمجة</td>
+    <td align="center">موجّه لـ تطبيق + واجهة برمجة تطبيق (API)</td>
+    <td align="center">برمجة Python</td>
+    <td align="center">موجه لتطبيق</td>
+    <td align="center">واجهة برمجة تطبيق (API)</td>
+  </tr>
+  <tr>
+    <td align="center">LLMs المدعومة</td>
+    <td align="center">تنوع غني</td>
+    <td align="center">تنوع غني</td>
+    <td align="center">تنوع غني</td>
+    <td align="center">فقط OpenAI</td>
+  </tr>
+  <tr>
+    <td align="center">محرك RAG</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+  </tr>
+  <tr>
+    <td align="center">الوكيل</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+    <td align="center">✅</td>
+  </tr>
+  <tr>
+    <td align="center">سير العمل</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+  </tr>
+  <tr>
+    <td align="center">الملاحظة</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+    <td align="center">❌</td>
+  </tr>
+  <tr>
+    <td align="center">ميزات الشركات (SSO / مراقبة الوصول)</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+    <td align="center">❌</td>
+    <td align="center">❌</td>
+  </tr>
+  <tr>
+    <td align="center">نشر محلي</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">✅</td>
+    <td align="center">❌</td>
+  </tr>
+</table>
+
+
+## استخدام Dify
+- **سحابة </br>**
+نحن نستضيف [خدمة Dify Cloud](https://dify.ai) لأي شخص لتجربتها بدون أي إعدادات. توفر كل قدرات النسخة التي تمت استضافتها ذاتيًا، وتتضمن 200 أمر GPT-4 مجانًا في خطة الصندوق الرملي.
+
+- **استضافة ذاتية لنسخة المجتمع Dify</br>**
+ابدأ سريعًا في تشغيل Dify في بيئتك باستخدام [دليل البدء السريع](#البدء السريع).
+استخدم [توثيقنا](https://docs.dify.ai) للمزيد من المراجع والتعليمات الأعمق.
+
+- **مشروع Dify للشركات / المؤسسات</br>**
+نحن نوفر ميزات إضافية مركزة على الشركات. [جدول اجتماع معنا](https://cal.com/guchenhe/30min) أو [أرسل لنا بريدًا إلكترونيًا](mailto:business@dify.ai?subject=[GitHub]Business%20License%20Inquiry) لمناقشة احتياجات الشركات. </br>
+> بالنسبة للشركات الناشئة والشركات الصغيرة التي تستخدم خدمات AWS، تحقق من [Dify Premium على AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6) ونشرها في شبكتك الخاصة على AWS VPC بنقرة واحدة. إنها عرض AMI بأسعار معقولة مع خيار إنشاء تطبيقات بشعار وعلامة تجارية مخصصة.
+## البقاء قدمًا
+
+قم بإضافة نجمة إلى Dify على GitHub وتلق تنبيهًا فوريًا بالإصدارات الجديدة.
+
+![نجمنا](https://github.com/langgenius/dify/assets/13230914/b823edc1-6388-4e25-ad45-2f6b187adbb4)
+## البداية السريعة
+> قبل تثبيت Dify، تأكد من أن جهازك يلبي الحد الأدنى من متطلبات النظام التالية:
+> 
+>- معالج >= 2 نواة
+>- ذاكرة وصول عشوائي (RAM) >= 4 جيجابايت
+
+</br>
+
+أسهل طريقة لبدء تشغيل خادم Dify هي تشغيل ملف [docker-compose.yml](docker/docker-compose.yaml) الخاص بنا. قبل تشغيل أمر التثبيت، تأكد من تثبيت [Docker](https://docs.docker.com/get-docker/) و [Docker Compose](https://docs.docker.com/compose/install/) على جهازك:
+
+```bash
+cd docker
+docker compose up -d
+```
+بعد التشغيل، يمكنك الوصول إلى لوحة تحكم Dify في متصفحك على [http://localhost/install](http://localhost/install) وبدء عملية التهيئة.
+
+> إذا كنت ترغب في المساهمة في Dify أو القيام بتطوير إضافي، فانظر إلى [دليلنا للنشر من الشفرة (code) المصدرية](https://docs.dify.ai/getting-started/install-self-hosted/local-source-code)
+
+## الخطوات التالية
+
+إذا كنت بحاجة إلى تخصيص التكوين، يرجى الرجوع إلى التعليقات في ملف [docker-compose.yml](docker/docker-compose.yaml) لدينا وتعيين التكوينات البيئية يدويًا. بعد إجراء التغييرات، يرجى تشغيل `docker-compose up -d` مرة أخرى. يمكنك رؤية قائمة كاملة بالمتغيرات البيئية [هنا](https://docs.dify.ai/getting-started/install-self-hosted/environments).
+
+إذا كنت ترغب في تكوين إعداد متوفر بشكل عالي، فهناك [رسوم بيانية Helm](https://helm.sh/) المساهمة من المجتمع تسمح بنشر Dify على Kubernetes.
+
+- [رسم بياني Helm من قبل @LeoQuote](https://github.com/douban/charts/tree/master/charts/dify)
+- [رسم بياني Helm من قبل @BorisPolonsky](https://github.com/BorisPolonsky/dify-helm)
+
+
+## المساهمة
+
+لأولئك الذين يرغبون في المساهمة، انظر إلى [دليل المساهمة](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md) لدينا. 
+في الوقت نفسه، يرجى النظر في دعم Dify عن طريق مشاركته على وسائل التواصل الاجتماعي وفي الفعاليات والمؤتمرات.
+
+
+> نحن نبحث عن مساهمين لمساعدة في ترجمة Dify إلى لغات أخرى غير اللغة الصينية المندرين أو الإنجليزية. إذا كنت مهتمًا بالمساعدة، يرجى الاطلاع على [README للترجمة](https://github.com/langgenius/dify/blob/main/web/i18n/README.md) لمزيد من المعلومات، واترك لنا تعليقًا في قناة `global-users` على [خادم المجتمع على Discord](https://discord.gg/8Tpq4AcN9c).
+
+**المساهمون**
+
+<a href="https://github.com/langgenius/dify/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=langgenius/dify" />
+</a>
+
+## المجتمع والاتصال
+* [مناقشة Github](https://github.com/langgenius/dify/discussions). الأفضل لـ: مشاركة التعليقات وطرح الأسئلة.
+* [المشكلات على GitHub](https://github.com/langgenius/dify/issues). الأفضل لـ: الأخطاء التي تواجهها في استخدام Dify.AI، واقتراحات الميزات. انظر [دليل المساهمة](https://github.com/langgenius/dify/blob/main/CONTRIBUTING.md).
+* [البريد الإلكتروني](mailto:support@dify.ai?subject=[GitHub]Questions%20About%20Dify). الأفضل لـ: الأسئلة التي تتعلق باستخدام Dify.AI.
+* [Discord](https://discord.gg/FngNHpbcY7). الأفضل لـ: مشاركة تطبيقاتك والترفيه مع المجتمع.
+* [تويتر](https://twitter.com/dify_ai). الأفضل لـ: مشاركة تطبيقاتك والترفيه مع المجتمع.
+
+أو، قم بجدولة اجتماع مباشرة مع أحد أعضاء الفريق:
+
+<table>
+  <tr>
+    <th>نقطة الاتصال</th>
+    <th>الغرض</th>
+  </tr>
+  <tr>
+    <td><a href='https://cal.com/guchenhe/15min' target='_blank'><img class="schedule-button" src='https://github.com/langgenius/dify/assets/13230914/9ebcd111-1205-4d71-83d5-948d70b809f5' alt='Git-Hub-README-Button-3x' style="width: 180px; height: auto; object-fit: contain;"/></a></td>
+    <td>استفسارات الأعمال واقتراحات حول المنتج</td>
+  </tr>
+  <tr>
+    <td><a href='https://cal.com/pinkbanana' target='_blank'><img class="schedule-button" src='https://github.com/langgenius/dify/assets/13230914/d1edd00a-d7e4-4513-be6c-e57038e143fd' alt='Git-Hub-README-Button-2x' style="width: 180px; height: auto; object-fit: contain;"/></a></td>
+    <td>المساهمات والمشكلات وطلبات الميزات</td>
+  </tr>
+</table>
+
+## تاريخ النجمة
+
+[![Star History Chart](https://api.star-history.com/svg?repos=langgenius/dify&type=Date)](https://star-history.com/#langgenius/dify&Date)
+
+
+## الكشف عن الأمان
+
+لحماية خصوصيتك، يرجى تجنب نشر مشكلات الأمان على GitHub. بدلاً من ذلك، أرسل أسئلتك إلى security@dify.ai وسنقدم لك إجابة أكثر تفصيلاً.
+
+## الرخصة
+
+هذا المستودع متاح تحت [رخصة البرنامج الحر Dify](LICENSE)، والتي تعتبر بشكل أساسي Apache 2.0 مع بعض القيود الإضافية.

api/.env.example

@@ -17,6 +17,9 @@ APP_WEB_URL=http://127.0.0.1:3000
 # Files URL
 FILES_URL=http://127.0.0.1:5001
 
+# The time in seconds after the signature is rejected
+FILES_ACCESS_TIMEOUT=300
+
 # celery configuration
 CELERY_BROKER_URL=redis://:difyai123456@localhost:6379/1
 
@@ -109,6 +112,21 @@ PGVECTOR_USER=postgres
 PGVECTOR_PASSWORD=postgres
 PGVECTOR_DATABASE=postgres
 
+# Tidb Vector configuration
+TIDB_VECTOR_HOST=xxx.eu-central-1.xxx.aws.tidbcloud.com
+TIDB_VECTOR_PORT=4000
+TIDB_VECTOR_USER=xxx.root
+TIDB_VECTOR_PASSWORD=xxxxxx
+TIDB_VECTOR_DATABASE=dify
+
+# Chroma configuration
+CHROMA_HOST=127.0.0.1
+CHROMA_PORT=8000
+CHROMA_TENANT=default_tenant
+CHROMA_DATABASE=default_database
+CHROMA_AUTH_PROVIDER=chromadb.auth.token_authn.TokenAuthenticationServerProvider
+CHROMA_AUTH_CREDENTIALS=difyai123456
+
 # Upload configuration
 UPLOAD_FILE_SIZE_LIMIT=15
 UPLOAD_FILE_BATCH_LIMIT=5
@@ -124,10 +142,11 @@ RESEND_API_KEY=
 RESEND_API_URL=https://api.resend.com
 # smtp configuration
 SMTP_SERVER=smtp.gmail.com
-SMTP_PORT=587
+SMTP_PORT=465
 SMTP_USERNAME=123
 SMTP_PASSWORD=abc
-SMTP_USE_TLS=false
+SMTP_USE_TLS=true
+SMTP_OPPORTUNISTIC_TLS=false
 
 # Sentry configuration
 SENTRY_DSN=
@@ -179,3 +198,8 @@ LOG_FILE=
 
 # Indexing configuration
 INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH=1000
+
+# Workflow runtime configuration
+WORKFLOW_MAX_EXECUTION_STEPS=500
+WORKFLOW_MAX_EXECUTION_TIME=1200
+WORKFLOW_CALL_MAX_DEPTH=5

api/.vscode/launch.json

@@ -17,7 +17,8 @@
                 "FLASK_DEBUG": "1",
                 "GEVENT_SUPPORT": "True"
             },
-            "console": "integratedTerminal"
+            "console": "integratedTerminal",
+            "python": "${command:python.interpreterPath}"
         },
         {
             "name": "Python: Flask",
@@ -36,7 +37,8 @@
                 "--debug"
             ],
             "jinja": true,
-            "justMyCode": true
+            "justMyCode": true,
+            "python": "${command:python.interpreterPath}"
         }
     ]
 }

api/README.md

@@ -17,15 +17,30 @@
    ```bash
    sed -i "/^SECRET_KEY=/c\SECRET_KEY=$(openssl rand -base64 42)" .env
    ```
-4. If you use Anaconda, create a new environment and activate it
+4. Create environment.
+   - Anaconda  
+   If you use Anaconda, create a new environment and activate it
    ```bash
    conda create --name dify python=3.10
    conda activate dify
    ```
+   - Poetry  
+   If you use Poetry, you don't need to manually create the environment. You can execute `poetry shell` to activate the environment.
 5. Install dependencies
+   - Anaconda  
    ```bash
    pip install -r requirements.txt
    ```
+   - Poetry  
+   ```bash
+   poetry install
+   ```
+   In case of contributors missing to update dependencies for `pyproject.toml`, you can perform the following shell instead.
+   ```base
+   poetry shell                                               # activate current environment
+   poetry add $(cat requirements.txt)           # install dependencies of production and update pyproject.toml
+   poetry add $(cat requirements-dev.txt) --group dev    # install dependencies of development and update pyproject.toml
+   ```
 6. Run migrate
 
    Before the first launch, migrate the database to the latest version.

api/commands.py

@@ -1,12 +1,15 @@
 import base64
 import json
 import secrets
+from typing import Optional
 
 import click
 from flask import current_app
 from werkzeug.exceptions import NotFound
 
+from constants.languages import languages
 from core.rag.datasource.vdb.vector_factory import Vector
+from core.rag.datasource.vdb.vector_type import VectorType
 from core.rag.models.document import Document
 from extensions.ext_database import db
 from libs.helper import email as email_validate
@@ -17,6 +20,7 @@ from models.dataset import Dataset, DatasetCollectionBinding, DocumentSegment
 from models.dataset import Document as DatasetDocument
 from models.model import Account, App, AppAnnotationSetting, AppMode, Conversation, MessageAnnotation
 from models.provider import Provider, ProviderModel
+from services.account_service import RegisterService, TenantService
 
 
 @click.command('reset-password', help='Reset the account password.')
@@ -57,7 +61,7 @@ def reset_password(email, new_password, password_confirm):
     account.password = base64_password_hashed
     account.password_salt = base64_salt
     db.session.commit()
-    click.echo(click.style('Congratulations!, password has been reset.', fg='green'))
+    click.echo(click.style('Congratulations! Password has been reset.', fg='green'))
 
 
 @click.command('reset-email', help='Reset the account email.')
@@ -263,15 +267,15 @@ def migrate_knowledge_vector_database():
                         skipped_count = skipped_count + 1
                         continue
                 collection_name = ''
-                if vector_type == "weaviate":
+                if vector_type == VectorType.WEAVIATE:
                     dataset_id = dataset.id
                     collection_name = Dataset.gen_collection_name_by_id(dataset_id)
                     index_struct_dict = {
-                        "type": 'weaviate',
+                        "type": VectorType.WEAVIATE,
                         "vector_store": {"class_prefix": collection_name}
                     }
                     dataset.index_struct = json.dumps(index_struct_dict)
-                elif vector_type == "qdrant":
+                elif vector_type == VectorType.QDRANT:
                     if dataset.collection_binding_id:
                         dataset_collection_binding = db.session.query(DatasetCollectionBinding). \
                             filter(DatasetCollectionBinding.id == dataset.collection_binding_id). \
@@ -284,20 +288,20 @@ def migrate_knowledge_vector_database():
                         dataset_id = dataset.id
                         collection_name = Dataset.gen_collection_name_by_id(dataset_id)
                     index_struct_dict = {
-                        "type": 'qdrant',
+                        "type": VectorType.QDRANT,
                         "vector_store": {"class_prefix": collection_name}
                     }
                     dataset.index_struct = json.dumps(index_struct_dict)
 
-                elif vector_type == "milvus":
+                elif vector_type == VectorType.MILVUS:
                     dataset_id = dataset.id
                     collection_name = Dataset.gen_collection_name_by_id(dataset_id)
                     index_struct_dict = {
-                        "type": 'milvus',
+                        "type": VectorType.MILVUS,
                         "vector_store": {"class_prefix": collection_name}
                     }
                     dataset.index_struct = json.dumps(index_struct_dict)
-                elif vector_type == "relyt":
+                elif vector_type == VectorType.RELYT:
                     dataset_id = dataset.id
                     collection_name = Dataset.gen_collection_name_by_id(dataset_id)
                     index_struct_dict = {
@@ -305,16 +309,16 @@ def migrate_knowledge_vector_database():
                         "vector_store": {"class_prefix": collection_name}
                     }
                     dataset.index_struct = json.dumps(index_struct_dict)
-                elif vector_type == "pgvector":
+                elif vector_type == VectorType.PGVECTOR:
                     dataset_id = dataset.id
                     collection_name = Dataset.gen_collection_name_by_id(dataset_id)
                     index_struct_dict = {
-                        "type": 'pgvector',
+                        "type": VectorType.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.")
+                    raise ValueError(f"Vector store {vector_type} is not supported.")
 
                 vector = Vector(dataset)
                 click.echo(f"Start to migrate dataset {dataset.id}.")
@@ -448,9 +452,105 @@ def convert_to_agent_apps():
     click.echo(click.style('Congratulations! Converted {} agent apps.'.format(len(proceeded_app_ids)), fg='green'))
 
 
+@click.command('add-qdrant-doc-id-index', help='add qdrant doc_id index.')
+@click.option('--field', default='metadata.doc_id', prompt=False, help='index field , default is metadata.doc_id.')
+def add_qdrant_doc_id_index(field: str):
+    click.echo(click.style('Start add qdrant doc_id index.', fg='green'))
+    config = current_app.config
+    vector_type = config.get('VECTOR_STORE')
+    if vector_type != "qdrant":
+        click.echo(click.style('Sorry, only support qdrant vector store.', fg='red'))
+        return
+    create_count = 0
+
+    try:
+        bindings = db.session.query(DatasetCollectionBinding).all()
+        if not bindings:
+            click.echo(click.style('Sorry, no dataset collection bindings found.', fg='red'))
+            return
+        import qdrant_client
+        from qdrant_client.http.exceptions import UnexpectedResponse
+        from qdrant_client.http.models import PayloadSchemaType
+
+        from core.rag.datasource.vdb.qdrant.qdrant_vector import QdrantConfig
+        for binding in bindings:
+            qdrant_config = QdrantConfig(
+                endpoint=config.get('QDRANT_URL'),
+                api_key=config.get('QDRANT_API_KEY'),
+                root_path=current_app.root_path,
+                timeout=config.get('QDRANT_CLIENT_TIMEOUT'),
+                grpc_port=config.get('QDRANT_GRPC_PORT'),
+                prefer_grpc=config.get('QDRANT_GRPC_ENABLED')
+            )
+            try:
+                client = qdrant_client.QdrantClient(**qdrant_config.to_qdrant_params())
+                # create payload index
+                client.create_payload_index(binding.collection_name, field,
+                                            field_schema=PayloadSchemaType.KEYWORD)
+                create_count += 1
+            except UnexpectedResponse as e:
+                # Collection does not exist, so return
+                if e.status_code == 404:
+                    click.echo(click.style(f'Collection not found, collection_name:{binding.collection_name}.', fg='red'))
+                    continue
+                # Some other error occurred, so re-raise the exception
+                else:
+                    click.echo(click.style(f'Failed to create qdrant index, collection_name:{binding.collection_name}.', fg='red'))
+
+    except Exception as e:
+        click.echo(click.style('Failed to create qdrant client.', fg='red'))
+
+    click.echo(
+        click.style(f'Congratulations! Create {create_count} collection indexes.',
+                    fg='green'))
+
+
+@click.command('create-tenant', help='Create account and tenant.')
+@click.option('--email', prompt=True, help='The email address of the tenant account.')
+@click.option('--language', prompt=True, help='Account language, default: en-US.')
+def create_tenant(email: str, language: Optional[str] = None):
+    """
+    Create tenant account
+    """
+    if not email:
+        click.echo(click.style('Sorry, email is required.', fg='red'))
+        return
+
+    # Create account
+    email = email.strip()
+
+    if '@' not in email:
+        click.echo(click.style('Sorry, invalid email address.', fg='red'))
+        return
+
+    account_name = email.split('@')[0]
+
+    if language not in languages:
+        language = 'en-US'
+
+    # generate random password
+    new_password = secrets.token_urlsafe(16)
+
+    # register account
+    account = RegisterService.register(
+        email=email,
+        name=account_name,
+        password=new_password,
+        language=language
+    )
+
+    TenantService.create_owner_tenant_if_not_exist(account)
+
+    click.echo(click.style('Congratulations! Account and tenant created.\n'
+                           'Account: {}\nPassword: {}'.format(email, new_password), fg='green'))
+
+
 def register_commands(app):
     app.cli.add_command(reset_password)
     app.cli.add_command(reset_email)
     app.cli.add_command(reset_encrypt_key_pair)
     app.cli.add_command(vdb_migrate)
     app.cli.add_command(convert_to_agent_apps)
+    app.cli.add_command(add_qdrant_doc_id_index)
+    app.cli.add_command(create_tenant)
+

api/config.py

@@ -23,6 +23,7 @@ DEFAULTS = {
     'SERVICE_API_URL': 'https://api.dify.ai',
     'APP_WEB_URL': 'https://udify.app',
     'FILES_URL': '',
+    'FILES_ACCESS_TIMEOUT': 300,
     'S3_ADDRESS_STYLE': 'auto',
     'STORAGE_TYPE': 'local',
     'STORAGE_LOCAL_PATH': 'storage',
@@ -69,6 +70,7 @@ DEFAULTS = {
     'INVITE_EXPIRY_HOURS': 72,
     'BILLING_ENABLED': 'False',
     'CAN_REPLACE_LOGO': 'False',
+    'MODEL_LB_ENABLED': 'False',
     'ETL_TYPE': 'dify',
     'KEYWORD_STORE': 'jieba',
     'BATCH_UPLOAD_LIMIT': 20,
@@ -80,6 +82,9 @@ DEFAULTS = {
     'INNER_API': 'False',
     'ENTERPRISE_ENABLED': 'False',
     'INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH': 1000,
+    'WORKFLOW_MAX_EXECUTION_STEPS': 500,
+    'WORKFLOW_MAX_EXECUTION_TIME': 1200,
+    'WORKFLOW_CALL_MAX_DEPTH': 5,
 }
 
 
@@ -110,7 +115,7 @@ class Config:
         # ------------------------
         # General Configurations.
         # ------------------------
-        self.CURRENT_VERSION = "0.6.8"
+        self.CURRENT_VERSION = "0.6.10"
         self.COMMIT_SHA = get_env('COMMIT_SHA')
         self.EDITION = get_env('EDITION')
         self.DEPLOY_ENV = get_env('DEPLOY_ENV')
@@ -119,6 +124,7 @@ class Config:
         self.LOG_FILE = get_env('LOG_FILE')
         self.LOG_FORMAT = get_env('LOG_FORMAT')
         self.LOG_DATEFORMAT = get_env('LOG_DATEFORMAT')
+        self.API_COMPRESSION_ENABLED = get_bool_env('API_COMPRESSION_ENABLED')
 
         # The backend URL prefix of the console API.
         # used to concatenate the login authorization callback or notion integration callback.
@@ -141,6 +147,10 @@ class Config:
         # Url is signed and has expiration time.
         self.FILES_URL = get_env('FILES_URL') if get_env('FILES_URL') else self.CONSOLE_API_URL
 
+        # File Access Time specifies a time interval in seconds for the file to be accessed.
+        # The default value is 300 seconds.
+        self.FILES_ACCESS_TIMEOUT = int(get_env('FILES_ACCESS_TIMEOUT'))
+
         # 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`.
@@ -177,7 +187,8 @@ class Config:
             'pool_size': int(get_env('SQLALCHEMY_POOL_SIZE')),
             'max_overflow': int(get_env('SQLALCHEMY_MAX_OVERFLOW')),
             'pool_recycle': int(get_env('SQLALCHEMY_POOL_RECYCLE')),
-            'pool_pre_ping': get_bool_env('SQLALCHEMY_POOL_PRE_PING')
+            'pool_pre_ping': get_bool_env('SQLALCHEMY_POOL_PRE_PING'),
+            'connect_args': {'options': '-c timezone=UTC'},
         }
 
         self.SQLALCHEMY_ECHO = get_bool_env('SQLALCHEMY_ECHO')
@@ -201,27 +212,41 @@ class Config:
             if self.CELERY_BACKEND == 'database' else self.CELERY_BROKER_URL
         self.BROKER_USE_SSL = self.CELERY_BROKER_URL.startswith('rediss://')
 
+        # ------------------------
+        # Code Execution Sandbox Configurations.
+        # ------------------------
+        self.CODE_EXECUTION_ENDPOINT = get_env('CODE_EXECUTION_ENDPOINT')
+        self.CODE_EXECUTION_API_KEY = get_env('CODE_EXECUTION_API_KEY')
+
         # ------------------------
         # File Storage Configurations.
         # ------------------------
         self.STORAGE_TYPE = get_env('STORAGE_TYPE')
         self.STORAGE_LOCAL_PATH = get_env('STORAGE_LOCAL_PATH')
+
+        # S3 Storage settings
         self.S3_ENDPOINT = get_env('S3_ENDPOINT')
         self.S3_BUCKET_NAME = get_env('S3_BUCKET_NAME')
         self.S3_ACCESS_KEY = get_env('S3_ACCESS_KEY')
         self.S3_SECRET_KEY = get_env('S3_SECRET_KEY')
         self.S3_REGION = get_env('S3_REGION')
         self.S3_ADDRESS_STYLE = get_env('S3_ADDRESS_STYLE')
+
+        # Azure Blob Storage settings
         self.AZURE_BLOB_ACCOUNT_NAME = get_env('AZURE_BLOB_ACCOUNT_NAME')
         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')
+
+        # Aliyun Storage settings
+        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')
+
+        # Google Cloud Storage settings
         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')
 
@@ -231,6 +256,7 @@ class Config:
         # ------------------------
         self.VECTOR_STORE = get_env('VECTOR_STORE')
         self.KEYWORD_STORE = get_env('KEYWORD_STORE')
+
         # qdrant settings
         self.QDRANT_URL = get_env('QDRANT_URL')
         self.QDRANT_API_KEY = get_env('QDRANT_API_KEY')
@@ -273,6 +299,21 @@ class Config:
         self.PGVECTOR_PASSWORD = get_env('PGVECTOR_PASSWORD')
         self.PGVECTOR_DATABASE = get_env('PGVECTOR_DATABASE')
 
+        # tidb-vector settings
+        self.TIDB_VECTOR_HOST = get_env('TIDB_VECTOR_HOST')
+        self.TIDB_VECTOR_PORT = get_env('TIDB_VECTOR_PORT')
+        self.TIDB_VECTOR_USER = get_env('TIDB_VECTOR_USER')
+        self.TIDB_VECTOR_PASSWORD = get_env('TIDB_VECTOR_PASSWORD')
+        self.TIDB_VECTOR_DATABASE = get_env('TIDB_VECTOR_DATABASE')
+
+        # chroma settings
+        self.CHROMA_HOST = get_env('CHROMA_HOST')
+        self.CHROMA_PORT = get_env('CHROMA_PORT')
+        self.CHROMA_TENANT = get_env('CHROMA_TENANT')
+        self.CHROMA_DATABASE = get_env('CHROMA_DATABASE')
+        self.CHROMA_AUTH_PROVIDER = get_env('CHROMA_AUTH_PROVIDER')
+        self.CHROMA_AUTH_CREDENTIALS = get_env('CHROMA_AUTH_CREDENTIALS')
+
         # ------------------------
         # Mail Configurations.
         # ------------------------
@@ -286,7 +327,8 @@ class Config:
         self.SMTP_USERNAME = get_env('SMTP_USERNAME')
         self.SMTP_PASSWORD = get_env('SMTP_PASSWORD')
         self.SMTP_USE_TLS = get_bool_env('SMTP_USE_TLS')
-        
+        self.SMTP_OPPORTUNISTIC_TLS = get_bool_env('SMTP_OPPORTUNISTIC_TLS')
+
         # ------------------------
         # Workspace Configurations.
         # ------------------------
@@ -313,6 +355,23 @@ class Config:
         self.UPLOAD_FILE_SIZE_LIMIT = int(get_env('UPLOAD_FILE_SIZE_LIMIT'))
         self.UPLOAD_FILE_BATCH_LIMIT = int(get_env('UPLOAD_FILE_BATCH_LIMIT'))
         self.UPLOAD_IMAGE_FILE_SIZE_LIMIT = int(get_env('UPLOAD_IMAGE_FILE_SIZE_LIMIT'))
+        self.BATCH_UPLOAD_LIMIT = get_env('BATCH_UPLOAD_LIMIT')
+
+        # RAG ETL Configurations.
+        self.ETL_TYPE = get_env('ETL_TYPE')
+        self.UNSTRUCTURED_API_URL = get_env('UNSTRUCTURED_API_URL')
+        self.UNSTRUCTURED_API_KEY = get_env('UNSTRUCTURED_API_KEY')
+        self.KEYWORD_DATA_SOURCE_TYPE = get_env('KEYWORD_DATA_SOURCE_TYPE')
+
+        # Indexing Configurations.
+        self.INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH = get_env('INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH')
+
+        # Tool Configurations.
+        self.TOOL_ICON_CACHE_MAX_AGE = get_env('TOOL_ICON_CACHE_MAX_AGE')
+
+        self.WORKFLOW_MAX_EXECUTION_STEPS = int(get_env('WORKFLOW_MAX_EXECUTION_STEPS'))
+        self.WORKFLOW_MAX_EXECUTION_TIME = int(get_env('WORKFLOW_MAX_EXECUTION_TIME'))
+        self.WORKFLOW_CALL_MAX_DEPTH = int(get_env('WORKFLOW_CALL_MAX_DEPTH'))
 
         # Moderation in app Configurations.
         self.OUTPUT_MODERATION_BUFFER_SIZE = int(get_env('OUTPUT_MODERATION_BUFFER_SIZE'))
@@ -364,24 +423,15 @@ class Config:
         self.HOSTED_FETCH_APP_TEMPLATES_MODE = get_env('HOSTED_FETCH_APP_TEMPLATES_MODE')
         self.HOSTED_FETCH_APP_TEMPLATES_REMOTE_DOMAIN = get_env('HOSTED_FETCH_APP_TEMPLATES_REMOTE_DOMAIN')
 
-        self.ETL_TYPE = get_env('ETL_TYPE')
-        self.UNSTRUCTURED_API_URL = get_env('UNSTRUCTURED_API_URL')
-        self.UNSTRUCTURED_API_KEY = get_env('UNSTRUCTURED_API_KEY')
+        # Model Load Balancing Configurations.
+        self.MODEL_LB_ENABLED = get_bool_env('MODEL_LB_ENABLED')
+
+        # Platform Billing Configurations.
         self.BILLING_ENABLED = get_bool_env('BILLING_ENABLED')
-        self.CAN_REPLACE_LOGO = get_bool_env('CAN_REPLACE_LOGO')
 
-        self.BATCH_UPLOAD_LIMIT = get_env('BATCH_UPLOAD_LIMIT')
-
-        self.CODE_EXECUTION_ENDPOINT = get_env('CODE_EXECUTION_ENDPOINT')
-        self.CODE_EXECUTION_API_KEY = get_env('CODE_EXECUTION_API_KEY')
-
-        self.API_COMPRESSION_ENABLED = get_bool_env('API_COMPRESSION_ENABLED')
-        self.TOOL_ICON_CACHE_MAX_AGE = get_env('TOOL_ICON_CACHE_MAX_AGE')
-
-        self.KEYWORD_DATA_SOURCE_TYPE = get_env('KEYWORD_DATA_SOURCE_TYPE')
+        # ------------------------
+        # Enterprise feature Configurations.
+        # **Before using, please contact business@dify.ai by email to inquire about licensing matters.**
+        # ------------------------
         self.ENTERPRISE_ENABLED = get_bool_env('ENTERPRISE_ENABLED')
-
-        # ------------------------
-        # Indexing Configurations.
-        # ------------------------
-        self.INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH = get_env('INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH')
+        self.CAN_REPLACE_LOGO = get_bool_env('CAN_REPLACE_LOGO')

api/controllers/console/__init__.py

@@ -54,4 +54,4 @@ from .explore import (
 from .tag import tags
 
 # Import workspace controllers
-from .workspace import account, members, model_providers, models, tool_providers, workspace
+from .workspace import account, load_balancing_config, members, model_providers, models, tool_providers, workspace

api/controllers/console/app/audio.py

@@ -85,7 +85,7 @@ class ChatMessageTextApi(Resource):
             response = AudioService.transcript_tts(
                 app_model=app_model,
                 text=request.form['text'],
-                voice=request.form.get('voice'),
+                voice=request.form['voice'],
                 streaming=False
             )
 

api/controllers/console/app/workflow.py

@@ -137,6 +137,71 @@ class AdvancedChatDraftWorkflowRunApi(Resource):
             logging.exception("internal server error.")
             raise InternalServerError()
 
+class AdvancedChatDraftRunIterationNodeApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT])
+    def post(self, app_model: App, node_id: str):
+        """
+        Run draft workflow iteration node
+        """
+        parser = reqparse.RequestParser()
+        parser.add_argument('inputs', type=dict, location='json')
+        args = parser.parse_args()
+
+        try:
+            response = AppGenerateService.generate_single_iteration(
+                app_model=app_model,
+                user=current_user,
+                node_id=node_id,
+                args=args,
+                streaming=True
+            )
+
+            return helper.compact_generate_response(response)
+        except services.errors.conversation.ConversationNotExistsError:
+            raise NotFound("Conversation Not Exists.")
+        except services.errors.conversation.ConversationCompletedError:
+            raise ConversationCompletedError()
+        except ValueError as e:
+            raise e
+        except Exception as e:
+            logging.exception("internal server error.")
+            raise InternalServerError()
+
+class WorkflowDraftRunIterationNodeApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.WORKFLOW])
+    def post(self, app_model: App, node_id: str):
+        """
+        Run draft workflow iteration node
+        """
+        parser = reqparse.RequestParser()
+        parser.add_argument('inputs', type=dict, location='json')
+        args = parser.parse_args()
+
+        try:
+            response = AppGenerateService.generate_single_iteration(
+                app_model=app_model,
+                user=current_user,
+                node_id=node_id,
+                args=args,
+                streaming=True
+            )
+
+            return helper.compact_generate_response(response)
+        except services.errors.conversation.ConversationNotExistsError:
+            raise NotFound("Conversation Not Exists.")
+        except services.errors.conversation.ConversationCompletedError:
+            raise ConversationCompletedError()
+        except ValueError as e:
+            raise e
+        except Exception as e:
+            logging.exception("internal server error.")
+            raise InternalServerError()
 
 class DraftWorkflowRunApi(Resource):
     @setup_required
@@ -326,6 +391,8 @@ api.add_resource(AdvancedChatDraftWorkflowRunApi, '/apps/<uuid:app_id>/advanced-
 api.add_resource(DraftWorkflowRunApi, '/apps/<uuid:app_id>/workflows/draft/run')
 api.add_resource(WorkflowTaskStopApi, '/apps/<uuid:app_id>/workflow-runs/tasks/<string:task_id>/stop')
 api.add_resource(DraftWorkflowNodeRunApi, '/apps/<uuid:app_id>/workflows/draft/nodes/<string:node_id>/run')
+api.add_resource(AdvancedChatDraftRunIterationNodeApi, '/apps/<uuid:app_id>/advanced-chat/workflows/draft/iteration/nodes/<string:node_id>/run')
+api.add_resource(WorkflowDraftRunIterationNodeApi, '/apps/<uuid:app_id>/workflows/draft/iteration/nodes/<string:node_id>/run')
 api.add_resource(PublishedWorkflowApi, '/apps/<uuid:app_id>/workflows/publish')
 api.add_resource(DefaultBlockConfigsApi, '/apps/<uuid:app_id>/workflows/default-workflow-block-configs')
 api.add_resource(DefaultBlockConfigApi, '/apps/<uuid:app_id>/workflows/default-workflow-block-configs'

api/controllers/console/datasets/datasets.py

@@ -15,6 +15,7 @@ from core.errors.error import LLMBadRequestError, ProviderTokenNotInitError
 from core.indexing_runner import IndexingRunner
 from core.model_runtime.entities.model_entities import ModelType
 from core.provider_manager import ProviderManager
+from core.rag.datasource.vdb.vector_type import VectorType
 from core.rag.extractor.entity.extract_setting import ExtractSetting
 from extensions.ext_database import db
 from fields.app_fields import related_app_list
@@ -476,20 +477,22 @@ class DatasetRetrievalSettingApi(Resource):
     @account_initialization_required
     def get(self):
         vector_type = current_app.config['VECTOR_STORE']
-        if vector_type in {"milvus", "relyt", "pgvector", "pgvecto_rs"}:
-            return {
-                'retrieval_method': [
-                    'semantic_search'
-                ]
-            }
-        elif vector_type in {"qdrant", "weaviate"}:
-            return {
-                'retrieval_method': [
-                    'semantic_search', 'full_text_search', 'hybrid_search'
-                ]
-            }
-        else:
-            raise ValueError("Unsupported vector db type.")
+
+        match vector_type:
+            case VectorType.MILVUS | VectorType.RELYT | VectorType.PGVECTOR | VectorType.TIDB_VECTOR | VectorType.CHROMA:
+                return {
+                    'retrieval_method': [
+                        'semantic_search'
+                    ]
+                }
+            case VectorType.QDRANT | VectorType.WEAVIATE:
+                return {
+                    'retrieval_method': [
+                        'semantic_search', 'full_text_search', 'hybrid_search'
+                    ]
+                }
+            case _:
+                raise ValueError(f"Unsupported vector db type {vector_type}.")
 
 
 class DatasetRetrievalSettingMockApi(Resource):
@@ -497,20 +500,22 @@ class DatasetRetrievalSettingMockApi(Resource):
     @login_required
     @account_initialization_required
     def get(self, vector_type):
-        if vector_type in {'milvus', 'relyt', 'pgvector'}:
-            return {
-                'retrieval_method': [
-                    'semantic_search'
-                ]
-            }
-        elif vector_type in {'qdrant', 'weaviate'}:
-            return {
-                'retrieval_method': [
-                    'semantic_search', 'full_text_search', 'hybrid_search'
-                ]
-            }
-        else:
-            raise ValueError("Unsupported vector db type.")
+        match vector_type:
+            case VectorType.MILVUS | VectorType.RELYT | VectorType.PGVECTOR | VectorType.TIDB_VECTOR | VectorType.CHROMA:
+                return {
+                    'retrieval_method': [
+                        'semantic_search'
+                    ]
+                }
+            case VectorType.QDRANT | VectorType.WEAVIATE:
+                return {
+                    'retrieval_method': [
+                        'semantic_search', 'full_text_search', 'hybrid_search'
+                    ]
+                }
+            case _:
+                raise ValueError(f"Unsupported vector db type {vector_type}.")
+
 
 class DatasetErrorDocs(Resource):
     @setup_required

api/controllers/console/datasets/datasets_document.py

@@ -1,10 +1,12 @@
 import logging
+from argparse import ArgumentTypeError
 from datetime import datetime, timezone
 
 from flask import request
 from flask_login import current_user
 from flask_restful import Resource, fields, marshal, marshal_with, reqparse
 from sqlalchemy import asc, desc
+from transformers.hf_argparser import string_to_bool
 from werkzeug.exceptions import Forbidden, NotFound
 
 import services
@@ -141,7 +143,11 @@ class DatasetDocumentListApi(Resource):
         limit = request.args.get('limit', default=20, type=int)
         search = request.args.get('keyword', default=None, type=str)
         sort = request.args.get('sort', default='-created_at', type=str)
-        fetch = request.args.get('fetch', default=False, type=bool)
+        # "yes", "true", "t", "y", "1" convert to True, while others convert to False.
+        try:
+            fetch = string_to_bool(request.args.get('fetch', default='false'))
+        except (ArgumentTypeError, ValueError, Exception) as e:
+            fetch = False
         dataset = DatasetService.get_dataset(dataset_id)
         if not dataset:
             raise NotFound('Dataset not found.')
@@ -924,6 +930,28 @@ class DocumentRetryApi(DocumentResource):
         return {'result': 'success'}, 204
 
 
+class DocumentRenameApi(DocumentResource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @marshal_with(document_fields)
+    def post(self, dataset_id, document_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', type=str, required=True, nullable=False, location='json')
+        args = parser.parse_args()
+
+        try:
+            document = DocumentService.rename_document(dataset_id, document_id, args['name'])
+        except services.errors.document.DocumentIndexingError:
+            raise DocumentIndexingError('Cannot delete document during indexing.')
+
+        return document
+
+
 api.add_resource(GetProcessRuleApi, '/datasets/process-rule')
 api.add_resource(DatasetDocumentListApi,
                  '/datasets/<uuid:dataset_id>/documents')
@@ -950,3 +978,5 @@ api.add_resource(DocumentStatusApi,
 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.add_resource(DocumentRenameApi,
+                 '/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/rename')

api/controllers/console/explore/audio.py

@@ -76,7 +76,7 @@ class ChatTextApi(InstalledAppResource):
             response = AudioService.transcript_tts(
                 app_model=app_model,
                 text=request.form['text'],
-                voice=request.form.get('voice'),
+                voice=request.form['voice'] if request.form.get('voice') else app_model.app_model_config.text_to_speech_dict.get('voice'),
                 streaming=False
             )
             return {'data': response.data.decode('latin1')}

api/controllers/console/feature.py

@@ -1,14 +1,19 @@
 from flask_login import current_user
 from flask_restful import Resource
 
+from libs.login import login_required
 from services.feature_service import FeatureService
 
 from . import api
-from .wraps import cloud_utm_record
+from .setup import setup_required
+from .wraps import account_initialization_required, cloud_utm_record
 
 
 class FeatureApi(Resource):
 
+    @setup_required
+    @login_required
+    @account_initialization_required
     @cloud_utm_record
     def get(self):
         return FeatureService.get_features(current_user.current_tenant_id).dict()

api/controllers/console/version.py

@@ -17,13 +17,19 @@ class VersionApi(Resource):
         args = parser.parse_args()
         check_update_url = current_app.config['CHECK_UPDATE_URL']
 
-        if not check_update_url:
-            return {
-                'version': '0.0.0',
-                'release_date': '',
-                'release_notes': '',
-                'can_auto_update': False
+        result = {
+            'version': current_app.config['CURRENT_VERSION'],
+            'release_date': '',
+            'release_notes': '',
+            'can_auto_update': False,
+            'features': {
+                'can_replace_logo': current_app.config['CAN_REPLACE_LOGO'],
+                'model_load_balancing_enabled': current_app.config['MODEL_LB_ENABLED']
             }
+        }
+
+        if not check_update_url:
+            return result
 
         try:
             response = requests.get(check_update_url, {
@@ -31,20 +37,15 @@ class VersionApi(Resource):
             })
         except Exception as error:
             logging.warning("Check update version error: {}.".format(str(error)))
-            return {
-                'version': args.get('current_version'),
-                'release_date': '',
-                'release_notes': '',
-                'can_auto_update': False
-            }
+            result['version'] = args.get('current_version')
+            return result
 
         content = json.loads(response.content)
-        return {
-            'version': content['version'],
-            'release_date': content['releaseDate'],
-            'release_notes': content['releaseNotes'],
-            'can_auto_update': content['canAutoUpdate']
-        }
+        result['version'] = content['version']
+        result['release_date'] = content['releaseDate']
+        result['release_notes'] = content['releaseNotes']
+        result['can_auto_update'] = content['canAutoUpdate']
+        return result
 
 
 api.add_resource(VersionApi, '/version')

api/controllers/console/workspace/load_balancing_config.py

@@ -0,0 +1,106 @@
+from flask_restful import Resource, 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 core.model_runtime.entities.model_entities import ModelType
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from libs.login import current_user, login_required
+from models.account import TenantAccountRole
+from services.model_load_balancing_service import ModelLoadBalancingService
+
+
+class LoadBalancingCredentialsValidateApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self, provider: str):
+        if not TenantAccountRole.is_privileged_role(current_user.current_tenant.current_role):
+            raise Forbidden()
+
+        tenant_id = current_user.current_tenant_id
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('model', type=str, required=True, nullable=False, location='json')
+        parser.add_argument('model_type', type=str, required=True, nullable=False,
+                            choices=[mt.value for mt in ModelType], location='json')
+        parser.add_argument('credentials', type=dict, required=True, nullable=False, location='json')
+        args = parser.parse_args()
+
+        # validate model load balancing credentials
+        model_load_balancing_service = ModelLoadBalancingService()
+
+        result = True
+        error = None
+
+        try:
+            model_load_balancing_service.validate_load_balancing_credentials(
+                tenant_id=tenant_id,
+                provider=provider,
+                model=args['model'],
+                model_type=args['model_type'],
+                credentials=args['credentials']
+            )
+        except CredentialsValidateFailedError as ex:
+            result = False
+            error = str(ex)
+
+        response = {'result': 'success' if result else 'error'}
+
+        if not result:
+            response['error'] = error
+
+        return response
+
+
+class LoadBalancingConfigCredentialsValidateApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self, provider: str, config_id: str):
+        if not TenantAccountRole.is_privileged_role(current_user.current_tenant.current_role):
+            raise Forbidden()
+
+        tenant_id = current_user.current_tenant_id
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('model', type=str, required=True, nullable=False, location='json')
+        parser.add_argument('model_type', type=str, required=True, nullable=False,
+                            choices=[mt.value for mt in ModelType], location='json')
+        parser.add_argument('credentials', type=dict, required=True, nullable=False, location='json')
+        args = parser.parse_args()
+
+        # validate model load balancing config credentials
+        model_load_balancing_service = ModelLoadBalancingService()
+
+        result = True
+        error = None
+
+        try:
+            model_load_balancing_service.validate_load_balancing_credentials(
+                tenant_id=tenant_id,
+                provider=provider,
+                model=args['model'],
+                model_type=args['model_type'],
+                credentials=args['credentials'],
+                config_id=config_id,
+            )
+        except CredentialsValidateFailedError as ex:
+            result = False
+            error = str(ex)
+
+        response = {'result': 'success' if result else 'error'}
+
+        if not result:
+            response['error'] = error
+
+        return response
+
+
+# Load Balancing Config
+api.add_resource(LoadBalancingCredentialsValidateApi,
+                 '/workspaces/current/model-providers/<string:provider>/models/load-balancing-configs/credentials-validate')
+
+api.add_resource(LoadBalancingConfigCredentialsValidateApi,
+                 '/workspaces/current/model-providers/<string:provider>/models/load-balancing-configs/<string:config_id>/credentials-validate')

api/controllers/console/workspace/models.py

@@ -12,6 +12,7 @@ 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_load_balancing_service import ModelLoadBalancingService
 from services.model_provider_service import ModelProviderService
 
 
@@ -104,21 +105,56 @@ class ModelProviderModelApi(Resource):
         parser.add_argument('model', type=str, required=True, nullable=False, location='json')
         parser.add_argument('model_type', type=str, required=True, nullable=False,
                             choices=[mt.value for mt in ModelType], location='json')
-        parser.add_argument('credentials', type=dict, required=True, nullable=False, location='json')
+        parser.add_argument('credentials', type=dict, required=False, nullable=True, location='json')
+        parser.add_argument('load_balancing', type=dict, required=False, nullable=True, location='json')
+        parser.add_argument('config_from', type=str, required=False, nullable=True, location='json')
         args = parser.parse_args()
 
-        model_provider_service = ModelProviderService()
+        model_load_balancing_service = ModelLoadBalancingService()
 
-        try:
-            model_provider_service.save_model_credentials(
+        if ('load_balancing' in args and args['load_balancing'] and
+                'enabled' in args['load_balancing'] and args['load_balancing']['enabled']):
+            if 'configs' not in args['load_balancing']:
+                raise ValueError('invalid load balancing configs')
+
+            # save load balancing configs
+            model_load_balancing_service.update_load_balancing_configs(
                 tenant_id=tenant_id,
                 provider=provider,
                 model=args['model'],
                 model_type=args['model_type'],
-                credentials=args['credentials']
+                configs=args['load_balancing']['configs']
             )
-        except CredentialsValidateFailedError as ex:
-            raise ValueError(str(ex))
+
+            # enable load balancing
+            model_load_balancing_service.enable_model_load_balancing(
+                tenant_id=tenant_id,
+                provider=provider,
+                model=args['model'],
+                model_type=args['model_type']
+            )
+        else:
+            # disable load balancing
+            model_load_balancing_service.disable_model_load_balancing(
+                tenant_id=tenant_id,
+                provider=provider,
+                model=args['model'],
+                model_type=args['model_type']
+            )
+
+            if args.get('config_from', '') != 'predefined-model':
+                model_provider_service = ModelProviderService()
+
+                try:
+                    model_provider_service.save_model_credentials(
+                        tenant_id=tenant_id,
+                        provider=provider,
+                        model=args['model'],
+                        model_type=args['model_type'],
+                        credentials=args['credentials']
+                    )
+                except CredentialsValidateFailedError as ex:
+                    raise ValueError(str(ex))
 
         return {'result': 'success'}, 200
 
@@ -170,11 +206,73 @@ class ModelProviderModelCredentialApi(Resource):
             model=args['model']
         )
 
+        model_load_balancing_service = ModelLoadBalancingService()
+        is_load_balancing_enabled, load_balancing_configs = model_load_balancing_service.get_load_balancing_configs(
+            tenant_id=tenant_id,
+            provider=provider,
+            model=args['model'],
+            model_type=args['model_type']
+        )
+
         return {
-            "credentials": credentials
+            "credentials": credentials,
+            "load_balancing": {
+                "enabled": is_load_balancing_enabled,
+                "configs": load_balancing_configs
+            }
         }
 
 
+class ModelProviderModelEnableApi(Resource):
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def patch(self, provider: str):
+        tenant_id = current_user.current_tenant_id
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('model', type=str, required=True, nullable=False, location='json')
+        parser.add_argument('model_type', type=str, required=True, nullable=False,
+                            choices=[mt.value for mt in ModelType], location='json')
+        args = parser.parse_args()
+
+        model_provider_service = ModelProviderService()
+        model_provider_service.enable_model(
+            tenant_id=tenant_id,
+            provider=provider,
+            model=args['model'],
+            model_type=args['model_type']
+        )
+
+        return {'result': 'success'}
+
+
+class ModelProviderModelDisableApi(Resource):
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def patch(self, provider: str):
+        tenant_id = current_user.current_tenant_id
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('model', type=str, required=True, nullable=False, location='json')
+        parser.add_argument('model_type', type=str, required=True, nullable=False,
+                            choices=[mt.value for mt in ModelType], location='json')
+        args = parser.parse_args()
+
+        model_provider_service = ModelProviderService()
+        model_provider_service.disable_model(
+            tenant_id=tenant_id,
+            provider=provider,
+            model=args['model'],
+            model_type=args['model_type']
+        )
+
+        return {'result': 'success'}
+
+
 class ModelProviderModelValidateApi(Resource):
 
     @setup_required
@@ -259,6 +357,10 @@ class ModelProviderAvailableModelApi(Resource):
 
 
 api.add_resource(ModelProviderModelApi, '/workspaces/current/model-providers/<string:provider>/models')
+api.add_resource(ModelProviderModelEnableApi, '/workspaces/current/model-providers/<string:provider>/models/enable',
+                 endpoint='model-provider-model-enable')
+api.add_resource(ModelProviderModelDisableApi, '/workspaces/current/model-providers/<string:provider>/models/disable',
+                 endpoint='model-provider-model-disable')
 api.add_resource(ModelProviderModelCredentialApi,
                  '/workspaces/current/model-providers/<string:provider>/models/credentials')
 api.add_resource(ModelProviderModelValidateApi,

api/controllers/console/workspace/tool_providers.py

@@ -9,8 +9,13 @@ from controllers.console import api
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required
 from core.model_runtime.utils.encoders import jsonable_encoder
+from libs.helper import alphanumeric, uuid_value
 from libs.login import login_required
-from services.tools_manage_service import ToolManageService
+from services.tools.api_tools_manage_service import ApiToolManageService
+from services.tools.builtin_tools_manage_service import BuiltinToolManageService
+from services.tools.tool_labels_service import ToolLabelsService
+from services.tools.tools_manage_service import ToolCommonService
+from services.tools.workflow_tools_manage_service import WorkflowToolManageService
 
 
 class ToolProviderListApi(Resource):
@@ -21,7 +26,11 @@ class ToolProviderListApi(Resource):
         user_id = current_user.id
         tenant_id = current_user.current_tenant_id
 
-        return ToolManageService.list_tool_providers(user_id, tenant_id)
+        req = reqparse.RequestParser()
+        req.add_argument('type', type=str, choices=['builtin', 'model', 'api', 'workflow'], required=False, nullable=True, location='args')
+        args = req.parse_args()
+
+        return ToolCommonService.list_tool_providers(user_id, tenant_id, args.get('type', None))
 
 class ToolBuiltinProviderListToolsApi(Resource):
     @setup_required
@@ -31,7 +40,7 @@ class ToolBuiltinProviderListToolsApi(Resource):
         user_id = current_user.id
         tenant_id = current_user.current_tenant_id
 
-        return jsonable_encoder(ToolManageService.list_builtin_tool_provider_tools(
+        return jsonable_encoder(BuiltinToolManageService.list_builtin_tool_provider_tools(
             user_id,
             tenant_id,
             provider,
@@ -48,7 +57,7 @@ class ToolBuiltinProviderDeleteApi(Resource):
         user_id = current_user.id
         tenant_id = current_user.current_tenant_id
 
-        return ToolManageService.delete_builtin_tool_provider(
+        return BuiltinToolManageService.delete_builtin_tool_provider(
             user_id,
             tenant_id,
             provider,
@@ -70,7 +79,7 @@ class ToolBuiltinProviderUpdateApi(Resource):
 
         args = parser.parse_args()
 
-        return ToolManageService.update_builtin_tool_provider(
+        return BuiltinToolManageService.update_builtin_tool_provider(
             user_id,
             tenant_id,
             provider,
@@ -85,7 +94,7 @@ class ToolBuiltinProviderGetCredentialsApi(Resource):
         user_id = current_user.id
         tenant_id = current_user.current_tenant_id
 
-        return ToolManageService.get_builtin_tool_provider_credentials(
+        return BuiltinToolManageService.get_builtin_tool_provider_credentials(
             user_id,
             tenant_id,
             provider,
@@ -94,7 +103,7 @@ class ToolBuiltinProviderGetCredentialsApi(Resource):
 class ToolBuiltinProviderIconApi(Resource):
     @setup_required
     def get(self, provider):
-        icon_bytes, mimetype = ToolManageService.get_builtin_tool_provider_icon(provider)
+        icon_bytes, mimetype = BuiltinToolManageService.get_builtin_tool_provider_icon(provider)
         icon_cache_max_age = int(current_app.config.get('TOOL_ICON_CACHE_MAX_AGE'))
         return send_file(io.BytesIO(icon_bytes), mimetype=mimetype, max_age=icon_cache_max_age)
 
@@ -116,11 +125,12 @@ class ToolApiProviderAddApi(Resource):
         parser.add_argument('provider', type=str, required=True, nullable=False, location='json')
         parser.add_argument('icon', type=dict, required=True, nullable=False, location='json')
         parser.add_argument('privacy_policy', type=str, required=False, nullable=True, location='json')
+        parser.add_argument('labels', type=list[str], required=False, nullable=True, location='json', default=[])
         parser.add_argument('custom_disclaimer', type=str, required=False, nullable=True, location='json')
 
         args = parser.parse_args()
 
-        return ToolManageService.create_api_tool_provider(
+        return ApiToolManageService.create_api_tool_provider(
             user_id,
             tenant_id,
             args['provider'],
@@ -130,6 +140,7 @@ class ToolApiProviderAddApi(Resource):
             args['schema'],
             args.get('privacy_policy', ''),
             args.get('custom_disclaimer', ''),
+            args.get('labels', []),
         )
 
 class ToolApiProviderGetRemoteSchemaApi(Resource):
@@ -143,7 +154,7 @@ class ToolApiProviderGetRemoteSchemaApi(Resource):
 
         args = parser.parse_args()
 
-        return ToolManageService.get_api_tool_provider_remote_schema(
+        return ApiToolManageService.get_api_tool_provider_remote_schema(
             current_user.id,
             current_user.current_tenant_id,
             args['url'],
@@ -163,7 +174,7 @@ class ToolApiProviderListToolsApi(Resource):
 
         args = parser.parse_args()
 
-        return jsonable_encoder(ToolManageService.list_api_tool_provider_tools(
+        return jsonable_encoder(ApiToolManageService.list_api_tool_provider_tools(
             user_id,
             tenant_id,
             args['provider'],
@@ -188,11 +199,12 @@ class ToolApiProviderUpdateApi(Resource):
         parser.add_argument('original_provider', type=str, required=True, nullable=False, location='json')
         parser.add_argument('icon', type=dict, required=True, nullable=False, location='json')
         parser.add_argument('privacy_policy', type=str, required=True, nullable=True, location='json')
+        parser.add_argument('labels', type=list[str], required=False, nullable=True, location='json')
         parser.add_argument('custom_disclaimer', type=str, required=True, nullable=True, location='json')
 
         args = parser.parse_args()
 
-        return ToolManageService.update_api_tool_provider(
+        return ApiToolManageService.update_api_tool_provider(
             user_id,
             tenant_id,
             args['provider'],
@@ -203,6 +215,7 @@ class ToolApiProviderUpdateApi(Resource):
             args['schema'],
             args['privacy_policy'],
             args['custom_disclaimer'],
+            args.get('labels', []),
         )
 
 class ToolApiProviderDeleteApi(Resource):
@@ -222,7 +235,7 @@ class ToolApiProviderDeleteApi(Resource):
 
         args = parser.parse_args()
 
-        return ToolManageService.delete_api_tool_provider(
+        return ApiToolManageService.delete_api_tool_provider(
             user_id,
             tenant_id,
             args['provider'],
@@ -242,7 +255,7 @@ class ToolApiProviderGetApi(Resource):
 
         args = parser.parse_args()
 
-        return ToolManageService.get_api_tool_provider(
+        return ApiToolManageService.get_api_tool_provider(
             user_id,
             tenant_id,
             args['provider'],
@@ -253,7 +266,7 @@ class ToolBuiltinProviderCredentialsSchemaApi(Resource):
     @login_required
     @account_initialization_required
     def get(self, provider):
-        return ToolManageService.list_builtin_provider_credentials_schema(provider)
+        return BuiltinToolManageService.list_builtin_provider_credentials_schema(provider)
 
 class ToolApiProviderSchemaApi(Resource):
     @setup_required
@@ -266,7 +279,7 @@ class ToolApiProviderSchemaApi(Resource):
 
         args = parser.parse_args()
 
-        return ToolManageService.parser_api_schema(
+        return ApiToolManageService.parser_api_schema(
             schema=args['schema'],
         )
 
@@ -286,7 +299,7 @@ class ToolApiProviderPreviousTestApi(Resource):
 
         args = parser.parse_args()
 
-        return ToolManageService.test_api_tool_preview(
+        return ApiToolManageService.test_api_tool_preview(
             current_user.current_tenant_id,
             args['provider_name'] if args['provider_name'] else '',
             args['tool_name'],
@@ -296,6 +309,153 @@ class ToolApiProviderPreviousTestApi(Resource):
             args['schema'],
         )
 
+class ToolWorkflowProviderCreateApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self):
+        if not current_user.is_admin_or_owner:
+            raise Forbidden()
+        
+        user_id = current_user.id
+        tenant_id = current_user.current_tenant_id
+
+        reqparser = reqparse.RequestParser()
+        reqparser.add_argument('workflow_app_id', type=uuid_value, required=True, nullable=False, location='json')
+        reqparser.add_argument('name', type=alphanumeric, required=True, nullable=False, location='json')
+        reqparser.add_argument('label', type=str, required=True, nullable=False, location='json')
+        reqparser.add_argument('description', type=str, required=True, nullable=False, location='json')
+        reqparser.add_argument('icon', type=dict, required=True, nullable=False, location='json')
+        reqparser.add_argument('parameters', type=list[dict], required=True, nullable=False, location='json')
+        reqparser.add_argument('privacy_policy', type=str, required=False, nullable=True, location='json', default='')
+        reqparser.add_argument('labels', type=list[str], required=False, nullable=True, location='json')
+
+        args = reqparser.parse_args()
+
+        return WorkflowToolManageService.create_workflow_tool(
+            user_id,
+            tenant_id,
+            args['workflow_app_id'],
+            args['name'],
+            args['label'],
+            args['icon'],
+            args['description'],
+            args['parameters'],
+            args['privacy_policy'],
+            args.get('labels', []),
+        )
+
+class ToolWorkflowProviderUpdateApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self):
+        if not current_user.is_admin_or_owner:
+            raise Forbidden()
+        
+        user_id = current_user.id
+        tenant_id = current_user.current_tenant_id
+
+        reqparser = reqparse.RequestParser()
+        reqparser.add_argument('workflow_tool_id', type=uuid_value, required=True, nullable=False, location='json')
+        reqparser.add_argument('name', type=alphanumeric, required=True, nullable=False, location='json')
+        reqparser.add_argument('label', type=str, required=True, nullable=False, location='json')
+        reqparser.add_argument('description', type=str, required=True, nullable=False, location='json')
+        reqparser.add_argument('icon', type=dict, required=True, nullable=False, location='json')
+        reqparser.add_argument('parameters', type=list[dict], required=True, nullable=False, location='json')
+        reqparser.add_argument('privacy_policy', type=str, required=False, nullable=True, location='json', default='')
+        reqparser.add_argument('labels', type=list[str], required=False, nullable=True, location='json')
+        
+        args = reqparser.parse_args()
+
+        if not args['workflow_tool_id']:
+            raise ValueError('incorrect workflow_tool_id')
+        
+        return WorkflowToolManageService.update_workflow_tool(
+            user_id,
+            tenant_id,
+            args['workflow_tool_id'],
+            args['name'],
+            args['label'],
+            args['icon'],
+            args['description'],
+            args['parameters'],
+            args['privacy_policy'],
+            args.get('labels', []),
+        )
+
+class ToolWorkflowProviderDeleteApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self):
+        if not current_user.is_admin_or_owner:
+            raise Forbidden()
+        
+        user_id = current_user.id
+        tenant_id = current_user.current_tenant_id
+
+        reqparser = reqparse.RequestParser()
+        reqparser.add_argument('workflow_tool_id', type=uuid_value, required=True, nullable=False, location='json')
+
+        args = reqparser.parse_args()
+
+        return WorkflowToolManageService.delete_workflow_tool(
+            user_id,
+            tenant_id,
+            args['workflow_tool_id'],
+        )
+        
+class ToolWorkflowProviderGetApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self):
+        user_id = current_user.id
+        tenant_id = current_user.current_tenant_id
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('workflow_tool_id', type=uuid_value, required=False, nullable=True, location='args')
+        parser.add_argument('workflow_app_id', type=uuid_value, required=False, nullable=True, location='args')
+
+        args = parser.parse_args()
+
+        if args.get('workflow_tool_id'):
+            tool = WorkflowToolManageService.get_workflow_tool_by_tool_id(
+                user_id,
+                tenant_id,
+                args['workflow_tool_id'],
+            )
+        elif args.get('workflow_app_id'):
+            tool = WorkflowToolManageService.get_workflow_tool_by_app_id(
+                user_id,
+                tenant_id,
+                args['workflow_app_id'],
+            )
+        else:
+            raise ValueError('incorrect workflow_tool_id or workflow_app_id')
+
+        return jsonable_encoder(tool)
+    
+class ToolWorkflowProviderListToolApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self):
+        user_id = current_user.id
+        tenant_id = current_user.current_tenant_id
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('workflow_tool_id', type=uuid_value, required=True, nullable=False, location='args')
+
+        args = parser.parse_args()
+
+        return jsonable_encoder(WorkflowToolManageService.list_single_workflow_tools(
+            user_id,
+            tenant_id,
+            args['workflow_tool_id'],
+        ))
+
 class ToolBuiltinListApi(Resource):
     @setup_required
     @login_required
@@ -304,7 +464,7 @@ class ToolBuiltinListApi(Resource):
         user_id = current_user.id
         tenant_id = current_user.current_tenant_id
 
-        return jsonable_encoder([provider.to_dict() for provider in ToolManageService.list_builtin_tools(
+        return jsonable_encoder([provider.to_dict() for provider in BuiltinToolManageService.list_builtin_tools(
             user_id,
             tenant_id,
         )])
@@ -317,18 +477,43 @@ class ToolApiListApi(Resource):
         user_id = current_user.id
         tenant_id = current_user.current_tenant_id
 
-        return jsonable_encoder([provider.to_dict() for provider in ToolManageService.list_api_tools(
+        return jsonable_encoder([provider.to_dict() for provider in ApiToolManageService.list_api_tools(
             user_id,
             tenant_id,
         )])
+    
+class ToolWorkflowListApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self):
+        user_id = current_user.id
+        tenant_id = current_user.current_tenant_id
 
+        return jsonable_encoder([provider.to_dict() for provider in WorkflowToolManageService.list_tenant_workflow_tools(
+            user_id,
+            tenant_id,
+        )])
+    
+class ToolLabelsApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self):
+        return jsonable_encoder(ToolLabelsService.list_tool_labels())
+
+# tool provider
 api.add_resource(ToolProviderListApi, '/workspaces/current/tool-providers')
+
+# builtin tool provider
 api.add_resource(ToolBuiltinProviderListToolsApi, '/workspaces/current/tool-provider/builtin/<provider>/tools')
 api.add_resource(ToolBuiltinProviderDeleteApi, '/workspaces/current/tool-provider/builtin/<provider>/delete')
 api.add_resource(ToolBuiltinProviderUpdateApi, '/workspaces/current/tool-provider/builtin/<provider>/update')
 api.add_resource(ToolBuiltinProviderGetCredentialsApi, '/workspaces/current/tool-provider/builtin/<provider>/credentials')
 api.add_resource(ToolBuiltinProviderCredentialsSchemaApi, '/workspaces/current/tool-provider/builtin/<provider>/credentials_schema')
 api.add_resource(ToolBuiltinProviderIconApi, '/workspaces/current/tool-provider/builtin/<provider>/icon')
+
+# api tool provider
 api.add_resource(ToolApiProviderAddApi, '/workspaces/current/tool-provider/api/add')
 api.add_resource(ToolApiProviderGetRemoteSchemaApi, '/workspaces/current/tool-provider/api/remote')
 api.add_resource(ToolApiProviderListToolsApi, '/workspaces/current/tool-provider/api/tools')
@@ -338,5 +523,15 @@ api.add_resource(ToolApiProviderGetApi, '/workspaces/current/tool-provider/api/g
 api.add_resource(ToolApiProviderSchemaApi, '/workspaces/current/tool-provider/api/schema')
 api.add_resource(ToolApiProviderPreviousTestApi, '/workspaces/current/tool-provider/api/test/pre')
 
+# workflow tool provider
+api.add_resource(ToolWorkflowProviderCreateApi, '/workspaces/current/tool-provider/workflow/create')
+api.add_resource(ToolWorkflowProviderUpdateApi, '/workspaces/current/tool-provider/workflow/update')
+api.add_resource(ToolWorkflowProviderDeleteApi, '/workspaces/current/tool-provider/workflow/delete')
+api.add_resource(ToolWorkflowProviderGetApi, '/workspaces/current/tool-provider/workflow/get')
+api.add_resource(ToolWorkflowProviderListToolApi, '/workspaces/current/tool-provider/workflow/tools')
+
 api.add_resource(ToolBuiltinListApi, '/workspaces/current/tools/builtin')
-api.add_resource(ToolApiListApi, '/workspaces/current/tools/api')
+api.add_resource(ToolApiListApi, '/workspaces/current/tools/api')
+api.add_resource(ToolWorkflowListApi, '/workspaces/current/tools/workflow')
+
+api.add_resource(ToolLabelsApi, '/workspaces/current/tool-labels')

api/controllers/service_api/app/message.py

@@ -97,7 +97,7 @@ class MessageListApi(Resource):
 
 
 class MessageFeedbackApi(Resource):
-    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON))
+    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON, required=True))
     def post(self, app_model: App, end_user: EndUser, message_id):
         message_id = str(message_id)
 
@@ -114,7 +114,7 @@ class MessageFeedbackApi(Resource):
 
 
 class MessageSuggestedApi(Resource):
-    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.QUERY))
+    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.QUERY, required=True))
     def get(self, app_model: App, end_user: EndUser, message_id):
         message_id = str(message_id)
         app_mode = AppMode.value_of(app_model.mode)

api/controllers/service_api/dataset/dataset.py

@@ -1,5 +1,6 @@
 from flask import request
 from flask_restful import marshal, reqparse
+from werkzeug.exceptions import NotFound
 
 import services.dataset_service
 from controllers.service_api import api
@@ -19,10 +20,12 @@ def _validate_name(name):
     return name
 
 
-class DatasetApi(DatasetApiResource):
-    """Resource for get datasets."""
+class DatasetListApi(DatasetApiResource):
+    """Resource for datasets."""
 
     def get(self, tenant_id):
+        """Resource for getting datasets."""
+
         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")
@@ -65,9 +68,9 @@ class DatasetApi(DatasetApiResource):
         }
         return response, 200
 
-    """Resource for datasets."""
 
     def post(self, tenant_id):
+        """Resource for creating datasets."""
         parser = reqparse.RequestParser()
         parser.add_argument('name', nullable=False, required=True,
                             help='type is required. Name must be between 1 to 40 characters.',
@@ -89,6 +92,31 @@ class DatasetApi(DatasetApiResource):
 
         return marshal(dataset, dataset_detail_fields), 200
 
+class DatasetApi(DatasetApiResource):
+    """Resource for dataset."""
 
-api.add_resource(DatasetApi, '/datasets')
+    def delete(self, _, dataset_id):
+        """
+        Deletes a dataset given its ID.
 
+        Args:
+            dataset_id (UUID): The ID of the dataset to be deleted.
+
+        Returns:
+            dict: A dictionary with a key 'result' and a value 'success' 
+                  if the dataset was successfully deleted. Omitted in HTTP response.
+            int: HTTP status code 204 indicating that the operation was successful.
+
+        Raises:
+            NotFound: If the dataset with the given ID does not exist.
+        """
+
+        dataset_id_str = str(dataset_id)
+
+        if DatasetService.delete_dataset(dataset_id_str, current_user):
+            return {'result': 'success'}, 204
+        else:
+            raise NotFound("Dataset not found.")
+
+api.add_resource(DatasetListApi, '/datasets')
+api.add_resource(DatasetApi, '/datasets/<uuid:dataset_id>')

api/controllers/service_api/wraps.py

@@ -8,7 +8,7 @@ from flask import current_app, request
 from flask_login import user_logged_in
 from flask_restful import Resource
 from pydantic import BaseModel
-from werkzeug.exceptions import Forbidden, NotFound, Unauthorized
+from werkzeug.exceptions import Forbidden, Unauthorized
 
 from extensions.ext_database import db
 from libs.login import _get_user
@@ -39,17 +39,17 @@ def validate_app_token(view: Optional[Callable] = None, *, fetch_user_arg: Optio
 
             app_model = db.session.query(App).filter(App.id == api_token.app_id).first()
             if not app_model:
-                raise NotFound()
+                raise Forbidden("The app no longer exists.")
 
             if app_model.status != 'normal':
-                raise NotFound()
+                raise Forbidden("The app's status is abnormal.")
 
             if not app_model.enable_api:
-                raise NotFound()
+                raise Forbidden("The app's API service has been disabled.")
 
             tenant = db.session.query(Tenant).filter(Tenant.id == app_model.tenant_id).first()
             if tenant.status == TenantStatus.ARCHIVE:
-                raise NotFound()
+                raise Forbidden("The workspace's status is archived.")
 
             kwargs['app_model'] = app_model
 

api/controllers/web/audio.py

@@ -74,7 +74,7 @@ class TextApi(WebApiResource):
                 app_model=app_model,
                 text=request.form['text'],
                 end_user=end_user.external_user_id,
-                voice=request.form.get('voice'),
+                voice=request.form['voice'] if request.form.get('voice') else None,
                 streaming=False
             )
 

api/core/agent/base_agent_runner.py

@@ -39,6 +39,7 @@ from core.tools.entities.tool_entities import (
 from core.tools.tool.dataset_retriever_tool import DatasetRetrieverTool
 from core.tools.tool.tool import Tool
 from core.tools.tool_manager import ToolManager
+from core.tools.utils.tool_parameter_converter import ToolParameterConverter
 from extensions.ext_database import db
 from models.model import Conversation, Message, MessageAgentThought
 from models.tools import ToolConversationVariables
@@ -128,6 +129,8 @@ class BaseAgentRunner(AppRunner):
             self.files = application_generate_entity.files
         else:
             self.files = []
+        self.query = None
+        self._current_thoughts: list[PromptMessage] = []
 
     def _repack_app_generate_entity(self, app_generate_entity: AgentChatAppGenerateEntity) \
             -> AgentChatAppGenerateEntity:
@@ -165,6 +168,7 @@ class BaseAgentRunner(AppRunner):
             tenant_id=self.tenant_id,
             app_id=self.app_config.app_id,
             agent_tool=tool,
+            invoke_from=self.application_generate_entity.invoke_from
         )
         tool_entity.load_variables(self.variables_pool)
 
@@ -183,21 +187,11 @@ class BaseAgentRunner(AppRunner):
             if parameter.form != ToolParameter.ToolParameterForm.LLM:
                 continue
 
-            parameter_type = 'string'
+            parameter_type = ToolParameterConverter.get_parameter_type(parameter.type)
             enum = []
-            if parameter.type == ToolParameter.ToolParameterType.STRING:
-                parameter_type = 'string'
-            elif parameter.type == ToolParameter.ToolParameterType.BOOLEAN:
-                parameter_type = 'boolean'
-            elif parameter.type == ToolParameter.ToolParameterType.NUMBER:
-                parameter_type = 'number'
-            elif parameter.type == ToolParameter.ToolParameterType.SELECT:
-                for option in parameter.options:
-                    enum.append(option.value)
-                parameter_type = 'string'
-            else:
-                raise ValueError(f"parameter type {parameter.type} is not supported")
-            
+            if parameter.type == ToolParameter.ToolParameterType.SELECT:
+                enum = [option.value for option in parameter.options]
+
             message_tool.parameters['properties'][parameter.name] = {
                 "type": parameter_type,
                 "description": parameter.llm_description or '',
@@ -278,20 +272,10 @@ class BaseAgentRunner(AppRunner):
             if parameter.form != ToolParameter.ToolParameterForm.LLM:
                 continue
 
-            parameter_type = 'string'
+            parameter_type = ToolParameterConverter.get_parameter_type(parameter.type)
             enum = []
-            if parameter.type == ToolParameter.ToolParameterType.STRING:
-                parameter_type = 'string'
-            elif parameter.type == ToolParameter.ToolParameterType.BOOLEAN:
-                parameter_type = 'boolean'
-            elif parameter.type == ToolParameter.ToolParameterType.NUMBER:
-                parameter_type = 'number'
-            elif parameter.type == ToolParameter.ToolParameterType.SELECT:
-                for option in parameter.options:
-                    enum.append(option.value)
-                parameter_type = 'string'
-            else:
-                raise ValueError(f"parameter type {parameter.type} is not supported")
+            if parameter.type == ToolParameter.ToolParameterType.SELECT:
+                enum = [option.value for option in parameter.options]
         
             prompt_tool.parameters['properties'][parameter.name] = {
                 "type": parameter_type,
@@ -463,7 +447,7 @@ class BaseAgentRunner(AppRunner):
         for message in messages:
             if message.id == self.message.id:
                 continue
-            
+
             result.append(self.organize_agent_user_prompt(message))
             agent_thoughts: list[MessageAgentThought] = message.agent_thoughts
             if agent_thoughts:

api/core/agent/cot_agent_runner.py

@@ -15,6 +15,7 @@ from core.model_runtime.entities.message_entities import (
     ToolPromptMessage,
     UserPromptMessage,
 )
+from core.prompt.agent_history_prompt_transform import AgentHistoryPromptTransform
 from core.tools.entities.tool_entities import ToolInvokeMeta
 from core.tools.tool.tool import Tool
 from core.tools.tool_engine import ToolEngine
@@ -373,7 +374,7 @@ class CotAgentRunner(BaseAgentRunner, ABC):
 
         return message
 
-    def _organize_historic_prompt_messages(self) -> list[PromptMessage]:
+    def _organize_historic_prompt_messages(self, current_session_messages: list[PromptMessage] = None) -> list[PromptMessage]:
         """
             organize historic prompt messages
         """
@@ -381,6 +382,13 @@ class CotAgentRunner(BaseAgentRunner, ABC):
         scratchpad: list[AgentScratchpadUnit] = []
         current_scratchpad: AgentScratchpadUnit = None
 
+        self.history_prompt_messages = AgentHistoryPromptTransform(
+            model_config=self.model_config,
+            prompt_messages=current_session_messages or [],
+            history_messages=self.history_prompt_messages,
+            memory=self.memory
+        ).get_prompt()
+
         for message in self.history_prompt_messages:
             if isinstance(message, AssistantPromptMessage):
                 current_scratchpad = AgentScratchpadUnit(

api/core/agent/cot_chat_agent_runner.py

@@ -32,9 +32,6 @@ class CotChatAgentRunner(CotAgentRunner):
         # organize system prompt
         system_message = self._organize_system_prompt()
 
-        # organize historic prompt messages
-        historic_messages = self._historic_prompt_messages
-
         # organize current assistant messages
         agent_scratchpad = self._agent_scratchpad
         if not agent_scratchpad:
@@ -57,6 +54,13 @@ class CotChatAgentRunner(CotAgentRunner):
         query_messages = UserPromptMessage(content=self._query)
 
         if assistant_messages:
+            # organize historic prompt messages
+            historic_messages = self._organize_historic_prompt_messages([
+                system_message,
+                query_messages,
+                *assistant_messages,
+                UserPromptMessage(content='continue')
+            ])            
             messages = [
                 system_message,
                 *historic_messages,
@@ -65,6 +69,8 @@ class CotChatAgentRunner(CotAgentRunner):
                 UserPromptMessage(content='continue')
             ]
         else:
+            # organize historic prompt messages
+            historic_messages = self._organize_historic_prompt_messages([system_message, query_messages])
             messages = [system_message, *historic_messages, query_messages]
 
         # join all messages

api/core/agent/cot_completion_agent_runner.py

@@ -19,11 +19,11 @@ class CotCompletionAgentRunner(CotAgentRunner):
         
         return system_prompt
 
-    def _organize_historic_prompt(self) -> str:
+    def _organize_historic_prompt(self, current_session_messages: list[PromptMessage] = None) -> str:
         """
         Organize historic prompt
         """
-        historic_prompt_messages = self._historic_prompt_messages
+        historic_prompt_messages = self._organize_historic_prompt_messages(current_session_messages)
         historic_prompt = ""
 
         for message in historic_prompt_messages:

api/core/agent/entities.py

@@ -8,7 +8,7 @@ class AgentToolEntity(BaseModel):
     """
     Agent Tool Entity.
     """
-    provider_type: Literal["builtin", "api"]
+    provider_type: Literal["builtin", "api", "workflow"]
     provider_id: str
     tool_name: str
     tool_parameters: dict[str, Any] = {}

api/core/agent/fc_agent_runner.py

@@ -17,6 +17,7 @@ from core.model_runtime.entities.message_entities import (
     ToolPromptMessage,
     UserPromptMessage,
 )
+from core.prompt.agent_history_prompt_transform import AgentHistoryPromptTransform
 from core.tools.entities.tool_entities import ToolInvokeMeta
 from core.tools.tool_engine import ToolEngine
 from models.model import Message
@@ -24,21 +25,18 @@ from models.model import Message
 logger = logging.getLogger(__name__)
 
 class FunctionCallAgentRunner(BaseAgentRunner):
+
     def run(self, 
             message: Message, query: str, **kwargs: Any
     ) -> Generator[LLMResultChunk, None, None]:
         """
         Run FunctionCall agent application
         """
+        self.query = query
         app_generate_entity = self.application_generate_entity
 
         app_config = self.app_config
 
-        prompt_template = app_config.prompt_template.simple_prompt_template or ''
-        prompt_messages = self.history_prompt_messages
-        prompt_messages = self._init_system_message(prompt_template, prompt_messages)
-        prompt_messages = self._organize_user_query(query, prompt_messages)
-
         # convert tools into ModelRuntime Tool format
         tool_instances, prompt_messages_tools = self._init_prompt_tools()
 
@@ -81,6 +79,7 @@ class FunctionCallAgentRunner(BaseAgentRunner):
             )
 
             # recalc llm max tokens
+            prompt_messages = self._organize_prompt_messages()
             self.recalc_llm_max_tokens(self.model_config, prompt_messages)
             # invoke model
             chunks: Union[Generator[LLMResultChunk, None, None], LLMResult] = model_instance.invoke_llm(
@@ -203,7 +202,7 @@ class FunctionCallAgentRunner(BaseAgentRunner):
             else:
                 assistant_message.content = response
             
-            prompt_messages.append(assistant_message)
+            self._current_thoughts.append(assistant_message)
 
             # save thought
             self.save_agent_thought(
@@ -265,12 +264,14 @@ class FunctionCallAgentRunner(BaseAgentRunner):
                     }
                 
                 tool_responses.append(tool_response)
-                prompt_messages = self._organize_assistant_message(
-                    tool_call_id=tool_call_id,
-                    tool_call_name=tool_call_name,
-                    tool_response=tool_response['tool_response'],
-                    prompt_messages=prompt_messages,
-                )
+                if tool_response['tool_response'] is not None:
+                    self._current_thoughts.append(
+                        ToolPromptMessage(
+                            content=tool_response['tool_response'],
+                            tool_call_id=tool_call_id,
+                            name=tool_call_name,
+                        )
+                    ) 
 
             if len(tool_responses) > 0:
                 # save agent thought
@@ -300,8 +301,6 @@ class FunctionCallAgentRunner(BaseAgentRunner):
 
             iteration_step += 1
 
-            prompt_messages = self._clear_user_prompt_image_messages(prompt_messages)
-
         self.update_db_variables(self.variables_pool, self.db_variables_pool)
         # publish end event
         self.queue_manager.publish(QueueMessageEndEvent(llm_result=LLMResult(
@@ -393,24 +392,6 @@ class FunctionCallAgentRunner(BaseAgentRunner):
 
         return prompt_messages
     
-    def _organize_assistant_message(self, tool_call_id: str = None, tool_call_name: str = None, tool_response: str = None, 
-                                    prompt_messages: list[PromptMessage] = None) -> list[PromptMessage]:
-        """
-        Organize assistant message
-        """
-        prompt_messages = deepcopy(prompt_messages)
-
-        if tool_response is not None:
-            prompt_messages.append(
-                ToolPromptMessage(
-                    content=tool_response,
-                    tool_call_id=tool_call_id,
-                    name=tool_call_name,
-                )
-            )
-
-        return prompt_messages
-    
     def _clear_user_prompt_image_messages(self, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
         """
         As for now, gpt supports both fc and vision at the first iteration.
@@ -428,4 +409,26 @@ class FunctionCallAgentRunner(BaseAgentRunner):
                         for content in prompt_message.content 
                     ])
 
-        return prompt_messages
+        return prompt_messages
+
+    def _organize_prompt_messages(self):
+        prompt_template = self.app_config.prompt_template.simple_prompt_template or ''
+        self.history_prompt_messages = self._init_system_message(prompt_template, self.history_prompt_messages)
+        query_prompt_messages = self._organize_user_query(self.query, [])
+
+        self.history_prompt_messages = AgentHistoryPromptTransform(
+            model_config=self.model_config,
+            prompt_messages=[*query_prompt_messages, *self._current_thoughts],
+            history_messages=self.history_prompt_messages,
+            memory=self.memory
+        ).get_prompt()
+
+        prompt_messages = [
+            *self.history_prompt_messages,
+            *query_prompt_messages,
+            *self._current_thoughts
+        ]
+        if len(self._current_thoughts) != 0:
+            # clear messages after the first iteration
+            prompt_messages = self._clear_user_prompt_image_messages(prompt_messages)
+        return prompt_messages

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

@@ -1,7 +1,7 @@
 from typing import Optional
 
 from core.agent.entities import AgentEntity, AgentPromptEntity, AgentToolEntity
-from core.tools.prompt.template import REACT_PROMPT_TEMPLATES
+from core.agent.prompt.template import REACT_PROMPT_TEMPLATES
 
 
 class AgentConfigManager:

api/core/app/app_config/entities.py

@@ -239,4 +239,4 @@ class WorkflowUIBasedAppConfig(AppConfig):
     """
     Workflow UI Based App Config Entity.
     """
-    workflow_id: str
+    workflow_id: str

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

@@ -98,6 +98,90 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             extras=extras
         )
 
+        return self._generate(
+            app_model=app_model,
+            workflow=workflow,
+            user=user,
+            invoke_from=invoke_from,
+            application_generate_entity=application_generate_entity,
+            conversation=conversation,
+            stream=stream
+        )
+    
+    def single_iteration_generate(self, app_model: App,
+                                  workflow: Workflow,
+                                  node_id: str,
+                                  user: Account,
+                                  args: dict,
+                                  stream: bool = True) \
+            -> Union[dict, Generator[dict, None, None]]:
+        """
+        Generate App response.
+
+        :param app_model: App
+        :param workflow: Workflow
+        :param user: account or end user
+        :param args: request args
+        :param invoke_from: invoke from source
+        :param stream: is stream
+        """
+        if not node_id:
+            raise ValueError('node_id is required')
+        
+        if args.get('inputs') is None:
+            raise ValueError('inputs is required')
+        
+        extras = {
+            "auto_generate_conversation_name": False
+        }
+
+        # get conversation
+        conversation = None
+        if args.get('conversation_id'):
+            conversation = self._get_conversation_by_user(app_model, args.get('conversation_id'), user)
+
+        # convert to app config
+        app_config = AdvancedChatAppConfigManager.get_app_config(
+            app_model=app_model,
+            workflow=workflow
+        )
+
+        # init application generate entity
+        application_generate_entity = AdvancedChatAppGenerateEntity(
+            task_id=str(uuid.uuid4()),
+            app_config=app_config,
+            conversation_id=conversation.id if conversation else None,
+            inputs={},
+            query='',
+            files=[],
+            user_id=user.id,
+            stream=stream,
+            invoke_from=InvokeFrom.DEBUGGER,
+            extras=extras,
+            single_iteration_run=AdvancedChatAppGenerateEntity.SingleIterationRunEntity(
+                node_id=node_id,
+                inputs=args['inputs']
+            )
+        )
+
+        return self._generate(
+            app_model=app_model,
+            workflow=workflow,
+            user=user,
+            invoke_from=InvokeFrom.DEBUGGER,
+            application_generate_entity=application_generate_entity,
+            conversation=conversation,
+            stream=stream
+        )
+
+    def _generate(self, app_model: App,
+                 workflow: Workflow,
+                 user: Union[Account, EndUser],
+                 invoke_from: InvokeFrom,
+                 application_generate_entity: AdvancedChatAppGenerateEntity,
+                 conversation: Conversation = None,
+                 stream: bool = True) \
+            -> Union[dict, Generator[dict, None, None]]:
         is_first_conversation = False
         if not conversation:
             is_first_conversation = True
@@ -167,18 +251,30 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
         """
         with flask_app.app_context():
             try:
-                # get conversation and message
-                conversation = self._get_conversation(conversation_id)
-                message = self._get_message(message_id)
-
-                # chatbot app
                 runner = AdvancedChatAppRunner()
-                runner.run(
-                    application_generate_entity=application_generate_entity,
-                    queue_manager=queue_manager,
-                    conversation=conversation,
-                    message=message
-                )
+                if application_generate_entity.single_iteration_run:
+                    single_iteration_run = application_generate_entity.single_iteration_run
+                    runner.single_iteration_run(
+                        app_id=application_generate_entity.app_config.app_id,
+                        workflow_id=application_generate_entity.app_config.workflow_id,
+                        queue_manager=queue_manager,
+                        inputs=single_iteration_run.inputs,
+                        node_id=single_iteration_run.node_id,
+                        user_id=application_generate_entity.user_id
+                    )
+                else:
+                    # get conversation and message
+                    conversation = self._get_conversation(conversation_id)
+                    message = self._get_message(message_id)
+
+                    # chatbot app
+                    runner = AdvancedChatAppRunner()
+                    runner.run(
+                        application_generate_entity=application_generate_entity,
+                        queue_manager=queue_manager,
+                        conversation=conversation,
+                        message=message
+                    )
             except GenerateTaskStoppedException:
                 pass
             except InvokeAuthorizationError:

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

@@ -102,6 +102,7 @@ class AdvancedChatAppRunner(AppRunner):
             user_from=UserFrom.ACCOUNT
             if application_generate_entity.invoke_from in [InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER]
             else UserFrom.END_USER,
+            invoke_from=application_generate_entity.invoke_from,
             user_inputs=inputs,
             system_inputs={
                 SystemVariable.QUERY: query,
@@ -109,6 +110,35 @@ class AdvancedChatAppRunner(AppRunner):
                 SystemVariable.CONVERSATION_ID: conversation.id,
                 SystemVariable.USER_ID: user_id
             },
+            callbacks=workflow_callbacks,
+            call_depth=application_generate_entity.call_depth
+        )
+
+    def single_iteration_run(self, app_id: str, workflow_id: str,
+                             queue_manager: AppQueueManager,
+                             inputs: dict, node_id: str, user_id: str) -> None:
+        """
+        Single iteration run
+        """
+        app_record: App = db.session.query(App).filter(App.id == app_id).first()
+        if not app_record:
+            raise ValueError("App not found")
+        
+        workflow = self.get_workflow(app_model=app_record, workflow_id=workflow_id)
+        if not workflow:
+            raise ValueError("Workflow not initialized")
+        
+        workflow_callbacks = [WorkflowEventTriggerCallback(
+            queue_manager=queue_manager,
+            workflow=workflow
+        )]
+
+        workflow_engine_manager = WorkflowEngineManager()
+        workflow_engine_manager.single_step_run_iteration_workflow_node(
+            workflow=workflow,
+            node_id=node_id,
+            user_id=user_id,
+            user_inputs=inputs,
             callbacks=workflow_callbacks
         )
 

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

@@ -12,6 +12,9 @@ from core.app.entities.queue_entities import (
     QueueAdvancedChatMessageEndEvent,
     QueueAnnotationReplyEvent,
     QueueErrorEvent,
+    QueueIterationCompletedEvent,
+    QueueIterationNextEvent,
+    QueueIterationStartEvent,
     QueueMessageReplaceEvent,
     QueueNodeFailedEvent,
     QueueNodeStartedEvent,
@@ -64,6 +67,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
     _workflow: Workflow
     _user: Union[Account, EndUser]
     _workflow_system_variables: dict[SystemVariable, Any]
+    _iteration_nested_relations: dict[str, list[str]]
 
     def __init__(self, application_generate_entity: AdvancedChatAppGenerateEntity,
                  workflow: Workflow,
@@ -103,6 +107,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
             usage=LLMUsage.empty_usage()
         )
 
+        self._iteration_nested_relations = self._get_iteration_nested_relations(self._workflow.graph_dict)
         self._stream_generate_routes = self._get_stream_generate_routes()
         self._conversation_name_generate_thread = None
 
@@ -204,6 +209,8 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                 # 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_routes:
                     self._task_state.current_stream_generate_state = self._stream_generate_routes[event.node_id]
+                    # reset current route position to 0
+                    self._task_state.current_stream_generate_state.current_route_position = 0
 
                     # generate stream outputs when node started
                     yield from self._generate_stream_outputs_when_node_started()
@@ -225,6 +232,22 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                     task_id=self._application_generate_entity.task_id,
                     workflow_node_execution=workflow_node_execution
                 )
+
+                if isinstance(event, QueueNodeFailedEvent):
+                    yield from self._handle_iteration_exception(
+                        task_id=self._application_generate_entity.task_id,
+                        error=f'Child node failed: {event.error}'
+                    )
+            elif isinstance(event, QueueIterationStartEvent | QueueIterationNextEvent | QueueIterationCompletedEvent):
+                if isinstance(event, QueueIterationNextEvent):
+                    # clear ran node execution infos of current iteration
+                    iteration_relations = self._iteration_nested_relations.get(event.node_id)
+                    if iteration_relations:
+                        for node_id in iteration_relations:
+                            self._task_state.ran_node_execution_infos.pop(node_id, None)
+
+                yield self._handle_iteration_to_stream_response(self._application_generate_entity.task_id, event)
+                self._handle_iteration_operation(event)
             elif isinstance(event, QueueStopEvent | QueueWorkflowSucceededEvent | QueueWorkflowFailedEvent):
                 workflow_run = self._handle_workflow_finished(event)
                 if workflow_run:
@@ -263,10 +286,6 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                 self._handle_retriever_resources(event)
             elif isinstance(event, QueueAnnotationReplyEvent):
                 self._handle_annotation_reply(event)
-            # elif isinstance(event, QueueMessageFileEvent):
-            #     response = self._message_file_to_stream_response(event)
-            #     if response:
-            #         yield response
             elif isinstance(event, QueueTextChunkEvent):
                 delta_text = event.text
                 if delta_text is None:
@@ -342,7 +361,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
             id=self._message.id,
             **extras
         )
-
+    
     def _get_stream_generate_routes(self) -> dict[str, ChatflowStreamGenerateRoute]:
         """
         Get stream generate routes.
@@ -372,7 +391,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                 )
 
         return stream_generate_routes
-
+    
     def _get_answer_start_at_node_ids(self, graph: dict, target_node_id: str) \
             -> list[str]:
         """
@@ -391,6 +410,18 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                 ingoing_edges.append(edge)
 
         if not ingoing_edges:
+            # check if it's the first node in the iteration
+            target_node = next((node for node in nodes if node.get('id') == target_node_id), None)
+            if not target_node:
+                return []
+            
+            node_iteration_id = target_node.get('data', {}).get('iteration_id')
+            # get iteration start node id
+            for node in nodes:
+                if node.get('id') == node_iteration_id:
+                    if node.get('data', {}).get('start_node_id') == target_node_id:
+                        return [target_node_id]
+                    
             return []
 
         start_node_ids = []
@@ -401,14 +432,23 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                 continue
 
             node_type = source_node.get('data', {}).get('type')
+            node_iteration_id = source_node.get('data', {}).get('iteration_id')
+            iteration_start_node_id = None
+            if node_iteration_id:
+                iteration_node = next((node for node in nodes if node.get('id') == node_iteration_id), None)
+                iteration_start_node_id = iteration_node.get('data', {}).get('start_node_id')
+
             if node_type in [
                 NodeType.ANSWER.value,
                 NodeType.IF_ELSE.value,
-                NodeType.QUESTION_CLASSIFIER.value
+                NodeType.QUESTION_CLASSIFIER.value,
+                NodeType.ITERATION.value,
+                NodeType.LOOP.value
             ]:
                 start_node_id = target_node_id
                 start_node_ids.append(start_node_id)
-            elif node_type == NodeType.START.value:
+            elif node_type == NodeType.START.value or \
+                node_iteration_id is not None and iteration_start_node_id == source_node.get('id'):
                 start_node_id = source_node_id
                 start_node_ids.append(start_node_id)
             else:
@@ -417,7 +457,27 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                     start_node_ids.extend(sub_start_node_ids)
 
         return start_node_ids
+    
+    def _get_iteration_nested_relations(self, graph: dict) -> dict[str, list[str]]:
+        """
+        Get iteration nested relations.
+        :param graph: graph
+        :return:
+        """
+        nodes = graph.get('nodes')
 
+        iteration_ids = [node.get('id') for node in nodes 
+                         if node.get('data', {}).get('type') in [
+                             NodeType.ITERATION.value,
+                             NodeType.LOOP.value,
+                        ]]
+
+        return {
+            iteration_id: [
+                node.get('id') for node in nodes if node.get('data', {}).get('iteration_id') == iteration_id
+            ] for iteration_id in iteration_ids
+        }
+    
     def _generate_stream_outputs_when_node_started(self) -> Generator:
         """
         Generate stream outputs.
@@ -425,7 +485,8 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
         """
         if self._task_state.current_stream_generate_state:
             route_chunks = self._task_state.current_stream_generate_state.generate_route[
-                           self._task_state.current_stream_generate_state.current_route_position:]
+                self._task_state.current_stream_generate_state.current_route_position:
+            ]
 
             for route_chunk in route_chunks:
                 if route_chunk.type == 'text':
@@ -458,13 +519,14 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
 
         route_chunks = self._task_state.current_stream_generate_state.generate_route[
                        self._task_state.current_stream_generate_state.current_route_position:]
-
+        
         for route_chunk in route_chunks:
             if route_chunk.type == 'text':
                 route_chunk = cast(TextGenerateRouteChunk, route_chunk)
                 self._task_state.answer += route_chunk.text
                 yield self._message_to_stream_response(route_chunk.text, self._message.id)
             else:
+                value = None
                 route_chunk = cast(VarGenerateRouteChunk, route_chunk)
                 value_selector = route_chunk.value_selector
                 if not value_selector:
@@ -476,6 +538,20 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                 if route_chunk_node_id == 'sys':
                     # system variable
                     value = self._workflow_system_variables.get(SystemVariable.value_of(value_selector[1]))
+                elif route_chunk_node_id in self._iteration_nested_relations:
+                    # it's a iteration variable
+                    if not self._iteration_state or route_chunk_node_id not in self._iteration_state.current_iterations:
+                        continue
+                    iteration_state = self._iteration_state.current_iterations[route_chunk_node_id]
+                    iterator = iteration_state.inputs
+                    if not iterator:
+                        continue
+                    iterator_selector = iterator.get('iterator_selector', [])
+                    if value_selector[1] == 'index':
+                        value = iteration_state.current_index
+                    elif value_selector[1] == 'item':
+                        value = iterator_selector[iteration_state.current_index] if iteration_state.current_index < len(
+                            iterator_selector) else None
                 else:
                     # check chunk node id is before current node id or equal to current node id
                     if route_chunk_node_id not in self._task_state.ran_node_execution_infos:
@@ -505,7 +581,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                         else:
                             value = value.get(key)
 
-                if value:
+                if value is not None:
                     text = ''
                     if isinstance(value, str | int | float):
                         text = str(value)

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

@@ -1,8 +1,11 @@
-from typing import Optional
+from typing import Any, Optional
 
 from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
 from core.app.entities.queue_entities import (
     AppQueueEvent,
+    QueueIterationCompletedEvent,
+    QueueIterationNextEvent,
+    QueueIterationStartEvent,
     QueueNodeFailedEvent,
     QueueNodeStartedEvent,
     QueueNodeSucceededEvent,
@@ -130,6 +133,66 @@ class WorkflowEventTriggerCallback(BaseWorkflowCallback):
             ), PublishFrom.APPLICATION_MANAGER
         )
 
+    def on_workflow_iteration_started(self, 
+                                      node_id: str,
+                                      node_type: NodeType,
+                                      node_run_index: int = 1,
+                                      node_data: Optional[BaseNodeData] = None,
+                                      inputs: dict = None,
+                                      predecessor_node_id: Optional[str] = None,
+                                      metadata: Optional[dict] = None) -> None:
+        """
+        Publish iteration started
+        """
+        self._queue_manager.publish(
+            QueueIterationStartEvent(
+                node_id=node_id,
+                node_type=node_type,
+                node_run_index=node_run_index,
+                node_data=node_data,
+                inputs=inputs,
+                predecessor_node_id=predecessor_node_id,
+                metadata=metadata
+            ),
+            PublishFrom.APPLICATION_MANAGER
+        )
+
+    def on_workflow_iteration_next(self, node_id: str, 
+                                   node_type: NodeType,
+                                   index: int, 
+                                   node_run_index: int,
+                                   output: Optional[Any]) -> None:
+        """
+        Publish iteration next
+        """
+        self._queue_manager._publish(
+            QueueIterationNextEvent(
+                node_id=node_id,
+                node_type=node_type,
+                index=index,
+                node_run_index=node_run_index,
+                output=output
+            ),
+            PublishFrom.APPLICATION_MANAGER
+        )
+
+    def on_workflow_iteration_completed(self, node_id: str, 
+                                        node_type: NodeType,
+                                        node_run_index: int,
+                                        outputs: dict) -> None:
+        """
+        Publish iteration completed
+        """
+        self._queue_manager._publish(
+            QueueIterationCompletedEvent(
+                node_id=node_id,
+                node_type=node_type,
+                node_run_index=node_run_index,
+                outputs=outputs
+            ),
+            PublishFrom.APPLICATION_MANAGER
+        )
+
     def on_event(self, event: AppQueueEvent) -> None:
         """
         Publish event

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

@@ -115,7 +115,8 @@ class AgentChatAppGenerator(MessageBasedAppGenerator):
             user_id=user.id,
             stream=stream,
             invoke_from=invoke_from,
-            extras=extras
+            extras=extras,
+            call_depth=0
         )
 
         # init generate records

api/core/app/apps/base_app_runner.py

@@ -1,6 +1,6 @@
 import time
 from collections.abc import Generator
-from typing import Optional, Union, cast
+from typing import Optional, Union
 
 from core.app.app_config.entities import ExternalDataVariableEntity, PromptTemplateEntity
 from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
@@ -16,11 +16,11 @@ from core.app.features.hosting_moderation.hosting_moderation import HostingModer
 from core.external_data_tool.external_data_fetch import ExternalDataFetch
 from core.file.file_obj import FileVar
 from core.memory.token_buffer_memory import TokenBufferMemory
+from core.model_manager import ModelInstance
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
 from core.model_runtime.entities.message_entities import AssistantPromptMessage, PromptMessage
 from core.model_runtime.entities.model_entities import ModelPropertyKey
 from core.model_runtime.errors.invoke import InvokeBadRequestError
-from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.moderation.input_moderation import InputModeration
 from core.prompt.advanced_prompt_transform import AdvancedPromptTransform
 from core.prompt.entities.advanced_prompt_entities import ChatModelMessage, CompletionModelPromptTemplate, MemoryConfig
@@ -45,8 +45,11 @@ class AppRunner:
         :param query: query
         :return:
         """
-        model_type_instance = model_config.provider_model_bundle.model_type_instance
-        model_type_instance = cast(LargeLanguageModel, model_type_instance)
+        # Invoke model
+        model_instance = ModelInstance(
+            provider_model_bundle=model_config.provider_model_bundle,
+            model=model_config.model
+        )
 
         model_context_tokens = model_config.model_schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE)
 
@@ -73,9 +76,7 @@ class AppRunner:
             query=query
         )
 
-        prompt_tokens = model_type_instance.get_num_tokens(
-            model_config.model,
-            model_config.credentials,
+        prompt_tokens = model_instance.get_llm_num_tokens(
             prompt_messages
         )
 
@@ -89,8 +90,10 @@ class AppRunner:
     def recalc_llm_max_tokens(self, model_config: ModelConfigWithCredentialsEntity,
                               prompt_messages: list[PromptMessage]):
         # recalc max_tokens if sum(prompt_token +  max_tokens) over model token limit
-        model_type_instance = model_config.provider_model_bundle.model_type_instance
-        model_type_instance = cast(LargeLanguageModel, model_type_instance)
+        model_instance = ModelInstance(
+            provider_model_bundle=model_config.provider_model_bundle,
+            model=model_config.model
+        )
 
         model_context_tokens = model_config.model_schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE)
 
@@ -107,9 +110,7 @@ class AppRunner:
         if max_tokens is None:
             max_tokens = 0
 
-        prompt_tokens = model_type_instance.get_num_tokens(
-            model_config.model,
-            model_config.credentials,
+        prompt_tokens = model_instance.get_llm_num_tokens(
             prompt_messages
         )
 

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

@@ -34,7 +34,8 @@ class WorkflowAppGenerator(BaseAppGenerator):
                  user: Union[Account, EndUser],
                  args: dict,
                  invoke_from: InvokeFrom,
-                 stream: bool = True) \
+                 stream: bool = True,
+                 call_depth: int = 0) \
             -> Union[dict, Generator[dict, None, None]]:
         """
         Generate App response.
@@ -75,9 +76,38 @@ class WorkflowAppGenerator(BaseAppGenerator):
             files=file_objs,
             user_id=user.id,
             stream=stream,
-            invoke_from=invoke_from
+            invoke_from=invoke_from,
+            call_depth=call_depth
         )
 
+        return self._generate(
+            app_model=app_model,
+            workflow=workflow,
+            user=user,
+            application_generate_entity=application_generate_entity,
+            invoke_from=invoke_from,
+            stream=stream,
+            call_depth=call_depth
+        )
+
+    def _generate(self, app_model: App,
+                 workflow: Workflow,
+                 user: Union[Account, EndUser],
+                 application_generate_entity: WorkflowAppGenerateEntity,
+                 invoke_from: InvokeFrom,
+                 stream: bool = True,
+                 call_depth: int = 0) \
+            -> Union[dict, Generator[dict, None, None]]:
+        """
+        Generate App response.
+
+        :param app_model: App
+        :param workflow: Workflow
+        :param user: account or end user
+        :param application_generate_entity: application generate entity
+        :param invoke_from: invoke from source
+        :param stream: is stream
+        """
         # init queue manager
         queue_manager = WorkflowAppQueueManager(
             task_id=application_generate_entity.task_id,
@@ -109,6 +139,64 @@ class WorkflowAppGenerator(BaseAppGenerator):
             invoke_from=invoke_from
         )
 
+    def single_iteration_generate(self, app_model: App,
+                                  workflow: Workflow,
+                                  node_id: str,
+                                  user: Account,
+                                  args: dict,
+                                  stream: bool = True) \
+            -> Union[dict, Generator[dict, None, None]]:
+        """
+        Generate App response.
+
+        :param app_model: App
+        :param workflow: Workflow
+        :param user: account or end user
+        :param args: request args
+        :param invoke_from: invoke from source
+        :param stream: is stream
+        """
+        if not node_id:
+            raise ValueError('node_id is required')
+        
+        if args.get('inputs') is None:
+            raise ValueError('inputs is required')
+        
+        extras = {
+            "auto_generate_conversation_name": False
+        }
+
+        # convert to app config
+        app_config = WorkflowAppConfigManager.get_app_config(
+            app_model=app_model,
+            workflow=workflow
+        )
+
+        # init application generate entity
+        application_generate_entity = WorkflowAppGenerateEntity(
+            task_id=str(uuid.uuid4()),
+            app_config=app_config,
+            inputs={},
+            files=[],
+            user_id=user.id,
+            stream=stream,
+            invoke_from=InvokeFrom.DEBUGGER,
+            extras=extras,
+            single_iteration_run=WorkflowAppGenerateEntity.SingleIterationRunEntity(
+                node_id=node_id,
+                inputs=args['inputs']
+            )
+        )
+
+        return self._generate(
+            app_model=app_model,
+            workflow=workflow,
+            user=user,
+            invoke_from=InvokeFrom.DEBUGGER,
+            application_generate_entity=application_generate_entity,
+            stream=stream
+        )
+
     def _generate_worker(self, flask_app: Flask,
                          application_generate_entity: WorkflowAppGenerateEntity,
                          queue_manager: AppQueueManager) -> None:
@@ -123,10 +211,21 @@ class WorkflowAppGenerator(BaseAppGenerator):
             try:
                 # workflow app
                 runner = WorkflowAppRunner()
-                runner.run(
-                    application_generate_entity=application_generate_entity,
-                    queue_manager=queue_manager
-                )
+                if application_generate_entity.single_iteration_run:
+                    single_iteration_run = application_generate_entity.single_iteration_run
+                    runner.single_iteration_run(
+                        app_id=application_generate_entity.app_config.app_id,
+                        workflow_id=application_generate_entity.app_config.workflow_id,
+                        queue_manager=queue_manager,
+                        inputs=single_iteration_run.inputs,
+                        node_id=single_iteration_run.node_id,
+                        user_id=application_generate_entity.user_id
+                    )
+                else:
+                    runner.run(
+                        application_generate_entity=application_generate_entity,
+                        queue_manager=queue_manager
+                    )
             except GenerateTaskStoppedException:
                 pass
             except InvokeAuthorizationError:

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

@@ -73,11 +73,44 @@ class WorkflowAppRunner:
             user_from=UserFrom.ACCOUNT
             if application_generate_entity.invoke_from in [InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER]
             else UserFrom.END_USER,
+            invoke_from=application_generate_entity.invoke_from,
             user_inputs=inputs,
             system_inputs={
                 SystemVariable.FILES: files,
                 SystemVariable.USER_ID: user_id
             },
+            callbacks=workflow_callbacks,
+            call_depth=application_generate_entity.call_depth
+        )
+
+    def single_iteration_run(self, app_id: str, workflow_id: str,
+                             queue_manager: AppQueueManager,
+                             inputs: dict, node_id: str, user_id: str) -> None:
+        """
+        Single iteration run
+        """
+        app_record: App = db.session.query(App).filter(App.id == app_id).first()
+        if not app_record:
+            raise ValueError("App not found")
+        
+        if not app_record.workflow_id:
+            raise ValueError("Workflow not initialized")
+
+        workflow = self.get_workflow(app_model=app_record, workflow_id=workflow_id)
+        if not workflow:
+            raise ValueError("Workflow not initialized")
+        
+        workflow_callbacks = [WorkflowEventTriggerCallback(
+            queue_manager=queue_manager,
+            workflow=workflow
+        )]
+
+        workflow_engine_manager = WorkflowEngineManager()
+        workflow_engine_manager.single_step_run_iteration_workflow_node(
+            workflow=workflow,
+            node_id=node_id,
+            user_id=user_id,
+            user_inputs=inputs,
             callbacks=workflow_callbacks
         )
 

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

@@ -9,6 +9,9 @@ from core.app.entities.app_invoke_entities import (
 )
 from core.app.entities.queue_entities import (
     QueueErrorEvent,
+    QueueIterationCompletedEvent,
+    QueueIterationNextEvent,
+    QueueIterationStartEvent,
     QueueMessageReplaceEvent,
     QueueNodeFailedEvent,
     QueueNodeStartedEvent,
@@ -58,6 +61,7 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
     _task_state: WorkflowTaskState
     _application_generate_entity: WorkflowAppGenerateEntity
     _workflow_system_variables: dict[SystemVariable, Any]
+    _iteration_nested_relations: dict[str, list[str]]
 
     def __init__(self, application_generate_entity: WorkflowAppGenerateEntity,
                  workflow: Workflow,
@@ -85,8 +89,11 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
             SystemVariable.USER_ID: user_id
         }
 
-        self._task_state = WorkflowTaskState()
+        self._task_state = WorkflowTaskState(
+            iteration_nested_node_ids=[]
+        )
         self._stream_generate_nodes = self._get_stream_generate_nodes()
+        self._iteration_nested_relations = self._get_iteration_nested_relations(self._workflow.graph_dict)
 
     def process(self) -> Union[WorkflowAppBlockingResponse, Generator[WorkflowAppStreamResponse, None, None]]:
         """
@@ -191,6 +198,22 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
                     task_id=self._application_generate_entity.task_id,
                     workflow_node_execution=workflow_node_execution
                 )
+
+                if isinstance(event, QueueNodeFailedEvent):
+                    yield from self._handle_iteration_exception(
+                        task_id=self._application_generate_entity.task_id,
+                        error=f'Child node failed: {event.error}'
+                    )
+            elif isinstance(event, QueueIterationStartEvent | QueueIterationNextEvent | QueueIterationCompletedEvent):
+                if isinstance(event, QueueIterationNextEvent):
+                    # clear ran node execution infos of current iteration
+                    iteration_relations = self._iteration_nested_relations.get(event.node_id)
+                    if iteration_relations:
+                        for node_id in iteration_relations:
+                            self._task_state.ran_node_execution_infos.pop(node_id, None)
+
+                yield self._handle_iteration_to_stream_response(self._application_generate_entity.task_id, event)
+                self._handle_iteration_operation(event)
             elif isinstance(event, QueueStopEvent | QueueWorkflowSucceededEvent | QueueWorkflowFailedEvent):
                 workflow_run = self._handle_workflow_finished(event)
 
@@ -331,13 +354,20 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
                 continue
 
             node_type = source_node.get('data', {}).get('type')
+            node_iteration_id = source_node.get('data', {}).get('iteration_id')
+            iteration_start_node_id = None
+            if node_iteration_id:
+                iteration_node = next((node for node in nodes if node.get('id') == node_iteration_id), None)
+                iteration_start_node_id = iteration_node.get('data', {}).get('start_node_id')
+
             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:
+            elif node_type == NodeType.START.value or \
+                node_iteration_id is not None and iteration_start_node_id == source_node.get('id'):
                 start_node_id = source_node_id
                 start_node_ids.append(start_node_id)
             else:
@@ -411,3 +441,24 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
             return False
 
         return True
+
+    def _get_iteration_nested_relations(self, graph: dict) -> dict[str, list[str]]:
+        """
+        Get iteration nested relations.
+        :param graph: graph
+        :return:
+        """
+        nodes = graph.get('nodes')
+
+        iteration_ids = [node.get('id') for node in nodes 
+                         if node.get('data', {}).get('type') in [
+                             NodeType.ITERATION.value,
+                             NodeType.LOOP.value,
+                        ]]
+
+        return {
+            iteration_id: [
+                node.get('id') for node in nodes if node.get('data', {}).get('iteration_id') == iteration_id
+            ] for iteration_id in iteration_ids
+        }
+    

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

@@ -1,8 +1,11 @@
-from typing import Optional
+from typing import Any, Optional
 
 from core.app.apps.base_app_queue_manager import AppQueueManager, PublishFrom
 from core.app.entities.queue_entities import (
     AppQueueEvent,
+    QueueIterationCompletedEvent,
+    QueueIterationNextEvent,
+    QueueIterationStartEvent,
     QueueNodeFailedEvent,
     QueueNodeStartedEvent,
     QueueNodeSucceededEvent,
@@ -130,6 +133,66 @@ class WorkflowEventTriggerCallback(BaseWorkflowCallback):
             ), PublishFrom.APPLICATION_MANAGER
         )
 
+    def on_workflow_iteration_started(self, 
+                                      node_id: str,
+                                      node_type: NodeType,
+                                      node_run_index: int = 1,
+                                      node_data: Optional[BaseNodeData] = None,
+                                      inputs: dict = None,
+                                      predecessor_node_id: Optional[str] = None,
+                                      metadata: Optional[dict] = None) -> None:
+        """
+        Publish iteration started
+        """
+        self._queue_manager.publish(
+            QueueIterationStartEvent(
+                node_id=node_id,
+                node_type=node_type,
+                node_run_index=node_run_index,
+                node_data=node_data,
+                inputs=inputs,
+                predecessor_node_id=predecessor_node_id,
+                metadata=metadata
+            ),
+            PublishFrom.APPLICATION_MANAGER
+        )
+
+    def on_workflow_iteration_next(self, node_id: str, 
+                                   node_type: NodeType,
+                                   index: int, 
+                                   node_run_index: int,
+                                   output: Optional[Any]) -> None:
+        """
+        Publish iteration next
+        """
+        self._queue_manager.publish(
+            QueueIterationNextEvent(
+                node_id=node_id,
+                node_type=node_type,
+                index=index,
+                node_run_index=node_run_index,
+                output=output
+            ),
+            PublishFrom.APPLICATION_MANAGER
+        )
+
+    def on_workflow_iteration_completed(self, node_id: str, 
+                                        node_type: NodeType,
+                                        node_run_index: int,
+                                        outputs: dict) -> None:
+        """
+        Publish iteration completed
+        """
+        self._queue_manager.publish(
+            QueueIterationCompletedEvent(
+                node_id=node_id,
+                node_type=node_type,
+                node_run_index=node_run_index,
+                outputs=outputs
+            ),
+            PublishFrom.APPLICATION_MANAGER
+        )
+        
     def on_event(self, event: AppQueueEvent) -> None:
         """
         Publish event

api/core/app/apps/workflow_logging_callback.py

@@ -102,6 +102,39 @@ class WorkflowLoggingCallback(BaseWorkflowCallback):
 
         self.print_text(text, color="pink", end="")
 
+    def on_workflow_iteration_started(self, 
+                                      node_id: str,
+                                      node_type: NodeType,
+                                      node_run_index: int = 1,
+                                      node_data: Optional[BaseNodeData] = None,
+                                      inputs: dict = None,
+                                      predecessor_node_id: Optional[str] = None,
+                                      metadata: Optional[dict] = None) -> None:
+        """
+        Publish iteration started
+        """
+        self.print_text("\n[on_workflow_iteration_started]", color='blue')
+        self.print_text(f"Node ID: {node_id}", color='blue')
+
+    def on_workflow_iteration_next(self, node_id: str, 
+                                   node_type: NodeType,
+                                   index: int, 
+                                   node_run_index: int,
+                                   output: Optional[dict]) -> None:
+        """
+        Publish iteration next
+        """
+        self.print_text("\n[on_workflow_iteration_next]", color='blue')
+
+    def on_workflow_iteration_completed(self, node_id: str, 
+                                        node_type: NodeType,
+                                        node_run_index: int,
+                                        outputs: dict) -> None:
+        """
+        Publish iteration completed
+        """
+        self.print_text("\n[on_workflow_iteration_completed]", color='blue')
+
     def on_event(self, event: AppQueueEvent) -> None:
         """
         Publish event

api/core/app/entities/app_invoke_entities.py

@@ -80,6 +80,9 @@ class AppGenerateEntity(BaseModel):
     stream: bool
     invoke_from: InvokeFrom
 
+    # invoke call depth
+    call_depth: int = 0
+
     # extra parameters, like: auto_generate_conversation_name
     extras: dict[str, Any] = {}
 
@@ -126,6 +129,14 @@ class AdvancedChatAppGenerateEntity(AppGenerateEntity):
     conversation_id: Optional[str] = None
     query: Optional[str] = None
 
+    class SingleIterationRunEntity(BaseModel):
+        """
+        Single Iteration Run Entity.
+        """
+        node_id: str
+        inputs: dict
+
+    single_iteration_run: Optional[SingleIterationRunEntity] = None
 
 class WorkflowAppGenerateEntity(AppGenerateEntity):
     """
@@ -133,3 +144,12 @@ class WorkflowAppGenerateEntity(AppGenerateEntity):
     """
     # app config
     app_config: WorkflowUIBasedAppConfig
+
+    class SingleIterationRunEntity(BaseModel):
+        """
+        Single Iteration Run Entity.
+        """
+        node_id: str
+        inputs: dict
+
+    single_iteration_run: Optional[SingleIterationRunEntity] = None

api/core/app/entities/queue_entities.py

@@ -1,7 +1,7 @@
 from enum import Enum
 from typing import Any, Optional
 
-from pydantic import BaseModel
+from pydantic import BaseModel, validator
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
 from core.workflow.entities.base_node_data_entities import BaseNodeData
@@ -21,6 +21,9 @@ class QueueEvent(Enum):
     WORKFLOW_STARTED = "workflow_started"
     WORKFLOW_SUCCEEDED = "workflow_succeeded"
     WORKFLOW_FAILED = "workflow_failed"
+    ITERATION_START = "iteration_start"
+    ITERATION_NEXT = "iteration_next"
+    ITERATION_COMPLETED = "iteration_completed"
     NODE_STARTED = "node_started"
     NODE_SUCCEEDED = "node_succeeded"
     NODE_FAILED = "node_failed"
@@ -47,6 +50,55 @@ class QueueLLMChunkEvent(AppQueueEvent):
     event = QueueEvent.LLM_CHUNK
     chunk: LLMResultChunk
 
+class QueueIterationStartEvent(AppQueueEvent):
+    """
+    QueueIterationStartEvent entity
+    """
+    event = QueueEvent.ITERATION_START
+    node_id: str
+    node_type: NodeType
+    node_data: BaseNodeData
+
+    node_run_index: int
+    inputs: dict = None
+    predecessor_node_id: Optional[str] = None
+    metadata: Optional[dict] = None
+
+class QueueIterationNextEvent(AppQueueEvent):
+    """
+    QueueIterationNextEvent entity
+    """
+    event = QueueEvent.ITERATION_NEXT
+
+    index: int
+    node_id: str
+    node_type: NodeType
+
+    node_run_index: int
+    output: Optional[Any] # output for the current iteration
+
+    @validator('output', pre=True, always=True)
+    def set_output(cls, v):
+        """
+        Set output
+        """
+        if v is None:
+            return None
+        if isinstance(v, int | float | str | bool | dict | list):
+            return v
+        raise ValueError('output must be a valid type')
+
+class QueueIterationCompletedEvent(AppQueueEvent):
+    """
+    QueueIterationCompletedEvent entity
+    """
+    event = QueueEvent.ITERATION_COMPLETED
+
+    node_id: str
+    node_type: NodeType
+    
+    node_run_index: int
+    outputs: dict
 
 class QueueTextChunkEvent(AppQueueEvent):
     """

api/core/app/entities/task_entities.py

@@ -1,12 +1,14 @@
 from enum import Enum
-from typing import Optional
+from typing import Any, Optional
 
 from pydantic import BaseModel
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage
 from core.model_runtime.utils.encoders import jsonable_encoder
+from core.workflow.entities.base_node_data_entities import BaseNodeData
 from core.workflow.entities.node_entities import NodeType
 from core.workflow.nodes.answer.entities import GenerateRouteChunk
+from models.workflow import WorkflowNodeExecutionStatus
 
 
 class WorkflowStreamGenerateNodes(BaseModel):
@@ -65,6 +67,7 @@ class WorkflowTaskState(TaskState):
 
     current_stream_generate_state: Optional[WorkflowStreamGenerateNodes] = None
 
+    iteration_nested_node_ids: list[str] = None
 
 class AdvancedChatTaskState(WorkflowTaskState):
     """
@@ -91,6 +94,9 @@ class StreamEvent(Enum):
     WORKFLOW_FINISHED = "workflow_finished"
     NODE_STARTED = "node_started"
     NODE_FINISHED = "node_finished"
+    ITERATION_STARTED = "iteration_started"
+    ITERATION_NEXT = "iteration_next"
+    ITERATION_COMPLETED = "iteration_completed"
     TEXT_CHUNK = "text_chunk"
     TEXT_REPLACE = "text_replace"
 
@@ -319,6 +325,74 @@ class NodeFinishStreamResponse(StreamResponse):
             }
         }
 
+class IterationNodeStartStreamResponse(StreamResponse):
+    """
+    NodeStartStreamResponse entity
+    """
+    class Data(BaseModel):
+        """
+        Data entity
+        """
+        id: str
+        node_id: str
+        node_type: str
+        title: str
+        created_at: int
+        extras: dict = {}
+        metadata: dict = {}
+        inputs: dict = {}
+
+    event: StreamEvent = StreamEvent.ITERATION_STARTED
+    workflow_run_id: str
+    data: Data
+
+class IterationNodeNextStreamResponse(StreamResponse):
+    """
+    NodeStartStreamResponse entity
+    """
+    class Data(BaseModel):
+        """
+        Data entity
+        """
+        id: str
+        node_id: str
+        node_type: str
+        title: str
+        index: int
+        created_at: int
+        pre_iteration_output: Optional[Any]
+        extras: dict = {}
+
+    event: StreamEvent = StreamEvent.ITERATION_NEXT
+    workflow_run_id: str
+    data: Data
+
+class IterationNodeCompletedStreamResponse(StreamResponse):
+    """
+    NodeStartStreamResponse entity
+    """
+    class Data(BaseModel):
+        """
+        Data entity
+        """
+        id: str
+        node_id: str
+        node_type: str
+        title: str
+        outputs: Optional[dict]
+        created_at: int
+        extras: dict = None
+        inputs: dict = None
+        status: WorkflowNodeExecutionStatus
+        error: Optional[str]
+        elapsed_time: float
+        total_tokens: int
+        finished_at: int
+        steps: int
+
+    event: StreamEvent = StreamEvent.ITERATION_COMPLETED
+    workflow_run_id: str
+    data: Data
 
 class TextChunkStreamResponse(StreamResponse):
     """
@@ -454,3 +528,23 @@ class WorkflowAppBlockingResponse(AppBlockingResponse):
 
     workflow_run_id: str
     data: Data
+
+class WorkflowIterationState(BaseModel):
+    """
+    WorkflowIterationState entity
+    """
+    class Data(BaseModel):
+        """
+        Data entity
+        """
+        parent_iteration_id: Optional[str] = None
+        iteration_id: str
+        current_index: int
+        iteration_steps_boundary: list[int] = None
+        node_execution_id: str
+        started_at: float
+        inputs: dict = None
+        total_tokens: int = 0
+        node_data: BaseNodeData
+
+    current_iterations: dict[str, Data] = None

api/core/app/task_pipeline/easy_ui_based_generate_task_pipeline.py

@@ -37,6 +37,7 @@ from core.app.entities.task_entities import (
 )
 from core.app.task_pipeline.based_generate_task_pipeline import BasedGenerateTaskPipeline
 from core.app.task_pipeline.message_cycle_manage import MessageCycleManage
+from core.model_manager import ModelInstance
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
 from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
@@ -317,29 +318,30 @@ class EasyUIBasedGenerateTaskPipeline(BasedGenerateTaskPipeline, MessageCycleMan
         """
         model_config = self._model_config
         model = model_config.model
-        model_type_instance = model_config.provider_model_bundle.model_type_instance
-        model_type_instance = cast(LargeLanguageModel, model_type_instance)
+
+        model_instance = ModelInstance(
+            provider_model_bundle=model_config.provider_model_bundle,
+            model=model_config.model
+        )
 
         # calculate num tokens
         prompt_tokens = 0
         if event.stopped_by != QueueStopEvent.StopBy.ANNOTATION_REPLY:
-            prompt_tokens = model_type_instance.get_num_tokens(
-                model,
-                model_config.credentials,
+            prompt_tokens = model_instance.get_llm_num_tokens(
                 self._task_state.llm_result.prompt_messages
             )
 
         completion_tokens = 0
         if event.stopped_by == QueueStopEvent.StopBy.USER_MANUAL:
-            completion_tokens = model_type_instance.get_num_tokens(
-                model,
-                model_config.credentials,
+            completion_tokens = model_instance.get_llm_num_tokens(
                 [self._task_state.llm_result.message]
             )
 
         credentials = model_config.credentials
 
         # transform usage
+        model_type_instance = model_config.provider_model_bundle.model_type_instance
+        model_type_instance = cast(LargeLanguageModel, model_type_instance)
         self._task_state.llm_result.usage = model_type_instance._calc_response_usage(
             model,
             credentials,

api/core/app/task_pipeline/workflow_cycle_manage.py

@@ -1,9 +1,9 @@
 import json
 import time
 from datetime import datetime, timezone
-from typing import Any, Optional, Union, cast
+from typing import Optional, Union, cast
 
-from core.app.entities.app_invoke_entities import AdvancedChatAppGenerateEntity, InvokeFrom, WorkflowAppGenerateEntity
+from core.app.entities.app_invoke_entities import InvokeFrom
 from core.app.entities.queue_entities import (
     QueueNodeFailedEvent,
     QueueNodeStartedEvent,
@@ -13,18 +13,17 @@ from core.app.entities.queue_entities import (
     QueueWorkflowSucceededEvent,
 )
 from core.app.entities.task_entities import (
-    AdvancedChatTaskState,
     NodeExecutionInfo,
     NodeFinishStreamResponse,
     NodeStartStreamResponse,
     WorkflowFinishStreamResponse,
     WorkflowStartStreamResponse,
-    WorkflowTaskState,
 )
+from core.app.task_pipeline.workflow_iteration_cycle_manage import WorkflowIterationCycleManage
 from core.file.file_obj import FileVar
 from core.model_runtime.utils.encoders import jsonable_encoder
 from core.tools.tool_manager import ToolManager
-from core.workflow.entities.node_entities import NodeRunMetadataKey, NodeType, SystemVariable
+from core.workflow.entities.node_entities import NodeRunMetadataKey, NodeType
 from core.workflow.nodes.tool.entities import ToolNodeData
 from core.workflow.workflow_engine_manager import WorkflowEngineManager
 from extensions.ext_database import db
@@ -42,13 +41,7 @@ from models.workflow import (
 )
 
 
-class WorkflowCycleManage:
-    _application_generate_entity: Union[AdvancedChatAppGenerateEntity, WorkflowAppGenerateEntity]
-    _workflow: Workflow
-    _user: Union[Account, EndUser]
-    _task_state: Union[AdvancedChatTaskState, WorkflowTaskState]
-    _workflow_system_variables: dict[SystemVariable, Any]
-
+class WorkflowCycleManage(WorkflowIterationCycleManage):
     def _init_workflow_run(self, workflow: Workflow,
                            triggered_from: WorkflowRunTriggeredFrom,
                            user: Union[Account, EndUser],
@@ -237,6 +230,7 @@ class WorkflowCycleManage:
                                         inputs: Optional[dict] = None,
                                         process_data: Optional[dict] = None,
                                         outputs: Optional[dict] = None,
+                                        execution_metadata: Optional[dict] = None
                                         ) -> WorkflowNodeExecution:
         """
         Workflow node execution failed
@@ -255,6 +249,8 @@ class WorkflowCycleManage:
         workflow_node_execution.inputs = json.dumps(inputs) if inputs else None
         workflow_node_execution.process_data = json.dumps(process_data) if process_data else None
         workflow_node_execution.outputs = json.dumps(outputs) if outputs else None
+        workflow_node_execution.execution_metadata = json.dumps(jsonable_encoder(execution_metadata)) \
+            if execution_metadata else None
 
         db.session.commit()
         db.session.refresh(workflow_node_execution)
@@ -444,6 +440,23 @@ class WorkflowCycleManage:
         current_node_execution = self._task_state.ran_node_execution_infos[event.node_id]
         workflow_node_execution = db.session.query(WorkflowNodeExecution).filter(
             WorkflowNodeExecution.id == current_node_execution.workflow_node_execution_id).first()
+        
+        execution_metadata = event.execution_metadata if isinstance(event, QueueNodeSucceededEvent) else None
+        
+        if self._iteration_state and self._iteration_state.current_iterations:
+            if not execution_metadata:
+                execution_metadata = {}
+            current_iteration_data = None
+            for iteration_node_id in self._iteration_state.current_iterations:
+                data = self._iteration_state.current_iterations[iteration_node_id]
+                if data.parent_iteration_id == None:
+                    current_iteration_data = data
+                    break
+
+            if current_iteration_data:
+                execution_metadata[NodeRunMetadataKey.ITERATION_ID] = current_iteration_data.iteration_id
+                execution_metadata[NodeRunMetadataKey.ITERATION_INDEX] = current_iteration_data.current_index
+
         if isinstance(event, QueueNodeSucceededEvent):
             workflow_node_execution = self._workflow_node_execution_success(
                 workflow_node_execution=workflow_node_execution,
@@ -451,12 +464,18 @@ class WorkflowCycleManage:
                 inputs=event.inputs,
                 process_data=event.process_data,
                 outputs=event.outputs,
-                execution_metadata=event.execution_metadata
+                execution_metadata=execution_metadata
             )
 
-            if event.execution_metadata and event.execution_metadata.get(NodeRunMetadataKey.TOTAL_TOKENS):
+            if execution_metadata and execution_metadata.get(NodeRunMetadataKey.TOTAL_TOKENS):
                 self._task_state.total_tokens += (
-                    int(event.execution_metadata.get(NodeRunMetadataKey.TOTAL_TOKENS)))
+                    int(execution_metadata.get(NodeRunMetadataKey.TOTAL_TOKENS)))
+                
+                if self._iteration_state:
+                    for iteration_node_id in self._iteration_state.current_iterations:
+                        data = self._iteration_state.current_iterations[iteration_node_id]
+                        if execution_metadata.get(NodeRunMetadataKey.TOTAL_TOKENS):
+                            data.total_tokens += int(execution_metadata.get(NodeRunMetadataKey.TOTAL_TOKENS))
 
             if workflow_node_execution.node_type == NodeType.LLM.value:
                 outputs = workflow_node_execution.outputs_dict
@@ -469,7 +488,8 @@ class WorkflowCycleManage:
                 error=event.error,
                 inputs=event.inputs,
                 process_data=event.process_data,
-                outputs=event.outputs
+                outputs=event.outputs,
+                execution_metadata=execution_metadata
             )
 
         db.session.close()

api/core/app/task_pipeline/workflow_cycle_state_manager.py

@@ -0,0 +1,16 @@
+from typing import Any, Union
+
+from core.app.entities.app_invoke_entities import AdvancedChatAppGenerateEntity, WorkflowAppGenerateEntity
+from core.app.entities.task_entities import AdvancedChatTaskState, WorkflowTaskState
+from core.workflow.entities.node_entities import SystemVariable
+from models.account import Account
+from models.model import EndUser
+from models.workflow import Workflow
+
+
+class WorkflowCycleStateManager:
+    _application_generate_entity: Union[AdvancedChatAppGenerateEntity, WorkflowAppGenerateEntity]
+    _workflow: Workflow
+    _user: Union[Account, EndUser]
+    _task_state: Union[AdvancedChatTaskState, WorkflowTaskState]
+    _workflow_system_variables: dict[SystemVariable, Any]

api/core/app/task_pipeline/workflow_iteration_cycle_manage.py

@@ -0,0 +1,281 @@
+import json
+import time
+from collections.abc import Generator
+from typing import Optional, Union
+
+from core.app.entities.queue_entities import (
+    QueueIterationCompletedEvent,
+    QueueIterationNextEvent,
+    QueueIterationStartEvent,
+)
+from core.app.entities.task_entities import (
+    IterationNodeCompletedStreamResponse,
+    IterationNodeNextStreamResponse,
+    IterationNodeStartStreamResponse,
+    NodeExecutionInfo,
+    WorkflowIterationState,
+)
+from core.app.task_pipeline.workflow_cycle_state_manager import WorkflowCycleStateManager
+from core.workflow.entities.node_entities import NodeType
+from extensions.ext_database import db
+from models.workflow import (
+    WorkflowNodeExecution,
+    WorkflowNodeExecutionStatus,
+    WorkflowNodeExecutionTriggeredFrom,
+    WorkflowRun,
+)
+
+
+class WorkflowIterationCycleManage(WorkflowCycleStateManager):
+    _iteration_state: WorkflowIterationState = None
+
+    def _init_iteration_state(self) -> WorkflowIterationState:
+        if not self._iteration_state:
+            self._iteration_state = WorkflowIterationState(
+                current_iterations={}
+            )
+
+    def _handle_iteration_to_stream_response(self, task_id: str, event: QueueIterationStartEvent | QueueIterationNextEvent | QueueIterationCompletedEvent) \
+    -> Union[IterationNodeStartStreamResponse, IterationNodeNextStreamResponse, IterationNodeCompletedStreamResponse]:
+        """
+        Handle iteration to stream response
+        :param task_id: task id
+        :param event: iteration event
+        :return:
+        """
+        if isinstance(event, QueueIterationStartEvent):
+            return IterationNodeStartStreamResponse(
+                task_id=task_id,
+                workflow_run_id=self._task_state.workflow_run_id,
+                data=IterationNodeStartStreamResponse.Data(
+                    id=event.node_id,
+                    node_id=event.node_id,
+                    node_type=event.node_type.value,
+                    title=event.node_data.title,
+                    created_at=int(time.time()),
+                    extras={},
+                    inputs=event.inputs,
+                    metadata=event.metadata
+                )
+            )
+        elif isinstance(event, QueueIterationNextEvent):
+            current_iteration = self._iteration_state.current_iterations[event.node_id]
+
+            return IterationNodeNextStreamResponse(
+                task_id=task_id,
+                workflow_run_id=self._task_state.workflow_run_id,
+                data=IterationNodeNextStreamResponse.Data(
+                    id=event.node_id,
+                    node_id=event.node_id,
+                    node_type=event.node_type.value,
+                    title=current_iteration.node_data.title,
+                    index=event.index,
+                    pre_iteration_output=event.output,
+                    created_at=int(time.time()),
+                    extras={}
+                )
+            )
+        elif isinstance(event, QueueIterationCompletedEvent):
+            current_iteration = self._iteration_state.current_iterations[event.node_id]
+
+            return IterationNodeCompletedStreamResponse(
+                task_id=task_id,
+                workflow_run_id=self._task_state.workflow_run_id,
+                data=IterationNodeCompletedStreamResponse.Data(
+                    id=event.node_id,
+                    node_id=event.node_id,
+                    node_type=event.node_type.value,
+                    title=current_iteration.node_data.title,
+                    outputs=event.outputs,
+                    created_at=int(time.time()),
+                    extras={},
+                    inputs=current_iteration.inputs,
+                    status=WorkflowNodeExecutionStatus.SUCCEEDED,
+                    error=None,
+                    elapsed_time=time.perf_counter() - current_iteration.started_at,
+                    total_tokens=current_iteration.total_tokens,
+                    finished_at=int(time.time()),
+                    steps=current_iteration.current_index
+                )
+            )
+        
+    def _init_iteration_execution_from_workflow_run(self, 
+        workflow_run: WorkflowRun,
+        node_id: str,
+        node_type: NodeType,
+        node_title: str,
+        node_run_index: int = 1,
+        inputs: Optional[dict] = None,
+        predecessor_node_id: Optional[str] = None
+    ) -> WorkflowNodeExecution:
+        workflow_node_execution = WorkflowNodeExecution(
+            tenant_id=workflow_run.tenant_id,
+            app_id=workflow_run.app_id,
+            workflow_id=workflow_run.workflow_id,
+            triggered_from=WorkflowNodeExecutionTriggeredFrom.WORKFLOW_RUN.value,
+            workflow_run_id=workflow_run.id,
+            predecessor_node_id=predecessor_node_id,
+            index=node_run_index,
+            node_id=node_id,
+            node_type=node_type.value,
+            inputs=json.dumps(inputs) if inputs else None,
+            title=node_title,
+            status=WorkflowNodeExecutionStatus.RUNNING.value,
+            created_by_role=workflow_run.created_by_role,
+            created_by=workflow_run.created_by,
+            execution_metadata=json.dumps({
+                'started_run_index': node_run_index + 1,
+                'current_index': 0,
+                'steps_boundary': [],
+            })
+        )
+
+        db.session.add(workflow_node_execution)
+        db.session.commit()
+        db.session.refresh(workflow_node_execution)
+        db.session.close()
+
+        return workflow_node_execution
+    
+    def _handle_iteration_operation(self, event: QueueIterationStartEvent | QueueIterationNextEvent | QueueIterationCompletedEvent) -> WorkflowNodeExecution:
+        if isinstance(event, QueueIterationStartEvent):
+            return self._handle_iteration_started(event)
+        elif isinstance(event, QueueIterationNextEvent):
+            return self._handle_iteration_next(event)
+        elif isinstance(event, QueueIterationCompletedEvent):
+            return self._handle_iteration_completed(event)
+    
+    def _handle_iteration_started(self, event: QueueIterationStartEvent) -> WorkflowNodeExecution:
+        self._init_iteration_state()
+
+        workflow_run = db.session.query(WorkflowRun).filter(WorkflowRun.id == self._task_state.workflow_run_id).first()
+        workflow_node_execution = self._init_iteration_execution_from_workflow_run(
+            workflow_run=workflow_run,
+            node_id=event.node_id,
+            node_type=NodeType.ITERATION,
+            node_title=event.node_data.title,
+            node_run_index=event.node_run_index,
+            inputs=event.inputs,
+            predecessor_node_id=event.predecessor_node_id
+        )
+
+        latest_node_execution_info = NodeExecutionInfo(
+            workflow_node_execution_id=workflow_node_execution.id,
+            node_type=NodeType.ITERATION,
+            start_at=time.perf_counter()
+        )
+
+        self._task_state.ran_node_execution_infos[event.node_id] = latest_node_execution_info
+        self._task_state.latest_node_execution_info = latest_node_execution_info
+
+        self._iteration_state.current_iterations[event.node_id] = WorkflowIterationState.Data(
+            parent_iteration_id=None,
+            iteration_id=event.node_id,
+            current_index=0,
+            iteration_steps_boundary=[],
+            node_execution_id=workflow_node_execution.id,
+            started_at=time.perf_counter(),
+            inputs=event.inputs,
+            total_tokens=0,
+            node_data=event.node_data
+        )
+
+        db.session.close()
+
+        return workflow_node_execution
+    
+    def _handle_iteration_next(self, event: QueueIterationNextEvent) -> WorkflowNodeExecution:
+        if event.node_id not in self._iteration_state.current_iterations:
+            return
+        current_iteration = self._iteration_state.current_iterations[event.node_id]
+        current_iteration.current_index = event.index
+        current_iteration.iteration_steps_boundary.append(event.node_run_index)
+        workflow_node_execution: WorkflowNodeExecution = db.session.query(WorkflowNodeExecution).filter(
+            WorkflowNodeExecution.id == current_iteration.node_execution_id
+        ).first()
+
+        original_node_execution_metadata = workflow_node_execution.execution_metadata_dict
+        if original_node_execution_metadata:
+            original_node_execution_metadata['current_index'] = event.index
+            original_node_execution_metadata['steps_boundary'] = current_iteration.iteration_steps_boundary
+            original_node_execution_metadata['total_tokens'] = current_iteration.total_tokens
+            workflow_node_execution.execution_metadata = json.dumps(original_node_execution_metadata)
+
+            db.session.commit()
+
+        db.session.close()
+
+    def _handle_iteration_completed(self, event: QueueIterationCompletedEvent) -> WorkflowNodeExecution:
+        if event.node_id not in self._iteration_state.current_iterations:
+            return
+        
+        current_iteration = self._iteration_state.current_iterations[event.node_id]
+        workflow_node_execution: WorkflowNodeExecution = db.session.query(WorkflowNodeExecution).filter(
+            WorkflowNodeExecution.id == current_iteration.node_execution_id
+        ).first()
+
+        workflow_node_execution.status = WorkflowNodeExecutionStatus.SUCCEEDED.value
+        workflow_node_execution.outputs = json.dumps(event.outputs) if event.outputs else None
+        workflow_node_execution.elapsed_time = time.perf_counter() - current_iteration.started_at
+        
+        original_node_execution_metadata = workflow_node_execution.execution_metadata_dict
+        if original_node_execution_metadata:
+            original_node_execution_metadata['steps_boundary'] = current_iteration.iteration_steps_boundary
+            original_node_execution_metadata['total_tokens'] = current_iteration.total_tokens
+            workflow_node_execution.execution_metadata = json.dumps(original_node_execution_metadata)
+
+        db.session.commit()
+
+        # remove current iteration
+        self._iteration_state.current_iterations.pop(event.node_id, None)
+
+        # set latest node execution info
+        latest_node_execution_info = NodeExecutionInfo(
+            workflow_node_execution_id=workflow_node_execution.id,
+            node_type=NodeType.ITERATION,
+            start_at=time.perf_counter()
+        )
+
+        self._task_state.latest_node_execution_info = latest_node_execution_info
+        
+        db.session.close()
+
+    def _handle_iteration_exception(self, task_id: str, error: str) -> Generator[IterationNodeCompletedStreamResponse, None, None]:
+        """
+        Handle iteration exception
+        """
+        if not self._iteration_state or not self._iteration_state.current_iterations:
+            return
+        
+        for node_id, current_iteration in self._iteration_state.current_iterations.items():
+            workflow_node_execution: WorkflowNodeExecution = db.session.query(WorkflowNodeExecution).filter(
+                WorkflowNodeExecution.id == current_iteration.node_execution_id
+            ).first()
+
+            workflow_node_execution.status = WorkflowNodeExecutionStatus.FAILED.value
+            workflow_node_execution.error = error
+            workflow_node_execution.elapsed_time = time.perf_counter() - current_iteration.started_at
+
+            db.session.commit()
+            db.session.close()
+
+            yield IterationNodeCompletedStreamResponse(
+                task_id=task_id,
+                workflow_run_id=self._task_state.workflow_run_id,
+                data=IterationNodeCompletedStreamResponse.Data(
+                    id=node_id,
+                    node_id=node_id,
+                    node_type=NodeType.ITERATION.value,
+                    title=current_iteration.node_data.title,
+                    outputs={},
+                    created_at=int(time.time()),
+                    extras={},
+                    inputs=current_iteration.inputs,
+                    status=WorkflowNodeExecutionStatus.FAILED,
+                    error=error,
+                    elapsed_time=time.perf_counter() - current_iteration.started_at,
+                    total_tokens=current_iteration.total_tokens,
+                    finished_at=int(time.time()),
+                    steps=current_iteration.current_index
+                )
+            )

api/core/entities/model_entities.py

@@ -16,6 +16,7 @@ class ModelStatus(Enum):
     NO_CONFIGURE = "no-configure"
     QUOTA_EXCEEDED = "quota-exceeded"
     NO_PERMISSION = "no-permission"
+    DISABLED = "disabled"
 
 
 class SimpleModelProviderEntity(BaseModel):
@@ -43,12 +44,19 @@ class SimpleModelProviderEntity(BaseModel):
         )
 
 
-class ModelWithProviderEntity(ProviderModel):
+class ProviderModelWithStatusEntity(ProviderModel):
+    """
+    Model class for model response.
+    """
+    status: ModelStatus
+    load_balancing_enabled: bool = False
+
+
+class ModelWithProviderEntity(ProviderModelWithStatusEntity):
     """
     Model with provider entity.
     """
     provider: SimpleModelProviderEntity
-    status: ModelStatus
 
 
 class DefaultModelProviderEntity(BaseModel):

api/core/entities/provider_configuration.py

@@ -1,6 +1,7 @@
 import datetime
 import json
 import logging
+from collections import defaultdict
 from collections.abc import Iterator
 from json import JSONDecodeError
 from typing import Optional
@@ -8,7 +9,12 @@ from typing import Optional
 from pydantic import BaseModel
 
 from core.entities.model_entities import ModelStatus, ModelWithProviderEntity, SimpleModelProviderEntity
-from core.entities.provider_entities import CustomConfiguration, SystemConfiguration, SystemConfigurationStatus
+from core.entities.provider_entities import (
+    CustomConfiguration,
+    ModelSettings,
+    SystemConfiguration,
+    SystemConfigurationStatus,
+)
 from core.helper import encrypter
 from core.helper.model_provider_cache import ProviderCredentialsCache, ProviderCredentialsCacheType
 from core.model_runtime.entities.model_entities import FetchFrom, ModelType
@@ -22,7 +28,14 @@ from core.model_runtime.model_providers import model_provider_factory
 from core.model_runtime.model_providers.__base.ai_model import AIModel
 from core.model_runtime.model_providers.__base.model_provider import ModelProvider
 from extensions.ext_database import db
-from models.provider import Provider, ProviderModel, ProviderType, TenantPreferredModelProvider
+from models.provider import (
+    LoadBalancingModelConfig,
+    Provider,
+    ProviderModel,
+    ProviderModelSetting,
+    ProviderType,
+    TenantPreferredModelProvider,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -39,6 +52,7 @@ class ProviderConfiguration(BaseModel):
     using_provider_type: ProviderType
     system_configuration: SystemConfiguration
     custom_configuration: CustomConfiguration
+    model_settings: list[ModelSettings]
 
     def __init__(self, **data):
         super().__init__(**data)
@@ -62,6 +76,14 @@ class ProviderConfiguration(BaseModel):
         :param model: model name
         :return:
         """
+        if self.model_settings:
+            # check if model is disabled by admin
+            for model_setting in self.model_settings:
+                if (model_setting.model_type == model_type
+                        and model_setting.model == model):
+                    if not model_setting.enabled:
+                        raise ValueError(f'Model {model} is disabled.')
+
         if self.using_provider_type == ProviderType.SYSTEM:
             restrict_models = []
             for quota_configuration in self.system_configuration.quota_configurations:
@@ -80,15 +102,17 @@ class ProviderConfiguration(BaseModel):
 
             return copy_credentials
         else:
+            credentials = None
             if self.custom_configuration.models:
                 for model_configuration in self.custom_configuration.models:
                     if model_configuration.model_type == model_type and model_configuration.model == model:
-                        return model_configuration.credentials
+                        credentials = model_configuration.credentials
+                        break
 
             if self.custom_configuration.provider:
-                return self.custom_configuration.provider.credentials
-            else:
-                return None
+                credentials = self.custom_configuration.provider.credentials
+
+            return credentials
 
     def get_system_configuration_status(self) -> SystemConfigurationStatus:
         """
@@ -130,7 +154,7 @@ class ProviderConfiguration(BaseModel):
             return credentials
 
         # Obfuscate credentials
-        return self._obfuscated_credentials(
+        return self.obfuscated_credentials(
             credentials=credentials,
             credential_form_schemas=self.provider.provider_credential_schema.credential_form_schemas
             if self.provider.provider_credential_schema else []
@@ -151,7 +175,7 @@ class ProviderConfiguration(BaseModel):
         ).first()
 
         # Get provider credential secret variables
-        provider_credential_secret_variables = self._extract_secret_variables(
+        provider_credential_secret_variables = self.extract_secret_variables(
             self.provider.provider_credential_schema.credential_form_schemas
             if self.provider.provider_credential_schema else []
         )
@@ -274,7 +298,7 @@ class ProviderConfiguration(BaseModel):
                     return credentials
 
                 # Obfuscate credentials
-                return self._obfuscated_credentials(
+                return self.obfuscated_credentials(
                     credentials=credentials,
                     credential_form_schemas=self.provider.model_credential_schema.credential_form_schemas
                     if self.provider.model_credential_schema else []
@@ -302,7 +326,7 @@ class ProviderConfiguration(BaseModel):
         ).first()
 
         # Get provider credential secret variables
-        provider_credential_secret_variables = self._extract_secret_variables(
+        provider_credential_secret_variables = self.extract_secret_variables(
             self.provider.model_credential_schema.credential_form_schemas
             if self.provider.model_credential_schema else []
         )
@@ -402,6 +426,160 @@ class ProviderConfiguration(BaseModel):
 
             provider_model_credentials_cache.delete()
 
+    def enable_model(self, model_type: ModelType, model: str) -> ProviderModelSetting:
+        """
+        Enable model.
+        :param model_type: model type
+        :param model: model name
+        :return:
+        """
+        model_setting = db.session.query(ProviderModelSetting) \
+            .filter(
+            ProviderModelSetting.tenant_id == self.tenant_id,
+            ProviderModelSetting.provider_name == self.provider.provider,
+            ProviderModelSetting.model_type == model_type.to_origin_model_type(),
+            ProviderModelSetting.model_name == model
+        ).first()
+
+        if model_setting:
+            model_setting.enabled = True
+            model_setting.updated_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
+            db.session.commit()
+        else:
+            model_setting = ProviderModelSetting(
+                tenant_id=self.tenant_id,
+                provider_name=self.provider.provider,
+                model_type=model_type.to_origin_model_type(),
+                model_name=model,
+                enabled=True
+            )
+            db.session.add(model_setting)
+            db.session.commit()
+
+        return model_setting
+
+    def disable_model(self, model_type: ModelType, model: str) -> ProviderModelSetting:
+        """
+        Disable model.
+        :param model_type: model type
+        :param model: model name
+        :return:
+        """
+        model_setting = db.session.query(ProviderModelSetting) \
+            .filter(
+            ProviderModelSetting.tenant_id == self.tenant_id,
+            ProviderModelSetting.provider_name == self.provider.provider,
+            ProviderModelSetting.model_type == model_type.to_origin_model_type(),
+            ProviderModelSetting.model_name == model
+        ).first()
+
+        if model_setting:
+            model_setting.enabled = False
+            model_setting.updated_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
+            db.session.commit()
+        else:
+            model_setting = ProviderModelSetting(
+                tenant_id=self.tenant_id,
+                provider_name=self.provider.provider,
+                model_type=model_type.to_origin_model_type(),
+                model_name=model,
+                enabled=False
+            )
+            db.session.add(model_setting)
+            db.session.commit()
+
+        return model_setting
+
+    def get_provider_model_setting(self, model_type: ModelType, model: str) -> Optional[ProviderModelSetting]:
+        """
+        Get provider model setting.
+        :param model_type: model type
+        :param model: model name
+        :return:
+        """
+        return db.session.query(ProviderModelSetting) \
+            .filter(
+            ProviderModelSetting.tenant_id == self.tenant_id,
+            ProviderModelSetting.provider_name == self.provider.provider,
+            ProviderModelSetting.model_type == model_type.to_origin_model_type(),
+            ProviderModelSetting.model_name == model
+        ).first()
+
+    def enable_model_load_balancing(self, model_type: ModelType, model: str) -> ProviderModelSetting:
+        """
+        Enable model load balancing.
+        :param model_type: model type
+        :param model: model name
+        :return:
+        """
+        load_balancing_config_count = db.session.query(LoadBalancingModelConfig) \
+            .filter(
+            LoadBalancingModelConfig.tenant_id == self.tenant_id,
+            LoadBalancingModelConfig.provider_name == self.provider.provider,
+            LoadBalancingModelConfig.model_type == model_type.to_origin_model_type(),
+            LoadBalancingModelConfig.model_name == model
+        ).count()
+
+        if load_balancing_config_count <= 1:
+            raise ValueError('Model load balancing configuration must be more than 1.')
+
+        model_setting = db.session.query(ProviderModelSetting) \
+            .filter(
+            ProviderModelSetting.tenant_id == self.tenant_id,
+            ProviderModelSetting.provider_name == self.provider.provider,
+            ProviderModelSetting.model_type == model_type.to_origin_model_type(),
+            ProviderModelSetting.model_name == model
+        ).first()
+
+        if model_setting:
+            model_setting.load_balancing_enabled = True
+            model_setting.updated_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
+            db.session.commit()
+        else:
+            model_setting = ProviderModelSetting(
+                tenant_id=self.tenant_id,
+                provider_name=self.provider.provider,
+                model_type=model_type.to_origin_model_type(),
+                model_name=model,
+                load_balancing_enabled=True
+            )
+            db.session.add(model_setting)
+            db.session.commit()
+
+        return model_setting
+
+    def disable_model_load_balancing(self, model_type: ModelType, model: str) -> ProviderModelSetting:
+        """
+        Disable model load balancing.
+        :param model_type: model type
+        :param model: model name
+        :return:
+        """
+        model_setting = db.session.query(ProviderModelSetting) \
+            .filter(
+            ProviderModelSetting.tenant_id == self.tenant_id,
+            ProviderModelSetting.provider_name == self.provider.provider,
+            ProviderModelSetting.model_type == model_type.to_origin_model_type(),
+            ProviderModelSetting.model_name == model
+        ).first()
+
+        if model_setting:
+            model_setting.load_balancing_enabled = False
+            model_setting.updated_at = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
+            db.session.commit()
+        else:
+            model_setting = ProviderModelSetting(
+                tenant_id=self.tenant_id,
+                provider_name=self.provider.provider,
+                model_type=model_type.to_origin_model_type(),
+                model_name=model,
+                load_balancing_enabled=False
+            )
+            db.session.add(model_setting)
+            db.session.commit()
+
+        return model_setting
+
     def get_provider_instance(self) -> ModelProvider:
         """
         Get provider instance.
@@ -453,7 +631,7 @@ class ProviderConfiguration(BaseModel):
 
         db.session.commit()
 
-    def _extract_secret_variables(self, credential_form_schemas: list[CredentialFormSchema]) -> list[str]:
+    def extract_secret_variables(self, credential_form_schemas: list[CredentialFormSchema]) -> list[str]:
         """
         Extract secret input form variables.
 
@@ -467,7 +645,7 @@ class ProviderConfiguration(BaseModel):
 
         return secret_input_form_variables
 
-    def _obfuscated_credentials(self, credentials: dict, credential_form_schemas: list[CredentialFormSchema]) -> dict:
+    def obfuscated_credentials(self, credentials: dict, credential_form_schemas: list[CredentialFormSchema]) -> dict:
         """
         Obfuscated credentials.
 
@@ -476,7 +654,7 @@ class ProviderConfiguration(BaseModel):
         :return:
         """
         # Get provider credential secret variables
-        credential_secret_variables = self._extract_secret_variables(
+        credential_secret_variables = self.extract_secret_variables(
             credential_form_schemas
         )
 
@@ -522,15 +700,22 @@ class ProviderConfiguration(BaseModel):
         else:
             model_types = provider_instance.get_provider_schema().supported_model_types
 
+        # Group model settings by model type and model
+        model_setting_map = defaultdict(dict)
+        for model_setting in self.model_settings:
+            model_setting_map[model_setting.model_type][model_setting.model] = model_setting
+
         if self.using_provider_type == ProviderType.SYSTEM:
             provider_models = self._get_system_provider_models(
                 model_types=model_types,
-                provider_instance=provider_instance
+                provider_instance=provider_instance,
+                model_setting_map=model_setting_map
             )
         else:
             provider_models = self._get_custom_provider_models(
                 model_types=model_types,
-                provider_instance=provider_instance
+                provider_instance=provider_instance,
+                model_setting_map=model_setting_map
             )
 
         if only_active:
@@ -541,18 +726,27 @@ class ProviderConfiguration(BaseModel):
 
     def _get_system_provider_models(self,
                                     model_types: list[ModelType],
-                                    provider_instance: ModelProvider) -> list[ModelWithProviderEntity]:
+                                    provider_instance: ModelProvider,
+                                    model_setting_map: dict[ModelType, dict[str, ModelSettings]]) \
+            -> list[ModelWithProviderEntity]:
         """
         Get system provider models.
 
         :param model_types: model types
         :param provider_instance: provider instance
+        :param model_setting_map: model setting map
         :return:
         """
         provider_models = []
         for model_type in model_types:
-            provider_models.extend(
-                [
+            for m in provider_instance.models(model_type):
+                status = ModelStatus.ACTIVE
+                if m.model_type in model_setting_map and m.model in model_setting_map[m.model_type]:
+                    model_setting = model_setting_map[m.model_type][m.model]
+                    if model_setting.enabled is False:
+                        status = ModelStatus.DISABLED
+
+                provider_models.append(
                     ModelWithProviderEntity(
                         model=m.model,
                         label=m.label,
@@ -562,11 +756,9 @@ class ProviderConfiguration(BaseModel):
                         model_properties=m.model_properties,
                         deprecated=m.deprecated,
                         provider=SimpleModelProviderEntity(self.provider),
-                        status=ModelStatus.ACTIVE
+                        status=status
                     )
-                    for m in provider_instance.models(model_type)
-                ]
-            )
+                )
 
         if self.provider.provider not in original_provider_configurate_methods:
             original_provider_configurate_methods[self.provider.provider] = []
@@ -586,7 +778,8 @@ class ProviderConfiguration(BaseModel):
                 break
 
             if should_use_custom_model:
-                if original_provider_configurate_methods[self.provider.provider] == [ConfigurateMethod.CUSTOMIZABLE_MODEL]:
+                if original_provider_configurate_methods[self.provider.provider] == [
+                    ConfigurateMethod.CUSTOMIZABLE_MODEL]:
                     # only customizable model
                     for restrict_model in restrict_models:
                         copy_credentials = self.system_configuration.credentials.copy()
@@ -611,6 +804,13 @@ class ProviderConfiguration(BaseModel):
                         if custom_model_schema.model_type not in model_types:
                             continue
 
+                        status = ModelStatus.ACTIVE
+                        if (custom_model_schema.model_type in model_setting_map
+                                and custom_model_schema.model in model_setting_map[custom_model_schema.model_type]):
+                            model_setting = model_setting_map[custom_model_schema.model_type][custom_model_schema.model]
+                            if model_setting.enabled is False:
+                                status = ModelStatus.DISABLED
+
                         provider_models.append(
                             ModelWithProviderEntity(
                                 model=custom_model_schema.model,
@@ -621,7 +821,7 @@ class ProviderConfiguration(BaseModel):
                                 model_properties=custom_model_schema.model_properties,
                                 deprecated=custom_model_schema.deprecated,
                                 provider=SimpleModelProviderEntity(self.provider),
-                                status=ModelStatus.ACTIVE
+                                status=status
                             )
                         )
 
@@ -632,16 +832,20 @@ class ProviderConfiguration(BaseModel):
                     m.status = ModelStatus.NO_PERMISSION
                 elif not quota_configuration.is_valid:
                     m.status = ModelStatus.QUOTA_EXCEEDED
+
         return provider_models
 
     def _get_custom_provider_models(self,
                                     model_types: list[ModelType],
-                                    provider_instance: ModelProvider) -> list[ModelWithProviderEntity]:
+                                    provider_instance: ModelProvider,
+                                    model_setting_map: dict[ModelType, dict[str, ModelSettings]]) \
+            -> list[ModelWithProviderEntity]:
         """
         Get custom provider models.
 
         :param model_types: model types
         :param provider_instance: provider instance
+        :param model_setting_map: model setting map
         :return:
         """
         provider_models = []
@@ -656,6 +860,16 @@ class ProviderConfiguration(BaseModel):
 
             models = provider_instance.models(model_type)
             for m in models:
+                status = ModelStatus.ACTIVE if credentials else ModelStatus.NO_CONFIGURE
+                load_balancing_enabled = False
+                if m.model_type in model_setting_map and m.model in model_setting_map[m.model_type]:
+                    model_setting = model_setting_map[m.model_type][m.model]
+                    if model_setting.enabled is False:
+                        status = ModelStatus.DISABLED
+
+                    if len(model_setting.load_balancing_configs) > 1:
+                        load_balancing_enabled = True
+
                 provider_models.append(
                     ModelWithProviderEntity(
                         model=m.model,
@@ -666,7 +880,8 @@ class ProviderConfiguration(BaseModel):
                         model_properties=m.model_properties,
                         deprecated=m.deprecated,
                         provider=SimpleModelProviderEntity(self.provider),
-                        status=ModelStatus.ACTIVE if credentials else ModelStatus.NO_CONFIGURE
+                        status=status,
+                        load_balancing_enabled=load_balancing_enabled
                     )
                 )
 
@@ -690,6 +905,17 @@ class ProviderConfiguration(BaseModel):
             if not custom_model_schema:
                 continue
 
+            status = ModelStatus.ACTIVE
+            load_balancing_enabled = False
+            if (custom_model_schema.model_type in model_setting_map
+                    and custom_model_schema.model in model_setting_map[custom_model_schema.model_type]):
+                model_setting = model_setting_map[custom_model_schema.model_type][custom_model_schema.model]
+                if model_setting.enabled is False:
+                    status = ModelStatus.DISABLED
+
+                if len(model_setting.load_balancing_configs) > 1:
+                    load_balancing_enabled = True
+
             provider_models.append(
                 ModelWithProviderEntity(
                     model=custom_model_schema.model,
@@ -700,7 +926,8 @@ class ProviderConfiguration(BaseModel):
                     model_properties=custom_model_schema.model_properties,
                     deprecated=custom_model_schema.deprecated,
                     provider=SimpleModelProviderEntity(self.provider),
-                    status=ModelStatus.ACTIVE
+                    status=status,
+                    load_balancing_enabled=load_balancing_enabled
                 )
             )
 

api/core/entities/provider_entities.py

@@ -72,3 +72,22 @@ class CustomConfiguration(BaseModel):
     """
     provider: Optional[CustomProviderConfiguration] = None
     models: list[CustomModelConfiguration] = []
+
+
+class ModelLoadBalancingConfiguration(BaseModel):
+    """
+    Class for model load balancing configuration.
+    """
+    id: str
+    name: str
+    credentials: dict
+
+
+class ModelSettings(BaseModel):
+    """
+    Model class for model settings.
+    """
+    model: str
+    model_type: ModelType
+    enabled: bool = True
+    load_balancing_configs: list[ModelLoadBalancingConfiguration] = []

api/core/extension/extensible.py

@@ -7,7 +7,7 @@ from typing import Any, Optional
 
 from pydantic import BaseModel
 
-from core.utils.position_helper import sort_to_dict_by_position_map
+from core.helper.position_helper import sort_to_dict_by_position_map
 
 
 class ExtensionModule(enum.Enum):

api/core/file/message_file_parser.py

@@ -42,6 +42,8 @@ class MessageFileParser:
                     raise ValueError('Invalid file url')
             if file.get('transfer_method') == FileTransferMethod.LOCAL_FILE.value and not file.get('upload_file_id'):
                 raise ValueError('Missing file upload_file_id')
+            if file.get('transform_method') == FileTransferMethod.TOOL_FILE.value and not file.get('tool_file_id'):
+                raise ValueError('Missing file tool_file_id')
 
         # transform files to file objs
         type_file_objs = self._to_file_objs(files, file_extra_config)
@@ -149,12 +151,21 @@ class MessageFileParser:
         """
         if isinstance(file, dict):
             transfer_method = FileTransferMethod.value_of(file.get('transfer_method'))
+            if transfer_method != FileTransferMethod.TOOL_FILE:
+                return FileVar(
+                    tenant_id=self.tenant_id,
+                    type=FileType.value_of(file.get('type')),
+                    transfer_method=transfer_method,
+                    url=file.get('url') if transfer_method == FileTransferMethod.REMOTE_URL else None,
+                    related_id=file.get('upload_file_id') if transfer_method == FileTransferMethod.LOCAL_FILE else None,
+                    extra_config=file_extra_config
+                )
             return FileVar(
                 tenant_id=self.tenant_id,
                 type=FileType.value_of(file.get('type')),
                 transfer_method=transfer_method,
-                url=file.get('url') if transfer_method == FileTransferMethod.REMOTE_URL else None,
-                related_id=file.get('upload_file_id') if transfer_method == FileTransferMethod.LOCAL_FILE else None,
+                url=None,
+                related_id=file.get('tool_file_id'),
                 extra_config=file_extra_config
             )
         else:

api/core/file/upload_file_parser.py

@@ -77,4 +77,4 @@ class UploadFileParser:
             return False
 
         current_time = int(time.time())
-        return current_time - int(timestamp) <= 300  # expired after 5 minutes
+        return current_time - int(timestamp) <= current_app.config.get('FILES_ACCESS_TIMEOUT')

api/core/helper/code_executor/code_executor.py

@@ -12,7 +12,7 @@ from config import get_env
 from core.helper.code_executor.entities import CodeDependency
 from core.helper.code_executor.javascript.javascript_transformer import NodeJsTemplateTransformer
 from core.helper.code_executor.jinja2.jinja2_transformer import Jinja2TemplateTransformer
-from core.helper.code_executor.python3.python3_transformer import PYTHON_STANDARD_PACKAGES, Python3TemplateTransformer
+from core.helper.code_executor.python3.python3_transformer import Python3TemplateTransformer
 from core.helper.code_executor.template_transformer import TemplateTransformer
 
 logger = logging.getLogger(__name__)
@@ -99,7 +99,9 @@ class CodeExecutor:
         except CodeExecutionException as e:
             raise e
         except Exception as e:
-            raise CodeExecutionException('Failed to execute code, this is likely a network issue, please check if the sandbox service is running')
+            raise CodeExecutionException('Failed to execute code, which is likely a network issue,'
+                                         ' please check if the sandbox service is running.'
+                                         f' ( Error: {str(e)} )')
         
         try:
             response = response.json()
@@ -187,7 +189,8 @@ class CodeExecutor:
             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
+                CodeDependency(**dependency) for dependency in dependencies
+                if dependency.get('name') not in Python3TemplateTransformer.get_standard_packages()
             ]
         except Exception as e:
             logger.exception(f'Failed to list dependencies: {e}')

api/core/helper/code_executor/javascript/javascript_transformer.py

@@ -1,58 +1,25 @@
-import json
-import re
-from typing import Optional
+from textwrap import dedent
 
-from core.helper.code_executor.entities import CodeDependency
 from core.helper.code_executor.template_transformer import TemplateTransformer
 
-NODEJS_RUNNER = """// declare main function here
-{{code}}
-
-// execute main function, and return the result
-// inputs is a dict, unstructured inputs
-output = main({{inputs}})
-
-// convert output to json and print
-output = JSON.stringify(output)
-
-result = `<<RESULT>>${output}<<RESULT>>`
-
-console.log(result)
-"""
-
-NODEJS_PRELOAD = """"""
-
 
 class NodeJsTemplateTransformer(TemplateTransformer):
     @classmethod
-    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
-        :param inputs: inputs
-        :return:
-        """
-
-        # transform inputs to json string
-        inputs_str = json.dumps(inputs, indent=4, ensure_ascii=False)
-
-        # replace code and inputs
-        runner = NODEJS_RUNNER.replace('{{code}}', code)
-        runner = runner.replace('{{inputs}}', inputs_str)
-
-        return runner, NODEJS_PRELOAD, []
-
-    @classmethod
-    def transform_response(cls, response: str) -> dict:
-        """
-        Transform response to dict
-        :param response: response
-        :return:
-        """
-        # extract result
-        result = re.search(r'<<RESULT>>(.*)<<RESULT>>', response, re.DOTALL)
-        if not result:
-            raise ValueError('Failed to parse result')
-        result = result.group(1)
-        return json.loads(result)
+    def get_runner_script(cls) -> str:
+        runner_script = dedent(
+            f"""
+            // declare main function
+            {cls._code_placeholder}
+            
+            // decode and prepare input object
+            var inputs_obj = JSON.parse(Buffer.from('{cls._inputs_placeholder}', 'base64').toString('utf-8'))
+            
+            // execute main function
+            var output_obj = main(inputs_obj)
+            
+            // convert output to json and print
+            var output_json = JSON.stringify(output_obj)
+            var result = `<<RESULT>>${{output_json}}<<RESULT>>`
+            console.log(result)
+            """)
+        return runner_script

api/core/helper/code_executor/jinja2/jinja2_transformer.py

@@ -1,94 +1,13 @@
-import json
-import re
-from base64 import b64encode
-from typing import Optional
+from textwrap import dedent
 
-from core.helper.code_executor.entities import CodeDependency
-from core.helper.code_executor.python3.python3_transformer import PYTHON_STANDARD_PACKAGES
+from core.helper.code_executor.python3.python3_transformer import Python3TemplateTransformer
 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}}''')
-
-def main(**inputs):
-    return template.render(**inputs)
-
-# execute main function, and return the result
-inputs = b64decode('{{inputs}}').decode('utf-8')
-output = main(**loads(inputs))
-
-result = f'''<<RESULT>>{output}<<RESULT>>'''
-
-print(result)
-
-"""
-
-JINJA2_PRELOAD_TEMPLATE = """{% set fruits = ['Apple'] %}
-{{ 'a' }}
-{% for fruit in fruits %}
-    <li>{{ fruit }}</li>
-{% endfor %}
-{% if fruits|length > 1 %}
-1
-{% endif %}
-{% for i in range(5) %}
-    {% if i == 3 %}{{ i }}{% else %}{% endif %}
-{% endfor %}
-    {% for i in range(3) %}
-        {{ i + 1 }}
-    {% endfor %}
-{% macro say_hello() %}a{{ 'b' }}{% endmacro %}
-{{ s }}{{ say_hello() }}"""
-
-JINJA2_PRELOAD = f"""
-import jinja2
-from base64 import b64decode
-
-def _jinja2_preload_():
-    # prepare jinja2 environment, load template and render before to avoid sandbox issue
-    template = jinja2.Template('''{JINJA2_PRELOAD_TEMPLATE}''')
-    template.render(s='a')
-
-if __name__ == '__main__':
-    _jinja2_preload_()
-
-"""
-
 
 class Jinja2TemplateTransformer(TemplateTransformer):
     @classmethod
-    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
-        :param inputs: inputs
-        :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}}', inputs_str)
-
-        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
+    def get_standard_packages(cls) -> set[str]:
+        return {'jinja2'} | Python3TemplateTransformer.get_standard_packages()
 
     @classmethod
     def transform_response(cls, response: str) -> dict:
@@ -97,12 +16,49 @@ class Jinja2TemplateTransformer(TemplateTransformer):
         :param response: response
         :return:
         """
-        # extract result
-        result = re.search(r'<<RESULT>>(.*)<<RESULT>>', response, re.DOTALL)
-        if not result:
-            raise ValueError('Failed to parse result')
-        result = result.group(1)
-
         return {
-            'result': result
+            'result': cls.extract_result_str_from_response(response)
         }
+
+    @classmethod
+    def get_runner_script(cls) -> str:
+        runner_script = dedent(f"""
+            # declare main function
+            def main(**inputs):
+                import jinja2
+                template = jinja2.Template('''{cls._code_placeholder}''')
+                return template.render(**inputs)
+                
+            import json
+            from base64 import b64decode
+            
+            # decode and prepare input dict
+            inputs_obj = json.loads(b64decode('{cls._inputs_placeholder}').decode('utf-8'))
+            
+            # execute main function
+            output = main(**inputs_obj)
+            
+            # convert output and print
+            result = f'''<<RESULT>>{{output}}<<RESULT>>'''
+            print(result)
+            
+            """)
+        return runner_script
+
+    @classmethod
+    def get_preload_script(cls) -> str:
+        preload_script = dedent("""
+            import jinja2
+            from base64 import b64decode
+            
+            def _jinja2_preload_():
+                # prepare jinja2 environment, load template and render before to avoid sandbox issue
+                template = jinja2.Template('{{s}}')
+                template.render(s='a')
+            
+            if __name__ == '__main__':
+                _jinja2_preload_()
+            
+            """)
+
+        return preload_script

api/core/helper/code_executor/python3/python3_transformer.py

@@ -1,83 +1,51 @@
-import json
-import re
-from base64 import b64encode
 from textwrap import dedent
-from typing import Optional
 
-from core.helper.code_executor.entities import CodeDependency
 from core.helper.code_executor.template_transformer import TemplateTransformer
 
-PYTHON_RUNNER = dedent("""
-# 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
-inputs = b64decode('{{inputs}}').decode('utf-8')
-output = main(**json.loads(inputs))
-
-# convert output to json and print
-output = dumps(output, indent=4)
-
-result = f'''<<RESULT>>
-{output}
-<<RESULT>>'''
-
-print(result)
-""")
-
-PYTHON_PRELOAD = """"""
-
-PYTHON_STANDARD_PACKAGES = {
-    'json', 'datetime', 'math', 'random', 're', 'string', 'sys', 'time', 'traceback', 'uuid', 'os', 'base64',
-    'hashlib', 'hmac', 'binascii', 'collections', 'functools', 'operator', 'itertools', 'uuid',
-}
-
 
 class Python3TemplateTransformer(TemplateTransformer):
     @classmethod
-    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
-        :param inputs: inputs
-        :return:
-        """
-        
-        # transform inputs to json string
-        inputs_str = b64encode(json.dumps(inputs, ensure_ascii=False).encode()).decode('utf-8')
+    def get_standard_packages(cls) -> set[str]:
+        return {
+            'base64',
+            'binascii',
+            'collections',
+            'datetime',
+            'functools',
+            'hashlib',
+            'hmac',
+            'itertools',
+            'json',
+            'math',
+            'operator',
+            'os',
+            'random',
+            're',
+            'string',
+            'sys',
+            'time',
+            'traceback',
+            'uuid',
+        }
 
-        # replace code and inputs
-        runner = PYTHON_RUNNER.replace('{{code}}', code)
-        runner = runner.replace('{{inputs}}', inputs_str)
-
-        # 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:
-        """
-        Transform response to dict
-        :param response: response
-        :return:
-        """
-        # extract result
-        result = re.search(r'<<RESULT>>(.*?)<<RESULT>>', response, re.DOTALL)
-        if not result:
-            raise ValueError('Failed to parse result')
-        result = result.group(1)
-        return json.loads(result)
+    def get_runner_script(cls) -> str:
+        runner_script = dedent(f"""
+            # declare main function
+            {cls._code_placeholder}
+            
+            import json
+            from base64 import b64decode
+            
+            # decode and prepare input dict
+            inputs_obj = json.loads(b64decode('{cls._inputs_placeholder}').decode('utf-8'))
+            
+            # execute main function
+            output_obj = main(**inputs_obj)
+            
+            # convert output to json and print
+            output_json = json.dumps(output_obj, indent=4)
+            result = f'''<<RESULT>>{{output_json}}<<RESULT>>'''
+            print(result)
+            """)
+        return runner_script

api/core/helper/code_executor/template_transformer.py

@@ -1,13 +1,25 @@
+import json
+import re
 from abc import ABC, abstractmethod
+from base64 import b64encode
 from typing import Optional
 
+from pydantic import BaseModel
+
 from core.helper.code_executor.entities import CodeDependency
 
 
-class TemplateTransformer(ABC):
+class TemplateTransformer(ABC, BaseModel):
+    _code_placeholder: str = '{{code}}'
+    _inputs_placeholder: str = '{{inputs}}'
+    _result_tag: str = '<<RESULT>>'
+
     @classmethod
-    @abstractmethod
-    def transform_caller(cls, code: str, inputs: dict, 
+    def get_standard_packages(cls) -> set[str]:
+        return set()
+
+    @classmethod
+    def transform_caller(cls, code: str, inputs: dict,
                          dependencies: Optional[list[CodeDependency]] = None) -> tuple[str, str, list[CodeDependency]]:
         """
         Transform code to python runner
@@ -15,14 +27,61 @@ class TemplateTransformer(ABC):
         :param inputs: inputs
         :return: runner, preload
         """
-        pass
-    
+        runner_script = cls.assemble_runner_script(code, inputs)
+        preload_script = cls.get_preload_script()
+
+        packages = dependencies or []
+        standard_packages = cls.get_standard_packages()
+        for package in standard_packages:
+            if package not in packages:
+                packages.append(CodeDependency(name=package, version=''))
+        packages = list({dep.name: dep for dep in packages if dep.name}.values())
+
+        return runner_script, preload_script, packages
+
+    @classmethod
+    def extract_result_str_from_response(cls, response: str) -> str:
+        result = re.search(rf'{cls._result_tag}(.*){cls._result_tag}', response, re.DOTALL)
+        if not result:
+            raise ValueError('Failed to parse result')
+        result = result.group(1)
+        return result
+
     @classmethod
-    @abstractmethod
     def transform_response(cls, response: str) -> dict:
         """
         Transform response to dict
         :param response: response
         :return:
         """
-        pass
+        return json.loads(cls.extract_result_str_from_response(response))
+
+    @classmethod
+    @abstractmethod
+    def get_runner_script(cls) -> str:
+        """
+        Get runner script
+        """
+        pass
+
+    @classmethod
+    def serialize_inputs(cls, inputs: dict) -> str:
+        inputs_json_str = json.dumps(inputs, ensure_ascii=False).encode()
+        input_base64_encoded = b64encode(inputs_json_str).decode('utf-8')
+        return input_base64_encoded
+
+    @classmethod
+    def assemble_runner_script(cls, code: str, inputs: dict) -> str:
+        # assemble runner script
+        script = cls.get_runner_script()
+        script = script.replace(cls._code_placeholder, code)
+        inputs_str = cls.serialize_inputs(inputs)
+        script = script.replace(cls._inputs_placeholder, inputs_str)
+        return script
+
+    @classmethod
+    def get_preload_script(cls) -> str:
+        """
+        Get preload script
+        """
+        return ''

api/core/helper/model_provider_cache.py

@@ -9,6 +9,7 @@ from extensions.ext_redis import redis_client
 class ProviderCredentialsCacheType(Enum):
     PROVIDER = "provider"
     MODEL = "provider_model"
+    LOAD_BALANCING_MODEL = "load_balancing_provider_model"
 
 
 class ProviderCredentialsCache:

api/core/helper/position_helper.py

@@ -1,10 +1,9 @@
-import logging
 import os
 from collections import OrderedDict
 from collections.abc import Callable
 from typing import Any, AnyStr
 
-import yaml
+from core.tools.utils.yaml_utils import load_yaml_file
 
 
 def get_position_map(
@@ -17,21 +16,15 @@ def get_position_map(
     :param file_name: the YAML file name, default to '_position.yaml'
     :return: a dict with name as key and index as value
     """
-    try:
-        position_file_name = os.path.join(folder_path, file_name)
-        if not os.path.exists(position_file_name):
-            return {}
-
-        with open(position_file_name, encoding='utf-8') as f:
-            positions = yaml.safe_load(f)
-        position_map = {}
-        for index, name in enumerate(positions):
-            if name and isinstance(name, str):
-                position_map[name.strip()] = index
-        return position_map
-    except:
-        logging.warning(f'Failed to load the YAML position file {folder_path}/{file_name}.')
-        return {}
+    position_file_name = os.path.join(folder_path, file_name)
+    positions = load_yaml_file(position_file_name, ignore_error=True)
+    position_map = {}
+    index = 0
+    for _, name in enumerate(positions):
+        if name and isinstance(name, str):
+            position_map[name.strip()] = index
+            index += 1
+    return position_map
 
 
 def sort_by_position_map(

api/core/indexing_runner.py

@@ -12,7 +12,6 @@ from flask import Flask, current_app
 from flask_login import current_user
 from sqlalchemy.orm.exc import ObjectDeletedError
 
-from core.docstore.dataset_docstore import DatasetDocumentStore
 from core.errors.error import ProviderTokenNotInitError
 from core.llm_generator.llm_generator import LLMGenerator
 from core.model_manager import ModelInstance, ModelManager
@@ -20,12 +19,16 @@ from core.model_runtime.entities.model_entities import ModelType, PriceType
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
 from core.rag.datasource.keyword.keyword_factory import Keyword
+from core.rag.docstore.dataset_docstore import DatasetDocumentStore
 from core.rag.extractor.entity.extract_setting import ExtractSetting
 from core.rag.index_processor.index_processor_base import BaseIndexProcessor
 from core.rag.index_processor.index_processor_factory import IndexProcessorFactory
 from core.rag.models.document import Document
-from core.splitter.fixed_text_splitter import EnhanceRecursiveCharacterTextSplitter, FixedRecursiveCharacterTextSplitter
-from core.splitter.text_splitter import TextSplitter
+from core.rag.splitter.fixed_text_splitter import (
+    EnhanceRecursiveCharacterTextSplitter,
+    FixedRecursiveCharacterTextSplitter,
+)
+from core.rag.splitter.text_splitter import TextSplitter
 from extensions.ext_database import db
 from extensions.ext_redis import redis_client
 from extensions.ext_storage import storage
@@ -283,11 +286,7 @@ class IndexingRunner:
                 if len(preview_texts) < 5:
                     preview_texts.append(document.page_content)
                 if indexing_technique == 'high_quality' or embedding_model_instance:
-                    embedding_model_type_instance = embedding_model_instance.model_type_instance
-                    embedding_model_type_instance = cast(TextEmbeddingModel, embedding_model_type_instance)
-                    tokens += embedding_model_type_instance.get_num_tokens(
-                        model=embedding_model_instance.model,
-                        credentials=embedding_model_instance.credentials,
+                    tokens += embedding_model_instance.get_text_embedding_num_tokens(
                         texts=[self.filter_string(document.page_content)]
                     )
 
@@ -428,7 +427,7 @@ class IndexingRunner:
                 chunk_size=segmentation["max_tokens"],
                 chunk_overlap=chunk_overlap,
                 fixed_separator=separator,
-                separators=["\n\n", "。", ".", " ", ""],
+                separators=["\n\n", "。", ". ", " ", ""],
                 embedding_model_instance=embedding_model_instance
             )
         else:
@@ -436,7 +435,7 @@ class IndexingRunner:
             character_splitter = EnhanceRecursiveCharacterTextSplitter.from_encoder(
                 chunk_size=DatasetProcessRule.AUTOMATIC_RULES['segmentation']['max_tokens'],
                 chunk_overlap=DatasetProcessRule.AUTOMATIC_RULES['segmentation']['chunk_overlap'],
-                separators=["\n\n", "。", ".", " ", ""],
+                separators=["\n\n", "。", ". ", " ", ""],
                 embedding_model_instance=embedding_model_instance
             )
 
@@ -655,10 +654,6 @@ class IndexingRunner:
         tokens = 0
         chunk_size = 10
 
-        embedding_model_type_instance = None
-        if embedding_model_instance:
-            embedding_model_type_instance = embedding_model_instance.model_type_instance
-            embedding_model_type_instance = cast(TextEmbeddingModel, embedding_model_type_instance)
         # create keyword index
         create_keyword_thread = threading.Thread(target=self._process_keyword_index,
                                                  args=(current_app._get_current_object(),
@@ -671,8 +666,7 @@ class IndexingRunner:
                     chunk_documents = documents[i:i + chunk_size]
                     futures.append(executor.submit(self._process_chunk, current_app._get_current_object(), index_processor,
                                                    chunk_documents, dataset,
-                                                   dataset_document, embedding_model_instance,
-                                                   embedding_model_type_instance))
+                                                   dataset_document, embedding_model_instance))
 
                 for future in futures:
                     tokens += future.result()
@@ -713,7 +707,7 @@ class IndexingRunner:
                 db.session.commit()
 
     def _process_chunk(self, flask_app, index_processor, chunk_documents, dataset, dataset_document,
-                       embedding_model_instance, embedding_model_type_instance):
+                       embedding_model_instance):
         with flask_app.app_context():
             # check document is paused
             self._check_document_paused_status(dataset_document.id)
@@ -721,9 +715,7 @@ class IndexingRunner:
             tokens = 0
             if dataset.indexing_technique == 'high_quality' or embedding_model_type_instance:
                 tokens += sum(
-                    embedding_model_type_instance.get_num_tokens(
-                        embedding_model_instance.model,
-                        embedding_model_instance.credentials,
+                    embedding_model_instance.get_text_embedding_num_tokens(
                         [document.page_content]
                     )
                     for document in chunk_documents

api/core/memory/token_buffer_memory.py

@@ -1,3 +1,5 @@
+from typing import Optional
+
 from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
 from core.file.message_file_parser import MessageFileParser
 from core.model_manager import ModelInstance
@@ -9,8 +11,6 @@ from core.model_runtime.entities.message_entities import (
     TextPromptMessageContent,
     UserPromptMessage,
 )
-from core.model_runtime.entities.model_entities import ModelType
-from core.model_runtime.model_providers import model_provider_factory
 from extensions.ext_database import db
 from models.model import AppMode, Conversation, Message
 
@@ -21,7 +21,7 @@ class TokenBufferMemory:
         self.model_instance = model_instance
 
     def get_history_prompt_messages(self, max_token_limit: int = 2000,
-                                    message_limit: int = 10) -> list[PromptMessage]:
+                                    message_limit: Optional[int] = None) -> list[PromptMessage]:
         """
         Get history prompt messages.
         :param max_token_limit: max token limit
@@ -30,10 +30,15 @@ class TokenBufferMemory:
         app_record = self.conversation.app
 
         # fetch limited messages, and return reversed
-        messages = db.session.query(Message).filter(
+        query = db.session.query(Message).filter(
             Message.conversation_id == self.conversation.id,
             Message.answer != ''
-        ).order_by(Message.created_at.desc()).limit(message_limit).all()
+        ).order_by(Message.created_at.desc())
+
+        if message_limit and message_limit > 0:
+            messages = query.limit(message_limit).all()
+        else:
+            messages = query.all()
 
         messages = list(reversed(messages))
         message_file_parser = MessageFileParser(
@@ -78,12 +83,7 @@ class TokenBufferMemory:
             return []
 
         # prune the chat message if it exceeds the max token limit
-        provider_instance = model_provider_factory.get_provider_instance(self.model_instance.provider)
-        model_type_instance = provider_instance.get_model_instance(ModelType.LLM)
-
-        curr_message_tokens = model_type_instance.get_num_tokens(
-            self.model_instance.model,
-            self.model_instance.credentials,
+        curr_message_tokens = self.model_instance.get_llm_num_tokens(
             prompt_messages
         )
 
@@ -91,9 +91,7 @@ class TokenBufferMemory:
             pruned_memory = []
             while curr_message_tokens > max_token_limit and prompt_messages:
                 pruned_memory.append(prompt_messages.pop(0))
-                curr_message_tokens = model_type_instance.get_num_tokens(
-                    self.model_instance.model,
-                    self.model_instance.credentials,
+                curr_message_tokens = self.model_instance.get_llm_num_tokens(
                     prompt_messages
                 )
 
@@ -102,7 +100,7 @@ class TokenBufferMemory:
     def get_history_prompt_text(self, human_prefix: str = "Human",
                                 ai_prefix: str = "Assistant",
                                 max_token_limit: int = 2000,
-                                message_limit: int = 10) -> str:
+                                message_limit: Optional[int] = None) -> str:
         """
         Get history prompt text.
         :param human_prefix: human prefix

api/core/model_manager.py

@@ -1,7 +1,10 @@
+import logging
+import os
 from collections.abc import Generator
 from typing import IO, Optional, Union, cast
 
-from core.entities.provider_configuration import ProviderModelBundle
+from core.entities.provider_configuration import ProviderConfiguration, ProviderModelBundle
+from core.entities.provider_entities import ModelLoadBalancingConfiguration
 from core.errors.error import ProviderTokenNotInitError
 from core.model_runtime.callbacks.base_callback import Callback
 from core.model_runtime.entities.llm_entities import LLMResult
@@ -9,6 +12,7 @@ from core.model_runtime.entities.message_entities import PromptMessage, PromptMe
 from core.model_runtime.entities.model_entities import ModelType
 from core.model_runtime.entities.rerank_entities import RerankResult
 from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult
+from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeConnectionError, InvokeRateLimitError
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.model_runtime.model_providers.__base.moderation_model import ModerationModel
 from core.model_runtime.model_providers.__base.rerank_model import RerankModel
@@ -16,6 +20,10 @@ from core.model_runtime.model_providers.__base.speech2text_model import Speech2T
 from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
 from core.model_runtime.model_providers.__base.tts_model import TTSModel
 from core.provider_manager import ProviderManager
+from extensions.ext_redis import redis_client
+from models.provider import ProviderType
+
+logger = logging.getLogger(__name__)
 
 
 class ModelInstance:
@@ -29,6 +37,12 @@ class ModelInstance:
         self.provider = provider_model_bundle.configuration.provider.provider
         self.credentials = self._fetch_credentials_from_bundle(provider_model_bundle, model)
         self.model_type_instance = self.provider_model_bundle.model_type_instance
+        self.load_balancing_manager = self._get_load_balancing_manager(
+            configuration=provider_model_bundle.configuration,
+            model_type=provider_model_bundle.model_type_instance.model_type,
+            model=model,
+            credentials=self.credentials
+        )
 
     def _fetch_credentials_from_bundle(self, provider_model_bundle: ProviderModelBundle, model: str) -> dict:
         """
@@ -37,8 +51,10 @@ class ModelInstance:
         :param model: model name
         :return:
         """
-        credentials = provider_model_bundle.configuration.get_current_credentials(
-            model_type=provider_model_bundle.model_type_instance.model_type,
+        configuration = provider_model_bundle.configuration
+        model_type = provider_model_bundle.model_type_instance.model_type
+        credentials = configuration.get_current_credentials(
+            model_type=model_type,
             model=model
         )
 
@@ -47,6 +63,43 @@ class ModelInstance:
 
         return credentials
 
+    def _get_load_balancing_manager(self, configuration: ProviderConfiguration,
+                                    model_type: ModelType,
+                                    model: str,
+                                    credentials: dict) -> Optional["LBModelManager"]:
+        """
+        Get load balancing model credentials
+        :param configuration: provider configuration
+        :param model_type: model type
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        if configuration.model_settings and configuration.using_provider_type == ProviderType.CUSTOM:
+            current_model_setting = None
+            # check if model is disabled by admin
+            for model_setting in configuration.model_settings:
+                if (model_setting.model_type == model_type
+                        and model_setting.model == model):
+                    current_model_setting = model_setting
+                    break
+
+            # check if load balancing is enabled
+            if current_model_setting and current_model_setting.load_balancing_configs:
+                # use load balancing proxy to choose credentials
+                lb_model_manager = LBModelManager(
+                    tenant_id=configuration.tenant_id,
+                    provider=configuration.provider.provider,
+                    model_type=model_type,
+                    model=model,
+                    load_balancing_configs=current_model_setting.load_balancing_configs,
+                    managed_credentials=credentials if configuration.custom_configuration.provider else None
+                )
+
+                return lb_model_manager
+
+        return None
+
     def invoke_llm(self, prompt_messages: list[PromptMessage], model_parameters: Optional[dict] = None,
                    tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
                    stream: bool = True, user: Optional[str] = None, callbacks: list[Callback] = None) \
@@ -67,7 +120,8 @@ class ModelInstance:
             raise Exception("Model type instance is not LargeLanguageModel")
 
         self.model_type_instance = cast(LargeLanguageModel, self.model_type_instance)
-        return self.model_type_instance.invoke(
+        return self._round_robin_invoke(
+            function=self.model_type_instance.invoke,
             model=self.model,
             credentials=self.credentials,
             prompt_messages=prompt_messages,
@@ -79,6 +133,27 @@ class ModelInstance:
             callbacks=callbacks
         )
 
+    def get_llm_num_tokens(self, prompt_messages: list[PromptMessage],
+                           tools: Optional[list[PromptMessageTool]] = None) -> int:
+        """
+        Get number of tokens for llm
+
+        :param prompt_messages: prompt messages
+        :param tools: tools for tool calling
+        :return:
+        """
+        if not isinstance(self.model_type_instance, LargeLanguageModel):
+            raise Exception("Model type instance is not LargeLanguageModel")
+
+        self.model_type_instance = cast(LargeLanguageModel, self.model_type_instance)
+        return self._round_robin_invoke(
+            function=self.model_type_instance.get_num_tokens,
+            model=self.model,
+            credentials=self.credentials,
+            prompt_messages=prompt_messages,
+            tools=tools
+        )
+
     def invoke_text_embedding(self, texts: list[str], user: Optional[str] = None) \
             -> TextEmbeddingResult:
         """
@@ -92,13 +167,32 @@ class ModelInstance:
             raise Exception("Model type instance is not TextEmbeddingModel")
 
         self.model_type_instance = cast(TextEmbeddingModel, self.model_type_instance)
-        return self.model_type_instance.invoke(
+        return self._round_robin_invoke(
+            function=self.model_type_instance.invoke,
             model=self.model,
             credentials=self.credentials,
             texts=texts,
             user=user
         )
 
+    def get_text_embedding_num_tokens(self, texts: list[str]) -> int:
+        """
+        Get number of tokens for text embedding
+
+        :param texts: texts to embed
+        :return:
+        """
+        if not isinstance(self.model_type_instance, TextEmbeddingModel):
+            raise Exception("Model type instance is not TextEmbeddingModel")
+
+        self.model_type_instance = cast(TextEmbeddingModel, self.model_type_instance)
+        return self._round_robin_invoke(
+            function=self.model_type_instance.get_num_tokens,
+            model=self.model,
+            credentials=self.credentials,
+            texts=texts
+        )
+
     def invoke_rerank(self, query: str, docs: list[str], score_threshold: Optional[float] = None,
                       top_n: Optional[int] = None,
                       user: Optional[str] = None) \
@@ -117,7 +211,8 @@ class ModelInstance:
             raise Exception("Model type instance is not RerankModel")
 
         self.model_type_instance = cast(RerankModel, self.model_type_instance)
-        return self.model_type_instance.invoke(
+        return self._round_robin_invoke(
+            function=self.model_type_instance.invoke,
             model=self.model,
             credentials=self.credentials,
             query=query,
@@ -140,7 +235,8 @@ class ModelInstance:
             raise Exception("Model type instance is not ModerationModel")
 
         self.model_type_instance = cast(ModerationModel, self.model_type_instance)
-        return self.model_type_instance.invoke(
+        return self._round_robin_invoke(
+            function=self.model_type_instance.invoke,
             model=self.model,
             credentials=self.credentials,
             text=text,
@@ -160,7 +256,8 @@ class ModelInstance:
             raise Exception("Model type instance is not Speech2TextModel")
 
         self.model_type_instance = cast(Speech2TextModel, self.model_type_instance)
-        return self.model_type_instance.invoke(
+        return self._round_robin_invoke(
+            function=self.model_type_instance.invoke,
             model=self.model,
             credentials=self.credentials,
             file=file,
@@ -183,7 +280,8 @@ class ModelInstance:
             raise Exception("Model type instance is not TTSModel")
 
         self.model_type_instance = cast(TTSModel, self.model_type_instance)
-        return self.model_type_instance.invoke(
+        return self._round_robin_invoke(
+            function=self.model_type_instance.invoke,
             model=self.model,
             credentials=self.credentials,
             content_text=content_text,
@@ -193,7 +291,44 @@ class ModelInstance:
             streaming=streaming
         )
 
-    def get_tts_voices(self, language: str) -> list:
+    def _round_robin_invoke(self, function: callable, *args, **kwargs):
+        """
+        Round-robin invoke
+        :param function: function to invoke
+        :param args: function args
+        :param kwargs: function kwargs
+        :return:
+        """
+        if not self.load_balancing_manager:
+            return function(*args, **kwargs)
+
+        last_exception = None
+        while True:
+            lb_config = self.load_balancing_manager.fetch_next()
+            if not lb_config:
+                if not last_exception:
+                    raise ProviderTokenNotInitError("Model credentials is not initialized.")
+                else:
+                    raise last_exception
+
+            try:
+                if 'credentials' in kwargs:
+                    del kwargs['credentials']
+                return function(*args, **kwargs, credentials=lb_config.credentials)
+            except InvokeRateLimitError as e:
+                # expire in 60 seconds
+                self.load_balancing_manager.cooldown(lb_config, expire=60)
+                last_exception = e
+                continue
+            except (InvokeAuthorizationError, InvokeConnectionError) as e:
+                # expire in 10 seconds
+                self.load_balancing_manager.cooldown(lb_config, expire=10)
+                last_exception = e
+                continue
+            except Exception as e:
+                raise e
+
+    def get_tts_voices(self, language: Optional[str] = None) -> list:
         """
         Invoke large language tts model voices
 
@@ -226,6 +361,7 @@ class ModelManager:
         """
         if not provider:
             return self.get_default_model_instance(tenant_id, model_type)
+
         provider_model_bundle = self._provider_manager.get_provider_model_bundle(
             tenant_id=tenant_id,
             provider=provider,
@@ -255,3 +391,141 @@ class ModelManager:
             model_type=model_type,
             model=default_model_entity.model
         )
+
+
+class LBModelManager:
+    def __init__(self, tenant_id: str,
+                 provider: str,
+                 model_type: ModelType,
+                 model: str,
+                 load_balancing_configs: list[ModelLoadBalancingConfiguration],
+                 managed_credentials: Optional[dict] = None) -> None:
+        """
+        Load balancing model manager
+        :param load_balancing_configs: all load balancing configurations
+        :param managed_credentials: credentials if load balancing configuration name is __inherit__
+        """
+        self._tenant_id = tenant_id
+        self._provider = provider
+        self._model_type = model_type
+        self._model = model
+        self._load_balancing_configs = load_balancing_configs
+
+        for load_balancing_config in self._load_balancing_configs:
+            if load_balancing_config.name == "__inherit__":
+                if not managed_credentials:
+                    # remove __inherit__ if managed credentials is not provided
+                    self._load_balancing_configs.remove(load_balancing_config)
+                else:
+                    load_balancing_config.credentials = managed_credentials
+
+    def fetch_next(self) -> Optional[ModelLoadBalancingConfiguration]:
+        """
+        Get next model load balancing config
+        Strategy: Round Robin
+        :return:
+        """
+        cache_key = "model_lb_index:{}:{}:{}:{}".format(
+            self._tenant_id,
+            self._provider,
+            self._model_type.value,
+            self._model
+        )
+
+        cooldown_load_balancing_configs = []
+        max_index = len(self._load_balancing_configs)
+
+        while True:
+            current_index = redis_client.incr(cache_key)
+            if current_index >= 10000000:
+                current_index = 1
+                redis_client.set(cache_key, current_index)
+
+            redis_client.expire(cache_key, 3600)
+            if current_index > max_index:
+                current_index = current_index % max_index
+
+            real_index = current_index - 1
+            if real_index > max_index:
+                real_index = 0
+
+            config = self._load_balancing_configs[real_index]
+
+            if self.in_cooldown(config):
+                cooldown_load_balancing_configs.append(config)
+                if len(cooldown_load_balancing_configs) >= len(self._load_balancing_configs):
+                    # all configs are in cooldown
+                    return None
+
+                continue
+
+            if bool(os.environ.get("DEBUG", 'False').lower() == 'true'):
+                logger.info(f"Model LB\nid: {config.id}\nname:{config.name}\n"
+                            f"tenant_id: {self._tenant_id}\nprovider: {self._provider}\n"
+                            f"model_type: {self._model_type.value}\nmodel: {self._model}")
+
+            return config
+
+        return None
+
+    def cooldown(self, config: ModelLoadBalancingConfiguration, expire: int = 60) -> None:
+        """
+        Cooldown model load balancing config
+        :param config: model load balancing config
+        :param expire: cooldown time
+        :return:
+        """
+        cooldown_cache_key = "model_lb_index:cooldown:{}:{}:{}:{}:{}".format(
+            self._tenant_id,
+            self._provider,
+            self._model_type.value,
+            self._model,
+            config.id
+        )
+
+        redis_client.setex(cooldown_cache_key, expire, 'true')
+
+    def in_cooldown(self, config: ModelLoadBalancingConfiguration) -> bool:
+        """
+        Check if model load balancing config is in cooldown
+        :param config: model load balancing config
+        :return:
+        """
+        cooldown_cache_key = "model_lb_index:cooldown:{}:{}:{}:{}:{}".format(
+            self._tenant_id,
+            self._provider,
+            self._model_type.value,
+            self._model,
+            config.id
+        )
+
+        return redis_client.exists(cooldown_cache_key)
+
+    @classmethod
+    def get_config_in_cooldown_and_ttl(cls, tenant_id: str,
+                                       provider: str,
+                                       model_type: ModelType,
+                                       model: str,
+                                       config_id: str) -> tuple[bool, int]:
+        """
+        Get model load balancing config is in cooldown and ttl
+        :param tenant_id: workspace id
+        :param provider: provider name
+        :param model_type: model type
+        :param model: model name
+        :param config_id: model load balancing config id
+        :return:
+        """
+        cooldown_cache_key = "model_lb_index:cooldown:{}:{}:{}:{}:{}".format(
+            tenant_id,
+            provider,
+            model_type.value,
+            model,
+            config_id
+        )
+
+        ttl = redis_client.ttl(cooldown_cache_key)
+        if ttl == -2:
+            return False, 0
+
+        return True, ttl

api/core/model_runtime/README.md

@@ -20,7 +20,7 @@ This module provides the interface for invoking and authenticating various model
 
   ![image-20231210143654461](./docs/en_US/images/index/image-20231210143654461.png)
 
-  Displays a list of all supported providers, including provider names, icons, supported model types list, predefined model list, configuration method, and credentials form rules, etc. For detailed rule design, see: [Schema](./schema.md).
+  Displays a list of all supported providers, including provider names, icons, supported model types list, predefined model list, configuration method, and credentials form rules, etc. For detailed rule design, see: [Schema](./docs/en_US/schema.md).
 
 - Selectable model list display
 

api/core/model_runtime/callbacks/base_callback.py

@@ -1,4 +1,3 @@
-from abc import ABC
 from typing import Optional
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
@@ -14,7 +13,7 @@ _TEXT_COLOR_MAPPING = {
 }
 
 
-class Callback(ABC):
+class Callback:
     """
     Base class for callbacks.
     Only for LLM.

api/core/model_runtime/docs/en_US/interfaces.md

@@ -336,7 +336,7 @@ Inherit the `__base.text2speech_model.Text2SpeechModel` base class and implement
 - Invoke Invocation
 
   ```python
-  def _invoke(elf, model: str, credentials: dict, content_text: str, streaming: bool, user: Optional[str] = None):
+  def _invoke(self, model: str, credentials: dict, content_text: str, streaming: bool, user: Optional[str] = None):
       """
       Invoke large language model
   

api/core/model_runtime/docs/zh_Hans/interfaces.md

@@ -376,7 +376,7 @@ class XinferenceProvider(Provider):
 - Invoke 调用
 
   ```python
-  def _invoke(elf, model: str, credentials: dict, content_text: str, streaming: bool, user: Optional[str] = None):
+  def _invoke(self, model: str, credentials: dict, content_text: str, streaming: bool, user: Optional[str] = None):
       """
       Invoke large language model
   

api/core/model_runtime/model_providers/__base/ai_model.py

@@ -3,8 +3,7 @@ import os
 from abc import ABC, abstractmethod
 from typing import Optional
 
-import yaml
-
+from core.helper.position_helper import get_position_map, sort_by_position_map
 from core.model_runtime.entities.common_entities import I18nObject
 from core.model_runtime.entities.defaults import PARAMETER_RULE_TEMPLATE
 from core.model_runtime.entities.model_entities import (
@@ -18,7 +17,7 @@ from core.model_runtime.entities.model_entities import (
 )
 from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
 from core.model_runtime.model_providers.__base.tokenizers.gpt2_tokenzier import GPT2Tokenizer
-from core.utils.position_helper import get_position_map, sort_by_position_map
+from core.tools.utils.yaml_utils import load_yaml_file
 
 
 class AIModel(ABC):
@@ -154,8 +153,7 @@ class AIModel(ABC):
         # traverse all model_schema_yaml_paths
         for model_schema_yaml_path in model_schema_yaml_paths:
             # read yaml data from yaml file
-            with open(model_schema_yaml_path, encoding='utf-8') as f:
-                yaml_data = yaml.safe_load(f)
+            yaml_data = load_yaml_file(model_schema_yaml_path, ignore_error=True)
 
             new_parameter_rules = []
             for parameter_rule in yaml_data.get('parameter_rules', []):

api/core/model_runtime/model_providers/__base/model_provider.py

@@ -1,12 +1,11 @@
 import os
 from abc import ABC, abstractmethod
 
-import yaml
-
+from core.helper.module_import_helper import get_subclasses_from_module, import_module_from_source
 from core.model_runtime.entities.model_entities import AIModelEntity, ModelType
 from core.model_runtime.entities.provider_entities import ProviderEntity
 from core.model_runtime.model_providers.__base.ai_model import AIModel
-from core.utils.module_import_helper import get_subclasses_from_module, import_module_from_source
+from core.tools.utils.yaml_utils import load_yaml_file
 
 
 class ModelProvider(ABC):
@@ -44,10 +43,7 @@ class ModelProvider(ABC):
 
         # read provider schema from yaml file
         yaml_path = os.path.join(current_path, f'{provider_name}.yaml')
-        yaml_data = {}
-        if os.path.exists(yaml_path):
-            with open(yaml_path, encoding='utf-8') as f:
-                yaml_data = yaml.safe_load(f)
+        yaml_data = load_yaml_file(yaml_path, ignore_error=True)
 
         try:
             # yaml_data to entity

api/core/model_runtime/model_providers/_position.yaml

@@ -2,7 +2,9 @@
 - anthropic
 - azure_openai
 - google
+- vertex_ai
 - nvidia
+- nvidia_nim
 - cohere
 - bedrock
 - togetherai
@@ -29,3 +31,5 @@
 - volcengine_maas
 - openai_api_compatible
 - deepseek
+- hunyuan
+- siliconflow

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

@@ -49,7 +49,7 @@ LLM_BASE_MODELS = [
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
                 ModelPropertyKey.MODE: LLMMode.CHAT.value,
-                ModelPropertyKey.CONTEXT_SIZE: 4096,
+                ModelPropertyKey.CONTEXT_SIZE: 16385,
             },
             parameter_rules=[
                 ParameterRule(
@@ -68,11 +68,25 @@ LLM_BASE_MODELS = [
                     name='frequency_penalty',
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
                 ),
-                _get_max_tokens(default=512, min_val=1, max_val=4096)
+                _get_max_tokens(default=512, min_val=1, max_val=4096),
+                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=0.002,
+                input=0.0005,
+                output=0.0015,
                 unit=0.001,
                 currency='USD',
             )
@@ -482,6 +496,158 @@ LLM_BASE_MODELS = [
             )
         )
     ),
+    AzureBaseModel(
+        base_model_name='gpt-4o',
+        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=5.00,
+                output=15.00,
+                unit=0.000001,
+                currency='USD',
+            )
+        )
+    ),
+    AzureBaseModel(
+        base_model_name='gpt-4o-2024-05-13',
+        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=5.00,
+                output=15.00,
+                unit=0.000001,
+                currency='USD',
+            )
+        )
+    ),
     AzureBaseModel(
         base_model_name='gpt-4-turbo',
         entity=AIModelEntity(
@@ -551,8 +717,8 @@ LLM_BASE_MODELS = [
                 ),
             ],
             pricing=PriceConfig(
-                input=0.001,
-                output=0.003,
+                input=0.01,
+                output=0.03,
                 unit=0.001,
                 currency='USD',
             )
@@ -627,8 +793,8 @@ LLM_BASE_MODELS = [
                 ),
             ],
             pricing=PriceConfig(
-                input=0.001,
-                output=0.003,
+                input=0.01,
+                output=0.03,
                 unit=0.001,
                 currency='USD',
             )

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

@@ -53,12 +53,24 @@ model_credential_schema:
       type: select
       required: true
       options:
+        - label:
+            en_US: 2024-05-01-preview
+          value: 2024-05-01-preview
+        - label:
+            en_US: 2024-04-01-preview
+          value: 2024-04-01-preview
+        - label:
+            en_US: 2024-03-01-preview
+          value: 2024-03-01-preview
         - label:
             en_US: 2024-02-15-preview
           value: 2024-02-15-preview
         - label:
             en_US: 2023-12-01-preview
           value: 2023-12-01-preview
+        - label:
+            en_US: '2024-02-01'
+          value: '2024-02-01'
       placeholder:
         zh_Hans: 在此选择您的 API 版本
         en_US: Select your API Version here
@@ -99,6 +111,18 @@ model_credential_schema:
           show_on:
             - variable: __model_type
               value: llm
+        - label:
+            en_US: gpt-4o
+          value: gpt-4o
+          show_on:
+            - variable: __model_type
+              value: llm
+        - label:
+            en_US: gpt-4o-2024-05-13
+          value: gpt-4o-2024-05-13
+          show_on:
+            - variable: __model_type
+              value: llm
         - label:
             en_US: gpt-4-turbo
           value: gpt-4-turbo

api/core/model_runtime/model_providers/baichuan/llm/baichuan2-53b.yaml

@@ -6,7 +6,7 @@ features:
   - agent-thought
 model_properties:
   mode: chat
-  context_size: 4000
+  context_size: 32000
 parameter_rules:
   - name: temperature
     use_template: temperature

api/core/model_runtime/model_providers/baichuan/llm/baichuan2-turbo.yaml

@@ -6,7 +6,7 @@ features:
   - agent-thought
 model_properties:
   mode: chat
-  context_size: 192000
+  context_size: 32000
 parameter_rules:
   - name: temperature
     use_template: temperature

api/core/model_runtime/model_providers/baichuan/llm/baichuan3-turbo-128k.yaml

@@ -0,0 +1,45 @@
+model: baichuan3-turbo-128k
+label:
+  en_US: Baichuan3-Turbo-128k
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 128000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    default: 8000
+    min: 1
+    max: 128000
+  - name: presence_penalty
+    use_template: presence_penalty
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    default: 1
+    min: 1
+    max: 2
+  - name: with_search_enhance
+    label:
+      zh_Hans: 搜索增强
+      en_US: Search Enhance
+    type: boolean
+    help:
+      zh_Hans: 允许模型自行进行外部搜索，以增强生成结果。
+      en_US: Allow the model to perform external search to enhance the generation results.
+    required: false

api/core/model_runtime/model_providers/baichuan/llm/baichuan3-turbo.yaml

@@ -0,0 +1,45 @@
+model: baichuan3-turbo
+label:
+  en_US: Baichuan3-Turbo
+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: 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
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    default: 8000
+    min: 1
+    max: 32000
+  - name: presence_penalty
+    use_template: presence_penalty
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    default: 1
+    min: 1
+    max: 2
+  - name: with_search_enhance
+    label:
+      zh_Hans: 搜索增强
+      en_US: Search Enhance
+    type: boolean
+    help:
+      zh_Hans: 允许模型自行进行外部搜索，以增强生成结果。
+      en_US: Allow the model to perform external search to enhance the generation results.
+    required: false

api/core/model_runtime/model_providers/baichuan/llm/baichuan4.yaml

@@ -0,0 +1,45 @@
+model: baichuan4
+label:
+  en_US: Baichuan4
+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: 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
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    default: 8000
+    min: 1
+    max: 32000
+  - name: presence_penalty
+    use_template: presence_penalty
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    default: 1
+    min: 1
+    max: 2
+  - name: with_search_enhance
+    label:
+      zh_Hans: 搜索增强
+      en_US: Search Enhance
+    type: boolean
+    help:
+      zh_Hans: 允许模型自行进行外部搜索，以增强生成结果。
+      en_US: Allow the model to perform external search to enhance the generation results.
+    required: false

api/core/model_runtime/model_providers/baichuan/llm/baichuan_turbo.py

@@ -51,26 +51,29 @@ class BaichuanModel:
             'baichuan2-turbo': 'Baichuan2-Turbo',
             'baichuan2-turbo-192k': 'Baichuan2-Turbo-192k',
             'baichuan2-53b': 'Baichuan2-53B',
+            'baichuan3-turbo': 'Baichuan3-Turbo',
+            'baichuan3-turbo-128k': 'Baichuan3-Turbo-128k',
+            'baichuan4': 'Baichuan4',
         }[model]
 
     def _handle_chat_generate_response(self, response) -> BaichuanMessage:
-            resp = response.json()
-            choices = resp.get('choices', [])
-            message = BaichuanMessage(content='', role='assistant')
-            for choice in choices:
-                message.content += choice['message']['content']
-                message.role = choice['message']['role']
-                if choice['finish_reason']:
-                    message.stop_reason = choice['finish_reason']
+        resp = response.json()
+        choices = resp.get('choices', [])
+        message = BaichuanMessage(content='', role='assistant')
+        for choice in choices:
+            message.content += choice['message']['content']
+            message.role = choice['message']['role']
+            if choice['finish_reason']:
+                message.stop_reason = choice['finish_reason']
 
-            if 'usage' in resp:
-                message.usage = {
-                    'prompt_tokens': resp['usage']['prompt_tokens'],
-                    'completion_tokens': resp['usage']['completion_tokens'],
-                    'total_tokens': resp['usage']['total_tokens'],
-                }
-            
-            return message
+        if 'usage' in resp:
+            message.usage = {
+                'prompt_tokens': resp['usage']['prompt_tokens'],
+                'completion_tokens': resp['usage']['completion_tokens'],
+                'total_tokens': resp['usage']['total_tokens'],
+            }
+
+        return message
     
     def _handle_chat_stream_generate_response(self, response) -> Generator:
         for line in response.iter_lines():
@@ -110,7 +113,8 @@ class BaichuanModel:
     def _build_parameters(self, model: str, stream: bool, messages: list[BaichuanMessage],
                                parameters: dict[str, Any]) \
         -> dict[str, Any]:
-        if model == 'baichuan2-turbo' or model == 'baichuan2-turbo-192k' or model == 'baichuan2-53b':
+        if (model == 'baichuan2-turbo' or model == 'baichuan2-turbo-192k' or model == 'baichuan2-53b'
+                or model == 'baichuan3-turbo' or model == 'baichuan3-turbo-128k'  or model == 'baichuan4'):
             prompt_messages = []
             for message in messages:
                 if message.role == BaichuanMessage.Role.USER.value or message.role == BaichuanMessage.Role._SYSTEM.value:
@@ -143,7 +147,8 @@ class BaichuanModel:
             raise BadRequestError(f"Unknown model: {model}")
         
     def _build_headers(self, model: str, data: dict[str, Any]) -> dict[str, Any]:
-        if model == 'baichuan2-turbo' or model == 'baichuan2-turbo-192k' or model == 'baichuan2-53b':
+        if (model == 'baichuan2-turbo' or model == 'baichuan2-turbo-192k' or model == 'baichuan2-53b'
+                or model == 'baichuan3-turbo' or model == 'baichuan3-turbo-128k'  or model == 'baichuan4'):
             # there is no secret key for turbo api
             return {
                 'Content-Type': 'application/json',
@@ -160,7 +165,8 @@ class BaichuanModel:
                  parameters: dict[str, Any], timeout: int) \
         -> Union[Generator, BaichuanMessage]:
         
-        if model == 'baichuan2-turbo' or model == 'baichuan2-turbo-192k' or model == 'baichuan2-53b':
+        if (model == 'baichuan2-turbo' or model == 'baichuan2-turbo-192k' or model == 'baichuan2-53b'
+                or model == 'baichuan3-turbo' or model == 'baichuan3-turbo-128k'  or model == 'baichuan4'):
             api_base = 'https://api.baichuan-ai.com/v1/chat/completions'
         else:
             raise BadRequestError(f"Unknown model: {model}")

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

@@ -7,6 +7,7 @@ from core.model_runtime.entities.message_entities import (
     PromptMessage,
     PromptMessageTool,
     SystemPromptMessage,
+    ToolPromptMessage,
     UserPromptMessage,
 )
 from core.model_runtime.errors.invoke import (
@@ -32,20 +33,21 @@ from core.model_runtime.model_providers.baichuan.llm.baichuan_turbo_errors impor
 
 
 class BaichuanLarguageModel(LargeLanguageModel):
-    def _invoke(self, model: str, credentials: dict, 
-                prompt_messages: list[PromptMessage], model_parameters: dict, 
-                tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None, 
+    def _invoke(self, model: str, credentials: dict,
+                prompt_messages: list[PromptMessage], model_parameters: dict,
+                tools: list[PromptMessageTool] | None = None, stop: list[str] | None = None,
                 stream: bool = True, user: str | None = None) \
             -> LLMResult | Generator:
         return self._generate(model=model, credentials=credentials, prompt_messages=prompt_messages,
-                                model_parameters=model_parameters, tools=tools, stop=stop, stream=stream, user=user)
+                              model_parameters=model_parameters, tools=tools, stop=stop, stream=stream, user=user)
 
     def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
                        tools: list[PromptMessageTool] | None = None) -> int:
         return self._num_tokens_from_messages(prompt_messages)
 
-    def _num_tokens_from_messages(self, messages: list[PromptMessage],) -> int:
+    def _num_tokens_from_messages(self, messages: list[PromptMessage], ) -> int:
         """Calculate num tokens for baichuan model"""
+
         def tokens(text: str):
             return BaichuanTokenizer._get_num_tokens(text)
 
@@ -85,9 +87,20 @@ class BaichuanLarguageModel(LargeLanguageModel):
         elif isinstance(message, SystemPromptMessage):
             message = cast(SystemPromptMessage, message)
             message_dict = {"role": "user", "content": message.content}
+        elif isinstance(message, ToolPromptMessage):
+            # copy from core/model_runtime/model_providers/anthropic/llm/llm.py
+            message = cast(ToolPromptMessage, message)
+            message_dict = {
+                "role": "user",
+                "content": [{
+                    "type": "tool_result",
+                    "tool_use_id": message.tool_call_id,
+                    "content": message.content
+                }]
+            }
         else:
             raise ValueError(f"Unknown message type {type(message)}")
-        
+
         return message_dict
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
@@ -106,13 +119,13 @@ class BaichuanLarguageModel(LargeLanguageModel):
         except Exception as e:
             raise CredentialsValidateFailedError(f"Invalid API key: {e}")
 
-    def _generate(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], 
-                 model_parameters: dict, tools: list[PromptMessageTool] | None = None, 
-                 stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
+    def _generate(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
+                  model_parameters: dict, tools: list[PromptMessageTool] | None = None,
+                  stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
             -> LLMResult | Generator:
         if tools is not None and len(tools) > 0:
             raise InvokeBadRequestError("Baichuan model doesn't support tools")
-        
+
         instance = BaichuanModel(
             api_key=credentials['api_key'],
             secret_key=credentials.get('secret_key', '')
@@ -129,11 +142,12 @@ class BaichuanLarguageModel(LargeLanguageModel):
         ]
 
         # invoke model
-        response = instance.generate(model=model, stream=stream, messages=messages, parameters=model_parameters, timeout=60)
+        response = instance.generate(model=model, stream=stream, messages=messages, parameters=model_parameters,
+                                     timeout=60)
 
         if stream:
             return self._handle_chat_generate_stream_response(model, prompt_messages, credentials, response)
-        
+
         return self._handle_chat_generate_response(model, prompt_messages, credentials, response)
 
     def _handle_chat_generate_response(self, model: str,
@@ -141,7 +155,9 @@ class BaichuanLarguageModel(LargeLanguageModel):
                                        credentials: dict,
                                        response: BaichuanMessage) -> LLMResult:
         # convert baichuan message to llm result
-        usage = self._calc_response_usage(model=model, credentials=credentials, prompt_tokens=response.usage['prompt_tokens'], completion_tokens=response.usage['completion_tokens'])
+        usage = self._calc_response_usage(model=model, credentials=credentials,
+                                          prompt_tokens=response.usage['prompt_tokens'],
+                                          completion_tokens=response.usage['completion_tokens'])
         return LLMResult(
             model=model,
             prompt_messages=prompt_messages,
@@ -158,7 +174,9 @@ class BaichuanLarguageModel(LargeLanguageModel):
                                               response: Generator[BaichuanMessage, None, None]) -> Generator:
         for message in response:
             if message.usage:
-                usage = self._calc_response_usage(model=model, credentials=credentials, prompt_tokens=message.usage['prompt_tokens'], completion_tokens=message.usage['completion_tokens'])
+                usage = self._calc_response_usage(model=model, credentials=credentials,
+                                                  prompt_tokens=message.usage['prompt_tokens'],
+                                                  completion_tokens=message.usage['completion_tokens'])
                 yield LLMResultChunk(
                     model=model,
                     prompt_messages=prompt_messages,

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

@@ -12,6 +12,7 @@
 - meta.llama3-70b-instruct-v1:0
 - meta.llama2-13b-chat-v1
 - meta.llama2-70b-chat-v1
+- mistral.mistral-small-2402-v1:0
 - 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/anthropic.claude-3-haiku-v1.yaml

@@ -51,7 +51,7 @@ parameter_rules:
       zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
       en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
 pricing:
-  input: '0.003'
-  output: '0.015'
+  input: '0.00025'
+  output: '0.00125'
   unit: '0.001'
   currency: USD

api/core/model_runtime/model_providers/bedrock/llm/anthropic.claude-3-sonnet-v1.yaml

@@ -50,7 +50,7 @@ parameter_rules:
       zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
       en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
 pricing:
-  input: '0.00025'
-  output: '0.00125'
+  input: '0.003'
+  output: '0.015'
   unit: '0.001'
   currency: USD

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

@@ -358,26 +358,25 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
 
         return message_dict
 
-    def get_num_tokens(self, model: str, credentials: dict, messages: list[PromptMessage] | str,
+    def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage] | str,
                        tools: Optional[list[PromptMessageTool]] = None) -> int:
         """
         Get number of tokens for given prompt messages
 
         :param model: model name
         :param credentials: model credentials
-        :param messages: prompt messages or message string
+        :param prompt_messages: prompt messages or message string
         :param tools: tools for tool calling
         :return:md = genai.GenerativeModel(model)
         """
         prefix = model.split('.')[0]
         model_name = model.split('.')[1]
-        if isinstance(messages, str):
-            prompt = messages
+        if isinstance(prompt_messages, str):
+            prompt = prompt_messages
         else:
-            prompt = self._convert_messages_to_prompt(messages, prefix, model_name)
+            prompt = self._convert_messages_to_prompt(prompt_messages, prefix, model_name)
 
         return self._get_num_tokens_by_gpt2(prompt)
-    
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
         """

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

@@ -0,0 +1,27 @@
+model: mistral.mistral-small-2402-v1:0
+label:
+  en_US: Mistral Small
+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: '0.001'
+  output: '0.03'
+  unit: '0.001'
+  currency: USD

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

@@ -1,3 +1,4 @@
 - amazon.titan-embed-text-v1
+- amazon.titan-embed-text-v2:0
 - cohere.embed-english-v3
 - cohere.embed-multilingual-v3

api/core/model_runtime/model_providers/bedrock/text_embedding/amazon.titan-embed-text-v1.yaml

@@ -4,5 +4,5 @@ model_properties:
   context_size: 8192
 pricing:
   input: '0.0001'
-  unit: '0.001'
+  unit: '0.0001'
   currency: USD

api/core/model_runtime/model_providers/bedrock/text_embedding/amazon.titan-embed-text-v2.yaml

@@ -0,0 +1,8 @@
+model: amazon.titan-embed-text-v2:0
+model_type: text-embedding
+model_properties:
+  context_size: 8192
+pricing:
+  input: '0.00002'
+  unit: '0.00001'
+  currency: USD

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

@@ -59,15 +59,15 @@ class BedrockTextEmbeddingModel(TextEmbeddingModel):
         model_prefix = model.split('.')[0]
          
         if model_prefix == "amazon" :
-           for text in texts:
-              body = {
+            for text in texts:
+                body = {
                  "inputText": text,
-              }
-              response_body = self._invoke_bedrock_embedding(model, bedrock_runtime, body)
-              embeddings.extend([response_body.get('embedding')])
-              token_usage += response_body.get('inputTextTokenCount')
-           logger.warning(f'Total Tokens: {token_usage}')
-           result = TextEmbeddingResult(
+                }
+                response_body = self._invoke_bedrock_embedding(model, bedrock_runtime, body)
+                embeddings.extend([response_body.get('embedding')])
+                token_usage += response_body.get('inputTextTokenCount')
+            logger.warning(f'Total Tokens: {token_usage}')
+            result = TextEmbeddingResult(
                 model=model,
                 embeddings=embeddings,
                 usage=self._calc_response_usage(
@@ -75,20 +75,20 @@ class BedrockTextEmbeddingModel(TextEmbeddingModel):
                     credentials=credentials,
                     tokens=token_usage
                 )
-           )
-           return result
-           
+            )
+            return result
+
         if model_prefix == "cohere" :
-           input_type = 'search_document' if len(texts) > 1 else 'search_query'
-           for text in texts:
-              body = {
+            input_type = 'search_document' if len(texts) > 1 else 'search_query'
+            for text in texts:
+                body = {
                  "texts": [text],
                  "input_type": input_type,
-              }
-              response_body = self._invoke_bedrock_embedding(model, bedrock_runtime, body)
-              embeddings.extend(response_body.get('embeddings'))
-              token_usage += len(text)
-           result = TextEmbeddingResult(
+                }
+                response_body = self._invoke_bedrock_embedding(model, bedrock_runtime, body)
+                embeddings.extend(response_body.get('embeddings'))
+                token_usage += len(text)
+            result = TextEmbeddingResult(
                 model=model,
                 embeddings=embeddings,
                 usage=self._calc_response_usage(
@@ -96,9 +96,9 @@ class BedrockTextEmbeddingModel(TextEmbeddingModel):
                     credentials=credentials,
                     tokens=token_usage
                 )
-           )
-           return result
-        
+            )
+            return result
+
         #others
         raise ValueError(f"Got unknown model prefix {model_prefix} when handling block response")
 
@@ -183,7 +183,7 @@ class BedrockTextEmbeddingModel(TextEmbeddingModel):
         )
 
         return usage
-    
+
     def _map_client_to_invoke_error(self, error_code: str, error_msg: str) -> type[InvokeError]:
         """
         Map client error to invoke error
@@ -212,9 +212,9 @@ class BedrockTextEmbeddingModel(TextEmbeddingModel):
         content_type = 'application/json'
         try:
             response = bedrock_runtime.invoke_model(
-                body=json.dumps(body), 
-                modelId=model, 
-                accept=accept, 
+                body=json.dumps(body),
+                modelId=model,
+                accept=accept,
                 contentType=content_type
             )
             response_body = json.loads(response.get('body').read().decode('utf-8'))

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-latest.yaml

@@ -0,0 +1,39 @@
+model: gemini-1.5-flash-latest
+label:
+  en_US: Gemini 1.5 Flash
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 1048576
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_tokens_to_sample
+    use_template: max_tokens
+    required: true
+    default: 8192
+    min: 1
+    max: 8192
+  - name: response_format
+    use_template: response_format
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

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

@@ -1,18 +1,22 @@
+import base64
 import json
 import logging
+import mimetypes
 from collections.abc import Generator
-from typing import Optional, Union
+from typing import Optional, Union, cast
 
 import google.ai.generativelanguage as glm
 import google.api_core.exceptions as exceptions
 import google.generativeai as genai
 import google.generativeai.client as client
+import requests
 from google.generativeai.types import ContentType, GenerateContentResponse, HarmBlockThreshold, HarmCategory
 from google.generativeai.types.content_types import to_part
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
 from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
+    ImagePromptMessageContent,
     PromptMessage,
     PromptMessageContentType,
     PromptMessageTool,
@@ -204,6 +208,7 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
             stream=stream,
             safety_settings=safety_settings,
             tools=self._convert_tools_to_glm_tool(tools) if tools else None,
+            request_options={"timeout": 600}
         )
 
         if stream:
@@ -360,11 +365,22 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
                 for c in message.content:
                     if c.type == PromptMessageContentType.TEXT:
                         glm_content['parts'].append(to_part(c.data))
-                    else:
-                        metadata, data = c.data.split(',', 1)
-                        mime_type = metadata.split(';', 1)[0].split(':')[1]
-                        blob = {"inline_data":{"mime_type":mime_type,"data":data}}
+                    elif c.type == PromptMessageContentType.IMAGE:
+                        message_content = cast(ImagePromptMessageContent, c)
+                        if message_content.data.startswith("data:"):
+                            metadata, base64_data = c.data.split(',', 1)
+                            mime_type = metadata.split(';', 1)[0].split(':')[1]
+                        else:
+                            # fetch image data from url
+                            try:
+                                image_content = requests.get(message_content.data).content
+                                mime_type, _ = mimetypes.guess_type(message_content.data)
+                                base64_data = base64.b64encode(image_content).decode('utf-8')
+                            except Exception as ex:
+                                raise ValueError(f"Failed to fetch image data from url {message_content.data}, {ex}")
+                        blob = {"inline_data":{"mime_type":mime_type,"data":base64_data}}
                         glm_content['parts'].append(blob)
+
             return glm_content
         elif isinstance(message, AssistantPromptMessage):
             glm_content = {
@@ -443,4 +459,4 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
                 exceptions.RequestRangeNotSatisfiable,
                 exceptions.Cancelled,
             ]
-        }
+        }