compatible query is None

Co-authored-by: jyong <jyong@dify.ai>

.github/pull_request_template.md

@@ -12,6 +12,8 @@ Please delete options that are not relevant.
 - [ ] New feature (non-breaking change which adds functionality)
 - [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
 - [ ] This change requires a documentation update, included: [Dify Document](https://github.com/langgenius/dify-docs)
+- [ ] Improvement, including but not limited to code refactoring, performance optimization, and UI/UX improvement
+- [ ] Dependency upgrade
 
 # How Has This Been Tested?
 

.github/workflows/api-workflow-tests.yaml

@@ -0,0 +1,31 @@
+name: Run Pytest
+
+on:
+  pull_request:
+    branches:
+      - main
+      - deploy/dev
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+
+    env:
+      MOCK_SWITCH: true
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.10'
+          cache: 'pip'
+          cache-dependency-path: ./api/requirements.txt
+
+      - name: Install dependencies
+        run: pip install -r ./api/requirements.txt
+
+      - name: Run pytest
+        run: pytest api/tests/integration_tests/workflow

.github/workflows/build-push.yml

@@ -1,17 +1,33 @@
-name: Build and Push API Image
+name: Build and Push API & Web
 
 on:
   push:
     branches:
-      - 'main'
-      - 'deploy/dev'
+      - "main"
+      - "deploy/dev"
+      - "feat/workflow"
   release:
-    types: [ published ]
+    types: [published]
+
+env:
+  DOCKERHUB_USER: ${{ secrets.DOCKERHUB_USER }}
+  DOCKERHUB_TOKEN: ${{ secrets.DOCKERHUB_TOKEN }}
+  DIFY_WEB_IMAGE_NAME: ${{ vars.DIFY_WEB_IMAGE_NAME || 'langgenius/dify-web' }}
+  DIFY_API_IMAGE_NAME: ${{ vars.DIFY_API_IMAGE_NAME || 'langgenius/dify-api' }}
 
 jobs:
   build-and-push:
     runs-on: ubuntu-latest
     if: github.event.pull_request.draft == false
+    strategy:
+      matrix:
+        include:
+          - service_name: "web"
+            image_name_env: "DIFY_WEB_IMAGE_NAME"
+            context: "web"
+          - service_name: "api"
+            image_name_env: "DIFY_API_IMAGE_NAME"
+            context: "api"
     steps:
       - name: Set up QEMU
         uses: docker/setup-qemu-action@v3
@@ -22,16 +38,16 @@ jobs:
       - name: Login to Docker Hub
         uses: docker/login-action@v2
         with:
-          username: ${{ secrets.DOCKERHUB_USER }}
-          password: ${{ secrets.DOCKERHUB_TOKEN }}
+          username: ${{ env.DOCKERHUB_USER }}
+          password: ${{ env.DOCKERHUB_TOKEN }}
 
       - name: Extract metadata (tags, labels) for Docker
         id: meta
         uses: docker/metadata-action@v5
         with:
-          images: langgenius/dify-api
+          images: ${{ env[matrix.image_name_env] }}
           tags: |
-            type=raw,value=latest,enable=${{ startsWith(github.ref, 'refs/tags/') }}
+            type=raw,value=latest,enable=${{ github.ref == 'refs/heads/main' && startsWith(github.ref, 'refs/tags/') }}
             type=ref,event=branch
             type=sha,enable=true,priority=100,prefix=,suffix=,format=long
             type=raw,value=${{ github.ref_name }},enable=${{ startsWith(github.ref, 'refs/tags/') }}
@@ -39,22 +55,11 @@ jobs:
       - name: Build and push
         uses: docker/build-push-action@v5
         with:
-          context: "{{defaultContext}}:api"
+          context: "{{defaultContext}}:${{ matrix.context }}"
           platforms: ${{ startsWith(github.ref, 'refs/tags/') && 'linux/amd64,linux/arm64' || 'linux/amd64' }}
-          build-args: |
-            COMMIT_SHA=${{ fromJSON(steps.meta.outputs.json).labels['org.opencontainers.image.revision'] }}
+          build-args: COMMIT_SHA=${{ fromJSON(steps.meta.outputs.json).labels['org.opencontainers.image.revision'] }}
           push: true
           tags: ${{ steps.meta.outputs.tags }}
           labels: ${{ steps.meta.outputs.labels }}
           cache-from: type=gha
           cache-to: type=gha,mode=max
-
-      - name: Deploy to server
-        if: github.ref == 'refs/heads/deploy/dev'
-        uses: appleboy/ssh-action@v0.1.8
-        with:
-          host: ${{ secrets.SSH_HOST }}
-          username: ${{ secrets.SSH_USER }}
-          key: ${{ secrets.SSH_PRIVATE_KEY }}
-          script: |
-            ${{ secrets.SSH_SCRIPT }}

.github/workflows/build-web-image.yml

@@ -1,60 +0,0 @@
-name: Build and Push WEB Image
-
-on:
-  push:
-    branches:
-      - 'main'
-      - 'deploy/dev'
-  release:
-    types: [ published ]
-
-jobs:
-  build-and-push:
-    runs-on: ubuntu-latest
-    if: github.event.pull_request.draft == false
-    steps:
-      - name: Set up QEMU
-        uses: docker/setup-qemu-action@v3
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Login to Docker Hub
-        uses: docker/login-action@v2
-        with:
-          username: ${{ secrets.DOCKERHUB_USER }}
-          password: ${{ secrets.DOCKERHUB_TOKEN }}
-
-      - name: Extract metadata (tags, labels) for Docker
-        id: meta
-        uses: docker/metadata-action@v5
-        with:
-          images: langgenius/dify-web
-          tags: |
-            type=raw,value=latest,enable=${{ startsWith(github.ref, 'refs/tags/') }}
-            type=ref,event=branch
-            type=sha,enable=true,priority=100,prefix=,suffix=,format=long
-            type=raw,value=${{ github.ref_name }},enable=${{ startsWith(github.ref, 'refs/tags/') }}
-
-      - name: Build and push
-        uses: docker/build-push-action@v5
-        with:
-          context: "{{defaultContext}}:web"
-          platforms: ${{ startsWith(github.ref, 'refs/tags/') && 'linux/amd64,linux/arm64' || 'linux/amd64' }}
-          build-args: |
-            COMMIT_SHA=${{ fromJSON(steps.meta.outputs.json).labels['org.opencontainers.image.revision'] }}
-          push: true
-          tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}
-          cache-from: type=gha
-          cache-to: type=gha,mode=max
-
-      - name: Deploy to server
-        if: github.ref == 'refs/heads/deploy/dev'
-        uses: appleboy/ssh-action@v0.1.8
-        with:
-          host: ${{ secrets.SSH_HOST }}
-          username: ${{ secrets.SSH_USER }}
-          key: ${{ secrets.SSH_PRIVATE_KEY }}
-          script: |
-            ${{ secrets.SSH_SCRIPT }}

.github/workflows/deploy-dev.yml

@@ -0,0 +1,24 @@
+name: Deploy Dev
+
+on:
+  workflow_run:
+    workflows: ["Build and Push API & Web"]
+    branches:
+      - "deploy/dev"
+    types:
+      - completed
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    if: |
+      github.event.workflow_run.conclusion == 'success'
+    steps:
+      - name: Deploy to server
+        uses: appleboy/ssh-action@v0.1.8
+        with:
+          host: ${{ secrets.SSH_HOST }}
+          username: ${{ secrets.SSH_USER }}
+          key: ${{ secrets.SSH_PRIVATE_KEY }}
+          script: |
+            ${{ vars.SSH_SCRIPT || secrets.SSH_SCRIPT }}

.github/workflows/style.yml

@@ -41,6 +41,8 @@ jobs:
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
 
       - name: Setup NodeJS
         uses: actions/setup-node@v4
@@ -60,11 +62,10 @@ jobs:
           yarn run lint
 
       - name: Super-linter
-        uses: super-linter/super-linter/slim@v5
+        uses: super-linter/super-linter/slim@v6
         env:
           BASH_SEVERITY: warning
           DEFAULT_BRANCH: main
-          ERROR_ON_MISSING_EXEC_BIT: true
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           IGNORE_GENERATED_FILES: true
           IGNORE_GITIGNORED_FILES: true

.gitignore

@@ -145,10 +145,14 @@ docker/volumes/db/data/*
 docker/volumes/redis/data/*
 docker/volumes/weaviate/*
 docker/volumes/qdrant/*
+docker/volumes/etcd/*
+docker/volumes/minio/*
+docker/volumes/milvus/*
 
 sdks/python-client/build
 sdks/python-client/dist
 sdks/python-client/dify_client.egg-info
 
 .vscode/*
-!.vscode/launch.json
+!.vscode/launch.json
+pyrightconfig.json

CONTRIBUTING.md

@@ -155,4 +155,4 @@ And that's it! Once your PR is merged, you will be featured as a contributor in
 
 ## Getting Help
 
-If you ever get stuck or got a burning question while contributing, simply shoot your queries our way via the related GitHub issue, or hop onto our [Discord](https://discord.gg/AhzKf7dNgk) for a quick chat. 
+If you ever get stuck or got a burning question while contributing, simply shoot your queries our way via the related GitHub issue, or hop onto our [Discord](https://discord.gg/8Tpq4AcN9c) for a quick chat. 

CONTRIBUTING_CN.md

@@ -152,4 +152,4 @@ Dify的后端使用Python编写，使用[Flask](https://flask.palletsprojects.co
 
 ## 获取帮助
 
-如果你在贡献过程中遇到困难或者有任何问题，可以通过相关的 GitHub 问题提出你的疑问，或者加入我们的 [Discord](https://discord.gg/AhzKf7dNgk) 进行快速交流。
+如果你在贡献过程中遇到困难或者有任何问题，可以通过相关的 GitHub 问题提出你的疑问，或者加入我们的 [Discord](https://discord.gg/8Tpq4AcN9c) 进行快速交流。

LICENSE

@@ -1,24 +1,26 @@
-# Dify Open Source License
+# Open Source License
 
-The Dify project is licensed under the Apache License 2.0, with the following additional conditions:
+Dify is licensed under the Apache License 2.0, with the following additional conditions:
 
-1. Dify is permitted to be used for commercialization, such as using Dify as a "backend-as-a-service" for your other applications, or delivering it to enterprises as an application development platform. However, when the following conditions are met, you must contact the producer to obtain a commercial license:
+1. Dify may be utilized commercially, including as a backend service for other applications or as an application development platform for enterprises. Should the conditions below be met, a commercial license must be obtained from the producer:
 
-a. Multi-tenant SaaS service: Unless explicitly authorized by Dify in writing, you may not use the Dify.AI source code to operate a multi-tenant SaaS service that is similar to the Dify.AI service edition.
-b. LOGO and copyright information: In the process of using Dify, you may not remove or modify the LOGO or copyright information in the Dify console.
+a. Multi-tenant SaaS service: Unless explicitly authorized by Dify in writing, you may not use the Dify source code to operate a multi-tenant environment. 
+    - Tenant Definition: Within the context of Dify, one tenant corresponds to one workspace. The workspace provides a separated area for each tenant's data and configurations.
+
+b. LOGO and copyright information: In the process of using Dify's frontend components, you may not remove or modify the LOGO or copyright information in the Dify console or applications. This restriction is inapplicable to uses of Dify that do not involve its frontend components.
 
 Please contact business@dify.ai by email to inquire about licensing matters.
 
-2. As a contributor, you should agree that your contributed code:
+2. As a contributor, you should agree that:
 
-a. The producer can adjust the open-source agreement to be more strict or relaxed.
-b. Can be used for commercial purposes, such as Dify's cloud business.
+a. The producer can adjust the open-source agreement to be more strict or relaxed as deemed necessary.
+b. Your contributed code may be used for commercial purposes, including but not limited to its cloud business operations.
 
-Apart from this, all other rights and restrictions follow the Apache License 2.0. If you need more detailed information, you can refer to the full version of Apache License 2.0.
+Apart from the specific conditions mentioned above, all other rights and restrictions follow the Apache License 2.0. Detailed information about the Apache License 2.0 can be found at http://www.apache.org/licenses/LICENSE-2.0.
 
 The interactive design of this product is protected by appearance patent.
 
-© 2023 LangGenius, Inc.
+© 2024 LangGenius, Inc.
 
 
 ----------

Makefile

@@ -0,0 +1,43 @@
+# Variables
+DOCKER_REGISTRY=langgenius
+WEB_IMAGE=$(DOCKER_REGISTRY)/dify-web
+API_IMAGE=$(DOCKER_REGISTRY)/dify-api
+VERSION=latest
+
+# Build Docker images
+build-web:
+	@echo "Building web Docker image: $(WEB_IMAGE):$(VERSION)..."
+	docker build -t $(WEB_IMAGE):$(VERSION) ./web
+	@echo "Web Docker image built successfully: $(WEB_IMAGE):$(VERSION)"
+
+build-api:
+	@echo "Building API Docker image: $(API_IMAGE):$(VERSION)..."
+	docker build -t $(API_IMAGE):$(VERSION) ./api
+	@echo "API Docker image built successfully: $(API_IMAGE):$(VERSION)"
+
+# Push Docker images
+push-web:
+	@echo "Pushing web Docker image: $(WEB_IMAGE):$(VERSION)..."
+	docker push $(WEB_IMAGE):$(VERSION)
+	@echo "Web Docker image pushed successfully: $(WEB_IMAGE):$(VERSION)"
+
+push-api:
+	@echo "Pushing API Docker image: $(API_IMAGE):$(VERSION)..."
+	docker push $(API_IMAGE):$(VERSION)
+	@echo "API Docker image pushed successfully: $(API_IMAGE):$(VERSION)"
+
+# Build all images
+build-all: build-web build-api
+
+# Push all images
+push-all: push-web push-api
+
+build-push-api: build-api push-api
+build-push-web: build-web push-web
+
+# Build and push all images
+build-push-all: build-all push-all
+	@echo "All Docker images have been built and pushed."
+
+# Phony targets
+.PHONY: build-web build-api push-web push-api build-all push-all build-push-all

README.md

@@ -22,23 +22,12 @@
 </p>
 
 <p align="center">
-  <a href="https://discord.com/events/1082486657678311454/1211724120996188220" target="_blank">
-   Dify.AI Upcoming Meetup Event [👉 Click to Join the Event Here 👈]
-  </a>
-   <ul align="center" style="text-decoration: none; list-style: none;">
-      <li> US EST: 09:00 (9:00 AM)</li>
-      <li> CET: 15:00 (3:00 PM)</li>
-      <li> CST:  22:00 (10:00 PM)</li>
-   </ul>
-</p>
-
-<p align="center">
-   <a href="https://dify.ai/blog/dify-ai-unveils-ai-agent-creating-gpts-and-assistants-with-various-llms" target="_blank">
-   Dify.AI Unveils AI Agent: Creating GPTs and Assistants with Various LLMs
+   <a href="https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6" target="_blank">
+   📌 Check out Dify Premium on AWS and deploy it to your own AWS VPC with one-click.
   </a>
 </p>
 
-**Dify** is an LLM application development platform that has helped built over **100,000** applications. It integrates BaaS and LLMOps, covering the essential tech stack for building generative AI-native applications, including a built-in RAG engine. Dify allows you to **deploy your own version of Assistants API and GPTs, based on any LLMs.**
+**Dify** is an open-source LLM app development platform. Dify's intuitive interface combines a RAG pipeline, AI workflow orchestration, agent capabilities, model management, observability features and more, letting you quickly go from prototype to production.
 
 ![](./images/demo.png)
 
@@ -48,6 +37,9 @@
 
 You can try out [Dify.AI Cloud](https://dify.ai) now. It provides all the capabilities of the self-deployed version, and includes 200 free requests to OpenAI GPT-3.5.
 
+### Looking to purchase via AWS?
+Check out [Dify Premium on AWS](https://aws.amazon.com/marketplace/pp/prodview-t22mebxzwjhu6) and deploy it to your own AWS VPC with one-click. 
+
 ## Dify vs. LangChain vs. Assistants API
 
 | Feature | Dify.AI | Assistants API | LangChain |
@@ -108,10 +100,12 @@ docker compose up -d
 
 After running, you can access the Dify dashboard in your browser at [http://localhost/install](http://localhost/install) and start the initialization installation process.
 
-### Helm Chart
+#### Deploy with Helm Chart
 
-Big thanks to @BorisPolonsky for providing us with a [Helm Chart](https://helm.sh/) version, which allows Dify to be deployed on Kubernetes.
-You can go to https://github.com/BorisPolonsky/dify-helm for deployment information.
+[Helm Chart](https://helm.sh/) version, which allows Dify to be deployed on Kubernetes.
+
+- [Helm Chart by @LeoQuote](https://github.com/douban/charts/tree/master/charts/dify)
+- [Helm Chart by @BorisPolonsky](https://github.com/BorisPolonsky/dify-helm)
 
 ### Configuration
 
@@ -128,6 +122,10 @@ For those who'd like to contribute code, see our [Contribution Guide](https://gi
 
 At the same time, please consider supporting Dify by sharing it on social media and at events and conferences.
 
+### Projects made by community
+
+- [Chatbot Chrome Extension by @charli117](https://github.com/langgenius/chatbot-chrome-extension)
+
 ### Contributors
 
 <a href="https://github.com/langgenius/dify/graphs/contributors">
@@ -136,7 +134,7 @@ At the same time, please consider supporting Dify by sharing it on social media
 
 ### Translations
 
-We are looking for contributors to help with translating Dify to languages other than Mandarin or English. If you are interested in helping, please see the [i18n README](https://github.com/langgenius/dify/blob/main/web/i18n/README_EN.md) for more information, and leave us a comment in the `global-users` channel of our [Discord Community Server](https://discord.gg/AhzKf7dNgk).
+We are looking for contributors to help with translating Dify to languages other than Mandarin or English. If you are interested in helping, please see the [i18n README](https://github.com/langgenius/dify/blob/main/web/i18n/README.md) for more information, and leave us a comment in the `global-users` channel of our [Discord Community Server](https://discord.gg/8Tpq4AcN9c).
 
 ## Community & Support
 

README_CN.md

@@ -94,10 +94,12 @@ docker compose up -d
 
 运行后，可以在浏览器上访问 [http://localhost/install](http://localhost/install) 进入 Dify 控制台并开始初始化安装操作。
 
-### Helm Chart
+#### 使用 Helm Chart 部署
 
-非常感谢 @BorisPolonsky 为我们提供了一个 [Helm Chart](https://helm.sh/) 版本，可以在 Kubernetes 上部署 Dify。
-您可以前往 https://github.com/BorisPolonsky/dify-helm 来获取部署信息。
+使用 [Helm Chart](https://helm.sh/) 版本，可以在 Kubernetes 上部署 Dify。
+
+- [Helm Chart by @LeoQuote](https://github.com/douban/charts/tree/master/charts/dify)
+- [Helm Chart by @BorisPolonsky](https://github.com/BorisPolonsky/dify-helm)
 
 ### 配置
 

api/.env.example

@@ -39,7 +39,7 @@ DB_DATABASE=dify
 
 # Storage configuration
 # use for store upload files, private keys...
-# storage type: local, s3
+# storage type: local, s3, azure-blob
 STORAGE_TYPE=local
 STORAGE_LOCAL_PATH=storage
 S3_ENDPOINT=https://your-bucket-name.storage.s3.clooudflare.com
@@ -47,6 +47,11 @@ S3_BUCKET_NAME=your-bucket-name
 S3_ACCESS_KEY=your-access-key
 S3_SECRET_KEY=your-secret-key
 S3_REGION=your-region
+# Azure Blob Storage configuration
+AZURE_BLOB_ACCOUNT_NAME=your-account-name
+AZURE_BLOB_ACCOUNT_KEY=your-account-key
+AZURE_BLOB_CONTAINER_NAME=yout-container-name
+AZURE_BLOB_ACCOUNT_URL=https://<your_account_name>.blob.core.windows.net
 
 # CORS configuration
 WEB_API_CORS_ALLOW_ORIGINS=http://127.0.0.1:3000,*
@@ -82,7 +87,7 @@ UPLOAD_IMAGE_FILE_SIZE_LIMIT=10
 MULTIMODAL_SEND_IMAGE_FORMAT=base64
 
 # Mail configuration, support: resend, smtp
-MAIL_TYPE=resend
+MAIL_TYPE=
 MAIL_DEFAULT_SEND_FROM=no-reply <no-reply@dify.ai>
 RESEND_API_KEY=
 RESEND_API_URL=https://api.resend.com
@@ -131,4 +136,16 @@ UNSTRUCTURED_API_URL=
 SSRF_PROXY_HTTP_URL=
 SSRF_PROXY_HTTPS_URL=
 
-BATCH_UPLOAD_LIMIT=10
+BATCH_UPLOAD_LIMIT=10
+KEYWORD_DATA_SOURCE_TYPE=database
+
+# CODE EXECUTION CONFIGURATION
+CODE_EXECUTION_ENDPOINT=http://127.0.0.1:8194
+CODE_EXECUTION_API_KEY=dify-sandbox
+CODE_MAX_NUMBER=9223372036854775807
+CODE_MIN_NUMBER=-9223372036854775808
+CODE_MAX_STRING_LENGTH=80000
+TEMPLATE_TRANSFORM_MAX_LENGTH=80000
+CODE_MAX_STRING_ARRAY_LENGTH=30
+CODE_MAX_OBJECT_ARRAY_LENGTH=30
+CODE_MAX_NUMBER_ARRAY_LENGTH=1000

api/README.md

@@ -5,7 +5,7 @@
 1. Start the docker-compose stack
 
    The backend require some middleware, including PostgreSQL, Redis, and Weaviate, which can be started together using `docker-compose`.
-   
+
    ```bash
    cd ../docker
    docker-compose -f docker-compose.middleware.yaml -p dify up -d
@@ -15,7 +15,7 @@
 3. Generate a `SECRET_KEY` in the `.env` file.
 
    ```bash
-   openssl rand -base64 42
+   sed -i "/^SECRET_KEY=/c\SECRET_KEY=$(openssl rand -base64 42)" .env
    ```
 3.5 If you use annaconda, create a new environment and activate it
    ```bash
@@ -46,7 +46,7 @@
    ```
    pip install -r requirements.txt --upgrade --force-reinstall
    ```
-   
+
 6. Start backend:
    ```bash
    flask run --host 0.0.0.0 --port=5001 --debug

api/app.py

@@ -26,6 +26,7 @@ from config import CloudEditionConfig, Config
 from extensions import (
     ext_celery,
     ext_code_based_extension,
+    ext_compress,
     ext_database,
     ext_hosting_provider,
     ext_login,
@@ -96,6 +97,7 @@ def create_app(test_config=None) -> Flask:
 def initialize_extensions(app):
     # Since the application instance is now created, pass it to each Flask
     # extension instance to bind it to the Flask application instance (app)
+    ext_compress.init_app(app)
     ext_code_based_extension.init()
     ext_database.init_app(app)
     ext_migrate.init(app, db)

api/commands.py

@@ -15,7 +15,7 @@ from libs.rsa import generate_key_pair
 from models.account import Tenant
 from models.dataset import Dataset, DatasetCollectionBinding, DocumentSegment
 from models.dataset import Document as DatasetDocument
-from models.model import Account
+from models.model import Account, App, AppAnnotationSetting, AppMode, Conversation, MessageAnnotation
 from models.provider import Provider, ProviderModel
 
 
@@ -109,28 +109,138 @@ def reset_encrypt_key_pair():
         click.echo(click.style('Sorry, only support SELF_HOSTED mode.', fg='red'))
         return
 
-    tenant = db.session.query(Tenant).first()
-    if not tenant:
-        click.echo(click.style('Sorry, no workspace found. Please enter /install to initialize.', fg='red'))
-        return
+    tenants = db.session.query(Tenant).all()
+    for tenant in tenants:
+        if not tenant:
+            click.echo(click.style('Sorry, no workspace found. Please enter /install to initialize.', fg='red'))
+            return
 
-    tenant.encrypt_public_key = generate_key_pair(tenant.id)
+        tenant.encrypt_public_key = generate_key_pair(tenant.id)
 
-    db.session.query(Provider).filter(Provider.provider_type == 'custom').delete()
-    db.session.query(ProviderModel).delete()
-    db.session.commit()
+        db.session.query(Provider).filter(Provider.provider_type == 'custom', Provider.tenant_id == tenant.id).delete()
+        db.session.query(ProviderModel).filter(ProviderModel.tenant_id == tenant.id).delete()
+        db.session.commit()
 
-    click.echo(click.style('Congratulations! '
-                           'the asymmetric key pair of workspace {} has been reset.'.format(tenant.id), fg='green'))
+        click.echo(click.style('Congratulations! '
+                               'the asymmetric key pair of workspace {} has been reset.'.format(tenant.id), fg='green'))
 
 
 @click.command('vdb-migrate', help='migrate vector db.')
-def vdb_migrate():
+@click.option('--scope', default='all', prompt=False, help='The scope of vector database to migrate, Default is All.')
+def vdb_migrate(scope: str):
+    if scope in ['knowledge', 'all']:
+        migrate_knowledge_vector_database()
+    if scope in ['annotation', 'all']:
+        migrate_annotation_vector_database()
+
+
+def migrate_annotation_vector_database():
+    """
+    Migrate annotation datas to target vector database .
+    """
+    click.echo(click.style('Start migrate annotation data.', fg='green'))
+    create_count = 0
+    skipped_count = 0
+    total_count = 0
+    page = 1
+    while True:
+        try:
+            # get apps info
+            apps = db.session.query(App).filter(
+                App.status == 'normal'
+            ).order_by(App.created_at.desc()).paginate(page=page, per_page=50)
+        except NotFound:
+            break
+
+        page += 1
+        for app in apps:
+            total_count = total_count + 1
+            click.echo(f'Processing the {total_count} app {app.id}. '
+                       + f'{create_count} created, {skipped_count} skipped.')
+            try:
+                click.echo('Create app annotation index: {}'.format(app.id))
+                app_annotation_setting = db.session.query(AppAnnotationSetting).filter(
+                    AppAnnotationSetting.app_id == app.id
+                ).first()
+
+                if not app_annotation_setting:
+                    skipped_count = skipped_count + 1
+                    click.echo('App annotation setting is disabled: {}'.format(app.id))
+                    continue
+                # get dataset_collection_binding info
+                dataset_collection_binding = db.session.query(DatasetCollectionBinding).filter(
+                    DatasetCollectionBinding.id == app_annotation_setting.collection_binding_id
+                ).first()
+                if not dataset_collection_binding:
+                    click.echo('App annotation collection binding is not exist: {}'.format(app.id))
+                    continue
+                annotations = db.session.query(MessageAnnotation).filter(MessageAnnotation.app_id == app.id).all()
+                dataset = Dataset(
+                    id=app.id,
+                    tenant_id=app.tenant_id,
+                    indexing_technique='high_quality',
+                    embedding_model_provider=dataset_collection_binding.provider_name,
+                    embedding_model=dataset_collection_binding.model_name,
+                    collection_binding_id=dataset_collection_binding.id
+                )
+                documents = []
+                if annotations:
+                    for annotation in annotations:
+                        document = Document(
+                            page_content=annotation.question,
+                            metadata={
+                                "annotation_id": annotation.id,
+                                "app_id": app.id,
+                                "doc_id": annotation.id
+                            }
+                        )
+                        documents.append(document)
+
+                vector = Vector(dataset, attributes=['doc_id', 'annotation_id', 'app_id'])
+                click.echo(f"Start to migrate annotation, app_id: {app.id}.")
+
+                try:
+                    vector.delete()
+                    click.echo(
+                        click.style(f'Successfully delete vector index for app: {app.id}.',
+                                    fg='green'))
+                except Exception as e:
+                    click.echo(
+                        click.style(f'Failed to delete vector index for app {app.id}.',
+                                    fg='red'))
+                    raise e
+                if documents:
+                    try:
+                        click.echo(click.style(
+                            f'Start to created vector index with {len(documents)} annotations for app {app.id}.',
+                            fg='green'))
+                        vector.create(documents)
+                        click.echo(
+                            click.style(f'Successfully created vector index for app {app.id}.', fg='green'))
+                    except Exception as e:
+                        click.echo(click.style(f'Failed to created vector index for app {app.id}.', fg='red'))
+                        raise e
+                click.echo(f'Successfully migrated app annotation {app.id}.')
+                create_count += 1
+            except Exception as e:
+                click.echo(
+                    click.style('Create app annotation index error: {} {}'.format(e.__class__.__name__, str(e)),
+                                fg='red'))
+                continue
+
+    click.echo(
+        click.style(f'Congratulations! Create {create_count} app annotation indexes, and skipped {skipped_count} apps.',
+                    fg='green'))
+
+
+def migrate_knowledge_vector_database():
     """
     Migrate vector database datas to target vector database .
     """
     click.echo(click.style('Start migrate vector db.', fg='green'))
     create_count = 0
+    skipped_count = 0
+    total_count = 0
     config = current_app.config
     vector_type = config.get('VECTOR_STORE')
     page = 1
@@ -143,14 +253,19 @@ def vdb_migrate():
 
         page += 1
         for dataset in datasets:
+            total_count = total_count + 1
+            click.echo(f'Processing the {total_count} dataset {dataset.id}. '
+                       + f'{create_count} created, {skipped_count} skipped.')
             try:
                 click.echo('Create dataset vdb index: {}'.format(dataset.id))
                 if dataset.index_struct_dict:
                     if dataset.index_struct_dict['type'] == vector_type:
+                        skipped_count = skipped_count + 1
                         continue
+                collection_name = ''
                 if vector_type == "weaviate":
                     dataset_id = dataset.id
-                    collection_name = "Vector_index_" + dataset_id.replace("-", "_") + '_Node'
+                    collection_name = Dataset.gen_collection_name_by_id(dataset_id)
                     index_struct_dict = {
                         "type": 'weaviate',
                         "vector_store": {"class_prefix": collection_name}
@@ -167,7 +282,7 @@ def vdb_migrate():
                             raise ValueError('Dataset Collection Bindings is not exist!')
                     else:
                         dataset_id = dataset.id
-                        collection_name = "Vector_index_" + dataset_id.replace("-", "_") + '_Node'
+                        collection_name = Dataset.gen_collection_name_by_id(dataset_id)
                     index_struct_dict = {
                         "type": 'qdrant',
                         "vector_store": {"class_prefix": collection_name}
@@ -176,7 +291,7 @@ def vdb_migrate():
 
                 elif vector_type == "milvus":
                     dataset_id = dataset.id
-                    collection_name = "Vector_index_" + dataset_id.replace("-", "_") + '_Node'
+                    collection_name = Dataset.gen_collection_name_by_id(dataset_id)
                     index_struct_dict = {
                         "type": 'milvus',
                         "vector_store": {"class_prefix": collection_name}
@@ -186,11 +301,17 @@ def vdb_migrate():
                     raise ValueError(f"Vector store {config.get('VECTOR_STORE')} is not supported.")
 
                 vector = Vector(dataset)
-                click.echo(f"vdb_migrate {dataset.id}")
+                click.echo(f"Start to migrate dataset {dataset.id}.")
 
                 try:
                     vector.delete()
+                    click.echo(
+                        click.style(f'Successfully delete vector index {collection_name} for dataset {dataset.id}.',
+                                    fg='green'))
                 except Exception as e:
+                    click.echo(
+                        click.style(f'Failed to delete vector index {collection_name} for dataset {dataset.id}.',
+                                    fg='red'))
                     raise e
 
                 dataset_documents = db.session.query(DatasetDocument).filter(
@@ -201,6 +322,7 @@ def vdb_migrate():
                 ).all()
 
                 documents = []
+                segments_count = 0
                 for dataset_document in dataset_documents:
                     segments = db.session.query(DocumentSegment).filter(
                         DocumentSegment.document_id == dataset_document.id,
@@ -220,15 +342,22 @@ def vdb_migrate():
                         )
 
                         documents.append(document)
+                        segments_count = segments_count + 1
 
                 if documents:
                     try:
+                        click.echo(click.style(
+                            f'Start to created vector index with {len(documents)} documents of {segments_count} segments for dataset {dataset.id}.',
+                            fg='green'))
                         vector.create(documents)
+                        click.echo(
+                            click.style(f'Successfully created vector index for dataset {dataset.id}.', fg='green'))
                     except Exception as e:
+                        click.echo(click.style(f'Failed to created vector index for dataset {dataset.id}.', fg='red'))
                         raise e
-                click.echo(f"Dataset {dataset.id} create successfully.")
                 db.session.add(dataset)
                 db.session.commit()
+                click.echo(f'Successfully migrated dataset {dataset.id}.')
                 create_count += 1
             except Exception as e:
                 db.session.rollback()
@@ -237,7 +366,70 @@ def vdb_migrate():
                                 fg='red'))
                 continue
 
-    click.echo(click.style('Congratulations! Create {} dataset indexes.'.format(create_count), fg='green'))
+    click.echo(
+        click.style(f'Congratulations! Create {create_count} dataset indexes, and skipped {skipped_count} datasets.',
+                    fg='green'))
+
+
+@click.command('convert-to-agent-apps', help='Convert Agent Assistant to Agent App.')
+def convert_to_agent_apps():
+    """
+    Convert Agent Assistant to Agent App.
+    """
+    click.echo(click.style('Start convert to agent apps.', fg='green'))
+
+    proceeded_app_ids = []
+
+    while True:
+        # fetch first 1000 apps
+        sql_query = """SELECT a.id AS id FROM apps a
+            INNER JOIN app_model_configs am ON a.app_model_config_id=am.id
+            WHERE a.mode = 'chat' 
+            AND am.agent_mode is not null 
+            AND (
+				am.agent_mode like '%"strategy": "function_call"%' 
+                OR am.agent_mode  like '%"strategy": "react"%'
+			) 
+            AND (
+				am.agent_mode like '{"enabled": true%' 
+                OR am.agent_mode like '{"max_iteration": %'
+			) ORDER BY a.created_at DESC LIMIT 1000
+        """
+
+        with db.engine.begin() as conn:
+            rs = conn.execute(db.text(sql_query))
+
+            apps = []
+            for i in rs:
+                app_id = str(i.id)
+                if app_id not in proceeded_app_ids:
+                    proceeded_app_ids.append(app_id)
+                    app = db.session.query(App).filter(App.id == app_id).first()
+                    apps.append(app)
+
+            if len(apps) == 0:
+                break
+
+        for app in apps:
+            click.echo('Converting app: {}'.format(app.id))
+
+            try:
+                app.mode = AppMode.AGENT_CHAT.value
+                db.session.commit()
+
+                # update conversation mode to agent
+                db.session.query(Conversation).filter(Conversation.app_id == app.id).update(
+                    {Conversation.mode: AppMode.AGENT_CHAT.value}
+                )
+
+                db.session.commit()
+                click.echo(click.style('Converted app: {}'.format(app.id), fg='green'))
+            except Exception as e:
+                click.echo(
+                    click.style('Convert app error: {} {}'.format(e.__class__.__name__,
+                                                                  str(e)), fg='red'))
+
+    click.echo(click.style('Congratulations! Converted {} agent apps.'.format(len(proceeded_app_ids)), fg='green'))
 
 
 def register_commands(app):
@@ -245,3 +437,4 @@ def register_commands(app):
     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)

api/config.py

@@ -22,11 +22,13 @@ DEFAULTS = {
     'SERVICE_API_URL': 'https://api.dify.ai',
     'APP_WEB_URL': 'https://udify.app',
     'FILES_URL': '',
+    'S3_ADDRESS_STYLE': 'auto',
     'STORAGE_TYPE': 'local',
     'STORAGE_LOCAL_PATH': 'storage',
     'CHECK_UPDATE_URL': 'https://updates.dify.ai',
     'DEPLOY_ENV': 'PRODUCTION',
     'SQLALCHEMY_POOL_SIZE': 30,
+    'SQLALCHEMY_MAX_OVERFLOW': 10,
     'SQLALCHEMY_POOL_RECYCLE': 3600,
     'SQLALCHEMY_ECHO': 'False',
     'SENTRY_TRACES_SAMPLE_RATE': 1.0,
@@ -48,6 +50,8 @@ DEFAULTS = {
     'HOSTED_ANTHROPIC_PAID_ENABLED': 'False',
     'HOSTED_MODERATION_ENABLED': 'False',
     'HOSTED_MODERATION_PROVIDERS': '',
+    'HOSTED_FETCH_APP_TEMPLATES_MODE': 'remote',
+    'HOSTED_FETCH_APP_TEMPLATES_REMOTE_DOMAIN': 'https://tmpl.dify.ai',
     'CLEAN_DAY_SETTING': 30,
     'UPLOAD_FILE_SIZE_LIMIT': 15,
     'UPLOAD_FILE_BATCH_LIMIT': 5,
@@ -59,7 +63,11 @@ DEFAULTS = {
     'CAN_REPLACE_LOGO': 'False',
     'ETL_TYPE': 'dify',
     'KEYWORD_STORE': 'jieba',
-    'BATCH_UPLOAD_LIMIT': 20
+    'BATCH_UPLOAD_LIMIT': 20,
+    'CODE_EXECUTION_ENDPOINT': '',
+    'CODE_EXECUTION_API_KEY': '',
+    'TOOL_ICON_CACHE_MAX_AGE': 3600,
+    'KEYWORD_DATA_SOURCE_TYPE': 'database',
 }
 
 
@@ -90,7 +98,7 @@ class Config:
         # ------------------------
         # General Configurations.
         # ------------------------
-        self.CURRENT_VERSION = "0.5.7"
+        self.CURRENT_VERSION = "0.6.0-preview-workflow.2"
         self.COMMIT_SHA = get_env('COMMIT_SHA')
         self.EDITION = "SELF_HOSTED"
         self.DEPLOY_ENV = get_env('DEPLOY_ENV')
@@ -146,6 +154,7 @@ class Config:
         self.SQLALCHEMY_DATABASE_URI = f"postgresql://{db_credentials['DB_USERNAME']}:{db_credentials['DB_PASSWORD']}@{db_credentials['DB_HOST']}:{db_credentials['DB_PORT']}/{db_credentials['DB_DATABASE']}{db_extras}"
         self.SQLALCHEMY_ENGINE_OPTIONS = {
             'pool_size': int(get_env('SQLALCHEMY_POOL_SIZE')),
+            'max_overflow': int(get_env('SQLALCHEMY_MAX_OVERFLOW')),
             'pool_recycle': int(get_env('SQLALCHEMY_POOL_RECYCLE'))
         }
 
@@ -180,6 +189,11 @@ class Config:
         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')
+        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')
 
         # ------------------------
         # Vector Store Configurations.
@@ -286,6 +300,10 @@ class Config:
         self.HOSTED_MODERATION_ENABLED = get_bool_env('HOSTED_MODERATION_ENABLED')
         self.HOSTED_MODERATION_PROVIDERS = get_env('HOSTED_MODERATION_PROVIDERS')
 
+        # fetch app templates mode, remote, builtin, db(only for dify SaaS), default: remote
+        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.BILLING_ENABLED = get_bool_env('BILLING_ENABLED')
@@ -293,6 +311,13 @@ class Config:
 
         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')
 
 class CloudEditionConfig(Config):
 

api/constants/languages.py

@@ -1,8 +1,6 @@
-import json
 
-from models.model import AppModelConfig
 
-languages = ['en-US', 'zh-Hans', 'pt-BR', 'es-ES', 'fr-FR', 'de-DE', 'ja-JP', 'ko-KR', 'ru-RU', 'it-IT', 'uk-UA']
+languages = ['en-US', 'zh-Hans', 'pt-BR', 'es-ES', 'fr-FR', 'de-DE', 'ja-JP', 'ko-KR', 'ru-RU', 'it-IT', 'uk-UA', 'vi-VN']
 
 language_timezone_mapping = {
     'en-US': 'America/New_York',
@@ -16,6 +14,7 @@ language_timezone_mapping = {
     'ru-RU': 'Europe/Moscow',
     'it-IT': 'Europe/Rome',
     'uk-UA': 'Europe/Kyiv',
+    'vi-VN': 'Asia/Ho_Chi_Minh',
 }
 
 
@@ -26,439 +25,3 @@ def supported_language(lang):
     error = ('{lang} is not a valid language.'
              .format(lang=lang))
     raise ValueError(error)
-
-
-user_input_form_template = {
-    "en-US": [
-        {
-            "paragraph": {
-                "label": "Query",
-                "variable": "default_input",
-                "required": False,
-                "default": ""
-            }
-        }
-    ],
-    "zh-Hans": [
-        {
-            "paragraph": {
-                "label": "查询内容",
-                "variable": "default_input",
-                "required": False,
-                "default": ""
-            }
-        }
-    ],
-    "pt-BR": [
-        {
-            "paragraph": {
-                "label": "Consulta",
-                "variable": "default_input",
-                "required": False,
-                "default": ""
-            }
-        }
-    ],
-    "es-ES": [
-        {
-            "paragraph": {
-                "label": "Consulta",
-                "variable": "default_input",
-                "required": False,
-                "default": ""
-            }
-        }
-    ],
-    "ua-UK": [
-        {
-            "paragraph": {
-                "label": "Запит",
-                "variable": "default_input",
-                "required": False,
-                "default": ""
-            }
-        }
-    ],
-}
-
-demo_model_templates = {
-    'en-US': [
-        {
-            'name': 'Translation Assistant',
-            'icon': '',
-            'icon_background': '',
-            'description': 'A multilingual translator that provides translation capabilities in multiple languages, translating user input into the language they need.',
-            'mode': 'completion',
-            'model_config': AppModelConfig(
-                provider='openai',
-                model_id='gpt-3.5-turbo-instruct',
-                configs={
-                    'prompt_template': "Please translate the following text into {{target_language}}:\n",
-                    'prompt_variables': [
-                        {
-                            "key": "target_language",
-                            "name": "Target Language",
-                            "description": "The language you want to translate into.",
-                            "type": "select",
-                            "default": "Chinese",
-                            'options': [
-                                'Chinese',
-                                'English',
-                                'Japanese',
-                                'French',
-                                'Russian',
-                                'German',
-                                'Spanish',
-                                'Korean',
-                                'Italian',
-                            ]
-                        }
-                    ],
-                    'completion_params': {
-                        'max_token': 1000,
-                        'temperature': 0,
-                        'top_p': 0,
-                        'presence_penalty': 0.1,
-                        'frequency_penalty': 0.1,
-                    }
-                },
-                opening_statement='',
-                suggested_questions=None,
-                pre_prompt="Please translate the following text into {{target_language}}:\n{{query}}\ntranslate:",
-                model=json.dumps({
-                    "provider": "openai",
-                    "name": "gpt-3.5-turbo-instruct",
-                    "mode": "completion",
-                    "completion_params": {
-                        "max_tokens": 1000,
-                        "temperature": 0,
-                        "top_p": 0,
-                        "presence_penalty": 0.1,
-                        "frequency_penalty": 0.1
-                    }
-                }),
-                user_input_form=json.dumps([
-                    {
-                        "select": {
-                            "label": "Target Language",
-                            "variable": "target_language",
-                            "description": "The language you want to translate into.",
-                            "default": "Chinese",
-                            "required": True,
-                            'options': [
-                                'Chinese',
-                                'English',
-                                'Japanese',
-                                'French',
-                                'Russian',
-                                'German',
-                                'Spanish',
-                                'Korean',
-                                'Italian',
-                            ]
-                        }
-                    }, {
-                        "paragraph": {
-                            "label": "Query",
-                            "variable": "query",
-                            "required": True,
-                            "default": ""
-                        }
-                    }
-                ])
-            )
-        },
-        {
-            'name': 'AI Front-end Interviewer',
-            'icon': '',
-            'icon_background': '',
-            'description': 'A simulated front-end interviewer that tests the skill level of front-end development through questioning.',
-            'mode': 'chat',
-            'model_config': AppModelConfig(
-                provider='openai',
-                model_id='gpt-3.5-turbo',
-                configs={
-                    'introduction': 'Hi, welcome to our interview. I am the interviewer for this technology company, and I will test your web front-end development skills. Next, I will ask you some technical questions. Please answer them as thoroughly as possible. ',
-                    'prompt_template': "You will play the role of an interviewer for a technology company, examining the user's web front-end development skills and posing 5-10 sharp technical questions.\n\nPlease note:\n- Only ask one question at a time.\n- After the user answers a question, ask the next question directly, without trying to correct any mistakes made by the candidate.\n- If you think the user has not answered correctly for several consecutive questions, ask fewer questions.\n- After asking the last question, you can ask this question: Why did you leave your last job? After the user answers this question, please express your understanding and support.\n",
-                    'prompt_variables': [],
-                    'completion_params': {
-                        'max_token': 300,
-                        'temperature': 0.8,
-                        'top_p': 0.9,
-                        'presence_penalty': 0.1,
-                        'frequency_penalty': 0.1,
-                    }
-                },
-                opening_statement='Hi, welcome to our interview. I am the interviewer for this technology company, and I will test your web front-end development skills. Next, I will ask you some technical questions. Please answer them as thoroughly as possible. ',
-                suggested_questions=None,
-                pre_prompt="You will play the role of an interviewer for a technology company, examining the user's web front-end development skills and posing 5-10 sharp technical questions.\n\nPlease note:\n- Only ask one question at a time.\n- After the user answers a question, ask the next question directly, without trying to correct any mistakes made by the candidate.\n- If you think the user has not answered correctly for several consecutive questions, ask fewer questions.\n- After asking the last question, you can ask this question: Why did you leave your last job? After the user answers this question, please express your understanding and support.\n",
-                model=json.dumps({
-                    "provider": "openai",
-                    "name": "gpt-3.5-turbo",
-                    "mode": "chat",
-                    "completion_params": {
-                        "max_tokens": 300,
-                        "temperature": 0.8,
-                        "top_p": 0.9,
-                        "presence_penalty": 0.1,
-                        "frequency_penalty": 0.1
-                    }
-                }),
-                user_input_form=None
-            )
-        }
-    ],
-
-    'zh-Hans': [
-        {
-            'name': '翻译助手',
-            'icon': '',
-            'icon_background': '',
-            'description': '一个多语言翻译器，提供多种语言翻译能力，将用户输入的文本翻译成他们需要的语言。',
-            'mode': 'completion',
-            'model_config': AppModelConfig(
-                provider='openai',
-                model_id='gpt-3.5-turbo-instruct',
-                configs={
-                    'prompt_template': "请将以下文本翻译为{{target_language}}:\n",
-                    'prompt_variables': [
-                        {
-                            "key": "target_language",
-                            "name": "目标语言",
-                            "description": "翻译的目标语言",
-                            "type": "select",
-                            "default": "中文",
-                            "options": [
-                                "中文",
-                                "英文",
-                                "日语",
-                                "法语",
-                                "俄语",
-                                "德语",
-                                "西班牙语",
-                                "韩语",
-                                "意大利语",
-                            ]
-                        }
-                    ],
-                    'completion_params': {
-                        'max_token': 1000,
-                        'temperature': 0,
-                        'top_p': 0,
-                        'presence_penalty': 0.1,
-                        'frequency_penalty': 0.1,
-                    }
-                },
-                opening_statement='',
-                suggested_questions=None,
-                pre_prompt="请将以下文本翻译为{{target_language}}:\n{{query}}\n翻译:",
-                model=json.dumps({
-                    "provider": "openai",
-                    "name": "gpt-3.5-turbo-instruct",
-                    "mode": "completion",
-                    "completion_params": {
-                        "max_tokens": 1000,
-                        "temperature": 0,
-                        "top_p": 0,
-                        "presence_penalty": 0.1,
-                        "frequency_penalty": 0.1
-                    }
-                }),
-                user_input_form=json.dumps([
-                    {
-                        "select": {
-                            "label": "目标语言",
-                            "variable": "target_language",
-                            "description": "翻译的目标语言",
-                            "default": "中文",
-                            "required": True,
-                            'options': [
-                                "中文",
-                                "英文",
-                                "日语",
-                                "法语",
-                                "俄语",
-                                "德语",
-                                "西班牙语",
-                                "韩语",
-                                "意大利语",
-                            ]
-                        }
-                    }, {
-                        "paragraph": {
-                            "label": "文本内容",
-                            "variable": "query",
-                            "required": True,
-                            "default": ""
-                        }
-                    }
-                ])
-            )
-        },
-        {
-            'name': 'AI 前端面试官',
-            'icon': '',
-            'icon_background': '',
-            'description': '一个模拟的前端面试官，通过提问的方式对前端开发的技能水平进行检验。',
-            'mode': 'chat',
-            'model_config': AppModelConfig(
-                provider='openai',
-                model_id='gpt-3.5-turbo',
-                configs={
-                    'introduction': '你好，欢迎来参加我们的面试，我是这家科技公司的面试官，我将考察你的 Web 前端开发技能。接下来我会向您提出一些技术问题，请您尽可能详尽地回答。',
-                    'prompt_template': "你将扮演一个科技公司的面试官，考察用户作为候选人的 Web 前端开发水平，提出 5-10 个犀利的技术问题。\n\n请注意：\n- 每次只问一个问题\n- 用户回答问题后请直接问下一个问题，而不要试图纠正候选人的错误；\n- 如果你认为用户连续几次回答的都不对，就少问一点；\n- 问完最后一个问题后，你可以问这样一个问题：上一份工作为什么离职？用户回答该问题后，请表示理解与支持。\n",
-                    'prompt_variables': [],
-                    'completion_params': {
-                        'max_token': 300,
-                        'temperature': 0.8,
-                        'top_p': 0.9,
-                        'presence_penalty': 0.1,
-                        'frequency_penalty': 0.1,
-                    }
-                },
-                opening_statement='你好，欢迎来参加我们的面试，我是这家科技公司的面试官，我将考察你的 Web 前端开发技能。接下来我会向您提出一些技术问题，请您尽可能详尽地回答。',
-                suggested_questions=None,
-                pre_prompt="你将扮演一个科技公司的面试官，考察用户作为候选人的 Web 前端开发水平，提出 5-10 个犀利的技术问题。\n\n请注意：\n- 每次只问一个问题\n- 用户回答问题后请直接问下一个问题，而不要试图纠正候选人的错误；\n- 如果你认为用户连续几次回答的都不对，就少问一点；\n- 问完最后一个问题后，你可以问这样一个问题：上一份工作为什么离职？用户回答该问题后，请表示理解与支持。\n",
-                model=json.dumps({
-                    "provider": "openai",
-                    "name": "gpt-3.5-turbo",
-                    "mode": "chat",
-                    "completion_params": {
-                        "max_tokens": 300,
-                        "temperature": 0.8,
-                        "top_p": 0.9,
-                        "presence_penalty": 0.1,
-                        "frequency_penalty": 0.1
-                    }
-                }),
-                user_input_form=None
-            )
-        }
-    ],
-    'uk-UA': [{
-        "name": "Помічник перекладу",
-        "icon": "",
-        "icon_background": "",
-        "description": "Багатомовний перекладач, який надає можливості перекладу різними мовами, перекладаючи введені користувачем дані на потрібну мову.",
-        "mode": "completion",
-        "model_config": AppModelConfig(
-            provider="openai",
-            model_id="gpt-3.5-turbo-instruct",
-            configs={
-                "prompt_template": "Будь ласка, перекладіть наступний текст на {{target_language}}:\n",
-                "prompt_variables": [
-                    {
-                        "key": "target_language",
-                        "name": "Цільова мова",
-                        "description": "Мова, на яку ви хочете перекласти.",
-                        "type": "select",
-                        "default": "Ukrainian",
-                        "options": [
-                            "Chinese",
-                            "English",
-                            "Japanese",
-                            "French",
-                            "Russian",
-                            "German",
-                            "Spanish",
-                            "Korean",
-                            "Italian",
-                        ],
-                    },
-                ],
-                "completion_params": {
-                    "max_token": 1000,
-                    "temperature": 0,
-                    "top_p": 0,
-                    "presence_penalty": 0.1,
-                    "frequency_penalty": 0.1,
-                },
-            },
-            opening_statement="",
-            suggested_questions=None,
-            pre_prompt="Будь ласка, перекладіть наступний текст на {{target_language}}:\n{{query}}\ntranslate:",
-            model=json.dumps({
-                "provider": "openai",
-                "name": "gpt-3.5-turbo-instruct",
-                "mode": "completion",
-                "completion_params": {
-                    "max_tokens": 1000,
-                    "temperature": 0,
-                    "top_p": 0,
-                    "presence_penalty": 0.1,
-                    "frequency_penalty": 0.1,
-                },
-            }),
-            user_input_form=json.dumps([
-                {
-                    "select": {
-                        "label": "Цільова мова",
-                        "variable": "target_language",
-                        "description": "Мова, на яку ви хочете перекласти.",
-                        "default": "Chinese",
-                        "required": True,
-                        'options': [
-                            'Chinese',
-                            'English',
-                            'Japanese',
-                            'French',
-                            'Russian',
-                            'German',
-                            'Spanish',
-                            'Korean',
-                            'Italian',
-                        ]
-                    }
-                }, {
-                    "paragraph": {
-                        "label": "Запит",
-                        "variable": "query",
-                        "required": True,
-                        "default": ""
-                    }
-                }
-            ])
-        )
-    },
-        {
-            "name": "AI інтерв’юер фронтенду",
-            "icon": "",
-            "icon_background": "",
-            "description": "Симульований інтерв’юер фронтенду, який перевіряє рівень кваліфікації у розробці фронтенду через опитування.",
-            "mode": "chat",
-            "model_config": AppModelConfig(
-                provider="openai",
-                model_id="gpt-3.5-turbo",
-                configs={
-                    "introduction": "Привіт, ласкаво просимо на наше співбесіду. Я інтерв'юер цієї технологічної компанії, і я перевірю ваші навички веб-розробки фронтенду. Далі я поставлю вам декілька технічних запитань. Будь ласка, відповідайте якомога ретельніше. ",
-                    "prompt_template": "Ви будете грати роль інтерв'юера технологічної компанії, перевіряючи навички розробки фронтенду користувача та ставлячи 5-10 чітких технічних питань.\n\nЗверніть увагу:\n- Ставте лише одне запитання за раз.\n- Після того, як користувач відповість на запитання, ставте наступне запитання безпосередньо, не намагаючись виправити будь-які помилки, допущені кандидатом.\n- Якщо ви вважаєте, що користувач не відповів правильно на кілька питань поспіль, задайте менше запитань.\n- Після того, як ви задали останнє запитання, ви можете поставити таке запитання: Чому ви залишили свою попередню роботу? Після того, як користувач відповість на це питання, висловіть своє розуміння та підтримку.\n",
-                    "prompt_variables": [],
-                    "completion_params": {
-                        "max_token": 300,
-                        "temperature": 0.8,
-                        "top_p": 0.9,
-                        "presence_penalty": 0.1,
-                        "frequency_penalty": 0.1,
-                    },
-                },
-                opening_statement="Привіт, ласкаво просимо на наше співбесіду. Я інтерв'юер цієї технологічної компанії, і я перевірю ваші навички веб-розробки фронтенду. Далі я поставлю вам декілька технічних запитань. Будь ласка, відповідайте якомога ретельніше. ",
-                suggested_questions=None,
-                pre_prompt="Ви будете грати роль інтерв'юера технологічної компанії, перевіряючи навички розробки фронтенду користувача та ставлячи 5-10 чітких технічних питань.\n\nЗверніть увагу:\n- Ставте лише одне запитання за раз.\n- Після того, як користувач відповість на запитання, ставте наступне запитання безпосередньо, не намагаючись виправити будь-які помилки, допущені кандидатом.\n- Якщо ви вважаєте, що користувач не відповів правильно на кілька питань поспіль, задайте менше запитань.\n- Після того, як ви задали останнє запитання, ви можете поставити таке запитання: Чому ви залишили свою попередню роботу? Після того, як користувач відповість на це питання, висловіть своє розуміння та підтримку.\n",
-                model=json.dumps({
-                    "provider": "openai",
-                    "name": "gpt-3.5-turbo",
-                    "mode": "chat",
-                    "completion_params": {
-                        "max_tokens": 300,
-                        "temperature": 0.8,
-                        "top_p": 0.9,
-                        "presence_penalty": 0.1,
-                        "frequency_penalty": 0.1,
-                    },
-                }),
-                user_input_form=None
-            ),
-        }
-    ],
-
-}

api/constants/model_template.py

@@ -1,43 +1,31 @@
 import json
 
-model_templates = {
-    # completion default mode
-    'completion_default': {
+from models.model import AppMode
+
+default_app_templates = {
+    # workflow default mode
+    AppMode.WORKFLOW: {
         'app': {
-            'mode': 'completion',
+            'mode': AppMode.WORKFLOW.value,
             'enable_site': True,
-            'enable_api': True,
-            'is_demo': False,
-            'api_rpm': 0,
-            'api_rph': 0,
-            'status': 'normal'
+            'enable_api': True
+        }
+    },
+
+    # completion default mode
+    AppMode.COMPLETION: {
+        'app': {
+            'mode': AppMode.COMPLETION.value,
+            'enable_site': True,
+            'enable_api': True
         },
         'model_config': {
-            'provider': 'openai',
-            'model_id': 'gpt-3.5-turbo-instruct',
-            'configs': {
-                'prompt_template': '',
-                'prompt_variables': [],
-                'completion_params': {
-                    'max_token': 512,
-                    'temperature': 1,
-                    'top_p': 1,
-                    'presence_penalty': 0,
-                    'frequency_penalty': 0,
-                }
-            },
-            'model': json.dumps({
+            'model': {
                 "provider": "openai",
-                "name": "gpt-3.5-turbo-instruct",
-                "mode": "completion",
-                "completion_params": {
-                    "max_tokens": 512,
-                    "temperature": 1,
-                    "top_p": 1,
-                    "presence_penalty": 0,
-                    "frequency_penalty": 0
-                }
-            }),
+                "name": "gpt-4",
+                "mode": "chat",
+                "completion_params": {}
+            },
             'user_input_form': json.dumps([
                 {
                     "paragraph": {
@@ -49,48 +37,50 @@ model_templates = {
                 }
             ]),
             'pre_prompt': '{{query}}'
-        }
+        },
+
     },
 
     # chat default mode
-    'chat_default': {
+    AppMode.CHAT: {
         'app': {
-            'mode': 'chat',
+            'mode': AppMode.CHAT.value,
             'enable_site': True,
-            'enable_api': True,
-            'is_demo': False,
-            'api_rpm': 0,
-            'api_rph': 0,
-            'status': 'normal'
+            'enable_api': True
         },
         'model_config': {
-            'provider': 'openai',
-            'model_id': 'gpt-3.5-turbo',
-            'configs': {
-                'prompt_template': '',
-                'prompt_variables': [],
-                'completion_params': {
-                    'max_token': 512,
-                    'temperature': 1,
-                    'top_p': 1,
-                    'presence_penalty': 0,
-                    'frequency_penalty': 0,
-                }
-            },
-            'model': json.dumps({
+            'model': {
                 "provider": "openai",
-                "name": "gpt-3.5-turbo",
+                "name": "gpt-4",
                 "mode": "chat",
-                "completion_params": {
-                    "max_tokens": 512,
-                    "temperature": 1,
-                    "top_p": 1,
-                    "presence_penalty": 0,
-                    "frequency_penalty": 0
-                }
-            })
+                "completion_params": {}
+            }
         }
     },
+
+    # advanced-chat default mode
+    AppMode.ADVANCED_CHAT: {
+        'app': {
+            'mode': AppMode.ADVANCED_CHAT.value,
+            'enable_site': True,
+            'enable_api': True
+        }
+    },
+
+    # agent-chat default mode
+    AppMode.AGENT_CHAT: {
+        'app': {
+            'mode': AppMode.AGENT_CHAT.value,
+            'enable_site': True,
+            'enable_api': True
+        },
+        'model_config': {
+            'model': {
+                "provider": "openai",
+                "name": "gpt-4",
+                "mode": "chat",
+                "completion_params": {}
+            }
+        }
+    }
 }
-
-

api/controllers/console/__init__.py

@@ -5,10 +5,10 @@ bp = Blueprint('console', __name__, url_prefix='/console/api')
 api = ExternalApi(bp)
 
 # Import other controllers
-from . import admin, apikey, extension, feature, setup, version
+from . import admin, apikey, extension, feature, setup, version, ping
 # Import app controllers
 from .app import (advanced_prompt_template, annotation, app, audio, completion, conversation, generator, message,
-                  model_config, site, statistic)
+                  model_config, site, statistic, workflow, workflow_run, workflow_app_log, workflow_statistic, agent)
 # Import auth controllers
 from .auth import activate, data_source_oauth, login, oauth
 # Import billing controllers
@@ -16,6 +16,7 @@ from .billing import billing
 # Import datasets controllers
 from .datasets import data_source, datasets, datasets_document, datasets_segments, file, hit_testing
 # Import explore controllers
-from .explore import audio, completion, conversation, installed_app, message, parameter, recommended_app, saved_message
+from .explore import (audio, completion, conversation, installed_app, message, parameter, recommended_app,
+                      saved_message, workflow)
 # Import workspace controllers
-from .workspace import account, members, model_providers, models, tool_providers, workspace
+from .workspace import account, members, model_providers, models, tool_providers, workspace

api/controllers/console/app/__init__.py

@@ -1,21 +0,0 @@
-from controllers.console.app.error import AppUnavailableError
-from extensions.ext_database import db
-from flask_login import current_user
-from models.model import App
-from werkzeug.exceptions import NotFound
-
-
-def _get_app(app_id, mode=None):
-    app = db.session.query(App).filter(
-        App.id == app_id,
-        App.tenant_id == current_user.current_tenant_id,
-        App.status == 'normal'
-    ).first()
-
-    if not app:
-        raise NotFound("App not found")
-
-    if mode and app.mode != mode:
-        raise NotFound("The {} app not found".format(mode))
-
-    return app

api/controllers/console/app/agent.py

@@ -0,0 +1,32 @@
+from flask_restful import Resource, reqparse
+
+from controllers.console import api
+from controllers.console.app.wraps import get_app_model
+from controllers.console.setup import setup_required
+from controllers.console.wraps import account_initialization_required
+from libs.helper import uuid_value
+from libs.login import login_required
+from models.model import AppMode
+from services.agent_service import AgentService
+
+
+class AgentLogApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.AGENT_CHAT])
+    def get(self, app_model):
+        """Get agent logs"""
+        parser = reqparse.RequestParser()
+        parser.add_argument('message_id', type=uuid_value, required=True, location='args')
+        parser.add_argument('conversation_id', type=uuid_value, required=True, location='args')
+
+        args = parser.parse_args()
+
+        return AgentService.get_agent_logs(
+            app_model,
+            args['conversation_id'],
+            args['message_id']
+        )
+    
+api.add_resource(AgentLogApi, '/apps/<uuid:app_id>/agent/logs')

api/controllers/console/app/app.py

@@ -1,39 +1,28 @@
 import json
-import logging
-from datetime import datetime
 
 from flask_login import current_user
-from flask_restful import Resource, abort, inputs, marshal_with, reqparse
-from werkzeug.exceptions import Forbidden
+from flask_restful import Resource, inputs, marshal_with, reqparse
+from werkzeug.exceptions import Forbidden, BadRequest
 
-from constants.languages import demo_model_templates, languages
-from constants.model_template import model_templates
 from controllers.console import api
-from controllers.console.app.error import AppNotFoundError, ProviderNotInitializeError
+from controllers.console.app.wraps import get_app_model
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required, cloud_edition_billing_resource_check
-from core.errors.error import LLMBadRequestError, ProviderTokenNotInitError
-from core.model_manager import ModelManager
-from core.model_runtime.entities.model_entities import ModelType
-from core.provider_manager import ProviderManager
-from events.app_event import app_was_created, app_was_deleted
+from core.agent.entities import AgentToolEntity
 from extensions.ext_database import db
 from fields.app_fields import (
     app_detail_fields,
     app_detail_fields_with_site,
     app_pagination_fields,
-    template_list_fields,
 )
 from libs.login import login_required
-from models.model import App, AppModelConfig, Site
-from services.app_model_config_service import AppModelConfigService
+from services.app_service import AppService
+from models.model import App, AppModelConfig, AppMode
+from core.tools.utils.configuration import ToolParameterConfigurationManager
+from core.tools.tool_manager import ToolManager
 
 
-def _get_app(app_id, tenant_id):
-    app = db.session.query(App).filter(App.id == app_id, App.tenant_id == tenant_id).first()
-    if not app:
-        raise AppNotFoundError
-    return app
+ALLOW_CREATE_APP_MODES = ['chat', 'agent-chat', 'advanced-chat', 'workflow', 'completion']
 
 
 class AppListApi(Resource):
@@ -47,33 +36,15 @@ class AppListApi(Resource):
         parser = reqparse.RequestParser()
         parser.add_argument('page', type=inputs.int_range(1, 99999), required=False, default=1, location='args')
         parser.add_argument('limit', type=inputs.int_range(1, 100), required=False, default=20, location='args')
-        parser.add_argument('mode', type=str, choices=['chat', 'completion', 'all'], default='all', location='args', required=False)
+        parser.add_argument('mode', type=str, choices=['chat', 'workflow', 'agent-chat', 'channel', 'all'], default='all', location='args', required=False)
         parser.add_argument('name', type=str, location='args', required=False)
         args = parser.parse_args()
 
-        filters = [
-            App.tenant_id == current_user.current_tenant_id,
-            App.is_universal == False
-        ]
+        # get app list
+        app_service = AppService()
+        app_pagination = app_service.get_paginate_apps(current_user.current_tenant_id, args)
 
-        if args['mode'] == 'completion':
-            filters.append(App.mode == 'completion')
-        elif args['mode'] == 'chat':
-            filters.append(App.mode == 'chat')
-        else:
-            pass
-
-        if 'name' in args and args['name']:
-            filters.append(App.name.ilike(f'%{args["name"]}%'))
-
-        app_models = db.paginate(
-            db.select(App).where(*filters).order_by(App.created_at.desc()),
-            page=args['page'],
-            per_page=args['limit'],
-            error_out=False
-        )
-
-        return app_models
+        return app_pagination
 
     @setup_required
     @login_required
@@ -84,147 +55,49 @@ class AppListApi(Resource):
         """Create app"""
         parser = reqparse.RequestParser()
         parser.add_argument('name', type=str, required=True, location='json')
-        parser.add_argument('mode', type=str, choices=['completion', 'chat', 'assistant'], location='json')
+        parser.add_argument('description', type=str, location='json')
+        parser.add_argument('mode', type=str, choices=ALLOW_CREATE_APP_MODES, location='json')
         parser.add_argument('icon', type=str, location='json')
         parser.add_argument('icon_background', type=str, location='json')
-        parser.add_argument('model_config', type=dict, location='json')
         args = parser.parse_args()
 
         # 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()
 
-        try:
-            provider_manager = ProviderManager()
-            default_model_entity = provider_manager.get_default_model(
-                tenant_id=current_user.current_tenant_id,
-                model_type=ModelType.LLM
-            )
-        except (ProviderTokenNotInitError, LLMBadRequestError):
-            default_model_entity = None
-        except Exception as e:
-            logging.exception(e)
-            default_model_entity = None
+        if 'mode' not in args or args['mode'] is None:
+            raise BadRequest("mode is required")
 
-        if args['model_config'] is not None:
-            # validate config
-            model_config_dict = args['model_config']
-
-            # Get provider configurations
-            provider_manager = ProviderManager()
-            provider_configurations = provider_manager.get_configurations(current_user.current_tenant_id)
-
-            # get available models from provider_configurations
-            available_models = provider_configurations.get_models(
-                model_type=ModelType.LLM,
-                only_active=True
-            )
-
-            # check if model is available
-            available_models_names = [f'{model.provider.provider}.{model.model}' for model in available_models]
-            provider_model = f"{model_config_dict['model']['provider']}.{model_config_dict['model']['name']}"
-            if provider_model not in available_models_names:
-                if not default_model_entity:
-                    raise ProviderNotInitializeError(
-                        "No Default System Reasoning Model available. Please configure "
-                        "in the Settings -> Model Provider.")
-                else:
-                    model_config_dict["model"]["provider"] = default_model_entity.provider
-                    model_config_dict["model"]["name"] = default_model_entity.model
-
-            model_configuration = AppModelConfigService.validate_configuration(
-                tenant_id=current_user.current_tenant_id,
-                account=current_user,
-                config=model_config_dict,
-                app_mode=args['mode']
-            )
-
-            app = App(
-                enable_site=True,
-                enable_api=True,
-                is_demo=False,
-                api_rpm=0,
-                api_rph=0,
-                status='normal'
-            )
-
-            app_model_config = AppModelConfig()
-            app_model_config = app_model_config.from_model_config_dict(model_configuration)
-        else:
-            if 'mode' not in args or args['mode'] is None:
-                abort(400, message="mode is required")
-
-            model_config_template = model_templates[args['mode'] + '_default']
-
-            app = App(**model_config_template['app'])
-            app_model_config = AppModelConfig(**model_config_template['model_config'])
-
-            # get model provider
-            model_manager = ModelManager()
-
-            try:
-                model_instance = model_manager.get_default_model_instance(
-                    tenant_id=current_user.current_tenant_id,
-                    model_type=ModelType.LLM
-                )
-            except ProviderTokenNotInitError:
-                model_instance = None
-
-            if model_instance:
-                model_dict = app_model_config.model_dict
-                model_dict['provider'] = model_instance.provider
-                model_dict['name'] = model_instance.model
-                app_model_config.model = json.dumps(model_dict)
-
-        app.name = args['name']
-        app.mode = args['mode']
-        app.icon = args['icon']
-        app.icon_background = args['icon_background']
-        app.tenant_id = current_user.current_tenant_id
-
-        db.session.add(app)
-        db.session.flush()
-
-        app_model_config.app_id = app.id
-        db.session.add(app_model_config)
-        db.session.flush()
-
-        app.app_model_config_id = app_model_config.id
-
-        account = current_user
-
-        site = Site(
-            app_id=app.id,
-            title=app.name,
-            default_language=account.interface_language,
-            customize_token_strategy='not_allow',
-            code=Site.generate_code(16)
-        )
-
-        db.session.add(site)
-        db.session.commit()
-
-        app_was_created.send(app)
+        app_service = AppService()
+        app = app_service.create_app(current_user.current_tenant_id, args, current_user)
 
         return app, 201
-    
 
-class AppTemplateApi(Resource):
 
+class AppImportApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    @marshal_with(template_list_fields)
-    def get(self):
-        """Get app demo templates"""
-        account = current_user
-        interface_language = account.interface_language
+    @marshal_with(app_detail_fields_with_site)
+    @cloud_edition_billing_resource_check('apps')
+    def post(self):
+        """Import app"""
+        # 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()
 
-        templates = demo_model_templates.get(interface_language)
-        if not templates:
-            templates = demo_model_templates.get(languages[0])
+        parser = reqparse.RequestParser()
+        parser.add_argument('data', type=str, required=True, nullable=False, location='json')
+        parser.add_argument('name', type=str, location='json')
+        parser.add_argument('description', type=str, location='json')
+        parser.add_argument('icon', type=str, location='json')
+        parser.add_argument('icon_background', type=str, location='json')
+        args = parser.parse_args()
 
-        return {'data': templates}
+        app_service = AppService()
+        app = app_service.import_app(current_user.current_tenant_id, args['data'], args, current_user)
+
+        return app, 201
 
 
 class AppApi(Resource):
@@ -232,176 +105,198 @@ class AppApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
+    @get_app_model
     @marshal_with(app_detail_fields_with_site)
-    def get(self, app_id):
+    def get(self, app_model):
         """Get app detail"""
-        app_id = str(app_id)
-        app = _get_app(app_id, current_user.current_tenant_id)
+        # get original app model config
+        if app_model.mode == AppMode.AGENT_CHAT.value or app_model.is_agent:
+            model_config: AppModelConfig = app_model.app_model_config
+            agent_mode = model_config.agent_mode_dict
+            # decrypt agent tool parameters if it's secret-input
+            for tool in agent_mode.get('tools') or []:
+                if not isinstance(tool, dict) or len(tool.keys()) <= 3:
+                    continue
+                agent_tool_entity = AgentToolEntity(**tool)
+                # get tool
+                try:
+                    tool_runtime = ToolManager.get_agent_tool_runtime(
+                        tenant_id=current_user.current_tenant_id,
+                        agent_tool=agent_tool_entity,
+                    )
+                    manager = ToolParameterConfigurationManager(
+                        tenant_id=current_user.current_tenant_id,
+                        tool_runtime=tool_runtime,
+                        provider_name=agent_tool_entity.provider_id,
+                        provider_type=agent_tool_entity.provider_type,
+                    )
 
-        return app
+                    # get decrypted parameters
+                    if agent_tool_entity.tool_parameters:
+                        parameters = manager.decrypt_tool_parameters(agent_tool_entity.tool_parameters or {})
+                        masked_parameter = manager.mask_tool_parameters(parameters or {})
+                    else:
+                        masked_parameter = {}
+
+                    # override tool parameters
+                    tool['tool_parameters'] = masked_parameter
+                except Exception as e:
+                    pass
+
+            # override agent mode
+            model_config.agent_mode = json.dumps(agent_mode)
+            db.session.commit()
+
+        return app_model
 
     @setup_required
     @login_required
     @account_initialization_required
-    def delete(self, app_id):
-        """Delete app"""
-        app_id = str(app_id)
+    @get_app_model
+    @marshal_with(app_detail_fields_with_site)
+    def put(self, app_model):
+        """Update app"""
+        parser = reqparse.RequestParser()
+        parser.add_argument('name', type=str, required=True, nullable=False, location='json')
+        parser.add_argument('description', type=str, location='json')
+        parser.add_argument('icon', type=str, location='json')
+        parser.add_argument('icon_background', type=str, location='json')
+        args = parser.parse_args()
 
+        app_service = AppService()
+        app_model = app_service.update_app(app_model, args)
+
+        return app_model
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model
+    def delete(self, app_model):
+        """Delete app"""
         if not current_user.is_admin_or_owner:
             raise Forbidden()
 
-        app = _get_app(app_id, current_user.current_tenant_id)
-
-        db.session.delete(app)
-        db.session.commit()
-
-        # todo delete related data??
-        # model_config, site, api_token, conversation, message, message_feedback, message_annotation
-
-        app_was_deleted.send(app)
+        app_service = AppService()
+        app_service.delete_app(app_model)
 
         return {'result': 'success'}, 204
 
 
+class AppCopyApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model
+    @marshal_with(app_detail_fields_with_site)
+    def post(self, app_model):
+        """Copy app"""
+        # 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, location='json')
+        parser.add_argument('description', type=str, location='json')
+        parser.add_argument('icon', type=str, location='json')
+        parser.add_argument('icon_background', type=str, location='json')
+        args = parser.parse_args()
+
+        app_service = AppService()
+        data = app_service.export_app(app_model)
+        app = app_service.import_app(current_user.current_tenant_id, data, args, current_user)
+
+        return app, 201
+
+
+class AppExportApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model
+    def get(self, app_model):
+        """Export app"""
+        app_service = AppService()
+
+        return {
+            "data": app_service.export_app(app_model)
+        }
+
+
 class AppNameApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
+    @get_app_model
     @marshal_with(app_detail_fields)
-    def post(self, app_id):
-        app_id = str(app_id)
-        app = _get_app(app_id, current_user.current_tenant_id)
-
+    def post(self, app_model):
         parser = reqparse.RequestParser()
         parser.add_argument('name', type=str, required=True, location='json')
         args = parser.parse_args()
 
-        app.name = args.get('name')
-        app.updated_at = datetime.utcnow()
-        db.session.commit()
-        return app
+        app_service = AppService()
+        app_model = app_service.update_app_name(app_model, args.get('name'))
+
+        return app_model
 
 
 class AppIconApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
+    @get_app_model
     @marshal_with(app_detail_fields)
-    def post(self, app_id):
-        app_id = str(app_id)
-        app = _get_app(app_id, current_user.current_tenant_id)
-
+    def post(self, app_model):
         parser = reqparse.RequestParser()
         parser.add_argument('icon', type=str, location='json')
         parser.add_argument('icon_background', type=str, location='json')
         args = parser.parse_args()
 
-        app.icon = args.get('icon')
-        app.icon_background = args.get('icon_background')
-        app.updated_at = datetime.utcnow()
-        db.session.commit()
+        app_service = AppService()
+        app_model = app_service.update_app_icon(app_model, args.get('icon'), args.get('icon_background'))
 
-        return app
+        return app_model
 
 
 class AppSiteStatus(Resource):
     @setup_required
     @login_required
     @account_initialization_required
+    @get_app_model
     @marshal_with(app_detail_fields)
-    def post(self, app_id):
+    def post(self, app_model):
         parser = reqparse.RequestParser()
         parser.add_argument('enable_site', type=bool, required=True, location='json')
         args = parser.parse_args()
-        app_id = str(app_id)
-        app = db.session.query(App).filter(App.id == app_id, App.tenant_id == current_user.current_tenant_id).first()
-        if not app:
-            raise AppNotFoundError
 
-        if args.get('enable_site') == app.enable_site:
-            return app
+        app_service = AppService()
+        app_model = app_service.update_app_site_status(app_model, args.get('enable_site'))
 
-        app.enable_site = args.get('enable_site')
-        app.updated_at = datetime.utcnow()
-        db.session.commit()
-        return app
+        return app_model
 
 
 class AppApiStatus(Resource):
     @setup_required
     @login_required
     @account_initialization_required
+    @get_app_model
     @marshal_with(app_detail_fields)
-    def post(self, app_id):
+    def post(self, app_model):
         parser = reqparse.RequestParser()
         parser.add_argument('enable_api', type=bool, required=True, location='json')
         args = parser.parse_args()
 
-        app_id = str(app_id)
-        app = _get_app(app_id, current_user.current_tenant_id)
+        app_service = AppService()
+        app_model = app_service.update_app_api_status(app_model, args.get('enable_api'))
 
-        if args.get('enable_api') == app.enable_api:
-            return app
-
-        app.enable_api = args.get('enable_api')
-        app.updated_at = datetime.utcnow()
-        db.session.commit()
-        return app
-
-
-class AppCopy(Resource):
-    @staticmethod
-    def create_app_copy(app):
-        copy_app = App(
-            name=app.name + ' copy',
-            icon=app.icon,
-            icon_background=app.icon_background,
-            tenant_id=app.tenant_id,
-            mode=app.mode,
-            app_model_config_id=app.app_model_config_id,
-            enable_site=app.enable_site,
-            enable_api=app.enable_api,
-            api_rpm=app.api_rpm,
-            api_rph=app.api_rph
-        )
-        return copy_app
-
-    @staticmethod
-    def create_app_model_config_copy(app_config, copy_app_id):
-        copy_app_model_config = app_config.copy()
-        copy_app_model_config.app_id = copy_app_id
-
-        return copy_app_model_config
-
-    @setup_required
-    @login_required
-    @account_initialization_required
-    @marshal_with(app_detail_fields)
-    def post(self, app_id):
-        app_id = str(app_id)
-        app = _get_app(app_id, current_user.current_tenant_id)
-
-        copy_app = self.create_app_copy(app)
-        db.session.add(copy_app)
-
-        app_config = db.session.query(AppModelConfig). \
-            filter(AppModelConfig.app_id == app_id). \
-            one_or_none()
-
-        if app_config:
-            copy_app_model_config = self.create_app_model_config_copy(app_config, copy_app.id)
-            db.session.add(copy_app_model_config)
-            db.session.commit()
-            copy_app.app_model_config_id = copy_app_model_config.id
-        db.session.commit()
-
-        return copy_app, 201
+        return app_model
 
 
 api.add_resource(AppListApi, '/apps')
-api.add_resource(AppTemplateApi, '/app-templates')
+api.add_resource(AppImportApi, '/apps/import')
 api.add_resource(AppApi, '/apps/<uuid:app_id>')
-api.add_resource(AppCopy, '/apps/<uuid:app_id>/copy')
+api.add_resource(AppCopyApi, '/apps/<uuid:app_id>/copy')
+api.add_resource(AppExportApi, '/apps/<uuid:app_id>/export')
 api.add_resource(AppNameApi, '/apps/<uuid:app_id>/name')
 api.add_resource(AppIconApi, '/apps/<uuid:app_id>/icon')
 api.add_resource(AppSiteStatus, '/apps/<uuid:app_id>/site-enable')

api/controllers/console/app/audio.py

@@ -6,7 +6,6 @@ from werkzeug.exceptions import InternalServerError
 
 import services
 from controllers.console import api
-from controllers.console.app import _get_app
 from controllers.console.app.error import (
     AppUnavailableError,
     AudioTooLargeError,
@@ -18,11 +17,13 @@ from controllers.console.app.error import (
     ProviderQuotaExceededError,
     UnsupportedAudioTypeError,
 )
+from controllers.console.app.wraps import get_app_model
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required
 from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
 from core.model_runtime.errors.invoke import InvokeError
 from libs.login import login_required
+from models.model import AppMode
 from services.audio_service import AudioService
 from services.errors.audio import (
     AudioTooLargeServiceError,
@@ -36,15 +37,13 @@ class ChatMessageAudioApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    def post(self, app_id):
-        app_id = str(app_id)
-        app_model = _get_app(app_id, 'chat')
-
+    @get_app_model(mode=[AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT])
+    def post(self, app_model):
         file = request.files['file']
 
         try:
             response = AudioService.transcript_asr(
-                tenant_id=app_model.tenant_id,
+                app_model=app_model,
                 file=file,
                 end_user=None,
             )
@@ -80,15 +79,13 @@ class ChatMessageTextApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    def post(self, app_id):
-        app_id = str(app_id)
-        app_model = _get_app(app_id, None)
-
+    @get_app_model
+    def post(self, app_model):
         try:
             response = AudioService.transcript_tts(
-                tenant_id=app_model.tenant_id,
+                app_model=app_model,
                 text=request.form['text'],
-                voice=app_model.app_model_config.text_to_speech_dict.get('voice'),
+                voice=request.form.get('voice'),
                 streaming=False
             )
 
@@ -120,9 +117,11 @@ class ChatMessageTextApi(Resource):
 
 
 class TextModesApi(Resource):
-    def get(self, app_id: str):
-        app_model = _get_app(str(app_id))
-
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model
+    def get(self, app_model):
         try:
             parser = reqparse.RequestParser()
             parser.add_argument('language', type=str, required=True, location='args')

api/controllers/console/app/completion.py

@@ -1,16 +1,11 @@
-import json
 import logging
-from collections.abc import Generator
-from typing import Union
 
 import flask_login
-from flask import Response, stream_with_context
 from flask_restful import Resource, reqparse
 from werkzeug.exceptions import InternalServerError, NotFound
 
 import services
 from controllers.console import api
-from controllers.console.app import _get_app
 from controllers.console.app.error import (
     AppUnavailableError,
     CompletionRequestError,
@@ -19,15 +14,18 @@ from controllers.console.app.error import (
     ProviderNotInitializeError,
     ProviderQuotaExceededError,
 )
+from controllers.console.app.wraps import get_app_model
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required
-from core.application_queue_manager import ApplicationQueueManager
-from core.entities.application_entities import InvokeFrom
+from core.app.apps.base_app_queue_manager import AppQueueManager
+from core.app.entities.app_invoke_entities import InvokeFrom
 from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
 from core.model_runtime.errors.invoke import InvokeError
+from libs import helper
 from libs.helper import uuid_value
 from libs.login import login_required
-from services.completion_service import CompletionService
+from models.model import AppMode
+from services.app_generate_service import AppGenerateService
 
 
 # define completion message api for user
@@ -36,12 +34,8 @@ class CompletionMessageApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    def post(self, app_id):
-        app_id = str(app_id)
-
-        # get app info
-        app_model = _get_app(app_id, 'completion')
-
+    @get_app_model(mode=AppMode.COMPLETION)
+    def post(self, app_model):
         parser = reqparse.RequestParser()
         parser.add_argument('inputs', type=dict, required=True, location='json')
         parser.add_argument('query', type=str, location='json', default='')
@@ -57,16 +51,15 @@ class CompletionMessageApi(Resource):
         account = flask_login.current_user
 
         try:
-            response = CompletionService.completion(
+            response = AppGenerateService.generate(
                 app_model=app_model,
                 user=account,
                 args=args,
                 invoke_from=InvokeFrom.DEBUGGER,
-                streaming=streaming,
-                is_model_config_override=True
+                streaming=streaming
             )
 
-            return compact_response(response)
+            return helper.compact_generate_response(response)
         except services.errors.conversation.ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")
         except services.errors.conversation.ConversationCompletedError:
@@ -93,15 +86,11 @@ class CompletionMessageStopApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    def post(self, app_id, task_id):
-        app_id = str(app_id)
-
-        # get app info
-        _get_app(app_id, 'completion')
-
+    @get_app_model(mode=AppMode.COMPLETION)
+    def post(self, app_model, task_id):
         account = flask_login.current_user
 
-        ApplicationQueueManager.set_stop_flag(task_id, InvokeFrom.DEBUGGER, account.id)
+        AppQueueManager.set_stop_flag(task_id, InvokeFrom.DEBUGGER, account.id)
 
         return {'result': 'success'}, 200
 
@@ -110,12 +99,8 @@ class ChatMessageApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    def post(self, app_id):
-        app_id = str(app_id)
-
-        # get app info
-        app_model = _get_app(app_id, 'chat')
-
+    @get_app_model(mode=[AppMode.CHAT, AppMode.AGENT_CHAT])
+    def post(self, app_model):
         parser = reqparse.RequestParser()
         parser.add_argument('inputs', type=dict, required=True, location='json')
         parser.add_argument('query', type=str, required=True, location='json')
@@ -132,16 +117,15 @@ class ChatMessageApi(Resource):
         account = flask_login.current_user
 
         try:
-            response = CompletionService.completion(
+            response = AppGenerateService.generate(
                 app_model=app_model,
                 user=account,
                 args=args,
                 invoke_from=InvokeFrom.DEBUGGER,
-                streaming=streaming,
-                is_model_config_override=True
+                streaming=streaming
             )
 
-            return compact_response(response)
+            return helper.compact_generate_response(response)
         except services.errors.conversation.ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")
         except services.errors.conversation.ConversationCompletedError:
@@ -164,30 +148,15 @@ class ChatMessageApi(Resource):
             raise InternalServerError()
 
 
-def compact_response(response: Union[dict, Generator]) -> Response:
-    if isinstance(response, dict):
-        return Response(response=json.dumps(response), status=200, mimetype='application/json')
-    else:
-        def generate() -> Generator:
-            yield from response
-
-        return Response(stream_with_context(generate()), status=200,
-                        mimetype='text/event-stream')
-
-
 class ChatMessageStopApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    def post(self, app_id, task_id):
-        app_id = str(app_id)
-
-        # get app info
-        _get_app(app_id, 'chat')
-
+    @get_app_model(mode=[AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT])
+    def post(self, app_model, task_id):
         account = flask_login.current_user
 
-        ApplicationQueueManager.set_stop_flag(task_id, InvokeFrom.DEBUGGER, account.id)
+        AppQueueManager.set_stop_flag(task_id, InvokeFrom.DEBUGGER, account.id)
 
         return {'result': 'success'}, 200
 

api/controllers/console/app/conversation.py

@@ -9,7 +9,7 @@ from sqlalchemy.orm import joinedload
 from werkzeug.exceptions import NotFound
 
 from controllers.console import api
-from controllers.console.app import _get_app
+from controllers.console.app.wraps import get_app_model
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required
 from extensions.ext_database import db
@@ -21,7 +21,7 @@ from fields.conversation_fields import (
 )
 from libs.helper import datetime_string
 from libs.login import login_required
-from models.model import Conversation, Message, MessageAnnotation
+from models.model import AppMode, Conversation, Message, MessageAnnotation
 
 
 class CompletionConversationApi(Resource):
@@ -29,10 +29,9 @@ class CompletionConversationApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
+    @get_app_model(mode=AppMode.COMPLETION)
     @marshal_with(conversation_pagination_fields)
-    def get(self, app_id):
-        app_id = str(app_id)
-
+    def get(self, app_model):
         parser = reqparse.RequestParser()
         parser.add_argument('keyword', type=str, location='args')
         parser.add_argument('start', type=datetime_string('%Y-%m-%d %H:%M'), location='args')
@@ -43,10 +42,7 @@ class CompletionConversationApi(Resource):
         parser.add_argument('limit', type=int_range(1, 100), default=20, location='args')
         args = parser.parse_args()
 
-        # get app info
-        app = _get_app(app_id, 'completion')
-
-        query = db.select(Conversation).where(Conversation.app_id == app.id, Conversation.mode == 'completion')
+        query = db.select(Conversation).where(Conversation.app_id == app_model.id, Conversation.mode == 'completion')
 
         if args['keyword']:
             query = query.join(
@@ -106,24 +102,22 @@ class CompletionConversationDetailApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
+    @get_app_model(mode=AppMode.COMPLETION)
     @marshal_with(conversation_message_detail_fields)
-    def get(self, app_id, conversation_id):
-        app_id = str(app_id)
+    def get(self, app_model, conversation_id):
         conversation_id = str(conversation_id)
 
-        return _get_conversation(app_id, conversation_id, 'completion')
+        return _get_conversation(app_model, conversation_id)
 
     @setup_required
     @login_required
     @account_initialization_required
-    def delete(self, app_id, conversation_id):
-        app_id = str(app_id)
+    @get_app_model(mode=[AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT])
+    def delete(self, app_model, conversation_id):
         conversation_id = str(conversation_id)
 
-        app = _get_app(app_id, 'chat')
-
         conversation = db.session.query(Conversation) \
-            .filter(Conversation.id == conversation_id, Conversation.app_id == app.id).first()
+            .filter(Conversation.id == conversation_id, Conversation.app_id == app_model.id).first()
 
         if not conversation:
             raise NotFound("Conversation Not Exists.")
@@ -139,10 +133,9 @@ class ChatConversationApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
+    @get_app_model(mode=[AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT])
     @marshal_with(conversation_with_summary_pagination_fields)
-    def get(self, app_id):
-        app_id = str(app_id)
-
+    def get(self, app_model):
         parser = reqparse.RequestParser()
         parser.add_argument('keyword', type=str, location='args')
         parser.add_argument('start', type=datetime_string('%Y-%m-%d %H:%M'), location='args')
@@ -154,10 +147,7 @@ class ChatConversationApi(Resource):
         parser.add_argument('limit', type=int_range(1, 100), required=False, default=20, location='args')
         args = parser.parse_args()
 
-        # get app info
-        app = _get_app(app_id, 'chat')
-
-        query = db.select(Conversation).where(Conversation.app_id == app.id, Conversation.mode == 'chat')
+        query = db.select(Conversation).where(Conversation.app_id == app_model.id)
 
         if args['keyword']:
             query = query.join(
@@ -211,6 +201,9 @@ class ChatConversationApi(Resource):
                 .having(func.count(Message.id) >= args['message_count_gte'])
             )
 
+        if app_model.mode == AppMode.ADVANCED_CHAT.value:
+            query = query.where(Conversation.override_model_configs.is_(None))
+
         query = query.order_by(Conversation.created_at.desc())
 
         conversations = db.paginate(
@@ -228,25 +221,22 @@ class ChatConversationDetailApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
+    @get_app_model(mode=[AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT])
     @marshal_with(conversation_detail_fields)
-    def get(self, app_id, conversation_id):
-        app_id = str(app_id)
+    def get(self, app_model, conversation_id):
         conversation_id = str(conversation_id)
 
-        return _get_conversation(app_id, conversation_id, 'chat')
+        return _get_conversation(app_model, conversation_id)
 
     @setup_required
     @login_required
+    @get_app_model(mode=[AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT])
     @account_initialization_required
-    def delete(self, app_id, conversation_id):
-        app_id = str(app_id)
+    def delete(self, app_model, conversation_id):
         conversation_id = str(conversation_id)
 
-        # get app info
-        app = _get_app(app_id, 'chat')
-
         conversation = db.session.query(Conversation) \
-            .filter(Conversation.id == conversation_id, Conversation.app_id == app.id).first()
+            .filter(Conversation.id == conversation_id, Conversation.app_id == app_model.id).first()
 
         if not conversation:
             raise NotFound("Conversation Not Exists.")
@@ -263,12 +253,9 @@ api.add_resource(ChatConversationApi, '/apps/<uuid:app_id>/chat-conversations')
 api.add_resource(ChatConversationDetailApi, '/apps/<uuid:app_id>/chat-conversations/<uuid:conversation_id>')
 
 
-def _get_conversation(app_id, conversation_id, mode):
-    # get app info
-    app = _get_app(app_id, mode)
-
+def _get_conversation(app_model, conversation_id):
     conversation = db.session.query(Conversation) \
-        .filter(Conversation.id == conversation_id, Conversation.app_id == app.id).first()
+        .filter(Conversation.id == conversation_id, Conversation.app_id == app_model.id).first()
 
     if not conversation:
         raise NotFound("Conversation Not Exists.")

api/controllers/console/app/error.py

@@ -85,3 +85,9 @@ class TooManyFilesError(BaseHTTPException):
     error_code = 'too_many_files'
     description = "Only one file is allowed."
     code = 400
+
+
+class DraftWorkflowNotExist(BaseHTTPException):
+    error_code = 'draft_workflow_not_exist'
+    description = "Draft workflow need to be initialized."
+    code = 400

api/controllers/console/app/generator.py

@@ -11,7 +11,7 @@ from controllers.console.app.error import (
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required
 from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
-from core.generator.llm_generator import LLMGenerator
+from core.llm_generator.llm_generator import LLMGenerator
 from core.model_runtime.errors.invoke import InvokeError
 from libs.login import login_required
 

api/controllers/console/app/message.py

@@ -1,26 +1,22 @@
-import json
 import logging
-from collections.abc import Generator
-from typing import Union
 
-from flask import Response, stream_with_context
 from flask_login import current_user
 from flask_restful import Resource, fields, marshal_with, reqparse
 from flask_restful.inputs import int_range
 from werkzeug.exceptions import Forbidden, InternalServerError, NotFound
 
 from controllers.console import api
-from controllers.console.app import _get_app
 from controllers.console.app.error import (
-    AppMoreLikeThisDisabledError,
     CompletionRequestError,
     ProviderModelCurrentlyNotSupportError,
     ProviderNotInitializeError,
     ProviderQuotaExceededError,
 )
+from controllers.console.app.wraps import get_app_model
+from controllers.console.explore.error import AppSuggestedQuestionsAfterAnswerDisabledError
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required, cloud_edition_billing_resource_check
-from core.entities.application_entities import InvokeFrom
+from core.app.entities.app_invoke_entities import InvokeFrom
 from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
 from core.model_runtime.errors.invoke import InvokeError
 from extensions.ext_database import db
@@ -28,12 +24,10 @@ from fields.conversation_fields import annotation_fields, message_detail_fields
 from libs.helper import uuid_value
 from libs.infinite_scroll_pagination import InfiniteScrollPagination
 from libs.login import login_required
-from models.model import Conversation, Message, MessageAnnotation, MessageFeedback
+from models.model import AppMode, Conversation, Message, MessageAnnotation, MessageFeedback
 from services.annotation_service import AppAnnotationService
-from services.completion_service import CompletionService
-from services.errors.app import MoreLikeThisDisabledError
 from services.errors.conversation import ConversationNotExistsError
-from services.errors.message import MessageNotExistsError
+from services.errors.message import MessageNotExistsError, SuggestedQuestionsAfterAnswerDisabledError
 from services.message_service import MessageService
 
 
@@ -46,14 +40,10 @@ class ChatMessageListApi(Resource):
 
     @setup_required
     @login_required
+    @get_app_model(mode=[AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT])
     @account_initialization_required
     @marshal_with(message_infinite_scroll_pagination_fields)
-    def get(self, app_id):
-        app_id = str(app_id)
-
-        # get app info
-        app = _get_app(app_id, 'chat')
-
+    def get(self, app_model):
         parser = reqparse.RequestParser()
         parser.add_argument('conversation_id', required=True, type=uuid_value, location='args')
         parser.add_argument('first_id', type=uuid_value, location='args')
@@ -62,7 +52,7 @@ class ChatMessageListApi(Resource):
 
         conversation = db.session.query(Conversation).filter(
             Conversation.id == args['conversation_id'],
-            Conversation.app_id == app.id
+            Conversation.app_id == app_model.id
         ).first()
 
         if not conversation:
@@ -110,12 +100,8 @@ class MessageFeedbackApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    def post(self, app_id):
-        app_id = str(app_id)
-
-        # get app info
-        app = _get_app(app_id)
-
+    @get_app_model
+    def post(self, app_model):
         parser = reqparse.RequestParser()
         parser.add_argument('message_id', required=True, type=uuid_value, location='json')
         parser.add_argument('rating', type=str, choices=['like', 'dislike', None], location='json')
@@ -125,7 +111,7 @@ class MessageFeedbackApi(Resource):
 
         message = db.session.query(Message).filter(
             Message.id == message_id,
-            Message.app_id == app.id
+            Message.app_id == app_model.id
         ).first()
 
         if not message:
@@ -141,7 +127,7 @@ class MessageFeedbackApi(Resource):
             raise ValueError('rating cannot be None when feedback not exists')
         else:
             feedback = MessageFeedback(
-                app_id=app.id,
+                app_id=app_model.id,
                 conversation_id=message.conversation_id,
                 message_id=message.id,
                 rating=args['rating'],
@@ -160,21 +146,20 @@ class MessageAnnotationApi(Resource):
     @login_required
     @account_initialization_required
     @cloud_edition_billing_resource_check('annotation')
+    @get_app_model
     @marshal_with(annotation_fields)
-    def post(self, app_id):
+    def post(self, app_model):
         # 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()
 
-        app_id = str(app_id)
-
         parser = reqparse.RequestParser()
         parser.add_argument('message_id', required=False, type=uuid_value, location='json')
         parser.add_argument('question', required=True, type=str, location='json')
         parser.add_argument('answer', required=True, type=str, location='json')
         parser.add_argument('annotation_reply', required=False, type=dict, location='json')
         args = parser.parse_args()
-        annotation = AppAnnotationService.up_insert_app_annotation_from_message(args, app_id)
+        annotation = AppAnnotationService.up_insert_app_annotation_from_message(args, app_model.id)
 
         return annotation
 
@@ -183,93 +168,29 @@ class MessageAnnotationCountApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    def get(self, app_id):
-        app_id = str(app_id)
-
-        # get app info
-        app = _get_app(app_id)
-
+    @get_app_model
+    def get(self, app_model):
         count = db.session.query(MessageAnnotation).filter(
-            MessageAnnotation.app_id == app.id
+            MessageAnnotation.app_id == app_model.id
         ).count()
 
         return {'count': count}
 
 
-class MessageMoreLikeThisApi(Resource):
-    @setup_required
-    @login_required
-    @account_initialization_required
-    def get(self, app_id, message_id):
-        app_id = str(app_id)
-        message_id = str(message_id)
-
-        parser = reqparse.RequestParser()
-        parser.add_argument('response_mode', type=str, required=True, choices=['blocking', 'streaming'],
-                            location='args')
-        args = parser.parse_args()
-
-        streaming = args['response_mode'] == 'streaming'
-
-        # get app info
-        app_model = _get_app(app_id, 'completion')
-
-        try:
-            response = CompletionService.generate_more_like_this(
-                app_model=app_model,
-                user=current_user,
-                message_id=message_id,
-                invoke_from=InvokeFrom.DEBUGGER,
-                streaming=streaming
-            )
-            return compact_response(response)
-        except MessageNotExistsError:
-            raise NotFound("Message Not Exists.")
-        except MoreLikeThisDisabledError:
-            raise AppMoreLikeThisDisabledError()
-        except ProviderTokenNotInitError as ex:
-            raise ProviderNotInitializeError(ex.description)
-        except QuotaExceededError:
-            raise ProviderQuotaExceededError()
-        except ModelCurrentlyNotSupportError:
-            raise ProviderModelCurrentlyNotSupportError()
-        except InvokeError as e:
-            raise CompletionRequestError(e.description)
-        except ValueError as e:
-            raise e
-        except Exception as e:
-            logging.exception("internal server error.")
-            raise InternalServerError()
-
-
-def compact_response(response: Union[dict, Generator]) -> Response:
-    if isinstance(response, dict):
-        return Response(response=json.dumps(response), status=200, mimetype='application/json')
-    else:
-        def generate() -> Generator:
-            yield from response
-
-        return Response(stream_with_context(generate()), status=200,
-                        mimetype='text/event-stream')
-
-
 class MessageSuggestedQuestionApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    def get(self, app_id, message_id):
-        app_id = str(app_id)
+    @get_app_model(mode=[AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT])
+    def get(self, app_model, message_id):
         message_id = str(message_id)
 
-        # get app info
-        app_model = _get_app(app_id, 'chat')
-
         try:
             questions = MessageService.get_suggested_questions_after_answer(
                 app_model=app_model,
                 message_id=message_id,
                 user=current_user,
-                check_enabled=False
+                invoke_from=InvokeFrom.DEBUGGER
             )
         except MessageNotExistsError:
             raise NotFound("Message not found")
@@ -283,6 +204,8 @@ class MessageSuggestedQuestionApi(Resource):
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
             raise CompletionRequestError(e.description)
+        except SuggestedQuestionsAfterAnswerDisabledError:
+            raise AppSuggestedQuestionsAfterAnswerDisabledError()
         except Exception:
             logging.exception("internal server error.")
             raise InternalServerError()
@@ -294,14 +217,11 @@ class MessageApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
+    @get_app_model
     @marshal_with(message_detail_fields)
-    def get(self, app_id, message_id):
-        app_id = str(app_id)
+    def get(self, app_model, message_id):
         message_id = str(message_id)
 
-        # get app info
-        app_model = _get_app(app_id)
-
         message = db.session.query(Message).filter(
             Message.id == message_id,
             Message.app_id == app_model.id
@@ -313,7 +233,6 @@ class MessageApi(Resource):
         return message
 
 
-api.add_resource(MessageMoreLikeThisApi, '/apps/<uuid:app_id>/completion-messages/<uuid:message_id>/more-like-this')
 api.add_resource(MessageSuggestedQuestionApi, '/apps/<uuid:app_id>/chat-messages/<uuid:message_id>/suggested-questions')
 api.add_resource(ChatMessageListApi, '/apps/<uuid:app_id>/chat-messages', endpoint='console_chat_messages')
 api.add_resource(MessageFeedbackApi, '/apps/<uuid:app_id>/feedbacks')

api/controllers/console/app/model_config.py

@@ -1,16 +1,20 @@
+import json
 
 from flask import request
 from flask_login import current_user
 from flask_restful import Resource
 
 from controllers.console import api
-from controllers.console.app import _get_app
+from controllers.console.app.wraps import get_app_model
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required
+from core.agent.entities import AgentToolEntity
+from core.tools.tool_manager import ToolManager
+from core.tools.utils.configuration import ToolParameterConfigurationManager
 from events.app_event import app_model_config_was_updated
 from extensions.ext_database import db
 from libs.login import login_required
-from models.model import AppModelConfig
+from models.model import AppMode, AppModelConfig
 from services.app_model_config_service import AppModelConfigService
 
 
@@ -19,33 +23,113 @@ class ModelConfigResource(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    def post(self, app_id):
+    @get_app_model(mode=[AppMode.AGENT_CHAT, AppMode.CHAT, AppMode.COMPLETION])
+    def post(self, app_model):
         """Modify app model config"""
-        app_id = str(app_id)
-
-        app = _get_app(app_id)
-
         # validate config
         model_configuration = AppModelConfigService.validate_configuration(
             tenant_id=current_user.current_tenant_id,
-            account=current_user,
             config=request.json,
-            app_mode=app.mode
+            app_mode=AppMode.value_of(app_model.mode)
         )
 
         new_app_model_config = AppModelConfig(
-            app_id=app.id,
+            app_id=app_model.id,
         )
         new_app_model_config = new_app_model_config.from_model_config_dict(model_configuration)
 
+        if app_model.mode == AppMode.AGENT_CHAT.value or app_model.is_agent:
+            # get original app model config
+            original_app_model_config: AppModelConfig = db.session.query(AppModelConfig).filter(
+                AppModelConfig.id == app_model.app_model_config_id
+            ).first()
+            agent_mode = original_app_model_config.agent_mode_dict
+            # decrypt agent tool parameters if it's secret-input
+            parameter_map = {}
+            masked_parameter_map = {}
+            tool_map = {}
+            for tool in agent_mode.get('tools') or []:
+                if not isinstance(tool, dict) or len(tool.keys()) <= 3:
+                    continue
+
+                agent_tool_entity = AgentToolEntity(**tool)
+                # get tool
+                try:
+                    tool_runtime = ToolManager.get_agent_tool_runtime(
+                        tenant_id=current_user.current_tenant_id,
+                        agent_tool=agent_tool_entity,
+                    )
+                    manager = ToolParameterConfigurationManager(
+                        tenant_id=current_user.current_tenant_id,
+                        tool_runtime=tool_runtime,
+                        provider_name=agent_tool_entity.provider_id,
+                        provider_type=agent_tool_entity.provider_type,
+                    )
+                except Exception as e:
+                    continue
+
+                # get decrypted parameters
+                if agent_tool_entity.tool_parameters:
+                    parameters = manager.decrypt_tool_parameters(agent_tool_entity.tool_parameters or {})
+                    masked_parameter = manager.mask_tool_parameters(parameters or {})
+                else:
+                    parameters = {}
+                    masked_parameter = {}
+
+                key = f'{agent_tool_entity.provider_id}.{agent_tool_entity.provider_type}.{agent_tool_entity.tool_name}'
+                masked_parameter_map[key] = masked_parameter
+                parameter_map[key] = parameters
+                tool_map[key] = tool_runtime
+
+            # encrypt agent tool parameters if it's secret-input
+            agent_mode = new_app_model_config.agent_mode_dict
+            for tool in agent_mode.get('tools') or []:
+                agent_tool_entity = AgentToolEntity(**tool)
+
+                # get tool
+                key = f'{agent_tool_entity.provider_id}.{agent_tool_entity.provider_type}.{agent_tool_entity.tool_name}'
+                if key in tool_map:
+                    tool_runtime = tool_map[key]
+                else:
+                    try:
+                        tool_runtime = ToolManager.get_agent_tool_runtime(
+                            tenant_id=current_user.current_tenant_id,
+                            agent_tool=agent_tool_entity,
+                        )
+                    except Exception as e:
+                        continue
+
+                manager = ToolParameterConfigurationManager(
+                    tenant_id=current_user.current_tenant_id,
+                    tool_runtime=tool_runtime,
+                    provider_name=agent_tool_entity.provider_id,
+                    provider_type=agent_tool_entity.provider_type,
+                )
+                manager.delete_tool_parameters_cache()
+
+                # override parameters if it equals to masked parameters
+                if agent_tool_entity.tool_parameters:
+                    if key not in masked_parameter_map:
+                        continue
+
+                    if agent_tool_entity.tool_parameters == masked_parameter_map[key]:
+                        agent_tool_entity.tool_parameters = parameter_map[key]
+
+                # encrypt parameters
+                if agent_tool_entity.tool_parameters:
+                    tool['tool_parameters'] = manager.encrypt_tool_parameters(agent_tool_entity.tool_parameters or {})
+
+            # update app model config
+            new_app_model_config.agent_mode = json.dumps(agent_mode)
+
         db.session.add(new_app_model_config)
         db.session.flush()
 
-        app.app_model_config_id = new_app_model_config.id
+        app_model.app_model_config_id = new_app_model_config.id
         db.session.commit()
 
         app_model_config_was_updated.send(
-            app,
+            app_model,
             app_model_config=new_app_model_config
         )
 

api/controllers/console/app/site.py

@@ -4,7 +4,7 @@ from werkzeug.exceptions import Forbidden, NotFound
 
 from constants.languages import supported_language
 from controllers.console import api
-from controllers.console.app import _get_app
+from controllers.console.app.wraps import get_app_model
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required
 from extensions.ext_database import db
@@ -34,13 +34,11 @@ class AppSite(Resource):
     @setup_required
     @login_required
     @account_initialization_required
+    @get_app_model
     @marshal_with(app_site_fields)
-    def post(self, app_id):
+    def post(self, app_model):
         args = parse_app_site_args()
 
-        app_id = str(app_id)
-        app_model = _get_app(app_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()
@@ -82,11 +80,9 @@ class AppSiteAccessTokenReset(Resource):
     @setup_required
     @login_required
     @account_initialization_required
+    @get_app_model
     @marshal_with(app_site_fields)
-    def post(self, app_id):
-        app_id = str(app_id)
-        app_model = _get_app(app_id)
-
+    def post(self, app_model):
         # 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()

api/controllers/console/app/statistic.py

@@ -7,12 +7,13 @@ from flask_login import current_user
 from flask_restful import Resource, reqparse
 
 from controllers.console import api
-from controllers.console.app import _get_app
+from controllers.console.app.wraps import get_app_model
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required
 from extensions.ext_database import db
 from libs.helper import datetime_string
 from libs.login import login_required
+from models.model import AppMode
 
 
 class DailyConversationStatistic(Resource):
@@ -20,10 +21,9 @@ class DailyConversationStatistic(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    def get(self, app_id):
+    @get_app_model
+    def get(self, app_model):
         account = current_user
-        app_id = str(app_id)
-        app_model = _get_app(app_id)
 
         parser = reqparse.RequestParser()
         parser.add_argument('start', type=datetime_string('%Y-%m-%d %H:%M'), location='args')
@@ -81,10 +81,9 @@ class DailyTerminalsStatistic(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    def get(self, app_id):
+    @get_app_model
+    def get(self, app_model):
         account = current_user
-        app_id = str(app_id)
-        app_model = _get_app(app_id)
 
         parser = reqparse.RequestParser()
         parser.add_argument('start', type=datetime_string('%Y-%m-%d %H:%M'), location='args')
@@ -141,10 +140,9 @@ class DailyTokenCostStatistic(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    def get(self, app_id):
+    @get_app_model
+    def get(self, app_model):
         account = current_user
-        app_id = str(app_id)
-        app_model = _get_app(app_id)
 
         parser = reqparse.RequestParser()
         parser.add_argument('start', type=datetime_string('%Y-%m-%d %H:%M'), location='args')
@@ -205,10 +203,9 @@ class AverageSessionInteractionStatistic(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    def get(self, app_id):
+    @get_app_model(mode=[AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT])
+    def get(self, app_model):
         account = current_user
-        app_id = str(app_id)
-        app_model = _get_app(app_id, 'chat')
 
         parser = reqparse.RequestParser()
         parser.add_argument('start', type=datetime_string('%Y-%m-%d %H:%M'), location='args')
@@ -271,10 +268,9 @@ class UserSatisfactionRateStatistic(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    def get(self, app_id):
+    @get_app_model
+    def get(self, app_model):
         account = current_user
-        app_id = str(app_id)
-        app_model = _get_app(app_id)
 
         parser = reqparse.RequestParser()
         parser.add_argument('start', type=datetime_string('%Y-%m-%d %H:%M'), location='args')
@@ -334,10 +330,9 @@ class AverageResponseTimeStatistic(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    def get(self, app_id):
+    @get_app_model(mode=AppMode.COMPLETION)
+    def get(self, app_model):
         account = current_user
-        app_id = str(app_id)
-        app_model = _get_app(app_id, 'completion')
 
         parser = reqparse.RequestParser()
         parser.add_argument('start', type=datetime_string('%Y-%m-%d %H:%M'), location='args')
@@ -396,10 +391,9 @@ class TokensPerSecondStatistic(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    def get(self, app_id):
+    @get_app_model
+    def get(self, app_model):
         account = current_user
-        app_id = str(app_id)
-        app_model = _get_app(app_id)
 
         parser = reqparse.RequestParser()
         parser.add_argument('start', type=datetime_string('%Y-%m-%d %H:%M'), location='args')

api/controllers/console/app/workflow.py

@@ -0,0 +1,324 @@
+import json
+import logging
+
+from flask import abort, request
+from flask_restful import Resource, marshal_with, reqparse
+from werkzeug.exceptions import InternalServerError, NotFound
+
+import services
+from controllers.console import api
+from controllers.console.app.error import ConversationCompletedError, DraftWorkflowNotExist
+from controllers.console.app.wraps import get_app_model
+from controllers.console.setup import setup_required
+from controllers.console.wraps import account_initialization_required
+from core.app.apps.base_app_queue_manager import AppQueueManager
+from core.app.entities.app_invoke_entities import InvokeFrom
+from fields.workflow_fields import workflow_fields
+from fields.workflow_run_fields import workflow_run_node_execution_fields
+from libs import helper
+from libs.helper import TimestampField, uuid_value
+from libs.login import current_user, login_required
+from models.model import App, AppMode
+from services.app_generate_service import AppGenerateService
+from services.workflow_service import WorkflowService
+
+logger = logging.getLogger(__name__)
+
+
+class DraftWorkflowApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    @marshal_with(workflow_fields)
+    def get(self, app_model: App):
+        """
+        Get draft workflow
+        """
+        # fetch draft workflow by app_model
+        workflow_service = WorkflowService()
+        workflow = workflow_service.get_draft_workflow(app_model=app_model)
+
+        if not workflow:
+            raise DraftWorkflowNotExist()
+
+        # return workflow, if not found, return None (initiate graph by frontend)
+        return workflow
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def post(self, app_model: App):
+        """
+        Sync draft workflow
+        """
+        content_type = request.headers.get('Content-Type')
+
+        if 'application/json' in content_type:
+            parser = reqparse.RequestParser()
+            parser.add_argument('graph', type=dict, required=True, nullable=False, location='json')
+            parser.add_argument('features', type=dict, required=True, nullable=False, location='json')
+            args = parser.parse_args()
+        elif 'text/plain' in content_type:
+            try:
+                data = json.loads(request.data.decode('utf-8'))
+                if 'graph' not in data or 'features' not in data:
+                    raise ValueError('graph or features not found in data')
+
+                if not isinstance(data.get('graph'), dict) or not isinstance(data.get('features'), dict):
+                    raise ValueError('graph or features is not a dict')
+
+                args = {
+                    'graph': data.get('graph'),
+                    'features': data.get('features')
+                }
+            except json.JSONDecodeError:
+                return {'message': 'Invalid JSON data'}, 400
+        else:
+            abort(415)
+
+        workflow_service = WorkflowService()
+        workflow = workflow_service.sync_draft_workflow(
+            app_model=app_model,
+            graph=args.get('graph'),
+            features=args.get('features'),
+            account=current_user
+        )
+
+        return {
+            "result": "success",
+            "updated_at": TimestampField().format(workflow.updated_at or workflow.created_at)
+        }
+
+
+class AdvancedChatDraftWorkflowRunApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT])
+    def post(self, app_model: App):
+        """
+        Run draft workflow
+        """
+        parser = reqparse.RequestParser()
+        parser.add_argument('inputs', type=dict, location='json')
+        parser.add_argument('query', type=str, required=True, location='json', default='')
+        parser.add_argument('files', type=list, location='json')
+        parser.add_argument('conversation_id', type=uuid_value, location='json')
+        args = parser.parse_args()
+
+        try:
+            response = AppGenerateService.generate(
+                app_model=app_model,
+                user=current_user,
+                args=args,
+                invoke_from=InvokeFrom.DEBUGGER,
+                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
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.WORKFLOW])
+    def post(self, app_model: App):
+        """
+        Run draft workflow
+        """
+        parser = reqparse.RequestParser()
+        parser.add_argument('inputs', type=dict, required=True, nullable=False, location='json')
+        parser.add_argument('files', type=list, required=False, location='json')
+        args = parser.parse_args()
+
+        try:
+            response = AppGenerateService.generate(
+                app_model=app_model,
+                user=current_user,
+                args=args,
+                invoke_from=InvokeFrom.DEBUGGER,
+                streaming=True
+            )
+
+            return helper.compact_generate_response(response)
+        except ValueError as e:
+            raise e
+        except Exception as e:
+            logging.exception("internal server error.")
+            raise InternalServerError()
+
+
+class WorkflowTaskStopApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def post(self, app_model: App, task_id: str):
+        """
+        Stop workflow task
+        """
+        AppQueueManager.set_stop_flag(task_id, InvokeFrom.DEBUGGER, current_user.id)
+
+        return {
+            "result": "success"
+        }
+
+
+class DraftWorkflowNodeRunApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    @marshal_with(workflow_run_node_execution_fields)
+    def post(self, app_model: App, node_id: str):
+        """
+        Run draft workflow node
+        """
+        parser = reqparse.RequestParser()
+        parser.add_argument('inputs', type=dict, required=True, nullable=False, location='json')
+        args = parser.parse_args()
+
+        workflow_service = WorkflowService()
+        workflow_node_execution = workflow_service.run_draft_workflow_node(
+            app_model=app_model,
+            node_id=node_id,
+            user_inputs=args.get('inputs'),
+            account=current_user
+        )
+
+        return workflow_node_execution
+
+
+class PublishedWorkflowApi(Resource):
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    @marshal_with(workflow_fields)
+    def get(self, app_model: App):
+        """
+        Get published workflow
+        """
+        # fetch published workflow by app_model
+        workflow_service = WorkflowService()
+        workflow = workflow_service.get_published_workflow(app_model=app_model)
+
+        # return workflow, if not found, return None
+        return workflow
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def post(self, app_model: App):
+        """
+        Publish workflow
+        """
+        workflow_service = WorkflowService()
+        workflow = workflow_service.publish_workflow(app_model=app_model, account=current_user)
+
+        return {
+            "result": "success",
+            "created_at": TimestampField().format(workflow.created_at)
+        }
+
+
+class DefaultBlockConfigsApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def get(self, app_model: App):
+        """
+        Get default block config
+        """
+        # Get default block configs
+        workflow_service = WorkflowService()
+        return workflow_service.get_default_block_configs()
+
+
+class DefaultBlockConfigApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    def get(self, app_model: App, block_type: str):
+        """
+        Get default block config
+        """
+        parser = reqparse.RequestParser()
+        parser.add_argument('q', type=str, location='args')
+        args = parser.parse_args()
+
+        filters = None
+        if args.get('q'):
+            try:
+                filters = json.loads(args.get('q'))
+            except json.JSONDecodeError:
+                raise ValueError('Invalid filters')
+
+        # Get default block configs
+        workflow_service = WorkflowService()
+        return workflow_service.get_default_block_config(
+            node_type=block_type,
+            filters=filters
+        )
+
+
+class ConvertToWorkflowApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.CHAT, AppMode.COMPLETION])
+    def post(self, app_model: App):
+        """
+        Convert basic mode of chatbot app to workflow mode
+        Convert expert mode of chatbot app to workflow mode
+        Convert Completion App to Workflow App
+        """
+        if request.data:
+            parser = reqparse.RequestParser()
+            parser.add_argument('name', type=str, required=False, nullable=True, location='json')
+            parser.add_argument('icon', type=str, required=False, nullable=True, location='json')
+            parser.add_argument('icon_background', type=str, required=False, nullable=True, location='json')
+            args = parser.parse_args()
+        else:
+            args = {}
+
+        # convert to workflow mode
+        workflow_service = WorkflowService()
+        new_app_model = workflow_service.convert_to_workflow(
+            app_model=app_model,
+            account=current_user,
+            args=args
+        )
+
+        # return app id
+        return {
+            'new_app_id': new_app_model.id,
+        }
+
+
+api.add_resource(DraftWorkflowApi, '/apps/<uuid:app_id>/workflows/draft')
+api.add_resource(AdvancedChatDraftWorkflowRunApi, '/apps/<uuid:app_id>/advanced-chat/workflows/draft/run')
+api.add_resource(DraftWorkflowRunApi, '/apps/<uuid:app_id>/workflows/draft/run')
+api.add_resource(WorkflowTaskStopApi, '/apps/<uuid:app_id>/workflow-runs/tasks/<string:task_id>/stop')
+api.add_resource(DraftWorkflowNodeRunApi, '/apps/<uuid:app_id>/workflows/draft/nodes/<string:node_id>/run')
+api.add_resource(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'
+                                        '/<string:block_type>')
+api.add_resource(ConvertToWorkflowApi, '/apps/<uuid:app_id>/convert-to-workflow')

api/controllers/console/app/workflow_app_log.py

@@ -0,0 +1,41 @@
+from flask_restful import Resource, marshal_with, reqparse
+from flask_restful.inputs import int_range
+
+from controllers.console import api
+from controllers.console.app.wraps import get_app_model
+from controllers.console.setup import setup_required
+from controllers.console.wraps import account_initialization_required
+from fields.workflow_app_log_fields import workflow_app_log_pagination_fields
+from libs.login import login_required
+from models.model import App, AppMode
+from services.workflow_app_service import WorkflowAppService
+
+
+class WorkflowAppLogApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.WORKFLOW])
+    @marshal_with(workflow_app_log_pagination_fields)
+    def get(self, app_model: App):
+        """
+        Get workflow app logs
+        """
+        parser = reqparse.RequestParser()
+        parser.add_argument('keyword', type=str, location='args')
+        parser.add_argument('status', type=str, choices=['succeeded', 'failed', 'stopped'], location='args')
+        parser.add_argument('page', type=int_range(1, 99999), default=1, location='args')
+        parser.add_argument('limit', type=int_range(1, 100), default=20, location='args')
+        args = parser.parse_args()
+
+        # get paginate workflow app logs
+        workflow_app_service = WorkflowAppService()
+        workflow_app_log_pagination = workflow_app_service.get_paginate_workflow_app_logs(
+            app_model=app_model,
+            args=args
+        )
+
+        return workflow_app_log_pagination
+
+
+api.add_resource(WorkflowAppLogApi, '/apps/<uuid:app_id>/workflow-app-logs')

api/controllers/console/app/workflow_run.py

@@ -0,0 +1,109 @@
+from flask_restful import Resource, marshal_with, reqparse
+from flask_restful.inputs import int_range
+
+from controllers.console import api
+from controllers.console.app.wraps import get_app_model
+from controllers.console.setup import setup_required
+from controllers.console.wraps import account_initialization_required
+from fields.workflow_run_fields import (
+    advanced_chat_workflow_run_pagination_fields,
+    workflow_run_detail_fields,
+    workflow_run_node_execution_list_fields,
+    workflow_run_pagination_fields,
+)
+from libs.helper import uuid_value
+from libs.login import login_required
+from models.model import App, AppMode
+from services.workflow_run_service import WorkflowRunService
+
+
+class AdvancedChatAppWorkflowRunListApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT])
+    @marshal_with(advanced_chat_workflow_run_pagination_fields)
+    def get(self, app_model: App):
+        """
+        Get advanced chat app workflow run list
+        """
+        parser = reqparse.RequestParser()
+        parser.add_argument('last_id', type=uuid_value, location='args')
+        parser.add_argument('limit', type=int_range(1, 100), required=False, default=20, location='args')
+        args = parser.parse_args()
+
+        workflow_run_service = WorkflowRunService()
+        result = workflow_run_service.get_paginate_advanced_chat_workflow_runs(
+            app_model=app_model,
+            args=args
+        )
+
+        return result
+
+
+class WorkflowRunListApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    @marshal_with(workflow_run_pagination_fields)
+    def get(self, app_model: App):
+        """
+        Get workflow run list
+        """
+        parser = reqparse.RequestParser()
+        parser.add_argument('last_id', type=uuid_value, location='args')
+        parser.add_argument('limit', type=int_range(1, 100), required=False, default=20, location='args')
+        args = parser.parse_args()
+
+        workflow_run_service = WorkflowRunService()
+        result = workflow_run_service.get_paginate_workflow_runs(
+            app_model=app_model,
+            args=args
+        )
+
+        return result
+
+
+class WorkflowRunDetailApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    @marshal_with(workflow_run_detail_fields)
+    def get(self, app_model: App, run_id):
+        """
+        Get workflow run detail
+        """
+        run_id = str(run_id)
+
+        workflow_run_service = WorkflowRunService()
+        workflow_run = workflow_run_service.get_workflow_run(app_model=app_model, run_id=run_id)
+
+        return workflow_run
+
+
+class WorkflowRunNodeExecutionListApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.ADVANCED_CHAT, AppMode.WORKFLOW])
+    @marshal_with(workflow_run_node_execution_list_fields)
+    def get(self, app_model: App, run_id):
+        """
+        Get workflow run node execution list
+        """
+        run_id = str(run_id)
+
+        workflow_run_service = WorkflowRunService()
+        node_executions = workflow_run_service.get_workflow_run_node_executions(app_model=app_model, run_id=run_id)
+
+        return {
+            'data': node_executions
+        }
+
+
+api.add_resource(AdvancedChatAppWorkflowRunListApi, '/apps/<uuid:app_id>/advanced-chat/workflow-runs')
+api.add_resource(WorkflowRunListApi, '/apps/<uuid:app_id>/workflow-runs')
+api.add_resource(WorkflowRunDetailApi, '/apps/<uuid:app_id>/workflow-runs/<uuid:run_id>')
+api.add_resource(WorkflowRunNodeExecutionListApi, '/apps/<uuid:app_id>/workflow-runs/<uuid:run_id>/node-executions')

api/controllers/console/app/workflow_statistic.py

@@ -0,0 +1,278 @@
+from datetime import datetime
+from decimal import Decimal
+
+import pytz
+from flask import jsonify
+from flask_login import current_user
+from flask_restful import Resource, reqparse
+
+from controllers.console import api
+from controllers.console.app.wraps import get_app_model
+from controllers.console.setup import setup_required
+from controllers.console.wraps import account_initialization_required
+from extensions.ext_database import db
+from libs.helper import datetime_string
+from libs.login import login_required
+from models.model import AppMode
+from models.workflow import WorkflowRunTriggeredFrom
+
+
+class WorkflowDailyRunsStatistic(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model
+    def get(self, app_model):
+        account = current_user
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('start', type=datetime_string('%Y-%m-%d %H:%M'), location='args')
+        parser.add_argument('end', type=datetime_string('%Y-%m-%d %H:%M'), location='args')
+        args = parser.parse_args()
+
+        sql_query = '''
+        SELECT date(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date, count(id) AS runs
+            FROM workflow_runs 
+            WHERE app_id = :app_id 
+                AND triggered_from = :triggered_from
+        '''
+        arg_dict = {'tz': account.timezone, 'app_id': app_model.id, 'triggered_from': WorkflowRunTriggeredFrom.APP_RUN.value}
+
+        timezone = pytz.timezone(account.timezone)
+        utc_timezone = pytz.utc
+
+        if args['start']:
+            start_datetime = datetime.strptime(args['start'], '%Y-%m-%d %H:%M')
+            start_datetime = start_datetime.replace(second=0)
+
+            start_datetime_timezone = timezone.localize(start_datetime)
+            start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
+
+            sql_query += ' and created_at >= :start'
+            arg_dict['start'] = start_datetime_utc
+
+        if args['end']:
+            end_datetime = datetime.strptime(args['end'], '%Y-%m-%d %H:%M')
+            end_datetime = end_datetime.replace(second=0)
+
+            end_datetime_timezone = timezone.localize(end_datetime)
+            end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
+
+            sql_query += ' and created_at < :end'
+            arg_dict['end'] = end_datetime_utc
+
+        sql_query += ' GROUP BY date order by date'
+
+        response_data = []
+
+        with db.engine.begin() as conn:
+            rs = conn.execute(db.text(sql_query), arg_dict)
+            for i in rs:
+                response_data.append({
+                    'date': str(i.date),
+                    'runs': i.runs
+                })
+
+        return jsonify({
+            'data': response_data
+        })
+
+class WorkflowDailyTerminalsStatistic(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model
+    def get(self, app_model):
+        account = current_user
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('start', type=datetime_string('%Y-%m-%d %H:%M'), location='args')
+        parser.add_argument('end', type=datetime_string('%Y-%m-%d %H:%M'), location='args')
+        args = parser.parse_args()
+
+        sql_query = '''
+                SELECT date(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date, count(distinct workflow_runs.created_by) AS terminal_count
+                    FROM workflow_runs 
+                    WHERE app_id = :app_id 
+                        AND triggered_from = :triggered_from
+                '''
+        arg_dict = {'tz': account.timezone, 'app_id': app_model.id, 'triggered_from': WorkflowRunTriggeredFrom.APP_RUN.value}
+
+        timezone = pytz.timezone(account.timezone)
+        utc_timezone = pytz.utc
+
+        if args['start']:
+            start_datetime = datetime.strptime(args['start'], '%Y-%m-%d %H:%M')
+            start_datetime = start_datetime.replace(second=0)
+
+            start_datetime_timezone = timezone.localize(start_datetime)
+            start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
+
+            sql_query += ' and created_at >= :start'
+            arg_dict['start'] = start_datetime_utc
+
+        if args['end']:
+            end_datetime = datetime.strptime(args['end'], '%Y-%m-%d %H:%M')
+            end_datetime = end_datetime.replace(second=0)
+
+            end_datetime_timezone = timezone.localize(end_datetime)
+            end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
+
+            sql_query += ' and created_at < :end'
+            arg_dict['end'] = end_datetime_utc
+
+        sql_query += ' GROUP BY date order by date'
+
+        response_data = []
+
+        with db.engine.begin() as conn:
+            rs = conn.execute(db.text(sql_query), arg_dict)            
+            for i in rs:
+                response_data.append({
+                    'date': str(i.date),
+                    'terminal_count': i.terminal_count
+                })
+
+        return jsonify({
+            'data': response_data
+        })
+
+class WorkflowDailyTokenCostStatistic(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model
+    def get(self, app_model):
+        account = current_user
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('start', type=datetime_string('%Y-%m-%d %H:%M'), location='args')
+        parser.add_argument('end', type=datetime_string('%Y-%m-%d %H:%M'), location='args')
+        args = parser.parse_args()
+
+        sql_query = '''
+                SELECT 
+                    date(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date, 
+                    SUM(workflow_runs.total_tokens) as token_count
+                FROM workflow_runs 
+                WHERE app_id = :app_id 
+                    AND triggered_from = :triggered_from
+                '''
+        arg_dict = {'tz': account.timezone, 'app_id': app_model.id, 'triggered_from': WorkflowRunTriggeredFrom.APP_RUN.value}
+
+        timezone = pytz.timezone(account.timezone)
+        utc_timezone = pytz.utc
+
+        if args['start']:
+            start_datetime = datetime.strptime(args['start'], '%Y-%m-%d %H:%M')
+            start_datetime = start_datetime.replace(second=0)
+
+            start_datetime_timezone = timezone.localize(start_datetime)
+            start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
+
+            sql_query += ' and created_at >= :start'
+            arg_dict['start'] = start_datetime_utc
+
+        if args['end']:
+            end_datetime = datetime.strptime(args['end'], '%Y-%m-%d %H:%M')
+            end_datetime = end_datetime.replace(second=0)
+
+            end_datetime_timezone = timezone.localize(end_datetime)
+            end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
+
+            sql_query += ' and created_at < :end'
+            arg_dict['end'] = end_datetime_utc
+
+        sql_query += ' GROUP BY date order by date'
+
+        response_data = []
+
+        with db.engine.begin() as conn:
+            rs = conn.execute(db.text(sql_query), arg_dict)
+            for i in rs:
+                response_data.append({
+                    'date': str(i.date),
+                    'token_count': i.token_count,
+                })
+
+        return jsonify({
+            'data': response_data
+        })
+
+class WorkflowAverageAppInteractionStatistic(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model(mode=[AppMode.WORKFLOW])
+    def get(self, app_model):
+        account = current_user
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('start', type=datetime_string('%Y-%m-%d %H:%M'), location='args')
+        parser.add_argument('end', type=datetime_string('%Y-%m-%d %H:%M'), location='args')
+        args = parser.parse_args()
+
+        sql_query = """
+            SELECT 
+                AVG(sub.interactions) as interactions,
+                sub.date
+            FROM
+                (SELECT 
+                    date(DATE_TRUNC('day', c.created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date, 
+                    c.created_by,
+                    COUNT(c.id) AS interactions
+                FROM workflow_runs c
+                WHERE c.app_id = :app_id
+                    AND c.triggered_from = :triggered_from
+                    {{start}}
+                    {{end}}
+                GROUP BY date, c.created_by) sub
+            GROUP BY sub.created_by, sub.date
+            """
+        arg_dict = {'tz': account.timezone, 'app_id': app_model.id, 'triggered_from': WorkflowRunTriggeredFrom.APP_RUN.value}
+
+        timezone = pytz.timezone(account.timezone)
+        utc_timezone = pytz.utc
+
+        if args['start']:
+            start_datetime = datetime.strptime(args['start'], '%Y-%m-%d %H:%M')
+            start_datetime = start_datetime.replace(second=0)
+
+            start_datetime_timezone = timezone.localize(start_datetime)
+            start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
+
+            sql_query = sql_query.replace('{{start}}', ' AND c.created_at >= :start')
+            arg_dict['start'] = start_datetime_utc
+        else:
+            sql_query = sql_query.replace('{{start}}', '')
+
+        if args['end']:
+            end_datetime = datetime.strptime(args['end'], '%Y-%m-%d %H:%M')
+            end_datetime = end_datetime.replace(second=0)
+
+            end_datetime_timezone = timezone.localize(end_datetime)
+            end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
+
+            sql_query = sql_query.replace('{{end}}', ' and c.created_at < :end')
+            arg_dict['end'] = end_datetime_utc
+        else:
+            sql_query = sql_query.replace('{{end}}', '')
+
+        response_data = []
+        
+        with db.engine.begin() as conn:
+            rs = conn.execute(db.text(sql_query), arg_dict)
+            for i in rs:
+                response_data.append({
+                    'date': str(i.date),
+                    'interactions': float(i.interactions.quantize(Decimal('0.01')))
+                })
+
+        return jsonify({
+            'data': response_data
+        })
+
+api.add_resource(WorkflowDailyRunsStatistic, '/apps/<uuid:app_id>/workflow/statistics/daily-conversations')
+api.add_resource(WorkflowDailyTerminalsStatistic, '/apps/<uuid:app_id>/workflow/statistics/daily-terminals')
+api.add_resource(WorkflowDailyTokenCostStatistic, '/apps/<uuid:app_id>/workflow/statistics/token-costs')
+api.add_resource(WorkflowAverageAppInteractionStatistic, '/apps/<uuid:app_id>/workflow/statistics/average-app-interactions')

api/controllers/console/app/wraps.py

@@ -0,0 +1,55 @@
+from collections.abc import Callable
+from functools import wraps
+from typing import Optional, Union
+
+from controllers.console.app.error import AppNotFoundError
+from extensions.ext_database import db
+from libs.login import current_user
+from models.model import App, AppMode
+
+
+def get_app_model(view: Optional[Callable] = None, *,
+                  mode: Union[AppMode, list[AppMode]] = None):
+    def decorator(view_func):
+        @wraps(view_func)
+        def decorated_view(*args, **kwargs):
+            if not kwargs.get('app_id'):
+                raise ValueError('missing app_id in path parameters')
+
+            app_id = kwargs.get('app_id')
+            app_id = str(app_id)
+
+            del kwargs['app_id']
+
+            app_model = db.session.query(App).filter(
+                App.id == app_id,
+                App.tenant_id == current_user.current_tenant_id,
+                App.status == 'normal'
+            ).first()
+
+            if not app_model:
+                raise AppNotFoundError()
+
+            app_mode = AppMode.value_of(app_model.mode)
+            if app_mode == AppMode.CHANNEL:
+                raise AppNotFoundError()
+
+            if mode is not None:
+                if isinstance(mode, list):
+                    modes = mode
+                else:
+                    modes = [mode]
+
+                if app_mode not in modes:
+                    mode_values = {m.value for m in modes}
+                    raise AppNotFoundError(f"App mode is not in the supported list: {mode_values}")
+
+            kwargs['app_model'] = app_model
+
+            return view_func(*args, **kwargs)
+        return decorated_view
+
+    if view is None:
+        return decorator
+    else:
+        return decorator(view)

api/controllers/console/datasets/datasets_segments.py

@@ -12,7 +12,11 @@ from controllers.console import api
 from controllers.console.app.error import ProviderNotInitializeError
 from controllers.console.datasets.error import InvalidActionError, NoFileUploadedError, TooManyFilesError
 from controllers.console.setup import setup_required
-from controllers.console.wraps import account_initialization_required, cloud_edition_billing_resource_check
+from controllers.console.wraps import (
+    account_initialization_required,
+    cloud_edition_billing_knowledge_limit_check,
+    cloud_edition_billing_resource_check,
+)
 from core.errors.error import LLMBadRequestError, ProviderTokenNotInitError
 from core.model_manager import ModelManager
 from core.model_runtime.entities.model_entities import ModelType
@@ -207,6 +211,7 @@ class DatasetDocumentSegmentAddApi(Resource):
     @login_required
     @account_initialization_required
     @cloud_edition_billing_resource_check('vector_space')
+    @cloud_edition_billing_knowledge_limit_check('add_segment')
     def post(self, dataset_id, document_id):
         # check dataset
         dataset_id = str(dataset_id)
@@ -357,6 +362,7 @@ class DatasetDocumentSegmentBatchImportApi(Resource):
     @login_required
     @account_initialization_required
     @cloud_edition_billing_resource_check('vector_space')
+    @cloud_edition_billing_knowledge_limit_check('add_segment')
     def post(self, dataset_id, document_id):
         # check dataset
         dataset_id = str(dataset_id)

api/controllers/console/datasets/file.py

@@ -11,7 +11,7 @@ from controllers.console.datasets.error import (
     UnsupportedFileTypeError,
 )
 from controllers.console.setup import setup_required
-from controllers.console.wraps import account_initialization_required
+from controllers.console.wraps import account_initialization_required, cloud_edition_billing_resource_check
 from fields.file_fields import file_fields, upload_config_fields
 from libs.login import login_required
 from services.file_service import ALLOWED_EXTENSIONS, UNSTRUSTURED_ALLOWED_EXTENSIONS, FileService
@@ -39,6 +39,7 @@ class FileApi(Resource):
     @login_required
     @account_initialization_required
     @marshal_with(file_fields)
+    @cloud_edition_billing_resource_check(resource='documents')
     def post(self):
 
         # get file from request

api/controllers/console/explore/audio.py

@@ -19,7 +19,6 @@ from controllers.console.app.error import (
 from controllers.console.explore.wraps import InstalledAppResource
 from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
 from core.model_runtime.errors.invoke import InvokeError
-from models.model import AppModelConfig
 from services.audio_service import AudioService
 from services.errors.audio import (
     AudioTooLargeServiceError,
@@ -32,16 +31,12 @@ from services.errors.audio import (
 class ChatAudioApi(InstalledAppResource):
     def post(self, installed_app):
         app_model = installed_app.app
-        app_model_config: AppModelConfig = app_model.app_model_config
-
-        if not app_model_config.speech_to_text_dict['enabled']:
-            raise AppUnavailableError()
 
         file = request.files['file']
 
         try:
             response = AudioService.transcript_asr(
-                tenant_id=app_model.tenant_id,
+                app_model=app_model,
                 file=file,
                 end_user=None
             )
@@ -76,16 +71,12 @@ class ChatAudioApi(InstalledAppResource):
 class ChatTextApi(InstalledAppResource):
     def post(self, installed_app):
         app_model = installed_app.app
-        app_model_config: AppModelConfig = app_model.app_model_config
-
-        if not app_model_config.text_to_speech_dict['enabled']:
-            raise AppUnavailableError()
 
         try:
             response = AudioService.transcript_tts(
-                tenant_id=app_model.tenant_id,
+                app_model=app_model,
                 text=request.form['text'],
-                voice=app_model.app_model_config.text_to_speech_dict.get('voice'),
+                voice=request.form.get('voice'),
                 streaming=False
             )
             return {'data': response.data.decode('latin1')}

api/controllers/console/explore/completion.py

@@ -1,10 +1,6 @@
-import json
 import logging
-from collections.abc import Generator
 from datetime import datetime
-from typing import Union
 
-from flask import Response, stream_with_context
 from flask_login import current_user
 from flask_restful import reqparse
 from werkzeug.exceptions import InternalServerError, NotFound
@@ -21,13 +17,15 @@ from controllers.console.app.error import (
 )
 from controllers.console.explore.error import NotChatAppError, NotCompletionAppError
 from controllers.console.explore.wraps import InstalledAppResource
-from core.application_queue_manager import ApplicationQueueManager
-from core.entities.application_entities import InvokeFrom
+from core.app.apps.base_app_queue_manager import AppQueueManager
+from core.app.entities.app_invoke_entities import InvokeFrom
 from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
 from core.model_runtime.errors.invoke import InvokeError
 from extensions.ext_database import db
+from libs import helper
 from libs.helper import uuid_value
-from services.completion_service import CompletionService
+from models.model import AppMode
+from services.app_generate_service import AppGenerateService
 
 
 # define completion api for user
@@ -53,7 +51,7 @@ class CompletionApi(InstalledAppResource):
         db.session.commit()
 
         try:
-            response = CompletionService.completion(
+            response = AppGenerateService.generate(
                 app_model=app_model,
                 user=current_user,
                 args=args,
@@ -61,7 +59,7 @@ class CompletionApi(InstalledAppResource):
                 streaming=streaming
             )
 
-            return compact_response(response)
+            return helper.compact_generate_response(response)
         except services.errors.conversation.ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")
         except services.errors.conversation.ConversationCompletedError:
@@ -90,7 +88,7 @@ class CompletionStopApi(InstalledAppResource):
         if app_model.mode != 'completion':
             raise NotCompletionAppError()
 
-        ApplicationQueueManager.set_stop_flag(task_id, InvokeFrom.EXPLORE, current_user.id)
+        AppQueueManager.set_stop_flag(task_id, InvokeFrom.EXPLORE, current_user.id)
 
         return {'result': 'success'}, 200
 
@@ -98,34 +96,33 @@ class CompletionStopApi(InstalledAppResource):
 class ChatApi(InstalledAppResource):
     def post(self, installed_app):
         app_model = installed_app.app
-        if app_model.mode != 'chat':
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
             raise NotChatAppError()
 
         parser = reqparse.RequestParser()
         parser.add_argument('inputs', type=dict, required=True, location='json')
         parser.add_argument('query', type=str, required=True, location='json')
         parser.add_argument('files', type=list, required=False, location='json')
-        parser.add_argument('response_mode', type=str, choices=['blocking', 'streaming'], location='json')
         parser.add_argument('conversation_id', type=uuid_value, location='json')
         parser.add_argument('retriever_from', type=str, required=False, default='explore_app', location='json')
         args = parser.parse_args()
 
-        streaming = args['response_mode'] == 'streaming'
         args['auto_generate_name'] = False
 
         installed_app.last_used_at = datetime.utcnow()
         db.session.commit()
 
         try:
-            response = CompletionService.completion(
+            response = AppGenerateService.generate(
                 app_model=app_model,
                 user=current_user,
                 args=args,
                 invoke_from=InvokeFrom.EXPLORE,
-                streaming=streaming
+                streaming=True
             )
 
-            return compact_response(response)
+            return helper.compact_generate_response(response)
         except services.errors.conversation.ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")
         except services.errors.conversation.ConversationCompletedError:
@@ -151,25 +148,15 @@ class ChatApi(InstalledAppResource):
 class ChatStopApi(InstalledAppResource):
     def post(self, installed_app, task_id):
         app_model = installed_app.app
-        if app_model.mode != 'chat':
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
             raise NotChatAppError()
 
-        ApplicationQueueManager.set_stop_flag(task_id, InvokeFrom.EXPLORE, current_user.id)
+        AppQueueManager.set_stop_flag(task_id, InvokeFrom.EXPLORE, current_user.id)
 
         return {'result': 'success'}, 200
 
 
-def compact_response(response: Union[dict, Generator]) -> Response:
-    if isinstance(response, dict):
-        return Response(response=json.dumps(response), status=200, mimetype='application/json')
-    else:
-        def generate() -> Generator:
-            yield from response
-
-        return Response(stream_with_context(generate()), status=200,
-                        mimetype='text/event-stream')
-
-
 api.add_resource(CompletionApi, '/installed-apps/<uuid:installed_app_id>/completion-messages', endpoint='installed_app_completion')
 api.add_resource(CompletionStopApi, '/installed-apps/<uuid:installed_app_id>/completion-messages/<string:task_id>/stop', endpoint='installed_app_stop_completion')
 api.add_resource(ChatApi, '/installed-apps/<uuid:installed_app_id>/chat-messages', endpoint='installed_app_chat_completion')

api/controllers/console/explore/conversation.py

@@ -8,6 +8,7 @@ from controllers.console.explore.error import NotChatAppError
 from controllers.console.explore.wraps import InstalledAppResource
 from fields.conversation_fields import conversation_infinite_scroll_pagination_fields, simple_conversation_fields
 from libs.helper import uuid_value
+from models.model import AppMode
 from services.conversation_service import ConversationService
 from services.errors.conversation import ConversationNotExistsError, LastConversationNotExistsError
 from services.web_conversation_service import WebConversationService
@@ -18,7 +19,8 @@ class ConversationListApi(InstalledAppResource):
     @marshal_with(conversation_infinite_scroll_pagination_fields)
     def get(self, installed_app):
         app_model = installed_app.app
-        if app_model.mode != 'chat':
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
             raise NotChatAppError()
 
         parser = reqparse.RequestParser()
@@ -47,7 +49,8 @@ class ConversationListApi(InstalledAppResource):
 class ConversationApi(InstalledAppResource):
     def delete(self, installed_app, c_id):
         app_model = installed_app.app
-        if app_model.mode != 'chat':
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
             raise NotChatAppError()
 
         conversation_id = str(c_id)
@@ -65,7 +68,8 @@ class ConversationRenameApi(InstalledAppResource):
     @marshal_with(simple_conversation_fields)
     def post(self, installed_app, c_id):
         app_model = installed_app.app
-        if app_model.mode != 'chat':
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
             raise NotChatAppError()
 
         conversation_id = str(c_id)
@@ -91,7 +95,8 @@ class ConversationPinApi(InstalledAppResource):
 
     def patch(self, installed_app, c_id):
         app_model = installed_app.app
-        if app_model.mode != 'chat':
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
             raise NotChatAppError()
 
         conversation_id = str(c_id)
@@ -107,7 +112,8 @@ class ConversationPinApi(InstalledAppResource):
 class ConversationUnPinApi(InstalledAppResource):
     def patch(self, installed_app, c_id):
         app_model = installed_app.app
-        if app_model.mode != 'chat':
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
             raise NotChatAppError()
 
         conversation_id = str(c_id)

api/controllers/console/explore/error.py

@@ -9,7 +9,13 @@ class NotCompletionAppError(BaseHTTPException):
 
 class NotChatAppError(BaseHTTPException):
     error_code = 'not_chat_app'
-    description = "Not Chat App"
+    description = "App mode is invalid."
+    code = 400
+
+
+class NotWorkflowAppError(BaseHTTPException):
+    error_code = 'not_workflow_app'
+    description = "Only support workflow app."
     code = 400
 
 

api/controllers/console/explore/installed_app.py

@@ -34,8 +34,7 @@ class InstalledAppsListApi(Resource):
                 'is_pinned': installed_app.is_pinned,
                 'last_used_at': installed_app.last_used_at,
                 'editable': current_user.role in ["owner", "admin"],
-                'uninstallable': current_tenant_id == installed_app.app_owner_tenant_id,
-                'is_agent': installed_app.is_agent
+                'uninstallable': current_tenant_id == installed_app.app_owner_tenant_id
             }
             for installed_app in installed_apps
         ]

api/controllers/console/explore/message.py

@@ -1,9 +1,5 @@
-import json
 import logging
-from collections.abc import Generator
-from typing import Union
 
-from flask import Response, stream_with_context
 from flask_login import current_user
 from flask_restful import marshal_with, reqparse
 from flask_restful.inputs import int_range
@@ -24,12 +20,14 @@ from controllers.console.explore.error import (
     NotCompletionAppError,
 )
 from controllers.console.explore.wraps import InstalledAppResource
-from core.entities.application_entities import InvokeFrom
+from core.app.entities.app_invoke_entities import InvokeFrom
 from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
 from core.model_runtime.errors.invoke import InvokeError
 from fields.message_fields import message_infinite_scroll_pagination_fields
+from libs import helper
 from libs.helper import uuid_value
-from services.completion_service import CompletionService
+from models.model import AppMode
+from services.app_generate_service import AppGenerateService
 from services.errors.app import MoreLikeThisDisabledError
 from services.errors.conversation import ConversationNotExistsError
 from services.errors.message import MessageNotExistsError, SuggestedQuestionsAfterAnswerDisabledError
@@ -41,7 +39,8 @@ class MessageListApi(InstalledAppResource):
     def get(self, installed_app):
         app_model = installed_app.app
 
-        if app_model.mode != 'chat':
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
             raise NotChatAppError()
 
         parser = reqparse.RequestParser()
@@ -91,14 +90,14 @@ class MessageMoreLikeThisApi(InstalledAppResource):
         streaming = args['response_mode'] == 'streaming'
 
         try:
-            response = CompletionService.generate_more_like_this(
+            response = AppGenerateService.generate_more_like_this(
                 app_model=app_model,
                 user=current_user,
                 message_id=message_id,
                 invoke_from=InvokeFrom.EXPLORE,
                 streaming=streaming
             )
-            return compact_response(response)
+            return helper.compact_generate_response(response)
         except MessageNotExistsError:
             raise NotFound("Message Not Exists.")
         except MoreLikeThisDisabledError:
@@ -118,22 +117,12 @@ class MessageMoreLikeThisApi(InstalledAppResource):
             raise InternalServerError()
 
 
-def compact_response(response: Union[dict, Generator]) -> Response:
-    if isinstance(response, dict):
-        return Response(response=json.dumps(response), status=200, mimetype='application/json')
-    else:
-        def generate() -> Generator:
-            yield from response
-
-        return Response(stream_with_context(generate()), status=200,
-                        mimetype='text/event-stream')
-
-
 class MessageSuggestedQuestionApi(InstalledAppResource):
     def get(self, installed_app, message_id):
         app_model = installed_app.app
-        if app_model.mode != 'chat':
-            raise NotCompletionAppError()
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
+            raise NotChatAppError()
 
         message_id = str(message_id)
 
@@ -141,7 +130,8 @@ class MessageSuggestedQuestionApi(InstalledAppResource):
             questions = MessageService.get_suggested_questions_after_answer(
                 app_model=app_model,
                 user=current_user,
-                message_id=message_id
+                message_id=message_id,
+                invoke_from=InvokeFrom.EXPLORE
             )
         except MessageNotExistsError:
             raise NotFound("Message not found")

api/controllers/console/explore/parameter.py

@@ -1,13 +1,12 @@
-import json
 
 from flask import current_app
 from flask_restful import fields, marshal_with
 
 from controllers.console import api
+from controllers.console.app.error import AppUnavailableError
 from controllers.console.explore.wraps import InstalledAppResource
-from extensions.ext_database import db
-from models.model import AppModelConfig, InstalledApp
-from models.tools import ApiToolProvider
+from models.model import AppMode, InstalledApp
+from services.app_service import AppService
 
 
 class AppParameterApi(InstalledAppResource):
@@ -45,61 +44,52 @@ class AppParameterApi(InstalledAppResource):
     def get(self, installed_app: InstalledApp):
         """Retrieve app parameters."""
         app_model = installed_app.app
-        app_model_config = app_model.app_model_config
+
+        if app_model.mode in [AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value]:
+            workflow = app_model.workflow
+            if workflow is None:
+                raise AppUnavailableError()
+
+            features_dict = workflow.features_dict
+            user_input_form = workflow.user_input_form(to_old_structure=True)
+        else:
+            app_model_config = app_model.app_model_config
+            features_dict = app_model_config.to_dict()
+
+            user_input_form = features_dict.get('user_input_form', [])
 
         return {
-            'opening_statement': app_model_config.opening_statement,
-            'suggested_questions': app_model_config.suggested_questions_list,
-            'suggested_questions_after_answer': app_model_config.suggested_questions_after_answer_dict,
-            'speech_to_text': app_model_config.speech_to_text_dict,
-            'text_to_speech': app_model_config.text_to_speech_dict,
-            'retriever_resource': app_model_config.retriever_resource_dict,
-            'annotation_reply': app_model_config.annotation_reply_dict,
-            'more_like_this': app_model_config.more_like_this_dict,
-            'user_input_form': app_model_config.user_input_form_list,
-            'sensitive_word_avoidance': app_model_config.sensitive_word_avoidance_dict,
-            'file_upload': app_model_config.file_upload_dict,
+            'opening_statement': features_dict.get('opening_statement'),
+            'suggested_questions': features_dict.get('suggested_questions', []),
+            'suggested_questions_after_answer': features_dict.get('suggested_questions_after_answer',
+                                                                  {"enabled": False}),
+            'speech_to_text': features_dict.get('speech_to_text', {"enabled": False}),
+            'text_to_speech': features_dict.get('text_to_speech', {"enabled": False}),
+            'retriever_resource': features_dict.get('retriever_resource', {"enabled": False}),
+            'annotation_reply': features_dict.get('annotation_reply', {"enabled": False}),
+            'more_like_this': features_dict.get('more_like_this', {"enabled": False}),
+            'user_input_form': user_input_form,
+            'sensitive_word_avoidance': features_dict.get('sensitive_word_avoidance',
+                                                          {"enabled": False, "type": "", "configs": []}),
+            'file_upload': features_dict.get('file_upload', {"image": {
+                                                     "enabled": False,
+                                                     "number_limits": 3,
+                                                     "detail": "high",
+                                                     "transfer_methods": ["remote_url", "local_file"]
+                                                 }}),
             'system_parameters': {
                 'image_file_size_limit': current_app.config.get('UPLOAD_IMAGE_FILE_SIZE_LIMIT')
             }
         }
 
+
 class ExploreAppMetaApi(InstalledAppResource):
     def get(self, installed_app: InstalledApp):
         """Get app meta"""
-        app_model_config: AppModelConfig = installed_app.app.app_model_config
+        app_model = installed_app.app
+        return AppService().get_app_meta(app_model)
 
-        agent_config = app_model_config.agent_mode_dict or {}
-        meta = {
-            'tool_icons': {}
-        }
 
-        # get all tools
-        tools = agent_config.get('tools', [])
-        url_prefix = (current_app.config.get("CONSOLE_API_URL")
-                  + "/console/api/workspaces/current/tool-provider/builtin/")
-        for tool in tools:
-            keys = list(tool.keys())
-            if len(keys) >= 4:
-                # current tool standard
-                provider_type = tool.get('provider_type')
-                provider_id = tool.get('provider_id')
-                tool_name = tool.get('tool_name')
-                if provider_type == 'builtin':
-                    meta['tool_icons'][tool_name] = url_prefix + provider_id + '/icon'
-                elif provider_type == 'api':
-                    try:
-                        provider: ApiToolProvider = db.session.query(ApiToolProvider).filter(
-                            ApiToolProvider.id == provider_id
-                        )
-                        meta['tool_icons'][tool_name] = json.loads(provider.icon)
-                    except:
-                        meta['tool_icons'][tool_name] =  {
-                            "background": "#252525",
-                            "content": "\ud83d\ude01"
-                        }
-
-        return meta
-
-api.add_resource(AppParameterApi, '/installed-apps/<uuid:installed_app_id>/parameters', endpoint='installed_app_parameters')
+api.add_resource(AppParameterApi, '/installed-apps/<uuid:installed_app_id>/parameters',
+                 endpoint='installed_app_parameters')
 api.add_resource(ExploreAppMetaApi, '/installed-apps/<uuid:installed_app_id>/meta', endpoint='installed_app_meta')

api/controllers/console/explore/recommended_app.py

@@ -1,15 +1,11 @@
 from flask_login import current_user
-from flask_restful import Resource, fields, marshal_with
-from sqlalchemy import and_
+from flask_restful import Resource, fields, marshal_with, reqparse
 
 from constants.languages import languages
 from controllers.console import api
-from controllers.console.app.error import AppNotFoundError
 from controllers.console.wraps import account_initialization_required
-from extensions.ext_database import db
 from libs.login import login_required
-from models.model import App, InstalledApp, RecommendedApp
-from services.account_service import TenantService
+from services.recommended_app_service import RecommendedAppService
 
 app_fields = {
     'id': fields.String,
@@ -27,11 +23,7 @@ recommended_app_fields = {
     'privacy_policy': fields.String,
     'category': fields.String,
     'position': fields.Integer,
-    'is_listed': fields.Boolean,
-    'install_count': fields.Integer,
-    'installed': fields.Boolean,
-    'editable': fields.Boolean,
-    'is_agent': fields.Boolean
+    'is_listed': fields.Boolean
 }
 
 recommended_app_list_fields = {
@@ -45,96 +37,27 @@ class RecommendedAppListApi(Resource):
     @account_initialization_required
     @marshal_with(recommended_app_list_fields)
     def get(self):
-        language_prefix = current_user.interface_language if current_user.interface_language else languages[0]
+        # language args
+        parser = reqparse.RequestParser()
+        parser.add_argument('language', type=str, location='args')
+        args = parser.parse_args()
 
-        recommended_apps = db.session.query(RecommendedApp).filter(
-            RecommendedApp.is_listed == True,
-            RecommendedApp.language == language_prefix
-        ).all()
+        if args.get('language') and args.get('language') in languages:
+            language_prefix = args.get('language')
+        elif current_user and current_user.interface_language:
+            language_prefix = current_user.interface_language
+        else:
+            language_prefix = languages[0]
 
-        categories = set()
-        current_user.role = TenantService.get_user_role(current_user, current_user.current_tenant)
-        recommended_apps_result = []
-        for recommended_app in recommended_apps:
-            installed = db.session.query(InstalledApp).filter(
-                and_(
-                    InstalledApp.app_id == recommended_app.app_id,
-                    InstalledApp.tenant_id == current_user.current_tenant_id
-                )
-            ).first() is not None
-
-            app = recommended_app.app
-            if not app or not app.is_public:
-                continue
-
-            site = app.site
-            if not site:
-                continue
-
-            recommended_app_result = {
-                'id': recommended_app.id,
-                'app': app,
-                'app_id': recommended_app.app_id,
-                'description': site.description,
-                'copyright': site.copyright,
-                'privacy_policy': site.privacy_policy,
-                'category': recommended_app.category,
-                'position': recommended_app.position,
-                'is_listed': recommended_app.is_listed,
-                'install_count': recommended_app.install_count,
-                'installed': installed,
-                'editable': current_user.role in ['owner', 'admin'],
-                "is_agent": app.is_agent
-            }
-            recommended_apps_result.append(recommended_app_result)
-
-            categories.add(recommended_app.category)  # add category to categories
-
-        return {'recommended_apps': recommended_apps_result, 'categories': list(categories)}
+        return RecommendedAppService.get_recommended_apps_and_categories(language_prefix)
 
 
 class RecommendedAppApi(Resource):
-    model_config_fields = {
-        'opening_statement': fields.String,
-        'suggested_questions': fields.Raw(attribute='suggested_questions_list'),
-        'suggested_questions_after_answer': fields.Raw(attribute='suggested_questions_after_answer_dict'),
-        'more_like_this': fields.Raw(attribute='more_like_this_dict'),
-        'model': fields.Raw(attribute='model_dict'),
-        'user_input_form': fields.Raw(attribute='user_input_form_list'),
-        'pre_prompt': fields.String,
-        'agent_mode': fields.Raw(attribute='agent_mode_dict'),
-    }
-
-    app_simple_detail_fields = {
-        'id': fields.String,
-        'name': fields.String,
-        'icon': fields.String,
-        'icon_background': fields.String,
-        'mode': fields.String,
-        'app_model_config': fields.Nested(model_config_fields),
-    }
-
     @login_required
     @account_initialization_required
-    @marshal_with(app_simple_detail_fields)
     def get(self, app_id):
         app_id = str(app_id)
-
-        # is in public recommended list
-        recommended_app = db.session.query(RecommendedApp).filter(
-            RecommendedApp.is_listed == True,
-            RecommendedApp.app_id == app_id
-        ).first()
-
-        if not recommended_app:
-            raise AppNotFoundError
-
-        # get app detail
-        app = db.session.query(App).filter(App.id == app_id).first()
-        if not app or not app.is_public:
-            raise AppNotFoundError
-
-        return app
+        return RecommendedAppService.get_recommend_app_detail(app_id)
 
 
 api.add_resource(RecommendedAppListApi, '/explore/apps')

api/controllers/console/explore/workflow.py

@@ -0,0 +1,85 @@
+import logging
+
+from flask_restful import reqparse
+from werkzeug.exceptions import InternalServerError
+
+from controllers.console import api
+from controllers.console.app.error import (
+    CompletionRequestError,
+    ProviderModelCurrentlyNotSupportError,
+    ProviderNotInitializeError,
+    ProviderQuotaExceededError,
+)
+from controllers.console.explore.error import NotWorkflowAppError
+from controllers.console.explore.wraps import InstalledAppResource
+from core.app.apps.base_app_queue_manager import AppQueueManager
+from core.app.entities.app_invoke_entities import InvokeFrom
+from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
+from core.model_runtime.errors.invoke import InvokeError
+from libs import helper
+from libs.login import current_user
+from models.model import AppMode, InstalledApp
+from services.app_generate_service import AppGenerateService
+
+logger = logging.getLogger(__name__)
+
+
+class InstalledAppWorkflowRunApi(InstalledAppResource):
+    def post(self, installed_app: InstalledApp):
+        """
+        Run workflow
+        """
+        app_model = installed_app.app
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode != AppMode.WORKFLOW:
+            raise NotWorkflowAppError()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('inputs', type=dict, required=True, nullable=False, location='json')
+        parser.add_argument('files', type=list, required=False, location='json')
+        args = parser.parse_args()
+
+        try:
+            response = AppGenerateService.generate(
+                app_model=app_model,
+                user=current_user,
+                args=args,
+                invoke_from=InvokeFrom.EXPLORE,
+                streaming=True
+            )
+
+            return helper.compact_generate_response(response)
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except InvokeError as e:
+            raise CompletionRequestError(e.description)
+        except ValueError as e:
+            raise e
+        except Exception as e:
+            logging.exception("internal server error.")
+            raise InternalServerError()
+
+
+class InstalledAppWorkflowTaskStopApi(InstalledAppResource):
+    def post(self, installed_app: InstalledApp, task_id: str):
+        """
+        Stop workflow task
+        """
+        app_model = installed_app.app
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode != AppMode.WORKFLOW:
+            raise NotWorkflowAppError()
+
+        AppQueueManager.set_stop_flag(task_id, InvokeFrom.EXPLORE, current_user.id)
+
+        return {
+            "result": "success"
+        }
+
+
+api.add_resource(InstalledAppWorkflowRunApi, '/installed-apps/<uuid:installed_app_id>/workflows/run')
+api.add_resource(InstalledAppWorkflowTaskStopApi, '/installed-apps/<uuid:installed_app_id>/workflows/tasks/<string:task_id>/stop')

api/controllers/console/ping.py

@@ -0,0 +1,17 @@
+from flask_restful import Resource
+
+from controllers.console import api
+
+
+class PingApi(Resource):
+
+    def get(self):
+        """
+        For connection health check
+        """
+        return {
+            "result": "pong"
+        }
+
+
+api.add_resource(PingApi, '/ping')

api/controllers/console/workspace/account.py

@@ -16,26 +16,13 @@ from controllers.console.workspace.error import (
 )
 from controllers.console.wraps import account_initialization_required
 from extensions.ext_database import db
+from fields.member_fields import account_fields
 from libs.helper import TimestampField, timezone
 from libs.login import login_required
 from models.account import AccountIntegrate, InvitationCode
 from services.account_service import AccountService
 from services.errors.account import CurrentPasswordIncorrectError as ServiceCurrentPasswordIncorrectError
 
-account_fields = {
-    'id': fields.String,
-    'name': fields.String,
-    'avatar': fields.String,
-    'email': fields.String,
-    'is_password_set': fields.Boolean,
-    'interface_language': fields.String,
-    'interface_theme': fields.String,
-    'timezone': fields.String,
-    'last_login_at': TimestampField,
-    'last_login_ip': fields.String,
-    'created_at': TimestampField
-}
-
 
 class AccountInitApi(Resource):
 

api/controllers/console/workspace/members.py

@@ -1,33 +1,18 @@
 from flask import current_app
 from flask_login import current_user
-from flask_restful import Resource, abort, fields, marshal_with, reqparse
+from flask_restful import Resource, abort, marshal_with, reqparse
 
 import services
 from controllers.console import api
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required, cloud_edition_billing_resource_check
 from extensions.ext_database import db
-from libs.helper import TimestampField
+from fields.member_fields import account_with_role_list_fields
 from libs.login import login_required
 from models.account import Account
 from services.account_service import RegisterService, TenantService
 from services.errors.account import AccountAlreadyInTenantError
 
-account_fields = {
-    'id': fields.String,
-    'name': fields.String,
-    'avatar': fields.String,
-    'email': fields.String,
-    'last_login_at': TimestampField,
-    'created_at': TimestampField,
-    'role': fields.String,
-    'status': fields.String,
-}
-
-account_list_fields = {
-    'accounts': fields.List(fields.Nested(account_fields))
-}
-
 
 class MemberListApi(Resource):
     """List all members of current tenant."""
@@ -35,7 +20,7 @@ class MemberListApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
-    @marshal_with(account_list_fields)
+    @marshal_with(account_with_role_list_fields)
     def get(self):
         members = TenantService.get_tenant_members(current_user.current_tenant)
         return {'result': 'success', 'accounts': members}, 200

api/controllers/console/workspace/tool_providers.py

@@ -1,6 +1,6 @@
 import io
 
-from flask import send_file
+from flask import current_app, send_file
 from flask_login import current_user
 from flask_restful import Resource, reqparse
 from werkzeug.exceptions import Forbidden
@@ -8,6 +8,7 @@ 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.utils.encoders import jsonable_encoder
 from libs.login import login_required
 from services.tools_manage_service import ToolManageService
 
@@ -30,11 +31,11 @@ class ToolBuiltinProviderListToolsApi(Resource):
         user_id = current_user.id
         tenant_id = current_user.current_tenant_id
 
-        return ToolManageService.list_builtin_tool_provider_tools(
+        return jsonable_encoder(ToolManageService.list_builtin_tool_provider_tools(
             user_id,
             tenant_id,
             provider,
-        )
+        ))
 
 class ToolBuiltinProviderDeleteApi(Resource):
     @setup_required
@@ -75,13 +76,52 @@ class ToolBuiltinProviderUpdateApi(Resource):
             provider,
             args['credentials'],
         )
+    
+class ToolBuiltinProviderGetCredentialsApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self, provider):
+        user_id = current_user.id
+        tenant_id = current_user.current_tenant_id
+
+        return ToolManageService.get_builtin_tool_provider_credentials(
+            user_id,
+            tenant_id,
+            provider,
+        )
 
 class ToolBuiltinProviderIconApi(Resource):
     @setup_required
     def get(self, provider):
-        icon_bytes, minetype = ToolManageService.get_builtin_tool_provider_icon(provider)
-        return send_file(io.BytesIO(icon_bytes), mimetype=minetype)
+        icon_bytes, mimetype = ToolManageService.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)
 
+class ToolModelProviderIconApi(Resource):
+    @setup_required
+    def get(self, provider):
+        icon_bytes, mimetype = ToolManageService.get_model_tool_provider_icon(provider)
+        return send_file(io.BytesIO(icon_bytes), mimetype=mimetype)
+    
+class ToolModelProviderListToolsApi(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('provider', type=str, required=True, nullable=False, location='args')
+
+        args = parser.parse_args()
+
+        return jsonable_encoder(ToolManageService.list_model_tool_provider_tools(
+            user_id,
+            tenant_id,
+            args['provider'],
+        ))
 
 class ToolApiProviderAddApi(Resource):
     @setup_required
@@ -146,11 +186,11 @@ class ToolApiProviderListToolsApi(Resource):
 
         args = parser.parse_args()
 
-        return ToolManageService.list_api_tool_provider_tools(
+        return jsonable_encoder(ToolManageService.list_api_tool_provider_tools(
             user_id,
             tenant_id,
             args['provider'],
-        )
+        ))
 
 class ToolApiProviderUpdateApi(Resource):
     @setup_required
@@ -259,6 +299,7 @@ class ToolApiProviderPreviousTestApi(Resource):
         parser = reqparse.RequestParser()
 
         parser.add_argument('tool_name', type=str, required=True, nullable=False, location='json')
+        parser.add_argument('provider_name', type=str, required=False, nullable=False, location='json')
         parser.add_argument('credentials', type=dict, required=True, nullable=False, location='json')
         parser.add_argument('parameters', type=dict, required=True, nullable=False, location='json')
         parser.add_argument('schema_type', type=str, required=True, nullable=False, location='json')
@@ -268,6 +309,7 @@ class ToolApiProviderPreviousTestApi(Resource):
 
         return ToolManageService.test_api_tool_preview(
             current_user.current_tenant_id,
+            args['provider_name'] if args['provider_name'] else '',
             args['tool_name'],
             args['credentials'],
             args['parameters'],
@@ -275,17 +317,49 @@ class ToolApiProviderPreviousTestApi(Resource):
             args['schema'],
         )
 
+class ToolBuiltinListApi(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 ToolManageService.list_builtin_tools(
+            user_id,
+            tenant_id,
+        )])
+    
+class ToolApiListApi(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 ToolManageService.list_api_tools(
+            user_id,
+            tenant_id,
+        )])
+
 api.add_resource(ToolProviderListApi, '/workspaces/current/tool-providers')
 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.add_resource(ToolModelProviderIconApi, '/workspaces/current/tool-provider/model/<provider>/icon')
+api.add_resource(ToolModelProviderListToolsApi, '/workspaces/current/tool-provider/model/tools')
 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')
-api.add_resource(ToolApiProviderUpdateApi, '/workspaces/current/tool-provider/api/update') 
+api.add_resource(ToolApiProviderUpdateApi, '/workspaces/current/tool-provider/api/update')
 api.add_resource(ToolApiProviderDeleteApi, '/workspaces/current/tool-provider/api/delete')
 api.add_resource(ToolApiProviderGetApi, '/workspaces/current/tool-provider/api/get')
 api.add_resource(ToolApiProviderSchemaApi, '/workspaces/current/tool-provider/api/schema')
 api.add_resource(ToolApiProviderPreviousTestApi, '/workspaces/current/tool-provider/api/test/pre')
+
+api.add_resource(ToolBuiltinListApi, '/workspaces/current/tools/builtin')
+api.add_resource(ToolApiListApi, '/workspaces/current/tools/api')

api/controllers/console/wraps.py

@@ -51,19 +51,25 @@ def cloud_edition_billing_resource_check(resource: str,
         @wraps(view)
         def decorated(*args, **kwargs):
             features = FeatureService.get_features(current_user.current_tenant_id)
-
             if features.billing.enabled:
                 members = features.members
                 apps = features.apps
                 vector_space = features.vector_space
+                documents_upload_quota = features.documents_upload_quota
                 annotation_quota_limit = features.annotation_quota_limit
-
                 if resource == 'members' and 0 < members.limit <= members.size:
                     abort(403, error_msg)
                 elif resource == 'apps' and 0 < apps.limit <= apps.size:
                     abort(403, error_msg)
                 elif resource == 'vector_space' and 0 < vector_space.limit <= vector_space.size:
                     abort(403, error_msg)
+                elif resource == 'documents' and 0 < documents_upload_quota.limit <= documents_upload_quota.size:
+                    # The api of file upload is used in the multiple places, so we need to check the source of the request from datasets
+                    source = request.args.get('source')
+                    if source == 'datasets':
+                        abort(403, error_msg)
+                    else:
+                        return view(*args, **kwargs)
                 elif resource == 'workspace_custom' and not features.can_replace_logo:
                     abort(403, error_msg)
                 elif resource == 'annotation' and 0 < annotation_quota_limit.limit < annotation_quota_limit.size:
@@ -72,7 +78,29 @@ def cloud_edition_billing_resource_check(resource: str,
                     return view(*args, **kwargs)
 
             return view(*args, **kwargs)
+
         return decorated
+
+    return interceptor
+
+
+def cloud_edition_billing_knowledge_limit_check(resource: str,
+                                                error_msg: str = "To unlock this feature and elevate your Dify experience, please upgrade to a paid plan."):
+    def interceptor(view):
+        @wraps(view)
+        def decorated(*args, **kwargs):
+            features = FeatureService.get_features(current_user.current_tenant_id)
+            if features.billing.enabled:
+                if resource == 'add_segment':
+                    if features.billing.subscription.plan == 'sandbox':
+                        abort(403, error_msg)
+                else:
+                    return view(*args, **kwargs)
+
+            return view(*args, **kwargs)
+
+        return decorated
+
     return interceptor
 
 
@@ -91,4 +119,5 @@ def cloud_utm_record(view):
         except Exception as e:
             pass
         return view(*args, **kwargs)
+
     return decorated

api/controllers/files/tool_files.py

@@ -27,7 +27,7 @@ class ToolFilePreviewApi(Resource):
             raise Forbidden('Invalid request.')
         
         try:
-            result = ToolFileManager.get_file_generator_by_message_file_id(
+            result = ToolFileManager.get_file_generator_by_tool_file_id(
                 file_id,
             )
 

api/controllers/service_api/__init__.py

@@ -7,5 +7,5 @@ api = ExternalApi(bp)
 
 
 from . import index
-from .app import app, audio, completion, conversation, file, message
+from .app import app, audio, completion, conversation, file, message, workflow
 from .dataset import dataset, document, segment

api/controllers/service_api/app/app.py

@@ -4,10 +4,12 @@ from flask import current_app
 from flask_restful import fields, marshal_with, Resource
 
 from controllers.service_api import api
+from controllers.service_api.app.error import AppUnavailableError
 from controllers.service_api.wraps import validate_app_token
 from extensions.ext_database import db
-from models.model import App, AppModelConfig
+from models.model import App, AppModelConfig, AppMode
 from models.tools import ApiToolProvider
+from services.app_service import AppService
 
 
 class AppParameterApi(Resource):
@@ -46,62 +48,50 @@ class AppParameterApi(Resource):
     @marshal_with(parameters_fields)
     def get(self, app_model: App):
         """Retrieve app parameters."""
-        app_model_config = app_model.app_model_config
+        if app_model.mode in [AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value]:
+            workflow = app_model.workflow
+            if workflow is None:
+                raise AppUnavailableError()
+
+            features_dict = workflow.features_dict
+            user_input_form = workflow.user_input_form(to_old_structure=True)
+        else:
+            app_model_config = app_model.app_model_config
+            features_dict = app_model_config.to_dict()
+
+            user_input_form = features_dict.get('user_input_form', [])
 
         return {
-            'opening_statement': app_model_config.opening_statement,
-            'suggested_questions': app_model_config.suggested_questions_list,
-            'suggested_questions_after_answer': app_model_config.suggested_questions_after_answer_dict,
-            'speech_to_text': app_model_config.speech_to_text_dict,
-            'text_to_speech': app_model_config.text_to_speech_dict,
-            'retriever_resource': app_model_config.retriever_resource_dict,
-            'annotation_reply': app_model_config.annotation_reply_dict,
-            'more_like_this': app_model_config.more_like_this_dict,
-            'user_input_form': app_model_config.user_input_form_list,
-            'sensitive_word_avoidance': app_model_config.sensitive_word_avoidance_dict,
-            'file_upload': app_model_config.file_upload_dict,
+            'opening_statement': features_dict.get('opening_statement'),
+            'suggested_questions': features_dict.get('suggested_questions', []),
+            'suggested_questions_after_answer': features_dict.get('suggested_questions_after_answer',
+                                                                  {"enabled": False}),
+            'speech_to_text': features_dict.get('speech_to_text', {"enabled": False}),
+            'text_to_speech': features_dict.get('text_to_speech', {"enabled": False}),
+            'retriever_resource': features_dict.get('retriever_resource', {"enabled": False}),
+            'annotation_reply': features_dict.get('annotation_reply', {"enabled": False}),
+            'more_like_this': features_dict.get('more_like_this', {"enabled": False}),
+            'user_input_form': user_input_form,
+            'sensitive_word_avoidance': features_dict.get('sensitive_word_avoidance',
+                                                          {"enabled": False, "type": "", "configs": []}),
+            'file_upload': features_dict.get('file_upload', {"image": {
+                                                     "enabled": False,
+                                                     "number_limits": 3,
+                                                     "detail": "high",
+                                                     "transfer_methods": ["remote_url", "local_file"]
+                                                 }}),
             'system_parameters': {
                 'image_file_size_limit': current_app.config.get('UPLOAD_IMAGE_FILE_SIZE_LIMIT')
             }
         }
 
+
 class AppMetaApi(Resource):
     @validate_app_token
     def get(self, app_model: App):
         """Get app meta"""
-        app_model_config: AppModelConfig = app_model.app_model_config
+        return AppService().get_app_meta(app_model)
 
-        agent_config = app_model_config.agent_mode_dict or {}
-        meta = {
-            'tool_icons': {}
-        }
-
-        # get all tools
-        tools = agent_config.get('tools', [])
-        url_prefix = (current_app.config.get("CONSOLE_API_URL")
-                  + "/console/api/workspaces/current/tool-provider/builtin/")
-        for tool in tools:
-            keys = list(tool.keys())
-            if len(keys) >= 4:
-                # current tool standard
-                provider_type = tool.get('provider_type')
-                provider_id = tool.get('provider_id')
-                tool_name = tool.get('tool_name')
-                if provider_type == 'builtin':
-                    meta['tool_icons'][tool_name] = url_prefix + provider_id + '/icon'
-                elif provider_type == 'api':
-                    try:
-                        provider: ApiToolProvider = db.session.query(ApiToolProvider).filter(
-                            ApiToolProvider.id == provider_id
-                        )
-                        meta['tool_icons'][tool_name] = json.loads(provider.icon)
-                    except:
-                        meta['tool_icons'][tool_name] =  {
-                            "background": "#252525",
-                            "content": "\ud83d\ude01"
-                        }
-
-        return meta
 
 api.add_resource(AppParameterApi, '/parameters')
 api.add_resource(AppMetaApi, '/meta')

api/controllers/service_api/app/audio.py

@@ -20,7 +20,7 @@ from controllers.service_api.app.error import (
 from controllers.service_api.wraps import FetchUserArg, WhereisUserArg, validate_app_token
 from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
 from core.model_runtime.errors.invoke import InvokeError
-from models.model import App, AppModelConfig, EndUser
+from models.model import App, EndUser
 from services.audio_service import AudioService
 from services.errors.audio import (
     AudioTooLargeServiceError,
@@ -33,16 +33,11 @@ from services.errors.audio import (
 class AudioApi(Resource):
     @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.FORM))
     def post(self, app_model: App, end_user: EndUser):
-        app_model_config: AppModelConfig = app_model.app_model_config
-
-        if not app_model_config.speech_to_text_dict['enabled']:
-            raise AppUnavailableError()
-
         file = request.files['file']
 
         try:
             response = AudioService.transcript_asr(
-                tenant_id=app_model.tenant_id,
+                app_model=app_model,
                 file=file,
                 end_user=end_user
             )
@@ -75,19 +70,20 @@ class AudioApi(Resource):
 
 
 class TextApi(Resource):
-    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON, required=True))
+    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON))
     def post(self, app_model: App, end_user: EndUser):
         parser = reqparse.RequestParser()
         parser.add_argument('text', type=str, required=True, nullable=False, location='json')
+        parser.add_argument('voice', type=str, location='json')
         parser.add_argument('streaming', type=bool, required=False, nullable=False, location='json')
         args = parser.parse_args()
 
         try:
             response = AudioService.transcript_tts(
-                tenant_id=app_model.tenant_id,
+                app_model=app_model,
                 text=args['text'],
                 end_user=end_user,
-                voice=app_model.app_model_config.text_to_speech_dict.get('voice'),
+                voice=args.get('voice'),
                 streaming=args['streaming']
             )
 

api/controllers/service_api/app/completion.py

@@ -1,9 +1,5 @@
-import json
 import logging
-from collections.abc import Generator
-from typing import Union
 
-from flask import Response, stream_with_context
 from flask_restful import Resource, reqparse
 from werkzeug.exceptions import InternalServerError, NotFound
 
@@ -19,13 +15,14 @@ from controllers.service_api.app.error import (
     ProviderQuotaExceededError,
 )
 from controllers.service_api.wraps import FetchUserArg, WhereisUserArg, validate_app_token
-from core.application_queue_manager import ApplicationQueueManager
-from core.entities.application_entities import InvokeFrom
+from core.app.apps.base_app_queue_manager import AppQueueManager
+from core.app.entities.app_invoke_entities import InvokeFrom
 from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
 from core.model_runtime.errors.invoke import InvokeError
+from libs import helper
 from libs.helper import uuid_value
-from models.model import App, EndUser
-from services.completion_service import CompletionService
+from models.model import App, AppMode, EndUser
+from services.app_generate_service import AppGenerateService
 
 
 class CompletionApi(Resource):
@@ -48,7 +45,7 @@ class CompletionApi(Resource):
         args['auto_generate_name'] = False
 
         try:
-            response = CompletionService.completion(
+            response = AppGenerateService.generate(
                 app_model=app_model,
                 user=end_user,
                 args=args,
@@ -56,7 +53,7 @@ class CompletionApi(Resource):
                 streaming=streaming,
             )
 
-            return compact_response(response)
+            return helper.compact_generate_response(response)
         except services.errors.conversation.ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")
         except services.errors.conversation.ConversationCompletedError:
@@ -85,7 +82,7 @@ class CompletionStopApi(Resource):
         if app_model.mode != 'completion':
             raise AppUnavailableError()
 
-        ApplicationQueueManager.set_stop_flag(task_id, InvokeFrom.SERVICE_API, end_user.id)
+        AppQueueManager.set_stop_flag(task_id, InvokeFrom.SERVICE_API, end_user.id)
 
         return {'result': 'success'}, 200
 
@@ -93,7 +90,8 @@ class CompletionStopApi(Resource):
 class ChatApi(Resource):
     @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON, required=True))
     def post(self, app_model: App, end_user: EndUser):
-        if app_model.mode != 'chat':
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
             raise NotChatAppError()
 
         parser = reqparse.RequestParser()
@@ -110,7 +108,7 @@ class ChatApi(Resource):
         streaming = args['response_mode'] == 'streaming'
 
         try:
-            response = CompletionService.completion(
+            response = AppGenerateService.generate(
                 app_model=app_model,
                 user=end_user,
                 args=args,
@@ -118,7 +116,7 @@ class ChatApi(Resource):
                 streaming=streaming
             )
 
-            return compact_response(response)
+            return helper.compact_generate_response(response)
         except services.errors.conversation.ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")
         except services.errors.conversation.ConversationCompletedError:
@@ -144,25 +142,15 @@ class ChatApi(Resource):
 class ChatStopApi(Resource):
     @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON, required=True))
     def post(self, app_model: App, end_user: EndUser, task_id):
-        if app_model.mode != 'chat':
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
             raise NotChatAppError()
 
-        ApplicationQueueManager.set_stop_flag(task_id, InvokeFrom.SERVICE_API, end_user.id)
+        AppQueueManager.set_stop_flag(task_id, InvokeFrom.SERVICE_API, end_user.id)
 
         return {'result': 'success'}, 200
 
 
-def compact_response(response: Union[dict, Generator]) -> Response:
-    if isinstance(response, dict):
-        return Response(response=json.dumps(response), status=200, mimetype='application/json')
-    else:
-        def generate() -> Generator:
-            yield from response
-
-        return Response(stream_with_context(generate()), status=200,
-                        mimetype='text/event-stream')
-
-
 api.add_resource(CompletionApi, '/completion-messages')
 api.add_resource(CompletionStopApi, '/completion-messages/<string:task_id>/stop')
 api.add_resource(ChatApi, '/chat-messages')

api/controllers/service_api/app/conversation.py

@@ -8,7 +8,7 @@ from controllers.service_api.app.error import NotChatAppError
 from controllers.service_api.wraps import FetchUserArg, WhereisUserArg, validate_app_token
 from fields.conversation_fields import conversation_infinite_scroll_pagination_fields, simple_conversation_fields
 from libs.helper import uuid_value
-from models.model import App, EndUser
+from models.model import App, AppMode, EndUser
 from services.conversation_service import ConversationService
 
 
@@ -17,7 +17,8 @@ class ConversationApi(Resource):
     @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.QUERY))
     @marshal_with(conversation_infinite_scroll_pagination_fields)
     def get(self, app_model: App, end_user: EndUser):
-        if app_model.mode != 'chat':
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
             raise NotChatAppError()
 
         parser = reqparse.RequestParser()
@@ -30,11 +31,13 @@ class ConversationApi(Resource):
         except services.errors.conversation.LastConversationNotExistsError:
             raise NotFound("Last Conversation Not Exists.")
 
+
 class ConversationDetailApi(Resource):
     @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON))
     @marshal_with(simple_conversation_fields)
     def delete(self, app_model: App, end_user: EndUser, c_id):
-        if app_model.mode != 'chat':
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
             raise NotChatAppError()
 
         conversation_id = str(c_id)
@@ -51,7 +54,8 @@ class ConversationRenameApi(Resource):
     @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON))
     @marshal_with(simple_conversation_fields)
     def post(self, app_model: App, end_user: EndUser, c_id):
-        if app_model.mode != 'chat':
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
             raise NotChatAppError()
 
         conversation_id = str(c_id)

api/controllers/service_api/app/error.py

@@ -15,7 +15,13 @@ class NotCompletionAppError(BaseHTTPException):
 
 class NotChatAppError(BaseHTTPException):
     error_code = 'not_chat_app'
-    description = "Please check if your Chat app mode matches the right API route."
+    description = "Please check if your app mode matches the right API route."
+    code = 400
+
+
+class NotWorkflowAppError(BaseHTTPException):
+    error_code = 'not_workflow_app'
+    description = "Please check if your app mode matches the right API route."
     code = 400
 
 

api/controllers/service_api/app/message.py

@@ -1,14 +1,18 @@
+import logging
+
 from flask_restful import Resource, fields, marshal_with, reqparse
 from flask_restful.inputs import int_range
-from werkzeug.exceptions import NotFound
+from werkzeug.exceptions import BadRequest, InternalServerError, NotFound
 
 import services
 from controllers.service_api import api
 from controllers.service_api.app.error import NotChatAppError
 from controllers.service_api.wraps import FetchUserArg, WhereisUserArg, validate_app_token
+from core.app.entities.app_invoke_entities import InvokeFrom
 from fields.conversation_fields import message_file_fields
 from libs.helper import TimestampField, uuid_value
-from models.model import App, EndUser
+from models.model import App, AppMode, EndUser
+from services.errors.message import SuggestedQuestionsAfterAnswerDisabledError
 from services.message_service import MessageService
 
 
@@ -54,12 +58,14 @@ class MessageListApi(Resource):
         'conversation_id': fields.String,
         'inputs': fields.Raw,
         'query': fields.String,
-        'answer': fields.String,
+        'answer': fields.String(attribute='re_sign_file_url_answer'),
         'message_files': fields.List(fields.Nested(message_file_fields), attribute='files'),
         'feedback': fields.Nested(feedback_fields, attribute='user_feedback', allow_null=True),
         'retriever_resources': fields.List(fields.Nested(retriever_resource_fields)),
         'created_at': TimestampField,
-        'agent_thoughts': fields.List(fields.Nested(agent_thought_fields))
+        'agent_thoughts': fields.List(fields.Nested(agent_thought_fields)),
+        'status': fields.String,
+        'error': fields.String,
     }
 
     message_infinite_scroll_pagination_fields = {
@@ -71,7 +77,8 @@ class MessageListApi(Resource):
     @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.QUERY))
     @marshal_with(message_infinite_scroll_pagination_fields)
     def get(self, app_model: App, end_user: EndUser):
-        if app_model.mode != 'chat':
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
             raise NotChatAppError()
 
         parser = reqparse.RequestParser()
@@ -110,7 +117,8 @@ class MessageSuggestedApi(Resource):
     @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.QUERY))
     def get(self, app_model: App, end_user: EndUser, message_id):
         message_id = str(message_id)
-        if app_model.mode != 'chat':
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
             raise NotChatAppError()
 
         try:
@@ -118,10 +126,15 @@ class MessageSuggestedApi(Resource):
                 app_model=app_model,
                 user=end_user,
                 message_id=message_id,
-                check_enabled=False
+                invoke_from=InvokeFrom.SERVICE_API
             )
         except services.errors.message.MessageNotExistsError:
             raise NotFound("Message Not Exists.")
+        except SuggestedQuestionsAfterAnswerDisabledError:
+            raise BadRequest("Message Not Exists.")
+        except Exception:
+            logging.exception("internal server error.")
+            raise InternalServerError()
 
         return {'result': 'success', 'data': questions}
 

api/controllers/service_api/app/workflow.py

@@ -0,0 +1,87 @@
+import logging
+
+from flask_restful import Resource, reqparse
+from werkzeug.exceptions import InternalServerError
+
+from controllers.service_api import api
+from controllers.service_api.app.error import (
+    CompletionRequestError,
+    NotWorkflowAppError,
+    ProviderModelCurrentlyNotSupportError,
+    ProviderNotInitializeError,
+    ProviderQuotaExceededError,
+)
+from controllers.service_api.wraps import FetchUserArg, WhereisUserArg, validate_app_token
+from core.app.apps.base_app_queue_manager import AppQueueManager
+from core.app.entities.app_invoke_entities import InvokeFrom
+from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
+from core.model_runtime.errors.invoke import InvokeError
+from libs import helper
+from models.model import App, AppMode, EndUser
+from services.app_generate_service import AppGenerateService
+
+logger = logging.getLogger(__name__)
+
+
+class WorkflowRunApi(Resource):
+    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON, required=True))
+    def post(self, app_model: App, end_user: EndUser):
+        """
+        Run workflow
+        """
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode != AppMode.WORKFLOW:
+            raise NotWorkflowAppError()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('inputs', type=dict, required=True, nullable=False, location='json')
+        parser.add_argument('files', type=list, required=False, location='json')
+        parser.add_argument('response_mode', type=str, choices=['blocking', 'streaming'], location='json')
+        args = parser.parse_args()
+
+        streaming = args.get('response_mode') == 'streaming'
+
+        try:
+            response = AppGenerateService.generate(
+                app_model=app_model,
+                user=end_user,
+                args=args,
+                invoke_from=InvokeFrom.SERVICE_API,
+                streaming=streaming
+            )
+
+            return helper.compact_generate_response(response)
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except InvokeError as e:
+            raise CompletionRequestError(e.description)
+        except ValueError as e:
+            raise e
+        except Exception as e:
+            logging.exception("internal server error.")
+            raise InternalServerError()
+
+
+class WorkflowTaskStopApi(Resource):
+    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON, required=True))
+    def post(self, app_model: App, end_user: EndUser, task_id: str):
+        """
+        Stop workflow task
+        """
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode != AppMode.WORKFLOW:
+            raise NotWorkflowAppError()
+
+        AppQueueManager.set_stop_flag(task_id, InvokeFrom.SERVICE_API, end_user.id)
+
+        return {
+            "result": "success"
+        }
+
+
+api.add_resource(WorkflowRunApi, '/workflows/run')
+api.add_resource(WorkflowTaskStopApi, '/workflows/tasks/<string:task_id>/stop')

api/controllers/service_api/dataset/document.py

@@ -28,6 +28,7 @@ class DocumentAddByTextApi(DatasetApiResource):
     """Resource for documents."""
 
     @cloud_edition_billing_resource_check('vector_space', 'dataset')
+    @cloud_edition_billing_resource_check('documents', 'dataset')
     def post(self, tenant_id, dataset_id):
         """Create document by text."""
         parser = reqparse.RequestParser()
@@ -153,6 +154,7 @@ class DocumentUpdateByTextApi(DatasetApiResource):
 class DocumentAddByFileApi(DatasetApiResource):
     """Resource for documents."""
     @cloud_edition_billing_resource_check('vector_space', 'dataset')
+    @cloud_edition_billing_resource_check('documents', 'dataset')
     def post(self, tenant_id, dataset_id):
         """Create document by upload file."""
         args = {}

api/controllers/service_api/dataset/segment.py

@@ -4,7 +4,11 @@ from werkzeug.exceptions import NotFound
 
 from controllers.service_api import api
 from controllers.service_api.app.error import ProviderNotInitializeError
-from controllers.service_api.wraps import DatasetApiResource, cloud_edition_billing_resource_check
+from controllers.service_api.wraps import (
+    DatasetApiResource,
+    cloud_edition_billing_knowledge_limit_check,
+    cloud_edition_billing_resource_check,
+)
 from core.errors.error import LLMBadRequestError, ProviderTokenNotInitError
 from core.model_manager import ModelManager
 from core.model_runtime.entities.model_entities import ModelType
@@ -18,6 +22,7 @@ class SegmentApi(DatasetApiResource):
     """Resource for segments."""
 
     @cloud_edition_billing_resource_check('vector_space', 'dataset')
+    @cloud_edition_billing_knowledge_limit_check('add_segment', 'dataset')
     def post(self, tenant_id, dataset_id, document_id):
         """Create single segment."""
         # check dataset
@@ -197,11 +202,11 @@ class DatasetSegmentApi(DatasetApiResource):
 
         # validate args
         parser = reqparse.RequestParser()
-        parser.add_argument('segments', type=dict, required=False, nullable=True, location='json')
+        parser.add_argument('segment', type=dict, required=False, nullable=True, location='json')
         args = parser.parse_args()
 
-        SegmentService.segment_create_args_validate(args['segments'], document)
-        segment = SegmentService.update_segment(args['segments'], segment, document, dataset)
+        SegmentService.segment_create_args_validate(args['segment'], document)
+        segment = SegmentService.update_segment(args['segment'], segment, document, dataset)
         return {
             'data': marshal(segment, segment_fields),
             'doc_form': document.doc_form

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 NotFound, Unauthorized
+from werkzeug.exceptions import Forbidden, NotFound, Unauthorized
 
 from extensions.ext_database import db
 from libs.login import _get_user
@@ -89,13 +89,16 @@ def cloud_edition_billing_resource_check(resource: str,
                 members = features.members
                 apps = features.apps
                 vector_space = features.vector_space
+                documents_upload_quota = features.documents_upload_quota
 
                 if resource == 'members' and 0 < members.limit <= members.size:
-                    raise Unauthorized(error_msg)
+                    raise Forbidden(error_msg)
                 elif resource == 'apps' and 0 < apps.limit <= apps.size:
-                    raise Unauthorized(error_msg)
+                    raise Forbidden(error_msg)
                 elif resource == 'vector_space' and 0 < vector_space.limit <= vector_space.size:
-                    raise Unauthorized(error_msg)
+                    raise Forbidden(error_msg)
+                elif resource == 'documents' and 0 < documents_upload_quota.limit <= documents_upload_quota.size:
+                    raise Forbidden(error_msg)
                 else:
                     return view(*args, **kwargs)
 
@@ -104,6 +107,27 @@ def cloud_edition_billing_resource_check(resource: str,
     return interceptor
 
 
+def cloud_edition_billing_knowledge_limit_check(resource: str,
+                                                api_token_type: str,
+                                                error_msg: str = "To unlock this feature and elevate your Dify experience, please upgrade to a paid plan."):
+    def interceptor(view):
+        @wraps(view)
+        def decorated(*args, **kwargs):
+            api_token = validate_and_get_api_token(api_token_type)
+            features = FeatureService.get_features(api_token.tenant_id)
+            if features.billing.enabled:
+                if resource == 'add_segment':
+                    if features.billing.subscription.plan == 'sandbox':
+                        raise Forbidden(error_msg)
+                else:
+                    return view(*args, **kwargs)
+
+            return view(*args, **kwargs)
+
+        return decorated
+
+    return interceptor
+
 def validate_dataset_token(view=None):
     def decorator(view):
         @wraps(view)

api/controllers/web/__init__.py

@@ -6,4 +6,4 @@ bp = Blueprint('web', __name__, url_prefix='/api')
 api = ExternalApi(bp)
 
 
-from . import app, audio, completion, conversation, file, message, passport, saved_message, site
+from . import app, audio, completion, conversation, file, message, passport, saved_message, site, workflow

api/controllers/web/app.py

@@ -4,10 +4,12 @@ from flask import current_app
 from flask_restful import fields, marshal_with
 
 from controllers.web import api
+from controllers.web.error import AppUnavailableError
 from controllers.web.wraps import WebApiResource
 from extensions.ext_database import db
-from models.model import App, AppModelConfig
+from models.model import App, AppModelConfig, AppMode
 from models.tools import ApiToolProvider
+from services.app_service import AppService
 
 
 class AppParameterApi(WebApiResource):
@@ -44,61 +46,49 @@ class AppParameterApi(WebApiResource):
     @marshal_with(parameters_fields)
     def get(self, app_model: App, end_user):
         """Retrieve app parameters."""
-        app_model_config = app_model.app_model_config
+        if app_model.mode in [AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value]:
+            workflow = app_model.workflow
+            if workflow is None:
+                raise AppUnavailableError()
+
+            features_dict = workflow.features_dict
+            user_input_form = workflow.user_input_form(to_old_structure=True)
+        else:
+            app_model_config = app_model.app_model_config
+            features_dict = app_model_config.to_dict()
+
+            user_input_form = features_dict.get('user_input_form', [])
 
         return {
-            'opening_statement': app_model_config.opening_statement,
-            'suggested_questions': app_model_config.suggested_questions_list,
-            'suggested_questions_after_answer': app_model_config.suggested_questions_after_answer_dict,
-            'speech_to_text': app_model_config.speech_to_text_dict,
-            'text_to_speech': app_model_config.text_to_speech_dict,
-            'retriever_resource': app_model_config.retriever_resource_dict,
-            'annotation_reply': app_model_config.annotation_reply_dict,
-            'more_like_this': app_model_config.more_like_this_dict,
-            'user_input_form': app_model_config.user_input_form_list,
-            'sensitive_word_avoidance': app_model_config.sensitive_word_avoidance_dict,
-            'file_upload': app_model_config.file_upload_dict,
+            'opening_statement': features_dict.get('opening_statement'),
+            'suggested_questions': features_dict.get('suggested_questions', []),
+            'suggested_questions_after_answer': features_dict.get('suggested_questions_after_answer',
+                                                                  {"enabled": False}),
+            'speech_to_text': features_dict.get('speech_to_text', {"enabled": False}),
+            'text_to_speech': features_dict.get('text_to_speech', {"enabled": False}),
+            'retriever_resource': features_dict.get('retriever_resource', {"enabled": False}),
+            'annotation_reply': features_dict.get('annotation_reply', {"enabled": False}),
+            'more_like_this': features_dict.get('more_like_this', {"enabled": False}),
+            'user_input_form': user_input_form,
+            'sensitive_word_avoidance': features_dict.get('sensitive_word_avoidance',
+                                                          {"enabled": False, "type": "", "configs": []}),
+            'file_upload': features_dict.get('file_upload', {"image": {
+                "enabled": False,
+                "number_limits": 3,
+                "detail": "high",
+                "transfer_methods": ["remote_url", "local_file"]
+            }}),
             'system_parameters': {
                 'image_file_size_limit': current_app.config.get('UPLOAD_IMAGE_FILE_SIZE_LIMIT')
             }
         }
 
+
 class AppMeta(WebApiResource):
     def get(self, app_model: App, end_user):
         """Get app meta"""
-        app_model_config: AppModelConfig = app_model.app_model_config
+        return AppService().get_app_meta(app_model)
 
-        agent_config = app_model_config.agent_mode_dict or {}
-        meta = {
-            'tool_icons': {}
-        }
-
-        # get all tools
-        tools = agent_config.get('tools', [])
-        url_prefix = (current_app.config.get("CONSOLE_API_URL")
-                  + "/console/api/workspaces/current/tool-provider/builtin/")
-        for tool in tools:
-            keys = list(tool.keys())
-            if len(keys) >= 4:
-                # current tool standard
-                provider_type = tool.get('provider_type')
-                provider_id = tool.get('provider_id')
-                tool_name = tool.get('tool_name')
-                if provider_type == 'builtin':
-                    meta['tool_icons'][tool_name] = url_prefix + provider_id + '/icon'
-                elif provider_type == 'api':
-                    try:
-                        provider: ApiToolProvider = db.session.query(ApiToolProvider).filter(
-                            ApiToolProvider.id == provider_id
-                        )
-                        meta['tool_icons'][tool_name] = json.loads(provider.icon)
-                    except:
-                        meta['tool_icons'][tool_name] =  {
-                            "background": "#252525",
-                            "content": "\ud83d\ude01"
-                        }
-
-        return meta
 
 api.add_resource(AppParameterApi, '/parameters')
 api.add_resource(AppMeta, '/meta')

api/controllers/web/audio.py

@@ -19,7 +19,7 @@ from controllers.web.error import (
 from controllers.web.wraps import WebApiResource
 from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
 from core.model_runtime.errors.invoke import InvokeError
-from models.model import App, AppModelConfig
+from models.model import App
 from services.audio_service import AudioService
 from services.errors.audio import (
     AudioTooLargeServiceError,
@@ -31,16 +31,11 @@ from services.errors.audio import (
 
 class AudioApi(WebApiResource):
     def post(self, app_model: App, end_user):
-        app_model_config: AppModelConfig = app_model.app_model_config
-
-        if not app_model_config.speech_to_text_dict['enabled']:
-            raise AppUnavailableError()
-
         file = request.files['file']
 
         try:
             response = AudioService.transcript_asr(
-                tenant_id=app_model.tenant_id,
+                app_model=app_model,
                 file=file,
                 end_user=end_user
             )
@@ -74,17 +69,12 @@ class AudioApi(WebApiResource):
 
 class TextApi(WebApiResource):
     def post(self, app_model: App, end_user):
-        app_model_config: AppModelConfig = app_model.app_model_config
-
-        if not app_model_config.text_to_speech_dict['enabled']:
-            raise AppUnavailableError()
-
         try:
             response = AudioService.transcript_tts(
-                tenant_id=app_model.tenant_id,
+                app_model=app_model,
                 text=request.form['text'],
                 end_user=end_user.external_user_id,
-                voice=app_model.app_model_config.text_to_speech_dict.get('voice'),
+                voice=request.form.get('voice'),
                 streaming=False
             )
 

api/controllers/web/completion.py

@@ -1,9 +1,5 @@
-import json
 import logging
-from collections.abc import Generator
-from typing import Union
 
-from flask import Response, stream_with_context
 from flask_restful import reqparse
 from werkzeug.exceptions import InternalServerError, NotFound
 
@@ -20,12 +16,14 @@ from controllers.web.error import (
     ProviderQuotaExceededError,
 )
 from controllers.web.wraps import WebApiResource
-from core.application_queue_manager import ApplicationQueueManager
-from core.entities.application_entities import InvokeFrom
+from core.app.apps.base_app_queue_manager import AppQueueManager
+from core.app.entities.app_invoke_entities import InvokeFrom
 from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
 from core.model_runtime.errors.invoke import InvokeError
+from libs import helper
 from libs.helper import uuid_value
-from services.completion_service import CompletionService
+from models.model import AppMode
+from services.app_generate_service import AppGenerateService
 
 
 # define completion api for user
@@ -48,7 +46,7 @@ class CompletionApi(WebApiResource):
         args['auto_generate_name'] = False
 
         try:
-            response = CompletionService.completion(
+            response = AppGenerateService.generate(
                 app_model=app_model,
                 user=end_user,
                 args=args,
@@ -56,7 +54,7 @@ class CompletionApi(WebApiResource):
                 streaming=streaming
             )
 
-            return compact_response(response)
+            return helper.compact_generate_response(response)
         except services.errors.conversation.ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")
         except services.errors.conversation.ConversationCompletedError:
@@ -84,14 +82,15 @@ class CompletionStopApi(WebApiResource):
         if app_model.mode != 'completion':
             raise NotCompletionAppError()
 
-        ApplicationQueueManager.set_stop_flag(task_id, InvokeFrom.WEB_APP, end_user.id)
+        AppQueueManager.set_stop_flag(task_id, InvokeFrom.WEB_APP, end_user.id)
 
         return {'result': 'success'}, 200
 
 
 class ChatApi(WebApiResource):
     def post(self, app_model, end_user):
-        if app_model.mode != 'chat':
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
             raise NotChatAppError()
 
         parser = reqparse.RequestParser()
@@ -108,7 +107,7 @@ class ChatApi(WebApiResource):
         args['auto_generate_name'] = False
 
         try:
-            response = CompletionService.completion(
+            response = AppGenerateService.generate(
                 app_model=app_model,
                 user=end_user,
                 args=args,
@@ -116,7 +115,7 @@ class ChatApi(WebApiResource):
                 streaming=streaming
             )
 
-            return compact_response(response)
+            return helper.compact_generate_response(response)
         except services.errors.conversation.ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")
         except services.errors.conversation.ConversationCompletedError:
@@ -141,25 +140,15 @@ class ChatApi(WebApiResource):
 
 class ChatStopApi(WebApiResource):
     def post(self, app_model, end_user, task_id):
-        if app_model.mode != 'chat':
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
             raise NotChatAppError()
 
-        ApplicationQueueManager.set_stop_flag(task_id, InvokeFrom.WEB_APP, end_user.id)
+        AppQueueManager.set_stop_flag(task_id, InvokeFrom.WEB_APP, end_user.id)
 
         return {'result': 'success'}, 200
 
 
-def compact_response(response: Union[dict, Generator]) -> Response:
-    if isinstance(response, dict):
-        return Response(response=json.dumps(response), status=200, mimetype='application/json')
-    else:
-        def generate() -> Generator:
-            yield from response
-
-        return Response(stream_with_context(generate()), status=200,
-                        mimetype='text/event-stream')
-
-
 api.add_resource(CompletionApi, '/completion-messages')
 api.add_resource(CompletionStopApi, '/completion-messages/<string:task_id>/stop')
 api.add_resource(ChatApi, '/chat-messages')

api/controllers/web/conversation.py

@@ -7,6 +7,7 @@ from controllers.web.error import NotChatAppError
 from controllers.web.wraps import WebApiResource
 from fields.conversation_fields import conversation_infinite_scroll_pagination_fields, simple_conversation_fields
 from libs.helper import uuid_value
+from models.model import AppMode
 from services.conversation_service import ConversationService
 from services.errors.conversation import ConversationNotExistsError, LastConversationNotExistsError
 from services.web_conversation_service import WebConversationService
@@ -16,7 +17,8 @@ class ConversationListApi(WebApiResource):
 
     @marshal_with(conversation_infinite_scroll_pagination_fields)
     def get(self, app_model, end_user):
-        if app_model.mode != 'chat':
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
             raise NotChatAppError()
 
         parser = reqparse.RequestParser()
@@ -43,7 +45,8 @@ class ConversationListApi(WebApiResource):
 
 class ConversationApi(WebApiResource):
     def delete(self, app_model, end_user, c_id):
-        if app_model.mode != 'chat':
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
             raise NotChatAppError()
 
         conversation_id = str(c_id)
@@ -60,7 +63,8 @@ class ConversationRenameApi(WebApiResource):
 
     @marshal_with(simple_conversation_fields)
     def post(self, app_model, end_user, c_id):
-        if app_model.mode != 'chat':
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
             raise NotChatAppError()
 
         conversation_id = str(c_id)
@@ -85,7 +89,8 @@ class ConversationRenameApi(WebApiResource):
 class ConversationPinApi(WebApiResource):
 
     def patch(self, app_model, end_user, c_id):
-        if app_model.mode != 'chat':
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
             raise NotChatAppError()
 
         conversation_id = str(c_id)
@@ -100,7 +105,8 @@ class ConversationPinApi(WebApiResource):
 
 class ConversationUnPinApi(WebApiResource):
     def patch(self, app_model, end_user, c_id):
-        if app_model.mode != 'chat':
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
             raise NotChatAppError()
 
         conversation_id = str(c_id)

api/controllers/web/error.py

@@ -15,7 +15,13 @@ class NotCompletionAppError(BaseHTTPException):
 
 class NotChatAppError(BaseHTTPException):
     error_code = 'not_chat_app'
-    description = "Please check if your Chat app mode matches the right API route."
+    description = "Please check if your app mode matches the right API route."
+    code = 400
+
+
+class NotWorkflowAppError(BaseHTTPException):
+    error_code = 'not_workflow_app'
+    description = "Please check if your Workflow app mode matches the right API route."
     code = 400
 
 

api/controllers/web/message.py

@@ -1,9 +1,5 @@
-import json
 import logging
-from collections.abc import Generator
-from typing import Union
 
-from flask import Response, stream_with_context
 from flask_restful import fields, marshal_with, reqparse
 from flask_restful.inputs import int_range
 from werkzeug.exceptions import InternalServerError, NotFound
@@ -21,13 +17,15 @@ from controllers.web.error import (
     ProviderQuotaExceededError,
 )
 from controllers.web.wraps import WebApiResource
-from core.entities.application_entities import InvokeFrom
+from core.app.entities.app_invoke_entities import InvokeFrom
 from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
 from core.model_runtime.errors.invoke import InvokeError
 from fields.conversation_fields import message_file_fields
 from fields.message_fields import agent_thought_fields
+from libs import helper
 from libs.helper import TimestampField, uuid_value
-from services.completion_service import CompletionService
+from models.model import AppMode
+from services.app_generate_service import AppGenerateService
 from services.errors.app import MoreLikeThisDisabledError
 from services.errors.conversation import ConversationNotExistsError
 from services.errors.message import MessageNotExistsError, SuggestedQuestionsAfterAnswerDisabledError
@@ -63,12 +61,14 @@ class MessageListApi(WebApiResource):
         'conversation_id': fields.String,
         'inputs': fields.Raw,
         'query': fields.String,
-        'answer': fields.String,
+        'answer': fields.String(attribute='re_sign_file_url_answer'),
         'message_files': fields.List(fields.Nested(message_file_fields), attribute='files'),
         'feedback': fields.Nested(feedback_fields, attribute='user_feedback', allow_null=True),
         'retriever_resources': fields.List(fields.Nested(retriever_resource_fields)),
         'created_at': TimestampField,
-        'agent_thoughts': fields.List(fields.Nested(agent_thought_fields))
+        'agent_thoughts': fields.List(fields.Nested(agent_thought_fields)),
+        'status': fields.String,
+        'error': fields.String,
     }
 
     message_infinite_scroll_pagination_fields = {
@@ -79,7 +79,8 @@ class MessageListApi(WebApiResource):
 
     @marshal_with(message_infinite_scroll_pagination_fields)
     def get(self, app_model, end_user):
-        if app_model.mode != 'chat':
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
             raise NotChatAppError()
 
         parser = reqparse.RequestParser()
@@ -127,7 +128,7 @@ class MessageMoreLikeThisApi(WebApiResource):
         streaming = args['response_mode'] == 'streaming'
 
         try:
-            response = CompletionService.generate_more_like_this(
+            response = AppGenerateService.generate_more_like_this(
                 app_model=app_model,
                 user=end_user,
                 message_id=message_id,
@@ -135,7 +136,7 @@ class MessageMoreLikeThisApi(WebApiResource):
                 streaming=streaming
             )
 
-            return compact_response(response)
+            return helper.compact_generate_response(response)
         except MessageNotExistsError:
             raise NotFound("Message Not Exists.")
         except MoreLikeThisDisabledError:
@@ -155,20 +156,10 @@ class MessageMoreLikeThisApi(WebApiResource):
             raise InternalServerError()
 
 
-def compact_response(response: Union[dict, Generator]) -> Response:
-    if isinstance(response, dict):
-        return Response(response=json.dumps(response), status=200, mimetype='application/json')
-    else:
-        def generate() -> Generator:
-            yield from response
-
-        return Response(stream_with_context(generate()), status=200,
-                        mimetype='text/event-stream')
-
-
 class MessageSuggestedQuestionApi(WebApiResource):
     def get(self, app_model, end_user, message_id):
-        if app_model.mode != 'chat':
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in [AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT]:
             raise NotCompletionAppError()
 
         message_id = str(message_id)
@@ -177,7 +168,8 @@ class MessageSuggestedQuestionApi(WebApiResource):
             questions = MessageService.get_suggested_questions_after_answer(
                 app_model=app_model,
                 user=end_user,
-                message_id=message_id
+                message_id=message_id,
+                invoke_from=InvokeFrom.WEB_APP
             )
         except MessageNotExistsError:
             raise NotFound("Message not found")

api/controllers/web/site.py

@@ -83,7 +83,3 @@ class AppSiteInfo:
                 'remove_webapp_brand': remove_webapp_brand,
                 'replace_webapp_logo': replace_webapp_logo,
             }
-
-        if app.enable_site and site.prompt_public:
-            app_model_config = app.app_model_config
-            self.model_config = app_model_config

api/controllers/web/workflow.py

@@ -0,0 +1,82 @@
+import logging
+
+from flask_restful import reqparse
+from werkzeug.exceptions import InternalServerError
+
+from controllers.web import api
+from controllers.web.error import (
+    CompletionRequestError,
+    NotWorkflowAppError,
+    ProviderModelCurrentlyNotSupportError,
+    ProviderNotInitializeError,
+    ProviderQuotaExceededError,
+)
+from controllers.web.wraps import WebApiResource
+from core.app.apps.base_app_queue_manager import AppQueueManager
+from core.app.entities.app_invoke_entities import InvokeFrom
+from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
+from core.model_runtime.errors.invoke import InvokeError
+from libs import helper
+from models.model import App, AppMode, EndUser
+from services.app_generate_service import AppGenerateService
+
+logger = logging.getLogger(__name__)
+
+
+class WorkflowRunApi(WebApiResource):
+    def post(self, app_model: App, end_user: EndUser):
+        """
+        Run workflow
+        """
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode != AppMode.WORKFLOW:
+            raise NotWorkflowAppError()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument('inputs', type=dict, required=True, nullable=False, location='json')
+        parser.add_argument('files', type=list, required=False, location='json')
+        args = parser.parse_args()
+
+        try:
+            response = AppGenerateService.generate(
+                app_model=app_model,
+                user=end_user,
+                args=args,
+                invoke_from=InvokeFrom.WEB_APP,
+                streaming=True
+            )
+
+            return helper.compact_generate_response(response)
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except InvokeError as e:
+            raise CompletionRequestError(e.description)
+        except ValueError as e:
+            raise e
+        except Exception as e:
+            logging.exception("internal server error.")
+            raise InternalServerError()
+
+
+class WorkflowTaskStopApi(WebApiResource):
+    def post(self, app_model: App, end_user: EndUser, task_id: str):
+        """
+        Stop workflow task
+        """
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode != AppMode.WORKFLOW:
+            raise NotWorkflowAppError()
+
+        AppQueueManager.set_stop_flag(task_id, InvokeFrom.WEB_APP, end_user.id)
+
+        return {
+            "result": "success"
+        }
+
+
+api.add_resource(WorkflowRunApi, '/workflows/run')
+api.add_resource(WorkflowTaskStopApi, '/workflows/tasks/<string:task_id>/stop')

api/core/agent/agent/agent_llm_callback.py

@@ -1,101 +0,0 @@
-import logging
-from typing import Optional
-
-from core.callback_handler.agent_loop_gather_callback_handler import AgentLoopGatherCallbackHandler
-from core.model_runtime.callbacks.base_callback import Callback
-from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
-from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool
-from core.model_runtime.model_providers.__base.ai_model import AIModel
-
-logger = logging.getLogger(__name__)
-
-
-class AgentLLMCallback(Callback):
-
-    def __init__(self, agent_callback: AgentLoopGatherCallbackHandler) -> None:
-        self.agent_callback = agent_callback
-
-    def on_before_invoke(self, llm_instance: AIModel, model: str, credentials: dict,
-                         prompt_messages: list[PromptMessage], model_parameters: dict,
-                         tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                         stream: bool = True, user: Optional[str] = None) -> None:
-        """
-        Before invoke callback
-
-        :param llm_instance: LLM instance
-        :param model: model name
-        :param credentials: model credentials
-        :param prompt_messages: prompt messages
-        :param model_parameters: model parameters
-        :param tools: tools for tool calling
-        :param stop: stop words
-        :param stream: is stream response
-        :param user: unique user id
-        """
-        self.agent_callback.on_llm_before_invoke(
-            prompt_messages=prompt_messages
-        )
-
-    def on_new_chunk(self, llm_instance: AIModel, chunk: LLMResultChunk, model: str, credentials: dict,
-                     prompt_messages: list[PromptMessage], model_parameters: dict,
-                     tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                     stream: bool = True, user: Optional[str] = None):
-        """
-        On new chunk callback
-
-        :param llm_instance: LLM instance
-        :param chunk: chunk
-        :param model: model name
-        :param credentials: model credentials
-        :param prompt_messages: prompt messages
-        :param model_parameters: model parameters
-        :param tools: tools for tool calling
-        :param stop: stop words
-        :param stream: is stream response
-        :param user: unique user id
-        """
-        pass
-
-    def on_after_invoke(self, llm_instance: AIModel, result: LLMResult, model: str, credentials: dict,
-                        prompt_messages: list[PromptMessage], model_parameters: dict,
-                        tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                        stream: bool = True, user: Optional[str] = None) -> None:
-        """
-        After invoke callback
-
-        :param llm_instance: LLM instance
-        :param result: result
-        :param model: model name
-        :param credentials: model credentials
-        :param prompt_messages: prompt messages
-        :param model_parameters: model parameters
-        :param tools: tools for tool calling
-        :param stop: stop words
-        :param stream: is stream response
-        :param user: unique user id
-        """
-        self.agent_callback.on_llm_after_invoke(
-            result=result
-        )
-
-    def on_invoke_error(self, llm_instance: AIModel, ex: Exception, model: str, credentials: dict,
-                        prompt_messages: list[PromptMessage], model_parameters: dict,
-                        tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
-                        stream: bool = True, user: Optional[str] = None) -> None:
-        """
-        Invoke error callback
-
-        :param llm_instance: LLM instance
-        :param ex: exception
-        :param model: model name
-        :param credentials: model credentials
-        :param prompt_messages: prompt messages
-        :param model_parameters: model parameters
-        :param tools: tools for tool calling
-        :param stop: stop words
-        :param stream: is stream response
-        :param user: unique user id
-        """
-        self.agent_callback.on_llm_error(
-            error=ex
-        )

api/core/agent/agent/calc_token_mixin.py

@@ -1,49 +0,0 @@
-from typing import cast
-
-from core.entities.application_entities import ModelConfigEntity
-from core.model_runtime.entities.message_entities import PromptMessage
-from core.model_runtime.entities.model_entities import ModelPropertyKey
-from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
-
-
-class CalcTokenMixin:
-
-    def get_message_rest_tokens(self, model_config: ModelConfigEntity, messages: list[PromptMessage], **kwargs) -> int:
-        """
-        Got the rest tokens available for the model after excluding messages tokens and completion max tokens
-
-        :param model_config:
-        :param messages:
-        :return:
-        """
-        model_type_instance = model_config.provider_model_bundle.model_type_instance
-        model_type_instance = cast(LargeLanguageModel, model_type_instance)
-
-        model_context_tokens = model_config.model_schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE)
-
-        max_tokens = 0
-        for parameter_rule in model_config.model_schema.parameter_rules:
-            if (parameter_rule.name == 'max_tokens'
-                    or (parameter_rule.use_template and parameter_rule.use_template == 'max_tokens')):
-                max_tokens = (model_config.parameters.get(parameter_rule.name)
-                              or model_config.parameters.get(parameter_rule.use_template)) or 0
-
-        if model_context_tokens is None:
-            return 0
-
-        if max_tokens is None:
-            max_tokens = 0
-
-        prompt_tokens = model_type_instance.get_num_tokens(
-            model_config.model,
-            model_config.credentials,
-            messages
-        )
-
-        rest_tokens = model_context_tokens - max_tokens - prompt_tokens
-
-        return rest_tokens
-
-
-class ExceededLLMTokensLimitError(Exception):
-    pass

api/core/agent/agent/openai_function_call.py

@@ -1,361 +0,0 @@
-from collections.abc import Sequence
-from typing import Any, Optional, Union
-
-from langchain.agents import BaseSingleActionAgent, OpenAIFunctionsAgent
-from langchain.agents.openai_functions_agent.base import _format_intermediate_steps, _parse_ai_message
-from langchain.callbacks.base import BaseCallbackManager
-from langchain.callbacks.manager import Callbacks
-from langchain.chat_models.openai import _convert_message_to_dict, _import_tiktoken
-from langchain.memory.prompt import SUMMARY_PROMPT
-from langchain.prompts.chat import BaseMessagePromptTemplate
-from langchain.schema import (
-    AgentAction,
-    AgentFinish,
-    AIMessage,
-    BaseMessage,
-    HumanMessage,
-    SystemMessage,
-    get_buffer_string,
-)
-from langchain.tools import BaseTool
-from pydantic import root_validator
-
-from core.agent.agent.agent_llm_callback import AgentLLMCallback
-from core.agent.agent.calc_token_mixin import CalcTokenMixin, ExceededLLMTokensLimitError
-from core.chain.llm_chain import LLMChain
-from core.entities.application_entities import ModelConfigEntity
-from core.entities.message_entities import lc_messages_to_prompt_messages
-from core.model_manager import ModelInstance
-from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool
-from core.third_party.langchain.llms.fake import FakeLLM
-
-
-class AutoSummarizingOpenAIFunctionCallAgent(OpenAIFunctionsAgent, CalcTokenMixin):
-    moving_summary_buffer: str = ""
-    moving_summary_index: int = 0
-    summary_model_config: ModelConfigEntity = None
-    model_config: ModelConfigEntity
-    agent_llm_callback: Optional[AgentLLMCallback] = None
-
-    class Config:
-        """Configuration for this pydantic object."""
-
-        arbitrary_types_allowed = True
-
-    @root_validator
-    def validate_llm(cls, values: dict) -> dict:
-        return values
-
-    @classmethod
-    def from_llm_and_tools(
-            cls,
-            model_config: ModelConfigEntity,
-            tools: Sequence[BaseTool],
-            callback_manager: Optional[BaseCallbackManager] = None,
-            extra_prompt_messages: Optional[list[BaseMessagePromptTemplate]] = None,
-            system_message: Optional[SystemMessage] = SystemMessage(
-                content="You are a helpful AI assistant."
-            ),
-            agent_llm_callback: Optional[AgentLLMCallback] = None,
-            **kwargs: Any,
-    ) -> BaseSingleActionAgent:
-        prompt = cls.create_prompt(
-            extra_prompt_messages=extra_prompt_messages,
-            system_message=system_message,
-        )
-        return cls(
-            model_config=model_config,
-            llm=FakeLLM(response=''),
-            prompt=prompt,
-            tools=tools,
-            callback_manager=callback_manager,
-            agent_llm_callback=agent_llm_callback,
-            **kwargs,
-        )
-
-    def should_use_agent(self, query: str):
-        """
-        return should use agent
-
-        :param query:
-        :return:
-        """
-        original_max_tokens = 0
-        for parameter_rule in self.model_config.model_schema.parameter_rules:
-            if (parameter_rule.name == 'max_tokens'
-                    or (parameter_rule.use_template and parameter_rule.use_template == 'max_tokens')):
-                original_max_tokens = (self.model_config.parameters.get(parameter_rule.name)
-                              or self.model_config.parameters.get(parameter_rule.use_template)) or 0
-
-        self.model_config.parameters['max_tokens'] = 40
-
-        prompt = self.prompt.format_prompt(input=query, agent_scratchpad=[])
-        messages = prompt.to_messages()
-
-        try:
-            prompt_messages = lc_messages_to_prompt_messages(messages)
-            model_instance = ModelInstance(
-                provider_model_bundle=self.model_config.provider_model_bundle,
-                model=self.model_config.model,
-            )
-
-            tools = []
-            for function in self.functions:
-                tool = PromptMessageTool(
-                    **function
-                )
-
-                tools.append(tool)
-
-            result = model_instance.invoke_llm(
-                prompt_messages=prompt_messages,
-                tools=tools,
-                stream=False,
-                model_parameters={
-                    'temperature': 0.2,
-                    'top_p': 0.3,
-                    'max_tokens': 1500
-                }
-            )
-        except Exception as e:
-            raise e
-
-        self.model_config.parameters['max_tokens'] = original_max_tokens
-
-        return True if result.message.tool_calls else False
-
-    def plan(
-            self,
-            intermediate_steps: list[tuple[AgentAction, str]],
-            callbacks: Callbacks = None,
-            **kwargs: Any,
-    ) -> Union[AgentAction, AgentFinish]:
-        """Given input, decided what to do.
-
-        Args:
-            intermediate_steps: Steps the LLM has taken to date, along with observations
-            **kwargs: User inputs.
-
-        Returns:
-            Action specifying what tool to use.
-        """
-        agent_scratchpad = _format_intermediate_steps(intermediate_steps)
-        selected_inputs = {
-            k: kwargs[k] for k in self.prompt.input_variables if k != "agent_scratchpad"
-        }
-        full_inputs = dict(**selected_inputs, agent_scratchpad=agent_scratchpad)
-        prompt = self.prompt.format_prompt(**full_inputs)
-        messages = prompt.to_messages()
-
-        prompt_messages = lc_messages_to_prompt_messages(messages)
-
-        # summarize messages if rest_tokens < 0
-        try:
-            prompt_messages = self.summarize_messages_if_needed(prompt_messages, functions=self.functions)
-        except ExceededLLMTokensLimitError as e:
-            return AgentFinish(return_values={"output": str(e)}, log=str(e))
-
-        model_instance = ModelInstance(
-            provider_model_bundle=self.model_config.provider_model_bundle,
-            model=self.model_config.model,
-        )
-
-        tools = []
-        for function in self.functions:
-            tool = PromptMessageTool(
-                **function
-            )
-
-            tools.append(tool)
-
-        result = model_instance.invoke_llm(
-            prompt_messages=prompt_messages,
-            tools=tools,
-            stream=False,
-            callbacks=[self.agent_llm_callback] if self.agent_llm_callback else [],
-            model_parameters={
-                'temperature': 0.2,
-                'top_p': 0.3,
-                'max_tokens': 1500
-            }
-        )
-
-        ai_message = AIMessage(
-            content=result.message.content or "",
-            additional_kwargs={
-                'function_call': {
-                    'id': result.message.tool_calls[0].id,
-                    **result.message.tool_calls[0].function.dict()
-                } if result.message.tool_calls else None
-            }
-        )
-        agent_decision = _parse_ai_message(ai_message)
-
-        if isinstance(agent_decision, AgentAction) and agent_decision.tool == 'dataset':
-            tool_inputs = agent_decision.tool_input
-            if isinstance(tool_inputs, dict) and 'query' in tool_inputs:
-                tool_inputs['query'] = kwargs['input']
-                agent_decision.tool_input = tool_inputs
-
-        return agent_decision
-
-    @classmethod
-    def get_system_message(cls):
-        return SystemMessage(content="You are a helpful AI assistant.\n"
-                                     "The current date or current time you know is wrong.\n"
-                                     "Respond directly if appropriate.")
-
-    def return_stopped_response(
-            self,
-            early_stopping_method: str,
-            intermediate_steps: list[tuple[AgentAction, str]],
-            **kwargs: Any,
-    ) -> AgentFinish:
-        try:
-            return super().return_stopped_response(early_stopping_method, intermediate_steps, **kwargs)
-        except ValueError:
-            return AgentFinish({"output": "I'm sorry, I don't know how to respond to that."}, "")
-
-    def summarize_messages_if_needed(self, messages: list[PromptMessage], **kwargs) -> list[PromptMessage]:
-        # calculate rest tokens and summarize previous function observation messages if rest_tokens < 0
-        rest_tokens = self.get_message_rest_tokens(
-            self.model_config,
-            messages,
-            **kwargs
-        )
-
-        rest_tokens = rest_tokens - 20  # to deal with the inaccuracy of rest_tokens
-        if rest_tokens >= 0:
-            return messages
-
-        system_message = None
-        human_message = None
-        should_summary_messages = []
-        for message in messages:
-            if isinstance(message, SystemMessage):
-                system_message = message
-            elif isinstance(message, HumanMessage):
-                human_message = message
-            else:
-                should_summary_messages.append(message)
-
-        if len(should_summary_messages) > 2:
-            ai_message = should_summary_messages[-2]
-            function_message = should_summary_messages[-1]
-            should_summary_messages = should_summary_messages[self.moving_summary_index:-2]
-            self.moving_summary_index = len(should_summary_messages)
-        else:
-            error_msg = "Exceeded LLM tokens limit, stopped."
-            raise ExceededLLMTokensLimitError(error_msg)
-
-        new_messages = [system_message, human_message]
-
-        if self.moving_summary_index == 0:
-            should_summary_messages.insert(0, human_message)
-
-        self.moving_summary_buffer = self.predict_new_summary(
-            messages=should_summary_messages,
-            existing_summary=self.moving_summary_buffer
-        )
-
-        new_messages.append(AIMessage(content=self.moving_summary_buffer))
-        new_messages.append(ai_message)
-        new_messages.append(function_message)
-
-        return new_messages
-
-    def predict_new_summary(
-        self, messages: list[BaseMessage], existing_summary: str
-    ) -> str:
-        new_lines = get_buffer_string(
-            messages,
-            human_prefix="Human",
-            ai_prefix="AI",
-        )
-
-        chain = LLMChain(model_config=self.summary_model_config, prompt=SUMMARY_PROMPT)
-        return chain.predict(summary=existing_summary, new_lines=new_lines)
-
-    def get_num_tokens_from_messages(self, model_config: ModelConfigEntity, messages: list[BaseMessage], **kwargs) -> int:
-        """Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
-
-        Official documentation: https://github.com/openai/openai-cookbook/blob/
-        main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
-        if model_config.provider == 'azure_openai':
-            model = model_config.model
-            model = model.replace("gpt-35", "gpt-3.5")
-        else:
-            model = model_config.credentials.get("base_model_name")
-
-        tiktoken_ = _import_tiktoken()
-        try:
-            encoding = tiktoken_.encoding_for_model(model)
-        except KeyError:
-            model = "cl100k_base"
-            encoding = tiktoken_.get_encoding(model)
-
-        if model.startswith("gpt-3.5-turbo"):
-            # every message follows <im_start>{role/name}\n{content}<im_end>\n
-            tokens_per_message = 4
-            # if there's a name, the role is omitted
-            tokens_per_name = -1
-        elif model.startswith("gpt-4"):
-            tokens_per_message = 3
-            tokens_per_name = 1
-        else:
-            raise NotImplementedError(
-                f"get_num_tokens_from_messages() is not presently implemented "
-                f"for model {model}."
-                "See https://github.com/openai/openai-python/blob/main/chatml.md for "
-                "information on how messages are converted to tokens."
-            )
-        num_tokens = 0
-        for m in messages:
-            message = _convert_message_to_dict(m)
-            num_tokens += tokens_per_message
-            for key, value in message.items():
-                if key == "function_call":
-                    for f_key, f_value in value.items():
-                        num_tokens += len(encoding.encode(f_key))
-                        num_tokens += len(encoding.encode(f_value))
-                else:
-                    num_tokens += len(encoding.encode(value))
-
-                if key == "name":
-                    num_tokens += tokens_per_name
-        # every reply is primed with <im_start>assistant
-        num_tokens += 3
-
-        if kwargs.get('functions'):
-            for function in kwargs.get('functions'):
-                num_tokens += len(encoding.encode('name'))
-                num_tokens += len(encoding.encode(function.get("name")))
-                num_tokens += len(encoding.encode('description'))
-                num_tokens += len(encoding.encode(function.get("description")))
-                parameters = function.get("parameters")
-                num_tokens += len(encoding.encode('parameters'))
-                if 'title' in parameters:
-                    num_tokens += len(encoding.encode('title'))
-                    num_tokens += len(encoding.encode(parameters.get("title")))
-                num_tokens += len(encoding.encode('type'))
-                num_tokens += len(encoding.encode(parameters.get("type")))
-                if 'properties' in parameters:
-                    num_tokens += len(encoding.encode('properties'))
-                    for key, value in parameters.get('properties').items():
-                        num_tokens += len(encoding.encode(key))
-                        for field_key, field_value in value.items():
-                            num_tokens += len(encoding.encode(field_key))
-                            if field_key == 'enum':
-                                for enum_field in field_value:
-                                    num_tokens += 3
-                                    num_tokens += len(encoding.encode(enum_field))
-                            else:
-                                num_tokens += len(encoding.encode(field_key))
-                                num_tokens += len(encoding.encode(str(field_value)))
-                if 'required' in parameters:
-                    num_tokens += len(encoding.encode('required'))
-                    for required_field in parameters['required']:
-                        num_tokens += 3
-                        num_tokens += len(encoding.encode(required_field))
-
-        return num_tokens

api/core/agent/agent/structured_chat.py

@@ -1,306 +0,0 @@
-import re
-from collections.abc import Sequence
-from typing import Any, Optional, Union, cast
-
-from langchain import BasePromptTemplate, PromptTemplate
-from langchain.agents import Agent, AgentOutputParser, StructuredChatAgent
-from langchain.agents.structured_chat.base import HUMAN_MESSAGE_TEMPLATE
-from langchain.agents.structured_chat.prompt import PREFIX, SUFFIX
-from langchain.callbacks.base import BaseCallbackManager
-from langchain.callbacks.manager import Callbacks
-from langchain.memory.prompt import SUMMARY_PROMPT
-from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate
-from langchain.schema import (
-    AgentAction,
-    AgentFinish,
-    AIMessage,
-    BaseMessage,
-    HumanMessage,
-    OutputParserException,
-    get_buffer_string,
-)
-from langchain.tools import BaseTool
-
-from core.agent.agent.agent_llm_callback import AgentLLMCallback
-from core.agent.agent.calc_token_mixin import CalcTokenMixin, ExceededLLMTokensLimitError
-from core.chain.llm_chain import LLMChain
-from core.entities.application_entities import ModelConfigEntity
-from core.entities.message_entities import lc_messages_to_prompt_messages
-
-FORMAT_INSTRUCTIONS = """Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
-The nouns in the format of "Thought", "Action", "Action Input", "Final Answer" must be expressed in English.
-Valid "action" values: "Final Answer" or {tool_names}
-
-Provide only ONE action per $JSON_BLOB, as shown:
-
-```
-{{{{
-  "action": $TOOL_NAME,
-  "action_input": $INPUT
-}}}}
-```
-
-Follow this format:
-
-Question: input question to answer
-Thought: consider previous and subsequent steps
-Action:
-```
-$JSON_BLOB
-```
-Observation: action result
-... (repeat Thought/Action/Observation N times)
-Thought: I know what to respond
-Action:
-```
-{{{{
-  "action": "Final Answer",
-  "action_input": "Final response to human"
-}}}}
-```"""
-
-
-class AutoSummarizingStructuredChatAgent(StructuredChatAgent, CalcTokenMixin):
-    moving_summary_buffer: str = ""
-    moving_summary_index: int = 0
-    summary_model_config: ModelConfigEntity = None
-
-    class Config:
-        """Configuration for this pydantic object."""
-
-        arbitrary_types_allowed = True
-
-    def should_use_agent(self, query: str):
-        """
-        return should use agent
-        Using the ReACT mode to determine whether an agent is needed is costly,
-        so it's better to just use an Agent for reasoning, which is cheaper.
-
-        :param query:
-        :return:
-        """
-        return True
-
-    def plan(
-        self,
-        intermediate_steps: list[tuple[AgentAction, str]],
-        callbacks: Callbacks = None,
-        **kwargs: Any,
-    ) -> Union[AgentAction, AgentFinish]:
-        """Given input, decided what to do.
-
-        Args:
-            intermediate_steps: Steps the LLM has taken to date,
-                along with observatons
-            callbacks: Callbacks to run.
-            **kwargs: User inputs.
-
-        Returns:
-            Action specifying what tool to use.
-        """
-        full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
-        prompts, _ = self.llm_chain.prep_prompts(input_list=[self.llm_chain.prep_inputs(full_inputs)])
-
-        messages = []
-        if prompts:
-            messages = prompts[0].to_messages()
-
-        prompt_messages = lc_messages_to_prompt_messages(messages)
-
-        rest_tokens = self.get_message_rest_tokens(self.llm_chain.model_config, prompt_messages)
-        if rest_tokens < 0:
-            full_inputs = self.summarize_messages(intermediate_steps, **kwargs)
-
-        try:
-            full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs)
-        except Exception as e:
-            raise e
-
-        try:
-            agent_decision = self.output_parser.parse(full_output)
-            if isinstance(agent_decision, AgentAction) and agent_decision.tool == 'dataset':
-                tool_inputs = agent_decision.tool_input
-                if isinstance(tool_inputs, dict) and 'query' in tool_inputs:
-                    tool_inputs['query'] = kwargs['input']
-                    agent_decision.tool_input = tool_inputs
-            return agent_decision
-        except OutputParserException:
-            return AgentFinish({"output": "I'm sorry, the answer of model is invalid, "
-                                          "I don't know how to respond to that."}, "")
-
-    def summarize_messages(self, intermediate_steps: list[tuple[AgentAction, str]], **kwargs):
-        if len(intermediate_steps) >= 2 and self.summary_model_config:
-            should_summary_intermediate_steps = intermediate_steps[self.moving_summary_index:-1]
-            should_summary_messages = [AIMessage(content=observation)
-                                       for _, observation in should_summary_intermediate_steps]
-            if self.moving_summary_index == 0:
-                should_summary_messages.insert(0, HumanMessage(content=kwargs.get("input")))
-
-            self.moving_summary_index = len(intermediate_steps)
-        else:
-            error_msg = "Exceeded LLM tokens limit, stopped."
-            raise ExceededLLMTokensLimitError(error_msg)
-
-        if self.moving_summary_buffer and 'chat_history' in kwargs:
-            kwargs["chat_history"].pop()
-
-        self.moving_summary_buffer = self.predict_new_summary(
-            messages=should_summary_messages,
-            existing_summary=self.moving_summary_buffer
-        )
-
-        if 'chat_history' in kwargs:
-            kwargs["chat_history"].append(AIMessage(content=self.moving_summary_buffer))
-
-        return self.get_full_inputs([intermediate_steps[-1]], **kwargs)
-
-    def predict_new_summary(
-        self, messages: list[BaseMessage], existing_summary: str
-    ) -> str:
-        new_lines = get_buffer_string(
-            messages,
-            human_prefix="Human",
-            ai_prefix="AI",
-        )
-
-        chain = LLMChain(model_config=self.summary_model_config, prompt=SUMMARY_PROMPT)
-        return chain.predict(summary=existing_summary, new_lines=new_lines)
-
-    @classmethod
-    def create_prompt(
-            cls,
-            tools: Sequence[BaseTool],
-            prefix: str = PREFIX,
-            suffix: str = SUFFIX,
-            human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
-            format_instructions: str = FORMAT_INSTRUCTIONS,
-            input_variables: Optional[list[str]] = None,
-            memory_prompts: Optional[list[BasePromptTemplate]] = None,
-    ) -> BasePromptTemplate:
-        tool_strings = []
-        for tool in tools:
-            args_schema = re.sub("}", "}}}}", re.sub("{", "{{{{", str(tool.args)))
-            tool_strings.append(f"{tool.name}: {tool.description}, args: {args_schema}")
-        formatted_tools = "\n".join(tool_strings)
-        tool_names = ", ".join([('"' + tool.name + '"') for tool in tools])
-        format_instructions = format_instructions.format(tool_names=tool_names)
-        template = "\n\n".join([prefix, formatted_tools, format_instructions, suffix])
-        if input_variables is None:
-            input_variables = ["input", "agent_scratchpad"]
-        _memory_prompts = memory_prompts or []
-        messages = [
-            SystemMessagePromptTemplate.from_template(template),
-            *_memory_prompts,
-            HumanMessagePromptTemplate.from_template(human_message_template),
-        ]
-        return ChatPromptTemplate(input_variables=input_variables, messages=messages)
-
-    @classmethod
-    def create_completion_prompt(
-            cls,
-            tools: Sequence[BaseTool],
-            prefix: str = PREFIX,
-            format_instructions: str = FORMAT_INSTRUCTIONS,
-            input_variables: Optional[list[str]] = None,
-    ) -> PromptTemplate:
-        """Create prompt in the style of the zero shot agent.
-
-        Args:
-            tools: List of tools the agent will have access to, used to format the
-                prompt.
-            prefix: String to put before the list of tools.
-            input_variables: List of input variables the final prompt will expect.
-
-        Returns:
-            A PromptTemplate with the template assembled from the pieces here.
-        """
-        suffix = """Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
-Question: {input}
-Thought: {agent_scratchpad}
-"""
-
-        tool_strings = "\n".join([f"{tool.name}: {tool.description}" for tool in tools])
-        tool_names = ", ".join([tool.name for tool in tools])
-        format_instructions = format_instructions.format(tool_names=tool_names)
-        template = "\n\n".join([prefix, tool_strings, format_instructions, suffix])
-        if input_variables is None:
-            input_variables = ["input", "agent_scratchpad"]
-        return PromptTemplate(template=template, input_variables=input_variables)
-
-    def _construct_scratchpad(
-        self, intermediate_steps: list[tuple[AgentAction, str]]
-    ) -> str:
-        agent_scratchpad = ""
-        for action, observation in intermediate_steps:
-            agent_scratchpad += action.log
-            agent_scratchpad += f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
-
-        if not isinstance(agent_scratchpad, str):
-            raise ValueError("agent_scratchpad should be of type string.")
-        if agent_scratchpad:
-            llm_chain = cast(LLMChain, self.llm_chain)
-            if llm_chain.model_config.mode == "chat":
-                return (
-                    f"This was your previous work "
-                    f"(but I haven't seen any of it! I only see what "
-                    f"you return as final answer):\n{agent_scratchpad}"
-                )
-            else:
-                return agent_scratchpad
-        else:
-            return agent_scratchpad
-
-    @classmethod
-    def from_llm_and_tools(
-            cls,
-            model_config: ModelConfigEntity,
-            tools: Sequence[BaseTool],
-            callback_manager: Optional[BaseCallbackManager] = None,
-            output_parser: Optional[AgentOutputParser] = None,
-            prefix: str = PREFIX,
-            suffix: str = SUFFIX,
-            human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
-            format_instructions: str = FORMAT_INSTRUCTIONS,
-            input_variables: Optional[list[str]] = None,
-            memory_prompts: Optional[list[BasePromptTemplate]] = None,
-            agent_llm_callback: Optional[AgentLLMCallback] = None,
-            **kwargs: Any,
-    ) -> Agent:
-        """Construct an agent from an LLM and tools."""
-        cls._validate_tools(tools)
-        if model_config.mode == "chat":
-            prompt = cls.create_prompt(
-                tools,
-                prefix=prefix,
-                suffix=suffix,
-                human_message_template=human_message_template,
-                format_instructions=format_instructions,
-                input_variables=input_variables,
-                memory_prompts=memory_prompts,
-            )
-        else:
-            prompt = cls.create_completion_prompt(
-                tools,
-                prefix=prefix,
-                format_instructions=format_instructions,
-                input_variables=input_variables,
-            )
-        llm_chain = LLMChain(
-            model_config=model_config,
-            prompt=prompt,
-            callback_manager=callback_manager,
-            agent_llm_callback=agent_llm_callback,
-            parameters={
-                'temperature': 0.2,
-                'top_p': 0.3,
-                'max_tokens': 1500
-            }
-        )
-        tool_names = [tool.name for tool in tools]
-        _output_parser = output_parser
-        return cls(
-            llm_chain=llm_chain,
-            allowed_tools=tool_names,
-            output_parser=_output_parser,
-            **kwargs,
-        )

api/core/agent/base_agent_runner.py

@@ -2,22 +2,18 @@ import json
 import logging
 import uuid
 from datetime import datetime
-from mimetypes import guess_extension
 from typing import Optional, Union, cast
 
-from core.app_runner.app_runner import AppRunner
-from core.application_queue_manager import ApplicationQueueManager
+from core.agent.entities import AgentEntity, AgentToolEntity
+from core.app.apps.agent_chat.app_config_manager import AgentChatAppConfig
+from core.app.apps.base_app_queue_manager import AppQueueManager
+from core.app.apps.base_app_runner import AppRunner
+from core.app.entities.app_invoke_entities import (
+    AgentChatAppGenerateEntity,
+    ModelConfigWithCredentialsEntity,
+)
 from core.callback_handler.agent_tool_callback_handler import DifyAgentCallbackHandler
 from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
-from core.entities.application_entities import (
-    AgentEntity,
-    AgentToolEntity,
-    ApplicationGenerateEntity,
-    AppOrchestrationConfigEntity,
-    InvokeFrom,
-    ModelConfigEntity,
-)
-from core.file.message_file_parser import FileTransferMethod
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance
 from core.model_runtime.entities.llm_entities import LLMUsage
@@ -34,27 +30,25 @@ from core.model_runtime.model_providers.__base.large_language_model import Large
 from core.model_runtime.utils.encoders import jsonable_encoder
 from core.tools.entities.tool_entities import (
     ToolInvokeMessage,
-    ToolInvokeMessageBinary,
     ToolParameter,
     ToolRuntimeVariablePool,
 )
 from core.tools.tool.dataset_retriever_tool import DatasetRetrieverTool
 from core.tools.tool.tool import Tool
-from core.tools.tool_file_manager import ToolFileManager
 from core.tools.tool_manager import ToolManager
 from extensions.ext_database import db
-from models.model import Message, MessageAgentThought, MessageFile
+from models.model import Message, MessageAgentThought
 from models.tools import ToolConversationVariables
 
 logger = logging.getLogger(__name__)
 
-class BaseAssistantApplicationRunner(AppRunner):
+class BaseAgentRunner(AppRunner):
     def __init__(self, tenant_id: str,
-                 application_generate_entity: ApplicationGenerateEntity,
-                 app_orchestration_config: AppOrchestrationConfigEntity,
-                 model_config: ModelConfigEntity,
+                 application_generate_entity: AgentChatAppGenerateEntity,
+                 app_config: AgentChatAppConfig,
+                 model_config: ModelConfigWithCredentialsEntity,
                  config: AgentEntity,
-                 queue_manager: ApplicationQueueManager,
+                 queue_manager: AppQueueManager,
                  message: Message,
                  user_id: str,
                  memory: Optional[TokenBufferMemory] = None,
@@ -66,7 +60,7 @@ class BaseAssistantApplicationRunner(AppRunner):
         """
         Agent runner
         :param tenant_id: tenant id
-        :param app_orchestration_config: app orchestration config
+        :param app_config: app generate entity
         :param model_config: model config
         :param config: dataset config
         :param queue_manager: queue manager
@@ -78,7 +72,7 @@ class BaseAssistantApplicationRunner(AppRunner):
         """
         self.tenant_id = tenant_id
         self.application_generate_entity = application_generate_entity
-        self.app_orchestration_config = app_orchestration_config
+        self.app_config = app_config
         self.model_config = model_config
         self.config = config
         self.queue_manager = queue_manager
@@ -97,16 +91,16 @@ class BaseAssistantApplicationRunner(AppRunner):
         # init dataset tools
         hit_callback = DatasetIndexToolCallbackHandler(
             queue_manager=queue_manager,
-            app_id=self.application_generate_entity.app_id,
+            app_id=self.app_config.app_id,
             message_id=message.id,
             user_id=user_id,
             invoke_from=self.application_generate_entity.invoke_from,
         )
         self.dataset_tools = DatasetRetrieverTool.get_dataset_tools(
             tenant_id=tenant_id,
-            dataset_ids=app_orchestration_config.dataset.dataset_ids if app_orchestration_config.dataset else [],
-            retrieve_config=app_orchestration_config.dataset.retrieve_config if app_orchestration_config.dataset else None,
-            return_resource=app_orchestration_config.show_retrieve_source,
+            dataset_ids=app_config.dataset.dataset_ids if app_config.dataset else [],
+            retrieve_config=app_config.dataset.retrieve_config if app_config.dataset else None,
+            return_resource=app_config.additional_features.show_retrieve_source,
             invoke_from=application_generate_entity.invoke_from,
             hit_callback=hit_callback
         )
@@ -114,6 +108,7 @@ class BaseAssistantApplicationRunner(AppRunner):
         self.agent_thought_count = db.session.query(MessageAgentThought).filter(
             MessageAgentThought.message_id == self.message.id,
         ).count()
+        db.session.close()
 
         # check if model supports stream tool call
         llm_model = cast(LargeLanguageModel, model_instance.model_type_instance)
@@ -123,14 +118,15 @@ class BaseAssistantApplicationRunner(AppRunner):
         else:
             self.stream_tool_call = False
 
-    def _repack_app_orchestration_config(self, app_orchestration_config: AppOrchestrationConfigEntity) -> AppOrchestrationConfigEntity:
+    def _repack_app_generate_entity(self, app_generate_entity: AgentChatAppGenerateEntity) \
+            -> AgentChatAppGenerateEntity:
         """
-        Repack app orchestration config
+        Repack app generate entity
         """
-        if app_orchestration_config.prompt_template.simple_prompt_template is None:
-            app_orchestration_config.prompt_template.simple_prompt_template = ''
+        if app_generate_entity.app_config.prompt_template.simple_prompt_template is None:
+            app_generate_entity.app_config.prompt_template.simple_prompt_template = ''
 
-        return app_orchestration_config
+        return app_generate_entity
 
     def _convert_tool_response_to_str(self, tool_response: list[ToolInvokeMessage]) -> str:
         """
@@ -144,7 +140,7 @@ class BaseAssistantApplicationRunner(AppRunner):
                 result += f"result link: {response.message}. please tell user to check it."
             elif response.type == ToolInvokeMessage.MessageType.IMAGE_LINK or \
                  response.type == ToolInvokeMessage.MessageType.IMAGE:
-                result += "image has been created and sent to user already, you should tell user to check it now."
+                result += "image has been created and sent to user already, you do not need to create it, just tell the user to check it now."
             else:
                 result += f"tool response: {response.message}."
 
@@ -154,10 +150,9 @@ class BaseAssistantApplicationRunner(AppRunner):
         """
             convert tool to prompt message tool
         """
-        tool_entity = ToolManager.get_tool_runtime(
-            provider_type=tool.provider_type, provider_name=tool.provider_id, tool_name=tool.tool_name, 
-            tenant_id=self.application_generate_entity.tenant_id,
-            agent_callback=self.agent_callback
+        tool_entity = ToolManager.get_agent_tool_runtime(
+            tenant_id=self.tenant_id,
+            agent_tool=tool,
         )
         tool_entity.load_variables(self.variables_pool)
 
@@ -171,33 +166,11 @@ class BaseAssistantApplicationRunner(AppRunner):
             }
         )
 
-        runtime_parameters = {}
-
-        parameters = tool_entity.parameters or []
-        user_parameters = tool_entity.get_runtime_parameters() or []
-
-        # override parameters
-        for parameter in user_parameters:
-            # check if parameter in tool parameters
-            found = False
-            for tool_parameter in parameters:
-                if tool_parameter.name == parameter.name:
-                    found = True
-                    break
-
-            if found:
-                # override parameter
-                tool_parameter.type = parameter.type
-                tool_parameter.form = parameter.form
-                tool_parameter.required = parameter.required
-                tool_parameter.default = parameter.default
-                tool_parameter.options = parameter.options
-                tool_parameter.llm_description = parameter.llm_description
-            else:
-                # add new parameter
-                parameters.append(parameter)
-
+        parameters = tool_entity.get_all_runtime_parameters()
         for parameter in parameters:
+            if parameter.form != ToolParameter.ToolParameterForm.LLM:
+                continue
+
             parameter_type = 'string'
             enum = []
             if parameter.type == ToolParameter.ToolParameterType.STRING:
@@ -213,59 +186,16 @@ class BaseAssistantApplicationRunner(AppRunner):
             else:
                 raise ValueError(f"parameter type {parameter.type} is not supported")
             
-            if parameter.form == ToolParameter.ToolParameterForm.FORM:
-                # get tool parameter from form
-                tool_parameter_config = tool.tool_parameters.get(parameter.name)
-                if not tool_parameter_config:
-                    # get default value
-                    tool_parameter_config = parameter.default
-                    if not tool_parameter_config and parameter.required:
-                        raise ValueError(f"tool parameter {parameter.name} not found in tool config")
-                    
-                if parameter.type == ToolParameter.ToolParameterType.SELECT:
-                    # check if tool_parameter_config in options
-                    options = list(map(lambda x: x.value, parameter.options))
-                    if tool_parameter_config not in options:
-                        raise ValueError(f"tool parameter {parameter.name} value {tool_parameter_config} not in options {options}")
-                    
-                # convert tool parameter config to correct type
-                try:
-                    if parameter.type == ToolParameter.ToolParameterType.NUMBER:
-                        # check if tool parameter is integer
-                        if isinstance(tool_parameter_config, int):
-                            tool_parameter_config = tool_parameter_config
-                        elif isinstance(tool_parameter_config, float):
-                            tool_parameter_config = tool_parameter_config
-                        elif isinstance(tool_parameter_config, str):
-                            if '.' in tool_parameter_config:
-                                tool_parameter_config = float(tool_parameter_config)
-                            else:
-                                tool_parameter_config = int(tool_parameter_config)
-                    elif parameter.type == ToolParameter.ToolParameterType.BOOLEAN:
-                        tool_parameter_config = bool(tool_parameter_config)
-                    elif parameter.type not in [ToolParameter.ToolParameterType.SELECT, ToolParameter.ToolParameterType.STRING]:
-                        tool_parameter_config = str(tool_parameter_config)
-                    elif parameter.type == ToolParameter.ToolParameterType:
-                        tool_parameter_config = str(tool_parameter_config)
-                except Exception as e:
-                    raise ValueError(f"tool parameter {parameter.name} value {tool_parameter_config} is not correct type")
-                
-                # save tool parameter to tool entity memory
-                runtime_parameters[parameter.name] = tool_parameter_config
-            
-            elif parameter.form == ToolParameter.ToolParameterForm.LLM:
-                message_tool.parameters['properties'][parameter.name] = {
-                    "type": parameter_type,
-                    "description": parameter.llm_description or '',
-                }
+            message_tool.parameters['properties'][parameter.name] = {
+                "type": parameter_type,
+                "description": parameter.llm_description or '',
+            }
 
-                if len(enum) > 0:
-                    message_tool.parameters['properties'][parameter.name]['enum'] = enum
+            if len(enum) > 0:
+                message_tool.parameters['properties'][parameter.name]['enum'] = enum
 
-                if parameter.required:
-                    message_tool.parameters['required'].append(parameter.name)
-
-        tool_entity.runtime.runtime_parameters.update(runtime_parameters)
+            if parameter.required:
+                message_tool.parameters['required'].append(parameter.name)
 
         return message_tool, tool_entity
     
@@ -305,6 +235,9 @@ class BaseAssistantApplicationRunner(AppRunner):
         tool_runtime_parameters = tool.get_runtime_parameters() or []
 
         for parameter in tool_runtime_parameters:
+            if parameter.form != ToolParameter.ToolParameterForm.LLM:
+                continue
+
             parameter_type = 'string'
             enum = []
             if parameter.type == ToolParameter.ToolParameterType.STRING:
@@ -320,98 +253,19 @@ class BaseAssistantApplicationRunner(AppRunner):
             else:
                 raise ValueError(f"parameter type {parameter.type} is not supported")
         
-            if parameter.form == ToolParameter.ToolParameterForm.LLM:
-                prompt_tool.parameters['properties'][parameter.name] = {
-                    "type": parameter_type,
-                    "description": parameter.llm_description or '',
-                }
+            prompt_tool.parameters['properties'][parameter.name] = {
+                "type": parameter_type,
+                "description": parameter.llm_description or '',
+            }
 
-                if len(enum) > 0:
-                    prompt_tool.parameters['properties'][parameter.name]['enum'] = enum
+            if len(enum) > 0:
+                prompt_tool.parameters['properties'][parameter.name]['enum'] = enum
 
-                if parameter.required:
-                    if parameter.name not in prompt_tool.parameters['required']:
-                        prompt_tool.parameters['required'].append(parameter.name)
+            if parameter.required:
+                if parameter.name not in prompt_tool.parameters['required']:
+                    prompt_tool.parameters['required'].append(parameter.name)
 
         return prompt_tool
-    
-    def extract_tool_response_binary(self, tool_response: list[ToolInvokeMessage]) -> list[ToolInvokeMessageBinary]:
-        """
-        Extract tool response binary
-        """
-        result = []
-
-        for response in tool_response:
-            if response.type == ToolInvokeMessage.MessageType.IMAGE_LINK or \
-                response.type == ToolInvokeMessage.MessageType.IMAGE:
-                result.append(ToolInvokeMessageBinary(
-                    mimetype=response.meta.get('mime_type', 'octet/stream'),
-                    url=response.message,
-                    save_as=response.save_as,
-                ))
-            elif response.type == ToolInvokeMessage.MessageType.BLOB:
-                result.append(ToolInvokeMessageBinary(
-                    mimetype=response.meta.get('mime_type', 'octet/stream'),
-                    url=response.message,
-                    save_as=response.save_as,
-                ))
-            elif response.type == ToolInvokeMessage.MessageType.LINK:
-                # check if there is a mime type in meta
-                if response.meta and 'mime_type' in response.meta:
-                    result.append(ToolInvokeMessageBinary(
-                        mimetype=response.meta.get('mime_type', 'octet/stream') if response.meta else 'octet/stream',
-                        url=response.message,
-                        save_as=response.save_as,
-                    ))
-
-        return result
-    
-    def create_message_files(self, messages: list[ToolInvokeMessageBinary]) -> list[tuple[MessageFile, bool]]:
-        """
-        Create message file
-
-        :param messages: messages
-        :return: message files, should save as variable
-        """
-        result = []
-
-        for message in messages:
-            file_type = 'bin'
-            if 'image' in message.mimetype:
-                file_type = 'image'
-            elif 'video' in message.mimetype:
-                file_type = 'video'
-            elif 'audio' in message.mimetype:
-                file_type = 'audio'
-            elif 'text' in message.mimetype:
-                file_type = 'text'
-            elif 'pdf' in message.mimetype:
-                file_type = 'pdf'
-            elif 'zip' in message.mimetype:
-                file_type = 'archive'
-            # ...
-
-            invoke_from = self.application_generate_entity.invoke_from
-
-            message_file = MessageFile(
-                message_id=self.message.id,
-                type=file_type,
-                transfer_method=FileTransferMethod.TOOL_FILE.value,
-                belongs_to='assistant',
-                url=message.url,
-                upload_file_id=None,
-                created_by_role=('account'if invoke_from in [InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER] else 'end_user'),
-                created_by=self.user_id,
-            )
-            db.session.add(message_file)
-            result.append((
-                message_file,
-                message.save_as
-            ))
-            
-        db.session.commit()
-
-        return result
         
     def create_agent_thought(self, message_id: str, message: str, 
                              tool_name: str, tool_input: str, messages_ids: list[str]
@@ -425,6 +279,7 @@ class BaseAssistantApplicationRunner(AppRunner):
             thought='',
             tool=tool_name,
             tool_labels_str='{}',
+            tool_meta_str='{}',
             tool_input=tool_input,
             message=message,
             message_token=0,
@@ -447,6 +302,8 @@ class BaseAssistantApplicationRunner(AppRunner):
 
         db.session.add(thought)
         db.session.commit()
+        db.session.refresh(thought)
+        db.session.close()
 
         self.agent_thought_count += 1
 
@@ -457,13 +314,18 @@ class BaseAssistantApplicationRunner(AppRunner):
                            tool_name: str,
                            tool_input: Union[str, dict],
                            thought: str, 
-                           observation: str, 
+                           observation: Union[str, str], 
+                           tool_invoke_meta: Union[str, dict],
                            answer: str,
                            messages_ids: list[str],
                            llm_usage: LLMUsage = None) -> MessageAgentThought:
         """
         Save agent thought
         """
+        agent_thought = db.session.query(MessageAgentThought).filter(
+            MessageAgentThought.id == agent_thought.id
+        ).first()
+
         if thought is not None:
             agent_thought.thought = thought
 
@@ -480,6 +342,12 @@ class BaseAssistantApplicationRunner(AppRunner):
             agent_thought.tool_input = tool_input
 
         if observation is not None:
+            if isinstance(observation, dict):
+                try:
+                    observation = json.dumps(observation, ensure_ascii=False)
+                except Exception as e:
+                    observation = json.dumps(observation)
+                    
             agent_thought.observation = observation
 
         if answer is not None:
@@ -513,82 +381,30 @@ class BaseAssistantApplicationRunner(AppRunner):
 
         agent_thought.tool_labels_str = json.dumps(labels)
 
-        db.session.commit()
-    
-    def transform_tool_invoke_messages(self, messages: list[ToolInvokeMessage]) -> list[ToolInvokeMessage]:
-        """
-        Transform tool message into agent thought
-        """
-        result = []
-
-        for message in messages:
-            if message.type == ToolInvokeMessage.MessageType.TEXT:
-                result.append(message)
-            elif message.type == ToolInvokeMessage.MessageType.LINK:
-                result.append(message)
-            elif message.type == ToolInvokeMessage.MessageType.IMAGE:
-                # try to download image
+        if tool_invoke_meta is not None:
+            if isinstance(tool_invoke_meta, dict):
                 try:
-                    file = ToolFileManager.create_file_by_url(user_id=self.user_id, tenant_id=self.tenant_id,
-                                                               conversation_id=self.message.conversation_id,
-                                                               file_url=message.message)
-                    
-                    url = f'/files/tools/{file.id}{guess_extension(file.mimetype) or ".png"}'
-
-                    result.append(ToolInvokeMessage(
-                        type=ToolInvokeMessage.MessageType.IMAGE_LINK,
-                        message=url,
-                        save_as=message.save_as,
-                        meta=message.meta.copy() if message.meta is not None else {},
-                    ))
+                    tool_invoke_meta = json.dumps(tool_invoke_meta, ensure_ascii=False)
                 except Exception as e:
-                    logger.exception(e)
-                    result.append(ToolInvokeMessage(
-                        type=ToolInvokeMessage.MessageType.TEXT,
-                        message=f"Failed to download image: {message.message}, you can try to download it yourself.",
-                        meta=message.meta.copy() if message.meta is not None else {},
-                        save_as=message.save_as,
-                    ))
-            elif message.type == ToolInvokeMessage.MessageType.BLOB:
-                # get mime type and save blob to storage
-                mimetype = message.meta.get('mime_type', 'octet/stream')
-                # if message is str, encode it to bytes
-                if isinstance(message.message, str):
-                    message.message = message.message.encode('utf-8')
-                file = ToolFileManager.create_file_by_raw(user_id=self.user_id, tenant_id=self.tenant_id,
-                                                            conversation_id=self.message.conversation_id,
-                                                            file_binary=message.message,
-                                                            mimetype=mimetype)
-                                                            
-                url = f'/files/tools/{file.id}{guess_extension(file.mimetype) or ".bin"}'
+                    tool_invoke_meta = json.dumps(tool_invoke_meta)
 
-                # check if file is image
-                if 'image' in mimetype:
-                    result.append(ToolInvokeMessage(
-                        type=ToolInvokeMessage.MessageType.IMAGE_LINK,
-                        message=url,
-                        save_as=message.save_as,
-                        meta=message.meta.copy() if message.meta is not None else {},
-                    ))
-                else:
-                    result.append(ToolInvokeMessage(
-                        type=ToolInvokeMessage.MessageType.LINK,
-                        message=url,
-                        save_as=message.save_as,
-                        meta=message.meta.copy() if message.meta is not None else {},
-                    ))
-            else:
-                result.append(message)
+            agent_thought.tool_meta_str = tool_invoke_meta
 
-        return result
+        db.session.commit()
+        db.session.close()
     
     def update_db_variables(self, tool_variables: ToolRuntimeVariablePool, db_variables: ToolConversationVariables):
         """
         convert tool variables to db variables
         """
+        db_variables = db.session.query(ToolConversationVariables).filter(
+            ToolConversationVariables.conversation_id == self.message.conversation_id,
+        ).first()
+
         db_variables.updated_at = datetime.utcnow()
         db_variables.variables_str = json.dumps(jsonable_encoder(tool_variables.pool))
         db.session.commit()
+        db.session.close()
 
     def organize_agent_history(self, prompt_messages: list[PromptMessage]) -> list[PromptMessage]:
         """
@@ -613,7 +429,15 @@ class BaseAssistantApplicationRunner(AppRunner):
                         tools = tools.split(';')
                         tool_calls: list[AssistantPromptMessage.ToolCall] = []
                         tool_call_response: list[ToolPromptMessage] = []
-                        tool_inputs = json.loads(agent_thought.tool_input)
+                        try:
+                            tool_inputs = json.loads(agent_thought.tool_input)
+                        except Exception as e:
+                            tool_inputs = { tool: {} for tool in tools }
+                        try:
+                            tool_responses = json.loads(agent_thought.observation)
+                        except Exception as e:
+                            tool_responses = { tool: agent_thought.observation for tool in tools }
+
                         for tool in tools:
                             # generate a uuid for tool call
                             tool_call_id = str(uuid.uuid4())
@@ -626,7 +450,7 @@ class BaseAssistantApplicationRunner(AppRunner):
                                 )
                             ))
                             tool_call_response.append(ToolPromptMessage(
-                                content=agent_thought.observation,
+                                content=tool_responses.get(tool, agent_thought.observation),
                                 name=tool,
                                 tool_call_id=tool_call_id,
                             ))
@@ -644,4 +468,6 @@ class BaseAssistantApplicationRunner(AppRunner):
                 if message.answer:
                     result.append(AssistantPromptMessage(content=message.answer))
 
-        return result
+        db.session.close()
+
+        return result

api/core/agent/cot_agent_runner.py

@@ -3,9 +3,10 @@ import re
 from collections.abc import Generator
 from typing import Literal, Union
 
-from core.application_queue_manager import PublishFrom
-from core.entities.application_entities import AgentPromptEntity, AgentScratchpadUnit
-from core.features.assistant_base_runner import BaseAssistantApplicationRunner
+from core.agent.base_agent_runner import BaseAgentRunner
+from core.agent.entities import AgentPromptEntity, AgentScratchpadUnit
+from core.app.apps.base_app_queue_manager import PublishFrom
+from core.app.entities.queue_entities import QueueAgentThoughtEvent, QueueMessageEndEvent, QueueMessageFileEvent
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
 from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
@@ -16,18 +17,15 @@ from core.model_runtime.entities.message_entities import (
     UserPromptMessage,
 )
 from core.model_runtime.utils.encoders import jsonable_encoder
-from core.tools.errors import (
-    ToolInvokeError,
-    ToolNotFoundError,
-    ToolNotSupportedError,
-    ToolParameterValidationError,
-    ToolProviderCredentialValidationError,
-    ToolProviderNotFoundError,
-)
+from core.tools.entities.tool_entities import ToolInvokeMeta
+from core.tools.tool_engine import ToolEngine
 from models.model import Conversation, Message
 
 
-class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
+class CotAgentRunner(BaseAgentRunner):
+    _is_first_iteration = True
+    _ignore_observation_providers = ['wenxin']
+
     def run(self, conversation: Conversation,
         message: Message,
         query: str,
@@ -36,32 +34,33 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
         """
         Run Cot agent application
         """
-        app_orchestration_config = self.app_orchestration_config
-        self._repack_app_orchestration_config(app_orchestration_config)
+        app_generate_entity = self.application_generate_entity
+        self._repack_app_generate_entity(app_generate_entity)
 
         agent_scratchpad: list[AgentScratchpadUnit] = []
         self._init_agent_scratchpad(agent_scratchpad, self.history_prompt_messages)
 
         # check model mode
-        if self.app_orchestration_config.model_config.mode == "completion":
-            # TODO: stop words
-            if 'Observation' not in app_orchestration_config.model_config.stop:
-                app_orchestration_config.model_config.stop.append('Observation')
+        if 'Observation' not in app_generate_entity.model_config.stop:
+            if app_generate_entity.model_config.provider not in self._ignore_observation_providers:
+                app_generate_entity.model_config.stop.append('Observation')
+
+        app_config = self.app_config
 
         # override inputs
         inputs = inputs or {}
-        instruction = self.app_orchestration_config.prompt_template.simple_prompt_template
+        instruction = app_config.prompt_template.simple_prompt_template
         instruction = self._fill_in_inputs_from_external_data_tools(instruction, inputs)
 
         iteration_step = 1
-        max_iteration_steps = min(self.app_orchestration_config.agent.max_iteration, 5) + 1
+        max_iteration_steps = min(app_config.agent.max_iteration, 5) + 1
 
         prompt_messages = self.history_prompt_messages
 
         # convert tools into ModelRuntime Tool format
         prompt_messages_tools: list[PromptMessageTool] = []
         tool_instances = {}
-        for tool in self.app_orchestration_config.agent.tools if self.app_orchestration_config.agent else []:
+        for tool in app_config.agent.tools if app_config.agent else []:
             try:
                 prompt_tool, tool_entity = self._convert_tool_to_prompt_message_tool(tool)
             except Exception:
@@ -117,27 +116,29 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
             )
 
             if iteration_step > 1:
-                self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
+                self.queue_manager.publish(QueueAgentThoughtEvent(
+                    agent_thought_id=agent_thought.id
+                ), PublishFrom.APPLICATION_MANAGER)
 
             # update prompt messages
             prompt_messages = self._organize_cot_prompt_messages(
-                mode=app_orchestration_config.model_config.mode,
+                mode=app_generate_entity.model_config.mode,
                 prompt_messages=prompt_messages,
                 tools=prompt_messages_tools,
                 agent_scratchpad=agent_scratchpad,
-                agent_prompt_message=app_orchestration_config.agent.prompt,
+                agent_prompt_message=app_config.agent.prompt,
                 instruction=instruction,
                 input=query
             )
 
-            # recale llm max tokens
-            self.recale_llm_max_tokens(self.model_config, prompt_messages)
+            # recalc llm max tokens
+            self.recalc_llm_max_tokens(self.model_config, prompt_messages)
             # invoke model
             chunks: Generator[LLMResultChunk, None, None] = model_instance.invoke_llm(
                 prompt_messages=prompt_messages,
-                model_parameters=app_orchestration_config.model_config.parameters,
+                model_parameters=app_generate_entity.model_config.parameters,
                 tools=[],
-                stop=app_orchestration_config.model_config.stop,
+                stop=app_generate_entity.model_config.stop,
                 stream=True,
                 user=self.user_id,
                 callbacks=[],
@@ -159,7 +160,9 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
 
             # publish agent thought if it's first iteration
             if iteration_step == 1:
-                self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
+                self.queue_manager.publish(QueueAgentThoughtEvent(
+                    agent_thought_id=agent_thought.id
+                ), PublishFrom.APPLICATION_MANAGER)
 
             for chunk in react_chunks:
                 if isinstance(chunk, dict):
@@ -181,7 +184,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
                             delta=LLMResultChunkDelta(
                                 index=0,
                                 message=AssistantPromptMessage(
-                                    content=json.dumps(chunk)
+                                    content=json.dumps(chunk, ensure_ascii=False) # if ensure_ascii=True, the text in webui maybe garbled text
                                 ),
                                 usage=None
                             )
@@ -202,6 +205,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
                         )
                     )
 
+            scratchpad.thought = scratchpad.thought.strip() or 'I am thinking about how to help you'
             agent_scratchpad.append(scratchpad)
                         
             # get llm usage
@@ -212,7 +216,10 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
             
             self.save_agent_thought(agent_thought=agent_thought,
                                     tool_name=scratchpad.action.action_name if scratchpad.action else '',
-                                    tool_input=scratchpad.action.action_input if scratchpad.action else '',
+                                    tool_input={
+                                        scratchpad.action.action_name: scratchpad.action.action_input
+                                    } if scratchpad.action else '',
+                                    tool_invoke_meta={},
                                     thought=scratchpad.thought,
                                     observation='',
                                     answer=scratchpad.agent_response,
@@ -220,7 +227,9 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
                                     llm_usage=usage_dict['usage'])
             
             if scratchpad.action and scratchpad.action.action_name.lower() != "final answer":
-                self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
+                self.queue_manager.publish(QueueAgentThoughtEvent(
+                    agent_thought_id=agent_thought.id
+                ), PublishFrom.APPLICATION_MANAGER)
 
             if not scratchpad.action:
                 # failed to extract action, return final answer directly
@@ -243,56 +252,65 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
                     tool_instance = tool_instances.get(tool_call_name)
                     if not tool_instance:
                         answer = f"there is not a tool named {tool_call_name}"
-                        self.save_agent_thought(agent_thought=agent_thought, 
-                                                tool_name='',
-                                                tool_input='',
-                                                thought=None, 
-                                                observation=answer, 
-                                                answer=answer,
-                                                messages_ids=[])
-                        self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
+                        self.save_agent_thought(
+                            agent_thought=agent_thought, 
+                            tool_name='',
+                            tool_input='',
+                            tool_invoke_meta=ToolInvokeMeta.error_instance(
+                                f"there is not a tool named {tool_call_name}"
+                            ).to_dict(),
+                            thought=None, 
+                            observation={
+                                tool_call_name: answer
+                            }, 
+                            answer=answer,
+                            messages_ids=[]
+                        )
+                        self.queue_manager.publish(QueueAgentThoughtEvent(
+                            agent_thought_id=agent_thought.id
+                        ), PublishFrom.APPLICATION_MANAGER)
                     else:
+                        if isinstance(tool_call_args, str):
+                            try:
+                                tool_call_args = json.loads(tool_call_args)
+                            except json.JSONDecodeError:
+                                pass
+
                         # invoke tool
-                        error_response = None
-                        try:
-                            tool_response = tool_instance.invoke(
-                                user_id=self.user_id, 
-                                tool_parameters=tool_call_args if isinstance(tool_call_args, dict) else json.loads(tool_call_args)
-                            )
-                            # transform tool response to llm friendly response
-                            tool_response = self.transform_tool_invoke_messages(tool_response)
-                            # extract binary data from tool invoke message
-                            binary_files = self.extract_tool_response_binary(tool_response)
-                            # create message file
-                            message_files = self.create_message_files(binary_files)
-                            # publish files
-                            for message_file, save_as in message_files:
-                                if save_as:
-                                    self.variables_pool.set_file(tool_name=tool_call_name,
-                                                                  value=message_file.id,
-                                                                  name=save_as)
-                                self.queue_manager.publish_message_file(message_file, PublishFrom.APPLICATION_MANAGER)
+                        tool_invoke_response, message_files, tool_invoke_meta = ToolEngine.agent_invoke(
+                            tool=tool_instance,
+                            tool_parameters=tool_call_args,
+                            user_id=self.user_id,
+                            tenant_id=self.tenant_id,
+                            message=self.message,
+                            invoke_from=self.application_generate_entity.invoke_from,
+                            agent_tool_callback=self.agent_callback
+                        )
+                        # publish files
+                        for message_file, save_as in message_files:
+                            if save_as:
+                                self.variables_pool.set_file(tool_name=tool_call_name, value=message_file.id, name=save_as)
 
-                            message_file_ids = [message_file.id for message_file, _ in message_files]
-                        except ToolProviderCredentialValidationError as e:
-                            error_response = "Please check your tool provider credentials"
-                        except (
-                            ToolNotFoundError, ToolNotSupportedError, ToolProviderNotFoundError
-                        ) as e:
-                            error_response = f"there is not a tool named {tool_call_name}"
-                        except (
-                            ToolParameterValidationError
-                        ) as e:
-                            error_response = f"tool parameters validation error: {e}, please check your tool parameters"
-                        except ToolInvokeError as e:
-                            error_response = f"tool invoke error: {e}"
-                        except Exception as e:
-                            error_response = f"unknown error: {e}"
+                            # publish message file
+                            self.queue_manager.publish(QueueMessageFileEvent(
+                                message_file_id=message_file.id
+                            ), PublishFrom.APPLICATION_MANAGER)
+                            # add message file ids
+                            message_file_ids.append(message_file.id)
 
-                        if error_response:
-                            observation = error_response
-                        else:
-                            observation = self._convert_tool_response_to_str(tool_response)
+                        # publish files
+                        for message_file, save_as in message_files:
+                            if save_as:
+                                self.variables_pool.set_file(tool_name=tool_call_name,
+                                                                value=message_file.id,
+                                                                name=save_as)
+                            self.queue_manager.publish(QueueMessageFileEvent(
+                                message_file_id=message_file.id
+                            ), PublishFrom.APPLICATION_MANAGER)
+
+                        message_file_ids = [message_file.id for message_file, _ in message_files]
+
+                        observation = tool_invoke_response
 
                         # save scratchpad
                         scratchpad.observation = observation
@@ -301,13 +319,22 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
                         self.save_agent_thought(
                             agent_thought=agent_thought, 
                             tool_name=tool_call_name,
-                            tool_input=tool_call_args,
+                            tool_input={
+                                tool_call_name: tool_call_args
+                            },
+                            tool_invoke_meta={
+                                tool_call_name: tool_invoke_meta.to_dict()
+                            },
                             thought=None,
-                            observation=observation, 
+                            observation={
+                                tool_call_name: observation
+                            }, 
                             answer=scratchpad.agent_response,
                             messages_ids=message_file_ids,
                         )
-                        self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
+                        self.queue_manager.publish(QueueAgentThoughtEvent(
+                            agent_thought_id=agent_thought.id
+                        ), PublishFrom.APPLICATION_MANAGER)
 
                 # update prompt tool message
                 for prompt_tool in prompt_messages_tools:
@@ -332,16 +359,17 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
         self.save_agent_thought(
             agent_thought=agent_thought, 
             tool_name='',
-            tool_input='',
+            tool_input={},
+            tool_invoke_meta={},
             thought=final_answer,
-            observation='', 
+            observation={}, 
             answer=final_answer,
             messages_ids=[]
         )
 
         self.update_db_variables(self.variables_pool, self.db_variables_pool)
         # publish end event
-        self.queue_manager.publish_message_end(LLMResult(
+        self.queue_manager.publish(QueueMessageEndEvent(llm_result=LLMResult(
             model=model_instance.model,
             prompt_messages=prompt_messages,
             message=AssistantPromptMessage(
@@ -349,7 +377,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
             ),
             usage=llm_usage['usage'] if llm_usage['usage'] else LLMUsage.empty_usage(),
             system_fingerprint=''
-        ), PublishFrom.APPLICATION_MANAGER)
+        )), PublishFrom.APPLICATION_MANAGER)
 
     def _handle_stream_react(self, llm_response: Generator[LLMResultChunk, None, None], usage: dict) \
         -> Generator[Union[str, dict], None, None]:
@@ -466,7 +494,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
             if isinstance(message, AssistantPromptMessage):
                 current_scratchpad = AgentScratchpadUnit(
                     agent_response=message.content,
-                    thought=message.content,
+                    thought=message.content or 'I am thinking about how to help you',
                     action_str='',
                     action=None,
                     observation=None,
@@ -542,11 +570,12 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
         """
             convert agent scratchpad list to str
         """
-        next_iteration = self.app_orchestration_config.agent.prompt.next_iteration
+        next_iteration = self.app_config.agent.prompt.next_iteration
 
         result = ''
         for scratchpad in agent_scratchpad:
-            result += scratchpad.thought + next_iteration.replace("{{observation}}", scratchpad.observation or '') + "\n"
+            result += (scratchpad.thought or '') + (scratchpad.action_str or '') + \
+                next_iteration.replace("{{observation}}", scratchpad.observation or 'It seems that no response is available')
 
         return result
     
@@ -621,21 +650,24 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
                 ))
 
             # add assistant message
-            if len(agent_scratchpad) > 0:
+            if len(agent_scratchpad) > 0 and not self._is_first_iteration:
                 prompt_messages.append(AssistantPromptMessage(
-                    content=(agent_scratchpad[-1].thought or '')
+                    content=(agent_scratchpad[-1].thought or '') + (agent_scratchpad[-1].action_str or ''),
                 ))
             
             # add user message
-            if len(agent_scratchpad) > 0:
+            if len(agent_scratchpad) > 0 and not self._is_first_iteration:
                 prompt_messages.append(UserPromptMessage(
-                    content=(agent_scratchpad[-1].observation or ''),
+                    content=(agent_scratchpad[-1].observation or 'It seems that no response is available'),
                 ))
 
+            self._is_first_iteration = False
+
             return prompt_messages
         elif mode == "completion":
             # parse agent scratchpad
             agent_scratchpad_str = self._convert_scratchpad_list_to_str(agent_scratchpad)
+            self._is_first_iteration = False
             # parse prompt messages
             return [UserPromptMessage(
                 content=first_prompt.replace("{{instruction}}", instruction)
@@ -655,4 +687,4 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
         try:
             return json.dumps(tools, ensure_ascii=False)
         except json.JSONDecodeError:
-            return json.dumps(tools)
+            return json.dumps(tools)

api/core/agent/entities.py

@@ -0,0 +1,61 @@
+from enum import Enum
+from typing import Any, Literal, Optional, Union
+
+from pydantic import BaseModel
+
+
+class AgentToolEntity(BaseModel):
+    """
+    Agent Tool Entity.
+    """
+    provider_type: Literal["builtin", "api"]
+    provider_id: str
+    tool_name: str
+    tool_parameters: dict[str, Any] = {}
+
+
+class AgentPromptEntity(BaseModel):
+    """
+    Agent Prompt Entity.
+    """
+    first_prompt: str
+    next_iteration: str
+
+
+class AgentScratchpadUnit(BaseModel):
+    """
+    Agent First Prompt Entity.
+    """
+
+    class Action(BaseModel):
+        """
+        Action Entity.
+        """
+        action_name: str
+        action_input: Union[dict, str]
+
+    agent_response: Optional[str] = None
+    thought: Optional[str] = None
+    action_str: Optional[str] = None
+    observation: Optional[str] = None
+    action: Optional[Action] = None
+
+
+class AgentEntity(BaseModel):
+    """
+    Agent Entity.
+    """
+
+    class Strategy(Enum):
+        """
+        Agent Strategy.
+        """
+        CHAIN_OF_THOUGHT = 'chain-of-thought'
+        FUNCTION_CALLING = 'function-calling'
+
+    provider: str
+    model: str
+    strategy: Strategy
+    prompt: Optional[AgentPromptEntity] = None
+    tools: list[AgentToolEntity] = None
+    max_iteration: int = 5

api/core/agent/fc_agent_runner.py

@@ -3,8 +3,9 @@ import logging
 from collections.abc import Generator
 from typing import Any, Union
 
-from core.application_queue_manager import PublishFrom
-from core.features.assistant_base_runner import BaseAssistantApplicationRunner
+from core.agent.base_agent_runner import BaseAgentRunner
+from core.app.apps.base_app_queue_manager import PublishFrom
+from core.app.entities.queue_entities import QueueAgentThoughtEvent, QueueMessageEndEvent, QueueMessageFileEvent
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
 from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
@@ -14,19 +15,13 @@ from core.model_runtime.entities.message_entities import (
     ToolPromptMessage,
     UserPromptMessage,
 )
-from core.tools.errors import (
-    ToolInvokeError,
-    ToolNotFoundError,
-    ToolNotSupportedError,
-    ToolParameterValidationError,
-    ToolProviderCredentialValidationError,
-    ToolProviderNotFoundError,
-)
+from core.tools.entities.tool_entities import ToolInvokeMeta
+from core.tools.tool_engine import ToolEngine
 from models.model import Conversation, Message, MessageAgentThought
 
 logger = logging.getLogger(__name__)
 
-class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
+class FunctionCallAgentRunner(BaseAgentRunner):
     def run(self, conversation: Conversation,
                 message: Message,
                 query: str,
@@ -34,9 +29,11 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
         """
         Run FunctionCall agent application
         """
-        app_orchestration_config = self.app_orchestration_config
+        app_generate_entity = self.application_generate_entity
 
-        prompt_template = self.app_orchestration_config.prompt_template.simple_prompt_template or ''
+        app_config = self.app_config
+
+        prompt_template = app_config.prompt_template.simple_prompt_template or ''
         prompt_messages = self.history_prompt_messages
         prompt_messages = self.organize_prompt_messages(
             prompt_template=prompt_template,
@@ -47,7 +44,7 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
         # convert tools into ModelRuntime Tool format
         prompt_messages_tools: list[PromptMessageTool] = []
         tool_instances = {}
-        for tool in self.app_orchestration_config.agent.tools if self.app_orchestration_config.agent else []:
+        for tool in app_config.agent.tools if app_config.agent else []:
             try:
                 prompt_tool, tool_entity = self._convert_tool_to_prompt_message_tool(tool)
             except Exception:
@@ -67,7 +64,7 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
             tool_instances[dataset_tool.identity.name] = dataset_tool
 
         iteration_step = 1
-        max_iteration_steps = min(app_orchestration_config.agent.max_iteration, 5) + 1
+        max_iteration_steps = min(app_config.agent.max_iteration, 5) + 1
 
         # continue to run until there is not any tool call
         function_call_state = True
@@ -105,14 +102,14 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
                 messages_ids=message_file_ids
             )
 
-            # recale llm max tokens
-            self.recale_llm_max_tokens(self.model_config, prompt_messages)
+            # recalc llm max tokens
+            self.recalc_llm_max_tokens(self.model_config, prompt_messages)
             # invoke model
             chunks: Union[Generator[LLMResultChunk, None, None], LLMResult] = model_instance.invoke_llm(
                 prompt_messages=prompt_messages,
-                model_parameters=app_orchestration_config.model_config.parameters,
+                model_parameters=app_generate_entity.model_config.parameters,
                 tools=prompt_messages_tools,
-                stop=app_orchestration_config.model_config.stop,
+                stop=app_generate_entity.model_config.stop,
                 stream=self.stream_tool_call,
                 user=self.user_id,
                 callbacks=[],
@@ -133,7 +130,9 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
                 is_first_chunk = True
                 for chunk in chunks:
                     if is_first_chunk:
-                        self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
+                        self.queue_manager.publish(QueueAgentThoughtEvent(
+                            agent_thought_id=agent_thought.id
+                        ), PublishFrom.APPLICATION_MANAGER)
                         is_first_chunk = False
                     # check if there is any tool call
                     if self.check_tool_calls(chunk):
@@ -193,7 +192,9 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
                 if not result.message.content:
                     result.message.content = ''
 
-                self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
+                self.queue_manager.publish(QueueAgentThoughtEvent(
+                    agent_thought_id=agent_thought.id
+                ), PublishFrom.APPLICATION_MANAGER)
                 
                 yield LLMResultChunk(
                     model=model_instance.model,
@@ -226,13 +227,15 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
                 tool_name=tool_call_names,
                 tool_input=tool_call_inputs,
                 thought=response,
+                tool_invoke_meta=None,
                 observation=None,
                 answer=response,
                 messages_ids=[],
                 llm_usage=current_llm_usage
             )
-
-            self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
+            self.queue_manager.publish(QueueAgentThoughtEvent(
+                agent_thought_id=agent_thought.id
+            ), PublishFrom.APPLICATION_MANAGER)
             
             final_answer += response + '\n'
 
@@ -250,65 +253,40 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
                     tool_response = {
                         "tool_call_id": tool_call_id,
                         "tool_call_name": tool_call_name,
-                        "tool_response": f"there is not a tool named {tool_call_name}"
+                        "tool_response": f"there is not a tool named {tool_call_name}",
+                        "meta": ToolInvokeMeta.error_instance(f"there is not a tool named {tool_call_name}").to_dict()
                     }
-                    tool_responses.append(tool_response)
                 else:
                     # invoke tool
-                    error_response = None
-                    try:
-                        tool_invoke_message = tool_instance.invoke(
-                            user_id=self.user_id, 
-                            tool_parameters=tool_call_args, 
-                        )
-                        # transform tool invoke message to get LLM friendly message
-                        tool_invoke_message = self.transform_tool_invoke_messages(tool_invoke_message)
-                        # extract binary data from tool invoke message
-                        binary_files = self.extract_tool_response_binary(tool_invoke_message)
-                        # create message file
-                        message_files = self.create_message_files(binary_files)
-                        # publish files
-                        for message_file, save_as in message_files:
-                            if save_as:
-                                self.variables_pool.set_file(tool_name=tool_call_name, value=message_file.id, name=save_as)
-
-                            # publish message file
-                            self.queue_manager.publish_message_file(message_file, PublishFrom.APPLICATION_MANAGER)
-                            # add message file ids
-                            message_file_ids.append(message_file.id)
-                            
-                    except ToolProviderCredentialValidationError as e:
-                        error_response = "Please check your tool provider credentials"
-                    except (
-                        ToolNotFoundError, ToolNotSupportedError, ToolProviderNotFoundError
-                    ) as e:
-                        error_response = f"there is not a tool named {tool_call_name}"
-                    except (
-                        ToolParameterValidationError
-                    ) as e:
-                        error_response = f"tool parameters validation error: {e}, please check your tool parameters"
-                    except ToolInvokeError as e:
-                        error_response = f"tool invoke error: {e}"
-                    except Exception as e:
-                        error_response = f"unknown error: {e}"
-
-                    if error_response:
-                        observation = error_response
-                        tool_response = {
-                            "tool_call_id": tool_call_id,
-                            "tool_call_name": tool_call_name,
-                            "tool_response": error_response
-                        }
-                        tool_responses.append(tool_response)
-                    else:
-                        observation = self._convert_tool_response_to_str(tool_invoke_message)
-                        tool_response = {
-                            "tool_call_id": tool_call_id,
-                            "tool_call_name": tool_call_name,
-                            "tool_response": observation
-                        }
-                        tool_responses.append(tool_response)
+                    tool_invoke_response, message_files, tool_invoke_meta = ToolEngine.agent_invoke(
+                        tool=tool_instance,
+                        tool_parameters=tool_call_args,
+                        user_id=self.user_id,
+                        tenant_id=self.tenant_id,
+                        message=self.message,
+                        invoke_from=self.application_generate_entity.invoke_from,
+                        agent_tool_callback=self.agent_callback,
+                    )
+                    # publish files
+                    for message_file, save_as in message_files:
+                        if save_as:
+                            self.variables_pool.set_file(tool_name=tool_call_name, value=message_file.id, name=save_as)
 
+                        # publish message file
+                        self.queue_manager.publish(QueueMessageFileEvent(
+                            message_file_id=message_file.id
+                        ), PublishFrom.APPLICATION_MANAGER)
+                        # add message file ids
+                        message_file_ids.append(message_file.id)
+                    
+                    tool_response = {
+                        "tool_call_id": tool_call_id,
+                        "tool_call_name": tool_call_name,
+                        "tool_response": tool_invoke_response,
+                        "meta": tool_invoke_meta.to_dict()
+                    }
+                
+                tool_responses.append(tool_response)
                 prompt_messages = self.organize_prompt_messages(
                     prompt_template=prompt_template,
                     query=None,
@@ -325,11 +303,20 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
                     tool_name=None,
                     tool_input=None,
                     thought=None, 
-                    observation=tool_response['tool_response'], 
+                    tool_invoke_meta={
+                        tool_response['tool_call_name']: tool_response['meta'] 
+                        for tool_response in tool_responses
+                    },
+                    observation={
+                        tool_response['tool_call_name']: tool_response['tool_response'] 
+                        for tool_response in tool_responses
+                    },
                     answer=None,
                     messages_ids=message_file_ids
                 )
-                self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
+                self.queue_manager.publish(QueueAgentThoughtEvent(
+                    agent_thought_id=agent_thought.id
+                ), PublishFrom.APPLICATION_MANAGER)
 
             # update prompt tool
             for prompt_tool in prompt_messages_tools:
@@ -339,15 +326,15 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
 
         self.update_db_variables(self.variables_pool, self.db_variables_pool)
         # publish end event
-        self.queue_manager.publish_message_end(LLMResult(
+        self.queue_manager.publish(QueueMessageEndEvent(llm_result=LLMResult(
             model=model_instance.model,
             prompt_messages=prompt_messages,
             message=AssistantPromptMessage(
-                content=final_answer,
+                content=final_answer
             ),
             usage=llm_usage['usage'] if llm_usage['usage'] else LLMUsage.empty_usage(),
             system_fingerprint=''
-        ), PublishFrom.APPLICATION_MANAGER)
+        )), PublishFrom.APPLICATION_MANAGER)
 
     def check_tool_calls(self, llm_result_chunk: LLMResultChunk) -> bool:
         """

api/core/app/app_config/base_app_config_manager.py

@@ -0,0 +1,76 @@
+from typing import Optional, Union
+
+from core.app.app_config.entities import AppAdditionalFeatures, EasyUIBasedAppModelConfigFrom
+from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
+from core.app.app_config.features.more_like_this.manager import MoreLikeThisConfigManager
+from core.app.app_config.features.opening_statement.manager import OpeningStatementConfigManager
+from core.app.app_config.features.retrieval_resource.manager import RetrievalResourceConfigManager
+from core.app.app_config.features.speech_to_text.manager import SpeechToTextConfigManager
+from core.app.app_config.features.suggested_questions_after_answer.manager import (
+    SuggestedQuestionsAfterAnswerConfigManager,
+)
+from core.app.app_config.features.text_to_speech.manager import TextToSpeechConfigManager
+from models.model import AppMode, AppModelConfig
+
+
+class BaseAppConfigManager:
+
+    @classmethod
+    def convert_to_config_dict(cls, config_from: EasyUIBasedAppModelConfigFrom,
+                               app_model_config: Union[AppModelConfig, dict],
+                               config_dict: Optional[dict] = None) -> dict:
+        """
+        Convert app model config to config dict
+        :param config_from: app model config from
+        :param app_model_config: app model config
+        :param config_dict: app model config dict
+        :return:
+        """
+        if config_from != EasyUIBasedAppModelConfigFrom.ARGS:
+            app_model_config_dict = app_model_config.to_dict()
+            config_dict = app_model_config_dict.copy()
+
+        return config_dict
+
+    @classmethod
+    def convert_features(cls, config_dict: dict, app_mode: AppMode) -> AppAdditionalFeatures:
+        """
+        Convert app config to app model config
+
+        :param config_dict: app config
+        :param app_mode: app mode
+        """
+        config_dict = config_dict.copy()
+
+        additional_features = AppAdditionalFeatures()
+        additional_features.show_retrieve_source = RetrievalResourceConfigManager.convert(
+            config=config_dict
+        )
+
+        additional_features.file_upload = FileUploadConfigManager.convert(
+            config=config_dict,
+            is_vision=app_mode in [AppMode.CHAT, AppMode.COMPLETION, AppMode.AGENT_CHAT]
+        )
+
+        additional_features.opening_statement, additional_features.suggested_questions = \
+            OpeningStatementConfigManager.convert(
+                config=config_dict
+            )
+
+        additional_features.suggested_questions_after_answer = SuggestedQuestionsAfterAnswerConfigManager.convert(
+            config=config_dict
+        )
+
+        additional_features.more_like_this = MoreLikeThisConfigManager.convert(
+            config=config_dict
+        )
+
+        additional_features.speech_to_text = SpeechToTextConfigManager.convert(
+            config=config_dict
+        )
+
+        additional_features.text_to_speech = TextToSpeechConfigManager.convert(
+            config=config_dict
+        )
+
+        return additional_features

api/core/app/app_config/common/sensitive_word_avoidance/manager.py

@@ -0,0 +1,50 @@
+from typing import Optional
+
+from core.app.app_config.entities import SensitiveWordAvoidanceEntity
+from core.moderation.factory import ModerationFactory
+
+
+class SensitiveWordAvoidanceConfigManager:
+    @classmethod
+    def convert(cls, config: dict) -> Optional[SensitiveWordAvoidanceEntity]:
+        sensitive_word_avoidance_dict = config.get('sensitive_word_avoidance')
+        if not sensitive_word_avoidance_dict:
+            return None
+
+        if 'enabled' in sensitive_word_avoidance_dict and sensitive_word_avoidance_dict['enabled']:
+            return SensitiveWordAvoidanceEntity(
+                type=sensitive_word_avoidance_dict.get('type'),
+                config=sensitive_word_avoidance_dict.get('config'),
+            )
+        else:
+            return None
+
+    @classmethod
+    def validate_and_set_defaults(cls, tenant_id, config: dict, only_structure_validate: bool = False) \
+            -> tuple[dict, list[str]]:
+        if not config.get("sensitive_word_avoidance"):
+            config["sensitive_word_avoidance"] = {
+                "enabled": False
+            }
+
+        if not isinstance(config["sensitive_word_avoidance"], dict):
+            raise ValueError("sensitive_word_avoidance must be of dict type")
+
+        if "enabled" not in config["sensitive_word_avoidance"] or not config["sensitive_word_avoidance"]["enabled"]:
+            config["sensitive_word_avoidance"]["enabled"] = False
+
+        if config["sensitive_word_avoidance"]["enabled"]:
+            if not config["sensitive_word_avoidance"].get("type"):
+                raise ValueError("sensitive_word_avoidance.type is required")
+
+            if not only_structure_validate:
+                typ = config["sensitive_word_avoidance"]["type"]
+                sensitive_word_avoidance_config = config["sensitive_word_avoidance"]["config"]
+
+                ModerationFactory.validate_config(
+                    name=typ,
+                    tenant_id=tenant_id,
+                    config=sensitive_word_avoidance_config
+                )
+
+        return config, ["sensitive_word_avoidance"]

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

@@ -0,0 +1,78 @@
+from typing import Optional
+
+from core.agent.entities import AgentEntity, AgentPromptEntity, AgentToolEntity
+from core.tools.prompt.template import REACT_PROMPT_TEMPLATES
+
+
+class AgentConfigManager:
+    @classmethod
+    def convert(cls, config: dict) -> Optional[AgentEntity]:
+        """
+        Convert model config to model config
+
+        :param config: model config args
+        """
+        if 'agent_mode' in config and config['agent_mode'] \
+                and 'enabled' in config['agent_mode']:
+
+            agent_dict = config.get('agent_mode', {})
+            agent_strategy = agent_dict.get('strategy', 'cot')
+
+            if agent_strategy == 'function_call':
+                strategy = AgentEntity.Strategy.FUNCTION_CALLING
+            elif agent_strategy == 'cot' or agent_strategy == 'react':
+                strategy = AgentEntity.Strategy.CHAIN_OF_THOUGHT
+            else:
+                # old configs, try to detect default strategy
+                if config['model']['provider'] == 'openai':
+                    strategy = AgentEntity.Strategy.FUNCTION_CALLING
+                else:
+                    strategy = AgentEntity.Strategy.CHAIN_OF_THOUGHT
+
+            agent_tools = []
+            for tool in agent_dict.get('tools', []):
+                keys = tool.keys()
+                if len(keys) >= 4:
+                    if "enabled" not in tool or not tool["enabled"]:
+                        continue
+
+                    agent_tool_properties = {
+                        'provider_type': tool['provider_type'],
+                        'provider_id': tool['provider_id'],
+                        'tool_name': tool['tool_name'],
+                        'tool_parameters': tool['tool_parameters'] if 'tool_parameters' in tool else {}
+                    }
+
+                    agent_tools.append(AgentToolEntity(**agent_tool_properties))
+
+            if 'strategy' in config['agent_mode'] and \
+                    config['agent_mode']['strategy'] not in ['react_router', 'router']:
+                agent_prompt = agent_dict.get('prompt', None) or {}
+                # check model mode
+                model_mode = config.get('model', {}).get('mode', 'completion')
+                if model_mode == 'completion':
+                    agent_prompt_entity = AgentPromptEntity(
+                        first_prompt=agent_prompt.get('first_prompt',
+                                                      REACT_PROMPT_TEMPLATES['english']['completion']['prompt']),
+                        next_iteration=agent_prompt.get('next_iteration',
+                                                        REACT_PROMPT_TEMPLATES['english']['completion'][
+                                                            'agent_scratchpad']),
+                    )
+                else:
+                    agent_prompt_entity = AgentPromptEntity(
+                        first_prompt=agent_prompt.get('first_prompt',
+                                                      REACT_PROMPT_TEMPLATES['english']['chat']['prompt']),
+                        next_iteration=agent_prompt.get('next_iteration',
+                                                        REACT_PROMPT_TEMPLATES['english']['chat']['agent_scratchpad']),
+                    )
+
+                return AgentEntity(
+                    provider=config['model']['provider'],
+                    model=config['model']['name'],
+                    strategy=strategy,
+                    prompt=agent_prompt_entity,
+                    tools=agent_tools,
+                    max_iteration=agent_dict.get('max_iteration', 5)
+                )
+
+        return None

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

@@ -0,0 +1,224 @@
+from typing import Optional
+
+from core.app.app_config.entities import DatasetEntity, DatasetRetrieveConfigEntity
+from core.entities.agent_entities import PlanningStrategy
+from models.model import AppMode
+from services.dataset_service import DatasetService
+
+
+class DatasetConfigManager:
+    @classmethod
+    def convert(cls, config: dict) -> Optional[DatasetEntity]:
+        """
+        Convert model config to model config
+
+        :param config: model config args
+        """
+        dataset_ids = []
+        if 'datasets' in config.get('dataset_configs', {}):
+            datasets = config.get('dataset_configs', {}).get('datasets', {
+                'strategy': 'router',
+                'datasets': []
+            })
+
+            for dataset in datasets.get('datasets', []):
+                keys = list(dataset.keys())
+                if len(keys) == 0 or keys[0] != 'dataset':
+                    continue
+
+                dataset = dataset['dataset']
+
+                if 'enabled' not in dataset or not dataset['enabled']:
+                    continue
+
+                dataset_id = dataset.get('id', None)
+                if dataset_id:
+                    dataset_ids.append(dataset_id)
+
+        if 'agent_mode' in config and config['agent_mode'] \
+                and 'enabled' in config['agent_mode'] \
+                and config['agent_mode']['enabled']:
+
+            agent_dict = config.get('agent_mode', {})
+
+            for tool in agent_dict.get('tools', []):
+                keys = tool.keys()
+                if len(keys) == 1:
+                    # old standard
+                    key = list(tool.keys())[0]
+
+                    if key != 'dataset':
+                        continue
+
+                    tool_item = tool[key]
+
+                    if "enabled" not in tool_item or not tool_item["enabled"]:
+                        continue
+
+                    dataset_id = tool_item['id']
+                    dataset_ids.append(dataset_id)
+
+        if len(dataset_ids) == 0:
+            return None
+
+        # dataset configs
+        dataset_configs = config.get('dataset_configs', {'retrieval_model': 'single'})
+        query_variable = config.get('dataset_query_variable')
+
+        if dataset_configs['retrieval_model'] == 'single':
+            return DatasetEntity(
+                dataset_ids=dataset_ids,
+                retrieve_config=DatasetRetrieveConfigEntity(
+                    query_variable=query_variable,
+                    retrieve_strategy=DatasetRetrieveConfigEntity.RetrieveStrategy.value_of(
+                        dataset_configs['retrieval_model']
+                    )
+                )
+            )
+        else:
+            return DatasetEntity(
+                dataset_ids=dataset_ids,
+                retrieve_config=DatasetRetrieveConfigEntity(
+                    query_variable=query_variable,
+                    retrieve_strategy=DatasetRetrieveConfigEntity.RetrieveStrategy.value_of(
+                        dataset_configs['retrieval_model']
+                    ),
+                    top_k=dataset_configs.get('top_k'),
+                    score_threshold=dataset_configs.get('score_threshold'),
+                    reranking_model=dataset_configs.get('reranking_model')
+                )
+            )
+
+    @classmethod
+    def validate_and_set_defaults(cls, tenant_id: str, app_mode: AppMode, config: dict) -> tuple[dict, list[str]]:
+        """
+        Validate and set defaults for dataset feature
+
+        :param tenant_id: tenant ID
+        :param app_mode: app mode
+        :param config: app model config args
+        """
+        # Extract dataset config for legacy compatibility
+        config = cls.extract_dataset_config_for_legacy_compatibility(tenant_id, app_mode, config)
+
+        # dataset_configs
+        if not config.get("dataset_configs"):
+            config["dataset_configs"] = {'retrieval_model': 'single'}
+
+        if not config["dataset_configs"].get("datasets"):
+            config["dataset_configs"]["datasets"] = {
+                "strategy": "router",
+                "datasets": []
+            }
+
+        if not isinstance(config["dataset_configs"], dict):
+            raise ValueError("dataset_configs must be of object type")
+
+        if config["dataset_configs"]['retrieval_model'] == 'multiple':
+            if not config["dataset_configs"]['reranking_model']:
+                raise ValueError("reranking_model has not been set")
+            if not isinstance(config["dataset_configs"]['reranking_model'], dict):
+                raise ValueError("reranking_model must be of object type")
+
+        if not isinstance(config["dataset_configs"], dict):
+            raise ValueError("dataset_configs must be of object type")
+
+        need_manual_query_datasets = (config.get("dataset_configs")
+                                      and config["dataset_configs"].get("datasets", {}).get("datasets"))
+
+        if need_manual_query_datasets and app_mode == AppMode.COMPLETION:
+            # Only check when mode is completion
+            dataset_query_variable = config.get("dataset_query_variable")
+
+            if not dataset_query_variable:
+                raise ValueError("Dataset query variable is required when dataset is exist")
+
+        return config, ["agent_mode", "dataset_configs", "dataset_query_variable"]
+
+    @classmethod
+    def extract_dataset_config_for_legacy_compatibility(cls, tenant_id: str, app_mode: AppMode, config: dict) -> dict:
+        """
+        Extract dataset config for legacy compatibility
+
+        :param tenant_id: tenant ID
+        :param app_mode: app mode
+        :param config: app model config args
+        """
+        # Extract dataset config for legacy compatibility
+        if not config.get("agent_mode"):
+            config["agent_mode"] = {
+                "enabled": False,
+                "tools": []
+            }
+
+        if not isinstance(config["agent_mode"], dict):
+            raise ValueError("agent_mode must be of object type")
+
+        # enabled
+        if "enabled" not in config["agent_mode"] or not config["agent_mode"]["enabled"]:
+            config["agent_mode"]["enabled"] = False
+
+        if not isinstance(config["agent_mode"]["enabled"], bool):
+            raise ValueError("enabled in agent_mode must be of boolean type")
+
+        # tools
+        if not config["agent_mode"].get("tools"):
+            config["agent_mode"]["tools"] = []
+
+        if not isinstance(config["agent_mode"]["tools"], list):
+            raise ValueError("tools in agent_mode must be a list of objects")
+
+        # strategy
+        if not config["agent_mode"].get("strategy"):
+            config["agent_mode"]["strategy"] = PlanningStrategy.ROUTER.value
+
+        has_datasets = False
+        if config["agent_mode"]["strategy"] in [PlanningStrategy.ROUTER.value, PlanningStrategy.REACT_ROUTER.value]:
+            for tool in config["agent_mode"]["tools"]:
+                key = list(tool.keys())[0]
+                if key == "dataset":
+                    # old style, use tool name as key
+                    tool_item = tool[key]
+
+                    if "enabled" not in tool_item or not tool_item["enabled"]:
+                        tool_item["enabled"] = False
+
+                    if not isinstance(tool_item["enabled"], bool):
+                        raise ValueError("enabled in agent_mode.tools must be of boolean type")
+
+                    if 'id' not in tool_item:
+                        raise ValueError("id is required in dataset")
+
+                    try:
+                        uuid.UUID(tool_item["id"])
+                    except ValueError:
+                        raise ValueError("id in dataset must be of UUID type")
+
+                    if not cls.is_dataset_exists(tenant_id, tool_item["id"]):
+                        raise ValueError("Dataset ID does not exist, please check your permission.")
+
+                    has_datasets = True
+
+        need_manual_query_datasets = has_datasets and config["agent_mode"]["enabled"]
+
+        if need_manual_query_datasets and app_mode == AppMode.COMPLETION:
+            # Only check when mode is completion
+            dataset_query_variable = config.get("dataset_query_variable")
+
+            if not dataset_query_variable:
+                raise ValueError("Dataset query variable is required when dataset is exist")
+
+        return config
+
+    @classmethod
+    def is_dataset_exists(cls, tenant_id: str, dataset_id: str) -> bool:
+        # verify if the dataset ID exists
+        dataset = DatasetService.get_dataset(dataset_id)
+
+        if not dataset:
+            return False
+
+        if dataset.tenant_id != tenant_id:
+            return False
+
+        return True

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

@@ -0,0 +1,103 @@
+from typing import cast
+
+from core.app.app_config.entities import EasyUIBasedAppConfig
+from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
+from core.entities.model_entities import ModelStatus
+from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
+from core.model_runtime.entities.model_entities import ModelType
+from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
+from core.provider_manager import ProviderManager
+
+
+class ModelConfigConverter:
+    @classmethod
+    def convert(cls, app_config: EasyUIBasedAppConfig,
+                skip_check: bool = False) \
+            -> ModelConfigWithCredentialsEntity:
+        """
+        Convert app model config dict to entity.
+        :param app_config: app config
+        :param skip_check: skip check
+        :raises ProviderTokenNotInitError: provider token not init error
+        :return: app orchestration config entity
+        """
+        model_config = app_config.model
+
+        provider_manager = ProviderManager()
+        provider_model_bundle = provider_manager.get_provider_model_bundle(
+            tenant_id=app_config.tenant_id,
+            provider=model_config.provider,
+            model_type=ModelType.LLM
+        )
+
+        provider_name = provider_model_bundle.configuration.provider.provider
+        model_name = model_config.model
+
+        model_type_instance = provider_model_bundle.model_type_instance
+        model_type_instance = cast(LargeLanguageModel, model_type_instance)
+
+        # check model credentials
+        model_credentials = provider_model_bundle.configuration.get_current_credentials(
+            model_type=ModelType.LLM,
+            model=model_config.model
+        )
+
+        if model_credentials is None:
+            if not skip_check:
+                raise ProviderTokenNotInitError(f"Model {model_name} credentials is not initialized.")
+            else:
+                model_credentials = {}
+
+        if not skip_check:
+            # check model
+            provider_model = provider_model_bundle.configuration.get_provider_model(
+                model=model_config.model,
+                model_type=ModelType.LLM
+            )
+
+            if provider_model is None:
+                model_name = model_config.model
+                raise ValueError(f"Model {model_name} not exist.")
+
+            if provider_model.status == ModelStatus.NO_CONFIGURE:
+                raise ProviderTokenNotInitError(f"Model {model_name} credentials is not initialized.")
+            elif provider_model.status == ModelStatus.NO_PERMISSION:
+                raise ModelCurrentlyNotSupportError(f"Dify Hosted OpenAI {model_name} currently not support.")
+            elif provider_model.status == ModelStatus.QUOTA_EXCEEDED:
+                raise QuotaExceededError(f"Model provider {provider_name} quota exceeded.")
+
+        # model config
+        completion_params = model_config.parameters
+        stop = []
+        if 'stop' in completion_params:
+            stop = completion_params['stop']
+            del completion_params['stop']
+
+        # get model mode
+        model_mode = model_config.mode
+        if not model_mode:
+            mode_enum = model_type_instance.get_model_mode(
+                model=model_config.model,
+                credentials=model_credentials
+            )
+
+            model_mode = mode_enum.value
+
+        model_schema = model_type_instance.get_model_schema(
+            model_config.model,
+            model_credentials
+        )
+
+        if not skip_check and not model_schema:
+            raise ValueError(f"Model {model_name} not exist.")
+
+        return ModelConfigWithCredentialsEntity(
+            provider=model_config.provider,
+            model=model_config.model,
+            model_schema=model_schema,
+            mode=model_mode,
+            provider_model_bundle=provider_model_bundle,
+            credentials=model_credentials,
+            parameters=completion_params,
+            stop=stop,
+        )

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

@@ -0,0 +1,112 @@
+from core.app.app_config.entities import ModelConfigEntity
+from core.model_runtime.entities.model_entities import ModelPropertyKey, ModelType
+from core.model_runtime.model_providers import model_provider_factory
+from core.provider_manager import ProviderManager
+
+
+class ModelConfigManager:
+    @classmethod
+    def convert(cls, config: dict) -> ModelConfigEntity:
+        """
+        Convert model config to model config
+
+        :param config: model config args
+        """
+        # model config
+        model_config = config.get('model')
+
+        if not model_config:
+            raise ValueError("model is required")
+
+        completion_params = model_config.get('completion_params')
+        stop = []
+        if 'stop' in completion_params:
+            stop = completion_params['stop']
+            del completion_params['stop']
+
+        # get model mode
+        model_mode = model_config.get('mode')
+
+        return ModelConfigEntity(
+            provider=config['model']['provider'],
+            model=config['model']['name'],
+            mode=model_mode,
+            parameters=completion_params,
+            stop=stop,
+        )
+
+    @classmethod
+    def validate_and_set_defaults(cls, tenant_id: str, config: dict) -> tuple[dict, list[str]]:
+        """
+        Validate and set defaults for model config
+
+        :param tenant_id: tenant id
+        :param config: app model config args
+        """
+        if 'model' not in config:
+            raise ValueError("model is required")
+
+        if not isinstance(config["model"], dict):
+            raise ValueError("model must be of object type")
+
+        # model.provider
+        provider_entities = model_provider_factory.get_providers()
+        model_provider_names = [provider.provider for provider in provider_entities]
+        if 'provider' not in config["model"] or config["model"]["provider"] not in model_provider_names:
+            raise ValueError(f"model.provider is required and must be in {str(model_provider_names)}")
+
+        # model.name
+        if 'name' not in config["model"]:
+            raise ValueError("model.name is required")
+
+        provider_manager = ProviderManager()
+        models = provider_manager.get_configurations(tenant_id).get_models(
+            provider=config["model"]["provider"],
+            model_type=ModelType.LLM
+        )
+
+        if not models:
+            raise ValueError("model.name must be in the specified model list")
+
+        model_ids = [m.model for m in models]
+        if config["model"]["name"] not in model_ids:
+            raise ValueError("model.name must be in the specified model list")
+
+        model_mode = None
+        for model in models:
+            if model.model == config["model"]["name"]:
+                model_mode = model.model_properties.get(ModelPropertyKey.MODE)
+                break
+
+        # model.mode
+        if model_mode:
+            config['model']["mode"] = model_mode
+        else:
+            config['model']["mode"] = "completion"
+
+        # model.completion_params
+        if 'completion_params' not in config["model"]:
+            raise ValueError("model.completion_params is required")
+
+        config["model"]["completion_params"] = cls.validate_model_completion_params(
+            config["model"]["completion_params"]
+        )
+
+        return config, ["model"]
+
+    @classmethod
+    def validate_model_completion_params(cls, cp: dict) -> dict:
+        # model.completion_params
+        if not isinstance(cp, dict):
+            raise ValueError("model.completion_params must be of object type")
+
+        # stop
+        if 'stop' not in cp:
+            cp["stop"] = []
+        elif not isinstance(cp["stop"], list):
+            raise ValueError("stop in model.completion_params must be of list type")
+
+        if len(cp["stop"]) > 4:
+            raise ValueError("stop sequences must be less than 4")
+
+        return cp