feat: bump version 0.4.5 (#1994)

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

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -1,56 +1,56 @@
 name: "🕷️ Bug report"
 description: Report errors or unexpected behavior
 labels:
-- bug
+  - bug
 body:
-- type: checkboxes
-  attributes:
-    label: Self Checks
-    description: "To make sure we get to you in time, please check the following :)"
-    options:
-      - label: I have searched for existing issues [search for existing issues](https://github.com/langgenius/dify/issues), including closed ones.
-        required: true
-      - label: I confirm that I am using English to file this report (我已阅读并同意 [Language Policy](https://github.com/langgenius/dify/issues/1542)).
-        required: true
+  - type: checkboxes
+    attributes:
+      label: Self Checks
+      description: "To make sure we get to you in time, please check the following :)"
+      options:
+        - label: I have searched for existing issues [search for existing issues](https://github.com/langgenius/dify/issues), including closed ones.
+          required: true
+        - label: I confirm that I am using English to file this report (我已阅读并同意 [Language Policy](https://github.com/langgenius/dify/issues/1542)).
+          required: true
 
-- type: input
-  attributes:
-    label: Dify version
-    placeholder: 0.3.21
-    description: See about section in Dify console
-  validations:
-    required: true
+  - type: input
+    attributes:
+      label: Dify version
+      placeholder: 0.3.21
+      description: See about section in Dify console
+    validations:
+      required: true
 
-- type: dropdown
-  attributes:
-    label: Cloud or Self Hosted
-    description: How / Where was Dify installed from?
-    multiple: true
-    options:
-      - Cloud
-      - Self Hosted (Docker)
-      - Self Hosted (Source)
-  validations:
-    required: true
+  - type: dropdown
+    attributes:
+      label: Cloud or Self Hosted
+      description: How / Where was Dify installed from?
+      multiple: true
+      options:
+        - Cloud
+        - Self Hosted (Docker)
+        - Self Hosted (Source)
+    validations:
+      required: true
 
-- type: textarea
-  attributes:
-    label: Steps to reproduce
-    description: We highly suggest including screenshots and a bug report log.
-    placeholder: Having detailed steps helps us reproduce the bug. 
-  validations:
-    required: true
+  - type: textarea
+    attributes:
+      label: Steps to reproduce
+      description: We highly suggest including screenshots and a bug report log.
+      placeholder: Having detailed steps helps us reproduce the bug.
+    validations:
+      required: true
 
-- type: textarea
-  attributes:
-    label: ✔️ Expected Behavior
-    placeholder: What were you expecting?
-  validations:
-    required: false
+  - type: textarea
+    attributes:
+      label: ✔️ Expected Behavior
+      placeholder: What were you expecting?
+    validations:
+      required: false
 
-- type: textarea
-  attributes:
-    label: ❌ Actual Behavior
-    placeholder: What happened instead?
-  validations:
-    required: false
+  - type: textarea
+    attributes:
+      label: ❌ Actual Behavior
+      placeholder: What happened instead?
+    validations:
+      required: false

.github/ISSUE_TEMPLATE/config.yml

@@ -5,4 +5,4 @@ contact_links:
     about: Documentation for users of Dify
   - name: "\U0001F4DA Dify dev documentation"
     url: https://docs.dify.ai/getting-started/install-self-hosted
-    about: Documentation for people interested in developing and contributing for Dify
+    about: Documentation for people interested in developing and contributing for Dify

.github/ISSUE_TEMPLATE/document_issue.yml

@@ -1,19 +1,20 @@
 name: "📚 Documentation Issue"
 description: Report issues in our documentation
-labels: 
-- ducumentation
+labels:
+  - ducumentation
 body:
-- type: checkboxes
-  attributes:
-    label: Self Checks
-    description: "To make sure we get to you in time, please check the following :)"
-      - label: I have searched for existing issues [search for existing issues](https://github.com/langgenius/dify/issues), including closed ones.
-        required: true
-      - label: I confirm that I am using English to file this report (我已阅读并同意 [Language Policy](https://github.com/langgenius/dify/issues/1542)).
-        required: true
-- type: textarea
-  attributes: 
-    label: Provide a description of requested docs changes
-    placeholder: Briefly describe which document needs to be corrected and why.
-  validations:
-    required: true
+  - type: checkboxes
+    attributes:
+      label: Self Checks
+      description: "To make sure we get to you in time, please check the following :)"
+      options:
+        - label: I have searched for existing issues [search for existing issues](https://github.com/langgenius/dify/issues), including closed ones.
+          required: true
+        - label: I confirm that I am using English to file this report (我已阅读并同意 [Language Policy](https://github.com/langgenius/dify/issues/1542)).
+          required: true
+  - type: textarea
+    attributes:
+      label: Provide a description of requested docs changes
+      placeholder: Briefly describe which document needs to be corrected and why.
+    validations:
+      required: true

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -1,35 +1,35 @@
 name: "⭐ Feature or enhancement request"
 description: Propose something new.
 labels:
-- enhancement
+  - enhancement
 body:
-- type: checkboxes
-  attributes:
-    label: Self Checks
-    description: "To make sure we get to you in time, please check the following :)"
-    options:
-      - label: I have searched for existing issues [search for existing issues](https://github.com/langgenius/dify/issues), including closed ones.
-        required: true
-      - label: I confirm that I am using English to file this report (我已阅读并同意 [Language Policy](https://github.com/langgenius/dify/issues/1542)).
-        required: true
-- type: textarea
-  attributes: 
-    label: Description of the new feature / enhancement
-    placeholder: What is the expected behavior of the proposed feature?
-  validations:
-    required: true
-- type: textarea
-  attributes:
-    label: Scenario when this would be used?
-    placeholder: What is the scenario this would be used? Why is this important to your workflow as a dify user?
-  validations:
-    required: true
-- type: textarea
-  attributes:
-    label: Supporting information
-    placeholder: "Having additional evidence, data, tweets, blog posts, research, ... anything is extremely helpful. This information provides context to the scenario that may otherwise be lost."
-  validations:
-    required: false
-- type: markdown
-  attributes:
-    value: Please limit one request per issue.
+  - type: checkboxes
+    attributes:
+      label: Self Checks
+      description: "To make sure we get to you in time, please check the following :)"
+      options:
+        - label: I have searched for existing issues [search for existing issues](https://github.com/langgenius/dify/issues), including closed ones.
+          required: true
+        - label: I confirm that I am using English to file this report (我已阅读并同意 [Language Policy](https://github.com/langgenius/dify/issues/1542)).
+          required: true
+  - type: textarea
+    attributes:
+      label: Description of the new feature / enhancement
+      placeholder: What is the expected behavior of the proposed feature?
+    validations:
+      required: true
+  - type: textarea
+    attributes:
+      label: Scenario when this would be used?
+      placeholder: What is the scenario this would be used? Why is this important to your workflow as a dify user?
+    validations:
+      required: true
+  - type: textarea
+    attributes:
+      label: Supporting information
+      placeholder: "Having additional evidence, data, tweets, blog posts, research, ... anything is extremely helpful. This information provides context to the scenario that may otherwise be lost."
+    validations:
+      required: false
+  - type: markdown
+    attributes:
+      value: Please limit one request per issue.

.github/ISSUE_TEMPLATE/help_wanted.yml

@@ -1,20 +1,20 @@
 name: "🤝 Help Wanted"
-description: "Request help from the community" [please use English :）]
+description: "Request help from the community [please use English :）]"
 labels:
-- help-wanted
+  - help-wanted
 body:
-- type: checkboxes
-  attributes:
-    label: Self Checks
-    description: "To make sure we get to you in time, please check the following :)"
-    options:
-      - label: I have searched for existing issues [search for existing issues](https://github.com/langgenius/dify/issues), including closed ones.
-        required: true
-      - label: I confirm that I am using English to file this report (我已阅读并同意 [Language Policy](https://github.com/langgenius/dify/issues/1542)).
-        required: true
-- type: textarea
-  attributes:
-    label: Provide a description of the help you need
-    placeholder: Briefly describe what you need help with.
-  validations:
-    required: true
+  - type: checkboxes
+    attributes:
+      label: Self Checks
+      description: "To make sure we get to you in time, please check the following :)"
+      options:
+        - label: I have searched for existing issues [search for existing issues](https://github.com/langgenius/dify/issues), including closed ones.
+          required: true
+        - label: I confirm that I am using English to file this report (我已阅读并同意 [Language Policy](https://github.com/langgenius/dify/issues/1542)).
+          required: true
+  - type: textarea
+    attributes:
+      label: Provide a description of the help you need
+      placeholder: Briefly describe what you need help with.
+    validations:
+      required: true

.github/ISSUE_TEMPLATE/translation_issue.yml

@@ -1,52 +1,52 @@
 name: "🌐 Localization/Translation issue"
 description: Report incorrect translations. [please use English :）]
 labels:
-- translation
+  - translation
 body:
-- type: checkboxes
-  attributes:
-    label: Self Checks
-    description: "To make sure we get to you in time, please check the following :)"
-    options:
-      - label: I have searched for existing issues [search for existing issues](https://github.com/langgenius/dify/issues), including closed ones.
-        required: true
-      - label: I confirm that I am using English to file this report (我已阅读并同意 [Language Policy](https://github.com/langgenius/dify/issues/1542)).
-        required: true
-- type: input
-  attributes:
-    label: Dify version
-    placeholder: 0.3.21
-    description: Hover over system tray icon or look at Settings
-  validations:
-    required: true
-- type: input
-  attributes:
-    label: Utility with translation issue
-    placeholder: Some area
-    description: Please input here the utility with the translation issue
-  validations:
-    required: true
-- type: input
-  attributes:
-    label: 🌐 Language affected
-    placeholder: "German"
-  validations:
-    required: true
-- type: textarea
-  attributes: 
-    label: ❌ Actual phrase(s)
-    placeholder: What is there? Please include a screenshot as that is extremely helpful.
-  validations:
-    required: true
-- type: textarea
-  attributes: 
-    label: ✔️ Expected phrase(s)
-    placeholder: What was expected?
-  validations:
-    required: true
-- type: textarea
-  attributes:
-    label: ℹ Why is the current translation wrong
-    placeholder: Why do you feel this is incorrect?
-  validations:
-    required: true
+  - type: checkboxes
+    attributes:
+      label: Self Checks
+      description: "To make sure we get to you in time, please check the following :)"
+      options:
+        - label: I have searched for existing issues [search for existing issues](https://github.com/langgenius/dify/issues), including closed ones.
+          required: true
+        - label: I confirm that I am using English to file this report (我已阅读并同意 [Language Policy](https://github.com/langgenius/dify/issues/1542)).
+          required: true
+  - type: input
+    attributes:
+      label: Dify version
+      placeholder: 0.3.21
+      description: Hover over system tray icon or look at Settings
+    validations:
+      required: true
+  - type: input
+    attributes:
+      label: Utility with translation issue
+      placeholder: Some area
+      description: Please input here the utility with the translation issue
+    validations:
+      required: true
+  - type: input
+    attributes:
+      label: 🌐 Language affected
+      placeholder: "German"
+    validations:
+      required: true
+  - type: textarea
+    attributes:
+      label: ❌ Actual phrase(s)
+      placeholder: What is there? Please include a screenshot as that is extremely helpful.
+    validations:
+      required: true
+  - type: textarea
+    attributes:
+      label: ✔️ Expected phrase(s)
+      placeholder: What was expected?
+    validations:
+      required: true
+  - type: textarea
+    attributes:
+      label: ℹ Why is the current translation wrong
+      placeholder: Why do you feel this is incorrect?
+    validations:
+      required: true

.github/linters/.hadolint.yaml

@@ -0,0 +1 @@
+failure-threshold: "error"

.github/linters/.yaml-lint.yml

@@ -0,0 +1,11 @@
+---
+
+extends: default
+
+rules:
+  brackets:
+    max-spaces-inside: 1
+  comments-indentation: disable
+  document-start: disable
+  line-length: disable
+  truthy: disable

.github/workflows/api-model-runtime-tests.yml

@@ -31,28 +31,19 @@ jobs:
       HUGGINGFACE_EMBEDDINGS_ENDPOINT_URL: c
       MOCK_SWITCH: true
 
-
     steps:
-    - name: Checkout code
-      uses: actions/checkout@v2
+      - name: Checkout code
+        uses: actions/checkout@v4
 
-    - name: Set up Python
-      uses: actions/setup-python@v2
-      with:
-        python-version: '3.10'
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.10'
+          cache: 'pip'
+          cache-dependency-path: ./api/requirements.txt
 
-    - name: Cache pip dependencies
-      uses: actions/cache@v2
-      with:
-        path: ~/.cache/pip
-        key: ${{ runner.os }}-pip-${{ hashFiles('api/requirements.txt') }}
-        restore-keys: ${{ runner.os }}-pip-
+      - name: Install dependencies
+        run: pip install -r ./api/requirements.txt
 
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install pytest
-        pip install -r api/requirements.txt
-
-    - name: Run pytest
-      run: pytest api/tests/integration_tests/model_runtime/anthropic api/tests/integration_tests/model_runtime/azure_openai api/tests/integration_tests/model_runtime/openai api/tests/integration_tests/model_runtime/chatglm api/tests/integration_tests/model_runtime/google api/tests/integration_tests/model_runtime/xinference api/tests/integration_tests/model_runtime/huggingface_hub/test_llm.py
+      - name: Run pytest
+        run: pytest api/tests/integration_tests/model_runtime/anthropic api/tests/integration_tests/model_runtime/azure_openai api/tests/integration_tests/model_runtime/openai api/tests/integration_tests/model_runtime/chatglm api/tests/integration_tests/model_runtime/google api/tests/integration_tests/model_runtime/xinference api/tests/integration_tests/model_runtime/huggingface_hub/test_llm.py

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

@@ -6,55 +6,55 @@ on:
       - 'main'
       - 'deploy/dev'
   release:
-    types: [published]
+    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 QEMU
+        uses: docker/setup-qemu-action@v3
 
-    - name: Set up Docker Buildx
-      uses: docker/setup-buildx-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: 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-api
-        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=${{ startsWith(github.ref, 'refs/tags/') }}
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          images: langgenius/dify-api
+          tags: |
+            type=raw,value=latest,enable=${{ 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}}:api"
-        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: Build and push
+        uses: docker/build-push-action@v5
+        with:
+          context: "{{defaultContext}}:api"
+          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 }}
+      - 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

@@ -6,55 +6,55 @@ on:
       - 'main'
       - 'deploy/dev'
   release:
-    types: [published]
+    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 QEMU
+        uses: docker/setup-qemu-action@v3
 
-    - name: Set up Docker Buildx
-      uses: docker/setup-buildx-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: 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=${{ startsWith(github.ref, 'refs/tags/') }}
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          images: langgenius/dify-web
+          tags: |
+            type=raw,value=latest,enable=${{ 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: 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 }}
+      - 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/stale.yml

@@ -7,7 +7,7 @@ name: Mark stale issues and pull requests
 
 on:
   schedule:
-  - cron: '0 3 * * *'
+    - cron: '0 3 * * *'
 
 jobs:
   stale:
@@ -18,13 +18,13 @@ jobs:
       pull-requests: write
 
     steps:
-    - uses: actions/stale@v5
-      with:
-        days-before-issue-stale: 15
-        days-before-issue-close: 3
-        repo-token: ${{ secrets.GITHUB_TOKEN }}
-        stale-issue-message: "Close due to it's no longer active, if you have any questions, you can reopen it."
-        stale-pr-message: "Close due to it's no longer active, if you have any questions, you can reopen it."
-        stale-issue-label: 'no-issue-activity'
-        stale-pr-label: 'no-pr-activity'
-        any-of-labels: 'duplicate,question,invalid,wontfix,no-issue-activity,no-pr-activity,enhancement,cant-reproduce,help-wanted'
+      - uses: actions/stale@v5
+        with:
+          days-before-issue-stale: 15
+          days-before-issue-close: 3
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+          stale-issue-message: "Close due to it's no longer active, if you have any questions, you can reopen it."
+          stale-pr-message: "Close due to it's no longer active, if you have any questions, you can reopen it."
+          stale-issue-label: 'no-issue-activity'
+          stale-pr-label: 'no-pr-activity'
+          any-of-labels: 'duplicate,question,invalid,wontfix,no-issue-activity,no-pr-activity,enhancement,cant-reproduce,help-wanted'

.github/workflows/style.yml

@@ -0,0 +1,54 @@
+name: Style check
+
+on:
+  pull_request:
+    branches:
+      - main
+  push:
+    branches:
+      - deploy/dev
+
+concurrency:
+  group: dep-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  test:
+    name: ESLint and SuperLinter
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Setup NodeJS
+        uses: actions/setup-node@v4
+        with:
+          node-version: 18
+          cache: yarn
+          cache-dependency-path: ./web/package.json
+
+      - name: Web dependencies
+        run: |
+          cd ./web
+          yarn install --frozen-lockfile
+
+      - name: Web style check
+        run: |
+          cd ./web
+          yarn run lint
+
+      - name: Super-linter
+        uses: super-linter/super-linter/slim@v5
+        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
+          VALIDATE_BASH: true
+          VALIDATE_BASH_EXEC: true
+          VALIDATE_GITHUB_ACTIONS: true
+          VALIDATE_DOCKERFILE_HADOLINT: true
+          VALIDATE_YAML: true

README.md

@@ -91,7 +91,7 @@ After running, you can access the Dify dashboard in your browser at [http://loca
 
 ### Helm Chart
 
-A big thanks to @BorisPolonsky for providing us with a [Helm Chart](https://helm.sh/) version, which allows Dify to be deployed on Kubernetes.
+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.
 
 ### Configuration

api/.env.example

@@ -65,6 +65,7 @@ WEAVIATE_BATCH_SIZE=100
 # Qdrant configuration, use `http://localhost:6333` for local mode or `https://your-qdrant-cluster-url.qdrant.io` for remote mode
 QDRANT_URL=http://localhost:6333
 QDRANT_API_KEY=difyai123456
+QDRANT_CLIENT_TIMEOUT=20
 
 # Milvus configuration
 MILVUS_HOST=127.0.0.1
@@ -85,6 +86,7 @@ MULTIMODAL_SEND_IMAGE_FORMAT=base64
 MAIL_TYPE=
 MAIL_DEFAULT_SEND_FROM=no-reply <no-reply@dify.ai>
 RESEND_API_KEY=
+RESEND_API_URL=https://api.resend.com
 
 # Sentry configuration
 SENTRY_DSN=

api/config.py

@@ -36,6 +36,7 @@ DEFAULTS = {
     'SENTRY_PROFILES_SAMPLE_RATE': 1.0,
     'WEAVIATE_GRPC_ENABLED': 'True',
     'WEAVIATE_BATCH_SIZE': 100,
+    'QDRANT_CLIENT_TIMEOUT': 20,
     'CELERY_BACKEND': 'database',
     'LOG_LEVEL': 'INFO',
     'HOSTED_OPENAI_QUOTA_LIMIT': 200,
@@ -87,7 +88,7 @@ class Config:
         # ------------------------
         # General Configurations.
         # ------------------------
-        self.CURRENT_VERSION = "0.4.0"
+        self.CURRENT_VERSION = "0.4.5"
         self.COMMIT_SHA = get_env('COMMIT_SHA')
         self.EDITION = "SELF_HOSTED"
         self.DEPLOY_ENV = get_env('DEPLOY_ENV')
@@ -197,6 +198,7 @@ class Config:
         # qdrant settings
         self.QDRANT_URL = get_env('QDRANT_URL')
         self.QDRANT_API_KEY = get_env('QDRANT_API_KEY')
+        self.QDRANT_CLIENT_TIMEOUT = get_env('QDRANT_CLIENT_TIMEOUT')
 
         # milvus / zilliz setting
         self.MILVUS_HOST = get_env('MILVUS_HOST')
@@ -217,6 +219,7 @@ class Config:
         self.MAIL_TYPE = get_env('MAIL_TYPE')
         self.MAIL_DEFAULT_SEND_FROM = get_env('MAIL_DEFAULT_SEND_FROM')
         self.RESEND_API_KEY = get_env('RESEND_API_KEY')
+        self.RESEND_API_URL = get_env('RESEND_API_URL')
         
         # ------------------------
         # Workpace Configurations.

api/controllers/console/app/app.py

@@ -141,15 +141,9 @@ class AppListApi(Resource):
                     model_type=ModelType.LLM
                 )
             except ProviderTokenNotInitError:
-                raise ProviderNotInitializeError(
-                    f"No Default System Reasoning Model available. Please configure "
-                    f"in the Settings -> Model Provider.")
+                model_instance = None
 
-            if not model_instance:
-                raise ProviderNotInitializeError(
-                    f"No Default System Reasoning Model available. Please configure "
-                    f"in the Settings -> Model Provider.")
-            else:
+            if model_instance:
                 model_dict = app_model_config.model_dict
                 model_dict['provider'] = model_instance.provider
                 model_dict['name'] = model_instance.model

api/controllers/console/app/audio.py

@@ -58,7 +58,7 @@ class ChatMessageAudioApi(Resource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:

api/controllers/console/app/completion.py

@@ -78,7 +78,7 @@ class CompletionMessageApi(Resource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:
@@ -153,7 +153,7 @@ class ChatMessageApi(Resource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:

api/controllers/console/app/generator.py

@@ -38,7 +38,7 @@ class RuleGenerateApi(Resource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
 
         return rules
 

api/controllers/console/app/message.py

@@ -228,7 +228,7 @@ class MessageMoreLikeThisApi(Resource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:
@@ -256,7 +256,7 @@ def compact_response(response: Union[dict, Generator]) -> Response:
                 yield "data: " + json.dumps(
                     api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n"
             except InvokeError as e:
-                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n"
+                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(e.description)).get_json()) + "\n\n"
             except ValueError as e:
                 yield "data: " + json.dumps(api.handle_error(e).get_json()) + "\n\n"
             except Exception:
@@ -296,7 +296,7 @@ class MessageSuggestedQuestionApi(Resource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except Exception:
             logging.exception("internal server error.")
             raise InternalServerError()

api/controllers/console/datasets/datasets_segments.py

@@ -156,6 +156,9 @@ class DatasetDocumentSegmentApi(Resource):
         if not segment:
             raise NotFound('Segment not found.')
 
+        if segment.status != 'completed':
+            raise NotFound('Segment is not completed, enable or disable function is not allowed')
+
         document_indexing_cache_key = 'document_{}_indexing'.format(segment.document_id)
         cache_result = redis_client.get(document_indexing_cache_key)
         if cache_result is not None:

api/controllers/console/datasets/hit_testing.py

@@ -1,6 +1,8 @@
 import logging
 
 from flask_login import current_user
+
+from core.model_runtime.errors.invoke import InvokeError
 from libs.login import login_required
 from flask_restful import Resource, reqparse, marshal
 from werkzeug.exceptions import InternalServerError, NotFound, Forbidden
@@ -8,7 +10,7 @@ from werkzeug.exceptions import InternalServerError, NotFound, Forbidden
 import services
 from controllers.console import api
 from controllers.console.app.error import ProviderNotInitializeError, ProviderQuotaExceededError, \
-    ProviderModelCurrentlyNotSupportError
+    ProviderModelCurrentlyNotSupportError, CompletionRequestError
 from controllers.console.datasets.error import HighQualityDatasetOnlyError, DatasetNotInitializedError
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required
@@ -69,6 +71,8 @@ class HitTestingApi(Resource):
             raise ProviderNotInitializeError(
                 f"No Embedding Model or Reranking Model available. Please configure a valid provider "
                 f"in the Settings -> Model Provider.")
+        except InvokeError as e:
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise ValueError(str(e))
         except Exception as e:

api/controllers/console/explore/audio.py

@@ -54,7 +54,7 @@ class ChatAudioApi(InstalledAppResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:

api/controllers/console/explore/completion.py

@@ -70,7 +70,7 @@ class CompletionApi(InstalledAppResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:
@@ -134,7 +134,7 @@ class ChatApi(InstalledAppResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:
@@ -175,7 +175,7 @@ def compact_response(response: Union[dict, Generator]) -> Response:
             except ModelCurrentlyNotSupportError:
                 yield "data: " + json.dumps(api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n"
             except InvokeError as e:
-                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n"
+                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(e.description)).get_json()) + "\n\n"
             except ValueError as e:
                 yield "data: " + json.dumps(api.handle_error(e).get_json()) + "\n\n"
             except Exception:

api/controllers/console/explore/message.py

@@ -104,7 +104,7 @@ class MessageMoreLikeThisApi(InstalledAppResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception:
@@ -131,7 +131,7 @@ def compact_response(response: Union[dict, Generator]) -> Response:
             except ModelCurrentlyNotSupportError:
                 yield "data: " + json.dumps(api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n"
             except InvokeError as e:
-                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n"
+                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(e.description)).get_json()) + "\n\n"
             except ValueError as e:
                 yield "data: " + json.dumps(api.handle_error(e).get_json()) + "\n\n"
             except Exception:
@@ -169,7 +169,7 @@ class MessageSuggestedQuestionApi(InstalledAppResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except Exception:
             logging.exception("internal server error.")
             raise InternalServerError()

api/controllers/console/universal_chat/audio.py

@@ -54,7 +54,7 @@ class UniversalChatAudioApi(UniversalChatResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:

api/controllers/console/universal_chat/chat.py

@@ -89,7 +89,7 @@ class UniversalChatApi(UniversalChatResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:
@@ -126,7 +126,7 @@ def compact_response(response: Union[dict, Generator]) -> Response:
             except ModelCurrentlyNotSupportError:
                 yield "data: " + json.dumps(api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n"
             except InvokeError as e:
-                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n"
+                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(e.description)).get_json()) + "\n\n"
             except ValueError as e:
                 yield "data: " + json.dumps(api.handle_error(e).get_json()) + "\n\n"
             except Exception:

api/controllers/console/universal_chat/message.py

@@ -133,7 +133,7 @@ class UniversalChatMessageSuggestedQuestionApi(UniversalChatResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except Exception:
             logging.exception("internal server error.")
             raise InternalServerError()

api/controllers/service_api/app/audio.py

@@ -50,7 +50,7 @@ class AudioApi(AppApiResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:

api/controllers/service_api/app/completion.py

@@ -31,7 +31,7 @@ class CompletionApi(AppApiResource):
         parser.add_argument('query', type=str, location='json', default='')
         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('user', type=str, location='json')
+        parser.add_argument('user', required=True, nullable=False, type=str, location='json')
         parser.add_argument('retriever_from', type=str, required=False, default='dev', location='json')
 
         args = parser.parse_args()
@@ -67,7 +67,7 @@ class CompletionApi(AppApiResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:
@@ -96,7 +96,7 @@ class ChatApi(AppApiResource):
         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('user', type=str, location='json')
+        parser.add_argument('user', type=str, required=True, nullable=False, location='json')
         parser.add_argument('retriever_from', type=str, required=False, default='dev', location='json')
         parser.add_argument('auto_generate_name', type=bool, required=False, default=True, location='json')
 
@@ -131,7 +131,7 @@ class ChatApi(AppApiResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:
@@ -171,7 +171,7 @@ def compact_response(response: Union[dict, Generator]) -> Response:
             except ModelCurrentlyNotSupportError:
                 yield "data: " + json.dumps(api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n"
             except InvokeError as e:
-                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n"
+                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(e.description)).get_json()) + "\n\n"
             except ValueError as e:
                 yield "data: " + json.dumps(api.handle_error(e).get_json()) + "\n\n"
             except Exception:

api/controllers/web/audio.py

@@ -52,7 +52,7 @@ class AudioApi(WebApiResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:

api/controllers/web/completion.py

@@ -64,7 +64,7 @@ class CompletionApi(WebApiResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:
@@ -124,7 +124,7 @@ class ChatApi(WebApiResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception as e:
@@ -164,7 +164,7 @@ def compact_response(response: Union[dict, Generator]) -> Response:
             except ModelCurrentlyNotSupportError:
                 yield "data: " + json.dumps(api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n"
             except InvokeError as e:
-                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n"
+                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(e.description)).get_json()) + "\n\n"
             except ValueError as e:
                 yield "data: " + json.dumps(api.handle_error(e).get_json()) + "\n\n"
             except Exception:

api/controllers/web/message.py

@@ -138,7 +138,7 @@ class MessageMoreLikeThisApi(WebApiResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except ValueError as e:
             raise e
         except Exception:
@@ -165,7 +165,7 @@ def compact_response(response: Union[dict, Generator]) -> Response:
             except ModelCurrentlyNotSupportError:
                 yield "data: " + json.dumps(api.handle_error(ProviderModelCurrentlyNotSupportError()).get_json()) + "\n\n"
             except InvokeError as e:
-                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(str(e))).get_json()) + "\n\n"
+                yield "data: " + json.dumps(api.handle_error(CompletionRequestError(e.description)).get_json()) + "\n\n"
             except ValueError as e:
                 yield "data: " + json.dumps(api.handle_error(e).get_json()) + "\n\n"
             except Exception:
@@ -202,7 +202,7 @@ class MessageSuggestedQuestionApi(WebApiResource):
         except ModelCurrentlyNotSupportError:
             raise ProviderModelCurrentlyNotSupportError()
         except InvokeError as e:
-            raise CompletionRequestError(str(e))
+            raise CompletionRequestError(e.description)
         except Exception:
             logging.exception("internal server error.")
             raise InternalServerError()

api/core/app_runner/agent_app_runner.py

@@ -75,7 +75,7 @@ class AgentApplicationRunner(AppRunner):
         # reorganize all inputs and template to prompt messages
         # Include: prompt template, inputs, query(optional), files(optional)
         #          memory(optional)
-        prompt_messages, stop = self.originze_prompt_messages(
+        prompt_messages, stop = self.organize_prompt_messages(
             app_record=app_record,
             model_config=app_orchestration_config.model_config,
             prompt_template_entity=app_orchestration_config.prompt_template,
@@ -153,7 +153,7 @@ class AgentApplicationRunner(AppRunner):
             # reorganize all inputs and template to prompt messages
             # Include: prompt template, inputs, query(optional), files(optional)
             #          memory(optional), external data, dataset context(optional)
-            prompt_messages, stop = self.originze_prompt_messages(
+            prompt_messages, stop = self.organize_prompt_messages(
                 app_record=app_record,
                 model_config=app_orchestration_config.model_config,
                 prompt_template_entity=app_orchestration_config.prompt_template,
@@ -237,8 +237,8 @@ class AgentApplicationRunner(AppRunner):
         all_message_tokens = 0
         all_answer_tokens = 0
         for agent_thought in agent_thoughts:
-            all_message_tokens += agent_thought.message_tokens
-            all_answer_tokens += agent_thought.answer_tokens
+            all_message_tokens += agent_thought.message_token
+            all_answer_tokens += agent_thought.answer_token
 
         model_type_instance = model_config.provider_model_bundle.model_type_instance
         model_type_instance = cast(LargeLanguageModel, model_type_instance)

api/core/app_runner/app_runner.py

@@ -1,7 +1,7 @@
 import time
 from typing import cast, Optional, List, Tuple, Generator, Union
 
-from core.application_queue_manager import ApplicationQueueManager
+from core.application_queue_manager import ApplicationQueueManager, PublishFrom
 from core.entities.application_entities import ModelConfigEntity, PromptTemplateEntity, AppOrchestrationConfigEntity
 from core.file.file_obj import FileObj
 from core.memory.token_buffer_memory import TokenBufferMemory
@@ -50,7 +50,7 @@ class AppRunner:
             max_tokens = 0
 
         # get prompt messages without memory and context
-        prompt_messages, stop = self.originze_prompt_messages(
+        prompt_messages, stop = self.organize_prompt_messages(
             app_record=app_record,
             model_config=model_config,
             prompt_template_entity=prompt_template_entity,
@@ -107,7 +107,7 @@ class AppRunner:
                         or (parameter_rule.use_template and parameter_rule.use_template == 'max_tokens')):
                     model_config.parameters[parameter_rule.name] = max_tokens
 
-    def originze_prompt_messages(self, app_record: App,
+    def organize_prompt_messages(self, app_record: App,
                                  model_config: ModelConfigEntity,
                                  prompt_template_entity: PromptTemplateEntity,
                                  inputs: dict[str, str],
@@ -183,7 +183,7 @@ class AppRunner:
                         index=index,
                         message=AssistantPromptMessage(content=token)
                     )
-                ))
+                ), PublishFrom.APPLICATION_MANAGER)
                 index += 1
                 time.sleep(0.01)
 
@@ -193,7 +193,8 @@ class AppRunner:
                 prompt_messages=prompt_messages,
                 message=AssistantPromptMessage(content=text),
                 usage=usage if usage else LLMUsage.empty_usage()
-            )
+            ),
+            pub_from=PublishFrom.APPLICATION_MANAGER
         )
 
     def _handle_invoke_result(self, invoke_result: Union[LLMResult, Generator],
@@ -226,7 +227,8 @@ class AppRunner:
         :return:
         """
         queue_manager.publish_message_end(
-            llm_result=invoke_result
+            llm_result=invoke_result,
+            pub_from=PublishFrom.APPLICATION_MANAGER
         )
 
     def _handle_invoke_result_stream(self, invoke_result: Generator,
@@ -242,7 +244,7 @@ class AppRunner:
         text = ''
         usage = None
         for result in invoke_result:
-            queue_manager.publish_chunk_message(result)
+            queue_manager.publish_chunk_message(result, PublishFrom.APPLICATION_MANAGER)
 
             text += result.delta.message.content
 
@@ -263,5 +265,6 @@ class AppRunner:
         )
 
         queue_manager.publish_message_end(
-            llm_result=llm_result
+            llm_result=llm_result,
+            pub_from=PublishFrom.APPLICATION_MANAGER
         )

api/core/app_runner/basic_app_runner.py

@@ -5,7 +5,7 @@ from core.app_runner.app_runner import AppRunner
 from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
 from core.entities.application_entities import ApplicationGenerateEntity, ModelConfigEntity, \
     AppOrchestrationConfigEntity, InvokeFrom, ExternalDataVariableEntity, DatasetEntity
-from core.application_queue_manager import ApplicationQueueManager
+from core.application_queue_manager import ApplicationQueueManager, PublishFrom
 from core.features.annotation_reply import AnnotationReplyFeature
 from core.features.dataset_retrieval import DatasetRetrievalFeature
 from core.features.external_data_fetch import ExternalDataFetchFeature
@@ -79,7 +79,7 @@ class BasicApplicationRunner(AppRunner):
         # organize all inputs and template to prompt messages
         # Include: prompt template, inputs, query(optional), files(optional)
         #          memory(optional)
-        prompt_messages, stop = self.originze_prompt_messages(
+        prompt_messages, stop = self.organize_prompt_messages(
             app_record=app_record,
             model_config=app_orchestration_config.model_config,
             prompt_template_entity=app_orchestration_config.prompt_template,
@@ -121,7 +121,8 @@ class BasicApplicationRunner(AppRunner):
 
             if annotation_reply:
                 queue_manager.publish_annotation_reply(
-                    message_annotation_id=annotation_reply.id
+                    message_annotation_id=annotation_reply.id,
+                    pub_from=PublishFrom.APPLICATION_MANAGER
                 )
                 self.direct_output(
                     queue_manager=queue_manager,
@@ -132,16 +133,16 @@ class BasicApplicationRunner(AppRunner):
                 )
                 return
 
-            # fill in variable inputs from external data tools if exists
-            external_data_tools = app_orchestration_config.external_data_variables
-            if external_data_tools:
-                inputs = self.fill_in_inputs_from_external_data_tools(
-                    tenant_id=app_record.tenant_id,
-                    app_id=app_record.id,
-                    external_data_tools=external_data_tools,
-                    inputs=inputs,
-                    query=query
-                )
+        # fill in variable inputs from external data tools if exists
+        external_data_tools = app_orchestration_config.external_data_variables
+        if external_data_tools:
+            inputs = self.fill_in_inputs_from_external_data_tools(
+                tenant_id=app_record.tenant_id,
+                app_id=app_record.id,
+                external_data_tools=external_data_tools,
+                inputs=inputs,
+                query=query
+            )
 
         # get context from datasets
         context = None
@@ -164,7 +165,7 @@ class BasicApplicationRunner(AppRunner):
         # reorganize all inputs and template to prompt messages
         # Include: prompt template, inputs, query(optional), files(optional)
         #          memory(optional), external data, dataset context(optional)
-        prompt_messages, stop = self.originze_prompt_messages(
+        prompt_messages, stop = self.organize_prompt_messages(
             app_record=app_record,
             model_config=app_orchestration_config.model_config,
             prompt_template_entity=app_orchestration_config.prompt_template,

api/core/app_runner/generate_task_pipeline.py

@@ -7,7 +7,7 @@ from pydantic import BaseModel
 
 from core.app_runner.moderation_handler import OutputModerationHandler, ModerationRule
 from core.entities.application_entities import ApplicationGenerateEntity
-from core.application_queue_manager import ApplicationQueueManager
+from core.application_queue_manager import ApplicationQueueManager, PublishFrom
 from core.entities.queue_entities import QueueErrorEvent, QueueStopEvent, QueueMessageEndEvent, \
     QueueRetrieverResourcesEvent, QueueAgentThoughtEvent, QueuePingEvent, QueueMessageEvent, QueueMessageReplaceEvent, \
     AnnotationReplyEvent
@@ -312,8 +312,11 @@ class GenerateTaskPipeline:
                                 index=0,
                                 message=AssistantPromptMessage(content=self._task_state.llm_result.message.content)
                             )
-                        ))
-                        self._queue_manager.publish(QueueStopEvent(stopped_by=QueueStopEvent.StopBy.OUTPUT_MODERATION))
+                        ), PublishFrom.TASK_PIPELINE)
+                        self._queue_manager.publish(
+                            QueueStopEvent(stopped_by=QueueStopEvent.StopBy.OUTPUT_MODERATION),
+                            PublishFrom.TASK_PIPELINE
+                        )
                         continue
                     else:
                         self._output_moderation_handler.append_new_token(delta_text)

api/core/app_runner/moderation_handler.py

@@ -6,6 +6,7 @@ from typing import Any, Optional, Dict
 from flask import current_app, Flask
 from pydantic import BaseModel
 
+from core.application_queue_manager import PublishFrom
 from core.moderation.base import ModerationAction, ModerationOutputsResult
 from core.moderation.factory import ModerationFactory
 
@@ -66,7 +67,7 @@ class OutputModerationHandler(BaseModel):
             final_output = result.text
 
         if public_event:
-            self.on_message_replace_func(final_output)
+            self.on_message_replace_func(final_output, PublishFrom.TASK_PIPELINE)
 
         return final_output
 

api/core/application_manager.py

@@ -23,7 +23,7 @@ from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeErr
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.prompt.prompt_template import PromptTemplateParser
 from core.provider_manager import ProviderManager
-from core.application_queue_manager import ApplicationQueueManager, ConversationTaskStoppedException
+from core.application_queue_manager import ApplicationQueueManager, ConversationTaskStoppedException, PublishFrom
 from extensions.ext_database import db
 from models.account import Account
 from models.model import EndUser, Conversation, Message, MessageFile, App
@@ -169,15 +169,18 @@ class ApplicationManager:
             except ConversationTaskStoppedException:
                 pass
             except InvokeAuthorizationError:
-                queue_manager.publish_error(InvokeAuthorizationError('Incorrect API key provided'))
+                queue_manager.publish_error(
+                    InvokeAuthorizationError('Incorrect API key provided'),
+                    PublishFrom.APPLICATION_MANAGER
+                )
             except ValidationError as e:
                 logger.exception("Validation Error when generating")
-                queue_manager.publish_error(e)
+                queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
             except (ValueError, InvokeError) as e:
-                queue_manager.publish_error(e)
+                queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
             except Exception as e:
                 logger.exception("Unknown Error when generating")
-                queue_manager.publish_error(e)
+                queue_manager.publish_error(e, PublishFrom.APPLICATION_MANAGER)
             finally:
                 db.session.remove()
 
@@ -376,7 +379,8 @@ class ApplicationManager:
                 and 'enabled' in copy_app_model_config_dict['agent_mode'] and copy_app_model_config_dict['agent_mode'][
             'enabled']:
             agent_dict = copy_app_model_config_dict.get('agent_mode')
-            if agent_dict['strategy'] in ['router', 'react_router']:
+            agent_strategy = agent_dict.get('strategy', 'router')
+            if agent_strategy in ['router', 'react_router']:
                 dataset_ids = []
                 for tool in agent_dict.get('tools', []):
                     key = list(tool.keys())[0]
@@ -402,7 +406,7 @@ class ApplicationManager:
                             retrieve_strategy=DatasetRetrieveConfigEntity.RetrieveStrategy.value_of(
                                 dataset_configs['retrieval_model']
                             ),
-                            single_strategy=agent_dict['strategy']
+                            single_strategy=agent_strategy
                         )
                     )
                 else:
@@ -419,7 +423,7 @@ class ApplicationManager:
                         )
                     )
             else:
-                if agent_dict['strategy'] == 'react':
+                if agent_strategy == 'react':
                     strategy = AgentEntity.Strategy.CHAIN_OF_THOUGHT
                 else:
                     strategy = AgentEntity.Strategy.FUNCTION_CALLING
@@ -472,7 +476,7 @@ class ApplicationManager:
         more_like_this_dict = copy_app_model_config_dict.get('more_like_this')
         if more_like_this_dict:
             if 'enabled' in more_like_this_dict and more_like_this_dict['enabled']:
-                properties['more_like_this'] = copy_app_model_config_dict.get('opening_statement')
+                properties['more_like_this'] = True
 
         # speech to text
         speech_to_text_dict = copy_app_model_config_dict.get('speech_to_text')

api/core/application_queue_manager.py

@@ -1,5 +1,6 @@
 import queue
 import time
+from enum import Enum
 from typing import Generator, Any
 
 from sqlalchemy.orm import DeclarativeMeta
@@ -13,6 +14,11 @@ from extensions.ext_redis import redis_client
 from models.model import MessageAgentThought
 
 
+class PublishFrom(Enum):
+    APPLICATION_MANAGER = 1
+    TASK_PIPELINE = 2
+
+
 class ApplicationQueueManager:
     def __init__(self, task_id: str,
                  user_id: str,
@@ -61,11 +67,14 @@ class ApplicationQueueManager:
                 if elapsed_time >= listen_timeout or self._is_stopped():
                     # publish two messages to make sure the client can receive the stop signal
                     # and stop listening after the stop signal processed
-                    self.publish(QueueStopEvent(stopped_by=QueueStopEvent.StopBy.USER_MANUAL))
+                    self.publish(
+                        QueueStopEvent(stopped_by=QueueStopEvent.StopBy.USER_MANUAL),
+                        PublishFrom.TASK_PIPELINE
+                    )
                     self.stop_listen()
 
                 if elapsed_time // 10 > last_ping_time:
-                    self.publish(QueuePingEvent())
+                    self.publish(QueuePingEvent(), PublishFrom.TASK_PIPELINE)
                     last_ping_time = elapsed_time // 10
 
     def stop_listen(self) -> None:
@@ -75,76 +84,83 @@ class ApplicationQueueManager:
         """
         self._q.put(None)
 
-    def publish_chunk_message(self, chunk: LLMResultChunk) -> None:
+    def publish_chunk_message(self, chunk: LLMResultChunk, pub_from: PublishFrom) -> None:
         """
         Publish chunk message to channel
 
         :param chunk: chunk
+        :param pub_from: publish from
         :return:
         """
         self.publish(QueueMessageEvent(
             chunk=chunk
-        ))
+        ), pub_from)
 
-    def publish_message_replace(self, text: str) -> None:
+    def publish_message_replace(self, text: str, pub_from: PublishFrom) -> None:
         """
         Publish message replace
         :param text: text
+        :param pub_from: publish from
         :return:
         """
         self.publish(QueueMessageReplaceEvent(
             text=text
-        ))
+        ), pub_from)
 
-    def publish_retriever_resources(self, retriever_resources: list[dict]) -> None:
+    def publish_retriever_resources(self, retriever_resources: list[dict], pub_from: PublishFrom) -> None:
         """
         Publish retriever resources
         :return:
         """
-        self.publish(QueueRetrieverResourcesEvent(retriever_resources=retriever_resources))
+        self.publish(QueueRetrieverResourcesEvent(retriever_resources=retriever_resources), pub_from)
 
-    def publish_annotation_reply(self, message_annotation_id: str) -> None:
+    def publish_annotation_reply(self, message_annotation_id: str, pub_from: PublishFrom) -> None:
         """
         Publish annotation reply
         :param message_annotation_id: message annotation id
+        :param pub_from: publish from
         :return:
         """
-        self.publish(AnnotationReplyEvent(message_annotation_id=message_annotation_id))
+        self.publish(AnnotationReplyEvent(message_annotation_id=message_annotation_id), pub_from)
 
-    def publish_message_end(self, llm_result: LLMResult) -> None:
+    def publish_message_end(self, llm_result: LLMResult, pub_from: PublishFrom) -> None:
         """
         Publish message end
         :param llm_result: llm result
+        :param pub_from: publish from
         :return:
         """
-        self.publish(QueueMessageEndEvent(llm_result=llm_result))
+        self.publish(QueueMessageEndEvent(llm_result=llm_result), pub_from)
         self.stop_listen()
 
-    def publish_agent_thought(self, message_agent_thought: MessageAgentThought) -> None:
+    def publish_agent_thought(self, message_agent_thought: MessageAgentThought, pub_from: PublishFrom) -> None:
         """
         Publish agent thought
         :param message_agent_thought: message agent thought
+        :param pub_from: publish from
         :return:
         """
         self.publish(QueueAgentThoughtEvent(
             agent_thought_id=message_agent_thought.id
-        ))
+        ), pub_from)
 
-    def publish_error(self, e) -> None:
+    def publish_error(self, e, pub_from: PublishFrom) -> None:
         """
         Publish error
         :param e: error
+        :param pub_from: publish from
         :return:
         """
         self.publish(QueueErrorEvent(
             error=e
-        ))
+        ), pub_from)
         self.stop_listen()
 
-    def publish(self, event: AppQueueEvent) -> None:
+    def publish(self, event: AppQueueEvent, pub_from: PublishFrom) -> None:
         """
         Publish event to queue
         :param event:
+        :param pub_from:
         :return:
         """
         self._check_for_sqlalchemy_models(event.dict())
@@ -162,6 +178,9 @@ class ApplicationQueueManager:
         if isinstance(event, QueueStopEvent):
             self.stop_listen()
 
+        if pub_from == PublishFrom.APPLICATION_MANAGER and self._is_stopped():
+            raise ConversationTaskStoppedException()
+
     @classmethod
     def set_stop_flag(cls, task_id: str, invoke_from: InvokeFrom, user_id: str) -> None:
         """
@@ -173,7 +192,7 @@ class ApplicationQueueManager:
             return
 
         user_prefix = 'account' if invoke_from in [InvokeFrom.EXPLORE, InvokeFrom.DEBUGGER] else 'end-user'
-        if result != f"{user_prefix}-{user_id}":
+        if result.decode('utf-8') != f"{user_prefix}-{user_id}":
             return
 
         stopped_cache_key = cls._generate_stopped_cache_key(task_id)
@@ -187,7 +206,6 @@ class ApplicationQueueManager:
         stopped_cache_key = ApplicationQueueManager._generate_stopped_cache_key(self._task_id)
         result = redis_client.get(stopped_cache_key)
         if result is not None:
-            redis_client.delete(stopped_cache_key)
             return True
 
         return False

api/core/callback_handler/agent_loop_gather_callback_handler.py

@@ -8,7 +8,7 @@ from langchain.agents import openai_functions_agent, openai_functions_multi_agen
 from langchain.callbacks.base import BaseCallbackHandler
 from langchain.schema import AgentAction, AgentFinish, LLMResult, ChatGeneration, BaseMessage
 
-from core.application_queue_manager import ApplicationQueueManager
+from core.application_queue_manager import ApplicationQueueManager, PublishFrom
 from core.callback_handler.entity.agent_loop import AgentLoop
 from core.entities.application_entities import ModelConfigEntity
 from core.model_runtime.entities.llm_entities import LLMResult as RuntimeLLMResult
@@ -232,7 +232,7 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
         db.session.add(message_agent_thought)
         db.session.commit()
 
-        self.queue_manager.publish_agent_thought(message_agent_thought)
+        self.queue_manager.publish_agent_thought(message_agent_thought, PublishFrom.APPLICATION_MANAGER)
 
         return message_agent_thought
 

api/core/callback_handler/index_tool_callback_handler.py

@@ -2,7 +2,7 @@ from typing import List, Union
 
 from langchain.schema import Document
 
-from core.application_queue_manager import ApplicationQueueManager
+from core.application_queue_manager import ApplicationQueueManager, PublishFrom
 from core.entities.application_entities import InvokeFrom
 from extensions.ext_database import db
 from models.dataset import DocumentSegment, DatasetQuery
@@ -80,4 +80,4 @@ class DatasetIndexToolCallbackHandler:
                 db.session.add(dataset_retriever_resource)
                 db.session.commit()
 
-        self._queue_manager.publish_retriever_resources(resource)
+        self._queue_manager.publish_retriever_resources(resource, PublishFrom.APPLICATION_MANAGER)

api/core/data_loader/file_extractor.py

@@ -65,7 +65,8 @@ class FileExtractor:
             elif file_extension == '.pdf':
                 loader = PdfLoader(file_path, upload_file=upload_file)
             elif file_extension in ['.md', '.markdown']:
-                loader = UnstructuredMarkdownLoader(file_path, unstructured_api_url)
+                loader = UnstructuredMarkdownLoader(file_path, unstructured_api_url) if is_automatic \
+                    else MarkdownLoader(file_path, autodetect_encoding=True)
             elif file_extension in ['.htm', '.html']:
                 loader = HTMLLoader(file_path)
             elif file_extension == '.docx':
@@ -84,7 +85,8 @@ class FileExtractor:
                 loader = UnstructuredXmlLoader(file_path, unstructured_api_url)
             else:
                 # txt
-                loader = UnstructuredTextLoader(file_path, unstructured_api_url)
+                loader = UnstructuredTextLoader(file_path, unstructured_api_url) if is_automatic \
+                    else TextLoader(file_path, autodetect_encoding=True)
         else:
             if file_extension == '.xlsx':
                 loader = ExcelLoader(file_path)

api/core/entities/provider_configuration.py

@@ -1,6 +1,7 @@
 import datetime
 import json
-import time
+import logging
+
 from json import JSONDecodeError
 from typing import Optional, List, Dict, Tuple, Iterator
 
@@ -9,8 +10,10 @@ from pydantic import BaseModel
 from core.entities.model_entities import ModelWithProviderEntity, ModelStatus, SimpleModelProviderEntity
 from core.entities.provider_entities import SystemConfiguration, CustomConfiguration, SystemConfigurationStatus
 from core.helper import encrypter
-from core.model_runtime.entities.model_entities import ModelType
-from core.model_runtime.entities.provider_entities import ProviderEntity, CredentialFormSchema, FormType
+from core.helper.model_provider_cache import ProviderCredentialsCache, ProviderCredentialsCacheType
+from core.model_runtime.entities.model_entities import ModelType, FetchFrom
+from core.model_runtime.entities.provider_entities import ProviderEntity, CredentialFormSchema, FormType, \
+    ConfigurateMethod
 from core.model_runtime.model_providers import model_provider_factory
 from core.model_runtime.model_providers.__base.ai_model import AIModel
 from core.model_runtime.model_providers.__base.model_provider import ModelProvider
@@ -18,6 +21,10 @@ from core.model_runtime.utils import encoders
 from extensions.ext_database import db
 from models.provider import ProviderType, Provider, ProviderModel, TenantPreferredModelProvider
 
+logger = logging.getLogger(__name__)
+
+original_provider_configurate_methods = {}
+
 
 class ProviderConfiguration(BaseModel):
     """
@@ -30,6 +37,20 @@ class ProviderConfiguration(BaseModel):
     system_configuration: SystemConfiguration
     custom_configuration: CustomConfiguration
 
+    def __init__(self, **data):
+        super().__init__(**data)
+
+        if self.provider.provider not in original_provider_configurate_methods:
+            original_provider_configurate_methods[self.provider.provider] = []
+            for configurate_method in self.provider.configurate_methods:
+                original_provider_configurate_methods[self.provider.provider].append(configurate_method)
+
+        if original_provider_configurate_methods[self.provider.provider] == [ConfigurateMethod.CUSTOMIZABLE_MODEL]:
+            if (any([len(quota_configuration.restrict_models) > 0
+                     for quota_configuration in self.system_configuration.quota_configurations])
+                    and ConfigurateMethod.PREDEFINED_MODEL not in self.provider.configurate_methods):
+                self.provider.configurate_methods.append(ConfigurateMethod.PREDEFINED_MODEL)
+
     def get_current_credentials(self, model_type: ModelType, model: str) -> Optional[dict]:
         """
         Get current credentials.
@@ -39,7 +60,22 @@ class ProviderConfiguration(BaseModel):
         :return:
         """
         if self.using_provider_type == ProviderType.SYSTEM:
-            return self.system_configuration.credentials
+            restrict_models = []
+            for quota_configuration in self.system_configuration.quota_configurations:
+                if self.system_configuration.current_quota_type != quota_configuration.quota_type:
+                    continue
+
+                restrict_models = quota_configuration.restrict_models
+
+            copy_credentials = self.system_configuration.credentials.copy()
+            if restrict_models:
+                for restrict_model in restrict_models:
+                    if (restrict_model.model_type == model_type
+                            and restrict_model.model == model
+                            and restrict_model.base_model_name):
+                        copy_credentials['base_model_name'] = restrict_model.base_model_name
+
+            return copy_credentials
         else:
             if self.custom_configuration.models:
                 for model_configuration in self.custom_configuration.models:
@@ -119,7 +155,8 @@ class ProviderConfiguration(BaseModel):
 
         if provider_record:
             try:
-                original_credentials = json.loads(provider_record.encrypted_config) if provider_record.encrypted_config else {}
+                original_credentials = json.loads(
+                    provider_record.encrypted_config) if provider_record.encrypted_config else {}
             except JSONDecodeError:
                 original_credentials = {}
 
@@ -168,6 +205,14 @@ class ProviderConfiguration(BaseModel):
             db.session.add(provider_record)
             db.session.commit()
 
+        provider_model_credentials_cache = ProviderCredentialsCache(
+            tenant_id=self.tenant_id,
+            identity_id=provider_record.id,
+            cache_type=ProviderCredentialsCacheType.PROVIDER
+        )
+
+        provider_model_credentials_cache.delete()
+
         self.switch_preferred_provider_type(ProviderType.CUSTOM)
 
     def delete_custom_credentials(self) -> None:
@@ -190,6 +235,14 @@ class ProviderConfiguration(BaseModel):
             db.session.delete(provider_record)
             db.session.commit()
 
+            provider_model_credentials_cache = ProviderCredentialsCache(
+                tenant_id=self.tenant_id,
+                identity_id=provider_record.id,
+                cache_type=ProviderCredentialsCacheType.PROVIDER
+            )
+
+            provider_model_credentials_cache.delete()
+
     def get_custom_model_credentials(self, model_type: ModelType, model: str, obfuscated: bool = False) \
             -> Optional[dict]:
         """
@@ -245,7 +298,8 @@ class ProviderConfiguration(BaseModel):
 
         if provider_model_record:
             try:
-                original_credentials = json.loads(provider_model_record.encrypted_config) if provider_model_record.encrypted_config else {}
+                original_credentials = json.loads(
+                    provider_model_record.encrypted_config) if provider_model_record.encrypted_config else {}
             except JSONDecodeError:
                 original_credentials = {}
 
@@ -311,6 +365,14 @@ class ProviderConfiguration(BaseModel):
             db.session.add(provider_model_record)
             db.session.commit()
 
+        provider_model_credentials_cache = ProviderCredentialsCache(
+            tenant_id=self.tenant_id,
+            identity_id=provider_model_record.id,
+            cache_type=ProviderCredentialsCacheType.MODEL
+        )
+
+        provider_model_credentials_cache.delete()
+
     def delete_custom_model_credentials(self, model_type: ModelType, model: str) -> None:
         """
         Delete custom model credentials.
@@ -332,6 +394,14 @@ class ProviderConfiguration(BaseModel):
             db.session.delete(provider_model_record)
             db.session.commit()
 
+            provider_model_credentials_cache = ProviderCredentialsCache(
+                tenant_id=self.tenant_id,
+                identity_id=provider_model_record.id,
+                cache_type=ProviderCredentialsCacheType.MODEL
+            )
+
+            provider_model_credentials_cache.delete()
+
     def get_provider_instance(self) -> ModelProvider:
         """
         Get provider instance.
@@ -484,7 +554,13 @@ class ProviderConfiguration(BaseModel):
             provider_models.extend(
                 [
                     ModelWithProviderEntity(
-                        **m.dict(),
+                        model=m.model,
+                        label=m.label,
+                        model_type=m.model_type,
+                        features=m.features,
+                        fetch_from=m.fetch_from,
+                        model_properties=m.model_properties,
+                        deprecated=m.deprecated,
                         provider=SimpleModelProviderEntity(self.provider),
                         status=ModelStatus.ACTIVE
                     )
@@ -492,21 +568,70 @@ class ProviderConfiguration(BaseModel):
                 ]
             )
 
+        if self.provider.provider not in original_provider_configurate_methods:
+            original_provider_configurate_methods[self.provider.provider] = []
+            for configurate_method in provider_instance.get_provider_schema().configurate_methods:
+                original_provider_configurate_methods[self.provider.provider].append(configurate_method)
+
+        should_use_custom_model = False
+        if original_provider_configurate_methods[self.provider.provider] == [ConfigurateMethod.CUSTOMIZABLE_MODEL]:
+            should_use_custom_model = True
+
         for quota_configuration in self.system_configuration.quota_configurations:
             if self.system_configuration.current_quota_type != quota_configuration.quota_type:
                 continue
 
-            restrict_llms = quota_configuration.restrict_llms
-            if not restrict_llms:
+            restrict_models = quota_configuration.restrict_models
+            if len(restrict_models) == 0:
                 break
 
+            if should_use_custom_model:
+                if original_provider_configurate_methods[self.provider.provider] == [ConfigurateMethod.CUSTOMIZABLE_MODEL]:
+                    # only customizable model
+                    for restrict_model in restrict_models:
+                        copy_credentials = self.system_configuration.credentials.copy()
+                        if restrict_model.base_model_name:
+                            copy_credentials['base_model_name'] = restrict_model.base_model_name
+
+                        try:
+                            custom_model_schema = (
+                                provider_instance.get_model_instance(restrict_model.model_type)
+                                .get_customizable_model_schema_from_credentials(
+                                    restrict_model.model,
+                                    copy_credentials
+                                )
+                            )
+                        except Exception as ex:
+                            logger.warning(f'get custom model schema failed, {ex}')
+                            continue
+
+                        if not custom_model_schema:
+                            continue
+
+                        if custom_model_schema.model_type not in model_types:
+                            continue
+
+                        provider_models.append(
+                            ModelWithProviderEntity(
+                                model=custom_model_schema.model,
+                                label=custom_model_schema.label,
+                                model_type=custom_model_schema.model_type,
+                                features=custom_model_schema.features,
+                                fetch_from=FetchFrom.PREDEFINED_MODEL,
+                                model_properties=custom_model_schema.model_properties,
+                                deprecated=custom_model_schema.deprecated,
+                                provider=SimpleModelProviderEntity(self.provider),
+                                status=ModelStatus.ACTIVE
+                            )
+                        )
+
             # if llm name not in restricted llm list, remove it
+            restrict_model_names = [rm.model for rm in restrict_models]
             for m in provider_models:
-                if m.model_type == ModelType.LLM and m.model not in restrict_llms:
+                if m.model_type == ModelType.LLM and m.model not in restrict_model_names:
                     m.status = ModelStatus.NO_PERMISSION
                 elif not quota_configuration.is_valid:
                     m.status = ModelStatus.QUOTA_EXCEEDED
-
         return provider_models
 
     def _get_custom_provider_models(self,
@@ -533,7 +658,13 @@ class ProviderConfiguration(BaseModel):
             for m in models:
                 provider_models.append(
                     ModelWithProviderEntity(
-                        **m.dict(),
+                        model=m.model,
+                        label=m.label,
+                        model_type=m.model_type,
+                        features=m.features,
+                        fetch_from=m.fetch_from,
+                        model_properties=m.model_properties,
+                        deprecated=m.deprecated,
                         provider=SimpleModelProviderEntity(self.provider),
                         status=ModelStatus.ACTIVE if credentials else ModelStatus.NO_CONFIGURE
                     )
@@ -544,20 +675,30 @@ class ProviderConfiguration(BaseModel):
             if model_configuration.model_type not in model_types:
                 continue
 
-            custom_model_schema = (
-                provider_instance.get_model_instance(model_configuration.model_type)
-                .get_customizable_model_schema_from_credentials(
-                    model_configuration.model,
-                    model_configuration.credentials
+            try:
+                custom_model_schema = (
+                    provider_instance.get_model_instance(model_configuration.model_type)
+                    .get_customizable_model_schema_from_credentials(
+                        model_configuration.model,
+                        model_configuration.credentials
+                    )
                 )
-            )
+            except Exception as ex:
+                logger.warning(f'get custom model schema failed, {ex}')
+                continue
 
             if not custom_model_schema:
                 continue
 
             provider_models.append(
                 ModelWithProviderEntity(
-                    **custom_model_schema.dict(),
+                    model=custom_model_schema.model,
+                    label=custom_model_schema.label,
+                    model_type=custom_model_schema.model_type,
+                    features=custom_model_schema.features,
+                    fetch_from=custom_model_schema.fetch_from,
+                    model_properties=custom_model_schema.model_properties,
+                    deprecated=custom_model_schema.deprecated,
                     provider=SimpleModelProviderEntity(self.provider),
                     status=ModelStatus.ACTIVE
                 )

api/core/entities/provider_entities.py

@@ -21,6 +21,12 @@ class SystemConfigurationStatus(Enum):
     UNSUPPORTED = 'unsupported'
 
 
+class RestrictModel(BaseModel):
+    model: str
+    base_model_name: Optional[str] = None
+    model_type: ModelType
+
+
 class QuotaConfiguration(BaseModel):
     """
     Model class for provider quota configuration.
@@ -30,7 +36,7 @@ class QuotaConfiguration(BaseModel):
     quota_limit: int
     quota_used: int
     is_valid: bool
-    restrict_llms: list[str] = []
+    restrict_models: list[RestrictModel] = []
 
 
 class SystemConfiguration(BaseModel):

api/core/extension/extensible.py

@@ -61,7 +61,7 @@ class Extensible:
 
                     builtin_file_path = os.path.join(subdir_path, '__builtin__')
                     if os.path.exists(builtin_file_path):
-                        with open(builtin_file_path, 'r') as f:
+                        with open(builtin_file_path, 'r', encoding='utf-8') as f:
                             position = int(f.read().strip())
 
                 if (extension_name + '.py') not in file_names:
@@ -93,7 +93,7 @@ class Extensible:
                     json_path = os.path.join(subdir_path, 'schema.json')
                     json_data = {}
                     if os.path.exists(json_path):
-                        with open(json_path, 'r') as f:
+                        with open(json_path, 'r', encoding='utf-8') as f:
                             json_data = json.load(f)
 
                 extensions[extension_name] = ModuleExtension(

api/core/external_data_tool/api/api.py

@@ -58,7 +58,7 @@ class ApiExternalDataTool(ExternalDataTool):
         if not api_based_extension:
             raise ValueError("[External data tool] API query failed, variable: {}, "
                              "error: api_based_extension_id is invalid"
-                             .format(self.config.get('variable')))
+                             .format(self.variable))
 
         # decrypt api_key
         api_key = encrypter.decrypt_token(
@@ -74,7 +74,7 @@ class ApiExternalDataTool(ExternalDataTool):
             )
         except Exception as e:
             raise ValueError("[External data tool] API query failed, variable: {}, error: {}".format(
-                self.config.get('variable'),
+                self.variable,
                 e
             ))
 
@@ -87,6 +87,10 @@ class ApiExternalDataTool(ExternalDataTool):
 
         if 'result' not in response_json:
             raise ValueError("[External data tool] API query failed, variable: {}, error: result not found in response"
-                             .format(self.config.get('variable')))
+                             .format(self.variable))
+
+        if not isinstance(response_json['result'], str):
+            raise ValueError("[External data tool] API query failed, variable: {}, error: result is not string"
+                             .format(self.variable))
 
         return response_json['result']

api/core/external_data_tool/weather_search/schema.json

@@ -1,35 +0,0 @@
-{
-    "label": {
-        "en-US": "Weather Search",
-        "zh-Hans": "天气查询"
-    },
-    "form_schema": [
-        {
-            "type": "select",
-            "label": {
-                "en-US": "Temperature Unit",
-                "zh-Hans": "温度单位"
-            },
-            "variable": "temperature_unit",
-            "required": true,
-            "options": [
-                {
-                    "label": {
-                        "en-US": "Fahrenheit",
-                        "zh-Hans": "华氏度"
-                    },
-                    "value": "fahrenheit"
-                },
-                {
-                    "label": {
-                        "en-US": "Centigrade",
-                        "zh-Hans": "摄氏度"
-                    },
-                    "value": "centigrade"
-                }
-            ],
-            "default": "centigrade",
-            "placeholder": "Please select temperature unit"
-        }
-    ]
-}

api/core/external_data_tool/weather_search/weather_search.py

@@ -1,45 +0,0 @@
-from typing import Optional
-
-from core.external_data_tool.base import ExternalDataTool
-
-
-class WeatherSearch(ExternalDataTool):
-    """
-    The name of custom type must be unique, keep the same with directory and file name.
-    """
-    name: str = "weather_search"
-
-    @classmethod
-    def validate_config(cls, tenant_id: str, config: dict) -> None:
-        """
-        schema.json validation. It will be called when user save the config.
-
-        Example:
-            .. code-block:: python
-                config = {
-                    "temperature_unit": "centigrade"
-                }
-
-        :param tenant_id: the id of workspace
-        :param config: the variables of form config
-        :return:
-        """
-
-        if not config.get('temperature_unit'):
-            raise ValueError('temperature unit is required')
-
-    def query(self, inputs: dict, query: Optional[str] = None) -> str:
-        """
-        Query the external data tool.
-
-        :param inputs: user inputs
-        :param query: the query of chat app
-        :return: the tool query result
-        """
-        city = inputs.get('city')
-        temperature_unit = self.config.get('temperature_unit')
-
-        if temperature_unit == 'fahrenheit':
-            return f'Weather in {city} is 32°F'
-        else:
-            return f'Weather in {city} is 0°C'

api/core/helper/model_provider_cache.py

@@ -0,0 +1,51 @@
+import json
+from enum import Enum
+from json import JSONDecodeError
+from typing import Optional
+
+from extensions.ext_redis import redis_client
+
+
+class ProviderCredentialsCacheType(Enum):
+    PROVIDER = "provider"
+    MODEL = "provider_model"
+
+
+class ProviderCredentialsCache:
+    def __init__(self, tenant_id: str, identity_id: str, cache_type: ProviderCredentialsCacheType):
+        self.cache_key = f"{cache_type.value}_credentials:tenant_id:{tenant_id}:id:{identity_id}"
+
+    def get(self) -> Optional[dict]:
+        """
+        Get cached model provider credentials.
+
+        :return:
+        """
+        cached_provider_credentials = redis_client.get(self.cache_key)
+        if cached_provider_credentials:
+            try:
+                cached_provider_credentials = cached_provider_credentials.decode('utf-8')
+                cached_provider_credentials = json.loads(cached_provider_credentials)
+            except JSONDecodeError:
+                return None
+
+            return cached_provider_credentials
+        else:
+            return None
+
+    def set(self, credentials: dict) -> None:
+        """
+        Cache model provider credentials.
+
+        :param credentials: provider credentials
+        :return:
+        """
+        redis_client.setex(self.cache_key, 86400, json.dumps(credentials))
+
+    def delete(self) -> None:
+        """
+        Delete cached model provider credentials.
+
+        :return:
+        """
+        redis_client.delete(self.cache_key)

api/core/hosting_configuration.py

@@ -4,13 +4,14 @@ from typing import Optional
 from flask import Flask
 from pydantic import BaseModel
 
-from core.entities.provider_entities import QuotaUnit
+from core.entities.provider_entities import QuotaUnit, RestrictModel
+from core.model_runtime.entities.model_entities import ModelType
 from models.provider import ProviderQuotaType
 
 
 class HostingQuota(BaseModel):
     quota_type: ProviderQuotaType
-    restrict_llms: list[str] = []
+    restrict_models: list[RestrictModel] = []
 
 
 class TrialHostingQuota(HostingQuota):
@@ -47,10 +48,11 @@ class HostingConfiguration:
     provider_map: dict[str, HostingProvider] = {}
     moderation_config: HostedModerationConfig = None
 
-    def init_app(self, app: Flask):
+    def init_app(self, app: Flask) -> None:
         if app.config.get('EDITION') != 'CLOUD':
             return
 
+        self.provider_map["azure_openai"] = self.init_azure_openai()
         self.provider_map["openai"] = self.init_openai()
         self.provider_map["anthropic"] = self.init_anthropic()
         self.provider_map["minimax"] = self.init_minimax()
@@ -59,6 +61,47 @@ class HostingConfiguration:
 
         self.moderation_config = self.init_moderation_config()
 
+    def init_azure_openai(self) -> HostingProvider:
+        quota_unit = QuotaUnit.TIMES
+        if os.environ.get("HOSTED_AZURE_OPENAI_ENABLED") and os.environ.get("HOSTED_AZURE_OPENAI_ENABLED").lower() == 'true':
+            credentials = {
+                "openai_api_key": os.environ.get("HOSTED_AZURE_OPENAI_API_KEY"),
+                "openai_api_base": os.environ.get("HOSTED_AZURE_OPENAI_API_BASE"),
+                "base_model_name": "gpt-35-turbo"
+            }
+
+            quotas = []
+            hosted_quota_limit = int(os.environ.get("HOSTED_AZURE_OPENAI_QUOTA_LIMIT", "1000"))
+            if hosted_quota_limit != -1 or hosted_quota_limit > 0:
+                trial_quota = TrialHostingQuota(
+                    quota_limit=hosted_quota_limit,
+                    restrict_models=[
+                        RestrictModel(model="gpt-4", base_model_name="gpt-4", model_type=ModelType.LLM),
+                        RestrictModel(model="gpt-4-32k", base_model_name="gpt-4-32k", model_type=ModelType.LLM),
+                        RestrictModel(model="gpt-4-1106-preview", base_model_name="gpt-4-1106-preview", model_type=ModelType.LLM),
+                        RestrictModel(model="gpt-4-vision-preview", base_model_name="gpt-4-vision-preview", model_type=ModelType.LLM),
+                        RestrictModel(model="gpt-35-turbo", base_model_name="gpt-35-turbo", model_type=ModelType.LLM),
+                        RestrictModel(model="gpt-35-turbo-1106", base_model_name="gpt-35-turbo-1106", model_type=ModelType.LLM),
+                        RestrictModel(model="gpt-35-turbo-instruct", base_model_name="gpt-35-turbo-instruct", model_type=ModelType.LLM),
+                        RestrictModel(model="gpt-35-turbo-16k", base_model_name="gpt-35-turbo-16k", model_type=ModelType.LLM),
+                        RestrictModel(model="text-davinci-003", base_model_name="text-davinci-003", model_type=ModelType.LLM),
+                        RestrictModel(model="text-embedding-ada-002", base_model_name="text-embedding-ada-002", model_type=ModelType.TEXT_EMBEDDING),
+                    ]
+                )
+                quotas.append(trial_quota)
+
+            return HostingProvider(
+                enabled=True,
+                credentials=credentials,
+                quota_unit=quota_unit,
+                quotas=quotas
+            )
+
+        return HostingProvider(
+            enabled=False,
+            quota_unit=quota_unit,
+        )
+
     def init_openai(self) -> HostingProvider:
         quota_unit = QuotaUnit.TIMES
         if os.environ.get("HOSTED_OPENAI_ENABLED") and os.environ.get("HOSTED_OPENAI_ENABLED").lower() == 'true':
@@ -77,12 +120,12 @@ class HostingConfiguration:
             if hosted_quota_limit != -1 or hosted_quota_limit > 0:
                 trial_quota = TrialHostingQuota(
                     quota_limit=hosted_quota_limit,
-                    restrict_llms=[
-                        "gpt-3.5-turbo",
-                        "gpt-3.5-turbo-1106",
-                        "gpt-3.5-turbo-instruct",
-                        "gpt-3.5-turbo-16k",
-                        "text-davinci-003"
+                    restrict_models=[
+                        RestrictModel(model="gpt-3.5-turbo", model_type=ModelType.LLM),
+                        RestrictModel(model="gpt-3.5-turbo-1106", model_type=ModelType.LLM),
+                        RestrictModel(model="gpt-3.5-turbo-instruct", model_type=ModelType.LLM),
+                        RestrictModel(model="gpt-3.5-turbo-16k", model_type=ModelType.LLM),
+                        RestrictModel(model="text-davinci-003", model_type=ModelType.LLM),
                     ]
                 )
                 quotas.append(trial_quota)

api/core/index/keyword_table_index/keyword_table_index.py

@@ -136,6 +136,7 @@ class KeywordTableIndex(BaseIndex):
                     page_content=segment.content,
                     metadata={
                         "doc_id": chunk_index,
+                        "doc_hash": segment.index_node_hash,
                         "document_id": segment.document_id,
                         "dataset_id": segment.dataset_id,
                     }

api/core/index/vector_index/qdrant_vector_index.py

@@ -18,6 +18,7 @@ from models.dataset import Dataset, DatasetCollectionBinding
 class QdrantConfig(BaseModel):
     endpoint: str
     api_key: Optional[str]
+    timeout: float = 20
     root_path: Optional[str]
 
     def to_qdrant_params(self):
@@ -33,6 +34,7 @@ class QdrantConfig(BaseModel):
             return {
                 'url': self.endpoint,
                 'api_key': self.api_key,
+                'timeout': self.timeout
             }
 
 

api/core/index/vector_index/vector_index.py

@@ -49,7 +49,8 @@ class VectorIndex:
                 config=QdrantConfig(
                     endpoint=config.get('QDRANT_URL'),
                     api_key=config.get('QDRANT_API_KEY'),
-                    root_path=current_app.root_path
+                    root_path=current_app.root_path,
+                    timeout=config.get('QDRANT_CLIENT_TIMEOUT')
                 ),
                 embeddings=embeddings
             )

api/core/indexing_runner.py

@@ -5,12 +5,12 @@ import re
 import threading
 import time
 import uuid
-from typing import Optional, List, cast
+from typing import Optional, List, cast, Type, Union, Literal, AbstractSet, Collection, Any
 
 from flask import current_app, Flask
 from flask_login import current_user
 from langchain.schema import Document
-from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter
+from langchain.text_splitter import TextSplitter, TS, TokenTextSplitter
 from sqlalchemy.orm.exc import ObjectDeletedError
 
 from core.data_loader.file_extractor import FileExtractor
@@ -23,7 +23,8 @@ from core.errors.error import ProviderTokenNotInitError
 from core.model_runtime.entities.model_entities import ModelType, PriceType
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
-from core.spiltter.fixed_text_splitter import FixedRecursiveCharacterTextSplitter
+from core.model_runtime.model_providers.__base.tokenizers.gpt2_tokenzier import GPT2Tokenizer
+from core.spiltter.fixed_text_splitter import FixedRecursiveCharacterTextSplitter, EnhanceRecursiveCharacterTextSplitter
 from extensions.ext_database import db
 from extensions.ext_redis import redis_client
 from extensions.ext_storage import storage
@@ -58,7 +59,7 @@ class IndexingRunner:
                     first()
 
                 # load file
-                text_docs = self._load_data(dataset_document)
+                text_docs = self._load_data(dataset_document, processing_rule.mode == 'automatic')
 
                 # get splitter
                 splitter = self._get_splitter(processing_rule)
@@ -112,15 +113,14 @@ class IndexingRunner:
             for document_segment in document_segments:
                 db.session.delete(document_segment)
             db.session.commit()
-
-            # load file
-            text_docs = self._load_data(dataset_document)
-
             # get the process rule
             processing_rule = db.session.query(DatasetProcessRule). \
                 filter(DatasetProcessRule.id == dataset_document.dataset_process_rule_id). \
                 first()
 
+            # load file
+            text_docs = self._load_data(dataset_document, processing_rule.mode == 'automatic')
+
             # get splitter
             splitter = self._get_splitter(processing_rule)
 
@@ -221,12 +221,18 @@ class IndexingRunner:
             if not dataset:
                 raise ValueError('Dataset not found.')
             if dataset.indexing_technique == 'high_quality' or indexing_technique == 'high_quality':
-                embedding_model_instance = self.model_manager.get_model_instance(
-                    tenant_id=tenant_id,
-                    provider=dataset.embedding_model_provider,
-                    model_type=ModelType.TEXT_EMBEDDING,
-                    model=dataset.embedding_model
-                )
+                if dataset.embedding_model_provider:
+                    embedding_model_instance = self.model_manager.get_model_instance(
+                        tenant_id=tenant_id,
+                        provider=dataset.embedding_model_provider,
+                        model_type=ModelType.TEXT_EMBEDDING,
+                        model=dataset.embedding_model
+                    )
+                else:
+                    embedding_model_instance = self.model_manager.get_default_model_instance(
+                        tenant_id=tenant_id,
+                        model_type=ModelType.TEXT_EMBEDDING,
+                    )
         else:
             if indexing_technique == 'high_quality':
                 embedding_model_instance = self.model_manager.get_default_model_instance(
@@ -237,14 +243,15 @@ class IndexingRunner:
         preview_texts = []
         total_segments = 0
         for file_detail in file_details:
-            # load data from file
-            text_docs = FileExtractor.load(file_detail)
 
             processing_rule = DatasetProcessRule(
                 mode=tmp_processing_rule["mode"],
                 rules=json.dumps(tmp_processing_rule["rules"])
             )
 
+            # load data from file
+            text_docs = FileExtractor.load(file_detail, is_automatic=processing_rule.mode == 'automatic')
+
             # get splitter
             splitter = self._get_splitter(processing_rule)
 
@@ -327,12 +334,18 @@ class IndexingRunner:
             if not dataset:
                 raise ValueError('Dataset not found.')
             if dataset.indexing_technique == 'high_quality' or indexing_technique == 'high_quality':
-                embedding_model_instance = self.model_manager.get_model_instance(
-                    tenant_id=tenant_id,
-                    provider=dataset.embedding_model_provider,
-                    model_type=ModelType.TEXT_EMBEDDING,
-                    model=dataset.embedding_model
-                )
+                if dataset.embedding_model_provider:
+                    embedding_model_instance = self.model_manager.get_model_instance(
+                        tenant_id=tenant_id,
+                        provider=dataset.embedding_model_provider,
+                        model_type=ModelType.TEXT_EMBEDDING,
+                        model=dataset.embedding_model
+                    )
+                else:
+                    embedding_model_instance = self.model_manager.get_default_model_instance(
+                        tenant_id=tenant_id,
+                        model_type=ModelType.TEXT_EMBEDDING,
+                    )
         else:
             if indexing_technique == 'high_quality':
                 embedding_model_instance = self.model_manager.get_default_model_instance(
@@ -381,13 +394,15 @@ class IndexingRunner:
                 )
                 total_segments += len(documents)
 
-                embedding_model_type_instance = embedding_model_instance.model_type_instance
-                embedding_model_type_instance = cast(TextEmbeddingModel, embedding_model_type_instance)
+                embedding_model_type_instance = None
+                if embedding_model_instance:
+                    embedding_model_type_instance = embedding_model_instance.model_type_instance
+                    embedding_model_type_instance = cast(TextEmbeddingModel, embedding_model_type_instance)
 
                 for document in documents:
                     if len(preview_texts) < 5:
                         preview_texts.append(document.page_content)
-                    if indexing_technique == 'high_quality' or embedding_model_instance:
+                    if indexing_technique == 'high_quality' and embedding_model_type_instance:
                         tokens += embedding_model_type_instance.get_num_tokens(
                             model=embedding_model_instance.model,
                             credentials=embedding_model_instance.credentials,
@@ -456,7 +471,7 @@ class IndexingRunner:
                 one_or_none()
 
             if file_detail:
-                text_docs = FileExtractor.load(file_detail, is_automatic=True)
+                text_docs = FileExtractor.load(file_detail, is_automatic=automatic)
         elif dataset_document.data_source_type == 'notion_import':
             loader = NotionLoader.from_document(dataset_document)
             text_docs = loader.load()
@@ -502,7 +517,8 @@ class IndexingRunner:
             if separator:
                 separator = separator.replace('\\n', '\n')
 
-            character_splitter = FixedRecursiveCharacterTextSplitter.from_tiktoken_encoder(
+
+            character_splitter = FixedRecursiveCharacterTextSplitter.from_gpt2_encoder(
                 chunk_size=segmentation["max_tokens"],
                 chunk_overlap=0,
                 fixed_separator=separator,
@@ -510,7 +526,7 @@ class IndexingRunner:
             )
         else:
             # Automatic segmentation
-            character_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
+            character_splitter = EnhanceRecursiveCharacterTextSplitter.from_gpt2_encoder(
                 chunk_size=DatasetProcessRule.AUTOMATIC_RULES['segmentation']['max_tokens'],
                 chunk_overlap=0,
                 separators=["\n\n", "。", ".", " ", ""]

api/core/model_manager.py

@@ -144,7 +144,7 @@ class ModelInstance:
             user=user
         )
 
-    def invoke_speech2text(self, file: IO[bytes], user: Optional[str] = None) \
+    def invoke_speech2text(self, file: IO[bytes], user: Optional[str] = None, **params) \
             -> str:
         """
         Invoke large language model
@@ -161,7 +161,8 @@ class ModelInstance:
             model=self.model,
             credentials=self.credentials,
             file=file,
-            user=user
+            user=user,
+            **params
         )
 
 
@@ -178,6 +179,8 @@ class ModelManager:
         :param model: model name
         :return:
         """
+        if not provider:
+            return self.get_default_model_instance(tenant_id, model_type)
         provider_model_bundle = self._provider_manager.get_provider_model_bundle(
             tenant_id=tenant_id,
             provider=provider,

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

@@ -30,7 +30,7 @@
 ```yaml
 provider: xinference #确定供应商标识
 label: # 供应商展示名称，可设置 en_US 英文、zh_Hans 中文两种语言，zh_Hans 不设置将默认使用 en_US。
-  en_US: Xorbots Inference
+  en_US: Xorbits Inference
 icon_small: # 小图标，可以参考其他供应商的图标，存储在对应供应商实现目录下的 _assets 目录，中英文策略同 label
   en_US: icon_s_en.svg
 icon_large: # 大图标
@@ -260,7 +260,7 @@ provider_credential_schema:
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=model_type,
             model_properties={ 
-                'mode':  ModelType.LLM,
+                ModelPropertyKey.MODE:  ModelType.LLM,
             },
             parameter_rules=rules
         )

api/core/model_runtime/entities/defaults.py

@@ -18,7 +18,7 @@ PARAMETER_RULE_TEMPLATE: Dict[DefaultParameterName, dict] = {
         'default': 0.0,
         'min': 0.0,
         'max': 1.0,
-        'precision': 1,
+        'precision': 2,
     },
     DefaultParameterName.TOP_P: {
         'label': {
@@ -34,7 +34,7 @@ PARAMETER_RULE_TEMPLATE: Dict[DefaultParameterName, dict] = {
         'default': 1.0,
         'min': 0.0,
         'max': 1.0,
-        'precision': 1,
+        'precision': 2,
     },
     DefaultParameterName.PRESENCE_PENALTY: {
         'label': {
@@ -50,7 +50,7 @@ PARAMETER_RULE_TEMPLATE: Dict[DefaultParameterName, dict] = {
         'default': 0.0,
         'min': 0.0,
         'max': 1.0,
-        'precision': 1,
+        'precision': 2,
     },
     DefaultParameterName.FREQUENCY_PENALTY: {
         'label': {
@@ -66,7 +66,7 @@ PARAMETER_RULE_TEMPLATE: Dict[DefaultParameterName, dict] = {
         'default': 0.0,
         'min': 0.0,
         'max': 1.0,
-        'precision': 1,
+        'precision': 2,
     },
     DefaultParameterName.MAX_TOKENS: {
         'label': {

api/core/model_runtime/entities/model_entities.py

@@ -32,7 +32,7 @@ class ModelType(Enum):
             return cls.TEXT_EMBEDDING
         elif origin_model_type == 'reranking' or origin_model_type == cls.RERANK.value:
             return cls.RERANK
-        elif origin_model_type == cls.SPEECH2TEXT.value:
+        elif origin_model_type == 'speech2text' or origin_model_type == cls.SPEECH2TEXT.value:
             return cls.SPEECH2TEXT
         elif origin_model_type == cls.MODERATION.value:
             return cls.MODERATION

api/core/model_runtime/errors/invoke.py

@@ -8,6 +8,9 @@ class InvokeError(Exception):
     def __init__(self, description: Optional[str] = None) -> None:
         self.description = description
 
+    def __str__(self):
+        return self.description or self.__class__.__name__
+
 
 class InvokeConnectionError(InvokeError):
     """Raised when the Invoke returns connection error."""

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

@@ -147,13 +147,15 @@ class AIModel(ABC):
         # read _position.yaml file
         position_map = {}
         if os.path.exists(position_file_path):
-            with open(position_file_path, 'r') as f:
-                position_map = yaml.safe_load(f)
+            with open(position_file_path, 'r', encoding='utf-8') as f:
+                positions = yaml.safe_load(f)
+                # convert list to dict with key as model provider name, value as index
+                position_map = {position: index for index, position in enumerate(positions)}
 
         # traverse all model_schema_yaml_paths
         for model_schema_yaml_path in model_schema_yaml_paths:
             # read yaml data from yaml file
-            with open(model_schema_yaml_path, 'r') as f:
+            with open(model_schema_yaml_path, 'r', encoding='utf-8') as f:
                 yaml_data = yaml.safe_load(f)
 
             new_parameter_rules = []
@@ -236,16 +238,6 @@ class AIModel(ABC):
         :param credentials: model credentials
         :return: model schema
         """
-        if 'schema' in credentials:
-            schema_dict = json.loads(credentials['schema'])
-
-            try:
-                model_instance = AIModelEntity.parse_obj(schema_dict)
-                return model_instance
-            except ValidationError as e:
-                logging.exception(f"Invalid model schema for {model}")
-                return self._get_customizable_model_schema(model, credentials)
-
         return self._get_customizable_model_schema(model, credentials)
     
     def _get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]:

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

@@ -132,8 +132,8 @@ class LargeLanguageModel(AIModel):
         system_fingerprint = None
         real_model = model
 
-        for chunk in result:
-            try:
+        try:
+            for chunk in result:
                 yield chunk
 
                 self._trigger_new_chunk_callbacks(
@@ -156,8 +156,8 @@ class LargeLanguageModel(AIModel):
 
                 if chunk.system_fingerprint:
                     system_fingerprint = chunk.system_fingerprint
-            except Exception as e:
-                raise self._transform_invoke_error(e)
+        except Exception as e:
+            raise self._transform_invoke_error(e)
 
         self._trigger_after_invoke_callbacks(
             model=model,
@@ -165,7 +165,7 @@ class LargeLanguageModel(AIModel):
                 model=real_model,
                 prompt_messages=prompt_messages,
                 message=prompt_message,
-                usage=usage,
+                usage=usage if usage else LLMUsage.empty_usage(),
                 system_fingerprint=system_fingerprint
             ),
             credentials=credentials,

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

@@ -47,7 +47,7 @@ class ModelProvider(ABC):
         yaml_path = os.path.join(current_path, f'{provider_name}.yaml')
         yaml_data = {}
         if os.path.exists(yaml_path):
-            with open(yaml_path, 'r') as f:
+            with open(yaml_path, 'r', encoding='utf-8') as f:
                 yaml_data = yaml.safe_load(f)
 
         try:
@@ -112,7 +112,7 @@ class ModelProvider(ABC):
         model_class = None
         for name, obj in vars(mod).items():
             if (isinstance(obj, type) and issubclass(obj, AIModel) and not obj.__abstractmethods__
-                    and obj != AIModel):
+                    and obj != AIModel and obj.__module__ == mod.__name__):
                 model_class = obj
                 break
 

api/core/model_runtime/model_providers/_position.yaml

@@ -1,19 +1,20 @@
-openai: 0
-anthropic: 1
-azure_openai: 2
-google: 3
-replicate: 4
-huggingface_hub: 5
-cohere: 6
-zhipuai: 7
-baichuan: 8
-spark: 9
-minimax: 10
-tongyi: 11
-wenxin: 12
-jina: 13
-chatglm: 14
-xinference: 15
-openllm: 16
-localai: 17
-openai_api_compatible: 18
+- openai
+- anthropic
+- azure_openai
+- google
+- replicate
+- huggingface_hub
+- cohere
+- togetherai
+- zhipuai
+- baichuan
+- spark
+- minimax
+- tongyi
+- wenxin
+- jina
+- chatglm
+- xinference
+- openllm
+- localai
+- openai_api_compatible

api/core/model_runtime/model_providers/anthropic/anthropic.yaml

@@ -16,24 +16,24 @@ help:
   url:
     en_US: https://console.anthropic.com/account/keys
 supported_model_types:
-- llm
+  - llm
 configurate_methods:
-- predefined-model
+  - predefined-model
 provider_credential_schema:
   credential_form_schemas:
-  - variable: anthropic_api_key
-    label:
-      en_US: API Key
-    type: secret-input
-    required: true
-    placeholder:
-      zh_Hans: 在此输入您的 API Key
-      en_US: Enter your API Key
-  - variable: anthropic_api_url
-    label:
-      en_US: API URL
-    type: text-input
-    required: false
-    placeholder:
-      zh_Hans: 在此输入您的 API URL
-      en_US: Enter your API URL
+    - variable: anthropic_api_key
+      label:
+        en_US: API Key
+      type: secret-input
+      required: true
+      placeholder:
+        zh_Hans: 在此输入您的 API Key
+        en_US: Enter your API Key
+    - variable: anthropic_api_url
+      label:
+        en_US: API URL
+      type: text-input
+      required: false
+      placeholder:
+        zh_Hans: 在此输入您的 API URL
+        en_US: Enter your API URL

api/core/model_runtime/model_providers/anthropic/llm/claude-2.1.yaml

@@ -3,32 +3,32 @@ label:
   en_US: claude-2.1
 model_type: llm
 features:
-- agent-thought
+  - agent-thought
 model_properties:
   mode: chat
   context_size: 200000
 parameter_rules:
-- name: temperature
-  use_template: temperature
-- name: top_p
-  use_template: top_p
-- name: top_k
-  label:
-    zh_Hans: 取样数量
-    en_US: Top k
-  type: int
-  help:
-    zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
-    en_US: Only sample from the top K options for each subsequent token.
-  required: false
-- name: max_tokens_to_sample
-  use_template: max_tokens
-  required: true
-  default: 4096
-  min: 1
-  max: 4096
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_tokens_to_sample
+    use_template: max_tokens
+    required: true
+    default: 4096
+    min: 1
+    max: 4096
 pricing:
   input: '8.00'
   output: '24.00'
   unit: '0.000001'
-  currency: USD
+  currency: USD

api/core/model_runtime/model_providers/anthropic/llm/claude-2.yaml

@@ -3,32 +3,32 @@ label:
   en_US: claude-2
 model_type: llm
 features:
-- agent-thought
+  - agent-thought
 model_properties:
   mode: chat
   context_size: 100000
 parameter_rules:
-- name: temperature
-  use_template: temperature
-- name: top_p
-  use_template: top_p
-- name: top_k
-  label:
-    zh_Hans: 取样数量
-    en_US: Top k
-  type: int
-  help:
-    zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
-    en_US: Only sample from the top K options for each subsequent token.
-  required: false
-- name: max_tokens_to_sample
-  use_template: max_tokens
-  required: true
-  default: 4096
-  min: 1
-  max: 4096
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_tokens_to_sample
+    use_template: max_tokens
+    required: true
+    default: 4096
+    min: 1
+    max: 4096
 pricing:
   input: '8.00'
   output: '24.00'
   unit: '0.000001'
-  currency: USD
+  currency: USD

api/core/model_runtime/model_providers/anthropic/llm/claude-instant-1.yaml

@@ -2,32 +2,32 @@ model: claude-instant-1
 label:
   en_US: claude-instant-1
 model_type: llm
-features: []
+features: [ ]
 model_properties:
   mode: chat
   context_size: 100000
 parameter_rules:
-- name: temperature
-  use_template: temperature
-- name: top_p
-  use_template: top_p
-- name: top_k
-  label:
-    zh_Hans: 取样数量
-    en_US: Top k
-  type: int
-  help:
-    zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
-    en_US: Only sample from the top K options for each subsequent token.
-  required: false
-- name: max_tokens_to_sample
-  use_template: max_tokens
-  required: true
-  default: 4096
-  min: 1
-  max: 4096
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_tokens_to_sample
+    use_template: max_tokens
+    required: true
+    default: 4096
+    min: 1
+    max: 4096
 pricing:
   input: '1.63'
   output: '5.51'
   unit: '0.000001'
-  currency: USD
+  currency: USD

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

@@ -252,6 +252,9 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
         :param messages: List of PromptMessage to combine.
         :return: Combined string with necessary human_prompt and ai_prompt tags.
         """
+        if not messages:
+            return ''
+
         messages = messages.copy()  # don't mutate the original list
         if not isinstance(messages[-1], AssistantPromptMessage):
             messages.append(AssistantPromptMessage(content=""))

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

@@ -2,7 +2,7 @@ from pydantic import BaseModel
 
 from core.model_runtime.entities.llm_entities import LLMMode
 from core.model_runtime.entities.model_entities import ModelFeature, ModelType, FetchFrom, ParameterRule, \
-    DefaultParameterName, PriceConfig
+    DefaultParameterName, PriceConfig, ModelPropertyKey
 from core.model_runtime.entities.model_entities import AIModelEntity, I18nObject
 from core.model_runtime.entities.defaults import PARAMETER_RULE_TEMPLATE
 
@@ -40,8 +40,8 @@ LLM_BASE_MODELS = [
             ],
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
-                'mode': LLMMode.CHAT.value,
-                'context_size': 4096,
+                ModelPropertyKey.MODE: LLMMode.CHAT.value,
+                ModelPropertyKey.CONTEXT_SIZE: 4096,
             },
             parameter_rules=[
                 ParameterRule(
@@ -84,8 +84,8 @@ LLM_BASE_MODELS = [
             ],
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
-                'mode': LLMMode.CHAT.value,
-                'context_size': 16385,
+                ModelPropertyKey.MODE: LLMMode.CHAT.value,
+                ModelPropertyKey.CONTEXT_SIZE: 16385,
             },
             parameter_rules=[
                 ParameterRule(
@@ -128,8 +128,8 @@ LLM_BASE_MODELS = [
             ],
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
-                'mode': LLMMode.CHAT.value,
-                'context_size': 8192,
+                ModelPropertyKey.MODE: LLMMode.CHAT.value,
+                ModelPropertyKey.CONTEXT_SIZE: 8192,
             },
             parameter_rules=[
                 ParameterRule(
@@ -202,8 +202,8 @@ LLM_BASE_MODELS = [
             ],
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
-                'mode': LLMMode.CHAT.value,
-                'context_size': 32768,
+                ModelPropertyKey.MODE: LLMMode.CHAT.value,
+                ModelPropertyKey.CONTEXT_SIZE: 32768,
             },
             parameter_rules=[
                 ParameterRule(
@@ -276,8 +276,8 @@ LLM_BASE_MODELS = [
             ],
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
-                'mode': LLMMode.CHAT.value,
-                'context_size': 128000,
+                ModelPropertyKey.MODE: LLMMode.CHAT.value,
+                ModelPropertyKey.CONTEXT_SIZE: 128000,
             },
             parameter_rules=[
                 ParameterRule(
@@ -296,7 +296,7 @@ LLM_BASE_MODELS = [
                     name='frequency_penalty',
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
                 ),
-                _get_max_tokens(default=512, min_val=1, max_val=128000),
+                _get_max_tokens(default=512, min_val=1, max_val=4096),
                 ParameterRule(
                     name='seed',
                     label=I18nObject(
@@ -349,8 +349,8 @@ LLM_BASE_MODELS = [
             ],
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
-                'mode': LLMMode.CHAT.value,
-                'context_size': 128000,
+                ModelPropertyKey.MODE: LLMMode.CHAT.value,
+                ModelPropertyKey.CONTEXT_SIZE: 128000,
             },
             parameter_rules=[
                 ParameterRule(
@@ -369,7 +369,7 @@ LLM_BASE_MODELS = [
                     name='frequency_penalty',
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
                 ),
-                _get_max_tokens(default=512, min_val=1, max_val=128000),
+                _get_max_tokens(default=512, min_val=1, max_val=4096),
                 ParameterRule(
                     name='seed',
                     label=I18nObject(
@@ -419,8 +419,8 @@ LLM_BASE_MODELS = [
             model_type=ModelType.LLM,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
-                'mode': LLMMode.COMPLETION.value,
-                'context_size': 4096,
+                ModelPropertyKey.MODE: LLMMode.COMPLETION.value,
+                ModelPropertyKey.CONTEXT_SIZE: 4096,
             },
             parameter_rules=[
                 ParameterRule(
@@ -448,6 +448,46 @@ LLM_BASE_MODELS = [
                 currency='USD',
             )
         )
+    ),
+    AzureBaseModel(
+        base_model_name='text-davinci-003',
+        entity=AIModelEntity(
+            model='fake-deployment-name',
+            label=I18nObject(
+                en_US='fake-deployment-name-label',
+            ),
+            model_type=ModelType.LLM,
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_properties={
+                ModelPropertyKey.MODE: LLMMode.COMPLETION.value,
+                ModelPropertyKey.CONTEXT_SIZE: 4096,
+            },
+            parameter_rules=[
+                ParameterRule(
+                    name='temperature',
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
+                ),
+                ParameterRule(
+                    name='top_p',
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
+                ),
+                ParameterRule(
+                    name='presence_penalty',
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.PRESENCE_PENALTY],
+                ),
+                ParameterRule(
+                    name='frequency_penalty',
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
+                ),
+                _get_max_tokens(default=512, min_val=1, max_val=4096),
+            ],
+            pricing=PriceConfig(
+                input=0.02,
+                output=0.02,
+                unit=0.001,
+                currency='USD',
+            )
+        )
     )
 ]
 
@@ -462,8 +502,8 @@ EMBEDDING_BASE_MODELS = [
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.TEXT_EMBEDDING,
             model_properties={
-                'context_size': 8097,
-                'max_chunks': 32,
+                ModelPropertyKey.CONTEXT_SIZE: 8097,
+                ModelPropertyKey.MAX_CHUNKS: 32,
             },
             pricing=PriceConfig(
                 input=0.0001,

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

@@ -13,10 +13,10 @@ help:
   url:
     en_US: https://azure.microsoft.com/en-us/products/ai-services/openai-service
 supported_model_types:
-- llm
-- text-embedding
+  - llm
+  - text-embedding
 configurate_methods:
-- customizable-model
+  - customizable-model
 model_credential_schema:
   model:
     label:
@@ -26,79 +26,79 @@ model_credential_schema:
       en_US: Enter your Deployment Name here, matching the Azure deployment name.
       zh_Hans: 在此输入您的部署名称，与 Azure 部署名称匹配。
   credential_form_schemas:
-  - variable: openai_api_base
-    label:
-      en_US: API Endpoint URL
-      zh_Hans: API 域名
-    type: text-input
-    required: true
-    placeholder:
-      zh_Hans: '在此输入您的 API 域名，如：https://example.com/xxx'
-      en_US: 'Enter your API Endpoint, eg: https://example.com/xxx'
-  - variable: openai_api_key
-    label:
-      en_US: API Key
-      zh_Hans: API Key
-    type: secret-input
-    required: true
-    placeholder:
-      zh_Hans: 在此输入您的 API Key
-      en_US: Enter your API key here
-  - variable: base_model_name
-    label:
-      en_US: Base Model
-      zh_Hans: 基础模型
-    type: select
-    required: true
-    options:
-    - label:
-        en_US: gpt-35-turbo
-      value: gpt-35-turbo
-      show_on:
-      - variable: __model_type
-        value: llm
-    - label:
-        en_US: gpt-35-turbo-16k
-      value: gpt-35-turbo-16k
-      show_on:
-      - variable: __model_type
-        value: llm
-    - label:
-        en_US: gpt-4
-      value: gpt-4
-      show_on:
-      - variable: __model_type
-        value: llm
-    - label:
-        en_US: gpt-4-32k
-      value: gpt-4-32k
-      show_on:
-      - variable: __model_type
-        value: llm
-    - label:
-        en_US: gpt-4-1106-preview
-      value: gpt-4-1106-preview
-      show_on:
-      - variable: __model_type
-        value: llm
-    - label:
-        en_US: gpt-4-vision-preview
-      value: gpt-4-vision-preview
-      show_on:
-      - variable: __model_type
-        value: llm
-    - label:
-        en_US: gpt-35-turbo-instruct
-      value: gpt-35-turbo-instruct
-      show_on:
-      - variable: __model_type
-        value: llm
-    - label:
-        en_US: text-embedding-ada-002
-      value: text-embedding-ada-002
-      show_on:
-        - variable: __model_type
-          value: text-embedding
-    placeholder:
-      zh_Hans: 在此输入您的模型版本
-      en_US: Enter your model version
+    - variable: openai_api_base
+      label:
+        en_US: API Endpoint URL
+        zh_Hans: API 域名
+      type: text-input
+      required: true
+      placeholder:
+        zh_Hans: '在此输入您的 API 域名，如：https://example.com/xxx'
+        en_US: 'Enter your API Endpoint, eg: https://example.com/xxx'
+    - variable: openai_api_key
+      label:
+        en_US: API Key
+        zh_Hans: API Key
+      type: secret-input
+      required: true
+      placeholder:
+        zh_Hans: 在此输入您的 API Key
+        en_US: Enter your API key here
+    - variable: base_model_name
+      label:
+        en_US: Base Model
+        zh_Hans: 基础模型
+      type: select
+      required: true
+      options:
+        - label:
+            en_US: gpt-35-turbo
+          value: gpt-35-turbo
+          show_on:
+            - variable: __model_type
+              value: llm
+        - label:
+            en_US: gpt-35-turbo-16k
+          value: gpt-35-turbo-16k
+          show_on:
+            - variable: __model_type
+              value: llm
+        - label:
+            en_US: gpt-4
+          value: gpt-4
+          show_on:
+            - variable: __model_type
+              value: llm
+        - label:
+            en_US: gpt-4-32k
+          value: gpt-4-32k
+          show_on:
+            - variable: __model_type
+              value: llm
+        - label:
+            en_US: gpt-4-1106-preview
+          value: gpt-4-1106-preview
+          show_on:
+            - variable: __model_type
+              value: llm
+        - label:
+            en_US: gpt-4-vision-preview
+          value: gpt-4-vision-preview
+          show_on:
+            - variable: __model_type
+              value: llm
+        - label:
+            en_US: gpt-35-turbo-instruct
+          value: gpt-35-turbo-instruct
+          show_on:
+            - variable: __model_type
+              value: llm
+        - label:
+            en_US: text-embedding-ada-002
+          value: text-embedding-ada-002
+          show_on:
+            - variable: __model_type
+              value: text-embedding
+      placeholder:
+        zh_Hans: 在此输入您的模型版本
+        en_US: Enter your model version

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

@@ -30,7 +30,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
                 stream: bool = True, user: Optional[str] = None) \
             -> Union[LLMResult, Generator]:
 
-        ai_model_entity = self._get_ai_model_entity(credentials['base_model_name'], model)
+        ai_model_entity = self._get_ai_model_entity(credentials.get('base_model_name'), model)
 
         if ai_model_entity.entity.model_properties.get(ModelPropertyKey.MODE) == LLMMode.CHAT.value:
             # chat model
@@ -59,7 +59,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
     def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
                        tools: Optional[list[PromptMessageTool]] = None) -> int:
 
-        model_mode = self._get_ai_model_entity(credentials['base_model_name'], model).entity.model_properties.get(
+        model_mode = self._get_ai_model_entity(credentials.get('base_model_name'), model).entity.model_properties.get(
             ModelPropertyKey.MODE)
 
         if model_mode == LLMMode.CHAT.value:
@@ -79,7 +79,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
         if 'base_model_name' not in credentials:
             raise CredentialsValidateFailedError('Base Model Name is required')
 
-        ai_model_entity = self._get_ai_model_entity(credentials['base_model_name'], model)
+        ai_model_entity = self._get_ai_model_entity(credentials.get('base_model_name'), model)
 
         if not ai_model_entity:
             raise CredentialsValidateFailedError(f'Base Model Name {credentials["base_model_name"]} is invalid')
@@ -109,8 +109,8 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
             raise CredentialsValidateFailedError(str(ex))
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> Optional[AIModelEntity]:
-        ai_model_entity = self._get_ai_model_entity(credentials['base_model_name'], model)
-        return ai_model_entity.entity
+        ai_model_entity = self._get_ai_model_entity(credentials.get('base_model_name'), model)
+        return ai_model_entity.entity if ai_model_entity else None
 
     def _generate(self, model: str, credentials: dict,
                   prompt_messages: list[PromptMessage], model_parameters: dict, stop: Optional[List[str]] = None,
@@ -309,7 +309,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
 
         # transform response
         response = LLMResult(
-            model=response.model,
+            model=response.model or model,
             prompt_messages=prompt_messages,
             message=assistant_prompt_message,
             usage=usage,

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

@@ -54,7 +54,7 @@ class AzureOpenAITextEmbeddingModel(_CommonAzureOpenAI, TextEmbeddingModel):
         _iter = range(0, len(tokens), max_chunks)
 
         for i in _iter:
-            embeddings, embedding_used_tokens = self._embedding_invoke(
+            embeddings_batch, embedding_used_tokens = self._embedding_invoke(
                 model=model,
                 client=client,
                 texts=tokens[i: i + max_chunks],
@@ -62,7 +62,7 @@ class AzureOpenAITextEmbeddingModel(_CommonAzureOpenAI, TextEmbeddingModel):
             )
 
             used_tokens += embedding_used_tokens
-            batched_embeddings += [data for data in embeddings]
+            batched_embeddings += embeddings_batch
 
         results: list[list[list[float]]] = [[] for _ in range(len(texts))]
         num_tokens_in_batch: list[list[int]] = [[] for _ in range(len(texts))]
@@ -73,7 +73,7 @@ class AzureOpenAITextEmbeddingModel(_CommonAzureOpenAI, TextEmbeddingModel):
         for i in range(len(texts)):
             _result = results[i]
             if len(_result) == 0:
-                embeddings, embedding_used_tokens = self._embedding_invoke(
+                embeddings_batch, embedding_used_tokens = self._embedding_invoke(
                     model=model,
                     client=client,
                     texts=[""],
@@ -81,7 +81,7 @@ class AzureOpenAITextEmbeddingModel(_CommonAzureOpenAI, TextEmbeddingModel):
                 )
 
                 used_tokens += embedding_used_tokens
-                average = embeddings[0]
+                average = embeddings_batch[0]
             else:
                 average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
             embeddings[i] = (average / np.linalg.norm(average)).tolist()

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

@@ -8,30 +8,30 @@ icon_large:
 background: "#FFF6F2"
 help:
   title:
-    en_US: Get your API Key from BAICHUAN AI 
+    en_US: Get your API Key from BAICHUAN AI
     zh_Hans: 从百川智能获取您的 API Key
   url:
     en_US: https://www.baichuan-ai.com
 supported_model_types:
-- llm
-- text-embedding
+  - llm
+  - text-embedding
 configurate_methods:
-- predefined-model
+  - predefined-model
 provider_credential_schema:
   credential_form_schemas:
-  - variable: api_key
-    label:
-      en_US: API Key
-    type: secret-input
-    required: true
-    placeholder:
-      zh_Hans: 在此输入您的 API Key
-      en_US: Enter your API Key
-  - variable: secret_key
-    label:
-      en_US: Secret Key
-    type: secret-input
-    required: false
-    placeholder:
-      zh_Hans: 在此输入您的 Secret Key
-      en_US: Enter your Secret Key
+    - variable: api_key
+      label:
+        en_US: API Key
+      type: secret-input
+      required: true
+      placeholder:
+        zh_Hans: 在此输入您的 API Key
+        en_US: Enter your API Key
+    - variable: secret_key
+      label:
+        en_US: Secret Key
+      type: secret-input
+      required: false
+      placeholder:
+        zh_Hans: 在此输入您的 Secret Key
+        en_US: Enter your Secret Key

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

@@ -3,40 +3,40 @@ label:
   en_US: Baichuan2-53B
 model_type: llm
 features:
-- agent-thought
+  - agent-thought
 model_properties:
   mode: chat
   context_size: 4000
 parameter_rules:
-- name: temperature
-  use_template: temperature
-- name: top_p
-  use_template: top_p
-- name: top_k
-  label:
-    zh_Hans: 取样数量
-    en_US: Top k
-  type: int
-  help:
-    zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
-    en_US: Only sample from the top K options for each subsequent token.
-  required: false
-- name: max_tokens
-  use_template: max_tokens
-  required: true
-  default: 1000
-  min: 1
-  max: 4000
-- name: presence_penalty
-  use_template: presence_penalty
-- name: frequency_penalty
-  use_template: frequency_penalty
-- name: with_search_enhance
-  label:
-    zh_Hans: 搜索增强
-    en_US: Search Enhance
-  type: boolean
-  help:
-    zh_Hans: 允许模型自行进行外部搜索，以增强生成结果。
-    en_US: Allow the model to perform external search to enhance the generation results.
-  required: false
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    default: 1000
+    min: 1
+    max: 4000
+  - name: presence_penalty
+    use_template: presence_penalty
+  - name: frequency_penalty
+    use_template: frequency_penalty
+  - name: with_search_enhance
+    label:
+      zh_Hans: 搜索增强
+      en_US: Search Enhance
+    type: boolean
+    help:
+      zh_Hans: 允许模型自行进行外部搜索，以增强生成结果。
+      en_US: Allow the model to perform external search to enhance the generation results.
+    required: false

api/core/model_runtime/model_providers/baichuan/llm/baichuan2-turbo-192k.yaml

@@ -3,40 +3,40 @@ label:
   en_US: Baichuan2-Turbo-192K
 model_type: llm
 features:
-- agent-thought
+  - agent-thought
 model_properties:
   mode: chat
   context_size: 192000
 parameter_rules:
-- name: temperature
-  use_template: temperature
-- name: top_p
-  use_template: top_p
-- name: top_k
-  label:
-    zh_Hans: 取样数量
-    en_US: Top k
-  type: int
-  help:
-    zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
-    en_US: Only sample from the top K options for each subsequent token.
-  required: false
-- name: max_tokens
-  use_template: max_tokens
-  required: true
-  default: 8000
-  min: 1
-  max: 192000
-- name: presence_penalty
-  use_template: presence_penalty
-- name: frequency_penalty
-  use_template: frequency_penalty
-- name: with_search_enhance
-  label:
-    zh_Hans: 搜索增强
-    en_US: Search Enhance
-  type: boolean
-  help:
-    zh_Hans: 允许模型自行进行外部搜索，以增强生成结果。
-    en_US: Allow the model to perform external search to enhance the generation results.
-  required: false
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    default: 8000
+    min: 1
+    max: 192000
+  - name: presence_penalty
+    use_template: presence_penalty
+  - name: frequency_penalty
+    use_template: frequency_penalty
+  - name: with_search_enhance
+    label:
+      zh_Hans: 搜索增强
+      en_US: Search Enhance
+    type: boolean
+    help:
+      zh_Hans: 允许模型自行进行外部搜索，以增强生成结果。
+      en_US: Allow the model to perform external search to enhance the generation results.
+    required: false

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

@@ -3,40 +3,40 @@ label:
   en_US: Baichuan2-Turbo
 model_type: llm
 features:
-- agent-thought
+  - agent-thought
 model_properties:
   mode: chat
   context_size: 192000
 parameter_rules:
-- name: temperature
-  use_template: temperature
-- name: top_p
-  use_template: top_p
-- name: top_k
-  label:
-    zh_Hans: 取样数量
-    en_US: Top k
-  type: int
-  help:
-    zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
-    en_US: Only sample from the top K options for each subsequent token.
-  required: false
-- name: max_tokens
-  use_template: max_tokens
-  required: true
-  default: 8000
-  min: 1
-  max: 192000
-- name: presence_penalty
-  use_template: presence_penalty
-- name: frequency_penalty
-  use_template: frequency_penalty
-- name: with_search_enhance
-  label:
-    zh_Hans: 搜索增强
-    en_US: Search Enhance
-  type: boolean
-  help:
-    zh_Hans: 允许模型自行进行外部搜索，以增强生成结果。
-    en_US: Allow the model to perform external search to enhance the generation results.
-  required: false
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    default: 8000
+    min: 1
+    max: 192000
+  - name: presence_penalty
+    use_template: presence_penalty
+  - name: frequency_penalty
+    use_template: frequency_penalty
+  - name: with_search_enhance
+    label:
+      zh_Hans: 搜索增强
+      en_US: Search Enhance
+    type: boolean
+    help:
+      zh_Hans: 允许模型自行进行外部搜索，以增强生成结果。
+      en_US: Allow the model to perform external search to enhance the generation results.
+    required: false

api/core/model_runtime/model_providers/baichuan/text_embedding/baichuan-text-embedding.yaml

@@ -2,4 +2,4 @@ model: baichuan-text-embedding
 model_type: text-embedding
 model_properties:
   context_size: 512
-  max_chunks: 16
+  max_chunks: 16

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

@@ -1,4 +1,4 @@
-from typing import Optional
+from typing import Optional, Tuple
 
 from core.model_runtime.entities.model_entities import PriceType
 from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult, EmbeddingUsage
@@ -38,6 +38,50 @@ class BaichuanTextEmbeddingModel(TextEmbeddingModel):
             raise ValueError('Invalid model name')
         if not api_key:
             raise CredentialsValidateFailedError('api_key is required')
+        
+        # split into chunks of batch size 16
+        chunks = []
+        for i in range(0, len(texts), 16):
+            chunks.append(texts[i:i + 16])
+
+        embeddings = []
+        token_usage = 0
+
+        for chunk in chunks:
+            # embeding chunk
+            chunk_embeddings, chunk_usage = self.embedding(
+                model=model,
+                api_key=api_key,
+                texts=chunk,
+                user=user
+            )
+
+            embeddings.extend(chunk_embeddings)
+            token_usage += chunk_usage
+
+        result = TextEmbeddingResult(
+            model=model,
+            embeddings=embeddings,
+            usage=self._calc_response_usage(
+                model=model,
+                credentials=credentials,
+                tokens=token_usage
+            )
+        )
+
+        return result
+    
+    def embedding(self, model: str, api_key, texts: list[str], user: Optional[str] = None) \
+            -> Tuple[list[list[float]], int]:
+        """
+        Embed given texts
+
+        :param model: model name
+        :param credentials: model credentials
+        :param texts: texts to embed
+        :param user: unique user id
+        :return: embeddings result
+        """
         url = self.api_base
         headers = {
             'Authorization': 'Bearer ' + api_key,
@@ -69,9 +113,9 @@ class BaichuanTextEmbeddingModel(TextEmbeddingModel):
                 raise InsufficientAccountBalance(msg)
             elif err == 'invalid_authentication':
                 raise InvalidAuthenticationError(msg)
-            elif 'rate' in err:
+            elif err and 'rate' in err:
                 raise RateLimitReachedError(msg)
-            elif 'internal' in err:
+            elif err and 'internal' in err:
                 raise InternalServerError(msg)
             elif err == 'api_key_empty':
                 raise InvalidAPIKeyError(msg)
@@ -85,17 +129,10 @@ class BaichuanTextEmbeddingModel(TextEmbeddingModel):
         except Exception as e:
             raise InternalServerError(f"Failed to convert response to json: {e} with text: {response.text}")
 
-        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=usage['total_tokens'])
+        return [
+            data['embedding'] for data in embeddings
+        ], usage['total_tokens']
 
-        result = TextEmbeddingResult(
-            model=model,
-            embeddings=[[
-                float(data) for data in x['embedding']
-            ] for x in embeddings],
-            usage=usage
-        )
-
-        return result
 
     def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
         """

api/core/model_runtime/model_providers/chatglm/chatglm.yaml

@@ -13,16 +13,16 @@ help:
   url:
     en_US: https://github.com/THUDM/ChatGLM3
 supported_model_types:
-- llm
+  - llm
 configurate_methods:
-- predefined-model
+  - predefined-model
 provider_credential_schema:
   credential_form_schemas:
-  - variable: api_base
-    label:
-      en_US: API URL
-    type: text-input
-    required: true
-    placeholder:
-      zh_Hans: 在此输入您的 API URL
-      en_US: Enter your API URL
+    - variable: api_base
+      label:
+        en_US: API URL
+      type: text-input
+      required: true
+      placeholder:
+        zh_Hans: 在此输入您的 API URL
+        en_US: Enter your API URL

api/core/model_runtime/model_providers/chatglm/llm/chatglm2-6b-32k.yaml

@@ -3,19 +3,19 @@ label:
   en_US: ChatGLM2-6B-32K
 model_type: llm
 features:
-- agent-thought
+  - agent-thought
 model_properties:
   mode: chat
   context_size: 32000
 parameter_rules:
-- name: temperature
-  use_template: temperature
-- name: top_p
-  use_template: top_p
-  required: false
-- name: max_tokens
-  use_template: max_tokens
-  required: true
-  default: 2000
-  min: 1
-  max: 32000
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+    required: false
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    default: 2000
+    min: 1
+    max: 32000

api/core/model_runtime/model_providers/chatglm/llm/chatglm2-6b.yaml

@@ -3,19 +3,19 @@ label:
   en_US: ChatGLM2-6B
 model_type: llm
 features:
-- agent-thought
+  - agent-thought
 model_properties:
   mode: chat
   context_size: 2000
 parameter_rules:
-- name: temperature
-  use_template: temperature
-- name: top_p
-  use_template: top_p
-  required: false
-- name: max_tokens
-  use_template: max_tokens
-  required: true
-  default: 256
-  min: 1
-  max: 2000
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+    required: false
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    default: 256
+    min: 1
+    max: 2000

api/core/model_runtime/model_providers/chatglm/llm/chatglm3-6b-32k.yaml

@@ -3,20 +3,20 @@ label:
   en_US: ChatGLM3-6B-32K
 model_type: llm
 features:
-- tool-call
-- agent-thought
+  - tool-call
+  - agent-thought
 model_properties:
   mode: chat
   context_size: 32000
 parameter_rules:
-- name: temperature
-  use_template: temperature
-- name: top_p
-  use_template: top_p
-  required: false
-- name: max_tokens
-  use_template: max_tokens
-  required: true
-  default: 8000
-  min: 1
-  max: 32000
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+    required: false
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    default: 8000
+    min: 1
+    max: 32000

api/core/model_runtime/model_providers/chatglm/llm/chatglm3-6b.yaml

@@ -3,20 +3,20 @@ label:
   en_US: ChatGLM3-6B
 model_type: llm
 features:
-- tool-call
-- agent-thought
+  - tool-call
+  - agent-thought
 model_properties:
   mode: chat
   context_size: 8000
 parameter_rules:
-- name: temperature
-  use_template: temperature
-- name: top_p
-  use_template: top_p
-  required: false
-- name: max_tokens
-  use_template: max_tokens
-  required: true
-  default: 256
-  min: 1
-  max: 8000
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+    required: false
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    default: 256
+    min: 1
+    max: 8000

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

@@ -14,18 +14,18 @@ help:
   url:
     en_US: https://dashboard.cohere.com/api-keys
 supported_model_types:
-- rerank
+  - rerank
 configurate_methods:
-- predefined-model
+  - predefined-model
 provider_credential_schema:
   credential_form_schemas:
-  - variable: api_key
-    label:
-      zh_Hans: API Key
-      en_US: API Key
-    type: secret-input
-    required: true
-    placeholder:
-      zh_Hans: 请填写 API Key
-      en_US: Please fill in API Key
-    show_on: []
+    - variable: api_key
+      label:
+        zh_Hans: API Key
+        en_US: API Key
+      type: secret-input
+      required: true
+      placeholder:
+        zh_Hans: 请填写 API Key
+        en_US: Please fill in API Key
+      show_on: [ ]

api/core/model_runtime/model_providers/cohere/rerank/rerank-multilingual-v2.0.yaml

@@ -1,4 +1,4 @@
 model: rerank-multilingual-v2.0
 model_type: rerank
 model_properties:
-  context_size: 5120
+  context_size: 5120

api/core/model_runtime/model_providers/google/google.yaml

@@ -16,17 +16,16 @@ help:
   url:
     en_US: https://ai.google.dev/
 supported_model_types:
-- llm
+  - llm
 configurate_methods:
-- predefined-model
+  - predefined-model
 provider_credential_schema:
   credential_form_schemas:
-  - variable: google_api_key
-    label:
-      en_US: API Key
-    type: secret-input
-    required: true
-    placeholder:
-      zh_Hans: 在此输入您的 API Key
-      en_US: Enter your API Key
-  
+    - variable: google_api_key
+      label:
+        en_US: API Key
+      type: secret-input
+      required: true
+      placeholder:
+        zh_Hans: 在此输入您的 API Key
+        en_US: Enter your API Key

api/core/model_runtime/model_providers/google/llm/gemini-pro-vision.yaml

@@ -3,32 +3,32 @@ label:
   en_US: Gemini Pro Vision
 model_type: llm
 features:
-- vision
+  - vision
 model_properties:
   mode: chat
   context_size: 12288
 parameter_rules:
-- name: temperature
-  use_template: temperature
-- name: top_p
-  use_template: top_p
-- name: top_k
-  label:
-    zh_Hans: 取样数量
-    en_US: Top k
-  type: int
-  help:
-    zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
-    en_US: Only sample from the top K options for each subsequent token.
-  required: false
-- name: max_tokens_to_sample
-  use_template: max_tokens
-  required: true
-  default: 4096
-  min: 1
-  max: 4096
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_tokens_to_sample
+    use_template: max_tokens
+    required: true
+    default: 4096
+    min: 1
+    max: 4096
 pricing:
   input: '0.00'
   output: '0.00'
   unit: '0.000001'
-  currency: USD
+  currency: USD

api/core/model_runtime/model_providers/google/llm/gemini-pro.yaml

@@ -3,32 +3,32 @@ label:
   en_US: Gemini Pro
 model_type: llm
 features:
-- agent-thought
+  - agent-thought
 model_properties:
   mode: chat
   context_size: 30720
 parameter_rules:
-- name: temperature
-  use_template: temperature
-- name: top_p
-  use_template: top_p
-- name: top_k
-  label:
-    zh_Hans: 取样数量
-    en_US: Top k
-  type: int
-  help:
-    zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
-    en_US: Only sample from the top K options for each subsequent token.
-  required: false
-- name: max_tokens_to_sample
-  use_template: max_tokens
-  required: true
-  default: 2048
-  min: 1
-  max: 2048
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_tokens_to_sample
+    use_template: max_tokens
+    required: true
+    default: 2048
+    min: 1
+    max: 2048
 pricing:
   input: '0.00'
   output: '0.00'
   unit: '0.000001'
-  currency: USD
+  currency: USD

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

@@ -3,6 +3,7 @@ from typing import Optional, Generator, Union, List
 import google.generativeai as genai
 import google.api_core.exceptions as exceptions
 import google.generativeai.client as client
+from google.generativeai.types import HarmCategory, HarmBlockThreshold
 
 from google.generativeai.types import GenerateContentResponse, ContentType
 from google.generativeai.types.content_types import to_part
@@ -124,7 +125,7 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
             last_msg = prompt_messages[-1]
             content = self._format_message_to_glm_content(last_msg)
             history.append(content)
-        else:    
+        else:
             for msg in prompt_messages:     # makes message roles strictly alternating
                 content = self._format_message_to_glm_content(msg)
                 if history and history[-1]["role"] == content["role"]:
@@ -139,13 +140,21 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
         new_custom_client = new_client_manager.make_client("generative")
 
         google_model._client = new_custom_client
+
+        safety_settings={
+            HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
+            HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
+            HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
+            HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
+        }
         
         response = google_model.generate_content(
             contents=history,
             generation_config=genai.types.GenerationConfig(
                 **config_kwargs
             ),
-            stream=stream
+            stream=stream,
+            safety_settings=safety_settings
         )
 
         if stream:
@@ -169,7 +178,6 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
             content=response.text
         )
 
-
         # calculate num tokens
         prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
         completion_tokens = self.get_num_tokens(model, credentials, [assistant_prompt_message])
@@ -202,11 +210,11 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
         for chunk in response:
             content = chunk.text
             index += 1
-
+           
             assistant_prompt_message = AssistantPromptMessage(
                 content=content if content else '',
             )
-
+  
             if not response._done:
                 
                 # transform assistant message to prompt message

api/core/model_runtime/model_providers/huggingface_hub/huggingface_hub.yaml

@@ -2,9 +2,9 @@ provider: huggingface_hub
 label:
   en_US: Hugging Face Model
 icon_small:
-    en_US: icon_s_en.svg
+  en_US: icon_s_en.svg
 icon_large:
-    en_US: icon_l_en.svg
+  en_US: icon_l_en.svg
 background: "#FFF8DC"
 help:
   title:
@@ -13,90 +13,90 @@ help:
   url:
     en_US: https://huggingface.co/settings/tokens
 supported_model_types:
-- llm
-- text-embedding
+  - llm
+  - text-embedding
 configurate_methods:
-- customizable-model
+  - customizable-model
 model_credential_schema:
   model:
     label:
       en_US: Model Name
       zh_Hans: 模型名称
   credential_form_schemas:
-  - variable: huggingfacehub_api_type
-    label:
-      en_US: Endpoint Type
-      zh_Hans: 端点类型
-    type: radio
-    required: true
-    default: hosted_inference_api
-    options:
-    - value: hosted_inference_api
-      label:
-        en_US: Hosted Inference API
-    - value: inference_endpoints
-      label:
-        en_US: Inference Endpoints
-  - variable: huggingfacehub_api_token
-    label:
-      en_US: API Token
-      zh_Hans: API Token
-    type: secret-input
-    required: true
-    placeholder:
-      en_US: Enter your Hugging Face Hub API Token here
-      zh_Hans: 在此输入您的 Hugging Face Hub API Token
-  - variable: huggingface_namespace
-    label:
-      en_US: 'User Name / Organization Name'
-      zh_Hans: '用户名 / 组织名称'
-    type: text-input
-    required: true
-    placeholder:
-      en_US: 'Enter your User Name / Organization Name here'
-      zh_Hans: '在此输入您的用户名 / 组织名称'
-    show_on:
-    - variable: __model_type
-      value: text-embedding
     - variable: huggingfacehub_api_type
-      value: inference_endpoints
-  - variable: huggingfacehub_endpoint_url
-    label:
-      en_US: Endpoint URL
-      zh_Hans: 端点 URL
-    type: text-input
-    required: true
-    placeholder:
-      en_US: Enter your Endpoint URL here
-      zh_Hans: 在此输入您的端点 URL
-    show_on:
-    - variable: huggingfacehub_api_type
-      value: inference_endpoints
-  - variable: task_type
-    label:
-      en_US: Task
-      zh_Hans: Task
-    type: select
-    options:
-    - value: text2text-generation
       label:
-        en_US: Text-to-Text Generation
-      show_on:
-      - variable: __model_type
-        value: llm
-    - value: text-generation
+        en_US: Endpoint Type
+        zh_Hans: 端点类型
+      type: radio
+      required: true
+      default: hosted_inference_api
+      options:
+        - value: hosted_inference_api
+          label:
+            en_US: Hosted Inference API
+        - value: inference_endpoints
+          label:
+            en_US: Inference Endpoints
+    - variable: huggingfacehub_api_token
       label:
-        en_US: Text Generation
-        zh_Hans: 文本生成
-      show_on:
-      - variable: __model_type
-        value: llm
-    - value: feature-extraction
+        en_US: API Token
+        zh_Hans: API Token
+      type: secret-input
+      required: true
+      placeholder:
+        en_US: Enter your Hugging Face Hub API Token here
+        zh_Hans: 在此输入您的 Hugging Face Hub API Token
+    - variable: huggingface_namespace
       label:
-        en_US: Feature Extraction
+        en_US: 'User Name / Organization Name'
+        zh_Hans: '用户名 / 组织名称'
+      type: text-input
+      required: true
+      placeholder:
+        en_US: 'Enter your User Name / Organization Name here'
+        zh_Hans: '在此输入您的用户名 / 组织名称'
       show_on:
-      - variable: __model_type
-        value: text-embedding
-    show_on:
-    - variable: huggingfacehub_api_type
-      value: inference_endpoints
+        - variable: __model_type
+          value: text-embedding
+        - variable: huggingfacehub_api_type
+          value: inference_endpoints
+    - variable: huggingfacehub_endpoint_url
+      label:
+        en_US: Endpoint URL
+        zh_Hans: 端点 URL
+      type: text-input
+      required: true
+      placeholder:
+        en_US: Enter your Endpoint URL here
+        zh_Hans: 在此输入您的端点 URL
+      show_on:
+        - variable: huggingfacehub_api_type
+          value: inference_endpoints
+    - variable: task_type
+      label:
+        en_US: Task
+        zh_Hans: Task
+      type: select
+      options:
+        - value: text2text-generation
+          label:
+            en_US: Text-to-Text Generation
+          show_on:
+            - variable: __model_type
+              value: llm
+        - value: text-generation
+          label:
+            en_US: Text Generation
+            zh_Hans: 文本生成
+          show_on:
+            - variable: __model_type
+              value: llm
+        - value: feature-extraction
+          label:
+            en_US: Feature Extraction
+          show_on:
+            - variable: __model_type
+              value: text-embedding
+      show_on:
+        - variable: huggingfacehub_api_type
+          value: inference_endpoints

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

@@ -10,7 +10,7 @@ from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk,
 from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool, AssistantPromptMessage, \
     UserPromptMessage, SystemPromptMessage
 from core.model_runtime.entities.model_entities import ParameterRule, DefaultParameterName, AIModelEntity, ModelType, \
-    FetchFrom
+    FetchFrom, ModelPropertyKey
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.model_runtime.model_providers.huggingface_hub._common import _CommonHuggingfaceHub
@@ -97,7 +97,7 @@ class HuggingfaceHubLargeLanguageModel(_CommonHuggingfaceHub, LargeLanguageModel
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.LLM,
             model_properties={
-                'mode': LLMMode.COMPLETION.value
+                ModelPropertyKey.MODE: LLMMode.COMPLETION.value
             },
             parameter_rules=self._get_customizable_model_parameter_rules()
         )
@@ -154,20 +154,31 @@ class HuggingfaceHubLargeLanguageModel(_CommonHuggingfaceHub, LargeLanguageModel
                 content=chunk.token.text
             )
 
-            prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
-            completion_tokens = self.get_num_tokens(model, credentials, [assistant_prompt_message])
+            if chunk.details:
+                prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
+                completion_tokens = self.get_num_tokens(model, credentials, [assistant_prompt_message])
 
-            usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
+                usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
 
-            yield LLMResultChunk(
-                model=model,
-                prompt_messages=prompt_messages,
-                delta=LLMResultChunkDelta(
-                    index=index,
-                    message=assistant_prompt_message,
-                    usage=usage,
-                ),
-            )
+                yield LLMResultChunk(
+                    model=model,
+                    prompt_messages=prompt_messages,
+                    delta=LLMResultChunkDelta(
+                        index=index,
+                        message=assistant_prompt_message,
+                        usage=usage,
+                        finish_reason=chunk.details.finish_reason,
+                    ),
+                )
+            else:
+                yield LLMResultChunk(
+                    model=model,
+                    prompt_messages=prompt_messages,
+                    delta=LLMResultChunkDelta(
+                        index=index,
+                        message=assistant_prompt_message,
+                    ),
+                )
 
     def _handle_generate_response(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], response: any) -> LLMResult:
         if isinstance(response, str):

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

@@ -2,7 +2,7 @@ provider: jina
 label:
   en_US: Jina
 description:
-    en_US: Embedding Model Supported
+  en_US: Embedding Model Supported
 icon_small:
   en_US: icon_s_en.svg
 icon_large:
@@ -15,16 +15,16 @@ help:
   url:
     en_US: https://jina.ai/embeddings/
 supported_model_types:
-- text-embedding
+  - text-embedding
 configurate_methods:
-- predefined-model
+  - predefined-model
 provider_credential_schema:
   credential_form_schemas:
-  - variable: api_key
-    label:
-      en_US: API Key
-    type: secret-input
-    required: true
-    placeholder:
-      zh_Hans: 在此输入您的 API Key
-      en_US: Enter your API Key
+    - variable: api_key
+      label:
+        en_US: API Key
+      type: secret-input
+      required: true
+      placeholder:
+        zh_Hans: 在此输入您的 API Key
+        en_US: Enter your API Key

api/core/model_runtime/model_providers/jina/text_embedding/jina-embeddings-v2-base-en.yaml

@@ -6,4 +6,4 @@ model_properties:
 pricing:
   input: '0.001'
   unit: '0.001'
-  currency: USD
+  currency: USD

api/core/model_runtime/model_providers/jina/text_embedding/jina-embeddings-v2-small-en.yaml

@@ -6,4 +6,4 @@ model_properties:
 pricing:
   input: '0.001'
   unit: '0.001'
-  currency: USD
+  currency: USD

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

@@ -1,7 +1,7 @@
 from typing import Generator, List, Optional, Union, cast
 from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage, LLMResultChunk, LLMResultChunkDelta, LLMMode
 from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool, AssistantPromptMessage, UserPromptMessage, SystemPromptMessage
-from core.model_runtime.entities.model_entities import AIModelEntity, ParameterRule, ParameterType, FetchFrom, ModelType
+from core.model_runtime.entities.model_entities import AIModelEntity, ParameterRule, ParameterType, FetchFrom, ModelType, ModelPropertyKey
 from core.model_runtime.entities.common_entities import I18nObject
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.model_runtime.errors.invoke import InvokeConnectionError, InvokeServerUnavailableError, InvokeRateLimitError, \
@@ -156,9 +156,9 @@ class LocalAILarguageModel(LargeLanguageModel):
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
         completion_model = None
         if credentials['completion_type'] == 'chat_completion':
-            completion_model = LLMMode.CHAT
+            completion_model = LLMMode.CHAT.value
         elif credentials['completion_type'] == 'completion':
-            completion_model = LLMMode.COMPLETION
+            completion_model = LLMMode.COMPLETION.value
         else:
             raise ValueError(f"Unknown completion type {credentials['completion_type']}")
             
@@ -202,7 +202,7 @@ class LocalAILarguageModel(LargeLanguageModel):
             ),
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.LLM,
-            model_properties={ 'mode': completion_model } if completion_model else {},
+            model_properties={ ModelPropertyKey.MODE: completion_model } if completion_model else {},
             parameter_rules=rules
         )
 

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

@@ -13,10 +13,10 @@ help:
   url:
     en_US: https://github.com/go-skynet/LocalAI
 supported_model_types:
-- llm
-- text-embedding
+  - llm
+  - text-embedding
 configurate_methods:
-- customizable-model
+  - customizable-model
 model_credential_schema:
   model:
     label:
@@ -26,33 +26,33 @@ model_credential_schema:
       en_US: Enter your model name
       zh_Hans: 输入模型名称
   credential_form_schemas:
-  - variable: completion_type
-    show_on:
-      - variable: __model_type
-        value: llm
-    label:
-      en_US: Completion type
-    type: select
-    required: false
-    default: chat_completion
-    placeholder:
-      zh_Hans: 选择对话类型
-      en_US: Select completion type
-    options:
-      - value: completion
-        label:
-          en_US: Completion
-          zh_Hans: 补全
-      - value: chat_completion
-        label:
-          en_US: ChatCompletion
-          zh_Hans: 对话
-  - variable: server_url
-    label:
-      zh_Hans: 服务器URL
-      en_US: Server url
-    type: text-input
-    required: true
-    placeholder:
-      zh_Hans: 在此输入LocalAI的服务器地址，如 https://example.com/xxx
-      en_US: Enter the url of your LocalAI, for example https://example.com/xxx
+    - variable: completion_type
+      show_on:
+        - variable: __model_type
+          value: llm
+      label:
+        en_US: Completion type
+      type: select
+      required: false
+      default: chat_completion
+      placeholder:
+        zh_Hans: 选择对话类型
+        en_US: Select completion type
+      options:
+        - value: completion
+          label:
+            en_US: Completion
+            zh_Hans: 补全
+        - value: chat_completion
+          label:
+            en_US: ChatCompletion
+            zh_Hans: 对话
+    - variable: server_url
+      label:
+        zh_Hans: 服务器URL
+        en_US: Server url
+      type: text-input
+      required: true
+      placeholder:
+        zh_Hans: 在此输入LocalAI的服务器地址，如 https://example.com/xxx
+        en_US: Enter the url of your LocalAI, for example https://example.com/xxx

api/core/model_runtime/model_providers/minimax/llm/abab5-chat.yaml

@@ -3,27 +3,27 @@ label:
   en_US: Abab5-Chat
 model_type: llm
 features:
-- agent-thought
+  - agent-thought
 model_properties:
   mode: chat
   context_size: 6144
 parameter_rules:
-- name: temperature
-  use_template: temperature
-- name: top_p
-  use_template: top_p
-- name: max_tokens
-  use_template: max_tokens
-  required: true
-  default: 6144
-  min: 1
-  max: 6144
-- name: presence_penalty
-  use_template: presence_penalty
-- name: frequency_penalty
-  use_template: frequency_penalty
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    default: 6144
+    min: 1
+    max: 6144
+  - name: presence_penalty
+    use_template: presence_penalty
+  - name: frequency_penalty
+    use_template: frequency_penalty
 pricing:
   input: '0.00'
   output: '0.015'
   unit: '0.001'
-  currency: RMB
+  currency: RMB

api/core/model_runtime/model_providers/minimax/llm/abab5.5-chat.yaml

@@ -3,34 +3,34 @@ label:
   en_US: Abab5.5-Chat
 model_type: llm
 features:
-- agent-thought
+  - agent-thought
 model_properties:
   mode: chat
   context_size: 16384
 parameter_rules:
-- name: temperature
-  use_template: temperature
-- name: top_p
-  use_template: top_p
-- name: max_tokens
-  use_template: max_tokens
-  required: true
-  default: 6144
-  min: 1
-  max: 16384
-- name: presence_penalty
-  use_template: presence_penalty
-- name: frequency_penalty
-  use_template: frequency_penalty
-- name: plugin_web_search
-  required: false
-  default: false
-  type: boolean
-  label:
-    en_US: Enable Web Search
-    zh_Hans: 开启网页搜索
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    default: 6144
+    min: 1
+    max: 16384
+  - name: presence_penalty
+    use_template: presence_penalty
+  - name: frequency_penalty
+    use_template: frequency_penalty
+  - name: plugin_web_search
+    required: false
+    default: false
+    type: boolean
+    label:
+      en_US: Enable Web Search
+      zh_Hans: 开启网页搜索
 pricing:
   input: '0.00'
   output: '0.015'
   unit: '0.001'
-  currency: RMB
+  currency: RMB