update action config.

Sgned-off-by: -LAN- <laipz8200@outlook.com>

.github/workflows/build-push.yml

@@ -5,6 +5,8 @@ on:
     branches:
       - "main"
       - "deploy/dev"
+      - "deploy/enterprise"
+      - "e-0156"
   release:
     types: [published]
 

.github/workflows/deploy-enterprise.yml

@@ -0,0 +1,29 @@
+name: Deploy Enterprise
+
+permissions:
+  contents: read
+
+on:
+  workflow_run:
+    workflows: ["Build and Push API & Web"]
+    branches:
+      - "deploy/enterprise"
+    types:
+      - completed
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    if: |
+      github.event.workflow_run.conclusion == 'success' &&
+      github.event.workflow_run.head_branch == 'deploy/enterprise'
+
+    steps:
+      - name: Deploy to server
+        uses: appleboy/ssh-action@v0.1.8
+        with:
+          host: ${{ secrets.ENTERPRISE_SSH_HOST }}
+          username: ${{ secrets.ENTERPRISE_SSH_USER }}
+          password: ${{ secrets.ENTERPRISE_SSH_PASSWORD }}
+          script: |
+            ${{ vars.ENTERPRISE_SSH_SCRIPT || secrets.ENTERPRISE_SSH_SCRIPT }}

.github/workflows/docker-build.yml

@@ -0,0 +1,47 @@
+name: Build docker image
+
+on:
+  pull_request:
+    branches:
+      - "main"
+    paths:
+      - api/Dockerfile
+      - web/Dockerfile
+
+concurrency:
+  group: docker-build-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  build-docker:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        include:
+          - service_name: "api-amd64"
+            platform: linux/amd64
+            context: "api"
+          - service_name: "api-arm64"
+            platform: linux/arm64
+            context: "api"
+          - service_name: "web-amd64"
+            platform: linux/amd64
+            context: "web"
+          - service_name: "web-arm64"
+            platform: linux/arm64
+            context: "web"
+    steps:
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v3
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Build Docker Image
+        uses: docker/build-push-action@v6
+        with:
+          push: false
+          context: "{{defaultContext}}:${{ matrix.context }}"
+          platforms: ${{ matrix.platform }}
+          cache-from: type=gha
+          cache-to: type=gha,mode=max

README.md

@@ -25,6 +25,9 @@
     <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
         <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
             alt="follow on X(Twitter)"></a>
+    <a href="https://www.linkedin.com/company/langgenius/" target="_blank">
+        <img src="https://custom-icon-badges.demolab.com/badge/LinkedIn-0A66C2?logo=linkedin-white&logoColor=fff"
+            alt="follow on LinkedIn"></a>
     <a href="https://hub.docker.com/u/langgenius" target="_blank">
         <img alt="Docker Pulls" src="https://img.shields.io/docker/pulls/langgenius/dify-web?labelColor=%20%23FDB062&color=%20%23f79009"></a>
     <a href="https://github.com/langgenius/dify/graphs/commit-activity" target="_blank">

README_AR.md

@@ -21,6 +21,9 @@
     <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
         <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
             alt="follow on X(Twitter)"></a>
+    <a href="https://www.linkedin.com/company/langgenius/" target="_blank">
+        <img src="https://custom-icon-badges.demolab.com/badge/LinkedIn-0A66C2?logo=linkedin-white&logoColor=fff"
+            alt="follow on LinkedIn"></a>
     <a href="https://hub.docker.com/u/langgenius" target="_blank">
         <img alt="Docker Pulls" src="https://img.shields.io/docker/pulls/langgenius/dify-web?labelColor=%20%23FDB062&color=%20%23f79009"></a>
     <a href="https://github.com/langgenius/dify/graphs/commit-activity" target="_blank">

README_CN.md

@@ -21,6 +21,9 @@
     <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
         <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
             alt="follow on X(Twitter)"></a>
+    <a href="https://www.linkedin.com/company/langgenius/" target="_blank">
+        <img src="https://custom-icon-badges.demolab.com/badge/LinkedIn-0A66C2?logo=linkedin-white&logoColor=fff"
+            alt="follow on LinkedIn"></a>
     <a href="https://hub.docker.com/u/langgenius" target="_blank">
         <img alt="Docker Pulls" src="https://img.shields.io/docker/pulls/langgenius/dify-web?labelColor=%20%23FDB062&color=%20%23f79009"></a>
     <a href="https://github.com/langgenius/dify/graphs/commit-activity" target="_blank">

README_ES.md

@@ -21,6 +21,9 @@
     <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
         <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
             alt="seguir en X(Twitter)"></a>
+    <a href="https://www.linkedin.com/company/langgenius/" target="_blank">
+        <img src="https://custom-icon-badges.demolab.com/badge/LinkedIn-0A66C2?logo=linkedin-white&logoColor=fff"
+            alt="seguir en LinkedIn"></a>
     <a href="https://hub.docker.com/u/langgenius" target="_blank">
         <img alt="Descargas de Docker" src="https://img.shields.io/docker/pulls/langgenius/dify-web?labelColor=%20%23FDB062&color=%20%23f79009"></a>
     <a href="https://github.com/langgenius/dify/graphs/commit-activity" target="_blank">

README_FR.md

@@ -21,6 +21,9 @@
     <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
         <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
             alt="suivre sur X(Twitter)"></a>
+    <a href="https://www.linkedin.com/company/langgenius/" target="_blank">
+        <img src="https://custom-icon-badges.demolab.com/badge/LinkedIn-0A66C2?logo=linkedin-white&logoColor=fff"
+            alt="suivre sur LinkedIn"></a>
     <a href="https://hub.docker.com/u/langgenius" target="_blank">
         <img alt="Tirages Docker" src="https://img.shields.io/docker/pulls/langgenius/dify-web?labelColor=%20%23FDB062&color=%20%23f79009"></a>
     <a href="https://github.com/langgenius/dify/graphs/commit-activity" target="_blank">

README_JA.md

@@ -21,6 +21,9 @@
     <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
         <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
             alt="X(Twitter)でフォロー"></a>
+    <a href="https://www.linkedin.com/company/langgenius/" target="_blank">
+        <img src="https://custom-icon-badges.demolab.com/badge/LinkedIn-0A66C2?logo=linkedin-white&logoColor=fff"
+            alt="LinkedInでフォロー"></a>
     <a href="https://hub.docker.com/u/langgenius" target="_blank">
         <img alt="Docker Pulls" src="https://img.shields.io/docker/pulls/langgenius/dify-web?labelColor=%20%23FDB062&color=%20%23f79009"></a>
     <a href="https://github.com/langgenius/dify/graphs/commit-activity" target="_blank">

README_KL.md

@@ -21,6 +21,9 @@
     <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
         <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
             alt="follow on X(Twitter)"></a>
+    <a href="https://www.linkedin.com/company/langgenius/" target="_blank">
+        <img src="https://custom-icon-badges.demolab.com/badge/LinkedIn-0A66C2?logo=linkedin-white&logoColor=fff"
+            alt="follow on LinkedIn"></a>
     <a href="https://hub.docker.com/u/langgenius" target="_blank">
         <img alt="Docker Pulls" src="https://img.shields.io/docker/pulls/langgenius/dify-web?labelColor=%20%23FDB062&color=%20%23f79009"></a>
     <a href="https://github.com/langgenius/dify/graphs/commit-activity" target="_blank">

README_KR.md

@@ -21,6 +21,9 @@
     <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
         <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
             alt="follow on X(Twitter)"></a>
+    <a href="https://www.linkedin.com/company/langgenius/" target="_blank">
+        <img src="https://custom-icon-badges.demolab.com/badge/LinkedIn-0A66C2?logo=linkedin-white&logoColor=fff"
+            alt="follow on LinkedIn"></a>
     <a href="https://hub.docker.com/u/langgenius" target="_blank">
         <img alt="Docker Pulls" src="https://img.shields.io/docker/pulls/langgenius/dify-web?labelColor=%20%23FDB062&color=%20%23f79009"></a>
     <a href="https://github.com/langgenius/dify/graphs/commit-activity" target="_blank">

README_PT.md

@@ -25,6 +25,9 @@
     <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
         <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
             alt="follow on X(Twitter)"></a>
+    <a href="https://www.linkedin.com/company/langgenius/" target="_blank">
+        <img src="https://custom-icon-badges.demolab.com/badge/LinkedIn-0A66C2?logo=linkedin-white&logoColor=fff"
+            alt="follow on LinkedIn"></a>
     <a href="https://hub.docker.com/u/langgenius" target="_blank">
         <img alt="Docker Pulls" src="https://img.shields.io/docker/pulls/langgenius/dify-web?labelColor=%20%23FDB062&color=%20%23f79009"></a>
     <a href="https://github.com/langgenius/dify/graphs/commit-activity" target="_blank">

README_SI.md

@@ -22,6 +22,9 @@
     <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
         <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
             alt="follow on X(Twitter)"></a>
+    <a href="https://www.linkedin.com/company/langgenius/" target="_blank">
+        <img src="https://custom-icon-badges.demolab.com/badge/LinkedIn-0A66C2?logo=linkedin-white&logoColor=fff"
+            alt="follow on LinkedIn"></a>
     <a href="https://hub.docker.com/u/langgenius" target="_blank">
         <img alt="Docker Pulls" src="https://img.shields.io/docker/pulls/langgenius/dify-web?labelColor=%20%23FDB062&color=%20%23f79009"></a>
     <a href="https://github.com/langgenius/dify/graphs/commit-activity" target="_blank">

README_TR.md

@@ -21,6 +21,9 @@
     <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
         <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
             alt="X(Twitter)'da takip et"></a>
+    <a href="https://www.linkedin.com/company/langgenius/" target="_blank">
+        <img src="https://custom-icon-badges.demolab.com/badge/LinkedIn-0A66C2?logo=linkedin-white&logoColor=fff"
+            alt="LinkedIn'da takip et"></a>
     <a href="https://hub.docker.com/u/langgenius" target="_blank">
         <img alt="Docker Çekmeleri" src="https://img.shields.io/docker/pulls/langgenius/dify-web?labelColor=%20%23FDB062&color=%20%23f79009"></a>
     <a href="https://github.com/langgenius/dify/graphs/commit-activity" target="_blank">
@@ -62,8 +65,6 @@ Görsel bir arayüz üzerinde güçlü AI iş akışları oluşturun ve test edi
 ![providers-v5](https://github.com/langgenius/dify/assets/13230914/5a17bdbe-097a-4100-8363-40255b70f6e3)
 
 
-Özür dilerim, haklısınız. Daha anlamlı ve akıcı bir çeviri yapmaya çalışayım. İşte güncellenmiş çeviri:
-
 **3. Prompt IDE**: 
   Komut istemlerini oluşturmak, model performansını karşılaştırmak ve sohbet tabanlı uygulamalara metin-konuşma gibi ek özellikler eklemek için kullanıcı dostu bir arayüz.
 
@@ -150,8 +151,6 @@ Görsel bir arayüz üzerinde güçlü AI iş akışları oluşturun ve test edi
 ## Dify'ı Kullanma
 
 - **Cloud </br>**
-İşte verdiğiniz metnin Türkçe çevirisi, kod bloğu içinde:
-- 
 Herkesin sıfır kurulumla denemesi için bir [Dify Cloud](https://dify.ai) hizmeti sunuyoruz. Bu hizmet, kendi kendine dağıtılan versiyonun tüm yeteneklerini sağlar ve sandbox planında 200 ücretsiz GPT-4 çağrısı içerir.
 
 - **Dify Topluluk Sürümünü Kendi Sunucunuzda Barındırma</br>**
@@ -177,8 +176,6 @@ GitHub'da Dify'a yıldız verin ve yeni sürümlerden anında haberdar olun.
 >- RAM >= 4GB
 
 </br>
-İşte verdiğiniz metnin Türkçe çevirisi, kod bloğu içinde:
-
 Dify sunucusunu başlatmanın en kolay yolu, [docker-compose.yml](docker/docker-compose.yaml) dosyamızı çalıştırmaktır. Kurulum komutunu çalıştırmadan önce, makinenizde [Docker](https://docs.docker.com/get-docker/) ve [Docker Compose](https://docs.docker.com/compose/install/)'un kurulu olduğundan emin olun:
 
 ```bash

README_VI.md

@@ -21,6 +21,9 @@
     <a href="https://twitter.com/intent/follow?screen_name=dify_ai" target="_blank">
         <img src="https://img.shields.io/twitter/follow/dify_ai?logo=X&color=%20%23f5f5f5"
             alt="theo dõi trên X(Twitter)"></a>
+    <a href="https://www.linkedin.com/company/langgenius/" target="_blank">
+        <img src="https://custom-icon-badges.demolab.com/badge/LinkedIn-0A66C2?logo=linkedin-white&logoColor=fff"
+            alt="theo dõi trên LinkedIn"></a>
     <a href="https://hub.docker.com/u/langgenius" target="_blank">
         <img alt="Docker Pulls" src="https://img.shields.io/docker/pulls/langgenius/dify-web?labelColor=%20%23FDB062&color=%20%23f79009"></a>
     <a href="https://github.com/langgenius/dify/graphs/commit-activity" target="_blank">

api/Dockerfile

@@ -48,18 +48,18 @@ ENV TZ=UTC
 
 WORKDIR /app/api
 
-RUN apt-get update \
-    && apt-get install -y --no-install-recommends curl nodejs libgmp-dev libmpfr-dev libmpc-dev \
-    # if you located in China, you can use aliyun mirror to speed up
-    # && echo "deb http://mirrors.aliyun.com/debian testing main" > /etc/apt/sources.list \
-    && echo "deb http://deb.debian.org/debian bookworm main" > /etc/apt/sources.list \
-    && apt-get update \
-    # For Security
-    && apt-get install -y --no-install-recommends expat libldap-2.5-0 perl libsqlite3-0 zlib1g \
-    # install a chinese font to support the use of tools like matplotlib
-    && apt-get install -y fonts-noto-cjk \
-    # install libmagic to support the use of python-magic guess MIMETYPE
-    && apt-get install -y libmagic1 \
+RUN \
+    apt-get update \
+    # Install dependencies
+    && apt-get install -y --no-install-recommends \
+        # basic environment
+        curl nodejs libgmp-dev libmpfr-dev libmpc-dev \
+        # For Security
+        expat libldap-2.5-0 perl libsqlite3-0 zlib1g \
+        # install a chinese font to support the use of tools like matplotlib
+        fonts-noto-cjk \
+        # install libmagic to support the use of python-magic guess MIMETYPE
+        libmagic1 \
     && apt-get autoremove -y \
     && rm -rf /var/lib/apt/lists/*
 
@@ -78,7 +78,6 @@ COPY . /app/api/
 COPY docker/entrypoint.sh /entrypoint.sh
 RUN chmod +x /entrypoint.sh
 
-
 ARG COMMIT_SHA
 ENV COMMIT_SHA=${COMMIT_SHA}
 

api/configs/feature/hosted_service/__init__.py

@@ -1,9 +1,40 @@
 from typing import Optional
 
-from pydantic import Field, NonNegativeInt
+from pydantic import Field, NonNegativeInt, computed_field
 from pydantic_settings import BaseSettings
 
 
+class HostedCreditConfig(BaseSettings):
+    HOSTED_MODEL_CREDIT_CONFIG: str = Field(
+        description="Model credit configuration in format 'model:credits,model:credits', e.g., 'gpt-4:20,gpt-4o:10'",
+        default="",
+    )
+
+    def get_model_credits(self, model_name: str) -> int:
+        """
+        Get credit value for a specific model name.
+        Returns 1 if model is not found in configuration (default credit).
+
+        :param model_name: The name of the model to search for
+        :return: The credit value for the model
+        """
+        if not self.HOSTED_MODEL_CREDIT_CONFIG:
+            return 1
+
+        try:
+            credit_map = dict(
+                item.strip().split(":", 1) for item in self.HOSTED_MODEL_CREDIT_CONFIG.split(",") if ":" in item
+            )
+
+            # Search for matching model pattern
+            for pattern, credit in credit_map.items():
+                if pattern.strip() == model_name:
+                    return int(credit)
+            return 1  # Default quota if no match found
+        except (ValueError, AttributeError):
+            return 1  # Return default quota if parsing fails
+
+
 class HostedOpenAiConfig(BaseSettings):
     """
     Configuration for hosted OpenAI service
@@ -202,5 +233,7 @@ class HostedServiceConfig(
     HostedZhipuAIConfig,
     # moderation
     HostedModerationConfig,
+    # credit config
+    HostedCreditConfig,
 ):
     pass

api/configs/packaging/__init__.py

@@ -9,7 +9,7 @@ class PackagingInfo(BaseSettings):
 
     CURRENT_VERSION: str = Field(
         description="Dify version",
-        default="0.15.2",
+        default="0.15.4",
     )
 
     COMMIT_SHA: str = Field(

api/controllers/console/wraps.py

@@ -39,6 +39,17 @@ def only_edition_cloud(view):
     return decorated
 
 
+def only_enterprise_edition(view):
+    @wraps(view)
+    def decorated(*args, **kwargs):
+        if not dify_config.ENTERPRISE_ENABLED:
+            abort(404)
+
+        return view(*args, **kwargs)
+
+    return decorated
+
+
 def only_edition_self_hosted(view):
     @wraps(view)
     def decorated(*args, **kwargs):

api/controllers/inner_api/__init__.py

@@ -6,3 +6,4 @@ bp = Blueprint("inner_api", __name__, url_prefix="/inner/api")
 api = ExternalApi(bp)
 
 from .workspace import workspace
+from . import mail

api/controllers/inner_api/mail.py

@@ -0,0 +1,25 @@
+from flask_restful import Resource  # type: ignore
+from flask_restful import reqparse
+
+from controllers.console.wraps import setup_required
+from controllers.inner_api import api
+from controllers.inner_api.wraps import inner_api_only
+from services.enterprise.mail_service import DifyMail, EnterpriseMailService
+
+
+class EnterpriseMail(Resource):
+    @setup_required
+    @inner_api_only
+    def post(self):
+        parser = reqparse.RequestParser()
+        parser.add_argument("to", type=str, action='append', required=True)
+        parser.add_argument("subject", type=str, required=True)
+        parser.add_argument("body", type=str, required=True)
+        parser.add_argument("substitutions", type=dict, required=False)
+        args = parser.parse_args()
+
+        EnterpriseMailService.send_mail(DifyMail(**args))
+        return {"message": "success"}, 200
+
+
+api.add_resource(EnterpriseMail, "/enterprise/mail")

api/core/helper/ssrf_proxy.py

@@ -11,15 +11,6 @@ from configs import dify_config
 
 SSRF_DEFAULT_MAX_RETRIES = dify_config.SSRF_DEFAULT_MAX_RETRIES
 
-proxy_mounts = (
-    {
-        "http://": httpx.HTTPTransport(proxy=dify_config.SSRF_PROXY_HTTP_URL),
-        "https://": httpx.HTTPTransport(proxy=dify_config.SSRF_PROXY_HTTPS_URL),
-    }
-    if dify_config.SSRF_PROXY_HTTP_URL and dify_config.SSRF_PROXY_HTTPS_URL
-    else None
-)
-
 BACKOFF_FACTOR = 0.5
 STATUS_FORCELIST = [429, 500, 502, 503, 504]
 
@@ -51,7 +42,11 @@ def make_request(method, url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
             if dify_config.SSRF_PROXY_ALL_URL:
                 with httpx.Client(proxy=dify_config.SSRF_PROXY_ALL_URL) as client:
                     response = client.request(method=method, url=url, **kwargs)
-            elif proxy_mounts:
+            elif dify_config.SSRF_PROXY_HTTP_URL and dify_config.SSRF_PROXY_HTTPS_URL:
+                proxy_mounts = {
+                    "http://": httpx.HTTPTransport(proxy=dify_config.SSRF_PROXY_HTTP_URL),
+                    "https://": httpx.HTTPTransport(proxy=dify_config.SSRF_PROXY_HTTPS_URL),
+                }
                 with httpx.Client(mounts=proxy_mounts) as client:
                     response = client.request(method=method, url=url, **kwargs)
             else:

api/core/model_runtime/entities/__init__.py

@@ -1,4 +1,4 @@
-from .llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
+from .llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
 from .message_entities import (
     AssistantPromptMessage,
     AudioPromptMessageContent,
@@ -23,6 +23,7 @@ __all__ = [
     "AudioPromptMessageContent",
     "DocumentPromptMessageContent",
     "ImagePromptMessageContent",
+    "LLMMode",
     "LLMResult",
     "LLMResultChunk",
     "LLMResultChunkDelta",

api/core/model_runtime/entities/llm_entities.py

@@ -1,5 +1,5 @@
 from decimal import Decimal
-from enum import Enum
+from enum import StrEnum
 from typing import Optional
 
 from pydantic import BaseModel
@@ -8,7 +8,7 @@ from core.model_runtime.entities.message_entities import AssistantPromptMessage,
 from core.model_runtime.entities.model_entities import ModelUsage, PriceInfo
 
 
-class LLMMode(Enum):
+class LLMMode(StrEnum):
     """
     Enum class for large language model mode.
     """

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

@@ -30,6 +30,11 @@ from core.model_runtime.model_providers.__base.ai_model import AIModel
 
 logger = logging.getLogger(__name__)
 
+HTML_THINKING_TAG = (
+    '<details style="color:gray;background-color: #f8f8f8;padding: 8px;border-radius: 4px;" open> '
+    "<summary> Thinking... </summary>"
+)
+
 
 class LargeLanguageModel(AIModel):
     """
@@ -400,6 +405,40 @@ if you are not sure about the structure.
                     ),
                 )
 
+    def _wrap_thinking_by_reasoning_content(self, delta: dict, is_reasoning: bool) -> tuple[str, bool]:
+        """
+        If the reasoning response is from delta.get("reasoning_content"), we wrap
+        it with HTML details tag.
+
+        :param delta: delta dictionary from LLM streaming response
+        :param is_reasoning: is reasoning
+        :return: tuple of (processed_content, is_reasoning)
+        """
+
+        content = delta.get("content") or ""
+        reasoning_content = delta.get("reasoning_content")
+
+        if reasoning_content:
+            if not is_reasoning:
+                content = HTML_THINKING_TAG + reasoning_content
+                is_reasoning = True
+            else:
+                content = reasoning_content
+        elif is_reasoning:
+            content = "</details>" + content
+            is_reasoning = False
+        return content, is_reasoning
+
+    def _wrap_thinking_by_tag(self, content: str) -> str:
+        """
+        if the reasoning response is a <think>...</think> block from delta.get("content"),
+        we replace <think> to <detail>.
+
+        :param content: delta.get("content")
+        :return: processed_content
+        """
+        return content.replace("<think>", HTML_THINKING_TAG).replace("</think>", "</details>")
+
     def _invoke_result_generator(
         self,
         model: str,

api/core/model_runtime/model_providers/_position.yaml

@@ -1,4 +1,5 @@
 - openai
+- deepseek
 - anthropic
 - azure_openai
 - google
@@ -32,7 +33,6 @@
 - localai
 - volcengine_maas
 - openai_api_compatible
-- deepseek
 - hunyuan
 - siliconflow
 - perfxcloud

api/core/model_runtime/model_providers/azure_ai_studio/azure_ai_studio.yaml

@@ -51,6 +51,40 @@ model_credential_schema:
       show_on:
         - variable: __model_type
           value: llm
+    - variable: mode
+      show_on:
+        - variable: __model_type
+          value: llm
+      label:
+        en_US: Completion mode
+      type: select
+      required: false
+      default: chat
+      placeholder:
+        zh_Hans: 选择对话类型
+        en_US: Select completion mode
+      options:
+        - value: completion
+          label:
+            en_US: Completion
+            zh_Hans: 补全
+        - value: chat
+          label:
+            en_US: Chat
+            zh_Hans: 对话
+    - variable: context_size
+      label:
+        zh_Hans: 模型上下文长度
+        en_US: Model context size
+      required: true
+      show_on:
+        - variable: __model_type
+          value: llm
+      type: text-input
+      default: "4096"
+      placeholder:
+        zh_Hans: 在此输入您的模型上下文长度
+        en_US: Enter your Model context size
     - variable: jwt_token
       required: true
       label:

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

@@ -1,9 +1,9 @@
 import logging
-from collections.abc import Generator
+from collections.abc import Generator, Sequence
 from typing import Any, Optional, Union
 
 from azure.ai.inference import ChatCompletionsClient
-from azure.ai.inference.models import StreamingChatCompletionsUpdate
+from azure.ai.inference.models import StreamingChatCompletionsUpdate, SystemMessage, UserMessage
 from azure.core.credentials import AzureKeyCredential
 from azure.core.exceptions import (
     ClientAuthenticationError,
@@ -20,7 +20,7 @@ from azure.core.exceptions import (
 )
 
 from core.model_runtime.callbacks.base_callback import Callback
-from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
+from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
 from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
     PromptMessage,
@@ -30,6 +30,7 @@ from core.model_runtime.entities.model_entities import (
     AIModelEntity,
     FetchFrom,
     I18nObject,
+    ModelPropertyKey,
     ModelType,
     ParameterRule,
     ParameterType,
@@ -60,10 +61,10 @@ class AzureAIStudioLargeLanguageModel(LargeLanguageModel):
         self,
         model: str,
         credentials: dict,
-        prompt_messages: list[PromptMessage],
+        prompt_messages: Sequence[PromptMessage],
         model_parameters: dict,
-        tools: Optional[list[PromptMessageTool]] = None,
-        stop: Optional[list[str]] = None,
+        tools: Optional[Sequence[PromptMessageTool]] = None,
+        stop: Optional[Sequence[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
     ) -> Union[LLMResult, Generator]:
@@ -82,8 +83,8 @@ class AzureAIStudioLargeLanguageModel(LargeLanguageModel):
         """
 
         if not self.client:
-            endpoint = credentials.get("endpoint")
-            api_key = credentials.get("api_key")
+            endpoint = str(credentials.get("endpoint"))
+            api_key = str(credentials.get("api_key"))
             self.client = ChatCompletionsClient(endpoint=endpoint, credential=AzureKeyCredential(api_key))
 
         messages = [{"role": msg.role.value, "content": msg.content} for msg in prompt_messages]
@@ -94,6 +95,7 @@ class AzureAIStudioLargeLanguageModel(LargeLanguageModel):
             "temperature": model_parameters.get("temperature", 0),
             "top_p": model_parameters.get("top_p", 1),
             "stream": stream,
+            "model": model,
         }
 
         if stop:
@@ -255,10 +257,16 @@ class AzureAIStudioLargeLanguageModel(LargeLanguageModel):
         :return:
         """
         try:
-            endpoint = credentials.get("endpoint")
-            api_key = credentials.get("api_key")
+            endpoint = str(credentials.get("endpoint"))
+            api_key = str(credentials.get("api_key"))
             client = ChatCompletionsClient(endpoint=endpoint, credential=AzureKeyCredential(api_key))
-            client.get_model_info()
+            client.complete(
+                messages=[
+                    SystemMessage(content="I say 'ping', you say 'pong'"),
+                    UserMessage(content="ping"),
+                ],
+                model=model,
+            )
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 
@@ -327,7 +335,10 @@ class AzureAIStudioLargeLanguageModel(LargeLanguageModel):
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_type=ModelType.LLM,
             features=[],
-            model_properties={},
+            model_properties={
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", "4096")),
+                ModelPropertyKey.MODE: credentials.get("mode", LLMMode.CHAT),
+            },
             parameter_rules=rules,
         )
 

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

@@ -138,6 +138,18 @@ model_credential_schema:
           show_on:
             - variable: __model_type
               value: llm
+        - label:
+            en_US: o3-mini
+          value: o3-mini
+          show_on:
+            - variable: __model_type
+              value: llm
+        - label:
+            en_US: o3-mini-2025-01-31
+          value: o3-mini-2025-01-31
+          show_on:
+            - variable: __model_type
+              value: llm
         - label:
             en_US: o1-preview
           value: o1-preview

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

@@ -123,6 +123,15 @@ provider_credential_schema:
             en_US: AWS GovCloud (US-West)
             zh_Hans: AWS GovCloud (US-West)
             ja_JP: AWS GovCloud (米国西部)
+    - variable: bedrock_endpoint_url
+      label:
+        zh_Hans: Bedrock Endpoint URL
+        en_US: Bedrock Endpoint URL
+      type: text-input
+      required: false
+      placeholder:
+        zh_Hans: 在此输入您的 Bedrock Endpoint URL, 如：https://123456.cloudfront.net
+        en_US: Enter your Bedrock Endpoint URL, e.g. https://123456.cloudfront.net
     - variable: model_for_validation
       required: false
       label:

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

@@ -13,6 +13,7 @@ def get_bedrock_client(service_name: str, credentials: Mapping[str, str]):
     client_config = Config(region_name=region_name)
     aws_access_key_id = credentials.get("aws_access_key_id")
     aws_secret_access_key = credentials.get("aws_secret_access_key")
+    bedrock_endpoint_url = credentials.get("bedrock_endpoint_url")
 
     if aws_access_key_id and aws_secret_access_key:
         # use aksk to call bedrock
@@ -21,6 +22,7 @@ def get_bedrock_client(service_name: str, credentials: Mapping[str, str]):
             config=client_config,
             aws_access_key_id=aws_access_key_id,
             aws_secret_access_key=aws_secret_access_key,
+            **({"endpoint_url": bedrock_endpoint_url} if bedrock_endpoint_url else {}),
         )
     else:
         # use iam without aksk to call

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

@@ -1,13 +1,10 @@
-import json
 from collections.abc import Generator
 from typing import Optional, Union
 
-import requests
 from yarl import URL
 
-from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta
+from core.model_runtime.entities.llm_entities import LLMMode, LLMResult
 from core.model_runtime.entities.message_entities import (
-    AssistantPromptMessage,
     PromptMessage,
     PromptMessageTool,
 )
@@ -39,208 +36,3 @@ class DeepseekLargeLanguageModel(OAIAPICompatLargeLanguageModel):
         credentials["mode"] = LLMMode.CHAT.value
         credentials["function_calling_type"] = "tool_call"
         credentials["stream_function_calling"] = "support"
-
-    def _handle_generate_stream_response(
-        self, model: str, credentials: dict, response: requests.Response, prompt_messages: list[PromptMessage]
-    ) -> Generator:
-        """
-        Handle llm stream response
-
-        :param model: model name
-        :param credentials: model credentials
-        :param response: streamed response
-        :param prompt_messages: prompt messages
-        :return: llm response chunk generator
-        """
-        full_assistant_content = ""
-        chunk_index = 0
-        is_reasoning_started = False  # Add flag to track reasoning state
-
-        def create_final_llm_result_chunk(
-            id: Optional[str], index: int, message: AssistantPromptMessage, finish_reason: str, usage: dict
-        ) -> LLMResultChunk:
-            # calculate num tokens
-            prompt_tokens = usage and usage.get("prompt_tokens")
-            if prompt_tokens is None:
-                prompt_tokens = self._num_tokens_from_string(model, prompt_messages[0].content)
-            completion_tokens = usage and usage.get("completion_tokens")
-            if completion_tokens is None:
-                completion_tokens = self._num_tokens_from_string(model, full_assistant_content)
-
-            # transform usage
-            usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
-
-            return LLMResultChunk(
-                id=id,
-                model=model,
-                prompt_messages=prompt_messages,
-                delta=LLMResultChunkDelta(index=index, message=message, finish_reason=finish_reason, usage=usage),
-            )
-
-        # delimiter for stream response, need unicode_escape
-        import codecs
-
-        delimiter = credentials.get("stream_mode_delimiter", "\n\n")
-        delimiter = codecs.decode(delimiter, "unicode_escape")
-
-        tools_calls: list[AssistantPromptMessage.ToolCall] = []
-
-        def increase_tool_call(new_tool_calls: list[AssistantPromptMessage.ToolCall]):
-            def get_tool_call(tool_call_id: str):
-                if not tool_call_id:
-                    return tools_calls[-1]
-
-                tool_call = next((tool_call for tool_call in tools_calls if tool_call.id == tool_call_id), None)
-                if tool_call is None:
-                    tool_call = AssistantPromptMessage.ToolCall(
-                        id=tool_call_id,
-                        type="function",
-                        function=AssistantPromptMessage.ToolCall.ToolCallFunction(name="", arguments=""),
-                    )
-                    tools_calls.append(tool_call)
-
-                return tool_call
-
-            for new_tool_call in new_tool_calls:
-                # get tool call
-                tool_call = get_tool_call(new_tool_call.function.name)
-                # update tool call
-                if new_tool_call.id:
-                    tool_call.id = new_tool_call.id
-                if new_tool_call.type:
-                    tool_call.type = new_tool_call.type
-                if new_tool_call.function.name:
-                    tool_call.function.name = new_tool_call.function.name
-                if new_tool_call.function.arguments:
-                    tool_call.function.arguments += new_tool_call.function.arguments
-
-        finish_reason = None  # The default value of finish_reason is None
-        message_id, usage = None, None
-        for chunk in response.iter_lines(decode_unicode=True, delimiter=delimiter):
-            chunk = chunk.strip()
-            if chunk:
-                # ignore sse comments
-                if chunk.startswith(":"):
-                    continue
-                decoded_chunk = chunk.strip().removeprefix("data:").lstrip()
-                if decoded_chunk == "[DONE]":  # Some provider returns "data: [DONE]"
-                    continue
-
-                try:
-                    chunk_json: dict = json.loads(decoded_chunk)
-                # stream ended
-                except json.JSONDecodeError as e:
-                    yield create_final_llm_result_chunk(
-                        id=message_id,
-                        index=chunk_index + 1,
-                        message=AssistantPromptMessage(content=""),
-                        finish_reason="Non-JSON encountered.",
-                        usage=usage,
-                    )
-                    break
-                # handle the error here. for issue #11629
-                if chunk_json.get("error") and chunk_json.get("choices") is None:
-                    raise ValueError(chunk_json.get("error"))
-
-                if chunk_json:
-                    if u := chunk_json.get("usage"):
-                        usage = u
-                if not chunk_json or len(chunk_json["choices"]) == 0:
-                    continue
-
-                choice = chunk_json["choices"][0]
-                finish_reason = chunk_json["choices"][0].get("finish_reason")
-                message_id = chunk_json.get("id")
-                chunk_index += 1
-
-                if "delta" in choice:
-                    delta = choice["delta"]
-                    is_reasoning = delta.get("reasoning_content")
-                    delta_content = delta.get("content") or delta.get("reasoning_content")
-
-                    assistant_message_tool_calls = None
-
-                    if "tool_calls" in delta and credentials.get("function_calling_type", "no_call") == "tool_call":
-                        assistant_message_tool_calls = delta.get("tool_calls", None)
-                    elif (
-                        "function_call" in delta
-                        and credentials.get("function_calling_type", "no_call") == "function_call"
-                    ):
-                        assistant_message_tool_calls = [
-                            {"id": "tool_call_id", "type": "function", "function": delta.get("function_call", {})}
-                        ]
-
-                    # assistant_message_function_call = delta.delta.function_call
-
-                    # extract tool calls from response
-                    if assistant_message_tool_calls:
-                        tool_calls = self._extract_response_tool_calls(assistant_message_tool_calls)
-                        increase_tool_call(tool_calls)
-
-                    if delta_content is None or delta_content == "":
-                        continue
-
-                    # Add markdown quote markers for reasoning content
-                    if is_reasoning:
-                        if not is_reasoning_started:
-                            delta_content = "> 💭 " + delta_content
-                            is_reasoning_started = True
-                        elif "\n\n" in delta_content:
-                            delta_content = delta_content.replace("\n\n", "\n> ")
-                        elif "\n" in delta_content:
-                            delta_content = delta_content.replace("\n", "\n> ")
-                    elif is_reasoning_started:
-                        # If we were in reasoning mode but now getting regular content,
-                        # add \n\n to close the reasoning block
-                        delta_content = "\n\n" + delta_content
-                        is_reasoning_started = False
-
-                    # transform assistant message to prompt message
-                    assistant_prompt_message = AssistantPromptMessage(
-                        content=delta_content,
-                    )
-
-                    # reset tool calls
-                    tool_calls = []
-                    full_assistant_content += delta_content
-                elif "text" in choice:
-                    choice_text = choice.get("text", "")
-                    if choice_text == "":
-                        continue
-
-                    # transform assistant message to prompt message
-                    assistant_prompt_message = AssistantPromptMessage(content=choice_text)
-                    full_assistant_content += choice_text
-                else:
-                    continue
-
-                yield LLMResultChunk(
-                    id=message_id,
-                    model=model,
-                    prompt_messages=prompt_messages,
-                    delta=LLMResultChunkDelta(
-                        index=chunk_index,
-                        message=assistant_prompt_message,
-                    ),
-                )
-
-            chunk_index += 1
-
-        if tools_calls:
-            yield LLMResultChunk(
-                id=message_id,
-                model=model,
-                prompt_messages=prompt_messages,
-                delta=LLMResultChunkDelta(
-                    index=chunk_index,
-                    message=AssistantPromptMessage(tool_calls=tools_calls, content=""),
-                ),
-            )
-
-        yield create_final_llm_result_chunk(
-            id=message_id,
-            index=chunk_index,
-            message=AssistantPromptMessage(content=""),
-            finish_reason=finish_reason,
-            usage=usage,
-        )

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

@@ -1,4 +1,6 @@
+- gemini-2.0-flash-001
 - gemini-2.0-flash-exp
+- gemini-2.0-pro-exp-02-05
 - gemini-2.0-flash-thinking-exp-1219
 - gemini-2.0-flash-thinking-exp-01-21
 - gemini-1.5-pro

api/core/model_runtime/model_providers/google/llm/gemini-2.0-flash-001.yml

@@ -0,0 +1,41 @@
+model: gemini-2.0-flash-001
+label:
+  en_US: Gemini 2.0 Flash 001
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+  - document
+  - video
+  - audio
+model_properties:
+  mode: chat
+  context_size: 1048576
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_output_tokens
+    use_template: max_tokens
+    default: 8192
+    min: 1
+    max: 8192
+  - name: json_schema
+    use_template: json_schema
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/google/llm/gemini-2.0-pro-exp-02-05.yaml

@@ -0,0 +1,41 @@
+model: gemini-2.0-pro-exp-02-05
+label:
+  en_US: Gemini 2.0 pro exp 02-05
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+  - document
+  - video
+  - audio
+model_properties:
+  mode: chat
+  context_size: 1048576
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_output_tokens
+    use_template: max_tokens
+    default: 8192
+    min: 1
+    max: 8192
+  - name: json_schema
+    use_template: json_schema
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

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

@@ -1,3 +1,4 @@
+- deepseek-r1-distill-llama-70b
 - llama-3.1-405b-reasoning
 - llama-3.3-70b-versatile
 - llama-3.1-70b-versatile

api/core/model_runtime/model_providers/groq/llm/deepseek-r1-distill-llama-70b.yaml

@@ -0,0 +1,36 @@
+model: deepseek-r1-distill-llama-70b
+label:
+  en_US: DeepSeek R1 Distill Llama 70b
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 128000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 512
+    min: 1
+    max: 8192
+  - name: response_format
+    label:
+      zh_Hans: 回复格式
+      en_US: Response Format
+    type: string
+    help:
+      zh_Hans: 指定模型必须输出的格式
+      en_US: specifying the format that the model must output
+    required: false
+    options:
+      - text
+      - json_object
+pricing:
+  input: '3.00'
+  output: '3.00'
+  unit: '0.000001'
+  currency: USD

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

@@ -1,3 +1,4 @@
+- deepseek-ai/deepseek-r1
 - google/gemma-7b
 - google/codegemma-7b
 - google/recurrentgemma-2b

api/core/model_runtime/model_providers/nvidia/llm/deepseek-r1.yaml

@@ -0,0 +1,35 @@
+model: deepseek-ai/deepseek-r1
+label:
+  en_US: deepseek-ai/deepseek-r1
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 128000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 1
+    default: 0.5
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 1024
+    default: 1024
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    min: -2
+    max: 2
+    default: 0
+  - name: presence_penalty
+    use_template: presence_penalty
+    min: -2
+    max: 2
+    default: 0

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

@@ -83,7 +83,7 @@ class NVIDIALargeLanguageModel(OAIAPICompatLargeLanguageModel):
     def _add_custom_parameters(self, credentials: dict, model: str) -> None:
         credentials["mode"] = "chat"
 
-        if self.MODEL_SUFFIX_MAP[model]:
+        if self.MODEL_SUFFIX_MAP.get(model):
             credentials["server_url"] = f"https://ai.api.nvidia.com/v1/{self.MODEL_SUFFIX_MAP[model]}"
             credentials.pop("endpoint_url")
         else:

api/core/model_runtime/model_providers/oci/llm/cohere.command-r-08-2024.yaml

@@ -0,0 +1,52 @@
+model: cohere.command-r-08-2024
+label:
+  en_US: cohere.command-r-08-2024 v1.7
+model_type: llm
+features:
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 128000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    default: 1
+    max: 1.0
+  - name: topP
+    use_template: top_p
+    default: 0.75
+    min: 0
+    max: 1
+  - name: topK
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+    default: 0
+    min: 0
+    max: 500
+  - name: presencePenalty
+    use_template: presence_penalty
+    min: 0
+    max: 1
+    default: 0
+  - name: frequencyPenalty
+    use_template: frequency_penalty
+    min: 0
+    max: 1
+    default: 0
+  - name: maxTokens
+    use_template: max_tokens
+    default: 600
+    max: 4000
+pricing:
+  input: '0.0009'
+  output: '0.0009'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/oci/llm/cohere.command-r-16k.yaml

@@ -50,3 +50,4 @@ pricing:
   output: '0.004'
   unit: '0.0001'
   currency: USD
+deprecated: true

api/core/model_runtime/model_providers/oci/llm/cohere.command-r-plus-08-2024.yaml

@@ -0,0 +1,52 @@
+model: cohere.command-r-plus-08-2024
+label:
+  en_US: cohere.command-r-plus-08-2024 v1.6
+model_type: llm
+features:
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 128000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    default: 1
+    max: 1.0
+  - name: topP
+    use_template: top_p
+    default: 0.75
+    min: 0
+    max: 1
+  - name: topK
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+    default: 0
+    min: 0
+    max: 500
+  - name: presencePenalty
+    use_template: presence_penalty
+    min: 0
+    max: 1
+    default: 0
+  - name: frequencyPenalty
+    use_template: frequency_penalty
+    min: 0
+    max: 1
+    default: 0
+  - name: maxTokens
+    use_template: max_tokens
+    default: 600
+    max: 4000
+pricing:
+  input: '0.0156'
+  output: '0.0156'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/oci/llm/cohere.command-r-plus.yaml

@@ -50,3 +50,4 @@ pricing:
   output: '0.0219'
   unit: '0.0001'
   currency: USD
+deprecated: true

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

@@ -33,7 +33,7 @@ logger = logging.getLogger(__name__)
 
 request_template = {
     "compartmentId": "",
-    "servingMode": {"modelId": "cohere.command-r-plus", "servingType": "ON_DEMAND"},
+    "servingMode": {"modelId": "cohere.command-r-plus-08-2024", "servingType": "ON_DEMAND"},
     "chatRequest": {
         "apiFormat": "COHERE",
         # "preambleOverride": "You are a helpful assistant.",
@@ -60,19 +60,19 @@ oci_config_template = {
 class OCILargeLanguageModel(LargeLanguageModel):
     # https://docs.oracle.com/en-us/iaas/Content/generative-ai/pretrained-models.htm
     _supported_models = {
-        "meta.llama-3-70b-instruct": {
+        "meta.llama-3.1-70b-instruct": {
             "system": True,
             "multimodal": False,
             "tool_call": False,
             "stream_tool_call": False,
         },
-        "cohere.command-r-16k": {
+        "cohere.command-r-08-2024": {
             "system": True,
             "multimodal": False,
             "tool_call": True,
             "stream_tool_call": False,
         },
-        "cohere.command-r-plus": {
+        "cohere.command-r-plus-08-2024": {
             "system": True,
             "multimodal": False,
             "tool_call": True,

api/core/model_runtime/model_providers/oci/llm/meta.llama-3-70b-instruct.yaml

@@ -49,3 +49,4 @@ pricing:
   output: '0.015'
   unit: '0.0001'
   currency: USD
+deprecated: true

api/core/model_runtime/model_providers/oci/llm/meta.llama-3.1-70b-instruct.yaml

@@ -0,0 +1,51 @@
+model: meta.llama-3.1-70b-instruct
+label:
+  zh_Hans: meta.llama-3.1-70b-instruct
+  en_US: meta.llama-3.1-70b-instruct
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 131072
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    default: 1
+    max: 2.0
+  - name: topP
+    use_template: top_p
+    default: 0.75
+    min: 0
+    max: 1
+  - name: topK
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+    default: 0
+    min: 0
+    max: 500
+  - name: presencePenalty
+    use_template: presence_penalty
+    min: -2
+    max: 2
+    default: 0
+  - name: frequencyPenalty
+    use_template: frequency_penalty
+    min: -2
+    max: 2
+    default: 0
+  - name: maxTokens
+    use_template: max_tokens
+    default: 600
+    max: 4000
+pricing:
+  input: '0.0075'
+  output: '0.0075'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/oci/oci.py

@@ -19,8 +19,8 @@ class OCIGENAIProvider(ModelProvider):
         try:
             model_instance = self.get_model_instance(ModelType.LLM)
 
-            # Use `cohere.command-r-plus` model for validate,
-            model_instance.validate_credentials(model="cohere.command-r-plus", credentials=credentials)
+            # Use `cohere.command-r-plus-08-2024` model for validate,
+            model_instance.validate_credentials(model="cohere.command-r-plus-08-2024", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:

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

@@ -367,6 +367,7 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
 
                 # transform assistant message to prompt message
                 text = chunk_json["response"]
+            text = self._wrap_thinking_by_tag(text)
 
             assistant_prompt_message = AssistantPromptMessage(content=text)
 

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

@@ -2,6 +2,8 @@
 - o1-2024-12-17
 - o1-mini
 - o1-mini-2024-09-12
+- o3-mini
+- o3-mini-2025-01-31
 - gpt-4
 - gpt-4o
 - gpt-4o-2024-05-13

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

@@ -619,9 +619,9 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
         # clear illegal prompt messages
         prompt_messages = self._clear_illegal_prompt_messages(model, prompt_messages)
 
-        # o1 compatibility
+        # o1, o3 compatibility
         block_as_stream = False
-        if model.startswith("o1"):
+        if model.startswith(("o1", "o3")):
             if "max_tokens" in model_parameters:
                 model_parameters["max_completion_tokens"] = model_parameters["max_tokens"]
                 del model_parameters["max_tokens"]
@@ -941,7 +941,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
                                 ]
                             )
 
-        if model.startswith("o1"):
+        if model.startswith(("o1", "o3")):
             system_message_count = len([m for m in prompt_messages if isinstance(m, SystemPromptMessage)])
             if system_message_count > 0:
                 new_prompt_messages = []
@@ -1053,7 +1053,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
             model = model.split(":")[1]
 
         # Currently, we can use gpt4o to calculate chatgpt-4o-latest's token.
-        if model == "chatgpt-4o-latest" or model.startswith("o1"):
+        if model == "chatgpt-4o-latest" or model.startswith(("o1", "o3")):
             model = "gpt-4o"
 
         try:
@@ -1068,7 +1068,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
             tokens_per_message = 4
             # if there's a name, the role is omitted
             tokens_per_name = -1
-        elif model.startswith("gpt-3.5-turbo") or model.startswith("gpt-4") or model.startswith("o1"):
+        elif model.startswith("gpt-3.5-turbo") or model.startswith("gpt-4") or model.startswith(("o1", "o3")):
             tokens_per_message = 3
             tokens_per_name = 1
         else:

api/core/model_runtime/model_providers/openai/llm/o1-2024-12-17.yaml

@@ -16,6 +16,19 @@ parameter_rules:
     default: 50000
     min: 1
     max: 50000
+  - name: reasoning_effort
+    label:
+      zh_Hans: 推理工作
+      en_US: reasoning_effort
+    type: string
+    help:
+      zh_Hans: 限制推理模型的推理工作
+      en_US: constrains effort on reasoning for reasoning models
+    required: false
+    options:
+      - low
+      - medium
+      - high
   - name: response_format
     label:
       zh_Hans: 回复格式

api/core/model_runtime/model_providers/openai/llm/o1.yaml

@@ -17,6 +17,19 @@ parameter_rules:
     default: 50000
     min: 1
     max: 50000
+  - name: reasoning_effort
+    label:
+      zh_Hans: 推理工作
+      en_US: reasoning_effort
+    type: string
+    help:
+      zh_Hans: 限制推理模型的推理工作
+      en_US: constrains effort on reasoning for reasoning models
+    required: false
+    options:
+      - low
+      - medium
+      - high
   - name: response_format
     label:
       zh_Hans: 回复格式

api/core/model_runtime/model_providers/openai/llm/o3-mini-2025-01-31.yaml

@@ -0,0 +1,46 @@
+model: o3-mini-2025-01-31
+label:
+  zh_Hans: o3-mini-2025-01-31
+  en_US: o3-mini-2025-01-31
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 200000
+parameter_rules:
+  - name: max_tokens
+    use_template: max_tokens
+    default: 100000
+    min: 1
+    max: 100000
+  - name: reasoning_effort
+    label:
+      zh_Hans: 推理工作
+      en_US: reasoning_effort
+    type: string
+    help:
+      zh_Hans: 限制推理模型的推理工作
+      en_US: constrains effort on reasoning for reasoning models
+    required: false
+    options:
+      - low
+      - medium
+      - high
+  - name: response_format
+    label:
+      zh_Hans: 回复格式
+      en_US: response_format
+    type: string
+    help:
+      zh_Hans: 指定模型必须输出的格式
+      en_US: specifying the format that the model must output
+    required: false
+    options:
+      - text
+      - json_object
+pricing:
+  input: '1.10'
+  output: '4.40'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/openai/llm/o3-mini.yaml

@@ -0,0 +1,46 @@
+model: o3-mini
+label:
+  zh_Hans: o3-mini
+  en_US: o3-mini
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 200000
+parameter_rules:
+  - name: max_tokens
+    use_template: max_tokens
+    default: 100000
+    min: 1
+    max: 100000
+  - name: reasoning_effort
+    label:
+      zh_Hans: 推理工作
+      en_US: reasoning_effort
+    type: string
+    help:
+      zh_Hans: 限制推理模型的推理工作
+      en_US: constrains effort on reasoning for reasoning models
+    required: false
+    options:
+      - low
+      - medium
+      - high
+  - name: response_format
+    label:
+      zh_Hans: 回复格式
+      en_US: response_format
+    type: string
+    help:
+      zh_Hans: 指定模型必须输出的格式
+      en_US: specifying the format that the model must output
+    required: false
+    options:
+      - text
+      - json_object
+pricing:
+  input: '1.10'
+  output: '4.40'
+  unit: '0.000001'
+  currency: USD

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

@@ -1,5 +1,5 @@
+import codecs
 import json
-import logging
 from collections.abc import Generator
 from decimal import Decimal
 from typing import Optional, Union, cast
@@ -38,8 +38,6 @@ from core.model_runtime.model_providers.__base.large_language_model import Large
 from core.model_runtime.model_providers.openai_api_compatible._common import _CommonOaiApiCompat
 from core.model_runtime.utils import helper
 
-logger = logging.getLogger(__name__)
-
 
 class OAIAPICompatLargeLanguageModel(_CommonOaiApiCompat, LargeLanguageModel):
     """
@@ -99,7 +97,7 @@ class OAIAPICompatLargeLanguageModel(_CommonOaiApiCompat, LargeLanguageModel):
         :param tools: tools for tool calling
         :return:
         """
-        return self._num_tokens_from_messages(model, prompt_messages, tools, credentials)
+        return self._num_tokens_from_messages(prompt_messages, tools, credentials)
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
         """
@@ -398,6 +396,73 @@ class OAIAPICompatLargeLanguageModel(_CommonOaiApiCompat, LargeLanguageModel):
 
         return self._handle_generate_response(model, credentials, response, prompt_messages)
 
+    def _create_final_llm_result_chunk(
+        self,
+        index: int,
+        message: AssistantPromptMessage,
+        finish_reason: str,
+        usage: dict,
+        model: str,
+        prompt_messages: list[PromptMessage],
+        credentials: dict,
+        full_content: str,
+    ) -> LLMResultChunk:
+        # calculate num tokens
+        prompt_tokens = usage and usage.get("prompt_tokens")
+        if prompt_tokens is None:
+            prompt_tokens = self._num_tokens_from_string(text=prompt_messages[0].content)
+        completion_tokens = usage and usage.get("completion_tokens")
+        if completion_tokens is None:
+            completion_tokens = self._num_tokens_from_string(text=full_content)
+
+        # transform usage
+        usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
+
+        return LLMResultChunk(
+            model=model,
+            prompt_messages=prompt_messages,
+            delta=LLMResultChunkDelta(index=index, message=message, finish_reason=finish_reason, usage=usage),
+        )
+
+    def _get_tool_call(self, tool_call_id: str, tools_calls: list[AssistantPromptMessage.ToolCall]):
+        """
+        Get or create a tool call by ID
+
+        :param tool_call_id: tool call ID
+        :param tools_calls: list of existing tool calls
+        :return: existing or new tool call, updated tools_calls
+        """
+        if not tool_call_id:
+            return tools_calls[-1], tools_calls
+
+        tool_call = next((tool_call for tool_call in tools_calls if tool_call.id == tool_call_id), None)
+        if tool_call is None:
+            tool_call = AssistantPromptMessage.ToolCall(
+                id=tool_call_id,
+                type="function",
+                function=AssistantPromptMessage.ToolCall.ToolCallFunction(name="", arguments=""),
+            )
+            tools_calls.append(tool_call)
+
+        return tool_call, tools_calls
+
+    def _increase_tool_call(
+        self, new_tool_calls: list[AssistantPromptMessage.ToolCall], tools_calls: list[AssistantPromptMessage.ToolCall]
+    ) -> list[AssistantPromptMessage.ToolCall]:
+        for new_tool_call in new_tool_calls:
+            # get tool call
+            tool_call, tools_calls = self._get_tool_call(new_tool_call.function.name, tools_calls)
+            # update tool call
+            if new_tool_call.id:
+                tool_call.id = new_tool_call.id
+            if new_tool_call.type:
+                tool_call.type = new_tool_call.type
+            if new_tool_call.function.name:
+                tool_call.function.name = new_tool_call.function.name
+            if new_tool_call.function.arguments:
+                tool_call.function.arguments += new_tool_call.function.arguments
+        return tools_calls
+
     def _handle_generate_stream_response(
         self, model: str, credentials: dict, response: requests.Response, prompt_messages: list[PromptMessage]
     ) -> Generator:
@@ -410,69 +475,15 @@ class OAIAPICompatLargeLanguageModel(_CommonOaiApiCompat, LargeLanguageModel):
         :param prompt_messages: prompt messages
         :return: llm response chunk generator
         """
-        full_assistant_content = ""
         chunk_index = 0
-
-        def create_final_llm_result_chunk(
-            id: Optional[str], index: int, message: AssistantPromptMessage, finish_reason: str, usage: dict
-        ) -> LLMResultChunk:
-            # calculate num tokens
-            prompt_tokens = usage and usage.get("prompt_tokens")
-            if prompt_tokens is None:
-                prompt_tokens = self._num_tokens_from_string(model, prompt_messages[0].content)
-            completion_tokens = usage and usage.get("completion_tokens")
-            if completion_tokens is None:
-                completion_tokens = self._num_tokens_from_string(model, full_assistant_content)
-
-            # transform usage
-            usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
-
-            return LLMResultChunk(
-                id=id,
-                model=model,
-                prompt_messages=prompt_messages,
-                delta=LLMResultChunkDelta(index=index, message=message, finish_reason=finish_reason, usage=usage),
-            )
-
+        full_assistant_content = ""
+        tools_calls: list[AssistantPromptMessage.ToolCall] = []
+        finish_reason = None
+        usage = None
+        is_reasoning_started = False
         # delimiter for stream response, need unicode_escape
-        import codecs
-
         delimiter = credentials.get("stream_mode_delimiter", "\n\n")
         delimiter = codecs.decode(delimiter, "unicode_escape")
-
-        tools_calls: list[AssistantPromptMessage.ToolCall] = []
-
-        def increase_tool_call(new_tool_calls: list[AssistantPromptMessage.ToolCall]):
-            def get_tool_call(tool_call_id: str):
-                if not tool_call_id:
-                    return tools_calls[-1]
-
-                tool_call = next((tool_call for tool_call in tools_calls if tool_call.id == tool_call_id), None)
-                if tool_call is None:
-                    tool_call = AssistantPromptMessage.ToolCall(
-                        id=tool_call_id,
-                        type="function",
-                        function=AssistantPromptMessage.ToolCall.ToolCallFunction(name="", arguments=""),
-                    )
-                    tools_calls.append(tool_call)
-
-                return tool_call
-
-            for new_tool_call in new_tool_calls:
-                # get tool call
-                tool_call = get_tool_call(new_tool_call.function.name)
-                # update tool call
-                if new_tool_call.id:
-                    tool_call.id = new_tool_call.id
-                if new_tool_call.type:
-                    tool_call.type = new_tool_call.type
-                if new_tool_call.function.name:
-                    tool_call.function.name = new_tool_call.function.name
-                if new_tool_call.function.arguments:
-                    tool_call.function.arguments += new_tool_call.function.arguments
-
-        finish_reason = None  # The default value of finish_reason is None
-        message_id, usage = None, None
         for chunk in response.iter_lines(decode_unicode=True, delimiter=delimiter):
             chunk = chunk.strip()
             if chunk:
@@ -487,12 +498,15 @@ class OAIAPICompatLargeLanguageModel(_CommonOaiApiCompat, LargeLanguageModel):
                     chunk_json: dict = json.loads(decoded_chunk)
                 # stream ended
                 except json.JSONDecodeError as e:
-                    yield create_final_llm_result_chunk(
-                        id=message_id,
+                    yield self._create_final_llm_result_chunk(
                         index=chunk_index + 1,
                         message=AssistantPromptMessage(content=""),
                         finish_reason="Non-JSON encountered.",
                         usage=usage,
+                        model=model,
+                        credentials=credentials,
+                        prompt_messages=prompt_messages,
+                        full_content=full_assistant_content,
                     )
                     break
                 # handle the error here. for issue #11629
@@ -507,12 +521,14 @@ class OAIAPICompatLargeLanguageModel(_CommonOaiApiCompat, LargeLanguageModel):
 
                 choice = chunk_json["choices"][0]
                 finish_reason = chunk_json["choices"][0].get("finish_reason")
-                message_id = chunk_json.get("id")
                 chunk_index += 1
 
                 if "delta" in choice:
                     delta = choice["delta"]
-                    delta_content = delta.get("content")
+                    delta_content, is_reasoning_started = self._wrap_thinking_by_reasoning_content(
+                        delta, is_reasoning_started
+                    )
+                    delta_content = self._wrap_thinking_by_tag(delta_content)
 
                     assistant_message_tool_calls = None
 
@@ -526,12 +542,10 @@ class OAIAPICompatLargeLanguageModel(_CommonOaiApiCompat, LargeLanguageModel):
                             {"id": "tool_call_id", "type": "function", "function": delta.get("function_call", {})}
                         ]
 
-                    # assistant_message_function_call = delta.delta.function_call
-
                     # extract tool calls from response
                     if assistant_message_tool_calls:
                         tool_calls = self._extract_response_tool_calls(assistant_message_tool_calls)
-                        increase_tool_call(tool_calls)
+                        tools_calls = self._increase_tool_call(tool_calls, tools_calls)
 
                     if delta_content is None or delta_content == "":
                         continue
@@ -556,7 +570,6 @@ class OAIAPICompatLargeLanguageModel(_CommonOaiApiCompat, LargeLanguageModel):
                     continue
 
                 yield LLMResultChunk(
-                    id=message_id,
                     model=model,
                     prompt_messages=prompt_messages,
                     delta=LLMResultChunkDelta(
@@ -569,7 +582,6 @@ class OAIAPICompatLargeLanguageModel(_CommonOaiApiCompat, LargeLanguageModel):
 
         if tools_calls:
             yield LLMResultChunk(
-                id=message_id,
                 model=model,
                 prompt_messages=prompt_messages,
                 delta=LLMResultChunkDelta(
@@ -578,12 +590,15 @@ class OAIAPICompatLargeLanguageModel(_CommonOaiApiCompat, LargeLanguageModel):
                 ),
             )
 
-        yield create_final_llm_result_chunk(
-            id=message_id,
+        yield self._create_final_llm_result_chunk(
             index=chunk_index,
             message=AssistantPromptMessage(content=""),
             finish_reason=finish_reason,
             usage=usage,
+            model=model,
+            credentials=credentials,
+            prompt_messages=prompt_messages,
+            full_content=full_assistant_content,
         )
 
     def _handle_generate_response(
@@ -697,12 +712,11 @@ class OAIAPICompatLargeLanguageModel(_CommonOaiApiCompat, LargeLanguageModel):
         return message_dict
 
     def _num_tokens_from_string(
-        self, model: str, text: Union[str, list[PromptMessageContent]], tools: Optional[list[PromptMessageTool]] = None
+        self, text: Union[str, list[PromptMessageContent]], tools: Optional[list[PromptMessageTool]] = None
     ) -> int:
         """
         Approximate num tokens for model with gpt2 tokenizer.
 
-        :param model: model name
         :param text: prompt text
         :param tools: tools for tool calling
         :return: number of tokens
@@ -725,7 +739,6 @@ class OAIAPICompatLargeLanguageModel(_CommonOaiApiCompat, LargeLanguageModel):
 
     def _num_tokens_from_messages(
         self,
-        model: str,
         messages: list[PromptMessage],
         tools: Optional[list[PromptMessageTool]] = None,
         credentials: Optional[dict] = None,

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

@@ -1,5 +1,7 @@
 - openai/o1-preview
 - openai/o1-mini
+- openai/o3-mini
+- openai/o3-mini-2025-01-31
 - openai/gpt-4o
 - openai/gpt-4o-mini
 - openai/gpt-4
@@ -28,5 +30,6 @@
 - mistralai/mistral-7b-instruct
 - qwen/qwen-2.5-72b-instruct
 - qwen/qwen-2-72b-instruct
+- deepseek/deepseek-r1
 - deepseek/deepseek-chat
 - deepseek/deepseek-coder

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

@@ -53,7 +53,7 @@ parameter_rules:
       zh_Hans: 介于 -2.0 和 2.0 之间的数字。如果该值为正，那么新 token 会根据其在已有文本中的出现频率受到相应的惩罚，降低模型重复相同内容的可能性。
       en_US: A number between -2.0 and 2.0. If the value is positive, new tokens are penalized based on their frequency of occurrence in existing text, reducing the likelihood that the model will repeat the same content.
 pricing:
-  input: "0.14"
-  output: "0.28"
+  input: "0.49"
+  output: "0.89"
   unit: "0.000001"
   currency: USD

api/core/model_runtime/model_providers/openrouter/llm/deepseek-r1.yaml

@@ -0,0 +1,59 @@
+model: deepseek/deepseek-r1
+label:
+  en_US: deepseek-r1
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 163840
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    type: float
+    default: 1
+    min: 0.0
+    max: 2.0
+    help:
+      zh_Hans: 控制生成结果的多样性和随机性。数值越小，越严谨；数值越大，越发散。
+      en_US: Control the diversity and randomness of generated results. The smaller the value, the more rigorous it is; the larger the value, the more divergent it is.
+  - name: max_tokens
+    use_template: max_tokens
+    type: int
+    default: 4096
+    min: 1
+    max: 4096
+    help:
+      zh_Hans: 指定生成结果长度的上限。如果生成结果截断，可以调大该参数。
+      en_US: Specifies the upper limit on the length of generated results. If the generated results are truncated, you can increase this parameter.
+  - name: top_p
+    use_template: top_p
+    type: float
+    default: 1
+    min: 0.01
+    max: 1.00
+    help:
+      zh_Hans: 控制生成结果的随机性。数值越小，随机性越弱；数值越大，随机性越强。一般而言，top_p 和 temperature 两个参数选择一个进行调整即可。
+      en_US: Control the randomness of generated results. The smaller the value, the weaker the randomness; the larger the value, the stronger the randomness. Generally speaking, you can adjust one of the two parameters top_p and temperature.
+  - name: 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: frequency_penalty
+    use_template: frequency_penalty
+    default: 0
+    min: -2.0
+    max: 2.0
+    help:
+      zh_Hans: 介于 -2.0 和 2.0 之间的数字。如果该值为正，那么新 token 会根据其在已有文本中的出现频率受到相应的惩罚，降低模型重复相同内容的可能性。
+      en_US: A number between -2.0 and 2.0. If the value is positive, new tokens are penalized based on their frequency of occurrence in existing text, reducing the likelihood that the model will repeat the same content.
+pricing:
+  input: "3"
+  output: "8"
+  unit: "0.000001"
+  currency: USD

api/core/model_runtime/model_providers/openrouter/llm/o3-mini-2025-01-31.yaml

@@ -0,0 +1,49 @@
+model: openai/o3-mini-2025-01-31
+label:
+  en_US: o3-mini-2025-01-31
+model_type: llm
+features:
+  - 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: presence_penalty
+    use_template: presence_penalty
+  - name: frequency_penalty
+    use_template: frequency_penalty
+  - name: max_tokens
+    use_template: max_tokens
+    default: 512
+    min: 1
+    max: 100000
+  - name: response_format
+    label:
+      zh_Hans: 回复格式
+      en_US: response_format
+    type: string
+    help:
+      zh_Hans: 指定模型必须输出的格式
+      en_US: specifying the format that the model must output
+    required: false
+    options:
+      - text
+      - json_object
+pricing:
+  input: "1.10"
+  output: "4.40"
+  unit: "0.000001"
+  currency: USD

api/core/model_runtime/model_providers/openrouter/llm/o3-mini.yaml

@@ -0,0 +1,49 @@
+model: openai/o3-mini
+label:
+  en_US: o3-mini
+model_type: llm
+features:
+  - 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: presence_penalty
+    use_template: presence_penalty
+  - name: frequency_penalty
+    use_template: frequency_penalty
+  - name: max_tokens
+    use_template: max_tokens
+    default: 512
+    min: 1
+    max: 100000
+  - name: response_format
+    label:
+      zh_Hans: 回复格式
+      en_US: response_format
+    type: string
+    help:
+      zh_Hans: 指定模型必须输出的格式
+      en_US: specifying the format that the model must output
+    required: false
+    options:
+      - text
+      - json_object
+pricing:
+  input: "1.10"
+  output: "4.40"
+  unit: "0.000001"
+  currency: USD

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

@@ -12,7 +12,11 @@
 - Pro/Qwen/Qwen2-VL-7B-Instruct
 - OpenGVLab/InternVL2-26B
 - Pro/OpenGVLab/InternVL2-8B
+- deepseek-ai/DeepSeek-R1
+- deepseek-ai/DeepSeek-V2-Chat
 - deepseek-ai/DeepSeek-V2.5
+- deepseek-ai/DeepSeek-V3
+- deepseek-ai/DeepSeek-Coder-V2-Instruct
 - THUDM/glm-4-9b-chat
 - 01-ai/Yi-1.5-34B-Chat-16K
 - 01-ai/Yi-1.5-9B-Chat-16K
@@ -25,3 +29,4 @@
 - meta-llama/Meta-Llama-3.1-8B-Instruct
 - google/gemma-2-27b-it
 - google/gemma-2-9b-it
+- Tencent/Hunyuan-A52B-Instruct

api/core/model_runtime/model_providers/siliconflow/llm/deepseek-r1.yaml

@@ -0,0 +1,21 @@
+model: deepseek-ai/DeepSeek-R1
+label:
+  zh_Hans: deepseek-ai/DeepSeek-R1
+  en_US: deepseek-ai/DeepSeek-R1
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 64000
+parameter_rules:
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 8192
+    default: 4096
+pricing:
+  input: "4"
+  output: "16"
+  unit: "0.000001"
+  currency: RMB

api/core/model_runtime/model_providers/siliconflow/llm/deepseek-v3.yaml

@@ -0,0 +1,53 @@
+model: deepseek-ai/DeepSeek-V3
+label:
+  en_US: deepseek-ai/DeepSeek-V3
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 64000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: max_tokens
+    use_template: max_tokens
+    type: int
+    default: 512
+    min: 1
+    max: 4096
+    help:
+      zh_Hans: 指定生成结果长度的上限。如果生成结果截断，可以调大该参数。
+      en_US: Specifies the upper limit on the length of generated results. If the generated results are truncated, you can increase this parameter.
+  - name: top_p
+    use_template: top_p
+  - name: 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: frequency_penalty
+    use_template: frequency_penalty
+  - name: response_format
+    label:
+      zh_Hans: 回复格式
+      en_US: Response Format
+    type: string
+    help:
+      zh_Hans: 指定模型必须输出的格式
+      en_US: specifying the format that the model must output
+    required: false
+    options:
+      - text
+      - json_object
+pricing:
+  input: "1"
+  output: "2"
+  unit: "0.000001"
+  currency: RMB

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

@@ -1,13 +1,9 @@
-import json
 from collections.abc import Generator
 from typing import Optional, Union
 
-import requests
-
 from core.model_runtime.entities.common_entities import I18nObject
-from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta
+from core.model_runtime.entities.llm_entities import LLMMode, LLMResult
 from core.model_runtime.entities.message_entities import (
-    AssistantPromptMessage,
     PromptMessage,
     PromptMessageTool,
 )
@@ -96,208 +92,3 @@ class SiliconflowLargeLanguageModel(OAIAPICompatLargeLanguageModel):
                 ),
             ],
         )
-
-    def _handle_generate_stream_response(
-        self, model: str, credentials: dict, response: requests.Response, prompt_messages: list[PromptMessage]
-    ) -> Generator:
-        """
-        Handle llm stream response
-
-        :param model: model name
-        :param credentials: model credentials
-        :param response: streamed response
-        :param prompt_messages: prompt messages
-        :return: llm response chunk generator
-        """
-        full_assistant_content = ""
-        chunk_index = 0
-        is_reasoning_started = False  # Add flag to track reasoning state
-
-        def create_final_llm_result_chunk(
-            id: Optional[str], index: int, message: AssistantPromptMessage, finish_reason: str, usage: dict
-        ) -> LLMResultChunk:
-            # calculate num tokens
-            prompt_tokens = usage and usage.get("prompt_tokens")
-            if prompt_tokens is None:
-                prompt_tokens = self._num_tokens_from_string(model, prompt_messages[0].content)
-            completion_tokens = usage and usage.get("completion_tokens")
-            if completion_tokens is None:
-                completion_tokens = self._num_tokens_from_string(model, full_assistant_content)
-
-            # transform usage
-            usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
-
-            return LLMResultChunk(
-                id=id,
-                model=model,
-                prompt_messages=prompt_messages,
-                delta=LLMResultChunkDelta(index=index, message=message, finish_reason=finish_reason, usage=usage),
-            )
-
-        # delimiter for stream response, need unicode_escape
-        import codecs
-
-        delimiter = credentials.get("stream_mode_delimiter", "\n\n")
-        delimiter = codecs.decode(delimiter, "unicode_escape")
-
-        tools_calls: list[AssistantPromptMessage.ToolCall] = []
-
-        def increase_tool_call(new_tool_calls: list[AssistantPromptMessage.ToolCall]):
-            def get_tool_call(tool_call_id: str):
-                if not tool_call_id:
-                    return tools_calls[-1]
-
-                tool_call = next((tool_call for tool_call in tools_calls if tool_call.id == tool_call_id), None)
-                if tool_call is None:
-                    tool_call = AssistantPromptMessage.ToolCall(
-                        id=tool_call_id,
-                        type="function",
-                        function=AssistantPromptMessage.ToolCall.ToolCallFunction(name="", arguments=""),
-                    )
-                    tools_calls.append(tool_call)
-
-                return tool_call
-
-            for new_tool_call in new_tool_calls:
-                # get tool call
-                tool_call = get_tool_call(new_tool_call.function.name)
-                # update tool call
-                if new_tool_call.id:
-                    tool_call.id = new_tool_call.id
-                if new_tool_call.type:
-                    tool_call.type = new_tool_call.type
-                if new_tool_call.function.name:
-                    tool_call.function.name = new_tool_call.function.name
-                if new_tool_call.function.arguments:
-                    tool_call.function.arguments += new_tool_call.function.arguments
-
-        finish_reason = None  # The default value of finish_reason is None
-        message_id, usage = None, None
-        for chunk in response.iter_lines(decode_unicode=True, delimiter=delimiter):
-            chunk = chunk.strip()
-            if chunk:
-                # ignore sse comments
-                if chunk.startswith(":"):
-                    continue
-                decoded_chunk = chunk.strip().removeprefix("data:").lstrip()
-                if decoded_chunk == "[DONE]":  # Some provider returns "data: [DONE]"
-                    continue
-
-                try:
-                    chunk_json: dict = json.loads(decoded_chunk)
-                # stream ended
-                except json.JSONDecodeError as e:
-                    yield create_final_llm_result_chunk(
-                        id=message_id,
-                        index=chunk_index + 1,
-                        message=AssistantPromptMessage(content=""),
-                        finish_reason="Non-JSON encountered.",
-                        usage=usage,
-                    )
-                    break
-                # handle the error here. for issue #11629
-                if chunk_json.get("error") and chunk_json.get("choices") is None:
-                    raise ValueError(chunk_json.get("error"))
-
-                if chunk_json:
-                    if u := chunk_json.get("usage"):
-                        usage = u
-                if not chunk_json or len(chunk_json["choices"]) == 0:
-                    continue
-
-                choice = chunk_json["choices"][0]
-                finish_reason = chunk_json["choices"][0].get("finish_reason")
-                message_id = chunk_json.get("id")
-                chunk_index += 1
-
-                if "delta" in choice:
-                    delta = choice["delta"]
-                    delta_content = delta.get("content")
-
-                    assistant_message_tool_calls = None
-
-                    if "tool_calls" in delta and credentials.get("function_calling_type", "no_call") == "tool_call":
-                        assistant_message_tool_calls = delta.get("tool_calls", None)
-                    elif (
-                        "function_call" in delta
-                        and credentials.get("function_calling_type", "no_call") == "function_call"
-                    ):
-                        assistant_message_tool_calls = [
-                            {"id": "tool_call_id", "type": "function", "function": delta.get("function_call", {})}
-                        ]
-
-                    # assistant_message_function_call = delta.delta.function_call
-
-                    # extract tool calls from response
-                    if assistant_message_tool_calls:
-                        tool_calls = self._extract_response_tool_calls(assistant_message_tool_calls)
-                        increase_tool_call(tool_calls)
-
-                    if delta_content is None or delta_content == "":
-                        continue
-
-                    # Check for think tags
-                    if "<think>" in delta_content:
-                        is_reasoning_started = True
-                        # Remove <think> tag and add markdown quote
-                        delta_content = "> 💭 " + delta_content.replace("<think>", "")
-                    elif "</think>" in delta_content:
-                        # Remove </think> tag and add newlines to end quote block
-                        delta_content = delta_content.replace("</think>", "") + "\n\n"
-                        is_reasoning_started = False
-                    elif is_reasoning_started:
-                        # Add quote markers for content within thinking block
-                        if "\n\n" in delta_content:
-                            delta_content = delta_content.replace("\n\n", "\n> ")
-                        elif "\n" in delta_content:
-                            delta_content = delta_content.replace("\n", "\n> ")
-
-                    # transform assistant message to prompt message
-                    assistant_prompt_message = AssistantPromptMessage(
-                        content=delta_content,
-                    )
-
-                    # reset tool calls
-                    tool_calls = []
-                    full_assistant_content += delta_content
-                elif "text" in choice:
-                    choice_text = choice.get("text", "")
-                    if choice_text == "":
-                        continue
-
-                    # transform assistant message to prompt message
-                    assistant_prompt_message = AssistantPromptMessage(content=choice_text)
-                    full_assistant_content += choice_text
-                else:
-                    continue
-
-                yield LLMResultChunk(
-                    id=message_id,
-                    model=model,
-                    prompt_messages=prompt_messages,
-                    delta=LLMResultChunkDelta(
-                        index=chunk_index,
-                        message=assistant_prompt_message,
-                    ),
-                )
-
-            chunk_index += 1
-
-        if tools_calls:
-            yield LLMResultChunk(
-                id=message_id,
-                model=model,
-                prompt_messages=prompt_messages,
-                delta=LLMResultChunkDelta(
-                    index=chunk_index,
-                    message=AssistantPromptMessage(tool_calls=tools_calls, content=""),
-                ),
-            )
-
-        yield create_final_llm_result_chunk(
-            id=message_id,
-            index=chunk_index,
-            message=AssistantPromptMessage(content=""),
-            finish_reason=finish_reason,
-            usage=usage,
-        )

api/core/model_runtime/model_providers/vertex_ai/llm/gemini-2.0-flash-001.yaml

@@ -0,0 +1,41 @@
+model: gemini-2.0-flash-001
+label:
+  en_US: Gemini 2.0 Flash 001
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+  - document
+  - video
+  - audio
+model_properties:
+  mode: chat
+  context_size: 1048576
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_output_tokens
+    use_template: max_tokens
+    default: 8192
+    min: 1
+    max: 8192
+  - name: json_schema
+    use_template: json_schema
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/vertex_ai/llm/gemini-2.0-flash-lite-preview-02-05.yaml

@@ -0,0 +1,41 @@
+model: gemini-2.0-flash-lite-preview-02-05
+label:
+  en_US: Gemini 2.0 Flash Lite Preview 0205
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+  - document
+  - video
+  - audio
+model_properties:
+  mode: chat
+  context_size: 1048576
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_output_tokens
+    use_template: max_tokens
+    default: 8192
+    min: 1
+    max: 8192
+  - name: json_schema
+    use_template: json_schema
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/vertex_ai/llm/gemini-2.0-flash-thinking-exp-01-21.yaml

@@ -0,0 +1,39 @@
+model: gemini-2.0-flash-thinking-exp-01-21
+label:
+  en_US: Gemini 2.0 Flash Thinking Exp 0121
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - document
+  - video
+  - audio
+model_properties:
+  mode: chat
+  context_size: 32767
+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_output_tokens
+    use_template: max_tokens
+    default: 8192
+    min: 1
+    max: 8192
+  - name: json_schema
+    use_template: json_schema
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/vertex_ai/llm/gemini-2.0-flash-thinking-exp-1219.yaml

@@ -0,0 +1,39 @@
+model: gemini-2.0-flash-thinking-exp-1219
+label:
+  en_US: Gemini 2.0 Flash Thinking Exp 1219
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - document
+  - video
+  - audio
+model_properties:
+  mode: chat
+  context_size: 32767
+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_output_tokens
+    use_template: max_tokens
+    default: 8192
+    min: 1
+    max: 8192
+  - name: json_schema
+    use_template: json_schema
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/vertex_ai/llm/gemini-2.0-pro-exp-02-05.yaml

@@ -0,0 +1,37 @@
+model: gemini-2.0-pro-exp-02-05
+label:
+  en_US: Gemini 2.0 Pro Exp 0205
+model_type: llm
+features:
+  - agent-thought
+  - document
+model_properties:
+  mode: chat
+  context_size: 2000000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      en_US: Top k
+    type: int
+    help:
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: presence_penalty
+    use_template: presence_penalty
+  - name: frequency_penalty
+    use_template: frequency_penalty
+  - name: max_output_tokens
+    use_template: max_tokens
+    required: true
+    default: 8192
+    min: 1
+    max: 8192
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/vertex_ai/llm/gemini-exp-1114.yaml

@@ -0,0 +1,41 @@
+model: gemini-exp-1114
+label:
+  en_US: Gemini exp 1114
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+  - document
+  - video
+  - audio
+model_properties:
+  mode: chat
+  context_size: 32767
+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_output_tokens
+    use_template: max_tokens
+    default: 8192
+    min: 1
+    max: 8192
+  - name: json_schema
+    use_template: json_schema
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/vertex_ai/llm/gemini-exp-1121.yaml

@@ -0,0 +1,41 @@
+model: gemini-exp-1121
+label:
+  en_US: Gemini exp 1121
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+  - document
+  - video
+  - audio
+model_properties:
+  mode: chat
+  context_size: 32767
+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_output_tokens
+    use_template: max_tokens
+    default: 8192
+    min: 1
+    max: 8192
+  - name: json_schema
+    use_template: json_schema
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/vertex_ai/llm/gemini-exp-1206.yaml

@@ -0,0 +1,41 @@
+model: gemini-exp-1206
+label:
+  en_US: Gemini exp 1206
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+  - document
+  - video
+  - audio
+model_properties:
+  mode: chat
+  context_size: 2097152
+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_output_tokens
+    use_template: max_tokens
+    default: 8192
+    min: 1
+    max: 8192
+  - name: json_schema
+    use_template: json_schema
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

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

@@ -1,4 +1,5 @@
 import logging
+import re
 from collections.abc import Generator
 from typing import Optional
 
@@ -247,15 +248,34 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
             req_params["tools"] = tools
 
         def _handle_stream_chat_response(chunks: Generator[ChatCompletionChunk]) -> Generator:
+            is_reasoning_started = False
             for chunk in chunks:
+                content = ""
+                if chunk.choices:
+                    delta = chunk.choices[0].delta
+                    if is_reasoning_started and not hasattr(delta, "reasoning_content") and not delta.content:
+                        content = ""
+                    elif hasattr(delta, "reasoning_content"):
+                        if not is_reasoning_started:
+                            is_reasoning_started = True
+                            content = "> 💭 " + delta.reasoning_content
+                        else:
+                            content = delta.reasoning_content
+
+                        if "\n" in content:
+                            content = re.sub(r"\n(?!(>|\n))", "\n> ", content)
+                    elif is_reasoning_started:
+                        content = "\n\n" + delta.content
+                        is_reasoning_started = False
+                    else:
+                        content = delta.content
+
                 yield LLMResultChunk(
                     model=model,
                     prompt_messages=prompt_messages,
                     delta=LLMResultChunkDelta(
                         index=0,
-                        message=AssistantPromptMessage(
-                            content=chunk.choices[0].delta.content if chunk.choices else "", tool_calls=[]
-                        ),
+                        message=AssistantPromptMessage(content=content, tool_calls=[]),
                         usage=self._calc_response_usage(
                             model=model,
                             credentials=credentials,

api/core/model_runtime/model_providers/volcengine_maas/llm/models.py

@@ -18,6 +18,22 @@ class ModelConfig(BaseModel):
 
 
 configs: dict[str, ModelConfig] = {
+    "DeepSeek-R1-Distill-Qwen-32B": ModelConfig(
+        properties=ModelProperties(context_size=64000, max_tokens=8192, mode=LLMMode.CHAT),
+        features=[ModelFeature.AGENT_THOUGHT],
+    ),
+    "DeepSeek-R1-Distill-Qwen-7B": ModelConfig(
+        properties=ModelProperties(context_size=64000, max_tokens=8192, mode=LLMMode.CHAT),
+        features=[ModelFeature.AGENT_THOUGHT],
+    ),
+    "DeepSeek-R1": ModelConfig(
+        properties=ModelProperties(context_size=64000, max_tokens=8192, mode=LLMMode.CHAT),
+        features=[ModelFeature.AGENT_THOUGHT],
+    ),
+    "DeepSeek-V3": ModelConfig(
+        properties=ModelProperties(context_size=64000, max_tokens=8192, mode=LLMMode.CHAT),
+        features=[ModelFeature.AGENT_THOUGHT, ModelFeature.TOOL_CALL, ModelFeature.STREAM_TOOL_CALL],
+    ),
     "Doubao-1.5-vision-pro-32k": ModelConfig(
         properties=ModelProperties(context_size=32768, max_tokens=12288, mode=LLMMode.CHAT),
         features=[ModelFeature.AGENT_THOUGHT, ModelFeature.VISION],

api/core/model_runtime/model_providers/volcengine_maas/volcengine_maas.yaml

@@ -118,6 +118,30 @@ model_credential_schema:
       type: select
       required: true
       options:
+        - label:
+            en_US: DeepSeek-R1-Distill-Qwen-32B
+          value: DeepSeek-R1-Distill-Qwen-32B
+          show_on:
+            - variable: __model_type
+              value: llm
+        - label:
+            en_US: DeepSeek-R1-Distill-Qwen-7B
+          value: DeepSeek-R1-Distill-Qwen-7B
+          show_on:
+            - variable: __model_type
+              value: llm
+        - label:
+            en_US: DeepSeek-R1
+          value: DeepSeek-R1
+          show_on:
+            - variable: __model_type
+              value: llm
+        - label:
+            en_US: DeepSeek-V3
+          value: DeepSeek-V3
+          show_on:
+            - variable: __model_type
+              value: llm
         - label:
             en_US: Doubao-1.5-vision-pro-32k
           value: Doubao-1.5-vision-pro-32k

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

@@ -635,16 +635,13 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
         handle stream chat generate response
         """
         full_response = ""
-
         for chunk in resp:
             if len(chunk.choices) == 0:
                 continue
-
             delta = chunk.choices[0]
-
             if delta.finish_reason is None and (delta.delta.content is None or delta.delta.content == ""):
                 continue
-
+            delta_content = delta.delta.content or ""
             # check if there is a tool call in the response
             function_call = None
             tool_calls = []
@@ -657,9 +654,10 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
             if function_call:
                 assistant_message_tool_calls += [self._extract_response_function_call(function_call)]
 
+            delta_content = self._wrap_thinking_by_tag(delta_content)
             # transform assistant message to prompt message
             assistant_prompt_message = AssistantPromptMessage(
-                content=delta.delta.content or "", tool_calls=assistant_message_tool_calls
+                content=delta_content or "", tool_calls=assistant_message_tool_calls
             )
 
             if delta.finish_reason is not None:
@@ -697,7 +695,7 @@ class XinferenceAILargeLanguageModel(LargeLanguageModel):
                     ),
                 )
 
-                full_response += delta.delta.content
+                full_response += delta_content
 
     def _handle_completion_generate_response(
         self,

api/core/tools/provider/_position.yaml

@@ -77,5 +77,4 @@
 - onebot
 - regex
 - trello
-- vanna
 - fal

api/core/tools/provider/builtin/aws/tools/bedrock_config.py

@@ -1,114 +0,0 @@
-"""
-Configuration classes for AWS Bedrock retrieve and generate API
-"""
-
-from dataclasses import dataclass
-from typing import Any, Literal, Optional
-
-
-@dataclass
-class TextInferenceConfig:
-    """Text inference configuration"""
-
-    maxTokens: Optional[int] = None
-    stopSequences: Optional[list[str]] = None
-    temperature: Optional[float] = None
-    topP: Optional[float] = None
-
-
-@dataclass
-class PerformanceConfig:
-    """Performance configuration"""
-
-    latency: Literal["standard", "optimized"]
-
-
-@dataclass
-class PromptTemplate:
-    """Prompt template configuration"""
-
-    textPromptTemplate: str
-
-
-@dataclass
-class GuardrailConfig:
-    """Guardrail configuration"""
-
-    guardrailId: str
-    guardrailVersion: str
-
-
-@dataclass
-class GenerationConfig:
-    """Generation configuration"""
-
-    additionalModelRequestFields: Optional[dict[str, Any]] = None
-    guardrailConfiguration: Optional[GuardrailConfig] = None
-    inferenceConfig: Optional[dict[str, TextInferenceConfig]] = None
-    performanceConfig: Optional[PerformanceConfig] = None
-    promptTemplate: Optional[PromptTemplate] = None
-
-
-@dataclass
-class VectorSearchConfig:
-    """Vector search configuration"""
-
-    filter: Optional[dict[str, Any]] = None
-    numberOfResults: Optional[int] = None
-    overrideSearchType: Optional[Literal["HYBRID", "SEMANTIC"]] = None
-
-
-@dataclass
-class RetrievalConfig:
-    """Retrieval configuration"""
-
-    vectorSearchConfiguration: VectorSearchConfig
-
-
-@dataclass
-class OrchestrationConfig:
-    """Orchestration configuration"""
-
-    additionalModelRequestFields: Optional[dict[str, Any]] = None
-    inferenceConfig: Optional[dict[str, TextInferenceConfig]] = None
-    performanceConfig: Optional[PerformanceConfig] = None
-    promptTemplate: Optional[PromptTemplate] = None
-
-
-@dataclass
-class KnowledgeBaseConfig:
-    """Knowledge base configuration"""
-
-    generationConfiguration: GenerationConfig
-    knowledgeBaseId: str
-    modelArn: str
-    orchestrationConfiguration: Optional[OrchestrationConfig] = None
-    retrievalConfiguration: Optional[RetrievalConfig] = None
-
-
-@dataclass
-class SessionConfig:
-    """Session configuration"""
-
-    kmsKeyArn: Optional[str] = None
-    sessionId: Optional[str] = None
-
-
-@dataclass
-class RetrieveAndGenerateConfiguration:
-    """Retrieve and generate configuration
-    The use of knowledgeBaseConfiguration or externalSourcesConfiguration depends on the type value
-    """
-
-    type: str = "KNOWLEDGE_BASE"
-    knowledgeBaseConfiguration: Optional[KnowledgeBaseConfig] = None
-
-
-@dataclass
-class RetrieveAndGenerateConfig:
-    """Retrieve and generate main configuration"""
-
-    input: dict[str, str]
-    retrieveAndGenerateConfiguration: RetrieveAndGenerateConfiguration
-    sessionConfiguration: Optional[SessionConfig] = None
-    sessionId: Optional[str] = None

api/core/tools/provider/builtin/aws/tools/bedrock_retrieve.py

@@ -77,15 +77,27 @@ class BedrockRetrieveTool(BuiltinTool):
         """
         invoke tools
         """
-        line = 0
         try:
+            line = 0
+            # Initialize Bedrock client if not already initialized
             if not self.bedrock_client:
                 aws_region = tool_parameters.get("aws_region")
-                if aws_region:
-                    self.bedrock_client = boto3.client("bedrock-agent-runtime", region_name=aws_region)
-                else:
-                    self.bedrock_client = boto3.client("bedrock-agent-runtime")
+                aws_access_key_id = tool_parameters.get("aws_access_key_id")
+                aws_secret_access_key = tool_parameters.get("aws_secret_access_key")
 
+                client_kwargs = {"service_name": "bedrock-agent-runtime", "region_name": aws_region or None}
+
+                # Only add credentials if both access key and secret key are provided
+                if aws_access_key_id and aws_secret_access_key:
+                    client_kwargs.update(
+                        {"aws_access_key_id": aws_access_key_id, "aws_secret_access_key": aws_secret_access_key}
+                    )
+
+                self.bedrock_client = boto3.client(**client_kwargs)
+        except Exception as e:
+            return self.create_text_message(f"Failed to initialize Bedrock client: {str(e)}")
+
+        try:
             line = 1
             if not self.knowledge_base_id:
                 self.knowledge_base_id = tool_parameters.get("knowledge_base_id")
@@ -123,7 +135,14 @@ class BedrockRetrieveTool(BuiltinTool):
             sorted_docs = sorted(retrieved_docs, key=operator.itemgetter("score"), reverse=True)
 
             line = 6
-            return [self.create_json_message(res) for res in sorted_docs]
+            result_type = tool_parameters.get("result_type")
+            if result_type == "json":
+                return [self.create_json_message(res) for res in sorted_docs]
+            else:
+                text = ""
+                for i, res in enumerate(sorted_docs):
+                    text += f"{i + 1}: {res['content']}\n"
+                return self.create_text_message(text)
 
         except Exception as e:
             return self.create_text_message(f"Exception {str(e)}, line : {line}")
@@ -138,7 +157,6 @@ class BedrockRetrieveTool(BuiltinTool):
         if not parameters.get("query"):
             raise ValueError("query is required")
 
-        # 可选：可以验证元数据过滤条件是否为有效的 JSON 字符串（如果提供）
         metadata_filter_str = parameters.get("metadata_filter")
         if metadata_filter_str and not isinstance(json.loads(metadata_filter_str), dict):
             raise ValueError("metadata_filter must be a valid JSON object")

api/core/tools/provider/builtin/aws/tools/bedrock_retrieve.yaml

@@ -15,6 +15,60 @@ description:
   llm: A tool for retrieving relevant information from Amazon Bedrock Knowledge Base. You can find deploy instructions on Github Repo - https://github.com/aws-samples/dify-aws-tool
 
 parameters:
+  - name: aws_region
+    type: string
+    required: false
+    label:
+      en_US: AWS Region
+      zh_Hans: AWS区域
+    human_description:
+      en_US: AWS region for the Bedrock service
+      zh_Hans: Bedrock服务的AWS区域
+    form: form
+
+  - name: aws_access_key_id
+    type: string
+    required: false
+    label:
+      en_US: AWS Access Key ID
+      zh_Hans: AWS访问密钥ID
+    human_description:
+      en_US: AWS access key ID for authentication (optional)
+      zh_Hans: 用于身份验证的AWS访问密钥ID（可选）
+    form: form
+
+  - name: aws_secret_access_key
+    type: string
+    required: false
+    label:
+      en_US: AWS Secret Access Key
+      zh_Hans: AWS秘密访问密钥
+    human_description:
+      en_US: AWS secret access key for authentication (optional)
+      zh_Hans: 用于身份验证的AWS秘密访问密钥（可选）
+    form: form
+
+  - name: result_type
+    type: select
+    required: true
+    label:
+      en_US: result type
+      zh_Hans: 结果类型
+    human_description:
+      en_US: return a list of json or texts
+      zh_Hans: 返回一个列表，内容是json还是纯文本
+    default: text
+    options:
+      - value: json
+        label:
+          en_US: JSON
+          zh_Hans: JSON
+      - value: text
+        label:
+          en_US: Text
+          zh_Hans: 文本
+    form: form
+
   - name: knowledge_base_id
     type: string
     required: true
@@ -95,6 +149,7 @@ parameters:
       zh_Hans: 重拍模型ID
       pt_BR: rerank model id
     llm_description: rerank model id
+    default: default
     options:
       - value: default
         label:
@@ -110,20 +165,6 @@ parameters:
           zh_Hans: amazon.rerank-v1:0
     form: form
 
-  - name: aws_region
-    type: string
-    required: false
-    label:
-      en_US: AWS Region
-      zh_Hans: AWS 区域
-      pt_BR: AWS Region
-    human_description:
-      en_US: AWS region where the Bedrock Knowledge Base is located
-      zh_Hans: Bedrock知识库所在的AWS区域
-      pt_BR: AWS region where the Bedrock Knowledge Base is located
-    llm_description: AWS region where the Bedrock Knowledge Base is located
-    form: form
-
   - name: metadata_filter   # Additional parameter for metadata filtering
     type: string            # String type, expects JSON-formatted filter conditions
     required: false         # Optional field - can be omitted

api/core/tools/provider/builtin/aws/tools/bedrock_retrieve_and_generate.py

@@ -1,5 +1,5 @@
 import json
-from typing import Any, Optional
+from typing import Any
 
 import boto3
 
@@ -10,193 +10,63 @@ from core.tools.tool.builtin_tool import BuiltinTool
 class BedrockRetrieveAndGenerateTool(BuiltinTool):
     bedrock_client: Any = None
 
-    def _create_text_inference_config(
+    def _invoke(
         self,
-        max_tokens: Optional[int] = None,
-        stop_sequences: Optional[str] = None,
-        temperature: Optional[float] = None,
-        top_p: Optional[float] = None,
-    ) -> Optional[dict]:
-        """Create text inference configuration"""
-        if any([max_tokens, stop_sequences, temperature, top_p]):
-            config = {}
-            if max_tokens is not None:
-                config["maxTokens"] = max_tokens
-            if stop_sequences:
-                try:
-                    config["stopSequences"] = json.loads(stop_sequences)
-                except json.JSONDecodeError:
-                    config["stopSequences"] = []
-            if temperature is not None:
-                config["temperature"] = temperature
-            if top_p is not None:
-                config["topP"] = top_p
-            return config
-        return None
-
-    def _create_guardrail_config(
-        self,
-        guardrail_id: Optional[str] = None,
-        guardrail_version: Optional[str] = None,
-    ) -> Optional[dict]:
-        """Create guardrail configuration"""
-        if guardrail_id and guardrail_version:
-            return {"guardrailId": guardrail_id, "guardrailVersion": guardrail_version}
-        return None
-
-    def _create_generation_config(
-        self,
-        additional_model_fields: Optional[str] = None,
-        guardrail_config: Optional[dict] = None,
-        text_inference_config: Optional[dict] = None,
-        performance_mode: Optional[str] = None,
-        prompt_template: Optional[str] = None,
-    ) -> dict:
-        """Create generation configuration"""
-        config = {}
-
-        if additional_model_fields:
-            try:
-                config["additionalModelRequestFields"] = json.loads(additional_model_fields)
-            except json.JSONDecodeError:
-                pass
-
-        if guardrail_config:
-            config["guardrailConfiguration"] = guardrail_config
-
-        if text_inference_config:
-            config["inferenceConfig"] = {"textInferenceConfig": text_inference_config}
-
-        if performance_mode:
-            config["performanceConfig"] = {"latency": performance_mode}
-
-        if prompt_template:
-            config["promptTemplate"] = {"textPromptTemplate": prompt_template}
-
-        return config
-
-    def _create_orchestration_config(
-        self,
-        orchestration_additional_model_fields: Optional[str] = None,
-        orchestration_text_inference_config: Optional[dict] = None,
-        orchestration_performance_mode: Optional[str] = None,
-        orchestration_prompt_template: Optional[str] = None,
-    ) -> dict:
-        """Create orchestration configuration"""
-        config = {}
-
-        if orchestration_additional_model_fields:
-            try:
-                config["additionalModelRequestFields"] = json.loads(orchestration_additional_model_fields)
-            except json.JSONDecodeError:
-                pass
-
-        if orchestration_text_inference_config:
-            config["inferenceConfig"] = {"textInferenceConfig": orchestration_text_inference_config}
-
-        if orchestration_performance_mode:
-            config["performanceConfig"] = {"latency": orchestration_performance_mode}
-
-        if orchestration_prompt_template:
-            config["promptTemplate"] = {"textPromptTemplate": orchestration_prompt_template}
-
-        return config
-
-    def _create_vector_search_config(
-        self,
-        number_of_results: int = 5,
-        search_type: str = "SEMANTIC",
-        metadata_filter: Optional[dict] = None,
-    ) -> dict:
-        """Create vector search configuration"""
-        config = {
-            "numberOfResults": number_of_results,
-            "overrideSearchType": search_type,
-        }
-
-        # Only add filter if metadata_filter is not empty
-        if metadata_filter:
-            config["filter"] = metadata_filter
-
-        return config
-
-    def _bedrock_retrieve_and_generate(
-        self,
-        query: str,
-        knowledge_base_id: str,
-        model_arn: str,
-        # Generation Configuration
-        additional_model_fields: Optional[str] = None,
-        guardrail_id: Optional[str] = None,
-        guardrail_version: Optional[str] = None,
-        max_tokens: Optional[int] = None,
-        stop_sequences: Optional[str] = None,
-        temperature: Optional[float] = None,
-        top_p: Optional[float] = None,
-        performance_mode: str = "standard",
-        prompt_template: Optional[str] = None,
-        # Orchestration Configuration
-        orchestration_additional_model_fields: Optional[str] = None,
-        orchestration_max_tokens: Optional[int] = None,
-        orchestration_stop_sequences: Optional[str] = None,
-        orchestration_temperature: Optional[float] = None,
-        orchestration_top_p: Optional[float] = None,
-        orchestration_performance_mode: Optional[str] = None,
-        orchestration_prompt_template: Optional[str] = None,
-        # Retrieval Configuration
-        number_of_results: int = 5,
-        search_type: str = "SEMANTIC",
-        metadata_filter: Optional[dict] = None,
-        # Additional Configuration
-        session_id: Optional[str] = None,
-    ) -> dict[str, Any]:
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> ToolInvokeMessage:
         try:
-            # Create text inference configurations
-            text_inference_config = self._create_text_inference_config(max_tokens, stop_sequences, temperature, top_p)
-            orchestration_text_inference_config = self._create_text_inference_config(
-                orchestration_max_tokens, orchestration_stop_sequences, orchestration_temperature, orchestration_top_p
-            )
+            # Initialize Bedrock client if not already initialized
+            if not self.bedrock_client:
+                aws_region = tool_parameters.get("aws_region")
+                aws_access_key_id = tool_parameters.get("aws_access_key_id")
+                aws_secret_access_key = tool_parameters.get("aws_secret_access_key")
 
-            # Create guardrail configuration
-            guardrail_config = self._create_guardrail_config(guardrail_id, guardrail_version)
+                client_kwargs = {"service_name": "bedrock-agent-runtime", "region_name": aws_region or None}
 
-            # Create vector search configuration
-            vector_search_config = self._create_vector_search_config(number_of_results, search_type, metadata_filter)
+                # Only add credentials if both access key and secret key are provided
+                if aws_access_key_id and aws_secret_access_key:
+                    client_kwargs.update(
+                        {"aws_access_key_id": aws_access_key_id, "aws_secret_access_key": aws_secret_access_key}
+                    )
 
-            # Create generation configuration
-            generation_config = self._create_generation_config(
-                additional_model_fields, guardrail_config, text_inference_config, performance_mode, prompt_template
-            )
+                self.bedrock_client = boto3.client(**client_kwargs)
+        except Exception as e:
+            return self.create_text_message(f"Failed to initialize Bedrock client: {str(e)}")
 
-            # Create orchestration configuration
-            orchestration_config = self._create_orchestration_config(
-                orchestration_additional_model_fields,
-                orchestration_text_inference_config,
-                orchestration_performance_mode,
-                orchestration_prompt_template,
-            )
+        try:
+            request_config = {}
 
-            # Create knowledge base configuration
-            knowledge_base_config = {
-                "knowledgeBaseId": knowledge_base_id,
-                "modelArn": model_arn,
-                "generationConfiguration": generation_config,
-                "orchestrationConfiguration": orchestration_config,
-                "retrievalConfiguration": {"vectorSearchConfiguration": vector_search_config},
-            }
+            # Set input configuration
+            input_text = tool_parameters.get("input")
+            if input_text:
+                request_config["input"] = {"text": input_text}
 
-            # Create request configuration
-            request_config = {
-                "input": {"text": query},
-                "retrieveAndGenerateConfiguration": {
-                    "type": "KNOWLEDGE_BASE",
-                    "knowledgeBaseConfiguration": knowledge_base_config,
-                },
-            }
+            # Build retrieve and generate configuration
+            config_type = tool_parameters.get("type")
+            retrieve_generate_config = {"type": config_type}
 
-            # Add session configuration if provided
-            if session_id and len(session_id) >= 2:
-                request_config["sessionConfiguration"] = {"sessionId": session_id}
+            # Add configuration based on type
+            if config_type == "KNOWLEDGE_BASE":
+                kb_config_str = tool_parameters.get("knowledge_base_configuration")
+                kb_config = json.loads(kb_config_str) if kb_config_str else None
+                retrieve_generate_config["knowledgeBaseConfiguration"] = kb_config
+            else:  # EXTERNAL_SOURCES
+                es_config_str = tool_parameters.get("external_sources_configuration")
+                es_config = json.loads(kb_config_str) if es_config_str else None
+                retrieve_generate_config["externalSourcesConfiguration"] = es_config
+
+            request_config["retrieveAndGenerateConfiguration"] = retrieve_generate_config
+
+            # Parse session configuration
+            session_config_str = tool_parameters.get("session_configuration")
+            session_config = json.loads(session_config_str) if session_config_str else None
+            if session_config:
+                request_config["sessionConfiguration"] = session_config
+
+            # Add session ID if provided
+            session_id = tool_parameters.get("session_id")
+            if session_id:
                 request_config["sessionId"] = session_id
 
             # Send request
@@ -226,99 +96,42 @@ class BedrockRetrieveAndGenerateTool(BuiltinTool):
                     citation_info["references"].append(reference)
 
                 result["citations"].append(citation_info)
-
-            return result
-
+            result_type = tool_parameters.get("result_type")
+            if result_type == "json":
+                return self.create_json_message(result)
+            elif result_type == "text-with-citations":
+                return self.create_text_message(result)
+            else:
+                return self.create_text_message(result.get("output"))
+        except json.JSONDecodeError as e:
+            return self.create_text_message(f"Invalid JSON format: {str(e)}")
         except Exception as e:
-            raise Exception(f"Error calling Bedrock service: {str(e)}")
-
-    def _invoke(
-        self,
-        user_id: str,
-        tool_parameters: dict[str, Any],
-    ) -> ToolInvokeMessage:
-        try:
-            # Initialize Bedrock client if not already initialized
-            if not self.bedrock_client:
-                aws_region = tool_parameters.get("aws_region")
-                aws_access_key_id = tool_parameters.get("aws_access_key_id")
-                aws_secret_access_key = tool_parameters.get("aws_secret_access_key")
-
-                client_kwargs = {
-                    "service_name": "bedrock-agent-runtime",
-                }
-                if aws_region:
-                    client_kwargs["region_name"] = aws_region
-                # Only add credentials if both access key and secret key are provided
-                if aws_access_key_id and aws_secret_access_key:
-                    client_kwargs.update(
-                        {"aws_access_key_id": aws_access_key_id, "aws_secret_access_key": aws_secret_access_key}
-                    )
-
-                try:
-                    self.bedrock_client = boto3.client(**client_kwargs)
-                except Exception as e:
-                    return self.create_text_message(f"Failed to initialize Bedrock client: {str(e)}")
-
-            # Parse metadata filter if provided
-            metadata_filter = None
-            if metadata_filter_str := tool_parameters.get("metadata_filter"):
-                try:
-                    parsed_filter = json.loads(metadata_filter_str)
-                    if parsed_filter:  # Only set if not empty
-                        metadata_filter = parsed_filter
-                except json.JSONDecodeError:
-                    return self.create_text_message("metadata_filter must be a valid JSON string")
-
-            try:
-                response = self._bedrock_retrieve_and_generate(
-                    query=tool_parameters["query"],
-                    knowledge_base_id=tool_parameters["knowledge_base_id"],
-                    model_arn=tool_parameters["model_arn"],
-                    # Generation Configuration
-                    additional_model_fields=tool_parameters.get("additional_model_fields"),
-                    guardrail_id=tool_parameters.get("guardrail_id"),
-                    guardrail_version=tool_parameters.get("guardrail_version"),
-                    max_tokens=tool_parameters.get("max_tokens"),
-                    stop_sequences=tool_parameters.get("stop_sequences"),
-                    temperature=tool_parameters.get("temperature"),
-                    top_p=tool_parameters.get("top_p"),
-                    performance_mode=tool_parameters.get("performance_mode", "standard"),
-                    prompt_template=tool_parameters.get("prompt_template"),
-                    # Orchestration Configuration
-                    orchestration_additional_model_fields=tool_parameters.get("orchestration_additional_model_fields"),
-                    orchestration_max_tokens=tool_parameters.get("orchestration_max_tokens"),
-                    orchestration_stop_sequences=tool_parameters.get("orchestration_stop_sequences"),
-                    orchestration_temperature=tool_parameters.get("orchestration_temperature"),
-                    orchestration_top_p=tool_parameters.get("orchestration_top_p"),
-                    orchestration_performance_mode=tool_parameters.get("orchestration_performance_mode"),
-                    orchestration_prompt_template=tool_parameters.get("orchestration_prompt_template"),
-                    # Retrieval Configuration
-                    number_of_results=tool_parameters.get("number_of_results", 5),
-                    search_type=tool_parameters.get("search_type", "SEMANTIC"),
-                    metadata_filter=metadata_filter,
-                    # Additional Configuration
-                    session_id=tool_parameters.get("session_id"),
-                )
-                return self.create_json_message(response)
-
-            except Exception as e:
-                return self.create_text_message(f"Tool invocation error: {str(e)}")
-
-        except Exception as e:
-            return self.create_text_message(f"Tool execution error: {str(e)}")
+            return self.create_text_message(f"Tool invocation error: {str(e)}")
 
     def validate_parameters(self, parameters: dict[str, Any]) -> None:
         """Validate the parameters"""
-        required_params = ["query", "model_arn", "knowledge_base_id"]
-        for param in required_params:
-            if not parameters.get(param):
-                raise ValueError(f"{param} is required")
+        # Validate required parameters
+        if not parameters.get("input"):
+            raise ValueError("input is required")
+        if not parameters.get("type"):
+            raise ValueError("type is required")
 
-        # Validate metadata filter if provided
-        if metadata_filter_str := parameters.get("metadata_filter"):
-            try:
-                if not isinstance(json.loads(metadata_filter_str), dict):
-                    raise ValueError("metadata_filter must be a valid JSON object")
-            except json.JSONDecodeError:
-                raise ValueError("metadata_filter must be a valid JSON string")
+        # Validate JSON configurations
+        json_configs = ["knowledge_base_configuration", "external_sources_configuration", "session_configuration"]
+        for config in json_configs:
+            if config_value := parameters.get(config):
+                try:
+                    json.loads(config_value)
+                except json.JSONDecodeError:
+                    raise ValueError(f"{config} must be a valid JSON string")
+
+        # Validate configuration type
+        config_type = parameters.get("type")
+        if config_type not in ["KNOWLEDGE_BASE", "EXTERNAL_SOURCES"]:
+            raise ValueError("type must be either KNOWLEDGE_BASE or EXTERNAL_SOURCES")
+
+        # Validate type-specific configuration
+        if config_type == "KNOWLEDGE_BASE" and not parameters.get("knowledge_base_configuration"):
+            raise ValueError("knowledge_base_configuration is required when type is KNOWLEDGE_BASE")
+        elif config_type == "EXTERNAL_SOURCES" and not parameters.get("external_sources_configuration"):
+            raise ValueError("external_sources_configuration is required when type is EXTERNAL_SOURCES")

api/core/tools/provider/builtin/aws/tools/bedrock_retrieve_and_generate.yaml

@@ -8,24 +8,11 @@ identity:
 
 description:
   human:
-    en_US: A tool for retrieving and generating information using Amazon Bedrock Knowledge Base
-    zh_Hans: 使用Amazon Bedrock知识库进行信息检索和生成的工具
+    en_US: "This is an advanced usage of Bedrock Retrieve. Please refer to the API documentation for detailed parameters and paste them into the corresponding Knowledge Base Configuration or External Sources Configuration"
+    zh_Hans: "这个工具为Bedrock Retrieve的高级用法，请参考API设置详细的参数，并粘贴到对应的知识库配置或者外部源配置"
   llm: A tool for retrieving and generating information using Amazon Bedrock Knowledge Base
 
 parameters:
-# Additional Configuration
-  - name: session_id
-    type: string
-    required: false
-    label:
-      en_US: Session ID
-      zh_Hans: 会话ID
-    human_description:
-      en_US: Optional session ID for continuous conversations
-      zh_Hans: 用于连续对话的可选会话ID
-    form: form
-
-  # AWS Configuration
   - name: aws_region
     type: string
     required: false
@@ -59,300 +46,103 @@ parameters:
       zh_Hans: 用于身份验证的AWS秘密访问密钥（可选）
     form: form
 
-  # Knowledge Base Configuration
-  - name: knowledge_base_id
-    type: string
+  - name: result_type
+    type: select
     required: true
     label:
-      en_US: Knowledge Base ID
-      zh_Hans: 知识库ID
+      en_US: result type
+      zh_Hans: 结果类型
     human_description:
-      en_US: ID of the Bedrock Knowledge Base
-      zh_Hans: Bedrock知识库的ID
+      en_US: return a list of json or texts
+      zh_Hans: 返回一个列表，内容是json还是纯文本
+    default: text
+    options:
+      - value: json
+        label:
+          en_US: JSON
+          zh_Hans: JSON
+      - value: text
+        label:
+          en_US: Text
+          zh_Hans: 文本
+      - value: text-with-citations
+        label:
+          en_US: Text With Citations
+          zh_Hans: 文本（包含引用）
     form: form
 
-  - name: model_arn
+  - name: input
     type: string
     required: true
     label:
-      en_US: Model ARN
-      zh_Hans: 模型ARN
+      en_US: Input Text
+      zh_Hans: 输入文本
     human_description:
-      en_US: The ARN of the model to use
-      zh_Hans: 要使用的模型ARN
-    form: form
-
-  # Retrieval Configuration
-  - name: query
-    type: string
-    required: true
-    label:
-      en_US: Query
-      zh_Hans: 查询
-    human_description:
-      en_US: The search query to retrieve information
-      zh_Hans: 用于检索信息的查询语句
+      en_US: The text query to retrieve information
+      zh_Hans: 用于检索信息的文本查询
     form: llm
 
-  - name: number_of_results
-    type: number
-    required: false
-    label:
-      en_US: Number of Results
-      zh_Hans: 结果数量
-    human_description:
-      en_US: Number of results to retrieve (1-10)
-      zh_Hans: 要检索的结果数量（1-10）
-    default: 5
-    min: 1
-    max: 10
-    form: form
-
-  - name: search_type
+  - name: type
     type: select
-    required: false
+    required: true
     label:
-      en_US: Search Type
-      zh_Hans: 搜索类型
+      en_US: Configuration Type
+      zh_Hans: 配置类型
     human_description:
-      en_US: Type of search to perform
-      zh_Hans: 要执行的搜索类型
-    default: SEMANTIC
+      en_US: Type of retrieve and generate configuration
+      zh_Hans: 检索和生成配置的类型
     options:
-      - value: SEMANTIC
+      - value: KNOWLEDGE_BASE
         label:
-          en_US: Semantic Search
-          zh_Hans: 语义搜索
-      - value: HYBRID
+          en_US: Knowledge Base
+          zh_Hans: 知识库
+      - value: EXTERNAL_SOURCES
         label:
-          en_US: Hybrid Search
-          zh_Hans: 混合搜索
+          en_US: External Sources
+          zh_Hans: 外部源
     form: form
 
-  - name: metadata_filter
+  - name: knowledge_base_configuration
     type: string
     required: false
     label:
-      en_US: Metadata Filter
-      zh_Hans: 元数据过滤器
+      en_US: Knowledge Base Configuration
+      zh_Hans: 知识库配置
     human_description:
-      en_US: JSON formatted filter conditions for metadata, supporting operations like equals, greaterThan, lessThan, etc.
-      zh_Hans: 元数据的JSON格式过滤条件，支持等于、大于、小于等操作
-    default: "{}"
+      en_US: Please refer to @https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/bedrock-agent-runtime/client/retrieve_and_generate.html#retrieve-and-generate for complete parameters and paste them here
+      zh_Hans: 请参考 https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/bedrock-agent-runtime/client/retrieve_and_generate.html#retrieve-and-generate 配置完整的参数并粘贴到这里
     form: form
 
-# Generation Configuration
-  - name: guardrail_id
+  - name: external_sources_configuration
     type: string
     required: false
     label:
-      en_US: Guardrail ID
-      zh_Hans: 防护栏ID
+      en_US: External Sources Configuration
+      zh_Hans: 外部源配置
     human_description:
-      en_US: ID of the guardrail to apply
-      zh_Hans: 要应用的防护栏ID
+      en_US: Please refer to https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/bedrock-agent-runtime/client/retrieve_and_generate.html#retrieve-and-generate for complete parameters and paste them here
+      zh_Hans: 请参考 https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/bedrock-agent-runtime/client/retrieve_and_generate.html#retrieve-and-generate 配置完整的参数并粘贴到这里
     form: form
 
-  - name: guardrail_version
+  - name: session_configuration
     type: string
     required: false
     label:
-      en_US: Guardrail Version
-      zh_Hans: 防护栏版本
+      en_US: Session Configuration
+      zh_Hans: 会话配置
     human_description:
-      en_US: Version of the guardrail to apply
-      zh_Hans: 要应用的防护栏版本
+      en_US: JSON formatted session configuration
+      zh_Hans: JSON格式的会话配置
+    default: ""
     form: form
 
-  - name: max_tokens
-    type: number
-    required: false
-    label:
-      en_US: Maximum Tokens
-      zh_Hans: 最大令牌数
-    human_description:
-      en_US: Maximum number of tokens to generate
-      zh_Hans: 生成的最大令牌数
-    default: 2048
-    form: form
-
-  - name: stop_sequences
+  - name: session_id
     type: string
     required: false
     label:
-      en_US: Stop Sequences
-      zh_Hans: 停止序列
+      en_US: Session ID
+      zh_Hans: 会话ID
     human_description:
-      en_US: JSON array of strings that will stop generation when encountered
-      zh_Hans: JSON数组格式的字符串，遇到这些序列时将停止生成
-    default: "[]"
-    form: form
-
-  - name: temperature
-    type: number
-    required: false
-    label:
-      en_US: Temperature
-      zh_Hans: 温度
-    human_description:
-      en_US: Controls randomness in the output (0-1)
-      zh_Hans: 控制输出的随机性（0-1）
-    default: 0.7
-    min: 0
-    max: 1
-    form: form
-
-  - name: top_p
-    type: number
-    required: false
-    label:
-      en_US: Top P
-      zh_Hans: Top P值
-    human_description:
-      en_US: Controls diversity via nucleus sampling (0-1)
-      zh_Hans: 通过核采样控制多样性（0-1）
-    default: 0.95
-    min: 0
-    max: 1
-    form: form
-
-  - name: performance_mode
-    type: select
-    required: false
-    label:
-      en_US: Performance Mode
-      zh_Hans: 性能模式
-    human_description:
-      en_US: Select performance optimization mode(performanceConfig.latency)
-      zh_Hans: 选择性能优化模式(performanceConfig.latency)
-    default: standard
-    options:
-      - value: standard
-        label:
-          en_US: Standard
-          zh_Hans: 标准
-      - value: optimized
-        label:
-          en_US: Optimized
-          zh_Hans: 优化
-    form: form
-
-  - name: prompt_template
-    type: string
-    required: false
-    label:
-      en_US: Prompt Template
-      zh_Hans: 提示模板
-    human_description:
-      en_US: Custom prompt template for generation
-      zh_Hans: 用于生成的自定义提示模板
-    form: form
-
-  - name: additional_model_fields
-    type: string
-    required: false
-    label:
-      en_US: Additional Model Fields
-      zh_Hans: 额外模型字段
-    human_description:
-      en_US: JSON formatted additional fields for model configuration
-      zh_Hans: JSON格式的额外模型配置字段
-    default: "{}"
-    form: form
-
-# Orchestration Configuration
-  - name: orchestration_max_tokens
-    type: number
-    required: false
-    label:
-      en_US: Orchestration Maximum Tokens
-      zh_Hans: 编排最大令牌数
-    human_description:
-      en_US: Maximum number of tokens for orchestration
-      zh_Hans: 编排过程的最大令牌数
-    default: 2048
-    form: form
-
-  - name: orchestration_stop_sequences
-    type: string
-    required: false
-    label:
-      en_US: Orchestration Stop Sequences
-      zh_Hans: 编排停止序列
-    human_description:
-      en_US: JSON array of strings that will stop orchestration when encountered
-      zh_Hans: JSON数组格式的字符串，遇到这些序列时将停止编排
-    default: "[]"
-    form: form
-
-  - name: orchestration_temperature
-    type: number
-    required: false
-    label:
-      en_US: Orchestration Temperature
-      zh_Hans: 编排温度
-    human_description:
-      en_US: Controls randomness in the orchestration output (0-1)
-      zh_Hans: 控制编排输出的随机性（0-1）
-    default: 0.7
-    min: 0
-    max: 1
-    form: form
-
-  - name: orchestration_top_p
-    type: number
-    required: false
-    label:
-      en_US: Orchestration Top P
-      zh_Hans: 编排Top P值
-    human_description:
-      en_US: Controls diversity via nucleus sampling in orchestration (0-1)
-      zh_Hans: 通过核采样控制编排的多样性（0-1）
-    default: 0.95
-    min: 0
-    max: 1
-    form: form
-
-  - name: orchestration_performance_mode
-    type: select
-    required: false
-    label:
-      en_US: Orchestration Performance Mode
-      zh_Hans: 编排性能模式
-    human_description:
-      en_US: Select performance optimization mode for orchestration
-      zh_Hans: 选择编排的性能优化模式
-    default: standard
-    options:
-      - value: standard
-        label:
-          en_US: Standard
-          zh_Hans: 标准
-      - value: optimized
-        label:
-          en_US: Optimized
-          zh_Hans: 优化
-    form: form
-
-  - name: orchestration_prompt_template
-    type: string
-    required: false
-    label:
-      en_US: Orchestration Prompt Template
-      zh_Hans: 编排提示模板
-    human_description:
-      en_US: Custom prompt template for orchestration
-      zh_Hans: 用于编排的自定义提示模板
-    form: form
-
-  - name: orchestration_additional_model_fields
-    type: string
-    required: false
-    label:
-      en_US: Orchestration Additional Model Fields
-      zh_Hans: 编排额外模型字段
-    human_description:
-      en_US: JSON formatted additional fields for orchestration model configuration
-      zh_Hans: JSON格式的编排模型额外配置字段
-    default: "{}"
+      en_US: Session ID for continuous conversations
+      zh_Hans: 用于连续对话的会话ID
     form: form

api/core/tools/provider/builtin/vanna/tools/vanna.py

@@ -1,134 +0,0 @@
-from typing import Any, Union
-
-from vanna.remote import VannaDefault  # type: ignore
-
-from core.tools.entities.tool_entities import ToolInvokeMessage
-from core.tools.errors import ToolProviderCredentialValidationError
-from core.tools.tool.builtin_tool import BuiltinTool
-
-
-class VannaTool(BuiltinTool):
-    def _invoke(
-        self, user_id: str, tool_parameters: dict[str, Any]
-    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
-        """
-        invoke tools
-        """
-        # Ensure runtime and credentials
-        if not self.runtime or not self.runtime.credentials:
-            raise ToolProviderCredentialValidationError("Tool runtime or credentials are missing")
-        api_key = self.runtime.credentials.get("api_key", None)
-        if not api_key:
-            raise ToolProviderCredentialValidationError("Please input api key")
-
-        model = tool_parameters.get("model", "")
-        if not model:
-            return self.create_text_message("Please input RAG model")
-
-        prompt = tool_parameters.get("prompt", "")
-        if not prompt:
-            return self.create_text_message("Please input prompt")
-
-        url = tool_parameters.get("url", "")
-        if not url:
-            return self.create_text_message("Please input URL/Host/DSN")
-
-        db_name = tool_parameters.get("db_name", "")
-        username = tool_parameters.get("username", "")
-        password = tool_parameters.get("password", "")
-        port = tool_parameters.get("port", 0)
-
-        base_url = self.runtime.credentials.get("base_url", None)
-        vn = VannaDefault(model=model, api_key=api_key, config={"endpoint": base_url})
-
-        db_type = tool_parameters.get("db_type", "")
-        if db_type in {"Postgres", "MySQL", "Hive", "ClickHouse"}:
-            if not db_name:
-                return self.create_text_message("Please input database name")
-            if not username:
-                return self.create_text_message("Please input username")
-            if port < 1:
-                return self.create_text_message("Please input port")
-
-        schema_sql = "SELECT * FROM INFORMATION_SCHEMA.COLUMNS"
-        match db_type:
-            case "SQLite":
-                schema_sql = "SELECT type, sql FROM sqlite_master WHERE sql is not null"
-                vn.connect_to_sqlite(url)
-            case "Postgres":
-                vn.connect_to_postgres(host=url, dbname=db_name, user=username, password=password, port=port)
-            case "DuckDB":
-                vn.connect_to_duckdb(url=url)
-            case "SQLServer":
-                vn.connect_to_mssql(url)
-            case "MySQL":
-                vn.connect_to_mysql(host=url, dbname=db_name, user=username, password=password, port=port)
-            case "Oracle":
-                vn.connect_to_oracle(user=username, password=password, dsn=url)
-            case "Hive":
-                vn.connect_to_hive(host=url, dbname=db_name, user=username, password=password, port=port)
-            case "ClickHouse":
-                vn.connect_to_clickhouse(host=url, dbname=db_name, user=username, password=password, port=port)
-
-        enable_training = tool_parameters.get("enable_training", False)
-        reset_training_data = tool_parameters.get("reset_training_data", False)
-        if enable_training:
-            if reset_training_data:
-                existing_training_data = vn.get_training_data()
-                if len(existing_training_data) > 0:
-                    for _, training_data in existing_training_data.iterrows():
-                        vn.remove_training_data(training_data["id"])
-
-            ddl = tool_parameters.get("ddl", "")
-            question = tool_parameters.get("question", "")
-            sql = tool_parameters.get("sql", "")
-            memos = tool_parameters.get("memos", "")
-            training_metadata = tool_parameters.get("training_metadata", False)
-
-            if training_metadata:
-                if db_type == "SQLite":
-                    df_ddl = vn.run_sql(schema_sql)
-                    for ddl in df_ddl["sql"].to_list():
-                        vn.train(ddl=ddl)
-                else:
-                    df_information_schema = vn.run_sql(schema_sql)
-                    plan = vn.get_training_plan_generic(df_information_schema)
-                    vn.train(plan=plan)
-
-            if ddl:
-                vn.train(ddl=ddl)
-
-            if sql:
-                if question:
-                    vn.train(question=question, sql=sql)
-                else:
-                    vn.train(sql=sql)
-            if memos:
-                vn.train(documentation=memos)
-
-        #########################################################################################
-        # Due to CVE-2024-5565, we have to disable the chart generation feature
-        # The Vanna library uses a prompt function to present the user with visualized results,
-        # it is possible to alter the prompt using prompt injection and run arbitrary Python code
-        # instead of the intended visualization code.
-        # Specifically - allowing external input to the library’s “ask” method
-        # with "visualize" set to True (default behavior) leads to remote code execution.
-        # Affected versions: <= 0.5.5
-        #########################################################################################
-        allow_llm_to_see_data = tool_parameters.get("allow_llm_to_see_data", False)
-        res = vn.ask(
-            prompt, print_results=False, auto_train=True, visualize=False, allow_llm_to_see_data=allow_llm_to_see_data
-        )
-
-        result = []
-
-        if res is not None:
-            result.append(self.create_text_message(res[0]))
-            if len(res) > 1 and res[1] is not None:
-                result.append(self.create_text_message(res[1].to_markdown()))
-            if len(res) > 2 and res[2] is not None:
-                result.append(
-                    self.create_blob_message(blob=res[2].to_image(format="svg"), meta={"mime_type": "image/svg+xml"})
-                )
-
-        return result

api/core/tools/provider/builtin/vanna/tools/vanna.yaml

@@ -1,213 +0,0 @@
-identity:
-  name: vanna
-  author: QCTC
-  label:
-    en_US: Vanna.AI
-    zh_Hans: Vanna.AI
-description:
-  human:
-    en_US: The fastest way to get actionable insights from your database just by asking questions.
-    zh_Hans: 一个基于大模型和RAG的Text2SQL工具。
-  llm: A tool for converting text to SQL.
-parameters:
-  - name: prompt
-    type: string
-    required: true
-    label:
-      en_US: Prompt
-      zh_Hans: 提示词
-      pt_BR: Prompt
-    human_description:
-      en_US: used for generating SQL
-      zh_Hans: 用于生成SQL
-    llm_description: key words for generating SQL
-    form: llm
-  - name: model
-    type: string
-    required: true
-    label:
-      en_US: RAG Model
-      zh_Hans: RAG模型
-    human_description:
-      en_US: RAG Model for your database DDL
-      zh_Hans: 存储数据库训练数据的RAG模型
-    llm_description: RAG Model for generating SQL
-    form: llm
-  - name: db_type
-    type: select
-    required: true
-    options:
-      - value: SQLite
-        label:
-          en_US: SQLite
-          zh_Hans: SQLite
-      - value: Postgres
-        label:
-          en_US: Postgres
-          zh_Hans: Postgres
-      - value: DuckDB
-        label:
-          en_US: DuckDB
-          zh_Hans: DuckDB
-      - value: SQLServer
-        label:
-          en_US: Microsoft SQL Server
-          zh_Hans: 微软 SQL Server
-      - value: MySQL
-        label:
-          en_US: MySQL
-          zh_Hans: MySQL
-      - value: Oracle
-        label:
-          en_US: Oracle
-          zh_Hans: Oracle
-      - value: Hive
-        label:
-          en_US: Hive
-          zh_Hans: Hive
-      - value: ClickHouse
-        label:
-          en_US: ClickHouse
-          zh_Hans: ClickHouse
-    default: SQLite
-    label:
-      en_US: DB Type
-      zh_Hans: 数据库类型
-    human_description:
-      en_US: Database type.
-      zh_Hans: 选择要链接的数据库类型。
-    form: form
-  - name: url
-    type: string
-    required: true
-    label:
-      en_US: URL/Host/DSN
-      zh_Hans: URL/Host/DSN
-    human_description:
-      en_US: Please input depending on DB type, visit https://vanna.ai/docs/ for more specification
-      zh_Hans: 请根据数据库类型，填入对应值，详情参考https://vanna.ai/docs/
-    form: form
-  - name: db_name
-    type: string
-    required: false
-    label:
-      en_US: DB name
-      zh_Hans: 数据库名
-    human_description:
-      en_US: Database name
-      zh_Hans: 数据库名
-    form: form
-  - name: username
-    type: string
-    required: false
-    label:
-      en_US: Username
-      zh_Hans: 用户名
-    human_description:
-      en_US: Username
-      zh_Hans: 用户名
-    form: form
-  - name: password
-    type: secret-input
-    required: false
-    label:
-      en_US: Password
-      zh_Hans: 密码
-    human_description:
-      en_US: Password
-      zh_Hans: 密码
-    form: form
-  - name: port
-    type: number
-    required: false
-    label:
-      en_US: Port
-      zh_Hans: 端口
-    human_description:
-      en_US: Port
-      zh_Hans: 端口
-    form: form
-  - name: ddl
-    type: string
-    required: false
-    label:
-      en_US: Training DDL
-      zh_Hans: 训练DDL
-    human_description:
-      en_US: DDL statements for training data
-      zh_Hans: 用于训练RAG Model的建表语句
-    form: llm
-  - name: question
-    type: string
-    required: false
-    label:
-      en_US: Training Question
-      zh_Hans: 训练问题
-    human_description:
-      en_US: Question-SQL Pairs
-      zh_Hans: Question-SQL中的问题
-    form: llm
-  - name: sql
-    type: string
-    required: false
-    label:
-      en_US: Training SQL
-      zh_Hans: 训练SQL
-    human_description:
-      en_US: SQL queries to your training data
-      zh_Hans: 用于训练RAG Model的SQL语句
-    form: llm
-  - name: memos
-    type: string
-    required: false
-    label:
-      en_US: Training Memos
-      zh_Hans: 训练说明
-    human_description:
-      en_US: Sometimes you may want to add documentation about your business terminology or definitions
-      zh_Hans: 添加更多关于数据库的业务说明
-    form: llm
-  - name: enable_training
-    type: boolean
-    required: false
-    default: false
-    label:
-      en_US: Training Data
-      zh_Hans: 训练数据
-    human_description:
-      en_US: You only need to train once. Do not train again unless you want to add more training data
-      zh_Hans: 训练数据无更新时，训练一次即可
-    form: form
-  - name: reset_training_data
-    type: boolean
-    required: false
-    default: false
-    label:
-      en_US: Reset Training Data
-      zh_Hans: 重置训练数据
-    human_description:
-      en_US: Remove all training data in the current RAG Model
-      zh_Hans: 删除当前RAG Model中的所有训练数据
-    form: form
-  - name: training_metadata
-    type: boolean
-    required: false
-    default: false
-    label:
-      en_US: Training Metadata
-      zh_Hans: 训练元数据
-    human_description:
-      en_US: If enabled, it will attempt to train on the metadata of that database
-      zh_Hans: 是否自动从数据库获取元数据来训练
-    form: form
-  - name: allow_llm_to_see_data
-    type: boolean
-    required: false
-    default: false
-    label:
-      en_US: Whether to allow the LLM to see the data
-      zh_Hans: 是否允许LLM查看数据
-    human_description:
-      en_US: Whether to allow the LLM to see the data
-      zh_Hans: 是否允许LLM查看数据
-    form: form

api/core/tools/provider/builtin/vanna/vanna.py

@@ -1,46 +0,0 @@
-import re
-from typing import Any
-from urllib.parse import urlparse
-
-from core.tools.errors import ToolProviderCredentialValidationError
-from core.tools.provider.builtin.vanna.tools.vanna import VannaTool
-from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
-
-
-class VannaProvider(BuiltinToolProviderController):
-    def _get_protocol_and_main_domain(self, url):
-        parsed_url = urlparse(url)
-        protocol = parsed_url.scheme
-        hostname = parsed_url.hostname
-        port = f":{parsed_url.port}" if parsed_url.port else ""
-
-        # Check if the hostname is an IP address
-        is_ip = re.match(r"^\d{1,3}(\.\d{1,3}){3}$", hostname) is not None
-
-        # Return the full hostname (with port if present) for IP addresses, otherwise return the main domain
-        main_domain = f"{hostname}{port}" if is_ip else ".".join(hostname.split(".")[-2:]) + port
-        return f"{protocol}://{main_domain}"
-
-    def _validate_credentials(self, credentials: dict[str, Any]) -> None:
-        base_url = credentials.get("base_url")
-        if not base_url:
-            base_url = "https://ask.vanna.ai/rpc"
-        else:
-            base_url = base_url.removesuffix("/")
-        credentials["base_url"] = base_url
-        try:
-            VannaTool().fork_tool_runtime(
-                runtime={
-                    "credentials": credentials,
-                }
-            ).invoke(
-                user_id="",
-                tool_parameters={
-                    "model": "chinook",
-                    "db_type": "SQLite",
-                    "url": f"{self._get_protocol_and_main_domain(credentials['base_url'])}/Chinook.sqlite",
-                    "query": "What are the top 10 customers by sales?",
-                },
-            )
-        except Exception as e:
-            raise ToolProviderCredentialValidationError(str(e))

api/core/tools/provider/builtin/vanna/vanna.yaml

@@ -1,35 +0,0 @@
-identity:
-  author: QCTC
-  name: vanna
-  label:
-    en_US: Vanna.AI
-    zh_Hans: Vanna.AI
-  description:
-    en_US: The fastest way to get actionable insights from your database just by asking questions.
-    zh_Hans: 一个基于大模型和RAG的Text2SQL工具。
-  icon: icon.png
-  tags:
-    - utilities
-    - productivity
-credentials_for_provider:
-  api_key:
-    type: secret-input
-    required: true
-    label:
-      en_US: API key
-      zh_Hans: API key
-    placeholder:
-      en_US: Please input your API key
-      zh_Hans: 请输入你的 API key
-      pt_BR: Please input your API key
-    help:
-      en_US: Get your API key from Vanna.AI
-      zh_Hans: 从 Vanna.AI 获取你的 API key
-    url: https://vanna.ai/account/profile
-  base_url:
-    type: text-input
-    required: false
-    label:
-      en_US: Vanna.AI Endpoint Base URL
-    placeholder:
-      en_US: https://ask.vanna.ai/rpc

api/core/workflow/graph_engine/entities/graph.py

@@ -590,6 +590,8 @@ class Graph(BaseModel):
                             start_node_id=node_id,
                             routes_node_ids=routes_node_ids,
                         )
+                        # Exclude conditional branch nodes
+                        and all(edge.run_condition is None for edge in reverse_edge_mapping.get(node_id, []))
                     ):
                         if node_id not in merge_branch_node_ids:
                             merge_branch_node_ids[node_id] = []

api/core/workflow/nodes/llm/entities.py

@@ -3,7 +3,7 @@ from typing import Any, Optional
 
 from pydantic import BaseModel, Field, field_validator
 
-from core.model_runtime.entities import ImagePromptMessageContent
+from core.model_runtime.entities import ImagePromptMessageContent, LLMMode
 from core.prompt.entities.advanced_prompt_entities import ChatModelMessage, CompletionModelPromptTemplate, MemoryConfig
 from core.workflow.entities.variable_entities import VariableSelector
 from core.workflow.nodes.base import BaseNodeData
@@ -12,7 +12,7 @@ from core.workflow.nodes.base import BaseNodeData
 class ModelConfig(BaseModel):
     provider: str
     name: str
-    mode: str
+    mode: LLMMode
     completion_params: dict[str, Any] = {}
 
 

api/core/workflow/nodes/llm/node.py

@@ -3,6 +3,7 @@ import logging
 from collections.abc import Generator, Mapping, Sequence
 from typing import TYPE_CHECKING, Any, Optional, cast
 
+from configs import dify_config
 from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
 from core.entities.model_entities import ModelStatus
 from core.entities.provider_entities import QuotaUnit
@@ -732,10 +733,7 @@ class LLMNode(BaseNode[LLMNodeData]):
             if quota_unit == QuotaUnit.TOKENS:
                 used_quota = usage.total_tokens
             elif quota_unit == QuotaUnit.CREDITS:
-                used_quota = 1
-
-                if "gpt-4" in model_instance.model:
-                    used_quota = 20
+                used_quota = dify_config.get_model_credits(model_instance.model)
             else:
                 used_quota = 1
 

api/events/event_handlers/deduct_quota_when_message_created.py

@@ -1,3 +1,4 @@
+from configs import dify_config
 from core.app.entities.app_invoke_entities import AgentChatAppGenerateEntity, ChatAppGenerateEntity
 from core.entities.provider_entities import QuotaUnit
 from events.message_event import message_was_created
@@ -37,10 +38,7 @@ def handle(sender, **kwargs):
         if quota_unit == QuotaUnit.TOKENS:
             used_quota = message.message_tokens + message.answer_tokens
         elif quota_unit == QuotaUnit.CREDITS:
-            used_quota = 1
-
-            if "gpt-4" in model_config.model:
-                used_quota = 20
+            used_quota = dify_config.get_model_credits(model_config.model)
         else:
             used_quota = 1
 

api/extensions/storage/azure_blob_storage.py

@@ -1,6 +1,8 @@
 from collections.abc import Generator
 from datetime import UTC, datetime, timedelta
+from typing import Optional
 
+from azure.identity import ChainedTokenCredential, DefaultAzureCredential
 from azure.storage.blob import AccountSasPermissions, BlobServiceClient, ResourceTypes, generate_account_sas
 
 from configs import dify_config
@@ -18,6 +20,12 @@ class AzureBlobStorage(BaseStorage):
         self.account_name = dify_config.AZURE_BLOB_ACCOUNT_NAME
         self.account_key = dify_config.AZURE_BLOB_ACCOUNT_KEY
 
+        self.credential: Optional[ChainedTokenCredential] = None
+        if self.account_key == "managedidentity":
+            self.credential = DefaultAzureCredential()
+        else:
+            self.credential = None
+
     def save(self, filename, data):
         client = self._sync_client()
         blob_container = client.get_container_client(container=self.bucket_name)
@@ -57,6 +65,9 @@ class AzureBlobStorage(BaseStorage):
         blob_container.delete_blob(filename)
 
     def _sync_client(self):
+        if self.account_key == "managedidentity":
+            return BlobServiceClient(account_url=self.account_url, credential=self.credential)  # type: ignore
+
         cache_key = "azure_blob_sas_token_{}_{}".format(self.account_name, self.account_key)
         cache_result = redis_client.get(cache_key)
         if cache_result is not None:

api/poetry.lock

@@ -627,15 +627,15 @@ cryptography = "*"
 
 [[package]]
 name = "azure-ai-inference"
-version = "1.0.0b6"
+version = "1.0.0b8"
 description = "Microsoft Azure AI Inference Client Library for Python"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 markers = "python_version == \"3.11\" or python_version >= \"3.12\""
 files = [
-    {file = "azure_ai_inference-1.0.0b6-py3-none-any.whl", hash = "sha256:5699ad78d70ec2d227a5eff2c1bafc845018f6624edc5b03589dfff861c54958"},
-    {file = "azure_ai_inference-1.0.0b6.tar.gz", hash = "sha256:b8ac941de1e69151bad464191e18856d4e74f962ae03235da137a9a326143676"},
+    {file = "azure_ai_inference-1.0.0b8-py3-none-any.whl", hash = "sha256:9bfcfe6ef5b1699fed6c70058027c253bcbc88f4730e7409fbfc675636ec05e4"},
+    {file = "azure_ai_inference-1.0.0b8.tar.gz", hash = "sha256:b7bcaaac5f53f2be06804ac6c755be9583ac6ba99df533a3970da081838b4cc1"},
 ]
 
 [package.dependencies]
@@ -931,19 +931,19 @@ files = [
 
 [[package]]
 name = "boto3"
-version = "1.36.4"
+version = "1.36.12"
 description = "The AWS SDK for Python"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 markers = "python_version == \"3.11\" or python_version >= \"3.12\""
 files = [
-    {file = "boto3-1.36.4-py3-none-any.whl", hash = "sha256:9f8f699e75ec63fcc98c4dd7290997c7c06c68d3ac8161ad4735fe71f5fe945c"},
-    {file = "boto3-1.36.4.tar.gz", hash = "sha256:eeceeb74ef8b65634d358c27aa074917f4449dc828f79301f1075232618eb502"},
+    {file = "boto3-1.36.12-py3-none-any.whl", hash = "sha256:32cdf0967287f3ec25a9dc09df0d29cb86b8900c3e0546a63d672775d8127abf"},
+    {file = "boto3-1.36.12.tar.gz", hash = "sha256:287d84f49bba3255a17b374578127d42b6251e72f55914a62e0ad9ca78c0954b"},
 ]
 
 [package.dependencies]
-botocore = ">=1.36.4,<1.37.0"
+botocore = ">=1.36.12,<1.37.0"
 jmespath = ">=0.7.1,<2.0.0"
 s3transfer = ">=0.11.0,<0.12.0"
 
@@ -952,15 +952,15 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.36.5"
+version = "1.36.12"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 markers = "python_version == \"3.11\" or python_version >= \"3.12\""
 files = [
-    {file = "botocore-1.36.5-py3-none-any.whl", hash = "sha256:6d9f70afa9bf9d21407089dc22b8cc8ec6fa44866d4660858c062c74fc8555eb"},
-    {file = "botocore-1.36.5.tar.gz", hash = "sha256:234ed3d29a8954c37a551c933453bf14c6ae44a69a4f273ffef377a2612ca6a6"},
+    {file = "botocore-1.36.12-py3-none-any.whl", hash = "sha256:5ae1ed362c8ed908a6ced8cdd12b21e2196c100bc79f9e95c9c1fc7f9ea74f5a"},
+    {file = "botocore-1.36.12.tar.gz", hash = "sha256:86ed88beb4f244c96529435c868d3940073c2774116f0023fb7691f6e7053bd9"},
 ]
 
 [package.dependencies]
@@ -969,7 +969,7 @@ python-dateutil = ">=2.1,<3.0.0"
 urllib3 = {version = ">=1.25.4,<2.2.0 || >2.2.0,<3", markers = "python_version >= \"3.10\""}
 
 [package.extras]
-crt = ["awscrt (==0.23.4)"]
+crt = ["awscrt (==0.23.8)"]
 
 [[package]]
 name = "bottleneck"
@@ -6522,15 +6522,15 @@ sympy = "*"
 
 [[package]]
 name = "openai"
-version = "1.52.2"
+version = "1.61.0"
 description = "The official Python library for the openai API"
 optional = false
-python-versions = ">=3.7.1"
+python-versions = ">=3.8"
 groups = ["main"]
 markers = "python_version == \"3.11\" or python_version >= \"3.12\""
 files = [
-    {file = "openai-1.52.2-py3-none-any.whl", hash = "sha256:57e9e37bc407f39bb6ec3a27d7e8fb9728b2779936daa1fcf95df17d3edfaccc"},
-    {file = "openai-1.52.2.tar.gz", hash = "sha256:87b7d0f69d85f5641678d414b7ee3082363647a5c66a462ed7f3ccb59582da0d"},
+    {file = "openai-1.61.0-py3-none-any.whl", hash = "sha256:e8c512c0743accbdbe77f3429a1490d862f8352045de8dc81969301eb4a4f666"},
+    {file = "openai-1.61.0.tar.gz", hash = "sha256:216f325a24ed8578e929b0f1b3fb2052165f3b04b0461818adaa51aa29c71f8a"},
 ]
 
 [package.dependencies]
@@ -6545,6 +6545,7 @@ typing-extensions = ">=4.11,<5"
 
 [package.extras]
 datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
+realtime = ["websockets (>=13,<15)"]
 
 [[package]]
 name = "opencensus"
@@ -12388,4 +12389,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.11,<3.13"
-content-hash = "6243573a26b9aa03558eb2c176d2477a08b1033a17065e870e4be83af0af644d"
+content-hash = "d197cdff507a70323c1d6aca11609188f54970f67715af744fe6def15b7776fd"

api/pyproject.toml

@@ -17,11 +17,11 @@ package-mode = false
 [tool.poetry.dependencies]
 anthropic = "~0.23.1"
 authlib = "1.3.1"
-azure-ai-inference = "~1.0.0b3"
+azure-ai-inference = "~1.0.0b8"
 azure-ai-ml = "~1.20.0"
 azure-identity = "1.16.1"
 beautifulsoup4 = "4.12.2"
-boto3 = "1.36.4"
+boto3 = "1.36.12"
 bs4 = "~0.0.1"
 cachetools = "~5.3.0"
 celery = "~5.4.0"
@@ -58,7 +58,7 @@ nomic = "~3.1.2"
 novita-client = "~0.5.7"
 numpy = "~1.26.4"
 oci = "~2.135.1"
-openai = "~1.52.0"
+openai = "~1.61.0"
 openpyxl = "~3.1.5"
 opik = "~1.3.4"
 pandas = { version = "~2.2.2", extras = ["performance", "excel"] }

api/services/enterprise/mail_service.py

@@ -0,0 +1,26 @@
+
+from typing import Dict, List
+
+from pydantic import BaseModel
+
+from tasks.mail_enterprise_task import send_enterprise_email_task
+
+
+class DifyMail(BaseModel):
+    to: List[str]
+    subject: str
+    body: str
+    substitutions: Dict[str, str] = {}
+
+
+class EnterpriseMailService:
+
+    @classmethod
+    def send_mail(cls, mail: DifyMail):
+
+        send_enterprise_email_task.delay(
+            to=mail.to,
+            subject=mail.subject,
+            body=mail.body,
+            substitutions=mail.substitutions
+        )

api/services/feature_service.py

@@ -36,6 +36,14 @@ class LicenseModel(BaseModel):
     expired_at: str = ""
 
 
+class BrandingModel(BaseModel):
+    enabled: bool = False
+    application_title: str = ""
+    login_page_logo: str = ""
+    workspace_logo: str = ""
+    favicon: str = ""
+
+
 class FeatureModel(BaseModel):
     billing: BillingModel = BillingModel()
     members: LimitationModel = LimitationModel(size=0, limit=1)
@@ -47,6 +55,7 @@ class FeatureModel(BaseModel):
     can_replace_logo: bool = False
     model_load_balancing_enabled: bool = False
     dataset_operator_enabled: bool = False
+    webapp_copyright_enabled: bool = False
 
     # pydantic configs
     model_config = ConfigDict(protected_namespaces=())
@@ -65,6 +74,7 @@ class SystemFeatureModel(BaseModel):
     is_allow_create_workspace: bool = False
     is_email_setup: bool = False
     license: LicenseModel = LicenseModel()
+    branding: BrandingModel = BrandingModel()
 
 
 class FeatureService:
@@ -77,6 +87,9 @@ class FeatureService:
         if dify_config.BILLING_ENABLED and tenant_id:
             cls._fulfill_params_from_billing_api(features, tenant_id)
 
+        if dify_config.ENTERPRISE_ENABLED:
+            features.webapp_copyright_enabled = True
+
         return features
 
     @classmethod
@@ -87,7 +100,7 @@ class FeatureService:
 
         if dify_config.ENTERPRISE_ENABLED:
             system_features.enable_web_sso_switch_component = True
-
+            system_features.branding.enabled = True
             cls._fulfill_params_from_enterprise(system_features)
 
         return system_features
@@ -115,6 +128,9 @@ class FeatureService:
         features.billing.subscription.plan = billing_info["subscription"]["plan"]
         features.billing.subscription.interval = billing_info["subscription"]["interval"]
 
+        if features.billing.subscription.plan != "sandbox":
+            features.webapp_copyright_enabled = True
+
         if "members" in billing_info:
             features.members.size = billing_info["members"]["size"]
             features.members.limit = billing_info["members"]["limit"]
@@ -148,35 +164,41 @@ class FeatureService:
     def _fulfill_params_from_enterprise(cls, features):
         enterprise_info = EnterpriseService.get_info()
 
-        if "sso_enforced_for_signin" in enterprise_info:
-            features.sso_enforced_for_signin = enterprise_info["sso_enforced_for_signin"]
+        if "SSOEnforcedForSignin" in enterprise_info:
+            features.sso_enforced_for_signin = enterprise_info["SSOEnforcedForSignin"]
 
-        if "sso_enforced_for_signin_protocol" in enterprise_info:
-            features.sso_enforced_for_signin_protocol = enterprise_info["sso_enforced_for_signin_protocol"]
+        if "SSOEnforcedForSigninProtocol" in enterprise_info:
+            features.sso_enforced_for_signin_protocol = enterprise_info["SSOEnforcedForSigninProtocol"]
 
-        if "sso_enforced_for_web" in enterprise_info:
-            features.sso_enforced_for_web = enterprise_info["sso_enforced_for_web"]
+        if "SSOEnforcedForWeb" in enterprise_info:
+            features.sso_enforced_for_web = enterprise_info["SSOEnforcedForWeb"]
 
-        if "sso_enforced_for_web_protocol" in enterprise_info:
-            features.sso_enforced_for_web_protocol = enterprise_info["sso_enforced_for_web_protocol"]
+        if "SSOEnforcedForWebProtocol" in enterprise_info:
+            features.sso_enforced_for_web_protocol = enterprise_info["SSOEnforcedForWebProtocol"]
 
-        if "enable_email_code_login" in enterprise_info:
-            features.enable_email_code_login = enterprise_info["enable_email_code_login"]
+        if "EnableEmailCodeLogin" in enterprise_info:
+            features.enable_email_code_login = enterprise_info["EnableEmailCodeLogin"]
 
-        if "enable_email_password_login" in enterprise_info:
-            features.enable_email_password_login = enterprise_info["enable_email_password_login"]
+        if "EnableEmailPasswordLogin" in enterprise_info:
+            features.enable_email_password_login = enterprise_info["EnableEmailPasswordLogin"]
 
-        if "is_allow_register" in enterprise_info:
-            features.is_allow_register = enterprise_info["is_allow_register"]
+        if "IsAllowRegister" in enterprise_info:
+            features.is_allow_register = enterprise_info["IsAllowRegister"]
 
-        if "is_allow_create_workspace" in enterprise_info:
-            features.is_allow_create_workspace = enterprise_info["is_allow_create_workspace"]
+        if "IsAllowCreateWorkspace" in enterprise_info:
+            features.is_allow_create_workspace = enterprise_info["IsAllowCreateWorkspace"]
 
-        if "license" in enterprise_info:
-            license_info = enterprise_info["license"]
+        if "Branding" in enterprise_info:
+            features.branding.application_title = enterprise_info["Branding"].get("applicationTitle", "")
+            features.branding.login_page_logo = enterprise_info["Branding"].get("loginPageLogo", "")
+            features.branding.workspace_logo = enterprise_info["Branding"].get("workspaceLogo", "")
+            features.branding.favicon = enterprise_info["Branding"].get("favicon", "")
+
+        if "License" in enterprise_info:
+            license_info = enterprise_info["License"]
 
             if "status" in license_info:
                 features.license.status = LicenseStatus(license_info.get("status", LicenseStatus.INACTIVE))
 
-            if "expired_at" in license_info:
-                features.license.expired_at = license_info["expired_at"]
+            if "expiredAt" in license_info:
+                features.license.expired_at = license_info["expiredAt"]

api/tasks/mail_email_code_login.py

@@ -6,6 +6,7 @@ from celery import shared_task  # type: ignore
 from flask import render_template
 
 from extensions.ext_mail import mail
+from services.feature_service import FeatureService
 
 
 @shared_task(queue="mail")
@@ -25,10 +26,24 @@ def send_email_code_login_mail_task(language: str, to: str, code: str):
     # send email code login mail using different languages
     try:
         if language == "zh-Hans":
-            html_content = render_template("email_code_login_mail_template_zh-CN.html", to=to, code=code)
+            template = "email_code_login_mail_template_zh-CN.html"
+            system_features = FeatureService.get_system_features()
+            if system_features.branding.enabled:
+                application_title = system_features.branding.application_title
+                template = "without-brand/email_code_login_mail_template_zh-CN.html"
+                html_content = render_template(template, to=to, code=code, application_title=application_title)
+            else:
+                html_content = render_template(template, to=to, code=code)
             mail.send(to=to, subject="邮箱验证码", html=html_content)
         else:
-            html_content = render_template("email_code_login_mail_template_en-US.html", to=to, code=code)
+            template = "email_code_login_mail_template_en-US.html"
+            system_features = FeatureService.get_system_features()
+            if system_features.branding.enabled:
+                application_title = system_features.branding.application_title
+                template = "without-brand/email_code_login_mail_template_en-US.html"
+                html_content = render_template(template, to=to, code=code, application_title=application_title)
+            else:
+                html_content = render_template(template, to=to, code=code)
             mail.send(to=to, subject="Email Code", html=html_content)
 
         end_at = time.perf_counter()

api/tasks/mail_enterprise_task.py

@@ -0,0 +1,37 @@
+import logging
+import time
+
+import click
+from celery import shared_task  # type: ignore
+from flask import render_template_string
+
+from extensions.ext_mail import mail
+
+
+@shared_task(queue="mail")
+def send_enterprise_email_task(to, subject, body, substitutions):
+    if not mail.is_inited():
+        return
+
+    logging.info(
+        click.style("Start enterprise mail to {} with subject {}".format(to, subject), fg="green")
+    )
+    start_at = time.perf_counter()
+
+    try:
+        html_content = render_template_string(body, **substitutions)
+
+        if isinstance(to, list):
+            for t in to:
+                mail.send(to=t, subject=subject, html=html_content)
+        else:
+            mail.send(to=to, subject=subject, html=html_content)
+
+        end_at = time.perf_counter()
+        logging.info(
+            click.style(
+                "Send enterprise mail to {} succeeded: latency: {}".format(to, end_at - start_at), fg="green"
+            )
+        )
+    except Exception:
+        logging.exception("Send enterprise mail to {} failed".format(to))

api/tasks/mail_invite_member_task.py

@@ -7,6 +7,7 @@ from flask import render_template
 
 from configs import dify_config
 from extensions.ext_mail import mail
+from services.feature_service import FeatureService
 
 
 @shared_task(queue="mail")
@@ -33,23 +34,45 @@ def send_invite_member_mail_task(language: str, to: str, token: str, inviter_nam
     try:
         url = f"{dify_config.CONSOLE_WEB_URL}/activate?token={token}"
         if language == "zh-Hans":
-            html_content = render_template(
-                "invite_member_mail_template_zh-CN.html",
-                to=to,
-                inviter_name=inviter_name,
-                workspace_name=workspace_name,
-                url=url,
-            )
-            mail.send(to=to, subject="立即加入 Dify 工作空间", html=html_content)
+            template = "invite_member_mail_template_zh-CN.html"
+            system_features = FeatureService.get_system_features()
+            if system_features.branding.enabled:
+                application_title = system_features.branding.application_title
+                template = "without-brand/invite_member_mail_template_zh-CN.html"
+                html_content = render_template(
+                    template,
+                    to=to,
+                    inviter_name=inviter_name,
+                    workspace_name=workspace_name,
+                    url=url,
+                    application_title=application_title,
+                )
+                mail.send(to=to, subject=f"立即加入 {application_title} 工作空间", html=html_content)
+            else:
+                html_content = render_template(
+                    template, to=to, inviter_name=inviter_name, workspace_name=workspace_name, url=url
+                )
+                mail.send(to=to, subject="立即加入 Dify 工作空间", html=html_content)
         else:
-            html_content = render_template(
-                "invite_member_mail_template_en-US.html",
-                to=to,
-                inviter_name=inviter_name,
-                workspace_name=workspace_name,
-                url=url,
-            )
-            mail.send(to=to, subject="Join Dify Workspace Now", html=html_content)
+            template = "invite_member_mail_template_en-US.html"
+            system_features = FeatureService.get_system_features()
+            if system_features.branding.enabled:
+                application_title = system_features.branding.application_title
+                template = "without-brand/invite_member_mail_template_en-US.html"
+                html_content = render_template(
+                    template,
+                    to=to,
+                    inviter_name=inviter_name,
+                    workspace_name=workspace_name,
+                    url=url,
+                    application_title=application_title,
+                )
+                mail.send(to=to, subject=f"Join {application_title} Workspace Now", html=html_content)
+            else:
+                html_content = render_template(
+                    template, to=to, inviter_name=inviter_name, workspace_name=workspace_name, url=url
+                )
+                mail.send(to=to, subject="Join Dify Workspace Now", html=html_content)
 
         end_at = time.perf_counter()
         logging.info(