chore: update version to 0.15.4 in configuration and docker files

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

.github/workflows/build-push.yml

@@ -5,7 +5,6 @@ on:
     branches:
       - "main"
       - "deploy/dev"
-      - "release/0.15.2-fix1"
   release:
     types: [published]
 

.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

@@ -48,16 +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 testing main" > /etc/apt/sources.list \
-    && apt-get update \
-    # For Security
-    && apt-get install -y --no-install-recommends expat=2.6.4-1 libldap-2.5-0=2.5.19+dfsg-1 perl=5.40.0-8 libsqlite3-0=3.46.1-1 zlib1g=1:1.3.dfsg+really1.3.1-1+b1 \
-    # install a chinese font to support the use of tools like matplotlib
-    && apt-get install -y fonts-noto-cjk \
+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/*
 
@@ -76,7 +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/common/helpers.py

@@ -1,12 +1,32 @@
 import mimetypes
 import os
+import platform
 import re
 import urllib.parse
+import warnings
 from collections.abc import Mapping
 from typing import Any
 from uuid import uuid4
 
 import httpx
+
+try:
+    import magic
+except ImportError:
+    if platform.system() == "Windows":
+        warnings.warn(
+            "To use python-magic guess MIMETYPE, you need to run `pip install python-magic-bin`", stacklevel=2
+        )
+    elif platform.system() == "Darwin":
+        warnings.warn("To use python-magic guess MIMETYPE, you need to run `brew install libmagic`", stacklevel=2)
+    elif platform.system() == "Linux":
+        warnings.warn(
+            "To use python-magic guess MIMETYPE, you need to run `sudo apt-get install libmagic1`", stacklevel=2
+        )
+    else:
+        warnings.warn("To use python-magic guess MIMETYPE, you need to install `libmagic`", stacklevel=2)
+    magic = None  # type: ignore
+
 from pydantic import BaseModel
 
 from configs import dify_config
@@ -47,6 +67,13 @@ def guess_file_info_from_response(response: httpx.Response):
         # If guessing fails, use Content-Type from response headers
         mimetype = response.headers.get("Content-Type", "application/octet-stream")
 
+    # Use python-magic to guess MIME type if still unknown or generic
+    if mimetype == "application/octet-stream" and magic is not None:
+        try:
+            mimetype = magic.from_buffer(response.content[:1024], mime=True)
+        except magic.MagicException:
+            pass
+
     extension = os.path.splitext(filename)[1]
 
     # Ensure filename has an extension

api/controllers/console/datasets/datasets.py

@@ -620,7 +620,6 @@ class DatasetRetrievalSettingApi(Resource):
         match vector_type:
             case (
                 VectorType.RELYT
-                | VectorType.PGVECTOR
                 | VectorType.TIDB_VECTOR
                 | VectorType.CHROMA
                 | VectorType.TENCENT

api/controllers/console/explore/message.py

@@ -50,7 +50,7 @@ class MessageListApi(InstalledAppResource):
 
         try:
             return MessageService.pagination_by_first_id(
-                app_model, current_user, args["conversation_id"], args["first_id"], args["limit"], "desc"
+                app_model, current_user, args["conversation_id"], args["first_id"], args["limit"]
             )
         except services.errors.conversation.ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")

api/controllers/inner_api/workspace/workspace.py

@@ -1,3 +1,5 @@
+import json
+
 from flask_restful import Resource, reqparse  # type: ignore
 
 from controllers.console.wraps import setup_required
@@ -29,4 +31,34 @@ class EnterpriseWorkspace(Resource):
         return {"message": "enterprise workspace created."}
 
 
+class EnterpriseWorkspaceNoOwnerEmail(Resource):
+    @setup_required
+    @inner_api_only
+    def post(self):
+        parser = reqparse.RequestParser()
+        parser.add_argument("name", type=str, required=True, location="json")
+        args = parser.parse_args()
+
+        tenant = TenantService.create_tenant(args["name"], is_from_dashboard=True)
+
+        tenant_was_created.send(tenant)
+
+        resp = {
+            "id": tenant.id,
+            "name": tenant.name,
+            "encrypt_public_key": tenant.encrypt_public_key,
+            "plan": tenant.plan,
+            "status": tenant.status,
+            "custom_config": json.loads(tenant.custom_config) if tenant.custom_config else {},
+            "created_at": tenant.created_at.isoformat() if tenant.created_at else None,
+            "updated_at": tenant.updated_at.isoformat() if tenant.updated_at else None,
+        }
+
+        return {
+            "message": "enterprise workspace created.",
+            "tenant": resp,
+        }
+
+
 api.add_resource(EnterpriseWorkspace, "/enterprise/workspace")
+api.add_resource(EnterpriseWorkspaceNoOwnerEmail, "/enterprise/workspace/ownerless")

api/controllers/service_api/dataset/document.py

@@ -18,6 +18,7 @@ from controllers.service_api.app.error import (
 from controllers.service_api.dataset.error import (
     ArchivedDocumentImmutableError,
     DocumentIndexingError,
+    InvalidMetadataError,
 )
 from controllers.service_api.wraps import DatasetApiResource, cloud_edition_billing_resource_check
 from core.errors.error import ProviderTokenNotInitError
@@ -50,6 +51,9 @@ class DocumentAddByTextApi(DatasetApiResource):
             "indexing_technique", type=str, choices=Dataset.INDEXING_TECHNIQUE_LIST, nullable=False, location="json"
         )
         parser.add_argument("retrieval_model", type=dict, required=False, nullable=False, location="json")
+        parser.add_argument("doc_type", type=str, required=False, nullable=True, location="json")
+        parser.add_argument("doc_metadata", type=dict, required=False, nullable=True, location="json")
+
         args = parser.parse_args()
         dataset_id = str(dataset_id)
         tenant_id = str(tenant_id)
@@ -61,6 +65,28 @@ class DocumentAddByTextApi(DatasetApiResource):
         if not dataset.indexing_technique and not args["indexing_technique"]:
             raise ValueError("indexing_technique is required.")
 
+        # Validate metadata if provided
+        if args.get("doc_type") or args.get("doc_metadata"):
+            if not args.get("doc_type") or not args.get("doc_metadata"):
+                raise InvalidMetadataError("Both doc_type and doc_metadata must be provided when adding metadata")
+
+            if args["doc_type"] not in DocumentService.DOCUMENT_METADATA_SCHEMA:
+                raise InvalidMetadataError(
+                    "Invalid doc_type. Must be one of: " + ", ".join(DocumentService.DOCUMENT_METADATA_SCHEMA.keys())
+                )
+
+            if not isinstance(args["doc_metadata"], dict):
+                raise InvalidMetadataError("doc_metadata must be a dictionary")
+
+            # Validate metadata schema based on doc_type
+            if args["doc_type"] != "others":
+                metadata_schema = DocumentService.DOCUMENT_METADATA_SCHEMA[args["doc_type"]]
+                for key, value in args["doc_metadata"].items():
+                    if key in metadata_schema and not isinstance(value, metadata_schema[key]):
+                        raise InvalidMetadataError(f"Invalid type for metadata field {key}")
+            # set to MetaDataConfig
+            args["metadata"] = {"doc_type": args["doc_type"], "doc_metadata": args["doc_metadata"]}
+
         text = args.get("text")
         name = args.get("name")
         if text is None or name is None:
@@ -107,6 +133,8 @@ class DocumentUpdateByTextApi(DatasetApiResource):
             "doc_language", type=str, default="English", required=False, nullable=False, location="json"
         )
         parser.add_argument("retrieval_model", type=dict, required=False, nullable=False, location="json")
+        parser.add_argument("doc_type", type=str, required=False, nullable=True, location="json")
+        parser.add_argument("doc_metadata", type=dict, required=False, nullable=True, location="json")
         args = parser.parse_args()
         dataset_id = str(dataset_id)
         tenant_id = str(tenant_id)
@@ -115,6 +143,32 @@ class DocumentUpdateByTextApi(DatasetApiResource):
         if not dataset:
             raise ValueError("Dataset is not exist.")
 
+        # indexing_technique is already set in dataset since this is an update
+        args["indexing_technique"] = dataset.indexing_technique
+
+        # Validate metadata if provided
+        if args.get("doc_type") or args.get("doc_metadata"):
+            if not args.get("doc_type") or not args.get("doc_metadata"):
+                raise InvalidMetadataError("Both doc_type and doc_metadata must be provided when adding metadata")
+
+            if args["doc_type"] not in DocumentService.DOCUMENT_METADATA_SCHEMA:
+                raise InvalidMetadataError(
+                    "Invalid doc_type. Must be one of: " + ", ".join(DocumentService.DOCUMENT_METADATA_SCHEMA.keys())
+                )
+
+            if not isinstance(args["doc_metadata"], dict):
+                raise InvalidMetadataError("doc_metadata must be a dictionary")
+
+            # Validate metadata schema based on doc_type
+            if args["doc_type"] != "others":
+                metadata_schema = DocumentService.DOCUMENT_METADATA_SCHEMA[args["doc_type"]]
+                for key, value in args["doc_metadata"].items():
+                    if key in metadata_schema and not isinstance(value, metadata_schema[key]):
+                        raise InvalidMetadataError(f"Invalid type for metadata field {key}")
+
+            # set to MetaDataConfig
+            args["metadata"] = {"doc_type": args["doc_type"], "doc_metadata": args["doc_metadata"]}
+
         if args["text"]:
             text = args.get("text")
             name = args.get("name")
@@ -161,6 +215,30 @@ class DocumentAddByFileApi(DatasetApiResource):
             args["doc_form"] = "text_model"
         if "doc_language" not in args:
             args["doc_language"] = "English"
+
+        # Validate metadata if provided
+        if args.get("doc_type") or args.get("doc_metadata"):
+            if not args.get("doc_type") or not args.get("doc_metadata"):
+                raise InvalidMetadataError("Both doc_type and doc_metadata must be provided when adding metadata")
+
+            if args["doc_type"] not in DocumentService.DOCUMENT_METADATA_SCHEMA:
+                raise InvalidMetadataError(
+                    "Invalid doc_type. Must be one of: " + ", ".join(DocumentService.DOCUMENT_METADATA_SCHEMA.keys())
+                )
+
+            if not isinstance(args["doc_metadata"], dict):
+                raise InvalidMetadataError("doc_metadata must be a dictionary")
+
+            # Validate metadata schema based on doc_type
+            if args["doc_type"] != "others":
+                metadata_schema = DocumentService.DOCUMENT_METADATA_SCHEMA[args["doc_type"]]
+                for key, value in args["doc_metadata"].items():
+                    if key in metadata_schema and not isinstance(value, metadata_schema[key]):
+                        raise InvalidMetadataError(f"Invalid type for metadata field {key}")
+
+            # set to MetaDataConfig
+            args["metadata"] = {"doc_type": args["doc_type"], "doc_metadata": args["doc_metadata"]}
+
         # get dataset info
         dataset_id = str(dataset_id)
         tenant_id = str(tenant_id)
@@ -228,6 +306,29 @@ class DocumentUpdateByFileApi(DatasetApiResource):
         if "doc_language" not in args:
             args["doc_language"] = "English"
 
+        # Validate metadata if provided
+        if args.get("doc_type") or args.get("doc_metadata"):
+            if not args.get("doc_type") or not args.get("doc_metadata"):
+                raise InvalidMetadataError("Both doc_type and doc_metadata must be provided when adding metadata")
+
+            if args["doc_type"] not in DocumentService.DOCUMENT_METADATA_SCHEMA:
+                raise InvalidMetadataError(
+                    "Invalid doc_type. Must be one of: " + ", ".join(DocumentService.DOCUMENT_METADATA_SCHEMA.keys())
+                )
+
+            if not isinstance(args["doc_metadata"], dict):
+                raise InvalidMetadataError("doc_metadata must be a dictionary")
+
+            # Validate metadata schema based on doc_type
+            if args["doc_type"] != "others":
+                metadata_schema = DocumentService.DOCUMENT_METADATA_SCHEMA[args["doc_type"]]
+                for key, value in args["doc_metadata"].items():
+                    if key in metadata_schema and not isinstance(value, metadata_schema[key]):
+                        raise InvalidMetadataError(f"Invalid type for metadata field {key}")
+
+            # set to MetaDataConfig
+            args["metadata"] = {"doc_type": args["doc_type"], "doc_metadata": args["doc_metadata"]}
+
         # get dataset info
         dataset_id = str(dataset_id)
         tenant_id = str(tenant_id)

api/controllers/web/message.py

@@ -91,7 +91,7 @@ class MessageListApi(WebApiResource):
 
         try:
             return MessageService.pagination_by_first_id(
-                app_model, end_user, args["conversation_id"], args["first_id"], args["limit"], "desc"
+                app_model, end_user, args["conversation_id"], args["first_id"], args["limit"]
             )
         except services.errors.conversation.ConversationNotExistsError:
             raise NotFound("Conversation Not Exists.")

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

@@ -202,7 +202,7 @@ class AgentChatAppRunner(AppRunner):
         # change function call strategy based on LLM model
         llm_model = cast(LargeLanguageModel, model_instance.model_type_instance)
         model_schema = llm_model.get_model_schema(model_instance.model, model_instance.credentials)
-        if not model_schema or not model_schema.features:
+        if not model_schema:
             raise ValueError("Model schema not found")
 
         if {ModelFeature.MULTI_TOOL_CALL, ModelFeature.TOOL_CALL}.intersection(model_schema.features or []):

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/ai_model.py

@@ -221,13 +221,12 @@ class AIModel(ABC):
         :param credentials: model credentials
         :return: model schema
         """
-        # get predefined models (predefined_models)
-        models = self.predefined_models()
-
-        model_map = {model.model: model for model in models}
-        if model in model_map:
-            return model_map[model]
+        # Try to get model schema from predefined models
+        for predefined_model in self.predefined_models():
+            if model == predefined_model.model:
+                return predefined_model
 
+        # Try to get model schema from credentials
         if credentials:
             model_schema = self.get_customizable_model_schema_from_credentials(model, credentials)
             if model_schema:

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

@@ -53,6 +53,9 @@ model_credential_schema:
       type: select
       required: true
       options:
+        - label:
+            en_US: 2024-12-01-preview
+          value: 2024-12-01-preview
         - label:
             en_US: 2024-10-01-preview
           value: 2024-10-01-preview
@@ -135,6 +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

@@ -44,6 +44,7 @@ provider_credential_schema:
       label:
         en_US: AWS Region
         zh_Hans: AWS 地区
+        ja_JP: AWS リージョン
       type: select
       default: us-east-1
       options:
@@ -51,62 +52,86 @@ provider_credential_schema:
           label:
             en_US: US East (N. Virginia)
             zh_Hans: 美国东部 (弗吉尼亚北部)
+            ja_JP: 米国 (バージニア北部)
         - value: us-east-2
           label:
             en_US: US East (Ohio)
-            zh_Hans: 美国东部 (弗吉尼亚北部)
+            zh_Hans: 美国东部 (俄亥俄)
+            ja_JP: 米国 (オハイオ)
         - value: us-west-2
           label:
             en_US: US West (Oregon)
             zh_Hans: 美国西部 (俄勒冈州)
+            ja_JP: 米国 (オレゴン)
         - value: ap-south-1
           label:
             en_US: Asia Pacific (Mumbai)
             zh_Hans: 亚太地区（孟买）
+            ja_JP: アジアパシフィック (ムンバイ)
         - value: ap-southeast-1
           label:
             en_US: Asia Pacific (Singapore)
             zh_Hans: 亚太地区 (新加坡)
+            ja_JP: アジアパシフィック (シンガポール)
         - value: ap-southeast-2
           label:
             en_US: Asia Pacific (Sydney)
             zh_Hans: 亚太地区 (悉尼)
+            ja_JP: アジアパシフィック (シドニー)
         - value: ap-northeast-1
           label:
             en_US: Asia Pacific (Tokyo)
             zh_Hans: 亚太地区 (东京)
+            ja_JP: アジアパシフィック (東京)
         - value: ap-northeast-2
           label:
             en_US: Asia Pacific (Seoul)
             zh_Hans: 亚太地区（首尔）
+            ja_JP: アジアパシフィック (ソウル)
         - value: ca-central-1
           label:
             en_US: Canada (Central)
             zh_Hans: 加拿大（中部）
+            ja_JP: カナダ (中部)
         - value: eu-central-1
           label:
             en_US: Europe (Frankfurt)
             zh_Hans: 欧洲 (法兰克福)
+            ja_JP: 欧州 (フランクフルト)
         - value: eu-west-1
           label:
             en_US: Europe (Ireland)
             zh_Hans: 欧洲（爱尔兰）
+            ja_JP: 欧州 (アイルランド)
         - value: eu-west-2
           label:
             en_US: Europe (London)
             zh_Hans: 欧洲西部 (伦敦)
+            ja_JP: 欧州 (ロンドン)
         - value: eu-west-3
           label:
             en_US: Europe (Paris)
             zh_Hans: 欧洲（巴黎）
+            ja_JP: 欧州 (パリ)
         - value: sa-east-1
           label:
             en_US: South America (São Paulo)
             zh_Hans: 南美洲（圣保罗）
+            ja_JP: 南米 (サンパウロ)
         - value: us-gov-west-1
           label:
             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/cohere/llm/llm.py

@@ -677,16 +677,17 @@ class CohereLargeLanguageModel(LargeLanguageModel):
 
         :return: model schema
         """
-        # get model schema
-        models = self.predefined_models()
-        model_map = {model.model: model for model in models}
-
         mode = credentials.get("mode")
+        base_model_schema = None
+        for predefined_model in self.predefined_models():
+            if (
+                mode == "chat" and predefined_model.model == "command-light-chat"
+            ) or predefined_model.model == "command-light":
+                base_model_schema = predefined_model
+                break
 
-        if mode == "chat":
-            base_model_schema = model_map["command-light-chat"]
-        else:
-            base_model_schema = model_map["command-light"]
+        if not base_model_schema:
+            raise ValueError("Model not found")
 
         base_model_schema = cast(AIModelEntity, base_model_schema)
 

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

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api/core/model_runtime/model_providers/novita/_assets/icon_s_en.svg

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api/core/model_runtime/model_providers/novita/llm/L3-8B-Stheno-v3.2.yaml

@@ -0,0 +1,41 @@
+model: Sao10K/L3-8B-Stheno-v3.2
+label:
+  zh_Hans: L3 8B Stheno V3.2
+  en_US: L3 8B Stheno V3.2
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 8192
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - 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
+pricing:
+  input: '0.0005'
+  output: '0.0005'
+  unit: '0.0001'
+  currency: USD

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

@@ -0,0 +1,41 @@
+# Deepseek Models
+- deepseek/deepseek-r1
+- deepseek/deepseek_v3
+
+# LLaMA Models
+- meta-llama/llama-3.3-70b-instruct
+- meta-llama/llama-3.2-11b-vision-instruct
+- meta-llama/llama-3.2-3b-instruct
+- meta-llama/llama-3.2-1b-instruct
+- meta-llama/llama-3.1-70b-instruct
+- meta-llama/llama-3.1-8b-instruct
+- meta-llama/llama-3.1-8b-instruct-max
+- meta-llama/llama-3.1-8b-instruct-bf16
+- meta-llama/llama-3-70b-instruct
+- meta-llama/llama-3-8b-instruct
+
+# Mistral Models
+- mistralai/mistral-nemo
+- mistralai/mistral-7b-instruct
+
+# Qwen Models
+- qwen/qwen-2.5-72b-instruct
+- qwen/qwen-2-72b-instruct
+- qwen/qwen-2-vl-72b-instruct
+- qwen/qwen-2-7b-instruct
+
+# Other Models
+- sao10k/L3-8B-Stheno-v3.2
+- sao10k/l3-70b-euryale-v2.1
+- sao10k/l31-70b-euryale-v2.2
+- sao10k/l3-8b-lunaris
+- jondurbin/airoboros-l2-70b
+- cognitivecomputations/dolphin-mixtral-8x22b
+- google/gemma-2-9b-it
+- nousresearch/hermes-2-pro-llama-3-8b
+- sophosympatheia/midnight-rose-70b
+- gryphe/mythomax-l2-13b
+- nousresearch/nous-hermes-llama2-13b
+- openchat/openchat-7b
+- teknium/openhermes-2.5-mistral-7b
+- microsoft/wizardlm-2-8x22b

api/core/model_runtime/model_providers/novita/llm/airoboros-l2-70b.yaml

@@ -1,7 +1,7 @@
 model: jondurbin/airoboros-l2-70b
 label:
-  zh_Hans: jondurbin/airoboros-l2-70b
-  en_US: jondurbin/airoboros-l2-70b
+  zh_Hans: Airoboros L2 70B
+  en_US: Airoboros L2 70B
 model_type: llm
 features:
   - agent-thought

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

@@ -0,0 +1,41 @@
+model: deepseek/deepseek-r1
+label:
+  zh_Hans: DeepSeek R1
+  en_US: DeepSeek R1
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 64000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - 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
+pricing:
+  input: '0.04'
+  output: '0.04'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/novita/llm/deepseek_v3.yaml

@@ -0,0 +1,41 @@
+model: deepseek/deepseek_v3
+label:
+  zh_Hans: DeepSeek V3
+  en_US: DeepSeek V3
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 64000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - 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
+pricing:
+  input: '0.0089'
+  output: '0.0089'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/novita/llm/dolphin-mixtral-8x22b.yaml

@@ -1,7 +1,7 @@
 model: cognitivecomputations/dolphin-mixtral-8x22b
 label:
-  zh_Hans: cognitivecomputations/dolphin-mixtral-8x22b
-  en_US: cognitivecomputations/dolphin-mixtral-8x22b
+  zh_Hans: Dolphin Mixtral 8x22B
+  en_US: Dolphin Mixtral 8x22B
 model_type: llm
 features:
   - agent-thought

api/core/model_runtime/model_providers/novita/llm/gemma-2-9b-it.yaml

@@ -1,7 +1,7 @@
 model: google/gemma-2-9b-it
 label:
-  zh_Hans: google/gemma-2-9b-it
-  en_US: google/gemma-2-9b-it
+  zh_Hans: Gemma 2 9B
+  en_US: Gemma 2 9B
 model_type: llm
 features:
   - agent-thought

api/core/model_runtime/model_providers/novita/llm/hermes-2-pro-llama-3-8b.yaml

@@ -1,7 +1,7 @@
 model: nousresearch/hermes-2-pro-llama-3-8b
 label:
-  zh_Hans: nousresearch/hermes-2-pro-llama-3-8b
-  en_US: nousresearch/hermes-2-pro-llama-3-8b
+  zh_Hans: Hermes 2 Pro Llama 3 8B
+  en_US: Hermes 2 Pro Llama 3 8B
 model_type: llm
 features:
   - agent-thought

api/core/model_runtime/model_providers/novita/llm/l3-70b-euryale-v2.1.yaml

@@ -1,7 +1,7 @@
 model: sao10k/l3-70b-euryale-v2.1
 label:
-  zh_Hans: sao10k/l3-70b-euryale-v2.1
-  en_US: sao10k/l3-70b-euryale-v2.1
+  zh_Hans: "L3 70B Euryale V2.1\t"
+  en_US: "L3 70B Euryale V2.1\t"
 model_type: llm
 features:
   - agent-thought

api/core/model_runtime/model_providers/novita/llm/l3-8b-lunaris.yaml

@@ -0,0 +1,41 @@
+model: sao10k/l3-8b-lunaris
+label:
+  zh_Hans: "Sao10k L3 8B Lunaris"
+  en_US: "Sao10k L3 8B Lunaris"
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 8192
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - 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
+pricing:
+  input: '0.0005'
+  output: '0.0005'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/novita/llm/l31-70b-euryale-v2.2.yaml

@@ -0,0 +1,41 @@
+model: sao10k/l31-70b-euryale-v2.2
+label:
+  zh_Hans: L31 70B Euryale V2.2
+  en_US: L31 70B Euryale V2.2
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 16000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - 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
+pricing:
+  input: '0.0148'
+  output: '0.0148'
+  unit: '0.0001'
+  currency: USD

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

@@ -1,7 +1,7 @@
 model: meta-llama/llama-3-70b-instruct
 label:
-  zh_Hans: meta-llama/llama-3-70b-instruct
-  en_US: meta-llama/llama-3-70b-instruct
+  zh_Hans: Llama3 70b Instruct
+  en_US: Llama3 70b Instruct
 model_type: llm
 features:
   - agent-thought

api/core/model_runtime/model_providers/novita/llm/llama-3-8b-instruct.yaml

@@ -1,7 +1,7 @@
 model: meta-llama/llama-3-8b-instruct
 label:
-  zh_Hans: meta-llama/llama-3-8b-instruct
-  en_US: meta-llama/llama-3-8b-instruct
+  zh_Hans: Llama 3 8B Instruct
+  en_US: Llama 3 8B Instruct
 model_type: llm
 features:
   - agent-thought
@@ -35,7 +35,7 @@ parameter_rules:
     max: 2
     default: 0
 pricing:
-  input: '0.00063'
-  output: '0.00063'
+  input: '0.0004'
+  output: '0.0004'
   unit: '0.0001'
   currency: USD

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

@@ -1,13 +1,13 @@
 model: meta-llama/llama-3.1-70b-instruct
 label:
-  zh_Hans: meta-llama/llama-3.1-70b-instruct
-  en_US: meta-llama/llama-3.1-70b-instruct
+  zh_Hans: Llama 3.1 70B Instruct
+  en_US: Llama 3.1 70B Instruct
 model_type: llm
 features:
   - agent-thought
 model_properties:
   mode: chat
-  context_size: 8192
+  context_size: 32768
 parameter_rules:
   - name: temperature
     use_template: temperature
@@ -35,7 +35,7 @@ parameter_rules:
     max: 2
     default: 0
 pricing:
-  input: '0.0055'
-  output: '0.0076'
+  input: '0.0034'
+  output: '0.0039'
   unit: '0.0001'
   currency: USD

api/core/model_runtime/model_providers/novita/llm/llama-3.1-8b-instruct-bf16.yaml

@@ -0,0 +1,41 @@
+model: meta-llama/llama-3.1-8b-instruct-bf16
+label:
+  zh_Hans: Llama 3.1 8B Instruct BF16
+  en_US: Llama 3.1 8B Instruct BF16
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 8192
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - 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
+pricing:
+  input: '0.0006'
+  output: '0.0006'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/novita/llm/llama-3.1-8b-instruct-max.yaml

@@ -0,0 +1,41 @@
+model: meta-llama/llama-3.1-8b-instruct-max
+label:
+  zh_Hans: "Llama3.1 8B Instruct Max\t"
+  en_US: "Llama3.1 8B Instruct Max\t"
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 16384
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - 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
+pricing:
+  input: '0.0005'
+  output: '0.0005'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/novita/llm/llama-3.1-8b-instruct.yaml

@@ -1,13 +1,13 @@
 model: meta-llama/llama-3.1-8b-instruct
 label:
-  zh_Hans: meta-llama/llama-3.1-8b-instruct
-  en_US: meta-llama/llama-3.1-8b-instruct
+  zh_Hans: Llama 3.1 8B Instruct
+  en_US: Llama 3.1 8B Instruct
 model_type: llm
 features:
   - agent-thought
 model_properties:
   mode: chat
-  context_size: 8192
+  context_size: 16384
 parameter_rules:
   - name: temperature
     use_template: temperature
@@ -35,7 +35,7 @@ parameter_rules:
     max: 2
     default: 0
 pricing:
-  input: '0.001'
-  output: '0.001'
+  input: '0.0005'
+  output: '0.0005'
   unit: '0.0001'
   currency: USD

api/core/model_runtime/model_providers/novita/llm/llama-3.2-11b-vision-instruct.yaml

@@ -0,0 +1,41 @@
+model: meta-llama/llama-3.2-11b-vision-instruct
+label:
+  zh_Hans: "Llama 3.2 11B Vision Instruct\t"
+  en_US: "Llama 3.2 11B Vision Instruct\t"
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 32768
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - 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
+pricing:
+  input: '0.0006'
+  output: '0.0006'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/novita/llm/llama-3.2-1b-instruct.yaml

@@ -0,0 +1,41 @@
+model: meta-llama/llama-3.2-1b-instruct
+label:
+  zh_Hans: "Llama 3.2 1B Instruct\t"
+  en_US: "Llama 3.2 1B Instruct\t"
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 131000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - 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
+pricing:
+  input: '0.0002'
+  output: '0.0002'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/novita/llm/llama-3.2-3b-instruct.yaml

@@ -1,7 +1,7 @@
-model: Nous-Hermes-2-Mixtral-8x7B-DPO
+model: meta-llama/llama-3.2-3b-instruct
 label:
-  zh_Hans: Nous-Hermes-2-Mixtral-8x7B-DPO
-  en_US: Nous-Hermes-2-Mixtral-8x7B-DPO
+  zh_Hans: Llama 3.2 3B Instruct
+  en_US: Llama 3.2 3B Instruct
 model_type: llm
 features:
   - agent-thought
@@ -35,7 +35,7 @@ parameter_rules:
     max: 2
     default: 0
 pricing:
-  input: '0.0027'
-  output: '0.0027'
+  input: '0.0003'
+  output: '0.0005'
   unit: '0.0001'
   currency: USD

api/core/model_runtime/model_providers/novita/llm/llama-3.3-70b-instruct.yaml

@@ -0,0 +1,41 @@
+model: meta-llama/llama-3.3-70b-instruct
+label:
+  zh_Hans: Llama 3.3 70B Instruct
+  en_US: Llama 3.3 70B Instruct
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 131072
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - 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
+pricing:
+  input: '0.0039'
+  output: '0.0039'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/novita/llm/midnight-rose-70b.yaml

@@ -1,7 +1,7 @@
 model: sophosympatheia/midnight-rose-70b
 label:
-  zh_Hans: sophosympatheia/midnight-rose-70b
-  en_US: sophosympatheia/midnight-rose-70b
+  zh_Hans: Midnight Rose 70B
+  en_US: Midnight Rose 70B
 model_type: llm
 features:
   - agent-thought

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

@@ -1,7 +1,7 @@
 model: mistralai/mistral-7b-instruct
 label:
-  zh_Hans: mistralai/mistral-7b-instruct
-  en_US: mistralai/mistral-7b-instruct
+  zh_Hans: Mistral 7B Instruct
+  en_US: Mistral 7B Instruct
 model_type: llm
 features:
   - agent-thought

api/core/model_runtime/model_providers/novita/llm/mistral-nemo.yaml

@@ -0,0 +1,41 @@
+model: mistralai/mistral-nemo
+label:
+  zh_Hans: Mistral Nemo
+  en_US: Mistral Nemo
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 131072
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - 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
+pricing:
+  input: '0.0017'
+  output: '0.0017'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/novita/llm/mythomax-l2-13b.yaml

@@ -1,7 +1,7 @@
 model: gryphe/mythomax-l2-13b
 label:
-  zh_Hans: gryphe/mythomax-l2-13b
-  en_US: gryphe/mythomax-l2-13b
+  zh_Hans: Mythomax L2 13B
+  en_US: Mythomax L2 13B
 model_type: llm
 features:
   - agent-thought
@@ -35,7 +35,7 @@ parameter_rules:
     max: 2
     default: 0
 pricing:
-  input: '0.00119'
-  output: '0.00119'
+  input: '0.0009'
+  output: '0.0009'
   unit: '0.0001'
   currency: USD

api/core/model_runtime/model_providers/novita/llm/nous-hermes-llama2-13b.yaml

@@ -1,7 +1,7 @@
 model: nousresearch/nous-hermes-llama2-13b
 label:
-  zh_Hans: nousresearch/nous-hermes-llama2-13b
-  en_US: nousresearch/nous-hermes-llama2-13b
+  zh_Hans: Nous Hermes Llama2 13B
+  en_US: Nous Hermes Llama2 13B
 model_type: llm
 features:
   - agent-thought

api/core/model_runtime/model_providers/novita/llm/openchat-7b.yaml

@@ -1,7 +1,7 @@
-model: lzlv_70b
+model: openchat/openchat-7b
 label:
-  zh_Hans: lzlv_70b
-  en_US: lzlv_70b
+  zh_Hans: OpenChat 7B
+  en_US: OpenChat 7B
 model_type: llm
 features:
   - agent-thought
@@ -35,7 +35,7 @@ parameter_rules:
     max: 2
     default: 0
 pricing:
-  input: '0.0058'
-  output: '0.0078'
+  input: '0.0006'
+  output: '0.0006'
   unit: '0.0001'
   currency: USD

api/core/model_runtime/model_providers/novita/llm/openhermes-2.5-mistral-7b.yaml

@@ -1,7 +1,7 @@
 model: teknium/openhermes-2.5-mistral-7b
 label:
-  zh_Hans: teknium/openhermes-2.5-mistral-7b
-  en_US: teknium/openhermes-2.5-mistral-7b
+  zh_Hans: Openhermes2.5 Mistral 7B
+  en_US: Openhermes2.5 Mistral 7B
 model_type: llm
 features:
   - agent-thought

api/core/model_runtime/model_providers/novita/llm/qwen-2-72b-instruct.yaml

@@ -1,7 +1,7 @@
-model: meta-llama/llama-3.1-405b-instruct
+model: qwen/qwen-2-72b-instruct
 label:
-  zh_Hans: meta-llama/llama-3.1-405b-instruct
-  en_US: meta-llama/llama-3.1-405b-instruct
+  zh_Hans: Qwen2 72B Instruct
+  en_US: Qwen2 72B Instruct
 model_type: llm
 features:
   - agent-thought
@@ -35,7 +35,7 @@ parameter_rules:
     max: 2
     default: 0
 pricing:
-  input: '0.03'
-  output: '0.05'
+  input: '0.0034'
+  output: '0.0039'
   unit: '0.0001'
   currency: USD

api/core/model_runtime/model_providers/novita/llm/qwen-2-7b-instruct.yaml

@@ -0,0 +1,41 @@
+model: qwen/qwen-2-7b-instruct
+label:
+  zh_Hans: Qwen 2 7B Instruct
+  en_US: Qwen 2 7B Instruct
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 32768
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - 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
+pricing:
+  input: '0.00054'
+  output: '0.00054'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/novita/llm/qwen-2-vl-72b-instruct.yaml

@@ -0,0 +1,41 @@
+model: qwen/qwen-2-vl-72b-instruct
+label:
+  zh_Hans: Qwen 2 VL 72B Instruct
+  en_US: Qwen 2 VL 72B Instruct
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 32768
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - 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
+pricing:
+  input: '0.0045'
+  output: '0.0045'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/novita/llm/qwen-2.5-72b-instruct.yaml

@@ -0,0 +1,41 @@
+model: qwen/qwen-2.5-72b-instruct
+label:
+  zh_Hans: Qwen 2.5 72B Instruct
+  en_US: Qwen 2.5 72B Instruct
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 32000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0
+    max: 2
+    default: 1
+  - name: top_p
+    use_template: top_p
+    min: 0
+    max: 1
+    default: 1
+  - name: max_tokens
+    use_template: max_tokens
+    min: 1
+    max: 2048
+    default: 512
+  - 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
+pricing:
+  input: '0.0038'
+  output: '0.004'
+  unit: '0.0001'
+  currency: USD

api/core/model_runtime/model_providers/novita/llm/wizardlm-2-8x22b.yaml

@@ -1,7 +1,7 @@
 model: microsoft/wizardlm-2-8x22b
 label:
-  zh_Hans: microsoft/wizardlm-2-8x22b
-  en_US: microsoft/wizardlm-2-8x22b
+  zh_Hans: Wizardlm 2 8x22B
+  en_US: Wizardlm 2 8x22B
 model_type: llm
 features:
   - agent-thought
@@ -35,7 +35,7 @@ parameter_rules:
     max: 2
     default: 0
 pricing:
-  input: '0.0064'
-  output: '0.0064'
+  input: '0.0062'
+  output: '0.0062'
   unit: '0.0001'
   currency: USD

api/core/model_runtime/model_providers/novita/novita.yaml

@@ -1,6 +1,6 @@
 provider: novita
 label:
-  en_US: novita.ai
+  en_US: Novita AI
 description:
   en_US: An LLM API that matches various application scenarios with high cost-effectiveness.
   zh_Hans: 适配多种海外应用场景的高性价比 LLM API
@@ -8,13 +8,13 @@ icon_small:
   en_US: icon_s_en.svg
 icon_large:
   en_US: icon_l_en.svg
-background: "#eadeff"
+background: "#c7fce2"
 help:
   title:
-    en_US: Get your API key from novita.ai
-    zh_Hans: 从 novita.ai 获取 API Key
+    en_US: Get your API key from Novita AI
+    zh_Hans: 从 Novita AI 获取 API Key
   url:
-    en_US: https://novita.ai/settings#key-management?utm_source=dify&utm_medium=ch&utm_campaign=api
+    en_US: https://novita.ai/settings/key-management?utm_source=dify&utm_medium=ch&utm_campaign=api
 supported_model_types:
   - llm
 configurate_methods:

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

@@ -341,9 +341,6 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
         :param credentials: provider credentials
         :return:
         """
-        # get predefined models
-        predefined_models = self.predefined_models()
-        predefined_models_map = {model.model: model for model in predefined_models}
 
         # transform credentials to kwargs for model instance
         credentials_kwargs = self._to_credential_kwargs(credentials)
@@ -359,9 +356,10 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
             base_model = model.id.split(":")[1]
 
             base_model_schema = None
-            for predefined_model_name, predefined_model in predefined_models_map.items():
-                if predefined_model_name in base_model:
+            for predefined_model in self.predefined_models():
+                if predefined_model.model in base_model:
                     base_model_schema = predefined_model
+                    break
 
             if not base_model_schema:
                 continue
@@ -621,9 +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"]
@@ -943,7 +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 = []
@@ -1055,7 +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:
@@ -1070,7 +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:
@@ -1186,12 +1184,14 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
             base_model = model.split(":")[1]
 
         # get model schema
-        models = self.predefined_models()
-        model_map = {model.model: model for model in models}
-        if base_model not in model_map:
-            raise ValueError(f"Base model {base_model} not found")
+        base_model_schema = None
+        for predefined_model in self.predefined_models():
+            if base_model == predefined_model.model:
+                base_model_schema = predefined_model
+                break
 
-        base_model_schema = model_map[base_model]
+        if not base_model_schema:
+            raise ValueError(f"Base model {base_model} not found")
 
         base_model_schema_features = base_model_schema.features or []
         base_model_schema_model_properties = base_model_schema.model_properties

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