downgrade boto3

chore: add ci
revert
2026-01-05 22:15:52 +00:00 · 2025-02-20 19:09:23 +08:00 · 2025-02-19 14:34:36 +08:00 · 2025-02-19 14:32:28 +08:00 · 2025-02-19 14:32:28 +08:00 · 2025-02-07 15:20:05 +08:00
211 changed files with 4985 additions and 4440 deletions
--- a/.github/workflows/build-push.yml
+++ b/.github/workflows/build-push.yml
@@ -5,6 +5,7 @@ on:
    branches:
      - "main"
      - "deploy/dev"
+      - "release/0.15.3-fix1"
  release:
    types: [published]

--- a/.github/workflows/docker-build.yml
+++ b/.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
--- a/README.md
+++ b/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">
--- a/README_AR.md
+++ b/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">
--- a/README_CN.md
+++ b/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">
--- a/README_ES.md
+++ b/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">
--- a/README_FR.md
+++ b/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">
--- a/README_JA.md
+++ b/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">
--- a/README_KL.md
+++ b/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">
--- a/README_KR.md
+++ b/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">
--- a/README_PT.md
+++ b/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">
--- a/README_SI.md
+++ b/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">
--- a/README_TR.md
+++ b/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
--- a/README_VI.md
+++ b/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">
--- a/api/Dockerfile
+++ b/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}

--- a/api/configs/feature/hosted_service/init.py
+++ b/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
--- a/api/configs/packaging/init.py
+++ b/api/configs/packaging/init.py
@@ -9,7 +9,7 @@ class PackagingInfo(BaseSettings):

    CURRENT_VERSION: str = Field(
        description="Dify version",
-        default="0.15.2",
+        default="0.15.3",
    )

    COMMIT_SHA: str = Field(
--- a/api/controllers/common/helpers.py
+++ b/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
--- a/api/controllers/console/datasets/datasets.py
+++ b/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
--- a/api/controllers/console/explore/message.py
+++ b/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.")
--- a/api/controllers/inner_api/workspace/workspace.py
+++ b/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")
--- a/api/controllers/service_api/dataset/document.py
+++ b/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)
--- a/api/controllers/web/message.py
+++ b/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.")
--- a/api/core/app/apps/agent_chat/app_runner.py
+++ b/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 []):
--- a/api/core/helper/ssrf_proxy.py
+++ b/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:
--- a/api/core/model_runtime/entities/init.py
+++ b/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",
--- a/api/core/model_runtime/entities/llm_entities.py
+++ b/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.
    """
--- a/api/core/model_runtime/model_providers/__base/ai_model.py
+++ b/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:
--- a/api/core/model_runtime/model_providers/__base/large_language_model.py
+++ b/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,
--- a/api/core/model_runtime/model_providers/_position.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/azure_ai_studio/azure_ai_studio.yaml
+++ b/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:
--- a/api/core/model_runtime/model_providers/azure_ai_studio/llm/llm.py
+++ b/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,
        )

--- a/api/core/model_runtime/model_providers/azure_openai/azure_openai.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/bedrock/bedrock.yaml
+++ b/api/core/model_runtime/model_providers/bedrock/bedrock.yaml
@@ -123,6 +123,15 @@ provider_credential_schema:
            en_US: AWS GovCloud (US-West)
            zh_Hans: AWS GovCloud (US-West)
            ja_JP: AWS GovCloud (米国西部)
+    - variable: bedrock_endpoint_url
+      label:
+        zh_Hans: Bedrock Endpoint URL
+        en_US: Bedrock Endpoint URL
+      type: text-input
+      required: false
+      placeholder:
+        zh_Hans: 在此输入您的 Bedrock Endpoint URL, 如：https://123456.cloudfront.net
+        en_US: Enter your Bedrock Endpoint URL, e.g. https://123456.cloudfront.net
    - variable: model_for_validation
      required: false
      label:
--- a/api/core/model_runtime/model_providers/bedrock/get_bedrock_client.py
+++ b/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
--- a/api/core/model_runtime/model_providers/cohere/llm/llm.py
+++ b/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)

--- a/api/core/model_runtime/model_providers/deepseek/llm/llm.py
+++ b/api/core/model_runtime/model_providers/deepseek/llm/llm.py
@@ -1,13 +1,10 @@
-import json
 from collections.abc import Generator
 from typing import Optional, Union

-import requests
 from yarl import URL

-from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta
+from core.model_runtime.entities.llm_entities import LLMMode, LLMResult
 from core.model_runtime.entities.message_entities import (
-    AssistantPromptMessage,
    PromptMessage,
    PromptMessageTool,
 )
@@ -39,208 +36,3 @@ class DeepseekLargeLanguageModel(OAIAPICompatLargeLanguageModel):
        credentials["mode"] = LLMMode.CHAT.value
        credentials["function_calling_type"] = "tool_call"
        credentials["stream_function_calling"] = "support"
-
-    def _handle_generate_stream_response(
-        self, model: str, credentials: dict, response: requests.Response, prompt_messages: list[PromptMessage]
-    ) -> Generator:
-        """
-        Handle llm stream response
-
-        :param model: model name
-        :param credentials: model credentials
-        :param response: streamed response
-        :param prompt_messages: prompt messages
-        :return: llm response chunk generator
-        """
-        full_assistant_content = ""
-        chunk_index = 0
-        is_reasoning_started = False  # Add flag to track reasoning state
-
-        def create_final_llm_result_chunk(
-            id: Optional[str], index: int, message: AssistantPromptMessage, finish_reason: str, usage: dict
-        ) -> LLMResultChunk:
-            # calculate num tokens
-            prompt_tokens = usage and usage.get("prompt_tokens")
-            if prompt_tokens is None:
-                prompt_tokens = self._num_tokens_from_string(model, prompt_messages[0].content)
-            completion_tokens = usage and usage.get("completion_tokens")
-            if completion_tokens is None:
-                completion_tokens = self._num_tokens_from_string(model, full_assistant_content)
-
-            # transform usage
-            usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
-
-            return LLMResultChunk(
-                id=id,
-                model=model,
-                prompt_messages=prompt_messages,
-                delta=LLMResultChunkDelta(index=index, message=message, finish_reason=finish_reason, usage=usage),
-            )
-
-        # delimiter for stream response, need unicode_escape
-        import codecs
-
-        delimiter = credentials.get("stream_mode_delimiter", "\n\n")
-        delimiter = codecs.decode(delimiter, "unicode_escape")
-
-        tools_calls: list[AssistantPromptMessage.ToolCall] = []
-
-        def increase_tool_call(new_tool_calls: list[AssistantPromptMessage.ToolCall]):
-            def get_tool_call(tool_call_id: str):
-                if not tool_call_id:
-                    return tools_calls[-1]
-
-                tool_call = next((tool_call for tool_call in tools_calls if tool_call.id == tool_call_id), None)
-                if tool_call is None:
-                    tool_call = AssistantPromptMessage.ToolCall(
-                        id=tool_call_id,
-                        type="function",
-                        function=AssistantPromptMessage.ToolCall.ToolCallFunction(name="", arguments=""),
-                    )
-                    tools_calls.append(tool_call)
-
-                return tool_call
-
-            for new_tool_call in new_tool_calls:
-                # get tool call
-                tool_call = get_tool_call(new_tool_call.function.name)
-                # update tool call
-                if new_tool_call.id:
-                    tool_call.id = new_tool_call.id
-                if new_tool_call.type:
-                    tool_call.type = new_tool_call.type
-                if new_tool_call.function.name:
-                    tool_call.function.name = new_tool_call.function.name
-                if new_tool_call.function.arguments:
-                    tool_call.function.arguments += new_tool_call.function.arguments
-
-        finish_reason = None  # The default value of finish_reason is None
-        message_id, usage = None, None
-        for chunk in response.iter_lines(decode_unicode=True, delimiter=delimiter):
-            chunk = chunk.strip()
-            if chunk:
-                # ignore sse comments
-                if chunk.startswith(":"):
-                    continue
-                decoded_chunk = chunk.strip().removeprefix("data:").lstrip()
-                if decoded_chunk == "[DONE]":  # Some provider returns "data: [DONE]"
-                    continue
-
-                try:
-                    chunk_json: dict = json.loads(decoded_chunk)
-                # stream ended
-                except json.JSONDecodeError as e:
-                    yield create_final_llm_result_chunk(
-                        id=message_id,
-                        index=chunk_index + 1,
-                        message=AssistantPromptMessage(content=""),
-                        finish_reason="Non-JSON encountered.",
-                        usage=usage,
-                    )
-                    break
-                # handle the error here. for issue #11629
-                if chunk_json.get("error") and chunk_json.get("choices") is None:
-                    raise ValueError(chunk_json.get("error"))
-
-                if chunk_json:
-                    if u := chunk_json.get("usage"):
-                        usage = u
-                if not chunk_json or len(chunk_json["choices"]) == 0:
-                    continue
-
-                choice = chunk_json["choices"][0]
-                finish_reason = chunk_json["choices"][0].get("finish_reason")
-                message_id = chunk_json.get("id")
-                chunk_index += 1
-
-                if "delta" in choice:
-                    delta = choice["delta"]
-                    is_reasoning = delta.get("reasoning_content")
-                    delta_content = delta.get("content") or delta.get("reasoning_content")
-
-                    assistant_message_tool_calls = None
-
-                    if "tool_calls" in delta and credentials.get("function_calling_type", "no_call") == "tool_call":
-                        assistant_message_tool_calls = delta.get("tool_calls", None)
-                    elif (
-                        "function_call" in delta
-                        and credentials.get("function_calling_type", "no_call") == "function_call"
-                    ):
-                        assistant_message_tool_calls = [
-                            {"id": "tool_call_id", "type": "function", "function": delta.get("function_call", {})}
-                        ]
-
-                    # assistant_message_function_call = delta.delta.function_call
-
-                    # extract tool calls from response
-                    if assistant_message_tool_calls:
-                        tool_calls = self._extract_response_tool_calls(assistant_message_tool_calls)
-                        increase_tool_call(tool_calls)
-
-                    if delta_content is None or delta_content == "":
-                        continue
-
-                    # Add markdown quote markers for reasoning content
-                    if is_reasoning:
-                        if not is_reasoning_started:
-                            delta_content = "> 💭 " + delta_content
-                            is_reasoning_started = True
-                        elif "\n\n" in delta_content:
-                            delta_content = delta_content.replace("\n\n", "\n> ")
-                        elif "\n" in delta_content:
-                            delta_content = delta_content.replace("\n", "\n> ")
-                    elif is_reasoning_started:
-                        # If we were in reasoning mode but now getting regular content,
-                        # add \n\n to close the reasoning block
-                        delta_content = "\n\n" + delta_content
-                        is_reasoning_started = False
-
-                    # transform assistant message to prompt message
-                    assistant_prompt_message = AssistantPromptMessage(
-                        content=delta_content,
-                    )
-
-                    # reset tool calls
-                    tool_calls = []
-                    full_assistant_content += delta_content
-                elif "text" in choice:
-                    choice_text = choice.get("text", "")
-                    if choice_text == "":
-                        continue
-
-                    # transform assistant message to prompt message
-                    assistant_prompt_message = AssistantPromptMessage(content=choice_text)
-                    full_assistant_content += choice_text
-                else:
-                    continue
-
-                yield LLMResultChunk(
-                    id=message_id,
-                    model=model,
-                    prompt_messages=prompt_messages,
-                    delta=LLMResultChunkDelta(
-                        index=chunk_index,
-                        message=assistant_prompt_message,
-                    ),
-                )
-
-            chunk_index += 1
-
-        if tools_calls:
-            yield LLMResultChunk(
-                id=message_id,
-                model=model,
-                prompt_messages=prompt_messages,
-                delta=LLMResultChunkDelta(
-                    index=chunk_index,
-                    message=AssistantPromptMessage(tool_calls=tools_calls, content=""),
-                ),
-            )
-
-        yield create_final_llm_result_chunk(
-            id=message_id,
-            index=chunk_index,
-            message=AssistantPromptMessage(content=""),
-            finish_reason=finish_reason,
-            usage=usage,
-        )
--- a/api/core/model_runtime/model_providers/google/llm/_position.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/google/llm/gemini-2.0-flash-001.yml
+++ b/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
--- a/api/core/model_runtime/model_providers/google/llm/gemini-2.0-pro-exp-02-05.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/groq/llm/_position.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/groq/llm/deepseek-r1-distill-llama-70b.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/novita/llm/L3-8B-Stheno-v3.2.yaml
+++ b/api/core/model_runtime/model_providers/novita/llm/L3-8B-Stheno-v3.2.yaml
@@ -1,7 +1,7 @@
 model: Sao10K/L3-8B-Stheno-v3.2
 label:
-  zh_Hans: Sao10K/L3-8B-Stheno-v3.2
-  en_US: Sao10K/L3-8B-Stheno-v3.2
+  zh_Hans: L3 8B Stheno V3.2
+  en_US: L3 8B Stheno V3.2
 model_type: llm
 features:
  - agent-thought
--- a/api/core/model_runtime/model_providers/novita/llm/_position.yaml
+++ b/api/core/model_runtime/model_providers/novita/llm/_position.yaml
@@ -1,4 +1,5 @@
 # Deepseek Models
+- deepseek/deepseek-r1
 - deepseek/deepseek_v3

 # LLaMA Models
--- a/api/core/model_runtime/model_providers/novita/llm/airoboros-l2-70b.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/novita/llm/deepseek-r1.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/novita/llm/deepseek_v3.yaml
+++ b/api/core/model_runtime/model_providers/novita/llm/deepseek_v3.yaml
@@ -1,7 +1,7 @@
 model: deepseek/deepseek_v3
 label:
-  zh_Hans: deepseek/deepseek_v3
-  en_US: deepseek/deepseek_v3
+  zh_Hans: DeepSeek V3
+  en_US: DeepSeek V3
 model_type: llm
 features:
  - agent-thought
--- a/api/core/model_runtime/model_providers/novita/llm/dolphin-mixtral-8x22b.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/novita/llm/gemma-2-9b-it.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/novita/llm/hermes-2-pro-llama-3-8b.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/novita/llm/l3-70b-euryale-v2.1.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/novita/llm/l3-8b-lunaris.yaml
+++ b/api/core/model_runtime/model_providers/novita/llm/l3-8b-lunaris.yaml
@@ -1,7 +1,7 @@
 model: sao10k/l3-8b-lunaris
 label:
-  zh_Hans: sao10k/l3-8b-lunaris
-  en_US: sao10k/l3-8b-lunaris
+  zh_Hans: "Sao10k L3 8B Lunaris"
+  en_US: "Sao10k L3 8B Lunaris"
 model_type: llm
 features:
  - agent-thought
--- a/api/core/model_runtime/model_providers/novita/llm/l31-70b-euryale-v2.2.yaml
+++ b/api/core/model_runtime/model_providers/novita/llm/l31-70b-euryale-v2.2.yaml
@@ -1,7 +1,7 @@
 model: sao10k/l31-70b-euryale-v2.2
 label:
-  zh_Hans: sao10k/l31-70b-euryale-v2.2
-  en_US: sao10k/l31-70b-euryale-v2.2
+  zh_Hans: L31 70B Euryale V2.2
+  en_US: L31 70B Euryale V2.2
 model_type: llm
 features:
  - agent-thought
--- a/api/core/model_runtime/model_providers/novita/llm/llama-3-70b-instruct.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/novita/llm/llama-3-8b-instruct.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/novita/llm/llama-3.1-70b-instruct.yaml
+++ b/api/core/model_runtime/model_providers/novita/llm/llama-3.1-70b-instruct.yaml
@@ -1,7 +1,7 @@
 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
--- a/api/core/model_runtime/model_providers/novita/llm/llama-3.1-8b-instruct-bf16.yaml
+++ b/api/core/model_runtime/model_providers/novita/llm/llama-3.1-8b-instruct-bf16.yaml
@@ -1,7 +1,7 @@
 model: meta-llama/llama-3.1-8b-instruct-bf16
 label:
-  zh_Hans: meta-llama/llama-3.1-8b-instruct-bf16
-  en_US: meta-llama/llama-3.1-8b-instruct-bf16
+  zh_Hans: Llama 3.1 8B Instruct BF16
+  en_US: Llama 3.1 8B Instruct BF16
 model_type: llm
 features:
  - agent-thought
--- a/api/core/model_runtime/model_providers/novita/llm/llama-3.1-8b-instruct-max.yaml
+++ b/api/core/model_runtime/model_providers/novita/llm/llama-3.1-8b-instruct-max.yaml
@@ -1,7 +1,7 @@
 model: meta-llama/llama-3.1-8b-instruct-max
 label:
-  zh_Hans: meta-llama/llama-3.1-8b-instruct-max
-  en_US: meta-llama/llama-3.1-8b-instruct-max
+  zh_Hans: "Llama3.1 8B Instruct Max\t"
+  en_US: "Llama3.1 8B Instruct Max\t"
 model_type: llm
 features:
  - agent-thought
--- a/api/core/model_runtime/model_providers/novita/llm/llama-3.1-8b-instruct.yaml
+++ b/api/core/model_runtime/model_providers/novita/llm/llama-3.1-8b-instruct.yaml
@@ -1,7 +1,7 @@
 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
--- a/api/core/model_runtime/model_providers/novita/llm/llama-3.2-11b-vision-instruct.yaml
+++ b/api/core/model_runtime/model_providers/novita/llm/llama-3.2-11b-vision-instruct.yaml
@@ -1,7 +1,7 @@
 model: meta-llama/llama-3.2-11b-vision-instruct
 label:
-  zh_Hans: meta-llama/llama-3.2-11b-vision-instruct
-  en_US: meta-llama/llama-3.2-11b-vision-instruct
+  zh_Hans: "Llama 3.2 11B Vision Instruct\t"
+  en_US: "Llama 3.2 11B Vision Instruct\t"
 model_type: llm
 features:
  - agent-thought
--- a/api/core/model_runtime/model_providers/novita/llm/llama-3.2-1b-instruct.yaml
+++ b/api/core/model_runtime/model_providers/novita/llm/llama-3.2-1b-instruct.yaml
@@ -1,7 +1,7 @@
 model: meta-llama/llama-3.2-1b-instruct
 label:
-  zh_Hans: meta-llama/llama-3.2-1b-instruct
-  en_US: meta-llama/llama-3.2-1b-instruct
+  zh_Hans: "Llama 3.2 1B Instruct\t"
+  en_US: "Llama 3.2 1B Instruct\t"
 model_type: llm
 features:
  - agent-thought
--- a/api/core/model_runtime/model_providers/novita/llm/llama-3.2-3b-instruct.yaml
+++ b/api/core/model_runtime/model_providers/novita/llm/llama-3.2-3b-instruct.yaml
@@ -1,7 +1,7 @@
 model: meta-llama/llama-3.2-3b-instruct
 label:
-  zh_Hans: meta-llama/llama-3.2-3b-instruct
-  en_US: meta-llama/llama-3.2-3b-instruct
+  zh_Hans: Llama 3.2 3B Instruct
+  en_US: Llama 3.2 3B Instruct
 model_type: llm
 features:
  - agent-thought
--- a/api/core/model_runtime/model_providers/novita/llm/llama-3.3-70b-instruct.yaml
+++ b/api/core/model_runtime/model_providers/novita/llm/llama-3.3-70b-instruct.yaml
@@ -1,7 +1,7 @@
 model: meta-llama/llama-3.3-70b-instruct
 label:
-  zh_Hans: meta-llama/llama-3.3-70b-instruct
-  en_US: meta-llama/llama-3.3-70b-instruct
+  zh_Hans: Llama 3.3 70B Instruct
+  en_US: Llama 3.3 70B Instruct
 model_type: llm
 features:
  - agent-thought
--- a/api/core/model_runtime/model_providers/novita/llm/midnight-rose-70b.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/novita/llm/mistral-7b-instruct.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/novita/llm/mistral-nemo.yaml
+++ b/api/core/model_runtime/model_providers/novita/llm/mistral-nemo.yaml
@@ -1,7 +1,7 @@
 model: mistralai/mistral-nemo
 label:
-  zh_Hans: mistralai/mistral-nemo
-  en_US: mistralai/mistral-nemo
+  zh_Hans: Mistral Nemo
+  en_US: Mistral Nemo
 model_type: llm
 features:
  - agent-thought
--- a/api/core/model_runtime/model_providers/novita/llm/mythomax-l2-13b.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/novita/llm/nous-hermes-llama2-13b.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/novita/llm/openchat-7b.yaml
+++ b/api/core/model_runtime/model_providers/novita/llm/openchat-7b.yaml
@@ -1,7 +1,7 @@
 model: openchat/openchat-7b
 label:
-  zh_Hans: openchat/openchat-7b
-  en_US: openchat/openchat-7b
+  zh_Hans: OpenChat 7B
+  en_US: OpenChat 7B
 model_type: llm
 features:
  - agent-thought
--- a/api/core/model_runtime/model_providers/novita/llm/openhermes-2.5-mistral-7b.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/novita/llm/qwen-2-72b-instruct.yaml
+++ b/api/core/model_runtime/model_providers/novita/llm/qwen-2-72b-instruct.yaml
@@ -1,7 +1,7 @@
 model: qwen/qwen-2-72b-instruct
 label:
-  zh_Hans: qwen/qwen-2-72b-instruct
-  en_US: qwen/qwen-2-72b-instruct
+  zh_Hans: Qwen2 72B Instruct
+  en_US: Qwen2 72B Instruct
 model_type: llm
 features:
  - agent-thought
--- a/api/core/model_runtime/model_providers/novita/llm/qwen-2-7b-instruct.yaml
+++ b/api/core/model_runtime/model_providers/novita/llm/qwen-2-7b-instruct.yaml
@@ -1,7 +1,7 @@
 model: qwen/qwen-2-7b-instruct
 label:
-  zh_Hans: qwen/qwen-2-7b-instruct
-  en_US: qwen/qwen-2-7b-instruct
+  zh_Hans: Qwen 2 7B Instruct
+  en_US: Qwen 2 7B Instruct
 model_type: llm
 features:
  - agent-thought
--- a/api/core/model_runtime/model_providers/novita/llm/qwen-2-vl-72b-instruct.yaml
+++ b/api/core/model_runtime/model_providers/novita/llm/qwen-2-vl-72b-instruct.yaml
@@ -1,7 +1,7 @@
 model: qwen/qwen-2-vl-72b-instruct
 label:
-  zh_Hans: qwen/qwen-2-vl-72b-instruct
-  en_US: qwen/qwen-2-vl-72b-instruct
+  zh_Hans: Qwen 2 VL 72B Instruct
+  en_US: Qwen 2 VL 72B Instruct
 model_type: llm
 features:
  - agent-thought
--- a/api/core/model_runtime/model_providers/novita/llm/qwen-2.5-72b-instruct.yaml
+++ b/api/core/model_runtime/model_providers/novita/llm/qwen-2.5-72b-instruct.yaml
@@ -1,7 +1,7 @@
 model: qwen/qwen-2.5-72b-instruct
 label:
-  zh_Hans: qwen/qwen-2.5-72b-instruct
-  en_US: qwen/qwen-2.5-72b-instruct
+  zh_Hans: Qwen 2.5 72B Instruct
+  en_US: Qwen 2.5 72B Instruct
 model_type: llm
 features:
  - agent-thought
--- a/api/core/model_runtime/model_providers/novita/llm/wizardlm-2-8x22b.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/novita/novita.yaml
+++ b/api/core/model_runtime/model_providers/novita/novita.yaml
@@ -8,7 +8,7 @@ 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
--- a/api/core/model_runtime/model_providers/nvidia/llm/_position.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/nvidia/llm/deepseek-r1.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/nvidia/llm/llm.py
+++ b/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:
--- a/api/core/model_runtime/model_providers/oci/llm/cohere.command-r-08-2024.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/oci/llm/cohere.command-r-16k.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/oci/llm/cohere.command-r-plus-08-2024.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/oci/llm/cohere.command-r-plus.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/oci/llm/llm.py
+++ b/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,
--- a/api/core/model_runtime/model_providers/oci/llm/meta.llama-3-70b-instruct.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/oci/llm/meta.llama-3.1-70b-instruct.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/oci/oci.py
+++ b/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:
--- a/api/core/model_runtime/model_providers/ollama/llm/llm.py
+++ b/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)

--- a/api/core/model_runtime/model_providers/openai/llm/_position.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/openai/llm/llm.py
+++ b/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
--- a/api/core/model_runtime/model_providers/openai/llm/o1-2024-12-17.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/openai/llm/o1.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/openai/llm/o3-mini-2025-01-31.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/openai/llm/o3-mini.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/openai_api_compatible/llm/llm.py
+++ b/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,
--- a/api/core/model_runtime/model_providers/openrouter/llm/_position.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/openrouter/llm/deepseek-chat.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/openrouter/llm/deepseek-r1.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/openrouter/llm/o3-mini-2025-01-31.yaml
+++ b/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
--- a/api/core/model_runtime/model_providers/openrouter/llm/o3-mini.yaml
+++ b/api/core/model_runtime/model_providers/openrouter/llm/o3-mini.yaml
@@ -0,0 +1,49 @@
+model: openai/o3-mini
+label:
+  en_US: o3-mini
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 200000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: presence_penalty
+    use_template: presence_penalty
+  - name: frequency_penalty
+    use_template: frequency_penalty
+  - name: max_tokens
+    use_template: max_tokens
+    default: 512
+    min: 1
+    max: 100000
+  - name: response_format
+    label:
+      zh_Hans: 回复格式
+      en_US: response_format
+    type: string
+    help:
+      zh_Hans: 指定模型必须输出的格式
+      en_US: specifying the format that the model must output
+    required: false
+    options:
+      - text
+      - json_object
+pricing:
+  input: "1.10"
+  output: "4.40"
+  unit: "0.000001"
+  currency: USD
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
jyong	cc53c9004a	downgrade boto3	2025-02-20 19:09:23 +08:00
crazywoola	0e33a3aa5f	chore: add ci	2025-02-19 14:34:36 +08:00
Hash Brown	d3895bcd6b	revert	2025-02-19 14:32:28 +08:00
Hash Brown	eeb390650b	fix: build failed	2025-02-19 14:32:28 +08:00
-LAN-	ca19bd31d4	chore(*): Bump version to 0.15.3 (#13308 ) Signed-off-by: -LAN- <laipz8200@outlook.com>	2025-02-07 15:20:05 +08:00
-LAN-	413dfd5628	feat: add completion mode and context size options for LLM configuration (#13325 ) Signed-off-by: -LAN- <laipz8200@outlook.com>	2025-02-07 15:08:53 +08:00
-LAN-	f9515901cc	fix: Azure AI Foundry model cannot be used in the workflow (#13323 ) Signed-off-by: -LAN- <laipz8200@outlook.com>	2025-02-07 14:52:57 +08:00
呆萌闷油瓶	3f42fabff8	chore:improve thinking display for llm from xinference and ollama pro… (#13318 )	2025-02-07 14:29:29 +08:00
-LAN-	1caa578771	chore(*): Update style of thinking (#13319 ) Signed-off-by: -LAN- <laipz8200@outlook.com>	2025-02-07 14:06:35 +08:00
Lazy_Frog	b7c11c1818	Fix the problem of Workflow terminates after parallel tasks execution, merge node not triggered (#12498 ) Co-authored-by: Novice Lee <novicelee@NoviPro.local>	2025-02-07 13:56:08 +08:00
非法操作	3eb3db0663	chore: refactor the OpenAICompatible and improve thinking display (#13299 )	2025-02-07 13:28:46 +08:00
-LAN-	be46f32056	fix(credits): require model name equals (#13314 ) Signed-off-by: -LAN- <laipz8200@outlook.com>	2025-02-07 13:28:17 +08:00
sino	6e5c915f96	feat(model): add deepseek-r1 for openrouter (#13312 )	2025-02-07 12:39:13 +08:00
-LAN-	04d13a8116	feat(credits): Allow to configure model-credit mapping (#13274 ) Signed-off-by: -LAN- <laipz8200@outlook.com>	2025-02-07 11:01:31 +08:00
Kemal	e638ede3f2	Update README_TR.md (#13294 )	2025-02-07 09:11:39 +08:00
Riddhimaan-Senapati	2348abe4bf	feat: added a couple of models not defined in vertex ai, that were already … (#13296 )	2025-02-07 09:11:25 +08:00
呆萌闷油瓶	f7e7a399d9	feat:add think tag display for xinference deepseek r1 (#13291 )	2025-02-06 22:04:58 +08:00
le0zh	ba91f34636	fix: incorrect transferMethod assignment for remote file (#13286 )	2025-02-06 19:32:21 +08:00
zhu-an	16865d43a8	feat: add deepseek models for volcengine provider (#13283 ) Co-authored-by: zhaoqingyu.1075 <zhaoqingyu.1075@bytedance.com>	2025-02-06 18:20:03 +08:00
呆萌闷油瓶	0d13aee15c	feat:add deepseek r1 think display for ollama provider (#13272 )	2025-02-06 15:32:10 +08:00
Wu Tianwei	49b4144ffd	fix: add dataset edit permissions (#13223 )	2025-02-06 14:26:16 +08:00
dependabot[bot]	186e2d972e	chore(deps): bump katex from 0.16.10 to 0.16.21 in /web (#13270 ) Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>	2025-02-06 13:27:07 +08:00
engchina	40dd63ecef	Upgrade oracle models (#13174 ) Co-authored-by: engchina <atjapan2015@gmail.com>	2025-02-06 13:24:27 +08:00
-LAN-	6d66d6da15	feat(model_providers): Support deepseek-r1 for Nvidia Catalog (#13269 ) Signed-off-by: -LAN- <laipz8200@outlook.com>	2025-02-06 13:03:19 +08:00
weiwenyan-dev	03ec3513f3	Fix bug large data no render (#12683 ) Co-authored-by: ex_wenyan.wei <ex_wenyan.wei@tcl.com>	2025-02-06 13:00:04 +08:00
-LAN-	87763fc234	feat(model_providers): Support deepseek for Azure AI Foundry (#13267 ) Signed-off-by: -LAN- <laipz8200@outlook.com>	2025-02-06 12:45:48 +08:00
JasonVV	f6c44cae2e	feat(model): add gemini-2.0 model (#13266 )	2025-02-06 12:28:59 +08:00
xhe	da2ee04fce	fix: correct linewrap think display in generic openai api (#13260 ) Signed-off-by: xhe <xw897002528@gmail.com>	2025-02-06 10:53:08 +08:00
JasonVV	7673c36af3	feat(model): add gemini-2.0-flash-thinking-exp-01-21 (#13230 )	2025-02-06 10:01:00 +08:00
Riddhimaan-Senapati	9457b2af2f	feat: added models :gemini 2.0 flash 001 and gemini 2.0 pro exp 02-05 (#13247 )	2025-02-06 09:58:39 +08:00
k-zaku	7203991032	feat: add parameter "reasoning_effort" and Openai o3-mini (#13243 )	2025-02-06 09:29:48 +08:00
xhe	5a685f7156	feat: add think display for volcengine and generic openapi (#13234 ) Signed-off-by: xhe <xw897002528@gmail.com>	2025-02-06 09:24:40 +08:00
Riddhimaan-Senapati	a6a25030ad	fix: updated _position.yaml to include the latest model already integ… (#13245 )	2025-02-06 09:21:51 +08:00
Riddhimaan-Senapati	00458a31d5	feat: added deepseek r1 and v3 to siliconflow (#13238 )	2025-02-05 21:59:18 +08:00
-LAN-	c6ddf6d6cc	feat(model_providers): Add Groq DeepSeek-R1-Distill-Llama-70b (#13229 ) Signed-off-by: -LAN- <laipz8200@outlook.com>	2025-02-05 19:15:29 +08:00
Joshbly	34b21b3065	feat: Add o3-mini and o3-mini-2025-01-31 model variants (#13129 ) Co-authored-by: crazywoola <427733928@qq.com>	2025-02-05 17:04:45 +08:00
Bowen Liang	8fbb355cd2	chore: squash system dependencies installation steps (#13206 )	2025-02-05 16:42:53 +08:00
HQidea	e8b3b7e578	Fix new variables in the conversation opener would override prompt_variables (#13191 )	2025-02-05 16:16:00 +08:00
-LAN-	59ca44f493	chore(model_runtime): Move deepseek ahead in the providers list. (#13197 ) Signed-off-by: -LAN- <laipz8200@outlook.com>	2025-02-05 16:08:28 +08:00
Bowen Liang	9e1457c2c3	fix: mypy checks violation in AzureBlobStorage (#13215 )	2025-02-05 15:56:23 +08:00
te-chan	fac83e14bc	Use DefaultAzureCredential for managed identity in azure blob extention (#11559 )	2025-02-05 13:43:43 +08:00
Nam Vu	a97cec57e4	fix: SSRF proxy file descriptor leak in concurrent requests (#13108 )	2025-02-05 13:10:27 +08:00
Riddhimaan-Senapati	38c10b47d3	Feat: add linkedin to readme (#13203 )	2025-02-05 12:27:58 +08:00
MaFee921	1a2523fd15	feat: bedrock_endpoint_url (#12838 )	2025-02-05 12:24:24 +08:00
Warren Chen	03243cb422	Modify params for bedrock retrieve generate (#13182 )	2025-02-05 12:17:42 +08:00
Bowen Liang	2ad7ee0344	chore: add tests for build docker image when dockerfile changed (#10732 )	2025-02-05 11:40:22 +08:00
Riddhimaan-Senapati	55ce3618ce	fix: Dollar Sign Handling in Markdown (#13178 ) Co-authored-by: crazywoola <427733928@qq.com>	2025-02-05 11:00:56 +08:00
TechnoHouse	e9e34c1ab2	Install apt dependencies using bookworm source, consistent with base image. Remove unnecessary, error-prone pins (#13176 )	2025-02-05 10:07:22 +08:00
-LAN-	d4c916b496	chore(pyproject): Add type stubs into `pyproject.toml` (#13145 ) Signed-off-by: -LAN- <laipz8200@outlook.com>	2025-02-04 12:01:28 +08:00
Obada Khalili	8fbc9c9342	Solve circular dependency issue between `workflow/constants.ts` file and `default.ts` file (#13165 )	2025-02-04 09:26:01 +08:00
aplio	1b6fd9dfe8	fix: set indexing technique from dataset during update-by-text (#13155 )	2025-02-03 11:06:03 +08:00
非法操作	304467e3f5	fix: not install libmagic raise error (#13146 )	2025-02-03 11:05:20 +08:00
Kei YAMAZAKI	7452032d81	add azure openai api version 2024-12-01-preview (#13135 )	2025-02-03 11:04:20 +08:00
aplio	87e2048f1b	nitpick: fix small typos in template.en.mdx (#13156 )	2025-02-03 11:03:11 +08:00
Nam Vu	d876084392	chore: upgrade libldap2 (#13158 )	2025-02-03 11:02:14 +08:00
非法操作	840729afa5	feat: the think tag display of siliconflow's deepseek r1 (#13153 )	2025-02-02 21:55:13 +08:00
Obada Khalili	941ad03f3c	pass model and cost so that langfuse can show cost (#13117 )	2025-02-02 15:27:27 +08:00
aplio	d73d191f99	feature. add feat to modify metadata via dataset api (#13116 )	2025-02-02 15:27:12 +08:00
Masashi Tomooka	c2664e0283	chore: fix wrong VectorType match case (#13123 )	2025-02-02 15:26:59 +08:00
-LAN-	ee61cede4e	test(huggingface_hub): Skip the failed test temporarily. (#13142 ) Signed-off-by: -LAN- <laipz8200@outlook.com>	2025-02-02 14:47:26 +08:00
-LAN-	b47669b80b	fix: deduct LLM quota after processing invoke result (#13075 ) Signed-off-by: -LAN- <laipz8200@outlook.com>	2025-02-02 12:05:11 +08:00
Hash Brown	c0d0c63592	feat: switch to chat messages before regenerated (#11301 ) Co-authored-by: zuodongxu <192560071+zuodongxu@users.noreply.github.com>	2025-01-31 13:05:10 +08:00
Yingchun Lai	b09c39c8dc	refactor: avoid to use extra space when finding model by name (#13043 )	2025-01-30 15:08:29 +08:00
heyszt	b4b09ddc3c	add tongyi qwen2.5-14b/7b-instruct-1m model (#13089 )	2025-01-29 11:58:01 +08:00
Ademílson Tonato	d0a21086bd	refactor: Update Firecrawl API parameters and default settings (#13082 )	2025-01-29 11:21:05 +08:00
Yingchun Lai	d44882c1b5	refactor: reduce duplciate code by inheritance (#13073 )	2025-01-28 10:52:01 +08:00
Yingchun Lai	23c68efa2d	fix: fix the formatter is not applied on log file (#12704 )	2025-01-28 10:49:58 +08:00
Jason	560c5de1b7	Fixed Novita AI color and added DeepSeek R1 model (#13074 )	2025-01-28 10:38:54 +08:00
Abdullah AlOsaimi	5d91dbd000	Set default LOG_LEVEL to INFO for celery workers and beat (#13066 ) Co-authored-by: Abdullah AlOsaimi <189027247+osaimi@users.noreply.github.com>	2025-01-27 17:09:41 +08:00
heyszt	6c31ee36cd	fix qwen-vl blocking mode (#13052 )	2025-01-27 11:35:23 +08:00
jiandanfeng	edc29780ed	fix: "Model schema not found" error only in agents (#12655 ) (#12760 )	2025-01-27 11:33:13 +08:00
yjc980121	aad7e4dd1c	fix:Improve MIME type detection for remote URL uploads using python-magic (#12693 )	2025-01-27 11:33:03 +08:00
Xin Zhang	a6a727e8a4	feat: add inner API to create workspace without requiring email (#13021 )	2025-01-26 15:36:56 +08:00
NFish	d1fc65fabc	fix: adjust iteration node dark style (#13051 )	2025-01-26 11:19:41 +08:00