Merge branch 'feat/tooltip' into deploy/dev

api/controllers/service_api/app/app.py

@@ -92,7 +92,7 @@ class AppMetaApi(Resource):
 class AppInfoApi(Resource):
     @validate_app_token
     def get(self, app_model: App):
-        """Get app infomation"""
+        """Get app information"""
         return {
             'name':app_model.name,
             'description':app_model.description

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

@@ -1,6 +1,6 @@
 import base64
+import io
 import json
-import mimetypes
 from collections.abc import Generator
 from typing import Optional, Union, cast
 
@@ -18,6 +18,7 @@ from anthropic.types import (
 )
 from anthropic.types.beta.tools import ToolsBetaMessage
 from httpx import Timeout
+from PIL import Image
 
 from core.model_runtime.callbacks.base_callback import Callback
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
@@ -462,7 +463,8 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
                                     # fetch image data from url
                                     try:
                                         image_content = requests.get(message_content.data).content
-                                        mime_type, _ = mimetypes.guess_type(message_content.data)
+                                        with Image.open(io.BytesIO(image_content)) as img:
+                                            mime_type = f"image/{img.format.lower()}"
                                         base64_data = base64.b64encode(image_content).decode('utf-8')
                                     except Exception as ex:
                                         raise ValueError(f"Failed to fetch image data from url {message_content.data}, {ex}")

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

@@ -1,8 +1,8 @@
 # standard import
 import base64
+import io
 import json
 import logging
-import mimetypes
 from collections.abc import Generator
 from typing import Optional, Union, cast
 
@@ -17,6 +17,7 @@ from botocore.exceptions import (
     ServiceNotInRegionError,
     UnknownServiceError,
 )
+from PIL.Image import Image
 
 # local import
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
@@ -381,9 +382,8 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
                             try:
                                 url = message_content.data
                                 image_content = requests.get(url).content
-                                if '?' in url:
-                                    url = url.split('?')[0]
-                                mime_type, _ = mimetypes.guess_type(url)
+                                with Image.open(io.BytesIO(image_content)) as img:
+                                    mime_type = f"image/{img.format.lower()}"
                                 base64_data = base64.b64encode(image_content).decode('utf-8')
                             except Exception as ex:
                                 raise ValueError(f"Failed to fetch image data from url {message_content.data}, {ex}")

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

@@ -1,7 +1,7 @@
 import base64
+import io
 import json
 import logging
-import mimetypes
 from collections.abc import Generator
 from typing import Optional, Union, cast
 
@@ -12,6 +12,7 @@ import google.generativeai.client as client
 import requests
 from google.generativeai.types import ContentType, GenerateContentResponse, HarmBlockThreshold, HarmCategory
 from google.generativeai.types.content_types import to_part
+from PIL import Image
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
 from core.model_runtime.entities.message_entities import (
@@ -371,7 +372,8 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
                             # fetch image data from url
                             try:
                                 image_content = requests.get(message_content.data).content
-                                mime_type, _ = mimetypes.guess_type(message_content.data)
+                                with Image.open(io.BytesIO(image_content)) as img:
+                                    mime_type = f"image/{img.format.lower()}"
                                 base64_data = base64.b64encode(image_content).decode('utf-8')
                             except Exception as ex:
                                 raise ValueError(f"Failed to fetch image data from url {message_content.data}, {ex}")

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

@@ -1,4 +1,5 @@
 import base64
+import io
 import json
 import logging
 from collections.abc import Generator
@@ -18,6 +19,7 @@ from anthropic.types import (
 )
 from google.cloud import aiplatform
 from google.oauth2 import service_account
+from PIL import Image
 from vertexai.generative_models import HarmBlockThreshold, HarmCategory
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
@@ -332,7 +334,8 @@ class VertexAiLargeLanguageModel(LargeLanguageModel):
                             # fetch image data from url
                             try:
                                 image_content = requests.get(message_content.data).content
-                                mime_type, _ = mimetypes.guess_type(message_content.data)
+                                with Image.open(io.BytesIO(image_content)) as img:
+                                    mime_type = f"image/{img.format.lower()}"
                                 base64_data = base64.b64encode(image_content).decode('utf-8')
                             except Exception as ex:
                                 raise ValueError(f"Failed to fetch image data from url {message_content.data}, {ex}")

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

@@ -1,6 +1,25 @@
 import re
-from collections.abc import Callable, Generator
-from typing import cast
+from collections.abc import Generator
+from typing import Optional, cast
+
+from volcenginesdkarkruntime import Ark
+from volcenginesdkarkruntime.types.chat import (
+    ChatCompletion,
+    ChatCompletionAssistantMessageParam,
+    ChatCompletionChunk,
+    ChatCompletionContentPartImageParam,
+    ChatCompletionContentPartTextParam,
+    ChatCompletionMessageParam,
+    ChatCompletionMessageToolCallParam,
+    ChatCompletionSystemMessageParam,
+    ChatCompletionToolMessageParam,
+    ChatCompletionToolParam,
+    ChatCompletionUserMessageParam,
+)
+from volcenginesdkarkruntime.types.chat.chat_completion_content_part_image_param import ImageURL
+from volcenginesdkarkruntime.types.chat.chat_completion_message_tool_call_param import Function
+from volcenginesdkarkruntime.types.create_embedding_response import CreateEmbeddingResponse
+from volcenginesdkarkruntime.types.shared_params import FunctionDefinition
 
 from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
@@ -12,123 +31,171 @@ from core.model_runtime.entities.message_entities import (
     ToolPromptMessage,
     UserPromptMessage,
 )
-from core.model_runtime.model_providers.volcengine_maas.errors import wrap_error
-from core.model_runtime.model_providers.volcengine_maas.volc_sdk import ChatRole, MaasException, MaasService
 
 
-class MaaSClient(MaasService):
-    def __init__(self, host: str, region: str):
+class ArkClientV3:
+    endpoint_id: Optional[str] = None
+    ark: Optional[Ark] = None
+
+    def __init__(self, *args, **kwargs):
+        self.ark = Ark(*args, **kwargs)
         self.endpoint_id = None
-        super().__init__(host, region)
-
-    def set_endpoint_id(self, endpoint_id: str):
-        self.endpoint_id = endpoint_id
-
-    @classmethod
-    def from_credential(cls, credentials: dict) -> 'MaaSClient':
-        host = credentials['api_endpoint_host']
-        region = credentials['volc_region']
-        ak = credentials['volc_access_key_id']
-        sk = credentials['volc_secret_access_key']
-        endpoint_id = credentials['endpoint_id']
-
-        client = cls(host, region)
-        client.set_endpoint_id(endpoint_id)
-        client.set_ak(ak)
-        client.set_sk(sk)
-        return client
-
-    def chat(self, params: dict, messages: list[PromptMessage], stream=False, **extra_model_kwargs) -> Generator | dict:
-        req = {
-            'parameters': params,
-            'messages': [self.convert_prompt_message_to_maas_message(prompt) for prompt in messages],
-            **extra_model_kwargs,
-        }
-        if not stream:
-            return super().chat(
-                self.endpoint_id,
-                req,
-            )
-        return super().stream_chat(
-            self.endpoint_id,
-            req,
-        )
-
-    def embeddings(self, texts: list[str]) -> dict:
-        req = {
-            'input': texts
-        }
-        return super().embeddings(self.endpoint_id, req)
 
     @staticmethod
-    def convert_prompt_message_to_maas_message(message: PromptMessage) -> dict:
+    def is_legacy(credentials: dict) -> bool:
+        if ArkClientV3.is_compatible_with_legacy(credentials):
+            return False
+        sdk_version = credentials.get("sdk_version", "v2")
+        return sdk_version != "v3"
+
+    @staticmethod
+    def is_compatible_with_legacy(credentials: dict) -> bool:
+        sdk_version = credentials.get("sdk_version")
+        endpoint = credentials.get("api_endpoint_host")
+        return sdk_version is None and endpoint == "maas-api.ml-platform-cn-beijing.volces.com"
+
+    @classmethod
+    def from_credentials(cls, credentials):
+        """Initialize the client using the credentials provided."""
+        args = {
+            "base_url": credentials['api_endpoint_host'],
+            "region": credentials['volc_region'],
+            "ak": credentials['volc_access_key_id'],
+            "sk": credentials['volc_secret_access_key'],
+        }
+        if cls.is_compatible_with_legacy(credentials):
+            args["base_url"] = "https://ark.cn-beijing.volces.com/api/v3"
+
+        client = ArkClientV3(
+            **args
+        )
+        client.endpoint_id = credentials['endpoint_id']
+        return client
+
+    @staticmethod
+    def convert_prompt_message(message: PromptMessage) -> ChatCompletionMessageParam:
+        """Converts a PromptMessage to a ChatCompletionMessageParam"""
         if isinstance(message, UserPromptMessage):
             message = cast(UserPromptMessage, message)
             if isinstance(message.content, str):
-                message_dict = {"role": ChatRole.USER,
-                                "content": message.content}
+                content = message.content
             else:
                 content = []
                 for message_content in message.content:
                     if message_content.type == PromptMessageContentType.TEXT:
-                        raise ValueError(
-                            'Content object type only support image_url')
+                        content.append(ChatCompletionContentPartTextParam(
+                            text=message_content.text,
+                            type='text',
+                        ))
                     elif message_content.type == PromptMessageContentType.IMAGE:
                         message_content = cast(
                             ImagePromptMessageContent, message_content)
                         image_data = re.sub(
                             r'^data:image\/[a-zA-Z]+;base64,', '', message_content.data)
-                        content.append({
-                            'type': 'image_url',
-                            'image_url': {
-                                'url': '',
-                                'image_bytes': image_data,
-                                'detail': message_content.detail,
-                            }
-                        })
-
-                message_dict = {'role': ChatRole.USER, 'content': content}
+                        content.append(ChatCompletionContentPartImageParam(
+                            image_url=ImageURL(
+                                url=image_data,
+                                detail=message_content.detail.value,
+                            ),
+                            type='image_url',
+                        ))
+            message_dict = ChatCompletionUserMessageParam(
+                role='user',
+                content=content
+            )
         elif isinstance(message, AssistantPromptMessage):
             message = cast(AssistantPromptMessage, message)
-            message_dict = {'role': ChatRole.ASSISTANT,
-                            'content': message.content}
-            if message.tool_calls:
-                message_dict['tool_calls'] = [
-                    {
-                        'name': call.function.name,
-                        'arguments': call.function.arguments
-                    } for call in message.tool_calls
+            message_dict = ChatCompletionAssistantMessageParam(
+                content=message.content,
+                role='assistant',
+                tool_calls=None if not message.tool_calls else [
+                    ChatCompletionMessageToolCallParam(
+                        id=call.id,
+                        function=Function(
+                            name=call.function.name,
+                            arguments=call.function.arguments
+                        ),
+                        type='function'
+                    ) for call in message.tool_calls
                 ]
+            )
         elif isinstance(message, SystemPromptMessage):
             message = cast(SystemPromptMessage, message)
-            message_dict = {'role': ChatRole.SYSTEM,
-                            'content': message.content}
+            message_dict = ChatCompletionSystemMessageParam(
+                content=message.content,
+                role='system'
+            )
         elif isinstance(message, ToolPromptMessage):
             message = cast(ToolPromptMessage, message)
-            message_dict = {'role': ChatRole.FUNCTION,
-                            'content': message.content,
-                            'name': message.tool_call_id}
+            message_dict = ChatCompletionToolMessageParam(
+                content=message.content,
+                role='tool',
+                tool_call_id=message.tool_call_id
+            )
         else:
             raise ValueError(f"Got unknown PromptMessage type {message}")
 
         return message_dict
 
     @staticmethod
-    def wrap_exception(fn: Callable[[], dict | Generator]) -> dict | Generator:
-        try:
-            resp = fn()
-        except MaasException as e:
-            raise wrap_error(e)
+    def _convert_tool_prompt(message: PromptMessageTool) -> ChatCompletionToolParam:
+        return ChatCompletionToolParam(
+            type='function',
+            function=FunctionDefinition(
+                name=message.name,
+                description=message.description,
+                parameters=message.parameters,
+            )
+        )
 
-        return resp
+    def chat(self, messages: list[PromptMessage],
+             tools: Optional[list[PromptMessageTool]] = None,
+             stop: Optional[list[str]] = None,
+             frequency_penalty: Optional[float] = None,
+             max_tokens: Optional[int] = None,
+             presence_penalty: Optional[float] = None,
+             top_p: Optional[float] = None,
+             temperature: Optional[float] = None,
+             ) -> ChatCompletion:
+        """Block chat"""
+        return self.ark.chat.completions.create(
+            model=self.endpoint_id,
+            messages=[self.convert_prompt_message(message) for message in messages],
+            tools=[self._convert_tool_prompt(tool) for tool in tools] if tools else None,
+            stop=stop,
+            frequency_penalty=frequency_penalty,
+            max_tokens=max_tokens,
+            presence_penalty=presence_penalty,
+            top_p=top_p,
+            temperature=temperature,
+        )
 
-    @staticmethod
-    def transform_tool_prompt_to_maas_config(tool: PromptMessageTool):
-        return {
-            "type": "function",
-            "function": {
-                "name": tool.name,
-                "description": tool.description,
-                "parameters": tool.parameters,
-            }
-        }
+    def stream_chat(self, messages: list[PromptMessage],
+                    tools: Optional[list[PromptMessageTool]] = None,
+                    stop: Optional[list[str]] = None,
+                    frequency_penalty: Optional[float] = None,
+                    max_tokens: Optional[int] = None,
+                    presence_penalty: Optional[float] = None,
+                    top_p: Optional[float] = None,
+                    temperature: Optional[float] = None,
+                    ) -> Generator[ChatCompletionChunk]:
+        """Stream chat"""
+        chunks = self.ark.chat.completions.create(
+            stream=True,
+            model=self.endpoint_id,
+            messages=[self.convert_prompt_message(message) for message in messages],
+            tools=[self._convert_tool_prompt(tool) for tool in tools] if tools else None,
+            stop=stop,
+            frequency_penalty=frequency_penalty,
+            max_tokens=max_tokens,
+            presence_penalty=presence_penalty,
+            top_p=top_p,
+            temperature=temperature,
+        )
+        for chunk in chunks:
+            if not chunk.choices:
+                continue
+            yield chunk
+
+    def embeddings(self, texts: list[str]) -> CreateEmbeddingResponse:
+        return self.ark.embeddings.create(model=self.endpoint_id, input=texts)

api/core/model_runtime/model_providers/volcengine_maas/legacy/client.py

@@ -0,0 +1,134 @@
+import re
+from collections.abc import Callable, Generator
+from typing import cast
+
+from core.model_runtime.entities.message_entities import (
+    AssistantPromptMessage,
+    ImagePromptMessageContent,
+    PromptMessage,
+    PromptMessageContentType,
+    PromptMessageTool,
+    SystemPromptMessage,
+    ToolPromptMessage,
+    UserPromptMessage,
+)
+from core.model_runtime.model_providers.volcengine_maas.legacy.errors import wrap_error
+from core.model_runtime.model_providers.volcengine_maas.legacy.volc_sdk import ChatRole, MaasException, MaasService
+
+
+class MaaSClient(MaasService):
+    def __init__(self, host: str, region: str):
+        self.endpoint_id = None
+        super().__init__(host, region)
+
+    def set_endpoint_id(self, endpoint_id: str):
+        self.endpoint_id = endpoint_id
+
+    @classmethod
+    def from_credential(cls, credentials: dict) -> 'MaaSClient':
+        host = credentials['api_endpoint_host']
+        region = credentials['volc_region']
+        ak = credentials['volc_access_key_id']
+        sk = credentials['volc_secret_access_key']
+        endpoint_id = credentials['endpoint_id']
+
+        client = cls(host, region)
+        client.set_endpoint_id(endpoint_id)
+        client.set_ak(ak)
+        client.set_sk(sk)
+        return client
+
+    def chat(self, params: dict, messages: list[PromptMessage], stream=False, **extra_model_kwargs) -> Generator | dict:
+        req = {
+            'parameters': params,
+            'messages': [self.convert_prompt_message_to_maas_message(prompt) for prompt in messages],
+            **extra_model_kwargs,
+        }
+        if not stream:
+            return super().chat(
+                self.endpoint_id,
+                req,
+            )
+        return super().stream_chat(
+            self.endpoint_id,
+            req,
+        )
+
+    def embeddings(self, texts: list[str]) -> dict:
+        req = {
+            'input': texts
+        }
+        return super().embeddings(self.endpoint_id, req)
+
+    @staticmethod
+    def convert_prompt_message_to_maas_message(message: PromptMessage) -> dict:
+        if isinstance(message, UserPromptMessage):
+            message = cast(UserPromptMessage, message)
+            if isinstance(message.content, str):
+                message_dict = {"role": ChatRole.USER,
+                                "content": message.content}
+            else:
+                content = []
+                for message_content in message.content:
+                    if message_content.type == PromptMessageContentType.TEXT:
+                        raise ValueError(
+                            'Content object type only support image_url')
+                    elif message_content.type == PromptMessageContentType.IMAGE:
+                        message_content = cast(
+                            ImagePromptMessageContent, message_content)
+                        image_data = re.sub(
+                            r'^data:image\/[a-zA-Z]+;base64,', '', message_content.data)
+                        content.append({
+                            'type': 'image_url',
+                            'image_url': {
+                                'url': '',
+                                'image_bytes': image_data,
+                                'detail': message_content.detail,
+                            }
+                        })
+
+                message_dict = {'role': ChatRole.USER, 'content': content}
+        elif isinstance(message, AssistantPromptMessage):
+            message = cast(AssistantPromptMessage, message)
+            message_dict = {'role': ChatRole.ASSISTANT,
+                            'content': message.content}
+            if message.tool_calls:
+                message_dict['tool_calls'] = [
+                    {
+                        'name': call.function.name,
+                        'arguments': call.function.arguments
+                    } for call in message.tool_calls
+                ]
+        elif isinstance(message, SystemPromptMessage):
+            message = cast(SystemPromptMessage, message)
+            message_dict = {'role': ChatRole.SYSTEM,
+                            'content': message.content}
+        elif isinstance(message, ToolPromptMessage):
+            message = cast(ToolPromptMessage, message)
+            message_dict = {'role': ChatRole.FUNCTION,
+                            'content': message.content,
+                            'name': message.tool_call_id}
+        else:
+            raise ValueError(f"Got unknown PromptMessage type {message}")
+
+        return message_dict
+
+    @staticmethod
+    def wrap_exception(fn: Callable[[], dict | Generator]) -> dict | Generator:
+        try:
+            resp = fn()
+        except MaasException as e:
+            raise wrap_error(e)
+
+        return resp
+
+    @staticmethod
+    def transform_tool_prompt_to_maas_config(tool: PromptMessageTool):
+        return {
+            "type": "function",
+            "function": {
+                "name": tool.name,
+                "description": tool.description,
+                "parameters": tool.parameters,
+            }
+        }

api/core/model_runtime/model_providers/volcengine_maas/legacy/errors.py

@@ -1,4 +1,4 @@
-from core.model_runtime.model_providers.volcengine_maas.volc_sdk import MaasException
+from core.model_runtime.model_providers.volcengine_maas.legacy.volc_sdk import MaasException
 
 
 class ClientSDKRequestError(MaasException):

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

@@ -1,8 +1,10 @@
 import logging
 from collections.abc import Generator
 
+from volcenginesdkarkruntime.types.chat import ChatCompletion, ChatCompletionChunk
+
 from core.model_runtime.entities.common_entities import I18nObject
-from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
+from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
 from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
     PromptMessage,
@@ -27,19 +29,21 @@ from core.model_runtime.errors.invoke import (
 )
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
-from core.model_runtime.model_providers.volcengine_maas.client import MaaSClient
-from core.model_runtime.model_providers.volcengine_maas.errors import (
+from core.model_runtime.model_providers.volcengine_maas.client import ArkClientV3
+from core.model_runtime.model_providers.volcengine_maas.legacy.client import MaaSClient
+from core.model_runtime.model_providers.volcengine_maas.legacy.errors import (
     AuthErrors,
     BadRequestErrors,
     ConnectionErrors,
+    MaasException,
     RateLimitErrors,
     ServerUnavailableErrors,
 )
 from core.model_runtime.model_providers.volcengine_maas.llm.models import (
     get_model_config,
     get_v2_req_params,
+    get_v3_req_params,
 )
-from core.model_runtime.model_providers.volcengine_maas.volc_sdk import MaasException
 
 logger = logging.getLogger(__name__)
 
@@ -49,13 +53,20 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
                 model_parameters: dict, tools: list[PromptMessageTool] | None = None,
                 stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
             -> LLMResult | Generator:
-        return self._generate(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
+        if ArkClientV3.is_legacy(credentials):
+            return self._generate_v2(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
+        return self._generate_v3(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
         """
         Validate credentials
         """
-        # ping
+        if ArkClientV3.is_legacy(credentials):
+            return self._validate_credentials_v2(credentials)
+        return self._validate_credentials_v3(credentials)
+
+    @staticmethod
+    def _validate_credentials_v2(credentials: dict) -> None:
         client = MaaSClient.from_credential(credentials)
         try:
             client.chat(
@@ -70,18 +81,24 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
         except MaasException as e:
             raise CredentialsValidateFailedError(e.message)
 
+    @staticmethod
+    def _validate_credentials_v3(credentials: dict) -> None:
+        client = ArkClientV3.from_credentials(credentials)
+        try:
+            client.chat(max_tokens=16, temperature=0.7, top_p=0.9,
+                        messages=[UserPromptMessage(content='ping\nAnswer: ')], )
+        except Exception as e:
+            raise CredentialsValidateFailedError(e)
+
     def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
                        tools: list[PromptMessageTool] | None = None) -> int:
-        if len(prompt_messages) == 0:
+        if ArkClientV3.is_legacy(credentials):
+            return self._get_num_tokens_v2(prompt_messages)
+        return self._get_num_tokens_v3(prompt_messages)
+
+    def _get_num_tokens_v2(self, messages: list[PromptMessage]) -> int:
+        if len(messages) == 0:
             return 0
-        return self._num_tokens_from_messages(prompt_messages)
-
-    def _num_tokens_from_messages(self, messages: list[PromptMessage]) -> int:
-        """
-        Calculate num tokens.
-
-        :param messages: messages
-        """
         num_tokens = 0
         messages_dict = [
             MaaSClient.convert_prompt_message_to_maas_message(m) for m in messages]
@@ -92,9 +109,22 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
 
         return num_tokens
 
-    def _generate(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
-                  model_parameters: dict, tools: list[PromptMessageTool] | None = None,
-                  stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
+    def _get_num_tokens_v3(self, messages: list[PromptMessage]) -> int:
+        if len(messages) == 0:
+            return 0
+        num_tokens = 0
+        messages_dict = [
+            ArkClientV3.convert_prompt_message(m) for m in messages]
+        for message in messages_dict:
+            for key, value in message.items():
+                num_tokens += self._get_num_tokens_by_gpt2(str(key))
+                num_tokens += self._get_num_tokens_by_gpt2(str(value))
+
+        return num_tokens
+
+    def _generate_v2(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
+                     model_parameters: dict, tools: list[PromptMessageTool] | None = None,
+                     stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
             -> LLMResult | Generator:
 
         client = MaaSClient.from_credential(credentials)
@@ -106,77 +136,151 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
             ]
         resp = MaaSClient.wrap_exception(
             lambda: client.chat(req_params, prompt_messages, stream, **extra_model_kwargs))
-        if not stream:
-            return self._handle_chat_response(model, credentials, prompt_messages, resp)
-        return self._handle_stream_chat_response(model, credentials, prompt_messages, resp)
 
-    def _handle_stream_chat_response(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], resp: Generator) -> Generator:
-        for index, r in enumerate(resp):
-            choices = r['choices']
+        def _handle_stream_chat_response() -> Generator:
+            for index, r in enumerate(resp):
+                choices = r['choices']
+                if not choices:
+                    continue
+                choice = choices[0]
+                message = choice['message']
+                usage = None
+                if r.get('usage'):
+                    usage = self._calc_response_usage(model=model, credentials=credentials,
+                                                      prompt_tokens=r['usage']['prompt_tokens'],
+                                                      completion_tokens=r['usage']['completion_tokens']
+                                                      )
+                yield LLMResultChunk(
+                    model=model,
+                    prompt_messages=prompt_messages,
+                    delta=LLMResultChunkDelta(
+                        index=index,
+                        message=AssistantPromptMessage(
+                            content=message['content'] if message['content'] else '',
+                            tool_calls=[]
+                        ),
+                        usage=usage,
+                        finish_reason=choice.get('finish_reason'),
+                    ),
+                )
+
+        def _handle_chat_response() -> LLMResult:
+            choices = resp['choices']
             if not choices:
-                continue
+                raise ValueError("No choices found")
+
             choice = choices[0]
             message = choice['message']
-            usage = None
-            if r.get('usage'):
-                usage = self._calc_usage(model, credentials, r['usage'])
-            yield LLMResultChunk(
+
+            # parse tool calls
+            tool_calls = []
+            if message['tool_calls']:
+                for call in message['tool_calls']:
+                    tool_call = AssistantPromptMessage.ToolCall(
+                        id=call['function']['name'],
+                        type=call['type'],
+                        function=AssistantPromptMessage.ToolCall.ToolCallFunction(
+                            name=call['function']['name'],
+                            arguments=call['function']['arguments']
+                        )
+                    )
+                    tool_calls.append(tool_call)
+
+            usage = resp['usage']
+            return LLMResult(
                 model=model,
                 prompt_messages=prompt_messages,
-                delta=LLMResultChunkDelta(
-                    index=index,
-                    message=AssistantPromptMessage(
-                        content=message['content'] if message['content'] else '',
-                        tool_calls=[]
-                    ),
-                    usage=usage,
-                    finish_reason=choice.get('finish_reason'),
+                message=AssistantPromptMessage(
+                    content=message['content'] if message['content'] else '',
+                    tool_calls=tool_calls,
                 ),
+                usage=self._calc_response_usage(model=model, credentials=credentials,
+                                                prompt_tokens=usage['prompt_tokens'],
+                                                completion_tokens=usage['completion_tokens']
+                                                ),
             )
 
-    def _handle_chat_response(self,  model: str, credentials: dict, prompt_messages: list[PromptMessage], resp: dict) -> LLMResult:
-        choices = resp['choices']
-        if not choices:
-            return
-        choice = choices[0]
-        message = choice['message']
+        if not stream:
+            return _handle_chat_response()
+        return _handle_stream_chat_response()
 
-        # parse tool calls
-        tool_calls = []
-        if message['tool_calls']:
-            for call in message['tool_calls']:
-                tool_call = AssistantPromptMessage.ToolCall(
-                    id=call['function']['name'],
-                    type=call['type'],
-                    function=AssistantPromptMessage.ToolCall.ToolCallFunction(
-                        name=call['function']['name'],
-                        arguments=call['function']['arguments']
-                    )
+    def _generate_v3(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
+                     model_parameters: dict, tools: list[PromptMessageTool] | None = None,
+                     stop: list[str] | None = None, stream: bool = True, user: str | None = None) \
+            -> LLMResult | Generator:
+
+        client = ArkClientV3.from_credentials(credentials)
+        req_params = get_v3_req_params(credentials, model_parameters, stop)
+        if tools:
+            req_params['tools'] = tools
+
+        def _handle_stream_chat_response(chunks: Generator[ChatCompletionChunk]) -> Generator:
+            for chunk in chunks:
+                if not chunk.choices:
+                    continue
+                choice = chunk.choices[0]
+
+                yield LLMResultChunk(
+                    model=model,
+                    prompt_messages=prompt_messages,
+                    delta=LLMResultChunkDelta(
+                        index=choice.index,
+                        message=AssistantPromptMessage(
+                            content=choice.delta.content,
+                            tool_calls=[]
+                        ),
+                        usage=self._calc_response_usage(model=model, credentials=credentials,
+                                                        prompt_tokens=chunk.usage.prompt_tokens,
+                                                        completion_tokens=chunk.usage.completion_tokens
+                                                        ) if chunk.usage else None,
+                        finish_reason=choice.finish_reason,
+                    ),
                 )
-                tool_calls.append(tool_call)
 
-        return LLMResult(
-            model=model,
-            prompt_messages=prompt_messages,
-            message=AssistantPromptMessage(
-                content=message['content'] if message['content'] else '',
-                tool_calls=tool_calls,
-            ),
-            usage=self._calc_usage(model, credentials, resp['usage']),
-        )
+        def _handle_chat_response(resp: ChatCompletion) -> LLMResult:
+            choice = resp.choices[0]
+            message = choice.message
+            # parse tool calls
+            tool_calls = []
+            if message.tool_calls:
+                for call in message.tool_calls:
+                    tool_call = AssistantPromptMessage.ToolCall(
+                        id=call.id,
+                        type=call.type,
+                        function=AssistantPromptMessage.ToolCall.ToolCallFunction(
+                            name=call.function.name,
+                            arguments=call.function.arguments
+                        )
+                    )
+                    tool_calls.append(tool_call)
 
-    def _calc_usage(self,  model: str, credentials: dict, usage: dict) -> LLMUsage:
-        return self._calc_response_usage(model=model, credentials=credentials,
-                                         prompt_tokens=usage['prompt_tokens'],
-                                         completion_tokens=usage['completion_tokens']
-                                         )
+            usage = resp.usage
+            return LLMResult(
+                model=model,
+                prompt_messages=prompt_messages,
+                message=AssistantPromptMessage(
+                    content=message.content if message.content else "",
+                    tool_calls=tool_calls,
+                ),
+                usage=self._calc_response_usage(model=model, credentials=credentials,
+                                                prompt_tokens=usage.prompt_tokens,
+                                                completion_tokens=usage.completion_tokens
+                                                ),
+            )
+
+        if not stream:
+            resp = client.chat(prompt_messages, **req_params)
+            return _handle_chat_response(resp)
+
+        chunks = client.stream_chat(prompt_messages, **req_params)
+        return _handle_stream_chat_response(chunks)
 
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
         """
             used to define customizable model schema
         """
         model_config = get_model_config(credentials)
-    
+
         rules = [
             ParameterRule(
                 name='temperature',
@@ -212,7 +316,7 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
                 use_template='presence_penalty',
                 label=I18nObject(
                     en_US='Presence Penalty',
-                    zh_Hans= '存在惩罚',
+                    zh_Hans='存在惩罚',
                 ),
                 min=-2.0,
                 max=2.0,
@@ -222,8 +326,8 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
                 type=ParameterType.FLOAT,
                 use_template='frequency_penalty',
                 label=I18nObject(
-                    en_US= 'Frequency Penalty',
-                    zh_Hans= '频率惩罚',
+                    en_US='Frequency Penalty',
+                    zh_Hans='频率惩罚',
                 ),
                 min=-2.0,
                 max=2.0,
@@ -245,7 +349,7 @@ class VolcengineMaaSLargeLanguageModel(LargeLanguageModel):
         model_properties = {}
         model_properties[ModelPropertyKey.CONTEXT_SIZE] = model_config.properties.context_size
         model_properties[ModelPropertyKey.MODE] = model_config.properties.mode.value
-       
+
         entity = AIModelEntity(
             model=model,
             label=I18nObject(

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

@@ -5,10 +5,11 @@ from core.model_runtime.entities.model_entities import ModelFeature
 
 
 class ModelProperties(BaseModel):
-    context_size: int 
-    max_tokens: int 
+    context_size: int
+    max_tokens: int
     mode: LLMMode
 
+
 class ModelConfig(BaseModel):
     properties: ModelProperties
     features: list[ModelFeature]
@@ -24,23 +25,23 @@ configs: dict[str, ModelConfig] = {
         features=[ModelFeature.TOOL_CALL]
     ),
     'Doubao-pro-32k': ModelConfig(
-        properties=ModelProperties(context_size=32768, max_tokens=32768, mode=LLMMode.CHAT),
+        properties=ModelProperties(context_size=32768, max_tokens=4096, mode=LLMMode.CHAT),
         features=[ModelFeature.TOOL_CALL]
     ),
     'Doubao-lite-32k': ModelConfig(
-        properties=ModelProperties(context_size=32768, max_tokens=32768, mode=LLMMode.CHAT),
+        properties=ModelProperties(context_size=32768, max_tokens=4096, mode=LLMMode.CHAT),
         features=[ModelFeature.TOOL_CALL]
     ),
     'Doubao-pro-128k': ModelConfig(
-        properties=ModelProperties(context_size=131072, max_tokens=131072, mode=LLMMode.CHAT),
+        properties=ModelProperties(context_size=131072, max_tokens=4096, mode=LLMMode.CHAT),
         features=[ModelFeature.TOOL_CALL]
     ),
     'Doubao-lite-128k': ModelConfig(
-        properties=ModelProperties(context_size=131072, max_tokens=131072, mode=LLMMode.CHAT),
+        properties=ModelProperties(context_size=131072, max_tokens=4096, mode=LLMMode.CHAT),
         features=[ModelFeature.TOOL_CALL]
     ),
     'Skylark2-pro-4k': ModelConfig(
-        properties=ModelProperties(context_size=4096, max_tokens=4000, mode=LLMMode.CHAT),
+        properties=ModelProperties(context_size=4096, max_tokens=4096, mode=LLMMode.CHAT),
         features=[]
     ),
     'Llama3-8B': ModelConfig(
@@ -77,23 +78,24 @@ configs: dict[str, ModelConfig] = {
     )
 }
 
-def get_model_config(credentials: dict)->ModelConfig:
+
+def get_model_config(credentials: dict) -> ModelConfig:
     base_model = credentials.get('base_model_name', '')
     model_configs = configs.get(base_model)
     if not model_configs:
         return ModelConfig(
-                properties=ModelProperties(
+            properties=ModelProperties(
                 context_size=int(credentials.get('context_size', 0)),
                 max_tokens=int(credentials.get('max_tokens', 0)),
-                mode= LLMMode.value_of(credentials.get('mode', 'chat')),
+                mode=LLMMode.value_of(credentials.get('mode', 'chat')),
             ),
             features=[]
         )
     return model_configs
 
 
-def get_v2_req_params(credentials: dict, model_parameters: dict, 
-                      stop: list[str] | None=None):
+def get_v2_req_params(credentials: dict, model_parameters: dict,
+                      stop: list[str] | None = None):
     req_params = {}
     # predefined properties
     model_configs = get_model_config(credentials)
@@ -116,8 +118,36 @@ def get_v2_req_params(credentials: dict, model_parameters: dict,
     if model_parameters.get('frequency_penalty'):
         req_params['frequency_penalty'] = model_parameters.get(
             'frequency_penalty')
-            
+
     if stop:
         req_params['stop'] = stop
 
-    return req_params
+    return req_params
+
+
+def get_v3_req_params(credentials: dict, model_parameters: dict,
+                      stop: list[str] | None = None):
+    req_params = {}
+    # predefined properties
+    model_configs = get_model_config(credentials)
+    if model_configs:
+        req_params['max_tokens'] = model_configs.properties.max_tokens
+
+    # model parameters
+    if model_parameters.get('max_tokens'):
+        req_params['max_tokens'] = model_parameters.get('max_tokens')
+    if model_parameters.get('temperature'):
+        req_params['temperature'] = model_parameters.get('temperature')
+    if model_parameters.get('top_p'):
+        req_params['top_p'] = model_parameters.get('top_p')
+    if model_parameters.get('presence_penalty'):
+        req_params['presence_penalty'] = model_parameters.get(
+            'presence_penalty')
+    if model_parameters.get('frequency_penalty'):
+        req_params['frequency_penalty'] = model_parameters.get(
+            'frequency_penalty')
+
+    if stop:
+        req_params['stop'] = stop
+
+    return req_params

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

@@ -2,26 +2,29 @@ from pydantic import BaseModel
 
 
 class ModelProperties(BaseModel):
-    context_size: int 
-    max_chunks: int 
+    context_size: int
+    max_chunks: int
+
 
 class ModelConfig(BaseModel):
     properties: ModelProperties
 
+
 ModelConfigs = {
     'Doubao-embedding': ModelConfig(
-        properties=ModelProperties(context_size=4096, max_chunks=1)
+        properties=ModelProperties(context_size=4096, max_chunks=32)
     ),
 }
 
-def get_model_config(credentials: dict)->ModelConfig:
+
+def get_model_config(credentials: dict) -> ModelConfig:
     base_model = credentials.get('base_model_name', '')
     model_configs = ModelConfigs.get(base_model)
     if not model_configs:
         return ModelConfig(
-                properties=ModelProperties(
+            properties=ModelProperties(
                 context_size=int(credentials.get('context_size', 0)),
                 max_chunks=int(credentials.get('max_chunks', 0)),
             )
         )
-    return model_configs
+    return model_configs

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

@@ -22,16 +22,17 @@ from core.model_runtime.errors.invoke import (
 )
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
-from core.model_runtime.model_providers.volcengine_maas.client import MaaSClient
-from core.model_runtime.model_providers.volcengine_maas.errors import (
+from core.model_runtime.model_providers.volcengine_maas.client import ArkClientV3
+from core.model_runtime.model_providers.volcengine_maas.legacy.client import MaaSClient
+from core.model_runtime.model_providers.volcengine_maas.legacy.errors import (
     AuthErrors,
     BadRequestErrors,
     ConnectionErrors,
+    MaasException,
     RateLimitErrors,
     ServerUnavailableErrors,
 )
 from core.model_runtime.model_providers.volcengine_maas.text_embedding.models import get_model_config
-from core.model_runtime.model_providers.volcengine_maas.volc_sdk import MaasException
 
 
 class VolcengineMaaSTextEmbeddingModel(TextEmbeddingModel):
@@ -51,6 +52,14 @@ class VolcengineMaaSTextEmbeddingModel(TextEmbeddingModel):
         :param user: unique user id
         :return: embeddings result
         """
+        if ArkClientV3.is_legacy(credentials):
+            return self._generate_v2(model, credentials, texts, user)
+
+        return self._generate_v3(model, credentials, texts, user)
+
+    def _generate_v2(self, model: str, credentials: dict,
+                     texts: list[str], user: Optional[str] = None) \
+            -> TextEmbeddingResult:
         client = MaaSClient.from_credential(credentials)
         resp = MaaSClient.wrap_exception(lambda: client.embeddings(texts))
 
@@ -65,6 +74,23 @@ class VolcengineMaaSTextEmbeddingModel(TextEmbeddingModel):
 
         return result
 
+    def _generate_v3(self, model: str, credentials: dict,
+                     texts: list[str], user: Optional[str] = None) \
+            -> TextEmbeddingResult:
+        client = ArkClientV3.from_credentials(credentials)
+        resp = client.embeddings(texts)
+
+        usage = self._calc_response_usage(
+            model=model, credentials=credentials, tokens=resp.usage.total_tokens)
+
+        result = TextEmbeddingResult(
+            model=model,
+            embeddings=[v.embedding for v in resp.data],
+            usage=usage
+        )
+
+        return result
+
     def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
         """
         Get number of tokens for given prompt messages
@@ -88,11 +114,22 @@ class VolcengineMaaSTextEmbeddingModel(TextEmbeddingModel):
         :param credentials: model credentials
         :return:
         """
+        if ArkClientV3.is_legacy(credentials):
+            return self._validate_credentials_v2(model, credentials)
+        return self._validate_credentials_v3(model, credentials)
+
+    def _validate_credentials_v2(self, model: str, credentials: dict) -> None:
         try:
             self._invoke(model=model, credentials=credentials, texts=['ping'])
         except MaasException as e:
             raise CredentialsValidateFailedError(e.message)
 
+    def _validate_credentials_v3(self, model: str, credentials: dict) -> None:
+        try:
+            self._invoke(model=model, credentials=credentials, texts=['ping'])
+        except Exception as e:
+            raise CredentialsValidateFailedError(e)
+
     @property
     def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
         """
@@ -116,9 +153,10 @@ class VolcengineMaaSTextEmbeddingModel(TextEmbeddingModel):
             generate custom model entities from credentials
         """
         model_config = get_model_config(credentials)
-        model_properties = {}
-        model_properties[ModelPropertyKey.CONTEXT_SIZE] = model_config.properties.context_size
-        model_properties[ModelPropertyKey.MAX_CHUNKS] = model_config.properties.max_chunks
+        model_properties = {
+            ModelPropertyKey.CONTEXT_SIZE: model_config.properties.context_size,
+            ModelPropertyKey.MAX_CHUNKS: model_config.properties.max_chunks
+        }
         entity = AIModelEntity(
             model=model,
             label=I18nObject(en_US=model),

api/core/tools/provider/builtin/siliconflow/_assets/icon.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" fill="none" version="1.1" width="128" height="128" viewBox="0 0 128 128"><g><g style="opacity:0;"><rect x="0" y="0" width="128" height="128" rx="0" fill="#FFFFFF" fill-opacity="1"/></g><g><path d="M100.74,12L93.2335,12C69.21260000000001,12,55.3672,27.3468,55.3672,50.8672L55.3672,54.8988C52.6011,54.1056,49.7377,53.7031,46.8601,53.7031C29.816499999999998,53.7031,16,67.5196,16,84.5632C16,101.6069,29.816499999999998,115.423,46.8601,115.423C63.9037,115.423,77.72030000000001,101.6069,77.72030000000001,84.5632C77.72030000000001,82.4902,77.51140000000001,80.4223,77.0967,78.3911L77.2197,78.3911L100.74,78.3911C106.9654,78.3681,112,73.3151,112,67.08959999999999C112,60.8642,106.9654,55.8111,100.74,55.7882L100.7362,55.7882L100.6985,55.7879L100.6606,55.7882L77.2197,55.7882L77.2195,49.8663C77.2195,40.8584,83.7252,34.352900000000005,93.2335,34.352900000000005L100.5653,34.352900000000005L100.5733,34.352900000000005L100.5812,34.352900000000005L100.74,34.352900000000005L100.74,34.352900000000005C106.8469,34.2605,111.7497,29.284,111.7497,23.1764C111.7497,17.06889,106.8469,12.0923454,100.74,12L100.74,12ZM56.0347,84.5632C56.0347,79.4962,51.9271,75.3885,46.8601,75.3885C41.793099999999995,75.3885,37.6854,79.4962,37.6854,84.5632C37.6854,89.6303,41.793099999999995,93.7378,46.8601,93.7378C51.9271,93.7378,56.0347,89.6303,56.0347,84.5632Z" fill-rule="evenodd" fill="#8358F6" fill-opacity="1"/></g></g></svg>

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

@@ -0,0 +1,19 @@
+import requests
+
+from core.tools.errors import ToolProviderCredentialValidationError
+from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
+
+
+class SiliconflowProvider(BuiltinToolProviderController):
+    def _validate_credentials(self, credentials: dict) -> None:
+        url = "https://api.siliconflow.cn/v1/models"
+        headers = {
+            "accept": "application/json",
+            "authorization": f"Bearer {credentials.get('siliconFlow_api_key')}",
+        }
+
+        response = requests.get(url, headers=headers)
+        if response.status_code != 200:
+            raise ToolProviderCredentialValidationError(
+                "SiliconFlow API key is invalid"
+            )

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

@@ -0,0 +1,21 @@
+identity:
+  author: hjlarry
+  name: siliconflow
+  label:
+    en_US: SiliconFlow
+    zh_CN: 硅基流动
+  description:
+    en_US: The image generation API provided by SiliconFlow includes Flux and Stable Diffusion models.
+    zh_CN: 硅基流动提供的图片生成 API，包含 Flux 和 Stable Diffusion 模型。
+  icon: icon.svg
+  tags:
+    - image
+credentials_for_provider:
+  siliconFlow_api_key:
+    type: secret-input
+    required: true
+    label:
+      en_US: SiliconFlow API Key
+    placeholder:
+      en_US: Please input your SiliconFlow API key
+    url: https://cloud.siliconflow.cn/account/ak

api/core/tools/provider/builtin/siliconflow/tools/flux.py

@@ -0,0 +1,44 @@
+from typing import Any, Union
+
+import requests
+
+from core.tools.entities.tool_entities import ToolInvokeMessage
+from core.tools.tool.builtin_tool import BuiltinTool
+
+FLUX_URL = (
+    "https://api.siliconflow.cn/v1/black-forest-labs/FLUX.1-schnell/text-to-image"
+)
+
+
+class FluxTool(BuiltinTool):
+
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+
+        headers = {
+            "accept": "application/json",
+            "content-type": "application/json",
+            "authorization": f"Bearer {self.runtime.credentials['siliconFlow_api_key']}",
+        }
+
+        payload = {
+            "prompt": tool_parameters.get("prompt"),
+            "image_size": tool_parameters.get("image_size", "1024x1024"),
+            "seed": tool_parameters.get("seed"),
+            "num_inference_steps": tool_parameters.get("num_inference_steps", 20),
+        }
+
+        response = requests.post(FLUX_URL, json=payload, headers=headers)
+        if response.status_code != 200:
+            return self.create_text_message(f"Got Error Response:{response.text}")
+
+        res = response.json()
+        result = [self.create_json_message(res)]
+        for image in res.get("images", []):
+            result.append(
+                self.create_image_message(
+                    image=image.get("url"), save_as=self.VARIABLE_KEY.IMAGE.value
+                )
+            )
+        return result

api/core/tools/provider/builtin/siliconflow/tools/flux.yaml

@@ -0,0 +1,73 @@
+identity:
+  name: flux
+  author: hjlarry
+  label:
+    en_US: Flux
+  icon: icon.svg
+description:
+  human:
+    en_US: Generate image via SiliconFlow's flux schnell.
+  llm: This tool is used to generate image from prompt via SiliconFlow's flux schnell model.
+parameters:
+  - name: prompt
+    type: string
+    required: true
+    label:
+      en_US: prompt
+      zh_Hans: 提示词
+    human_description:
+      en_US: The text prompt used to generate the image.
+      zh_Hans: 用于生成图片的文字提示词
+    llm_description: this prompt text will be used to generate image.
+    form: llm
+  - name: image_size
+    type: select
+    required: true
+    options:
+      - value: 1024x1024
+        label:
+          en_US: 1024x1024
+      - value: 768x1024
+        label:
+          en_US: 768x1024
+      - value: 576x1024
+        label:
+          en_US: 576x1024
+      - value: 512x1024
+        label:
+          en_US: 512x1024
+      - value: 1024x576
+        label:
+          en_US: 1024x576
+      - value: 768x512
+        label:
+          en_US: 768x512
+    default: 1024x1024
+    label:
+      en_US: Choose Image Size
+      zh_Hans: 选择生成的图片大小
+    form: form
+  - name: num_inference_steps
+    type: number
+    required: true
+    default: 20
+    min: 1
+    max: 100
+    label:
+      en_US: Num Inference Steps
+      zh_Hans: 生成图片的步数
+    form: form
+    human_description:
+      en_US: The number of inference steps to perform. More steps produce higher quality but take longer.
+      zh_Hans: 执行的推理步骤数量。更多的步骤可以产生更高质量的结果，但需要更长的时间。
+  - name: seed
+    type: number
+    min: 0
+    max: 9999999999
+    label:
+      en_US: Seed
+      zh_Hans: 种子
+    human_description:
+      en_US: The same seed and prompt can produce similar images.
+      zh_Hans: 相同的种子和提示可以产生相似的图像。
+    form: form

api/core/tools/provider/builtin/siliconflow/tools/stable_diffusion.py

@@ -0,0 +1,51 @@
+from typing import Any, Union
+
+import requests
+
+from core.tools.entities.tool_entities import ToolInvokeMessage
+from core.tools.tool.builtin_tool import BuiltinTool
+
+SDURL = {
+    "sd_3": "https://api.siliconflow.cn/v1/stabilityai/stable-diffusion-3-medium/text-to-image",
+    "sd_xl": "https://api.siliconflow.cn/v1/stabilityai/stable-diffusion-xl-base-1.0/text-to-image",
+}
+
+
+class StableDiffusionTool(BuiltinTool):
+
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+
+        headers = {
+            "accept": "application/json",
+            "content-type": "application/json",
+            "authorization": f"Bearer {self.runtime.credentials['siliconFlow_api_key']}",
+        }
+
+        model = tool_parameters.get("model", "sd_3")
+        url = SDURL.get(model)
+
+        payload = {
+            "prompt": tool_parameters.get("prompt"),
+            "negative_prompt": tool_parameters.get("negative_prompt", ""),
+            "image_size": tool_parameters.get("image_size", "1024x1024"),
+            "batch_size": tool_parameters.get("batch_size", 1),
+            "seed": tool_parameters.get("seed"),
+            "guidance_scale": tool_parameters.get("guidance_scale", 7.5),
+            "num_inference_steps": tool_parameters.get("num_inference_steps", 20),
+        }
+
+        response = requests.post(url, json=payload, headers=headers)
+        if response.status_code != 200:
+            return self.create_text_message(f"Got Error Response:{response.text}")
+
+        res = response.json()
+        result = [self.create_json_message(res)]
+        for image in res.get("images", []):
+            result.append(
+                self.create_image_message(
+                    image=image.get("url"), save_as=self.VARIABLE_KEY.IMAGE.value
+                )
+            )
+        return result

api/core/tools/provider/builtin/siliconflow/tools/stable_diffusion.yaml

@@ -0,0 +1,121 @@
+identity:
+  name: stable_diffusion
+  author: hjlarry
+  label:
+    en_US: Stable Diffusion
+  icon: icon.svg
+description:
+  human:
+    en_US: Generate image via SiliconFlow's stable diffusion model.
+  llm: This tool is used to generate image from prompt via SiliconFlow's stable diffusion model.
+parameters:
+  - name: prompt
+    type: string
+    required: true
+    label:
+      en_US: prompt
+      zh_Hans: 提示词
+    human_description:
+      en_US: The text prompt used to generate the image.
+      zh_Hans: 用于生成图片的文字提示词
+    llm_description: this prompt text will be used to generate image.
+    form: llm
+  - name: negative_prompt
+    type: string
+    label:
+      en_US: negative prompt
+      zh_Hans: 负面提示词
+    human_description:
+      en_US: Describe what you don't want included in the image.
+      zh_Hans: 描述您不希望包含在图片中的内容。
+    llm_description: Describe what you don't want included in the image.
+    form: llm
+  - name: model
+    type: select
+    required: true
+    options:
+      - value: sd_3
+        label:
+          en_US: Stable Diffusion 3
+      - value: sd_xl
+        label:
+          en_US: Stable Diffusion XL
+    default: sd_3
+    label:
+      en_US: Choose Image Model
+      zh_Hans: 选择生成图片的模型
+    form: form
+  - name: image_size
+    type: select
+    required: true
+    options:
+      - value: 1024x1024
+        label:
+          en_US: 1024x1024
+      - value: 1024x2048
+        label:
+          en_US: 1024x2048
+      - value: 1152x2048
+        label:
+          en_US: 1152x2048
+      - value: 1536x1024
+        label:
+          en_US: 1536x1024
+      - value: 1536x2048
+        label:
+          en_US: 1536x2048
+      - value: 2048x1152
+        label:
+          en_US: 2048x1152
+    default: 1024x1024
+    label:
+      en_US: Choose Image Size
+      zh_Hans: 选择生成图片的大小
+    form: form
+  - name: batch_size
+    type: number
+    required: true
+    default: 1
+    min: 1
+    max: 4
+    label:
+      en_US: Number Images
+      zh_Hans: 生成图片的数量
+    form: form
+  - name: guidance_scale
+    type: number
+    required: true
+    default: 7
+    min: 0
+    max: 100
+    label:
+      en_US: Guidance Scale
+      zh_Hans: 与提示词紧密性
+    human_description:
+      en_US: Classifier Free Guidance. How close you want the model to stick to your prompt when looking for a related image to show you.
+      zh_Hans: 无分类器引导。您希望模型在寻找相关图片向您展示时，与您的提示保持多紧密的关联度。
+    form: form
+  - name: num_inference_steps
+    type: number
+    required: true
+    default: 20
+    min: 1
+    max: 100
+    label:
+      en_US: Num Inference Steps
+      zh_Hans: 生成图片的步数
+    human_description:
+      en_US: The number of inference steps to perform. More steps produce higher quality but take longer.
+      zh_Hans: 执行的推理步骤数量。更多的步骤可以产生更高质量的结果，但需要更长的时间。
+    form: form
+  - name: seed
+    type: number
+    min: 0
+    max: 9999999999
+    label:
+      en_US: Seed
+      zh_Hans: 种子
+    human_description:
+      en_US: The same seed and prompt can produce similar images.
+      zh_Hans: 相同的种子和提示可以产生相似的图像。
+    form: form

api/poetry.lock

@@ -6143,6 +6143,19 @@ files = [
     {file = "pyarrow-17.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:392bc9feabc647338e6c89267635e111d71edad5fcffba204425a7c8d13610d7"},
     {file = "pyarrow-17.0.0-cp38-cp38-macosx_10_15_x86_64.whl", hash = "sha256:af5ff82a04b2171415f1410cff7ebb79861afc5dae50be73ce06d6e870615204"},
     {file = "pyarrow-17.0.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:edca18eaca89cd6382dfbcff3dd2d87633433043650c07375d095cd3517561d8"},
+    {file = "pyarrow-17.0.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7c7916bff914ac5d4a8fe25b7a25e432ff921e72f6f2b7547d1e325c1ad9d155"},
+    {file = "pyarrow-17.0.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f553ca691b9e94b202ff741bdd40f6ccb70cdd5fbf65c187af132f1317de6145"},
+    {file = "pyarrow-17.0.0-cp38-cp38-manylinux_2_28_aarch64.whl", hash = "sha256:0cdb0e627c86c373205a2f94a510ac4376fdc523f8bb36beab2e7f204416163c"},
+    {file = "pyarrow-17.0.0-cp38-cp38-manylinux_2_28_x86_64.whl", hash = "sha256:d7d192305d9d8bc9082d10f361fc70a73590a4c65cf31c3e6926cd72b76bc35c"},
+    {file = "pyarrow-17.0.0-cp38-cp38-win_amd64.whl", hash = "sha256:02dae06ce212d8b3244dd3e7d12d9c4d3046945a5933d28026598e9dbbda1fca"},
+    {file = "pyarrow-17.0.0-cp39-cp39-macosx_10_15_x86_64.whl", hash = "sha256:13d7a460b412f31e4c0efa1148e1d29bdf18ad1411eb6757d38f8fbdcc8645fb"},
+    {file = "pyarrow-17.0.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:9b564a51fbccfab5a04a80453e5ac6c9954a9c5ef2890d1bcf63741909c3f8df"},
+    {file = "pyarrow-17.0.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:32503827abbc5aadedfa235f5ece8c4f8f8b0a3cf01066bc8d29de7539532687"},
+    {file = "pyarrow-17.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a155acc7f154b9ffcc85497509bcd0d43efb80d6f733b0dc3bb14e281f131c8b"},
+    {file = "pyarrow-17.0.0-cp39-cp39-manylinux_2_28_aarch64.whl", hash = "sha256:dec8d129254d0188a49f8a1fc99e0560dc1b85f60af729f47de4046015f9b0a5"},
+    {file = "pyarrow-17.0.0-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:a48ddf5c3c6a6c505904545c25a4ae13646ae1f8ba703c4df4a1bfe4f4006bda"},
+    {file = "pyarrow-17.0.0-cp39-cp39-win_amd64.whl", hash = "sha256:42bf93249a083aca230ba7e2786c5f673507fa97bbd9725a1e2754715151a204"},
+    {file = "pyarrow-17.0.0.tar.gz", hash = "sha256:4beca9521ed2c0921c1023e68d097d0299b62c362639ea315572a58f3f50fd28"},
 ]
 
 [package.dependencies]
@@ -8854,6 +8867,28 @@ files = [
     {file = "vine-5.1.0.tar.gz", hash = "sha256:8b62e981d35c41049211cf62a0a1242d8c1ee9bd15bb196ce38aefd6799e61e0"},
 ]
 
+[[package]]
+name = "volcengine-python-sdk"
+version = "1.0.98"
+description = "Volcengine SDK for Python"
+optional = false
+python-versions = "*"
+files = [
+    {file = "volcengine-python-sdk-1.0.98.tar.gz", hash = "sha256:1515e8d46cdcda387f9b45abbcaf0b04b982f7be68068de83f1e388281441784"},
+]
+
+[package.dependencies]
+anyio = {version = ">=3.5.0,<5", optional = true, markers = "extra == \"ark\""}
+certifi = ">=2017.4.17"
+httpx = {version = ">=0.23.0,<1", optional = true, markers = "extra == \"ark\""}
+pydantic = {version = ">=1.9.0,<3", optional = true, markers = "extra == \"ark\""}
+python-dateutil = ">=2.1"
+six = ">=1.10"
+urllib3 = ">=1.23"
+
+[package.extras]
+ark = ["anyio (>=3.5.0,<5)", "cached-property", "httpx (>=0.23.0,<1)", "pydantic (>=1.9.0,<3)"]
+
 [[package]]
 name = "watchfiles"
 version = "0.23.0"
@@ -9634,4 +9669,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "d7336115709114c2a4ff09b392f717e9c3547ae82b6a111d0c885c7a44269f02"
+content-hash = "04f970820de691f40fc9fb30f5ff0618b0f1a04d3315b14467fb88e475fa1243"

api/pyproject.toml

@@ -191,6 +191,7 @@ zhipuai = "1.0.7"
 # Related transparent dependencies with pinned verion
 # required by main implementations
 ############################################################
+volcengine-python-sdk = {extras = ["ark"], version = "^1.0.98"}
 [tool.poetry.group.indriect.dependencies]
 kaleido = "0.2.1"
 rank-bm25 = "~0.2.2"

web/app/(commonLayout)/app/(appDetailLayout)/[appId]/overview/cardView.tsx

@@ -34,16 +34,17 @@ const CardView: FC<ICardViewProps> = ({ appId }) => {
   const systemFeatures = useContextSelector(AppContext, state => state.systemFeatures)
 
   const updateAppDetail = async () => {
-    fetchAppDetail({ url: '/apps', id: appId }).then((res) => {
+    try {
+      const res = await fetchAppDetail({ url: '/apps', id: appId })
       if (systemFeatures.enable_web_sso_switch_component) {
-        fetchAppSSO({ appId }).then((ssoRes) => {
-          setAppDetail({ ...res, enable_sso: ssoRes.enabled })
-        })
+        const ssoRes = await fetchAppSSO({ appId })
+        setAppDetail({ ...res, enable_sso: ssoRes.enabled })
       }
       else {
         setAppDetail({ ...res })
       }
-    })
+    }
+    catch (error) { console.error(error) }
   }
 
   const handleCallbackResult = (err: Error | null, message?: string) => {

web/app/components/app/overview/settings/index.tsx

@@ -43,7 +43,7 @@ export type ConfigParams = {
   icon: string
   icon_background?: string
   show_workflow_steps: boolean
-  enable_sso: boolean
+  enable_sso?: boolean
 }
 
 const prefixSettings = 'appOverview.overview.appInfo.settings'
@@ -157,7 +157,7 @@ const SettingsModal: FC<ISettingsModalProps> = ({
       icon: appIcon.type === 'emoji' ? appIcon.icon : appIcon.fileId,
       icon_background: appIcon.type === 'emoji' ? appIcon.background : undefined,
       show_workflow_steps: inputInfo.show_workflow_steps,
-      enable_sso: inputInfo.enable_sso!,
+      enable_sso: inputInfo.enable_sso,
     }
     await onSave?.(params)
     setSaveLoading(false)
@@ -235,7 +235,11 @@ const SettingsModal: FC<ISettingsModalProps> = ({
           <p className='system-xs-medium text-gray-500'>{t(`${prefixSettings}.sso.label`)}</p>
           <div className='flex justify-between items-center'>
             <div className='font-medium system-sm-semibold flex-grow text-gray-900'>{t(`${prefixSettings}.sso.title`)}</div>
-            <Tooltip asChild={false} disabled={systemFeatures.sso_enforced_for_web} popupContent={<div className='w-[180px]'>{t(`${prefixSettings}.sso.tooltip`)}</div>}>
+            <Tooltip
+              disabled={systemFeatures.sso_enforced_for_web}
+              popupContent={<div className='w-[180px]'>{t(`${prefixSettings}.sso.tooltip`)}</div>}
+              asChild={false}
+            >
               <Switch disabled={!systemFeatures.sso_enforced_for_web} defaultValue={systemFeatures.sso_enforced_for_web && inputInfo.enable_sso} onChange={v => setInputInfo({ ...inputInfo, enable_sso: v })}></Switch>
             </Tooltip>
           </div>

web/app/components/base/tooltip/index.tsx

@@ -89,7 +89,7 @@ const Tooltip: FC<TooltipProps> = ({
         onMouseLeave={() => triggerMethod === 'hover' && handleLeave(true)}
         asChild={asChild}
       >
-        {children || <div className={triggerClassName || 'p-[1px] w-3.5 h-3.5'}><RiQuestionLine className='text-text-quaternary hover:text-text-tertiary w-full h-full' /></div>}
+        {children || <div className={triggerClassName || 'p-[1px] w-3.5 h-3.5 shrink-0'}><RiQuestionLine className='text-text-quaternary hover:text-text-tertiary w-full h-full' /></div>}
       </PortalToFollowElemTrigger>
       <PortalToFollowElemContent
         className="z-[9999]"

web/app/components/header/account-setting/model-provider-page/model-modal/Form.tsx

@@ -1,6 +1,5 @@
 import { Fragment, useState } from 'react'
 import type { FC } from 'react'
-
 import { ValidatingTip } from '../../key-validator/ValidateStatus'
 import type {
   CredentialFormSchema,

web/app/components/header/account-setting/model-provider-page/model-parameter-modal/parameter-item.tsx

@@ -242,13 +242,14 @@ const ParameterItem: FC<ParameterItemProps> = ({
                   <div className='w-[200px] whitespace-pre-wrap'>{parameterRule.help[language] || parameterRule.help.en_US}</div>
                 )}
                 popupClassName='mr-1'
-                triggerClassName='mr-1 w-4 h-4'
+                triggerClassName='mr-1 w-4 h-4 shrink-0'
               />
             )
           }
           {
             !parameterRule.required && parameterRule.name !== 'stop' && (
               <Switch
+                className='mr-1'
                 defaultValue={!isNullOrUndefined(value)}
                 onChange={handleSwitch}
                 size='md'

web/app/components/workflow/nodes/_base/components/option-card.tsx

@@ -3,9 +3,8 @@ import type { FC } from 'react'
 import React, { useCallback } from 'react'
 import type { VariantProps } from 'class-variance-authority'
 import { cva } from 'class-variance-authority'
-import { RiQuestionLine } from '@remixicon/react'
 import cn from '@/utils/classnames'
-import TooltipPlus from '@/app/components/base/tooltip'
+import Tooltip from '@/app/components/base/tooltip'
 
 const variants = cva([], {
   variants: {
@@ -59,15 +58,16 @@ const OptionCard: FC<Props> = ({
       onClick={handleSelect}
     >
       <span>{title}</span>
-      {tooltip && <TooltipPlus
-        popupContent={<div className='w-[240px]'
-        >
-          {tooltip}
-        </div>}
-        asChild={false}
-      >
-        <RiQuestionLine className='ml-0.5 w-[14px] h-[14px] text-text-quaternary' />
-      </TooltipPlus>}
+      {tooltip
+        && <Tooltip
+          popupContent={
+            <div className='w-[240px]'>
+              {tooltip}
+            </div>
+          }
+          asChild={false}
+        />
+      }
     </div>
   )
 }

web/i18n/en-US/app-overview.ts

@@ -54,7 +54,7 @@ const translation = {
         chatColorThemeInverted: 'Inverted',
         invalidHexMessage: 'Invalid hex value',
         sso: {
-          label: 'SSO ENFORCEMENT',
+          label: 'SSO Authentication',
           title: 'WebApp SSO',
           description: 'All users are required to login with SSO before using WebApp',
           tooltip: 'Contact the administrator to enable WebApp SSO',

web/service/apps.ts

@@ -16,7 +16,7 @@ export const fetchAppDetail = ({ url, id }: { url: string; id: string }) => {
 export const fetchAppSSO = async ({ appId }: { appId: string }) => {
   return get<AppSSOResponse>(`/enterprise/app-setting/sso?appID=${appId}`)
 }
-export const updateAppSSO = async ({ id, enabled }: { id: string;enabled: boolean }) => {
+export const updateAppSSO = async ({ id, enabled }: { id: string; enabled: boolean }) => {
   return post('/enterprise/app-setting/sso', { body: { app_id: id, enabled } })
 }