Merge remote-tracking branch 'origin/main' into feat/ai-ppt-tool

.gitignore

@@ -145,6 +145,9 @@ docker/volumes/db/data/*
 docker/volumes/redis/data/*
 docker/volumes/weaviate/*
 docker/volumes/qdrant/*
+docker/volumes/etcd/*
+docker/volumes/minio/*
+docker/volumes/milvus/*
 
 sdks/python-client/build
 sdks/python-client/dist

LICENSE

-# Dify Open Source License
+# Open Source License
 
-The Dify project is licensed under the Apache License 2.0, with the following additional conditions:
+Dify is licensed under the Apache License 2.0, with the following additional conditions:
 
-1. Dify is permitted to be used for commercialization, such as using Dify as a "backend-as-a-service" for your other applications, or delivering it to enterprises as an application development platform. However, when the following conditions are met, you must contact the producer to obtain a commercial license:
+1. Dify may be utilized commercially, including as a backend service for other applications or as an application development platform for enterprises. Should the conditions below be met, a commercial license must be obtained from the producer:
 
-a. Multi-tenant SaaS service: Unless explicitly authorized by Dify in writing, you may not use the Dify.AI source code to operate a multi-tenant SaaS service that is similar to the Dify.AI service edition.
-b. LOGO and copyright information: In the process of using Dify, you may not remove or modify the LOGO or copyright information in the Dify console.
+a. Multi-tenant SaaS service: Unless explicitly authorized by Dify in writing, you may not use the Dify source code to operate a multi-tenant environment. 
+    - Tenant Definition: Within the context of Dify, one tenant corresponds to one workspace. The workspace provides a separated area for each tenant's data and configurations.
+
+b. LOGO and copyright information: In the process of using Dify's frontend components, you may not remove or modify the LOGO or copyright information in the Dify console or applications. This restriction is inapplicable to uses of Dify that do not involve its frontend components.
 
 Please contact business@dify.ai by email to inquire about licensing matters.
 
-2. As a contributor, you should agree that your contributed code:
+2. As a contributor, you should agree that:
 
-a. The producer can adjust the open-source agreement to be more strict or relaxed.
-b. Can be used for commercial purposes, such as Dify's cloud business.
+a. The producer can adjust the open-source agreement to be more strict or relaxed as deemed necessary.
+b. Your contributed code may be used for commercial purposes, including but not limited to its cloud business operations.
 
-Apart from this, all other rights and restrictions follow the Apache License 2.0. If you need more detailed information, you can refer to the full version of Apache License 2.0.
+Apart from the specific conditions mentioned above, all other rights and restrictions follow the Apache License 2.0. Detailed information about the Apache License 2.0 can be found at http://www.apache.org/licenses/LICENSE-2.0.
 
 The interactive design of this product is protected by appearance patent.
 
-© 2023 LangGenius, Inc.
+© 2024 LangGenius, Inc.
 
 
 ----------

api/app.py

@@ -26,6 +26,7 @@ from config import CloudEditionConfig, Config
 from extensions import (
     ext_celery,
     ext_code_based_extension,
+    ext_compress,
     ext_database,
     ext_hosting_provider,
     ext_login,
@@ -96,6 +97,7 @@ def create_app(test_config=None) -> Flask:
 def initialize_extensions(app):
     # Since the application instance is now created, pass it to each Flask
     # extension instance to bind it to the Flask application instance (app)
+    ext_compress.init_app(app)
     ext_code_based_extension.init()
     ext_database.init_app(app)
     ext_migrate.init(app, db)

api/commands.py

@@ -15,7 +15,7 @@ from libs.rsa import generate_key_pair
 from models.account import Tenant
 from models.dataset import Dataset, DatasetCollectionBinding, DocumentSegment
 from models.dataset import Document as DatasetDocument
-from models.model import Account
+from models.model import Account, App, AppAnnotationSetting, MessageAnnotation
 from models.provider import Provider, ProviderModel
 
 
@@ -125,7 +125,114 @@ def reset_encrypt_key_pair():
 
 
 @click.command('vdb-migrate', help='migrate vector db.')
-def vdb_migrate():
+@click.option('--scope', default='all', prompt=False, help='The scope of vector database to migrate, Default is All.')
+def vdb_migrate(scope: str):
+    if scope in ['knowledge', 'all']:
+        migrate_knowledge_vector_database()
+    if scope in ['annotation', 'all']:
+        migrate_annotation_vector_database()
+
+
+def migrate_annotation_vector_database():
+    """
+    Migrate annotation datas to target vector database .
+    """
+    click.echo(click.style('Start migrate annotation data.', fg='green'))
+    create_count = 0
+    skipped_count = 0
+    total_count = 0
+    page = 1
+    while True:
+        try:
+            # get apps info
+            apps = db.session.query(App).filter(
+                App.status == 'normal'
+            ).order_by(App.created_at.desc()).paginate(page=page, per_page=50)
+        except NotFound:
+            break
+
+        page += 1
+        for app in apps:
+            total_count = total_count + 1
+            click.echo(f'Processing the {total_count} app {app.id}. '
+                       + f'{create_count} created, {skipped_count} skipped.')
+            try:
+                click.echo('Create app annotation index: {}'.format(app.id))
+                app_annotation_setting = db.session.query(AppAnnotationSetting).filter(
+                    AppAnnotationSetting.app_id == app.id
+                ).first()
+
+                if not app_annotation_setting:
+                    skipped_count = skipped_count + 1
+                    click.echo('App annotation setting is disabled: {}'.format(app.id))
+                    continue
+                # get dataset_collection_binding info
+                dataset_collection_binding = db.session.query(DatasetCollectionBinding).filter(
+                    DatasetCollectionBinding.id == app_annotation_setting.collection_binding_id
+                ).first()
+                if not dataset_collection_binding:
+                    click.echo('App annotation collection binding is not exist: {}'.format(app.id))
+                    continue
+                annotations = db.session.query(MessageAnnotation).filter(MessageAnnotation.app_id == app.id).all()
+                dataset = Dataset(
+                    id=app.id,
+                    tenant_id=app.tenant_id,
+                    indexing_technique='high_quality',
+                    embedding_model_provider=dataset_collection_binding.provider_name,
+                    embedding_model=dataset_collection_binding.model_name,
+                    collection_binding_id=dataset_collection_binding.id
+                )
+                documents = []
+                if annotations:
+                    for annotation in annotations:
+                        document = Document(
+                            page_content=annotation.question,
+                            metadata={
+                                "annotation_id": annotation.id,
+                                "app_id": app.id,
+                                "doc_id": annotation.id
+                            }
+                        )
+                        documents.append(document)
+
+                vector = Vector(dataset, attributes=['doc_id', 'annotation_id', 'app_id'])
+                click.echo(f"Start to migrate annotation, app_id: {app.id}.")
+
+                try:
+                    vector.delete()
+                    click.echo(
+                        click.style(f'Successfully delete vector index for app: {app.id}.',
+                                    fg='green'))
+                except Exception as e:
+                    click.echo(
+                        click.style(f'Failed to delete vector index for app {app.id}.',
+                                    fg='red'))
+                    raise e
+                if documents:
+                    try:
+                        click.echo(click.style(
+                            f'Start to created vector index with {len(documents)} annotations for app {app.id}.',
+                            fg='green'))
+                        vector.create(documents)
+                        click.echo(
+                            click.style(f'Successfully created vector index for app {app.id}.', fg='green'))
+                    except Exception as e:
+                        click.echo(click.style(f'Failed to created vector index for app {app.id}.', fg='red'))
+                        raise e
+                click.echo(f'Successfully migrated app annotation {app.id}.')
+                create_count += 1
+            except Exception as e:
+                click.echo(
+                    click.style('Create app annotation index error: {} {}'.format(e.__class__.__name__, str(e)),
+                                fg='red'))
+                continue
+
+    click.echo(
+        click.style(f'Congratulations! Create {create_count} app annotation indexes, and skipped {skipped_count} apps.',
+                    fg='green'))
+
+
+def migrate_knowledge_vector_database():
     """
     Migrate vector database datas to target vector database .
     """

api/config.py

@@ -90,7 +90,7 @@ class Config:
         # ------------------------
         # General Configurations.
         # ------------------------
-        self.CURRENT_VERSION = "0.5.7"
+        self.CURRENT_VERSION = "0.5.8"
         self.COMMIT_SHA = get_env('COMMIT_SHA')
         self.EDITION = "SELF_HOSTED"
         self.DEPLOY_ENV = get_env('DEPLOY_ENV')
@@ -293,6 +293,8 @@ class Config:
 
         self.BATCH_UPLOAD_LIMIT = get_env('BATCH_UPLOAD_LIMIT')
 
+        self.API_COMPRESSION_ENABLED = get_bool_env('API_COMPRESSION_ENABLED')
+
 
 class CloudEditionConfig(Config):
 

api/controllers/console/app/app.py

@@ -129,7 +129,7 @@ class AppListApi(Resource):
                         "No Default System Reasoning Model available. Please configure "
                         "in the Settings -> Model Provider.")
                 else:
-                    model_config_dict["model"]["provider"] = default_model_entity.provider
+                    model_config_dict["model"]["provider"] = default_model_entity.provider.provider
                     model_config_dict["model"]["name"] = default_model_entity.model
 
             model_configuration = AppModelConfigService.validate_configuration(

api/controllers/console/app/audio.py

@@ -88,7 +88,7 @@ class ChatMessageTextApi(Resource):
             response = AudioService.transcript_tts(
                 tenant_id=app_model.tenant_id,
                 text=request.form['text'],
-                voice=app_model.app_model_config.text_to_speech_dict.get('voice'),
+                voice=request.form['voice'] if request.form['voice'] else app_model.app_model_config.text_to_speech_dict.get('voice'),
                 streaming=False
             )
 

api/controllers/console/explore/audio.py

@@ -85,7 +85,7 @@ class ChatTextApi(InstalledAppResource):
             response = AudioService.transcript_tts(
                 tenant_id=app_model.tenant_id,
                 text=request.form['text'],
-                voice=app_model.app_model_config.text_to_speech_dict.get('voice'),
+                voice=request.form['voice'] if request.form['voice'] else app_model.app_model_config.text_to_speech_dict.get('voice'),
                 streaming=False
             )
             return {'data': response.data.decode('latin1')}

api/controllers/console/workspace/tool_providers.py

@@ -259,6 +259,7 @@ class ToolApiProviderPreviousTestApi(Resource):
         parser = reqparse.RequestParser()
 
         parser.add_argument('tool_name', type=str, required=True, nullable=False, location='json')
+        parser.add_argument('provider_name', type=str, required=False, nullable=False, location='json')
         parser.add_argument('credentials', type=dict, required=True, nullable=False, location='json')
         parser.add_argument('parameters', type=dict, required=True, nullable=False, location='json')
         parser.add_argument('schema_type', type=str, required=True, nullable=False, location='json')
@@ -268,6 +269,7 @@ class ToolApiProviderPreviousTestApi(Resource):
 
         return ToolManageService.test_api_tool_preview(
             current_user.current_tenant_id,
+            args['provider_name'] if args['provider_name'] else '',
             args['tool_name'],
             args['credentials'],
             args['parameters'],

api/controllers/service_api/app/audio.py

@@ -87,7 +87,7 @@ class TextApi(Resource):
                 tenant_id=app_model.tenant_id,
                 text=args['text'],
                 end_user=end_user,
-                voice=app_model.app_model_config.text_to_speech_dict.get('voice'),
+                voice=args['voice'] if args['voice'] else app_model.app_model_config.text_to_speech_dict.get('voice'),
                 streaming=args['streaming']
             )
 

api/controllers/web/audio.py

@@ -84,7 +84,7 @@ class TextApi(WebApiResource):
                 tenant_id=app_model.tenant_id,
                 text=request.form['text'],
                 end_user=end_user.external_user_id,
-                voice=app_model.app_model_config.text_to_speech_dict.get('voice'),
+                voice=request.form['voice'] if request.form['voice'] else app_model.app_model_config.text_to_speech_dict.get('voice'),
                 streaming=False
             )
 

api/core/app_runner/app_runner.py

@@ -84,7 +84,7 @@ class AppRunner:
 
         return rest_tokens
 
-    def recale_llm_max_tokens(self, model_config: ModelConfigEntity,
+    def recalc_llm_max_tokens(self, model_config: ModelConfigEntity,
                               prompt_messages: list[PromptMessage]):
         # recalc max_tokens if sum(prompt_token +  max_tokens) over model token limit
         model_type_instance = model_config.provider_model_bundle.model_type_instance

api/core/app_runner/basic_app_runner.py

@@ -181,7 +181,7 @@ class BasicApplicationRunner(AppRunner):
             return
 
         # Re-calculate the max tokens if sum(prompt_token +  max_tokens) over model token limit
-        self.recale_llm_max_tokens(
+        self.recalc_llm_max_tokens(
             model_config=app_orchestration_config.model_config,
             prompt_messages=prompt_messages
         )

api/core/features/assistant_cot_runner.py

@@ -28,6 +28,9 @@ from models.model import Conversation, Message
 
 
 class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
+    _is_first_iteration = True
+    _ignore_observation_providers = ['wenxin']
+
     def run(self, conversation: Conversation,
         message: Message,
         query: str,
@@ -42,10 +45,8 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
         agent_scratchpad: list[AgentScratchpadUnit] = []
         self._init_agent_scratchpad(agent_scratchpad, self.history_prompt_messages)
 
-        # check model mode
-        if self.app_orchestration_config.model_config.mode == "completion":
-            # TODO: stop words
         if 'Observation' not in app_orchestration_config.model_config.stop:
+            if app_orchestration_config.model_config.provider not in self._ignore_observation_providers:
                 app_orchestration_config.model_config.stop.append('Observation')
 
         # override inputs
@@ -130,8 +131,8 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
                 input=query
             )
 
-            # recale llm max tokens
-            self.recale_llm_max_tokens(self.model_config, prompt_messages)
+            # recalc llm max tokens
+            self.recalc_llm_max_tokens(self.model_config, prompt_messages)
             # invoke model
             chunks: Generator[LLMResultChunk, None, None] = model_instance.invoke_llm(
                 prompt_messages=prompt_messages,
@@ -202,6 +203,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
                         )
                     )
 
+            scratchpad.thought = scratchpad.thought.strip() or 'I am thinking about how to help you'
             agent_scratchpad.append(scratchpad)
                         
             # get llm usage
@@ -255,9 +257,15 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
                         # invoke tool
                         error_response = None
                         try:
+                            if isinstance(tool_call_args, str):
+                                try:
+                                    tool_call_args = json.loads(tool_call_args)
+                                except json.JSONDecodeError:
+                                    pass
+                            
                             tool_response = tool_instance.invoke(
                                 user_id=self.user_id, 
-                                tool_parameters=tool_call_args if isinstance(tool_call_args, dict) else json.loads(tool_call_args)
+                                tool_parameters=tool_call_args
                             )
                             # transform tool response to llm friendly response
                             tool_response = self.transform_tool_invoke_messages(tool_response)
@@ -466,7 +474,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
             if isinstance(message, AssistantPromptMessage):
                 current_scratchpad = AgentScratchpadUnit(
                     agent_response=message.content,
-                    thought=message.content,
+                    thought=message.content or 'I am thinking about how to help you',
                     action_str='',
                     action=None,
                     observation=None,
@@ -546,7 +554,8 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
 
         result = ''
         for scratchpad in agent_scratchpad:
-            result += scratchpad.thought + next_iteration.replace("{{observation}}", scratchpad.observation or '') + "\n"
+            result += (scratchpad.thought or '') + (scratchpad.action_str or '') + \
+                next_iteration.replace("{{observation}}", scratchpad.observation or 'It seems that no response is available')
 
         return result
     
@@ -621,21 +630,24 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
                 ))
 
             # add assistant message
-            if len(agent_scratchpad) > 0:
+            if len(agent_scratchpad) > 0 and not self._is_first_iteration:
                 prompt_messages.append(AssistantPromptMessage(
-                    content=(agent_scratchpad[-1].thought or '')
+                    content=(agent_scratchpad[-1].thought or '') + (agent_scratchpad[-1].action_str or ''),
                 ))
             
             # add user message
-            if len(agent_scratchpad) > 0:
+            if len(agent_scratchpad) > 0 and not self._is_first_iteration:
                 prompt_messages.append(UserPromptMessage(
-                    content=(agent_scratchpad[-1].observation or ''),
+                    content=(agent_scratchpad[-1].observation or 'It seems that no response is available'),
                 ))
 
+            self._is_first_iteration = False
+
             return prompt_messages
         elif mode == "completion":
             # parse agent scratchpad
             agent_scratchpad_str = self._convert_scratchpad_list_to_str(agent_scratchpad)
+            self._is_first_iteration = False
             # parse prompt messages
             return [UserPromptMessage(
                 content=first_prompt.replace("{{instruction}}", instruction)

api/core/features/assistant_fc_runner.py

@@ -105,8 +105,8 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
                 messages_ids=message_file_ids
             )
 
-            # recale llm max tokens
-            self.recale_llm_max_tokens(self.model_config, prompt_messages)
+            # recalc llm max tokens
+            self.recalc_llm_max_tokens(self.model_config, prompt_messages)
             # invoke model
             chunks: Union[Generator[LLMResultChunk, None, None], LLMResult] = model_instance.invoke_llm(
                 prompt_messages=prompt_messages,

api/core/indexing_runner.py

@@ -186,7 +186,7 @@ class IndexingRunner:
                 first()
 
             index_type = dataset_document.doc_form
-            index_processor = IndexProcessorFactory(index_type, processing_rule.to_dict()).init_index_processor()
+            index_processor = IndexProcessorFactory(index_type).init_index_processor()
             self._load(
                 index_processor=index_processor,
                 dataset=dataset,

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

@@ -21,7 +21,7 @@ class AnthropicProvider(ModelProvider):
 
             # Use `claude-instant-1` model for validate,
             model_instance.validate_credentials(
-                model='claude-instant-1',
+                model='claude-instant-1.2',
                 credentials=credentials
             )
         except CredentialsValidateFailedError as ex:

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

@@ -2,8 +2,8 @@ provider: anthropic
 label:
   en_US: Anthropic
 description:
-  en_US: Anthropic’s powerful models, such as Claude 2 and Claude Instant.
-  zh_Hans: Anthropic 的强大模型，例如 Claude 2 和 Claude Instant。
+  en_US: Anthropic’s powerful models, such as Claude 3.
+  zh_Hans: Anthropic 的强大模型，例如 Claude 3。
 icon_small:
   en_US: icon_s_en.svg
 icon_large:

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

+- claude-3-opus-20240229
+- claude-3-sonnet-20240229
+- claude-2.1
+- claude-instant-1.2
+- claude-2
+- claude-instant-1

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

@@ -34,3 +34,4 @@ pricing:
   output: '24.00'
   unit: '0.000001'
   currency: USD
+deprecated: true

api/core/model_runtime/model_providers/anthropic/llm/claude-3-opus-20240229.yaml

+model: claude-3-opus-20240229
+label:
+  en_US: claude-3-opus-20240229
+model_type: llm
+features:
+  - agent-thought
+  - vision
+model_properties:
+  mode: chat
+  context_size: 200000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    default: 4096
+    min: 1
+    max: 4096
+  - name: response_format
+    use_template: response_format
+pricing:
+  input: '15.00'
+  output: '75.00'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/anthropic/llm/claude-3-sonnet-20240229.yaml

+model: claude-3-sonnet-20240229
+label:
+  en_US: claude-3-sonnet-20240229
+model_type: llm
+features:
+  - agent-thought
+  - vision
+model_properties:
+  mode: chat
+  context_size: 200000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    default: 4096
+    min: 1
+    max: 4096
+  - name: response_format
+    use_template: response_format
+pricing:
+  input: '3.00'
+  output: '15.00'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/anthropic/llm/claude-instant-1.2.yaml

+model: claude-instant-1.2
+label:
+  en_US: claude-instant-1.2
+model_type: llm
+features: [ ]
+model_properties:
+  mode: chat
+  context_size: 100000
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    default: 4096
+    min: 1
+    max: 4096
+  - name: response_format
+    use_template: response_format
+pricing:
+  input: '1.63'
+  output: '5.51'
+  unit: '0.000001'
+  currency: USD

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

@@ -33,3 +33,4 @@ pricing:
   output: '5.51'
   unit: '0.000001'
   currency: USD
+deprecated: true

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

+import base64
+import mimetypes
 from collections.abc import Generator
-from typing import Optional, Union
+from typing import Optional, Union, cast
 
 import anthropic
+import requests
 from anthropic import Anthropic, Stream
-from anthropic.types import Completion, completion_create_params
+from anthropic.types import (
+    ContentBlockDeltaEvent,
+    Message,
+    MessageDeltaEvent,
+    MessageStartEvent,
+    MessageStopEvent,
+    MessageStreamEvent,
+    completion_create_params,
+)
 from httpx import Timeout
 
 from core.model_runtime.callbacks.base_callback import Callback
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
 from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
+    ImagePromptMessageContent,
     PromptMessage,
+    PromptMessageContentType,
     PromptMessageTool,
     SystemPromptMessage,
+    TextPromptMessageContent,
     UserPromptMessage,
 )
 from core.model_runtime.errors.invoke import (
@@ -35,6 +49,7 @@ if you are not sure about the structure.
 </instructions>
 """
 
+
 class AnthropicLargeLanguageModel(LargeLanguageModel):
     def _invoke(self, model: str, credentials: dict,
                 prompt_messages: list[PromptMessage], model_parameters: dict,
@@ -55,7 +70,58 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
         :return: full response or stream response chunk generator result
         """
         # invoke model
-        return self._generate(model, credentials, prompt_messages, model_parameters, stop, stream, user)
+        return self._chat_generate(model, credentials, prompt_messages, model_parameters, stop, stream, user)
+
+    def _chat_generate(self, model: str, credentials: dict,
+                       prompt_messages: list[PromptMessage], model_parameters: dict, stop: Optional[list[str]] = None,
+                       stream: bool = True, user: Optional[str] = None) -> Union[LLMResult, Generator]:
+        """
+        Invoke llm chat model
+
+        :param model: model name
+        :param credentials: credentials
+        :param prompt_messages: prompt messages
+        :param model_parameters: model parameters
+        :param stop: stop words
+        :param stream: is stream response
+        :param user: unique user id
+        :return: full response or stream response chunk generator result
+        """
+        # transform credentials to kwargs for model instance
+        credentials_kwargs = self._to_credential_kwargs(credentials)
+
+        # transform model parameters from completion api of anthropic to chat api
+        if 'max_tokens_to_sample' in model_parameters:
+            model_parameters['max_tokens'] = model_parameters.pop('max_tokens_to_sample')
+
+        # init model client
+        client = Anthropic(**credentials_kwargs)
+
+        extra_model_kwargs = {}
+        if stop:
+            extra_model_kwargs['stop_sequences'] = stop
+
+        if user:
+            extra_model_kwargs['metadata'] = completion_create_params.Metadata(user_id=user)
+
+        system, prompt_message_dicts = self._convert_prompt_messages(prompt_messages)
+
+        if system:
+            extra_model_kwargs['system'] = system
+
+        # chat model
+        response = client.messages.create(
+            model=model,
+            messages=prompt_message_dicts,
+            stream=stream,
+            **model_parameters,
+            **extra_model_kwargs
+        )
+
+        if stream:
+            return self._handle_chat_generate_stream_response(model, credentials, response, prompt_messages)
+
+        return self._handle_chat_generate_response(model, credentials, response, prompt_messages)
 
     def _code_block_mode_wrapper(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
                                  model_parameters: dict, tools: Optional[list[PromptMessageTool]] = None,
@@ -66,14 +132,23 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
         """
         if 'response_format' in model_parameters and model_parameters['response_format']:
             stop = stop or []
-            self._transform_json_prompts(
-                model, credentials, prompt_messages, model_parameters, tools, stop, stream, user, model_parameters['response_format']
+            # chat model
+            self._transform_chat_json_prompts(
+                model=model,
+                credentials=credentials,
+                prompt_messages=prompt_messages,
+                model_parameters=model_parameters,
+                tools=tools,
+                stop=stop,
+                stream=stream,
+                user=user,
+                response_format=model_parameters['response_format']
             )
             model_parameters.pop('response_format')
 
         return self._invoke(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
 
-    def _transform_json_prompts(self, model: str, credentials: dict, 
+    def _transform_chat_json_prompts(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, response_format: str = 'JSON') \
@@ -83,6 +158,8 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
         """
         if "```\n" not in stop:
             stop.append("```\n")
+        if "\n```" not in stop:
+            stop.append("\n```")
 
         # check if there is a system message
         if len(prompt_messages) > 0 and isinstance(prompt_messages[0], SystemPromptMessage):
@@ -92,6 +169,7 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
                 .replace("{{instructions}}", prompt_messages[0].content)
                 .replace("{{block}}", response_format)
             )
+            prompt_messages.append(AssistantPromptMessage(content=f"\n```{response_format}"))
         else:
             # insert the system message
             prompt_messages.insert(0, SystemPromptMessage(
@@ -99,10 +177,7 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
                 .replace("{{instructions}}", f"Please output a valid {response_format} object.")
                 .replace("{{block}}", response_format)
             ))
-
-        prompt_messages.append(AssistantPromptMessage(
-            content=f"```{response_format}\n"
-        ))
+            prompt_messages.append(AssistantPromptMessage(content=f"\n```{response_format}"))
 
     def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
                        tools: Optional[list[PromptMessageTool]] = None) -> int:
@@ -129,7 +204,7 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
         :return:
         """
         try:
-            self._generate(
+            self._chat_generate(
                 model=model,
                 credentials=credentials,
                 prompt_messages=[
@@ -137,58 +212,17 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
                 ],
                 model_parameters={
                     "temperature": 0,
-                    "max_tokens_to_sample": 20,
+                    "max_tokens": 20,
                 },
                 stream=False
             )
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
 
-    def _generate(self, model: str, credentials: dict,
-                  prompt_messages: list[PromptMessage], model_parameters: dict,
-                  stop: Optional[list[str]] = None, stream: bool = True,
-                  user: Optional[str] = None) -> Union[LLMResult, Generator]:
-        """
-        Invoke large language model
-
-        :param model: model name
-        :param credentials: credentials kwargs
-        :param prompt_messages: prompt messages
-        :param model_parameters: model parameters
-        :param stop: stop words
-        :param stream: is stream response
-        :param user: unique user id
-        :return: full response or stream response chunk generator result
-        """
-        # transform credentials to kwargs for model instance
-        credentials_kwargs = self._to_credential_kwargs(credentials)
-
-        client = Anthropic(**credentials_kwargs)
-
-        extra_model_kwargs = {}
-        if stop:
-            extra_model_kwargs['stop_sequences'] = stop
-
-        if user:
-            extra_model_kwargs['metadata'] = completion_create_params.Metadata(user_id=user)
-
-        response = client.completions.create(
-            model=model,
-            prompt=self._convert_messages_to_prompt_anthropic(prompt_messages),
-            stream=stream,
-            **model_parameters,
-            **extra_model_kwargs
-        )
-
-        if stream:
-            return self._handle_generate_stream_response(model, credentials, response, prompt_messages)
-
-        return self._handle_generate_response(model, credentials, response, prompt_messages)
-
-    def _handle_generate_response(self, model: str, credentials: dict, response: Completion,
+    def _handle_chat_generate_response(self, model: str, credentials: dict, response: Message,
                                        prompt_messages: list[PromptMessage]) -> LLMResult:
         """
-        Handle llm response
+        Handle llm chat response
 
         :param model: model name
         :param credentials: credentials
@@ -198,9 +232,15 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
         """
         # transform assistant message to prompt message
         assistant_prompt_message = AssistantPromptMessage(
-            content=response.completion
+            content=response.content[0].text
         )
 
+        # calculate num tokens
+        if response.usage:
+            # transform usage
+            prompt_tokens = response.usage.input_tokens
+            completion_tokens = response.usage.output_tokens
+        else:
             # calculate num tokens
             prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
             completion_tokens = self.get_num_tokens(model, credentials, [assistant_prompt_message])
@@ -209,64 +249,72 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
         usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
 
         # transform response
-        result = LLMResult(
+        response = LLMResult(
             model=response.model,
             prompt_messages=prompt_messages,
             message=assistant_prompt_message,
-            usage=usage,
+            usage=usage
         )
 
-        return result
+        return response
 
-    def _handle_generate_stream_response(self, model: str, credentials: dict, response: Stream[Completion],
+    def _handle_chat_generate_stream_response(self, model: str, credentials: dict,
+                                              response: Stream[MessageStreamEvent],
                                               prompt_messages: list[PromptMessage]) -> Generator:
         """
-        Handle llm stream response
+        Handle llm chat stream response
 
         :param model: model name
-        :param credentials: credentials
         :param response: response
         :param prompt_messages: prompt messages
-        :return: llm response chunk generator result
+        :return: llm response chunk generator
         """
-        index = -1
+        full_assistant_content = ''
+        return_model = None
+        input_tokens = 0
+        output_tokens = 0
+        finish_reason = None
+        index = 0
         for chunk in response:
-            content = chunk.completion
-            if chunk.stop_reason is None and (content is None or content == ''):
-                continue
-
-            # transform assistant message to prompt message
-            assistant_prompt_message = AssistantPromptMessage(
-                content=content if content else '',
-            )
-
-            index += 1
-
-            if chunk.stop_reason is not None:
-                # calculate num tokens
-                prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
-                completion_tokens = self.get_num_tokens(model, credentials, [assistant_prompt_message])
-
+            if isinstance(chunk, MessageStartEvent):
+                return_model = chunk.message.model
+                input_tokens = chunk.message.usage.input_tokens
+            elif isinstance(chunk, MessageDeltaEvent):
+                output_tokens = chunk.usage.output_tokens
+                finish_reason = chunk.delta.stop_reason
+            elif isinstance(chunk, MessageStopEvent):
                 # transform usage
-                usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
+                usage = self._calc_response_usage(model, credentials, input_tokens, output_tokens)
 
                 yield LLMResultChunk(
-                    model=chunk.model,
+                    model=return_model,
                     prompt_messages=prompt_messages,
                     delta=LLMResultChunkDelta(
-                        index=index,
-                        message=assistant_prompt_message,
-                        finish_reason=chunk.stop_reason,
+                        index=index + 1,
+                        message=AssistantPromptMessage(
+                            content=''
+                        ),
+                        finish_reason=finish_reason,
                         usage=usage
                     )
                 )
-            else:
+            elif isinstance(chunk, ContentBlockDeltaEvent):
+                chunk_text = chunk.delta.text if chunk.delta.text else ''
+                full_assistant_content += chunk_text
+
+                # transform assistant message to prompt message
+                assistant_prompt_message = AssistantPromptMessage(
+                    content=chunk_text
+                )
+
+                index = chunk.index
+
                 yield LLMResultChunk(
-                    model=chunk.model,
+                    model=return_model,
                     prompt_messages=prompt_messages,
                     delta=LLMResultChunkDelta(
-                        index=index,
-                        message=assistant_prompt_message
+                        index=chunk.index,
+                        message=assistant_prompt_message,
                     )
                 )
 
@@ -289,6 +337,80 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
 
         return credentials_kwargs
 
+    def _convert_prompt_messages(self, prompt_messages: list[PromptMessage]) -> tuple[str, list[dict]]:
+        """
+        Convert prompt messages to dict list and system
+        """
+        system = ""
+        prompt_message_dicts = []
+
+        for message in prompt_messages:
+            if isinstance(message, SystemPromptMessage):
+                system += message.content + ("\n" if not system else "")
+            else:
+                prompt_message_dicts.append(self._convert_prompt_message_to_dict(message))
+
+        return system, prompt_message_dicts
+
+    def _convert_prompt_message_to_dict(self, message: PromptMessage) -> dict:
+        """
+        Convert PromptMessage to dict
+        """
+        if isinstance(message, UserPromptMessage):
+            message = cast(UserPromptMessage, message)
+            if isinstance(message.content, str):
+                message_dict = {"role": "user", "content": message.content}
+            else:
+                sub_messages = []
+                for message_content in message.content:
+                    if message_content.type == PromptMessageContentType.TEXT:
+                        message_content = cast(TextPromptMessageContent, message_content)
+                        sub_message_dict = {
+                            "type": "text",
+                            "text": message_content.data
+                        }
+                        sub_messages.append(sub_message_dict)
+                    elif message_content.type == PromptMessageContentType.IMAGE:
+                        message_content = cast(ImagePromptMessageContent, message_content)
+                        if not message_content.data.startswith("data:"):
+                            # fetch image data from url
+                            try:
+                                image_content = requests.get(message_content.data).content
+                                mime_type, _ = mimetypes.guess_type(message_content.data)
+                                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}")
+                        else:
+                            data_split = message_content.data.split(";base64,")
+                            mime_type = data_split[0].replace("data:", "")
+                            base64_data = data_split[1]
+
+                        if mime_type not in ["image/jpeg", "image/png", "image/gif", "image/webp"]:
+                            raise ValueError(f"Unsupported image type {mime_type}, "
+                                             f"only support image/jpeg, image/png, image/gif, and image/webp")
+
+                        sub_message_dict = {
+                            "type": "image",
+                            "source": {
+                                "type": "base64",
+                                "media_type": mime_type,
+                                "data": base64_data
+                            }
+                        }
+                        sub_messages.append(sub_message_dict)
+
+                message_dict = {"role": "user", "content": sub_messages}
+        elif isinstance(message, AssistantPromptMessage):
+            message = cast(AssistantPromptMessage, message)
+            message_dict = {"role": "assistant", "content": message.content}
+        elif isinstance(message, SystemPromptMessage):
+            message = cast(SystemPromptMessage, message)
+            message_dict = {"role": "system", "content": message.content}
+        else:
+            raise ValueError(f"Got unknown type {message}")
+
+        return message_dict
+
     def _convert_one_message_to_text(self, message: PromptMessage) -> str:
         """
         Convert a single message to a string.

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

@@ -2,7 +2,7 @@ provider: jina
 label:
   en_US: Jina
 description:
-  en_US: Embedding Model Supported
+  en_US: Embedding and Rerank Model Supported
 icon_small:
   en_US: icon_s_en.svg
 icon_large:
@@ -13,9 +13,10 @@ help:
     en_US: Get your API key from Jina AI
     zh_Hans: 从 Jina 获取 API Key
   url:
-    en_US: https://jina.ai/embeddings/
+    en_US: https://jina.ai/
 supported_model_types:
   - text-embedding
+  - rerank
 configurate_methods:
   - predefined-model
 provider_credential_schema:

api/core/model_runtime/model_providers/jina/rerank/jina-reranker-v1-base-en.yaml

+model: jina-reranker-v1-base-en
+model_type: rerank
+model_properties:
+  context_size: 8192

api/core/model_runtime/model_providers/jina/rerank/rerank.py

+from typing import Optional
+
+import httpx
+
+from core.model_runtime.entities.rerank_entities import RerankDocument, RerankResult
+from core.model_runtime.errors.invoke import (
+    InvokeAuthorizationError,
+    InvokeBadRequestError,
+    InvokeConnectionError,
+    InvokeError,
+    InvokeRateLimitError,
+    InvokeServerUnavailableError,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.rerank_model import RerankModel
+
+
+class JinaRerankModel(RerankModel):
+    """
+    Model class for Jina rerank model.
+    """
+
+    def _invoke(self, model: str, credentials: dict,
+                query: str, docs: list[str], score_threshold: Optional[float] = None, top_n: Optional[int] = None,
+                user: Optional[str] = None) -> RerankResult:
+        """
+        Invoke rerank model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param query: search query
+        :param docs: docs for reranking
+        :param score_threshold: score threshold
+        :param top_n: top n documents to return
+        :param user: unique user id
+        :return: rerank result
+        """
+        if len(docs) == 0:
+            return RerankResult(model=model, docs=[])
+
+        try:
+            response = httpx.post(
+                "https://api.jina.ai/v1/rerank",
+                json={
+                    "model": model,
+                    "query": query,
+                    "documents": docs,
+                    "top_n": top_n
+                },
+                headers={"Authorization": f"Bearer {credentials.get('api_key')}"}  
+            )
+            response.raise_for_status() 
+            results = response.json()
+
+            rerank_documents = []
+            for result in results['results']:  
+                rerank_document = RerankDocument(
+                    index=result['index'],
+                    text=result['document']['text'],
+                    score=result['relevance_score'],
+                )
+                if score_threshold is None or result['relevance_score'] >= score_threshold:
+                    rerank_documents.append(rerank_document)
+
+            return RerankResult(model=model, docs=rerank_documents)
+        except httpx.HTTPStatusError as e:
+            raise InvokeServerUnavailableError(str(e))  
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            
+            self._invoke(
+                model=model,
+                credentials=credentials,
+                query="What is the capital of the United States?",
+                docs=[
+                    "Carson City is the capital city of the American state of Nevada. At the 2010 United States "
+                    "Census, Carson City had a population of 55,274.",
+                    "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that "
+                    "are a political division controlled by the United States. Its capital is Saipan.",
+                ],
+                score_threshold=0.8
+            )
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    @property
+    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
+        """
+        Map model invoke error to unified error
+        """
+        return {
+            InvokeConnectionError: [httpx.ConnectError],
+            InvokeServerUnavailableError: [httpx.RemoteProtocolError],
+            InvokeRateLimitError: [], 
+            InvokeAuthorizationError: [httpx.HTTPStatusError],  
+            InvokeBadRequestError: [httpx.RequestError]
+        }

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

@@ -34,7 +34,7 @@ class OpenAIText2SpeechModel(_CommonOpenAI, TTSModel):
         :return: text translated to audio file
         """
         audio_type = self._get_model_audio_type(model, credentials)
-        if not voice:
+        if not voice or voice not in [d['value'] for d in self.get_tts_model_voices(model=model, credentials=credentials)]:
             voice = self._get_model_default_voice(model, credentials)
         if streaming:
             return Response(stream_with_context(self._tts_invoke_streaming(model=model,

api/core/model_runtime/model_providers/tongyi/tts/tts.py

@@ -34,7 +34,7 @@ class TongyiText2SpeechModel(_CommonTongyi, TTSModel):
         :return: text translated to audio file
         """
         audio_type = self._get_model_audio_type(model, credentials)
-        if not voice or voice not in self.get_tts_model_voices(model=model, credentials=credentials):
+        if not voice or voice not in [d['value'] for d in self.get_tts_model_voices(model=model, credentials=credentials)]:
             voice = self._get_model_default_voice(model, credentials)
         if streaming:
             return Response(stream_with_context(self._tts_invoke_streaming(model=model,

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

-from os import path
 from threading import Lock
 from time import time
 
 from requests.adapters import HTTPAdapter
 from requests.exceptions import ConnectionError, MissingSchema, Timeout
 from requests.sessions import Session
+from yarl import URL
 
 
 class XinferenceModelExtraParameter:
@@ -55,7 +55,10 @@ class XinferenceHelper:
             get xinference model extra parameter like model_format and model_handle_type
         """
 
-        url = path.join(server_url, 'v1/models', model_uid)
+        if not model_uid or not model_uid.strip() or not server_url or not server_url.strip():
+            raise RuntimeError('model_uid is empty')
+
+        url = str(URL(server_url) / 'v1' / 'models' / model_uid)
 
         # this method is surrounded by a lock, and default requests may hang forever, so we just set a Adapter with max_retries=3
         session = Session()
@@ -66,7 +69,6 @@ class XinferenceHelper:
             response = session.get(url, timeout=10)
         except (MissingSchema, ConnectionError, Timeout) as e:
             raise RuntimeError(f'get xinference model extra parameter failed, url: {url}, error: {e}')
-
         if response.status_code != 200:
             raise RuntimeError(f'get xinference model extra parameter failed, status code: {response.status_code}, response: {response.text}')
         

api/core/rag/datasource/vdb/milvus/milvus_vector.py

@@ -140,6 +140,7 @@ class MilvusVector(BaseVector):
         connections.connect(alias=alias, uri=uri, user=self._client_config.user, password=self._client_config.password)
 
         from pymilvus import utility
+        if utility.has_collection(self._collection_name, using=alias):
             utility.drop_collection(self._collection_name, None, using=alias)
 
     def text_exists(self, id: str) -> bool:

api/core/rag/datasource/vdb/qdrant/qdrant_vector.py

@@ -231,6 +231,9 @@ class QdrantVector(BaseVector):
 
     def delete(self):
         from qdrant_client.http import models
+        from qdrant_client.http.exceptions import UnexpectedResponse
+        
+        try:
             filter = models.Filter(
                 must=[
                     models.FieldCondition(
@@ -245,7 +248,13 @@ class QdrantVector(BaseVector):
                     filter=filter
                 ),
             )
-
+        except UnexpectedResponse as e:
+            # Collection does not exist, so return
+            if e.status_code == 404:                
+                return
+            # Some other error occurred, so re-raise the exception
+            else:
+                raise e
     def delete_by_ids(self, ids: list[str]) -> None:
 
         from qdrant_client.http import models

api/core/rag/extractor/csv_extractor.py

@@ -3,6 +3,7 @@ import csv
 from typing import Optional
 
 from core.rag.extractor.extractor_base import BaseExtractor
+from core.rag.extractor.helpers import detect_file_encodings
 from core.rag.models.document import Document
 
 
@@ -36,7 +37,7 @@ class CSVExtractor(BaseExtractor):
                 docs = self._read_from_file(csvfile)
         except UnicodeDecodeError as e:
             if self._autodetect_encoding:
-                detected_encodings = detect_filze_encodings(self._file_path)
+                detected_encodings = detect_file_encodings(self._file_path)
                 for encoding in detected_encodings:
                     try:
                         with open(self._file_path, newline="", encoding=encoding.encoding) as csvfile:

api/core/tools/provider/_position.yaml

@@ -18,3 +18,4 @@
 - vectorizer
 - gaode
 - wecom
+- qrcode

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

@@ -16,7 +16,8 @@ class BingProvider(BuiltinToolProviderController):
                 user_id='',
                 tool_parameters={
                     "query": "test",
-                    "result_type": "link"
+                    "result_type": "link",
+                    "enable_webpages": True,
                 },
             )
         except Exception as e:

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

+<?xml version="1.0" encoding="utf-8"?>
+<!-- Uploaded to: SVG Repo, www.svgrepo.com, Generator: SVG Repo Mixer Tools -->
+<svg width="800px" height="800px" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg">
+    <g>
+        <path fill="none" d="M0 0h24v24H0z"/>
+        <path d="M16 17v-1h-3v-3h3v2h2v2h-1v2h-2v2h-2v-3h2v-1h1zm5 4h-4v-2h2v-2h2v4zM3 3h8v8H3V3zm2 2v4h4V5H5zm8-2h8v8h-8V3zm2 2v4h4V5h-4zM3 13h8v8H3v-8zm2 2v4h4v-4H5zm13-2h3v2h-3v-2zM6 6h2v2H6V6zm0 10h2v2H6v-2zM16 6h2v2h-2V6z"/>
+    </g>
+</svg>
\ No newline at end of file

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

+from typing import Any
+
+from core.tools.errors import ToolProviderCredentialValidationError
+from core.tools.provider.builtin.qrcode.tools.qrcode_generator import QRCodeGeneratorTool
+from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
+
+
+class QRCodeProvider(BuiltinToolProviderController):
+    def _validate_credentials(self, credentials: dict[str, Any]) -> None:
+        try:
+            QRCodeGeneratorTool().invoke(user_id='',
+                                         tool_parameters={
+                                            'content': 'Dify 123 😊'
+                                        })
+        except Exception as e:
+            raise ToolProviderCredentialValidationError(str(e))

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

+identity:
+  author: Bowen Liang
+  name: qrcode
+  label:
+    en_US: QRCode
+    zh_Hans: 二维码工具
+    pt_BR: QRCode
+  description:
+    en_US: A tool for generating QR code (quick-response code) image.
+    zh_Hans: 一个二维码工具
+    pt_BR: A tool for generating QR code (quick-response code) image.
+  icon: icon.svg

api/core/tools/provider/builtin/qrcode/tools/qrcode_generator.py

+import io
+import logging
+from typing import Any, Union
+
+import qrcode
+from qrcode.image.pure import PyPNGImage
+
+from core.tools.entities.tool_entities import ToolInvokeMessage
+from core.tools.tool.builtin_tool import BuiltinTool
+
+
+class QRCodeGeneratorTool(BuiltinTool):
+    def _invoke(self,
+                user_id: str,
+                tool_parameters: dict[str, Any],
+                ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        """
+            invoke tools
+        """
+        # get expression
+        content = tool_parameters.get('content', '')
+        if not content:
+            return self.create_text_message('Invalid parameter content')
+
+        try:
+            img = qrcode.make(data=content, image_factory=PyPNGImage)
+            byte_stream = io.BytesIO()
+            img.save(byte_stream)
+            byte_array = byte_stream.getvalue()
+            return self.create_blob_message(blob=byte_array,
+                                            meta={'mime_type': 'image/png'},
+                                            save_as=self.VARIABLE_KEY.IMAGE.value)
+        except Exception:
+            logging.exception(f'Failed to generate QR code for content: {content}')
+            return self.create_text_message('Failed to generate QR code')

api/core/tools/provider/builtin/qrcode/tools/qrcode_generator.yaml

+identity:
+  name: qrcode_generator
+  author: Bowen Liang
+  label:
+    en_US: QR Code Generator
+    zh_Hans: 二维码生成器
+    pt_BR: QR Code Generator
+description:
+  human:
+    en_US: A tool for generating QR code image
+    zh_Hans: 一个用于生成二维码的工具
+    pt_BR: A tool for generating QR code image
+  llm: A tool for generating QR code image
+parameters:
+  - name: content
+    type: string
+    required: true
+    label:
+      en_US: content text for QR code
+      zh_Hans: 二维码文本内容
+      pt_BR: content text for QR code
+    human_description:
+      en_US: content text for QR code
+      zh_Hans: 二维码文本内容
+      pt_BR: 二维码文本内容
+    form: llm

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

+from typing import Any
+
+from core.tools.errors import ToolProviderCredentialValidationError
+from core.tools.provider.builtin.tavily.tools.tavily_search import TavilySearchTool
+from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
+
+
+class TavilyProvider(BuiltinToolProviderController):
+    def _validate_credentials(self, credentials: dict[str, Any]) -> None:
+        try:
+            TavilySearchTool().fork_tool_runtime(
+                meta={
+                    "credentials": credentials,
+                }
+            ).invoke(
+                user_id='',
+                tool_parameters={
+                    "query": "Sachin Tendulkar",
+                },
+            )
+        except Exception as e:
+            raise ToolProviderCredentialValidationError(str(e))
\ No newline at end of file

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

+identity:
+  author: Yash Parmar
+  name: tavily
+  label:
+    en_US: Tavily
+    zh_Hans: Tavily
+    pt_BR: Tavily
+  description:
+    en_US: Tavily
+    zh_Hans: Tavily
+    pt_BR: Tavily
+  icon: icon.png
+credentials_for_provider:
+  tavily_api_key:
+    type: secret-input
+    required: true
+    label:
+      en_US: Tavily API key
+      zh_Hans: Tavily API key
+      pt_BR: Tavily API key
+    placeholder:
+      en_US: Please input your Tavily API key
+      zh_Hans: 请输入你的 Tavily API key
+      pt_BR: Please input your Tavily API key
+    help:
+      en_US: Get your Tavily API key from Tavily
+      zh_Hans: 从 TavilyApi 获取您的 Tavily API key
+      pt_BR: Get your Tavily API key from Tavily
+    url: https://docs.tavily.com/docs/tavily-api/introduction

api/core/tools/provider/builtin/tavily/tools/tavily_search.py

+from typing import Any, Optional
+
+import requests
+
+from core.tools.entities.tool_entities import ToolInvokeMessage
+from core.tools.tool.builtin_tool import BuiltinTool
+
+TAVILY_API_URL = "https://api.tavily.com"
+
+
+class TavilySearch:
+    """
+    A class for performing search operations using the Tavily Search API.
+
+    Args:
+        api_key (str): The API key for accessing the Tavily Search API.
+
+    Methods:
+        raw_results: Retrieves raw search results from the Tavily Search API.
+        results: Retrieves cleaned search results from the Tavily Search API.
+        clean_results: Cleans the raw search results.
+    """
+
+    def __init__(self, api_key: str) -> None:
+        self.api_key = api_key
+
+    def raw_results(
+        self,
+        query: str,
+        max_results: Optional[int] = 3,
+        search_depth: Optional[str] = "advanced",
+        include_domains: Optional[list[str]] = [],
+        exclude_domains: Optional[list[str]] = [],
+        include_answer: Optional[bool] = False,
+        include_raw_content: Optional[bool] = False,
+        include_images: Optional[bool] = False,
+    ) -> dict:
+        """
+        Retrieves raw search results from the Tavily Search API.
+
+        Args:
+            query (str): The search query.
+            max_results (int, optional): The maximum number of results to retrieve. Defaults to 3.
+            search_depth (str, optional): The search depth. Defaults to "advanced".
+            include_domains (List[str], optional): The domains to include in the search. Defaults to [].
+            exclude_domains (List[str], optional): The domains to exclude from the search. Defaults to [].
+            include_answer (bool, optional): Whether to include answer in the search results. Defaults to False.
+            include_raw_content (bool, optional): Whether to include raw content in the search results. Defaults to False.
+            include_images (bool, optional): Whether to include images in the search results. Defaults to False.
+
+        Returns:
+            dict: The raw search results.
+
+        """
+        params = {
+            "api_key": self.api_key,
+            "query": query,
+            "max_results": max_results,
+            "search_depth": search_depth,
+            "include_domains": include_domains,
+            "exclude_domains": exclude_domains,
+            "include_answer": include_answer,
+            "include_raw_content": include_raw_content,
+            "include_images": include_images,
+        }
+        response = requests.post(f"{TAVILY_API_URL}/search", json=params)
+        response.raise_for_status()
+        return response.json()
+
+    def results(
+        self,
+        query: str,
+        max_results: Optional[int] = 3,
+        search_depth: Optional[str] = "advanced",
+        include_domains: Optional[list[str]] = [],
+        exclude_domains: Optional[list[str]] = [],
+        include_answer: Optional[bool] = False,
+        include_raw_content: Optional[bool] = False,
+        include_images: Optional[bool] = False,
+    ) -> list[dict]:
+        """
+        Retrieves cleaned search results from the Tavily Search API.
+
+        Args:
+            query (str): The search query.
+            max_results (int, optional): The maximum number of results to retrieve. Defaults to 3.
+            search_depth (str, optional): The search depth. Defaults to "advanced".
+            include_domains (List[str], optional): The domains to include in the search. Defaults to [].
+            exclude_domains (List[str], optional): The domains to exclude from the search. Defaults to [].
+            include_answer (bool, optional): Whether to include answer in the search results. Defaults to False.
+            include_raw_content (bool, optional): Whether to include raw content in the search results. Defaults to False.
+            include_images (bool, optional): Whether to include images in the search results. Defaults to False.
+
+        Returns:
+            list: The cleaned search results.
+
+        """
+        raw_search_results = self.raw_results(
+            query,
+            max_results=max_results,
+            search_depth=search_depth,
+            include_domains=include_domains,
+            exclude_domains=exclude_domains,
+            include_answer=include_answer,
+            include_raw_content=include_raw_content,
+            include_images=include_images,
+        )
+        return self.clean_results(raw_search_results["results"])
+
+    def clean_results(self, results: list[dict]) -> list[dict]:
+        """
+        Cleans the raw search results.
+
+        Args:
+            results (list): The raw search results.
+
+        Returns:
+            list: The cleaned search results.
+
+        """
+        clean_results = []
+        for result in results:
+            clean_results.append(
+                {
+                    "url": result["url"],
+                    "content": result["content"],
+                }
+            )
+        # return clean results as a string
+        return "\n".join([f"{res['url']}\n{res['content']}" for res in clean_results])
+
+
+class TavilySearchTool(BuiltinTool):
+    """
+    A tool for searching Tavily using a given query.
+    """
+
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> ToolInvokeMessage | list[ToolInvokeMessage]:
+        """
+        Invokes the Tavily search tool with the given user ID and tool parameters.
+
+        Args:
+            user_id (str): The ID of the user invoking the tool.
+            tool_parameters (Dict[str, Any]): The parameters for the Tavily search tool.
+
+        Returns:
+            ToolInvokeMessage | list[ToolInvokeMessage]: The result of the Tavily search tool invocation.
+        """
+        query = tool_parameters.get("query", "")
+        api_key = self.runtime.credentials["tavily_api_key"]
+        if not query:
+            return self.create_text_message("Please input query")
+        tavily_search = TavilySearch(api_key)
+        results = tavily_search.results(query)
+        print(results)
+        if not results:
+            return self.create_text_message(f"No results found for '{query}' in Tavily")
+        else:
+            return self.create_text_message(text=results)

api/core/tools/provider/builtin/tavily/tools/tavily_search.yaml

+identity:
+  name: tavily_search
+  author: Yash Parmar
+  label:
+    en_US: TavilySearch
+    zh_Hans: TavilySearch
+    pt_BR: TavilySearch
+description:
+  human:
+    en_US: A tool for  search engine built specifically for AI agents (LLMs), delivering real-time, accurate, and factual results at speed.
+    zh_Hans: 专为人工智能代理 (LLM) 构建的搜索引擎工具，可快速提供实时、准确和真实的结果。
+    pt_BR: A tool for search engine built specifically for AI agents (LLMs), delivering real-time, accurate, and factual results at speed.
+  llm: A tool for search engine built specifically for AI agents (LLMs), delivering real-time, accurate, and factual results at speed.
+parameters:
+  - name: query
+    type: string
+    required: true
+    label:
+      en_US: Query string
+      zh_Hans: 查询语句
+      pt_BR: Query string
+    human_description:
+      en_US: used for searching
+      zh_Hans: 用于搜索网页内容
+      pt_BR: used for searching
+    llm_description: key words for searching
+    form: llm

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

+<svg width="2500" height="2500" viewBox="0 0 256 256" xmlns="http://www.w3.org/2000/svg" preserveAspectRatio="xMidYMid"><g fill="#CF272D"><path d="M127.86 222.304c-52.005 0-94.164-42.159-94.164-94.163 0-52.005 42.159-94.163 94.164-94.163 52.004 0 94.162 42.158 94.162 94.163 0 52.004-42.158 94.163-94.162 94.163zm0-222.023C57.245.281 0 57.527 0 128.141 0 198.756 57.245 256 127.86 256c70.614 0 127.859-57.244 127.859-127.859 0-70.614-57.245-127.86-127.86-127.86z"/><path d="M133.116 96.297c0-14.682 11.903-26.585 26.586-26.585 14.683 0 26.585 11.903 26.585 26.585 0 14.684-11.902 26.586-26.585 26.586-14.683 0-26.586-11.902-26.586-26.586M133.116 159.983c0-14.682 11.903-26.586 26.586-26.586 14.683 0 26.585 11.904 26.585 26.586 0 14.683-11.902 26.586-26.585 26.586-14.683 0-26.586-11.903-26.586-26.586M69.431 159.983c0-14.682 11.904-26.586 26.586-26.586 14.683 0 26.586 11.904 26.586 26.586 0 14.683-11.903 26.586-26.586 26.586-14.682 0-26.586-11.903-26.586-26.586M69.431 96.298c0-14.683 11.904-26.585 26.586-26.585 14.683 0 26.586 11.902 26.586 26.585 0 14.684-11.903 26.586-26.586 26.586-14.682 0-26.586-11.902-26.586-26.586"/></g></svg>
\ No newline at end of file

api/core/tools/provider/builtin/twilio/tools/send_message.py

+from typing import Any, Union
+
+from langchain.utilities import TwilioAPIWrapper
+
+from core.tools.entities.tool_entities import ToolInvokeMessage
+from core.tools.tool.builtin_tool import BuiltinTool
+
+
+class SendMessageTool(BuiltinTool):
+    """
+    A tool for sending messages using Twilio API.
+
+    Args:
+        user_id (str): The ID of the user invoking the tool.
+        tool_parameters (Dict[str, Any]): The parameters required for sending the message.
+
+    Returns:
+        Union[ToolInvokeMessage, List[ToolInvokeMessage]]: The result of invoking the tool, which includes the status of the message sending operation.
+    """
+
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        account_sid = self.runtime.credentials["account_sid"]
+        auth_token = self.runtime.credentials["auth_token"]
+        from_number = self.runtime.credentials["from_number"]
+
+        message = tool_parameters["message"]
+        to_number = tool_parameters["to_number"]
+
+        if to_number.startswith("whatsapp:"):
+            from_number = f"whatsapp: {from_number}"
+
+        twilio = TwilioAPIWrapper(
+            account_sid=account_sid, auth_token=auth_token, from_number=from_number
+        )
+
+        # Sending the message through Twilio
+        result = twilio.run(message, to_number)
+
+        return self.create_text_message(text="Message sent successfully.")

api/core/tools/provider/builtin/twilio/tools/send_message.yaml

+identity:
+  name: send_message
+  author: Yash Parmar
+  label:
+    en_US: SendMessage
+    zh_Hans: 发送消息
+    pt_BR: SendMessage
+description:
+  human:
+    en_US: Send SMS or Twilio Messaging Channels messages.
+    zh_Hans: 发送SMS或Twilio消息通道消息。
+    pt_BR: Send SMS or Twilio Messaging Channels messages.
+  llm: Send SMS or Twilio Messaging Channels messages. Supports different channels including WhatsApp.
+parameters:
+  - name: message
+    type: string
+    required: true
+    label:
+      en_US: Message
+      zh_Hans: 消息内容
+      pt_BR: Message
+    human_description:
+      en_US: The content of the message to be sent.
+      zh_Hans: 要发送的消息内容。
+      pt_BR: The content of the message to be sent.
+    llm_description: The content of the message to be sent.
+    form: llm
+  - name: to_number
+    type: string
+    required: true
+    label:
+      en_US: To Number
+      zh_Hans: 收信号码
+      pt_BR: Para Número
+    human_description:
+      en_US: The recipient's phone number. Prefix with 'whatsapp:' for WhatsApp messages, e.g., "whatsapp:+1234567890".
+      zh_Hans: 收件人的电话号码。WhatsApp消息前缀为'whatsapp:'，例如，"whatsapp:+1234567890"。
+      pt_BR: The recipient's phone number. Prefix with 'whatsapp:' for WhatsApp messages, e.g., "whatsapp:+1234567890".
+    llm_description: The recipient's phone number. Prefix with 'whatsapp:' for WhatsApp messages, e.g., "whatsapp:+1234567890".
+    form: llm

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

+from typing import Any
+
+from core.tools.errors import ToolProviderCredentialValidationError
+from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
+
+
+class TwilioProvider(BuiltinToolProviderController):
+    def _validate_credentials(self, credentials: dict[str, Any]) -> None:
+        try:
+            """
+            SendMessageTool().fork_tool_runtime(
+                meta={
+                    "credentials": credentials,
+                }
+            ).invoke(
+                user_id="",
+                tool_parameters={
+                    "message": "Credential validation message",
+                    "to_number": "+14846624384",
+                },
+            )
+            """
+            pass
+        except Exception as e:
+            raise ToolProviderCredentialValidationError(str(e))

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

+identity:
+  author: Yash Parmar
+  name: twilio
+  label:
+    en_US: Twilio
+    zh_Hans: Twilio
+    pt_BR: Twilio
+  description:
+    en_US: Send messages through SMS or Twilio Messaging Channels.
+    zh_Hans: 通过SMS或Twilio消息通道发送消息。
+    pt_BR: Send messages through SMS or Twilio Messaging Channels.
+  icon: icon.svg
+credentials_for_provider:
+  account_sid:
+    type: secret-input
+    required: true
+    label:
+      en_US: Account SID
+      zh_Hans: 账户SID
+      pt_BR: Account SID
+    placeholder:
+      en_US: Please input your Twilio Account SID
+      zh_Hans: 请输入您的Twilio账户SID
+      pt_BR: Please input your Twilio Account SID
+  auth_token:
+    type: secret-input
+    required: true
+    label:
+      en_US: Auth Token
+      zh_Hans: 认证令牌
+      pt_BR: Auth Token
+    placeholder:
+      en_US: Please input your Twilio Auth Token
+      zh_Hans: 请输入您的Twilio认证令牌
+      pt_BR: Please input your Twilio Auth Token
+  from_number:
+    type: secret-input
+    required: true
+    label:
+      en_US: From Number
+      zh_Hans: 发信号码
+      pt_BR: De Número
+    placeholder:
+      en_US: Please input your Twilio phone number
+      zh_Hans: 请输入您的Twilio电话号码
+      pt_BR: Please input your Twilio phone number

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

@@ -2,7 +2,7 @@ from core.tools.provider.builtin.wecom.tools.wecom_group_bot import WecomReposit
 from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
 
 
-class GaodeProvider(BuiltinToolProviderController):
+class WecomProvider(BuiltinToolProviderController):
     def _validate_credentials(self, credentials: dict) -> None:
         WecomRepositoriesTool()
         pass

api/core/tools/tool/api_tool.py

 import json
 from json import dumps
 from typing import Any, Union
+from urllib.parse import urlencode
 
 import httpx
 import requests
@@ -203,6 +204,8 @@ class ApiTool(Tool):
         if 'Content-Type' in headers:
             if headers['Content-Type'] == 'application/json':
                 body = dumps(body)
+            elif headers['Content-Type'] == 'application/x-www-form-urlencoded':
+                body = urlencode(body)
             else:
                 body = body
         

api/core/tools/tool/tool.py

@@ -174,7 +174,18 @@ class Tool(BaseModel, ABC):
 
         return result
 
-    def invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> list[ToolInvokeMessage]:
+    def invoke(self, user_id: str, tool_parameters: Union[dict[str, Any], str]) -> list[ToolInvokeMessage]:
+        # check if tool_parameters is a string
+        if isinstance(tool_parameters, str):
+            # check if this tool has only one parameter
+            parameters = [parameter for parameter in self.parameters if parameter.form == ToolParameter.ToolParameterForm.LLM]
+            if parameters and len(parameters) == 1:
+                tool_parameters = {
+                    parameters[0].name: tool_parameters
+                }
+            else:
+                raise ValueError(f"tool_parameters should be a dict, but got a string: {tool_parameters}")
+
         # update tool_parameters
         if self.runtime.runtime_parameters:
             tool_parameters.update(self.runtime.runtime_parameters)

api/extensions/ext_compress.py

+from flask import Flask
+
+
+def init_app(app: Flask):
+    if app.config.get('API_COMPRESSION_ENABLED', False):
+        from flask_compress import Compress
+
+        compress = Compress()
+        compress.init_app(app)
+

api/requirements.txt

@@ -3,6 +3,7 @@ beautifulsoup4==4.12.2
 flask~=3.0.1
 Flask-SQLAlchemy~=3.0.5
 SQLAlchemy~=1.4.28
+Flask-Compress~=1.14
 flask-login~=0.6.3
 flask-migrate~=4.0.5
 flask-restful~=0.3.10
@@ -35,7 +36,7 @@ docx2txt==0.8
 pypdfium2==4.16.0
 resend~=0.7.0
 pyjwt~=2.8.0
-anthropic~=0.7.7
+anthropic~=0.17.0
 newspaper3k==0.2.8
 google-api-python-client==2.90.0
 wikipedia==1.4.0
@@ -52,7 +53,7 @@ safetensors==0.3.2
 zhipuai==1.0.7
 werkzeug~=3.0.1
 pymilvus==2.3.0
-qdrant-client==1.6.4
+qdrant-client==1.7.3
 cohere~=4.44
 pyyaml~=6.0.1
 numpy~=1.25.2
@@ -68,3 +69,6 @@ gmpy2~=2.1.5
 numexpr~=2.9.0
 duckduckgo-search==4.4.3
 arxiv==2.1.0
+yarl~=1.9.4
+twilio==9.0.0
+qrcode~=7.4.2

api/services/tools_manage_service.py

@@ -536,12 +536,16 @@ class ToolManageService:
     
     @staticmethod
     def test_api_tool_preview(
-        tenant_id: str, tool_name: str, credentials: dict, parameters: dict, schema_type: str, schema: str
+        tenant_id: str, 
+        provider_name: str,
+        tool_name: str, 
+        credentials: dict, 
+        parameters: dict, 
+        schema_type: str, 
+        schema: str
     ):
         """
             test api tool before adding api tool provider
-
-            1. parse schema into tool bundle
         """
         if schema_type not in [member.value for member in ApiProviderSchemaType]:
             raise ValueError(f'invalid schema type {schema_type}')
@@ -556,6 +560,12 @@ class ToolManageService:
         if tool_bundle is None:
             raise ValueError(f'invalid tool name {tool_name}')
         
+        db_provider: ApiToolProvider = db.session.query(ApiToolProvider).filter(
+            ApiToolProvider.tenant_id == tenant_id,
+            ApiToolProvider.name == provider_name,
+        ).first()
+
+        if not db_provider:
             # create a fake db provider
             db_provider = ApiToolProvider(
                 tenant_id='', user_id='', name='', icon='',
@@ -577,6 +587,19 @@ class ToolManageService:
         # load tools into provider entity
         provider_controller.load_bundled_tools(tool_bundles)
 
+        # decrypt credentials
+        if db_provider.id:
+            tool_configuration = ToolConfiguration(
+                tenant_id=tenant_id, 
+                provider_controller=provider_controller
+            )
+            decrypted_credentials = tool_configuration.decrypt_tool_credentials(credentials)
+            # check if the credential has changed, save the original credential
+            masked_credentials = tool_configuration.mask_tool_credentials(decrypted_credentials)
+            for name, value in credentials.items():
+                if name in masked_credentials and value == masked_credentials[name]:
+                    credentials[name] = decrypted_credentials[name]
+
         try:
             provider_controller.validate_credentials_format(credentials)
             # get tool

api/tests/integration_tests/model_runtime/__mock/anthropic.py

 import os
 from time import sleep
-from typing import Any, Generator, List, Literal, Union
+from typing import Any, Literal, Union, Iterable
+
+from anthropic.resources import Messages
+from anthropic.types.message_delta_event import Delta
 
 import anthropic
 import pytest
 from _pytest.monkeypatch import MonkeyPatch
-from anthropic import Anthropic
-from anthropic._types import NOT_GIVEN, Body, Headers, NotGiven, Query
-from anthropic.resources.completions import Completions
-from anthropic.types import Completion, completion_create_params
+from anthropic import Anthropic, Stream
+from anthropic.types import MessageParam, Message, MessageStreamEvent, \
+    ContentBlock, MessageStartEvent, Usage, TextDelta, MessageDeltaEvent, MessageStopEvent, ContentBlockDeltaEvent, \
+    MessageDeltaUsage
 
 MOCK = os.getenv('MOCK_SWITCH', 'false') == 'true'
 
+
 class MockAnthropicClass(object):
     @staticmethod
-    def mocked_anthropic_chat_create_sync(model: str) -> Completion:
-        return Completion(
-            completion='hello, I\'m a chatbot from anthropic',
+    def mocked_anthropic_chat_create_sync(model: str) -> Message:
+        return Message(
+            id='msg-123',
+            type='message',
+            role='assistant',
+            content=[ContentBlock(text='hello, I\'m a chatbot from anthropic', type='text')],
             model=model,
-            stop_reason='stop_sequence'
+            stop_reason='stop_sequence',
+            usage=Usage(
+                input_tokens=1,
+                output_tokens=1
+            )
         )
 
     @staticmethod
-    def mocked_anthropic_chat_create_stream(model: str) -> Generator[Completion, None, None]:
+    def mocked_anthropic_chat_create_stream(model: str) -> Stream[MessageStreamEvent]:
         full_response_text = "hello, I'm a chatbot from anthropic"
 
-        for i in range(0, len(full_response_text) + 1):
-            sleep(0.1)
-            if i == len(full_response_text):
-                yield Completion(
-                    completion='',
+        yield MessageStartEvent(
+            type='message_start',
+            message=Message(
+                id='msg-123',
+                content=[],
+                role='assistant',
                 model=model,
+                stop_reason=None,
+                type='message',
+                usage=Usage(
+                    input_tokens=1,
+                    output_tokens=1
+                )
+            )
+        )
+
+        index = 0
+        for i in range(0, len(full_response_text)):
+            sleep(0.1)
+            yield ContentBlockDeltaEvent(
+                type='content_block_delta',
+                delta=TextDelta(text=full_response_text[i], type='text_delta'),
+                index=index
+            )
+
+            index += 1
+
+        yield MessageDeltaEvent(
+            type='message_delta',
+            delta=Delta(
                 stop_reason='stop_sequence'
+            ),
+            usage=MessageDeltaUsage(
+                output_tokens=1
             )
-            else:
-                yield Completion(
-                    completion=full_response_text[i],
-                    model=model,
-                    stop_reason=''
         )
 
-    def mocked_anthropic(self: Completions, *,
-        max_tokens_to_sample: int,
-        model: Union[str, Literal["claude-2.1", "claude-instant-1"]],
-        prompt: str,
+        yield MessageStopEvent(type='message_stop')
+
+    def mocked_anthropic(self: Messages, *,
+                         max_tokens: int,
+                         messages: Iterable[MessageParam],
+                         model: str,
                          stream: Literal[True],
                          **kwargs: Any
-    ) -> Union[Completion, Generator[Completion, None, None]]:
+                         ) -> Union[Message, Stream[MessageStreamEvent]]:
         if len(self._client.api_key) < 18:
             raise anthropic.AuthenticationError('Invalid API key')
 
@@ -55,10 +90,11 @@ class MockAnthropicClass(object):
         else:
             return MockAnthropicClass.mocked_anthropic_chat_create_sync(model=model)
 
+
 @pytest.fixture
 def setup_anthropic_mock(request, monkeypatch: MonkeyPatch):
     if MOCK:
-        monkeypatch.setattr(Completions, 'create', MockAnthropicClass.mocked_anthropic)
+        monkeypatch.setattr(Messages, 'create', MockAnthropicClass.mocked_anthropic)
 
     yield
 

api/tests/integration_tests/model_runtime/__mock/xinference.py

@@ -32,15 +32,17 @@ class MockXinferenceClass(object):
         response = Response()
         if 'v1/models/' in url:
             # get model uid
-            model_uid = url.split('/')[-1]
+            model_uid = url.split('/')[-1] or ''
             if not re.match(r'[a-f0-9]{8}-[a-f0-9]{4}-[a-f0-9]{4}-[a-f0-9]{4}-[a-f0-9]{12}', model_uid) and \
                 model_uid not in ['generate', 'chat', 'embedding', 'rerank']:
                 response.status_code = 404
+                response._content = b'{}'
                 return response
 
             # check if url is valid
             if not re.match(r'^(https?):\/\/[^\s\/$.?#].[^\s]*$', url):
                 response.status_code = 404
+                response._content = b'{}'
                 return response
             
             if model_uid in ['generate', 'chat']:
@@ -86,14 +88,14 @@ class MockXinferenceClass(object):
                     ],
                     "revision": null,
                     "max_tokens": 512
-}'''
+                }'''
                 return response
             
         elif 'v1/cluster/auth' in url:
             response.status_code = 200
             response._content = b'''{
                 "auth": true
-}'''
+            }'''
             return response
         
     def _check_cluster_authenticated(self):

api/tests/integration_tests/model_runtime/anthropic/test_llm.py

@@ -15,14 +15,14 @@ def test_validate_credentials(setup_anthropic_mock):
 
     with pytest.raises(CredentialsValidateFailedError):
         model.validate_credentials(
-            model='claude-instant-1',
+            model='claude-instant-1.2',
             credentials={
                 'anthropic_api_key': 'invalid_key'
             }
         )
 
     model.validate_credentials(
-        model='claude-instant-1',
+        model='claude-instant-1.2',
         credentials={
             'anthropic_api_key': os.environ.get('ANTHROPIC_API_KEY')
         }
@@ -33,7 +33,7 @@ def test_invoke_model(setup_anthropic_mock):
     model = AnthropicLargeLanguageModel()
 
     response = model.invoke(
-        model='claude-instant-1',
+        model='claude-instant-1.2',
         credentials={
             'anthropic_api_key': os.environ.get('ANTHROPIC_API_KEY'),
             'anthropic_api_url': os.environ.get('ANTHROPIC_API_URL')
@@ -49,7 +49,7 @@ def test_invoke_model(setup_anthropic_mock):
         model_parameters={
             'temperature': 0.0,
             'top_p': 1.0,
-            'max_tokens_to_sample': 10
+            'max_tokens': 10
         },
         stop=['How'],
         stream=False,
@@ -64,7 +64,7 @@ def test_invoke_stream_model(setup_anthropic_mock):
     model = AnthropicLargeLanguageModel()
 
     response = model.invoke(
-        model='claude-instant-1',
+        model='claude-instant-1.2',
         credentials={
             'anthropic_api_key': os.environ.get('ANTHROPIC_API_KEY')
         },
@@ -78,7 +78,7 @@ def test_invoke_stream_model(setup_anthropic_mock):
         ],
         model_parameters={
             'temperature': 0.0,
-            'max_tokens_to_sample': 100
+            'max_tokens': 100
         },
         stream=True,
         user="abc-123"
@@ -97,7 +97,7 @@ def test_get_num_tokens():
     model = AnthropicLargeLanguageModel()
 
     num_tokens = model.get_num_tokens(
-        model='claude-instant-1',
+        model='claude-instant-1.2',
         credentials={
             'anthropic_api_key': os.environ.get('ANTHROPIC_API_KEY')
         },

docker/docker-compose.yaml

@@ -2,7 +2,7 @@ version: '3.1'
 services:
   # API service
   api:
-    image: langgenius/dify-api:0.5.7
+    image: langgenius/dify-api:0.5.8
     restart: always
     environment:
       # Startup mode, 'api' starts the API server.
@@ -135,7 +135,7 @@ services:
   # worker service
   # The Celery worker for processing the queue.
   worker:
-    image: langgenius/dify-api:0.5.7
+    image: langgenius/dify-api:0.5.8
     restart: always
     environment:
       # Startup mode, 'worker' starts the Celery worker for processing the queue.
@@ -206,7 +206,7 @@ services:
 
   # Frontend web application.
   web:
-    image: langgenius/dify-web:0.5.7
+    image: langgenius/dify-web:0.5.8
     restart: always
     environment:
       EDITION: SELF_HOSTED

web/app/components/app/configuration/config/automatic/get-automatic-res.tsx

@@ -20,7 +20,7 @@ import useBreakpoints, { MediaType } from '@/hooks/use-breakpoints'
 
 const noDataIcon = (
   <svg width="56" height="56" viewBox="0 0 56 56" fill="none" xmlns="http://www.w3.org/2000/svg">
-    <path d="M10.4998 51.3333V39.6666M10.4998 16.3333V4.66663M4.6665 10.5H16.3332M4.6665 45.5H16.3332M30.3332 6.99996L26.2868 17.5206C25.6287 19.2315 25.2997 20.0869 24.7881 20.8065C24.3346 21.4442 23.7774 22.0014 23.1397 22.4549C22.4202 22.9665 21.5647 23.2955 19.8538 23.9535L9.33317 28L19.8539 32.0464C21.5647 32.7044 22.4202 33.0334 23.1397 33.5451C23.7774 33.9985 24.3346 34.5557 24.7881 35.1934C25.2997 35.913 25.6287 36.7684 26.2868 38.4793L30.3332 49L34.3796 38.4793C35.0376 36.7684 35.3666 35.913 35.8783 35.1934C36.3317 34.5557 36.8889 33.9985 37.5266 33.5451C38.2462 33.0334 39.1016 32.7044 40.8125 32.0464L51.3332 28L40.8125 23.9535C39.1016 23.2955 38.2462 22.9665 37.5266 22.4549C36.8889 22.0014 36.3317 21.4442 35.8783 20.8065C35.3666 20.0869 35.0376 19.2315 34.3796 17.5206L30.3332 6.99996Z" stroke="#EAECF0" strokeWidth="3" strokeLinecap="round" strokeLinejoin="round"/>
+    <path d="M10.4998 51.3333V39.6666M10.4998 16.3333V4.66663M4.6665 10.5H16.3332M4.6665 45.5H16.3332M30.3332 6.99996L26.2868 17.5206C25.6287 19.2315 25.2997 20.0869 24.7881 20.8065C24.3346 21.4442 23.7774 22.0014 23.1397 22.4549C22.4202 22.9665 21.5647 23.2955 19.8538 23.9535L9.33317 28L19.8539 32.0464C21.5647 32.7044 22.4202 33.0334 23.1397 33.5451C23.7774 33.9985 24.3346 34.5557 24.7881 35.1934C25.2997 35.913 25.6287 36.7684 26.2868 38.4793L30.3332 49L34.3796 38.4793C35.0376 36.7684 35.3666 35.913 35.8783 35.1934C36.3317 34.5557 36.8889 33.9985 37.5266 33.5451C38.2462 33.0334 39.1016 32.7044 40.8125 32.0464L51.3332 28L40.8125 23.9535C39.1016 23.2955 38.2462 22.9665 37.5266 22.4549C36.8889 22.0014 36.3317 21.4442 35.8783 20.8065C35.3666 20.0869 35.0376 19.2315 34.3796 17.5206L30.3332 6.99996Z" stroke="#EAECF0" strokeWidth="3" strokeLinecap="round" strokeLinejoin="round" />
   </svg>
 )
 
@@ -33,9 +33,9 @@ export type IGetAutomaticResProps = {
 
 const genIcon = (
   <svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg">
-    <path d="M3.6665 1.33332C3.6665 0.965133 3.36803 0.666656 2.99984 0.666656C2.63165 0.666656 2.33317 0.965133 2.33317 1.33332V2.33332H1.33317C0.964981 2.33332 0.666504 2.6318 0.666504 2.99999C0.666504 3.36818 0.964981 3.66666 1.33317 3.66666H2.33317V4.66666C2.33317 5.03485 2.63165 5.33332 2.99984 5.33332C3.36803 5.33332 3.6665 5.03485 3.6665 4.66666V3.66666H4.6665C5.03469 3.66666 5.33317 3.36818 5.33317 2.99999C5.33317 2.6318 5.03469 2.33332 4.6665 2.33332H3.6665V1.33332Z" fill="white"/>
-    <path d="M3.6665 11.3333C3.6665 10.9651 3.36803 10.6667 2.99984 10.6667C2.63165 10.6667 2.33317 10.9651 2.33317 11.3333V12.3333H1.33317C0.964981 12.3333 0.666504 12.6318 0.666504 13C0.666504 13.3682 0.964981 13.6667 1.33317 13.6667H2.33317V14.6667C2.33317 15.0348 2.63165 15.3333 2.99984 15.3333C3.36803 15.3333 3.6665 15.0348 3.6665 14.6667V13.6667H4.6665C5.03469 13.6667 5.33317 13.3682 5.33317 13C5.33317 12.6318 5.03469 12.3333 4.6665 12.3333H3.6665V11.3333Z" fill="white"/>
-    <path d="M9.28873 1.76067C9.18971 1.50321 8.94235 1.33332 8.6665 1.33332C8.39066 1.33332 8.1433 1.50321 8.04427 1.76067L6.88815 4.76658C6.68789 5.28727 6.62495 5.43732 6.53887 5.55838C6.4525 5.67986 6.34637 5.78599 6.2249 5.87236C6.10384 5.95844 5.95379 6.02137 5.43309 6.22164L2.42718 7.37776C2.16972 7.47678 1.99984 7.72414 1.99984 7.99999C1.99984 8.27584 2.16972 8.5232 2.42718 8.62222L5.43309 9.77834C5.95379 9.97861 6.10384 10.0415 6.2249 10.1276C6.34637 10.214 6.4525 10.3201 6.53887 10.4416C6.62495 10.5627 6.68789 10.7127 6.88816 11.2334L8.04427 14.2393C8.1433 14.4968 8.39066 14.6667 8.6665 14.6667C8.94235 14.6667 9.18971 14.4968 9.28873 14.2393L10.4449 11.2334C10.6451 10.7127 10.7081 10.5627 10.7941 10.4416C10.8805 10.3201 10.9866 10.214 11.1081 10.1276C11.2292 10.0415 11.3792 9.97861 11.8999 9.77834L14.9058 8.62222C15.1633 8.5232 15.3332 8.27584 15.3332 7.99999C15.3332 7.72414 15.1633 7.47678 14.9058 7.37776L11.8999 6.22164C11.3792 6.02137 11.2292 5.95844 11.1081 5.87236C10.9866 5.78599 10.8805 5.67986 10.7941 5.55838C10.7081 5.43732 10.6451 5.28727 10.4449 4.76658L9.28873 1.76067Z" fill="white"/>
+    <path d="M3.6665 1.33332C3.6665 0.965133 3.36803 0.666656 2.99984 0.666656C2.63165 0.666656 2.33317 0.965133 2.33317 1.33332V2.33332H1.33317C0.964981 2.33332 0.666504 2.6318 0.666504 2.99999C0.666504 3.36818 0.964981 3.66666 1.33317 3.66666H2.33317V4.66666C2.33317 5.03485 2.63165 5.33332 2.99984 5.33332C3.36803 5.33332 3.6665 5.03485 3.6665 4.66666V3.66666H4.6665C5.03469 3.66666 5.33317 3.36818 5.33317 2.99999C5.33317 2.6318 5.03469 2.33332 4.6665 2.33332H3.6665V1.33332Z" fill="white" />
+    <path d="M3.6665 11.3333C3.6665 10.9651 3.36803 10.6667 2.99984 10.6667C2.63165 10.6667 2.33317 10.9651 2.33317 11.3333V12.3333H1.33317C0.964981 12.3333 0.666504 12.6318 0.666504 13C0.666504 13.3682 0.964981 13.6667 1.33317 13.6667H2.33317V14.6667C2.33317 15.0348 2.63165 15.3333 2.99984 15.3333C3.36803 15.3333 3.6665 15.0348 3.6665 14.6667V13.6667H4.6665C5.03469 13.6667 5.33317 13.3682 5.33317 13C5.33317 12.6318 5.03469 12.3333 4.6665 12.3333H3.6665V11.3333Z" fill="white" />
+    <path d="M9.28873 1.76067C9.18971 1.50321 8.94235 1.33332 8.6665 1.33332C8.39066 1.33332 8.1433 1.50321 8.04427 1.76067L6.88815 4.76658C6.68789 5.28727 6.62495 5.43732 6.53887 5.55838C6.4525 5.67986 6.34637 5.78599 6.2249 5.87236C6.10384 5.95844 5.95379 6.02137 5.43309 6.22164L2.42718 7.37776C2.16972 7.47678 1.99984 7.72414 1.99984 7.99999C1.99984 8.27584 2.16972 8.5232 2.42718 8.62222L5.43309 9.77834C5.95379 9.97861 6.10384 10.0415 6.2249 10.1276C6.34637 10.214 6.4525 10.3201 6.53887 10.4416C6.62495 10.5627 6.68789 10.7127 6.88816 11.2334L8.04427 14.2393C8.1433 14.4968 8.39066 14.6667 8.6665 14.6667C8.94235 14.6667 9.18971 14.4968 9.28873 14.2393L10.4449 11.2334C10.6451 10.7127 10.7081 10.5627 10.7941 10.4416C10.8805 10.3201 10.9866 10.214 11.1081 10.1276C11.2292 10.0415 11.3792 9.97861 11.8999 9.77834L14.9058 8.62222C15.1633 8.5232 15.3332 8.27584 15.3332 7.99999C15.3332 7.72414 15.1633 7.47678 14.9058 7.37776L11.8999 6.22164C11.3792 6.02137 11.2292 5.95844 11.1081 5.87236C10.9866 5.78599 10.8805 5.67986 10.7941 5.55838C10.7081 5.43732 10.6451 5.28727 10.4449 4.76658L9.28873 1.76067Z" fill="white" />
   </svg>
 )
 
@@ -74,14 +74,14 @@ const GetAutomaticRes: FC<IGetAutomaticResProps> = ({
   const [res, setRes] = React.useState<AutomaticRes | null>(null)
 
   const renderLoading = (
-    <div className='grow flex flex-col items-center justify-center h-full space-y-3'>
+    <div className='w-0 grow flex flex-col items-center justify-center h-full space-y-3'>
       <Loading />
       <div className='text-[13px] text-gray-400'>{t('appDebug.automatic.loading')}</div>
     </div>
   )
 
   const renderNoData = (
-    <div className='grow flex flex-col items-center justify-center h-full space-y-3'>
+    <div className='w-0 grow flex flex-col items-center px-8 justify-center h-full space-y-3'>
       {noDataIcon}
       <div className='text-[13px] text-gray-400'>{t('appDebug.automatic.noData')}</div>
     </div>
@@ -142,7 +142,7 @@ const GetAutomaticRes: FC<IGetAutomaticResProps> = ({
             <div className='text-[13px] font-normal text-gray-500'>{t('appDebug.automatic.description')}</div>
           </div>
           {/* inputs */}
-          <div className='mt-12 space-y-5'>
+          <div className='mt-2 space-y-5'>
             <div className='space-y-2'>
               <div className='text-[13px] font-medium text-gray-900'>{t('appDebug.automatic.intendedAudience')}</div>
               <input className="w-full h-8 px-3 text-[13px] font-normal bg-gray-50 rounded-lg" placeholder={t('appDebug.automatic.intendedAudiencePlaceHolder') as string} value={audiences} onChange={e => setAudiences(e.target.value)} />
@@ -167,8 +167,8 @@ const GetAutomaticRes: FC<IGetAutomaticResProps> = ({
         </div>}
 
         {(!isLoading && res) && (
-          <div className='grow px-8 pt-6 h-full overflow-y-auto'>
-            <div className='mb-4 w-1/2 text-lg font-medium text-gray-900'>{t('appDebug.automatic.resTitle')}</div>
+          <div className='w-0 grow px-8 pt-6 h-full overflow-y-auto'>
+            <div className='mb-4 text-lg font-medium text-gray-900'>{t('appDebug.automatic.resTitle')}</div>
 
             <ConfigPrompt
               mode={mode}
@@ -196,7 +196,7 @@ const GetAutomaticRes: FC<IGetAutomaticResProps> = ({
               </div>
             )}
 
-            <div className='sticky bottom-0 flex justify-end right-0 py-4'>
+            <div className='sticky bottom-0 flex justify-end right-0 py-4 bg-white'>
               <Button onClick={onClose}>{t('common.operation.cancel')}</Button>
               <Button type='primary' className='ml-2' onClick={() => {
                 setShowConfirmOverwrite(true)

web/app/components/app/configuration/features/chat-group/text-to-speech/index.tsx

@@ -40,6 +40,7 @@ const TextToSpeech: FC = () => {
           { languageInfo?.example && (
             <AudioBtn
               value={languageInfo?.example}
+              voice={voiceItem?.value}
               isAudition={true}
             />
           )}

web/app/components/base/audio-btn/index.tsx

@@ -9,12 +9,14 @@ import { textToAudio } from '@/service/share'
 
 type AudioBtnProps = {
   value: string
+  voice?: string
   className?: string
   isAudition?: boolean
 }
 
 const AudioBtn = ({
   value,
+  voice,
   className,
   isAudition,
 }: AudioBtnProps) => {
@@ -27,13 +29,16 @@ const AudioBtn = ({
   const pathname = usePathname()
   const removeCodeBlocks = (inputText: any) => {
     const codeBlockRegex = /```[\s\S]*?```/g
+    if (inputText)
       return inputText.replace(codeBlockRegex, '')
+    return ''
   }
 
   const playAudio = async () => {
     const formData = new FormData()
     if (value !== '') {
       formData.append('text', removeCodeBlocks(value))
+      formData.append('voice', removeCodeBlocks(voice))
 
       let url = ''
       let isPublic = false
@@ -56,13 +61,14 @@ const AudioBtn = ({
         const audioUrl = URL.createObjectURL(blob)
         const audio = new Audio(audioUrl)
         audioRef.current = audio
-        audio.play().then(() => {
-          setIsPlaying(true)
-        }).catch(() => {
+        audio.play().then(() => {}).catch(() => {
           setIsPlaying(false)
           URL.revokeObjectURL(audioUrl)
         })
-        audio.onended = () => setHasEnded(true)
+        audio.onended = () => {
+          setHasEnded(true)
+          setIsPlaying(false)
+        }
       }
       catch (error) {
         setIsPlaying(false)
@@ -70,23 +76,33 @@ const AudioBtn = ({
       }
     }
   }
-
   const togglePlayPause = () => {
     if (audioRef.current) {
       if (isPlaying) {
+        if (!hasEnded) {
+          setPause(false)
+          audioRef.current.play()
+        }
+        if (!isPause) {
           setPause(true)
           audioRef.current.pause()
         }
-      else if (!hasEnded) {
+      }
+      else if (!isPlaying) {
+        if (isPause) {
           setPause(false)
           audioRef.current.play()
         }
-      else if (!isPlaying) {
+        else {
+          setHasEnded(false)
           playAudio().then()
         }
+      }
       setIsPlaying(prevIsPlaying => !prevIsPlaying)
     }
     else {
+      setIsPlaying(true)
+      if (!isPlaying)
         playAudio().then()
     }
   }
@@ -102,7 +118,7 @@ const AudioBtn = ({
           className={`box-border p-0.5 flex items-center justify-center cursor-pointer ${isAudition || 'rounded-md bg-white'}`}
           style={{ boxShadow: !isAudition ? '0px 4px 8px -2px rgba(16, 24, 40, 0.1), 0px 2px 4px -2px rgba(16, 24, 40, 0.06)' : '' }}
           onClick={togglePlayPause}>
-          <div className={`w-6 h-6 rounded-md ${!isAudition ? 'hover:bg-gray-200' : 'hover:bg-gray-50'} ${!isPause ? ((isPlaying && !hasEnded) ? s.playIcon : s.stopIcon) : s.pauseIcon}`}></div>
+          <div className={`w-6 h-6 rounded-md ${!isAudition ? 'hover:bg-gray-200' : 'hover:bg-gray-50'} ${(isPlaying && !hasEnded) ? s.pauseIcon : s.playIcon}`}></div>
         </div>
       </Tooltip>
     </div>

web/app/components/base/audio-btn/style.module.css

@@ -8,9 +8,3 @@
   background-position: center;
   background-repeat: no-repeat;
 }
-
-.stopIcon {
-  background-position: center;
-  background-repeat: no-repeat;
-  background-image: url(~@/app/components/develop/secret-key/assets/stop.svg);
-}
\ No newline at end of file

web/app/components/base/chat/chat/answer/operation.tsx

@@ -77,6 +77,7 @@ const Operation: FC<OperationProps> = ({
       {(!isOpeningStatement && config?.text_to_speech?.enabled) && (
         <AudioBtn
           value={content}
+          voice={config?.text_to_speech?.voice}
           className='hidden group-hover:block'
         />
       )}

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

@@ -9,6 +9,7 @@ import {
 } from 'react'
 import { useTranslation } from 'react-i18next'
 import { useThrottleEffect } from 'ahooks'
+import { debounce } from 'lodash-es'
 import type {
   ChatConfig,
   ChatItem,
@@ -81,16 +82,24 @@ const Chat: FC<ChatProps> = ({
       chatContainerRef.current.scrollTop = chatContainerRef.current.scrollHeight
   }
 
-  useThrottleEffect(() => {
-    handleScrolltoBottom()
-
+  const handleWindowResize = () => {
     if (chatContainerRef.current && chatFooterRef.current)
       chatFooterRef.current.style.width = `${chatContainerRef.current.clientWidth}px`
 
     if (chatContainerInnerRef.current && chatFooterInnerRef.current)
       chatFooterInnerRef.current.style.width = `${chatContainerInnerRef.current.clientWidth}px`
+  }
+
+  useThrottleEffect(() => {
+    handleScrolltoBottom()
+    handleWindowResize()
   }, [chatList], { wait: 500 })
 
+  useEffect(() => {
+    window.addEventListener('resize', debounce(handleWindowResize))
+    return () => window.removeEventListener('resize', handleWindowResize)
+  }, [])
+
   useEffect(() => {
     if (chatFooterRef.current && chatContainerRef.current) {
       const resizeObserver = new ResizeObserver((entries) => {

web/app/components/datasets/create/file-uploader/index.tsx

@@ -13,6 +13,7 @@ import { fetchFileUploadConfig } from '@/service/common'
 import { fetchSupportFileTypes } from '@/service/datasets'
 import I18n from '@/context/i18n'
 import { LanguagesSupported } from '@/i18n/language'
+import { IS_CE_EDITION } from '@/config'
 
 const FILES_NUMBER_LIMIT = 20
 
@@ -180,7 +181,7 @@ const FileUploader = ({
     if (!files.length)
       return false
 
-    if (files.length + fileList.length > FILES_NUMBER_LIMIT) {
+    if (files.length + fileList.length > FILES_NUMBER_LIMIT && !IS_CE_EDITION) {
       notify({ type: 'error', message: t('datasetCreation.stepOne.uploader.validation.filesNumber', { filesNumber: FILES_NUMBER_LIMIT }) })
       return false
     }

web/app/components/develop/secret-key/assets/play.svg

 <svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg">
 <g clip-path="url(#clip0_129_107)">
-<path d="M7.99991 14.6666C11.6819 14.6666 14.6666 11.6819 14.6666 7.99998C14.6666 4.31808 11.6819 1.33331 7.99998 1.33331C4.31808 1.33331 1.33331 4.31808 1.33331 7.99998C1.33331 11.6819 4.31808 14.6666 7.99998 14.6666Z" stroke="#155EEF" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/>
-<path d="M6.66665 5.33331L10.6666 7.99998L6.66665 10.6666V5.33331Z" stroke="#155EEF" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/>
+<path d="M7.99998 14.6666C11.6819 14.6666 14.6666 11.6819 14.6666 7.99998C14.6666 4.31808 11.6819 1.33331 7.99998 1.33331C4.31808 1.33331 1.33331 4.31808 1.33331 7.99998C1.33331 11.6819 4.31808 14.6666 7.99998 14.6666Z" stroke="#667085" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/>
+<path d="M6.66665 5.33331L10.6666 7.99998L6.66665 10.6666V5.33331Z" stroke="#667085" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/>
 </g>
 <defs>
 <clipPath id="clip0_129_107">

web/app/components/develop/secret-key/assets/stop.svg

-<svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://www.w3.org/2000/svg">
-<g clip-path="url(#clip0_129_107)">
-<path d="M7.99998 14.6666C11.6819 14.6666 14.6666 11.6819 14.6666 7.99998C14.6666 4.31808 11.6819 1.33331 7.99998 1.33331C4.31808 1.33331 1.33331 4.31808 1.33331 7.99998C1.33331 11.6819 4.31808 14.6666 7.99998 14.6666Z" stroke="#667085" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/>
-<path d="M6.66665 5.33331L10.6666 7.99998L6.66665 10.6666V5.33331Z" stroke="#667085" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round"/>
-</g>
-<defs>
-<clipPath id="clip0_129_107">
-<rect width="16" height="16" fill="white"/>
-</clipPath>
-</defs>
-</svg>

web/app/components/develop/template/template_chat.en.mdx

@@ -71,8 +71,8 @@ Chat applications support session persistence, allowing previous chat history to
           - `upload_file_id` (string) Uploaded file ID, which must be obtained by uploading through the File Upload API in advance (when the transfer method is `local_file`)
       </Property>
       <Property name='auto_generate_name' type='bool' key='auto_generate_name'>
-      Auto-generate title, default is `false`.
-      Can achieve async title generation by calling the conversation rename API and setting `auto_generate` to true.
+      Auto-generate title, default is `true`.
+      If set to `false`, can achieve async title generation by calling the conversation rename API and setting `auto_generate` to `true`.
       </Property>
     </Properties>
 

web/app/components/develop/template/template_chat.zh.mdx

@@ -71,7 +71,7 @@ import { Row, Col, Properties, Property, Heading, SubProperty } from '../md.tsx'
           - `upload_file_id` 上传文件 ID。（仅当传递方式为 `local_file `时）。
       </Property>
       <Property name='auto_generate_name' type='bool' key='auto_generate_name'>
-      （选填）自动生成标题，默认 `false`。 可通过调用会话重命名接口并设置 `auto_generate` 为 `true` 实现异步生成标题。
+      （选填）自动生成标题，默认 `true`。 若设置为 `false`，则可通过调用会话重命名接口并设置 `auto_generate` 为 `true` 实现异步生成标题。
       </Property>
     </Properties>
 

web/app/components/tools/edit-custom-collection-modal/index.tsx

@@ -2,10 +2,9 @@
 import type { FC } from 'react'
 import React, { useEffect, useState } from 'react'
 import { useTranslation } from 'react-i18next'
-import produce from 'immer'
 import { useDebounce, useGetState } from 'ahooks'
-import { clone } from 'lodash-es'
 import cn from 'classnames'
+import produce from 'immer'
 import { LinkExternal02, Settings01 } from '../../base/icons/src/vender/line/general'
 import type { Credential, CustomCollectionBackend, CustomParamSchema, Emoji } from '../types'
 import { AuthHeaderPrefix, AuthType } from '../types'
@@ -116,14 +115,16 @@ const EditCustomCollectionModal: FC<Props> = ({
   const [isShowTestApi, setIsShowTestApi] = useState(false)
 
   const handleSave = () => {
-    const postData = clone(customCollection)
-    delete postData.tools
+    // const postData = clone(customCollection)
+    const postData = produce(customCollection, (draft) => {
+      delete draft.tools
 
-    if (postData.credentials.auth_type === AuthType.none) {
-      delete postData.credentials.api_key_header
-      delete postData.credentials.api_key_header_prefix
-      delete postData.credentials.api_key_value
+      if (draft.credentials.auth_type === AuthType.none) {
+        delete draft.credentials.api_key_header
+        delete draft.credentials.api_key_header_prefix
+        delete draft.credentials.api_key_value
       }
+    })
 
     if (isAdd) {
       onAdd?.(postData)

web/app/components/tools/edit-custom-collection-modal/test-api.tsx

@@ -42,6 +42,7 @@ const TestApi: FC<Props> = ({
       delete credentials.api_key_value
     }
     const data = {
+      provider_name: customCollection.provider,
       tool_name: toolName,
       credentials,
       schema_type: customCollection.schema_type,

web/package.json

 {
   "name": "dify-web",
-  "version": "0.5.7",
+  "version": "0.5.8",
   "private": true,
   "scripts": {
     "dev": "next dev",