feat: implement NotImplementedError for token counting in LLMs and reintroduce disabled token count method

Signed-off-by: -LAN- <[email protected]>

.gitignore

@@ -175,6 +175,7 @@ docker/volumes/pgvector/data/*
 docker/volumes/pgvecto_rs/data/*
 docker/volumes/couchbase/*
 docker/volumes/oceanbase/*
+docker/volumes/plugin_daemon/*
 !docker/volumes/oceanbase/init.d
 
 docker/nginx/conf.d/default.conf

.markdownlint.json

@@ -1,3 +0,0 @@
-{
-    "MD024": false
-}

CHANGELOG.md

@@ -1,32 +0,0 @@
-# Changelog
-
-All notable changes to Dify will be documented in this file.
-
-The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
-and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
-
-## [0.15.7] - 2025-04-27
-
-### Added
-
-- Added support for GPT-4.1 in model providers (#18912)
-- Added support for Amazon Bedrock DeepSeek-R1 model (#18908)
-- Added support for Amazon Bedrock Claude Sonnet 3.7 model (#18788)
-- Refined version compatibility logic in app DSL service
-
-### Fixed
-
-- Fixed issue with creating apps from template categories (#18807, #18868)
-- Fixed DSL version check when creating apps from explore templates (#18872, #18878)
-
-## [0.15.6] - 2025-04-22
-
-### Security
-
-- Fixed clickjacking vulnerability (#18552)
-- Fixed reset password security issue (#18366)
-- Updated reset password token when email code verification succeeds (#18362)
-
-### Fixed
-
-- Fixed Vertex AI Gemini 2.0 Flash 001 schema (#18405)

api/.env.example

@@ -430,7 +430,4 @@ CREATE_TIDB_SERVICE_JOB_ENABLED=false
 # Maximum number of submitted thread count in a ThreadPool for parallel node execution
 MAX_SUBMIT_COUNT=100
 # Lockout duration in seconds
-LOGIN_LOCKOUT_DURATION=86400
-
-# Prevent Clickjacking
-ALLOW_EMBED=false
+LOGIN_LOCKOUT_DURATION=86400

api/configs/packaging/__init__.py

@@ -9,7 +9,7 @@ class PackagingInfo(BaseSettings):
 
     CURRENT_VERSION: str = Field(
         description="Dify version",
-        default="0.15.7",
+        default="0.15.6-alpha.1",
     )
 
     COMMIT_SHA: str = Field(

api/controllers/console/auth/forgot_password.py

@@ -8,7 +8,7 @@ from constants.languages import languages
 from controllers.console import api
 from controllers.console.auth.error import EmailCodeError, InvalidEmailError, InvalidTokenError, PasswordMismatchError
 from controllers.console.error import AccountInFreezeError, AccountNotFound, EmailSendIpLimitError
-from controllers.console.wraps import email_password_login_enabled, setup_required
+from controllers.console.wraps import setup_required
 from events.tenant_event import tenant_was_created
 from extensions.ext_database import db
 from libs.helper import email, extract_remote_ip
@@ -22,7 +22,6 @@ from services.feature_service import FeatureService
 
 class ForgotPasswordSendEmailApi(Resource):
     @setup_required
-    @email_password_login_enabled
     def post(self):
         parser = reqparse.RequestParser()
         parser.add_argument("email", type=email, required=True, location="json")
@@ -54,7 +53,6 @@ class ForgotPasswordSendEmailApi(Resource):
 
 class ForgotPasswordCheckApi(Resource):
     @setup_required
-    @email_password_login_enabled
     def post(self):
         parser = reqparse.RequestParser()
         parser.add_argument("email", type=str, required=True, location="json")
@@ -74,20 +72,11 @@ class ForgotPasswordCheckApi(Resource):
         if args["code"] != token_data.get("code"):
             raise EmailCodeError()
 
-        # Verified, revoke the first token
-        AccountService.revoke_reset_password_token(args["token"])
-
-        # Refresh token data by generating a new token
-        _, new_token = AccountService.generate_reset_password_token(
-            user_email, code=args["code"], additional_data={"phase": "reset"}
-        )
-
-        return {"is_valid": True, "email": token_data.get("email"), "token": new_token}
+        return {"is_valid": True, "email": token_data.get("email")}
 
 
 class ForgotPasswordResetApi(Resource):
     @setup_required
-    @email_password_login_enabled
     def post(self):
         parser = reqparse.RequestParser()
         parser.add_argument("token", type=str, required=True, nullable=False, location="json")
@@ -106,9 +95,6 @@ class ForgotPasswordResetApi(Resource):
 
         if reset_data is None:
             raise InvalidTokenError()
-        # Must use token in reset phase
-        if reset_data.get("phase", "") != "reset":
-            raise InvalidTokenError()
 
         AccountService.revoke_reset_password_token(token)
 

api/controllers/console/auth/login.py

@@ -22,7 +22,7 @@ from controllers.console.error import (
     EmailSendIpLimitError,
     NotAllowedCreateWorkspace,
 )
-from controllers.console.wraps import email_password_login_enabled, setup_required
+from controllers.console.wraps import setup_required
 from events.tenant_event import tenant_was_created
 from libs.helper import email, extract_remote_ip
 from libs.password import valid_password
@@ -38,7 +38,6 @@ class LoginApi(Resource):
     """Resource for user login."""
 
     @setup_required
-    @email_password_login_enabled
     def post(self):
         """Authenticate user and login."""
         parser = reqparse.RequestParser()
@@ -111,7 +110,6 @@ class LogoutApi(Resource):
 
 class ResetPasswordSendEmailApi(Resource):
     @setup_required
-    @email_password_login_enabled
     def post(self):
         parser = reqparse.RequestParser()
         parser.add_argument("email", type=email, required=True, location="json")

api/controllers/console/wraps.py

@@ -154,16 +154,3 @@ def enterprise_license_required(view):
         return view(*args, **kwargs)
 
     return decorated
-
-
-def email_password_login_enabled(view):
-    @wraps(view)
-    def decorated(*args, **kwargs):
-        features = FeatureService.get_system_features()
-        if features.enable_email_password_login:
-            return view(*args, **kwargs)
-
-        # otherwise, return 403
-        abort(403)
-
-    return decorated

api/core/agent/cot_agent_runner.py

@@ -104,6 +104,7 @@ class CotAgentRunner(BaseAgentRunner, ABC):
 
             # recalc llm max tokens
             prompt_messages = self._organize_prompt_messages()
+            self.recalc_llm_max_tokens(self.model_config, prompt_messages)
             # invoke model
             chunks = model_instance.invoke_llm(
                 prompt_messages=prompt_messages,

api/core/agent/fc_agent_runner.py

@@ -84,6 +84,7 @@ class FunctionCallAgentRunner(BaseAgentRunner):
 
             # recalc llm max tokens
             prompt_messages = self._organize_prompt_messages()
+            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/app/apps/agent_chat/app_runner.py

@@ -55,6 +55,20 @@ class AgentChatAppRunner(AppRunner):
         query = application_generate_entity.query
         files = application_generate_entity.files
 
+        # Pre-calculate the number of tokens of the prompt messages,
+        # and return the rest number of tokens by model context token size limit and max token size limit.
+        # If the rest number of tokens is not enough, raise exception.
+        # Include: prompt template, inputs, query(optional), files(optional)
+        # Not Include: memory, external data, dataset context
+        self.get_pre_calculate_rest_tokens(
+            app_record=app_record,
+            model_config=application_generate_entity.model_conf,
+            prompt_template_entity=app_config.prompt_template,
+            inputs=inputs,
+            files=files,
+            query=query,
+        )
+
         memory = None
         if application_generate_entity.conversation_id:
             # get memory of conversation (read-only)

api/core/app/apps/base_app_runner.py

@@ -15,8 +15,10 @@ from core.app.features.annotation_reply.annotation_reply import AnnotationReplyF
 from core.app.features.hosting_moderation.hosting_moderation import HostingModerationFeature
 from core.external_data_tool.external_data_fetch import ExternalDataFetch
 from core.memory.token_buffer_memory import TokenBufferMemory
+from core.model_manager import ModelInstance
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
 from core.model_runtime.entities.message_entities import AssistantPromptMessage, PromptMessage
+from core.model_runtime.entities.model_entities import ModelPropertyKey
 from core.model_runtime.errors.invoke import InvokeBadRequestError
 from core.moderation.input_moderation import InputModeration
 from core.prompt.advanced_prompt_transform import AdvancedPromptTransform
@@ -29,6 +31,106 @@ if TYPE_CHECKING:
 
 
 class AppRunner:
+    def get_pre_calculate_rest_tokens(
+        self,
+        app_record: App,
+        model_config: ModelConfigWithCredentialsEntity,
+        prompt_template_entity: PromptTemplateEntity,
+        inputs: Mapping[str, str],
+        files: Sequence["File"],
+        query: Optional[str] = None,
+    ) -> int:
+        """
+        Get pre calculate rest tokens
+        :param app_record: app record
+        :param model_config: model config entity
+        :param prompt_template_entity: prompt template entity
+        :param inputs: inputs
+        :param files: files
+        :param query: query
+        :return:
+        """
+        # Invoke model
+        model_instance = ModelInstance(
+            provider_model_bundle=model_config.provider_model_bundle, model=model_config.model
+        )
+
+        model_context_tokens = model_config.model_schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE)
+
+        max_tokens = 0
+        for parameter_rule in model_config.model_schema.parameter_rules:
+            if parameter_rule.name == "max_tokens" or (
+                parameter_rule.use_template and parameter_rule.use_template == "max_tokens"
+            ):
+                max_tokens = (
+                    model_config.parameters.get(parameter_rule.name)
+                    or model_config.parameters.get(parameter_rule.use_template or "")
+                ) or 0
+
+        if model_context_tokens is None:
+            return -1
+
+        if max_tokens is None:
+            max_tokens = 0
+
+        # get prompt messages without memory and context
+        prompt_messages, stop = self.organize_prompt_messages(
+            app_record=app_record,
+            model_config=model_config,
+            prompt_template_entity=prompt_template_entity,
+            inputs=inputs,
+            files=files,
+            query=query,
+        )
+
+        prompt_tokens = model_instance.get_llm_num_tokens(prompt_messages)
+
+        rest_tokens: int = model_context_tokens - max_tokens - prompt_tokens
+        if rest_tokens < 0:
+            raise InvokeBadRequestError(
+                "Query or prefix prompt is too long, you can reduce the prefix prompt, "
+                "or shrink the max token, or switch to a llm with a larger token limit size."
+            )
+
+        return rest_tokens
+
+    def recalc_llm_max_tokens(
+        self, model_config: ModelConfigWithCredentialsEntity, prompt_messages: list[PromptMessage]
+    ):
+        # recalc max_tokens if sum(prompt_token +  max_tokens) over model token limit
+        model_instance = ModelInstance(
+            provider_model_bundle=model_config.provider_model_bundle, model=model_config.model
+        )
+
+        model_context_tokens = model_config.model_schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE)
+
+        max_tokens = 0
+        for parameter_rule in model_config.model_schema.parameter_rules:
+            if parameter_rule.name == "max_tokens" or (
+                parameter_rule.use_template and parameter_rule.use_template == "max_tokens"
+            ):
+                max_tokens = (
+                    model_config.parameters.get(parameter_rule.name)
+                    or model_config.parameters.get(parameter_rule.use_template or "")
+                ) or 0
+
+        if model_context_tokens is None:
+            return -1
+
+        if max_tokens is None:
+            max_tokens = 0
+
+        prompt_tokens = model_instance.get_llm_num_tokens(prompt_messages)
+
+        if prompt_tokens + max_tokens > model_context_tokens:
+            max_tokens = max(model_context_tokens - prompt_tokens, 16)
+
+            for parameter_rule in model_config.model_schema.parameter_rules:
+                if parameter_rule.name == "max_tokens" or (
+                    parameter_rule.use_template and parameter_rule.use_template == "max_tokens"
+                ):
+                    model_config.parameters[parameter_rule.name] = max_tokens
+
     def organize_prompt_messages(
         self,
         app_record: App,

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

@@ -50,6 +50,20 @@ class ChatAppRunner(AppRunner):
         query = application_generate_entity.query
         files = application_generate_entity.files
 
+        # Pre-calculate the number of tokens of the prompt messages,
+        # and return the rest number of tokens by model context token size limit and max token size limit.
+        # If the rest number of tokens is not enough, raise exception.
+        # Include: prompt template, inputs, query(optional), files(optional)
+        # Not Include: memory, external data, dataset context
+        self.get_pre_calculate_rest_tokens(
+            app_record=app_record,
+            model_config=application_generate_entity.model_conf,
+            prompt_template_entity=app_config.prompt_template,
+            inputs=inputs,
+            files=files,
+            query=query,
+        )
+
         memory = None
         if application_generate_entity.conversation_id:
             # get memory of conversation (read-only)
@@ -180,6 +194,9 @@ class ChatAppRunner(AppRunner):
         if hosting_moderation_result:
             return
 
+        # Re-calculate the max tokens if sum(prompt_token +  max_tokens) over model token limit
+        self.recalc_llm_max_tokens(model_config=application_generate_entity.model_conf, prompt_messages=prompt_messages)
+
         # Invoke model
         model_instance = ModelInstance(
             provider_model_bundle=application_generate_entity.model_conf.provider_model_bundle,

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

@@ -43,6 +43,20 @@ class CompletionAppRunner(AppRunner):
         query = application_generate_entity.query
         files = application_generate_entity.files
 
+        # Pre-calculate the number of tokens of the prompt messages,
+        # and return the rest number of tokens by model context token size limit and max token size limit.
+        # If the rest number of tokens is not enough, raise exception.
+        # Include: prompt template, inputs, query(optional), files(optional)
+        # Not Include: memory, external data, dataset context
+        self.get_pre_calculate_rest_tokens(
+            app_record=app_record,
+            model_config=application_generate_entity.model_conf,
+            prompt_template_entity=app_config.prompt_template,
+            inputs=inputs,
+            files=files,
+            query=query,
+        )
+
         # organize all inputs and template to prompt messages
         # Include: prompt template, inputs, query(optional), files(optional)
         prompt_messages, stop = self.organize_prompt_messages(
@@ -138,6 +152,9 @@ class CompletionAppRunner(AppRunner):
         if hosting_moderation_result:
             return
 
+        # Re-calculate the max tokens if sum(prompt_token +  max_tokens) over model token limit
+        self.recalc_llm_max_tokens(model_config=application_generate_entity.model_conf, prompt_messages=prompt_messages)
+
         # Invoke model
         model_instance = ModelInstance(
             provider_model_bundle=application_generate_entity.model_conf.provider_model_bundle,

api/core/memory/token_buffer_memory.py

@@ -26,7 +26,7 @@ class TokenBufferMemory:
         self.model_instance = model_instance
 
     def get_history_prompt_messages(
-        self, max_token_limit: int = 100000, message_limit: Optional[int] = None
+        self, max_token_limit: int = 2000, message_limit: Optional[int] = None
     ) -> Sequence[PromptMessage]:
         """
         Get history prompt messages.

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

@@ -537,7 +537,6 @@ if you are not sure about the structure.
         """
         raise NotImplementedError
 
-    @abstractmethod
     def get_num_tokens(
         self,
         model: str,
@@ -915,3 +914,7 @@ if you are not sure about the structure.
             filtered_model_parameters[parameter_name] = parameter_value
 
         return filtered_model_parameters
+
+    def _get_num_tokens_by_gpt2(self, text: str) -> int:
+        # Disable the token count in LLMs for profermance testing.
+        return 0

api/core/model_runtime/model_providers/bedrock/llm/eu.anthropic.claude-3.7-sonnet-v1.yaml

@@ -1,115 +0,0 @@
-model: us.anthropic.claude-3-7-sonnet-20250219-v1:0
-label:
-  en_US: Claude 3.7 Sonnet(US.Cross Region Inference)
-icon: icon_s_en.svg
-model_type: llm
-features:
-  - agent-thought
-  - vision
-  - tool-call
-  - stream-tool-call
-model_properties:
-  mode: chat
-  context_size: 200000
-# docs: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html
-parameter_rules:
-  - name: enable_cache
-    label:
-      zh_Hans: 启用提示缓存
-      en_US: Enable Prompt Cache
-    type: boolean
-    required: false
-    default: true
-    help:
-      zh_Hans: 启用提示缓存可以提高性能并降低成本。Claude 3.7 Sonnet支持在system、messages和tools字段中使用缓存检查点。
-      en_US: Enable prompt caching to improve performance and reduce costs. Claude 3.7 Sonnet supports cache checkpoints in system, messages, and tools fields.
-  - name: reasoning_type
-    label:
-      zh_Hans: 推理配置
-      en_US: Reasoning Type
-    type: boolean
-    required: false
-    default: false
-    placeholder:
-      zh_Hans: 设置推理配置
-      en_US: Set reasoning configuration
-    help:
-      zh_Hans: 控制模型的推理能力。启用时，temperature将固定为1且top_p将被禁用。
-      en_US: Controls the model's reasoning capability. When enabled, temperature will be fixed to 1 and top_p will be disabled.
-  - name: reasoning_budget
-    show_on:
-      - variable: reasoning_type
-        value: true
-    label:
-      zh_Hans: 推理预算
-      en_US: Reasoning Budget
-    type: int
-    default: 1024
-    min: 0
-    max: 128000
-    help:
-      zh_Hans: 推理的预算限制（最小1024），必须小于max_tokens。仅在推理类型为enabled时可用。
-      en_US: Budget limit for reasoning (minimum 1024), must be less than max_tokens. Only available when reasoning type is enabled.
-
-  - name: max_tokens
-    use_template: max_tokens
-    required: true
-    label:
-      zh_Hans: 最大token数
-      en_US: Max Tokens
-    type: int
-    default: 8192
-    min: 1
-    max: 128000
-    help:
-      zh_Hans: 停止前生成的最大令牌数。请注意，Anthropic Claude 模型可能会在达到 max_tokens 的值之前停止生成令牌。不同的 Anthropic Claude 模型对此参数具有不同的最大值。
-      en_US: The maximum number of tokens to generate before stopping. Note that Anthropic Claude models might stop generating tokens before reaching the value of max_tokens. Different Anthropic Claude models have different maximum values for this parameter.
-  - name: temperature
-    use_template: temperature
-    required: false
-    label:
-      zh_Hans: 模型温度
-      en_US: Model Temperature
-    type: float
-    default: 1
-    min: 0.0
-    max: 1.0
-    help:
-      zh_Hans: 生成内容的随机性。当推理功能启用时，该值将被固定为1。
-      en_US: The amount of randomness injected into the response. When reasoning is enabled, this value will be fixed to 1.
-  - name: top_p
-    show_on:
-      - variable: reasoning_type
-        value: disabled
-    use_template: top_p
-    label:
-      zh_Hans: Top P
-      en_US: Top P
-    required: false
-    type: float
-    default: 0.999
-    min: 0.000
-    max: 1.000
-    help:
-      zh_Hans: 在核采样中的概率阈值。当推理功能启用时，该参数将被禁用。
-      en_US: The probability threshold in nucleus sampling. When reasoning is enabled, this parameter will be disabled.
-  - name: top_k
-    label:
-      zh_Hans: 取样数量
-      en_US: Top k
-    required: false
-    type: int
-    default: 0
-    min: 0
-    # tip docs from aws has error, max value is 500
-    max: 500
-    help:
-      zh_Hans: 对于每个后续标记，仅从前 K 个选项中进行采样。使用 top_k 删除长尾低概率响应。
-      en_US: Only sample from the top K options for each subsequent token. Use top_k to remove long tail low probability responses.
-  - name: response_format
-    use_template: response_format
-pricing:
-  input: '0.003'
-  output: '0.015'
-  unit: '0.001'
-  currency: USD

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

@@ -58,7 +58,6 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
     # TODO There is invoke issue: context limit on Cohere Model, will add them after fixed.
     CONVERSE_API_ENABLED_MODEL_INFO = [
         {"prefix": "anthropic.claude-v2", "support_system_prompts": True, "support_tool_use": False},
-        {"prefix": "us.deepseek", "support_system_prompts": True, "support_tool_use": False},
         {"prefix": "anthropic.claude-v1", "support_system_prompts": True, "support_tool_use": False},
         {"prefix": "us.anthropic.claude-3", "support_system_prompts": True, "support_tool_use": True},
         {"prefix": "eu.anthropic.claude-3", "support_system_prompts": True, "support_tool_use": True},

api/core/model_runtime/model_providers/bedrock/llm/us.deepseek-r1.yaml

@@ -1,63 +0,0 @@
-model: us.deepseek.r1-v1:0
-label:
-  en_US: DeepSeek-R1(US.Cross Region Inference)
-icon: icon_s_en.svg
-model_type: llm
-features:
-  - agent-thought
-  - vision
-  - tool-call
-  - stream-tool-call
-model_properties:
-  mode: chat
-  context_size: 32768
-parameter_rules:
-  - name: max_tokens
-    use_template: max_tokens
-    required: true
-    label:
-      zh_Hans: 最大token数
-      en_US: Max Tokens
-    type: int
-    default: 8192
-    min: 1
-    max: 128000
-    help:
-      zh_Hans: 停止前生成的最大令牌数。
-      en_US: The maximum number of tokens to generate before stopping.
-  - name: temperature
-    use_template: temperature
-    required: false
-    label:
-      zh_Hans: 模型温度
-      en_US: Model Temperature
-    type: float
-    default: 1
-    min: 0.0
-    max: 1.0
-    help:
-      zh_Hans: 生成内容的随机性。当推理功能启用时，该值将被固定为1。
-      en_US: The amount of randomness injected into the response. When reasoning is enabled, this value will be fixed to 1.
-  - name: top_p
-    show_on:
-      - variable: reasoning_type
-        value: disabled
-    use_template: top_p
-    label:
-      zh_Hans: Top P
-      en_US: Top P
-    required: false
-    type: float
-    default: 0.999
-    min: 0.000
-    max: 1.000
-    help:
-      zh_Hans: 在核采样中的概率阈值。当推理功能启用时，该参数将被禁用。
-      en_US: The probability threshold in nucleus sampling. When reasoning is enabled, this parameter will be disabled.
-  - name: response_format
-    use_template: response_format
-pricing:
-  input: '0.001'
-  output: '0.005'
-  unit: '0.001'
-  currency: USD

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

@@ -19,8 +19,8 @@ class GoogleProvider(ModelProvider):
         try:
             model_instance = self.get_model_instance(ModelType.LLM)
 
-            # Use `gemini-2.0-flash` model for validate,
-            model_instance.validate_credentials(model="gemini-2.0-flash", credentials=credentials)
+            # Use `gemini-pro` model for validate,
+            model_instance.validate_credentials(model="gemini-pro", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:

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

@@ -19,3 +19,5 @@
 - gemini-exp-1206
 - gemini-exp-1121
 - gemini-exp-1114
+- gemini-pro
+- gemini-pro-vision

api/core/model_runtime/model_providers/google/llm/gemini-pro-vision.yaml

@@ -0,0 +1,35 @@
+model: gemini-pro-vision
+label:
+  en_US: Gemini Pro Vision
+model_type: llm
+features:
+  - vision
+model_properties:
+  mode: chat
+  context_size: 12288
+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_to_sample
+    use_template: max_tokens
+    required: true
+    default: 4096
+    min: 1
+    max: 4096
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD
+deprecated: true

api/core/model_runtime/model_providers/google/llm/gemini-pro.yaml

@@ -0,0 +1,39 @@
+model: gemini-pro
+label:
+  en_US: Gemini Pro
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 30720
+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_to_sample
+    use_template: max_tokens
+    required: true
+    default: 2048
+    min: 1
+    max: 2048
+  - name: response_format
+    use_template: response_format
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD
+deprecated: true

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

@@ -1,4 +1,3 @@
-- gpt-4.1
 - o1
 - o1-2024-12-17
 - o1-mini

api/core/model_runtime/model_providers/openai/llm/gpt-4-1.yaml

@@ -1,60 +0,0 @@
-model: gpt-4.1
-label:
-  zh_Hans: gpt-4.1
-  en_US: gpt-4.1
-model_type: llm
-features:
-  - multi-tool-call
-  - agent-thought
-  - stream-tool-call
-  - vision
-model_properties:
-  mode: chat
-  context_size: 1047576
-parameter_rules:
-  - name: temperature
-    use_template: temperature
-  - name: top_p
-    use_template: top_p
-  - name: presence_penalty
-    use_template: presence_penalty
-  - name: frequency_penalty
-    use_template: frequency_penalty
-  - name: max_tokens
-    use_template: max_tokens
-    default: 512
-    min: 1
-    max: 32768
-  - name: reasoning_effort
-    label:
-      zh_Hans: 推理工作
-      en_US: Reasoning Effort
-    type: string
-    help:
-      zh_Hans: 限制推理模型的推理工作
-      en_US: Constrains effort on reasoning for reasoning models
-    required: false
-    options:
-      - low
-      - medium
-      - high
-  - name: response_format
-    label:
-      zh_Hans: 回复格式
-      en_US: Response Format
-    type: string
-    help:
-      zh_Hans: 指定模型必须输出的格式
-      en_US: specifying the format that the model must output
-    required: false
-    options:
-      - text
-      - json_object
-      - json_schema
-  - name: json_schema
-    use_template: json_schema
-pricing:
-  input: '2.00'
-  output: '8.00'
-  unit: '0.000001'
-  currency: USD

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

@@ -1057,7 +1057,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
             model = "gpt-4o"
 
         try:
-            encoding = tiktoken.get_encoding(model)
+            encoding = tiktoken.encoding_for_model(model)
         except KeyError:
             logger.warning("Warning: model not found. Using cl100k_base encoding.")
             model = "cl100k_base"

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

@@ -5,6 +5,11 @@ model_type: llm
 features:
   - agent-thought
   - vision
+  - tool-call
+  - stream-tool-call
+  - document
+  - video
+  - audio
 model_properties:
   mode: chat
   context_size: 1048576
@@ -15,21 +20,20 @@ parameter_rules:
     use_template: top_p
   - name: top_k
     label:
+      zh_Hans: 取样数量
       en_US: Top k
     type: int
     help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
       en_US: Only sample from the top K options for each subsequent token.
     required: false
-  - name: presence_penalty
-    use_template: presence_penalty
-  - name: frequency_penalty
-    use_template: frequency_penalty
   - name: max_output_tokens
     use_template: max_tokens
-    required: true
     default: 8192
     min: 1
     max: 8192
+  - name: json_schema
+    use_template: json_schema
 pricing:
   input: '0.00'
   output: '0.00'

api/core/workflow/nodes/code/code_node.py

@@ -195,7 +195,7 @@ class CodeNode(BaseNode[CodeNodeData]):
             if output_config.type == "object":
                 # check if output is object
                 if not isinstance(result.get(output_name), dict):
-                    if result.get(output_name) is None:
+                    if isinstance(result.get(output_name), type(None)):
                         transformed_result[output_name] = None
                     else:
                         raise OutputValidationError(
@@ -223,7 +223,7 @@ class CodeNode(BaseNode[CodeNodeData]):
             elif output_config.type == "array[number]":
                 # check if array of number available
                 if not isinstance(result[output_name], list):
-                    if result[output_name] is None:
+                    if isinstance(result[output_name], type(None)):
                         transformed_result[output_name] = None
                     else:
                         raise OutputValidationError(
@@ -244,7 +244,7 @@ class CodeNode(BaseNode[CodeNodeData]):
             elif output_config.type == "array[string]":
                 # check if array of string available
                 if not isinstance(result[output_name], list):
-                    if result[output_name] is None:
+                    if isinstance(result[output_name], type(None)):
                         transformed_result[output_name] = None
                     else:
                         raise OutputValidationError(
@@ -265,7 +265,7 @@ class CodeNode(BaseNode[CodeNodeData]):
             elif output_config.type == "array[object]":
                 # check if array of object available
                 if not isinstance(result[output_name], list):
-                    if result[output_name] is None:
+                    if isinstance(result[output_name], type(None)):
                         transformed_result[output_name] = None
                     else:
                         raise OutputValidationError(

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

@@ -968,12 +968,14 @@ def _handle_memory_chat_mode(
     *,
     memory: TokenBufferMemory | None,
     memory_config: MemoryConfig | None,
-    model_config: ModelConfigWithCredentialsEntity,  # TODO(-LAN-): Needs to remove
+    model_config: ModelConfigWithCredentialsEntity,
 ) -> Sequence[PromptMessage]:
     memory_messages: Sequence[PromptMessage] = []
     # Get messages from memory for chat model
     if memory and memory_config:
+        rest_tokens = _calculate_rest_token(prompt_messages=[], model_config=model_config)
         memory_messages = memory.get_history_prompt_messages(
+            max_token_limit=rest_tokens,
             message_limit=memory_config.window.size if memory_config.window.enabled else None,
         )
     return memory_messages

api/poetry.lock

@@ -10473,44 +10473,44 @@ client = ["SQLAlchemy (>=1.4,<3)"]
 
 [[package]]
 name = "tiktoken"
-version = "0.9.0"
+version = "0.8.0"
 description = "tiktoken is a fast BPE tokeniser for use with OpenAI's models"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 markers = "python_version == \"3.11\" or python_version >= \"3.12\""
 files = [
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-    {file = "tiktoken-0.9.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f0968d5beeafbca2a72c595e8385a1a1f8af58feaebb02b227229b69ca5357fd"},
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+    {file = "tiktoken-0.8.0-cp39-cp39-win_amd64.whl", hash = "sha256:1473cfe584252dc3fa62adceb5b1c763c1874e04511b197da4e6de51d6ce5a02"},
+    {file = "tiktoken-0.8.0.tar.gz", hash = "sha256:9ccbb2740f24542534369c5635cfd9b2b3c2490754a78ac8831d99f89f94eeb2"},
 ]
 
 [package.dependencies]
@@ -12389,4 +12389,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.11,<3.13"
-content-hash = "0df8aef68385b6596306fd18af317a835023d648eb5028cd57ec463f176e4c0f"
+content-hash = "d197cdff507a70323c1d6aca11609188f54970f67715af744fe6def15b7776fd"

api/pyproject.toml

@@ -85,7 +85,7 @@ sentry-sdk = { version = "~1.44.1", extras = ["flask"] }
 sqlalchemy = "~2.0.29"
 starlette = "0.41.0"
 tencentcloud-sdk-python-hunyuan = "~3.0.1294"
-tiktoken = "^0.9.0"
+tiktoken = "~0.8.0"
 tokenizers = "~0.15.0"
 transformers = "~4.35.0"
 unstructured = { version = "~0.16.1", extras = ["docx", "epub", "md", "msg", "ppt", "pptx"] }

api/services/account_service.py

@@ -406,8 +406,10 @@ class AccountService:
 
             raise PasswordResetRateLimitExceededError()
 
-        code, token = cls.generate_reset_password_token(account_email, account)
-
+        code = "".join([str(random.randint(0, 9)) for _ in range(6)])
+        token = TokenManager.generate_token(
+            account=account, email=email, token_type="reset_password", additional_data={"code": code}
+        )
         send_reset_password_mail_task.delay(
             language=language,
             to=account_email,
@@ -416,22 +418,6 @@ class AccountService:
         cls.reset_password_rate_limiter.increment_rate_limit(account_email)
         return token
 
-    @classmethod
-    def generate_reset_password_token(
-        cls,
-        email: str,
-        account: Optional[Account] = None,
-        code: Optional[str] = None,
-        additional_data: dict[str, Any] = {},
-    ):
-        if not code:
-            code = "".join([str(random.randint(0, 9)) for _ in range(6)])
-        additional_data["code"] = code
-        token = TokenManager.generate_token(
-            account=account, email=email, token_type="reset_password", additional_data=additional_data
-        )
-        return code, token
-
     @classmethod
     def revoke_reset_password_token(cls, token: str):
         TokenManager.revoke_token(token, "reset_password")

api/services/app_dsl_service.py

@@ -55,19 +55,13 @@ def _check_version_compatibility(imported_version: str) -> ImportStatus:
     except version.InvalidVersion:
         return ImportStatus.FAILED
 
-    # If imported version is newer than current, always return PENDING
-    if imported_ver > current_ver:
+    # Compare major version and minor version
+    if current_ver.major != imported_ver.major or current_ver.minor != imported_ver.minor:
         return ImportStatus.PENDING
 
-    # If imported version is older than current's major, return PENDING
-    if imported_ver.major < current_ver.major:
-        return ImportStatus.PENDING
-
-    # If imported version is older than current's minor, return COMPLETED_WITH_WARNINGS
-    if imported_ver.minor < current_ver.minor:
+    if current_ver.micro != imported_ver.micro:
         return ImportStatus.COMPLETED_WITH_WARNINGS
 
-    # If imported version equals or is older than current's micro, return COMPLETED
     return ImportStatus.COMPLETED
 
 

docker/.env.example

@@ -932,6 +932,3 @@ MAX_SUBMIT_COUNT=100
 
 # The maximum number of top-k value for RAG.
 TOP_K_MAX_VALUE=10
-
-# Prevent Clickjacking
-ALLOW_EMBED=false

docker/docker-compose-template.yaml

@@ -1,8 +1,8 @@
-x-shared-env: &shared-api-worker-env 
+x-shared-env: &shared-api-worker-env
 services:
   # API service
   api:
-    image: langgenius/dify-api:0.15.7
+    image: langgenius/dify-api:0.15.6-alpha.1
     restart: always
     environment:
       # Use the shared environment variables.
@@ -25,7 +25,7 @@ services:
   # worker service
   # The Celery worker for processing the queue.
   worker:
-    image: langgenius/dify-api:0.15.7
+    image: langgenius/dify-api:0.15.6-alpha.1
     restart: always
     environment:
       # Use the shared environment variables.
@@ -47,7 +47,7 @@ services:
 
   # Frontend web application.
   web:
-    image: langgenius/dify-web:0.15.7
+    image: langgenius/dify-web:0.15.6-alpha.1
     restart: always
     environment:
       CONSOLE_API_URL: ${CONSOLE_API_URL:-}
@@ -56,7 +56,6 @@ services:
       NEXT_TELEMETRY_DISABLED: ${NEXT_TELEMETRY_DISABLED:-0}
       TEXT_GENERATION_TIMEOUT_MS: ${TEXT_GENERATION_TIMEOUT_MS:-60000}
       CSP_WHITELIST: ${CSP_WHITELIST:-}
-      ALLOW_EMBED: ${ALLOW_EMBED:-false}
       TOP_K_MAX_VALUE: ${TOP_K_MAX_VALUE:-}
       INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH: ${INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH:-}
 

docker/docker-compose.yaml

@@ -389,12 +389,11 @@ x-shared-env: &shared-api-worker-env
   CREATE_TIDB_SERVICE_JOB_ENABLED: ${CREATE_TIDB_SERVICE_JOB_ENABLED:-false}
   MAX_SUBMIT_COUNT: ${MAX_SUBMIT_COUNT:-100}
   TOP_K_MAX_VALUE: ${TOP_K_MAX_VALUE:-10}
-  ALLOW_EMBED: ${ALLOW_EMBED:-false}
 
 services:
   # API service
   api:
-    image: langgenius/dify-api:0.15.7
+    image: langgenius/dify-api:0.15.5-alpha.1
     restart: always
     environment:
       # Use the shared environment variables.
@@ -417,7 +416,7 @@ services:
   # worker service
   # The Celery worker for processing the queue.
   worker:
-    image: langgenius/dify-api:0.15.7
+    image: langgenius/dify-api:0.15.5-alpha.1
     restart: always
     environment:
       # Use the shared environment variables.
@@ -439,7 +438,7 @@ services:
 
   # Frontend web application.
   web:
-    image: langgenius/dify-web:0.15.7
+    image: langgenius/dify-web:0.15.5-alpha.1
     restart: always
     environment:
       CONSOLE_API_URL: ${CONSOLE_API_URL:-}
@@ -448,7 +447,6 @@ services:
       NEXT_TELEMETRY_DISABLED: ${NEXT_TELEMETRY_DISABLED:-0}
       TEXT_GENERATION_TIMEOUT_MS: ${TEXT_GENERATION_TIMEOUT_MS:-60000}
       CSP_WHITELIST: ${CSP_WHITELIST:-}
-      ALLOW_EMBED: ${ALLOW_EMBED:-false}
       TOP_K_MAX_VALUE: ${TOP_K_MAX_VALUE:-}
       INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH: ${INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH:-}
 

web/.env.example

@@ -31,6 +31,3 @@ NEXT_PUBLIC_TOP_K_MAX_VALUE=10
 
 # The maximum number of tokens for segmentation
 NEXT_PUBLIC_INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH=4000
-
-# Default is not allow to embed into iframe to prevent Clickjacking: https://owasp.org/www-community/attacks/Clickjacking
-NEXT_PUBLIC_ALLOW_EMBED=

web/app/components/app/create-app-dialog/app-list/index.tsx

@@ -186,17 +186,15 @@ const Apps = ({
         <div className='w-[180px] h-8'></div>
       </div>
       <div className='relative flex flex-1 overflow-y-auto'>
-        {!searchKeywords && <div className='h-full w-[200px] p-4'>
-          <Sidebar current={currCategory as AppCategories} categories={categories} onClick={(category) => { setCurrCategory(category) }} onCreateFromBlank={onCreateFromBlank} />
+        {!searchKeywords && <div className='w-[200px] h-full p-4'>
+          <Sidebar current={currCategory as AppCategories} onClick={(category) => { setCurrCategory(category) }} onCreateFromBlank={onCreateFromBlank} />
         </div>}
         <div className='flex-1 h-full overflow-auto shrink-0 grow p-6 pt-2 border-l border-divider-burn'>
           {searchFilteredList && searchFilteredList.length > 0 && <>
             <div className='pt-4 pb-1'>
               {searchKeywords
                 ? <p className='title-md-semi-bold text-text-tertiary'>{searchFilteredList.length > 1 ? t('app.newApp.foundResults', { count: searchFilteredList.length }) : t('app.newApp.foundResult', { count: searchFilteredList.length })}</p>
-                : <div className='flex h-[22px] items-center'>
-                  <AppCategoryLabel category={currCategory as AppCategories} className='title-md-semi-bold text-text-primary' />
-                </div>}
+                : <AppCategoryLabel category={currCategory as AppCategories} className='title-md-semi-bold text-text-primary' />}
             </div>
             <div
               className={cn(

web/app/components/app/create-app-dialog/app-list/sidebar.tsx

@@ -1,29 +1,39 @@
 'use client'
-import { RiStickyNoteAddLine, RiThumbUpLine } from '@remixicon/react'
+import { RiAppsFill, RiChatSmileAiFill, RiExchange2Fill, RiPassPendingFill, RiQuillPenAiFill, RiSpeakAiFill, RiStickyNoteAddLine, RiTerminalBoxFill, RiThumbUpFill } from '@remixicon/react'
 import { useTranslation } from 'react-i18next'
 import classNames from '@/utils/classnames'
 import Divider from '@/app/components/base/divider'
 
 export enum AppCategories {
   RECOMMENDED = 'Recommended',
+  ASSISTANT = 'Assistant',
+  AGENT = 'Agent',
+  HR = 'HR',
+  PROGRAMMING = 'Programming',
+  WORKFLOW = 'Workflow',
+  WRITING = 'Writing',
 }
 
 type SidebarProps = {
-  current: AppCategories | string
-  categories: string[]
-  onClick?: (category: AppCategories | string) => void
+  current: AppCategories
+  onClick?: (category: AppCategories) => void
   onCreateFromBlank?: () => void
 }
 
-export default function Sidebar({ current, categories, onClick, onCreateFromBlank }: SidebarProps) {
+export default function Sidebar({ current, onClick, onCreateFromBlank }: SidebarProps) {
   const { t } = useTranslation()
-  return <div className="flex h-full w-full flex-col">
-    <ul className='pt-0.5'>
+  return <div className="w-full h-full flex flex-col">
+    <ul>
       <CategoryItem category={AppCategories.RECOMMENDED} active={current === AppCategories.RECOMMENDED} onClick={onClick} />
     </ul>
-    <div className='system-xs-medium-uppercase mb-0.5 mt-3 px-3 pb-1 pt-2 text-text-tertiary'>{t('app.newAppFromTemplate.byCategories')}</div>
-    <ul className='flex grow flex-col gap-0.5'>
-      {categories.map(category => (<CategoryItem key={category} category={category} active={current === category} onClick={onClick} />))}
+    <div className='px-3 pt-2 pb-1 system-xs-medium-uppercase text-text-tertiary'>{t('app.newAppFromTemplate.byCategories')}</div>
+    <ul className='flex-grow flex flex-col gap-0.5'>
+      <CategoryItem category={AppCategories.ASSISTANT} active={current === AppCategories.ASSISTANT} onClick={onClick} />
+      <CategoryItem category={AppCategories.AGENT} active={current === AppCategories.AGENT} onClick={onClick} />
+      <CategoryItem category={AppCategories.HR} active={current === AppCategories.HR} onClick={onClick} />
+      <CategoryItem category={AppCategories.PROGRAMMING} active={current === AppCategories.PROGRAMMING} onClick={onClick} />
+      <CategoryItem category={AppCategories.WORKFLOW} active={current === AppCategories.WORKFLOW} onClick={onClick} />
+      <CategoryItem category={AppCategories.WRITING} active={current === AppCategories.WRITING} onClick={onClick} />
     </ul>
     <Divider bgStyle='gradient' />
     <div className='px-3 py-1 flex items-center gap-1 text-text-tertiary cursor-pointer' onClick={onCreateFromBlank}>
@@ -35,26 +45,47 @@ export default function Sidebar({ current, categories, onClick, onCreateFromBlan
 
 type CategoryItemProps = {
   active: boolean
-  category: AppCategories | string
-  onClick?: (category: AppCategories | string) => void
+  category: AppCategories
+  onClick?: (category: AppCategories) => void
 }
 function CategoryItem({ category, active, onClick }: CategoryItemProps) {
   return <li
-    className={classNames('p-1 pl-3 h-8 rounded-lg flex items-center gap-2 group cursor-pointer hover:bg-state-base-hover [&.active]:bg-state-base-active', active && 'active')}
+    className={classNames('p-1 pl-3 rounded-lg flex items-center gap-2 group cursor-pointer hover:bg-state-base-hover [&.active]:bg-state-base-active', active && 'active')}
     onClick={() => { onClick?.(category) }}>
-    {category === AppCategories.RECOMMENDED && <div className='inline-flex h-5 w-5 items-center justify-center rounded-md'>
-      <RiThumbUpLine className='h-4 w-4 text-components-menu-item-text group-[.active]:text-components-menu-item-text-active' />
-    </div>}
+    <div className='w-5 h-5 inline-flex items-center justify-center rounded-md border border-divider-regular bg-components-icon-bg-midnight-solid group-[.active]:bg-components-icon-bg-blue-solid'>
+      <AppCategoryIcon category={category} />
+    </div>
     <AppCategoryLabel category={category}
       className={classNames('system-sm-medium text-components-menu-item-text group-[.active]:text-components-menu-item-text-active group-hover:text-components-menu-item-text-hover', active && 'system-sm-semibold')} />
   </li >
 }
 
 type AppCategoryLabelProps = {
-  category: AppCategories | string
+  category: AppCategories
   className?: string
 }
 export function AppCategoryLabel({ category, className }: AppCategoryLabelProps) {
   const { t } = useTranslation()
-  return <span className={className}>{category === AppCategories.RECOMMENDED ? t('app.newAppFromTemplate.sidebar.Recommended') : category}</span>
+  return <span className={className}>{t(`app.newAppFromTemplate.sidebar.${category}`)}</span>
+}
+
+type AppCategoryIconProps = {
+  category: AppCategories
+}
+function AppCategoryIcon({ category }: AppCategoryIconProps) {
+  if (category === AppCategories.AGENT)
+    return <RiSpeakAiFill className='w-3.5 h-3.5 text-components-avatar-shape-fill-stop-100' />
+  if (category === AppCategories.ASSISTANT)
+    return <RiChatSmileAiFill className='w-3.5 h-3.5 text-components-avatar-shape-fill-stop-100' />
+  if (category === AppCategories.HR)
+    return <RiPassPendingFill className='w-3.5 h-3.5 text-components-avatar-shape-fill-stop-100' />
+  if (category === AppCategories.PROGRAMMING)
+    return <RiTerminalBoxFill className='w-3.5 h-3.5 text-components-avatar-shape-fill-stop-100' />
+  if (category === AppCategories.RECOMMENDED)
+    return <RiThumbUpFill className='w-3.5 h-3.5 text-components-avatar-shape-fill-stop-100' />
+  if (category === AppCategories.WRITING)
+    return <RiQuillPenAiFill className='w-3.5 h-3.5 text-components-avatar-shape-fill-stop-100' />
+  if (category === AppCategories.WORKFLOW)
+    return <RiExchange2Fill className='w-3.5 h-3.5 text-components-avatar-shape-fill-stop-100' />
+  return <RiAppsFill className='w-3.5 h-3.5 text-components-avatar-shape-fill-stop-100' />
 }

web/app/components/app/create-from-dsl-modal/dsl-confirm-modal.tsx

@@ -1,46 +0,0 @@
-import { useTranslation } from 'react-i18next'
-import Modal from '@/app/components/base/modal'
-import Button from '@/app/components/base/button'
-
-type DSLConfirmModalProps = {
-  versions?: {
-    importedVersion: string
-    systemVersion: string
-  }
-  onCancel: () => void
-  onConfirm: () => void
-  confirmDisabled?: boolean
-}
-const DSLConfirmModal = ({
-  versions = { importedVersion: '', systemVersion: '' },
-  onCancel,
-  onConfirm,
-  confirmDisabled = false,
-}: DSLConfirmModalProps) => {
-  const { t } = useTranslation()
-
-  return (
-    <Modal
-      isShow
-      onClose={() => onCancel()}
-      className='w-[480px]'
-    >
-      <div className='flex flex-col items-start gap-2 self-stretch pb-4'>
-        <div className='title-2xl-semi-bold text-text-primary'>{t('app.newApp.appCreateDSLErrorTitle')}</div>
-        <div className='system-md-regular flex grow flex-col text-text-secondary'>
-          <div>{t('app.newApp.appCreateDSLErrorPart1')}</div>
-          <div>{t('app.newApp.appCreateDSLErrorPart2')}</div>
-          <br />
-          <div>{t('app.newApp.appCreateDSLErrorPart3')}<span className='system-md-medium'>{versions.importedVersion}</span></div>
-          <div>{t('app.newApp.appCreateDSLErrorPart4')}<span className='system-md-medium'>{versions.systemVersion}</span></div>
-        </div>
-      </div>
-      <div className='flex items-start justify-end gap-2 self-stretch pt-6'>
-        <Button variant='secondary' onClick={() => onCancel()}>{t('app.newApp.Cancel')}</Button>
-        <Button variant='primary' destructive onClick={onConfirm} disabled={confirmDisabled}>{t('app.newApp.Confirm')}</Button>
-      </div>
-    </Modal>
-  )
-}
-
-export default DSLConfirmModal

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

@@ -24,7 +24,7 @@ const OPTION_MAP = {
   iframe: {
     getContent: (url: string, token: string) =>
       `<iframe
- src="${url}/chat/${token}"
+ src="${url}/chatbot/${token}"
  style="width: 100%; height: 100%; min-height: 700px"
  frameborder="0"
  allow="microphone">
@@ -35,12 +35,12 @@ const OPTION_MAP = {
       `<script>
  window.difyChatbotConfig = {
   token: '${token}'${isTestEnv
-        ? `,
+  ? `,
   isDev: true`
-        : ''}${IS_CE_EDITION
-          ? `,
+  : ''}${IS_CE_EDITION
+  ? `,
   baseUrl: '${url}'`
-          : ''}
+  : ''}
  }
 </script>
 <script

web/app/components/base/chat/utils.ts

@@ -1,8 +1,6 @@
 import { UUID_NIL } from './constants'
 import type { IChatItem } from './chat/type'
 import type { ChatItem, ChatItemInTree } from './types'
-import { addFileInfos, sortAgentSorts } from '../../tools/utils'
-import { getProcessedFilesFromResponse } from '../file-uploader/utils'
 
 async function decodeBase64AndDecompress(base64String: string) {
   const binaryString = atob(base64String)
@@ -21,60 +19,6 @@ function getProcessedInputsFromUrlParams(): Record<string, any> {
   return inputs
 }
 
-function appendQAToChatList(chatList: ChatItem[], item: any) {
-  // we append answer first and then question since will reverse the whole chatList later
-  const answerFiles = item.message_files?.filter((file: any) => file.belongs_to === 'assistant') || []
-  chatList.push({
-    id: item.id,
-    content: item.answer,
-    agent_thoughts: addFileInfos(item.agent_thoughts ? sortAgentSorts(item.agent_thoughts) : item.agent_thoughts, item.message_files),
-    feedback: item.feedback,
-    isAnswer: true,
-    citation: item.retriever_resources,
-    message_files: getProcessedFilesFromResponse(answerFiles.map((item: any) => ({ ...item, related_id: item.id }))),
-  })
-  const questionFiles = item.message_files?.filter((file: any) => file.belongs_to === 'user') || []
-  chatList.push({
-    id: `question-${item.id}`,
-    content: item.query,
-    isAnswer: false,
-    message_files: getProcessedFilesFromResponse(questionFiles.map((item: any) => ({ ...item, related_id: item.id }))),
-  })
-}
-
-/**
- * Computes the latest thread messages from all messages of the conversation.
- * Same logic as backend codebase `api/core/prompt/utils/extract_thread_messages.py`
- *
- * @param fetchedMessages - The history chat list data from the backend, sorted by created_at in descending order. This includes all flattened history messages of the conversation.
- * @returns An array of ChatItems representing the latest thread.
- */
-
-
-function getPrevChatList(fetchedMessages: any[]) {
-  const ret: ChatItem[] = []
-  let nextMessageId = null
-
-  for (const item of fetchedMessages) {
-    if (!item.parent_message_id) {
-      appendQAToChatList(ret, item)
-      break
-    }
-
-    if (!nextMessageId) {
-      appendQAToChatList(ret, item)
-      nextMessageId = item.parent_message_id
-    }
-    else {
-      if (item.id === nextMessageId || nextMessageId === UUID_NIL) {
-        appendQAToChatList(ret, item)
-        nextMessageId = item.parent_message_id
-      }
-    }
-  }
-  return ret.reverse()
-}
-
 function isValidGeneratedAnswer(item?: ChatItem | ChatItemInTree): boolean {
   return !!item && item.isAnswer && !item.id.startsWith('answer-placeholder-') && !item.isOpeningStatement
 }
@@ -220,7 +164,6 @@ function getThreadMessages(tree: ChatItemInTree[], targetMessageId?: string): Ch
 export {
   getProcessedInputsFromUrlParams,
   isValidGeneratedAnswer,
-  getPrevChatList,
   getLastAnswer,
   buildChatItemTree,
   getThreadMessages,

web/app/components/explore/app-list/index.tsx

@@ -1,10 +1,12 @@
 'use client'
 
-import React, { useCallback, useMemo, useState } from 'react'
+import React, { useMemo, useState } from 'react'
+import { useRouter } from 'next/navigation'
 import { useTranslation } from 'react-i18next'
 import { useContext } from 'use-context-selector'
 import useSWR from 'swr'
 import { useDebounceFn } from 'ahooks'
+import Toast from '../../base/toast'
 import s from './style.module.css'
 import cn from '@/utils/classnames'
 import ExploreContext from '@/context/explore-context'
@@ -12,17 +14,17 @@ import type { App } from '@/models/explore'
 import Category from '@/app/components/explore/category'
 import AppCard from '@/app/components/explore/app-card'
 import { fetchAppDetail, fetchAppList } from '@/service/explore'
+import { importDSL } from '@/service/apps'
 import { useTabSearchParams } from '@/hooks/use-tab-searchparams'
 import CreateAppModal from '@/app/components/explore/create-app-modal'
 import AppTypeSelector from '@/app/components/app/type-selector'
 import type { CreateAppModalProps } from '@/app/components/explore/create-app-modal'
 import Loading from '@/app/components/base/loading'
+import { NEED_REFRESH_APP_LIST_KEY } from '@/config'
+import { useAppContext } from '@/context/app-context'
+import { getRedirection } from '@/utils/app-redirection'
 import Input from '@/app/components/base/input'
-import {
-  DSLImportMode,
-} from '@/models/app'
-import { useImportDSL } from '@/hooks/use-import-dsl'
-import DSLConfirmModal from '@/app/components/app/create-from-dsl-modal/dsl-confirm-modal'
+import { DSLImportMode } from '@/models/app'
 
 type AppsProps = {
   pageType?: PageType
@@ -39,6 +41,8 @@ const Apps = ({
   onSuccess,
 }: AppsProps) => {
   const { t } = useTranslation()
+  const { isCurrentWorkspaceEditor } = useAppContext()
+  const { push } = useRouter()
   const { hasEditPermission } = useContext(ExploreContext)
   const allCategoriesEn = t('explore.apps.allCategories', { lng: 'en' })
 
@@ -113,14 +117,6 @@ const Apps = ({
 
   const [currApp, setCurrApp] = React.useState<App | null>(null)
   const [isShowCreateModal, setIsShowCreateModal] = React.useState(false)
-
-  const {
-    handleImportDSL,
-    handleImportDSLConfirm,
-    versions,
-    isFetching,
-  } = useImportDSL()
-  const [showDSLConfirmModal, setShowDSLConfirmModal] = useState(false)
   const onCreate: CreateAppModalProps['onConfirm'] = async ({
     name,
     icon_type,
@@ -131,31 +127,31 @@ const Apps = ({
     const { export_data } = await fetchAppDetail(
       currApp?.app.id as string,
     )
-    const payload = {
-      mode: DSLImportMode.YAML_CONTENT,
-      yaml_content: export_data,
-      name,
-      icon_type,
-      icon,
-      icon_background,
-      description,
+    try {
+      const app = await importDSL({
+        mode: DSLImportMode.YAML_CONTENT,
+        yaml_content: export_data,
+        name,
+        icon_type,
+        icon,
+        icon_background,
+        description,
+      })
+      setIsShowCreateModal(false)
+      Toast.notify({
+        type: 'success',
+        message: t('app.newApp.appCreated'),
+      })
+      if (onSuccess)
+        onSuccess()
+      localStorage.setItem(NEED_REFRESH_APP_LIST_KEY, '1')
+      getRedirection(isCurrentWorkspaceEditor, { id: app.app_id }, push)
+    }
+    catch (e) {
+      Toast.notify({ type: 'error', message: t('app.newApp.appCreateFailed') })
     }
-    await handleImportDSL(payload, {
-      onSuccess: () => {
-        setIsShowCreateModal(false)
-      },
-      onPending: () => {
-        setShowDSLConfirmModal(true)
-      },
-    })
   }
 
-  const onConfirmDSL = useCallback(async () => {
-    await handleImportDSLConfirm({
-      onSuccess,
-    })
-  }, [handleImportDSLConfirm, onSuccess])
-
   if (!categories || categories.length === 0) {
     return (
       <div className="flex h-full items-center">
@@ -238,20 +234,9 @@ const Apps = ({
           appDescription={currApp?.app.description || ''}
           show={isShowCreateModal}
           onConfirm={onCreate}
-          confirmDisabled={isFetching}
           onHide={() => setIsShowCreateModal(false)}
         />
       )}
-      {
-        showDSLConfirmModal && (
-          <DSLConfirmModal
-            versions={versions}
-            onCancel={() => setShowDSLConfirmModal(false)}
-            onConfirm={onConfirmDSL}
-            confirmDisabled={isFetching}
-          />
-        )
-      }
     </div>
   )
 }

web/app/components/explore/create-app-modal/index.tsx

@@ -33,7 +33,6 @@ export type CreateAppModalProps = {
     description: string
     use_icon_as_answer_icon?: boolean
   }) => Promise<void>
-  confirmDisabled?: boolean
   onHide: () => void
 }
 
@@ -49,7 +48,6 @@ const CreateAppModal = ({
   appMode,
   appUseIconAsAnswerIcon,
   onConfirm,
-  confirmDisabled,
   onHide,
 }: CreateAppModalProps) => {
   const { t } = useTranslation()
@@ -147,7 +145,7 @@ const CreateAppModal = ({
           {!isEditModal && isAppsFull && <AppsFull loc='app-explore-create' />}
         </div>
         <div className='flex flex-row-reverse'>
-          <Button disabled={(!isEditModal && isAppsFull) || !name.trim() || confirmDisabled} className='w-24 ml-2' variant='primary' onClick={submit}>{!isEditModal ? t('common.operation.create') : t('common.operation.save')}</Button>
+          <Button disabled={!isEditModal && isAppsFull} className='w-24 ml-2' variant='primary' onClick={submit}>{!isEditModal ? t('common.operation.create') : t('common.operation.save')}</Button>
           <Button className='w-24' onClick={onHide}>{t('common.operation.cancel')}</Button>
         </div>
       </Modal>

web/app/reset-password/check-code/page.tsx

@@ -39,11 +39,7 @@ export default function CheckCode() {
       }
       setIsLoading(true)
       const ret = await verifyResetPasswordCode({ email, code, token })
-      if (ret.is_valid) {
-        const params = new URLSearchParams(searchParams)
-        params.set('token', encodeURIComponent(ret.token))
-        router.push(`/reset-password/set-password?${params.toString()}`)
-      }
+      ret.is_valid && router.push(`/reset-password/set-password?${searchParams.toString()}`)
     }
     catch (error) { console.error(error) }
     finally {

web/docker/entrypoint.sh

@@ -23,7 +23,6 @@ export NEXT_TELEMETRY_DISABLED=${NEXT_TELEMETRY_DISABLED}
 
 export NEXT_PUBLIC_TEXT_GENERATION_TIMEOUT_MS=${TEXT_GENERATION_TIMEOUT_MS}
 export NEXT_PUBLIC_CSP_WHITELIST=${CSP_WHITELIST}
-export NEXT_PUBLIC_ALLOW_EMBED=${ALLOW_EMBED}
 export NEXT_PUBLIC_TOP_K_MAX_VALUE=${TOP_K_MAX_VALUE}
 export NEXT_PUBLIC_INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH=${INDEXING_MAX_SEGMENTATION_TOKENS_LENGTH}
 

web/hooks/use-import-dsl.ts

@@ -1,158 +0,0 @@
-import {
-  useCallback,
-  useRef,
-  useState,
-} from 'react'
-import { useTranslation } from 'react-i18next'
-import { useRouter } from 'next/navigation'
-import type {
-  DSLImportMode,
-  DSLImportResponse,
-} from '@/models/app'
-import { DSLImportStatus } from '@/models/app'
-import {
-  importDSL,
-  importDSLConfirm,
-} from '@/service/apps'
-import type { AppIconType } from '@/types/app'
-import { useToastContext } from '@/app/components/base/toast'
-import { getRedirection } from '@/utils/app-redirection'
-import { useSelector } from '@/context/app-context'
-import { NEED_REFRESH_APP_LIST_KEY } from '@/config'
-
-type DSLPayload = {
-  mode: DSLImportMode
-  yaml_content?: string
-  yaml_url?: string
-  name?: string
-  icon_type?: AppIconType
-  icon?: string
-  icon_background?: string
-  description?: string
-}
-type ResponseCallback = {
-  onSuccess?: () => void
-  onPending?: (payload: DSLImportResponse) => void
-  onFailed?: () => void
-}
-export const useImportDSL = () => {
-  const { t } = useTranslation()
-  const { notify } = useToastContext()
-  const [isFetching, setIsFetching] = useState(false)
-  const isCurrentWorkspaceEditor = useSelector(s => s.isCurrentWorkspaceEditor)
-  const { push } = useRouter()
-  const [versions, setVersions] = useState<{ importedVersion: string; systemVersion: string }>()
-  const importIdRef = useRef<string>('')
-
-  const handleImportDSL = useCallback(async (
-    payload: DSLPayload,
-    {
-      onSuccess,
-      onPending,
-      onFailed,
-    }: ResponseCallback,
-  ) => {
-    if (isFetching)
-      return
-    setIsFetching(true)
-
-    try {
-      const response = await importDSL(payload)
-
-      if (!response)
-        return
-
-      const {
-        id,
-        status,
-        app_id,
-        imported_dsl_version,
-        current_dsl_version,
-      } = response
-
-      if (status === DSLImportStatus.COMPLETED || status === DSLImportStatus.COMPLETED_WITH_WARNINGS) {
-        if (!app_id)
-          return
-
-        notify({
-          type: status === DSLImportStatus.COMPLETED ? 'success' : 'warning',
-          message: t(status === DSLImportStatus.COMPLETED ? 'app.newApp.appCreated' : 'app.newApp.caution'),
-          children: status === DSLImportStatus.COMPLETED_WITH_WARNINGS && t('app.newApp.appCreateDSLWarning'),
-        })
-        onSuccess?.()
-        localStorage.setItem(NEED_REFRESH_APP_LIST_KEY, '1')
-        getRedirection(isCurrentWorkspaceEditor, { id: app_id }, push)
-      }
-      else if (status === DSLImportStatus.PENDING) {
-        setVersions({
-          importedVersion: imported_dsl_version ?? '',
-          systemVersion: current_dsl_version ?? '',
-        })
-        importIdRef.current = id
-        onPending?.(response)
-      }
-      else {
-        notify({ type: 'error', message: t('app.newApp.appCreateFailed') })
-        onFailed?.()
-      }
-    }
-    catch {
-      notify({ type: 'error', message: t('app.newApp.appCreateFailed') })
-      onFailed?.()
-    }
-    finally {
-      setIsFetching(false)
-    }
-  }, [t, notify, isCurrentWorkspaceEditor, push, isFetching])
-
-  const handleImportDSLConfirm = useCallback(async (
-    {
-      onSuccess,
-      onFailed,
-    }: Pick<ResponseCallback, 'onSuccess' | 'onFailed'>,
-  ) => {
-    if (isFetching)
-      return
-    setIsFetching(true)
-    if (!importIdRef.current)
-      return
-
-    try {
-      const response = await importDSLConfirm({
-        import_id: importIdRef.current,
-      })
-
-      const { status, app_id } = response
-      if (!app_id)
-        return
-
-      if (status === DSLImportStatus.COMPLETED) {
-        onSuccess?.()
-        notify({
-          type: 'success',
-          message: t('app.newApp.appCreated'),
-        })
-        localStorage.setItem(NEED_REFRESH_APP_LIST_KEY, '1')
-        getRedirection(isCurrentWorkspaceEditor, { id: app_id! }, push)
-      }
-      else if (status === DSLImportStatus.FAILED) {
-        notify({ type: 'error', message: t('app.newApp.appCreateFailed') })
-        onFailed?.()
-      }
-    }
-    catch {
-      notify({ type: 'error', message: t('app.newApp.appCreateFailed') })
-      onFailed?.()
-    }
-    finally {
-      setIsFetching(false)
-    }
-  }, [t, notify, isCurrentWorkspaceEditor, push, isFetching])
-
-  return {
-    handleImportDSL,
-    handleImportDSLConfirm,
-    versions,
-    isFetching,
-  }
-}

web/middleware.ts

@@ -3,26 +3,10 @@ import { NextResponse } from 'next/server'
 
 const NECESSARY_DOMAIN = '*.sentry.io http://localhost:* http://127.0.0.1:* https://analytics.google.com googletagmanager.com *.googletagmanager.com https://www.google-analytics.com https://api.github.com'
 
-const wrapResponseWithXFrameOptions = (response: NextResponse, pathname: string) => {
-  // prevent clickjacking: https://owasp.org/www-community/attacks/Clickjacking
-  // Chatbot page should be allowed to be embedded in iframe. It's a feature
-  if (process.env.NEXT_PUBLIC_ALLOW_EMBED !== 'true' && !pathname.startsWith('/chat'))
-    response.headers.set('X-Frame-Options', 'DENY')
-
-  return response
-}
 export function middleware(request: NextRequest) {
-  const { pathname } = request.nextUrl
-  const requestHeaders = new Headers(request.headers)
-  const response = NextResponse.next({
-    request: {
-      headers: requestHeaders,
-    },
-  })
-
   const isWhiteListEnabled = !!process.env.NEXT_PUBLIC_CSP_WHITELIST && process.env.NODE_ENV === 'production'
   if (!isWhiteListEnabled)
-    return wrapResponseWithXFrameOptions(response, pathname)
+    return NextResponse.next()
 
   const whiteList = `${process.env.NEXT_PUBLIC_CSP_WHITELIST} ${NECESSARY_DOMAIN}`
   const nonce = Buffer.from(crypto.randomUUID()).toString('base64')
@@ -49,6 +33,7 @@ export function middleware(request: NextRequest) {
     .replace(/\s{2,}/g, ' ')
     .trim()
 
+  const requestHeaders = new Headers(request.headers)
   requestHeaders.set('x-nonce', nonce)
 
   requestHeaders.set(
@@ -56,12 +41,17 @@ export function middleware(request: NextRequest) {
     contentSecurityPolicyHeaderValue,
   )
 
+  const response = NextResponse.next({
+    request: {
+      headers: requestHeaders,
+    },
+  })
   response.headers.set(
     'Content-Security-Policy',
     contentSecurityPolicyHeaderValue,
   )
 
-  return wrapResponseWithXFrameOptions(response, pathname)
+  return response
 }
 
 export const config = {
@@ -83,4 +73,4 @@ export const config = {
       // ],
     },
   ],
-}
+}

web/package.json

@@ -1,6 +1,6 @@
 {
   "name": "dify-web",
-  "version": "0.15.7",
+  "version": "0.15.6-alpha.1",
   "private": true,
   "engines": {
     "node": ">=18.17.0"

web/service/common.ts

@@ -40,7 +40,7 @@ import type { SystemFeatures } from '@/types/feature'
 
 type LoginSuccess = {
   result: 'success'
-  data: { access_token: string; refresh_token: string }
+  data: { access_token: string;refresh_token: string }
 }
 type LoginFail = {
   result: 'fail'
@@ -331,20 +331,20 @@ export const uploadRemoteFileInfo = (url: string, isPublic?: boolean) => {
 export const sendEMailLoginCode = (email: string, language = 'en-US') =>
   post<CommonResponse & { data: string }>('/email-code-login', { body: { email, language } })
 
-export const emailLoginWithCode = (data: { email: string; code: string; token: string }) =>
+export const emailLoginWithCode = (data: { email: string;code: string;token: string }) =>
   post<LoginResponse>('/email-code-login/validity', { body: data })
 
 export const sendResetPasswordCode = (email: string, language = 'en-US') =>
-  post<CommonResponse & { data: string; message?: string; code?: string }>('/forgot-password', { body: { email, language } })
+  post<CommonResponse & { data: string;message?: string ;code?: string }>('/forgot-password', { body: { email, language } })
 
-export const verifyResetPasswordCode = (body: { email: string; code: string; token: string }) =>
-  post<CommonResponse & { is_valid: boolean; token: string }>('/forgot-password/validity', { body })
+export const verifyResetPasswordCode = (body: { email: string;code: string;token: string }) =>
+  post<CommonResponse & { is_valid: boolean }>('/forgot-password/validity', { body })
 
 export const sendDeleteAccountCode = () =>
   get<CommonResponse & { data: string }>('/account/delete/verify')
 
-export const verifyDeleteAccountCode = (body: { code: string; token: string }) =>
+export const verifyDeleteAccountCode = (body: { code: string;token: string }) =>
   post<CommonResponse & { is_valid: boolean }>('/account/delete', { body })
 
-export const submitDeleteAccountFeedback = (body: { feedback: string; email: string }) =>
+export const submitDeleteAccountFeedback = (body: { feedback: string;email: string }) =>
   post<CommonResponse>('/account/delete/feedback', { body })