fix the tooltip for the knowledge base's firecrawl max depth attribute

Co-authored-by: 莫岳恒 <moyueheng@datagrand.com>

api/Dockerfile

@@ -55,7 +55,7 @@ RUN apt-get update \
     && echo "deb http://deb.debian.org/debian testing main" > /etc/apt/sources.list \
     && apt-get update \
     # For Security
-    && apt-get install -y --no-install-recommends zlib1g=1:1.3.dfsg+really1.3.1-1 expat=2.6.2-1 libldap-2.5-0=2.5.18+dfsg-2 perl=5.38.2-5 libsqlite3-0=3.46.0-1 \
+    && apt-get install -y --no-install-recommends zlib1g=1:1.3.dfsg+really1.3.1-1 expat=2.6.2-1 libldap-2.5-0=2.5.18+dfsg-3 perl=5.38.2-5 libsqlite3-0=3.46.0-1 \
     && apt-get autoremove -y \
     && rm -rf /var/lib/apt/lists/*
 

api/configs/feature/__init__.py

@@ -1,6 +1,6 @@
 from typing import Optional
 
-from pydantic import AliasChoices, Field, NegativeInt, NonNegativeInt, PositiveInt, computed_field
+from pydantic import AliasChoices, Field, HttpUrl, NegativeInt, NonNegativeInt, PositiveInt, computed_field
 from pydantic_settings import BaseSettings
 
 from configs.feature.hosted_service import HostedServiceConfig
@@ -45,7 +45,7 @@ class CodeExecutionSandboxConfig(BaseSettings):
     Code Execution Sandbox configs
     """
 
-    CODE_EXECUTION_ENDPOINT: str = Field(
+    CODE_EXECUTION_ENDPOINT: HttpUrl = Field(
         description="endpoint URL of code execution servcie",
         default="http://sandbox:8194",
     )
@@ -55,6 +55,21 @@ class CodeExecutionSandboxConfig(BaseSettings):
         default="dify-sandbox",
     )
 
+    CODE_EXECUTION_CONNECT_TIMEOUT: Optional[float] = Field(
+        description="connect timeout in seconds for code execution request",
+        default=10.0,
+    )
+
+    CODE_EXECUTION_READ_TIMEOUT: Optional[float] = Field(
+        description="read timeout in seconds for code execution request",
+        default=60.0,
+    )
+
+    CODE_EXECUTION_WRITE_TIMEOUT: Optional[float] = Field(
+        description="write timeout in seconds for code execution request",
+        default=10.0,
+    )
+
     CODE_MAX_NUMBER: PositiveInt = Field(
         description="max depth for code execution",
         default=9223372036854775807,

api/configs/middleware/__init__.py

@@ -13,6 +13,7 @@ from configs.middleware.storage.oci_storage_config import OCIStorageConfig
 from configs.middleware.storage.tencent_cos_storage_config import TencentCloudCOSStorageConfig
 from configs.middleware.vdb.analyticdb_config import AnalyticdbConfig
 from configs.middleware.vdb.chroma_config import ChromaConfig
+from configs.middleware.vdb.elasticsearch_config import ElasticsearchConfig
 from configs.middleware.vdb.milvus_config import MilvusConfig
 from configs.middleware.vdb.myscale_config import MyScaleConfig
 from configs.middleware.vdb.opensearch_config import OpenSearchConfig
@@ -200,5 +201,6 @@ class MiddlewareConfig(
     TencentVectorDBConfig,
     TiDBVectorConfig,
     WeaviateConfig,
+    ElasticsearchConfig,
 ):
     pass

api/configs/middleware/vdb/elasticsearch_config.py

@@ -0,0 +1,30 @@
+from typing import Optional
+
+from pydantic import Field, PositiveInt
+from pydantic_settings import BaseSettings
+
+
+class ElasticsearchConfig(BaseSettings):
+    """
+    Elasticsearch configs
+    """
+
+    ELASTICSEARCH_HOST: Optional[str] = Field(
+        description="Elasticsearch host",
+        default="127.0.0.1",
+    )
+
+    ELASTICSEARCH_PORT: PositiveInt = Field(
+        description="Elasticsearch port",
+        default=9200,
+    )
+
+    ELASTICSEARCH_USERNAME: Optional[str] = Field(
+        description="Elasticsearch username",
+        default="elastic",
+    )
+
+    ELASTICSEARCH_PASSWORD: Optional[str] = Field(
+        description="Elasticsearch password",
+        default="elastic",
+    )

api/controllers/console/app/model_config.py

@@ -32,6 +32,8 @@ class ModelConfigResource(Resource):
 
         new_app_model_config = AppModelConfig(
             app_id=app_model.id,
+            created_by=current_user.id,
+            updated_by=current_user.id,
         )
         new_app_model_config = new_app_model_config.from_model_config_dict(model_configuration)
 

api/controllers/console/app/site.py

@@ -1,3 +1,5 @@
+from datetime import datetime, timezone
+
 from flask_login import current_user
 from flask_restful import Resource, marshal_with, reqparse
 from werkzeug.exceptions import Forbidden, NotFound
@@ -71,6 +73,8 @@ class AppSite(Resource):
             if value is not None:
                 setattr(site, attr_name, value)
 
+        site.updated_by = current_user.id
+        site.updated_at = datetime.now(timezone.utc).replace(tzinfo=None)
         db.session.commit()
 
         return site
@@ -93,6 +97,8 @@ class AppSiteAccessTokenReset(Resource):
             raise NotFound
 
         site.code = Site.generate_code(16)
+        site.updated_by = current_user.id
+        site.updated_at = datetime.now(timezone.utc).replace(tzinfo=None)
         db.session.commit()
 
         return site

api/controllers/console/app/statistic.py

@@ -16,6 +16,60 @@ from libs.login import login_required
 from models.model import AppMode
 
 
+class DailyMessageStatistic(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    @get_app_model
+    def get(self, app_model):
+        account = current_user
+
+        parser = reqparse.RequestParser()
+        parser.add_argument("start", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
+        parser.add_argument("end", type=datetime_string("%Y-%m-%d %H:%M"), location="args")
+        args = parser.parse_args()
+
+        sql_query = """
+        SELECT date(DATE_TRUNC('day', created_at AT TIME ZONE 'UTC' AT TIME ZONE :tz )) AS date, count(*) AS message_count
+            FROM messages where app_id = :app_id 
+        """
+        arg_dict = {"tz": account.timezone, "app_id": app_model.id}
+
+        timezone = pytz.timezone(account.timezone)
+        utc_timezone = pytz.utc
+
+        if args["start"]:
+            start_datetime = datetime.strptime(args["start"], "%Y-%m-%d %H:%M")
+            start_datetime = start_datetime.replace(second=0)
+
+            start_datetime_timezone = timezone.localize(start_datetime)
+            start_datetime_utc = start_datetime_timezone.astimezone(utc_timezone)
+
+            sql_query += " and created_at >= :start"
+            arg_dict["start"] = start_datetime_utc
+
+        if args["end"]:
+            end_datetime = datetime.strptime(args["end"], "%Y-%m-%d %H:%M")
+            end_datetime = end_datetime.replace(second=0)
+
+            end_datetime_timezone = timezone.localize(end_datetime)
+            end_datetime_utc = end_datetime_timezone.astimezone(utc_timezone)
+
+            sql_query += " and created_at < :end"
+            arg_dict["end"] = end_datetime_utc
+
+        sql_query += " GROUP BY date order by date"
+
+        response_data = []
+
+        with db.engine.begin() as conn:
+            rs = conn.execute(db.text(sql_query), arg_dict)
+            for i in rs:
+                response_data.append({"date": str(i.date), "message_count": i.message_count})
+
+        return jsonify({"data": response_data})
+
+
 class DailyConversationStatistic(Resource):
     @setup_required
     @login_required
@@ -419,6 +473,7 @@ WHERE app_id = :app_id"""
         return jsonify({"data": response_data})
 
 
+api.add_resource(DailyMessageStatistic, "/apps/<uuid:app_id>/statistics/daily-messages")
 api.add_resource(DailyConversationStatistic, "/apps/<uuid:app_id>/statistics/daily-conversations")
 api.add_resource(DailyTerminalsStatistic, "/apps/<uuid:app_id>/statistics/daily-end-users")
 api.add_resource(DailyTokenCostStatistic, "/apps/<uuid:app_id>/statistics/token-costs")

api/controllers/console/datasets/datasets_document.py

@@ -599,6 +599,7 @@ class DocumentDetailApi(DocumentResource):
                 "hit_count": document.hit_count,
                 "display_status": document.display_status,
                 "doc_form": document.doc_form,
+                "doc_language": document.doc_language,
             }
         else:
             process_rules = DatasetService.get_process_rules(dataset_id)
@@ -631,6 +632,7 @@ class DocumentDetailApi(DocumentResource):
                 "hit_count": document.hit_count,
                 "display_status": document.display_status,
                 "doc_form": document.doc_form,
+                "doc_language": document.doc_language,
             }
 
         return response, 200

api/core/helper/code_executor/code_executor.py

@@ -15,12 +15,6 @@ from core.helper.code_executor.template_transformer import TemplateTransformer
 
 logger = logging.getLogger(__name__)
 
-# Code Executor
-CODE_EXECUTION_ENDPOINT = dify_config.CODE_EXECUTION_ENDPOINT
-CODE_EXECUTION_API_KEY = dify_config.CODE_EXECUTION_API_KEY
-
-CODE_EXECUTION_TIMEOUT = Timeout(connect=10, write=10, read=60, pool=None)
-
 class CodeExecutionException(Exception):
     pass
 
@@ -71,10 +65,10 @@ class CodeExecutor:
         :param code: code
         :return:
         """
-        url = URL(CODE_EXECUTION_ENDPOINT) / 'v1' / 'sandbox' / 'run'
+        url = URL(str(dify_config.CODE_EXECUTION_ENDPOINT)) / 'v1' / 'sandbox' / 'run'
 
         headers = {
-            'X-Api-Key': CODE_EXECUTION_API_KEY
+            'X-Api-Key': dify_config.CODE_EXECUTION_API_KEY
         }
 
         data = {
@@ -85,7 +79,12 @@ class CodeExecutor:
         }
 
         try:
-            response = post(str(url), json=data, headers=headers, timeout=CODE_EXECUTION_TIMEOUT)
+            response = post(str(url), json=data, headers=headers,
+                            timeout=Timeout(
+                                connect=dify_config.CODE_EXECUTION_CONNECT_TIMEOUT,
+                                read=dify_config.CODE_EXECUTION_READ_TIMEOUT,
+                                write=dify_config.CODE_EXECUTION_WRITE_TIMEOUT,
+                                pool=None))
             if response.status_code == 503:
                 raise CodeExecutionException('Code execution service is unavailable')
             elif response.status_code != 200:
@@ -96,7 +95,7 @@ class CodeExecutor:
             raise CodeExecutionException('Failed to execute code, which is likely a network issue,'
                                          ' please check if the sandbox service is running.'
                                          f' ( Error: {str(e)} )')
-        
+
         try:
             response = response.json()
         except:
@@ -104,12 +103,12 @@ class CodeExecutor:
 
         if (code := response.get('code')) != 0:
             raise CodeExecutionException(f"Got error code: {code}. Got error msg: {response.get('message')}")
-        
+
         response = CodeExecutionResponse(**response)
-        
+
         if response.data.error:
             raise CodeExecutionException(response.data.error)
-        
+
         return response.data.stdout or ''
 
     @classmethod
@@ -133,4 +132,3 @@ class CodeExecutor:
             raise e
 
         return template_transformer.transform_response(response)
-    

api/core/indexing_runner.py

@@ -720,6 +720,7 @@ class IndexingRunner:
                 document_ids = [document.metadata['doc_id'] for document in documents]
                 db.session.query(DocumentSegment).filter(
                     DocumentSegment.document_id == document_id,
+                    DocumentSegment.dataset_id == dataset_id,
                     DocumentSegment.index_node_id.in_(document_ids),
                     DocumentSegment.status == "indexing"
                 ).update({
@@ -751,6 +752,7 @@ class IndexingRunner:
             document_ids = [document.metadata['doc_id'] for document in chunk_documents]
             db.session.query(DocumentSegment).filter(
                 DocumentSegment.document_id == dataset_document.id,
+                DocumentSegment.dataset_id == dataset.id,
                 DocumentSegment.index_node_id.in_(document_ids),
                 DocumentSegment.status == "indexing"
             ).update({

api/core/model_runtime/model_providers/azure_ai_studio/azure_ai_studio.py

@@ -0,0 +1,17 @@
+import logging
+
+from core.model_runtime.model_providers.__base.model_provider import ModelProvider
+
+logger = logging.getLogger(__name__)
+
+
+class AzureAIStudioProvider(ModelProvider):
+    def validate_provider_credentials(self, credentials: dict) -> None:
+        """
+        Validate provider credentials
+
+        if validate failed, raise exception
+
+        :param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
+        """
+        pass

api/core/model_runtime/model_providers/azure_ai_studio/azure_ai_studio.yaml

@@ -0,0 +1,65 @@
+provider: azure_ai_studio
+label:
+  zh_Hans: Azure AI Studio
+  en_US: Azure AI Studio
+icon_small:
+  en_US: icon_s_en.png
+icon_large:
+  en_US: icon_l_en.png
+description:
+  en_US: Azure AI Studio
+  zh_Hans: Azure AI Studio
+background: "#93c5fd"
+help:
+  title:
+    en_US: How to deploy customized model on Azure AI Studio
+    zh_Hans: 如何在Azure AI Studio上的私有化部署的模型
+  url:
+    en_US: https://learn.microsoft.com/en-us/azure/ai-studio/how-to/deploy-models
+    zh_Hans: https://learn.microsoft.com/zh-cn/azure/ai-studio/how-to/deploy-models
+supported_model_types:
+  - llm
+  - rerank
+configurate_methods:
+  - customizable-model
+model_credential_schema:
+  model:
+    label:
+      en_US: Model Name
+      zh_Hans: 模型名称
+    placeholder:
+      en_US: Enter your model name
+      zh_Hans: 输入模型名称
+  credential_form_schemas:
+    - variable: endpoint
+      label:
+        en_US: Azure AI Studio Endpoint
+      type: text-input
+      required: true
+      placeholder:
+        zh_Hans: 请输入你的Azure AI Studio推理端点
+        en_US: 'Enter your API Endpoint, eg: https://example.com'
+    - variable: api_key
+      required: true
+      label:
+        en_US: API Key
+        zh_Hans: API Key
+      type: secret-input
+      placeholder:
+        en_US: Enter your Azure AI Studio API Key
+        zh_Hans: 在此输入您的 Azure AI Studio API Key
+      show_on:
+        - variable: __model_type
+          value: llm
+    - variable: jwt_token
+      required: true
+      label:
+        en_US: JWT Token
+        zh_Hans: JWT令牌
+      type: secret-input
+      placeholder:
+        en_US: Enter your Azure AI Studio JWT Token
+        zh_Hans: 在此输入您的 Azure AI Studio 推理 API Key
+      show_on:
+        - variable: __model_type
+          value: rerank

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

@@ -0,0 +1,334 @@
+import logging
+from collections.abc import Generator
+from typing import Any, Optional, Union
+
+from azure.ai.inference import ChatCompletionsClient
+from azure.ai.inference.models import StreamingChatCompletionsUpdate
+from azure.core.credentials import AzureKeyCredential
+from azure.core.exceptions import (
+    ClientAuthenticationError,
+    DecodeError,
+    DeserializationError,
+    HttpResponseError,
+    ResourceExistsError,
+    ResourceModifiedError,
+    ResourceNotFoundError,
+    ResourceNotModifiedError,
+    SerializationError,
+    ServiceRequestError,
+    ServiceResponseError,
+)
+
+from core.model_runtime.callbacks.base_callback import Callback
+from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage
+from core.model_runtime.entities.message_entities import (
+    AssistantPromptMessage,
+    PromptMessage,
+    PromptMessageTool,
+)
+from core.model_runtime.entities.model_entities import (
+    AIModelEntity,
+    FetchFrom,
+    I18nObject,
+    ModelType,
+    ParameterRule,
+    ParameterType,
+)
+from core.model_runtime.errors.invoke import (
+    InvokeAuthorizationError,
+    InvokeBadRequestError,
+    InvokeConnectionError,
+    InvokeError,
+    InvokeServerUnavailableError,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
+
+logger = logging.getLogger(__name__)
+
+
+class AzureAIStudioLargeLanguageModel(LargeLanguageModel):
+    """
+    Model class for Azure AI Studio large language model.
+    """
+
+    client: Any = None
+
+    from azure.ai.inference.models import StreamingChatCompletionsUpdate
+
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        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: model credentials
+        :param prompt_messages: prompt messages
+        :param model_parameters: model parameters
+        :param tools: tools for tool calling
+        :param stop: stop words
+        :param stream: is stream response
+        :param user: unique user id
+        :return: full response or stream response chunk generator result
+        """
+
+        if not self.client:
+            endpoint = credentials.get("endpoint")
+            api_key = credentials.get("api_key")
+            self.client = ChatCompletionsClient(endpoint=endpoint, credential=AzureKeyCredential(api_key))
+
+        messages = [{"role": msg.role.value, "content": msg.content} for msg in prompt_messages]
+
+        payload = {
+            "messages": messages,
+            "max_tokens": model_parameters.get("max_tokens", 4096),
+            "temperature": model_parameters.get("temperature", 0),
+            "top_p": model_parameters.get("top_p", 1),
+            "stream": stream,
+        }
+
+        if stop:
+            payload["stop"] = stop
+
+        if tools:
+            payload["tools"] = [tool.model_dump() for tool in tools]
+
+        try:
+            response = self.client.complete(**payload)
+
+            if stream:
+                return self._handle_stream_response(response, model, prompt_messages)
+            else:
+                return self._handle_non_stream_response(response, model, prompt_messages, credentials)
+        except Exception as e:
+            raise self._transform_invoke_error(e)
+
+    def _handle_stream_response(self, response, model: str, prompt_messages: list[PromptMessage]) -> Generator:
+        for chunk in response:
+            if isinstance(chunk, StreamingChatCompletionsUpdate):
+                if chunk.choices:
+                    delta = chunk.choices[0].delta
+                    if delta.content:
+                        yield LLMResultChunk(
+                            model=model,
+                            prompt_messages=prompt_messages,
+                            delta=LLMResultChunkDelta(
+                                index=0,
+                                message=AssistantPromptMessage(content=delta.content, tool_calls=[]),
+                            ),
+                        )
+
+    def _handle_non_stream_response(
+        self, response, model: str, prompt_messages: list[PromptMessage], credentials: dict
+    ) -> LLMResult:
+        assistant_text = response.choices[0].message.content
+        assistant_prompt_message = AssistantPromptMessage(content=assistant_text)
+        usage = self._calc_response_usage(
+            model, credentials, response.usage.prompt_tokens, response.usage.completion_tokens
+        )
+        result = LLMResult(model=model, prompt_messages=prompt_messages, message=assistant_prompt_message, usage=usage)
+
+        if hasattr(response, "system_fingerprint"):
+            result.system_fingerprint = response.system_fingerprint
+
+        return result
+
+    def _invoke_result_generator(
+        self,
+        model: str,
+        result: Generator,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
+        stop: Optional[list[str]] = None,
+        stream: bool = True,
+        user: Optional[str] = None,
+        callbacks: Optional[list[Callback]] = None,
+    ) -> Generator:
+        """
+        Invoke result generator
+
+        :param result: result generator
+        :return: result generator
+        """
+        callbacks = callbacks or []
+        prompt_message = AssistantPromptMessage(content="")
+        usage = None
+        system_fingerprint = None
+        real_model = model
+
+        try:
+            for chunk in result:
+                if isinstance(chunk, dict):
+                    content = chunk["choices"][0]["message"]["content"]
+                    usage = chunk["usage"]
+                    chunk = LLMResultChunk(
+                        model=model,
+                        prompt_messages=prompt_messages,
+                        delta=LLMResultChunkDelta(
+                            index=0,
+                            message=AssistantPromptMessage(content=content, tool_calls=[]),
+                        ),
+                        system_fingerprint=chunk.get("system_fingerprint"),
+                    )
+
+                yield chunk
+
+                self._trigger_new_chunk_callbacks(
+                    chunk=chunk,
+                    model=model,
+                    credentials=credentials,
+                    prompt_messages=prompt_messages,
+                    model_parameters=model_parameters,
+                    tools=tools,
+                    stop=stop,
+                    stream=stream,
+                    user=user,
+                    callbacks=callbacks,
+                )
+
+                prompt_message.content += chunk.delta.message.content
+                real_model = chunk.model
+                if hasattr(chunk.delta, "usage"):
+                    usage = chunk.delta.usage
+
+                if chunk.system_fingerprint:
+                    system_fingerprint = chunk.system_fingerprint
+        except Exception as e:
+            raise self._transform_invoke_error(e)
+
+        self._trigger_after_invoke_callbacks(
+            model=model,
+            result=LLMResult(
+                model=real_model,
+                prompt_messages=prompt_messages,
+                message=prompt_message,
+                usage=usage if usage else LLMUsage.empty_usage(),
+                system_fingerprint=system_fingerprint,
+            ),
+            credentials=credentials,
+            prompt_messages=prompt_messages,
+            model_parameters=model_parameters,
+            tools=tools,
+            stop=stop,
+            stream=stream,
+            user=user,
+            callbacks=callbacks,
+        )
+
+    def get_num_tokens(
+        self,
+        model: str,
+        credentials: dict,
+        prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]] = None,
+    ) -> int:
+        """
+        Get number of tokens for given prompt messages
+
+        :param model: model name
+        :param credentials: model credentials
+        :param prompt_messages: prompt messages
+        :param tools: tools for tool calling
+        :return:
+        """
+        # Implement token counting logic here
+        # Might need to use a tokenizer specific to the Azure AI Studio model
+        return 0
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            endpoint = credentials.get("endpoint")
+            api_key = credentials.get("api_key")
+            client = ChatCompletionsClient(endpoint=endpoint, credential=AzureKeyCredential(api_key))
+            client.get_model_info()
+        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
+        The key is the error type thrown to the caller
+        The value is the error type thrown by the model,
+        which needs to be converted into a unified error type for the caller.
+
+        :return: Invoke error mapping
+        """
+        return {
+            InvokeConnectionError: [
+                ServiceRequestError,
+            ],
+            InvokeServerUnavailableError: [
+                ServiceResponseError,
+            ],
+            InvokeAuthorizationError: [
+                ClientAuthenticationError,
+            ],
+            InvokeBadRequestError: [
+                HttpResponseError,
+                DecodeError,
+                ResourceExistsError,
+                ResourceNotFoundError,
+                ResourceModifiedError,
+                ResourceNotModifiedError,
+                SerializationError,
+                DeserializationError,
+            ],
+        }
+
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
+        """
+        Used to define customizable model schema
+        """
+        rules = [
+            ParameterRule(
+                name="temperature",
+                type=ParameterType.FLOAT,
+                use_template="temperature",
+                label=I18nObject(zh_Hans="温度", en_US="Temperature"),
+            ),
+            ParameterRule(
+                name="top_p",
+                type=ParameterType.FLOAT,
+                use_template="top_p",
+                label=I18nObject(zh_Hans="Top P", en_US="Top P"),
+            ),
+            ParameterRule(
+                name="max_tokens",
+                type=ParameterType.INT,
+                use_template="max_tokens",
+                min=1,
+                default=512,
+                label=I18nObject(zh_Hans="最大生成长度", en_US="Max Tokens"),
+            ),
+        ]
+
+        entity = AIModelEntity(
+            model=model,
+            label=I18nObject(en_US=model),
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_type=ModelType.LLM,
+            features=[],
+            model_properties={},
+            parameter_rules=rules,
+        )
+
+        return entity

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

@@ -0,0 +1,164 @@
+import json
+import logging
+import os
+import ssl
+import urllib.request
+from typing import Optional
+
+from core.model_runtime.entities.common_entities import I18nObject
+from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelType
+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
+
+logger = logging.getLogger(__name__)
+
+
+class AzureRerankModel(RerankModel):
+    """
+    Model class for Azure AI Studio rerank model.
+    """
+
+    def _allow_self_signed_https(self, allowed):
+        # bypass the server certificate verification on client side
+        if allowed and not os.environ.get("PYTHONHTTPSVERIFY", "") and getattr(ssl, "_create_unverified_context", None):
+            ssl._create_default_https_context = ssl._create_unverified_context
+
+    def _azure_rerank(self, query_input: str, docs: list[str], endpoint: str, api_key: str):
+        #   self._allow_self_signed_https(True)  # Enable if using self-signed certificate
+
+        data = {"inputs": query_input, "docs": docs}
+
+        body = json.dumps(data).encode("utf-8")
+        headers = {"Content-Type": "application/json", "Authorization": f"Bearer {api_key}"}
+
+        req = urllib.request.Request(endpoint, body, headers)
+
+        try:
+            with urllib.request.urlopen(req) as response:
+                result = response.read()
+                return json.loads(result)
+        except urllib.error.HTTPError as error:
+            logger.error(f"The request failed with status code: {error.code}")
+            logger.error(error.info())
+            logger.error(error.read().decode("utf8", "ignore"))
+            raise
+
+    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
+        :param user: unique user id
+        :return: rerank result
+        """
+        try:
+            if len(docs) == 0:
+                return RerankResult(model=model, docs=[])
+
+            endpoint = credentials.get("endpoint")
+            api_key = credentials.get("jwt_token")
+
+            if not endpoint or not api_key:
+                raise ValueError("Azure endpoint and API key must be provided in credentials")
+
+            result = self._azure_rerank(query, docs, endpoint, api_key)
+            logger.info(f"Azure rerank result: {result}")
+
+            rerank_documents = []
+            for idx, (doc, score_dict) in enumerate(zip(docs, result)):
+                score = score_dict["score"]
+                rerank_document = RerankDocument(index=idx, text=doc, score=score)
+
+                if score_threshold is None or score >= score_threshold:
+                    rerank_documents.append(rerank_document)
+
+            rerank_documents.sort(key=lambda x: x.score, reverse=True)
+
+            if top_n:
+                rerank_documents = rerank_documents[:top_n]
+
+            return RerankResult(model=model, docs=rerank_documents)
+
+        except Exception as e:
+            logger.exception(f"Exception in Azure rerank: {e}")
+            raise
+
+    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
+        The key is the error type thrown to the caller
+        The value is the error type thrown by the model,
+        which needs to be converted into a unified error type for the caller.
+
+        :return: Invoke error mapping
+        """
+        return {
+            InvokeConnectionError: [urllib.error.URLError],
+            InvokeServerUnavailableError: [urllib.error.HTTPError],
+            InvokeRateLimitError: [InvokeRateLimitError],
+            InvokeAuthorizationError: [InvokeAuthorizationError],
+            InvokeBadRequestError: [InvokeBadRequestError, KeyError, ValueError, json.JSONDecodeError],
+        }
+
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
+        """
+        used to define customizable model schema
+        """
+        entity = AIModelEntity(
+            model=model,
+            label=I18nObject(en_US=model),
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_type=ModelType.RERANK,
+            model_properties={},
+            parameter_rules=[],
+        )
+
+        return entity

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

@@ -137,9 +137,19 @@ class TongyiTextEmbeddingModel(_CommonTongyi, TextEmbeddingModel):
                 input=text,
                 text_type="document",
             )
-            data = response.output["embeddings"][0]
-            embeddings.append(data["embedding"])
-            embedding_used_tokens += response.usage["total_tokens"]
+            if response.output and "embeddings" in response.output and response.output["embeddings"]:
+                data = response.output["embeddings"][0]
+                if "embedding" in data:
+                    embeddings.append(data["embedding"])
+                else:
+                    raise ValueError("Embedding data is missing in the response.")
+            else:
+                raise ValueError("Response output is missing or does not contain embeddings.")
+            
+            if response.usage and "total_tokens" in response.usage:
+                embedding_used_tokens += response.usage["total_tokens"]
+            else:
+                raise ValueError("Response usage is missing or does not contain total tokens.")
 
         return [list(map(float, e)) for e in embeddings], embedding_used_tokens
 

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

@@ -32,6 +32,9 @@ from core.model_runtime.entities.message_entities import (
     UserPromptMessage,
 )
 
+DEFAULT_V2_ENDPOINT = "maas-api.ml-platform-cn-beijing.volces.com"
+DEFAULT_V3_ENDPOINT = "https://ark.cn-beijing.volces.com/api/v3"
+
 
 class ArkClientV3:
     endpoint_id: Optional[str] = None
@@ -43,16 +46,24 @@ class ArkClientV3:
 
     @staticmethod
     def is_legacy(credentials: dict) -> bool:
+        # match default v2 endpoint
         if ArkClientV3.is_compatible_with_legacy(credentials):
             return False
-        sdk_version = credentials.get("sdk_version", "v2")
-        return sdk_version != "v3"
+        # match default v3 endpoint
+        if credentials.get("api_endpoint_host") == DEFAULT_V3_ENDPOINT:
+            return False
+        # only v3 support api_key
+        if credentials.get("auth_method") == "api_key":
+            return False
+        # these cases are considered as sdk v2
+        # - modified default v2 endpoint
+        # - modified default v3 endpoint and auth without api_key
+        return True
 
     @staticmethod
     def is_compatible_with_legacy(credentials: dict) -> bool:
-        sdk_version = credentials.get("sdk_version")
         endpoint = credentials.get("api_endpoint_host")
-        return sdk_version is None and endpoint == "maas-api.ml-platform-cn-beijing.volces.com"
+        return endpoint == DEFAULT_V2_ENDPOINT
 
     @classmethod
     def from_credentials(cls, credentials):
@@ -64,7 +75,7 @@ class ArkClientV3:
             "sk": credentials['volc_secret_access_key'],
         }
         if cls.is_compatible_with_legacy(credentials):
-            args["base_url"] = "https://ark.cn-beijing.volces.com/api/v3"
+            args["base_url"] = DEFAULT_V3_ENDPOINT
 
         client = ArkClientV3(
             **args

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

@@ -38,7 +38,7 @@ configs: dict[str, ModelConfig] = {
     ),
     'Doubao-lite-128k': ModelConfig(
         properties=ModelProperties(context_size=131072, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[ModelFeature.TOOL_CALL]
+        features=[]
     ),
     'Skylark2-pro-4k': ModelConfig(
         properties=ModelProperties(context_size=4096, max_tokens=4096, mode=LLMMode.CHAT),
@@ -54,23 +54,23 @@ configs: dict[str, ModelConfig] = {
     ),
     'Moonshot-v1-8k': ModelConfig(
         properties=ModelProperties(context_size=8192, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[]
+        features=[ModelFeature.TOOL_CALL]
     ),
     'Moonshot-v1-32k': ModelConfig(
         properties=ModelProperties(context_size=32768, max_tokens=16384, mode=LLMMode.CHAT),
-        features=[]
+        features=[ModelFeature.TOOL_CALL]
     ),
     'Moonshot-v1-128k': ModelConfig(
         properties=ModelProperties(context_size=131072, max_tokens=65536, mode=LLMMode.CHAT),
-        features=[]
+        features=[ModelFeature.TOOL_CALL]
     ),
     'GLM3-130B': ModelConfig(
         properties=ModelProperties(context_size=8192, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[]
+        features=[ModelFeature.TOOL_CALL]
     ),
     'GLM3-130B-Fin': ModelConfig(
         properties=ModelProperties(context_size=8192, max_tokens=4096, mode=LLMMode.CHAT),
-        features=[]
+        features=[ModelFeature.TOOL_CALL]
     ),
     'Mistral-7B': ModelConfig(
         properties=ModelProperties(context_size=8192, max_tokens=2048, mode=LLMMode.CHAT),

api/core/model_runtime/model_providers/volcengine_maas/volcengine_maas.yaml

@@ -64,7 +64,7 @@ model_credential_schema:
         en_US: API Endpoint Host
         zh_Hans: API Endpoint Host
       type: text-input
-      default: maas-api.ml-platform-cn-beijing.volces.com
+      default: https://ark.cn-beijing.volces.com/api/v3
       placeholder:
         en_US: Enter your API Endpoint Host
         zh_Hans: 输入 API Endpoint Host

api/core/ops/entities/config_entity.py

@@ -21,7 +21,6 @@ class LangfuseConfig(BaseTracingConfig):
     """
     public_key: str
     secret_key: str
-    project_key: str
     host: str = 'https://api.langfuse.com'
 
     @field_validator("host")

api/core/rag/datasource/vdb/elasticsearch/elasticsearch_vector.py

@@ -1,5 +1,7 @@
 import json
-from typing import Any
+import logging
+from typing import Any, Optional
+from urllib.parse import urlparse
 
 import requests
 from elasticsearch import Elasticsearch
@@ -7,16 +9,20 @@ from flask import current_app
 from pydantic import BaseModel, model_validator
 
 from core.rag.datasource.entity.embedding import Embeddings
+from core.rag.datasource.vdb.field import Field
 from core.rag.datasource.vdb.vector_base import BaseVector
 from core.rag.datasource.vdb.vector_factory import AbstractVectorFactory
 from core.rag.datasource.vdb.vector_type import VectorType
 from core.rag.models.document import Document
+from extensions.ext_redis import redis_client
 from models.dataset import Dataset
 
+logger = logging.getLogger(__name__)
+
 
 class ElasticSearchConfig(BaseModel):
     host: str
-    port: str
+    port: int
     username: str
     password: str
 
@@ -37,12 +43,19 @@ class ElasticSearchVector(BaseVector):
     def __init__(self, index_name: str, config: ElasticSearchConfig, attributes: list):
         super().__init__(index_name.lower())
         self._client = self._init_client(config)
+        self._version = self._get_version()
+        self._check_version()
         self._attributes = attributes
 
     def _init_client(self, config: ElasticSearchConfig) -> Elasticsearch:
         try:
+            parsed_url = urlparse(config.host)
+            if parsed_url.scheme in ['http', 'https']:
+                hosts = f'{config.host}:{config.port}'
+            else:
+                hosts = f'http://{config.host}:{config.port}'
             client = Elasticsearch(
-                hosts=f'{config.host}:{config.port}',
+                hosts=hosts,
                 basic_auth=(config.username, config.password),
                 request_timeout=100000,
                 retry_on_timeout=True,
@@ -53,42 +66,27 @@ class ElasticSearchVector(BaseVector):
 
         return client
 
+    def _get_version(self) -> str:
+        info = self._client.info()
+        return info['version']['number']
+
+    def _check_version(self):
+        if self._version < '8.0.0':
+            raise ValueError("Elasticsearch vector database version must be greater than 8.0.0")
+
     def get_type(self) -> str:
         return 'elasticsearch'
 
     def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
         uuids = self._get_uuids(documents)
-        texts = [d.page_content for d in documents]
-        metadatas = [d.metadata for d in documents]
-
-        if not self._client.indices.exists(index=self._collection_name):
-            dim = len(embeddings[0])
-            mapping = {
-                "properties": {
-                    "text": {
-                        "type": "text"
-                    },
-                    "vector": {
-                        "type": "dense_vector",
-                        "index": True,
-                        "dims": dim,
-                        "similarity": "l2_norm"
-                    },
-                }
-            }
-            self._client.indices.create(index=self._collection_name, mappings=mapping)
-
-        added_ids = []
-        for i, text in enumerate(texts):
+        for i in range(len(documents)):
             self._client.index(index=self._collection_name,
                                id=uuids[i],
                                document={
-                                   "text": text,
-                                   "vector": embeddings[i] if embeddings[i] else None,
-                                   "metadata": metadatas[i] if metadatas[i] else {},
+                                   Field.CONTENT_KEY.value: documents[i].page_content,
+                                   Field.VECTOR.value: embeddings[i] if embeddings[i] else None,
+                                   Field.METADATA_KEY.value: documents[i].metadata if documents[i].metadata else {}
                                })
-            added_ids.append(uuids[i])
-
         self._client.indices.refresh(index=self._collection_name)
         return uuids
 
@@ -116,28 +114,21 @@ class ElasticSearchVector(BaseVector):
         self._client.indices.delete(index=self._collection_name)
 
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
-        query_str = {
-            "query": {
-                "script_score": {
-                    "query": {
-                        "match_all": {}
-                    },
-                    "script": {
-                        "source": "cosineSimilarity(params.query_vector, 'vector') + 1.0",
-                        "params": {
-                            "query_vector": query_vector
-                        }
-                    }
-                }
-            }
+        top_k = kwargs.get("top_k", 10)
+        knn = {
+            "field": Field.VECTOR.value,
+            "query_vector": query_vector,
+            "k": top_k
         }
 
-        results = self._client.search(index=self._collection_name, body=query_str)
+        results = self._client.search(index=self._collection_name, knn=knn, size=top_k)
 
         docs_and_scores = []
         for hit in results['hits']['hits']:
             docs_and_scores.append(
-                (Document(page_content=hit['_source']['text'], metadata=hit['_source']['metadata']), hit['_score']))
+                (Document(page_content=hit['_source'][Field.CONTENT_KEY.value],
+                          vector=hit['_source'][Field.VECTOR.value],
+                          metadata=hit['_source'][Field.METADATA_KEY.value]), hit['_score']))
 
         docs = []
         for doc, score in docs_and_scores:
@@ -146,25 +137,61 @@ class ElasticSearchVector(BaseVector):
                 doc.metadata['score'] = score
             docs.append(doc)
 
-        # Sort the documents by score in descending order
-        docs = sorted(docs, key=lambda x: x.metadata['score'], reverse=True)
-
         return docs
+
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
         query_str = {
             "match": {
-                "text": query
+                Field.CONTENT_KEY.value: query
             }
         }
         results = self._client.search(index=self._collection_name, query=query_str)
         docs = []
         for hit in results['hits']['hits']:
-            docs.append(Document(page_content=hit['_source']['text'], metadata=hit['_source']['metadata']))
+            docs.append(Document(
+                page_content=hit['_source'][Field.CONTENT_KEY.value],
+                vector=hit['_source'][Field.VECTOR.value],
+                metadata=hit['_source'][Field.METADATA_KEY.value],
+            ))
 
         return docs
 
     def create(self, texts: list[Document], embeddings: list[list[float]], **kwargs):
-        return self.add_texts(texts, embeddings, **kwargs)
+        metadatas = [d.metadata for d in texts]
+        self.create_collection(embeddings, metadatas)
+        self.add_texts(texts, embeddings, **kwargs)
+
+    def create_collection(
+            self, embeddings: list, metadatas: Optional[list[dict]] = None, index_params: Optional[dict] = None
+    ):
+        lock_name = f'vector_indexing_lock_{self._collection_name}'
+        with redis_client.lock(lock_name, timeout=20):
+            collection_exist_cache_key = f'vector_indexing_{self._collection_name}'
+            if redis_client.get(collection_exist_cache_key):
+                logger.info(f"Collection {self._collection_name} already exists.")
+                return
+
+            if not self._client.indices.exists(index=self._collection_name):
+                dim = len(embeddings[0])
+                mappings = {
+                    "properties": {
+                        Field.CONTENT_KEY.value: {"type": "text"},
+                        Field.VECTOR.value: {  # Make sure the dimension is correct here
+                            "type": "dense_vector",
+                            "dims": dim,
+                            "similarity": "cosine"
+                        },
+                        Field.METADATA_KEY.value: {
+                            "type": "object",
+                            "properties": {
+                                "doc_id": {"type": "keyword"}  # Map doc_id to keyword type
+                            }
+                        }
+                    }
+                }
+                self._client.indices.create(index=self._collection_name, mappings=mappings)
+
+            redis_client.set(collection_exist_cache_key, 1, ex=3600)
 
 
 class ElasticSearchVectorFactory(AbstractVectorFactory):

api/core/rag/datasource/vdb/myscale/myscale_vector.py

@@ -122,7 +122,7 @@ class MyScaleVector(BaseVector):
 
     def _search(self, dist: str, order: SortOrder, **kwargs: Any) -> list[Document]:
         top_k = kwargs.get("top_k", 5)
-        score_threshold = kwargs.get("score_threshold", 0.0)
+        score_threshold = kwargs.get('score_threshold') or 0.0
         where_str = f"WHERE dist < {1 - score_threshold}" if \
             self._metric.upper() == "COSINE" and order == SortOrder.ASC and score_threshold > 0.0 else ""
         sql = f"""

api/core/rag/index_processor/index_processor_base.py

@@ -57,7 +57,7 @@ class BaseIndexProcessor(ABC):
 
             character_splitter = FixedRecursiveCharacterTextSplitter.from_encoder(
                 chunk_size=segmentation["max_tokens"],
-                chunk_overlap=segmentation.get('chunk_overlap', 0),
+                chunk_overlap=segmentation.get('chunk_overlap', 0) or 0,
                 fixed_separator=separator,
                 separators=["\n\n", "。", ". ", " ", ""],
                 embedding_model_instance=embedding_model_instance

api/core/tools/provider/_position.yaml

@@ -30,5 +30,7 @@
 - dingtalk
 - feishu
 - feishu_base
+- feishu_document
+- feishu_message
 - slack
 - tianditu

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

@@ -0,0 +1,9 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<svg xmlns="http://www.w3.org/2000/svg" version="1.1" width="64px" height="64px" style="shape-rendering:geometricPrecision; text-rendering:geometricPrecision; image-rendering:optimizeQuality; fill-rule:evenodd; clip-rule:evenodd" xmlns:xlink="http://www.w3.org/1999/xlink">
+<g><path style="opacity:1" fill="#fefefe" d="M -0.5,-0.5 C 20.8333,-0.5 42.1667,-0.5 63.5,-0.5C 63.5,20.8333 63.5,42.1667 63.5,63.5C 42.1667,63.5 20.8333,63.5 -0.5,63.5C -0.5,42.1667 -0.5,20.8333 -0.5,-0.5 Z"/></g>
+<g><path style="opacity:1" fill="#346df3" d="M 47.5,33.5 C 43.3272,29.8779 38.9939,29.7112 34.5,33C 32.682,35.4897 30.3487,37.3231 27.5,38.5C 23.5003,43.5136 24.167,47.847 29.5,51.5C 24.1563,51.666 18.8229,51.4994 13.5,51C 13,50.5 12.5,50 12,49.5C 11.3333,36.8333 11.3333,24.1667 12,11.5C 12.5,11 13,10.5 13.5,10C 24.1667,9.33333 34.8333,9.33333 45.5,10C 46,10.5 46.5,11 47,11.5C 47.4997,18.8258 47.6663,26.1591 47.5,33.5 Z"/></g>
+<g><path style="opacity:1" fill="#f9fafe" d="M 20.5,19.5 C 25.1785,19.3342 29.8452,19.5008 34.5,20C 35.8333,21 35.8333,22 34.5,23C 29.8333,23.6667 25.1667,23.6667 20.5,23C 19.3157,21.8545 19.3157,20.6879 20.5,19.5 Z"/></g>
+<g><path style="opacity:1" fill="#f3f6fe" d="M 20.5,27.5 C 22.5273,27.3379 24.5273,27.5045 26.5,28C 27.8333,29 27.8333,30 26.5,31C 24.5,31.6667 22.5,31.6667 20.5,31C 19.3157,29.8545 19.3157,28.6879 20.5,27.5 Z"/></g>
+<g><path style="opacity:1" fill="#36d4c1" d="M 47.5,33.5 C 48.7298,35.2972 49.3964,37.2972 49.5,39.5C 51.3904,39.2965 52.8904,39.9632 54,41.5C 55.1825,45.2739 54.3492,48.4406 51.5,51C 44.1742,51.4997 36.8409,51.6663 29.5,51.5C 24.167,47.847 23.5003,43.5136 27.5,38.5C 30.3487,37.3231 32.682,35.4897 34.5,33C 38.9939,29.7112 43.3272,29.8779 47.5,33.5 Z"/></g>
+</svg>

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

@@ -0,0 +1,15 @@
+from core.tools.errors import ToolProviderCredentialValidationError
+from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
+from core.tools.utils.feishu_api_utils import FeishuRequest
+
+
+class FeishuDocumentProvider(BuiltinToolProviderController):
+    def _validate_credentials(self, credentials: dict) -> None:
+        app_id = credentials.get('app_id')
+        app_secret = credentials.get('app_secret')
+        if not app_id or not app_secret:
+            raise ToolProviderCredentialValidationError("app_id and app_secret is required")
+        try:
+            assert FeishuRequest(app_id, app_secret).tenant_access_token is not None
+        except Exception as e:
+            raise ToolProviderCredentialValidationError(str(e))

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

@@ -0,0 +1,34 @@
+identity:
+  author: Doug Lea
+  name: feishu_document
+  label:
+    en_US: Lark Cloud Document
+    zh_Hans: 飞书云文档
+  description:
+    en_US: Lark Cloud Document
+    zh_Hans: 飞书云文档
+  icon: icon.svg
+  tags:
+    - social
+    - productivity
+credentials_for_provider:
+  app_id:
+    type: text-input
+    required: true
+    label:
+      en_US: APP ID
+    placeholder:
+      en_US: Please input your feishu app id
+      zh_Hans: 请输入你的飞书 app id
+    help:
+      en_US: Get your app_id and app_secret from Feishu
+      zh_Hans: 从飞书获取您的 app_id 和 app_secret
+    url: https://open.feishu.cn
+  app_secret:
+    type: secret-input
+    required: true
+    label:
+      en_US: APP Secret
+    placeholder:
+      en_US: Please input your app secret
+      zh_Hans: 请输入你的飞书 app secret

api/core/tools/provider/builtin/feishu_document/tools/create_document.py

@@ -0,0 +1,19 @@
+from typing import Any
+
+from core.tools.entities.tool_entities import ToolInvokeMessage
+from core.tools.tool.builtin_tool import BuiltinTool
+from core.tools.utils.feishu_api_utils import FeishuRequest
+
+
+class CreateDocumentTool(BuiltinTool):
+    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage:
+        app_id = self.runtime.credentials.get('app_id')
+        app_secret = self.runtime.credentials.get('app_secret')
+        client = FeishuRequest(app_id, app_secret)
+
+        title = tool_parameters.get('title')
+        content = tool_parameters.get('content')
+        folder_token = tool_parameters.get('folder_token')
+
+        res = client.create_document(title, content, folder_token)
+        return self.create_json_message(res)

api/core/tools/provider/builtin/feishu_document/tools/create_document.yaml

@@ -0,0 +1,47 @@
+identity:
+  name: create_document
+  author: Doug Lea
+  label:
+    en_US: Create Lark document
+    zh_Hans: 创建飞书文档
+description:
+  human:
+    en_US: Create Lark document
+    zh_Hans: 创建飞书文档，支持创建空文档和带内容的文档，支持 markdown 语法创建。
+  llm: A tool for creating Feishu documents.
+parameters:
+  - name: title
+    type: string
+    required: false
+    label:
+      en_US: Document title
+      zh_Hans: 文档标题
+    human_description:
+      en_US: Document title, only supports plain text content.
+      zh_Hans: 文档标题，只支持纯文本内容。
+    llm_description: 文档标题，只支持纯文本内容，可以为空。
+    form: llm
+
+  - name: content
+    type: string
+    required: false
+    label:
+      en_US: Document content
+      zh_Hans: 文档内容
+    human_description:
+      en_US: Document content, supports markdown syntax, can be empty.
+      zh_Hans: 文档内容，支持 markdown 语法，可以为空。
+    llm_description: 文档内容，支持 markdown 语法，可以为空。
+    form: llm
+
+  - name: folder_token
+    type: string
+    required: false
+    label:
+      en_US: folder_token
+      zh_Hans: 文档所在文件夹的 Token
+    human_description:
+      en_US: The token of the folder where the document is located. If it is not passed or is empty, it means the root directory.
+      zh_Hans: 文档所在文件夹的 Token，不传或传空表示根目录。
+    llm_description: 文档所在文件夹的 Token，不传或传空表示根目录。
+    form: llm

api/core/tools/provider/builtin/feishu_document/tools/get_document_raw_content.py

@@ -0,0 +1,17 @@
+from typing import Any
+
+from core.tools.entities.tool_entities import ToolInvokeMessage
+from core.tools.tool.builtin_tool import BuiltinTool
+from core.tools.utils.feishu_api_utils import FeishuRequest
+
+
+class GetDocumentRawContentTool(BuiltinTool):
+    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage:
+        app_id = self.runtime.credentials.get('app_id')
+        app_secret = self.runtime.credentials.get('app_secret')
+        client = FeishuRequest(app_id, app_secret)
+
+        document_id = tool_parameters.get('document_id')
+
+        res = client.get_document_raw_content(document_id)
+        return self.create_json_message(res)

api/core/tools/provider/builtin/feishu_document/tools/get_document_raw_content.yaml

@@ -0,0 +1,23 @@
+identity:
+  name: get_document_raw_content
+  author: Doug Lea
+  label:
+    en_US: Get Document Raw Content
+    zh_Hans: 获取文档纯文本内容
+description:
+  human:
+    en_US: Get document raw content
+    zh_Hans: 获取文档纯文本内容
+  llm: A tool for getting the plain text content of Feishu documents
+parameters:
+  - name: document_id
+    type: string
+    required: true
+    label:
+      en_US: document_id
+      zh_Hans: 飞书文档的唯一标识
+    human_description:
+      en_US: Unique ID of Feishu document document_id
+      zh_Hans: 飞书文档的唯一标识 document_id
+    llm_description: 飞书文档的唯一标识 document_id
+    form: llm

api/core/tools/provider/builtin/feishu_document/tools/list_document_block.py

@@ -0,0 +1,19 @@
+from typing import Any
+
+from core.tools.entities.tool_entities import ToolInvokeMessage
+from core.tools.tool.builtin_tool import BuiltinTool
+from core.tools.utils.feishu_api_utils import FeishuRequest
+
+
+class ListDocumentBlockTool(BuiltinTool):
+    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage:
+        app_id = self.runtime.credentials.get('app_id')
+        app_secret = self.runtime.credentials.get('app_secret')
+        client = FeishuRequest(app_id, app_secret)
+
+        document_id = tool_parameters.get('document_id')
+        page_size = tool_parameters.get('page_size', 500)
+        page_token = tool_parameters.get('page_token', '')
+
+        res = client.list_document_block(document_id, page_token, page_size)
+        return self.create_json_message(res)

api/core/tools/provider/builtin/feishu_document/tools/list_document_block.yaml

@@ -0,0 +1,48 @@
+identity:
+  name: list_document_block
+  author: Doug Lea
+  label:
+    en_US: List Document Block
+    zh_Hans: 获取飞书文档所有块
+description:
+  human:
+    en_US: List document block
+    zh_Hans: 获取飞书文档所有块的富文本内容并分页返回。
+  llm: A tool to get all blocks of Feishu documents
+parameters:
+  - name: document_id
+    type: string
+    required: true
+    label:
+      en_US: document_id
+      zh_Hans: 飞书文档的唯一标识
+    human_description:
+      en_US: Unique ID of Feishu document document_id
+      zh_Hans: 飞书文档的唯一标识 document_id
+    llm_description: 飞书文档的唯一标识 document_id
+    form: llm
+
+  - name: page_size
+    type: number
+    required: false
+    default: 500
+    label:
+      en_US: page_size
+      zh_Hans: 分页大小
+    human_description:
+      en_US: Paging size, the default and maximum value is 500.
+      zh_Hans: 分页大小, 默认值和最大值为 500。
+    llm_description: 分页大小, 表示一次请求最多返回多少条数据，默认值和最大值为 500。
+    form: llm
+
+  - name: page_token
+    type: string
+    required: false
+    label:
+      en_US: page_token
+      zh_Hans: 分页标记
+    human_description:
+      en_US: Pagination tag, used to paginate query results so that more items can be obtained in the next traversal.
+      zh_Hans: 分页标记，用于分页查询结果，以便下次遍历时获取更多项。
+    llm_description: 分页标记，第一次请求不填，表示从头开始遍历；分页查询结果还有更多项时会同时返回新的 page_token，下次遍历可采用该 page_token 获取查询结果。
+    form: llm

api/core/tools/provider/builtin/feishu_document/tools/write_document.py

@@ -0,0 +1,19 @@
+from typing import Any
+
+from core.tools.entities.tool_entities import ToolInvokeMessage
+from core.tools.tool.builtin_tool import BuiltinTool
+from core.tools.utils.feishu_api_utils import FeishuRequest
+
+
+class CreateDocumentTool(BuiltinTool):
+    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage:
+        app_id = self.runtime.credentials.get('app_id')
+        app_secret = self.runtime.credentials.get('app_secret')
+        client = FeishuRequest(app_id, app_secret)
+
+        document_id = tool_parameters.get('document_id')
+        content = tool_parameters.get('content')
+        position = tool_parameters.get('position')
+
+        res = client.write_document(document_id, content, position)
+        return self.create_json_message(res)

api/core/tools/provider/builtin/feishu_document/tools/write_document.yaml

@@ -0,0 +1,56 @@
+identity:
+  name: write_document
+  author: Doug Lea
+  label:
+    en_US: Write Document
+    zh_Hans: 在飞书文档中新增内容
+description:
+  human:
+    en_US: Adding new content to Lark documents
+    zh_Hans: 在飞书文档中新增内容
+  llm: A tool for adding new content to Lark documents.
+parameters:
+  - name: document_id
+    type: string
+    required: true
+    label:
+      en_US: document_id
+      zh_Hans: 飞书文档的唯一标识
+    human_description:
+      en_US: Unique ID of Feishu document document_id
+      zh_Hans: 飞书文档的唯一标识 document_id
+    llm_description: 飞书文档的唯一标识 document_id
+    form: llm
+
+  - name: content
+    type: string
+    required: true
+    label:
+      en_US: document content
+      zh_Hans: 文档内容
+    human_description:
+      en_US: Document content, supports markdown syntax, can be empty.
+      zh_Hans: 文档内容，支持 markdown 语法，可以为空。
+    llm_description:
+    form: llm
+
+  - name: position
+    type: select
+    required: true
+    default: start
+    label:
+      en_US: Choose where to add content
+      zh_Hans: 选择添加内容的位置
+    human_description:
+      en_US: Please fill in start or end to add content at the beginning or end of the document respectively.
+      zh_Hans: 请填入 start 或 end, 分别表示在文档开头(start)或结尾(end)添加内容。
+    form: llm
+    options:
+      - value: start
+        label:
+          en_US: start
+          zh_Hans: 在文档开头添加内容
+      - value: end
+        label:
+          en_US: end
+          zh_Hans: 在文档结尾添加内容

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

@@ -0,0 +1,19 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px" width="64px" height="64px" viewBox="0 0 64 64" enable-background="new 0 0 64 64" xml:space="preserve">  <image id="image0" width="64" height="64" x="0" y="0"
+    xlink:href="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAMAAACdt4HsAAAAIGNIUk0AAHomAACAhAAA+gAAAIDo
+AAB1MAAA6mAAADqYAAAXcJy6UTwAAAC9UExURf///////+bs/vL2/qa/+n+j+E1/9TNt9FmI9nOa
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+U/4a4wd9AAAAAWJLR0QAiAUdSAAAAAlwSFlzAAAWJQAAFiUBSVIk8AAAAAd0SU1FB+gHEggfEk4D
+XiUAAAFOSURBVFjD7dVZU8IwFAXgpq2NtFFRUVTKtYC4gCvu6///WcCMI9Cc3CR2fLLn/XyT3KRp
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+</svg>

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

@@ -0,0 +1,15 @@
+from core.tools.errors import ToolProviderCredentialValidationError
+from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController
+from core.tools.utils.feishu_api_utils import FeishuRequest
+
+
+class FeishuMessageProvider(BuiltinToolProviderController):
+    def _validate_credentials(self, credentials: dict) -> None:
+        app_id = credentials.get('app_id')
+        app_secret = credentials.get('app_secret')
+        if not app_id or not app_secret:
+            raise ToolProviderCredentialValidationError("app_id and app_secret is required")
+        try:
+            assert FeishuRequest(app_id, app_secret).tenant_access_token is not None
+        except Exception as e:
+            raise ToolProviderCredentialValidationError(str(e))

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

@@ -0,0 +1,34 @@
+identity:
+  author: Doug Lea
+  name: feishu_message
+  label:
+    en_US: Lark Message
+    zh_Hans: 飞书消息
+  description:
+    en_US: Lark Message
+    zh_Hans: 飞书消息
+  icon: icon.svg
+  tags:
+    - social
+    - productivity
+credentials_for_provider:
+  app_id:
+    type: text-input
+    required: true
+    label:
+      en_US: APP ID
+    placeholder:
+      en_US: Please input your feishu app id
+      zh_Hans: 请输入你的飞书 app id
+    help:
+      en_US: Get your app_id and app_secret from Feishu
+      zh_Hans: 从飞书获取您的 app_id 和 app_secret
+    url: https://open.feishu.cn
+  app_secret:
+    type: secret-input
+    required: true
+    label:
+      en_US: APP Secret
+    placeholder:
+      en_US: Please input your app secret
+      zh_Hans: 请输入你的飞书 app secret

api/core/tools/provider/builtin/feishu_message/tools/send_bot_message.py

@@ -0,0 +1,20 @@
+from typing import Any
+
+from core.tools.entities.tool_entities import ToolInvokeMessage
+from core.tools.tool.builtin_tool import BuiltinTool
+from core.tools.utils.feishu_api_utils import FeishuRequest
+
+
+class SendBotMessageTool(BuiltinTool):
+    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage:
+        app_id = self.runtime.credentials.get('app_id')
+        app_secret = self.runtime.credentials.get('app_secret')
+        client = FeishuRequest(app_id, app_secret)
+
+        receive_id_type = tool_parameters.get('receive_id_type')
+        receive_id = tool_parameters.get('receive_id')
+        msg_type = tool_parameters.get('msg_type')
+        content = tool_parameters.get('content')
+
+        res = client.send_bot_message(receive_id_type, receive_id, msg_type, content)
+        return self.create_json_message(res)

api/core/tools/provider/builtin/feishu_message/tools/send_bot_message.yaml

@@ -0,0 +1,91 @@
+identity:
+  name: send_bot_message
+  author: Doug Lea
+  label:
+    en_US: Send Bot Message
+    zh_Hans: 发送飞书应用消息
+description:
+  human:
+    en_US: Send bot message
+    zh_Hans: 发送飞书应用消息
+  llm: A tool for sending Feishu application messages.
+parameters:
+  - name: receive_id_type
+    type: select
+    required: true
+    options:
+      - value: open_id
+        label:
+          en_US: open id
+          zh_Hans: open id
+      - value: union_id
+        label:
+          en_US: union id
+          zh_Hans: union id
+      - value: user_id
+        label:
+          en_US: user id
+          zh_Hans: user id
+      - value: email
+        label:
+          en_US: email
+          zh_Hans: email
+      - value: chat_id
+        label:
+          en_US: chat id
+          zh_Hans: chat id
+    label:
+      en_US: User ID Type
+      zh_Hans: 用户 ID 类型
+    human_description:
+      en_US: User ID Type
+      zh_Hans: 用户 ID 类型，可选值有 open_id、union_id、user_id、email、chat_id。
+    llm_description: 用户 ID 类型，可选值有 open_id、union_id、user_id、email、chat_id。
+    form: llm
+
+  - name: receive_id
+    type: string
+    required: true
+    label:
+      en_US: Receive Id
+      zh_Hans: 消息接收者的 ID
+    human_description:
+      en_US: The ID of the message receiver. The ID type should correspond to the query parameter receive_id_type.
+      zh_Hans: 消息接收者的 ID，ID 类型应与查询参数 receive_id_type 对应。
+    llm_description: 消息接收者的 ID，ID 类型应与查询参数 receive_id_type 对应。
+    form: llm
+
+  - name: msg_type
+    type: string
+    required: true
+    options:
+      - value: text
+        label:
+          en_US: text
+          zh_Hans: 文本
+      - value: interactive
+        label:
+          en_US: message card
+          zh_Hans: 消息卡片
+    label:
+      en_US: Message type
+      zh_Hans: 消息类型
+    human_description:
+      en_US: Message type, optional values are, text (text), interactive (message card).
+      zh_Hans: 消息类型，可选值有：text(文本)、interactive(消息卡片)。
+    llm_description: 消息类型，可选值有：text(文本)、interactive(消息卡片)。
+    form: llm
+
+  - name: content
+    type: string
+    required: true
+    label:
+      en_US: Message content
+      zh_Hans: 消息内容
+    human_description:
+      en_US: Message content
+      zh_Hans: |
+        消息内容，JSON 结构序列化后的字符串。不同 msg_type 对应不同内容，
+        具体格式说明参考：https://open.larkoffice.com/document/server-docs/im-v1/message-content-description/create_json
+    llm_description: 消息内容，JSON 结构序列化后的字符串。不同 msg_type 对应不同内容。
+    form: llm

api/core/tools/provider/builtin/feishu_message/tools/send_webhook_message.py

@@ -0,0 +1,19 @@
+from typing import Any
+
+from core.tools.entities.tool_entities import ToolInvokeMessage
+from core.tools.tool.builtin_tool import BuiltinTool
+from core.tools.utils.feishu_api_utils import FeishuRequest
+
+
+class SendWebhookMessageTool(BuiltinTool):
+    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) ->ToolInvokeMessage:
+        app_id = self.runtime.credentials.get('app_id')
+        app_secret = self.runtime.credentials.get('app_secret')
+        client = FeishuRequest(app_id, app_secret)
+
+        webhook = tool_parameters.get('webhook')
+        msg_type = tool_parameters.get('msg_type')
+        content = tool_parameters.get('content')
+
+        res = client.send_webhook_message(webhook, msg_type, content)
+        return self.create_json_message(res)

api/core/tools/provider/builtin/feishu_message/tools/send_webhook_message.yaml

@@ -0,0 +1,58 @@
+identity:
+  name: send_webhook_message
+  author: Doug Lea
+  label:
+    en_US: Send Webhook Message
+    zh_Hans: 使用自定义机器人发送飞书消息
+description:
+  human:
+    en_US: Send webhook message
+    zh_Hans: 使用自定义机器人发送飞书消息
+  llm: A tool for sending Lark messages using a custom robot.
+parameters:
+  - name: webhook
+    type: string
+    required: true
+    label:
+      en_US: webhook
+      zh_Hans: webhook 的地址
+    human_description:
+      en_US: The address of the webhook
+      zh_Hans: webhook 的地址
+    llm_description: webhook 的地址
+    form: llm
+
+  - name: msg_type
+    type: string
+    required: true
+    options:
+      - value: text
+        label:
+          en_US: text
+          zh_Hans: 文本
+      - value: interactive
+        label:
+          en_US: message card
+          zh_Hans: 消息卡片
+    label:
+      en_US: Message type
+      zh_Hans: 消息类型
+    human_description:
+      en_US: Message type, optional values are, text (text), interactive (message card).
+      zh_Hans: 消息类型，可选值有：text(文本)、interactive(消息卡片)。
+    llm_description: 消息类型，可选值有：text(文本)、interactive(消息卡片)。
+    form: llm
+
+  - name: content
+    type: string
+    required: true
+    label:
+      en_US: Message content
+      zh_Hans: 消息内容
+    human_description:
+      en_US: Message content
+      zh_Hans: |
+        消息内容，JSON 结构序列化后的字符串。不同 msg_type 对应不同内容，
+        具体格式说明参考：https://open.larkoffice.com/document/server-docs/im-v1/message-content-description/create_json
+    llm_description: 消息内容，JSON 结构序列化后的字符串。不同 msg_type 对应不同内容。
+    form: llm

api/core/tools/utils/feishu_api_utils.py

@@ -0,0 +1,143 @@
+import httpx
+
+from extensions.ext_redis import redis_client
+
+
+class FeishuRequest:
+    def __init__(self, app_id: str, app_secret: str):
+        self.app_id = app_id
+        self.app_secret = app_secret
+
+    @property
+    def tenant_access_token(self):
+        feishu_tenant_access_token = f"tools:{self.app_id}:feishu_tenant_access_token"
+        if redis_client.exists(feishu_tenant_access_token):
+            return redis_client.get(feishu_tenant_access_token).decode()
+        res = self.get_tenant_access_token(self.app_id, self.app_secret)
+        redis_client.setex(feishu_tenant_access_token, res.get("expire"), res.get("tenant_access_token"))
+        return res.get("tenant_access_token")
+
+    def _send_request(self, url: str, method: str = "post", require_token: bool = True, payload: dict = None,
+                      params: dict = None):
+        headers = {
+            "Content-Type": "application/json",
+            "user-agent": "Dify",
+        }
+        if require_token:
+            headers["tenant-access-token"] = f"{self.tenant_access_token}"
+        res = httpx.request(method=method, url=url, headers=headers, json=payload, params=params, timeout=30).json()
+        if res.get("code") != 0:
+            raise Exception(res)
+        return res
+
+    def get_tenant_access_token(self, app_id: str, app_secret: str) -> dict:
+        """
+        API url: https://open.feishu.cn/document/server-docs/authentication-management/access-token/tenant_access_token_internal
+        Example Response:
+        {
+            "code": 0,
+            "msg": "ok",
+            "tenant_access_token": "t-caecc734c2e3328a62489fe0648c4b98779515d3",
+            "expire": 7200
+        }
+        """
+        url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/access_token/get_tenant_access_token"
+        payload = {
+            "app_id": app_id,
+            "app_secret": app_secret
+        }
+        res = self._send_request(url, require_token=False, payload=payload)
+        return res
+
+    def create_document(self, title: str, content: str, folder_token: str) -> dict:
+        """
+        API url: https://open.larkoffice.com/document/server-docs/docs/docs/docx-v1/document/create
+        Example Response:
+        {
+            "data": {
+                "title": "title",
+                "url": "https://svi136aogf123.feishu.cn/docx/VWbvd4fEdoW0WSxaY1McQTz8n7d",
+                "type": "docx",
+                "token": "VWbvd4fEdoW0WSxaY1McQTz8n7d"
+            },
+            "log_id": "021721281231575fdbddc0200ff00060a9258ec0000103df61b5d",
+            "code": 0,
+            "msg": "创建飞书文档成功，请查看"
+        }
+        """
+        url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/document/create_document"
+        payload = {
+            "title": title,
+            "content": content,
+            "folder_token": folder_token,
+        }
+        res = self._send_request(url, payload=payload)
+        return res.get("data")
+
+    def write_document(self, document_id: str, content: str, position: str = "start") -> dict:
+        url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/document/write_document"
+        payload = {
+            "document_id": document_id,
+            "content": content,
+            "position": position
+        }
+        res = self._send_request(url, payload=payload)
+        return res.get("data")
+
+    def get_document_raw_content(self, document_id: str) -> dict:
+        """
+        API url: https://open.larkoffice.com/document/server-docs/docs/docs/docx-v1/document/raw_content
+        Example Response:
+        {
+            "code": 0,
+            "msg": "success",
+            "data": {
+                "content": "云文档\n多人实时协同，插入一切元素。不仅是在线文档，更是强大的创作和互动工具\n云文档：专为协作而生\n"
+            }
+        }
+        """
+        params = {
+            "document_id": document_id,
+        }
+        url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/document/get_document_raw_content"
+        res = self._send_request(url, method="get", params=params)
+        return res.get("data").get("content")
+
+    def list_document_block(self, document_id: str, page_token: str, page_size: int = 500) -> dict:
+        """
+        API url: https://open.larkoffice.com/document/server-docs/docs/docs/docx-v1/document/list
+        """
+        url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/document/list_document_block"
+        params = {
+            "document_id": document_id,
+            "page_size": page_size,
+            "page_token": page_token,
+        }
+        res = self._send_request(url, method="get", params=params)
+        return res.get("data")
+
+    def send_bot_message(self, receive_id_type: str, receive_id: str, msg_type: str, content: str) -> dict:
+        """
+        API url: https://open.larkoffice.com/document/server-docs/im-v1/message/create
+        """
+        url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/message/send_bot_message"
+        params = {
+            "receive_id_type": receive_id_type,
+        }
+        payload = {
+            "receive_id": receive_id,
+            "msg_type": msg_type,
+            "content": content,
+        }
+        res = self._send_request(url, params=params, payload=payload)
+        return res.get("data")
+
+    def send_webhook_message(self, webhook: str, msg_type: str, content: str) -> dict:
+        url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/message/send_webhook_message"
+        payload = {
+            "webhook": webhook,
+            "msg_type": msg_type,
+            "content": content,
+        }
+        res = self._send_request(url, require_token=False, payload=payload)
+        return res

api/core/tools/utils/yaml_utils.py

@@ -26,7 +26,6 @@ def load_yaml_file(file_path: str, ignore_error: bool = True, default_value: Any
                 raise YAMLError(f'Failed to load YAML file {file_path}: {e}')
     except Exception as e:
         if ignore_error:
-            logger.debug(f'Failed to load YAML file {file_path}: {e}')
             return default_value
         else:
             raise e

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

@@ -88,9 +88,9 @@ class CodeNode(BaseNode):
             else:
                 raise ValueError(f"Output variable `{variable}` must be a string")
         
-        if len(value) > dify_config.CODE_MAX_STRING_ARRAY_LENGTH:
+        if len(value) > dify_config.CODE_MAX_STRING_LENGTH:
             raise ValueError(f'The length of output variable `{variable}` must be'
-                             f' less than {dify_config.CODE_MAX_STRING_ARRAY_LENGTH} characters')
+                             f' less than {dify_config.CODE_MAX_STRING_LENGTH} characters')
 
         return value.replace('\x00', '')
 

api/events/event_handlers/create_site_record_when_app_created.py

@@ -17,6 +17,8 @@ def handle(sender, **kwargs):
         default_language=account.interface_language,
         customize_token_strategy="not_allow",
         code=Site.generate_code(16),
+        created_by=app.created_by,
+        updated_by=app.updated_by,
     )
 
     db.session.add(site)

api/extensions/storage/s3_storage.py

@@ -35,6 +35,9 @@ class S3Storage(BaseStorage):
             # if bucket not exists, create it
             if e.response["Error"]["Code"] == "404":
                 self.client.create_bucket(Bucket=self.bucket_name)
+            # if bucket is not accessible, pass, maybe the bucket is existing but not accessible
+            elif e.response["Error"]["Code"] == "403":
+                pass
             else:
                 # other error, raise exception
                 raise

api/fields/app_fields.py

@@ -1,5 +1,6 @@
 from flask_restful import fields
 
+from fields.workflow_fields import workflow_partial_fields
 from libs.helper import AppIconUrlField, TimestampField
 
 app_detail_kernel_fields = {
@@ -39,7 +40,10 @@ model_config_fields = {
     "completion_prompt_config": fields.Raw(attribute="completion_prompt_config_dict"),
     "dataset_configs": fields.Raw(attribute="dataset_configs_dict"),
     "file_upload": fields.Raw(attribute="file_upload_dict"),
+    "created_by": fields.String,
     "created_at": TimestampField,
+    "updated_by": fields.String,
+    "updated_at": TimestampField,
 }
 
 app_detail_fields = {
@@ -52,8 +56,12 @@ app_detail_fields = {
     "enable_site": fields.Boolean,
     "enable_api": fields.Boolean,
     "model_config": fields.Nested(model_config_fields, attribute="app_model_config", allow_null=True),
+    "workflow": fields.Nested(workflow_partial_fields, allow_null=True),
     "tracing": fields.Raw,
+    "created_by": fields.String,
     "created_at": TimestampField,
+    "updated_by": fields.String,
+    "updated_at": TimestampField,
 }
 
 prompt_config_fields = {
@@ -63,6 +71,10 @@ prompt_config_fields = {
 model_config_partial_fields = {
     "model": fields.Raw(attribute="model_dict"),
     "pre_prompt": fields.String,
+    "created_by": fields.String,
+    "created_at": TimestampField,
+    "updated_by": fields.String,
+    "updated_at": TimestampField,
 }
 
 tag_fields = {"id": fields.String, "name": fields.String, "type": fields.String}
@@ -78,7 +90,11 @@ app_partial_fields = {
     "icon_background": fields.String,
     "icon_url": AppIconUrlField,
     "model_config": fields.Nested(model_config_partial_fields, attribute="app_model_config", allow_null=True),
+    "workflow": fields.Nested(workflow_partial_fields, allow_null=True),
+    "created_by": fields.String,
     "created_at": TimestampField,
+    "updated_by": fields.String,
+    "updated_at": TimestampField,
     "tags": fields.List(fields.Nested(tag_fields)),
 }
 
@@ -124,6 +140,10 @@ site_fields = {
     "prompt_public": fields.Boolean,
     "app_base_url": fields.String,
     "show_workflow_steps": fields.Boolean,
+    "created_by": fields.String,
+    "created_at": TimestampField,
+    "updated_by": fields.String,
+    "updated_at": TimestampField,
 }
 
 app_detail_fields_with_site = {
@@ -138,9 +158,13 @@ app_detail_fields_with_site = {
     "enable_site": fields.Boolean,
     "enable_api": fields.Boolean,
     "model_config": fields.Nested(model_config_fields, attribute="app_model_config", allow_null=True),
+    "workflow": fields.Nested(workflow_partial_fields, allow_null=True),
     "site": fields.Nested(site_fields),
     "api_base_url": fields.String,
+    "created_by": fields.String,
     "created_at": TimestampField,
+    "updated_by": fields.String,
+    "updated_at": TimestampField,
     "deleted_tools": fields.List(fields.String),
 }
 

api/fields/conversation_fields.py

@@ -111,6 +111,7 @@ conversation_fields = {
     "from_end_user_id": fields.String,
     "from_end_user_session_id": fields.String(),
     "from_account_id": fields.String,
+    "from_account_name": fields.String,
     "read_at": TimestampField,
     "created_at": TimestampField,
     "annotation": fields.Nested(annotation_fields, allow_null=True),
@@ -146,6 +147,7 @@ conversation_with_summary_fields = {
     "from_end_user_id": fields.String,
     "from_end_user_session_id": fields.String,
     "from_account_id": fields.String,
+    "from_account_name": fields.String,
     "name": fields.String,
     "summary": fields.String(attribute="summary_or_query"),
     "read_at": TimestampField,

api/fields/workflow_fields.py

@@ -53,3 +53,11 @@ workflow_fields = {
     "environment_variables": fields.List(EnvironmentVariableField()),
     "conversation_variables": fields.List(fields.Nested(conversation_variable_fields)),
 }
+
+workflow_partial_fields = {
+    "id": fields.String,
+    "created_by": fields.String,
+    "created_at": TimestampField,
+    "updated_by": fields.String,
+    "updated_at": TimestampField,
+}

api/migrations/versions/2024_08_25_0441-d0187d6a88dd_add_created_by_and_updated_by_to_app_.py

@@ -0,0 +1,52 @@
+"""add created_by and updated_by to app, modelconfig, and site
+
+Revision ID: d0187d6a88dd
+Revises: 2dbe42621d96
+Create Date: 2024-08-25 04:41:18.157397
+
+"""
+
+import sqlalchemy as sa
+from alembic import op
+
+import models as models
+
+# revision identifiers, used by Alembic.
+revision = "d0187d6a88dd"
+down_revision = "2dbe42621d96"
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table("app_model_configs", schema=None) as batch_op:
+        batch_op.add_column(sa.Column("created_by", models.types.StringUUID(), nullable=True))
+        batch_op.add_column(sa.Column("updated_by", models.types.StringUUID(), nullable=True))
+
+    with op.batch_alter_table("apps", schema=None) as batch_op:
+        batch_op.add_column(sa.Column("created_by", models.types.StringUUID(), nullable=True))
+        batch_op.add_column(sa.Column("updated_by", models.types.StringUUID(), nullable=True))
+
+    with op.batch_alter_table("sites", schema=None) as batch_op:
+        batch_op.add_column(sa.Column("created_by", models.types.StringUUID(), nullable=True))
+        batch_op.add_column(sa.Column("updated_by", models.types.StringUUID(), nullable=True))
+
+    # ### end Alembic commands ###
+
+
+def downgrade():
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table("sites", schema=None) as batch_op:
+        batch_op.drop_column("updated_by")
+        batch_op.drop_column("created_by")
+
+    with op.batch_alter_table("apps", schema=None) as batch_op:
+        batch_op.drop_column("updated_by")
+        batch_op.drop_column("created_by")
+
+    with op.batch_alter_table("app_model_configs", schema=None) as batch_op:
+        batch_op.drop_column("updated_by")
+        batch_op.drop_column("created_by")
+
+    # ### end Alembic commands ###

api/models/model.py

@@ -82,7 +82,9 @@ class App(db.Model):
     is_universal = db.Column(db.Boolean, nullable=False, server_default=db.text('false'))
     tracing = db.Column(db.Text, nullable=True)
     max_active_requests = db.Column(db.Integer, nullable=True)
+    created_by = db.Column(StringUUID, nullable=True)
     created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    updated_by = db.Column(StringUUID, nullable=True)
     updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
 
     @property
@@ -221,7 +223,9 @@ class AppModelConfig(db.Model):
     provider = db.Column(db.String(255), nullable=True)
     model_id = db.Column(db.String(255), nullable=True)
     configs = db.Column(db.JSON, nullable=True)
+    created_by = db.Column(StringUUID, nullable=True)
     created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    updated_by = db.Column(StringUUID, nullable=True)
     updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
     opening_statement = db.Column(db.Text)
     suggested_questions = db.Column(db.Text)
@@ -490,7 +494,6 @@ class InstalledApp(db.Model):
         return tenant
 
 
-
 class Conversation(db.Model):
     __tablename__ = 'conversations'
     __table_args__ = (
@@ -623,6 +626,15 @@ class Conversation(db.Model):
 
         return None
 
+    @property
+    def from_account_name(self):
+        if self.from_account_id:
+            account = db.session.query(Account).filter(Account.id == self.from_account_id).first()
+            if account:
+                return account.name
+
+        return None
+
     @property
     def in_debug_mode(self):
         return self.override_model_configs is not None
@@ -1107,7 +1119,9 @@ class Site(db.Model):
     customize_token_strategy = db.Column(db.String(255), nullable=False)
     prompt_public = db.Column(db.Boolean, nullable=False, server_default=db.text('false'))
     status = db.Column(db.String(255), nullable=False, server_default=db.text("'normal'::character varying"))
+    created_by = db.Column(StringUUID, nullable=True)
     created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
+    updated_by = db.Column(StringUUID, nullable=True)
     updated_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
     code = db.Column(db.String(255))
 

api/poetry.lock

@@ -551,6 +551,69 @@ files = [
 [package.dependencies]
 cryptography = "*"
 
+[[package]]
+name = "azure-ai-inference"
+version = "1.0.0b3"
+description = "Microsoft Azure Ai Inference Client Library for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "azure-ai-inference-1.0.0b3.tar.gz", hash = "sha256:1e99dc74c3b335a457500311bbbadb348f54dc4c12252a93cb8ab78d6d217ff0"},
+    {file = "azure_ai_inference-1.0.0b3-py3-none-any.whl", hash = "sha256:6734ca7334c809a170beb767f1f1455724ab3f006cb60045e42a833c0e764403"},
+]
+
+[package.dependencies]
+azure-core = ">=1.30.0"
+isodate = ">=0.6.1"
+typing-extensions = ">=4.6.0"
+
+[[package]]
+name = "azure-ai-ml"
+version = "1.19.0"
+description = "Microsoft Azure Machine Learning Client Library for Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "azure-ai-ml-1.19.0.tar.gz", hash = "sha256:94bb1afbb0497e539ae75455fc4a51b6942b5b68b3a275727ecce6ceb250eff9"},
+    {file = "azure_ai_ml-1.19.0-py3-none-any.whl", hash = "sha256:f0385af06efbeae1f83113613e45343508d1288fd2f05857619e7c7d4d4f5302"},
+]
+
+[package.dependencies]
+azure-common = ">=1.1"
+azure-core = ">=1.23.0"
+azure-mgmt-core = ">=1.3.0"
+azure-storage-blob = ">=12.10.0"
+azure-storage-file-datalake = ">=12.2.0"
+azure-storage-file-share = "*"
+colorama = "*"
+isodate = "*"
+jsonschema = ">=4.0.0"
+marshmallow = ">=3.5"
+msrest = ">=0.6.18"
+opencensus-ext-azure = "*"
+opencensus-ext-logging = "*"
+pydash = ">=6.0.0"
+pyjwt = "*"
+pyyaml = ">=5.1.0"
+strictyaml = "*"
+tqdm = "*"
+typing-extensions = "*"
+
+[package.extras]
+designer = ["mldesigner"]
+mount = ["azureml-dataprep-rslex (>=2.22.0)"]
+
+[[package]]
+name = "azure-common"
+version = "1.1.28"
+description = "Microsoft Azure Client Library for Python (Common)"
+optional = false
+python-versions = "*"
+files = [
+    {file = "azure-common-1.1.28.zip", hash = "sha256:4ac0cd3214e36b6a1b6a442686722a5d8cc449603aa833f3f0f40bda836704a3"},
+    {file = "azure_common-1.1.28-py2.py3-none-any.whl", hash = "sha256:5c12d3dcf4ec20599ca6b0d3e09e86e146353d443e7fcc050c9a19c1f9df20ad"},
+]
+
 [[package]]
 name = "azure-core"
 version = "1.30.2"
@@ -587,6 +650,20 @@ cryptography = ">=2.5"
 msal = ">=1.24.0"
 msal-extensions = ">=0.3.0"
 
+[[package]]
+name = "azure-mgmt-core"
+version = "1.4.0"
+description = "Microsoft Azure Management Core Library for Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "azure-mgmt-core-1.4.0.zip", hash = "sha256:d195208340094f98e5a6661b781cde6f6a051e79ce317caabd8ff97030a9b3ae"},
+    {file = "azure_mgmt_core-1.4.0-py3-none-any.whl", hash = "sha256:81071675f186a585555ef01816f2774d49c1c9024cb76e5720c3c0f6b337bb7d"},
+]
+
+[package.dependencies]
+azure-core = ">=1.26.2,<2.0.0"
+
 [[package]]
 name = "azure-storage-blob"
 version = "12.13.0"
@@ -603,6 +680,42 @@ azure-core = ">=1.23.1,<2.0.0"
 cryptography = ">=2.1.4"
 msrest = ">=0.6.21"
 
+[[package]]
+name = "azure-storage-file-datalake"
+version = "12.8.0"
+description = "Microsoft Azure File DataLake Storage Client Library for Python"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "azure-storage-file-datalake-12.8.0.zip", hash = "sha256:12e6306e5efb5ca28e0ccd9fa79a2c61acd589866d6109fe5601b18509da92f4"},
+    {file = "azure_storage_file_datalake-12.8.0-py3-none-any.whl", hash = "sha256:b6cf5733fe794bf3c866efbe3ce1941409e35b6b125028ac558b436bf90f2de7"},
+]
+
+[package.dependencies]
+azure-core = ">=1.23.1,<2.0.0"
+azure-storage-blob = ">=12.13.0,<13.0.0"
+msrest = ">=0.6.21"
+
+[[package]]
+name = "azure-storage-file-share"
+version = "12.17.0"
+description = "Microsoft Azure Azure File Share Storage Client Library for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "azure-storage-file-share-12.17.0.tar.gz", hash = "sha256:f7b2c6cfc1b7cb80097a53b1ed2efa9e545b49a291430d369cdb49fafbc841d6"},
+    {file = "azure_storage_file_share-12.17.0-py3-none-any.whl", hash = "sha256:c4652759a9d529bf08881bb53275bf38774bb643746b849d27c47118f9cf923d"},
+]
+
+[package.dependencies]
+azure-core = ">=1.28.0"
+cryptography = ">=2.1.4"
+isodate = ">=0.6.1"
+typing-extensions = ">=4.6.0"
+
+[package.extras]
+aio = ["azure-core[aio] (>=1.28.0)"]
+
 [[package]]
 name = "backoff"
 version = "2.2.1"
@@ -3952,6 +4065,41 @@ files = [
 [package.dependencies]
 ply = "*"
 
+[[package]]
+name = "jsonschema"
+version = "4.23.0"
+description = "An implementation of JSON Schema validation for Python"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "jsonschema-4.23.0.tar.gz", hash = "sha256:d71497fef26351a33265337fa77ffeb82423f3ea21283cd9467bb03999266bc4"},
+]
+
+[package.dependencies]
+attrs = ">=22.2.0"
+jsonschema-specifications = ">=2023.03.6"
+referencing = ">=0.28.4"
+rpds-py = ">=0.7.1"
+
+[package.extras]
+format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
+format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=24.6.0)"]
+
+[[package]]
+name = "jsonschema-specifications"
+version = "2023.12.1"
+description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jsonschema_specifications-2023.12.1-py3-none-any.whl", hash = "sha256:87e4fdf3a94858b8a2ba2778d9ba57d8a9cafca7c7489c46ba0d30a8bc6a9c3c"},
+    {file = "jsonschema_specifications-2023.12.1.tar.gz", hash = "sha256:48a76787b3e70f5ed53f1160d2b81f586e4ca6d1548c5de7085d1682674764cc"},
+]
+
+[package.dependencies]
+referencing = ">=0.31.0"
+
 [[package]]
 name = "kaleido"
 version = "0.2.1"
@@ -5277,6 +5425,65 @@ typing-extensions = ">=4.7,<5"
 [package.extras]
 datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
 
+[[package]]
+name = "opencensus"
+version = "0.11.4"
+description = "A stats collection and distributed tracing framework"
+optional = false
+python-versions = "*"
+files = [
+    {file = "opencensus-0.11.4-py2.py3-none-any.whl", hash = "sha256:a18487ce68bc19900336e0ff4655c5a116daf10c1b3685ece8d971bddad6a864"},
+    {file = "opencensus-0.11.4.tar.gz", hash = "sha256:cbef87d8b8773064ab60e5c2a1ced58bbaa38a6d052c41aec224958ce544eff2"},
+]
+
+[package.dependencies]
+google-api-core = {version = ">=1.0.0,<3.0.0", markers = "python_version >= \"3.6\""}
+opencensus-context = ">=0.1.3"
+six = ">=1.16,<2.0"
+
+[[package]]
+name = "opencensus-context"
+version = "0.1.3"
+description = "OpenCensus Runtime Context"
+optional = false
+python-versions = "*"
+files = [
+    {file = "opencensus-context-0.1.3.tar.gz", hash = "sha256:a03108c3c10d8c80bb5ddf5c8a1f033161fa61972a9917f9b9b3a18517f0088c"},
+    {file = "opencensus_context-0.1.3-py2.py3-none-any.whl", hash = "sha256:073bb0590007af276853009fac7e4bab1d523c3f03baf4cb4511ca38967c6039"},
+]
+
+[[package]]
+name = "opencensus-ext-azure"
+version = "1.1.13"
+description = "OpenCensus Azure Monitor Exporter"
+optional = false
+python-versions = "*"
+files = [
+    {file = "opencensus-ext-azure-1.1.13.tar.gz", hash = "sha256:aec30472177005379ba56a702a097d618c5f57558e1bb6676ec75f948130692a"},
+    {file = "opencensus_ext_azure-1.1.13-py2.py3-none-any.whl", hash = "sha256:06001fac6f8588ba00726a3a7c6c7f2fc88bc8ad12a65afdca657923085393dd"},
+]
+
+[package.dependencies]
+azure-core = ">=1.12.0,<2.0.0"
+azure-identity = ">=1.5.0,<2.0.0"
+opencensus = ">=0.11.4,<1.0.0"
+psutil = ">=5.6.3"
+requests = ">=2.19.0"
+
+[[package]]
+name = "opencensus-ext-logging"
+version = "0.1.1"
+description = "OpenCensus logging Integration"
+optional = false
+python-versions = "*"
+files = [
+    {file = "opencensus-ext-logging-0.1.1.tar.gz", hash = "sha256:c203b70f034151dada529f543af330ba17aaffec27d8a5267d03c713eb1de334"},
+    {file = "opencensus_ext_logging-0.1.1-py2.py3-none-any.whl", hash = "sha256:cfdaf5da5d8b195ff3d1af87a4066a6621a28046173f6be4b0b6caec4a3ca89f"},
+]
+
+[package.dependencies]
+opencensus = ">=0.8.0,<1.0.0"
+
 [[package]]
 name = "openpyxl"
 version = "3.1.5"
@@ -6021,6 +6228,35 @@ files = [
     {file = "protobuf-4.25.4.tar.gz", hash = "sha256:0dc4a62cc4052a036ee2204d26fe4d835c62827c855c8a03f29fe6da146b380d"},
 ]
 
+[[package]]
+name = "psutil"
+version = "6.0.0"
+description = "Cross-platform lib for process and system monitoring in Python."
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
+files = [
+    {file = "psutil-6.0.0-cp27-cp27m-macosx_10_9_x86_64.whl", hash = "sha256:a021da3e881cd935e64a3d0a20983bda0bb4cf80e4f74fa9bfcb1bc5785360c6"},
+    {file = "psutil-6.0.0-cp27-cp27m-manylinux2010_i686.whl", hash = "sha256:1287c2b95f1c0a364d23bc6f2ea2365a8d4d9b726a3be7294296ff7ba97c17f0"},
+    {file = "psutil-6.0.0-cp27-cp27m-manylinux2010_x86_64.whl", hash = "sha256:a9a3dbfb4de4f18174528d87cc352d1f788b7496991cca33c6996f40c9e3c92c"},
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+    {file = "psutil-6.0.0-cp27-none-win32.whl", hash = "sha256:02b69001f44cc73c1c5279d02b30a817e339ceb258ad75997325e0e6169d8b35"},
+    {file = "psutil-6.0.0-cp27-none-win_amd64.whl", hash = "sha256:21f1fb635deccd510f69f485b87433460a603919b45e2a324ad65b0cc74f8fb1"},
+    {file = "psutil-6.0.0-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:c588a7e9b1173b6e866756dde596fd4cad94f9399daf99ad8c3258b3cb2b47a0"},
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+    {file = "psutil-6.0.0-cp36-cp36m-win_amd64.whl", hash = "sha256:34859b8d8f423b86e4385ff3665d3f4d94be3cdf48221fbe476e883514fdb71c"},
+    {file = "psutil-6.0.0-cp37-abi3-win32.whl", hash = "sha256:a495580d6bae27291324fe60cea0b5a7c23fa36a7cd35035a16d93bdcf076b9d"},
+    {file = "psutil-6.0.0-cp37-abi3-win_amd64.whl", hash = "sha256:33ea5e1c975250a720b3a6609c490db40dae5d83a4eb315170c4fe0d8b1f34b3"},
+    {file = "psutil-6.0.0-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:ffe7fc9b6b36beadc8c322f84e1caff51e8703b88eee1da46d1e3a6ae11b4fd0"},
+    {file = "psutil-6.0.0.tar.gz", hash = "sha256:8faae4f310b6d969fa26ca0545338b21f73c6b15db7c4a8d934a5482faa818f2"},
+]
+
+[package.extras]
+test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
+
 [[package]]
 name = "psycopg2-binary"
 version = "2.9.9"
@@ -6403,6 +6639,23 @@ azure-key-vault = ["azure-identity (>=1.16.0)", "azure-keyvault-secrets (>=4.8.0
 toml = ["tomli (>=2.0.1)"]
 yaml = ["pyyaml (>=6.0.1)"]
 
+[[package]]
+name = "pydash"
+version = "8.0.3"
+description = "The kitchen sink of Python utility libraries for doing \"stuff\" in a functional way. Based on the Lo-Dash Javascript library."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pydash-8.0.3-py3-none-any.whl", hash = "sha256:c16871476822ee6b59b87e206dd27888240eff50a7b4cd72a4b80b43b6b994d7"},
+    {file = "pydash-8.0.3.tar.gz", hash = "sha256:1b27cd3da05b72f0e5ff786c523afd82af796936462e631ffd1b228d91f8b9aa"},
+]
+
+[package.dependencies]
+typing-extensions = ">3.10,<4.6.0 || >4.6.0"
+
+[package.extras]
+dev = ["build", "coverage", "furo", "invoke", "mypy", "pytest", "pytest-cov", "pytest-mypy-testing", "ruff", "sphinx", "sphinx-autodoc-typehints", "tox", "twine", "wheel"]
+
 [[package]]
 name = "pygments"
 version = "2.18.0"
@@ -6568,13 +6821,13 @@ files = [
 
 [[package]]
 name = "pytest"
-version = "8.1.2"
+version = "8.3.2"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pytest-8.1.2-py3-none-any.whl", hash = "sha256:6c06dc309ff46a05721e6fd48e492a775ed8165d2ecdf57f156a80c7e95bb142"},
-    {file = "pytest-8.1.2.tar.gz", hash = "sha256:f3c45d1d5eed96b01a2aea70dee6a4a366d51d38f9957768083e4fecfc77f3ef"},
+    {file = "pytest-8.3.2-py3-none-any.whl", hash = "sha256:4ba08f9ae7dcf84ded419494d229b48d0903ea6407b030eaec46df5e6a73bba5"},
+    {file = "pytest-8.3.2.tar.gz", hash = "sha256:c132345d12ce551242c87269de812483f5bcc87cdbb4722e48487ba194f9fdce"},
 ]
 
 [package.dependencies]
@@ -6582,11 +6835,11 @@ colorama = {version = "*", markers = "sys_platform == \"win32\""}
 exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""}
 iniconfig = "*"
 packaging = "*"
-pluggy = ">=1.4,<2.0"
+pluggy = ">=1.5,<2"
 tomli = {version = ">=1", markers = "python_version < \"3.11\""}
 
 [package.extras]
-testing = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
+dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
 [[package]]
 name = "pytest-benchmark"
@@ -7170,6 +7423,21 @@ hiredis = {version = ">1.0.0", optional = true, markers = "extra == \"hiredis\""
 hiredis = ["hiredis (>1.0.0)"]
 ocsp = ["cryptography (>=36.0.1)", "pyopenssl (==20.0.1)", "requests (>=2.26.0)"]
 
+[[package]]
+name = "referencing"
+version = "0.35.1"
+description = "JSON Referencing + Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "referencing-0.35.1-py3-none-any.whl", hash = "sha256:eda6d3234d62814d1c64e305c1331c9a3a6132da475ab6382eaa997b21ee75de"},
+    {file = "referencing-0.35.1.tar.gz", hash = "sha256:25b42124a6c8b632a425174f24087783efb348a6f1e0008e63cd4466fedf703c"},
+]
+
+[package.dependencies]
+attrs = ">=22.2.0"
+rpds-py = ">=0.7.0"
+
 [[package]]
 name = "regex"
 version = "2024.7.24"
@@ -7377,6 +7645,118 @@ pygments = ">=2.13.0,<3.0.0"
 [package.extras]
 jupyter = ["ipywidgets (>=7.5.1,<9)"]
 
+[[package]]
+name = "rpds-py"
+version = "0.20.0"
+description = "Python bindings to Rust's persistent data structures (rpds)"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "rpds_py-0.20.0.tar.gz", hash = "sha256:d72a210824facfdaf8768cf2d7ca25a042c30320b3020de2fa04640920d4e121"},
+]
+
 [[package]]
 name = "rsa"
 version = "4.9"
@@ -7987,6 +8367,20 @@ anyio = ">=3.4.0,<5"
 [package.extras]
 full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.7)", "pyyaml"]
 
+[[package]]
+name = "strictyaml"
+version = "1.7.3"
+description = "Strict, typed YAML parser"
+optional = false
+python-versions = ">=3.7.0"
+files = [
+    {file = "strictyaml-1.7.3-py3-none-any.whl", hash = "sha256:fb5c8a4edb43bebb765959e420f9b3978d7f1af88c80606c03fb420888f5d1c7"},
+    {file = "strictyaml-1.7.3.tar.gz", hash = "sha256:22f854a5fcab42b5ddba8030a0e4be51ca89af0267961c8d6cfa86395586c407"},
+]
+
+[package.dependencies]
+python-dateutil = ">=2.6.0"
+
 [[package]]
 name = "sympy"
 version = "1.13.2"
@@ -9669,4 +10063,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "04f970820de691f40fc9fb30f5ff0618b0f1a04d3315b14467fb88e475fa1243"
+content-hash = "e4c00268514d26bd07c6b72925e0e3b4558ec972895d252e60e9571e3ac38895"

api/pyproject.toml

@@ -188,6 +188,8 @@ zhipuai = "1.0.7"
 # Related transparent dependencies with pinned verion
 # required by main implementations
 ############################################################
+azure-ai-ml = "^1.19.0"
+azure-ai-inference = "^1.0.0b3"
 volcengine-python-sdk = {extras = ["ark"], version = "^1.0.98"}
 [tool.poetry.group.indriect.dependencies]
 kaleido = "0.2.1"
@@ -241,7 +243,7 @@ optional = true
 
 [tool.poetry.group.dev.dependencies]
 coverage = "~7.2.4"
-pytest = "~8.1.1"
+pytest = "~8.3.2"
 pytest-benchmark = "~4.0.0"
 pytest-env = "~1.1.3"
 pytest-mock = "~3.14.0"

api/services/app_dsl_service.py

@@ -346,6 +346,8 @@ class AppDslService:
         app_model_config = AppModelConfig()
         app_model_config = app_model_config.from_model_config_dict(model_config_data)
         app_model_config.app_id = app.id
+        app_model_config.created_by = account.id
+        app_model_config.updated_by = account.id
 
         db.session.add(app_model_config)
         db.session.commit()
@@ -390,6 +392,8 @@ class AppDslService:
             icon_background=icon_background,
             enable_site=True,
             enable_api=True,
+            created_by=account.id,
+            updated_by=account.id,
         )
 
         db.session.add(app)

api/services/app_service.py

@@ -127,6 +127,8 @@ class AppService:
         app.tenant_id = tenant_id
         app.api_rph = args.get("api_rph", 0)
         app.api_rpm = args.get("api_rpm", 0)
+        app.created_by = account.id
+        app.updated_by = account.id
 
         db.session.add(app)
         db.session.flush()
@@ -134,6 +136,8 @@ class AppService:
         if default_model_config:
             app_model_config = AppModelConfig(**default_model_config)
             app_model_config.app_id = app.id
+            app_model_config.created_by = account.id
+            app_model_config.updated_by = account.id
             db.session.add(app_model_config)
             db.session.flush()
 
@@ -217,6 +221,7 @@ class AppService:
         app.icon_type = args.get("icon_type", "emoji")
         app.icon = args.get("icon")
         app.icon_background = args.get("icon_background")
+        app.updated_by = current_user.id
         app.updated_at = datetime.now(timezone.utc).replace(tzinfo=None)
         db.session.commit()
 
@@ -233,6 +238,7 @@ class AppService:
         :return: App instance
         """
         app.name = name
+        app.updated_by = current_user.id
         app.updated_at = datetime.now(timezone.utc).replace(tzinfo=None)
         db.session.commit()
 
@@ -248,6 +254,7 @@ class AppService:
         """
         app.icon = icon
         app.icon_background = icon_background
+        app.updated_by = current_user.id
         app.updated_at = datetime.now(timezone.utc).replace(tzinfo=None)
         db.session.commit()
 
@@ -264,6 +271,7 @@ class AppService:
             return app
 
         app.enable_site = enable_site
+        app.updated_by = current_user.id
         app.updated_at = datetime.now(timezone.utc).replace(tzinfo=None)
         db.session.commit()
 
@@ -280,6 +288,7 @@ class AppService:
             return app
 
         app.enable_api = enable_api
+        app.updated_by = current_user.id
         app.updated_at = datetime.now(timezone.utc).replace(tzinfo=None)
         db.session.commit()
 

api/services/dataset_service.py

@@ -136,7 +136,9 @@ class DatasetService:
         return datasets.items, datasets.total
 
     @staticmethod
-    def create_empty_dataset(tenant_id: str, name: str, indexing_technique: Optional[str], account: Account):
+    def create_empty_dataset(
+        tenant_id: str, name: str, indexing_technique: Optional[str], account: Account, permission: Optional[str]
+    ):
         # check if dataset name already exists
         if Dataset.query.filter_by(name=name, tenant_id=tenant_id).first():
             raise DatasetNameDuplicateError(f"Dataset with name {name} already exists.")
@@ -153,6 +155,7 @@ class DatasetService:
         dataset.tenant_id = tenant_id
         dataset.embedding_model_provider = embedding_model.provider if embedding_model else None
         dataset.embedding_model = embedding_model.model if embedding_model else None
+        dataset.permission = permission if permission else DatasetPermissionEnum.ONLY_ME
         db.session.add(dataset)
         db.session.commit()
         return dataset

api/services/ops_service.py

@@ -26,16 +26,15 @@ class OpsService:
         decrypt_tracing_config = OpsTraceManager.decrypt_tracing_config(
             tenant_id, tracing_provider, trace_config_data.tracing_config
         )
+        new_decrypt_tracing_config = OpsTraceManager.obfuscated_decrypt_token(tracing_provider, decrypt_tracing_config)
+
         if tracing_provider == "langfuse" and (
             "project_key" not in decrypt_tracing_config or not decrypt_tracing_config.get("project_key")
         ):
             project_key = OpsTraceManager.get_trace_config_project_key(decrypt_tracing_config, tracing_provider)
-            decrypt_tracing_config["project_key"] = project_key
-
-        decrypt_tracing_config = OpsTraceManager.obfuscated_decrypt_token(tracing_provider, decrypt_tracing_config)
-
-        trace_config_data.tracing_config = decrypt_tracing_config
+            new_decrypt_tracing_config.update({"project_key": project_key})
 
+        trace_config_data.tracing_config = new_decrypt_tracing_config
         return trace_config_data.to_dict()
 
     @classmethod
@@ -79,7 +78,7 @@ class OpsService:
         # get tenant id
         tenant_id = db.session.query(App).filter(App.id == app_id).first().tenant_id
         tracing_config = OpsTraceManager.encrypt_tracing_config(tenant_id, tracing_provider, tracing_config)
-        if tracing_provider == "langfuse":
+        if tracing_provider == "langfuse" and project_key:
             tracing_config["project_key"] = project_key
         trace_config_data = TraceAppConfig(
             app_id=app_id,

api/services/workflow/workflow_converter.py

@@ -74,6 +74,8 @@ class WorkflowConverter:
         new_app.api_rph = app_model.api_rph
         new_app.is_demo = False
         new_app.is_public = app_model.is_public
+        new_app.created_by = account.id
+        new_app.updated_by = account.id
         db.session.add(new_app)
         db.session.flush()
         db.session.commit()

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

@@ -0,0 +1,113 @@
+import os
+from collections.abc import Generator
+
+import pytest
+
+from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
+from core.model_runtime.entities.message_entities import (
+    AssistantPromptMessage,
+    ImagePromptMessageContent,
+    PromptMessageTool,
+    SystemPromptMessage,
+    TextPromptMessageContent,
+    UserPromptMessage,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.azure_ai_studio.llm.llm import AzureAIStudioLargeLanguageModel
+from tests.integration_tests.model_runtime.__mock.azure_ai_studio import setup_azure_ai_studio_mock
+
+
+@pytest.mark.parametrize("setup_azure_ai_studio_mock", [["chat"]], indirect=True)
+def test_validate_credentials(setup_azure_ai_studio_mock):
+    model = AzureAIStudioLargeLanguageModel()
+
+    with pytest.raises(CredentialsValidateFailedError):
+        model.validate_credentials(
+            model="gpt-35-turbo",
+            credentials={"api_key": "invalid_key", "api_base": os.getenv("AZURE_AI_STUDIO_API_BASE")},
+        )
+
+    model.validate_credentials(
+        model="gpt-35-turbo",
+        credentials={
+            "api_key": os.getenv("AZURE_AI_STUDIO_API_KEY"),
+            "api_base": os.getenv("AZURE_AI_STUDIO_API_BASE"),
+        },
+    )
+
+
+@pytest.mark.parametrize("setup_azure_ai_studio_mock", [["chat"]], indirect=True)
+def test_invoke_model(setup_azure_ai_studio_mock):
+    model = AzureAIStudioLargeLanguageModel()
+
+    result = model.invoke(
+        model="gpt-35-turbo",
+        credentials={
+            "api_key": os.getenv("AZURE_AI_STUDIO_API_KEY"),
+            "api_base": os.getenv("AZURE_AI_STUDIO_API_BASE"),
+        },
+        prompt_messages=[
+            SystemPromptMessage(
+                content="You are a helpful AI assistant.",
+            ),
+            UserPromptMessage(content="Hello World!"),
+        ],
+        model_parameters={"temperature": 0.0, "max_tokens": 100},
+        stream=False,
+        user="abc-123",
+    )
+
+    assert isinstance(result, LLMResult)
+    assert len(result.message.content) > 0
+
+
+@pytest.mark.parametrize("setup_azure_ai_studio_mock", [["chat"]], indirect=True)
+def test_invoke_stream_model(setup_azure_ai_studio_mock):
+    model = AzureAIStudioLargeLanguageModel()
+
+    result = model.invoke(
+        model="gpt-35-turbo",
+        credentials={
+            "api_key": os.getenv("AZURE_AI_STUDIO_API_KEY"),
+            "api_base": os.getenv("AZURE_AI_STUDIO_API_BASE"),
+        },
+        prompt_messages=[
+            SystemPromptMessage(
+                content="You are a helpful AI assistant.",
+            ),
+            UserPromptMessage(content="Hello World!"),
+        ],
+        model_parameters={"temperature": 0.0, "max_tokens": 100},
+        stream=True,
+        user="abc-123",
+    )
+
+    assert isinstance(result, Generator)
+
+    for chunk in result:
+        assert isinstance(chunk, LLMResultChunk)
+        assert isinstance(chunk.delta, LLMResultChunkDelta)
+        assert isinstance(chunk.delta.message, AssistantPromptMessage)
+        if chunk.delta.finish_reason is not None:
+            assert chunk.delta.usage is not None
+            assert chunk.delta.usage.completion_tokens > 0
+
+
+def test_get_num_tokens():
+    model = AzureAIStudioLargeLanguageModel()
+
+    num_tokens = model.get_num_tokens(
+        model="gpt-35-turbo",
+        credentials={
+            "api_key": os.getenv("AZURE_AI_STUDIO_API_KEY"),
+            "api_base": os.getenv("AZURE_AI_STUDIO_API_BASE"),
+        },
+        prompt_messages=[
+            SystemPromptMessage(
+                content="You are a helpful AI assistant.",
+            ),
+            UserPromptMessage(content="Hello World!"),
+        ],
+    )
+
+    assert num_tokens == 21

api/tests/integration_tests/model_runtime/azure_ai_studio/test_provider.py

@@ -0,0 +1,17 @@
+import os
+
+import pytest
+
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.azure_ai_studio.azure_ai_studio import AzureAIStudioProvider
+
+
+def test_validate_provider_credentials():
+    provider = AzureAIStudioProvider()
+
+    with pytest.raises(CredentialsValidateFailedError):
+        provider.validate_provider_credentials(credentials={})
+
+    provider.validate_provider_credentials(
+        credentials={"api_key": os.getenv("AZURE_AI_STUDIO_API_KEY"), "api_base": os.getenv("AZURE_AI_STUDIO_API_BASE")}
+    )

api/tests/integration_tests/model_runtime/azure_ai_studio/test_rerank.py

@@ -0,0 +1,50 @@
+import os
+
+import pytest
+
+from core.model_runtime.entities.rerank_entities import RerankResult
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.azure_ai_studio.rerank.rerank import AzureAIStudioRerankModel
+
+
+def test_validate_credentials():
+    model = AzureAIStudioRerankModel()
+
+    with pytest.raises(CredentialsValidateFailedError):
+        model.validate_credentials(
+            model="azure-ai-studio-rerank-v1",
+            credentials={"api_key": "invalid_key", "api_base": os.getenv("AZURE_AI_STUDIO_API_BASE")},
+            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,
+        )
+
+
+def test_invoke_model():
+    model = AzureAIStudioRerankModel()
+
+    result = model.invoke(
+        model="azure-ai-studio-rerank-v1",
+        credentials={
+            "api_key": os.getenv("AZURE_AI_STUDIO_JWT_TOKEN"),
+            "api_base": os.getenv("AZURE_AI_STUDIO_API_BASE"),
+        },
+        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,
+    )
+
+    assert isinstance(result, RerankResult)
+    assert len(result.docs) == 1
+    assert result.docs[0].index == 1
+    assert result.docs[0].score >= 0.8

api/tests/unit_tests/configs/test_dify_config.py

@@ -3,6 +3,7 @@ from textwrap import dedent
 
 import pytest
 from flask import Flask
+from yarl import URL
 
 from configs.app_config import DifyConfig
 
@@ -84,3 +85,6 @@ def test_flask_configs(example_env_file):
     assert config["CONSOLE_WEB_URL"] == "https://example.com"
     assert config["CONSOLE_CORS_ALLOW_ORIGINS"] == ["https://example.com"]
     assert config["WEB_API_CORS_ALLOW_ORIGINS"] == ["*"]
+
+    assert str(config["CODE_EXECUTION_ENDPOINT"]) == "http://sandbox:8194/"
+    assert str(URL(str(config["CODE_EXECUTION_ENDPOINT"])) / "v1") == "http://sandbox:8194/v1"

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

@@ -4,7 +4,7 @@ import dayjs from 'dayjs'
 import quarterOfYear from 'dayjs/plugin/quarterOfYear'
 import { useTranslation } from 'react-i18next'
 import type { PeriodParams } from '@/app/components/app/overview/appChart'
-import { AvgResponseTime, AvgSessionInteractions, AvgUserInteractions, ConversationsChart, CostChart, EndUsersChart, TokenPerSecond, UserSatisfactionRate, WorkflowCostChart, WorkflowDailyTerminalsChart, WorkflowMessagesChart } from '@/app/components/app/overview/appChart'
+import { AvgResponseTime, AvgSessionInteractions, AvgUserInteractions, ConversationsChart, CostChart, EndUsersChart, MessagesChart, TokenPerSecond, UserSatisfactionRate, WorkflowCostChart, WorkflowDailyTerminalsChart, WorkflowMessagesChart } from '@/app/components/app/overview/appChart'
 import type { Item } from '@/app/components/base/select'
 import { SimpleSelect } from '@/app/components/base/select'
 import { TIME_PERIOD_LIST } from '@/app/components/app/log/filter'
@@ -79,6 +79,11 @@ export default function ChartView({ appId }: IChartViewProps) {
           <CostChart period={period} id={appId} />
         </div>
       )}
+      {!isWorkflow && isChatApp && (
+        <div className='grid gap-6 grid-cols-1 xl:grid-cols-2 w-full mb-6'>
+          <MessagesChart period={period} id={appId} />
+        </div>
+      )}
       {isWorkflow && (
         <div className='grid gap-6 grid-cols-1 xl:grid-cols-2 w-full mb-6'>
           <WorkflowMessagesChart period={period} id={appId} />

web/app/components/app/annotation/index.tsx

@@ -280,7 +280,7 @@ const Annotation: FC<Props> = ({
             onSave={async (embeddingModel, score) => {
               if (
                 embeddingModel.embedding_model_name !== annotationConfig?.embedding_model?.embedding_model_name
-                && embeddingModel.embedding_provider_name !== annotationConfig?.embedding_model?.embedding_provider_name
+                || embeddingModel.embedding_provider_name !== annotationConfig?.embedding_model?.embedding_provider_name
               ) {
                 const { job_id: jobId }: any = await updateAnnotationStatus(appDetail.id, AnnotationEnableStatus.enable, embeddingModel, score)
                 await ensureJobCompleted(jobId, AnnotationEnableStatus.enable)

web/app/components/app/configuration/toolbox/annotation/config-param.tsx

@@ -98,7 +98,7 @@ const AnnotationReplyConfig: FC<Props> = ({
             let isEmbeddingModelChanged = false
             if (
               embeddingModel.embedding_model_name !== annotationConfig.embedding_model.embedding_model_name
-              && embeddingModel.embedding_provider_name !== annotationConfig.embedding_model.embedding_provider_name
+              || embeddingModel.embedding_provider_name !== annotationConfig.embedding_model.embedding_provider_name
             ) {
               await onEmbeddingChange(embeddingModel)
               isEmbeddingModelChanged = true

web/app/components/app/configuration/toolbox/index.tsx

@@ -32,7 +32,7 @@ const Toolbox: FC<ToolboxProps> = ({
         )
       }
       {
-        (showAnnotation || true) && (
+        showAnnotation && (
           <Annotation
             onEmbeddingChange={onEmbeddingChange}
             onScoreChange={onScoreChange}

web/app/components/app/log-annotation/index.tsx

@@ -1,6 +1,6 @@
 'use client'
 import type { FC } from 'react'
-import React from 'react'
+import React, { useMemo } from 'react'
 import { useTranslation } from 'react-i18next'
 import { useRouter } from 'next/navigation'
 import cn from '@/utils/classnames'
@@ -23,10 +23,14 @@ const LogAnnotation: FC<Props> = ({
   const router = useRouter()
   const appDetail = useAppStore(state => state.appDetail)
 
-  const options = [
-    { value: PageType.log, text: t('appLog.title') },
-    { value: PageType.annotation, text: t('appAnnotation.title') },
-  ]
+  const options = useMemo(() => {
+    if (appDetail?.mode === 'completion')
+      return [{ value: PageType.log, text: t('appLog.title') }]
+    return [
+      { value: PageType.log, text: t('appLog.title') },
+      { value: PageType.annotation, text: t('appAnnotation.title') },
+    ]
+  }, [appDetail])
 
   if (!appDetail) {
     return (

web/app/components/app/log/list.tsx

@@ -678,7 +678,7 @@ const ConversationList: FC<IConversationList> = ({ logs, appDetail, onRefresh })
         </thead>
         <tbody className="text-gray-500">
           {logs.data.map((log: any) => {
-            const endUser = log.from_end_user_session_id
+            const endUser = log.from_end_user_session_id || log.from_account_name
             const leftValue = get(log, isChatMode ? 'name' : 'message.inputs.query') || (!isChatMode ? (get(log, 'message.query') || get(log, 'message.inputs.default_input')) : '') || ''
             const rightValue = get(log, isChatMode ? 'message_count' : 'message.answer')
             return <tr

web/app/components/app/overview/appChart.tsx

@@ -10,8 +10,8 @@ import { useTranslation } from 'react-i18next'
 import { formatNumber } from '@/utils/format'
 import Basic from '@/app/components/app-sidebar/basic'
 import Loading from '@/app/components/base/loading'
-import type { AppDailyConversationsResponse, AppDailyEndUsersResponse, AppTokenCostsResponse } from '@/models/app'
-import { getAppDailyConversations, getAppDailyEndUsers, getAppStatistics, getAppTokenCosts, getWorkflowDailyConversations } from '@/service/apps'
+import type { AppDailyConversationsResponse, AppDailyEndUsersResponse, AppDailyMessagesResponse, AppTokenCostsResponse } from '@/models/app'
+import { getAppDailyConversations, getAppDailyEndUsers, getAppDailyMessages, getAppStatistics, getAppTokenCosts, getWorkflowDailyConversations } from '@/service/apps'
 const valueFormatter = (v: string | number) => v
 
 const COLOR_TYPE_MAP = {
@@ -36,12 +36,15 @@ const COMMON_COLOR_MAP = {
 }
 
 type IColorType = 'green' | 'orange' | 'blue'
-type IChartType = 'conversations' | 'endUsers' | 'costs' | 'workflowCosts'
+type IChartType = 'messages' | 'conversations' | 'endUsers' | 'costs' | 'workflowCosts'
 type IChartConfigType = { colorType: IColorType; showTokens?: boolean }
 
 const commonDateFormat = 'MMM D, YYYY'
 
 const CHART_TYPE_CONFIG: Record<string, IChartConfigType> = {
+  messages: {
+    colorType: 'green',
+  },
   conversations: {
     colorType: 'green',
   },
@@ -89,7 +92,7 @@ export type IChartProps = {
   unit?: string
   yMax?: number
   chartType: IChartType
-  chartData: AppDailyConversationsResponse | AppDailyEndUsersResponse | AppTokenCostsResponse | { data: Array<{ date: string; count: number }> }
+  chartData: AppDailyMessagesResponse | AppDailyConversationsResponse | AppDailyEndUsersResponse | AppTokenCostsResponse | { data: Array<{ date: string; count: number }> }
 }
 
 const Chart: React.FC<IChartProps> = ({
@@ -258,6 +261,20 @@ const getDefaultChartData = ({ start, end, key = 'count' }: { start: string; end
   })
 }
 
+export const MessagesChart: FC<IBizChartProps> = ({ id, period }) => {
+  const { t } = useTranslation()
+  const { data: response } = useSWR({ url: `/apps/${id}/statistics/daily-messages`, params: period.query }, getAppDailyMessages)
+  if (!response)
+    return <Loading />
+  const noDataFlag = !response.data || response.data.length === 0
+  return <Chart
+    basicInfo={{ title: t('appOverview.analysis.totalMessages.title'), explanation: t('appOverview.analysis.totalMessages.explanation'), timePeriod: period.name }}
+    chartData={!noDataFlag ? response : { data: getDefaultChartData(period.query ?? defaultPeriod) }}
+    chartType='messages'
+    {...(noDataFlag && { yMax: 500 })}
+  />
+}
+
 export const ConversationsChart: FC<IBizChartProps> = ({ id, period }) => {
   const { t } = useTranslation()
   const { data: response } = useSWR({ url: `/apps/${id}/statistics/daily-conversations`, params: period.query }, getAppDailyConversations)
@@ -265,7 +282,7 @@ export const ConversationsChart: FC<IBizChartProps> = ({ id, period }) => {
     return <Loading />
   const noDataFlag = !response.data || response.data.length === 0
   return <Chart
-    basicInfo={{ title: t('appOverview.analysis.totalMessages.title'), explanation: t('appOverview.analysis.totalMessages.explanation'), timePeriod: period.name }}
+    basicInfo={{ title: t('appOverview.analysis.totalConversations.title'), explanation: t('appOverview.analysis.totalConversations.explanation'), timePeriod: period.name }}
     chartData={!noDataFlag ? response : { data: getDefaultChartData(period.query ?? defaultPeriod) }}
     chartType='conversations'
     {...(noDataFlag && { yMax: 500 })}

web/app/components/app/workflow-log/list.tsx

@@ -91,7 +91,7 @@ const WorkflowAppLogList: FC<ILogs> = ({ logs, appDetail, onRefresh }) => {
         </thead>
         <tbody className="text-gray-700 text-[13px]">
           {logs.data.map((log: WorkflowAppLogDetail) => {
-            const endUser = log.created_by_end_user ? log.created_by_end_user.session_id : defaultValue
+            const endUser = log.created_by_end_user ? log.created_by_end_user.session_id : log.created_by_account ? log.created_by_account.name : defaultValue
             return <tr
               key={log.id}
               className={`border-b border-gray-200 h-8 hover:bg-gray-50 cursor-pointer ${currentLog?.id !== log.id ? '' : 'bg-gray-50'}`}

web/app/components/custom/custom-app-header-brand/index.tsx

@@ -1,62 +0,0 @@
-import { useTranslation } from 'react-i18next'
-import s from './style.module.css'
-import Button from '@/app/components/base/button'
-import { Grid01 } from '@/app/components/base/icons/src/vender/solid/layout'
-import { Container, Database01 } from '@/app/components/base/icons/src/vender/line/development'
-import { ImagePlus } from '@/app/components/base/icons/src/vender/line/images'
-import { useProviderContext } from '@/context/provider-context'
-import { Plan } from '@/app/components/billing/type'
-
-const CustomAppHeaderBrand = () => {
-  const { t } = useTranslation()
-  const { plan } = useProviderContext()
-
-  return (
-    <div className='py-3'>
-      <div className='mb-2 text-sm font-medium text-gray-900'>{t('custom.app.title')}</div>
-      <div className='relative mb-4 rounded-xl bg-gray-100 border-[0.5px] border-black/8 shadow-xs'>
-        <div className={`${s.mask} absolute inset-0 rounded-xl`}></div>
-        <div className='flex items-center pl-5 h-14 rounded-t-xl'>
-          <div className='relative flex items-center mr-[199px] w-[120px] h-10 bg-[rgba(217,45,32,0.12)]'>
-            <div className='ml-[1px] mr-[3px] w-[34px] h-[34px] border-8 border-black/[0.16] rounded-full'></div>
-            <div className='text-[13px] font-bold text-black/[0.24]'>YOUR LOGO</div>
-            <div className='absolute top-0 bottom-0 left-0.5 w-[0.5px] bg-[#F97066] opacity-50'></div>
-            <div className='absolute top-0 bottom-0 right-0.5 w-[0.5px] bg-[#F97066] opacity-50'></div>
-            <div className='absolute left-0 right-0 top-0.5 h-[0.5px] bg-[#F97066] opacity-50'></div>
-            <div className='absolute left-0 right-0 bottom-0.5 h-[0.5px] bg-[#F97066] opacity-50'></div>
-          </div>
-          <div className='flex items-center mr-3 px-3 h-7 rounded-xl bg-white shadow-xs'>
-            <Grid01 className='shrink-0 mr-2 w-4 h-4 text-[#155eef]' />
-            <div className='w-12 h-1.5 rounded-[5px] bg-[#155eef] opacity-80'></div>
-          </div>
-          <div className='flex items-center mr-3 px-3 h-7'>
-            <Container className='shrink-0 mr-2 w-4 h-4 text-gray-500' />
-            <div className='w-[50px] h-1.5 rounded-[5px] bg-gray-300'></div>
-          </div>
-          <div className='flex items-center px-3 h-7'>
-            <Database01 className='shrink-0 mr-2 w-4 h-4 text-gray-500' />
-            <div className='w-14 h-1.5 rounded-[5px] bg-gray-300 opacity-80'></div>
-          </div>
-        </div>
-        <div className='h-8 border-t border-t-gray-200 rounded-b-xl'></div>
-      </div>
-      <div className='flex items-center mb-2'>
-        <Button
-          disabled={plan.type === Plan.sandbox}
-        >
-          <ImagePlus className='mr-2 w-4 h-4' />
-          {t('custom.upload')}
-        </Button>
-        <div className='mx-2 h-5 w-[1px] bg-black/5'></div>
-        <Button
-          disabled={plan.type === Plan.sandbox}
-        >
-          {t('custom.restore')}
-        </Button>
-      </div>
-      <div className='text-xs text-gray-500'>{t('custom.app.changeLogoTip')}</div>
-    </div>
-  )
-}
-
-export default CustomAppHeaderBrand

web/app/components/custom/custom-app-header-brand/style.module.css

@@ -1,3 +0,0 @@
-.mask {
-  background: linear-gradient(95deg, rgba(255, 255, 255, 0.00) 43.9%, rgba(255, 255, 255, 0.80) 95.76%);  ;
-}

web/app/components/custom/custom-page/index.tsx

@@ -1,6 +1,5 @@
 import { useTranslation } from 'react-i18next'
 import CustomWebAppBrand from '../custom-web-app-brand'
-import CustomAppHeaderBrand from '../custom-app-header-brand'
 import s from '../style.module.css'
 import GridMask from '@/app/components/base/grid-mask'
 import UpgradeBtn from '@/app/components/billing/upgrade-btn'
@@ -13,7 +12,6 @@ const CustomPage = () => {
   const { plan, enableBilling } = useProviderContext()
 
   const showBillingTip = enableBilling && plan.type === Plan.sandbox
-  const showCustomAppHeaderBrand = enableBilling && plan.type === Plan.sandbox
   const showContact = enableBilling && (plan.type === Plan.professional || plan.type === Plan.team)
 
   return (
@@ -32,14 +30,6 @@ const CustomPage = () => {
         )
       }
       <CustomWebAppBrand />
-      {
-        showCustomAppHeaderBrand && (
-          <>
-            <div className='my-2 h-[0.5px] bg-gray-100'></div>
-            <CustomAppHeaderBrand />
-          </>
-        )
-      }
       {
         showContact && (
           <div className='absolute bottom-0 h-[50px] leading-[50px] text-xs text-gray-500'>

web/app/components/datasets/create/step-two/index.tsx

@@ -10,7 +10,6 @@ import {
 } from '@remixicon/react'
 import Link from 'next/link'
 import { groupBy } from 'lodash-es'
-import RetrievalMethodInfo from '../../common/retrieval-method-info'
 import PreviewItem, { PreviewType } from './preview-item'
 import LanguageSelect from './language-select'
 import s from './index.module.css'
@@ -124,7 +123,9 @@ const StepTwo = ({
   const [docForm, setDocForm] = useState<DocForm | string>(
     (datasetId && documentDetail) ? documentDetail.doc_form : DocForm.TEXT,
   )
-  const [docLanguage, setDocLanguage] = useState<string>(locale !== LanguagesSupported[1] ? 'English' : 'Chinese')
+  const [docLanguage, setDocLanguage] = useState<string>(
+    (datasetId && documentDetail) ? documentDetail.doc_language : (locale !== LanguagesSupported[1] ? 'English' : 'Chinese'),
+  )
   const [QATipHide, setQATipHide] = useState(false)
   const [previewSwitched, setPreviewSwitched] = useState(false)
   const [showPreview, { setTrue: setShowPreview, setFalse: hidePreview }] = useBoolean()
@@ -785,34 +786,21 @@ const StepTwo = ({
                 )}
 
               <div className='max-w-[640px]'>
-                {!datasetId
-                  ? (<>
-                    {getIndexing_technique() === IndexingType.QUALIFIED
-                      ? (
-                        <RetrievalMethodConfig
-                          value={retrievalConfig}
-                          onChange={setRetrievalConfig}
-                        />
-                      )
-                      : (
-                        <EconomicalRetrievalMethodConfig
-                          value={retrievalConfig}
-                          onChange={setRetrievalConfig}
-                        />
-                      )}
-                  </>)
-                  : (
-                    <div>
-                      <RetrievalMethodInfo
+                {
+                  getIndexing_technique() === IndexingType.QUALIFIED
+                    ? (
+                      <RetrievalMethodConfig
                         value={retrievalConfig}
+                        onChange={setRetrievalConfig}
                       />
-                      <div className='mt-2 text-xs text-gray-500 font-medium'>
-                        {t('datasetCreation.stepTwo.retrivalSettedTip')}
-                        <Link className='text-[#155EEF]' href={`/datasets/${datasetId}/settings`}>{t('datasetCreation.stepTwo.datasetSettingLink')}</Link>
-                      </div>
-                    </div>
-                  )}
-
+                    )
+                    : (
+                      <EconomicalRetrievalMethodConfig
+                        value={retrievalConfig}
+                        onChange={setRetrievalConfig}
+                      />
+                    )
+                }
               </div>
             </div>
 

web/app/components/datasets/create/website/firecrawl/base/field.tsx

@@ -38,7 +38,7 @@ const Field: FC<Props> = ({
             popupContent={
               <div className='w-[200px]'>{tooltip}</div>
             }
-            popupClassName='relative top-[3px] w-3 h-3 ml-1'
+            triggerClassName='ml-0.5 w-4 h-4'
           />
         )}
       </div>

web/app/components/datasets/documents/list.tsx

@@ -405,7 +405,7 @@ const DocumentList: FC<IDocumentListProps> = ({ embeddingAvailable, documents =
         <tbody className="text-gray-700">
           {localDocs.map((doc) => {
             const isFile = doc.data_source_type === DataSourceType.FILE
-            const fileType = isFile ? doc.data_source_detail_dict?.upload_file.extension : ''
+            const fileType = isFile ? doc.data_source_detail_dict?.upload_file?.extension : ''
             return <tr
               key={doc.id}
               className={'border-b border-gray-200 h-8 hover:bg-gray-50 cursor-pointer'}

web/i18n/de-DE/app-log.ts

@@ -6,7 +6,7 @@ const translation = {
     header: {
       updatedTime: 'Aktualisierungszeit',
       time: 'Erstellungszeit',
-      endUser: 'Endbenutzer',
+      endUser: 'Endbenutzer oder Konto',
       input: 'Eingabe',
       output: 'Ausgabe',
       summary: 'Titel',

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

@@ -6,7 +6,7 @@ const translation = {
     header: {
       updatedTime: 'Updated time',
       time: 'Created time',
-      endUser: 'End User',
+      endUser: 'End User or Account',
       input: 'Input',
       output: 'Output',
       summary: 'Title',
@@ -17,7 +17,7 @@ const translation = {
       status: 'STATUS',
       runtime: 'RUN TIME',
       tokens: 'TOKENS',
-      user: 'END-USER',
+      user: 'End User or Account',
       version: 'VERSION',
     },
     pagination: {

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

@@ -127,7 +127,11 @@ const translation = {
     tokenPS: 'Token/s',
     totalMessages: {
       title: 'Total Messages',
-      explanation: 'Daily AI interactions count; prompt engineering/debugging excluded.',
+      explanation: 'Daily AI interactions count.',
+    },
+    totalConversations: {
+      title: 'Total Conversations',
+      explanation: 'Daily AI conversations count; prompt engineering/debugging excluded.',
     },
     activeUsers: {
       title: 'Active Users',

web/i18n/es-ES/app-log.ts

@@ -6,7 +6,7 @@ const translation = {
     header: {
       updatedTime: 'Hora actualizada',
       time: 'Hora creada',
-      endUser: 'Usuario Final',
+      endUser: 'Usuario Final o Cuenta',
       input: 'Entrada',
       output: 'Salida',
       summary: 'Título',
@@ -17,7 +17,7 @@ const translation = {
       status: 'ESTADO',
       runtime: 'TIEMPO DE EJECUCIÓN',
       tokens: 'TOKENS',
-      user: 'USUARIO FINAL',
+      user: 'USUARIO FINAL O CUENTA',
       version: 'VERSIÓN',
     },
     pagination: {

web/i18n/fa-IR/app-log.ts

@@ -6,7 +6,7 @@ const translation = {
     header: {
       updatedTime: 'زمان به‌روزرسانی',
       time: 'زمان ایجاد',
-      endUser: 'کاربر نهایی',
+      endUser: 'کاربر نهایی یا حساب',
       input: 'ورودی',
       output: 'خروجی',
       summary: 'عنوان',
@@ -17,7 +17,7 @@ const translation = {
       status: 'وضعیت',
       runtime: 'زمان اجرا',
       tokens: 'توکن‌ها',
-      user: 'کاربر نهایی',
+      user: 'کاربر نهایی یا حساب',
       version: 'نسخه',
     },
     pagination: {

web/i18n/fr-FR/app-log.ts

@@ -6,7 +6,7 @@ const translation = {
     header: {
       updatedTime: 'Heure de mise à jour',
       time: 'Heure de création',
-      endUser: 'Utilisateur final',
+      endUser: 'Utilisateur final ou compte',
       input: 'Entrée',
       output: 'Sortie',
       summary: 'Titre',
@@ -17,7 +17,7 @@ const translation = {
       status: 'STATUT',
       runtime: 'TEMPS D\'EXÉCUTION',
       tokens: 'JETONS',
-      user: 'UTILISATEUR FINAL',
+      user: 'UTILISATEUR FINAL OU COMPTE',
       version: 'VERSION',
     },
     pagination: {

web/i18n/hi-IN/app-log.ts

@@ -6,7 +6,7 @@ const translation = {
     header: {
       updatedTime: 'अपडेट का समय',
       time: 'बनाने का समय',
-      endUser: 'अंतिम उपयोगकर्ता',
+      endUser: 'अंतिम उपयोगकर्ता या खाता',
       input: 'इनपुट',
       output: 'आउटपुट',
       summary: 'शीर्षक',
@@ -17,7 +17,7 @@ const translation = {
       status: 'स्थिति',
       runtime: 'रन टाइम',
       tokens: 'टोकन',
-      user: 'अंतिम उपयोगकर्ता',
+      user: 'अंतिम उपयोगकर्ता या खाता',
       version: 'संस्करण',
     },
     pagination: {

web/i18n/it-IT/app-log.ts

@@ -7,7 +7,7 @@ const translation = {
     header: {
       updatedTime: 'Ora di aggiornamento',
       time: 'Ora di creazione',
-      endUser: 'Utente Finale',
+      endUser: 'Utente Finale o Account',
       input: 'Input',
       output: 'Output',
       summary: 'Titolo',
@@ -18,7 +18,7 @@ const translation = {
       status: 'STATO',
       runtime: 'TEMPO DI ESECUZIONE',
       tokens: 'TOKEN',
-      user: 'UTENTE FINALE',
+      user: 'UTENTE FINALE O ACCOUNT',
       version: 'VERSIONE',
     },
     pagination: {

web/i18n/ja-JP/app-log.ts

@@ -6,7 +6,7 @@ const translation = {
     header: {
       updatedTime: '更新時間',
       time: '作成時間',
-      endUser: 'エンドユーザー',
+      endUser: 'エンドユーザーまたはアカウント',
       input: '入力',
       output: '出力',
       summary: 'タイトル',
@@ -17,7 +17,7 @@ const translation = {
       status: 'ステータス',
       runtime: 'ランタイム',
       tokens: 'トークン',
-      user: 'エンドユーザー',
+      user: 'エンドユーザーまたはアカウント',
       version: 'バージョン',
     },
     pagination: {

web/i18n/ko-KR/app-log.ts

@@ -6,7 +6,7 @@ const translation = {
     header: {
       updatedTime: '업데이트 시간',
       time: '생성 시간',
-      endUser: '엔드 유저',
+      endUser: '엔드 유저 또는 계정',
       input: '입력',
       output: '출력',
       summary: '요약',
@@ -17,7 +17,7 @@ const translation = {
       status: '상태',
       runtime: '실행 시간',
       tokens: '토큰',
-      user: '엔드 유저',
+      user: '엔드 유저 또는 계정',
       version: '버전',
     },
     pagination: {

web/i18n/pl-PL/app-log.ts

@@ -7,7 +7,7 @@ const translation = {
     header: {
       updatedTime: 'Czas aktualizacji',
       time: 'Czas utworzenia',
-      endUser: 'Użytkownik końcowy',
+      endUser: 'Użytkownik końcowy lub konto',
       input: 'Wejście',
       output: 'Wyjście',
       summary: 'Tytuł',
@@ -18,7 +18,7 @@ const translation = {
       status: 'STATUS',
       runtime: 'CZAS DZIAŁANIA',
       tokens: 'TOKENY',
-      user: 'UŻYTKOWNIK KOŃCOWY',
+      user: 'UŻYTKOWNIK KOŃCOWY LUB KONTO',
       version: 'WERSJA',
     },
     pagination: {

web/i18n/pt-BR/app-log.ts

@@ -6,7 +6,7 @@ const translation = {
     header: {
       updatedTime: 'Hora de atualização',
       time: 'Hora de criação',
-      endUser: 'Usuário Final',
+      endUser: 'Usuário final ou conta',
       input: 'Entrada',
       output: 'Saída',
       summary: 'Título',
@@ -17,7 +17,7 @@ const translation = {
       status: 'STATUS',
       runtime: 'TEMPO DE EXECUÇÃO',
       tokens: 'TOKENS',
-      user: 'USUÁRIO FINAL',
+      user: 'USUÁRIO FINAL OU CONTA',
       version: 'VERSÃO',
     },
     pagination: {

web/i18n/ro-RO/app-log.ts

@@ -6,7 +6,7 @@ const translation = {
     header: {
       updatedTime: 'Timp actualizare',
       time: 'Timp creare',
-      endUser: 'Utilizator final',
+      endUser: 'Utilizator final sau cont',
       input: 'Intrare',
       output: 'Ieșire',
       summary: 'Titlu',
@@ -17,7 +17,7 @@ const translation = {
       status: 'STARE',
       runtime: 'TIMP DE RULARE',
       tokens: 'JETOANE',
-      user: 'UTILIZATOR FINAL',
+      user: 'UTILIZATOR FINAL SAU CONT',
       version: 'VERSIUNE',
     },
     pagination: {

web/i18n/tr-TR/app-log.ts

@@ -6,7 +6,7 @@ const translation = {
     header: {
       updatedTime: 'Güncellenme zamanı',
       time: 'Oluşturulma zamanı',
-      endUser: 'Son Kullanıcı',
+      endUser: 'Son Kullanıcı veya Hesap',
       input: 'Girdi',
       output: 'Çıktı',
       summary: 'Başlık',
@@ -17,7 +17,7 @@ const translation = {
       status: 'DURUM',
       runtime: 'ÇALIŞMA SÜRESİ',
       tokens: 'TOKENLAR',
-      user: 'SON KULLANICI',
+      user: 'SON KULLANICI VEYA HESAP',
       version: 'VERSİYON',
     },
     pagination: {

web/i18n/uk-UA/app-log.ts

@@ -6,7 +6,7 @@ const translation = {
     header: {
       updatedTime: 'Час оновлення',
       time: 'Час створення',
-      endUser: 'Кінцевий Користувач',
+      endUser: 'Кінцевий Користувач або Обліковий Запис',
       input: 'Введення',
       output: 'Виведення',
       summary: 'Заголовок',
@@ -17,7 +17,7 @@ const translation = {
       status: 'СТАТУС',
       runtime: 'ЧАС ВИКОНАННЯ',
       tokens: 'ТОКЕНИ',
-      user: 'КІНЦЕВИЙ КОРИСТУВАЧ',
+      user: 'КІНЦЕВИЙ КОРИСТУВАЧ АБО ОБЛІКОВИЙ ЗАПИС',
       version: 'ВЕРСІЯ',
     },
     pagination: {

web/i18n/vi-VN/app-log.ts

@@ -6,7 +6,7 @@ const translation = {
     header: {
       updatedTime: 'Thời gian cập nhật',
       time: 'Thời gian tạo',
-      endUser: 'Người dùng cuối',
+      endUser: 'Người dùng cuối hoặc tài khoản',
       input: 'Đầu vào',
       output: 'Đầu ra',
       summary: 'Tóm tắt',
@@ -17,7 +17,7 @@ const translation = {
       status: 'TRẠNG THÁI',
       runtime: 'THỜI GIAN CHẠY',
       tokens: 'TOKEN',
-      user: 'NGƯỜI DÙNG CUỐI',
+      user: 'NGƯỜI DÙNG CUỐI HOẶC TÀI KHOẢN',
       version: 'PHIÊN BẢN',
     },
     pagination: {

web/i18n/zh-Hans/app-log.ts

@@ -6,7 +6,7 @@ const translation = {
     header: {
       updatedTime: '更新时间',
       time: '创建时间',
-      endUser: '用户',
+      endUser: '用户或账户',
       input: '输入',
       output: '输出',
       summary: '标题',
@@ -17,7 +17,7 @@ const translation = {
       status: '状态',
       runtime: '运行时间',
       tokens: 'TOKENS',
-      user: '用户',
+      user: '用户或账户',
       version: '版本',
     },
     pagination: {

web/i18n/zh-Hans/app-overview.ts

@@ -127,7 +127,11 @@ const translation = {
     tokenPS: 'Token/秒',
     totalMessages: {
       title: '全部消息数',
-      explanation: '反映 AI 每天的互动总次数，每回答用户一个问题算一条 Message。提示词编排和调试的消息不计入。',
+      explanation: '反映 AI 每天的互动总次数，每回答用户一个问题算一条 Message。',
+    },
+    totalConversations: {
+      title: '全部会话数',
+      explanation: '反映 AI 每天的会话总次数，提示词编排和调试的消息不计入。',
     },
     activeUsers: {
       title: '活跃用户数',