Initial commit

dify_0.15.3/api/controllers/service_api/__init__.py

@@ -0,0 +1,10 @@
+from flask import Blueprint
+
+from libs.external_api import ExternalApi
+
+bp = Blueprint("service_api", __name__, url_prefix="/v1")
+api = ExternalApi(bp)
+
+from . import index
+from .app import app, audio, completion, conversation, file, message, workflow
+from .dataset import dataset, document, hit_testing, segment, upload_file

dify_0.15.3/api/controllers/service_api/app/app.py

@@ -0,0 +1,57 @@
+from flask_restful import Resource, marshal_with  # type: ignore
+
+from controllers.common import fields
+from controllers.common import helpers as controller_helpers
+from controllers.service_api import api
+from controllers.service_api.app.error import AppUnavailableError
+from controllers.service_api.wraps import validate_app_token
+from models.model import App, AppMode
+from services.app_service import AppService
+
+
+class AppParameterApi(Resource):
+    """Resource for app variables."""
+
+    @validate_app_token
+    @marshal_with(fields.parameters_fields)
+    def get(self, app_model: App):
+        """Retrieve app parameters."""
+        if app_model.mode in {AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value}:
+            workflow = app_model.workflow
+            if workflow is None:
+                raise AppUnavailableError()
+
+            features_dict = workflow.features_dict
+            user_input_form = workflow.user_input_form(to_old_structure=True)
+        else:
+            app_model_config = app_model.app_model_config
+            if app_model_config is None:
+                raise AppUnavailableError()
+
+            features_dict = app_model_config.to_dict()
+
+            user_input_form = features_dict.get("user_input_form", [])
+
+        return controller_helpers.get_parameters_from_feature_dict(
+            features_dict=features_dict, user_input_form=user_input_form
+        )
+
+
+class AppMetaApi(Resource):
+    @validate_app_token
+    def get(self, app_model: App):
+        """Get app meta"""
+        return AppService().get_app_meta(app_model)
+
+
+class AppInfoApi(Resource):
+    @validate_app_token
+    def get(self, app_model: App):
+        """Get app information"""
+        tags = [tag.name for tag in app_model.tags]
+        return {"name": app_model.name, "description": app_model.description, "tags": tags}
+
+
+api.add_resource(AppParameterApi, "/parameters")
+api.add_resource(AppMetaApi, "/meta")
+api.add_resource(AppInfoApi, "/info")

dify_0.15.3/api/controllers/service_api/app/audio.py

@@ -0,0 +1,125 @@
+import logging
+
+from flask import request
+from flask_restful import Resource, reqparse  # type: ignore
+from werkzeug.exceptions import InternalServerError
+
+import services
+from controllers.service_api import api
+from controllers.service_api.app.error import (
+    AppUnavailableError,
+    AudioTooLargeError,
+    CompletionRequestError,
+    NoAudioUploadedError,
+    ProviderModelCurrentlyNotSupportError,
+    ProviderNotInitializeError,
+    ProviderNotSupportSpeechToTextError,
+    ProviderQuotaExceededError,
+    UnsupportedAudioTypeError,
+)
+from controllers.service_api.wraps import FetchUserArg, WhereisUserArg, validate_app_token
+from core.errors.error import ModelCurrentlyNotSupportError, ProviderTokenNotInitError, QuotaExceededError
+from core.model_runtime.errors.invoke import InvokeError
+from models.model import App, AppMode, EndUser
+from services.audio_service import AudioService
+from services.errors.audio import (
+    AudioTooLargeServiceError,
+    NoAudioUploadedServiceError,
+    ProviderNotSupportSpeechToTextServiceError,
+    UnsupportedAudioTypeServiceError,
+)
+
+
+class AudioApi(Resource):
+    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.FORM))
+    def post(self, app_model: App, end_user: EndUser):
+        file = request.files["file"]
+
+        try:
+            response = AudioService.transcript_asr(app_model=app_model, file=file, end_user=end_user)
+
+            return response
+        except services.errors.app_model_config.AppModelConfigBrokenError:
+            logging.exception("App model config broken.")
+            raise AppUnavailableError()
+        except NoAudioUploadedServiceError:
+            raise NoAudioUploadedError()
+        except AudioTooLargeServiceError as e:
+            raise AudioTooLargeError(str(e))
+        except UnsupportedAudioTypeServiceError:
+            raise UnsupportedAudioTypeError()
+        except ProviderNotSupportSpeechToTextServiceError:
+            raise ProviderNotSupportSpeechToTextError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except InvokeError as e:
+            raise CompletionRequestError(e.description)
+        except ValueError as e:
+            raise e
+        except Exception as e:
+            logging.exception("internal server error.")
+            raise InternalServerError()
+
+
+class TextApi(Resource):
+    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON))
+    def post(self, app_model: App, end_user: EndUser):
+        try:
+            parser = reqparse.RequestParser()
+            parser.add_argument("message_id", type=str, required=False, location="json")
+            parser.add_argument("voice", type=str, location="json")
+            parser.add_argument("text", type=str, location="json")
+            parser.add_argument("streaming", type=bool, location="json")
+            args = parser.parse_args()
+
+            message_id = args.get("message_id", None)
+            text = args.get("text", None)
+            if (
+                app_model.mode in {AppMode.ADVANCED_CHAT.value, AppMode.WORKFLOW.value}
+                and app_model.workflow
+                and app_model.workflow.features_dict
+            ):
+                text_to_speech = app_model.workflow.features_dict.get("text_to_speech", {})
+                voice = args.get("voice") or text_to_speech.get("voice")
+            else:
+                try:
+                    voice = args.get("voice") or app_model.app_model_config.text_to_speech_dict.get("voice")
+                except Exception:
+                    voice = None
+            response = AudioService.transcript_tts(
+                app_model=app_model, message_id=message_id, end_user=end_user.external_user_id, voice=voice, text=text
+            )
+
+            return response
+        except services.errors.app_model_config.AppModelConfigBrokenError:
+            logging.exception("App model config broken.")
+            raise AppUnavailableError()
+        except NoAudioUploadedServiceError:
+            raise NoAudioUploadedError()
+        except AudioTooLargeServiceError as e:
+            raise AudioTooLargeError(str(e))
+        except UnsupportedAudioTypeServiceError:
+            raise UnsupportedAudioTypeError()
+        except ProviderNotSupportSpeechToTextServiceError:
+            raise ProviderNotSupportSpeechToTextError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except InvokeError as e:
+            raise CompletionRequestError(e.description)
+        except ValueError as e:
+            raise e
+        except Exception as e:
+            logging.exception("internal server error.")
+            raise InternalServerError()
+
+
+api.add_resource(AudioApi, "/audio-to-text")
+api.add_resource(TextApi, "/text-to-audio")

dify_0.15.3/api/controllers/service_api/app/completion.py

@@ -0,0 +1,157 @@
+import logging
+
+from flask_restful import Resource, reqparse  # type: ignore
+from werkzeug.exceptions import InternalServerError, NotFound
+
+import services
+from controllers.service_api import api
+from controllers.service_api.app.error import (
+    AppUnavailableError,
+    CompletionRequestError,
+    ConversationCompletedError,
+    NotChatAppError,
+    ProviderModelCurrentlyNotSupportError,
+    ProviderNotInitializeError,
+    ProviderQuotaExceededError,
+)
+from controllers.service_api.wraps import FetchUserArg, WhereisUserArg, validate_app_token
+from core.app.apps.base_app_queue_manager import AppQueueManager
+from core.app.entities.app_invoke_entities import InvokeFrom
+from core.errors.error import (
+    ModelCurrentlyNotSupportError,
+    ProviderTokenNotInitError,
+    QuotaExceededError,
+)
+from core.model_runtime.errors.invoke import InvokeError
+from libs import helper
+from libs.helper import uuid_value
+from models.model import App, AppMode, EndUser
+from services.app_generate_service import AppGenerateService
+
+
+class CompletionApi(Resource):
+    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON, required=True))
+    def post(self, app_model: App, end_user: EndUser):
+        if app_model.mode != "completion":
+            raise AppUnavailableError()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument("inputs", type=dict, required=True, location="json")
+        parser.add_argument("query", type=str, location="json", default="")
+        parser.add_argument("files", type=list, required=False, location="json")
+        parser.add_argument("response_mode", type=str, choices=["blocking", "streaming"], location="json")
+        parser.add_argument("retriever_from", type=str, required=False, default="dev", location="json")
+
+        args = parser.parse_args()
+
+        streaming = args["response_mode"] == "streaming"
+
+        args["auto_generate_name"] = False
+
+        try:
+            response = AppGenerateService.generate(
+                app_model=app_model,
+                user=end_user,
+                args=args,
+                invoke_from=InvokeFrom.SERVICE_API,
+                streaming=streaming,
+            )
+
+            return helper.compact_generate_response(response)
+        except services.errors.conversation.ConversationNotExistsError:
+            raise NotFound("Conversation Not Exists.")
+        except services.errors.conversation.ConversationCompletedError:
+            raise ConversationCompletedError()
+        except services.errors.app_model_config.AppModelConfigBrokenError:
+            logging.exception("App model config broken.")
+            raise AppUnavailableError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except InvokeError as e:
+            raise CompletionRequestError(e.description)
+        except ValueError as e:
+            raise e
+        except Exception as e:
+            logging.exception("internal server error.")
+            raise InternalServerError()
+
+
+class CompletionStopApi(Resource):
+    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON, required=True))
+    def post(self, app_model: App, end_user: EndUser, task_id):
+        if app_model.mode != "completion":
+            raise AppUnavailableError()
+
+        AppQueueManager.set_stop_flag(task_id, InvokeFrom.SERVICE_API, end_user.id)
+
+        return {"result": "success"}, 200
+
+
+class ChatApi(Resource):
+    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON, required=True))
+    def post(self, app_model: App, end_user: EndUser):
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
+            raise NotChatAppError()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument("inputs", type=dict, required=True, location="json")
+        parser.add_argument("query", type=str, required=True, location="json")
+        parser.add_argument("files", type=list, required=False, location="json")
+        parser.add_argument("response_mode", type=str, choices=["blocking", "streaming"], location="json")
+        parser.add_argument("conversation_id", type=uuid_value, location="json")
+        parser.add_argument("retriever_from", type=str, required=False, default="dev", location="json")
+        parser.add_argument("auto_generate_name", type=bool, required=False, default=True, location="json")
+
+        args = parser.parse_args()
+
+        streaming = args["response_mode"] == "streaming"
+
+        try:
+            response = AppGenerateService.generate(
+                app_model=app_model, user=end_user, args=args, invoke_from=InvokeFrom.SERVICE_API, streaming=streaming
+            )
+
+            return helper.compact_generate_response(response)
+        except services.errors.conversation.ConversationNotExistsError:
+            raise NotFound("Conversation Not Exists.")
+        except services.errors.conversation.ConversationCompletedError:
+            raise ConversationCompletedError()
+        except services.errors.app_model_config.AppModelConfigBrokenError:
+            logging.exception("App model config broken.")
+            raise AppUnavailableError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except InvokeError as e:
+            raise CompletionRequestError(e.description)
+        except ValueError as e:
+            raise e
+        except Exception as e:
+            logging.exception("internal server error.")
+            raise InternalServerError()
+
+
+class ChatStopApi(Resource):
+    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON, required=True))
+    def post(self, app_model: App, end_user: EndUser, task_id):
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
+            raise NotChatAppError()
+
+        AppQueueManager.set_stop_flag(task_id, InvokeFrom.SERVICE_API, end_user.id)
+
+        return {"result": "success"}, 200
+
+
+api.add_resource(CompletionApi, "/completion-messages")
+api.add_resource(CompletionStopApi, "/completion-messages/<string:task_id>/stop")
+api.add_resource(ChatApi, "/chat-messages")
+api.add_resource(ChatStopApi, "/chat-messages/<string:task_id>/stop")

dify_0.15.3/api/controllers/service_api/app/conversation.py

@@ -0,0 +1,98 @@
+from flask_restful import Resource, marshal_with, reqparse  # type: ignore
+from flask_restful.inputs import int_range  # type: ignore
+from sqlalchemy.orm import Session
+from werkzeug.exceptions import NotFound
+
+import services
+from controllers.service_api import api
+from controllers.service_api.app.error import NotChatAppError
+from controllers.service_api.wraps import FetchUserArg, WhereisUserArg, validate_app_token
+from core.app.entities.app_invoke_entities import InvokeFrom
+from extensions.ext_database import db
+from fields.conversation_fields import (
+    conversation_delete_fields,
+    conversation_infinite_scroll_pagination_fields,
+    simple_conversation_fields,
+)
+from libs.helper import uuid_value
+from models.model import App, AppMode, EndUser
+from services.conversation_service import ConversationService
+
+
+class ConversationApi(Resource):
+    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.QUERY))
+    @marshal_with(conversation_infinite_scroll_pagination_fields)
+    def get(self, app_model: App, end_user: EndUser):
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
+            raise NotChatAppError()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument("last_id", type=uuid_value, location="args")
+        parser.add_argument("limit", type=int_range(1, 100), required=False, default=20, location="args")
+        parser.add_argument(
+            "sort_by",
+            type=str,
+            choices=["created_at", "-created_at", "updated_at", "-updated_at"],
+            required=False,
+            default="-updated_at",
+            location="args",
+        )
+        args = parser.parse_args()
+
+        try:
+            with Session(db.engine) as session:
+                return ConversationService.pagination_by_last_id(
+                    session=session,
+                    app_model=app_model,
+                    user=end_user,
+                    last_id=args["last_id"],
+                    limit=args["limit"],
+                    invoke_from=InvokeFrom.SERVICE_API,
+                    sort_by=args["sort_by"],
+                )
+        except services.errors.conversation.LastConversationNotExistsError:
+            raise NotFound("Last Conversation Not Exists.")
+
+
+class ConversationDetailApi(Resource):
+    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON))
+    @marshal_with(conversation_delete_fields)
+    def delete(self, app_model: App, end_user: EndUser, c_id):
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
+            raise NotChatAppError()
+
+        conversation_id = str(c_id)
+
+        try:
+            ConversationService.delete(app_model, conversation_id, end_user)
+        except services.errors.conversation.ConversationNotExistsError:
+            raise NotFound("Conversation Not Exists.")
+        return {"result": "success"}, 200
+
+
+class ConversationRenameApi(Resource):
+    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON))
+    @marshal_with(simple_conversation_fields)
+    def post(self, app_model: App, end_user: EndUser, c_id):
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
+            raise NotChatAppError()
+
+        conversation_id = str(c_id)
+
+        parser = reqparse.RequestParser()
+        parser.add_argument("name", type=str, required=False, location="json")
+        parser.add_argument("auto_generate", type=bool, required=False, default=False, location="json")
+        args = parser.parse_args()
+
+        try:
+            return ConversationService.rename(app_model, conversation_id, end_user, args["name"], args["auto_generate"])
+        except services.errors.conversation.ConversationNotExistsError:
+            raise NotFound("Conversation Not Exists.")
+
+
+api.add_resource(ConversationRenameApi, "/conversations/<uuid:c_id>/name", endpoint="conversation_name")
+api.add_resource(ConversationApi, "/conversations")
+api.add_resource(ConversationDetailApi, "/conversations/<uuid:c_id>", endpoint="conversation_detail")

dify_0.15.3/api/controllers/service_api/app/error.py

@@ -0,0 +1,109 @@
+from libs.exception import BaseHTTPException
+
+
+class AppUnavailableError(BaseHTTPException):
+    error_code = "app_unavailable"
+    description = "App unavailable, please check your app configurations."
+    code = 400
+
+
+class NotCompletionAppError(BaseHTTPException):
+    error_code = "not_completion_app"
+    description = "Please check if your Completion app mode matches the right API route."
+    code = 400
+
+
+class NotChatAppError(BaseHTTPException):
+    error_code = "not_chat_app"
+    description = "Please check if your app mode matches the right API route."
+    code = 400
+
+
+class NotWorkflowAppError(BaseHTTPException):
+    error_code = "not_workflow_app"
+    description = "Please check if your app mode matches the right API route."
+    code = 400
+
+
+class ConversationCompletedError(BaseHTTPException):
+    error_code = "conversation_completed"
+    description = "The conversation has ended. Please start a new conversation."
+    code = 400
+
+
+class ProviderNotInitializeError(BaseHTTPException):
+    error_code = "provider_not_initialize"
+    description = (
+        "No valid model provider credentials found. "
+        "Please go to Settings -> Model Provider to complete your provider credentials."
+    )
+    code = 400
+
+
+class ProviderQuotaExceededError(BaseHTTPException):
+    error_code = "provider_quota_exceeded"
+    description = (
+        "Your quota for Dify Hosted OpenAI has been exhausted. "
+        "Please go to Settings -> Model Provider to complete your own provider credentials."
+    )
+    code = 400
+
+
+class ProviderModelCurrentlyNotSupportError(BaseHTTPException):
+    error_code = "model_currently_not_support"
+    description = "Dify Hosted OpenAI trial currently not support the GPT-4 model."
+    code = 400
+
+
+class CompletionRequestError(BaseHTTPException):
+    error_code = "completion_request_error"
+    description = "Completion request failed."
+    code = 400
+
+
+class NoAudioUploadedError(BaseHTTPException):
+    error_code = "no_audio_uploaded"
+    description = "Please upload your audio."
+    code = 400
+
+
+class AudioTooLargeError(BaseHTTPException):
+    error_code = "audio_too_large"
+    description = "Audio size exceeded. {message}"
+    code = 413
+
+
+class UnsupportedAudioTypeError(BaseHTTPException):
+    error_code = "unsupported_audio_type"
+    description = "Audio type not allowed."
+    code = 415
+
+
+class ProviderNotSupportSpeechToTextError(BaseHTTPException):
+    error_code = "provider_not_support_speech_to_text"
+    description = "Provider not support speech to text."
+    code = 400
+
+
+class NoFileUploadedError(BaseHTTPException):
+    error_code = "no_file_uploaded"
+    description = "Please upload your file."
+    code = 400
+
+
+class TooManyFilesError(BaseHTTPException):
+    error_code = "too_many_files"
+    description = "Only one file is allowed."
+    code = 400
+
+
+class FileTooLargeError(BaseHTTPException):
+    error_code = "file_too_large"
+    description = "File size exceeded. {message}"
+    code = 413
+
+
+class UnsupportedFileTypeError(BaseHTTPException):
+    error_code = "unsupported_file_type"
+    description = "File type not allowed."
+    code = 415

dify_0.15.3/api/controllers/service_api/app/file.py

@@ -0,0 +1,53 @@
+from flask import request
+from flask_restful import Resource, marshal_with  # type: ignore
+
+import services
+from controllers.common.errors import FilenameNotExistsError
+from controllers.service_api import api
+from controllers.service_api.app.error import (
+    FileTooLargeError,
+    NoFileUploadedError,
+    TooManyFilesError,
+    UnsupportedFileTypeError,
+)
+from controllers.service_api.wraps import FetchUserArg, WhereisUserArg, validate_app_token
+from fields.file_fields import file_fields
+from models.model import App, EndUser
+from services.file_service import FileService
+
+
+class FileApi(Resource):
+    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.FORM))
+    @marshal_with(file_fields)
+    def post(self, app_model: App, end_user: EndUser):
+        file = request.files["file"]
+
+        # check file
+        if "file" not in request.files:
+            raise NoFileUploadedError()
+
+        if not file.mimetype:
+            raise UnsupportedFileTypeError()
+
+        if len(request.files) > 1:
+            raise TooManyFilesError()
+
+        if not file.filename:
+            raise FilenameNotExistsError
+
+        try:
+            upload_file = FileService.upload_file(
+                filename=file.filename,
+                content=file.read(),
+                mimetype=file.mimetype,
+                user=end_user,
+            )
+        except services.errors.file.FileTooLargeError as file_too_large_error:
+            raise FileTooLargeError(file_too_large_error.description)
+        except services.errors.file.UnsupportedFileTypeError:
+            raise UnsupportedFileTypeError()
+
+        return upload_file, 201
+
+
+api.add_resource(FileApi, "/files/upload")

dify_0.15.3/api/controllers/service_api/app/message.py

@@ -0,0 +1,149 @@
+import logging
+
+from flask_restful import Resource, fields, marshal_with, reqparse  # type: ignore
+from flask_restful.inputs import int_range  # type: ignore
+from werkzeug.exceptions import BadRequest, InternalServerError, NotFound
+
+import services
+from controllers.service_api import api
+from controllers.service_api.app.error import NotChatAppError
+from controllers.service_api.wraps import FetchUserArg, WhereisUserArg, validate_app_token
+from core.app.entities.app_invoke_entities import InvokeFrom
+from fields.conversation_fields import message_file_fields
+from fields.raws import FilesContainedField
+from libs.helper import TimestampField, uuid_value
+from models.model import App, AppMode, EndUser
+from services.errors.message import SuggestedQuestionsAfterAnswerDisabledError
+from services.message_service import MessageService
+
+
+class MessageListApi(Resource):
+    feedback_fields = {"rating": fields.String}
+    retriever_resource_fields = {
+        "id": fields.String,
+        "message_id": fields.String,
+        "position": fields.Integer,
+        "dataset_id": fields.String,
+        "dataset_name": fields.String,
+        "document_id": fields.String,
+        "document_name": fields.String,
+        "data_source_type": fields.String,
+        "segment_id": fields.String,
+        "score": fields.Float,
+        "hit_count": fields.Integer,
+        "word_count": fields.Integer,
+        "segment_position": fields.Integer,
+        "index_node_hash": fields.String,
+        "content": fields.String,
+        "created_at": TimestampField,
+    }
+
+    agent_thought_fields = {
+        "id": fields.String,
+        "chain_id": fields.String,
+        "message_id": fields.String,
+        "position": fields.Integer,
+        "thought": fields.String,
+        "tool": fields.String,
+        "tool_labels": fields.Raw,
+        "tool_input": fields.String,
+        "created_at": TimestampField,
+        "observation": fields.String,
+        "message_files": fields.List(fields.Nested(message_file_fields)),
+    }
+
+    message_fields = {
+        "id": fields.String,
+        "conversation_id": fields.String,
+        "parent_message_id": fields.String,
+        "inputs": FilesContainedField,
+        "query": fields.String,
+        "answer": fields.String(attribute="re_sign_file_url_answer"),
+        "message_files": fields.List(fields.Nested(message_file_fields)),
+        "feedback": fields.Nested(feedback_fields, attribute="user_feedback", allow_null=True),
+        "retriever_resources": fields.List(fields.Nested(retriever_resource_fields)),
+        "created_at": TimestampField,
+        "agent_thoughts": fields.List(fields.Nested(agent_thought_fields)),
+        "status": fields.String,
+        "error": fields.String,
+    }
+
+    message_infinite_scroll_pagination_fields = {
+        "limit": fields.Integer,
+        "has_more": fields.Boolean,
+        "data": fields.List(fields.Nested(message_fields)),
+    }
+
+    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.QUERY))
+    @marshal_with(message_infinite_scroll_pagination_fields)
+    def get(self, app_model: App, end_user: EndUser):
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
+            raise NotChatAppError()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument("conversation_id", required=True, type=uuid_value, location="args")
+        parser.add_argument("first_id", type=uuid_value, location="args")
+        parser.add_argument("limit", type=int_range(1, 100), required=False, default=20, location="args")
+        args = parser.parse_args()
+
+        try:
+            return MessageService.pagination_by_first_id(
+                app_model, end_user, args["conversation_id"], args["first_id"], args["limit"]
+            )
+        except services.errors.conversation.ConversationNotExistsError:
+            raise NotFound("Conversation Not Exists.")
+        except services.errors.message.FirstMessageNotExistsError:
+            raise NotFound("First Message Not Exists.")
+
+
+class MessageFeedbackApi(Resource):
+    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON, required=True))
+    def post(self, app_model: App, end_user: EndUser, message_id):
+        message_id = str(message_id)
+
+        parser = reqparse.RequestParser()
+        parser.add_argument("rating", type=str, choices=["like", "dislike", None], location="json")
+        parser.add_argument("content", type=str, location="json")
+        args = parser.parse_args()
+
+        try:
+            MessageService.create_feedback(
+                app_model=app_model,
+                message_id=message_id,
+                user=end_user,
+                rating=args.get("rating"),
+                content=args.get("content"),
+            )
+        except services.errors.message.MessageNotExistsError:
+            raise NotFound("Message Not Exists.")
+
+        return {"result": "success"}
+
+
+class MessageSuggestedApi(Resource):
+    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.QUERY, required=True))
+    def get(self, app_model: App, end_user: EndUser, message_id):
+        message_id = str(message_id)
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode not in {AppMode.CHAT, AppMode.AGENT_CHAT, AppMode.ADVANCED_CHAT}:
+            raise NotChatAppError()
+
+        try:
+            questions = MessageService.get_suggested_questions_after_answer(
+                app_model=app_model, user=end_user, message_id=message_id, invoke_from=InvokeFrom.SERVICE_API
+            )
+        except services.errors.message.MessageNotExistsError:
+            raise NotFound("Message Not Exists.")
+        except SuggestedQuestionsAfterAnswerDisabledError:
+            raise BadRequest("Suggested Questions Is Disabled.")
+        except Exception:
+            logging.exception("internal server error.")
+            raise InternalServerError()
+
+        return {"result": "success", "data": questions}
+
+
+api.add_resource(MessageListApi, "/messages")
+api.add_resource(MessageFeedbackApi, "/messages/<uuid:message_id>/feedbacks")
+api.add_resource(MessageSuggestedApi, "/messages/<uuid:message_id>/suggested")

dify_0.15.3/api/controllers/service_api/app/workflow.py

@@ -0,0 +1,144 @@
+import logging
+
+from flask_restful import Resource, fields, marshal_with, reqparse  # type: ignore
+from flask_restful.inputs import int_range  # type: ignore
+from werkzeug.exceptions import InternalServerError
+
+from controllers.service_api import api
+from controllers.service_api.app.error import (
+    CompletionRequestError,
+    NotWorkflowAppError,
+    ProviderModelCurrentlyNotSupportError,
+    ProviderNotInitializeError,
+    ProviderQuotaExceededError,
+)
+from controllers.service_api.wraps import FetchUserArg, WhereisUserArg, validate_app_token
+from core.app.apps.base_app_queue_manager import AppQueueManager
+from core.app.entities.app_invoke_entities import InvokeFrom
+from core.errors.error import (
+    ModelCurrentlyNotSupportError,
+    ProviderTokenNotInitError,
+    QuotaExceededError,
+)
+from core.model_runtime.errors.invoke import InvokeError
+from extensions.ext_database import db
+from fields.workflow_app_log_fields import workflow_app_log_pagination_fields
+from libs import helper
+from models.model import App, AppMode, EndUser
+from models.workflow import WorkflowRun
+from services.app_generate_service import AppGenerateService
+from services.workflow_app_service import WorkflowAppService
+
+logger = logging.getLogger(__name__)
+
+workflow_run_fields = {
+    "id": fields.String,
+    "workflow_id": fields.String,
+    "status": fields.String,
+    "inputs": fields.Raw,
+    "outputs": fields.Raw,
+    "error": fields.String,
+    "total_steps": fields.Integer,
+    "total_tokens": fields.Integer,
+    "created_at": fields.DateTime,
+    "finished_at": fields.DateTime,
+    "elapsed_time": fields.Float,
+}
+
+
+class WorkflowRunDetailApi(Resource):
+    @validate_app_token
+    @marshal_with(workflow_run_fields)
+    def get(self, app_model: App, workflow_id: str):
+        """
+        Get a workflow task running detail
+        """
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode != AppMode.WORKFLOW:
+            raise NotWorkflowAppError()
+
+        workflow_run = db.session.query(WorkflowRun).filter(WorkflowRun.id == workflow_id).first()
+        return workflow_run
+
+
+class WorkflowRunApi(Resource):
+    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON, required=True))
+    def post(self, app_model: App, end_user: EndUser):
+        """
+        Run workflow
+        """
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode != AppMode.WORKFLOW:
+            raise NotWorkflowAppError()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument("inputs", type=dict, required=True, nullable=False, location="json")
+        parser.add_argument("files", type=list, required=False, location="json")
+        parser.add_argument("response_mode", type=str, choices=["blocking", "streaming"], location="json")
+        args = parser.parse_args()
+
+        streaming = args.get("response_mode") == "streaming"
+
+        try:
+            response = AppGenerateService.generate(
+                app_model=app_model, user=end_user, args=args, invoke_from=InvokeFrom.SERVICE_API, streaming=streaming
+            )
+
+            return helper.compact_generate_response(response)
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except InvokeError as e:
+            raise CompletionRequestError(e.description)
+        except ValueError as e:
+            raise e
+        except Exception as e:
+            logging.exception("internal server error.")
+            raise InternalServerError()
+
+
+class WorkflowTaskStopApi(Resource):
+    @validate_app_token(fetch_user_arg=FetchUserArg(fetch_from=WhereisUserArg.JSON, required=True))
+    def post(self, app_model: App, end_user: EndUser, task_id: str):
+        """
+        Stop workflow task
+        """
+        app_mode = AppMode.value_of(app_model.mode)
+        if app_mode != AppMode.WORKFLOW:
+            raise NotWorkflowAppError()
+
+        AppQueueManager.set_stop_flag(task_id, InvokeFrom.SERVICE_API, end_user.id)
+
+        return {"result": "success"}
+
+
+class WorkflowAppLogApi(Resource):
+    @validate_app_token
+    @marshal_with(workflow_app_log_pagination_fields)
+    def get(self, app_model: App):
+        """
+        Get workflow app logs
+        """
+        parser = reqparse.RequestParser()
+        parser.add_argument("keyword", type=str, location="args")
+        parser.add_argument("status", type=str, choices=["succeeded", "failed", "stopped"], location="args")
+        parser.add_argument("page", type=int_range(1, 99999), default=1, location="args")
+        parser.add_argument("limit", type=int_range(1, 100), default=20, location="args")
+        args = parser.parse_args()
+
+        # get paginate workflow app logs
+        workflow_app_service = WorkflowAppService()
+        workflow_app_log_pagination = workflow_app_service.get_paginate_workflow_app_logs(
+            app_model=app_model, args=args
+        )
+
+        return workflow_app_log_pagination
+
+
+api.add_resource(WorkflowRunApi, "/workflows/run")
+api.add_resource(WorkflowRunDetailApi, "/workflows/run/<string:workflow_id>")
+api.add_resource(WorkflowTaskStopApi, "/workflows/tasks/<string:task_id>/stop")
+api.add_resource(WorkflowAppLogApi, "/workflows/logs")

dify_0.15.3/api/controllers/service_api/dataset/dataset.py

@@ -0,0 +1,166 @@
+from flask import request
+from flask_restful import marshal, reqparse  # type: ignore
+from werkzeug.exceptions import NotFound
+
+import services.dataset_service
+from controllers.service_api import api
+from controllers.service_api.dataset.error import DatasetInUseError, DatasetNameDuplicateError
+from controllers.service_api.wraps import DatasetApiResource
+from core.model_runtime.entities.model_entities import ModelType
+from core.provider_manager import ProviderManager
+from fields.dataset_fields import dataset_detail_fields
+from libs.login import current_user
+from models.dataset import Dataset, DatasetPermissionEnum
+from services.dataset_service import DatasetService
+
+
+def _validate_name(name):
+    if not name or len(name) < 1 or len(name) > 40:
+        raise ValueError("Name must be between 1 to 40 characters.")
+    return name
+
+
+class DatasetListApi(DatasetApiResource):
+    """Resource for datasets."""
+
+    def get(self, tenant_id):
+        """Resource for getting datasets."""
+
+        page = request.args.get("page", default=1, type=int)
+        limit = request.args.get("limit", default=20, type=int)
+        # provider = request.args.get("provider", default="vendor")
+        search = request.args.get("keyword", default=None, type=str)
+        tag_ids = request.args.getlist("tag_ids")
+        include_all = request.args.get("include_all", default="false").lower() == "true"
+
+        datasets, total = DatasetService.get_datasets(
+            page, limit, tenant_id, current_user, search, tag_ids, include_all
+        )
+        # check embedding setting
+        provider_manager = ProviderManager()
+        configurations = provider_manager.get_configurations(tenant_id=current_user.current_tenant_id)
+
+        embedding_models = configurations.get_models(model_type=ModelType.TEXT_EMBEDDING, only_active=True)
+
+        model_names = []
+        for embedding_model in embedding_models:
+            model_names.append(f"{embedding_model.model}:{embedding_model.provider.provider}")
+
+        data = marshal(datasets, dataset_detail_fields)
+        for item in data:
+            if item["indexing_technique"] == "high_quality":
+                item_model = f"{item['embedding_model']}:{item['embedding_model_provider']}"
+                if item_model in model_names:
+                    item["embedding_available"] = True
+                else:
+                    item["embedding_available"] = False
+            else:
+                item["embedding_available"] = True
+        response = {"data": data, "has_more": len(datasets) == limit, "limit": limit, "total": total, "page": page}
+        return response, 200
+
+    def post(self, tenant_id):
+        """Resource for creating datasets."""
+        parser = reqparse.RequestParser()
+        parser.add_argument(
+            "name",
+            nullable=False,
+            required=True,
+            help="type is required. Name must be between 1 to 40 characters.",
+            type=_validate_name,
+        )
+        parser.add_argument(
+            "description",
+            type=str,
+            nullable=True,
+            required=False,
+            default="",
+        )
+        parser.add_argument(
+            "indexing_technique",
+            type=str,
+            location="json",
+            choices=Dataset.INDEXING_TECHNIQUE_LIST,
+            help="Invalid indexing technique.",
+        )
+        parser.add_argument(
+            "permission",
+            type=str,
+            location="json",
+            choices=(DatasetPermissionEnum.ONLY_ME, DatasetPermissionEnum.ALL_TEAM, DatasetPermissionEnum.PARTIAL_TEAM),
+            help="Invalid permission.",
+            required=False,
+            nullable=False,
+        )
+        parser.add_argument(
+            "external_knowledge_api_id",
+            type=str,
+            nullable=True,
+            required=False,
+            default="_validate_name",
+        )
+        parser.add_argument(
+            "provider",
+            type=str,
+            nullable=True,
+            required=False,
+            default="vendor",
+        )
+        parser.add_argument(
+            "external_knowledge_id",
+            type=str,
+            nullable=True,
+            required=False,
+        )
+        args = parser.parse_args()
+
+        try:
+            dataset = DatasetService.create_empty_dataset(
+                tenant_id=tenant_id,
+                name=args["name"],
+                description=args["description"],
+                indexing_technique=args["indexing_technique"],
+                account=current_user,
+                permission=args["permission"],
+                provider=args["provider"],
+                external_knowledge_api_id=args["external_knowledge_api_id"],
+                external_knowledge_id=args["external_knowledge_id"],
+            )
+        except services.errors.dataset.DatasetNameDuplicateError:
+            raise DatasetNameDuplicateError()
+
+        return marshal(dataset, dataset_detail_fields), 200
+
+
+class DatasetApi(DatasetApiResource):
+    """Resource for dataset."""
+
+    def delete(self, _, dataset_id):
+        """
+        Deletes a dataset given its ID.
+
+        Args:
+            dataset_id (UUID): The ID of the dataset to be deleted.
+
+        Returns:
+            dict: A dictionary with a key 'result' and a value 'success'
+                  if the dataset was successfully deleted. Omitted in HTTP response.
+            int: HTTP status code 204 indicating that the operation was successful.
+
+        Raises:
+            NotFound: If the dataset with the given ID does not exist.
+        """
+
+        dataset_id_str = str(dataset_id)
+
+        try:
+            if DatasetService.delete_dataset(dataset_id_str, current_user):
+                return {"result": "success"}, 204
+            else:
+                raise NotFound("Dataset not found.")
+        except services.errors.dataset.DatasetInUseError:
+            raise DatasetInUseError()
+
+
+api.add_resource(DatasetListApi, "/datasets")
+api.add_resource(DatasetApi, "/datasets/<uuid:dataset_id>")

dify_0.15.3/api/controllers/service_api/dataset/document.py

@@ -0,0 +1,506 @@
+import json
+
+from flask import request
+from flask_restful import marshal, reqparse  # type: ignore
+from sqlalchemy import desc
+from werkzeug.exceptions import NotFound
+
+import services.dataset_service
+from controllers.common.errors import FilenameNotExistsError
+from controllers.service_api import api
+from controllers.service_api.app.error import (
+    FileTooLargeError,
+    NoFileUploadedError,
+    ProviderNotInitializeError,
+    TooManyFilesError,
+    UnsupportedFileTypeError,
+)
+from controllers.service_api.dataset.error import (
+    ArchivedDocumentImmutableError,
+    DocumentIndexingError,
+    InvalidMetadataError,
+)
+from controllers.service_api.wraps import DatasetApiResource, cloud_edition_billing_resource_check
+from core.errors.error import ProviderTokenNotInitError
+from extensions.ext_database import db
+from fields.document_fields import document_fields, document_status_fields
+from libs.login import current_user
+from models.dataset import Dataset, Document, DocumentSegment
+from services.dataset_service import DocumentService
+from services.entities.knowledge_entities.knowledge_entities import KnowledgeConfig
+from services.file_service import FileService
+
+
+class DocumentAddByTextApi(DatasetApiResource):
+    """Resource for documents."""
+
+    @cloud_edition_billing_resource_check("vector_space", "dataset")
+    @cloud_edition_billing_resource_check("documents", "dataset")
+    def post(self, tenant_id, dataset_id):
+        """Create document by text."""
+        parser = reqparse.RequestParser()
+        parser.add_argument("name", type=str, required=True, nullable=False, location="json")
+        parser.add_argument("text", type=str, required=True, nullable=False, location="json")
+        parser.add_argument("process_rule", type=dict, required=False, nullable=True, location="json")
+        parser.add_argument("original_document_id", type=str, required=False, location="json")
+        parser.add_argument("doc_form", type=str, default="text_model", required=False, nullable=False, location="json")
+        parser.add_argument(
+            "doc_language", type=str, default="English", required=False, nullable=False, location="json"
+        )
+        parser.add_argument(
+            "indexing_technique", type=str, choices=Dataset.INDEXING_TECHNIQUE_LIST, nullable=False, location="json"
+        )
+        parser.add_argument("retrieval_model", type=dict, required=False, nullable=False, location="json")
+        parser.add_argument("doc_type", type=str, required=False, nullable=True, location="json")
+        parser.add_argument("doc_metadata", type=dict, required=False, nullable=True, location="json")
+
+        args = parser.parse_args()
+        dataset_id = str(dataset_id)
+        tenant_id = str(tenant_id)
+        dataset = db.session.query(Dataset).filter(Dataset.tenant_id == tenant_id, Dataset.id == dataset_id).first()
+
+        if not dataset:
+            raise ValueError("Dataset is not exist.")
+
+        if not dataset.indexing_technique and not args["indexing_technique"]:
+            raise ValueError("indexing_technique is required.")
+
+        # Validate metadata if provided
+        if args.get("doc_type") or args.get("doc_metadata"):
+            if not args.get("doc_type") or not args.get("doc_metadata"):
+                raise InvalidMetadataError("Both doc_type and doc_metadata must be provided when adding metadata")
+
+            if args["doc_type"] not in DocumentService.DOCUMENT_METADATA_SCHEMA:
+                raise InvalidMetadataError(
+                    "Invalid doc_type. Must be one of: " + ", ".join(DocumentService.DOCUMENT_METADATA_SCHEMA.keys())
+                )
+
+            if not isinstance(args["doc_metadata"], dict):
+                raise InvalidMetadataError("doc_metadata must be a dictionary")
+
+            # Validate metadata schema based on doc_type
+            if args["doc_type"] != "others":
+                metadata_schema = DocumentService.DOCUMENT_METADATA_SCHEMA[args["doc_type"]]
+                for key, value in args["doc_metadata"].items():
+                    if key in metadata_schema and not isinstance(value, metadata_schema[key]):
+                        raise InvalidMetadataError(f"Invalid type for metadata field {key}")
+            # set to MetaDataConfig
+            args["metadata"] = {"doc_type": args["doc_type"], "doc_metadata": args["doc_metadata"]}
+
+        text = args.get("text")
+        name = args.get("name")
+        if text is None or name is None:
+            raise ValueError("Both 'text' and 'name' must be non-null values.")
+
+        upload_file = FileService.upload_text(text=str(text), text_name=str(name))
+        data_source = {
+            "type": "upload_file",
+            "info_list": {"data_source_type": "upload_file", "file_info_list": {"file_ids": [upload_file.id]}},
+        }
+        args["data_source"] = data_source
+        knowledge_config = KnowledgeConfig(**args)
+        # validate args
+        DocumentService.document_create_args_validate(knowledge_config)
+
+        try:
+            documents, batch = DocumentService.save_document_with_dataset_id(
+                dataset=dataset,
+                knowledge_config=knowledge_config,
+                account=current_user,
+                dataset_process_rule=dataset.latest_process_rule if "process_rule" not in args else None,
+                created_from="api",
+            )
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        document = documents[0]
+
+        documents_and_batch_fields = {"document": marshal(document, document_fields), "batch": batch}
+        return documents_and_batch_fields, 200
+
+
+class DocumentUpdateByTextApi(DatasetApiResource):
+    """Resource for update documents."""
+
+    @cloud_edition_billing_resource_check("vector_space", "dataset")
+    def post(self, tenant_id, dataset_id, document_id):
+        """Update document by text."""
+        parser = reqparse.RequestParser()
+        parser.add_argument("name", type=str, required=False, nullable=True, location="json")
+        parser.add_argument("text", type=str, required=False, nullable=True, location="json")
+        parser.add_argument("process_rule", type=dict, required=False, nullable=True, location="json")
+        parser.add_argument("doc_form", type=str, default="text_model", required=False, nullable=False, location="json")
+        parser.add_argument(
+            "doc_language", type=str, default="English", required=False, nullable=False, location="json"
+        )
+        parser.add_argument("retrieval_model", type=dict, required=False, nullable=False, location="json")
+        parser.add_argument("doc_type", type=str, required=False, nullable=True, location="json")
+        parser.add_argument("doc_metadata", type=dict, required=False, nullable=True, location="json")
+        args = parser.parse_args()
+        dataset_id = str(dataset_id)
+        tenant_id = str(tenant_id)
+        dataset = db.session.query(Dataset).filter(Dataset.tenant_id == tenant_id, Dataset.id == dataset_id).first()
+
+        if not dataset:
+            raise ValueError("Dataset is not exist.")
+
+        # indexing_technique is already set in dataset since this is an update
+        args["indexing_technique"] = dataset.indexing_technique
+
+        # Validate metadata if provided
+        if args.get("doc_type") or args.get("doc_metadata"):
+            if not args.get("doc_type") or not args.get("doc_metadata"):
+                raise InvalidMetadataError("Both doc_type and doc_metadata must be provided when adding metadata")
+
+            if args["doc_type"] not in DocumentService.DOCUMENT_METADATA_SCHEMA:
+                raise InvalidMetadataError(
+                    "Invalid doc_type. Must be one of: " + ", ".join(DocumentService.DOCUMENT_METADATA_SCHEMA.keys())
+                )
+
+            if not isinstance(args["doc_metadata"], dict):
+                raise InvalidMetadataError("doc_metadata must be a dictionary")
+
+            # Validate metadata schema based on doc_type
+            if args["doc_type"] != "others":
+                metadata_schema = DocumentService.DOCUMENT_METADATA_SCHEMA[args["doc_type"]]
+                for key, value in args["doc_metadata"].items():
+                    if key in metadata_schema and not isinstance(value, metadata_schema[key]):
+                        raise InvalidMetadataError(f"Invalid type for metadata field {key}")
+
+            # set to MetaDataConfig
+            args["metadata"] = {"doc_type": args["doc_type"], "doc_metadata": args["doc_metadata"]}
+
+        if args["text"]:
+            text = args.get("text")
+            name = args.get("name")
+            if text is None or name is None:
+                raise ValueError("Both text and name must be strings.")
+            upload_file = FileService.upload_text(text=str(text), text_name=str(name))
+            data_source = {
+                "type": "upload_file",
+                "info_list": {"data_source_type": "upload_file", "file_info_list": {"file_ids": [upload_file.id]}},
+            }
+            args["data_source"] = data_source
+        # validate args
+        args["original_document_id"] = str(document_id)
+        knowledge_config = KnowledgeConfig(**args)
+        DocumentService.document_create_args_validate(knowledge_config)
+
+        try:
+            documents, batch = DocumentService.save_document_with_dataset_id(
+                dataset=dataset,
+                knowledge_config=knowledge_config,
+                account=current_user,
+                dataset_process_rule=dataset.latest_process_rule if "process_rule" not in args else None,
+                created_from="api",
+            )
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        document = documents[0]
+
+        documents_and_batch_fields = {"document": marshal(document, document_fields), "batch": batch}
+        return documents_and_batch_fields, 200
+
+
+class DocumentAddByFileApi(DatasetApiResource):
+    """Resource for documents."""
+
+    @cloud_edition_billing_resource_check("vector_space", "dataset")
+    @cloud_edition_billing_resource_check("documents", "dataset")
+    def post(self, tenant_id, dataset_id):
+        """Create document by upload file."""
+        args = {}
+        if "data" in request.form:
+            args = json.loads(request.form["data"])
+        if "doc_form" not in args:
+            args["doc_form"] = "text_model"
+        if "doc_language" not in args:
+            args["doc_language"] = "English"
+
+        # Validate metadata if provided
+        if args.get("doc_type") or args.get("doc_metadata"):
+            if not args.get("doc_type") or not args.get("doc_metadata"):
+                raise InvalidMetadataError("Both doc_type and doc_metadata must be provided when adding metadata")
+
+            if args["doc_type"] not in DocumentService.DOCUMENT_METADATA_SCHEMA:
+                raise InvalidMetadataError(
+                    "Invalid doc_type. Must be one of: " + ", ".join(DocumentService.DOCUMENT_METADATA_SCHEMA.keys())
+                )
+
+            if not isinstance(args["doc_metadata"], dict):
+                raise InvalidMetadataError("doc_metadata must be a dictionary")
+
+            # Validate metadata schema based on doc_type
+            if args["doc_type"] != "others":
+                metadata_schema = DocumentService.DOCUMENT_METADATA_SCHEMA[args["doc_type"]]
+                for key, value in args["doc_metadata"].items():
+                    if key in metadata_schema and not isinstance(value, metadata_schema[key]):
+                        raise InvalidMetadataError(f"Invalid type for metadata field {key}")
+
+            # set to MetaDataConfig
+            args["metadata"] = {"doc_type": args["doc_type"], "doc_metadata": args["doc_metadata"]}
+
+        # get dataset info
+        dataset_id = str(dataset_id)
+        tenant_id = str(tenant_id)
+        dataset = db.session.query(Dataset).filter(Dataset.tenant_id == tenant_id, Dataset.id == dataset_id).first()
+
+        if not dataset:
+            raise ValueError("Dataset is not exist.")
+        if not dataset.indexing_technique and not args.get("indexing_technique"):
+            raise ValueError("indexing_technique is required.")
+
+        # save file info
+        file = request.files["file"]
+        # check file
+        if "file" not in request.files:
+            raise NoFileUploadedError()
+
+        if len(request.files) > 1:
+            raise TooManyFilesError()
+
+        if not file.filename:
+            raise FilenameNotExistsError
+
+        upload_file = FileService.upload_file(
+            filename=file.filename,
+            content=file.read(),
+            mimetype=file.mimetype,
+            user=current_user,
+            source="datasets",
+        )
+        data_source = {
+            "type": "upload_file",
+            "info_list": {"data_source_type": "upload_file", "file_info_list": {"file_ids": [upload_file.id]}},
+        }
+        args["data_source"] = data_source
+        # validate args
+        knowledge_config = KnowledgeConfig(**args)
+        DocumentService.document_create_args_validate(knowledge_config)
+
+        try:
+            documents, batch = DocumentService.save_document_with_dataset_id(
+                dataset=dataset,
+                knowledge_config=knowledge_config,
+                account=dataset.created_by_account,
+                dataset_process_rule=dataset.latest_process_rule if "process_rule" not in args else None,
+                created_from="api",
+            )
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        document = documents[0]
+        documents_and_batch_fields = {"document": marshal(document, document_fields), "batch": batch}
+        return documents_and_batch_fields, 200
+
+
+class DocumentUpdateByFileApi(DatasetApiResource):
+    """Resource for update documents."""
+
+    @cloud_edition_billing_resource_check("vector_space", "dataset")
+    def post(self, tenant_id, dataset_id, document_id):
+        """Update document by upload file."""
+        args = {}
+        if "data" in request.form:
+            args = json.loads(request.form["data"])
+        if "doc_form" not in args:
+            args["doc_form"] = "text_model"
+        if "doc_language" not in args:
+            args["doc_language"] = "English"
+
+        # Validate metadata if provided
+        if args.get("doc_type") or args.get("doc_metadata"):
+            if not args.get("doc_type") or not args.get("doc_metadata"):
+                raise InvalidMetadataError("Both doc_type and doc_metadata must be provided when adding metadata")
+
+            if args["doc_type"] not in DocumentService.DOCUMENT_METADATA_SCHEMA:
+                raise InvalidMetadataError(
+                    "Invalid doc_type. Must be one of: " + ", ".join(DocumentService.DOCUMENT_METADATA_SCHEMA.keys())
+                )
+
+            if not isinstance(args["doc_metadata"], dict):
+                raise InvalidMetadataError("doc_metadata must be a dictionary")
+
+            # Validate metadata schema based on doc_type
+            if args["doc_type"] != "others":
+                metadata_schema = DocumentService.DOCUMENT_METADATA_SCHEMA[args["doc_type"]]
+                for key, value in args["doc_metadata"].items():
+                    if key in metadata_schema and not isinstance(value, metadata_schema[key]):
+                        raise InvalidMetadataError(f"Invalid type for metadata field {key}")
+
+            # set to MetaDataConfig
+            args["metadata"] = {"doc_type": args["doc_type"], "doc_metadata": args["doc_metadata"]}
+
+        # get dataset info
+        dataset_id = str(dataset_id)
+        tenant_id = str(tenant_id)
+        dataset = db.session.query(Dataset).filter(Dataset.tenant_id == tenant_id, Dataset.id == dataset_id).first()
+
+        if not dataset:
+            raise ValueError("Dataset is not exist.")
+        if "file" in request.files:
+            # save file info
+            file = request.files["file"]
+
+            if len(request.files) > 1:
+                raise TooManyFilesError()
+
+            if not file.filename:
+                raise FilenameNotExistsError
+
+            try:
+                upload_file = FileService.upload_file(
+                    filename=file.filename,
+                    content=file.read(),
+                    mimetype=file.mimetype,
+                    user=current_user,
+                    source="datasets",
+                )
+            except services.errors.file.FileTooLargeError as file_too_large_error:
+                raise FileTooLargeError(file_too_large_error.description)
+            except services.errors.file.UnsupportedFileTypeError:
+                raise UnsupportedFileTypeError()
+            data_source = {
+                "type": "upload_file",
+                "info_list": {"data_source_type": "upload_file", "file_info_list": {"file_ids": [upload_file.id]}},
+            }
+            args["data_source"] = data_source
+        # validate args
+        args["original_document_id"] = str(document_id)
+
+        knowledge_config = KnowledgeConfig(**args)
+        DocumentService.document_create_args_validate(knowledge_config)
+
+        try:
+            documents, batch = DocumentService.save_document_with_dataset_id(
+                dataset=dataset,
+                knowledge_config=knowledge_config,
+                account=dataset.created_by_account,
+                dataset_process_rule=dataset.latest_process_rule if "process_rule" not in args else None,
+                created_from="api",
+            )
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        document = documents[0]
+        documents_and_batch_fields = {"document": marshal(document, document_fields), "batch": document.batch}
+        return documents_and_batch_fields, 200
+
+
+class DocumentDeleteApi(DatasetApiResource):
+    def delete(self, tenant_id, dataset_id, document_id):
+        """Delete document."""
+        document_id = str(document_id)
+        dataset_id = str(dataset_id)
+        tenant_id = str(tenant_id)
+
+        # get dataset info
+        dataset = db.session.query(Dataset).filter(Dataset.tenant_id == tenant_id, Dataset.id == dataset_id).first()
+
+        if not dataset:
+            raise ValueError("Dataset is not exist.")
+
+        document = DocumentService.get_document(dataset.id, document_id)
+
+        # 404 if document not found
+        if document is None:
+            raise NotFound("Document Not Exists.")
+
+        # 403 if document is archived
+        if DocumentService.check_archived(document):
+            raise ArchivedDocumentImmutableError()
+
+        try:
+            # delete document
+            DocumentService.delete_document(document)
+        except services.errors.document.DocumentIndexingError:
+            raise DocumentIndexingError("Cannot delete document during indexing.")
+
+        return {"result": "success"}, 200
+
+
+class DocumentListApi(DatasetApiResource):
+    def get(self, tenant_id, dataset_id):
+        dataset_id = str(dataset_id)
+        tenant_id = str(tenant_id)
+        page = request.args.get("page", default=1, type=int)
+        limit = request.args.get("limit", default=20, type=int)
+        search = request.args.get("keyword", default=None, type=str)
+        dataset = db.session.query(Dataset).filter(Dataset.tenant_id == tenant_id, Dataset.id == dataset_id).first()
+        if not dataset:
+            raise NotFound("Dataset not found.")
+
+        query = Document.query.filter_by(dataset_id=str(dataset_id), tenant_id=tenant_id)
+
+        if search:
+            search = f"%{search}%"
+            query = query.filter(Document.name.like(search))
+
+        query = query.order_by(desc(Document.created_at))
+
+        paginated_documents = query.paginate(page=page, per_page=limit, max_per_page=100, error_out=False)
+        documents = paginated_documents.items
+
+        response = {
+            "data": marshal(documents, document_fields),
+            "has_more": len(documents) == limit,
+            "limit": limit,
+            "total": paginated_documents.total,
+            "page": page,
+        }
+
+        return response
+
+
+class DocumentIndexingStatusApi(DatasetApiResource):
+    def get(self, tenant_id, dataset_id, batch):
+        dataset_id = str(dataset_id)
+        batch = str(batch)
+        tenant_id = str(tenant_id)
+        # get dataset
+        dataset = db.session.query(Dataset).filter(Dataset.tenant_id == tenant_id, Dataset.id == dataset_id).first()
+        if not dataset:
+            raise NotFound("Dataset not found.")
+        # get documents
+        documents = DocumentService.get_batch_documents(dataset_id, batch)
+        if not documents:
+            raise NotFound("Documents not found.")
+        documents_status = []
+        for document in documents:
+            completed_segments = DocumentSegment.query.filter(
+                DocumentSegment.completed_at.isnot(None),
+                DocumentSegment.document_id == str(document.id),
+                DocumentSegment.status != "re_segment",
+            ).count()
+            total_segments = DocumentSegment.query.filter(
+                DocumentSegment.document_id == str(document.id), DocumentSegment.status != "re_segment"
+            ).count()
+            document.completed_segments = completed_segments
+            document.total_segments = total_segments
+            if document.is_paused:
+                document.indexing_status = "paused"
+            documents_status.append(marshal(document, document_status_fields))
+        data = {"data": documents_status}
+        return data
+
+
+api.add_resource(
+    DocumentAddByTextApi,
+    "/datasets/<uuid:dataset_id>/document/create_by_text",
+    "/datasets/<uuid:dataset_id>/document/create-by-text",
+)
+api.add_resource(
+    DocumentAddByFileApi,
+    "/datasets/<uuid:dataset_id>/document/create_by_file",
+    "/datasets/<uuid:dataset_id>/document/create-by-file",
+)
+api.add_resource(
+    DocumentUpdateByTextApi,
+    "/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/update_by_text",
+    "/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/update-by-text",
+)
+api.add_resource(
+    DocumentUpdateByFileApi,
+    "/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/update_by_file",
+    "/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/update-by-file",
+)
+api.add_resource(DocumentDeleteApi, "/datasets/<uuid:dataset_id>/documents/<uuid:document_id>")
+api.add_resource(DocumentListApi, "/datasets/<uuid:dataset_id>/documents")
+api.add_resource(DocumentIndexingStatusApi, "/datasets/<uuid:dataset_id>/documents/<string:batch>/indexing-status")

dify_0.15.3/api/controllers/service_api/dataset/error.py

@@ -0,0 +1,79 @@
+from libs.exception import BaseHTTPException
+
+
+class NoFileUploadedError(BaseHTTPException):
+    error_code = "no_file_uploaded"
+    description = "Please upload your file."
+    code = 400
+
+
+class TooManyFilesError(BaseHTTPException):
+    error_code = "too_many_files"
+    description = "Only one file is allowed."
+    code = 400
+
+
+class FileTooLargeError(BaseHTTPException):
+    error_code = "file_too_large"
+    description = "File size exceeded. {message}"
+    code = 413
+
+
+class UnsupportedFileTypeError(BaseHTTPException):
+    error_code = "unsupported_file_type"
+    description = "File type not allowed."
+    code = 415
+
+
+class HighQualityDatasetOnlyError(BaseHTTPException):
+    error_code = "high_quality_dataset_only"
+    description = "Current operation only supports 'high-quality' datasets."
+    code = 400
+
+
+class DatasetNotInitializedError(BaseHTTPException):
+    error_code = "dataset_not_initialized"
+    description = "The dataset is still being initialized or indexing. Please wait a moment."
+    code = 400
+
+
+class ArchivedDocumentImmutableError(BaseHTTPException):
+    error_code = "archived_document_immutable"
+    description = "The archived document is not editable."
+    code = 403
+
+
+class DatasetNameDuplicateError(BaseHTTPException):
+    error_code = "dataset_name_duplicate"
+    description = "The dataset name already exists. Please modify your dataset name."
+    code = 409
+
+
+class InvalidActionError(BaseHTTPException):
+    error_code = "invalid_action"
+    description = "Invalid action."
+    code = 400
+
+
+class DocumentAlreadyFinishedError(BaseHTTPException):
+    error_code = "document_already_finished"
+    description = "The document has been processed. Please refresh the page or go to the document details."
+    code = 400
+
+
+class DocumentIndexingError(BaseHTTPException):
+    error_code = "document_indexing"
+    description = "The document is being processed and cannot be edited."
+    code = 400
+
+
+class InvalidMetadataError(BaseHTTPException):
+    error_code = "invalid_metadata"
+    description = "The metadata content is incorrect. Please check and verify."
+    code = 400
+
+
+class DatasetInUseError(BaseHTTPException):
+    error_code = "dataset_in_use"
+    description = "The dataset is being used by some apps. Please remove the dataset from the apps before deleting it."
+    code = 409

dify_0.15.3/api/controllers/service_api/dataset/hit_testing.py

@@ -0,0 +1,17 @@
+from controllers.console.datasets.hit_testing_base import DatasetsHitTestingBase
+from controllers.service_api import api
+from controllers.service_api.wraps import DatasetApiResource
+
+
+class HitTestingApi(DatasetApiResource, DatasetsHitTestingBase):
+    def post(self, tenant_id, dataset_id):
+        dataset_id_str = str(dataset_id)
+
+        dataset = self.get_and_validate_dataset(dataset_id_str)
+        args = self.parse_args()
+        self.hit_testing_args_check(args)
+
+        return self.perform_hit_testing(dataset, args)
+
+
+api.add_resource(HitTestingApi, "/datasets/<uuid:dataset_id>/hit-testing", "/datasets/<uuid:dataset_id>/retrieve")

dify_0.15.3/api/controllers/service_api/dataset/segment.py

@@ -0,0 +1,201 @@
+from flask_login import current_user  # type: ignore
+from flask_restful import marshal, reqparse  # type: ignore
+from werkzeug.exceptions import NotFound
+
+from controllers.service_api import api
+from controllers.service_api.app.error import ProviderNotInitializeError
+from controllers.service_api.wraps import (
+    DatasetApiResource,
+    cloud_edition_billing_knowledge_limit_check,
+    cloud_edition_billing_resource_check,
+)
+from core.errors.error import LLMBadRequestError, ProviderTokenNotInitError
+from core.model_manager import ModelManager
+from core.model_runtime.entities.model_entities import ModelType
+from extensions.ext_database import db
+from fields.segment_fields import segment_fields
+from models.dataset import Dataset, DocumentSegment
+from services.dataset_service import DatasetService, DocumentService, SegmentService
+from services.entities.knowledge_entities.knowledge_entities import SegmentUpdateArgs
+
+
+class SegmentApi(DatasetApiResource):
+    """Resource for segments."""
+
+    @cloud_edition_billing_resource_check("vector_space", "dataset")
+    @cloud_edition_billing_knowledge_limit_check("add_segment", "dataset")
+    def post(self, tenant_id, dataset_id, document_id):
+        """Create single segment."""
+        # check dataset
+        dataset_id = str(dataset_id)
+        tenant_id = str(tenant_id)
+        dataset = db.session.query(Dataset).filter(Dataset.tenant_id == tenant_id, Dataset.id == dataset_id).first()
+        if not dataset:
+            raise NotFound("Dataset not found.")
+        # check document
+        document_id = str(document_id)
+        document = DocumentService.get_document(dataset.id, document_id)
+        if not document:
+            raise NotFound("Document not found.")
+        if document.indexing_status != "completed":
+            raise NotFound("Document is not completed.")
+        if not document.enabled:
+            raise NotFound("Document is disabled.")
+        # check embedding model setting
+        if dataset.indexing_technique == "high_quality":
+            try:
+                model_manager = ModelManager()
+                model_manager.get_model_instance(
+                    tenant_id=current_user.current_tenant_id,
+                    provider=dataset.embedding_model_provider,
+                    model_type=ModelType.TEXT_EMBEDDING,
+                    model=dataset.embedding_model,
+                )
+            except LLMBadRequestError:
+                raise ProviderNotInitializeError(
+                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
+                )
+            except ProviderTokenNotInitError as ex:
+                raise ProviderNotInitializeError(ex.description)
+        # validate args
+        parser = reqparse.RequestParser()
+        parser.add_argument("segments", type=list, required=False, nullable=True, location="json")
+        args = parser.parse_args()
+        if args["segments"] is not None:
+            for args_item in args["segments"]:
+                SegmentService.segment_create_args_validate(args_item, document)
+            segments = SegmentService.multi_create_segment(args["segments"], document, dataset)
+            return {"data": marshal(segments, segment_fields), "doc_form": document.doc_form}, 200
+        else:
+            return {"error": "Segments is required"}, 400
+
+    def get(self, tenant_id, dataset_id, document_id):
+        """Create single segment."""
+        # check dataset
+        dataset_id = str(dataset_id)
+        tenant_id = str(tenant_id)
+        dataset = db.session.query(Dataset).filter(Dataset.tenant_id == tenant_id, Dataset.id == dataset_id).first()
+        if not dataset:
+            raise NotFound("Dataset not found.")
+        # check document
+        document_id = str(document_id)
+        document = DocumentService.get_document(dataset.id, document_id)
+        if not document:
+            raise NotFound("Document not found.")
+        # check embedding model setting
+        if dataset.indexing_technique == "high_quality":
+            try:
+                model_manager = ModelManager()
+                model_manager.get_model_instance(
+                    tenant_id=current_user.current_tenant_id,
+                    provider=dataset.embedding_model_provider,
+                    model_type=ModelType.TEXT_EMBEDDING,
+                    model=dataset.embedding_model,
+                )
+            except LLMBadRequestError:
+                raise ProviderNotInitializeError(
+                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
+                )
+            except ProviderTokenNotInitError as ex:
+                raise ProviderNotInitializeError(ex.description)
+
+        parser = reqparse.RequestParser()
+        parser.add_argument("status", type=str, action="append", default=[], location="args")
+        parser.add_argument("keyword", type=str, default=None, location="args")
+        args = parser.parse_args()
+
+        status_list = args["status"]
+        keyword = args["keyword"]
+
+        query = DocumentSegment.query.filter(
+            DocumentSegment.document_id == str(document_id), DocumentSegment.tenant_id == current_user.current_tenant_id
+        )
+
+        if status_list:
+            query = query.filter(DocumentSegment.status.in_(status_list))
+
+        if keyword:
+            query = query.where(DocumentSegment.content.ilike(f"%{keyword}%"))
+
+        total = query.count()
+        segments = query.order_by(DocumentSegment.position).all()
+        return {"data": marshal(segments, segment_fields), "doc_form": document.doc_form, "total": total}, 200
+
+
+class DatasetSegmentApi(DatasetApiResource):
+    def delete(self, tenant_id, dataset_id, document_id, segment_id):
+        # check dataset
+        dataset_id = str(dataset_id)
+        tenant_id = str(tenant_id)
+        dataset = db.session.query(Dataset).filter(Dataset.tenant_id == tenant_id, Dataset.id == dataset_id).first()
+        if not dataset:
+            raise NotFound("Dataset not found.")
+        # check user's model setting
+        DatasetService.check_dataset_model_setting(dataset)
+        # check document
+        document_id = str(document_id)
+        document = DocumentService.get_document(dataset_id, document_id)
+        if not document:
+            raise NotFound("Document not found.")
+        # check segment
+        segment = DocumentSegment.query.filter(
+            DocumentSegment.id == str(segment_id), DocumentSegment.tenant_id == current_user.current_tenant_id
+        ).first()
+        if not segment:
+            raise NotFound("Segment not found.")
+        SegmentService.delete_segment(segment, document, dataset)
+        return {"result": "success"}, 200
+
+    @cloud_edition_billing_resource_check("vector_space", "dataset")
+    def post(self, tenant_id, dataset_id, document_id, segment_id):
+        # check dataset
+        dataset_id = str(dataset_id)
+        tenant_id = str(tenant_id)
+        dataset = db.session.query(Dataset).filter(Dataset.tenant_id == tenant_id, Dataset.id == dataset_id).first()
+        if not dataset:
+            raise NotFound("Dataset not found.")
+        # check user's model setting
+        DatasetService.check_dataset_model_setting(dataset)
+        # check document
+        document_id = str(document_id)
+        document = DocumentService.get_document(dataset_id, document_id)
+        if not document:
+            raise NotFound("Document not found.")
+        if dataset.indexing_technique == "high_quality":
+            # check embedding model setting
+            try:
+                model_manager = ModelManager()
+                model_manager.get_model_instance(
+                    tenant_id=current_user.current_tenant_id,
+                    provider=dataset.embedding_model_provider,
+                    model_type=ModelType.TEXT_EMBEDDING,
+                    model=dataset.embedding_model,
+                )
+            except LLMBadRequestError:
+                raise ProviderNotInitializeError(
+                    "No Embedding Model available. Please configure a valid provider in the Settings -> Model Provider."
+                )
+            except ProviderTokenNotInitError as ex:
+                raise ProviderNotInitializeError(ex.description)
+            # check segment
+        segment_id = str(segment_id)
+        segment = DocumentSegment.query.filter(
+            DocumentSegment.id == str(segment_id), DocumentSegment.tenant_id == current_user.current_tenant_id
+        ).first()
+        if not segment:
+            raise NotFound("Segment not found.")
+
+        # validate args
+        parser = reqparse.RequestParser()
+        parser.add_argument("segment", type=dict, required=False, nullable=True, location="json")
+        args = parser.parse_args()
+
+        SegmentService.segment_create_args_validate(args["segment"], document)
+        segment = SegmentService.update_segment(SegmentUpdateArgs(**args["segment"]), segment, document, dataset)
+        return {"data": marshal(segment, segment_fields), "doc_form": document.doc_form}, 200
+
+
+api.add_resource(SegmentApi, "/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/segments")
+api.add_resource(
+    DatasetSegmentApi, "/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/segments/<uuid:segment_id>"
+)

dify_0.15.3/api/controllers/service_api/dataset/upload_file.py

@@ -0,0 +1,54 @@
+from werkzeug.exceptions import NotFound
+
+from controllers.service_api import api
+from controllers.service_api.wraps import (
+    DatasetApiResource,
+)
+from core.file import helpers as file_helpers
+from extensions.ext_database import db
+from models.dataset import Dataset
+from models.model import UploadFile
+from services.dataset_service import DocumentService
+
+
+class UploadFileApi(DatasetApiResource):
+    def get(self, tenant_id, dataset_id, document_id):
+        """Get upload file."""
+        # check dataset
+        dataset_id = str(dataset_id)
+        tenant_id = str(tenant_id)
+        dataset = db.session.query(Dataset).filter(Dataset.tenant_id == tenant_id, Dataset.id == dataset_id).first()
+        if not dataset:
+            raise NotFound("Dataset not found.")
+        # check document
+        document_id = str(document_id)
+        document = DocumentService.get_document(dataset.id, document_id)
+        if not document:
+            raise NotFound("Document not found.")
+        # check upload file
+        if document.data_source_type != "upload_file":
+            raise ValueError(f"Document data source type ({document.data_source_type}) is not upload_file.")
+        data_source_info = document.data_source_info_dict
+        if data_source_info and "upload_file_id" in data_source_info:
+            file_id = data_source_info["upload_file_id"]
+            upload_file = db.session.query(UploadFile).filter(UploadFile.id == file_id).first()
+            if not upload_file:
+                raise NotFound("UploadFile not found.")
+        else:
+            raise ValueError("Upload file id not found in document data source info.")
+
+        url = file_helpers.get_signed_file_url(upload_file_id=upload_file.id)
+        return {
+            "id": upload_file.id,
+            "name": upload_file.name,
+            "size": upload_file.size,
+            "extension": upload_file.extension,
+            "url": url,
+            "download_url": f"{url}&as_attachment=true",
+            "mime_type": upload_file.mime_type,
+            "created_by": upload_file.created_by,
+            "created_at": upload_file.created_at.timestamp(),
+        }, 200
+
+
+api.add_resource(UploadFileApi, "/datasets/<uuid:dataset_id>/documents/<uuid:document_id>/upload-file")

dify_0.15.3/api/controllers/service_api/index.py

@@ -0,0 +1,16 @@
+from flask_restful import Resource  # type: ignore
+
+from configs import dify_config
+from controllers.service_api import api
+
+
+class IndexApi(Resource):
+    def get(self):
+        return {
+            "welcome": "Dify OpenAPI",
+            "api_version": "v1",
+            "server_version": dify_config.CURRENT_VERSION,
+        }
+
+
+api.add_resource(IndexApi, "/")

dify_0.15.3/api/controllers/service_api/wraps.py

@@ -0,0 +1,253 @@
+from collections.abc import Callable
+from datetime import UTC, datetime, timedelta
+from enum import Enum
+from functools import wraps
+from typing import Optional
+
+from flask import current_app, request
+from flask_login import user_logged_in  # type: ignore
+from flask_restful import Resource  # type: ignore
+from pydantic import BaseModel
+from sqlalchemy import select, update
+from sqlalchemy.orm import Session
+from werkzeug.exceptions import Forbidden, Unauthorized
+
+from extensions.ext_database import db
+from libs.login import _get_user
+from models.account import Account, Tenant, TenantAccountJoin, TenantStatus
+from models.model import ApiToken, App, EndUser
+from services.feature_service import FeatureService
+
+
+class WhereisUserArg(Enum):
+    """
+    Enum for whereis_user_arg.
+    """
+
+    QUERY = "query"
+    JSON = "json"
+    FORM = "form"
+
+
+class FetchUserArg(BaseModel):
+    fetch_from: WhereisUserArg
+    required: bool = False
+
+
+def validate_app_token(view: Optional[Callable] = None, *, fetch_user_arg: Optional[FetchUserArg] = None):
+    def decorator(view_func):
+        @wraps(view_func)
+        def decorated_view(*args, **kwargs):
+            api_token = validate_and_get_api_token("app")
+
+            app_model = db.session.query(App).filter(App.id == api_token.app_id).first()
+            if not app_model:
+                raise Forbidden("The app no longer exists.")
+
+            if app_model.status != "normal":
+                raise Forbidden("The app's status is abnormal.")
+
+            if not app_model.enable_api:
+                raise Forbidden("The app's API service has been disabled.")
+
+            tenant = db.session.query(Tenant).filter(Tenant.id == app_model.tenant_id).first()
+            if tenant is None:
+                raise ValueError("Tenant does not exist.")
+            if tenant.status == TenantStatus.ARCHIVE:
+                raise Forbidden("The workspace's status is archived.")
+
+            kwargs["app_model"] = app_model
+
+            if fetch_user_arg:
+                if fetch_user_arg.fetch_from == WhereisUserArg.QUERY:
+                    user_id = request.args.get("user")
+                elif fetch_user_arg.fetch_from == WhereisUserArg.JSON:
+                    user_id = request.get_json().get("user")
+                elif fetch_user_arg.fetch_from == WhereisUserArg.FORM:
+                    user_id = request.form.get("user")
+                else:
+                    # use default-user
+                    user_id = None
+
+                if not user_id and fetch_user_arg.required:
+                    raise ValueError("Arg user must be provided.")
+
+                if user_id:
+                    user_id = str(user_id)
+
+                kwargs["end_user"] = create_or_update_end_user_for_user_id(app_model, user_id)
+
+            return view_func(*args, **kwargs)
+
+        return decorated_view
+
+    if view is None:
+        return decorator
+    else:
+        return decorator(view)
+
+
+def cloud_edition_billing_resource_check(resource: str, api_token_type: str):
+    def interceptor(view):
+        def decorated(*args, **kwargs):
+            api_token = validate_and_get_api_token(api_token_type)
+            features = FeatureService.get_features(api_token.tenant_id)
+
+            if features.billing.enabled:
+                members = features.members
+                apps = features.apps
+                vector_space = features.vector_space
+                documents_upload_quota = features.documents_upload_quota
+
+                if resource == "members" and 0 < members.limit <= members.size:
+                    raise Forbidden("The number of members has reached the limit of your subscription.")
+                elif resource == "apps" and 0 < apps.limit <= apps.size:
+                    raise Forbidden("The number of apps has reached the limit of your subscription.")
+                elif resource == "vector_space" and 0 < vector_space.limit <= vector_space.size:
+                    raise Forbidden("The capacity of the vector space has reached the limit of your subscription.")
+                elif resource == "documents" and 0 < documents_upload_quota.limit <= documents_upload_quota.size:
+                    raise Forbidden("The number of documents has reached the limit of your subscription.")
+                else:
+                    return view(*args, **kwargs)
+
+            return view(*args, **kwargs)
+
+        return decorated
+
+    return interceptor
+
+
+def cloud_edition_billing_knowledge_limit_check(resource: str, api_token_type: str):
+    def interceptor(view):
+        @wraps(view)
+        def decorated(*args, **kwargs):
+            api_token = validate_and_get_api_token(api_token_type)
+            features = FeatureService.get_features(api_token.tenant_id)
+            if features.billing.enabled:
+                if resource == "add_segment":
+                    if features.billing.subscription.plan == "sandbox":
+                        raise Forbidden(
+                            "To unlock this feature and elevate your Dify experience, please upgrade to a paid plan."
+                        )
+                else:
+                    return view(*args, **kwargs)
+
+            return view(*args, **kwargs)
+
+        return decorated
+
+    return interceptor
+
+
+def validate_dataset_token(view=None):
+    def decorator(view):
+        @wraps(view)
+        def decorated(*args, **kwargs):
+            api_token = validate_and_get_api_token("dataset")
+            tenant_account_join = (
+                db.session.query(Tenant, TenantAccountJoin)
+                .filter(Tenant.id == api_token.tenant_id)
+                .filter(TenantAccountJoin.tenant_id == Tenant.id)
+                .filter(TenantAccountJoin.role.in_(["owner"]))
+                .filter(Tenant.status == TenantStatus.NORMAL)
+                .one_or_none()
+            )  # TODO: only owner information is required, so only one is returned.
+            if tenant_account_join:
+                tenant, ta = tenant_account_join
+                account = Account.query.filter_by(id=ta.account_id).first()
+                # Login admin
+                if account:
+                    account.current_tenant = tenant
+                    current_app.login_manager._update_request_context_with_user(account)  # type: ignore
+                    user_logged_in.send(current_app._get_current_object(), user=_get_user())  # type: ignore
+                else:
+                    raise Unauthorized("Tenant owner account does not exist.")
+            else:
+                raise Unauthorized("Tenant does not exist.")
+            return view(api_token.tenant_id, *args, **kwargs)
+
+        return decorated
+
+    if view:
+        return decorator(view)
+
+    # if view is None, it means that the decorator is used without parentheses
+    # use the decorator as a function for method_decorators
+    return decorator
+
+
+def validate_and_get_api_token(scope: str | None = None):
+    """
+    Validate and get API token.
+    """
+    auth_header = request.headers.get("Authorization")
+    if auth_header is None or " " not in auth_header:
+        raise Unauthorized("Authorization header must be provided and start with 'Bearer'")
+
+    auth_scheme, auth_token = auth_header.split(None, 1)
+    auth_scheme = auth_scheme.lower()
+
+    if auth_scheme != "bearer":
+        raise Unauthorized("Authorization scheme must be 'Bearer'")
+
+    current_time = datetime.now(UTC).replace(tzinfo=None)
+    cutoff_time = current_time - timedelta(minutes=1)
+    with Session(db.engine, expire_on_commit=False) as session:
+        update_stmt = (
+            update(ApiToken)
+            .where(
+                ApiToken.token == auth_token,
+                (ApiToken.last_used_at.is_(None) | (ApiToken.last_used_at < cutoff_time)),
+                ApiToken.type == scope,
+            )
+            .values(last_used_at=current_time)
+            .returning(ApiToken)
+        )
+        result = session.execute(update_stmt)
+        api_token = result.scalar_one_or_none()
+
+        if not api_token:
+            stmt = select(ApiToken).where(ApiToken.token == auth_token, ApiToken.type == scope)
+            api_token = session.scalar(stmt)
+            if not api_token:
+                raise Unauthorized("Access token is invalid")
+        else:
+            session.commit()
+
+    return api_token
+
+
+def create_or_update_end_user_for_user_id(app_model: App, user_id: Optional[str] = None) -> EndUser:
+    """
+    Create or update session terminal based on user ID.
+    """
+    if not user_id:
+        user_id = "DEFAULT-USER"
+
+    end_user = (
+        db.session.query(EndUser)
+        .filter(
+            EndUser.tenant_id == app_model.tenant_id,
+            EndUser.app_id == app_model.id,
+            EndUser.session_id == user_id,
+            EndUser.type == "service_api",
+        )
+        .first()
+    )
+
+    if end_user is None:
+        end_user = EndUser(
+            tenant_id=app_model.tenant_id,
+            app_id=app_model.id,
+            type="service_api",
+            is_anonymous=user_id == "DEFAULT-USER",
+            session_id=user_id,
+        )
+        db.session.add(end_user)
+        db.session.commit()
+
+    return end_user
+
+
+class DatasetApiResource(Resource):
+    method_decorators = [validate_dataset_token]