Merge remote-tracking branch 'origin/main'

pull/18136/head
JF.Hsiong 1 year ago
commit 298d0d6d0b

@ -8,7 +8,7 @@
<a href="https://cloud.dify.ai">Dify Cloud</a> ·
<a href="https://docs.dify.ai/getting-started/install-self-hosted">Self-hosting</a> ·
<a href="https://docs.dify.ai">Documentation</a> ·
<a href="https://udify.app/chat/22L1zSxg6yW1cWQg">Enterprise inquiry</a>
<a href="https://dify.ai/pricing">Dify edition overview</a>
</p>
<p align="center">

@ -4,7 +4,7 @@
<a href="https://cloud.dify.ai">Dify Cloud</a> ·
<a href="https://docs.dify.ai/getting-started/install-self-hosted">الاستضافة الذاتية</a> ·
<a href="https://docs.dify.ai">التوثيق</a> ·
<a href="https://udify.app/chat/22L1zSxg6yW1cWQg">استفسار الشركات (للإنجليزية فقط)</a>
<a href="https://dify.ai/pricing">نظرة عامة على منتجات Dify</a>
</p>
<p align="center">

@ -8,7 +8,7 @@
<a href="https://cloud.dify.ai">ডিফাই ক্লাউড</a> ·
<a href="https://docs.dify.ai/getting-started/install-self-hosted">সেল্ফ-হোস্টিং</a> ·
<a href="https://docs.dify.ai">ডকুমেন্টেশন</a> ·
<a href="https://udify.app/chat/22L1zSxg6yW1cWQg">ব্যাবসায়িক অনুসন্ধান</a>
<a href="https://dify.ai/pricing">Dify পণ্যের রূপভেদ</a>
</p>
<p align="center">

@ -4,7 +4,7 @@
<a href="https://cloud.dify.ai">Dify 云服务</a> ·
<a href="https://docs.dify.ai/getting-started/install-self-hosted">自托管</a> ·
<a href="https://docs.dify.ai">文档</a> ·
<a href="https://udify.app/chat/22L1zSxg6yW1cWQg">(需用英文)常见问题解答 / 联系团队</a>
<a href="https://dify.ai/pricing">Dify 产品形态总览</a>
</div>
<p align="center">

@ -8,7 +8,7 @@
<a href="https://cloud.dify.ai">Dify Cloud</a> ·
<a href="https://docs.dify.ai/getting-started/install-self-hosted">Selbstgehostetes</a> ·
<a href="https://docs.dify.ai">Dokumentation</a> ·
<a href="https://udify.app/chat/22L1zSxg6yW1cWQg">Anfrage an Unternehmen</a>
<a href="https://dify.ai/pricing">Überblick über die Dify-Produkte</a>
</p>
<p align="center">

@ -4,7 +4,7 @@
<a href="https://cloud.dify.ai">Dify Cloud</a> ·
<a href="https://docs.dify.ai/getting-started/install-self-hosted">Auto-alojamiento</a> ·
<a href="https://docs.dify.ai">Documentación</a> ·
<a href="https://udify.app/chat/22L1zSxg6yW1cWQg">Consultas empresariales (en inglés)</a>
<a href="https://dify.ai/pricing">Resumen de las ediciones de Dify</a>
</p>
<p align="center">

@ -4,7 +4,7 @@
<a href="https://cloud.dify.ai">Dify Cloud</a> ·
<a href="https://docs.dify.ai/getting-started/install-self-hosted">Auto-hébergement</a> ·
<a href="https://docs.dify.ai">Documentation</a> ·
<a href="https://udify.app/chat/22L1zSxg6yW1cWQg">Demande dentreprise (en anglais seulement)</a>
<a href="https://dify.ai/pricing">Présentation des différentes offres Dify</a>
</p>
<p align="center">

@ -4,7 +4,7 @@
<a href="https://cloud.dify.ai">Dify Cloud</a> ·
<a href="https://docs.dify.ai/getting-started/install-self-hosted">セルフホスティング</a> ·
<a href="https://docs.dify.ai">ドキュメント</a> ·
<a href="https://udify.app/chat/22L1zSxg6yW1cWQg">企業のお問い合わせ(英語のみ)</a>
<a href="https://dify.ai/pricing">Difyの各種エディションについて</a>
</p>
<p align="center">

@ -4,7 +4,7 @@
<a href="https://cloud.dify.ai">Dify Cloud</a> ·
<a href="https://docs.dify.ai/getting-started/install-self-hosted">Self-hosting</a> ·
<a href="https://docs.dify.ai">Documentation</a> ·
<a href="https://udify.app/chat/22L1zSxg6yW1cWQg">Commercial enquiries</a>
<a href="https://dify.ai/pricing">Dify product editions</a>
</p>
<p align="center">

@ -4,7 +4,7 @@
<a href="https://cloud.dify.ai">Dify 클라우드</a> ·
<a href="https://docs.dify.ai/getting-started/install-self-hosted">셀프-호스팅</a> ·
<a href="https://docs.dify.ai">문서</a> ·
<a href="https://udify.app/chat/22L1zSxg6yW1cWQg">기업 문의 (영어만 가능)</a>
<a href="https://dify.ai/pricing">Dify 제품 에디션 안내</a>
</p>
<p align="center">

@ -8,7 +8,7 @@
<a href="https://cloud.dify.ai">Dify Cloud</a> ·
<a href="https://docs.dify.ai/getting-started/install-self-hosted">Auto-hospedagem</a> ·
<a href="https://docs.dify.ai">Documentação</a> ·
<a href="https://udify.app/chat/22L1zSxg6yW1cWQg">Consultas empresariais</a>
<a href="https://dify.ai/pricing">Visão geral das edições do Dify</a>
</p>
<p align="center">

@ -8,7 +8,7 @@
<a href="https://cloud.dify.ai">Dify Cloud</a> ·
<a href="https://docs.dify.ai/getting-started/install-self-hosted">Samostojno gostovanje</a> ·
<a href="https://docs.dify.ai">Dokumentacija</a> ·
<a href="https://udify.app/chat/22L1zSxg6yW1cWQg">Povpraševanje za podjetja</a>
<a href="https://dify.ai/pricing">Pregled ponudb izdelkov Dify</a>
</p>
<p align="center">

@ -4,7 +4,7 @@
<a href="https://cloud.dify.ai">Dify Bulut</a> ·
<a href="https://docs.dify.ai/getting-started/install-self-hosted">Kendi Sunucunuzda Barındırma</a> ·
<a href="https://docs.dify.ai">Dokümantasyon</a> ·
<a href="https://udify.app/chat/22L1zSxg6yW1cWQg">Yalnızca İngilizce: Kurumsal Sorgulama</a>
<a href="https://dify.ai/pricing">Dify ürün seçeneklerine genel bakış</a>
</p>
<p align="center">

@ -8,7 +8,7 @@
<a href="https://cloud.dify.ai">Dify 雲端服務</a> ·
<a href="https://docs.dify.ai/getting-started/install-self-hosted">自行託管</a> ·
<a href="https://docs.dify.ai">說明文件</a> ·
<a href="https://udify.app/chat/22L1zSxg6yW1cWQg">企業諮詢</a>
<a href="https://dify.ai/pricing">產品方案概覽</a>
</p>
<p align="center">

@ -4,7 +4,7 @@
<a href="https://cloud.dify.ai">Dify Cloud</a> ·
<a href="https://docs.dify.ai/getting-started/install-self-hosted">Tự triển khai</a> ·
<a href="https://docs.dify.ai">Tài liệu</a> ·
<a href="https://udify.app/chat/22L1zSxg6yW1cWQg">Yêu cầu doanh nghiệp</a>
<a href="https://dify.ai/pricing">Tổng quan các lựa chọn sản phẩm Dify</a>
</p>
<p align="center">

@ -482,4 +482,7 @@ OTEL_MAX_QUEUE_SIZE=2048
OTEL_MAX_EXPORT_BATCH_SIZE=512
OTEL_METRIC_EXPORT_INTERVAL=60000
OTEL_BATCH_EXPORT_TIMEOUT=10000
OTEL_METRIC_EXPORT_TIMEOUT=30000
OTEL_METRIC_EXPORT_TIMEOUT=30000
# Prevent Clickjacking
ALLOW_EMBED=false

@ -52,6 +52,7 @@ def initialize_extensions(app: DifyApp):
ext_mail,
ext_migrate,
ext_otel,
ext_otel_patch,
ext_proxy_fix,
ext_redis,
ext_repositories,
@ -84,6 +85,7 @@ def initialize_extensions(app: DifyApp):
ext_proxy_fix,
ext_blueprints,
ext_commands,
ext_otel_patch, # Apply patch before initializing OpenTelemetry
ext_otel,
]
for ext in extensions:

@ -13,6 +13,7 @@ from .observability import ObservabilityConfig
from .packaging import PackagingInfo
from .remote_settings_sources import RemoteSettingsSource, RemoteSettingsSourceConfig, RemoteSettingsSourceName
from .remote_settings_sources.apollo import ApolloSettingsSource
from .remote_settings_sources.nacos import NacosSettingsSource
logger = logging.getLogger(__name__)
@ -34,6 +35,8 @@ class RemoteSettingsSourceFactory(PydanticBaseSettingsSource):
match remote_source_name:
case RemoteSettingsSourceName.APOLLO:
remote_source = ApolloSettingsSource(current_state)
case RemoteSettingsSourceName.NACOS:
remote_source = NacosSettingsSource(current_state)
case _:
logger.warning(f"Unsupported remote source: {remote_source_name}")
return {}

@ -22,6 +22,7 @@ from .vdb.baidu_vector_config import BaiduVectorDBConfig
from .vdb.chroma_config import ChromaConfig
from .vdb.couchbase_config import CouchbaseConfig
from .vdb.elasticsearch_config import ElasticsearchConfig
from .vdb.huawei_cloud_config import HuaweiCloudConfig
from .vdb.lindorm_config import LindormConfig
from .vdb.milvus_config import MilvusConfig
from .vdb.myscale_config import MyScaleConfig
@ -263,6 +264,7 @@ class MiddlewareConfig(
VectorStoreConfig,
AnalyticdbConfig,
ChromaConfig,
HuaweiCloudConfig,
MilvusConfig,
MyScaleConfig,
OpenSearchConfig,

@ -0,0 +1,25 @@
from typing import Optional
from pydantic import Field
from pydantic_settings import BaseSettings
class HuaweiCloudConfig(BaseSettings):
"""
Configuration settings for Huawei cloud search service
"""
HUAWEI_CLOUD_HOSTS: Optional[str] = Field(
description="Hostname or IP address of the Huawei cloud search service instance",
default=None,
)
HUAWEI_CLOUD_USER: Optional[str] = Field(
description="Username for authenticating with Huawei cloud search service",
default=None,
)
HUAWEI_CLOUD_PASSWORD: Optional[str] = Field(
description="Password for authenticating with Huawei cloud search service",
default=None,
)

@ -3,3 +3,4 @@ from enum import StrEnum
class RemoteSettingsSourceName(StrEnum):
APOLLO = "apollo"
NACOS = "nacos"

@ -0,0 +1,52 @@
import logging
import os
from collections.abc import Mapping
from typing import Any
from pydantic.fields import FieldInfo
from .http_request import NacosHttpClient
logger = logging.getLogger(__name__)
from configs.remote_settings_sources.base import RemoteSettingsSource
from .utils import _parse_config
class NacosSettingsSource(RemoteSettingsSource):
def __init__(self, configs: Mapping[str, Any]):
self.configs = configs
self.remote_configs: dict[str, Any] = {}
self.async_init()
def async_init(self):
data_id = os.getenv("DIFY_ENV_NACOS_DATA_ID", "dify-api-env.properties")
group = os.getenv("DIFY_ENV_NACOS_GROUP", "nacos-dify")
tenant = os.getenv("DIFY_ENV_NACOS_NAMESPACE", "")
params = {"dataId": data_id, "group": group, "tenant": tenant}
try:
content = NacosHttpClient().http_request("/nacos/v1/cs/configs", method="GET", headers={}, params=params)
self.remote_configs = self._parse_config(content)
except Exception as e:
logger.exception("[get-access-token] exception occurred")
raise
def _parse_config(self, content: str) -> dict:
if not content:
return {}
try:
return _parse_config(self, content)
except Exception as e:
raise RuntimeError(f"Failed to parse config: {e}")
def get_field_value(self, field: FieldInfo, field_name: str) -> tuple[Any, str, bool]:
if not isinstance(self.remote_configs, dict):
raise ValueError(f"remote configs is not dict, but {type(self.remote_configs)}")
field_value = self.remote_configs.get(field_name)
if field_value is None:
return None, field_name, False
return field_value, field_name, False

@ -0,0 +1,83 @@
import base64
import hashlib
import hmac
import logging
import os
import time
import requests
logger = logging.getLogger(__name__)
class NacosHttpClient:
def __init__(self):
self.username = os.getenv("DIFY_ENV_NACOS_USERNAME")
self.password = os.getenv("DIFY_ENV_NACOS_PASSWORD")
self.ak = os.getenv("DIFY_ENV_NACOS_ACCESS_KEY")
self.sk = os.getenv("DIFY_ENV_NACOS_SECRET_KEY")
self.server = os.getenv("DIFY_ENV_NACOS_SERVER_ADDR", "localhost:8848")
self.token = None
self.token_ttl = 18000
self.token_expire_time: float = 0
def http_request(self, url, method="GET", headers=None, params=None):
try:
self._inject_auth_info(headers, params)
response = requests.request(method, url="http://" + self.server + url, headers=headers, params=params)
response.raise_for_status()
return response.text
except requests.exceptions.RequestException as e:
return f"Request to Nacos failed: {e}"
def _inject_auth_info(self, headers, params, module="config"):
headers.update({"User-Agent": "Nacos-Http-Client-In-Dify:v0.0.1"})
if module == "login":
return
ts = str(int(time.time() * 1000))
if self.ak and self.sk:
sign_str = self.get_sign_str(params["group"], params["tenant"], ts)
headers["Spas-AccessKey"] = self.ak
headers["Spas-Signature"] = self.__do_sign(sign_str, self.sk)
headers["timeStamp"] = ts
if self.username and self.password:
self.get_access_token(force_refresh=False)
params["accessToken"] = self.token
def __do_sign(self, sign_str, sk):
return (
base64.encodebytes(hmac.new(sk.encode(), sign_str.encode(), digestmod=hashlib.sha1).digest())
.decode()
.strip()
)
def get_sign_str(self, group, tenant, ts):
sign_str = ""
if tenant:
sign_str = tenant + "+"
if group:
sign_str = sign_str + group + "+"
if sign_str:
sign_str += ts
return sign_str
def get_access_token(self, force_refresh=False):
current_time = time.time()
if self.token and not force_refresh and self.token_expire_time > current_time:
return self.token
params = {"username": self.username, "password": self.password}
url = "http://" + self.server + "/nacos/v1/auth/login"
try:
resp = requests.request("POST", url, headers=None, params=params)
resp.raise_for_status()
response_data = resp.json()
self.token = response_data.get("accessToken")
self.token_ttl = response_data.get("tokenTtl", 18000)
self.token_expire_time = current_time + self.token_ttl - 10
except Exception as e:
logger.exception("[get-access-token] exception occur")
raise

@ -0,0 +1,31 @@
def _parse_config(self, content: str) -> dict[str, str]:
config: dict[str, str] = {}
if not content:
return config
for line in content.splitlines():
cleaned_line = line.strip()
if not cleaned_line or cleaned_line.startswith(("#", "!")):
continue
separator_index = -1
for i, c in enumerate(cleaned_line):
if c in ("=", ":") and (i == 0 or cleaned_line[i - 1] != "\\"):
separator_index = i
break
if separator_index == -1:
continue
key = cleaned_line[:separator_index].strip()
raw_value = cleaned_line[separator_index + 1 :].strip()
try:
decoded_value = bytes(raw_value, "utf-8").decode("unicode_escape")
decoded_value = decoded_value.replace(r"\=", "=").replace(r"\:", ":")
except UnicodeDecodeError:
decoded_value = raw_value
config[key] = decoded_value
return config

@ -85,5 +85,35 @@ class RuleCodeGenerateApi(Resource):
return code_result
class RuleStructuredOutputGenerateApi(Resource):
@setup_required
@login_required
@account_initialization_required
def post(self):
parser = reqparse.RequestParser()
parser.add_argument("instruction", type=str, required=True, nullable=False, location="json")
parser.add_argument("model_config", type=dict, required=True, nullable=False, location="json")
args = parser.parse_args()
account = current_user
try:
structured_output = LLMGenerator.generate_structured_output(
tenant_id=account.current_tenant_id,
instruction=args["instruction"],
model_config=args["model_config"],
)
except ProviderTokenNotInitError as ex:
raise ProviderNotInitializeError(ex.description)
except QuotaExceededError:
raise ProviderQuotaExceededError()
except ModelCurrentlyNotSupportError:
raise ProviderModelCurrentlyNotSupportError()
except InvokeError as e:
raise CompletionRequestError(e.description)
return structured_output
api.add_resource(RuleGenerateApi, "/rule-generate")
api.add_resource(RuleCodeGenerateApi, "/rule-code-generate")
api.add_resource(RuleStructuredOutputGenerateApi, "/rule-structured-output-generate")

@ -16,7 +16,7 @@ from controllers.console.auth.error import (
PasswordMismatchError,
)
from controllers.console.error import AccountInFreezeError, AccountNotFound, EmailSendIpLimitError
from controllers.console.wraps import setup_required
from controllers.console.wraps import email_password_login_enabled, setup_required
from events.tenant_event import tenant_was_created
from extensions.ext_database import db
from libs.helper import email, extract_remote_ip
@ -30,6 +30,7 @@ from services.feature_service import FeatureService
class ForgotPasswordSendEmailApi(Resource):
@setup_required
@email_password_login_enabled
def post(self):
parser = reqparse.RequestParser()
parser.add_argument("email", type=email, required=True, location="json")
@ -62,6 +63,7 @@ class ForgotPasswordSendEmailApi(Resource):
class ForgotPasswordCheckApi(Resource):
@setup_required
@email_password_login_enabled
def post(self):
parser = reqparse.RequestParser()
parser.add_argument("email", type=str, required=True, location="json")
@ -86,12 +88,21 @@ class ForgotPasswordCheckApi(Resource):
AccountService.add_forgot_password_error_rate_limit(args["email"])
raise EmailCodeError()
# Verified, revoke the first token
AccountService.revoke_reset_password_token(args["token"])
# Refresh token data by generating a new token
_, new_token = AccountService.generate_reset_password_token(
user_email, code=args["code"], additional_data={"phase": "reset"}
)
AccountService.reset_forgot_password_error_rate_limit(args["email"])
return {"is_valid": True, "email": token_data.get("email")}
return {"is_valid": True, "email": token_data.get("email"), "token": new_token}
class ForgotPasswordResetApi(Resource):
@setup_required
@email_password_login_enabled
def post(self):
parser = reqparse.RequestParser()
parser.add_argument("token", type=str, required=True, nullable=False, location="json")
@ -107,6 +118,9 @@ class ForgotPasswordResetApi(Resource):
reset_data = AccountService.get_reset_password_data(args["token"])
if not reset_data:
raise InvalidTokenError()
# Must use token in reset phase
if reset_data.get("phase", "") != "reset":
raise InvalidTokenError()
# Revoke token to prevent reuse
AccountService.revoke_reset_password_token(args["token"])

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

@ -664,6 +664,7 @@ class DatasetRetrievalSettingApi(Resource):
| VectorType.OPENGAUSS
| VectorType.OCEANBASE
| VectorType.TABLESTORE
| VectorType.HUAWEI_CLOUD
| VectorType.TENCENT
):
return {
@ -710,6 +711,7 @@ class DatasetRetrievalSettingMockApi(Resource):
| VectorType.OCEANBASE
| VectorType.TABLESTORE
| VectorType.TENCENT
| VectorType.HUAWEI_CLOUD
):
return {
"retrieval_method": [

@ -210,3 +210,16 @@ def enterprise_license_required(view):
return view(*args, **kwargs)
return decorated
def email_password_login_enabled(view):
@wraps(view)
def decorated(*args, **kwargs):
features = FeatureService.get_system_features()
if features.enable_email_password_login:
return view(*args, **kwargs)
# otherwise, return 403
abort(403)
return decorated

@ -1,3 +1,5 @@
from mimetypes import guess_extension
from flask import request
from flask_restful import Resource, marshal_with # type: ignore
from werkzeug.exceptions import Forbidden
@ -9,8 +11,8 @@ from controllers.files.error import UnsupportedFileTypeError
from controllers.inner_api.plugin.wraps import get_user
from controllers.service_api.app.error import FileTooLargeError
from core.file.helpers import verify_plugin_file_signature
from core.tools.tool_file_manager import ToolFileManager
from fields.file_fields import file_fields
from services.file_service import FileService
class PluginUploadFileApi(Resource):
@ -51,19 +53,26 @@ class PluginUploadFileApi(Resource):
raise Forbidden("Invalid request.")
try:
upload_file = FileService.upload_file(
filename=filename,
content=file.read(),
tool_file = ToolFileManager.create_file_by_raw(
user_id=user.id,
tenant_id=tenant_id,
file_binary=file.read(),
mimetype=mimetype,
user=user,
source=None,
filename=filename,
conversation_id=None,
)
extension = guess_extension(tool_file.mimetype) or ".bin"
preview_url = ToolFileManager.sign_file(tool_file_id=tool_file.id, extension=extension)
tool_file.mime_type = mimetype
tool_file.extension = extension
tool_file.preview_url = preview_url
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
return tool_file, 201
api.add_resource(PluginUploadFileApi, "/files/upload/for-plugin")

@ -21,14 +21,13 @@ from core.model_runtime.entities import (
AssistantPromptMessage,
LLMUsage,
PromptMessage,
PromptMessageContent,
PromptMessageTool,
SystemPromptMessage,
TextPromptMessageContent,
ToolPromptMessage,
UserPromptMessage,
)
from core.model_runtime.entities.message_entities import ImagePromptMessageContent
from core.model_runtime.entities.message_entities import ImagePromptMessageContent, PromptMessageContentUnionTypes
from core.model_runtime.entities.model_entities import ModelFeature
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from core.prompt.utils.extract_thread_messages import extract_thread_messages
@ -501,7 +500,7 @@ class BaseAgentRunner(AppRunner):
)
if not file_objs:
return UserPromptMessage(content=message.query)
prompt_message_contents: list[PromptMessageContent] = []
prompt_message_contents: list[PromptMessageContentUnionTypes] = []
prompt_message_contents.append(TextPromptMessageContent(data=message.query))
for file in file_objs:
prompt_message_contents.append(

@ -5,12 +5,11 @@ from core.file import file_manager
from core.model_runtime.entities import (
AssistantPromptMessage,
PromptMessage,
PromptMessageContent,
SystemPromptMessage,
TextPromptMessageContent,
UserPromptMessage,
)
from core.model_runtime.entities.message_entities import ImagePromptMessageContent
from core.model_runtime.entities.message_entities import ImagePromptMessageContent, PromptMessageContentUnionTypes
from core.model_runtime.utils.encoders import jsonable_encoder
@ -40,7 +39,7 @@ class CotChatAgentRunner(CotAgentRunner):
Organize user query
"""
if self.files:
prompt_message_contents: list[PromptMessageContent] = []
prompt_message_contents: list[PromptMessageContentUnionTypes] = []
prompt_message_contents.append(TextPromptMessageContent(data=query))
# get image detail config

@ -15,14 +15,13 @@ from core.model_runtime.entities import (
LLMResultChunkDelta,
LLMUsage,
PromptMessage,
PromptMessageContent,
PromptMessageContentType,
SystemPromptMessage,
TextPromptMessageContent,
ToolPromptMessage,
UserPromptMessage,
)
from core.model_runtime.entities.message_entities import ImagePromptMessageContent
from core.model_runtime.entities.message_entities import ImagePromptMessageContent, PromptMessageContentUnionTypes
from core.prompt.agent_history_prompt_transform import AgentHistoryPromptTransform
from core.tools.entities.tool_entities import ToolInvokeMeta
from core.tools.tool_engine import ToolEngine
@ -395,7 +394,7 @@ class FunctionCallAgentRunner(BaseAgentRunner):
Organize user query
"""
if self.files:
prompt_message_contents: list[PromptMessageContent] = []
prompt_message_contents: list[PromptMessageContentUnionTypes] = []
prompt_message_contents.append(TextPromptMessageContent(data=query))
# get image detail config

@ -7,9 +7,9 @@ from core.model_runtime.entities import (
AudioPromptMessageContent,
DocumentPromptMessageContent,
ImagePromptMessageContent,
MultiModalPromptMessageContent,
VideoPromptMessageContent,
)
from core.model_runtime.entities.message_entities import PromptMessageContentUnionTypes
from extensions.ext_storage import storage
from . import helpers
@ -43,7 +43,7 @@ def to_prompt_message_content(
/,
*,
image_detail_config: ImagePromptMessageContent.DETAIL | None = None,
) -> MultiModalPromptMessageContent:
) -> PromptMessageContentUnionTypes:
if f.extension is None:
raise ValueError("Missing file extension")
if f.mime_type is None:
@ -58,7 +58,7 @@ def to_prompt_message_content(
if f.type == FileType.IMAGE:
params["detail"] = image_detail_config or ImagePromptMessageContent.DETAIL.LOW
prompt_class_map: Mapping[FileType, type[MultiModalPromptMessageContent]] = {
prompt_class_map: Mapping[FileType, type[PromptMessageContentUnionTypes]] = {
FileType.IMAGE: ImagePromptMessageContent,
FileType.AUDIO: AudioPromptMessageContent,
FileType.VIDEO: VideoPromptMessageContent,

@ -10,6 +10,7 @@ from core.llm_generator.prompts import (
GENERATOR_QA_PROMPT,
JAVASCRIPT_CODE_GENERATOR_PROMPT_TEMPLATE,
PYTHON_CODE_GENERATOR_PROMPT_TEMPLATE,
SYSTEM_STRUCTURED_OUTPUT_GENERATE,
WORKFLOW_RULE_CONFIG_PROMPT_GENERATE_TEMPLATE,
)
from core.model_manager import ModelManager
@ -340,3 +341,37 @@ class LLMGenerator:
answer = cast(str, response.message.content)
return answer.strip()
@classmethod
def generate_structured_output(cls, tenant_id: str, instruction: str, model_config: dict):
model_manager = ModelManager()
model_instance = model_manager.get_model_instance(
tenant_id=tenant_id,
model_type=ModelType.LLM,
provider=model_config.get("provider", ""),
model=model_config.get("name", ""),
)
prompt_messages = [
SystemPromptMessage(content=SYSTEM_STRUCTURED_OUTPUT_GENERATE),
UserPromptMessage(content=instruction),
]
model_parameters = model_config.get("model_parameters", {})
try:
response = cast(
LLMResult,
model_instance.invoke_llm(
prompt_messages=list(prompt_messages), model_parameters=model_parameters, stream=False
),
)
generated_json_schema = cast(str, response.message.content)
return {"output": generated_json_schema, "error": ""}
except InvokeError as e:
error = str(e)
return {"output": "", "error": f"Failed to generate JSON Schema. Error: {error}"}
except Exception as e:
logging.exception(f"Failed to invoke LLM model, model: {model_config.get('name')}")
return {"output": "", "error": f"An unexpected error occurred: {str(e)}"}

@ -220,3 +220,110 @@ Here is the task description: {{INPUT_TEXT}}
You just need to generate the output
""" # noqa: E501
SYSTEM_STRUCTURED_OUTPUT_GENERATE = """
Your task is to convert simple user descriptions into properly formatted JSON Schema definitions. When a user describes data fields they need, generate a complete, valid JSON Schema that accurately represents those fields with appropriate types and requirements.
## Instructions:
1. Analyze the user's description of their data needs
2. Identify each property that should be included in the schema
3. Determine the appropriate data type for each property
4. Decide which properties should be required
5. Generate a complete JSON Schema with proper syntax
6. Include appropriate constraints when specified (min/max values, patterns, formats)
7. Provide ONLY the JSON Schema without any additional explanations, comments, or markdown formatting.
8. DO NOT use markdown code blocks (``` or ``` json). Return the raw JSON Schema directly.
## Examples:
### Example 1:
**User Input:** I need name and age
**JSON Schema Output:**
{
"type": "object",
"properties": {
"name": { "type": "string" },
"age": { "type": "number" }
},
"required": ["name", "age"]
}
### Example 2:
**User Input:** I want to store information about books including title, author, publication year and optional page count
**JSON Schema Output:**
{
"type": "object",
"properties": {
"title": { "type": "string" },
"author": { "type": "string" },
"publicationYear": { "type": "integer" },
"pageCount": { "type": "integer" }
},
"required": ["title", "author", "publicationYear"]
}
### Example 3:
**User Input:** Create a schema for user profiles with email, password, and age (must be at least 18)
**JSON Schema Output:**
{
"type": "object",
"properties": {
"email": {
"type": "string",
"format": "email"
},
"password": {
"type": "string",
"minLength": 8
},
"age": {
"type": "integer",
"minimum": 18
}
},
"required": ["email", "password", "age"]
}
### Example 4:
**User Input:** I need album schema, the ablum has songs, and each song has name, duration, and artist.
**JSON Schema Output:**
{
"type": "object",
"properties": {
"properties": {
"songs": {
"type": "array",
"items": {
"type": "object",
"properties": {
"name": {
"type": "string"
},
"id": {
"type": "string"
},
"duration": {
"type": "string"
},
"aritst": {
"type": "string"
}
},
"required": [
"name",
"id",
"duration",
"aritst"
]
}
}
}
},
"required": [
"songs"
]
}
Now, generate a JSON Schema based on my description
""" # noqa: E501

@ -8,11 +8,11 @@ from core.model_runtime.entities import (
AssistantPromptMessage,
ImagePromptMessageContent,
PromptMessage,
PromptMessageContent,
PromptMessageRole,
TextPromptMessageContent,
UserPromptMessage,
)
from core.model_runtime.entities.message_entities import PromptMessageContentUnionTypes
from core.prompt.utils.extract_thread_messages import extract_thread_messages
from extensions.ext_database import db
from factories import file_factory
@ -100,7 +100,7 @@ class TokenBufferMemory:
if not file_objs:
prompt_messages.append(UserPromptMessage(content=message.query))
else:
prompt_message_contents: list[PromptMessageContent] = []
prompt_message_contents: list[PromptMessageContentUnionTypes] = []
prompt_message_contents.append(TextPromptMessageContent(data=message.query))
for file in file_objs:
prompt_message = file_manager.to_prompt_message_content(

@ -1,8 +1,8 @@
from collections.abc import Sequence
from enum import Enum, StrEnum
from typing import Optional
from typing import Annotated, Any, Literal, Optional, Union
from pydantic import BaseModel, Field, field_validator
from pydantic import BaseModel, Field, field_serializer, field_validator
class PromptMessageRole(Enum):
@ -61,11 +61,7 @@ class PromptMessageContentType(StrEnum):
class PromptMessageContent(BaseModel):
"""
Model class for prompt message content.
"""
type: PromptMessageContentType
pass
class TextPromptMessageContent(PromptMessageContent):
@ -73,7 +69,7 @@ class TextPromptMessageContent(PromptMessageContent):
Model class for text prompt message content.
"""
type: PromptMessageContentType = PromptMessageContentType.TEXT
type: Literal[PromptMessageContentType.TEXT] = PromptMessageContentType.TEXT
data: str
@ -82,7 +78,6 @@ class MultiModalPromptMessageContent(PromptMessageContent):
Model class for multi-modal prompt message content.
"""
type: PromptMessageContentType
format: str = Field(default=..., description="the format of multi-modal file")
base64_data: str = Field(default="", description="the base64 data of multi-modal file")
url: str = Field(default="", description="the url of multi-modal file")
@ -94,11 +89,11 @@ class MultiModalPromptMessageContent(PromptMessageContent):
class VideoPromptMessageContent(MultiModalPromptMessageContent):
type: PromptMessageContentType = PromptMessageContentType.VIDEO
type: Literal[PromptMessageContentType.VIDEO] = PromptMessageContentType.VIDEO
class AudioPromptMessageContent(MultiModalPromptMessageContent):
type: PromptMessageContentType = PromptMessageContentType.AUDIO
type: Literal[PromptMessageContentType.AUDIO] = PromptMessageContentType.AUDIO
class ImagePromptMessageContent(MultiModalPromptMessageContent):
@ -110,12 +105,24 @@ class ImagePromptMessageContent(MultiModalPromptMessageContent):
LOW = "low"
HIGH = "high"
type: PromptMessageContentType = PromptMessageContentType.IMAGE
type: Literal[PromptMessageContentType.IMAGE] = PromptMessageContentType.IMAGE
detail: DETAIL = DETAIL.LOW
class DocumentPromptMessageContent(MultiModalPromptMessageContent):
type: PromptMessageContentType = PromptMessageContentType.DOCUMENT
type: Literal[PromptMessageContentType.DOCUMENT] = PromptMessageContentType.DOCUMENT
PromptMessageContentUnionTypes = Annotated[
Union[
TextPromptMessageContent,
ImagePromptMessageContent,
DocumentPromptMessageContent,
AudioPromptMessageContent,
VideoPromptMessageContent,
],
Field(discriminator="type"),
]
class PromptMessage(BaseModel):
@ -124,7 +131,7 @@ class PromptMessage(BaseModel):
"""
role: PromptMessageRole
content: Optional[str | Sequence[PromptMessageContent]] = None
content: Optional[str | list[PromptMessageContentUnionTypes]] = None
name: Optional[str] = None
def is_empty(self) -> bool:
@ -135,6 +142,16 @@ class PromptMessage(BaseModel):
"""
return not self.content
@field_serializer("content")
def serialize_content(
self, content: Optional[Union[str, Sequence[PromptMessageContent]]]
) -> Optional[str | list[dict[str, Any] | PromptMessageContent] | Sequence[PromptMessageContent]]:
if content is None or isinstance(content, str):
return content
if isinstance(content, list):
return [item.model_dump() if hasattr(item, "model_dump") else item for item in content]
return content
class UserPromptMessage(PromptMessage):
"""

@ -2,7 +2,7 @@ from decimal import Decimal
from enum import Enum, StrEnum
from typing import Any, Optional
from pydantic import BaseModel, ConfigDict
from pydantic import BaseModel, ConfigDict, model_validator
from core.model_runtime.entities.common_entities import I18nObject
@ -85,6 +85,7 @@ class ModelFeature(Enum):
DOCUMENT = "document"
VIDEO = "video"
AUDIO = "audio"
STRUCTURED_OUTPUT = "structured-output"
class DefaultParameterName(StrEnum):
@ -197,6 +198,19 @@ class AIModelEntity(ProviderModel):
parameter_rules: list[ParameterRule] = []
pricing: Optional[PriceConfig] = None
@model_validator(mode="after")
def validate_model(self):
supported_schema_keys = ["json_schema"]
schema_key = next((rule.name for rule in self.parameter_rules if rule.name in supported_schema_keys), None)
if not schema_key:
return self
if self.features is None:
self.features = [ModelFeature.STRUCTURED_OUTPUT]
else:
if ModelFeature.STRUCTURED_OUTPUT not in self.features:
self.features.append(ModelFeature.STRUCTURED_OUTPUT)
return self
class ModelUsage(BaseModel):
pass

@ -39,6 +39,7 @@ class PluginNodeBackwardsInvocation(BaseBackwardsInvocation):
:param query: str
:return: dict
"""
# FIXME(-LAN-): Avoid import service into core
workflow_service = WorkflowService()
node_id = "1919810"
node_data = ParameterExtractorNodeData(
@ -89,6 +90,7 @@ class PluginNodeBackwardsInvocation(BaseBackwardsInvocation):
:param query: str
:return: dict
"""
# FIXME(-LAN-): Avoid import service into core
workflow_service = WorkflowService()
node_id = "1919810"
node_data = QuestionClassifierNodeData(

@ -9,13 +9,12 @@ from core.memory.token_buffer_memory import TokenBufferMemory
from core.model_runtime.entities import (
AssistantPromptMessage,
PromptMessage,
PromptMessageContent,
PromptMessageRole,
SystemPromptMessage,
TextPromptMessageContent,
UserPromptMessage,
)
from core.model_runtime.entities.message_entities import ImagePromptMessageContent
from core.model_runtime.entities.message_entities import ImagePromptMessageContent, PromptMessageContentUnionTypes
from core.prompt.entities.advanced_prompt_entities import ChatModelMessage, CompletionModelPromptTemplate, MemoryConfig
from core.prompt.prompt_transform import PromptTransform
from core.prompt.utils.prompt_template_parser import PromptTemplateParser
@ -125,7 +124,7 @@ class AdvancedPromptTransform(PromptTransform):
prompt = Jinja2Formatter.format(prompt, prompt_inputs)
if files:
prompt_message_contents: list[PromptMessageContent] = []
prompt_message_contents: list[PromptMessageContentUnionTypes] = []
prompt_message_contents.append(TextPromptMessageContent(data=prompt))
for file in files:
prompt_message_contents.append(
@ -201,7 +200,7 @@ class AdvancedPromptTransform(PromptTransform):
prompt_messages = self._append_chat_histories(memory, memory_config, prompt_messages, model_config)
if files and query is not None:
prompt_message_contents: list[PromptMessageContent] = []
prompt_message_contents: list[PromptMessageContentUnionTypes] = []
prompt_message_contents.append(TextPromptMessageContent(data=query))
for file in files:
prompt_message_contents.append(

@ -11,7 +11,7 @@ from core.memory.token_buffer_memory import TokenBufferMemory
from core.model_runtime.entities.message_entities import (
ImagePromptMessageContent,
PromptMessage,
PromptMessageContent,
PromptMessageContentUnionTypes,
SystemPromptMessage,
TextPromptMessageContent,
UserPromptMessage,
@ -277,7 +277,7 @@ class SimplePromptTransform(PromptTransform):
image_detail_config: Optional[ImagePromptMessageContent.DETAIL] = None,
) -> UserPromptMessage:
if files:
prompt_message_contents: list[PromptMessageContent] = []
prompt_message_contents: list[PromptMessageContentUnionTypes] = []
prompt_message_contents.append(TextPromptMessageContent(data=prompt))
for file in files:
prompt_message_contents.append(

@ -0,0 +1,215 @@
import json
import logging
import ssl
from typing import Any, Optional
from elasticsearch import Elasticsearch
from pydantic import BaseModel, model_validator
from configs import dify_config
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.embedding.embedding_base import Embeddings
from core.rag.models.document import Document
from extensions.ext_redis import redis_client
from models.dataset import Dataset
logger = logging.getLogger(__name__)
def create_ssl_context() -> ssl.SSLContext:
ssl_context = ssl.create_default_context()
ssl_context.check_hostname = False
ssl_context.verify_mode = ssl.CERT_NONE
return ssl_context
class HuaweiCloudVectorConfig(BaseModel):
hosts: str
username: str | None
password: str | None
@model_validator(mode="before")
@classmethod
def validate_config(cls, values: dict) -> dict:
if not values["hosts"]:
raise ValueError("config HOSTS is required")
return values
def to_elasticsearch_params(self) -> dict[str, Any]:
params = {
"hosts": self.hosts.split(","),
"verify_certs": False,
"ssl_show_warn": False,
"request_timeout": 30000,
"retry_on_timeout": True,
"max_retries": 10,
}
if self.username and self.password:
params["basic_auth"] = (self.username, self.password)
return params
class HuaweiCloudVector(BaseVector):
def __init__(self, index_name: str, config: HuaweiCloudVectorConfig):
super().__init__(index_name.lower())
self._client = Elasticsearch(**config.to_elasticsearch_params())
def get_type(self) -> str:
return VectorType.HUAWEI_CLOUD
def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
uuids = self._get_uuids(documents)
for i in range(len(documents)):
self._client.index(
index=self._collection_name,
id=uuids[i],
document={
Field.CONTENT_KEY.value: documents[i].page_content,
Field.VECTOR.value: embeddings[i] or None,
Field.METADATA_KEY.value: documents[i].metadata or {},
},
)
self._client.indices.refresh(index=self._collection_name)
return uuids
def text_exists(self, id: str) -> bool:
return bool(self._client.exists(index=self._collection_name, id=id))
def delete_by_ids(self, ids: list[str]) -> None:
if not ids:
return
for id in ids:
self._client.delete(index=self._collection_name, id=id)
def delete_by_metadata_field(self, key: str, value: str) -> None:
query_str = {"query": {"match": {f"metadata.{key}": f"{value}"}}}
results = self._client.search(index=self._collection_name, body=query_str)
ids = [hit["_id"] for hit in results["hits"]["hits"]]
if ids:
self.delete_by_ids(ids)
def delete(self) -> None:
self._client.indices.delete(index=self._collection_name)
def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
top_k = kwargs.get("top_k", 4)
query = {
"size": top_k,
"query": {
"vector": {
Field.VECTOR.value: {
"vector": query_vector,
"topk": top_k,
}
}
},
}
results = self._client.search(index=self._collection_name, body=query)
docs_and_scores = []
for hit in results["hits"]["hits"]:
docs_and_scores.append(
(
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:
score_threshold = float(kwargs.get("score_threshold") or 0.0)
if score > score_threshold:
if doc.metadata is not None:
doc.metadata["score"] = score
docs.append(doc)
return docs
def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
query_str = {"match": {Field.CONTENT_KEY.value: query}}
results = self._client.search(index=self._collection_name, query=query_str, size=kwargs.get("top_k", 4))
docs = []
for hit in results["hits"]["hits"]:
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):
metadatas = [d.metadata if d.metadata is not None else {} for d in texts]
self.create_collection(embeddings, metadatas)
self.add_texts(texts, embeddings, **kwargs)
def create_collection(
self,
embeddings: list[list[float]],
metadatas: Optional[list[dict[Any, Any]]] = 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": "vector",
"dimension": dim,
"indexing": True,
"algorithm": "GRAPH",
"metric": "cosine",
"neighbors": 32,
"efc": 128,
},
Field.METADATA_KEY.value: {
"type": "object",
"properties": {
"doc_id": {"type": "keyword"} # Map doc_id to keyword type
},
},
}
}
settings = {"index.vector": True}
self._client.indices.create(index=self._collection_name, mappings=mappings, settings=settings)
redis_client.set(collection_exist_cache_key, 1, ex=3600)
class HuaweiCloudVectorFactory(AbstractVectorFactory):
def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> HuaweiCloudVector:
if dataset.index_struct_dict:
class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
collection_name = class_prefix.lower()
else:
dataset_id = dataset.id
collection_name = Dataset.gen_collection_name_by_id(dataset_id).lower()
dataset.index_struct = json.dumps(self.gen_index_struct_dict(VectorType.HUAWEI_CLOUD, collection_name))
return HuaweiCloudVector(
index_name=collection_name,
config=HuaweiCloudVectorConfig(
hosts=dify_config.HUAWEI_CLOUD_HOSTS or "http://localhost:9200",
username=dify_config.HUAWEI_CLOUD_USER,
password=dify_config.HUAWEI_CLOUD_PASSWORD,
),
)

@ -2,12 +2,12 @@ import array
import json
import re
import uuid
from contextlib import contextmanager
from typing import Any
import jieba.posseg as pseg # type: ignore
import numpy
import oracledb
from oracledb.connection import Connection
from pydantic import BaseModel, model_validator
from configs import dify_config
@ -70,6 +70,7 @@ class OracleVector(BaseVector):
super().__init__(collection_name)
self.pool = self._create_connection_pool(config)
self.table_name = f"embedding_{collection_name}"
self.config = config
def get_type(self) -> str:
return VectorType.ORACLE
@ -107,16 +108,19 @@ class OracleVector(BaseVector):
outconverter=self.numpy_converter_out,
)
def _get_connection(self) -> Connection:
connection = oracledb.connect(user=self.config.user, password=self.config.password, dsn=self.config.dsn)
return connection
def _create_connection_pool(self, config: OracleVectorConfig):
pool_params = {
"user": config.user,
"password": config.password,
"dsn": config.dsn,
"min": 1,
"max": 50,
"max": 5,
"increment": 1,
}
if config.is_autonomous:
pool_params.update(
{
@ -125,22 +129,8 @@ class OracleVector(BaseVector):
"wallet_password": config.wallet_password,
}
)
return oracledb.create_pool(**pool_params)
@contextmanager
def _get_cursor(self):
conn = self.pool.acquire()
conn.inputtypehandler = self.input_type_handler
conn.outputtypehandler = self.output_type_handler
cur = conn.cursor()
try:
yield cur
finally:
cur.close()
conn.commit()
conn.close()
def create(self, texts: list[Document], embeddings: list[list[float]], **kwargs):
dimension = len(embeddings[0])
self._create_collection(dimension)
@ -162,41 +152,68 @@ class OracleVector(BaseVector):
numpy.array(embeddings[i]),
)
)
# print(f"INSERT INTO {self.table_name} (id, text, meta, embedding) VALUES (:1, :2, :3, :4)")
with self._get_cursor() as cur:
cur.executemany(
f"INSERT INTO {self.table_name} (id, text, meta, embedding) VALUES (:1, :2, :3, :4)", values
)
with self._get_connection() as conn:
conn.inputtypehandler = self.input_type_handler
conn.outputtypehandler = self.output_type_handler
# with conn.cursor() as cur:
# cur.executemany(
# f"INSERT INTO {self.table_name} (id, text, meta, embedding) VALUES (:1, :2, :3, :4)", values
# )
# conn.commit()
for value in values:
with conn.cursor() as cur:
try:
cur.execute(
f"""INSERT INTO {self.table_name} (id, text, meta, embedding)
VALUES (:1, :2, :3, :4)""",
value,
)
conn.commit()
except Exception as e:
print(e)
conn.close()
return pks
def text_exists(self, id: str) -> bool:
with self._get_cursor() as cur:
cur.execute(f"SELECT id FROM {self.table_name} WHERE id = '%s'" % (id,))
return cur.fetchone() is not None
with self._get_connection() as conn:
with conn.cursor() as cur:
cur.execute(f"SELECT id FROM {self.table_name} WHERE id = '%s'" % (id,))
return cur.fetchone() is not None
conn.close()
def get_by_ids(self, ids: list[str]) -> list[Document]:
with self._get_cursor() as cur:
cur.execute(f"SELECT meta, text FROM {self.table_name} WHERE id IN %s", (tuple(ids),))
docs = []
for record in cur:
docs.append(Document(page_content=record[1], metadata=record[0]))
with self._get_connection() as conn:
with conn.cursor() as cur:
cur.execute(f"SELECT meta, text FROM {self.table_name} WHERE id IN %s", (tuple(ids),))
docs = []
for record in cur:
docs.append(Document(page_content=record[1], metadata=record[0]))
self.pool.release(connection=conn)
conn.close()
return docs
def delete_by_ids(self, ids: list[str]) -> None:
if not ids:
return
with self._get_cursor() as cur:
cur.execute(f"DELETE FROM {self.table_name} WHERE id IN %s" % (tuple(ids),))
with self._get_connection() as conn:
with conn.cursor() as cur:
cur.execute(f"DELETE FROM {self.table_name} WHERE id IN %s" % (tuple(ids),))
conn.commit()
conn.close()
def delete_by_metadata_field(self, key: str, value: str) -> None:
with self._get_cursor() as cur:
cur.execute(f"DELETE FROM {self.table_name} WHERE meta->>%s = %s", (key, value))
with self._get_connection() as conn:
with conn.cursor() as cur:
cur.execute(f"DELETE FROM {self.table_name} WHERE meta->>%s = %s", (key, value))
conn.commit()
conn.close()
def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
"""
Search the nearest neighbors to a vector.
:param query_vector: The input vector to search for similar items.
:param top_k: The number of nearest neighbors to return, default is 5.
:return: List of Documents that are nearest to the query vector.
"""
top_k = kwargs.get("top_k", 4)
@ -205,20 +222,25 @@ class OracleVector(BaseVector):
if document_ids_filter:
document_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
where_clause = f"WHERE metadata->>'document_id' in ({document_ids})"
with self._get_cursor() as cur:
cur.execute(
f"SELECT meta, text, vector_distance(embedding,:1) AS distance FROM {self.table_name}"
f" {where_clause} ORDER BY distance fetch first {top_k} rows only",
[numpy.array(query_vector)],
)
docs = []
score_threshold = float(kwargs.get("score_threshold") or 0.0)
for record in cur:
metadata, text, distance = record
score = 1 - distance
metadata["score"] = score
if score > score_threshold:
docs.append(Document(page_content=text, metadata=metadata))
with self._get_connection() as conn:
conn.inputtypehandler = self.input_type_handler
conn.outputtypehandler = self.output_type_handler
with conn.cursor() as cur:
cur.execute(
f"""SELECT meta, text, vector_distance(embedding,(select to_vector(:1) from dual),cosine)
AS distance FROM {self.table_name}
{where_clause} ORDER BY distance fetch first {top_k} rows only""",
[numpy.array(query_vector)],
)
docs = []
score_threshold = float(kwargs.get("score_threshold") or 0.0)
for record in cur:
metadata, text, distance = record
score = 1 - distance
metadata["score"] = score
if score > score_threshold:
docs.append(Document(page_content=text, metadata=metadata))
conn.close()
return docs
def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
@ -228,7 +250,7 @@ class OracleVector(BaseVector):
top_k = kwargs.get("top_k", 5)
# just not implement fetch by score_threshold now, may be later
# score_threshold = float(kwargs.get("score_threshold") or 0.0)
score_threshold = float(kwargs.get("score_threshold") or 0.0)
if len(query) > 0:
# Check which language the query is in
zh_pattern = re.compile("[\u4e00-\u9fa5]+")
@ -239,7 +261,7 @@ class OracleVector(BaseVector):
words = pseg.cut(query)
current_entity = ""
for word, pos in words:
if pos in {"nr", "Ng", "eng", "nz", "n", "ORG", "v"}: # nr: 人名ns: 地名,nt: 机构名
if pos in {"nr", "Ng", "eng", "nz", "n", "ORG", "v"}: # nr: 人名, ns: 地名, nt: 机构名
current_entity += word
else:
if current_entity:
@ -260,30 +282,35 @@ class OracleVector(BaseVector):
for token in all_tokens:
if token not in stop_words:
entities.append(token)
with self._get_cursor() as cur:
document_ids_filter = kwargs.get("document_ids_filter")
where_clause = ""
if document_ids_filter:
document_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
where_clause = f" AND metadata->>'document_id' in ({document_ids}) "
cur.execute(
f"select meta, text, embedding FROM {self.table_name}"
f"WHERE CONTAINS(text, :1, 1) > 0 {where_clause} "
f"order by score(1) desc fetch first {top_k} rows only",
[" ACCUM ".join(entities)],
)
docs = []
for record in cur:
metadata, text, embedding = record
docs.append(Document(page_content=text, vector=embedding, metadata=metadata))
with self._get_connection() as conn:
with conn.cursor() as cur:
document_ids_filter = kwargs.get("document_ids_filter")
where_clause = ""
if document_ids_filter:
document_ids = ", ".join(f"'{id}'" for id in document_ids_filter)
where_clause = f" AND metadata->>'document_id' in ({document_ids}) "
cur.execute(
f"""select meta, text, embedding FROM {self.table_name}
WHERE CONTAINS(text, :kk, 1) > 0 {where_clause}
order by score(1) desc fetch first {top_k} rows only""",
kk=" ACCUM ".join(entities),
)
docs = []
for record in cur:
metadata, text, embedding = record
docs.append(Document(page_content=text, vector=embedding, metadata=metadata))
conn.close()
return docs
else:
return [Document(page_content="", metadata={})]
return []
def delete(self) -> None:
with self._get_cursor() as cur:
cur.execute(f"DROP TABLE IF EXISTS {self.table_name} cascade constraints")
with self._get_connection() as conn:
with conn.cursor() as cur:
cur.execute(f"DROP TABLE IF EXISTS {self.table_name} cascade constraints")
conn.commit()
conn.close()
def _create_collection(self, dimension: int):
cache_key = f"vector_indexing_{self._collection_name}"
@ -293,11 +320,14 @@ class OracleVector(BaseVector):
if redis_client.get(collection_exist_cache_key):
return
with self._get_cursor() as cur:
cur.execute(SQL_CREATE_TABLE.format(table_name=self.table_name))
redis_client.set(collection_exist_cache_key, 1, ex=3600)
with self._get_cursor() as cur:
cur.execute(SQL_CREATE_INDEX.format(table_name=self.table_name))
with self._get_connection() as conn:
with conn.cursor() as cur:
cur.execute(SQL_CREATE_TABLE.format(table_name=self.table_name))
redis_client.set(collection_exist_cache_key, 1, ex=3600)
with conn.cursor() as cur:
cur.execute(SQL_CREATE_INDEX.format(table_name=self.table_name))
conn.commit()
conn.close()
class OracleVectorFactory(AbstractVectorFactory):

@ -156,6 +156,10 @@ class Vector:
from core.rag.datasource.vdb.tablestore.tablestore_vector import TableStoreVectorFactory
return TableStoreVectorFactory
case VectorType.HUAWEI_CLOUD:
from core.rag.datasource.vdb.huawei.huawei_cloud_vector import HuaweiCloudVectorFactory
return HuaweiCloudVectorFactory
case _:
raise ValueError(f"Vector store {vector_type} is not supported.")

@ -26,3 +26,4 @@ class VectorType(StrEnum):
OCEANBASE = "oceanbase"
OPENGAUSS = "opengauss"
TABLESTORE = "tablestore"
HUAWEI_CLOUD = "huawei_cloud"

@ -126,9 +126,7 @@ class WordExtractor(BaseExtractor):
db.session.add(upload_file)
db.session.commit()
image_map[rel.target_part] = (
f"![image]({dify_config.CONSOLE_API_URL}/files/{upload_file.id}/file-preview)"
)
image_map[rel.target_part] = f"![image]({dify_config.FILES_URL}/files/{upload_file.id}/file-preview)"
return image_map

@ -869,7 +869,9 @@ class DatasetRetrieval:
)
)
metadata_condition = MetadataCondition(
logical_operator=metadata_filtering_conditions.logical_operator, # type: ignore
logical_operator=metadata_filtering_conditions.logical_operator
if metadata_filtering_conditions
else "or", # type: ignore
conditions=conditions,
)
elif metadata_filtering_mode == "manual":
@ -891,10 +893,10 @@ class DatasetRetrieval:
else:
raise ValueError("Invalid metadata filtering mode")
if filters:
if metadata_filtering_conditions.logical_operator == "or": # type: ignore
document_query = document_query.filter(or_(*filters))
else:
if metadata_filtering_conditions and metadata_filtering_conditions.logical_operator == "and": # type: ignore
document_query = document_query.filter(and_(*filters))
else:
document_query = document_query.filter(or_(*filters))
documents = document_query.all()
# group by dataset_id
metadata_filter_document_ids = defaultdict(list) if documents else None # type: ignore

@ -86,3 +86,12 @@ class WorkflowNodeExecutionRepository(Protocol):
execution: The WorkflowNodeExecution instance to update
"""
...
def clear(self) -> None:
"""
Clear all WorkflowNodeExecution records based on implementation-specific criteria.
This method is intended to be used for bulk deletion operations, such as removing
all records associated with a specific app_id and tenant_id in multi-tenant implementations.
"""
...

@ -94,7 +94,7 @@ class DatasetRetrieverTool(DatasetRetrieverBaseTool):
"title": item.metadata.get("title"),
"content": item.page_content,
}
context_list.append(source)
context_list.append(source)
for hit_callback in self.hit_callbacks:
hit_callback.return_retriever_resource_info(context_list)

@ -16,7 +16,7 @@ from core.variables.segments import StringSegment
from core.workflow.entities.node_entities import NodeRunResult
from core.workflow.entities.variable_pool import VariablePool
from core.workflow.enums import SystemVariableKey
from core.workflow.nodes.agent.entities import AgentNodeData, ParamsAutoGenerated
from core.workflow.nodes.agent.entities import AgentNodeData, AgentOldVersionModelFeatures, ParamsAutoGenerated
from core.workflow.nodes.base.entities import BaseNodeData
from core.workflow.nodes.enums import NodeType
from core.workflow.nodes.event.event import RunCompletedEvent
@ -251,7 +251,12 @@ class AgentNode(ToolNode):
prompt_message.model_dump(mode="json") for prompt_message in prompt_messages
]
value["history_prompt_messages"] = history_prompt_messages
value["entity"] = model_schema.model_dump(mode="json") if model_schema else None
if model_schema:
# remove structured output feature to support old version agent plugin
model_schema = self._remove_unsupported_model_features_for_old_version(model_schema)
value["entity"] = model_schema.model_dump(mode="json")
else:
value["entity"] = None
result[parameter_name] = value
return result
@ -348,3 +353,10 @@ class AgentNode(ToolNode):
)
model_schema = model_type_instance.get_model_schema(model_name, model_credentials)
return model_instance, model_schema
def _remove_unsupported_model_features_for_old_version(self, model_schema: AIModelEntity) -> AIModelEntity:
if model_schema.features:
for feature in model_schema.features:
if feature.value not in AgentOldVersionModelFeatures:
model_schema.features.remove(feature)
return model_schema

@ -24,3 +24,18 @@ class AgentNodeData(BaseNodeData):
class ParamsAutoGenerated(Enum):
CLOSE = 0
OPEN = 1
class AgentOldVersionModelFeatures(Enum):
"""
Enum class for old SDK version llm feature.
"""
TOOL_CALL = "tool-call"
MULTI_TOOL_CALL = "multi-tool-call"
AGENT_THOUGHT = "agent-thought"
VISION = "vision"
STREAM_TOOL_CALL = "stream-tool-call"
DOCUMENT = "document"
VIDEO = "video"
AUDIO = "audio"

@ -438,7 +438,9 @@ class KnowledgeRetrievalNode(LLMNode):
)
)
metadata_condition = MetadataCondition(
logical_operator=node_data.metadata_filtering_conditions.logical_operator, # type: ignore
logical_operator=node_data.metadata_filtering_conditions.logical_operator
if node_data.metadata_filtering_conditions
else "or", # type: ignore
conditions=conditions,
)
elif node_data.metadata_filtering_mode == "manual":
@ -469,7 +471,10 @@ class KnowledgeRetrievalNode(LLMNode):
else:
raise ValueError("Invalid metadata filtering mode")
if filters:
if node_data.metadata_filtering_conditions.logical_operator == "and": # type: ignore
if (
node_data.metadata_filtering_conditions
and node_data.metadata_filtering_conditions.logical_operator == "and"
): # type: ignore
document_query = document_query.filter(and_(*filters))
else:
document_query = document_query.filter(or_(*filters))

@ -65,6 +65,8 @@ class LLMNodeData(BaseNodeData):
memory: Optional[MemoryConfig] = None
context: ContextConfig
vision: VisionConfig = Field(default_factory=VisionConfig)
structured_output: dict | None = None
structured_output_enabled: bool = False
@field_validator("prompt_config", mode="before")
@classmethod

@ -4,6 +4,8 @@ from collections.abc import Generator, Mapping, Sequence
from datetime import UTC, datetime
from typing import TYPE_CHECKING, Any, Optional, cast
import json_repair
from configs import dify_config
from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
from core.entities.model_entities import ModelStatus
@ -22,12 +24,18 @@ from core.model_runtime.entities import (
from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage
from core.model_runtime.entities.message_entities import (
AssistantPromptMessage,
PromptMessageContent,
PromptMessageContentUnionTypes,
PromptMessageRole,
SystemPromptMessage,
UserPromptMessage,
)
from core.model_runtime.entities.model_entities import ModelFeature, ModelPropertyKey, ModelType
from core.model_runtime.entities.model_entities import (
AIModelEntity,
ModelFeature,
ModelPropertyKey,
ModelType,
ParameterRule,
)
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from core.model_runtime.utils.encoders import jsonable_encoder
from core.plugin.entities.plugin import ModelProviderID
@ -57,6 +65,12 @@ from core.workflow.nodes.event import (
RunRetrieverResourceEvent,
RunStreamChunkEvent,
)
from core.workflow.utils.structured_output.entities import (
ResponseFormat,
SpecialModelType,
SupportStructuredOutputStatus,
)
from core.workflow.utils.structured_output.prompt import STRUCTURED_OUTPUT_PROMPT
from core.workflow.utils.variable_template_parser import VariableTemplateParser
from extensions.ext_database import db
from models.model import Conversation
@ -92,6 +106,12 @@ class LLMNode(BaseNode[LLMNodeData]):
_node_type = NodeType.LLM
def _run(self) -> Generator[NodeEvent | InNodeEvent, None, None]:
def process_structured_output(text: str) -> Optional[dict[str, Any] | list[Any]]:
"""Process structured output if enabled"""
if not self.node_data.structured_output_enabled or not self.node_data.structured_output:
return None
return self._parse_structured_output(text)
node_inputs: Optional[dict[str, Any]] = None
process_data = None
result_text = ""
@ -130,7 +150,6 @@ class LLMNode(BaseNode[LLMNodeData]):
if isinstance(event, RunRetrieverResourceEvent):
context = event.context
yield event
if context:
node_inputs["#context#"] = context
@ -192,7 +211,9 @@ class LLMNode(BaseNode[LLMNodeData]):
self.deduct_llm_quota(tenant_id=self.tenant_id, model_instance=model_instance, usage=usage)
break
outputs = {"text": result_text, "usage": jsonable_encoder(usage), "finish_reason": finish_reason}
structured_output = process_structured_output(result_text)
if structured_output:
outputs["structured_output"] = structured_output
yield RunCompletedEvent(
run_result=NodeRunResult(
status=WorkflowNodeExecutionStatus.SUCCEEDED,
@ -513,7 +534,12 @@ class LLMNode(BaseNode[LLMNodeData]):
if not model_schema:
raise ModelNotExistError(f"Model {model_name} not exist.")
support_structured_output = self._check_model_structured_output_support()
if support_structured_output == SupportStructuredOutputStatus.SUPPORTED:
completion_params = self._handle_native_json_schema(completion_params, model_schema.parameter_rules)
elif support_structured_output == SupportStructuredOutputStatus.UNSUPPORTED:
# Set appropriate response format based on model capabilities
self._set_response_format(completion_params, model_schema.parameter_rules)
return model_instance, ModelConfigWithCredentialsEntity(
provider=provider_name,
model=model_name,
@ -568,8 +594,7 @@ class LLMNode(BaseNode[LLMNodeData]):
variable_pool: VariablePool,
jinja2_variables: Sequence[VariableSelector],
) -> tuple[Sequence[PromptMessage], Optional[Sequence[str]]]:
# FIXME: fix the type error cause prompt_messages is type quick a few times
prompt_messages: list[Any] = []
prompt_messages: list[PromptMessage] = []
if isinstance(prompt_template, list):
# For chat model
@ -631,12 +656,14 @@ class LLMNode(BaseNode[LLMNodeData]):
# For issue #11247 - Check if prompt content is a string or a list
prompt_content_type = type(prompt_content)
if prompt_content_type == str:
prompt_content = str(prompt_content)
if "#histories#" in prompt_content:
prompt_content = prompt_content.replace("#histories#", memory_text)
else:
prompt_content = memory_text + "\n" + prompt_content
prompt_messages[0].content = prompt_content
elif prompt_content_type == list:
prompt_content = prompt_content if isinstance(prompt_content, list) else []
for content_item in prompt_content:
if content_item.type == PromptMessageContentType.TEXT:
if "#histories#" in content_item.data:
@ -649,9 +676,10 @@ class LLMNode(BaseNode[LLMNodeData]):
# Add current query to the prompt message
if sys_query:
if prompt_content_type == str:
prompt_content = prompt_messages[0].content.replace("#sys.query#", sys_query)
prompt_content = str(prompt_messages[0].content).replace("#sys.query#", sys_query)
prompt_messages[0].content = prompt_content
elif prompt_content_type == list:
prompt_content = prompt_content if isinstance(prompt_content, list) else []
for content_item in prompt_content:
if content_item.type == PromptMessageContentType.TEXT:
content_item.data = sys_query + "\n" + content_item.data
@ -681,7 +709,7 @@ class LLMNode(BaseNode[LLMNodeData]):
filtered_prompt_messages = []
for prompt_message in prompt_messages:
if isinstance(prompt_message.content, list):
prompt_message_content = []
prompt_message_content: list[PromptMessageContentUnionTypes] = []
for content_item in prompt_message.content:
# Skip content if features are not defined
if not model_config.model_schema.features:
@ -724,10 +752,29 @@ class LLMNode(BaseNode[LLMNodeData]):
"No prompt found in the LLM configuration. "
"Please ensure a prompt is properly configured before proceeding."
)
support_structured_output = self._check_model_structured_output_support()
if support_structured_output == SupportStructuredOutputStatus.UNSUPPORTED:
filtered_prompt_messages = self._handle_prompt_based_schema(
prompt_messages=filtered_prompt_messages,
)
stop = model_config.stop
return filtered_prompt_messages, stop
def _parse_structured_output(self, result_text: str) -> dict[str, Any] | list[Any]:
structured_output: dict[str, Any] | list[Any] = {}
try:
parsed = json.loads(result_text)
if not isinstance(parsed, (dict | list)):
raise LLMNodeError(f"Failed to parse structured output: {result_text}")
structured_output = parsed
except json.JSONDecodeError as e:
# if the result_text is not a valid json, try to repair it
parsed = json_repair.loads(result_text)
if not isinstance(parsed, (dict | list)):
raise LLMNodeError(f"Failed to parse structured output: {result_text}")
structured_output = parsed
return structured_output
@classmethod
def deduct_llm_quota(cls, tenant_id: str, model_instance: ModelInstance, usage: LLMUsage) -> None:
provider_model_bundle = model_instance.provider_model_bundle
@ -926,8 +973,170 @@ class LLMNode(BaseNode[LLMNodeData]):
return prompt_messages
def _handle_native_json_schema(self, model_parameters: dict, rules: list[ParameterRule]) -> dict:
"""
Handle structured output for models with native JSON schema support.
:param model_parameters: Model parameters to update
:param rules: Model parameter rules
:return: Updated model parameters with JSON schema configuration
"""
# Process schema according to model requirements
schema = self._fetch_structured_output_schema()
schema_json = self._prepare_schema_for_model(schema)
# Set JSON schema in parameters
model_parameters["json_schema"] = json.dumps(schema_json, ensure_ascii=False)
# Set appropriate response format if required by the model
for rule in rules:
if rule.name == "response_format" and ResponseFormat.JSON_SCHEMA.value in rule.options:
model_parameters["response_format"] = ResponseFormat.JSON_SCHEMA.value
return model_parameters
def _handle_prompt_based_schema(self, prompt_messages: Sequence[PromptMessage]) -> list[PromptMessage]:
"""
Handle structured output for models without native JSON schema support.
This function modifies the prompt messages to include schema-based output requirements.
Args:
prompt_messages: Original sequence of prompt messages
Returns:
list[PromptMessage]: Updated prompt messages with structured output requirements
"""
# Convert schema to string format
schema_str = json.dumps(self._fetch_structured_output_schema(), ensure_ascii=False)
# Find existing system prompt with schema placeholder
system_prompt = next(
(prompt for prompt in prompt_messages if isinstance(prompt, SystemPromptMessage)),
None,
)
structured_output_prompt = STRUCTURED_OUTPUT_PROMPT.replace("{{schema}}", schema_str)
# Prepare system prompt content
system_prompt_content = (
structured_output_prompt + "\n\n" + system_prompt.content
if system_prompt and isinstance(system_prompt.content, str)
else structured_output_prompt
)
system_prompt = SystemPromptMessage(content=system_prompt_content)
# Extract content from the last user message
filtered_prompts = [prompt for prompt in prompt_messages if not isinstance(prompt, SystemPromptMessage)]
updated_prompt = [system_prompt] + filtered_prompts
return updated_prompt
def _set_response_format(self, model_parameters: dict, rules: list) -> None:
"""
Set the appropriate response format parameter based on model rules.
:param model_parameters: Model parameters to update
:param rules: Model parameter rules
"""
for rule in rules:
if rule.name == "response_format":
if ResponseFormat.JSON.value in rule.options:
model_parameters["response_format"] = ResponseFormat.JSON.value
elif ResponseFormat.JSON_OBJECT.value in rule.options:
model_parameters["response_format"] = ResponseFormat.JSON_OBJECT.value
def _prepare_schema_for_model(self, schema: dict) -> dict:
"""
Prepare JSON schema based on model requirements.
Different models have different requirements for JSON schema formatting.
This function handles these differences.
:param schema: The original JSON schema
:return: Processed schema compatible with the current model
"""
# Deep copy to avoid modifying the original schema
processed_schema = schema.copy()
# Convert boolean types to string types (common requirement)
convert_boolean_to_string(processed_schema)
# Apply model-specific transformations
if SpecialModelType.GEMINI in self.node_data.model.name:
remove_additional_properties(processed_schema)
return processed_schema
elif SpecialModelType.OLLAMA in self.node_data.model.provider:
return processed_schema
else:
# Default format with name field
return {"schema": processed_schema, "name": "llm_response"}
def _fetch_model_schema(self, provider: str) -> AIModelEntity | None:
"""
Fetch model schema
"""
model_name = self.node_data.model.name
model_manager = ModelManager()
model_instance = model_manager.get_model_instance(
tenant_id=self.tenant_id, model_type=ModelType.LLM, provider=provider, model=model_name
)
model_type_instance = model_instance.model_type_instance
model_type_instance = cast(LargeLanguageModel, model_type_instance)
model_credentials = model_instance.credentials
model_schema = model_type_instance.get_model_schema(model_name, model_credentials)
return model_schema
def _fetch_structured_output_schema(self) -> dict[str, Any]:
"""
Fetch the structured output schema from the node data.
Returns:
dict[str, Any]: The structured output schema
"""
if not self.node_data.structured_output:
raise LLMNodeError("Please provide a valid structured output schema")
structured_output_schema = json.dumps(self.node_data.structured_output.get("schema", {}), ensure_ascii=False)
if not structured_output_schema:
raise LLMNodeError("Please provide a valid structured output schema")
try:
schema = json.loads(structured_output_schema)
if not isinstance(schema, dict):
raise LLMNodeError("structured_output_schema must be a JSON object")
return schema
except json.JSONDecodeError:
raise LLMNodeError("structured_output_schema is not valid JSON format")
def _check_model_structured_output_support(self) -> SupportStructuredOutputStatus:
"""
Check if the current model supports structured output.
Returns:
SupportStructuredOutput: The support status of structured output
"""
# Early return if structured output is disabled
if (
not isinstance(self.node_data, LLMNodeData)
or not self.node_data.structured_output_enabled
or not self.node_data.structured_output
):
return SupportStructuredOutputStatus.DISABLED
# Get model schema and check if it exists
model_schema = self._fetch_model_schema(self.node_data.model.provider)
if not model_schema:
return SupportStructuredOutputStatus.DISABLED
# Check if model supports structured output feature
return (
SupportStructuredOutputStatus.SUPPORTED
if bool(model_schema.features and ModelFeature.STRUCTURED_OUTPUT in model_schema.features)
else SupportStructuredOutputStatus.UNSUPPORTED
)
def _combine_message_content_with_role(*, contents: Sequence[PromptMessageContent], role: PromptMessageRole):
def _combine_message_content_with_role(
*, contents: Optional[str | list[PromptMessageContentUnionTypes]] = None, role: PromptMessageRole
):
match role:
case PromptMessageRole.USER:
return UserPromptMessage(content=contents)
@ -1064,3 +1273,49 @@ def _handle_completion_template(
)
prompt_messages.append(prompt_message)
return prompt_messages
def remove_additional_properties(schema: dict) -> None:
"""
Remove additionalProperties fields from JSON schema.
Used for models like Gemini that don't support this property.
:param schema: JSON schema to modify in-place
"""
if not isinstance(schema, dict):
return
# Remove additionalProperties at current level
schema.pop("additionalProperties", None)
# Process nested structures recursively
for value in schema.values():
if isinstance(value, dict):
remove_additional_properties(value)
elif isinstance(value, list):
for item in value:
if isinstance(item, dict):
remove_additional_properties(item)
def convert_boolean_to_string(schema: dict) -> None:
"""
Convert boolean type specifications to string in JSON schema.
:param schema: JSON schema to modify in-place
"""
if not isinstance(schema, dict):
return
# Check for boolean type at current level
if schema.get("type") == "boolean":
schema["type"] = "string"
# Process nested dictionaries and lists recursively
for value in schema.values():
if isinstance(value, dict):
convert_boolean_to_string(value)
elif isinstance(value, list):
for item in value:
if isinstance(item, dict):
convert_boolean_to_string(item)

@ -0,0 +1,24 @@
from enum import StrEnum
class ResponseFormat(StrEnum):
"""Constants for model response formats"""
JSON_SCHEMA = "json_schema" # model's structured output mode. some model like gemini, gpt-4o, support this mode.
JSON = "JSON" # model's json mode. some model like claude support this mode.
JSON_OBJECT = "json_object" # json mode's another alias. some model like deepseek-chat, qwen use this alias.
class SpecialModelType(StrEnum):
"""Constants for identifying model types"""
GEMINI = "gemini"
OLLAMA = "ollama"
class SupportStructuredOutputStatus(StrEnum):
"""Constants for structured output support status"""
SUPPORTED = "supported"
UNSUPPORTED = "unsupported"
DISABLED = "disabled"

@ -0,0 +1,17 @@
STRUCTURED_OUTPUT_PROMPT = """Youre a helpful AI assistant. You could answer questions and output in JSON format.
constraints:
- You must output in JSON format.
- Do not output boolean value, use string type instead.
- Do not output integer or float value, use number type instead.
eg:
Here is the JSON schema:
{"additionalProperties": false, "properties": {"age": {"type": "number"}, "name": {"type": "string"}}, "required": ["name", "age"], "type": "object"}
Here is the user's question:
My name is John Doe and I am 30 years old.
output:
{"name": "John Doe", "age": 30}
Here is the JSON schema:
{{schema}}
""" # noqa: E501

@ -14,7 +14,7 @@ from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExport
from opentelemetry.instrumentation.celery import CeleryInstrumentor
from opentelemetry.instrumentation.flask import FlaskInstrumentor
from opentelemetry.instrumentation.sqlalchemy import SQLAlchemyInstrumentor
from opentelemetry.metrics import get_meter_provider, set_meter_provider
from opentelemetry.metrics import get_meter, get_meter_provider, set_meter_provider
from opentelemetry.propagate import set_global_textmap
from opentelemetry.propagators.b3 import B3Format
from opentelemetry.propagators.composite import CompositePropagator
@ -112,6 +112,11 @@ def is_celery_worker():
def init_flask_instrumentor(app: DifyApp):
meter = get_meter("http_metrics", version=dify_config.CURRENT_VERSION)
_http_response_counter = meter.create_counter(
"http.server.response.count", description="Total number of HTTP responses by status code", unit="{response}"
)
def response_hook(span: Span, status: str, response_headers: list):
if span and span.is_recording():
if status.startswith("2"):
@ -119,6 +124,11 @@ def init_flask_instrumentor(app: DifyApp):
else:
span.set_status(StatusCode.ERROR, status)
status = status.split(" ")[0]
status_code = int(status)
status_class = f"{status_code // 100}xx"
_http_response_counter.add(1, {"status_code": status_code, "status_class": status_class})
instrumentor = FlaskInstrumentor()
if dify_config.DEBUG:
logging.info("Initializing Flask instrumentor")

@ -0,0 +1,63 @@
"""
Patch for OpenTelemetry context detach method to handle None tokens gracefully.
This patch addresses the issue where OpenTelemetry's context.detach() method raises a TypeError
when called with a None token. The error occurs in the contextvars_context.py file where it tries
to call reset() on a None token.
Related GitHub issue: https://github.com/langgenius/dify/issues/18496
Error being fixed:
```
Traceback (most recent call last):
File "opentelemetry/context/__init__.py", line 154, in detach
_RUNTIME_CONTEXT.detach(token)
File "opentelemetry/context/contextvars_context.py", line 50, in detach
self._current_context.reset(token) # type: ignore
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: expected an instance of Token, got None
```
Instead of modifying the third-party package directly, this patch monkey-patches the
context.detach method to gracefully handle None tokens.
"""
import logging
from functools import wraps
from opentelemetry import context
logger = logging.getLogger(__name__)
# Store the original detach method
original_detach = context.detach
# Create a patched version that handles None tokens
@wraps(original_detach)
def patched_detach(token):
"""
A patched version of context.detach that handles None tokens gracefully.
"""
if token is None:
logger.debug("Attempted to detach a None token, skipping")
return
return original_detach(token)
def is_enabled():
"""
Check if the extension is enabled.
Always enable this patch to prevent errors even when OpenTelemetry is disabled.
"""
return True
def init_app(app):
"""
Initialize the OpenTelemetry context patch.
"""
# Replace the original detach method with our patched version
context.detach = patched_detach
logger.info("OpenTelemetry context.detach patched to handle None tokens")

@ -19,6 +19,7 @@ file_fields = {
"mime_type": fields.String,
"created_by": fields.String,
"created_at": TimestampField,
"preview_url": fields.String,
}
remote_file_info_fields = {

@ -245,6 +245,13 @@ class Workflow(Base):
@property
def tool_published(self) -> bool:
"""
DEPRECATED: This property is not accurate for determining if a workflow is published as a tool.
It only checks if there's a WorkflowToolProvider for the app, not if this specific workflow version
is the one being used by the tool.
For accurate checking, use a direct query with tenant_id, app_id, and version.
"""
from models.tools import WorkflowToolProvider
return (
@ -630,6 +637,7 @@ class WorkflowNodeExecution(Base):
@property
def created_by_account(self):
created_by_role = CreatedByRole(self.created_by_role)
# TODO(-LAN-): Avoid using db.session.get() here.
return db.session.get(Account, self.created_by) if created_by_role == CreatedByRole.ACCOUNT else None
@property
@ -637,6 +645,7 @@ class WorkflowNodeExecution(Base):
from models.model import EndUser
created_by_role = CreatedByRole(self.created_by_role)
# TODO(-LAN-): Avoid using db.session.get() here.
return db.session.get(EndUser, self.created_by) if created_by_role == CreatedByRole.END_USER else None
@property

@ -30,6 +30,7 @@ dependencies = [
"gunicorn~=23.0.0",
"httpx[socks]~=0.27.0",
"jieba==0.42.1",
"json-repair>=0.41.1",
"langfuse~=2.51.3",
"langsmith~=0.1.77",
"mailchimp-transactional~=1.0.50",
@ -163,10 +164,7 @@ storage = [
############################################################
# [ Tools ] dependency group
############################################################
tools = [
"cloudscraper~=1.2.71",
"nltk~=3.9.1",
]
tools = ["cloudscraper~=1.2.71", "nltk~=3.9.1"]
############################################################
# [ VDB ] dependency group
@ -180,7 +178,7 @@ vdb = [
"couchbase~=4.3.0",
"elasticsearch==8.14.0",
"opensearch-py==2.4.0",
"oracledb~=2.2.1",
"oracledb==3.0.0",
"pgvecto-rs[sqlalchemy]~=0.2.1",
"pgvector==0.2.5",
"pymilvus~=2.5.0",

@ -6,7 +6,7 @@ import logging
from collections.abc import Sequence
from typing import Optional
from sqlalchemy import UnaryExpression, asc, desc, select
from sqlalchemy import UnaryExpression, asc, delete, desc, select
from sqlalchemy.engine import Engine
from sqlalchemy.orm import sessionmaker
@ -37,8 +37,12 @@ class SQLAlchemyWorkflowNodeExecutionRepository:
# If an engine is provided, create a sessionmaker from it
if isinstance(session_factory, Engine):
self._session_factory = sessionmaker(bind=session_factory, expire_on_commit=False)
else:
elif isinstance(session_factory, sessionmaker):
self._session_factory = session_factory
else:
raise ValueError(
f"Invalid session_factory type {type(session_factory).__name__}; expected sessionmaker or Engine"
)
self._tenant_id = tenant_id
self._app_id = app_id
@ -168,3 +172,25 @@ class SQLAlchemyWorkflowNodeExecutionRepository:
session.merge(execution)
session.commit()
def clear(self) -> None:
"""
Clear all WorkflowNodeExecution records for the current tenant_id and app_id.
This method deletes all WorkflowNodeExecution records that match the tenant_id
and app_id (if provided) associated with this repository instance.
"""
with self._session_factory() as session:
stmt = delete(WorkflowNodeExecution).where(WorkflowNodeExecution.tenant_id == self._tenant_id)
if self._app_id:
stmt = stmt.where(WorkflowNodeExecution.app_id == self._app_id)
result = session.execute(stmt)
session.commit()
deleted_count = result.rowcount
logger.info(
f"Cleared {deleted_count} workflow node execution records for tenant {self._tenant_id}"
+ (f" and app {self._app_id}" if self._app_id else "")
)

@ -407,10 +407,8 @@ class AccountService:
raise PasswordResetRateLimitExceededError()
code = "".join([str(random.randint(0, 9)) for _ in range(6)])
token = TokenManager.generate_token(
account=account, email=email, token_type="reset_password", additional_data={"code": code}
)
code, token = cls.generate_reset_password_token(account_email, account)
send_reset_password_mail_task.delay(
language=language,
to=account_email,
@ -419,6 +417,22 @@ class AccountService:
cls.reset_password_rate_limiter.increment_rate_limit(account_email)
return token
@classmethod
def generate_reset_password_token(
cls,
email: str,
account: Optional[Account] = None,
code: Optional[str] = None,
additional_data: dict[str, Any] = {},
):
if not code:
code = "".join([str(random.randint(0, 9)) for _ in range(6)])
additional_data["code"] = code
token = TokenManager.generate_token(
account=account, email=email, token_type="reset_password", additional_data=additional_data
)
return code, token
@classmethod
def revoke_reset_password_token(cls, token: str):
TokenManager.revoke_token(token, "reset_password")

@ -2,13 +2,14 @@ import threading
from typing import Optional
import contexts
from core.repository import RepositoryFactory
from core.repository.workflow_node_execution_repository import OrderConfig
from extensions.ext_database import db
from libs.infinite_scroll_pagination import InfiniteScrollPagination
from models.enums import WorkflowRunTriggeredFrom
from models.model import App
from models.workflow import (
WorkflowNodeExecution,
WorkflowNodeExecutionTriggeredFrom,
WorkflowRun,
)
@ -127,17 +128,17 @@ class WorkflowRunService:
if not workflow_run:
return []
node_executions = (
db.session.query(WorkflowNodeExecution)
.filter(
WorkflowNodeExecution.tenant_id == app_model.tenant_id,
WorkflowNodeExecution.app_id == app_model.id,
WorkflowNodeExecution.workflow_id == workflow_run.workflow_id,
WorkflowNodeExecution.triggered_from == WorkflowNodeExecutionTriggeredFrom.WORKFLOW_RUN.value,
WorkflowNodeExecution.workflow_run_id == run_id,
)
.order_by(WorkflowNodeExecution.index.desc())
.all()
# Use the repository to get the node executions
repository = RepositoryFactory.create_workflow_node_execution_repository(
params={
"tenant_id": app_model.tenant_id,
"app_id": app_model.id,
"session_factory": db.session.get_bind(),
}
)
return node_executions
# Use the repository to get the node executions with ordering
order_config = OrderConfig(order_by=["index"], order_direction="desc")
node_executions = repository.get_by_workflow_run(workflow_run_id=run_id, order_config=order_config)
return list(node_executions)

@ -11,6 +11,7 @@ from sqlalchemy.orm import Session
from core.app.apps.advanced_chat.app_config_manager import AdvancedChatAppConfigManager
from core.app.apps.workflow.app_config_manager import WorkflowAppConfigManager
from core.model_runtime.utils.encoders import jsonable_encoder
from core.repository import RepositoryFactory
from core.variables import Variable
from core.workflow.entities.node_entities import NodeRunResult
from core.workflow.errors import WorkflowNodeRunFailedError
@ -27,6 +28,7 @@ from extensions.ext_database import db
from models.account import Account
from models.enums import CreatedByRole
from models.model import App, AppMode
from models.tools import WorkflowToolProvider
from models.workflow import (
Workflow,
WorkflowNodeExecution,
@ -282,8 +284,15 @@ class WorkflowService:
workflow_node_execution.created_by = account.id
workflow_node_execution.workflow_id = draft_workflow.id
db.session.add(workflow_node_execution)
db.session.commit()
# Use the repository to save the workflow node execution
repository = RepositoryFactory.create_workflow_node_execution_repository(
params={
"tenant_id": app_model.tenant_id,
"app_id": app_model.id,
"session_factory": db.session.get_bind(),
}
)
repository.save(workflow_node_execution)
return workflow_node_execution
@ -515,8 +524,19 @@ class WorkflowService:
# Cannot delete a workflow that's currently in use by an app
raise WorkflowInUseError(f"Cannot delete workflow that is currently in use by app '{app.name}'")
# Check if this workflow is published as a tool
if workflow.tool_published:
# Don't use workflow.tool_published as it's not accurate for specific workflow versions
# Check if there's a tool provider using this specific workflow version
tool_provider = (
session.query(WorkflowToolProvider)
.filter(
WorkflowToolProvider.tenant_id == workflow.tenant_id,
WorkflowToolProvider.app_id == workflow.app_id,
WorkflowToolProvider.version == workflow.version,
)
.first()
)
if tool_provider:
# Cannot delete a workflow that's published as a tool
raise WorkflowInUseError("Cannot delete workflow that is published as a tool")

@ -7,6 +7,7 @@ from celery import shared_task # type: ignore
from sqlalchemy import delete
from sqlalchemy.exc import SQLAlchemyError
from core.repository import RepositoryFactory
from extensions.ext_database import db
from models.dataset import AppDatasetJoin
from models.model import (
@ -30,7 +31,7 @@ from models.model import (
)
from models.tools import WorkflowToolProvider
from models.web import PinnedConversation, SavedMessage
from models.workflow import ConversationVariable, Workflow, WorkflowAppLog, WorkflowNodeExecution, WorkflowRun
from models.workflow import ConversationVariable, Workflow, WorkflowAppLog, WorkflowRun
@shared_task(queue="app_deletion", bind=True, max_retries=3)
@ -187,18 +188,20 @@ def _delete_app_workflow_runs(tenant_id: str, app_id: str):
def _delete_app_workflow_node_executions(tenant_id: str, app_id: str):
def del_workflow_node_execution(workflow_node_execution_id: str):
db.session.query(WorkflowNodeExecution).filter(WorkflowNodeExecution.id == workflow_node_execution_id).delete(
synchronize_session=False
)
_delete_records(
"""select id from workflow_node_executions where tenant_id=:tenant_id and app_id=:app_id limit 1000""",
{"tenant_id": tenant_id, "app_id": app_id},
del_workflow_node_execution,
"workflow node execution",
# Create a repository instance for WorkflowNodeExecution
repository = RepositoryFactory.create_workflow_node_execution_repository(
params={
"tenant_id": tenant_id,
"app_id": app_id,
"session_factory": db.session.get_bind(),
}
)
# Use the clear method to delete all records for this tenant_id and app_id
repository.clear()
logging.info(click.style(f"Deleted workflow node executions for tenant {tenant_id} and app {app_id}", fg="green"))
def _delete_app_workflow_app_logs(tenant_id: str, app_id: str):
def del_workflow_app_log(workflow_app_log_id: str):

@ -0,0 +1,88 @@
import os
import pytest
from _pytest.monkeypatch import MonkeyPatch
from api.core.rag.datasource.vdb.field import Field
from elasticsearch import Elasticsearch
class MockIndicesClient:
def __init__(self):
pass
def create(self, index, mappings, settings):
return {"acknowledge": True}
def refresh(self, index):
return {"acknowledge": True}
def delete(self, index):
return {"acknowledge": True}
def exists(self, index):
return True
class MockClient:
def __init__(self, **kwargs):
self.indices = MockIndicesClient()
def index(self, **kwargs):
return {"acknowledge": True}
def exists(self, **kwargs):
return True
def delete(self, **kwargs):
return {"acknowledge": True}
def search(self, **kwargs):
return {
"took": 1,
"hits": {
"hits": [
{
"_source": {
Field.CONTENT_KEY.value: "abcdef",
Field.VECTOR.value: [1, 2],
Field.METADATA_KEY.value: {},
},
"_score": 1.0,
},
{
"_source": {
Field.CONTENT_KEY.value: "123456",
Field.VECTOR.value: [2, 2],
Field.METADATA_KEY.value: {},
},
"_score": 0.9,
},
{
"_source": {
Field.CONTENT_KEY.value: "a1b2c3",
Field.VECTOR.value: [3, 2],
Field.METADATA_KEY.value: {},
},
"_score": 0.8,
},
]
},
}
MOCK = os.getenv("MOCK_SWITCH", "false").lower() == "true"
@pytest.fixture
def setup_client_mock(request, monkeypatch: MonkeyPatch):
if MOCK:
monkeypatch.setattr(Elasticsearch, "__init__", MockClient.__init__)
monkeypatch.setattr(Elasticsearch, "index", MockClient.index)
monkeypatch.setattr(Elasticsearch, "exists", MockClient.exists)
monkeypatch.setattr(Elasticsearch, "delete", MockClient.delete)
monkeypatch.setattr(Elasticsearch, "search", MockClient.search)
yield
if MOCK:
monkeypatch.undo()

@ -0,0 +1,28 @@
from core.rag.datasource.vdb.huawei.huawei_cloud_vector import HuaweiCloudVector, HuaweiCloudVectorConfig
from tests.integration_tests.vdb.__mock.huaweicloudvectordb import setup_client_mock
from tests.integration_tests.vdb.test_vector_store import AbstractVectorTest, get_example_text, setup_mock_redis
class HuaweiCloudVectorTest(AbstractVectorTest):
def __init__(self):
super().__init__()
self.vector = HuaweiCloudVector(
"dify",
HuaweiCloudVectorConfig(
hosts="https://127.0.0.1:9200",
username="dify",
password="dify",
),
)
def search_by_vector(self):
hits_by_vector = self.vector.search_by_vector(query_vector=self.example_embedding)
assert len(hits_by_vector) == 3
def search_by_full_text(self):
hits_by_full_text = self.vector.search_by_full_text(query=get_example_text())
assert len(hits_by_full_text) == 3
def test_huawei_cloud_vector(setup_mock_redis, setup_client_mock):
HuaweiCloudVectorTest().run_all_tests()

@ -0,0 +1,27 @@
from core.model_runtime.entities.message_entities import (
ImagePromptMessageContent,
TextPromptMessageContent,
UserPromptMessage,
)
def test_build_prompt_message_with_prompt_message_contents():
prompt = UserPromptMessage(content=[TextPromptMessageContent(data="Hello, World!")])
assert isinstance(prompt.content, list)
assert isinstance(prompt.content[0], TextPromptMessageContent)
assert prompt.content[0].data == "Hello, World!"
def test_dump_prompt_message():
example_url = "https://example.com/image.jpg"
prompt = UserPromptMessage(
content=[
ImagePromptMessageContent(
url=example_url,
format="jpeg",
mime_type="image/jpeg",
)
]
)
data = prompt.model_dump()
assert data["content"][0].get("url") == example_url

@ -152,3 +152,27 @@ def test_update(repository, session):
# Assert session.merge was called
session_obj.merge.assert_called_once_with(execution)
def test_clear(repository, session, mocker: MockerFixture):
"""Test clear method."""
session_obj, _ = session
# Set up mock
mock_delete = mocker.patch("repositories.workflow_node_execution.sqlalchemy_repository.delete")
mock_stmt = mocker.MagicMock()
mock_delete.return_value = mock_stmt
mock_stmt.where.return_value = mock_stmt
# Mock the execute result with rowcount
mock_result = mocker.MagicMock()
mock_result.rowcount = 5 # Simulate 5 records deleted
session_obj.execute.return_value = mock_result
# Call method
repository.clear()
# Assert delete was called with correct parameters
mock_delete.assert_called_once_with(WorkflowNodeExecution)
mock_stmt.where.assert_called()
session_obj.execute.assert_called_once_with(mock_stmt)
session_obj.commit.assert_called_once()

@ -40,6 +40,10 @@ def workflow_setup():
def test_delete_workflow_success(workflow_setup):
# Setup mocks
# Mock the tool provider query to return None (not published as a tool)
workflow_setup["session"].query.return_value.filter.return_value.first.return_value = None
workflow_setup["session"].scalar = MagicMock(
side_effect=[workflow_setup["workflow"], None]
) # Return workflow first, then None for app
@ -97,7 +101,12 @@ def test_delete_workflow_in_use_by_app_error(workflow_setup):
def test_delete_workflow_published_as_tool_error(workflow_setup):
# Setup mocks
workflow_setup["workflow"].tool_published = True
from models.tools import WorkflowToolProvider
# Mock the tool provider query
mock_tool_provider = MagicMock(spec=WorkflowToolProvider)
workflow_setup["session"].query.return_value.filter.return_value.first.return_value = mock_tool_provider
workflow_setup["session"].scalar = MagicMock(
side_effect=[workflow_setup["workflow"], None]
) # Return workflow first, then None for app

@ -1,5 +1,4 @@
version = 1
revision = 1
requires-python = ">=3.11, <3.13"
resolution-markers = [
"python_full_version >= '3.12.4' and platform_python_implementation != 'PyPy'",
@ -1178,6 +1177,7 @@ dependencies = [
{ name = "gunicorn" },
{ name = "httpx", extra = ["socks"] },
{ name = "jieba" },
{ name = "json-repair" },
{ name = "langfuse" },
{ name = "langsmith" },
{ name = "mailchimp-transactional" },
@ -1346,6 +1346,7 @@ requires-dist = [
{ name = "gunicorn", specifier = "~=23.0.0" },
{ name = "httpx", extras = ["socks"], specifier = "~=0.27.0" },
{ name = "jieba", specifier = "==0.42.1" },
{ name = "json-repair", specifier = ">=0.41.1" },
{ name = "langfuse", specifier = "~=2.51.3" },
{ name = "langsmith", specifier = "~=0.1.77" },
{ name = "mailchimp-transactional", specifier = "~=1.0.50" },
@ -1470,7 +1471,7 @@ vdb = [
{ name = "couchbase", specifier = "~=4.3.0" },
{ name = "elasticsearch", specifier = "==8.14.0" },
{ name = "opensearch-py", specifier = "==2.4.0" },
{ name = "oracledb", specifier = "~=2.2.1" },
{ name = "oracledb", specifier = "==3.0.0" },
{ name = "pgvecto-rs", extras = ["sqlalchemy"], specifier = "~=0.2.1" },
{ name = "pgvector", specifier = "==0.2.5" },
{ name = "pymilvus", specifier = "~=2.5.0" },
@ -2524,6 +2525,15 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/91/29/df4b9b42f2be0b623cbd5e2140cafcaa2bef0759a00b7b70104dcfe2fb51/joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6", size = 301817 },
]
[[package]]
name = "json-repair"
version = "0.41.1"
source = { registry = "https://pypi.org/simple" }
sdist = { url = "https://files.pythonhosted.org/packages/6d/6a/6c7a75a10da6dc807b582f2449034da1ed74415e8899746bdfff97109012/json_repair-0.41.1.tar.gz", hash = "sha256:bba404b0888c84a6b86ecc02ec43b71b673cfee463baf6da94e079c55b136565", size = 31208 }
wheels = [
{ url = "https://files.pythonhosted.org/packages/10/5c/abd7495c934d9af5c263c2245ae30cfaa716c3c0cf027b2b8fa686ee7bd4/json_repair-0.41.1-py3-none-any.whl", hash = "sha256:0e181fd43a696887881fe19fed23422a54b3e4c558b6ff27a86a8c3ddde9ae79", size = 21578 },
]
[[package]]
name = "jsonpath-python"
version = "1.0.6"
@ -3590,23 +3600,23 @@ wheels = [
[[package]]
name = "oracledb"
version = "2.2.1"
version = "3.0.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "cryptography" },
]
sdist = { url = "https://files.pythonhosted.org/packages/36/fb/3fbacb351833dd794abb184303a5761c4bb33df9d770fd15d01ead2ff738/oracledb-2.2.1.tar.gz", hash = "sha256:8464c6f0295f3318daf6c2c72c83c2dcbc37e13f8fd44e3e39ff8665f442d6b6", size = 580818 }
sdist = { url = "https://files.pythonhosted.org/packages/bf/39/712f797b75705c21148fa1d98651f63c2e5cc6876e509a0a9e2f5b406572/oracledb-3.0.0.tar.gz", hash = "sha256:64dc86ee5c032febc556798b06e7b000ef6828bb0252084f6addacad3363db85", size = 840431 }
wheels = [
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[[package]]
@ -4074,6 +4084,8 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/af/cd/ed6e429fb0792ce368f66e83246264dd3a7a045b0b1e63043ed22a063ce5/pycryptodome-3.19.1-cp35-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:7c9e222d0976f68d0cf6409cfea896676ddc1d98485d601e9508f90f60e2b0a2", size = 2144914 },
{ url = "https://files.pythonhosted.org/packages/f6/23/b064bd4cfbf2cc5f25afcde0e7c880df5b20798172793137ba4b62d82e72/pycryptodome-3.19.1-cp35-abi3-win32.whl", hash = "sha256:4805e053571140cb37cf153b5c72cd324bb1e3e837cbe590a19f69b6cf85fd03", size = 1713105 },
{ url = "https://files.pythonhosted.org/packages/7d/e0/ded1968a5257ab34216a0f8db7433897a2337d59e6d03be113713b346ea2/pycryptodome-3.19.1-cp35-abi3-win_amd64.whl", hash = "sha256:a470237ee71a1efd63f9becebc0ad84b88ec28e6784a2047684b693f458f41b7", size = 1749222 },
{ url = "https://files.pythonhosted.org/packages/1d/e3/0c9679cd66cf5604b1f070bdf4525a0c01a15187be287d8348b2eafb718e/pycryptodome-3.19.1-pp27-pypy_73-manylinux2010_x86_64.whl", hash = "sha256:ed932eb6c2b1c4391e166e1a562c9d2f020bfff44a0e1b108f67af38b390ea89", size = 1629005 },
{ url = "https://files.pythonhosted.org/packages/13/75/0d63bf0daafd0580b17202d8a9dd57f28c8487f26146b3e2799b0c5a059c/pycryptodome-3.19.1-pp27-pypy_73-win32.whl", hash = "sha256:81e9d23c0316fc1b45d984a44881b220062336bbdc340aa9218e8d0656587934", size = 1697997 },
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[[package]]

@ -15,3 +15,4 @@ pytest api/tests/integration_tests/vdb/chroma \
api/tests/integration_tests/vdb/couchbase \
api/tests/integration_tests/vdb/oceanbase \
api/tests/integration_tests/vdb/tidb_vector \
api/tests/integration_tests/vdb/huawei \

@ -574,6 +574,11 @@ OPENGAUSS_MIN_CONNECTION=1
OPENGAUSS_MAX_CONNECTION=5
OPENGAUSS_ENABLE_PQ=false
# huawei cloud search service vector configurations, only available when VECTOR_STORE is `huawei_cloud`
HUAWEI_CLOUD_HOSTS=https://127.0.0.1:9200
HUAWEI_CLOUD_USER=admin
HUAWEI_CLOUD_PASSWORD=admin
# Upstash Vector configuration, only available when VECTOR_STORE is `upstash`
UPSTASH_VECTOR_URL=https://xxx-vector.upstash.io
UPSTASH_VECTOR_TOKEN=dify
@ -1063,3 +1068,6 @@ OTEL_MAX_EXPORT_BATCH_SIZE=512
OTEL_METRIC_EXPORT_INTERVAL=60000
OTEL_BATCH_EXPORT_TIMEOUT=10000
OTEL_METRIC_EXPORT_TIMEOUT=30000
# Prevent Clickjacking
ALLOW_EMBED=false

@ -66,6 +66,7 @@ services:
NEXT_TELEMETRY_DISABLED: ${NEXT_TELEMETRY_DISABLED:-0}
TEXT_GENERATION_TIMEOUT_MS: ${TEXT_GENERATION_TIMEOUT_MS:-60000}
CSP_WHITELIST: ${CSP_WHITELIST:-}
ALLOW_EMBED: ${ALLOW_EMBED:-false}
MARKETPLACE_API_URL: ${MARKETPLACE_API_URL:-https://marketplace.dify.ai}
MARKETPLACE_URL: ${MARKETPLACE_URL:-https://marketplace.dify.ai}
TOP_K_MAX_VALUE: ${TOP_K_MAX_VALUE:-}
@ -130,6 +131,7 @@ services:
HTTP_PROXY: ${SANDBOX_HTTP_PROXY:-http://ssrf_proxy:3128}
HTTPS_PROXY: ${SANDBOX_HTTPS_PROXY:-http://ssrf_proxy:3128}
SANDBOX_PORT: ${SANDBOX_PORT:-8194}
PIP_MIRROR_URL: ${PIP_MIRROR_URL:-}
volumes:
- ./volumes/sandbox/dependencies:/dependencies
- ./volumes/sandbox/conf:/conf
@ -551,7 +553,7 @@ services:
volumes:
- ./volumes/opengauss/data:/var/lib/opengauss/data
healthcheck:
test: ["CMD-SHELL", "netstat -lntp | grep tcp6 > /dev/null 2>&1"]
test: [ "CMD-SHELL", "netstat -lntp | grep tcp6 > /dev/null 2>&1" ]
interval: 10s
timeout: 10s
retries: 10

@ -60,6 +60,7 @@ services:
HTTP_PROXY: ${SANDBOX_HTTP_PROXY:-http://ssrf_proxy:3128}
HTTPS_PROXY: ${SANDBOX_HTTPS_PROXY:-http://ssrf_proxy:3128}
SANDBOX_PORT: ${SANDBOX_PORT:-8194}
PIP_MIRROR_URL: ${PIP_MIRROR_URL:-}
volumes:
- ./volumes/sandbox/dependencies:/dependencies
- ./volumes/sandbox/conf:/conf

@ -266,6 +266,9 @@ x-shared-env: &shared-api-worker-env
OPENGAUSS_MIN_CONNECTION: ${OPENGAUSS_MIN_CONNECTION:-1}
OPENGAUSS_MAX_CONNECTION: ${OPENGAUSS_MAX_CONNECTION:-5}
OPENGAUSS_ENABLE_PQ: ${OPENGAUSS_ENABLE_PQ:-false}
HUAWEI_CLOUD_HOSTS: ${HUAWEI_CLOUD_HOSTS:-https://127.0.0.1:9200}
HUAWEI_CLOUD_USER: ${HUAWEI_CLOUD_USER:-admin}
HUAWEI_CLOUD_PASSWORD: ${HUAWEI_CLOUD_PASSWORD:-admin}
UPSTASH_VECTOR_URL: ${UPSTASH_VECTOR_URL:-https://xxx-vector.upstash.io}
UPSTASH_VECTOR_TOKEN: ${UPSTASH_VECTOR_TOKEN:-dify}
TABLESTORE_ENDPOINT: ${TABLESTORE_ENDPOINT:-https://instance-name.cn-hangzhou.ots.aliyuncs.com}
@ -471,6 +474,7 @@ x-shared-env: &shared-api-worker-env
OTEL_METRIC_EXPORT_INTERVAL: ${OTEL_METRIC_EXPORT_INTERVAL:-60000}
OTEL_BATCH_EXPORT_TIMEOUT: ${OTEL_BATCH_EXPORT_TIMEOUT:-10000}
OTEL_METRIC_EXPORT_TIMEOUT: ${OTEL_METRIC_EXPORT_TIMEOUT:-30000}
ALLOW_EMBED: ${ALLOW_EMBED:-false}
services:
# API service
@ -539,6 +543,7 @@ services:
NEXT_TELEMETRY_DISABLED: ${NEXT_TELEMETRY_DISABLED:-0}
TEXT_GENERATION_TIMEOUT_MS: ${TEXT_GENERATION_TIMEOUT_MS:-60000}
CSP_WHITELIST: ${CSP_WHITELIST:-}
ALLOW_EMBED: ${ALLOW_EMBED:-false}
MARKETPLACE_API_URL: ${MARKETPLACE_API_URL:-https://marketplace.dify.ai}
MARKETPLACE_URL: ${MARKETPLACE_URL:-https://marketplace.dify.ai}
TOP_K_MAX_VALUE: ${TOP_K_MAX_VALUE:-}
@ -603,6 +608,7 @@ services:
HTTP_PROXY: ${SANDBOX_HTTP_PROXY:-http://ssrf_proxy:3128}
HTTPS_PROXY: ${SANDBOX_HTTPS_PROXY:-http://ssrf_proxy:3128}
SANDBOX_PORT: ${SANDBOX_PORT:-8194}
PIP_MIRROR_URL: ${PIP_MIRROR_URL:-}
volumes:
- ./volumes/sandbox/dependencies:/dependencies
- ./volumes/sandbox/conf:/conf
@ -1024,7 +1030,7 @@ services:
volumes:
- ./volumes/opengauss/data:/var/lib/opengauss/data
healthcheck:
test: ["CMD-SHELL", "netstat -lntp | grep tcp6 > /dev/null 2>&1"]
test: [ "CMD-SHELL", "netstat -lntp | grep tcp6 > /dev/null 2>&1" ]
interval: 10s
timeout: 10s
retries: 10

@ -29,6 +29,8 @@ NEXT_PUBLIC_TEXT_GENERATION_TIMEOUT_MS=60000
# CSP https://developer.mozilla.org/en-US/docs/Web/HTTP/CSP
NEXT_PUBLIC_CSP_WHITELIST=
# Default is not allow to embed into iframe to prevent Clickjacking: https://owasp.org/www-community/attacks/Clickjacking
NEXT_PUBLIC_ALLOW_EMBED=
# Github Access Token, used for invoking Github API
NEXT_PUBLIC_GITHUB_ACCESS_TOKEN=

@ -7,7 +7,7 @@ This is a [Next.js](https://nextjs.org/) project bootstrapped with [`create-next
### Run by source code
Before starting the web frontend service, please make sure the following environment is ready.
- [Node.js](https://nodejs.org) >= v18.x
- [Node.js](https://nodejs.org) >= v22.11.x
- [pnpm](https://pnpm.io) v10.x
First, install the dependencies:

@ -0,0 +1,82 @@
import { fireEvent, render, screen } from '@testing-library/react'
import ConfigSelect from './index'
jest.mock('react-sortablejs', () => ({
ReactSortable: ({ children }: { children: React.ReactNode }) => <div>{children}</div>,
}))
jest.mock('react-i18next', () => ({
useTranslation: () => ({
t: (key: string) => key,
}),
}))
describe('ConfigSelect Component', () => {
const defaultProps = {
options: ['Option 1', 'Option 2'],
onChange: jest.fn(),
}
afterEach(() => {
jest.clearAllMocks()
})
it('renders all options', () => {
render(<ConfigSelect {...defaultProps} />)
defaultProps.options.forEach((option) => {
expect(screen.getByDisplayValue(option)).toBeInTheDocument()
})
})
it('renders add button', () => {
render(<ConfigSelect {...defaultProps} />)
expect(screen.getByText('appDebug.variableConfig.addOption')).toBeInTheDocument()
})
it('handles option deletion', () => {
render(<ConfigSelect {...defaultProps} />)
const optionContainer = screen.getByDisplayValue('Option 1').closest('div')
const deleteButton = optionContainer?.querySelector('div[role="button"]')
if (!deleteButton) return
fireEvent.click(deleteButton)
expect(defaultProps.onChange).toHaveBeenCalledWith(['Option 2'])
})
it('handles adding new option', () => {
render(<ConfigSelect {...defaultProps} />)
const addButton = screen.getByText('appDebug.variableConfig.addOption')
fireEvent.click(addButton)
expect(defaultProps.onChange).toHaveBeenCalledWith([...defaultProps.options, ''])
})
it('applies focus styles on input focus', () => {
render(<ConfigSelect {...defaultProps} />)
const firstInput = screen.getByDisplayValue('Option 1')
fireEvent.focus(firstInput)
expect(firstInput.closest('div')).toHaveClass('border-components-input-border-active')
})
it('applies delete hover styles', () => {
render(<ConfigSelect {...defaultProps} />)
const optionContainer = screen.getByDisplayValue('Option 1').closest('div')
const deleteButton = optionContainer?.querySelector('div[role="button"]')
if (!deleteButton) return
fireEvent.mouseEnter(deleteButton)
expect(optionContainer).toHaveClass('border-components-input-border-destructive')
})
it('renders empty state correctly', () => {
render(<ConfigSelect options={[]} onChange={defaultProps.onChange} />)
expect(screen.queryByRole('textbox')).not.toBeInTheDocument()
expect(screen.getByText('appDebug.variableConfig.addOption')).toBeInTheDocument()
})
})

@ -51,7 +51,7 @@ const ConfigSelect: FC<IConfigSelectProps> = ({
<RiDraggable className='handle h-4 w-4 cursor-grab text-text-quaternary' />
<input
key={index}
type="input"
type='input'
value={o || ''}
onChange={(e) => {
const value = e.target.value
@ -67,6 +67,7 @@ const ConfigSelect: FC<IConfigSelectProps> = ({
onBlur={() => setFocusID(null)}
/>
<div
role='button'
className='absolute right-1.5 top-1/2 block translate-y-[-50%] cursor-pointer rounded-md p-1 text-text-tertiary hover:bg-state-destructive-hover hover:text-text-destructive'
onClick={() => {
onChange(options.filter((_, i) => index !== i))

@ -163,7 +163,7 @@ const SettingBuiltInTool: FC<Props> = ({
footer={null}
mask={false}
positionCenter={false}
panelClassname={cn('mb-2 mr-2 mt-[64px] !w-[420px] !max-w-[420px] justify-start rounded-2xl border-[0.5px] border-components-panel-border !bg-components-panel-bg !p-0 shadow-xl')}
panelClassName={cn('mb-2 mr-2 mt-[64px] !w-[420px] !max-w-[420px] justify-start rounded-2xl border-[0.5px] border-components-panel-border !bg-components-panel-bg !p-0 shadow-xl')}
>
<>
{isLoading && <Loading type='app' />}

@ -97,7 +97,7 @@ const Item: FC<ItemProps> = ({
<RiDeleteBinLine className='h-4 w-4' />
</div>
</div>
<Drawer isOpen={showSettingsModal} onClose={() => setShowSettingsModal(false)} footer={null} mask={isMobile} panelClassname='mt-16 mx-2 sm:mr-2 mb-3 !p-0 !max-w-[640px] rounded-xl'>
<Drawer isOpen={showSettingsModal} onClose={() => setShowSettingsModal(false)} footer={null} mask={isMobile} panelClassName='mt-16 mx-2 sm:mr-2 mb-3 !p-0 !max-w-[640px] rounded-xl'>
<SettingsModal
currentDataset={config}
onCancel={() => setShowSettingsModal(false)}

@ -62,13 +62,13 @@ const SettingsModal: FC<SettingsModalProps> = ({
const { notify } = useToastContext()
const ref = useRef(null)
const isExternal = currentDataset.provider === 'external'
const [topK, setTopK] = useState(currentDataset?.external_retrieval_model.top_k ?? 2)
const [scoreThreshold, setScoreThreshold] = useState(currentDataset?.external_retrieval_model.score_threshold ?? 0.5)
const [scoreThresholdEnabled, setScoreThresholdEnabled] = useState(currentDataset?.external_retrieval_model.score_threshold_enabled ?? false)
const { setShowAccountSettingModal } = useModalContext()
const [loading, setLoading] = useState(false)
const { isCurrentWorkspaceDatasetOperator } = useAppContext()
const [localeCurrentDataset, setLocaleCurrentDataset] = useState({ ...currentDataset })
const [topK, setTopK] = useState(localeCurrentDataset?.external_retrieval_model.top_k ?? 2)
const [scoreThreshold, setScoreThreshold] = useState(localeCurrentDataset?.external_retrieval_model.score_threshold ?? 0.5)
const [scoreThresholdEnabled, setScoreThresholdEnabled] = useState(localeCurrentDataset?.external_retrieval_model.score_threshold_enabled ?? false)
const [selectedMemberIDs, setSelectedMemberIDs] = useState<string[]>(currentDataset.partial_member_list || [])
const [memberList, setMemberList] = useState<Member[]>([])
@ -88,6 +88,14 @@ const SettingsModal: FC<SettingsModalProps> = ({
setScoreThreshold(data.score_threshold)
if (data.score_threshold_enabled !== undefined)
setScoreThresholdEnabled(data.score_threshold_enabled)
setLocaleCurrentDataset({
...localeCurrentDataset,
external_retrieval_model: {
...localeCurrentDataset?.external_retrieval_model,
...data,
},
})
}
const handleSave = async () => {

@ -743,7 +743,7 @@ const ConversationList: FC<IConversationList> = ({ logs, appDetail, onRefresh })
onClose={onCloseDrawer}
mask={isMobile}
footer={null}
panelClassname='mt-16 mx-2 sm:mr-2 mb-4 !p-0 !max-w-[640px] rounded-xl bg-components-panel-bg'
panelClassName='mt-16 mx-2 sm:mr-2 mb-4 !p-0 !max-w-[640px] rounded-xl bg-components-panel-bg'
>
<DrawerContext.Provider value={{
onClose: onCloseDrawer,

@ -29,7 +29,7 @@ const OPTION_MAP = {
iframe: {
getContent: (url: string, token: string) =>
`<iframe
src="${url}${basePath}/chatbot/${token}"
src="${url}${basePath}/chat/${token}"
style="width: 100%; height: 100%; min-height: 700px"
frameborder="0"
allow="microphone">
@ -42,10 +42,10 @@ const OPTION_MAP = {
token: '${token}'${isTestEnv
? `,
isDev: true`
: ''}${IS_CE_EDITION
? `,
: ''}${IS_CE_EDITION
? `,
baseUrl: '${url}${basePath}'`
: ''},
: ''},
systemVariables: {
// user_id: 'YOU CAN DEFINE USER ID HERE',
},

@ -162,11 +162,22 @@ const SettingsModal: FC<ISettingsModalProps> = ({
return check
}
const validatePrivacyPolicy = (privacyPolicy: string | null) => {
if (privacyPolicy === null || privacyPolicy?.length === 0)
return true
return privacyPolicy.startsWith('http://') || privacyPolicy.startsWith('https://')
}
if (inputInfo !== null) {
if (!validateColorHex(inputInfo.chatColorTheme)) {
notify({ type: 'error', message: t(`${prefixSettings}.invalidHexMessage`) })
return
}
if (!validatePrivacyPolicy(inputInfo.privacyPolicy)) {
notify({ type: 'error', message: t(`${prefixSettings}.invalidPrivacyPolicy`) })
return
}
}
setSaveLoading(true)
@ -410,7 +421,7 @@ const SettingsModal: FC<ISettingsModalProps> = ({
<p className={cn('body-xs-regular pb-0.5 text-text-tertiary')}>
<Trans
i18nKey={`${prefixSettings}.more.privacyPolicyTip`}
components={{ privacyPolicyLink: <Link href={'https://docs.dify.ai/user-agreement/privacy-policy'} target='_blank' rel='noopener noreferrer' className='text-text-accent' /> }}
components={{ privacyPolicyLink: <Link href={'https://dify.ai/privacy'} target='_blank' rel='noopener noreferrer' className='text-text-accent' /> }}
/>
</p>
<Input

@ -134,7 +134,7 @@ const WorkflowAppLogList: FC<ILogs> = ({ logs, appDetail, onRefresh }) => {
onClose={onCloseDrawer}
mask={isMobile}
footer={null}
panelClassname='mt-16 mx-2 sm:mr-2 mb-3 !p-0 !max-w-[600px] rounded-xl border border-components-panel-border'
panelClassName='mt-16 mx-2 sm:mr-2 mb-3 !p-0 !max-w-[600px] rounded-xl border border-components-panel-border'
>
<DetailPanel onClose={onCloseDrawer} runID={currentLog?.workflow_run.id || ''} />
</Drawer>

@ -234,4 +234,6 @@ const Answer: FC<AnswerProps> = ({
)
}
export default memo(Answer)
export default memo(Answer, (prevProps, nextProps) =>
prevProps.responding === false && nextProps.responding === false,
)

@ -80,8 +80,30 @@ export const useEmbeddedChatbot = () => {
}, [])
useEffect(() => {
if (appInfo?.site.default_language)
changeLanguage(appInfo.site.default_language)
const setLanguageFromParams = async () => {
// Check URL parameters for language override
const urlParams = new URLSearchParams(window.location.search)
const localeParam = urlParams.get('locale')
// Check for encoded system variables
const systemVariables = await getProcessedSystemVariablesFromUrlParams()
const localeFromSysVar = systemVariables.locale
if (localeParam) {
// If locale parameter exists in URL, use it instead of default
changeLanguage(localeParam)
}
else if (localeFromSysVar) {
// If locale is set as a system variable, use that
changeLanguage(localeFromSysVar)
}
else if (appInfo?.site.default_language) {
// Otherwise use the default from app config
changeLanguage(appInfo.site.default_language)
}
}
setLanguageFromParams()
}, [appInfo])
const [conversationIdInfo, setConversationIdInfo] = useLocalStorageState<Record<string, Record<string, string>>>(CONVERSATION_ID_INFO, {

@ -0,0 +1,11 @@
const IndeterminateIcon = () => {
return (
<div data-testid='indeterminate-icon'>
<svg xmlns="http://www.w3.org/2000/svg" width="12" height="12" viewBox="0 0 12 12" fill="none">
<path d="M2.5 6H9.5" stroke="currentColor" strokeWidth="1.5" strokeLinecap="round"/>
</svg>
</div>
)
}
export default IndeterminateIcon

@ -1,5 +0,0 @@
<svg width="12" height="12" viewBox="0 0 12 12" fill="none" xmlns="http://www.w3.org/2000/svg">
<g id="check">
<path id="Vector 1" d="M2.5 6H9.5" stroke="white" stroke-width="1.5" stroke-linecap="round"/>
</g>
</svg>

Before

Width:  |  Height:  |  Size: 217 B

@ -1,10 +0,0 @@
.mixed {
background: var(--color-components-checkbox-bg) url(./assets/mixed.svg) center center no-repeat;
background-size: 12px 12px;
border: none;
}
.checked.disabled {
background-color: #d0d5dd;
border-color: #d0d5dd;
}

@ -0,0 +1,67 @@
import { fireEvent, render, screen } from '@testing-library/react'
import Checkbox from './index'
describe('Checkbox Component', () => {
const mockProps = {
id: 'test',
}
it('renders unchecked checkbox by default', () => {
render(<Checkbox {...mockProps} />)
const checkbox = screen.getByTestId('checkbox-test')
expect(checkbox).toBeInTheDocument()
expect(checkbox).not.toHaveClass('bg-components-checkbox-bg')
})
it('renders checked checkbox when checked prop is true', () => {
render(<Checkbox {...mockProps} checked />)
const checkbox = screen.getByTestId('checkbox-test')
expect(checkbox).toHaveClass('bg-components-checkbox-bg')
expect(screen.getByTestId('check-icon-test')).toBeInTheDocument()
})
it('renders indeterminate state correctly', () => {
render(<Checkbox {...mockProps} indeterminate />)
expect(screen.getByTestId('indeterminate-icon')).toBeInTheDocument()
})
it('handles click events when not disabled', () => {
const onCheck = jest.fn()
render(<Checkbox {...mockProps} onCheck={onCheck} />)
const checkbox = screen.getByTestId('checkbox-test')
fireEvent.click(checkbox)
expect(onCheck).toHaveBeenCalledTimes(1)
})
it('does not handle click events when disabled', () => {
const onCheck = jest.fn()
render(<Checkbox {...mockProps} disabled onCheck={onCheck} />)
const checkbox = screen.getByTestId('checkbox-test')
fireEvent.click(checkbox)
expect(onCheck).not.toHaveBeenCalled()
expect(checkbox).toHaveClass('cursor-not-allowed')
})
it('applies custom className when provided', () => {
const customClass = 'custom-class'
render(<Checkbox {...mockProps} className={customClass} />)
const checkbox = screen.getByTestId('checkbox-test')
expect(checkbox).toHaveClass(customClass)
})
it('applies correct styles for disabled checked state', () => {
render(<Checkbox {...mockProps} checked disabled />)
const checkbox = screen.getByTestId('checkbox-test')
expect(checkbox).toHaveClass('bg-components-checkbox-bg-disabled-checked')
expect(checkbox).toHaveClass('cursor-not-allowed')
})
it('applies correct styles for disabled unchecked state', () => {
render(<Checkbox {...mockProps} disabled />)
const checkbox = screen.getByTestId('checkbox-test')
expect(checkbox).toHaveClass('bg-components-checkbox-bg-disabled')
expect(checkbox).toHaveClass('cursor-not-allowed')
})
})

@ -1,48 +1,49 @@
import { RiCheckLine } from '@remixicon/react'
import s from './index.module.css'
import cn from '@/utils/classnames'
import IndeterminateIcon from './assets/indeterminate-icon'
type CheckboxProps = {
id?: string
checked?: boolean
onCheck?: () => void
className?: string
disabled?: boolean
mixed?: boolean
indeterminate?: boolean
}
const Checkbox = ({ checked, onCheck, className, disabled, mixed }: CheckboxProps) => {
if (!checked) {
return (
<div
className={cn(
'h-4 w-4 cursor-pointer rounded-[4px] border border-components-checkbox-border bg-components-checkbox-bg-unchecked shadow-xs hover:border-components-checkbox-border-hover',
mixed ? s.mixed : 'hover:bg-components-checkbox-bg-unchecked-hover',
disabled && 'cursor-not-allowed border-components-checkbox-border-disabled bg-components-checkbox-bg-disabled hover:border-components-checkbox-border-disabled hover:bg-components-checkbox-bg-disabled',
className,
)}
onClick={() => {
if (disabled)
return
onCheck?.()
}}
></div>
)
}
const Checkbox = ({
id,
checked,
onCheck,
className,
disabled,
indeterminate,
}: CheckboxProps) => {
const checkClassName = (checked || indeterminate)
? 'bg-components-checkbox-bg text-components-checkbox-icon hover:bg-components-checkbox-bg-hover'
: 'border border-components-checkbox-border bg-components-checkbox-bg-unchecked hover:bg-components-checkbox-bg-unchecked-hover hover:border-components-checkbox-border-hover'
const disabledClassName = (checked || indeterminate)
? 'cursor-not-allowed bg-components-checkbox-bg-disabled-checked text-components-checkbox-icon-disabled hover:bg-components-checkbox-bg-disabled-checked'
: 'cursor-not-allowed border-components-checkbox-border-disabled bg-components-checkbox-bg-disabled hover:border-components-checkbox-border-disabled hover:bg-components-checkbox-bg-disabled'
return (
<div
id={id}
className={cn(
'flex h-4 w-4 cursor-pointer items-center justify-center rounded-[4px] bg-components-checkbox-bg text-components-checkbox-icon shadow-xs hover:bg-components-checkbox-bg-hover',
disabled && 'cursor-not-allowed bg-components-checkbox-bg-disabled-checked text-components-checkbox-icon-disabled hover:bg-components-checkbox-bg-disabled-checked',
'flex h-4 w-4 cursor-pointer items-center justify-center rounded-[4px] shadow-xs shadow-shadow-shadow-3',
checkClassName,
disabled && disabledClassName,
className,
)}
onClick={() => {
if (disabled)
return
onCheck?.()
}}
data-testid={`checkbox-${id}`}
>
<RiCheckLine className={cn('h-3 w-3')} />
{!checked && indeterminate && <IndeterminateIcon />}
{checked && <RiCheckLine className='h-3 w-3' data-testid={`check-icon-${id}`} />}
</div>
)
}

@ -9,6 +9,8 @@ import useBreakpoints, { MediaType } from '@/hooks/use-breakpoints'
type Props = {
isShow: boolean
onHide: () => void
dialogClassName?: string
dialogBackdropClassName?: string
panelClassName?: string
maxWidthClassName?: string
contentClassName?: string
@ -26,6 +28,8 @@ type Props = {
const DrawerPlus: FC<Props> = ({
isShow,
onHide,
dialogClassName = '',
dialogBackdropClassName = '',
panelClassName = '',
maxWidthClassName = '!max-w-[640px]',
height = 'calc(100vh - 72px)',
@ -55,7 +59,9 @@ const DrawerPlus: FC<Props> = ({
footer={null}
mask={isMobile || isShowMask}
positionCenter={positionCenter}
panelClassname={cn('mx-2 mb-3 mt-16 rounded-xl !p-0 sm:mr-2', panelClassName, maxWidthClassName)}
dialogClassName={dialogClassName}
dialogBackdropClassName={dialogBackdropClassName}
panelClassName={cn('mx-2 mb-3 mt-16 rounded-xl !p-0 sm:mr-2', panelClassName, maxWidthClassName)}
>
<div
className={cn(contentClassName, 'flex w-full flex-col rounded-xl border-[0.5px] border-divider-subtle bg-components-panel-bg shadow-xl')}

@ -8,7 +8,9 @@ import cn from '@/utils/classnames'
export type IDrawerProps = {
title?: string
description?: string
panelClassname?: string
dialogClassName?: string
dialogBackdropClassName?: string
panelClassName?: string
children: React.ReactNode
footer?: React.ReactNode
mask?: boolean
@ -25,7 +27,9 @@ export type IDrawerProps = {
export default function Drawer({
title = '',
description = '',
panelClassname = '',
dialogClassName = '',
dialogBackdropClassName = '',
panelClassName = '',
children,
footer,
mask = true,
@ -44,17 +48,17 @@ export default function Drawer({
unmount={unmount}
open={isOpen}
onClose={() => !clickOutsideNotOpen && onClose()}
className="fixed inset-0 z-[80] overflow-y-auto"
className={cn('fixed inset-0 z-[30] overflow-y-auto', dialogClassName)}
>
<div className={cn('flex h-screen w-screen justify-end', positionCenter && '!justify-center')}>
{/* mask */}
<DialogBackdrop
className={cn('fixed inset-0 z-[90]', mask && 'bg-black bg-opacity-30')}
className={cn('fixed inset-0 z-[40]', mask && 'bg-black/30', dialogBackdropClassName)}
onClick={() => {
!clickOutsideNotOpen && onClose()
}}
/>
<div className={cn('relative z-[100] flex w-full max-w-sm flex-col justify-between overflow-hidden bg-components-panel-bg p-6 text-left align-middle shadow-xl', panelClassname)}>
<div className={cn('relative z-[50] flex w-full max-w-sm flex-col justify-between overflow-hidden bg-components-panel-bg p-6 text-left align-middle shadow-xl', panelClassName)}>
<>
<div className='flex justify-between'>
{title && <DialogTitle

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