diff --git a/README_AR.md b/README_AR.md index e58f59da5d..4f93802fda 100644 --- a/README_AR.md +++ b/README_AR.md @@ -4,7 +4,7 @@ Dify Cloud · الاستضافة الذاتية · التوثيق · - استفسار الشركات (للإنجليزية فقط) + نظرة عامة على منتجات Dify
diff --git a/README_BN.md b/README_BN.md index 3ebc81af5d..7599fae9ff 100644 --- a/README_BN.md +++ b/README_BN.md @@ -8,7 +8,7 @@ ডিফাই ক্লাউড · সেল্ফ-হোস্টিং · ডকুমেন্টেশন · - ব্যাবসায়িক অনুসন্ধান + Dify পণ্যের রূপভেদ
diff --git a/README_CN.md b/README_CN.md index 6d3c601100..973629f459 100644 --- a/README_CN.md +++ b/README_CN.md @@ -4,7 +4,7 @@ Dify 云服务 · 自托管 · 文档 · - （需用英文）常见问题解答 / 联系团队 + Dify 产品形态总览
diff --git a/README_DE.md b/README_DE.md index b3b9bf3221..738c0e3b67 100644 --- a/README_DE.md +++ b/README_DE.md @@ -8,7 +8,7 @@ Dify Cloud · Selbstgehostetes · Dokumentation · - Anfrage an Unternehmen + Überblick über die Dify-Produkte
diff --git a/README_ES.md b/README_ES.md index d14afdd2eb..212268b73d 100644 --- a/README_ES.md +++ b/README_ES.md @@ -4,7 +4,7 @@ Dify Cloud · Auto-alojamiento · Documentación · - Consultas empresariales (en inglés) + Resumen de las ediciones de Dify
diff --git a/README_FR.md b/README_FR.md index 031196303e..89eea7d058 100644 --- a/README_FR.md +++ b/README_FR.md @@ -4,7 +4,7 @@ Dify Cloud · Auto-hébergement · Documentation · - Demande d’entreprise (en anglais seulement) + Présentation des différentes offres Dify
diff --git a/README_JA.md b/README_JA.md index 3b7a6f50db..adca219753 100644 --- a/README_JA.md +++ b/README_JA.md @@ -4,7 +4,7 @@ Dify Cloud · セルフホスティング · ドキュメント · - 企業のお問い合わせ（英語のみ） + Difyの各種エディションについて
diff --git a/README_KL.md b/README_KL.md index ccadb77274..17e6c9d509 100644 --- a/README_KL.md +++ b/README_KL.md @@ -4,7 +4,7 @@ Dify Cloud · Self-hosting · Documentation · - Commercial enquiries + Dify product editions
diff --git a/README_KR.md b/README_KR.md index c1a98f8b68..d44723f9b6 100644 --- a/README_KR.md +++ b/README_KR.md @@ -4,7 +4,7 @@ Dify 클라우드 · 셀프-호스팅 · 문서 · - 기업 문의 (영어만 가능) + Dify 제품 에디션 안내
diff --git a/README_PT.md b/README_PT.md index 5b3c782645..9dc2207279 100644 --- a/README_PT.md +++ b/README_PT.md @@ -8,7 +8,7 @@ Dify Cloud · Auto-hospedagem · Documentação · - Consultas empresariais + Visão geral das edições do Dify
diff --git a/README_SI.md b/README_SI.md index 7c0867c776..caa5975973 100644 --- a/README_SI.md +++ b/README_SI.md @@ -8,7 +8,7 @@ Dify Cloud · Samostojno gostovanje · Dokumentacija · - Povpraševanje za podjetja + Pregled ponudb izdelkov Dify
diff --git a/README_TR.md b/README_TR.md index f8890b00ef..ab2853a019 100644 --- a/README_TR.md +++ b/README_TR.md @@ -4,7 +4,7 @@ Dify Bulut · Kendi Sunucunuzda Barındırma · Dokümantasyon · - Yalnızca İngilizce: Kurumsal Sorgulama + Dify ürün seçeneklerine genel bakış
diff --git a/README_TW.md b/README_TW.md index 260f1e80ac..8263a22b64 100644 --- a/README_TW.md +++ b/README_TW.md @@ -8,7 +8,7 @@ Dify 雲端服務 · 自行託管 · 說明文件 · - 企業諮詢 + 產品方案概覽
diff --git a/README_VI.md b/README_VI.md index 15d2d5ae80..852ed7aaa0 100644 --- a/README_VI.md +++ b/README_VI.md @@ -4,7 +4,7 @@ Dify Cloud · Tự triển khai · Tài liệu · - Yêu cầu doanh nghiệp + Tổng quan các lựa chọn sản phẩm Dify
diff --git a/api/.env.example b/api/.env.example index 01ddb4adfd..b5820fcdc2 100644 --- a/api/.env.example +++ b/api/.env.example @@ -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 \ No newline at end of file +OTEL_METRIC_EXPORT_TIMEOUT=30000 + +# Prevent Clickjacking +ALLOW_EMBED=false diff --git a/api/app_factory.py b/api/app_factory.py index 586f2ded9e..9648d770ab 100644 --- a/api/app_factory.py +++ b/api/app_factory.py @@ -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: diff --git a/api/configs/app_config.py b/api/configs/app_config.py index cb0adb751c..3a3ad35ee7 100644 --- a/api/configs/app_config.py +++ b/api/configs/app_config.py @@ -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 {} diff --git a/api/configs/middleware/__init__.py b/api/configs/middleware/__init__.py index 15dfe0063b..c2ad24094a 100644 --- a/api/configs/middleware/__init__.py +++ b/api/configs/middleware/__init__.py @@ -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, diff --git a/api/configs/middleware/vdb/huawei_cloud_config.py b/api/configs/middleware/vdb/huawei_cloud_config.py new file mode 100644 index 0000000000..2290c60499 --- /dev/null +++ b/api/configs/middleware/vdb/huawei_cloud_config.py @@ -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, + ) diff --git a/api/configs/remote_settings_sources/enums.py b/api/configs/remote_settings_sources/enums.py index 3081f2950f..dd998cac64 100644 --- a/api/configs/remote_settings_sources/enums.py +++ b/api/configs/remote_settings_sources/enums.py @@ -3,3 +3,4 @@ from enum import StrEnum class RemoteSettingsSourceName(StrEnum): APOLLO = "apollo" + NACOS = "nacos" diff --git a/api/configs/remote_settings_sources/nacos/__init__.py b/api/configs/remote_settings_sources/nacos/__init__.py new file mode 100644 index 0000000000..b1ce8e87bc --- /dev/null +++ b/api/configs/remote_settings_sources/nacos/__init__.py @@ -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 diff --git a/api/configs/remote_settings_sources/nacos/http_request.py b/api/configs/remote_settings_sources/nacos/http_request.py new file mode 100644 index 0000000000..2785bd955b --- /dev/null +++ b/api/configs/remote_settings_sources/nacos/http_request.py @@ -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 diff --git a/api/configs/remote_settings_sources/nacos/utils.py b/api/configs/remote_settings_sources/nacos/utils.py new file mode 100644 index 0000000000..f3372563b1 --- /dev/null +++ b/api/configs/remote_settings_sources/nacos/utils.py @@ -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 diff --git a/api/controllers/console/app/generator.py b/api/controllers/console/app/generator.py index 8518d34a8e..4046417076 100644 --- a/api/controllers/console/app/generator.py +++ b/api/controllers/console/app/generator.py @@ -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") diff --git a/api/controllers/console/auth/forgot_password.py b/api/controllers/console/auth/forgot_password.py index dc0009f36e..d4a33645ab 100644 --- a/api/controllers/console/auth/forgot_password.py +++ b/api/controllers/console/auth/forgot_password.py @@ -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"]) diff --git a/api/controllers/console/auth/login.py b/api/controllers/console/auth/login.py index 41362e9fa2..16c1dcc441 100644 --- a/api/controllers/console/auth/login.py +++ b/api/controllers/console/auth/login.py @@ -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") diff --git a/api/controllers/console/datasets/datasets.py b/api/controllers/console/datasets/datasets.py index 4644ac6299..752d124735 100644 --- a/api/controllers/console/datasets/datasets.py +++ b/api/controllers/console/datasets/datasets.py @@ -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": [ diff --git a/api/controllers/console/wraps.py b/api/controllers/console/wraps.py index 6caaae87f4..e5e8038ad7 100644 --- a/api/controllers/console/wraps.py +++ b/api/controllers/console/wraps.py @@ -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 diff --git a/api/controllers/files/upload.py b/api/controllers/files/upload.py index ca5ea54435..28ee0eecf4 100644 --- a/api/controllers/files/upload.py +++ b/api/controllers/files/upload.py @@ -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") diff --git a/api/core/agent/base_agent_runner.py b/api/core/agent/base_agent_runner.py index 48c92ea2db..e648613605 100644 --- a/api/core/agent/base_agent_runner.py +++ b/api/core/agent/base_agent_runner.py @@ -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( diff --git a/api/core/agent/cot_chat_agent_runner.py b/api/core/agent/cot_chat_agent_runner.py index 7d407a4976..5ff89bdacb 100644 --- a/api/core/agent/cot_chat_agent_runner.py +++ b/api/core/agent/cot_chat_agent_runner.py @@ -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 diff --git a/api/core/agent/fc_agent_runner.py b/api/core/agent/fc_agent_runner.py index f45fa5c66e..a1110e7709 100644 --- a/api/core/agent/fc_agent_runner.py +++ b/api/core/agent/fc_agent_runner.py @@ -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 diff --git a/api/core/file/file_manager.py b/api/core/file/file_manager.py index 4ebe997ac5..9a204e9ff6 100644 --- a/api/core/file/file_manager.py +++ b/api/core/file/file_manager.py @@ -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, diff --git a/api/core/llm_generator/llm_generator.py b/api/core/llm_generator/llm_generator.py index 75687f9ae3..d5d2ca60fa 100644 --- a/api/core/llm_generator/llm_generator.py +++ b/api/core/llm_generator/llm_generator.py @@ -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)}"} diff --git a/api/core/llm_generator/prompts.py b/api/core/llm_generator/prompts.py index cf20e60c82..82d22d7f89 100644 --- a/api/core/llm_generator/prompts.py +++ b/api/core/llm_generator/prompts.py @@ -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 diff --git a/api/core/memory/token_buffer_memory.py b/api/core/memory/token_buffer_memory.py index 3c90dd22a2..2254b3d4d5 100644 --- a/api/core/memory/token_buffer_memory.py +++ b/api/core/memory/token_buffer_memory.py @@ -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( diff --git a/api/core/model_runtime/entities/message_entities.py b/api/core/model_runtime/entities/message_entities.py index 977678b893..b1c43d1455 100644 --- a/api/core/model_runtime/entities/message_entities.py +++ b/api/core/model_runtime/entities/message_entities.py @@ -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): """ diff --git a/api/core/model_runtime/entities/model_entities.py b/api/core/model_runtime/entities/model_entities.py index 3225f03fbd..373ef2bbe2 100644 --- a/api/core/model_runtime/entities/model_entities.py +++ b/api/core/model_runtime/entities/model_entities.py @@ -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 diff --git a/api/core/plugin/backwards_invocation/node.py b/api/core/plugin/backwards_invocation/node.py index f402da030f..db07e52f3f 100644 --- a/api/core/plugin/backwards_invocation/node.py +++ b/api/core/plugin/backwards_invocation/node.py @@ -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( diff --git a/api/core/prompt/advanced_prompt_transform.py b/api/core/prompt/advanced_prompt_transform.py index c7427f797e..25964ae063 100644 --- a/api/core/prompt/advanced_prompt_transform.py +++ b/api/core/prompt/advanced_prompt_transform.py @@ -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( diff --git a/api/core/prompt/simple_prompt_transform.py b/api/core/prompt/simple_prompt_transform.py index ad56d84cb6..47808928f7 100644 --- a/api/core/prompt/simple_prompt_transform.py +++ b/api/core/prompt/simple_prompt_transform.py @@ -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( diff --git a/api/core/rag/datasource/vdb/huawei/__init__.py b/api/core/rag/datasource/vdb/huawei/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/api/core/rag/datasource/vdb/huawei/huawei_cloud_vector.py b/api/core/rag/datasource/vdb/huawei/huawei_cloud_vector.py new file mode 100644 index 0000000000..89423eb160 --- /dev/null +++ b/api/core/rag/datasource/vdb/huawei/huawei_cloud_vector.py @@ -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, + ), + ) diff --git a/api/core/rag/datasource/vdb/oracle/oraclevector.py b/api/core/rag/datasource/vdb/oracle/oraclevector.py index 4af2578197..63695e6f3f 100644 --- a/api/core/rag/datasource/vdb/oracle/oraclevector.py +++ b/api/core/rag/datasource/vdb/oracle/oraclevector.py @@ -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): diff --git a/api/core/rag/datasource/vdb/vector_factory.py b/api/core/rag/datasource/vdb/vector_factory.py index 00601c38a1..05158cc7ca 100644 --- a/api/core/rag/datasource/vdb/vector_factory.py +++ b/api/core/rag/datasource/vdb/vector_factory.py @@ -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.") diff --git a/api/core/rag/datasource/vdb/vector_type.py b/api/core/rag/datasource/vdb/vector_type.py index 940f12caef..0421be3458 100644 --- a/api/core/rag/datasource/vdb/vector_type.py +++ b/api/core/rag/datasource/vdb/vector_type.py @@ -26,3 +26,4 @@ class VectorType(StrEnum): OCEANBASE = "oceanbase" OPENGAUSS = "opengauss" TABLESTORE = "tablestore" + HUAWEI_CLOUD = "huawei_cloud" diff --git a/api/core/rag/extractor/word_extractor.py b/api/core/rag/extractor/word_extractor.py index 70c618a631..edaa8c92fa 100644 --- a/api/core/rag/extractor/word_extractor.py +++ b/api/core/rag/extractor/word_extractor.py @@ -126,9 +126,7 @@ class WordExtractor(BaseExtractor): db.session.add(upload_file) db.session.commit() - image_map[rel.target_part] = ( - f"" - ) + image_map[rel.target_part] = f"" return image_map diff --git a/api/core/rag/retrieval/dataset_retrieval.py b/api/core/rag/retrieval/dataset_retrieval.py index 23ea775dec..4869a21e80 100644 --- a/api/core/rag/retrieval/dataset_retrieval.py +++ b/api/core/rag/retrieval/dataset_retrieval.py @@ -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 diff --git a/api/core/repository/workflow_node_execution_repository.py b/api/core/repository/workflow_node_execution_repository.py index 6dea4566de..9bb790cb0f 100644 --- a/api/core/repository/workflow_node_execution_repository.py +++ b/api/core/repository/workflow_node_execution_repository.py @@ -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. + """ + ... diff --git a/api/core/tools/utils/dataset_retriever/dataset_retriever_tool.py b/api/core/tools/utils/dataset_retriever/dataset_retriever_tool.py index f661294ec4..f5838c3b76 100644 --- a/api/core/tools/utils/dataset_retriever/dataset_retriever_tool.py +++ b/api/core/tools/utils/dataset_retriever/dataset_retriever_tool.py @@ -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) diff --git a/api/core/workflow/nodes/agent/agent_node.py b/api/core/workflow/nodes/agent/agent_node.py index 7c8960fe49..da40cbcdea 100644 --- a/api/core/workflow/nodes/agent/agent_node.py +++ b/api/core/workflow/nodes/agent/agent_node.py @@ -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 diff --git a/api/core/workflow/nodes/agent/entities.py b/api/core/workflow/nodes/agent/entities.py index 87cc7e9824..77e94375bf 100644 --- a/api/core/workflow/nodes/agent/entities.py +++ b/api/core/workflow/nodes/agent/entities.py @@ -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" diff --git a/api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py b/api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py index 9f95b0466b..f1798ccedd 100644 --- a/api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py +++ b/api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py @@ -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)) diff --git a/api/core/workflow/nodes/llm/entities.py b/api/core/workflow/nodes/llm/entities.py index bf54fdb80c..486b4b01af 100644 --- a/api/core/workflow/nodes/llm/entities.py +++ b/api/core/workflow/nodes/llm/entities.py @@ -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 diff --git a/api/core/workflow/nodes/llm/node.py b/api/core/workflow/nodes/llm/node.py index fe0ed3e564..1089e7168e 100644 --- a/api/core/workflow/nodes/llm/node.py +++ b/api/core/workflow/nodes/llm/node.py @@ -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) diff --git a/api/core/workflow/utils/structured_output/entities.py b/api/core/workflow/utils/structured_output/entities.py new file mode 100644 index 0000000000..7954acbaee --- /dev/null +++ b/api/core/workflow/utils/structured_output/entities.py @@ -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" diff --git a/api/core/workflow/utils/structured_output/prompt.py b/api/core/workflow/utils/structured_output/prompt.py new file mode 100644 index 0000000000..06d9b2056e --- /dev/null +++ b/api/core/workflow/utils/structured_output/prompt.py @@ -0,0 +1,17 @@ +STRUCTURED_OUTPUT_PROMPT = """You’re 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 diff --git a/api/extensions/ext_otel.py b/api/extensions/ext_otel.py index 59ec0d0686..a2edd832ec 100644 --- a/api/extensions/ext_otel.py +++ b/api/extensions/ext_otel.py @@ -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") diff --git a/api/extensions/ext_otel_patch.py b/api/extensions/ext_otel_patch.py new file mode 100644 index 0000000000..58309fe4d1 --- /dev/null +++ b/api/extensions/ext_otel_patch.py @@ -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") diff --git a/api/fields/file_fields.py b/api/fields/file_fields.py index f896c15f0f..dfc1b623d5 100644 --- a/api/fields/file_fields.py +++ b/api/fields/file_fields.py @@ -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 = { diff --git a/api/models/workflow.py b/api/models/workflow.py index 045fa0aaa0..5a67fa47a8 100644 --- a/api/models/workflow.py +++ b/api/models/workflow.py @@ -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 diff --git a/api/pyproject.toml b/api/pyproject.toml index 85679a6359..4992178423 100644 --- a/api/pyproject.toml +++ b/api/pyproject.toml @@ -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", diff --git a/api/repositories/workflow_node_execution/sqlalchemy_repository.py b/api/repositories/workflow_node_execution/sqlalchemy_repository.py index c9c6e70ff3..e0ad384be6 100644 --- a/api/repositories/workflow_node_execution/sqlalchemy_repository.py +++ b/api/repositories/workflow_node_execution/sqlalchemy_repository.py @@ -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 "") + ) diff --git a/api/services/account_service.py b/api/services/account_service.py index ada8109067..f930ef910b 100644 --- a/api/services/account_service.py +++ b/api/services/account_service.py @@ -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") diff --git a/api/services/workflow_run_service.py b/api/services/workflow_run_service.py index 0ddd18ea27..8b7213eefb 100644 --- a/api/services/workflow_run_service.py +++ b/api/services/workflow_run_service.py @@ -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) diff --git a/api/services/workflow_service.py b/api/services/workflow_service.py index 992942fc70..63e3791147 100644 --- a/api/services/workflow_service.py +++ b/api/services/workflow_service.py @@ -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") diff --git a/api/tasks/remove_app_and_related_data_task.py b/api/tasks/remove_app_and_related_data_task.py index c3910e2be3..cd8981abf6 100644 --- a/api/tasks/remove_app_and_related_data_task.py +++ b/api/tasks/remove_app_and_related_data_task.py @@ -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): diff --git a/api/tests/integration_tests/vdb/__mock/huaweicloudvectordb.py b/api/tests/integration_tests/vdb/__mock/huaweicloudvectordb.py new file mode 100644 index 0000000000..e1aba4e2c1 --- /dev/null +++ b/api/tests/integration_tests/vdb/__mock/huaweicloudvectordb.py @@ -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() diff --git a/api/tests/integration_tests/vdb/huawei/__init__.py b/api/tests/integration_tests/vdb/huawei/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/api/tests/integration_tests/vdb/huawei/test_huawei_cloud.py b/api/tests/integration_tests/vdb/huawei/test_huawei_cloud.py new file mode 100644 index 0000000000..943b2bc877 --- /dev/null +++ b/api/tests/integration_tests/vdb/huawei/test_huawei_cloud.py @@ -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() diff --git a/api/tests/unit_tests/core/prompt/test_prompt_message.py b/api/tests/unit_tests/core/prompt/test_prompt_message.py new file mode 100644 index 0000000000..e5da51d733 --- /dev/null +++ b/api/tests/unit_tests/core/prompt/test_prompt_message.py @@ -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 diff --git a/api/tests/unit_tests/repositories/workflow_node_execution/test_sqlalchemy_repository.py b/api/tests/unit_tests/repositories/workflow_node_execution/test_sqlalchemy_repository.py index f31adab2a8..36847f8a13 100644 --- a/api/tests/unit_tests/repositories/workflow_node_execution/test_sqlalchemy_repository.py +++ b/api/tests/unit_tests/repositories/workflow_node_execution/test_sqlalchemy_repository.py @@ -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() diff --git a/api/tests/unit_tests/services/workflow/test_workflow_deletion.py b/api/tests/unit_tests/services/workflow/test_workflow_deletion.py index 56efcccc78..223020c2c5 100644 --- a/api/tests/unit_tests/services/workflow/test_workflow_deletion.py +++ b/api/tests/unit_tests/services/workflow/test_workflow_deletion.py @@ -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 diff --git a/api/uv.lock b/api/uv.lock index 4ff9c34446..6c8699dd7c 100644 --- a/api/uv.lock +++ b/api/uv.lock @@ -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" 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api/tests/integration_tests/vdb/tidb_vector \ + api/tests/integration_tests/vdb/huawei \ diff --git a/docker/.env.example b/docker/.env.example index 82ef4174c2..0b80dccb37 100644 --- a/docker/.env.example +++ b/docker/.env.example @@ -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 diff --git a/docker/docker-compose-template.yaml b/docker/docker-compose-template.yaml index a8f7b755fb..377ff9c117 100644 --- a/docker/docker-compose-template.yaml +++ b/docker/docker-compose-template.yaml @@ -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 diff --git a/docker/docker-compose.middleware.yaml b/docker/docker-compose.middleware.yaml index 27d6d660d0..1702a5395f 100644 --- a/docker/docker-compose.middleware.yaml +++ b/docker/docker-compose.middleware.yaml @@ -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 diff --git a/docker/docker-compose.yaml b/docker/docker-compose.yaml index e01b9f7e9a..81fa651ed9 100644 --- a/docker/docker-compose.yaml +++ b/docker/docker-compose.yaml @@ -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 diff --git a/web/.env.example b/web/.env.example index 1c3f42ddfc..51631c2437 100644 --- a/web/.env.example +++ b/web/.env.example @@ -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= diff --git a/web/README.md b/web/README.md index 3236347e80..3d9fd2de87 100644 --- a/web/README.md +++ b/web/README.md @@ -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: diff --git a/web/app/components/app/configuration/config-var/config-select/index.spec.tsx b/web/app/components/app/configuration/config-var/config-select/index.spec.tsx new file mode 100644 index 0000000000..18df318de3 --- /dev/null +++ b/web/app/components/app/configuration/config-var/config-select/index.spec.tsx @@ -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 }) =>