merge main

api/.env.example

@@ -42,6 +42,11 @@ REDIS_SENTINEL_USERNAME=
 REDIS_SENTINEL_PASSWORD=
 REDIS_SENTINEL_SOCKET_TIMEOUT=0.1
 
+# redis Cluster configuration.
+REDIS_USE_CLUSTERS=false
+REDIS_CLUSTERS=
+REDIS_CLUSTERS_PASSWORD=
+
 # PostgreSQL database configuration
 DB_USERNAME=postgres
 DB_PASSWORD=difyai123456
@@ -234,6 +239,10 @@ ANALYTICDB_ACCOUNT=testaccount
 ANALYTICDB_PASSWORD=testpassword
 ANALYTICDB_NAMESPACE=dify
 ANALYTICDB_NAMESPACE_PASSWORD=difypassword
+ANALYTICDB_HOST=gp-test.aliyuncs.com
+ANALYTICDB_PORT=5432
+ANALYTICDB_MIN_CONNECTION=1
+ANALYTICDB_MAX_CONNECTION=5
 
 # OpenSearch configuration
 OPENSEARCH_HOST=127.0.0.1

api/commands.py

@@ -589,7 +589,7 @@ def upgrade_db():
             click.echo(click.style("Database migration successful!", fg="green"))
 
         except Exception as e:
-            logging.exception(f"Database migration failed: {e}")
+            logging.exception("Failed to execute database migration")
         finally:
             lock.release()
     else:
@@ -633,7 +633,7 @@ where sites.id is null limit 1000"""
                 except Exception as e:
                     failed_app_ids.append(app_id)
                     click.echo(click.style("Failed to fix missing site for app {}".format(app_id), fg="red"))
-                    logging.exception(f"Fix app related site missing issue failed, error: {e}")
+                    logging.exception(f"Failed to fix app related site missing issue, app_id: {app_id}")
                     continue
 
             if not processed_count:

api/configs/feature/__init__.py

@@ -616,6 +616,11 @@ class DataSetConfig(BaseSettings):
         default=False,
     )
 
+    PLAN_SANDBOX_CLEAN_MESSAGE_DAY_SETTING: PositiveInt = Field(
+        description="Interval in days for message cleanup operations - plan: sandbox",
+        default=30,
+    )
+
 
 class WorkspaceConfig(BaseSettings):
     """

api/configs/middleware/cache/redis_config.py

@@ -68,3 +68,18 @@ class RedisConfig(BaseSettings):
         description="Socket timeout in seconds for Redis Sentinel connections",
         default=0.1,
     )
+
+    REDIS_USE_CLUSTERS: bool = Field(
+        description="Enable Redis Clusters mode for high availability",
+        default=False,
+    )
+
+    REDIS_CLUSTERS: Optional[str] = Field(
+        description="Comma-separated list of Redis Clusters nodes (host:port)",
+        default=None,
+    )
+
+    REDIS_CLUSTERS_PASSWORD: Optional[str] = Field(
+        description="Password for Redis Clusters authentication (if required)",
+        default=None,
+    )

api/configs/middleware/vdb/analyticdb_config.py

@@ -1,6 +1,6 @@
 from typing import Optional
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, PositiveInt
 
 
 class AnalyticdbConfig(BaseModel):
@@ -40,3 +40,11 @@ class AnalyticdbConfig(BaseModel):
         description="The password for accessing the specified namespace within the AnalyticDB instance"
         " (if namespace feature is enabled).",
     )
+    ANALYTICDB_HOST: Optional[str] = Field(
+        default=None, description="The host of the AnalyticDB instance you want to connect to."
+    )
+    ANALYTICDB_PORT: PositiveInt = Field(
+        default=5432, description="The port of the AnalyticDB instance you want to connect to."
+    )
+    ANALYTICDB_MIN_CONNECTION: PositiveInt = Field(default=1, description="Min connection of the AnalyticDB database.")
+    ANALYTICDB_MAX_CONNECTION: PositiveInt = Field(default=5, description="Max connection of the AnalyticDB database.")

api/configs/packaging/__init__.py

@@ -9,7 +9,7 @@ class PackagingInfo(BaseSettings):
 
     CURRENT_VERSION: str = Field(
         description="Dify version",
-        default="0.11.1",
+        default="0.11.2",
     )
 
     COMMIT_SHA: str = Field(

api/controllers/console/app/app.py

@@ -9,6 +9,7 @@ from controllers.console.app.wraps import get_app_model
 from controllers.console.wraps import (
     account_initialization_required,
     cloud_edition_billing_resource_check,
+    enterprise_license_required,
     setup_required,
 )
 from core.ops.ops_trace_manager import OpsTraceManager
@@ -28,6 +29,7 @@ class AppListApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
+    @enterprise_license_required
     def get(self):
         """Get app list"""
 
@@ -149,6 +151,7 @@ class AppApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
+    @enterprise_license_required
     @get_app_model
     @marshal_with(app_detail_fields_with_site)
     def get(self, app_model):

api/controllers/console/app/audio.py

@@ -70,7 +70,7 @@ class ChatMessageAudioApi(Resource):
         except ValueError as e:
             raise e
         except Exception as e:
-            logging.exception(f"internal server error, {str(e)}.")
+            logging.exception("Failed to handle post request to ChatMessageAudioApi")
             raise InternalServerError()
 
 
@@ -128,7 +128,7 @@ class ChatMessageTextApi(Resource):
         except ValueError as e:
             raise e
         except Exception as e:
-            logging.exception(f"internal server error, {str(e)}.")
+            logging.exception("Failed to handle post request to ChatMessageTextApi")
             raise InternalServerError()
 
 
@@ -170,7 +170,7 @@ class TextModesApi(Resource):
         except ValueError as e:
             raise e
         except Exception as e:
-            logging.exception(f"internal server error, {str(e)}.")
+            logging.exception("Failed to handle get request to TextModesApi")
             raise InternalServerError()
 
 

api/controllers/console/auth/forgot_password.py

@@ -12,7 +12,7 @@ from controllers.console.auth.error import (
     InvalidTokenError,
     PasswordMismatchError,
 )
-from controllers.console.error import EmailSendIpLimitError, NotAllowedRegister
+from controllers.console.error import AccountNotFound, EmailSendIpLimitError
 from controllers.console.wraps import setup_required
 from events.tenant_event import tenant_was_created
 from extensions.ext_database import db
@@ -48,7 +48,7 @@ class ForgotPasswordSendEmailApi(Resource):
                 token = AccountService.send_reset_password_email(email=args["email"], language=language)
                 return {"result": "fail", "data": token, "code": "account_not_found"}
             else:
-                raise NotAllowedRegister()
+                raise AccountNotFound()
         else:
             token = AccountService.send_reset_password_email(account=account, email=args["email"], language=language)
 

api/controllers/console/auth/login.py

@@ -16,9 +16,9 @@ from controllers.console.auth.error import (
 )
 from controllers.console.error import (
     AccountBannedError,
+    AccountNotFound,
     EmailSendIpLimitError,
     NotAllowedCreateWorkspace,
-    NotAllowedRegister,
 )
 from controllers.console.wraps import setup_required
 from events.tenant_event import tenant_was_created
@@ -76,7 +76,7 @@ class LoginApi(Resource):
                 token = AccountService.send_reset_password_email(email=args["email"], language=language)
                 return {"result": "fail", "data": token, "code": "account_not_found"}
             else:
-                raise NotAllowedRegister()
+                raise AccountNotFound()
         # SELF_HOSTED only have one workspace
         tenants = TenantService.get_join_tenants(account)
         if len(tenants) == 0:
@@ -119,7 +119,7 @@ class ResetPasswordSendEmailApi(Resource):
             if FeatureService.get_system_features().is_allow_register:
                 token = AccountService.send_reset_password_email(email=args["email"], language=language)
             else:
-                raise NotAllowedRegister()
+                raise AccountNotFound()
         else:
             token = AccountService.send_reset_password_email(account=account, language=language)
 
@@ -148,7 +148,7 @@ class EmailCodeLoginSendEmailApi(Resource):
             if FeatureService.get_system_features().is_allow_register:
                 token = AccountService.send_email_code_login_email(email=args["email"], language=language)
             else:
-                raise NotAllowedRegister()
+                raise AccountNotFound()
         else:
             token = AccountService.send_email_code_login_email(account=account, language=language)
 

api/controllers/console/datasets/datasets.py

@@ -10,7 +10,7 @@ from controllers.console import api
 from controllers.console.apikey import api_key_fields, api_key_list
 from controllers.console.app.error import ProviderNotInitializeError
 from controllers.console.datasets.error import DatasetInUseError, DatasetNameDuplicateError, IndexingEstimateError
-from controllers.console.wraps import account_initialization_required, setup_required
+from controllers.console.wraps import account_initialization_required, enterprise_license_required, setup_required
 from core.errors.error import LLMBadRequestError, ProviderTokenNotInitError
 from core.indexing_runner import IndexingRunner
 from core.model_runtime.entities.model_entities import ModelType
@@ -44,6 +44,7 @@ class DatasetListApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
+    @enterprise_license_required
     def get(self):
         page = request.args.get("page", default=1, type=int)
         limit = request.args.get("limit", default=20, type=int)

api/controllers/console/datasets/datasets_document.py

@@ -948,7 +948,7 @@ class DocumentRetryApi(DocumentResource):
                     raise DocumentAlreadyFinishedError()
                 retry_documents.append(document)
             except Exception as e:
-                logging.exception(f"Document {document_id} retry failed: {str(e)}")
+                logging.exception(f"Failed to retry document, document id: {document_id}")
                 continue
         # retry document
         DocumentService.retry_document(dataset_id, retry_documents)

api/controllers/console/error.py

@@ -52,8 +52,8 @@ class AccountBannedError(BaseHTTPException):
     code = 400
 
 
-class NotAllowedRegister(BaseHTTPException):
-    error_code = "unauthorized"
+class AccountNotFound(BaseHTTPException):
+    error_code = "account_not_found"
     description = "Account not found."
     code = 400
 
@@ -86,3 +86,9 @@ class NoFileUploadedError(BaseHTTPException):
     error_code = "no_file_uploaded"
     description = "Please upload your file."
     code = 400
+
+
+class UnauthorizedAndForceLogout(BaseHTTPException):
+    error_code = "unauthorized_and_force_logout"
+    description = "Unauthorized and force logout."
+    code = 401

api/controllers/console/remote_files.py

@@ -45,7 +45,7 @@ class RemoteFileUploadApi(Resource):
 
         resp = ssrf_proxy.head(url=url)
         if resp.status_code != httpx.codes.OK:
-            resp = ssrf_proxy.get(url=url, timeout=3)
+            resp = ssrf_proxy.get(url=url, timeout=3, follow_redirects=True)
         resp.raise_for_status()
 
         file_info = helpers.guess_file_info_from_response(resp)

api/controllers/console/workspace/account.py

@@ -14,7 +14,7 @@ from controllers.console.workspace.error import (
     InvalidInvitationCodeError,
     RepeatPasswordNotMatchError,
 )
-from controllers.console.wraps import account_initialization_required, setup_required
+from controllers.console.wraps import account_initialization_required, enterprise_license_required, setup_required
 from extensions.ext_database import db
 from fields.member_fields import account_fields
 from libs.helper import TimestampField, timezone
@@ -79,6 +79,7 @@ class AccountProfileApi(Resource):
     @login_required
     @account_initialization_required
     @marshal_with(account_fields)
+    @enterprise_license_required
     def get(self):
         return current_user
 

api/controllers/console/workspace/members.py

@@ -1,3 +1,5 @@
+from urllib import parse
+
 from flask_login import current_user
 from flask_restful import Resource, abort, marshal_with, reqparse
 
@@ -57,11 +59,12 @@ class MemberInviteEmailApi(Resource):
                 token = RegisterService.invite_new_member(
                     inviter.current_tenant, invitee_email, interface_language, role=invitee_role, inviter=inviter
                 )
+                encoded_invitee_email = parse.quote(invitee_email)
                 invitation_results.append(
                     {
                         "status": "success",
                         "email": invitee_email,
-                        "url": f"{console_web_url}/activate?email={invitee_email}&token={token}",
+                        "url": f"{console_web_url}/activate?email={encoded_invitee_email}&token={token}",
                     }
                 )
             except AccountAlreadyInTenantError:

api/controllers/console/workspace/models.py

@@ -72,7 +72,10 @@ class DefaultModelApi(Resource):
                     model=model_setting["model"],
                 )
             except Exception as ex:
-                logging.exception(f"{model_setting['model_type']} save error: {ex}")
+                logging.exception(
+                    f"Failed to update default model, model type: {model_setting['model_type']},"
+                    f" model:{model_setting.get('model')}"
+                )
                 raise ex
 
         return {"result": "success"}
@@ -156,7 +159,10 @@ class ModelProviderModelApi(Resource):
                         credentials=args["credentials"],
                     )
                 except CredentialsValidateFailedError as ex:
-                    logging.exception(f"save model credentials error: {ex}")
+                    logging.exception(
+                        f"Failed to save model credentials, tenant_id: {tenant_id},"
+                        f" model: {args.get('model')}, model_type: {args.get('model_type')}"
+                    )
                     raise ValueError(str(ex))
 
         return {"result": "success"}, 200

api/controllers/console/workspace/tool_providers.py

@@ -7,7 +7,7 @@ from werkzeug.exceptions import Forbidden
 
 from configs import dify_config
 from controllers.console import api
-from controllers.console.wraps import account_initialization_required, setup_required
+from controllers.console.wraps import account_initialization_required, enterprise_license_required, setup_required
 from core.model_runtime.utils.encoders import jsonable_encoder
 from libs.helper import alphanumeric, uuid_value
 from libs.login import login_required
@@ -549,6 +549,7 @@ class ToolLabelsApi(Resource):
     @setup_required
     @login_required
     @account_initialization_required
+    @enterprise_license_required
     def get(self):
         return jsonable_encoder(ToolLabelsService.list_tool_labels())
 

api/controllers/console/wraps.py

@@ -8,10 +8,10 @@ from flask_login import current_user
 from configs import dify_config
 from controllers.console.workspace.error import AccountNotInitializedError
 from models.model import DifySetup
-from services.feature_service import FeatureService
+from services.feature_service import FeatureService, LicenseStatus
 from services.operation_service import OperationService
 
-from .error import NotInitValidateError, NotSetupError
+from .error import NotInitValidateError, NotSetupError, UnauthorizedAndForceLogout
 
 
 def account_initialization_required(view):
@@ -142,3 +142,15 @@ def setup_required(view):
         return view(*args, **kwargs)
 
     return decorated
+
+
+def enterprise_license_required(view):
+    @wraps(view)
+    def decorated(*args, **kwargs):
+        settings = FeatureService.get_system_features()
+        if settings.license.status in [LicenseStatus.INACTIVE, LicenseStatus.EXPIRED, LicenseStatus.LOST]:
+            raise UnauthorizedAndForceLogout("Your license is invalid. Please contact your administrator.")
+
+        return view(*args, **kwargs)
+
+    return decorated

api/controllers/web/audio.py

@@ -59,7 +59,7 @@ class AudioApi(WebApiResource):
         except ValueError as e:
             raise e
         except Exception as e:
-            logging.exception(f"internal server error: {str(e)}")
+            logging.exception("Failed to handle post request to AudioApi")
             raise InternalServerError()
 
 
@@ -117,7 +117,7 @@ class TextApi(WebApiResource):
         except ValueError as e:
             raise e
         except Exception as e:
-            logging.exception(f"internal server error: {str(e)}")
+            logging.exception("Failed to handle post request to TextApi")
             raise InternalServerError()
 
 

api/core/app/app_config/features/file_upload/manager.py

@@ -16,9 +16,7 @@ class FileUploadConfigManager:
         file_upload_dict = config.get("file_upload")
         if file_upload_dict:
             if file_upload_dict.get("enabled"):
-                transform_methods = file_upload_dict.get("allowed_file_upload_methods") or file_upload_dict.get(
-                    "allowed_upload_methods", []
-                )
+                transform_methods = file_upload_dict.get("allowed_file_upload_methods", [])
                 data = {
                     "image_config": {
                         "number_limits": file_upload_dict["number_limits"],

api/core/app/apps/advanced_chat/app_generator.py

@@ -362,5 +362,5 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             if e.args[0] == "I/O operation on closed file.":  # ignore this error
                 raise GenerateTaskStoppedError()
             else:
-                logger.exception(e)
+                logger.exception(f"Failed to process generate task pipeline, conversation_id: {conversation.id}")
                 raise e

api/core/app/apps/advanced_chat/generate_task_pipeline.py

@@ -242,7 +242,7 @@ class AdvancedChatAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCyc
                     start_listener_time = time.time()
                     yield MessageAudioStreamResponse(audio=audio_trunk.audio, task_id=task_id)
             except Exception as e:
-                logger.exception(e)
+                logger.exception(f"Failed to listen audio message, task_id: {task_id}")
                 break
         if tts_publisher:
             yield MessageAudioEndStreamResponse(audio="", task_id=task_id)

api/core/app/apps/base_app_generator.py

@@ -33,8 +33,8 @@ class BaseAppGenerator:
                 tenant_id=app_config.tenant_id,
                 config=FileUploadConfig(
                     allowed_file_types=entity_dictionary[k].allowed_file_types,
-                    allowed_extensions=entity_dictionary[k].allowed_file_extensions,
-                    allowed_upload_methods=entity_dictionary[k].allowed_file_upload_methods,
+                    allowed_file_extensions=entity_dictionary[k].allowed_file_extensions,
+                    allowed_file_upload_methods=entity_dictionary[k].allowed_file_upload_methods,
                 ),
             )
             for k, v in user_inputs.items()
@@ -47,8 +47,8 @@ class BaseAppGenerator:
                 tenant_id=app_config.tenant_id,
                 config=FileUploadConfig(
                     allowed_file_types=entity_dictionary[k].allowed_file_types,
-                    allowed_extensions=entity_dictionary[k].allowed_file_extensions,
-                    allowed_upload_methods=entity_dictionary[k].allowed_file_upload_methods,
+                    allowed_file_extensions=entity_dictionary[k].allowed_file_extensions,
+                    allowed_file_upload_methods=entity_dictionary[k].allowed_file_upload_methods,
                 ),
             )
             for k, v in user_inputs.items()
@@ -91,6 +91,9 @@ class BaseAppGenerator:
             )
 
         if variable_entity.type == VariableEntityType.NUMBER and isinstance(value, str):
+            # handle empty string case
+            if not value.strip():
+                return None
             # may raise ValueError if user_input_value is not a valid number
             try:
                 if "." in value:

api/core/app/apps/message_based_app_generator.py

@@ -80,7 +80,7 @@ class MessageBasedAppGenerator(BaseAppGenerator):
             if e.args[0] == "I/O operation on closed file.":  # ignore this error
                 raise GenerateTaskStoppedError()
             else:
-                logger.exception(e)
+                logger.exception(f"Failed to handle response, conversation_id: {conversation.id}")
                 raise e
 
     def _get_conversation_by_user(

api/core/app/apps/workflow/app_generator.py

@@ -298,5 +298,7 @@ class WorkflowAppGenerator(BaseAppGenerator):
             if e.args[0] == "I/O operation on closed file.":  # ignore this error
                 raise GenerateTaskStoppedError()
             else:
-                logger.exception(e)
+                logger.exception(
+                    f"Fails to process generate task pipeline, task_id: {application_generate_entity.task_id}"
+                )
                 raise e

api/core/app/apps/workflow/generate_task_pipeline.py

@@ -216,7 +216,7 @@ class WorkflowAppGenerateTaskPipeline(BasedGenerateTaskPipeline, WorkflowCycleMa
                 else:
                     yield MessageAudioStreamResponse(audio=audio_trunk.audio, task_id=task_id)
             except Exception as e:
-                logger.exception(e)
+                logger.exception(f"Fails to get audio trunk, task_id: {task_id}")
                 break
         if tts_publisher:
             yield MessageAudioEndStreamResponse(audio="", task_id=task_id)

api/core/app/task_pipeline/message_cycle_manage.py

@@ -86,7 +86,7 @@ class MessageCycleManage:
                     conversation.name = name
                 except Exception as e:
                     if dify_config.DEBUG:
-                        logging.exception(f"generate conversation name failed: {e}")
+                        logging.exception(f"generate conversation name failed, conversation_id: {conversation_id}")
                     pass
 
                 db.session.merge(conversation)

api/core/file/models.py

@@ -28,8 +28,8 @@ class FileUploadConfig(BaseModel):
 
     image_config: Optional[ImageConfig] = None
     allowed_file_types: Sequence[FileType] = Field(default_factory=list)
-    allowed_extensions: Sequence[str] = Field(default_factory=list)
-    allowed_upload_methods: Sequence[FileTransferMethod] = Field(default_factory=list)
+    allowed_file_extensions: Sequence[str] = Field(default_factory=list)
+    allowed_file_upload_methods: Sequence[FileTransferMethod] = Field(default_factory=list)
     number_limits: int = 0
 
 

api/core/helper/moderation.py

@@ -41,7 +41,7 @@ def check_moderation(model_config: ModelConfigWithCredentialsEntity, text: str)
                 if moderation_result is True:
                     return True
             except Exception as ex:
-                logger.exception(ex)
+                logger.exception(f"Fails to check moderation, provider_name: {provider_name}")
                 raise InvokeBadRequestError("Rate limit exceeded, please try again later.")
 
     return False

api/core/helper/module_import_helper.py

@@ -29,7 +29,7 @@ def import_module_from_source(*, module_name: str, py_file_path: AnyStr, use_laz
         spec.loader.exec_module(module)
         return module
     except Exception as e:
-        logging.exception(f"Failed to load module {module_name} from {py_file_path}: {str(e)}")
+        logging.exception(f"Failed to load module {module_name} from script file '{py_file_path}'")
         raise e
 
 

api/core/helper/ssrf_proxy.py

@@ -39,6 +39,7 @@ def make_request(method, url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
         )
 
     retries = 0
+    stream = kwargs.pop("stream", False)
     while retries <= max_retries:
         try:
             if dify_config.SSRF_PROXY_ALL_URL:
@@ -52,6 +53,8 @@ def make_request(method, url, max_retries=SSRF_DEFAULT_MAX_RETRIES, **kwargs):
                     response = client.request(method=method, url=url, **kwargs)
 
             if response.status_code not in STATUS_FORCELIST:
+                if stream:
+                    return response.iter_bytes()
                 return response
             else:
                 logging.warning(

api/core/indexing_runner.py

@@ -29,6 +29,7 @@ from core.rag.splitter.fixed_text_splitter import (
     FixedRecursiveCharacterTextSplitter,
 )
 from core.rag.splitter.text_splitter import TextSplitter
+from core.tools.utils.text_processing_utils import remove_leading_symbols
 from extensions.ext_database import db
 from extensions.ext_redis import redis_client
 from extensions.ext_storage import storage
@@ -500,11 +501,7 @@ class IndexingRunner:
                     document_node.metadata["doc_hash"] = hash
                     # delete Splitter character
                     page_content = document_node.page_content
-                    if page_content.startswith(".") or page_content.startswith("。"):
-                        page_content = page_content[1:]
-                    else:
-                        page_content = page_content
-                    document_node.page_content = page_content
+                    document_node.page_content = remove_leading_symbols(page_content)
 
                     if document_node.page_content:
                         split_documents.append(document_node)
@@ -554,7 +551,7 @@ class IndexingRunner:
                     qa_documents.append(qa_document)
                 format_documents.extend(qa_documents)
             except Exception as e:
-                logging.exception(e)
+                logging.exception("Failed to format qa document")
 
             all_qa_documents.extend(format_documents)
 

api/core/llm_generator/llm_generator.py

@@ -102,7 +102,7 @@ class LLMGenerator:
         except InvokeError:
             questions = []
         except Exception as e:
-            logging.exception(e)
+            logging.exception("Failed to generate suggested questions after answer")
             questions = []
 
         return questions
@@ -148,7 +148,7 @@ class LLMGenerator:
                 error = str(e)
                 error_step = "generate rule config"
             except Exception as e:
-                logging.exception(e)
+                logging.exception(f"Failed to generate rule config, model: {model_config.get('name')}")
                 rule_config["error"] = str(e)
 
             rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
@@ -234,7 +234,7 @@ class LLMGenerator:
                 error_step = "generate conversation opener"
 
         except Exception as e:
-            logging.exception(e)
+            logging.exception(f"Failed to generate rule config, model: {model_config.get('name')}")
             rule_config["error"] = str(e)
 
         rule_config["error"] = f"Failed to {error_step}. Error: {error}" if error else ""
@@ -286,7 +286,9 @@ class LLMGenerator:
             error = str(e)
             return {"code": "", "language": code_language, "error": f"Failed to generate code. Error: {error}"}
         except Exception as e:
-            logging.exception(e)
+            logging.exception(
+                f"Failed to invoke LLM model, model: {model_config.get('name')}, language: {code_language}"
+            )
             return {"code": "", "language": code_language, "error": f"An unexpected error occurred: {str(e)}"}
 
     @classmethod

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

@@ -325,14 +325,13 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
                 assistant_prompt_message.tool_calls.append(tool_call)
 
         # calculate num tokens
-        if response.usage:
-            # transform usage
-            prompt_tokens = response.usage.input_tokens
-            completion_tokens = response.usage.output_tokens
-        else:
-            # calculate num tokens
-            prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages)
-            completion_tokens = self.get_num_tokens(model, credentials, [assistant_prompt_message])
+        prompt_tokens = (response.usage and response.usage.input_tokens) or self.get_num_tokens(
+            model, credentials, prompt_messages
+        )
+
+        completion_tokens = (response.usage and response.usage.output_tokens) or self.get_num_tokens(
+            model, credentials, [assistant_prompt_message]
+        )
 
         # transform usage
         usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)

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

@@ -103,7 +103,7 @@ class AzureRerankModel(RerankModel):
             return RerankResult(model=model, docs=rerank_documents)
 
         except Exception as e:
-            logger.exception(f"Exception in Azure rerank: {e}")
+            logger.exception(f"Failed to invoke rerank model, model: {model}")
             raise
 
     def validate_credentials(self, model: str, credentials: dict) -> None:

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

@@ -2,13 +2,11 @@
 import base64
 import json
 import logging
-import mimetypes
 from collections.abc import Generator
 from typing import Optional, Union, cast
 
 # 3rd import
 import boto3
-import requests
 from botocore.config import Config
 from botocore.exceptions import (
     ClientError,
@@ -439,22 +437,10 @@ class BedrockLargeLanguageModel(LargeLanguageModel):
                         sub_messages.append(sub_message_dict)
                     elif message_content.type == PromptMessageContentType.IMAGE:
                         message_content = cast(ImagePromptMessageContent, message_content)
-                        if not message_content.data.startswith("data:"):
-                            # fetch image data from url
-                            try:
-                                url = message_content.data
-                                image_content = requests.get(url).content
-                                if "?" in url:
-                                    url = url.split("?")[0]
-                                mime_type, _ = mimetypes.guess_type(url)
-                                base64_data = base64.b64encode(image_content).decode("utf-8")
-                            except Exception as ex:
-                                raise ValueError(f"Failed to fetch image data from url {message_content.data}, {ex}")
-                        else:
-                            data_split = message_content.data.split(";base64,")
-                            mime_type = data_split[0].replace("data:", "")
-                            base64_data = data_split[1]
-                            image_content = base64.b64decode(base64_data)
+                        data_split = message_content.data.split(";base64,")
+                        mime_type = data_split[0].replace("data:", "")
+                        base64_data = data_split[1]
+                        image_content = base64.b64decode(base64_data)
 
                         if mime_type not in {"image/jpeg", "image/png", "image/gif", "image/webp"}:
                             raise ValueError(

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

@@ -11,5 +11,6 @@
 - gemini-1.5-flash-exp-0827
 - gemini-1.5-flash-8b-exp-0827
 - gemini-1.5-flash-8b-exp-0924
+- gemini-exp-1114
 - gemini-pro
 - gemini-pro-vision

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-001.yaml

@@ -24,14 +24,13 @@ parameter_rules:
       zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
       en_US: Only sample from the top K options for each subsequent token.
     required: false
-  - name: max_tokens_to_sample
+  - name: max_output_tokens
     use_template: max_tokens
-    required: true
     default: 8192
     min: 1
     max: 8192
-  - name: response_format
-    use_template: response_format
+  - name: json_schema
+    use_template: json_schema
 pricing:
   input: '0.00'
   output: '0.00'

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-002.yaml

@@ -24,14 +24,13 @@ parameter_rules:
       zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
       en_US: Only sample from the top K options for each subsequent token.
     required: false
-  - name: max_tokens_to_sample
+  - name: max_output_tokens
     use_template: max_tokens
-    required: true
     default: 8192
     min: 1
     max: 8192
-  - name: response_format
-    use_template: response_format
+  - name: json_schema
+    use_template: json_schema
 pricing:
   input: '0.00'
   output: '0.00'

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-8b-exp-0827.yaml

@@ -24,14 +24,13 @@ parameter_rules:
       zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
       en_US: Only sample from the top K options for each subsequent token.
     required: false
-  - name: max_tokens_to_sample
+  - name: max_output_tokens
     use_template: max_tokens
-    required: true
     default: 8192
     min: 1
     max: 8192
-  - name: response_format
-    use_template: response_format
+  - name: json_schema
+    use_template: json_schema
 pricing:
   input: '0.00'
   output: '0.00'

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-8b-exp-0924.yaml

@@ -24,14 +24,13 @@ parameter_rules:
       zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
       en_US: Only sample from the top K options for each subsequent token.
     required: false
-  - name: max_tokens_to_sample
+  - name: max_output_tokens
     use_template: max_tokens
-    required: true
     default: 8192
     min: 1
     max: 8192
-  - name: response_format
-    use_template: response_format
+  - name: json_schema
+    use_template: json_schema
 pricing:
   input: '0.00'
   output: '0.00'

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-exp-0827.yaml

@@ -24,14 +24,13 @@ parameter_rules:
       zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
       en_US: Only sample from the top K options for each subsequent token.
     required: false
-  - name: max_tokens_to_sample
+  - name: max_output_tokens
     use_template: max_tokens
-    required: true
     default: 8192
     min: 1
     max: 8192
-  - name: response_format
-    use_template: response_format
+  - name: json_schema
+    use_template: json_schema
 pricing:
   input: '0.00'
   output: '0.00'

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-latest.yaml

@@ -24,14 +24,13 @@ parameter_rules:
       zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
       en_US: Only sample from the top K options for each subsequent token.
     required: false
-  - name: max_tokens_to_sample
+  - name: max_output_tokens
     use_template: max_tokens
-    required: true
     default: 8192
     min: 1
     max: 8192
-  - name: response_format
-    use_template: response_format
+  - name: json_schema
+    use_template: json_schema
 pricing:
   input: '0.00'
   output: '0.00'

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash.yaml

@@ -24,14 +24,13 @@ parameter_rules:
       zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
       en_US: Only sample from the top K options for each subsequent token.
     required: false
-  - name: max_tokens_to_sample
+  - name: max_output_tokens
     use_template: max_tokens
-    required: true
     default: 8192
     min: 1
     max: 8192
-  - name: response_format
-    use_template: response_format
+  - name: json_schema
+    use_template: json_schema
 pricing:
   input: '0.00'
   output: '0.00'

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-001.yaml

@@ -24,14 +24,13 @@ parameter_rules:
       zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
       en_US: Only sample from the top K options for each subsequent token.
     required: false
-  - name: max_tokens_to_sample
+  - name: max_output_tokens
     use_template: max_tokens
-    required: true
     default: 8192
     min: 1
     max: 8192
-  - name: response_format
-    use_template: response_format
+  - name: json_schema
+    use_template: json_schema
 pricing:
   input: '0.00'
   output: '0.00'

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-002.yaml

@@ -24,14 +24,13 @@ parameter_rules:
       zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
       en_US: Only sample from the top K options for each subsequent token.
     required: false
-  - name: max_tokens_to_sample
+  - name: max_output_tokens
     use_template: max_tokens
-    required: true
     default: 8192
     min: 1
     max: 8192
-  - name: response_format
-    use_template: response_format
+  - name: json_schema
+    use_template: json_schema
 pricing:
   input: '0.00'
   output: '0.00'

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-exp-0801.yaml

@@ -24,14 +24,13 @@ parameter_rules:
       zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
       en_US: Only sample from the top K options for each subsequent token.
     required: false
-  - name: max_tokens_to_sample
+  - name: max_output_tokens
     use_template: max_tokens
-    required: true
     default: 8192
     min: 1
     max: 8192
-  - name: response_format
-    use_template: response_format
+  - name: json_schema
+    use_template: json_schema
 pricing:
   input: '0.00'
   output: '0.00'

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-exp-0827.yaml

@@ -24,14 +24,13 @@ parameter_rules:
       zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
       en_US: Only sample from the top K options for each subsequent token.
     required: false
-  - name: max_tokens_to_sample
+  - name: max_output_tokens
     use_template: max_tokens
-    required: true
     default: 8192
     min: 1
     max: 8192
-  - name: response_format
-    use_template: response_format
+  - name: json_schema
+    use_template: json_schema
 pricing:
   input: '0.00'
   output: '0.00'

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-latest.yaml

@@ -24,14 +24,13 @@ parameter_rules:
       zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
       en_US: Only sample from the top K options for each subsequent token.
     required: false
-  - name: max_tokens_to_sample
+  - name: max_output_tokens
     use_template: max_tokens
-    required: true
     default: 8192
     min: 1
     max: 8192
-  - name: response_format
-    use_template: response_format
+  - name: json_schema
+    use_template: json_schema
 pricing:
   input: '0.00'
   output: '0.00'

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro.yaml

@@ -24,14 +24,13 @@ parameter_rules:
       zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
       en_US: Only sample from the top K options for each subsequent token.
     required: false
-  - name: max_tokens_to_sample
+  - name: max_output_tokens
     use_template: max_tokens
-    required: true
     default: 8192
     min: 1
     max: 8192
-  - name: response_format
-    use_template: response_format
+  - name: json_schema
+    use_template: json_schema
 pricing:
   input: '0.00'
   output: '0.00'

api/core/model_runtime/model_providers/google/llm/gemini-exp-1114.yaml

@@ -0,0 +1,38 @@
+model: gemini-exp-1114
+label:
+  en_US: Gemini exp 1114
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 2097152
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_output_tokens
+    use_template: max_tokens
+    default: 8192
+    min: 1
+    max: 8192
+  - name: json_schema
+    use_template: json_schema
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

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

@@ -32,3 +32,4 @@ pricing:
   output: '0.00'
   unit: '0.000001'
   currency: USD
+deprecated: true

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

@@ -36,3 +36,4 @@ pricing:
   output: '0.00'
   unit: '0.000001'
   currency: USD
+deprecated: true

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

@@ -1,7 +1,6 @@
 import base64
 import io
 import json
-import logging
 from collections.abc import Generator
 from typing import Optional, Union, cast
 
@@ -36,17 +35,6 @@ from core.model_runtime.errors.invoke import (
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 
-logger = logging.getLogger(__name__)
-
-GEMINI_BLOCK_MODE_PROMPT = """You should always follow the instructions and output a valid {{block}} object.
-The structure of the {{block}} object you can found in the instructions, use {"answer": "$your_answer"} as the default structure
-if you are not sure about the structure.
-
-<instructions>
-{{instructions}}
-</instructions>
-"""  # noqa: E501
-
 
 class GoogleLargeLanguageModel(LargeLanguageModel):
     def _invoke(
@@ -155,7 +143,7 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
 
         try:
             ping_message = SystemPromptMessage(content="ping")
-            self._generate(model, credentials, [ping_message], {"max_tokens_to_sample": 5})
+            self._generate(model, credentials, [ping_message], {"max_output_tokens": 5})
 
         except Exception as ex:
             raise CredentialsValidateFailedError(str(ex))
@@ -184,7 +172,15 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
         :return: full response or stream response chunk generator result
         """
         config_kwargs = model_parameters.copy()
-        config_kwargs["max_output_tokens"] = config_kwargs.pop("max_tokens_to_sample", None)
+        if schema := config_kwargs.pop("json_schema", None):
+            try:
+                schema = json.loads(schema)
+            except:
+                raise exceptions.InvalidArgument("Invalid JSON Schema")
+            if tools:
+                raise exceptions.InvalidArgument("gemini not support use Tools and JSON Schema at same time")
+            config_kwargs["response_schema"] = schema
+            config_kwargs["response_mime_type"] = "application/json"
 
         if stop:
             config_kwargs["stop_sequences"] = stop

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

@@ -22,6 +22,7 @@ from core.model_runtime.entities.message_entities import (
     PromptMessageTool,
     SystemPromptMessage,
     TextPromptMessageContent,
+    ToolPromptMessage,
     UserPromptMessage,
 )
 from core.model_runtime.entities.model_entities import (
@@ -86,6 +87,7 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
             credentials=credentials,
             prompt_messages=prompt_messages,
             model_parameters=model_parameters,
+            tools=tools,
             stop=stop,
             stream=stream,
             user=user,
@@ -153,6 +155,7 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
         credentials: dict,
         prompt_messages: list[PromptMessage],
         model_parameters: dict,
+        tools: Optional[list[PromptMessageTool]] = None,
         stop: Optional[list[str]] = None,
         stream: bool = True,
         user: Optional[str] = None,
@@ -196,6 +199,8 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
         if completion_type is LLMMode.CHAT:
             endpoint_url = urljoin(endpoint_url, "api/chat")
             data["messages"] = [self._convert_prompt_message_to_dict(m) for m in prompt_messages]
+            if tools:
+                data["tools"] = [self._convert_prompt_message_tool_to_dict(tool) for tool in tools]
         else:
             endpoint_url = urljoin(endpoint_url, "api/generate")
             first_prompt_message = prompt_messages[0]
@@ -232,7 +237,7 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
         if stream:
             return self._handle_generate_stream_response(model, credentials, completion_type, response, prompt_messages)
 
-        return self._handle_generate_response(model, credentials, completion_type, response, prompt_messages)
+        return self._handle_generate_response(model, credentials, completion_type, response, prompt_messages, tools)
 
     def _handle_generate_response(
         self,
@@ -241,6 +246,7 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
         completion_type: LLMMode,
         response: requests.Response,
         prompt_messages: list[PromptMessage],
+        tools: Optional[list[PromptMessageTool]],
     ) -> LLMResult:
         """
         Handle llm completion response
@@ -253,14 +259,16 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
         :return: llm result
         """
         response_json = response.json()
-
+        tool_calls = []
         if completion_type is LLMMode.CHAT:
             message = response_json.get("message", {})
             response_content = message.get("content", "")
+            response_tool_calls = message.get("tool_calls", [])
+            tool_calls = [self._extract_response_tool_call(tool_call) for tool_call in response_tool_calls]
         else:
             response_content = response_json["response"]
 
-        assistant_message = AssistantPromptMessage(content=response_content)
+        assistant_message = AssistantPromptMessage(content=response_content, tool_calls=tool_calls)
 
         if "prompt_eval_count" in response_json and "eval_count" in response_json:
             # transform usage
@@ -405,9 +413,28 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
 
             chunk_index += 1
 
+    def _convert_prompt_message_tool_to_dict(self, tool: PromptMessageTool) -> dict:
+        """
+        Convert PromptMessageTool to dict for Ollama API
+
+        :param tool: tool
+        :return: tool dict
+        """
+        return {
+            "type": "function",
+            "function": {
+                "name": tool.name,
+                "description": tool.description,
+                "parameters": tool.parameters,
+            },
+        }
+
     def _convert_prompt_message_to_dict(self, message: PromptMessage) -> dict:
         """
         Convert PromptMessage to dict for Ollama API
+
+        :param message: prompt message
+        :return: message dict
         """
         if isinstance(message, UserPromptMessage):
             message = cast(UserPromptMessage, message)
@@ -432,6 +459,9 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
         elif isinstance(message, SystemPromptMessage):
             message = cast(SystemPromptMessage, message)
             message_dict = {"role": "system", "content": message.content}
+        elif isinstance(message, ToolPromptMessage):
+            message = cast(ToolPromptMessage, message)
+            message_dict = {"role": "tool", "content": message.content}
         else:
             raise ValueError(f"Got unknown type {message}")
 
@@ -452,6 +482,29 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
 
         return num_tokens
 
+    def _extract_response_tool_call(self, response_tool_call: dict) -> AssistantPromptMessage.ToolCall:
+        """
+        Extract response tool call
+        """
+        tool_call = None
+        if response_tool_call and "function" in response_tool_call:
+            # Convert arguments to JSON string if it's a dict
+            arguments = response_tool_call.get("function").get("arguments")
+            if isinstance(arguments, dict):
+                arguments = json.dumps(arguments)
+
+            function = AssistantPromptMessage.ToolCall.ToolCallFunction(
+                name=response_tool_call.get("function").get("name"),
+                arguments=arguments,
+            )
+            tool_call = AssistantPromptMessage.ToolCall(
+                id=response_tool_call.get("function").get("name"),
+                type="function",
+                function=function,
+            )
+
+        return tool_call
+
     def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
         """
         Get customizable model schema.
@@ -461,10 +514,15 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
 
         :return: model schema
         """
-        extras = {}
+        extras = {
+            "features": [],
+        }
 
         if "vision_support" in credentials and credentials["vision_support"] == "true":
-            extras["features"] = [ModelFeature.VISION]
+            extras["features"].append(ModelFeature.VISION)
+        if "function_call_support" in credentials and credentials["function_call_support"] == "true":
+            extras["features"].append(ModelFeature.TOOL_CALL)
+            extras["features"].append(ModelFeature.MULTI_TOOL_CALL)
 
         entity = AIModelEntity(
             model=model,

api/core/model_runtime/model_providers/ollama/ollama.yaml

@@ -96,3 +96,22 @@ model_credential_schema:
           label:
             en_US: 'No'
             zh_Hans: 否
+    - variable: function_call_support
+      label:
+        zh_Hans: 是否支持函数调用
+        en_US: Function call support
+      show_on:
+        - variable: __model_type
+          value: llm
+      default: 'false'
+      type: radio
+      required: false
+      options:
+        - value: 'true'
+          label:
+            en_US: 'Yes'
+            zh_Hans: 是
+        - value: 'false'
+          label:
+            en_US: 'No'
+            zh_Hans: 否

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

@@ -615,19 +615,11 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
         prompt_messages = self._clear_illegal_prompt_messages(model, prompt_messages)
 
         # o1 compatibility
-        block_as_stream = False
         if model.startswith("o1"):
             if "max_tokens" in model_parameters:
                 model_parameters["max_completion_tokens"] = model_parameters["max_tokens"]
                 del model_parameters["max_tokens"]
 
-            if stream:
-                block_as_stream = True
-                stream = False
-
-                if "stream_options" in extra_model_kwargs:
-                    del extra_model_kwargs["stream_options"]
-
             if "stop" in extra_model_kwargs:
                 del extra_model_kwargs["stop"]
 
@@ -644,47 +636,7 @@ class OpenAILargeLanguageModel(_CommonOpenAI, LargeLanguageModel):
         if stream:
             return self._handle_chat_generate_stream_response(model, credentials, response, prompt_messages, tools)
 
-        block_result = self._handle_chat_generate_response(model, credentials, response, prompt_messages, tools)
-
-        if block_as_stream:
-            return self._handle_chat_block_as_stream_response(block_result, prompt_messages, stop)
-
-        return block_result
-
-    def _handle_chat_block_as_stream_response(
-        self,
-        block_result: LLMResult,
-        prompt_messages: list[PromptMessage],
-        stop: Optional[list[str]] = None,
-    ) -> Generator[LLMResultChunk, None, None]:
-        """
-        Handle llm chat response
-
-        :param model: model name
-        :param credentials: credentials
-        :param response: response
-        :param prompt_messages: prompt messages
-        :param tools: tools for tool calling
-        :param stop: stop words
-        :return: llm response chunk generator
-        """
-        text = block_result.message.content
-        text = cast(str, text)
-
-        if stop:
-            text = self.enforce_stop_tokens(text, stop)
-
-        yield LLMResultChunk(
-            model=block_result.model,
-            prompt_messages=prompt_messages,
-            system_fingerprint=block_result.system_fingerprint,
-            delta=LLMResultChunkDelta(
-                index=0,
-                message=AssistantPromptMessage(content=text),
-                finish_reason="stop",
-                usage=block_result.usage,
-            ),
-        )
+        return self._handle_chat_generate_response(model, credentials, response, prompt_messages, tools)
 
     def _handle_chat_generate_response(
         self,

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

@@ -45,19 +45,7 @@ class OpenRouterLargeLanguageModel(OAIAPICompatLargeLanguageModel):
         user: Optional[str] = None,
     ) -> Union[LLMResult, Generator]:
         self._update_credential(model, credentials)
-
-        block_as_stream = False
-        if model.startswith("openai/o1"):
-            block_as_stream = True
-            stop = None
-
-        # invoke block as stream
-        if stream and block_as_stream:
-            return self._generate_block_as_stream(
-                model, credentials, prompt_messages, model_parameters, tools, stop, user
-            )
-        else:
-            return super()._generate(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
+        return super()._generate(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
 
     def _generate_block_as_stream(
         self,
@@ -69,9 +57,7 @@ class OpenRouterLargeLanguageModel(OAIAPICompatLargeLanguageModel):
         stop: Optional[list[str]] = None,
         user: Optional[str] = None,
     ) -> Generator:
-        resp: LLMResult = super()._generate(
-            model, credentials, prompt_messages, model_parameters, tools, stop, False, user
-        )
+        resp = super()._generate(model, credentials, prompt_messages, model_parameters, tools, stop, False, user)
 
         yield LLMResultChunk(
             model=model,

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

@@ -113,7 +113,7 @@ class SageMakerRerankModel(RerankModel):
             return RerankResult(model=model, docs=rerank_documents)
 
         except Exception as e:
-            logger.exception(f"Exception {e}, line : {line}")
+            logger.exception(f"Failed to invoke rerank model, model: {model}")
 
     def validate_credentials(self, model: str, credentials: dict) -> None:
         """

api/core/model_runtime/model_providers/sagemaker/speech2text/speech2text.py

@@ -78,7 +78,7 @@ class SageMakerSpeech2TextModel(Speech2TextModel):
             json_obj = json.loads(json_str)
             asr_text = json_obj["text"]
         except Exception as e:
-            logger.exception(f"failed to invoke speech2text model, {e}")
+            logger.exception(f"failed to invoke speech2text model, model: {model}")
             raise CredentialsValidateFailedError(str(e))
 
         return asr_text

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

@@ -117,7 +117,7 @@ class SageMakerEmbeddingModel(TextEmbeddingModel):
             return TextEmbeddingResult(embeddings=all_embeddings, usage=usage, model=model)
 
         except Exception as e:
-            logger.exception(f"Exception {e}, line : {line}")
+            logger.exception(f"Failed to invoke text embedding model, model: {model}, line: {line}")
 
     def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
         """

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

@@ -65,6 +65,8 @@ class GTERerankModel(RerankModel):
         )
 
         rerank_documents = []
+        if not response.output:
+            return RerankResult(model=model, docs=rerank_documents)
         for _, result in enumerate(response.output.results):
             # format document
             rerank_document = RerankDocument(

api/core/moderation/keywords/keywords.py

@@ -1,3 +1,6 @@
+from collections.abc import Sequence
+from typing import Any
+
 from core.moderation.base import Moderation, ModerationAction, ModerationInputsResult, ModerationOutputsResult
 
 
@@ -62,5 +65,5 @@ class KeywordsModeration(Moderation):
     def _is_violated(self, inputs: dict, keywords_list: list) -> bool:
         return any(self._check_keywords_in_value(keywords_list, value) for value in inputs.values())
 
-    def _check_keywords_in_value(self, keywords_list, value) -> bool:
-        return any(keyword.lower() in value.lower() for keyword in keywords_list)
+    def _check_keywords_in_value(self, keywords_list: Sequence[str], value: Any) -> bool:
+        return any(keyword.lower() in str(value).lower() for keyword in keywords_list)

api/core/moderation/output_moderation.py

@@ -126,6 +126,6 @@ class OutputModeration(BaseModel):
             result: ModerationOutputsResult = moderation_factory.moderation_for_outputs(moderation_buffer)
             return result
         except Exception as e:
-            logger.exception("Moderation Output error: %s", e)
+            logger.exception(f"Moderation Output error, app_id: {app_id}")
 
         return None

api/core/ops/langsmith_trace/entities/langsmith_trace_entity.py

@@ -49,6 +49,7 @@ class LangSmithRunModel(LangSmithTokenUsage, LangSmithMultiModel):
     reference_example_id: Optional[str] = Field(None, description="Reference example ID associated with the run")
     input_attachments: Optional[dict[str, Any]] = Field(None, description="Input attachments of the run")
     output_attachments: Optional[dict[str, Any]] = Field(None, description="Output attachments of the run")
+    dotted_order: Optional[str] = Field(None, description="Dotted order of the run")
 
     @field_validator("inputs", "outputs")
     @classmethod

api/core/ops/langsmith_trace/langsmith_trace.py

@@ -25,7 +25,7 @@ from core.ops.langsmith_trace.entities.langsmith_trace_entity import (
     LangSmithRunType,
     LangSmithRunUpdateModel,
 )
-from core.ops.utils import filter_none_values
+from core.ops.utils import filter_none_values, generate_dotted_order
 from extensions.ext_database import db
 from models.model import EndUser, MessageFile
 from models.workflow import WorkflowNodeExecution
@@ -62,6 +62,16 @@ class LangSmithDataTrace(BaseTraceInstance):
             self.generate_name_trace(trace_info)
 
     def workflow_trace(self, trace_info: WorkflowTraceInfo):
+        trace_id = trace_info.message_id or trace_info.workflow_app_log_id or trace_info.workflow_run_id
+        message_dotted_order = (
+            generate_dotted_order(trace_info.message_id, trace_info.start_time) if trace_info.message_id else None
+        )
+        workflow_dotted_order = generate_dotted_order(
+            trace_info.workflow_app_log_id or trace_info.workflow_run_id,
+            trace_info.workflow_data.created_at,
+            message_dotted_order,
+        )
+
         if trace_info.message_id:
             message_run = LangSmithRunModel(
                 id=trace_info.message_id,
@@ -76,6 +86,8 @@ class LangSmithDataTrace(BaseTraceInstance):
                 },
                 tags=["message", "workflow"],
                 error=trace_info.error,
+                trace_id=trace_id,
+                dotted_order=message_dotted_order,
             )
             self.add_run(message_run)
 
@@ -95,6 +107,8 @@ class LangSmithDataTrace(BaseTraceInstance):
             error=trace_info.error,
             tags=["workflow"],
             parent_run_id=trace_info.message_id or None,
+            trace_id=trace_id,
+            dotted_order=workflow_dotted_order,
         )
 
         self.add_run(langsmith_run)
@@ -177,6 +191,7 @@ class LangSmithDataTrace(BaseTraceInstance):
             else:
                 run_type = LangSmithRunType.tool
 
+            node_dotted_order = generate_dotted_order(node_execution_id, created_at, workflow_dotted_order)
             langsmith_run = LangSmithRunModel(
                 total_tokens=node_total_tokens,
                 name=node_type,
@@ -191,6 +206,9 @@ class LangSmithDataTrace(BaseTraceInstance):
                 },
                 parent_run_id=trace_info.workflow_app_log_id or trace_info.workflow_run_id,
                 tags=["node_execution"],
+                id=node_execution_id,
+                trace_id=trace_id,
+                dotted_order=node_dotted_order,
             )
 
             self.add_run(langsmith_run)

api/core/ops/ops_trace_manager.py

@@ -711,7 +711,7 @@ class TraceQueueManager:
                 trace_task.app_id = self.app_id
                 trace_manager_queue.put(trace_task)
         except Exception as e:
-            logging.exception(f"Error adding trace task: {e}")
+            logging.exception(f"Error adding trace task, trace_type {trace_task.trace_type}")
         finally:
             self.start_timer()
 
@@ -730,7 +730,7 @@ class TraceQueueManager:
             if tasks:
                 self.send_to_celery(tasks)
         except Exception as e:
-            logging.exception(f"Error processing trace tasks: {e}")
+            logging.exception("Error processing trace tasks")
 
     def start_timer(self):
         global trace_manager_timer

api/core/ops/utils.py

@@ -1,5 +1,6 @@
 from contextlib import contextmanager
 from datetime import datetime
+from typing import Optional, Union
 
 from extensions.ext_database import db
 from models.model import Message
@@ -43,3 +44,19 @@ def replace_text_with_content(data):
         return [replace_text_with_content(item) for item in data]
     else:
         return data
+
+
+def generate_dotted_order(
+    run_id: str, start_time: Union[str, datetime], parent_dotted_order: Optional[str] = None
+) -> str:
+    """
+    generate dotted_order for langsmith
+    """
+    start_time = datetime.fromisoformat(start_time) if isinstance(start_time, str) else start_time
+    timestamp = start_time.strftime("%Y%m%dT%H%M%S%f")[:-3] + "Z"
+    current_segment = f"{timestamp}{run_id}"
+
+    if parent_dotted_order is None:
+        return current_segment
+
+    return f"{parent_dotted_order}.{current_segment}"

api/core/rag/datasource/vdb/analyticdb/analyticdb_vector.py

@@ -1,310 +1,62 @@
 import json
 from typing import Any
 
-from pydantic import BaseModel
-
-_import_err_msg = (
-    "`alibabacloud_gpdb20160503` and `alibabacloud_tea_openapi` packages not found, "
-    "please run `pip install alibabacloud_gpdb20160503 alibabacloud_tea_openapi`"
-)
-
 from configs import dify_config
+from core.rag.datasource.vdb.analyticdb.analyticdb_vector_openapi import (
+    AnalyticdbVectorOpenAPI,
+    AnalyticdbVectorOpenAPIConfig,
+)
+from core.rag.datasource.vdb.analyticdb.analyticdb_vector_sql import AnalyticdbVectorBySql, AnalyticdbVectorBySqlConfig
 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
 
 
-class AnalyticdbConfig(BaseModel):
-    access_key_id: str
-    access_key_secret: str
-    region_id: str
-    instance_id: str
-    account: str
-    account_password: str
-    namespace: str = ("dify",)
-    namespace_password: str = (None,)
-    metrics: str = ("cosine",)
-    read_timeout: int = 60000
-
-    def to_analyticdb_client_params(self):
-        return {
-            "access_key_id": self.access_key_id,
-            "access_key_secret": self.access_key_secret,
-            "region_id": self.region_id,
-            "read_timeout": self.read_timeout,
-        }
-
-
 class AnalyticdbVector(BaseVector):
-    def __init__(self, collection_name: str, config: AnalyticdbConfig):
-        self._collection_name = collection_name.lower()
-        try:
-            from alibabacloud_gpdb20160503.client import Client
-            from alibabacloud_tea_openapi import models as open_api_models
-        except:
-            raise ImportError(_import_err_msg)
-        self.config = config
-        self._client_config = open_api_models.Config(user_agent="dify", **config.to_analyticdb_client_params())
-        self._client = Client(self._client_config)
-        self._initialize()
-
-    def _initialize(self) -> None:
-        cache_key = f"vector_indexing_{self.config.instance_id}"
-        lock_name = f"{cache_key}_lock"
-        with redis_client.lock(lock_name, timeout=20):
-            collection_exist_cache_key = f"vector_indexing_{self.config.instance_id}"
-            if redis_client.get(collection_exist_cache_key):
-                return
-            self._initialize_vector_database()
-            self._create_namespace_if_not_exists()
-            redis_client.set(collection_exist_cache_key, 1, ex=3600)
-
-    def _initialize_vector_database(self) -> None:
-        from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
-
-        request = gpdb_20160503_models.InitVectorDatabaseRequest(
-            dbinstance_id=self.config.instance_id,
-            region_id=self.config.region_id,
-            manager_account=self.config.account,
-            manager_account_password=self.config.account_password,
-        )
-        self._client.init_vector_database(request)
-
-    def _create_namespace_if_not_exists(self) -> None:
-        from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
-        from Tea.exceptions import TeaException
-
-        try:
-            request = gpdb_20160503_models.DescribeNamespaceRequest(
-                dbinstance_id=self.config.instance_id,
-                region_id=self.config.region_id,
-                namespace=self.config.namespace,
-                manager_account=self.config.account,
-                manager_account_password=self.config.account_password,
-            )
-            self._client.describe_namespace(request)
-        except TeaException as e:
-            if e.statusCode == 404:
-                request = gpdb_20160503_models.CreateNamespaceRequest(
-                    dbinstance_id=self.config.instance_id,
-                    region_id=self.config.region_id,
-                    manager_account=self.config.account,
-                    manager_account_password=self.config.account_password,
-                    namespace=self.config.namespace,
-                    namespace_password=self.config.namespace_password,
-                )
-                self._client.create_namespace(request)
-            else:
-                raise ValueError(f"failed to create namespace {self.config.namespace}: {e}")
-
-    def _create_collection_if_not_exists(self, embedding_dimension: int):
-        from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
-        from Tea.exceptions import TeaException
-
-        cache_key = f"vector_indexing_{self._collection_name}"
-        lock_name = f"{cache_key}_lock"
-        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):
-                return
-            try:
-                request = gpdb_20160503_models.DescribeCollectionRequest(
-                    dbinstance_id=self.config.instance_id,
-                    region_id=self.config.region_id,
-                    namespace=self.config.namespace,
-                    namespace_password=self.config.namespace_password,
-                    collection=self._collection_name,
-                )
-                self._client.describe_collection(request)
-            except TeaException as e:
-                if e.statusCode == 404:
-                    metadata = '{"ref_doc_id":"text","page_content":"text","metadata_":"jsonb"}'
-                    full_text_retrieval_fields = "page_content"
-                    request = gpdb_20160503_models.CreateCollectionRequest(
-                        dbinstance_id=self.config.instance_id,
-                        region_id=self.config.region_id,
-                        manager_account=self.config.account,
-                        manager_account_password=self.config.account_password,
-                        namespace=self.config.namespace,
-                        collection=self._collection_name,
-                        dimension=embedding_dimension,
-                        metrics=self.config.metrics,
-                        metadata=metadata,
-                        full_text_retrieval_fields=full_text_retrieval_fields,
-                    )
-                    self._client.create_collection(request)
-                else:
-                    raise ValueError(f"failed to create collection {self._collection_name}: {e}")
-            redis_client.set(collection_exist_cache_key, 1, ex=3600)
+    def __init__(
+        self, collection_name: str, api_config: AnalyticdbVectorOpenAPIConfig, sql_config: AnalyticdbVectorBySqlConfig
+    ):
+        super().__init__(collection_name)
+        if api_config is not None:
+            self.analyticdb_vector = AnalyticdbVectorOpenAPI(collection_name, api_config)
+        else:
+            self.analyticdb_vector = AnalyticdbVectorBySql(collection_name, sql_config)
 
     def get_type(self) -> str:
         return VectorType.ANALYTICDB
 
     def create(self, texts: list[Document], embeddings: list[list[float]], **kwargs):
         dimension = len(embeddings[0])
-        self._create_collection_if_not_exists(dimension)
-        self.add_texts(texts, embeddings)
+        self.analyticdb_vector._create_collection_if_not_exists(dimension)
+        self.analyticdb_vector.add_texts(texts, embeddings)
 
-    def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
-        from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
-
-        rows: list[gpdb_20160503_models.UpsertCollectionDataRequestRows] = []
-        for doc, embedding in zip(documents, embeddings, strict=True):
-            metadata = {
-                "ref_doc_id": doc.metadata["doc_id"],
-                "page_content": doc.page_content,
-                "metadata_": json.dumps(doc.metadata),
-            }
-            rows.append(
-                gpdb_20160503_models.UpsertCollectionDataRequestRows(
-                    vector=embedding,
-                    metadata=metadata,
-                )
-            )
-        request = gpdb_20160503_models.UpsertCollectionDataRequest(
-            dbinstance_id=self.config.instance_id,
-            region_id=self.config.region_id,
-            namespace=self.config.namespace,
-            namespace_password=self.config.namespace_password,
-            collection=self._collection_name,
-            rows=rows,
-        )
-        self._client.upsert_collection_data(request)
+    def add_texts(self, texts: list[Document], embeddings: list[list[float]], **kwargs):
+        self.analyticdb_vector.add_texts(texts, embeddings)
 
     def text_exists(self, id: str) -> bool:
-        from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
-
-        request = gpdb_20160503_models.QueryCollectionDataRequest(
-            dbinstance_id=self.config.instance_id,
-            region_id=self.config.region_id,
-            namespace=self.config.namespace,
-            namespace_password=self.config.namespace_password,
-            collection=self._collection_name,
-            metrics=self.config.metrics,
-            include_values=True,
-            vector=None,
-            content=None,
-            top_k=1,
-            filter=f"ref_doc_id='{id}'",
-        )
-        response = self._client.query_collection_data(request)
-        return len(response.body.matches.match) > 0
+        return self.analyticdb_vector.text_exists(id)
 
     def delete_by_ids(self, ids: list[str]) -> None:
-        from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
-
-        ids_str = ",".join(f"'{id}'" for id in ids)
-        ids_str = f"({ids_str})"
-        request = gpdb_20160503_models.DeleteCollectionDataRequest(
-            dbinstance_id=self.config.instance_id,
-            region_id=self.config.region_id,
-            namespace=self.config.namespace,
-            namespace_password=self.config.namespace_password,
-            collection=self._collection_name,
-            collection_data=None,
-            collection_data_filter=f"ref_doc_id IN {ids_str}",
-        )
-        self._client.delete_collection_data(request)
+        self.analyticdb_vector.delete_by_ids(ids)
 
     def delete_by_metadata_field(self, key: str, value: str) -> None:
-        from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
-
-        request = gpdb_20160503_models.DeleteCollectionDataRequest(
-            dbinstance_id=self.config.instance_id,
-            region_id=self.config.region_id,
-            namespace=self.config.namespace,
-            namespace_password=self.config.namespace_password,
-            collection=self._collection_name,
-            collection_data=None,
-            collection_data_filter=f"metadata_ ->> '{key}' = '{value}'",
-        )
-        self._client.delete_collection_data(request)
+        self.analyticdb_vector.delete_by_metadata_field(key, value)
 
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
-        from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
-
-        score_threshold = kwargs.get("score_threshold") or 0.0
-        request = gpdb_20160503_models.QueryCollectionDataRequest(
-            dbinstance_id=self.config.instance_id,
-            region_id=self.config.region_id,
-            namespace=self.config.namespace,
-            namespace_password=self.config.namespace_password,
-            collection=self._collection_name,
-            include_values=kwargs.pop("include_values", True),
-            metrics=self.config.metrics,
-            vector=query_vector,
-            content=None,
-            top_k=kwargs.get("top_k", 4),
-            filter=None,
-        )
-        response = self._client.query_collection_data(request)
-        documents = []
-        for match in response.body.matches.match:
-            if match.score > score_threshold:
-                metadata = json.loads(match.metadata.get("metadata_"))
-                metadata["score"] = match.score
-                doc = Document(
-                    page_content=match.metadata.get("page_content"),
-                    metadata=metadata,
-                )
-                documents.append(doc)
-        documents = sorted(documents, key=lambda x: x.metadata["score"], reverse=True)
-        return documents
+        return self.analyticdb_vector.search_by_vector(query_vector)
 
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
-        from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
-
-        score_threshold = float(kwargs.get("score_threshold") or 0.0)
-        request = gpdb_20160503_models.QueryCollectionDataRequest(
-            dbinstance_id=self.config.instance_id,
-            region_id=self.config.region_id,
-            namespace=self.config.namespace,
-            namespace_password=self.config.namespace_password,
-            collection=self._collection_name,
-            include_values=kwargs.pop("include_values", True),
-            metrics=self.config.metrics,
-            vector=None,
-            content=query,
-            top_k=kwargs.get("top_k", 4),
-            filter=None,
-        )
-        response = self._client.query_collection_data(request)
-        documents = []
-        for match in response.body.matches.match:
-            if match.score > score_threshold:
-                metadata = json.loads(match.metadata.get("metadata_"))
-                metadata["score"] = match.score
-                doc = Document(
-                    page_content=match.metadata.get("page_content"),
-                    vector=match.metadata.get("vector"),
-                    metadata=metadata,
-                )
-                documents.append(doc)
-        documents = sorted(documents, key=lambda x: x.metadata["score"], reverse=True)
-        return documents
+        return self.analyticdb_vector.search_by_full_text(query, **kwargs)
 
     def delete(self) -> None:
-        try:
-            from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
-
-            request = gpdb_20160503_models.DeleteCollectionRequest(
-                collection=self._collection_name,
-                dbinstance_id=self.config.instance_id,
-                namespace=self.config.namespace,
-                namespace_password=self.config.namespace_password,
-                region_id=self.config.region_id,
-            )
-            self._client.delete_collection(request)
-        except Exception as e:
-            raise e
+        self.analyticdb_vector.delete()
 
 
 class AnalyticdbVectorFactory(AbstractVectorFactory):
-    def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings):
+    def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> AnalyticdbVector:
         if dataset.index_struct_dict:
             class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
             collection_name = class_prefix.lower()
@@ -313,26 +65,9 @@ class AnalyticdbVectorFactory(AbstractVectorFactory):
             collection_name = Dataset.gen_collection_name_by_id(dataset_id).lower()
             dataset.index_struct = json.dumps(self.gen_index_struct_dict(VectorType.ANALYTICDB, collection_name))
 
-        # handle optional params
-        if dify_config.ANALYTICDB_KEY_ID is None:
-            raise ValueError("ANALYTICDB_KEY_ID should not be None")
-        if dify_config.ANALYTICDB_KEY_SECRET is None:
-            raise ValueError("ANALYTICDB_KEY_SECRET should not be None")
-        if dify_config.ANALYTICDB_REGION_ID is None:
-            raise ValueError("ANALYTICDB_REGION_ID should not be None")
-        if dify_config.ANALYTICDB_INSTANCE_ID is None:
-            raise ValueError("ANALYTICDB_INSTANCE_ID should not be None")
-        if dify_config.ANALYTICDB_ACCOUNT is None:
-            raise ValueError("ANALYTICDB_ACCOUNT should not be None")
-        if dify_config.ANALYTICDB_PASSWORD is None:
-            raise ValueError("ANALYTICDB_PASSWORD should not be None")
-        if dify_config.ANALYTICDB_NAMESPACE is None:
-            raise ValueError("ANALYTICDB_NAMESPACE should not be None")
-        if dify_config.ANALYTICDB_NAMESPACE_PASSWORD is None:
-            raise ValueError("ANALYTICDB_NAMESPACE_PASSWORD should not be None")
-        return AnalyticdbVector(
-            collection_name,
-            AnalyticdbConfig(
+        if dify_config.ANALYTICDB_HOST is None:
+            # implemented through OpenAPI
+            apiConfig = AnalyticdbVectorOpenAPIConfig(
                 access_key_id=dify_config.ANALYTICDB_KEY_ID,
                 access_key_secret=dify_config.ANALYTICDB_KEY_SECRET,
                 region_id=dify_config.ANALYTICDB_REGION_ID,
@@ -341,5 +76,22 @@ class AnalyticdbVectorFactory(AbstractVectorFactory):
                 account_password=dify_config.ANALYTICDB_PASSWORD,
                 namespace=dify_config.ANALYTICDB_NAMESPACE,
                 namespace_password=dify_config.ANALYTICDB_NAMESPACE_PASSWORD,
-            ),
+            )
+            sqlConfig = None
+        else:
+            # implemented through sql
+            sqlConfig = AnalyticdbVectorBySqlConfig(
+                host=dify_config.ANALYTICDB_HOST,
+                port=dify_config.ANALYTICDB_PORT,
+                account=dify_config.ANALYTICDB_ACCOUNT,
+                account_password=dify_config.ANALYTICDB_PASSWORD,
+                min_connection=dify_config.ANALYTICDB_MIN_CONNECTION,
+                max_connection=dify_config.ANALYTICDB_MAX_CONNECTION,
+                namespace=dify_config.ANALYTICDB_NAMESPACE,
+            )
+            apiConfig = None
+        return AnalyticdbVector(
+            collection_name,
+            apiConfig,
+            sqlConfig,
         )

api/core/rag/datasource/vdb/analyticdb/analyticdb_vector_openapi.py

@@ -0,0 +1,309 @@
+import json
+from typing import Any
+
+from pydantic import BaseModel, model_validator
+
+_import_err_msg = (
+    "`alibabacloud_gpdb20160503` and `alibabacloud_tea_openapi` packages not found, "
+    "please run `pip install alibabacloud_gpdb20160503 alibabacloud_tea_openapi`"
+)
+
+from core.rag.models.document import Document
+from extensions.ext_redis import redis_client
+
+
+class AnalyticdbVectorOpenAPIConfig(BaseModel):
+    access_key_id: str
+    access_key_secret: str
+    region_id: str
+    instance_id: str
+    account: str
+    account_password: str
+    namespace: str = "dify"
+    namespace_password: str = (None,)
+    metrics: str = "cosine"
+    read_timeout: int = 60000
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_config(cls, values: dict) -> dict:
+        if not values["access_key_id"]:
+            raise ValueError("config ANALYTICDB_KEY_ID is required")
+        if not values["access_key_secret"]:
+            raise ValueError("config ANALYTICDB_KEY_SECRET is required")
+        if not values["region_id"]:
+            raise ValueError("config ANALYTICDB_REGION_ID is required")
+        if not values["instance_id"]:
+            raise ValueError("config ANALYTICDB_INSTANCE_ID is required")
+        if not values["account"]:
+            raise ValueError("config ANALYTICDB_ACCOUNT is required")
+        if not values["account_password"]:
+            raise ValueError("config ANALYTICDB_PASSWORD is required")
+        if not values["namespace_password"]:
+            raise ValueError("config ANALYTICDB_NAMESPACE_PASSWORD is required")
+        return values
+
+    def to_analyticdb_client_params(self):
+        return {
+            "access_key_id": self.access_key_id,
+            "access_key_secret": self.access_key_secret,
+            "region_id": self.region_id,
+            "read_timeout": self.read_timeout,
+        }
+
+
+class AnalyticdbVectorOpenAPI:
+    def __init__(self, collection_name: str, config: AnalyticdbVectorOpenAPIConfig):
+        try:
+            from alibabacloud_gpdb20160503.client import Client
+            from alibabacloud_tea_openapi import models as open_api_models
+        except:
+            raise ImportError(_import_err_msg)
+        self._collection_name = collection_name.lower()
+        self.config = config
+        self._client_config = open_api_models.Config(user_agent="dify", **config.to_analyticdb_client_params())
+        self._client = Client(self._client_config)
+        self._initialize()
+
+    def _initialize(self) -> None:
+        cache_key = f"vector_initialize_{self.config.instance_id}"
+        lock_name = f"{cache_key}_lock"
+        with redis_client.lock(lock_name, timeout=20):
+            database_exist_cache_key = f"vector_initialize_{self.config.instance_id}"
+            if redis_client.get(database_exist_cache_key):
+                return
+            self._initialize_vector_database()
+            self._create_namespace_if_not_exists()
+            redis_client.set(database_exist_cache_key, 1, ex=3600)
+
+    def _initialize_vector_database(self) -> None:
+        from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
+
+        request = gpdb_20160503_models.InitVectorDatabaseRequest(
+            dbinstance_id=self.config.instance_id,
+            region_id=self.config.region_id,
+            manager_account=self.config.account,
+            manager_account_password=self.config.account_password,
+        )
+        self._client.init_vector_database(request)
+
+    def _create_namespace_if_not_exists(self) -> None:
+        from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
+        from Tea.exceptions import TeaException
+
+        try:
+            request = gpdb_20160503_models.DescribeNamespaceRequest(
+                dbinstance_id=self.config.instance_id,
+                region_id=self.config.region_id,
+                namespace=self.config.namespace,
+                manager_account=self.config.account,
+                manager_account_password=self.config.account_password,
+            )
+            self._client.describe_namespace(request)
+        except TeaException as e:
+            if e.statusCode == 404:
+                request = gpdb_20160503_models.CreateNamespaceRequest(
+                    dbinstance_id=self.config.instance_id,
+                    region_id=self.config.region_id,
+                    manager_account=self.config.account,
+                    manager_account_password=self.config.account_password,
+                    namespace=self.config.namespace,
+                    namespace_password=self.config.namespace_password,
+                )
+                self._client.create_namespace(request)
+            else:
+                raise ValueError(f"failed to create namespace {self.config.namespace}: {e}")
+
+    def _create_collection_if_not_exists(self, embedding_dimension: int):
+        from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
+        from Tea.exceptions import TeaException
+
+        cache_key = f"vector_indexing_{self._collection_name}"
+        lock_name = f"{cache_key}_lock"
+        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):
+                return
+            try:
+                request = gpdb_20160503_models.DescribeCollectionRequest(
+                    dbinstance_id=self.config.instance_id,
+                    region_id=self.config.region_id,
+                    namespace=self.config.namespace,
+                    namespace_password=self.config.namespace_password,
+                    collection=self._collection_name,
+                )
+                self._client.describe_collection(request)
+            except TeaException as e:
+                if e.statusCode == 404:
+                    metadata = '{"ref_doc_id":"text","page_content":"text","metadata_":"jsonb"}'
+                    full_text_retrieval_fields = "page_content"
+                    request = gpdb_20160503_models.CreateCollectionRequest(
+                        dbinstance_id=self.config.instance_id,
+                        region_id=self.config.region_id,
+                        manager_account=self.config.account,
+                        manager_account_password=self.config.account_password,
+                        namespace=self.config.namespace,
+                        collection=self._collection_name,
+                        dimension=embedding_dimension,
+                        metrics=self.config.metrics,
+                        metadata=metadata,
+                        full_text_retrieval_fields=full_text_retrieval_fields,
+                    )
+                    self._client.create_collection(request)
+                else:
+                    raise ValueError(f"failed to create collection {self._collection_name}: {e}")
+            redis_client.set(collection_exist_cache_key, 1, ex=3600)
+
+    def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
+        from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
+
+        rows: list[gpdb_20160503_models.UpsertCollectionDataRequestRows] = []
+        for doc, embedding in zip(documents, embeddings, strict=True):
+            metadata = {
+                "ref_doc_id": doc.metadata["doc_id"],
+                "page_content": doc.page_content,
+                "metadata_": json.dumps(doc.metadata),
+            }
+            rows.append(
+                gpdb_20160503_models.UpsertCollectionDataRequestRows(
+                    vector=embedding,
+                    metadata=metadata,
+                )
+            )
+        request = gpdb_20160503_models.UpsertCollectionDataRequest(
+            dbinstance_id=self.config.instance_id,
+            region_id=self.config.region_id,
+            namespace=self.config.namespace,
+            namespace_password=self.config.namespace_password,
+            collection=self._collection_name,
+            rows=rows,
+        )
+        self._client.upsert_collection_data(request)
+
+    def text_exists(self, id: str) -> bool:
+        from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
+
+        request = gpdb_20160503_models.QueryCollectionDataRequest(
+            dbinstance_id=self.config.instance_id,
+            region_id=self.config.region_id,
+            namespace=self.config.namespace,
+            namespace_password=self.config.namespace_password,
+            collection=self._collection_name,
+            metrics=self.config.metrics,
+            include_values=True,
+            vector=None,
+            content=None,
+            top_k=1,
+            filter=f"ref_doc_id='{id}'",
+        )
+        response = self._client.query_collection_data(request)
+        return len(response.body.matches.match) > 0
+
+    def delete_by_ids(self, ids: list[str]) -> None:
+        from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
+
+        ids_str = ",".join(f"'{id}'" for id in ids)
+        ids_str = f"({ids_str})"
+        request = gpdb_20160503_models.DeleteCollectionDataRequest(
+            dbinstance_id=self.config.instance_id,
+            region_id=self.config.region_id,
+            namespace=self.config.namespace,
+            namespace_password=self.config.namespace_password,
+            collection=self._collection_name,
+            collection_data=None,
+            collection_data_filter=f"ref_doc_id IN {ids_str}",
+        )
+        self._client.delete_collection_data(request)
+
+    def delete_by_metadata_field(self, key: str, value: str) -> None:
+        from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
+
+        request = gpdb_20160503_models.DeleteCollectionDataRequest(
+            dbinstance_id=self.config.instance_id,
+            region_id=self.config.region_id,
+            namespace=self.config.namespace,
+            namespace_password=self.config.namespace_password,
+            collection=self._collection_name,
+            collection_data=None,
+            collection_data_filter=f"metadata_ ->> '{key}' = '{value}'",
+        )
+        self._client.delete_collection_data(request)
+
+    def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
+        from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
+
+        score_threshold = kwargs.get("score_threshold") or 0.0
+        request = gpdb_20160503_models.QueryCollectionDataRequest(
+            dbinstance_id=self.config.instance_id,
+            region_id=self.config.region_id,
+            namespace=self.config.namespace,
+            namespace_password=self.config.namespace_password,
+            collection=self._collection_name,
+            include_values=kwargs.pop("include_values", True),
+            metrics=self.config.metrics,
+            vector=query_vector,
+            content=None,
+            top_k=kwargs.get("top_k", 4),
+            filter=None,
+        )
+        response = self._client.query_collection_data(request)
+        documents = []
+        for match in response.body.matches.match:
+            if match.score > score_threshold:
+                metadata = json.loads(match.metadata.get("metadata_"))
+                metadata["score"] = match.score
+                doc = Document(
+                    page_content=match.metadata.get("page_content"),
+                    vector=match.values.value,
+                    metadata=metadata,
+                )
+                documents.append(doc)
+        documents = sorted(documents, key=lambda x: x.metadata["score"], reverse=True)
+        return documents
+
+    def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
+        from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
+
+        score_threshold = float(kwargs.get("score_threshold") or 0.0)
+        request = gpdb_20160503_models.QueryCollectionDataRequest(
+            dbinstance_id=self.config.instance_id,
+            region_id=self.config.region_id,
+            namespace=self.config.namespace,
+            namespace_password=self.config.namespace_password,
+            collection=self._collection_name,
+            include_values=kwargs.pop("include_values", True),
+            metrics=self.config.metrics,
+            vector=None,
+            content=query,
+            top_k=kwargs.get("top_k", 4),
+            filter=None,
+        )
+        response = self._client.query_collection_data(request)
+        documents = []
+        for match in response.body.matches.match:
+            if match.score > score_threshold:
+                metadata = json.loads(match.metadata.get("metadata_"))
+                metadata["score"] = match.score
+                doc = Document(
+                    page_content=match.metadata.get("page_content"),
+                    vector=match.values.value,
+                    metadata=metadata,
+                )
+                documents.append(doc)
+        documents = sorted(documents, key=lambda x: x.metadata["score"], reverse=True)
+        return documents
+
+    def delete(self) -> None:
+        try:
+            from alibabacloud_gpdb20160503 import models as gpdb_20160503_models
+
+            request = gpdb_20160503_models.DeleteCollectionRequest(
+                collection=self._collection_name,
+                dbinstance_id=self.config.instance_id,
+                namespace=self.config.namespace,
+                namespace_password=self.config.namespace_password,
+                region_id=self.config.region_id,
+            )
+            self._client.delete_collection(request)
+        except Exception as e:
+            raise e

api/core/rag/datasource/vdb/analyticdb/analyticdb_vector_sql.py

@@ -0,0 +1,245 @@
+import json
+import uuid
+from contextlib import contextmanager
+from typing import Any
+
+import psycopg2.extras
+import psycopg2.pool
+from pydantic import BaseModel, model_validator
+
+from core.rag.models.document import Document
+from extensions.ext_redis import redis_client
+
+
+class AnalyticdbVectorBySqlConfig(BaseModel):
+    host: str
+    port: int
+    account: str
+    account_password: str
+    min_connection: int
+    max_connection: int
+    namespace: str = "dify"
+    metrics: str = "cosine"
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_config(cls, values: dict) -> dict:
+        if not values["host"]:
+            raise ValueError("config ANALYTICDB_HOST is required")
+        if not values["port"]:
+            raise ValueError("config ANALYTICDB_PORT is required")
+        if not values["account"]:
+            raise ValueError("config ANALYTICDB_ACCOUNT is required")
+        if not values["account_password"]:
+            raise ValueError("config ANALYTICDB_PASSWORD is required")
+        if not values["min_connection"]:
+            raise ValueError("config ANALYTICDB_MIN_CONNECTION is required")
+        if not values["max_connection"]:
+            raise ValueError("config ANALYTICDB_MAX_CONNECTION is required")
+        if values["min_connection"] > values["max_connection"]:
+            raise ValueError("config ANALYTICDB_MIN_CONNECTION should less than ANALYTICDB_MAX_CONNECTION")
+        return values
+
+
+class AnalyticdbVectorBySql:
+    def __init__(self, collection_name: str, config: AnalyticdbVectorBySqlConfig):
+        self._collection_name = collection_name.lower()
+        self.databaseName = "knowledgebase"
+        self.config = config
+        self.table_name = f"{self.config.namespace}.{self._collection_name}"
+        self.pool = None
+        self._initialize()
+        if not self.pool:
+            self.pool = self._create_connection_pool()
+
+    def _initialize(self) -> None:
+        cache_key = f"vector_initialize_{self.config.host}"
+        lock_name = f"{cache_key}_lock"
+        with redis_client.lock(lock_name, timeout=20):
+            database_exist_cache_key = f"vector_initialize_{self.config.host}"
+            if redis_client.get(database_exist_cache_key):
+                return
+            self._initialize_vector_database()
+            redis_client.set(database_exist_cache_key, 1, ex=3600)
+
+    def _create_connection_pool(self):
+        return psycopg2.pool.SimpleConnectionPool(
+            self.config.min_connection,
+            self.config.max_connection,
+            host=self.config.host,
+            port=self.config.port,
+            user=self.config.account,
+            password=self.config.account_password,
+            database=self.databaseName,
+        )
+
+    @contextmanager
+    def _get_cursor(self):
+        conn = self.pool.getconn()
+        cur = conn.cursor()
+        try:
+            yield cur
+        finally:
+            cur.close()
+            conn.commit()
+            self.pool.putconn(conn)
+
+    def _initialize_vector_database(self) -> None:
+        conn = psycopg2.connect(
+            host=self.config.host,
+            port=self.config.port,
+            user=self.config.account,
+            password=self.config.account_password,
+            database="postgres",
+        )
+        conn.autocommit = True
+        cur = conn.cursor()
+        try:
+            cur.execute(f"CREATE DATABASE {self.databaseName}")
+        except Exception as e:
+            if "already exists" in str(e):
+                return
+            raise e
+        finally:
+            cur.close()
+            conn.close()
+        self.pool = self._create_connection_pool()
+        with self._get_cursor() as cur:
+            try:
+                cur.execute("CREATE TEXT SEARCH CONFIGURATION zh_cn (PARSER = zhparser)")
+                cur.execute("ALTER TEXT SEARCH CONFIGURATION zh_cn ADD MAPPING FOR n,v,a,i,e,l,x WITH simple")
+            except Exception as e:
+                if "already exists" not in str(e):
+                    raise e
+            cur.execute(
+                "CREATE OR REPLACE FUNCTION "
+                "public.to_tsquery_from_text(txt text, lang regconfig DEFAULT 'english'::regconfig) "
+                "RETURNS tsquery LANGUAGE sql IMMUTABLE STRICT AS $function$ "
+                "SELECT to_tsquery(lang, COALESCE(string_agg(split_part(word, ':', 1), ' | '), '')) "
+                "FROM (SELECT unnest(string_to_array(to_tsvector(lang, txt)::text, ' ')) AS word) "
+                "AS words_only;$function$"
+            )
+            cur.execute(f"CREATE SCHEMA IF NOT EXISTS {self.config.namespace}")
+
+    def _create_collection_if_not_exists(self, embedding_dimension: int):
+        cache_key = f"vector_indexing_{self._collection_name}"
+        lock_name = f"{cache_key}_lock"
+        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):
+                return
+            with self._get_cursor() as cur:
+                cur.execute(
+                    f"CREATE TABLE IF NOT EXISTS {self.table_name}("
+                    f"id text PRIMARY KEY,"
+                    f"vector real[], ref_doc_id text, page_content text, metadata_ jsonb, "
+                    f"to_tsvector TSVECTOR"
+                    f") WITH (fillfactor=70) DISTRIBUTED BY (id);"
+                )
+                if embedding_dimension is not None:
+                    index_name = f"{self._collection_name}_embedding_idx"
+                    cur.execute(f"ALTER TABLE {self.table_name} ALTER COLUMN vector SET STORAGE PLAIN")
+                    cur.execute(
+                        f"CREATE INDEX {index_name} ON {self.table_name} USING ann(vector) "
+                        f"WITH(dim='{embedding_dimension}', distancemeasure='{self.config.metrics}', "
+                        f"pq_enable=0, external_storage=0)"
+                    )
+                    cur.execute(f"CREATE INDEX ON {self.table_name} USING gin(to_tsvector)")
+            redis_client.set(collection_exist_cache_key, 1, ex=3600)
+
+    def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
+        values = []
+        id_prefix = str(uuid.uuid4()) + "_"
+        sql = f"""
+                INSERT INTO {self.table_name} 
+                (id, ref_doc_id, vector, page_content, metadata_, to_tsvector) 
+                VALUES (%s, %s, %s, %s, %s, to_tsvector('zh_cn',  %s));
+            """
+        for i, doc in enumerate(documents):
+            values.append(
+                (
+                    id_prefix + str(i),
+                    doc.metadata.get("doc_id", str(uuid.uuid4())),
+                    embeddings[i],
+                    doc.page_content,
+                    json.dumps(doc.metadata),
+                    doc.page_content,
+                )
+            )
+        with self._get_cursor() as cur:
+            psycopg2.extras.execute_batch(cur, sql, values)
+
+    def text_exists(self, id: str) -> bool:
+        with self._get_cursor() as cur:
+            cur.execute(f"SELECT id FROM {self.table_name} WHERE ref_doc_id = %s", (id,))
+            return cur.fetchone() is not None
+
+    def delete_by_ids(self, ids: list[str]) -> None:
+        with self._get_cursor() as cur:
+            try:
+                cur.execute(f"DELETE FROM {self.table_name} WHERE ref_doc_id IN %s", (tuple(ids),))
+            except Exception as e:
+                if "does not exist" not in str(e):
+                    raise e
+
+    def delete_by_metadata_field(self, key: str, value: str) -> None:
+        with self._get_cursor() as cur:
+            try:
+                cur.execute(f"DELETE FROM {self.table_name} WHERE metadata_->>%s = %s", (key, value))
+            except Exception as e:
+                if "does not exist" not in str(e):
+                    raise e
+
+    def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
+        top_k = kwargs.get("top_k", 4)
+        score_threshold = float(kwargs.get("score_threshold") or 0.0)
+        with self._get_cursor() as cur:
+            query_vector_str = json.dumps(query_vector)
+            query_vector_str = "{" + query_vector_str[1:-1] + "}"
+            cur.execute(
+                f"SELECT t.id AS id, t.vector AS vector, (1.0 - t.score) AS score, "
+                f"t.page_content as page_content, t.metadata_ AS metadata_ "
+                f"FROM (SELECT id, vector, page_content, metadata_, vector <=> %s AS score "
+                f"FROM {self.table_name} ORDER BY score LIMIT {top_k} ) t",
+                (query_vector_str,),
+            )
+            documents = []
+            for record in cur:
+                id, vector, score, page_content, metadata = record
+                if score > score_threshold:
+                    metadata["score"] = score
+                    doc = Document(
+                        page_content=page_content,
+                        vector=vector,
+                        metadata=metadata,
+                    )
+                    documents.append(doc)
+        return documents
+
+    def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
+        top_k = kwargs.get("top_k", 4)
+        with self._get_cursor() as cur:
+            cur.execute(
+                f"""SELECT id, vector, page_content, metadata_, 
+                ts_rank(to_tsvector, to_tsquery_from_text(%s, 'zh_cn'), 32) AS score
+                FROM {self.table_name}
+                WHERE to_tsvector@@to_tsquery_from_text(%s, 'zh_cn')
+                ORDER BY score DESC
+                LIMIT {top_k}""",
+                (f"'{query}'", f"'{query}'"),
+            )
+            documents = []
+            for record in cur:
+                id, vector, page_content, metadata, score = record
+                metadata["score"] = score
+                doc = Document(
+                    page_content=page_content,
+                    vector=vector,
+                    metadata=metadata,
+                )
+                documents.append(doc)
+        return documents
+
+    def delete(self) -> None:
+        with self._get_cursor() as cur:
+            cur.execute(f"DROP TABLE IF EXISTS {self.table_name}")

api/core/rag/datasource/vdb/couchbase/couchbase_vector.py

@@ -242,7 +242,7 @@ class CouchbaseVector(BaseVector):
         try:
             self._cluster.query(query, named_parameters={"doc_ids": ids}).execute()
         except Exception as e:
-            logger.exception(e)
+            logger.exception(f"Failed to delete documents, ids: {ids}")
 
     def delete_by_document_id(self, document_id: str):
         query = f"""

api/core/rag/datasource/vdb/lindorm/lindorm_vector.py

@@ -81,7 +81,7 @@ class LindormVectorStore(BaseVector):
                                                   "ids": batch_ids}, _source=False)
                 return {doc["_id"] for doc in existing_docs["docs"] if doc["found"]}
             except Exception as e:
-                logger.exception(f"Error fetching batch {batch_ids}: {e}")
+                logger.exception(f"Error fetching batch {batch_ids}")
                 return set()
 
         @retry(stop=stop_after_attempt(3), wait=wait_fixed(60))
@@ -99,7 +99,7 @@ class LindormVectorStore(BaseVector):
                 )
                 return {doc["_id"] for doc in existing_docs["docs"] if doc["found"]}
             except Exception as e:
-                logger.exception(f"Error fetching batch {batch_ids}: {e}")
+                logger.exception(f"Error fetching batch ids: {batch_ids}")
                 return set()
 
         if ids is None:
@@ -187,7 +187,7 @@ class LindormVectorStore(BaseVector):
                 logger.warning(
                     f"Index '{self._collection_name}' does not exist. No deletion performed.")
         except Exception as e:
-            logger.exception(f"Error occurred while deleting the index: {e}")
+            logger.exception(f"Error occurred while deleting the index: {self._collection_name}")
             raise e
 
     def text_exists(self, id: str) -> bool:
@@ -213,7 +213,7 @@ class LindormVectorStore(BaseVector):
             response = self._client.search(
                 index=self._collection_name, body=query)
         except Exception as e:
-            logger.exception(f"Error executing search: {e}")
+            logger.exception(f"Error executing vector search, query: {query}")
             raise
 
         docs_and_scores = []

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

@@ -142,7 +142,7 @@ class MyScaleVector(BaseVector):
                 for r in self._client.query(sql).named_results()
             ]
         except Exception as e:
-            logging.exception(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")
+            logging.exception(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m")  # noqa:TRY401
             return []
 
     def delete(self) -> None:

api/core/rag/datasource/vdb/opensearch/opensearch_vector.py

@@ -158,7 +158,7 @@ class OpenSearchVector(BaseVector):
         try:
             response = self._client.search(index=self._collection_name.lower(), body=query)
         except Exception as e:
-            logger.exception(f"Error executing search: {e}")
+            logger.exception(f"Error executing vector search, query: {query}")
             raise
 
         docs = []

api/core/rag/embedding/cached_embedding.py

@@ -69,7 +69,7 @@ class CacheEmbedding(Embeddings):
                         except IntegrityError:
                             db.session.rollback()
                         except Exception as e:
-                            logging.exception("Failed transform embedding: %s", e)
+                            logging.exception("Failed transform embedding")
                 cache_embeddings = []
                 try:
                     for i, embedding in zip(embedding_queue_indices, embedding_queue_embeddings):
@@ -89,7 +89,7 @@ class CacheEmbedding(Embeddings):
                     db.session.rollback()
             except Exception as ex:
                 db.session.rollback()
-                logger.exception("Failed to embed documents: %s", ex)
+                logger.exception("Failed to embed documents: %s")
                 raise ex
 
         return text_embeddings
@@ -112,7 +112,7 @@ class CacheEmbedding(Embeddings):
             embedding_results = (embedding_results / np.linalg.norm(embedding_results)).tolist()
         except Exception as ex:
             if dify_config.DEBUG:
-                logging.exception(f"Failed to embed query text: {ex}")
+                logging.exception(f"Failed to embed query text '{text[:10]}...({len(text)} chars)'")
             raise ex
 
         try:
@@ -126,7 +126,7 @@ class CacheEmbedding(Embeddings):
             redis_client.setex(embedding_cache_key, 600, encoded_str)
         except Exception as ex:
             if dify_config.DEBUG:
-                logging.exception("Failed to add embedding to redis %s", ex)
+                logging.exception(f"Failed to add embedding to redis for the text '{text[:10]}...({len(text)} chars)'")
             raise ex
 
         return embedding_results

api/core/rag/extractor/word_extractor.py

@@ -229,7 +229,7 @@ class WordExtractor(BaseExtractor):
                                 for i in url_pattern.findall(x.text):
                                     hyperlinks_url = str(i)
                     except Exception as e:
-                        logger.exception(e)
+                        logger.exception("Failed to parse HYPERLINK xml")
 
         def parse_paragraph(paragraph):
             paragraph_content = []

api/core/rag/index_processor/processor/paragraph_index_processor.py

@@ -11,6 +11,7 @@ from core.rag.extractor.entity.extract_setting import ExtractSetting
 from core.rag.extractor.extract_processor import ExtractProcessor
 from core.rag.index_processor.index_processor_base import BaseIndexProcessor
 from core.rag.models.document import Document
+from core.tools.utils.text_processing_utils import remove_leading_symbols
 from libs import helper
 from models.dataset import Dataset
 
@@ -43,11 +44,7 @@ class ParagraphIndexProcessor(BaseIndexProcessor):
                     document_node.metadata["doc_id"] = doc_id
                     document_node.metadata["doc_hash"] = hash
                     # delete Splitter character
-                    page_content = document_node.page_content
-                    if page_content.startswith(".") or page_content.startswith("。"):
-                        page_content = page_content[1:].strip()
-                    else:
-                        page_content = page_content
+                    page_content = remove_leading_symbols(document_node.page_content).strip()
                     if len(page_content) > 0:
                         document_node.page_content = page_content
                         split_documents.append(document_node)

api/core/rag/index_processor/processor/qa_index_processor.py

@@ -18,6 +18,7 @@ from core.rag.extractor.entity.extract_setting import ExtractSetting
 from core.rag.extractor.extract_processor import ExtractProcessor
 from core.rag.index_processor.index_processor_base import BaseIndexProcessor
 from core.rag.models.document import Document
+from core.tools.utils.text_processing_utils import remove_leading_symbols
 from libs import helper
 from models.dataset import Dataset
 
@@ -53,11 +54,7 @@ class QAIndexProcessor(BaseIndexProcessor):
                     document_node.metadata["doc_hash"] = hash
                     # delete Splitter character
                     page_content = document_node.page_content
-                    if page_content.startswith(".") or page_content.startswith("。"):
-                        page_content = page_content[1:]
-                    else:
-                        page_content = page_content
-                    document_node.page_content = page_content
+                    document_node.page_content = remove_leading_symbols(page_content)
                     split_documents.append(document_node)
             all_documents.extend(split_documents)
         for i in range(0, len(all_documents), 10):
@@ -159,7 +156,7 @@ class QAIndexProcessor(BaseIndexProcessor):
                     qa_documents.append(qa_document)
                 format_documents.extend(qa_documents)
             except Exception as e:
-                logging.exception(e)
+                logging.exception("Failed to format qa document")
 
             all_qa_documents.extend(format_documents)
 

api/core/rag/rerank/weight_rerank.py

@@ -36,23 +36,21 @@ class WeightRerankRunner(BaseRerankRunner):
 
         :return:
         """
-        docs = []
-        doc_id = []
         unique_documents = []
+        doc_id = set()
         for document in documents:
-            if document.metadata["doc_id"] not in doc_id:
-                doc_id.append(document.metadata["doc_id"])
-                docs.append(document.page_content)
+            doc_id = document.metadata.get("doc_id")
+            if doc_id not in doc_id:
+                doc_id.add(doc_id)
                 unique_documents.append(document)
 
         documents = unique_documents
 
-        rerank_documents = []
         query_scores = self._calculate_keyword_score(query, documents)
-
         query_vector_scores = self._calculate_cosine(self.tenant_id, query, documents, self.weights.vector_setting)
+
+        rerank_documents = []
         for document, query_score, query_vector_score in zip(documents, query_scores, query_vector_scores):
-            # format document
             score = (
                 self.weights.vector_setting.vector_weight * query_vector_score
                 + self.weights.keyword_setting.keyword_weight * query_score
@@ -61,7 +59,8 @@ class WeightRerankRunner(BaseRerankRunner):
                 continue
             document.metadata["score"] = score
             rerank_documents.append(document)
-        rerank_documents = sorted(rerank_documents, key=lambda x: x.metadata["score"], reverse=True)
+
+        rerank_documents.sort(key=lambda x: x.metadata["score"], reverse=True)
         return rerank_documents[:top_n] if top_n else rerank_documents
 
     def _calculate_keyword_score(self, query: str, documents: list[Document]) -> list[float]:

api/core/tools/provider/builtin/duckduckgo/tools/ddgo_news.py

@@ -0,0 +1,87 @@
+from typing import Any
+
+from duckduckgo_search import DDGS
+
+from core.model_runtime.entities.message_entities import SystemPromptMessage
+from core.tools.entities.tool_entities import ToolInvokeMessage
+from core.tools.tool.builtin_tool import BuiltinTool
+
+SUMMARY_PROMPT = """
+User's query: 
+{query}
+
+Here are the news results:
+{content}
+
+Please summarize the news in a few sentences.
+"""
+
+
+class DuckDuckGoNewsSearchTool(BuiltinTool):
+    """
+    Tool for performing a news search using DuckDuckGo search engine.
+    """
+
+    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage | list[ToolInvokeMessage]:
+        query_dict = {
+            "keywords": tool_parameters.get("query"),
+            "timelimit": tool_parameters.get("timelimit"),
+            "max_results": tool_parameters.get("max_results"),
+            "safesearch": "moderate",
+            "region": "wt-wt",
+        }
+        try:
+            response = list(DDGS().news(**query_dict))
+            if not response:
+                return [self.create_text_message("No news found matching your criteria.")]
+        except Exception as e:
+            return [self.create_text_message(f"Error searching news: {str(e)}")]
+
+        require_summary = tool_parameters.get("require_summary", False)
+
+        if require_summary:
+            results = "\n".join([f"{res.get('title')}: {res.get('body')}" for res in response])
+            results = self.summary_results(user_id=user_id, content=results, query=query_dict["keywords"])
+            return self.create_text_message(text=results)
+
+        # Create rich markdown content for each news item
+        markdown_result = "\n\n"
+        json_result = []
+
+        for res in response:
+            markdown_result += f"### {res.get('title', 'Untitled')}\n\n"
+            if res.get("date"):
+                markdown_result += f"**Date:** {res.get('date')}\n\n"
+            if res.get("body"):
+                markdown_result += f"{res.get('body')}\n\n"
+            if res.get("source"):
+                markdown_result += f"*Source: {res.get('source')}*\n\n"
+            if res.get("image"):
+                markdown_result += f"![{res.get('title', '')}]({res.get('image')})\n\n"
+            markdown_result += f"[Read more]({res.get('url', '')})\n\n---\n\n"
+
+            json_result.append(
+                self.create_json_message(
+                    {
+                        "title": res.get("title", ""),
+                        "date": res.get("date", ""),
+                        "body": res.get("body", ""),
+                        "url": res.get("url", ""),
+                        "image": res.get("image", ""),
+                        "source": res.get("source", ""),
+                    }
+                )
+            )
+
+        return [self.create_text_message(markdown_result)] + json_result
+
+    def summary_results(self, user_id: str, content: str, query: str) -> str:
+        prompt = SUMMARY_PROMPT.format(query=query, content=content)
+        summary = self.invoke_model(
+            user_id=user_id,
+            prompt_messages=[
+                SystemPromptMessage(content=prompt),
+            ],
+            stop=[],
+        )
+        return summary.message.content

api/core/tools/provider/builtin/duckduckgo/tools/ddgo_news.yaml

@@ -0,0 +1,71 @@
+identity:
+  name: ddgo_news
+  author: Assistant
+  label:
+    en_US: DuckDuckGo News Search
+    zh_Hans: DuckDuckGo 新闻搜索
+description:
+  human:
+    en_US: Perform news searches on DuckDuckGo and get results.
+    zh_Hans: 在 DuckDuckGo 上进行新闻搜索并获取结果。
+  llm: Perform news searches on DuckDuckGo and get results.
+parameters:
+  - name: query
+    type: string
+    required: true
+    label:
+      en_US: Query String
+      zh_Hans: 查询语句
+    human_description:
+      en_US: Search Query.
+      zh_Hans: 搜索查询语句。
+    llm_description: Key words for searching
+    form: llm
+  - name: max_results
+    type: number
+    required: true
+    default: 5
+    label:
+      en_US: Max Results
+      zh_Hans: 最大结果数量
+    human_description:
+      en_US: The Max Results
+      zh_Hans: 最大结果数量
+    form: form
+  - name: timelimit
+    type: select
+    required: false
+    options:
+      - value: Day
+        label:
+          en_US: Current Day
+          zh_Hans: 当天
+      - value: Week
+        label:
+          en_US: Current Week
+          zh_Hans: 本周
+      - value: Month
+        label:
+          en_US: Current Month
+          zh_Hans: 当月
+      - value: Year
+        label:
+          en_US: Current Year
+          zh_Hans: 今年
+    label:
+      en_US: Result Time Limit
+      zh_Hans: 结果时间限制
+    human_description:
+      en_US: Use when querying results within a specific time range only.
+      zh_Hans: 只查询一定时间范围内的结果时使用
+    form: form
+  - name: require_summary
+    type: boolean
+    default: false
+    label:
+      en_US: Require Summary
+      zh_Hans: 是否总结
+    human_description:
+      en_US: Whether to pass the news results to llm for summarization.
+      zh_Hans: 是否需要将新闻结果传给大模型总结
+    form: form

api/core/tools/provider/builtin/duckduckgo/tools/ddgo_video.py

@@ -0,0 +1,75 @@
+from typing import Any, ClassVar
+
+from duckduckgo_search import DDGS
+
+from core.tools.entities.tool_entities import ToolInvokeMessage
+from core.tools.tool.builtin_tool import BuiltinTool
+
+
+class DuckDuckGoVideoSearchTool(BuiltinTool):
+    """
+    Tool for performing a video search using DuckDuckGo search engine.
+    """
+
+    IFRAME_TEMPLATE: ClassVar[str] = """
+<div style="position: relative; padding-bottom: 56.25%; height: 0; overflow: hidden; \
+max-width: 100%; border-radius: 8px;">
+    <iframe
+        style="position: absolute; top: 0; left: 0; width: 100%; height: 100%;"
+        src="{src}"
+        frameborder="0"
+        allowfullscreen>
+    </iframe>
+</div>"""
+
+    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> list[ToolInvokeMessage]:
+        query_dict = {
+            "keywords": tool_parameters.get("query"),
+            "region": tool_parameters.get("region", "wt-wt"),
+            "safesearch": tool_parameters.get("safesearch", "moderate"),
+            "timelimit": tool_parameters.get("timelimit"),
+            "resolution": tool_parameters.get("resolution"),
+            "duration": tool_parameters.get("duration"),
+            "license_videos": tool_parameters.get("license_videos"),
+            "max_results": tool_parameters.get("max_results"),
+        }
+
+        # Remove None values to use API defaults
+        query_dict = {k: v for k, v in query_dict.items() if v is not None}
+
+        # Get proxy URL from parameters
+        proxy_url = tool_parameters.get("proxy_url", "").strip()
+
+        response = DDGS().videos(**query_dict)
+
+        # Create HTML result with embedded iframes
+        markdown_result = "\n\n"
+        json_result = []
+
+        for res in response:
+            title = res.get("title", "")
+            embed_html = res.get("embed_html", "")
+            description = res.get("description", "")
+            content_url = res.get("content", "")
+
+            # Handle TED.com videos
+            if not embed_html and "ted.com/talks" in content_url:
+                embed_url = content_url.replace("www.ted.com", "embed.ted.com")
+                if proxy_url:
+                    embed_url = f"{proxy_url}{embed_url}"
+                embed_html = self.IFRAME_TEMPLATE.format(src=embed_url)
+
+            # Original YouTube/other platform handling
+            elif embed_html:
+                embed_url = res.get("embed_url", "")
+                if proxy_url and embed_url:
+                    embed_url = f"{proxy_url}{embed_url}"
+                embed_html = self.IFRAME_TEMPLATE.format(src=embed_url)
+
+            markdown_result += f"{title}\n\n"
+            markdown_result += f"{embed_html}\n\n"
+            markdown_result += "---\n\n"
+
+            json_result.append(self.create_json_message(res))
+
+        return [self.create_text_message(markdown_result)] + json_result

api/core/tools/provider/builtin/duckduckgo/tools/ddgo_video.yaml

@@ -0,0 +1,97 @@
+identity:
+  name: ddgo_video
+  author: Tao Wang
+  label:
+    en_US: DuckDuckGo Video Search
+    zh_Hans: DuckDuckGo 视频搜索
+description:
+  human:
+    en_US: Search and embedded videos.
+    zh_Hans: 搜索并嵌入视频
+  llm: Search videos on duckduckgo and embed videos in iframe
+parameters:
+  - name: query
+    label:
+      en_US: Query String
+      zh_Hans: 查询语句
+    type: string
+    required: true
+    human_description:
+      en_US: Search Query
+      zh_Hans: 搜索查询语句
+    llm_description: Key words for searching
+    form: llm
+  - name: max_results
+    label:
+      en_US: Max Results
+      zh_Hans: 最大结果数量
+    type: number
+    required: true
+    default: 3
+    minimum: 1
+    maximum: 10
+    human_description:
+      en_US: The max results (1-10)
+      zh_Hans: 最大结果数量（1-10）
+    form: form
+  - name: timelimit
+    label:
+      en_US: Result Time Limit
+      zh_Hans: 结果时间限制
+    type: select
+    required: false
+    options:
+      - value: Day
+        label:
+          en_US: Current Day
+          zh_Hans: 当天
+      - value: Week
+        label:
+          en_US: Current Week
+          zh_Hans: 本周
+      - value: Month
+        label:
+          en_US: Current Month
+          zh_Hans: 当月
+      - value: Year
+        label:
+          en_US: Current Year
+          zh_Hans: 今年
+    human_description:
+      en_US: Query results within a specific time range only
+      zh_Hans: 只查询一定时间范围内的结果时使用
+    form: form
+  - name: duration
+    label:
+      en_US: Video Duration
+      zh_Hans: 视频时长
+    type: select
+    required: false
+    options:
+      - value: short
+        label:
+          en_US: Short (<4 minutes)
+          zh_Hans: 短视频（<4分钟）
+      - value: medium
+        label:
+          en_US: Medium (4-20 minutes)
+          zh_Hans: 中等（4-20分钟）
+      - value: long
+        label:
+          en_US: Long (>20 minutes)
+          zh_Hans: 长视频（>20分钟）
+    human_description:
+      en_US: Filter videos by duration
+      zh_Hans: 按时长筛选视频
+    form: form
+  - name: proxy_url
+    label:
+      en_US: Proxy URL
+      zh_Hans: 视频代理地址
+    type: string
+    required: false
+    default: ""
+    human_description:
+      en_US: Proxy URL
+      zh_Hans: 视频代理地址
+    form: form

api/core/tools/provider/builtin/email/tools/send.py

@@ -38,7 +38,7 @@ def send_mail(parmas: SendEmailToolParameters):
                 server.sendmail(parmas.email_account, parmas.sender_to, msg.as_string())
                 return True
         except Exception as e:
-            logging.exception("send email failed: %s", e)
+            logging.exception("send email failed")
             return False
     else:  # NONE or TLS
         try:
@@ -49,5 +49,5 @@ def send_mail(parmas: SendEmailToolParameters):
                 server.sendmail(parmas.email_account, parmas.sender_to, msg.as_string())
                 return True
         except Exception as e:
-            logging.exception("send email failed: %s", e)
+            logging.exception("send email failed")
             return False

api/core/tools/provider/builtin/email/tools/send_mail.py

@@ -17,7 +17,7 @@ class SendMailTool(BuiltinTool):
         invoke tools
         """
         sender = self.runtime.credentials.get("email_account", "")
-        email_rgx = re.compile(r"^[a-zA-Z0-9_-]+@[a-zA-Z0-9_-]+(\.[a-zA-Z0-9_-]+)+$")
+        email_rgx = re.compile(r"^[a-zA-Z0-9._-]+@[a-zA-Z0-9_-]+(\.[a-zA-Z0-9_-]+)+$")
         password = self.runtime.credentials.get("email_password", "")
         smtp_server = self.runtime.credentials.get("smtp_server", "")
         if not smtp_server:

api/core/tools/provider/builtin/email/tools/send_mail_batch.py

@@ -18,7 +18,7 @@ class SendMailTool(BuiltinTool):
         invoke tools
         """
         sender = self.runtime.credentials.get("email_account", "")
-        email_rgx = re.compile(r"^[a-zA-Z0-9_-]+@[a-zA-Z0-9_-]+(\.[a-zA-Z0-9_-]+)+$")
+        email_rgx = re.compile(r"^[a-zA-Z0-9._-]+@[a-zA-Z0-9_-]+(\.[a-zA-Z0-9_-]+)+$")
         password = self.runtime.credentials.get("email_password", "")
         smtp_server = self.runtime.credentials.get("smtp_server", "")
         if not smtp_server:

api/core/tools/provider/builtin/fal/tools/wizper.py

@@ -19,7 +19,7 @@ class WizperTool(BuiltinTool):
         version = tool_parameters.get("version", "3")
 
         if audio_file.type != FileType.AUDIO:
-            return [self.create_text_message("Not a valid audio file.")]
+            return self.create_text_message("Not a valid audio file.")
 
         api_key = self.runtime.credentials["fal_api_key"]
 
@@ -31,9 +31,8 @@ class WizperTool(BuiltinTool):
 
         try:
             audio_url = fal_client.upload(file_data, mime_type)
-
         except Exception as e:
-            return [self.create_text_message(f"Error uploading audio file: {str(e)}")]
+            return self.create_text_message(f"Error uploading audio file: {str(e)}")
 
         arguments = {
             "audio_url": audio_url,
@@ -49,4 +48,9 @@ class WizperTool(BuiltinTool):
             with_logs=False,
         )
 
-        return self.create_json_message(result)
+        json_message = self.create_json_message(result)
+
+        text = result.get("text", "")
+        text_message = self.create_text_message(text)
+
+        return [json_message, text_message]

api/core/tools/provider/builtin/gitee_ai/tools/embedding.py

@@ -0,0 +1,25 @@
+from typing import Any, Union
+
+import requests
+
+from core.tools.entities.tool_entities import ToolInvokeMessage
+from core.tools.tool.builtin_tool import BuiltinTool
+
+
+class GiteeAIToolEmbedding(BuiltinTool):
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        headers = {
+            "content-type": "application/json",
+            "authorization": f"Bearer {self.runtime.credentials['api_key']}",
+        }
+
+        payload = {"inputs": tool_parameters.get("inputs")}
+        model = tool_parameters.get("model", "bge-m3")
+        url = f"https://ai.gitee.com/api/serverless/{model}/embeddings"
+        response = requests.post(url, json=payload, headers=headers)
+        if response.status_code != 200:
+            return self.create_text_message(f"Got Error Response:{response.text}")
+
+        return [self.create_text_message(response.content.decode("utf-8"))]

api/core/tools/provider/builtin/gitee_ai/tools/embedding.yaml

@@ -0,0 +1,37 @@
+identity:
+  name: embedding
+  author: gitee_ai
+  label:
+    en_US: embedding
+  icon: icon.svg
+description:
+  human:
+    en_US: Generate word embeddings using Serverless-supported models (compatible with OpenAI)
+  llm: This tool is used to generate word embeddings from text input.
+parameters:
+  - name: model
+    type: string
+    required: true
+    in: path
+    description:
+      en_US: Supported Embedding (compatible with OpenAI) interface models
+    enum:
+      - bge-m3
+      - bge-large-zh-v1.5
+      - bge-small-zh-v1.5
+    label:
+      en_US: Service Model
+      zh_Hans: 服务模型
+    default: bge-m3
+    form: form
+  - name: inputs
+    type: string
+    required: true
+    label:
+      en_US: Input Text
+      zh_Hans: 输入文本
+    human_description:
+      en_US: The text input used to generate embeddings.
+      zh_Hans: 用于生成词向量的输入文本。
+    llm_description: This text input will be used to generate embeddings.
+    form: llm

api/core/tools/provider/builtin/gitee_ai/tools/text-to-image.py

@@ -6,7 +6,7 @@ from core.tools.entities.tool_entities import ToolInvokeMessage
 from core.tools.tool.builtin_tool import BuiltinTool
 
 
-class GiteeAITool(BuiltinTool):
+class GiteeAIToolText2Image(BuiltinTool):
     def _invoke(
         self, user_id: str, tool_parameters: dict[str, Any]
     ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:

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

@@ -1,14 +1,12 @@
 identity:
-  author: Yash Parmar
+  author: Yash Parmar, Kalo Chin
   name: tavily
   label:
-    en_US: Tavily
-    zh_Hans: Tavily
-    pt_BR: Tavily
+    en_US: Tavily Search & Extract
+    zh_Hans: Tavily 搜索和提取
   description:
-    en_US: Tavily
-    zh_Hans: Tavily
-    pt_BR: Tavily
+    en_US: A powerful AI-native search engine and web content extraction tool that provides highly relevant search results and raw content extraction from web pages.
+    zh_Hans: 一个强大的原生AI搜索引擎和网页内容提取工具，提供高度相关的搜索结果和网页原始内容提取。
   icon: icon.png
   tags:
     - search
@@ -19,13 +17,10 @@ credentials_for_provider:
     label:
       en_US: Tavily API key
       zh_Hans: Tavily API key
-      pt_BR: Tavily API key
     placeholder:
       en_US: Please input your Tavily API key
       zh_Hans: 请输入你的 Tavily API key
-      pt_BR: Please input your Tavily API key
     help:
       en_US: Get your Tavily API key from Tavily
       zh_Hans: 从 TavilyApi 获取您的 Tavily API key
-      pt_BR: Get your Tavily API key from Tavily
-    url: https://docs.tavily.com/docs/welcome
+    url: https://app.tavily.com/home

api/core/tools/provider/builtin/tavily/tools/tavily_extract.py

@@ -0,0 +1,145 @@
+from typing import Any
+
+import requests
+
+from core.tools.entities.tool_entities import ToolInvokeMessage
+from core.tools.tool.builtin_tool import BuiltinTool
+
+TAVILY_API_URL = "https://api.tavily.com"
+
+
+class TavilyExtract:
+    """
+    A class for extracting content from web pages using the Tavily Extract API.
+
+    Args:
+        api_key (str): The API key for accessing the Tavily Extract API.
+
+    Methods:
+        extract_content: Retrieves extracted content from the Tavily Extract API.
+    """
+
+    def __init__(self, api_key: str) -> None:
+        self.api_key = api_key
+
+    def extract_content(self, params: dict[str, Any]) -> dict:
+        """
+        Retrieves extracted content from the Tavily Extract API.
+
+        Args:
+            params (Dict[str, Any]): The extraction parameters.
+
+        Returns:
+            dict: The extracted content.
+
+        """
+        # Ensure required parameters are set
+        if "api_key" not in params:
+            params["api_key"] = self.api_key
+
+        # Process parameters
+        processed_params = self._process_params(params)
+
+        response = requests.post(f"{TAVILY_API_URL}/extract", json=processed_params)
+        response.raise_for_status()
+        return response.json()
+
+    def _process_params(self, params: dict[str, Any]) -> dict:
+        """
+        Processes and validates the extraction parameters.
+
+        Args:
+            params (Dict[str, Any]): The extraction parameters.
+
+        Returns:
+            dict: The processed parameters.
+        """
+        processed_params = {}
+
+        # Process 'urls'
+        if "urls" in params:
+            urls = params["urls"]
+            if isinstance(urls, str):
+                processed_params["urls"] = [url.strip() for url in urls.replace(",", " ").split()]
+            elif isinstance(urls, list):
+                processed_params["urls"] = urls
+        else:
+            raise ValueError("The 'urls' parameter is required.")
+
+        # Only include 'api_key'
+        processed_params["api_key"] = params.get("api_key", self.api_key)
+
+        return processed_params
+
+
+class TavilyExtractTool(BuiltinTool):
+    """
+    A tool for extracting content from web pages using Tavily Extract.
+    """
+
+    def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage | list[ToolInvokeMessage]:
+        """
+        Invokes the Tavily Extract tool with the given user ID and tool parameters.
+
+        Args:
+            user_id (str): The ID of the user invoking the tool.
+            tool_parameters (Dict[str, Any]): The parameters for the Tavily Extract tool.
+
+        Returns:
+            ToolInvokeMessage | list[ToolInvokeMessage]: The result of the Tavily Extract tool invocation.
+        """
+        urls = tool_parameters.get("urls", "")
+        api_key = self.runtime.credentials.get("tavily_api_key")
+        if not api_key:
+            return self.create_text_message(
+                "Tavily API key is missing. Please set the 'tavily_api_key' in credentials."
+            )
+        if not urls:
+            return self.create_text_message("Please input at least one URL to extract.")
+
+        tavily_extract = TavilyExtract(api_key)
+        try:
+            raw_results = tavily_extract.extract_content(tool_parameters)
+        except requests.HTTPError as e:
+            return self.create_text_message(f"Error occurred while extracting content: {str(e)}")
+
+        if not raw_results.get("results"):
+            return self.create_text_message("No content could be extracted from the provided URLs.")
+        else:
+            # Always return JSON message with all data
+            json_message = self.create_json_message(raw_results)
+
+            # Create text message based on user-selected parameters
+            text_message_content = self._format_results_as_text(raw_results)
+            text_message = self.create_text_message(text=text_message_content)
+
+            return [json_message, text_message]
+
+    def _format_results_as_text(self, raw_results: dict) -> str:
+        """
+        Formats the raw extraction results into a markdown text based on user-selected parameters.
+
+        Args:
+            raw_results (dict): The raw extraction results.
+
+        Returns:
+            str: The formatted markdown text.
+        """
+        output_lines = []
+
+        for idx, result in enumerate(raw_results.get("results", []), 1):
+            url = result.get("url", "")
+            raw_content = result.get("raw_content", "")
+
+            output_lines.append(f"## Extracted Content {idx}: {url}\n")
+            output_lines.append(f"**Raw Content:**\n{raw_content}\n")
+            output_lines.append("---\n")
+
+        if raw_results.get("failed_results"):
+            output_lines.append("## Failed URLs:\n")
+            for failed in raw_results["failed_results"]:
+                url = failed.get("url", "")
+                error = failed.get("error", "Unknown error")
+                output_lines.append(f"- {url}: {error}\n")
+
+        return "\n".join(output_lines)

api/core/tools/provider/builtin/tavily/tools/tavily_extract.yaml

@@ -0,0 +1,23 @@
+identity:
+  name: tavily_extract
+  author: Kalo Chin
+  label:
+    en_US: Tavily Extract
+    zh_Hans: Tavily Extract
+description:
+  human:
+    en_US: A web extraction tool built specifically for AI agents (LLMs), delivering raw content from web pages.
+    zh_Hans: 专为人工智能代理 (LLM) 构建的网页提取工具，提供网页的原始内容。
+  llm: A tool for extracting raw content from web pages, designed for AI agents (LLMs).
+parameters:
+  - name: urls
+    type: string
+    required: true
+    label:
+      en_US: URLs
+      zh_Hans: URLs
+    human_description:
+      en_US: A comma-separated list of URLs to extract content from.
+      zh_Hans: 要从中提取内容的 URL 的逗号分隔列表。
+    llm_description: A comma-separated list of URLs to extract content from.
+    form: llm

api/core/tools/provider/builtin/tavily/tools/tavily_search.py

@@ -17,8 +17,6 @@ class TavilySearch:
 
     Methods:
         raw_results: Retrieves raw search results from the Tavily Search API.
-        results: Retrieves cleaned search results from the Tavily Search API.
-        clean_results: Cleans the raw search results.
     """
 
     def __init__(self, api_key: str) -> None:
@@ -35,63 +33,62 @@ class TavilySearch:
             dict: The raw search results.
 
         """
+        # Ensure required parameters are set
         params["api_key"] = self.api_key
-        if (
-            "exclude_domains" in params
-            and isinstance(params["exclude_domains"], str)
-            and params["exclude_domains"] != "None"
-        ):
-            params["exclude_domains"] = params["exclude_domains"].split()
-        else:
-            params["exclude_domains"] = []
-        if (
-            "include_domains" in params
-            and isinstance(params["include_domains"], str)
-            and params["include_domains"] != "None"
-        ):
-            params["include_domains"] = params["include_domains"].split()
-        else:
-            params["include_domains"] = []
 
-        response = requests.post(f"{TAVILY_API_URL}/search", json=params)
+        # Process parameters to ensure correct types
+        processed_params = self._process_params(params)
+
+        response = requests.post(f"{TAVILY_API_URL}/search", json=processed_params)
         response.raise_for_status()
         return response.json()
 
-    def results(self, params: dict[str, Any]) -> list[dict]:
+    def _process_params(self, params: dict[str, Any]) -> dict:
         """
-        Retrieves cleaned search results from the Tavily Search API.
+        Processes and validates the search parameters.
 
         Args:
             params (Dict[str, Any]): The search parameters.
 
         Returns:
-            list: The cleaned search results.
-
+            dict: The processed parameters.
         """
-        raw_search_results = self.raw_results(params)
-        return self.clean_results(raw_search_results["results"])
+        processed_params = {}
 
-    def clean_results(self, results: list[dict]) -> list[dict]:
-        """
-        Cleans the raw search results.
+        for key, value in params.items():
+            if value is None or value == "None":
+                continue
+            if key in ["include_domains", "exclude_domains"]:
+                if isinstance(value, str):
+                    # Split the string by commas or spaces and strip whitespace
+                    processed_params[key] = [domain.strip() for domain in value.replace(",", " ").split()]
+            elif key in ["include_images", "include_image_descriptions", "include_answer", "include_raw_content"]:
+                # Ensure boolean type
+                if isinstance(value, str):
+                    processed_params[key] = value.lower() == "true"
+                else:
+                    processed_params[key] = bool(value)
+            elif key in ["max_results", "days"]:
+                if isinstance(value, str):
+                    processed_params[key] = int(value)
+                else:
+                    processed_params[key] = value
+            elif key in ["search_depth", "topic", "query", "api_key"]:
+                processed_params[key] = value
+            else:
+                # Unrecognized parameter
+                pass
 
-        Args:
-            results (list): The raw search results.
+        # Set defaults if not present
+        processed_params.setdefault("search_depth", "basic")
+        processed_params.setdefault("topic", "general")
+        processed_params.setdefault("max_results", 5)
 
-        Returns:
-            list: The cleaned search results.
+        # If topic is 'news', ensure 'days' is set
+        if processed_params.get("topic") == "news":
+            processed_params.setdefault("days", 3)
 
-        """
-        clean_results = []
-        for result in results:
-            clean_results.append(
-                {
-                    "url": result["url"],
-                    "content": result["content"],
-                }
-            )
-        # return clean results as a string
-        return "\n".join([f"{res['url']}\n{res['content']}" for res in clean_results])
+        return processed_params
 
 
 class TavilySearchTool(BuiltinTool):
@@ -111,14 +108,88 @@ class TavilySearchTool(BuiltinTool):
             ToolInvokeMessage | list[ToolInvokeMessage]: The result of the Tavily search tool invocation.
         """
         query = tool_parameters.get("query", "")
-
-        api_key = self.runtime.credentials["tavily_api_key"]
+        api_key = self.runtime.credentials.get("tavily_api_key")
+        if not api_key:
+            return self.create_text_message(
+                "Tavily API key is missing. Please set the 'tavily_api_key' in credentials."
+            )
         if not query:
-            return self.create_text_message("Please input query")
+            return self.create_text_message("Please input a query.")
+
         tavily_search = TavilySearch(api_key)
-        results = tavily_search.results(tool_parameters)
-        print(results)
-        if not results:
-            return self.create_text_message(f"No results found for '{query}' in Tavily")
+        try:
+            raw_results = tavily_search.raw_results(tool_parameters)
+        except requests.HTTPError as e:
+            return self.create_text_message(f"Error occurred while searching: {str(e)}")
+
+        if not raw_results.get("results"):
+            return self.create_text_message(f"No results found for '{query}' in Tavily.")
         else:
-            return self.create_text_message(text=results)
+            # Always return JSON message with all data
+            json_message = self.create_json_message(raw_results)
+
+            # Create text message based on user-selected parameters
+            text_message_content = self._format_results_as_text(raw_results, tool_parameters)
+            text_message = self.create_text_message(text=text_message_content)
+
+            return [json_message, text_message]
+
+    def _format_results_as_text(self, raw_results: dict, tool_parameters: dict[str, Any]) -> str:
+        """
+        Formats the raw results into a markdown text based on user-selected parameters.
+
+        Args:
+            raw_results (dict): The raw search results.
+            tool_parameters (dict): The tool parameters selected by the user.
+
+        Returns:
+            str: The formatted markdown text.
+        """
+        output_lines = []
+
+        # Include answer if requested
+        if tool_parameters.get("include_answer", False) and raw_results.get("answer"):
+            output_lines.append(f"**Answer:** {raw_results['answer']}\n")
+
+        # Include images if requested
+        if tool_parameters.get("include_images", False) and raw_results.get("images"):
+            output_lines.append("**Images:**\n")
+            for image in raw_results["images"]:
+                if tool_parameters.get("include_image_descriptions", False) and "description" in image:
+                    output_lines.append(f"![{image['description']}]({image['url']})\n")
+                else:
+                    output_lines.append(f"![]({image['url']})\n")
+
+        # Process each result
+        if "results" in raw_results:
+            for idx, result in enumerate(raw_results["results"], 1):
+                title = result.get("title", "No Title")
+                url = result.get("url", "")
+                content = result.get("content", "")
+                published_date = result.get("published_date", "")
+                score = result.get("score", "")
+
+                output_lines.append(f"### Result {idx}: [{title}]({url})\n")
+
+                # Include published date if available and topic is 'news'
+                if tool_parameters.get("topic") == "news" and published_date:
+                    output_lines.append(f"**Published Date:** {published_date}\n")
+
+                output_lines.append(f"**URL:** {url}\n")
+
+                # Include score (relevance)
+                if score:
+                    output_lines.append(f"**Relevance Score:** {score}\n")
+
+                # Include content
+                if content:
+                    output_lines.append(f"**Content:**\n{content}\n")
+
+                # Include raw content if requested
+                if tool_parameters.get("include_raw_content", False) and result.get("raw_content"):
+                    output_lines.append(f"**Raw Content:**\n{result['raw_content']}\n")
+
+                # Add a separator
+                output_lines.append("---\n")
+
+        return "\n".join(output_lines)

api/core/tools/provider/builtin/tavily/tools/tavily_search.yaml

@@ -2,28 +2,24 @@ identity:
   name: tavily_search
   author: Yash Parmar
   label:
-    en_US: TavilySearch
-    zh_Hans: TavilySearch
-    pt_BR: TavilySearch
+    en_US: Tavily Search
+    zh_Hans: Tavily Search
 description:
   human:
-    en_US: A tool for  search engine built specifically for AI agents (LLMs), delivering real-time, accurate, and factual results at speed.
+    en_US: A search engine tool built specifically for AI agents (LLMs), delivering real-time, accurate, and factual results at speed.
     zh_Hans: 专为人工智能代理 (LLM) 构建的搜索引擎工具，可快速提供实时、准确和真实的结果。
-    pt_BR: A tool for search engine built specifically for AI agents (LLMs), delivering real-time, accurate, and factual results at speed.
   llm: A tool for search engine built specifically for AI agents (LLMs), delivering real-time, accurate, and factual results at speed.
 parameters:
   - name: query
     type: string
     required: true
     label:
-      en_US: Query string
-      zh_Hans: 查询语句
-      pt_BR: Query string
+      en_US: Query
+      zh_Hans: 查询
     human_description:
-      en_US: used for searching
-      zh_Hans: 用于搜索网页内容
-      pt_BR: used for searching
-    llm_description: key words for searching
+      en_US: The search query you want to execute with Tavily.
+      zh_Hans: 您想用 Tavily 执行的搜索查询。
+    llm_description: The search query.
     form: llm
   - name: search_depth
     type: select
@@ -31,122 +27,118 @@ parameters:
     label:
       en_US: Search Depth
       zh_Hans: 搜索深度
-      pt_BR: Search Depth
     human_description:
-      en_US: The depth of search results
-      zh_Hans: 搜索结果的深度
-      pt_BR: The depth of search results
+      en_US: The depth of the search.
+      zh_Hans: 搜索的深度。
     form: form
     options:
       - value: basic
         label:
           en_US: Basic
           zh_Hans: 基本
-          pt_BR: Basic
       - value: advanced
         label:
           en_US: Advanced
           zh_Hans: 高级
-          pt_BR: Advanced
     default: basic
+  - name: topic
+    type: select
+    required: false
+    label:
+      en_US: Topic
+      zh_Hans: 主题
+    human_description:
+      en_US: The category of the search.
+      zh_Hans: 搜索的类别。
+    form: form
+    options:
+      - value: general
+        label:
+          en_US: General
+          zh_Hans: 一般
+      - value: news
+        label:
+          en_US: News
+          zh_Hans: 新闻
+    default: general
+  - name: days
+    type: number
+    required: false
+    label:
+      en_US: Days
+      zh_Hans: 天数
+    human_description:
+      en_US: The number of days back from the current date to include in the search results (only applicable when "topic" is "news").
+      zh_Hans: 从当前日期起向前追溯的天数，以包含在搜索结果中（仅当“topic”为“news”时适用）。
+    form: form
+    min: 1
+    default: 3
+  - name: max_results
+    type: number
+    required: false
+    label:
+      en_US: Max Results
+      zh_Hans: 最大结果数
+    human_description:
+      en_US: The maximum number of search results to return.
+      zh_Hans: 要返回的最大搜索结果数。
+    form: form
+    min: 1
+    max: 20
+    default: 5
   - name: include_images
     type: boolean
     required: false
     label:
       en_US: Include Images
       zh_Hans: 包含图片
-      pt_BR: Include Images
     human_description:
-      en_US: Include images in the search results
-      zh_Hans: 在搜索结果中包含图片
-      pt_BR: Include images in the search results
+      en_US: Include a list of query-related images in the response.
+      zh_Hans: 在响应中包含与查询相关的图片列表。
     form: form
-    options:
-      - value: 'true'
-        label:
-          en_US: 'Yes'
-          zh_Hans: 是
-          pt_BR: 'Yes'
-      - value: 'false'
-        label:
-          en_US: 'No'
-          zh_Hans: 否
-          pt_BR: 'No'
-    default: 'false'
+    default: false
+  - name: include_image_descriptions
+    type: boolean
+    required: false
+    label:
+      en_US: Include Image Descriptions
+      zh_Hans: 包含图片描述
+    human_description:
+      en_US: When include_images is True, adds descriptive text for each image.
+      zh_Hans: 当 include_images 为 True 时，为每个图像添加描述文本。
+    form: form
+    default: false
   - name: include_answer
     type: boolean
     required: false
     label:
       en_US: Include Answer
       zh_Hans: 包含答案
-      pt_BR: Include Answer
     human_description:
-      en_US: Include answers in the search results
-      zh_Hans: 在搜索结果中包含答案
-      pt_BR: Include answers in the search results
+      en_US: Include a short answer to the original query in the response.
+      zh_Hans: 在响应中包含对原始查询的简短回答。
     form: form
-    options:
-      - value: 'true'
-        label:
-          en_US: 'Yes'
-          zh_Hans: 是
-          pt_BR: 'Yes'
-      - value: 'false'
-        label:
-          en_US: 'No'
-          zh_Hans: 否
-          pt_BR: 'No'
-    default: 'false'
+    default: false
   - name: include_raw_content
     type: boolean
     required: false
     label:
       en_US: Include Raw Content
       zh_Hans: 包含原始内容
-      pt_BR: Include Raw Content
     human_description:
-      en_US: Include raw content in the search results
-      zh_Hans: 在搜索结果中包含原始内容
-      pt_BR: Include raw content in the search results
+      en_US: Include the cleaned and parsed HTML content of each search result.
+      zh_Hans: 包含每个搜索结果的已清理和解析的HTML内容。
     form: form
-    options:
-      - value: 'true'
-        label:
-          en_US: 'Yes'
-          zh_Hans: 是
-          pt_BR: 'Yes'
-      - value: 'false'
-        label:
-          en_US: 'No'
-          zh_Hans: 否
-          pt_BR: 'No'
-    default: 'false'
-  - name: max_results
-    type: number
-    required: false
-    label:
-      en_US: Max Results
-      zh_Hans: 最大结果
-      pt_BR: Max Results
-    human_description:
-      en_US: The number of maximum search results to return
-      zh_Hans: 返回的最大搜索结果数
-      pt_BR: The number of maximum search results to return
-    form: form
-    min: 1
-    max: 20
-    default: 5
+    default: false
   - name: include_domains
     type: string
     required: false
     label:
       en_US: Include Domains
       zh_Hans: 包含域
-      pt_BR: Include Domains
     human_description:
-      en_US: A list of domains to specifically include in the search results
-      zh_Hans: 在搜索结果中特别包含的域名列表
-      pt_BR: A list of domains to specifically include in the search results
+      en_US: A comma-separated list of domains to specifically include in the search results.
+      zh_Hans: 要在搜索结果中特别包含的域的逗号分隔列表。
     form: form
   - name: exclude_domains
     type: string
@@ -154,9 +146,7 @@ parameters:
     label:
       en_US: Exclude Domains
       zh_Hans: 排除域
-      pt_BR: Exclude Domains
     human_description:
-      en_US: A list of domains to specifically exclude from the search results
-      zh_Hans: 从搜索结果中特别排除的域名列表
-      pt_BR: A list of domains to specifically exclude from the search results
+      en_US: A comma-separated list of domains to specifically exclude from the search results.
+      zh_Hans: 要从搜索结果中特别排除的域的逗号分隔列表。
     form: form

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

@@ -0,0 +1,11 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<svg width="800px" height="800px" viewBox="0 -38 256 256" version="1.1" xmlns="http://www.w3.org/2000/svg"
+     xmlns:xlink="http://www.w3.org/1999/xlink" preserveAspectRatio="xMidYMid">
+    <g>
+        <path d="M250.346231,28.0746923 C247.358133,17.0320558 238.732098,8.40602109 227.689461,5.41792308 C207.823743,0 127.868333,0 127.868333,0 C127.868333,0 47.9129229,0.164179487 28.0472049,5.58210256 C17.0045684,8.57020058 8.37853373,17.1962353 5.39043571,28.2388718 C-0.618533519,63.5374615 -2.94988224,117.322662 5.5546152,151.209308 C8.54271322,162.251944 17.1687479,170.877979 28.2113844,173.866077 C48.0771024,179.284 128.032513,179.284 128.032513,179.284 C128.032513,179.284 207.987923,179.284 227.853641,173.866077 C238.896277,170.877979 247.522312,162.251944 250.51041,151.209308 C256.847738,115.861464 258.801474,62.1091 250.346231,28.0746923 Z"
+              fill="#FF0000">
+        </path>
+        <polygon fill="#FFFFFF" points="102.420513 128.06 168.749025 89.642 102.420513 51.224">
+        </polygon>
+    </g>
+</svg>

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

@@ -0,0 +1,81 @@
+from typing import Any, Union
+from urllib.parse import parse_qs, urlparse
+
+from youtube_transcript_api import YouTubeTranscriptApi
+
+from core.tools.entities.tool_entities import ToolInvokeMessage
+from core.tools.tool.builtin_tool import BuiltinTool
+
+
+class YouTubeTranscriptTool(BuiltinTool):
+    def _invoke(
+        self, user_id: str, tool_parameters: dict[str, Any]
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        """
+        Invoke the YouTube transcript tool
+        """
+        try:
+            # Extract parameters with defaults
+            video_input = tool_parameters["video_id"]
+            language = tool_parameters.get("language")
+            output_format = tool_parameters.get("format", "text")
+            preserve_formatting = tool_parameters.get("preserve_formatting", False)
+            proxy = tool_parameters.get("proxy")
+            cookies = tool_parameters.get("cookies")
+
+            # Extract video ID from URL if needed
+            video_id = self._extract_video_id(video_input)
+
+            # Common kwargs for API calls
+            kwargs = {"proxies": {"https": proxy} if proxy else None, "cookies": cookies}
+
+            try:
+                if language:
+                    transcript_list = YouTubeTranscriptApi.list_transcripts(video_id, **kwargs)
+                    try:
+                        transcript = transcript_list.find_transcript([language])
+                    except:
+                        # If requested language not found, try translating from English
+                        transcript = transcript_list.find_transcript(["en"]).translate(language)
+                    transcript_data = transcript.fetch()
+                else:
+                    transcript_data = YouTubeTranscriptApi.get_transcript(
+                        video_id, preserve_formatting=preserve_formatting, **kwargs
+                    )
+
+                # Format output
+                formatter_class = {
+                    "json": "JSONFormatter",
+                    "pretty": "PrettyPrintFormatter",
+                    "srt": "SRTFormatter",
+                    "vtt": "WebVTTFormatter",
+                }.get(output_format)
+
+                if formatter_class:
+                    from youtube_transcript_api import formatters
+
+                    formatter = getattr(formatters, formatter_class)()
+                    formatted_transcript = formatter.format_transcript(transcript_data)
+                else:
+                    formatted_transcript = " ".join(entry["text"] for entry in transcript_data)
+
+                return self.create_text_message(text=formatted_transcript)
+
+            except Exception as e:
+                return self.create_text_message(text=f"Error getting transcript: {str(e)}")
+
+        except Exception as e:
+            return self.create_text_message(text=f"Error processing request: {str(e)}")
+
+    def _extract_video_id(self, video_input: str) -> str:
+        """
+        Extract video ID from URL or return as-is if already an ID
+        """
+        if "youtube.com" in video_input or "youtu.be" in video_input:
+            # Parse URL
+            parsed_url = urlparse(video_input)
+            if "youtube.com" in parsed_url.netloc:
+                return parse_qs(parsed_url.query)["v"][0]
+            else:  # youtu.be
+                return parsed_url.path[1:]
+        return video_input  # Assume it's already a video ID

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

@@ -0,0 +1,101 @@
+identity:
+  name: free_youtube_transcript
+  author: Tao Wang
+  label:
+    en_US: Free YouTube Transcript API
+    zh_Hans: 免费获取 YouTube 转录
+description:
+  human:
+    en_US: Get transcript from a YouTube video for free.
+    zh_Hans: 免费获取 YouTube 视频的转录文案。
+  llm: A tool for retrieving transcript from YouTube videos.
+parameters:
+  - name: video_id
+    type: string
+    required: true
+    label:
+      en_US: Video ID/URL
+      zh_Hans: 视频ID
+    human_description:
+      en_US: Used to define the video from which the transcript will be fetched. You can find the id in the video url. For example - https://www.youtube.com/watch?v=video_id.
+      zh_Hans: 您要哪条视频的转录文案？您可以在视频链接中找到id。例如 - https://www.youtube.com/watch?v=video_id。
+    llm_description: Used to define the video from which the transcript will be fetched. For example - https://www.youtube.com/watch?v=video_id.
+    form: llm
+  - name: language
+    type: string
+    required: false
+    label:
+      en_US: Language Code
+      zh_Hans: 语言
+    human_description:
+      en_US: Language code (e.g. 'en', 'zh') for the transcript.
+      zh_Hans: 字幕语言代码（如'en'、'zh'）。留空则自动选择。
+    llm_description: Used to set the language for transcripts.
+    form: form
+  - name: format
+    type: select
+    required: false
+    default: text
+    options:
+      - value: text
+        label:
+          en_US: Plain Text
+          zh_Hans: 纯文本
+      - value: json
+        label:
+          en_US: JSON Format
+          zh_Hans: JSON 格式
+      - value: pretty
+        label:
+          en_US: Pretty Print Format
+          zh_Hans: 美化格式
+      - value: srt
+        label:
+          en_US: SRT Format
+          zh_Hans: SRT 格式
+      - value: vtt
+        label:
+          en_US: WebVTT Format
+          zh_Hans: WebVTT 格式
+    label:
+      en_US: Output Format
+      zh_Hans: 输出格式
+    human_description:
+      en_US: Format of the transcript output
+      zh_Hans: 字幕输出格式
+    llm_description: The format to output the transcript in. Options are text (plain text), json (raw transcript data), srt (SubRip format), or vtt (WebVTT format)
+    form: form
+  - name: preserve_formatting
+    type: boolean
+    required: false
+    default: false
+    label:
+      en_US: Preserve Formatting
+      zh_Hans: 保留格式
+    human_description:
+      en_US: Keep HTML formatting elements like <i> (italics) and <b> (bold)
+      zh_Hans: 保留HTML格式元素，如<i>（斜体）和<b>（粗体）
+    llm_description: Whether to preserve HTML formatting elements in the transcript text
+    form: form
+  - name: proxy
+    type: string
+    required: false
+    label:
+      en_US: HTTPS Proxy
+      zh_Hans: HTTPS 代理
+    human_description:
+      en_US: HTTPS proxy URL (e.g. https://user:pass@domain:port)
+      zh_Hans: HTTPS 代理地址（如 https://user:pass@domain:port）
+    llm_description: HTTPS proxy to use for the request. Format should be https://user:pass@domain:port
+    form: form
+  - name: cookies
+    type: string
+    required: false
+    label:
+      en_US: Cookies File Path
+      zh_Hans: Cookies 文件路径
+    human_description:
+      en_US: Path to cookies.txt file for accessing age-restricted videos
+      zh_Hans: 用于访问年龄限制视频的 cookies.txt 文件路径
+    llm_description: Path to a cookies.txt file containing YouTube cookies, needed for accessing age-restricted videos
+    form: form