Revert "add clean 7 days datasets (#9424)"

This reverts commit bd678f9ca1.

.github/workflows/api-tests.yml

@@ -27,18 +27,17 @@ jobs:
       - name: Checkout code
         uses: actions/checkout@v4
 
-      - name: Install Poetry
-        uses: abatilo/actions-poetry@v3
-
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
-          cache: 'poetry'
           cache-dependency-path: |
             api/pyproject.toml
             api/poetry.lock
 
+      - name: Install Poetry
+        uses: abatilo/actions-poetry@v3
+
       - name: Check Poetry lockfile
         run: |
           poetry check -C api --lock

.github/workflows/db-migration-test.yml

@@ -23,18 +23,17 @@ jobs:
       - name: Checkout code
         uses: actions/checkout@v4
 
-      - name: Install Poetry
-        uses: abatilo/actions-poetry@v3
-
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
-          cache: 'poetry'
           cache-dependency-path: |
             api/pyproject.toml
             api/poetry.lock
 
+      - name: Install Poetry
+        uses: abatilo/actions-poetry@v3
+
       - name: Install dependencies
         run: poetry install -C api
 

.github/workflows/style.yml

@@ -24,15 +24,16 @@ jobs:
         with:
           files: api/**
 
-      - name: Install Poetry
-        uses: abatilo/actions-poetry@v3
-
       - name: Set up Python
         uses: actions/setup-python@v5
         if: steps.changed-files.outputs.any_changed == 'true'
         with:
           python-version: '3.10'
 
+      - name: Install Poetry
+        if: steps.changed-files.outputs.any_changed == 'true'
+        uses: abatilo/actions-poetry@v3
+
       - name: Python dependencies
         if: steps.changed-files.outputs.any_changed == 'true'
         run: poetry install -C api --only lint

api/README.md

@@ -85,3 +85,4 @@
    cd ../
    poetry run -C api bash dev/pytest/pytest_all_tests.sh
    ```
+

api/configs/middleware/vdb/oracle_config.py

@@ -14,7 +14,7 @@ class OracleConfig(BaseSettings):
         default=None,
     )
 
-    ORACLE_PORT: Optional[PositiveInt] = Field(
+    ORACLE_PORT: PositiveInt = Field(
         description="Port number on which the Oracle database server is listening (default is 1521)",
         default=1521,
     )

api/configs/middleware/vdb/pgvector_config.py

@@ -14,7 +14,7 @@ class PGVectorConfig(BaseSettings):
         default=None,
     )
 
-    PGVECTOR_PORT: Optional[PositiveInt] = Field(
+    PGVECTOR_PORT: PositiveInt = Field(
         description="Port number on which the PostgreSQL server is listening (default is 5433)",
         default=5433,
     )

api/configs/middleware/vdb/pgvectors_config.py

@@ -14,7 +14,7 @@ class PGVectoRSConfig(BaseSettings):
         default=None,
     )
 
-    PGVECTO_RS_PORT: Optional[PositiveInt] = Field(
+    PGVECTO_RS_PORT: PositiveInt = Field(
         description="Port number on which the PostgreSQL server with PGVecto.RS is listening (default is 5431)",
         default=5431,
     )

api/configs/middleware/vdb/vikingdb_config.py

@@ -11,27 +11,39 @@ class VikingDBConfig(BaseModel):
     """
 
     VIKINGDB_ACCESS_KEY: Optional[str] = Field(
-        default=None, description="The Access Key provided by Volcengine VikingDB for API authentication."
+        description="The Access Key provided by Volcengine VikingDB for API authentication."
+        "Refer to the following documentation for details on obtaining credentials:"
+        "https://www.volcengine.com/docs/6291/65568",
+        default=None,
     )
+
     VIKINGDB_SECRET_KEY: Optional[str] = Field(
-        default=None, description="The Secret Key provided by Volcengine VikingDB for API authentication."
+        description="The Secret Key provided by Volcengine VikingDB for API authentication.",
+        default=None,
     )
-    VIKINGDB_REGION: Optional[str] = Field(
-        default="cn-shanghai",
+
+    VIKINGDB_REGION: str = Field(
         description="The region of the Volcengine VikingDB service.(e.g., 'cn-shanghai', 'cn-beijing').",
+        default="cn-shanghai",
     )
-    VIKINGDB_HOST: Optional[str] = Field(
-        default="api-vikingdb.mlp.cn-shanghai.volces.com",
+
+    VIKINGDB_HOST: str = Field(
         description="The host of the Volcengine VikingDB service.(e.g., 'api-vikingdb.volces.com', \
             'api-vikingdb.mlp.cn-shanghai.volces.com')",
+        default="api-vikingdb.mlp.cn-shanghai.volces.com",
     )
-    VIKINGDB_SCHEME: Optional[str] = Field(
-        default="http",
+
+    VIKINGDB_SCHEME: str = Field(
         description="The scheme of the Volcengine VikingDB service.(e.g., 'http', 'https').",
+        default="http",
     )
-    VIKINGDB_CONNECTION_TIMEOUT: Optional[int] = Field(
-        default=30, description="The connection timeout of the Volcengine VikingDB service."
+
+    VIKINGDB_CONNECTION_TIMEOUT: int = Field(
+        description="The connection timeout of the Volcengine VikingDB service.",
+        default=30,
     )
-    VIKINGDB_SOCKET_TIMEOUT: Optional[int] = Field(
-        default=30, description="The socket timeout of the Volcengine VikingDB service."
+
+    VIKINGDB_SOCKET_TIMEOUT: int = Field(
+        description="The socket timeout of the Volcengine VikingDB service.",
+        default=30,
     )

api/controllers/console/datasets/hit_testing.py

@@ -1,88 +1,24 @@
-import logging
+from flask_restful import Resource
 
-from flask_login import current_user
-from flask_restful import Resource, marshal, reqparse
-from werkzeug.exceptions import Forbidden, InternalServerError, NotFound
-
-import services
 from controllers.console import api
-from controllers.console.app.error import (
-    CompletionRequestError,
-    ProviderModelCurrentlyNotSupportError,
-    ProviderNotInitializeError,
-    ProviderQuotaExceededError,
-)
-from controllers.console.datasets.error import DatasetNotInitializedError
+from controllers.console.datasets.hit_testing_base import DatasetsHitTestingBase
 from controllers.console.setup import setup_required
 from controllers.console.wraps import account_initialization_required
-from core.errors.error import (
-    LLMBadRequestError,
-    ModelCurrentlyNotSupportError,
-    ProviderTokenNotInitError,
-    QuotaExceededError,
-)
-from core.model_runtime.errors.invoke import InvokeError
-from fields.hit_testing_fields import hit_testing_record_fields
 from libs.login import login_required
-from services.dataset_service import DatasetService
-from services.hit_testing_service import HitTestingService
 
 
-class HitTestingApi(Resource):
+class HitTestingApi(Resource, DatasetsHitTestingBase):
     @setup_required
     @login_required
     @account_initialization_required
     def post(self, dataset_id):
         dataset_id_str = str(dataset_id)
 
-        dataset = DatasetService.get_dataset(dataset_id_str)
-        if dataset is None:
-            raise NotFound("Dataset not found.")
+        dataset = self.get_and_validate_dataset(dataset_id_str)
+        args = self.parse_args()
+        self.hit_testing_args_check(args)
 
-        try:
-            DatasetService.check_dataset_permission(dataset, current_user)
-        except services.errors.account.NoPermissionError as e:
-            raise Forbidden(str(e))
-
-        parser = reqparse.RequestParser()
-        parser.add_argument("query", type=str, location="json")
-        parser.add_argument("retrieval_model", type=dict, required=False, location="json")
-        parser.add_argument("external_retrieval_model", type=dict, required=False, location="json")
-        args = parser.parse_args()
-
-        HitTestingService.hit_testing_args_check(args)
-
-        try:
-            response = HitTestingService.retrieve(
-                dataset=dataset,
-                query=args["query"],
-                account=current_user,
-                retrieval_model=args["retrieval_model"],
-                external_retrieval_model=args["external_retrieval_model"],
-                limit=10,
-            )
-
-            return {"query": response["query"], "records": marshal(response["records"], hit_testing_record_fields)}
-        except services.errors.index.IndexNotInitializedError:
-            raise DatasetNotInitializedError()
-        except ProviderTokenNotInitError as ex:
-            raise ProviderNotInitializeError(ex.description)
-        except QuotaExceededError:
-            raise ProviderQuotaExceededError()
-        except ModelCurrentlyNotSupportError:
-            raise ProviderModelCurrentlyNotSupportError()
-        except LLMBadRequestError:
-            raise ProviderNotInitializeError(
-                "No Embedding Model or Reranking Model available. Please configure a valid provider "
-                "in the Settings -> Model Provider."
-            )
-        except InvokeError as e:
-            raise CompletionRequestError(e.description)
-        except ValueError as e:
-            raise ValueError(str(e))
-        except Exception as e:
-            logging.exception("Hit testing failed.")
-            raise InternalServerError(str(e))
+        return self.perform_hit_testing(dataset, args)
 
 
 api.add_resource(HitTestingApi, "/datasets/<uuid:dataset_id>/hit-testing")

api/controllers/console/datasets/hit_testing_base.py

@@ -0,0 +1,85 @@
+import logging
+
+from flask_login import current_user
+from flask_restful import marshal, reqparse
+from werkzeug.exceptions import Forbidden, InternalServerError, NotFound
+
+import services.dataset_service
+from controllers.console.app.error import (
+    CompletionRequestError,
+    ProviderModelCurrentlyNotSupportError,
+    ProviderNotInitializeError,
+    ProviderQuotaExceededError,
+)
+from controllers.console.datasets.error import DatasetNotInitializedError
+from core.errors.error import (
+    LLMBadRequestError,
+    ModelCurrentlyNotSupportError,
+    ProviderTokenNotInitError,
+    QuotaExceededError,
+)
+from core.model_runtime.errors.invoke import InvokeError
+from fields.hit_testing_fields import hit_testing_record_fields
+from services.dataset_service import DatasetService
+from services.hit_testing_service import HitTestingService
+
+
+class DatasetsHitTestingBase:
+    @staticmethod
+    def get_and_validate_dataset(dataset_id: str):
+        dataset = DatasetService.get_dataset(dataset_id)
+        if dataset is None:
+            raise NotFound("Dataset not found.")
+
+        try:
+            DatasetService.check_dataset_permission(dataset, current_user)
+        except services.errors.account.NoPermissionError as e:
+            raise Forbidden(str(e))
+
+        return dataset
+
+    @staticmethod
+    def hit_testing_args_check(args):
+        HitTestingService.hit_testing_args_check(args)
+
+    @staticmethod
+    def parse_args():
+        parser = reqparse.RequestParser()
+
+        parser.add_argument("query", type=str, location="json")
+        parser.add_argument("retrieval_model", type=dict, required=False, location="json")
+        parser.add_argument("external_retrieval_model", type=dict, required=False, location="json")
+        return parser.parse_args()
+
+    @staticmethod
+    def perform_hit_testing(dataset, args):
+        try:
+            response = HitTestingService.retrieve(
+                dataset=dataset,
+                query=args["query"],
+                account=current_user,
+                retrieval_model=args["retrieval_model"],
+                external_retrieval_model=args["external_retrieval_model"],
+                limit=10,
+            )
+            return {"query": response["query"], "records": marshal(response["records"], hit_testing_record_fields)}
+        except services.errors.index.IndexNotInitializedError:
+            raise DatasetNotInitializedError()
+        except ProviderTokenNotInitError as ex:
+            raise ProviderNotInitializeError(ex.description)
+        except QuotaExceededError:
+            raise ProviderQuotaExceededError()
+        except ModelCurrentlyNotSupportError:
+            raise ProviderModelCurrentlyNotSupportError()
+        except LLMBadRequestError:
+            raise ProviderNotInitializeError(
+                "No Embedding Model or Reranking Model available. Please configure a valid provider "
+                "in the Settings -> Model Provider."
+            )
+        except InvokeError as e:
+            raise CompletionRequestError(e.description)
+        except ValueError as e:
+            raise ValueError(str(e))
+        except Exception as e:
+            logging.exception("Hit testing failed.")
+            raise InternalServerError(str(e))

api/controllers/service_api/__init__.py

@@ -5,7 +5,6 @@ from libs.external_api import ExternalApi
 bp = Blueprint("service_api", __name__, url_prefix="/v1")
 api = ExternalApi(bp)
 
-
 from . import index
 from .app import app, audio, completion, conversation, file, message, workflow
-from .dataset import dataset, document, segment
+from .dataset import dataset, document, hit_testing, segment

api/controllers/service_api/app/completion.py

@@ -4,7 +4,6 @@ from flask_restful import Resource, reqparse
 from werkzeug.exceptions import InternalServerError, NotFound
 
 import services
-from constants import UUID_NIL
 from controllers.service_api import api
 from controllers.service_api.app.error import (
     AppUnavailableError,
@@ -108,7 +107,6 @@ class ChatApi(Resource):
         parser.add_argument("conversation_id", type=uuid_value, location="json")
         parser.add_argument("retriever_from", type=str, required=False, default="dev", location="json")
         parser.add_argument("auto_generate_name", type=bool, required=False, default=True, location="json")
-        parser.add_argument("parent_message_id", type=uuid_value, required=False, default=UUID_NIL, location="json")
 
         args = parser.parse_args()
 

api/controllers/service_api/dataset/hit_testing.py

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

api/core/agent/output_parser/cot_output_parser.py

@@ -62,6 +62,8 @@ class CotAgentOutputParser:
         thought_str = "thought:"
         thought_idx = 0
 
+        last_character = ""
+
         for response in llm_response:
             if response.delta.usage:
                 usage_dict["usage"] = response.delta.usage
@@ -74,35 +76,38 @@ class CotAgentOutputParser:
             while index < len(response):
                 steps = 1
                 delta = response[index : index + steps]
-                last_character = response[index - 1] if index > 0 else ""
+                yield_delta = False
 
                 if delta == "`":
+                    last_character = delta
                     code_block_cache += delta
                     code_block_delimiter_count += 1
                 else:
                     if not in_code_block:
                         if code_block_delimiter_count > 0:
+                            last_character = delta
                             yield code_block_cache
                         code_block_cache = ""
                     else:
+                        last_character = delta
                         code_block_cache += delta
                     code_block_delimiter_count = 0
 
                 if not in_code_block and not in_json:
                     if delta.lower() == action_str[action_idx] and action_idx == 0:
                         if last_character not in {"\n", " ", ""}:
+                            yield_delta = True
+                        else:
+                            last_character = delta
+                            action_cache += delta
+                            action_idx += 1
+                            if action_idx == len(action_str):
+                                action_cache = ""
+                                action_idx = 0
                             index += steps
-                            yield delta
                             continue
-
-                        action_cache += delta
-                        action_idx += 1
-                        if action_idx == len(action_str):
-                            action_cache = ""
-                            action_idx = 0
-                        index += steps
-                        continue
                     elif delta.lower() == action_str[action_idx] and action_idx > 0:
+                        last_character = delta
                         action_cache += delta
                         action_idx += 1
                         if action_idx == len(action_str):
@@ -112,24 +117,25 @@ class CotAgentOutputParser:
                         continue
                     else:
                         if action_cache:
+                            last_character = delta
                             yield action_cache
                             action_cache = ""
                             action_idx = 0
 
                     if delta.lower() == thought_str[thought_idx] and thought_idx == 0:
                         if last_character not in {"\n", " ", ""}:
+                            yield_delta = True
+                        else:
+                            last_character = delta
+                            thought_cache += delta
+                            thought_idx += 1
+                            if thought_idx == len(thought_str):
+                                thought_cache = ""
+                                thought_idx = 0
                             index += steps
-                            yield delta
                             continue
-
-                        thought_cache += delta
-                        thought_idx += 1
-                        if thought_idx == len(thought_str):
-                            thought_cache = ""
-                            thought_idx = 0
-                        index += steps
-                        continue
                     elif delta.lower() == thought_str[thought_idx] and thought_idx > 0:
+                        last_character = delta
                         thought_cache += delta
                         thought_idx += 1
                         if thought_idx == len(thought_str):
@@ -139,12 +145,20 @@ class CotAgentOutputParser:
                         continue
                     else:
                         if thought_cache:
+                            last_character = delta
                             yield thought_cache
                             thought_cache = ""
                             thought_idx = 0
 
+                    if yield_delta:
+                        index += steps
+                        last_character = delta
+                        yield delta
+                        continue
+
                 if code_block_delimiter_count == 3:
                     if in_code_block:
+                        last_character = delta
                         yield from extra_json_from_code_block(code_block_cache)
                         code_block_cache = ""
 
@@ -156,8 +170,10 @@ class CotAgentOutputParser:
                     if delta == "{":
                         json_quote_count += 1
                         in_json = True
+                        last_character = delta
                         json_cache += delta
                     elif delta == "}":
+                        last_character = delta
                         json_cache += delta
                         if json_quote_count > 0:
                             json_quote_count -= 1
@@ -168,16 +184,19 @@ class CotAgentOutputParser:
                                 continue
                     else:
                         if in_json:
+                            last_character = delta
                             json_cache += delta
 
                     if got_json:
                         got_json = False
+                        last_character = delta
                         yield parse_action(json_cache)
                         json_cache = ""
                         json_quote_count = 0
                         in_json = False
 
                 if not in_code_block and not in_json:
+                    last_character = delta
                     yield delta.replace("`", "")
 
                 index += steps

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

@@ -10,6 +10,7 @@ from flask import Flask, current_app
 from pydantic import ValidationError
 
 import contexts
+from constants import UUID_NIL
 from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
 from core.app.apps.advanced_chat.app_config_manager import AdvancedChatAppConfigManager
 from core.app.apps.advanced_chat.app_runner import AdvancedChatAppRunner
@@ -122,7 +123,7 @@ class AdvancedChatAppGenerator(MessageBasedAppGenerator):
             inputs=conversation.inputs if conversation else self._get_cleaned_inputs(inputs, app_config),
             query=query,
             files=file_objs,
-            parent_message_id=args.get("parent_message_id"),
+            parent_message_id=args.get("parent_message_id") if invoke_from != InvokeFrom.SERVICE_API else UUID_NIL,
             user_id=user.id,
             stream=stream,
             invoke_from=invoke_from,

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

@@ -8,6 +8,7 @@ from typing import Any, Literal, Union, overload
 from flask import Flask, current_app
 from pydantic import ValidationError
 
+from constants import UUID_NIL
 from core.app.app_config.easy_ui_based_app.model_config.converter import ModelConfigConverter
 from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
 from core.app.apps.agent_chat.app_config_manager import AgentChatAppConfigManager
@@ -127,7 +128,7 @@ class AgentChatAppGenerator(MessageBasedAppGenerator):
             inputs=conversation.inputs if conversation else self._get_cleaned_inputs(inputs, app_config),
             query=query,
             files=file_objs,
-            parent_message_id=args.get("parent_message_id"),
+            parent_message_id=args.get("parent_message_id") if invoke_from != InvokeFrom.SERVICE_API else UUID_NIL,
             user_id=user.id,
             stream=stream,
             invoke_from=invoke_from,

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

@@ -8,6 +8,7 @@ from typing import Any, Literal, Union, overload
 from flask import Flask, current_app
 from pydantic import ValidationError
 
+from constants import UUID_NIL
 from core.app.app_config.easy_ui_based_app.model_config.converter import ModelConfigConverter
 from core.app.app_config.features.file_upload.manager import FileUploadConfigManager
 from core.app.apps.base_app_queue_manager import AppQueueManager, GenerateTaskStoppedError, PublishFrom
@@ -128,7 +129,7 @@ class ChatAppGenerator(MessageBasedAppGenerator):
             inputs=conversation.inputs if conversation else self._get_cleaned_inputs(inputs, app_config),
             query=query,
             files=file_objs,
-            parent_message_id=args.get("parent_message_id"),
+            parent_message_id=args.get("parent_message_id") if invoke_from != InvokeFrom.SERVICE_API else UUID_NIL,
             user_id=user.id,
             stream=stream,
             invoke_from=invoke_from,

api/core/app/entities/app_invoke_entities.py

@@ -2,8 +2,9 @@ from collections.abc import Mapping
 from enum import Enum
 from typing import Any, Optional
 
-from pydantic import BaseModel, ConfigDict
+from pydantic import BaseModel, ConfigDict, Field, ValidationInfo, field_validator
 
+from constants import UUID_NIL
 from core.app.app_config.entities import AppConfig, EasyUIBasedAppConfig, WorkflowUIBasedAppConfig
 from core.entities.provider_configuration import ProviderModelBundle
 from core.file.file_obj import FileVar
@@ -116,13 +117,36 @@ class EasyUIBasedAppGenerateEntity(AppGenerateEntity):
     model_config = ConfigDict(protected_namespaces=())
 
 
-class ChatAppGenerateEntity(EasyUIBasedAppGenerateEntity):
+class ConversationAppGenerateEntity(AppGenerateEntity):
+    """
+    Base entity for conversation-based app generation.
+    """
+
+    conversation_id: Optional[str] = None
+    parent_message_id: Optional[str] = Field(
+        default=None,
+        description=(
+            "Starting from v0.9.0, parent_message_id is used to support message regeneration for internal chat API."
+            "For service API, we need to ensure its forward compatibility, "
+            "so passing in the parent_message_id as request arg is not supported for now. "
+            "It needs to be set to UUID_NIL so that the subsequent processing will treat it as legacy messages."
+        ),
+    )
+
+    @field_validator("parent_message_id")
+    @classmethod
+    def validate_parent_message_id(cls, v, info: ValidationInfo):
+        if info.data.get("invoke_from") == InvokeFrom.SERVICE_API and v != UUID_NIL:
+            raise ValueError("parent_message_id should be UUID_NIL for service API")
+        return v
+
+
+class ChatAppGenerateEntity(ConversationAppGenerateEntity, EasyUIBasedAppGenerateEntity):
     """
     Chat Application Generate Entity.
     """
 
-    conversation_id: Optional[str] = None
-    parent_message_id: Optional[str] = None
+    pass
 
 
 class CompletionAppGenerateEntity(EasyUIBasedAppGenerateEntity):
@@ -133,16 +157,15 @@ class CompletionAppGenerateEntity(EasyUIBasedAppGenerateEntity):
     pass
 
 
-class AgentChatAppGenerateEntity(EasyUIBasedAppGenerateEntity):
+class AgentChatAppGenerateEntity(ConversationAppGenerateEntity, EasyUIBasedAppGenerateEntity):
     """
     Agent Chat Application Generate Entity.
     """
 
-    conversation_id: Optional[str] = None
-    parent_message_id: Optional[str] = None
+    pass
 
 
-class AdvancedChatAppGenerateEntity(AppGenerateEntity):
+class AdvancedChatAppGenerateEntity(ConversationAppGenerateEntity):
     """
     Advanced Chat Application Generate Entity.
     """
@@ -150,8 +173,6 @@ class AdvancedChatAppGenerateEntity(AppGenerateEntity):
     # app config
     app_config: WorkflowUIBasedAppConfig
 
-    conversation_id: Optional[str] = None
-    parent_message_id: Optional[str] = None
     workflow_run_id: Optional[str] = None
     query: str
 

api/core/model_runtime/model_providers/azure_openai/_constant.py

@@ -1098,6 +1098,14 @@ LLM_BASE_MODELS = [
                 ModelPropertyKey.CONTEXT_SIZE: 128000,
             },
             parameter_rules=[
+                ParameterRule(
+                    name="temperature",
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
+                ),
+                ParameterRule(
+                    name="top_p",
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
+                ),
                 ParameterRule(
                     name="response_format",
                     label=I18nObject(zh_Hans="回复格式", en_US="response_format"),
@@ -1135,6 +1143,14 @@ LLM_BASE_MODELS = [
                 ModelPropertyKey.CONTEXT_SIZE: 128000,
             },
             parameter_rules=[
+                ParameterRule(
+                    name="temperature",
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TEMPERATURE],
+                ),
+                ParameterRule(
+                    name="top_p",
+                    **PARAMETER_RULE_TEMPLATE[DefaultParameterName.TOP_P],
+                ),
                 ParameterRule(
                     name="response_format",
                     label=I18nObject(zh_Hans="回复格式", en_US="response_format"),

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

@@ -119,7 +119,15 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
         try:
             client = AzureOpenAI(**self._to_credential_kwargs(credentials))
 
-            if ai_model_entity.entity.model_properties.get(ModelPropertyKey.MODE) == LLMMode.CHAT.value:
+            if model.startswith("o1"):
+                client.chat.completions.create(
+                    messages=[{"role": "user", "content": "ping"}],
+                    model=model,
+                    temperature=1,
+                    max_completion_tokens=20,
+                    stream=False,
+                )
+            elif ai_model_entity.entity.model_properties.get(ModelPropertyKey.MODE) == LLMMode.CHAT.value:
                 # chat model
                 client.chat.completions.create(
                     messages=[{"role": "user", "content": "ping"}],

api/core/model_runtime/model_providers/fireworks/fireworks.yaml

@@ -18,6 +18,7 @@ supported_model_types:
   - text-embedding
 configurate_methods:
   - predefined-model
+  - customizable-model
 provider_credential_schema:
   credential_form_schemas:
     - variable: fireworks_api_key
@@ -28,3 +29,75 @@ provider_credential_schema:
       placeholder:
         zh_Hans: 在此输入您的 API Key
         en_US: Enter your API Key
+model_credential_schema:
+  model:
+    label:
+      en_US: Model URL
+      zh_Hans: 模型URL
+    placeholder:
+      en_US: Enter your Model URL
+      zh_Hans: 输入模型URL
+  credential_form_schemas:
+    - variable: model_label_zh_Hanns
+      label:
+        zh_Hans: 模型中文名称
+        en_US: The zh_Hans of Model
+      required: true
+      type: text-input
+      placeholder:
+        zh_Hans: 在此输入您的模型中文名称
+        en_US: Enter your zh_Hans of Model
+    - variable: model_label_en_US
+      label:
+        zh_Hans: 模型英文名称
+        en_US: The en_US of Model
+      required: true
+      type: text-input
+      placeholder:
+        zh_Hans: 在此输入您的模型英文名称
+        en_US: Enter your en_US of Model
+    - variable: fireworks_api_key
+      label:
+        en_US: API Key
+      type: secret-input
+      required: true
+      placeholder:
+        zh_Hans: 在此输入您的 API Key
+        en_US: Enter your API Key
+    - variable: context_size
+      label:
+        zh_Hans: 模型上下文长度
+        en_US: Model context size
+      required: true
+      type: text-input
+      default: '4096'
+      placeholder:
+        zh_Hans: 在此输入您的模型上下文长度
+        en_US: Enter your Model context size
+    - variable: max_tokens
+      label:
+        zh_Hans: 最大 token 上限
+        en_US: Upper bound for max tokens
+      default: '4096'
+      type: text-input
+      show_on:
+        - variable: __model_type
+          value: llm
+    - variable: function_calling_type
+      label:
+        en_US: Function calling
+      type: select
+      required: false
+      default: no_call
+      options:
+        - value: no_call
+          label:
+            en_US: Not Support
+            zh_Hans: 不支持
+        - value: function_call
+          label:
+            en_US: Support
+            zh_Hans: 支持
+      show_on:
+        - variable: __model_type
+          value: llm

api/core/model_runtime/model_providers/fireworks/llm/gemma2-9b-it.yaml

@@ -43,3 +43,4 @@ pricing:
   output: '0.2'
   unit: '0.000001'
   currency: USD
+deprecated: true

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

@@ -8,7 +8,8 @@ from openai.types.chat.chat_completion_chunk import ChoiceDeltaFunctionCall, Cho
 from openai.types.chat.chat_completion_message import FunctionCall
 
 from core.model_runtime.callbacks.base_callback import Callback
-from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
+from core.model_runtime.entities.common_entities import I18nObject
+from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta
 from core.model_runtime.entities.message_entities import (
     AssistantPromptMessage,
     ImagePromptMessageContent,
@@ -20,6 +21,15 @@ from core.model_runtime.entities.message_entities import (
     ToolPromptMessage,
     UserPromptMessage,
 )
+from core.model_runtime.entities.model_entities import (
+    AIModelEntity,
+    FetchFrom,
+    ModelFeature,
+    ModelPropertyKey,
+    ModelType,
+    ParameterRule,
+    ParameterType,
+)
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.model_runtime.model_providers.fireworks._common import _CommonFireworks
@@ -608,3 +618,50 @@ class FireworksLargeLanguageModel(_CommonFireworks, LargeLanguageModel):
                     num_tokens += self._get_num_tokens_by_gpt2(required_field)
 
         return num_tokens
+
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
+        return AIModelEntity(
+            model=model,
+            label=I18nObject(
+                en_US=credentials.get("model_label_en_US", model),
+                zh_Hans=credentials.get("model_label_zh_Hanns", model),
+            ),
+            model_type=ModelType.LLM,
+            features=[ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL, ModelFeature.STREAM_TOOL_CALL]
+            if credentials.get("function_calling_type") == "function_call"
+            else [],
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_properties={
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 4096)),
+                ModelPropertyKey.MODE: LLMMode.CHAT.value,
+            },
+            parameter_rules=[
+                ParameterRule(
+                    name="temperature",
+                    use_template="temperature",
+                    label=I18nObject(en_US="Temperature", zh_Hans="温度"),
+                    type=ParameterType.FLOAT,
+                ),
+                ParameterRule(
+                    name="max_tokens",
+                    use_template="max_tokens",
+                    default=512,
+                    min=1,
+                    max=int(credentials.get("max_tokens", 4096)),
+                    label=I18nObject(en_US="Max Tokens", zh_Hans="最大标记"),
+                    type=ParameterType.INT,
+                ),
+                ParameterRule(
+                    name="top_p",
+                    use_template="top_p",
+                    label=I18nObject(en_US="Top P", zh_Hans="Top P"),
+                    type=ParameterType.FLOAT,
+                ),
+                ParameterRule(
+                    name="top_k",
+                    use_template="top_k",
+                    label=I18nObject(en_US="Top K", zh_Hans="Top K"),
+                    type=ParameterType.FLOAT,
+                ),
+            ],
+        )

api/core/model_runtime/model_providers/fireworks/llm/qwen2p5-72b-instruct.yaml

@@ -0,0 +1,46 @@
+model: accounts/fireworks/models/qwen2p5-72b-instruct
+label:
+  zh_Hans: Qwen2.5 72B Instruct
+  en_US: Qwen2.5 72B Instruct
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+model_properties:
+  mode: chat
+  context_size: 32768
+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.
+  - name: max_tokens
+    use_template: max_tokens
+  - name: context_length_exceeded_behavior
+    default: None
+    label:
+      zh_Hans: 上下文长度超出行为
+      en_US: Context Length Exceeded Behavior
+    help:
+      zh_Hans: 上下文长度超出行为
+      en_US: Context Length Exceeded Behavior
+    type: string
+    options:
+      - None
+      - truncate
+      - error
+  - name: response_format
+    use_template: response_format
+pricing:
+  input: '0.9'
+  output: '0.9'
+  unit: '0.000001'
+  currency: USD

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

@@ -32,15 +32,6 @@ parameter_rules:
     max: 8192
   - name: response_format
     use_template: response_format
-  - name: stream
-    label:
-      zh_Hans: 流式输出
-      en_US: Stream
-    type: boolean
-    help:
-      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
-      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
-    default: false
 pricing:
   input: '0.00'
   output: '0.00'

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

@@ -32,15 +32,6 @@ parameter_rules:
     max: 8192
   - name: response_format
     use_template: response_format
-  - name: stream
-    label:
-      zh_Hans: 流式输出
-      en_US: Stream
-    type: boolean
-    help:
-      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
-      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
-    default: false
 pricing:
   input: '0.00'
   output: '0.00'

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

@@ -32,15 +32,6 @@ parameter_rules:
     max: 8192
   - name: response_format
     use_template: response_format
-  - name: stream
-    label:
-      zh_Hans: 流式输出
-      en_US: Stream
-    type: boolean
-    help:
-      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
-      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
-    default: false
 pricing:
   input: '0.00'
   output: '0.00'

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

@@ -32,15 +32,6 @@ parameter_rules:
     max: 8192
   - name: response_format
     use_template: response_format
-  - name: stream
-    label:
-      zh_Hans: 流式输出
-      en_US: Stream
-    type: boolean
-    help:
-      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
-      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
-    default: false
 pricing:
   input: '0.00'
   output: '0.00'

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

@@ -32,15 +32,6 @@ parameter_rules:
     max: 8192
   - name: response_format
     use_template: response_format
-  - name: stream
-    label:
-      zh_Hans: 流式输出
-      en_US: Stream
-    type: boolean
-    help:
-      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
-      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
-    default: false
 pricing:
   input: '0.00'
   output: '0.00'

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

@@ -32,15 +32,6 @@ parameter_rules:
     max: 8192
   - name: response_format
     use_template: response_format
-  - name: stream
-    label:
-      zh_Hans: 流式输出
-      en_US: Stream
-    type: boolean
-    help:
-      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
-      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
-    default: false
 pricing:
   input: '0.00'
   output: '0.00'

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

@@ -32,15 +32,6 @@ parameter_rules:
     max: 8192
   - name: response_format
     use_template: response_format
-  - name: stream
-    label:
-      zh_Hans: 流式输出
-      en_US: Stream
-    type: boolean
-    help:
-      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
-      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
-    default: false
 pricing:
   input: '0.00'
   output: '0.00'

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

@@ -32,15 +32,6 @@ parameter_rules:
     max: 8192
   - name: response_format
     use_template: response_format
-  - name: stream
-    label:
-      zh_Hans: 流式输出
-      en_US: Stream
-    type: boolean
-    help:
-      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
-      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
-    default: false
 pricing:
   input: '0.00'
   output: '0.00'

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

@@ -32,15 +32,6 @@ parameter_rules:
     max: 8192
   - name: response_format
     use_template: response_format
-  - name: stream
-    label:
-      zh_Hans: 流式输出
-      en_US: Stream
-    type: boolean
-    help:
-      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
-      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
-    default: false
 pricing:
   input: '0.00'
   output: '0.00'

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

@@ -32,15 +32,6 @@ parameter_rules:
     max: 8192
   - name: response_format
     use_template: response_format
-  - name: stream
-    label:
-      zh_Hans: 流式输出
-      en_US: Stream
-    type: boolean
-    help:
-      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
-      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
-    default: false
 pricing:
   input: '0.00'
   output: '0.00'

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

@@ -32,15 +32,6 @@ parameter_rules:
     max: 8192
   - name: response_format
     use_template: response_format
-  - name: stream
-    label:
-      zh_Hans: 流式输出
-      en_US: Stream
-    type: boolean
-    help:
-      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
-      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
-    default: false
 pricing:
   input: '0.00'
   output: '0.00'

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

@@ -32,15 +32,6 @@ parameter_rules:
     max: 8192
   - name: response_format
     use_template: response_format
-  - name: stream
-    label:
-      zh_Hans: 流式输出
-      en_US: Stream
-    type: boolean
-    help:
-      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
-      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
-    default: false
 pricing:
   input: '0.00'
   output: '0.00'

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

@@ -32,15 +32,6 @@ parameter_rules:
     max: 8192
   - name: response_format
     use_template: response_format
-  - name: stream
-    label:
-      zh_Hans: 流式输出
-      en_US: Stream
-    type: boolean
-    help:
-      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
-      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
-    default: false
 pricing:
   input: '0.00'
   output: '0.00'

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

@@ -27,15 +27,6 @@ parameter_rules:
     default: 4096
     min: 1
     max: 4096
-  - name: stream
-    label:
-      zh_Hans: 流式输出
-      en_US: Stream
-    type: boolean
-    help:
-      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
-      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
-    default: false
 pricing:
   input: '0.00'
   output: '0.00'

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

@@ -31,15 +31,6 @@ parameter_rules:
     max: 2048
   - name: response_format
     use_template: response_format
-  - name: stream
-    label:
-      zh_Hans: 流式输出
-      en_US: Stream
-    type: boolean
-    help:
-      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
-      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
-    default: false
 pricing:
   input: '0.00'
   output: '0.00'

api/core/model_runtime/model_providers/minimax/llm/abab6.5t-chat.yaml

@@ -0,0 +1,44 @@
+model: abab6.5t-chat
+label:
+  en_US: Abab6.5t-Chat
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 8192
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    min: 0.01
+    max: 1
+    default: 0.9
+  - name: top_p
+    use_template: top_p
+    min: 0.01
+    max: 1
+    default: 0.95
+  - name: max_tokens
+    use_template: max_tokens
+    required: true
+    default: 3072
+    min: 1
+    max: 8192
+  - name: mask_sensitive_info
+    type: boolean
+    default: true
+    label:
+      zh_Hans: 隐私保护
+      en_US: Moderate
+    help:
+      zh_Hans: 对输出中易涉及隐私问题的文本信息进行打码，目前包括但不限于邮箱、域名、链接、证件号、家庭住址等，默认true，即开启打码
+      en_US: Mask the sensitive info of the generated content, such as email/domain/link/address/phone/id..
+  - name: presence_penalty
+    use_template: presence_penalty
+  - name: frequency_penalty
+    use_template: frequency_penalty
+pricing:
+  input: '0.005'
+  output: '0.005'
+  unit: '0.001'
+  currency: RMB

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

@@ -61,7 +61,8 @@ class MinimaxTextEmbeddingModel(TextEmbeddingModel):
         url = f"{self.api_base}?GroupId={group_id}"
         headers = {"Authorization": "Bearer " + api_key, "Content-Type": "application/json"}
 
-        data = {"model": "embo-01", "texts": texts, "type": "db"}
+        embedding_type = "db" if input_type == EmbeddingInputType.DOCUMENT else "query"
+        data = {"model": "embo-01", "texts": texts, "type": embedding_type}
 
         try:
             response = post(url, headers=headers, data=dumps(data))

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

@@ -19,9 +19,9 @@ class OpenAIProvider(ModelProvider):
         try:
             model_instance = self.get_model_instance(ModelType.LLM)
 
-            # Use `gpt-3.5-turbo` model for validate,
+            # Use `gpt-4o-mini` model for validate,
             # no matter what model you pass in, text completion model or chat model
-            model_instance.validate_credentials(model="gpt-3.5-turbo", credentials=credentials)
+            model_instance.validate_credentials(model="gpt-4o-mini", credentials=credentials)
         except CredentialsValidateFailedError as ex:
             raise ex
         except Exception as ex:

api/core/model_runtime/model_providers/zhipuai/llm/chatglm_turbo.yaml

@@ -28,15 +28,6 @@ parameter_rules:
       zh_Hans: do_sample 为 true 时启用采样策略，do_sample 为 false 时采样策略 temperature、top_p 将不生效。默认值为 true。
       en_US: When `do_sample` is set to true, the sampling strategy is enabled. When `do_sample` is set to false, the sampling strategies such as `temperature` and `top_p` will not take effect. The default value is true.
     default: true
-  - name: stream
-    label:
-      zh_Hans: 流处理
-      en_US: Event Stream
-    type: boolean
-    help:
-      zh_Hans: 使用同步调用时，此参数应当设置为 fasle 或者省略。表示模型生成完所有内容后一次性返回所有内容。默认值为 false。如果设置为 true，模型将通过标准 Event Stream ，逐块返回模型生成内容。Event Stream 结束时会返回一条data：[DONE]消息。注意：在模型流式输出生成内容的过程中，我们会分批对模型生成内容进行检测，当检测到违法及不良信息时，API会返回错误码（1301）。开发者识别到错误码（1301），应及时采取（清屏、重启对话）等措施删除生成内容，并确保不将含有违法及不良信息的内容传递给模型继续生成，避免其造成负面影响。
-      en_US: When using synchronous invocation, this parameter should be set to false or omitted. It indicates that the model will return all the generated content at once after the generation is complete. The default value is false. If set to true, the model will return the generated content in chunks via the standard Event Stream. A data：[DONE] message will be sent at the end of the Event Stream.Note：During the model's streaming output process, we will batch check the generated content. If illegal or harmful information is detected, the API will return an error code (1301). Developers who identify error code (1301) should promptly take actions such as clearing the screen or restarting the conversation to delete the generated content. They should also ensure that no illegal or harmful content is passed back to the model for continued generation to avoid negative impacts.
-    default: false
   - name: return_type
     label:
       zh_Hans: 回复类型
@@ -49,3 +40,4 @@ parameter_rules:
     options:
       - text
       - json_string
+deprecated: true

api/core/model_runtime/model_providers/zhipuai/llm/glm-4-0520.yaml

@@ -32,15 +32,6 @@ parameter_rules:
       zh_Hans: do_sample 为 true 时启用采样策略，do_sample 为 false 时采样策略 temperature、top_p 将不生效。默认值为 true。
       en_US: When `do_sample` is set to true, the sampling strategy is enabled. When `do_sample` is set to false, the sampling strategies such as `temperature` and `top_p` will not take effect. The default value is true.
     default: true
-  - name: stream
-    label:
-      zh_Hans: 流处理
-      en_US: Event Stream
-    type: boolean
-    help:
-      zh_Hans: 使用同步调用时，此参数应当设置为 fasle 或者省略。表示模型生成完所有内容后一次性返回所有内容。默认值为 false。如果设置为 true，模型将通过标准 Event Stream ，逐块返回模型生成内容。Event Stream 结束时会返回一条data：[DONE]消息。注意：在模型流式输出生成内容的过程中，我们会分批对模型生成内容进行检测，当检测到违法及不良信息时，API会返回错误码（1301）。开发者识别到错误码（1301），应及时采取（清屏、重启对话）等措施删除生成内容，并确保不将含有违法及不良信息的内容传递给模型继续生成，避免其造成负面影响。
-      en_US: When using synchronous invocation, this parameter should be set to false or omitted. It indicates that the model will return all the generated content at once after the generation is complete. The default value is false. If set to true, the model will return the generated content in chunks via the standard Event Stream. A data：[DONE] message will be sent at the end of the Event Stream.Note：During the model's streaming output process, we will batch check the generated content. If illegal or harmful information is detected, the API will return an error code (1301). Developers who identify error code (1301) should promptly take actions such as clearing the screen or restarting the conversation to delete the generated content. They should also ensure that no illegal or harmful content is passed back to the model for continued generation to avoid negative impacts.
-    default: false
   - name: max_tokens
     use_template: max_tokens
     default: 1024

api/core/model_runtime/model_providers/zhipuai/llm/glm-4-air.yaml

@@ -32,15 +32,6 @@ parameter_rules:
       zh_Hans: do_sample 为 true 时启用采样策略，do_sample 为 false 时采样策略 temperature、top_p 将不生效。默认值为 true。
       en_US: When `do_sample` is set to true, the sampling strategy is enabled. When `do_sample` is set to false, the sampling strategies such as `temperature` and `top_p` will not take effect. The default value is true.
     default: true
-  - name: stream
-    label:
-      zh_Hans: 流处理
-      en_US: Event Stream
-    type: boolean
-    help:
-      zh_Hans: 使用同步调用时，此参数应当设置为 fasle 或者省略。表示模型生成完所有内容后一次性返回所有内容。默认值为 false。如果设置为 true，模型将通过标准 Event Stream ，逐块返回模型生成内容。Event Stream 结束时会返回一条data：[DONE]消息。注意：在模型流式输出生成内容的过程中，我们会分批对模型生成内容进行检测，当检测到违法及不良信息时，API会返回错误码（1301）。开发者识别到错误码（1301），应及时采取（清屏、重启对话）等措施删除生成内容，并确保不将含有违法及不良信息的内容传递给模型继续生成，避免其造成负面影响。
-      en_US: When using synchronous invocation, this parameter should be set to false or omitted. It indicates that the model will return all the generated content at once after the generation is complete. The default value is false. If set to true, the model will return the generated content in chunks via the standard Event Stream. A data：[DONE] message will be sent at the end of the Event Stream.Note：During the model's streaming output process, we will batch check the generated content. If illegal or harmful information is detected, the API will return an error code (1301). Developers who identify error code (1301) should promptly take actions such as clearing the screen or restarting the conversation to delete the generated content. They should also ensure that no illegal or harmful content is passed back to the model for continued generation to avoid negative impacts.
-    default: false
   - name: max_tokens
     use_template: max_tokens
     default: 1024

api/core/model_runtime/model_providers/zhipuai/llm/glm-4-airx.yaml

@@ -32,15 +32,6 @@ parameter_rules:
       zh_Hans: do_sample 为 true 时启用采样策略，do_sample 为 false 时采样策略 temperature、top_p 将不生效。默认值为 true。
       en_US: When `do_sample` is set to true, the sampling strategy is enabled. When `do_sample` is set to false, the sampling strategies such as `temperature` and `top_p` will not take effect. The default value is true.
     default: true
-  - name: stream
-    label:
-      zh_Hans: 流处理
-      en_US: Event Stream
-    type: boolean
-    help:
-      zh_Hans: 使用同步调用时，此参数应当设置为 fasle 或者省略。表示模型生成完所有内容后一次性返回所有内容。默认值为 false。如果设置为 true，模型将通过标准 Event Stream ，逐块返回模型生成内容。Event Stream 结束时会返回一条data：[DONE]消息。注意：在模型流式输出生成内容的过程中，我们会分批对模型生成内容进行检测，当检测到违法及不良信息时，API会返回错误码（1301）。开发者识别到错误码（1301），应及时采取（清屏、重启对话）等措施删除生成内容，并确保不将含有违法及不良信息的内容传递给模型继续生成，避免其造成负面影响。
-      en_US: When using synchronous invocation, this parameter should be set to false or omitted. It indicates that the model will return all the generated content at once after the generation is complete. The default value is false. If set to true, the model will return the generated content in chunks via the standard Event Stream. A data：[DONE] message will be sent at the end of the Event Stream.Note：During the model's streaming output process, we will batch check the generated content. If illegal or harmful information is detected, the API will return an error code (1301). Developers who identify error code (1301) should promptly take actions such as clearing the screen or restarting the conversation to delete the generated content. They should also ensure that no illegal or harmful content is passed back to the model for continued generation to avoid negative impacts.
-    default: false
   - name: max_tokens
     use_template: max_tokens
     default: 1024

api/core/model_runtime/model_providers/zhipuai/llm/glm-4-flash.yaml

@@ -32,15 +32,6 @@ parameter_rules:
       zh_Hans: do_sample 为 true 时启用采样策略，do_sample 为 false 时采样策略 temperature、top_p 将不生效。默认值为 true。
       en_US: When `do_sample` is set to true, the sampling strategy is enabled. When `do_sample` is set to false, the sampling strategies such as `temperature` and `top_p` will not take effect. The default value is true.
     default: true
-  - name: stream
-    label:
-      zh_Hans: 流处理
-      en_US: Event Stream
-    type: boolean
-    help:
-      zh_Hans: 使用同步调用时，此参数应当设置为 fasle 或者省略。表示模型生成完所有内容后一次性返回所有内容。默认值为 false。如果设置为 true，模型将通过标准 Event Stream ，逐块返回模型生成内容。Event Stream 结束时会返回一条data：[DONE]消息。注意：在模型流式输出生成内容的过程中，我们会分批对模型生成内容进行检测，当检测到违法及不良信息时，API会返回错误码（1301）。开发者识别到错误码（1301），应及时采取（清屏、重启对话）等措施删除生成内容，并确保不将含有违法及不良信息的内容传递给模型继续生成，避免其造成负面影响。
-      en_US: When using synchronous invocation, this parameter should be set to false or omitted. It indicates that the model will return all the generated content at once after the generation is complete. The default value is false. If set to true, the model will return the generated content in chunks via the standard Event Stream. A data：[DONE] message will be sent at the end of the Event Stream.Note：During the model's streaming output process, we will batch check the generated content. If illegal or harmful information is detected, the API will return an error code (1301). Developers who identify error code (1301) should promptly take actions such as clearing the screen or restarting the conversation to delete the generated content. They should also ensure that no illegal or harmful content is passed back to the model for continued generation to avoid negative impacts.
-    default: false
   - name: max_tokens
     use_template: max_tokens
     default: 1024

api/core/model_runtime/model_providers/zhipuai/llm/glm-4-flashx.yaml

@@ -0,0 +1,53 @@
+model: glm-4-flashx
+label:
+  en_US: glm-4-flashx
+model_type: llm
+features:
+  - multi-tool-call
+  - agent-thought
+  - stream-tool-call
+model_properties:
+  mode: chat
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+    default: 0.95
+    min: 0.0
+    max: 1.0
+    help:
+      zh_Hans: 采样温度，控制输出的随机性，必须为正数取值范围是：(0.0,1.0]，不能等于 0,默认值为 0.95 值越大，会使输出更随机，更具创造性；值越小，输出会更加稳定或确定建议您根据应用场景调整 top_p 或 temperature 参数，但不要同时调整两个参数。
+      en_US: Sampling temperature, controls the randomness of the output, must be a positive number. The value range is (0.0,1.0], which cannot be equal to 0. The default value is 0.95. The larger the value, the more random and creative the output will be; the smaller the value, The output will be more stable or certain. It is recommended that you adjust the top_p or temperature parameters according to the application scenario, but do not adjust both parameters at the same time.
+  - name: top_p
+    use_template: top_p
+    default: 0.7
+    help:
+      zh_Hans: 用温度取样的另一种方法，称为核取样取值范围是：(0.0, 1.0) 开区间，不能等于 0 或 1，默认值为 0.7 模型考虑具有 top_p 概率质量tokens的结果例如：0.1 意味着模型解码器只考虑从前 10% 的概率的候选集中取 tokens 建议您根据应用场景调整 top_p 或 temperature 参数，但不要同时调整两个参数。
+      en_US: Another method of temperature sampling is called kernel sampling. The value range is (0.0, 1.0) open interval, which cannot be equal to 0 or 1. The default value is 0.7. The model considers the results with top_p probability mass tokens. For example 0.1 means The model decoder only considers tokens from the candidate set with the top 10% probability. It is recommended that you adjust the top_p or temperature parameters according to the application scenario, but do not adjust both parameters at the same time.
+  - name: do_sample
+    label:
+      zh_Hans: 采样策略
+      en_US: Sampling strategy
+    type: boolean
+    help:
+      zh_Hans: do_sample 为 true 时启用采样策略，do_sample 为 false 时采样策略 temperature、top_p 将不生效。默认值为 true。
+      en_US: When `do_sample` is set to true, the sampling strategy is enabled. When `do_sample` is set to false, the sampling strategies such as `temperature` and `top_p` will not take effect. The default value is true.
+    default: true
+  - name: max_tokens
+    use_template: max_tokens
+    default: 1024
+    min: 1
+    max: 4095
+  - name: web_search
+    type: boolean
+    label:
+      zh_Hans: 联网搜索
+      en_US: Web Search
+    default: false
+    help:
+      zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。
+      en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic.
+pricing:
+  input: '0'
+  output: '0'
+  unit: '0.001'
+  currency: RMB

api/core/model_runtime/model_providers/zhipuai/llm/glm_3_turbo.yaml

@@ -32,15 +32,6 @@ parameter_rules:
       zh_Hans: do_sample 为 true 时启用采样策略，do_sample 为 false 时采样策略 temperature、top_p 将不生效。默认值为 true。
       en_US: When `do_sample` is set to true, the sampling strategy is enabled. When `do_sample` is set to false, the sampling strategies such as `temperature` and `top_p` will not take effect. The default value is true.
     default: true
-  - name: stream
-    label:
-      zh_Hans: 流处理
-      en_US: Event Stream
-    type: boolean
-    help:
-      zh_Hans: 使用同步调用时，此参数应当设置为 fasle 或者省略。表示模型生成完所有内容后一次性返回所有内容。默认值为 false。如果设置为 true，模型将通过标准 Event Stream ，逐块返回模型生成内容。Event Stream 结束时会返回一条data：[DONE]消息。注意：在模型流式输出生成内容的过程中，我们会分批对模型生成内容进行检测，当检测到违法及不良信息时，API会返回错误码（1301）。开发者识别到错误码（1301），应及时采取（清屏、重启对话）等措施删除生成内容，并确保不将含有违法及不良信息的内容传递给模型继续生成，避免其造成负面影响。
-      en_US: When using synchronous invocation, this parameter should be set to false or omitted. It indicates that the model will return all the generated content at once after the generation is complete. The default value is false. If set to true, the model will return the generated content in chunks via the standard Event Stream. A data：[DONE] message will be sent at the end of the Event Stream.Note：During the model's streaming output process, we will batch check the generated content. If illegal or harmful information is detected, the API will return an error code (1301). Developers who identify error code (1301) should promptly take actions such as clearing the screen or restarting the conversation to delete the generated content. They should also ensure that no illegal or harmful content is passed back to the model for continued generation to avoid negative impacts.
-    default: false
   - name: max_tokens
     use_template: max_tokens
     default: 1024

api/core/model_runtime/model_providers/zhipuai/llm/glm_4.yaml

@@ -32,15 +32,6 @@ parameter_rules:
       zh_Hans: do_sample 为 true 时启用采样策略，do_sample 为 false 时采样策略 temperature、top_p 将不生效。默认值为 true。
       en_US: When `do_sample` is set to true, the sampling strategy is enabled. When `do_sample` is set to false, the sampling strategies such as `temperature` and `top_p` will not take effect. The default value is true.
     default: true
-  - name: stream
-    label:
-      zh_Hans: 流处理
-      en_US: Event Stream
-    type: boolean
-    help:
-      zh_Hans: 使用同步调用时，此参数应当设置为 fasle 或者省略。表示模型生成完所有内容后一次性返回所有内容。默认值为 false。如果设置为 true，模型将通过标准 Event Stream ，逐块返回模型生成内容。Event Stream 结束时会返回一条data：[DONE]消息。注意：在模型流式输出生成内容的过程中，我们会分批对模型生成内容进行检测，当检测到违法及不良信息时，API会返回错误码（1301）。开发者识别到错误码（1301），应及时采取（清屏、重启对话）等措施删除生成内容，并确保不将含有违法及不良信息的内容传递给模型继续生成，避免其造成负面影响。
-      en_US: When using synchronous invocation, this parameter should be set to false or omitted. It indicates that the model will return all the generated content at once after the generation is complete. The default value is false. If set to true, the model will return the generated content in chunks via the standard Event Stream. A data：[DONE] message will be sent at the end of the Event Stream.Note：During the model's streaming output process, we will batch check the generated content. If illegal or harmful information is detected, the API will return an error code (1301). Developers who identify error code (1301) should promptly take actions such as clearing the screen or restarting the conversation to delete the generated content. They should also ensure that no illegal or harmful content is passed back to the model for continued generation to avoid negative impacts.
-    default: false
   - name: max_tokens
     use_template: max_tokens
     default: 1024

api/core/model_runtime/model_providers/zhipuai/llm/glm_4_long.yaml

@@ -35,15 +35,6 @@ parameter_rules:
       zh_Hans: do_sample 为 true 时启用采样策略，do_sample 为 false 时采样策略 temperature、top_p 将不生效。默认值为 true。
       en_US: When `do_sample` is set to true, the sampling strategy is enabled. When `do_sample` is set to false, the sampling strategies such as `temperature` and `top_p` will not take effect. The default value is true.
     default: true
-  - name: stream
-    label:
-      zh_Hans: 流处理
-      en_US: Event Stream
-    type: boolean
-    help:
-      zh_Hans: 使用同步调用时，此参数应当设置为 fasle 或者省略。表示模型生成完所有内容后一次性返回所有内容。默认值为 false。如果设置为 true，模型将通过标准 Event Stream ，逐块返回模型生成内容。Event Stream 结束时会返回一条data：[DONE]消息。注意：在模型流式输出生成内容的过程中，我们会分批对模型生成内容进行检测，当检测到违法及不良信息时，API会返回错误码（1301）。开发者识别到错误码（1301），应及时采取（清屏、重启对话）等措施删除生成内容，并确保不将含有违法及不良信息的内容传递给模型继续生成，避免其造成负面影响。
-      en_US: When using synchronous invocation, this parameter should be set to false or omitted. It indicates that the model will return all the generated content at once after the generation is complete. The default value is false. If set to true, the model will return the generated content in chunks via the standard Event Stream. A data：[DONE] message will be sent at the end of the Event Stream.Note：During the model's streaming output process, we will batch check the generated content. If illegal or harmful information is detected, the API will return an error code (1301). Developers who identify error code (1301) should promptly take actions such as clearing the screen or restarting the conversation to delete the generated content. They should also ensure that no illegal or harmful content is passed back to the model for continued generation to avoid negative impacts.
-    default: false
   - name: max_tokens
     use_template: max_tokens
     default: 1024

api/core/model_runtime/model_providers/zhipuai/llm/glm_4_plus.yaml

@@ -32,15 +32,6 @@ parameter_rules:
       zh_Hans: do_sample 为 true 时启用采样策略，do_sample 为 false 时采样策略 temperature、top_p 将不生效。默认值为 true。
       en_US: When `do_sample` is set to true, the sampling strategy is enabled. When `do_sample` is set to false, the sampling strategies such as `temperature` and `top_p` will not take effect. The default value is true.
     default: true
-  - name: stream
-    label:
-      zh_Hans: 流处理
-      en_US: Event Stream
-    type: boolean
-    help:
-      zh_Hans: 使用同步调用时，此参数应当设置为 fasle 或者省略。表示模型生成完所有内容后一次性返回所有内容。默认值为 false。如果设置为 true，模型将通过标准 Event Stream ，逐块返回模型生成内容。Event Stream 结束时会返回一条data：[DONE]消息。注意：在模型流式输出生成内容的过程中，我们会分批对模型生成内容进行检测，当检测到违法及不良信息时，API会返回错误码（1301）。开发者识别到错误码（1301），应及时采取（清屏、重启对话）等措施删除生成内容，并确保不将含有违法及不良信息的内容传递给模型继续生成，避免其造成负面影响。
-      en_US: When using synchronous invocation, this parameter should be set to false or omitted. It indicates that the model will return all the generated content at once after the generation is complete. The default value is false. If set to true, the model will return the generated content in chunks via the standard Event Stream. A data：[DONE] message will be sent at the end of the Event Stream.Note：During the model's streaming output process, we will batch check the generated content. If illegal or harmful information is detected, the API will return an error code (1301). Developers who identify error code (1301) should promptly take actions such as clearing the screen or restarting the conversation to delete the generated content. They should also ensure that no illegal or harmful content is passed back to the model for continued generation to avoid negative impacts.
-    default: false
   - name: max_tokens
     use_template: max_tokens
     default: 1024

api/core/model_runtime/model_providers/zhipuai/llm/glm_4v.yaml

@@ -30,15 +30,6 @@ parameter_rules:
       zh_Hans: do_sample 为 true 时启用采样策略，do_sample 为 false 时采样策略 temperature、top_p 将不生效。默认值为 true。
       en_US: When `do_sample` is set to true, the sampling strategy is enabled. When `do_sample` is set to false, the sampling strategies such as `temperature` and `top_p` will not take effect. The default value is true.
     default: true
-  - name: stream
-    label:
-      zh_Hans: 流处理
-      en_US: Event Stream
-    type: boolean
-    help:
-      zh_Hans: 使用同步调用时，此参数应当设置为 fasle 或者省略。表示模型生成完所有内容后一次性返回所有内容。默认值为 false。如果设置为 true，模型将通过标准 Event Stream ，逐块返回模型生成内容。Event Stream 结束时会返回一条data：[DONE]消息。注意：在模型流式输出生成内容的过程中，我们会分批对模型生成内容进行检测，当检测到违法及不良信息时，API会返回错误码（1301）。开发者识别到错误码（1301），应及时采取（清屏、重启对话）等措施删除生成内容，并确保不将含有违法及不良信息的内容传递给模型继续生成，避免其造成负面影响。
-      en_US: When using synchronous invocation, this parameter should be set to false or omitted. It indicates that the model will return all the generated content at once after the generation is complete. The default value is false. If set to true, the model will return the generated content in chunks via the standard Event Stream. A data：[DONE] message will be sent at the end of the Event Stream.Note：During the model's streaming output process, we will batch check the generated content. If illegal or harmful information is detected, the API will return an error code (1301). Developers who identify error code (1301) should promptly take actions such as clearing the screen or restarting the conversation to delete the generated content. They should also ensure that no illegal or harmful content is passed back to the model for continued generation to avoid negative impacts.
-    default: false
   - name: max_tokens
     use_template: max_tokens
     default: 1024

api/core/model_runtime/model_providers/zhipuai/llm/glm_4v_plus.yaml

@@ -30,15 +30,6 @@ parameter_rules:
       zh_Hans: do_sample 为 true 时启用采样策略，do_sample 为 false 时采样策略 temperature、top_p 将不生效。默认值为 true。
       en_US: When `do_sample` is set to true, the sampling strategy is enabled. When `do_sample` is set to false, the sampling strategies such as `temperature` and `top_p` will not take effect. The default value is true.
     default: true
-  - name: stream
-    label:
-      zh_Hans: 流处理
-      en_US: Event Stream
-    type: boolean
-    help:
-      zh_Hans: 使用同步调用时，此参数应当设置为 fasle 或者省略。表示模型生成完所有内容后一次性返回所有内容。默认值为 false。如果设置为 true，模型将通过标准 Event Stream ，逐块返回模型生成内容。Event Stream 结束时会返回一条data：[DONE]消息。注意：在模型流式输出生成内容的过程中，我们会分批对模型生成内容进行检测，当检测到违法及不良信息时，API会返回错误码（1301）。开发者识别到错误码（1301），应及时采取（清屏、重启对话）等措施删除生成内容，并确保不将含有违法及不良信息的内容传递给模型继续生成，避免其造成负面影响。
-      en_US: When using synchronous invocation, this parameter should be set to false or omitted. It indicates that the model will return all the generated content at once after the generation is complete. The default value is false. If set to true, the model will return the generated content in chunks via the standard Event Stream. A data：[DONE] message will be sent at the end of the Event Stream.Note：During the model's streaming output process, we will batch check the generated content. If illegal or harmful information is detected, the API will return an error code (1301). Developers who identify error code (1301) should promptly take actions such as clearing the screen or restarting the conversation to delete the generated content. They should also ensure that no illegal or harmful content is passed back to the model for continued generation to avoid negative impacts.
-    default: false
   - name: max_tokens
     use_template: max_tokens
     default: 1024

api/core/rag/datasource/keyword/keyword_factory.py

@@ -1,8 +1,8 @@
 from typing import Any
 
 from configs import dify_config
-from core.rag.datasource.keyword.jieba.jieba import Jieba
 from core.rag.datasource.keyword.keyword_base import BaseKeyword
+from core.rag.datasource.keyword.keyword_type import KeyWordType
 from core.rag.models.document import Document
 from models.dataset import Dataset
 
@@ -13,16 +13,19 @@ class Keyword:
         self._keyword_processor = self._init_keyword()
 
     def _init_keyword(self) -> BaseKeyword:
-        config = dify_config
-        keyword_type = config.KEYWORD_STORE
+        keyword_type = dify_config.KEYWORD_STORE
+        keyword_factory = self.get_keyword_factory(keyword_type)
+        return keyword_factory(self._dataset)
 
-        if not keyword_type:
-            raise ValueError("Keyword store must be specified.")
+    @staticmethod
+    def get_keyword_factory(keyword_type: str) -> type[BaseKeyword]:
+        match keyword_type:
+            case KeyWordType.JIEBA:
+                from core.rag.datasource.keyword.jieba.jieba import Jieba
 
-        if keyword_type == "jieba":
-            return Jieba(dataset=self._dataset)
-        else:
-            raise ValueError(f"Keyword store {keyword_type} is not supported.")
+                return Jieba
+            case _:
+                raise ValueError(f"Keyword store {keyword_type} is not supported.")
 
     def create(self, texts: list[Document], **kwargs):
         self._keyword_processor.create(texts, **kwargs)

api/core/rag/datasource/keyword/keyword_type.py

@@ -0,0 +1,5 @@
+from enum import Enum
+
+
+class KeyWordType(str, Enum):
+    JIEBA = "jieba"

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

@@ -112,7 +112,7 @@ class ElasticSearchVector(BaseVector):
         self._client.indices.delete(index=self._collection_name)
 
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
-        top_k = kwargs.get("top_k", 10)
+        top_k = kwargs.get("top_k", 4)
         num_candidates = math.ceil(top_k * 1.5)
         knn = {"field": Field.VECTOR.value, "query_vector": query_vector, "k": top_k, "num_candidates": num_candidates}
 

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

@@ -121,7 +121,7 @@ class MyScaleVector(BaseVector):
         return self._search(f"TextSearch('enable_nlq=false')(text, '{query}')", SortOrder.DESC, **kwargs)
 
     def _search(self, dist: str, order: SortOrder, **kwargs: Any) -> list[Document]:
-        top_k = kwargs.get("top_k", 5)
+        top_k = kwargs.get("top_k", 4)
         score_threshold = float(kwargs.get("score_threshold") or 0.0)
         where_str = (
             f"WHERE dist < {1 - score_threshold}"

api/core/rag/datasource/vdb/oracle/oraclevector.py

@@ -168,14 +168,6 @@ class OracleVector(BaseVector):
                 docs.append(Document(page_content=record[1], metadata=record[0]))
         return docs
 
-    # def get_ids_by_metadata_field(self, key: str, value: str):
-    #    with self._get_cursor() as cur:
-    #        cur.execute(f"SELECT id FROM {self.table_name} d WHERE d.meta.{key}='{value}'" )
-    #        idss = []
-    #        for record in cur:
-    #            idss.append(record[0])
-    #    return idss
-
     def delete_by_ids(self, ids: list[str]) -> None:
         with self._get_cursor() as cur:
             cur.execute(f"DELETE FROM {self.table_name} WHERE id IN %s" % (tuple(ids),))
@@ -192,7 +184,7 @@ class OracleVector(BaseVector):
         :param top_k: The number of nearest neighbors to return, default is 5.
         :return: List of Documents that are nearest to the query vector.
         """
-        top_k = kwargs.get("top_k", 5)
+        top_k = kwargs.get("top_k", 4)
         with self._get_cursor() as cur:
             cur.execute(
                 f"SELECT meta, text, vector_distance(embedding,:1) AS distance FROM {self.table_name}"

api/core/rag/datasource/vdb/pgvecto_rs/pgvecto_rs.py

@@ -186,7 +186,7 @@ class PGVectoRS(BaseVector):
                         query_vector,
                     ).label("distance"),
                 )
-                .limit(kwargs.get("top_k", 2))
+                .limit(kwargs.get("top_k", 4))
                 .order_by("distance")
             )
             res = session.execute(stmt)
@@ -205,18 +205,6 @@ class PGVectoRS(BaseVector):
         return docs
 
     def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
-        # with Session(self._client) as session:
-        #     select_statement = sql_text(
-        #         f"SELECT text, meta FROM {self._collection_name} WHERE to_tsvector(text) @@ '{query}'::tsquery"
-        #     )
-        #     results = session.execute(select_statement).fetchall()
-        # if results:
-        #     docs = []
-        #     for result in results:
-        #         doc = Document(page_content=result[0],
-        #                        metadata=result[1])
-        #         docs.append(doc)
-        #     return docs
         return []
 
 

api/core/rag/datasource/vdb/pgvector/pgvector.py

@@ -143,7 +143,7 @@ class PGVector(BaseVector):
         :param top_k: The number of nearest neighbors to return, default is 5.
         :return: List of Documents that are nearest to the query vector.
         """
-        top_k = kwargs.get("top_k", 5)
+        top_k = kwargs.get("top_k", 4)
 
         with self._get_cursor() as cur:
             cur.execute(

api/core/rag/datasource/vdb/relyt/relyt_vector.py

@@ -224,7 +224,7 @@ class RelytVector(BaseVector):
 
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
         results = self.similarity_search_with_score_by_vector(
-            k=int(kwargs.get("top_k")), embedding=query_vector, filter=kwargs.get("filter")
+            k=int(kwargs.get("top_k", 4)), embedding=query_vector, filter=kwargs.get("filter")
         )
 
         # Organize results.

api/core/rag/datasource/vdb/tidb_vector/tidb_vector.py

@@ -184,7 +184,7 @@ class TiDBVector(BaseVector):
             self._delete_by_ids(ids)
 
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
-        top_k = kwargs.get("top_k", 5)
+        top_k = kwargs.get("top_k", 4)
         score_threshold = float(kwargs.get("score_threshold") or 0.0)
         filter = kwargs.get("filter")
         distance = 1 - score_threshold

api/core/rag/datasource/vdb/vikingdb/vikingdb_vector.py

@@ -173,7 +173,7 @@ class VikingDBVector(BaseVector):
 
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
         results = self._client.get_index(self._collection_name, self._index_name).search_by_vector(
-            query_vector, limit=kwargs.get("top_k", 50)
+            query_vector, limit=kwargs.get("top_k", 4)
         )
         score_threshold = float(kwargs.get("score_threshold") or 0.0)
         return self._get_search_res(results, score_threshold)

api/core/rag/datasource/vdb/weaviate/weaviate_vector.py

@@ -235,7 +235,7 @@ class WeaviateVector(BaseVector):
             query_obj = query_obj.with_where(kwargs.get("where_filter"))
         query_obj = query_obj.with_additional(["vector"])
         properties = ["text"]
-        result = query_obj.with_bm25(query=query, properties=properties).with_limit(kwargs.get("top_k", 2)).do()
+        result = query_obj.with_bm25(query=query, properties=properties).with_limit(kwargs.get("top_k", 4)).do()
         if "errors" in result:
             raise ValueError(f"Error during query: {result['errors']}")
         docs = []

api/core/rag/retrieval/dataset_retrieval.py

@@ -217,7 +217,7 @@ class DatasetRetrieval:
                                 "data_source_type": document.data_source_type,
                                 "segment_id": segment.id,
                                 "retriever_from": invoke_from.to_source(),
-                                "score": document_score_list.get(segment.index_node_id, None),
+                                "score": document_score_list.get(segment.index_node_id, 0.0),
                             }
 
                             if invoke_from.to_source() == "dev":
@@ -231,12 +231,12 @@ class DatasetRetrieval:
                                 source["content"] = segment.content
                             retrieval_resource_list.append(source)
         if hit_callback and retrieval_resource_list:
-            retrieval_resource_list = sorted(retrieval_resource_list, key=lambda x: x.get("score"), reverse=True)
+            retrieval_resource_list = sorted(retrieval_resource_list, key=lambda x: x.get("score") or 0.0, reverse=True)
             for position, item in enumerate(retrieval_resource_list, start=1):
                 item["position"] = position
             hit_callback.return_retriever_resource_info(retrieval_resource_list)
         if document_context_list:
-            document_context_list = sorted(document_context_list, key=lambda x: x.score, reverse=True)
+            document_context_list = sorted(document_context_list, key=lambda x: x.score or 0.0, reverse=True)
             return str("\n".join([document_context.content for document_context in document_context_list]))
         return ""
 

api/core/tools/provider/builtin/time/tools/timezone_conversion.py

@@ -0,0 +1,48 @@
+from datetime import datetime
+from typing import Any, Union
+
+import pytz
+
+from core.tools.entities.tool_entities import ToolInvokeMessage
+from core.tools.errors import ToolInvokeError
+from core.tools.tool.builtin_tool import BuiltinTool
+
+
+class TimezoneConversionTool(BuiltinTool):
+    def _invoke(
+        self,
+        user_id: str,
+        tool_parameters: dict[str, Any],
+    ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]:
+        """
+        Convert time to equivalent time zone
+        """
+        current_time = tool_parameters.get("current_time")
+        current_timezone = tool_parameters.get("current_timezone", "Asia/Shanghai")
+        target_timezone = tool_parameters.get("target_timezone", "Asia/Tokyo")
+        target_time = self.timezone_convert(current_time, current_timezone, target_timezone)
+        if not target_time:
+            return self.create_text_message(
+                f"Invalid datatime and timezone: {current_time},{current_timezone},{target_timezone}"
+            )
+
+        return self.create_text_message(f"{target_time}")
+
+    @staticmethod
+    def timezone_convert(current_time: str, source_timezone: str, target_timezone: str) -> str:
+        """
+        Convert a time string from source timezone to target timezone.
+        """
+        time_format = "%Y-%m-%d %H:%M:%S"
+        try:
+            # get source timezone
+            input_timezone = pytz.timezone(source_timezone)
+            # get target timezone
+            output_timezone = pytz.timezone(target_timezone)
+            local_time = datetime.strptime(current_time, time_format)
+            datetime_with_tz = input_timezone.localize(local_time)
+            # timezone convert
+            converted_datetime = datetime_with_tz.astimezone(output_timezone)
+            return converted_datetime.strftime(format=time_format)
+        except Exception as e:
+            raise ToolInvokeError(str(e))

api/core/tools/provider/builtin/time/tools/timezone_conversion.yaml

@@ -0,0 +1,44 @@
+identity:
+  name: timezone_conversion
+  author: zhuhao
+  label:
+    en_US: convert time to equivalent time zone
+    zh_Hans: 时区转换
+description:
+  human:
+    en_US: A tool to convert time to equivalent time zone
+    zh_Hans: 时区转换
+  llm: A tool to convert time to equivalent time zone
+parameters:
+  - name: current_time
+    type: string
+    required: true
+    form: llm
+    label:
+      en_US: current time
+      zh_Hans: 当前时间
+    human_description:
+      en_US: current time, such as 2024-1-1 0:0:0
+      zh_Hans: 当前时间, 比如2024-1-1 0:0:0
+  - name: current_timezone
+    type: string
+    required: true
+    form: llm
+    label:
+      en_US: Current Timezone
+      zh_Hans: 当前时区
+    human_description:
+      en_US: Current Timezone, such as Asia/Shanghai
+      zh_Hans: 当前时区, 比如Asia/Shanghai
+    default: Asia/Shanghai
+  - name: target_timezone
+    type: string
+    required: true
+    form: llm
+    label:
+      en_US: Target Timezone
+      zh_Hans: 目标时区
+    human_description:
+      en_US: Target Timezone, such as Asia/Tokyo
+      zh_Hans: 目标时区, 比如Asia/Tokyo
+    default: Asia/Tokyo

api/core/workflow/nodes/knowledge_retrieval/knowledge_retrieval_node.py

@@ -234,7 +234,7 @@ class KnowledgeRetrievalNode(BaseNode):
                         retrieval_resource_list.append(source)
         if retrieval_resource_list:
             retrieval_resource_list = sorted(
-                retrieval_resource_list, key=lambda x: x.get("metadata").get("score"), reverse=True
+                retrieval_resource_list, key=lambda x: x.get("metadata").get("score") or 0.0, reverse=True
             )
             position = 1
             for item in retrieval_resource_list:

api/extensions/storage/aliyun_oss_storage.py

@@ -1,5 +1,4 @@
 from collections.abc import Generator
-from contextlib import closing
 
 import oss2 as aliyun_s3
 from flask import Flask
@@ -34,15 +33,15 @@ class AliyunOssStorage(BaseStorage):
         self.client.put_object(self.__wrapper_folder_filename(filename), data)
 
     def load_once(self, filename: str) -> bytes:
-        with closing(self.client.get_object(self.__wrapper_folder_filename(filename))) as obj:
-            data = obj.read()
+        obj = self.client.get_object(self.__wrapper_folder_filename(filename))
+        data = obj.read()
         return data
 
     def load_stream(self, filename: str) -> Generator:
         def generate(filename: str = filename) -> Generator:
-            with closing(self.client.get_object(self.__wrapper_folder_filename(filename))) as obj:
-                while chunk := obj.read(4096):
-                    yield chunk
+            obj = self.client.get_object(self.__wrapper_folder_filename(filename))
+            while chunk := obj.read(4096):
+                yield chunk
 
         return generate()
 

api/extensions/storage/aws_s3_storage.py

@@ -1,6 +1,5 @@
 import logging
 from collections.abc import Generator
-from contextlib import closing
 
 import boto3
 from botocore.client import Config
@@ -55,8 +54,7 @@ class AwsS3Storage(BaseStorage):
 
     def load_once(self, filename: str) -> bytes:
         try:
-            with closing(self.client) as client:
-                data = client.get_object(Bucket=self.bucket_name, Key=filename)["Body"].read()
+            data = self.client.get_object(Bucket=self.bucket_name, Key=filename)["Body"].read()
         except ClientError as ex:
             if ex.response["Error"]["Code"] == "NoSuchKey":
                 raise FileNotFoundError("File not found")
@@ -67,9 +65,8 @@ class AwsS3Storage(BaseStorage):
     def load_stream(self, filename: str) -> Generator:
         def generate(filename: str = filename) -> Generator:
             try:
-                with closing(self.client) as client:
-                    response = client.get_object(Bucket=self.bucket_name, Key=filename)
-                    yield from response["Body"].iter_chunks()
+                response = self.client.get_object(Bucket=self.bucket_name, Key=filename)
+                yield from response["Body"].iter_chunks()
             except ClientError as ex:
                 if ex.response["Error"]["Code"] == "NoSuchKey":
                     raise FileNotFoundError("File not found")
@@ -79,16 +76,14 @@ class AwsS3Storage(BaseStorage):
         return generate()
 
     def download(self, filename, target_filepath):
-        with closing(self.client) as client:
-            client.download_file(self.bucket_name, filename, target_filepath)
+        self.client.download_file(self.bucket_name, filename, target_filepath)
 
     def exists(self, filename):
-        with closing(self.client) as client:
-            try:
-                client.head_object(Bucket=self.bucket_name, Key=filename)
-                return True
-            except:
-                return False
+        try:
+            self.client.head_object(Bucket=self.bucket_name, Key=filename)
+            return True
+        except:
+            return False
 
     def delete(self, filename):
         self.client.delete_object(Bucket=self.bucket_name, Key=filename)

api/extensions/storage/google_cloud_storage.py

@@ -2,7 +2,6 @@ import base64
 import io
 import json
 from collections.abc import Generator
-from contextlib import closing
 
 from flask import Flask
 from google.cloud import storage as google_cloud_storage
@@ -43,7 +42,7 @@ class GoogleCloudStorage(BaseStorage):
         def generate(filename: str = filename) -> Generator:
             bucket = self.client.get_bucket(self.bucket_name)
             blob = bucket.get_blob(filename)
-            with closing(blob.open(mode="rb")) as blob_stream:
+            with blob.open(mode="rb") as blob_stream:
                 while chunk := blob_stream.read(4096):
                     yield chunk
 

api/extensions/storage/oracle_oci_storage.py

@@ -1,5 +1,4 @@
 from collections.abc import Generator
-from contextlib import closing
 
 import boto3
 from botocore.exceptions import ClientError
@@ -28,8 +27,7 @@ class OracleOCIStorage(BaseStorage):
 
     def load_once(self, filename: str) -> bytes:
         try:
-            with closing(self.client) as client:
-                data = client.get_object(Bucket=self.bucket_name, Key=filename)["Body"].read()
+            data = self.client.get_object(Bucket=self.bucket_name, Key=filename)["Body"].read()
         except ClientError as ex:
             if ex.response["Error"]["Code"] == "NoSuchKey":
                 raise FileNotFoundError("File not found")
@@ -40,9 +38,8 @@ class OracleOCIStorage(BaseStorage):
     def load_stream(self, filename: str) -> Generator:
         def generate(filename: str = filename) -> Generator:
             try:
-                with closing(self.client) as client:
-                    response = client.get_object(Bucket=self.bucket_name, Key=filename)
-                    yield from response["Body"].iter_chunks()
+                response = self.client.get_object(Bucket=self.bucket_name, Key=filename)
+                yield from response["Body"].iter_chunks()
             except ClientError as ex:
                 if ex.response["Error"]["Code"] == "NoSuchKey":
                     raise FileNotFoundError("File not found")
@@ -52,16 +49,14 @@ class OracleOCIStorage(BaseStorage):
         return generate()
 
     def download(self, filename, target_filepath):
-        with closing(self.client) as client:
-            client.download_file(self.bucket_name, filename, target_filepath)
+        self.client.download_file(self.bucket_name, filename, target_filepath)
 
     def exists(self, filename):
-        with closing(self.client) as client:
-            try:
-                client.head_object(Bucket=self.bucket_name, Key=filename)
-                return True
-            except:
-                return False
+        try:
+            self.client.head_object(Bucket=self.bucket_name, Key=filename)
+            return True
+        except:
+            return False
 
     def delete(self, filename):
         self.client.delete_object(Bucket=self.bucket_name, Key=filename)

api/poetry.lock

@@ -455,13 +455,13 @@ vertex = ["google-auth (>=2,<3)"]
 
 [[package]]
 name = "anyio"
-version = "4.6.0"
+version = "4.6.2.post1"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "anyio-4.6.0-py3-none-any.whl", hash = "sha256:c7d2e9d63e31599eeb636c8c5c03a7e108d73b345f064f1c19fdc87b79036a9a"},
-    {file = "anyio-4.6.0.tar.gz", hash = "sha256:137b4559cbb034c477165047febb6ff83f390fc3b20bf181c1fc0a728cb8beeb"},
+    {file = "anyio-4.6.2.post1-py3-none-any.whl", hash = "sha256:6d170c36fba3bdd840c73d3868c1e777e33676a69c3a72cf0a0d5d6d8009b61d"},
+    {file = "anyio-4.6.2.post1.tar.gz", hash = "sha256:4c8bc31ccdb51c7f7bd251f51c609e038d63e34219b44aa86e47576389880b4c"},
 ]
 
 [package.dependencies]
@@ -472,7 +472,7 @@ typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
 
 [package.extras]
 doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.21.0b1)"]
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 trio = ["trio (>=0.26.1)"]
 
 [[package]]
@@ -844,13 +844,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.35.38"
+version = "1.35.40"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -863,53 +863,47 @@ crt = ["awscrt (==0.22.0)"]
 
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 name = "bottleneck"
-version = "1.4.0"
+version = "1.4.1"
 description = "Fast NumPy array functions written in C"
 optional = false
 python-versions = "*"
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 ]
 
 [package.dependencies]
@@ -2159,63 +2153,63 @@ typing_extensions = ">=4.0,<5.0"
 
 [[package]]
 name = "duckdb"
-version = "1.1.1"
+version = "1.1.2"
 description = "DuckDB in-process database"
 optional = false
 python-versions = ">=3.7.0"
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 [[package]]
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 [[package]]
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 [[package]]
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 ]
 
 [package.extras]
-test = ["Cython (>=0.29.24,<0.30.0)"]
+test = ["Cython (>=0.29.24)"]
 
 [[package]]
 name = "httpx"
@@ -4127,6 +4129,88 @@ MarkupSafe = ">=2.0"
 [package.extras]
 i18n = ["Babel (>=2.7)"]
 
+[[package]]
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+version = "0.6.1"
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 [[package]]
 name = "langsmith"
-version = "0.1.134"
+version = "0.1.135"
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 python-versions = "<4.0,>=3.8.1"
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 [package.dependencies]
@@ -5678,23 +5762,24 @@ sympy = "*"
 
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 tqdm = ">4"
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 [package.extras]
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 [[package]]
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 [package.dependencies]
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 [[package]]
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 [package.dependencies]
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 [[package]]
 name = "threadpoolctl"
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 [package.dependencies]
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 [package.extras]
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 docs = ["Sphinx (>=4.1.2,<4.2.0)", "sphinx-rtd-theme (>=0.5.2,<0.6.0)", "sphinxcontrib-asyncio (>=0.3.0,<0.4.0)"]
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 [[package]]
 name = "validators"
@@ -10697,4 +10784,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "75a7e7eab36b9386c11a3e9808da28102ad20a43a0e8ae08c37594ecf50da02b"
+content-hash = "51f048197baebf9ffdc393e5990b9a90185bc5ff515b8b5d2d9b72de900cf6e2"

api/pyproject.toml

@@ -86,10 +86,19 @@ ignore = [
 ]
 "tests/*" = [
     "F811", # redefined-while-unused
+    "F401", # unused-import
+    "PT001", # missing-function-docstring
+    "PT004", # missing-parameter-docstring
+]
+"core/rag/extractor/word_extractor.py" = [
+    "RUF100", # Unused `noqa` directive
+]
+"core/tools/provider/builtin/gitlab/tools/gitlab_commits.py" = [
+    "PLR1714", # Consider merging multiple comparisons
 ]
 
 [tool.ruff.lint.pyflakes]
-allowed-unused-imports=[
+extend-generics=[
     "_pytest.monkeypatch",
     "tests.integration_tests",
 ]
@@ -149,7 +158,7 @@ nomic = "~3.1.2"
 novita-client = "~0.5.7"
 numpy = "~1.26.4"
 oci = "~2.135.1"
-openai = "~1.29.0"
+openai = "~1.51.2"
 openpyxl = "~3.1.5"
 pandas = { version = "~2.2.2", extras = ["performance", "excel"] }
 psycopg2-binary = "~2.9.6"
@@ -172,7 +181,7 @@ scikit-learn = "~1.5.1"
 sentry-sdk = { version = "~1.44.1", extras = ["flask"] }
 sqlalchemy = "~2.0.29"
 tencentcloud-sdk-python-hunyuan = "~3.0.1158"
-tiktoken = "~0.7.0"
+tiktoken = "~0.8.0"
 tokenizers = "~0.15.0"
 transformers = "~4.35.0"
 unstructured = { version = "~0.10.27", extras = ["docx", "epub", "md", "msg", "ppt", "pptx"] }

api/services/auth/api_key_auth_factory.py

@@ -1,15 +1,25 @@
-from services.auth.firecrawl import FirecrawlAuth
-from services.auth.jina import JinaAuth
+from services.auth.api_key_auth_base import ApiKeyAuthBase
+from services.auth.auth_type import AuthType
 
 
 class ApiKeyAuthFactory:
     def __init__(self, provider: str, credentials: dict):
-        if provider == "firecrawl":
-            self.auth = FirecrawlAuth(credentials)
-        elif provider == "jinareader":
-            self.auth = JinaAuth(credentials)
-        else:
-            raise ValueError("Invalid provider")
+        auth_factory = self.get_apikey_auth_factory(provider)
+        self.auth = auth_factory(credentials)
 
     def validate_credentials(self):
         return self.auth.validate_credentials()
+
+    @staticmethod
+    def get_apikey_auth_factory(provider: str) -> type[ApiKeyAuthBase]:
+        match provider:
+            case AuthType.FIRECRAWL:
+                from services.auth.firecrawl.firecrawl import FirecrawlAuth
+
+                return FirecrawlAuth
+            case AuthType.JINA:
+                from services.auth.jina.jina import JinaAuth
+
+                return JinaAuth
+            case _:
+                raise ValueError("Invalid provider")

api/services/auth/auth_type.py

@@ -0,0 +1,6 @@
+from enum import Enum
+
+
+class AuthType(str, Enum):
+    FIRECRAWL = "firecrawl"
+    JINA = "jinareader"

api/tests/integration_tests/model_runtime/__mock/openai_moderation.py

@@ -41,6 +41,8 @@ class MockModerationClass:
                     "sexual/minors": False,
                     "violence": False,
                     "violence/graphic": False,
+                    "illicit": False,
+                    "illicit/violent": False,
                 }
                 moderation_categories_scores = {
                     "harassment": 1.0,
@@ -54,13 +56,30 @@ class MockModerationClass:
                     "sexual/minors": 1.0,
                     "violence": 1.0,
                     "violence/graphic": 1.0,
+                    "illicit": 1.0,
+                    "illicit/violent": 1.0,
+                }
+                category_applied_input_types = {
+                    "sexual": ["text", "image"],
+                    "hate": ["text"],
+                    "harassment": ["text"],
+                    "self-harm": ["text", "image"],
+                    "sexual/minors": ["text"],
+                    "hate/threatening": ["text"],
+                    "violence/graphic": ["text", "image"],
+                    "self-harm/intent": ["text", "image"],
+                    "self-harm/instructions": ["text", "image"],
+                    "harassment/threatening": ["text"],
+                    "violence": ["text", "image"],
+                    "illicit": ["text"],
+                    "illicit/violent": ["text"],
                 }
-
                 result.append(
                     Moderation(
                         flagged=True,
                         categories=Categories(**moderation_categories),
                         category_scores=CategoryScores(**moderation_categories_scores),
+                        category_applied_input_types=category_applied_input_types,
                     )
                 )
             else:
@@ -76,6 +95,8 @@ class MockModerationClass:
                     "sexual/minors": False,
                     "violence": False,
                     "violence/graphic": False,
+                    "illicit": False,
+                    "illicit/violent": False,
                 }
                 moderation_categories_scores = {
                     "harassment": 0.0,
@@ -89,12 +110,30 @@ class MockModerationClass:
                     "sexual/minors": 0.0,
                     "violence": 0.0,
                     "violence/graphic": 0.0,
+                    "illicit": 0.0,
+                    "illicit/violent": 0.0,
+                }
+                category_applied_input_types = {
+                    "sexual": ["text", "image"],
+                    "hate": ["text"],
+                    "harassment": ["text"],
+                    "self-harm": ["text", "image"],
+                    "sexual/minors": ["text"],
+                    "hate/threatening": ["text"],
+                    "violence/graphic": ["text", "image"],
+                    "self-harm/intent": ["text", "image"],
+                    "self-harm/instructions": ["text", "image"],
+                    "harassment/threatening": ["text"],
+                    "violence": ["text", "image"],
+                    "illicit": ["text"],
+                    "illicit/violent": ["text"],
                 }
                 result.append(
                     Moderation(
                         flagged=False,
                         categories=Categories(**moderation_categories),
                         category_scores=CategoryScores(**moderation_categories_scores),
+                        category_applied_input_types=category_applied_input_types,
                     )
                 )
 

api/tests/integration_tests/vdb/__mock/baiduvectordb.py

@@ -1,4 +1,5 @@
 import os
+from unittest.mock import MagicMock
 
 import pytest
 from _pytest.monkeypatch import MonkeyPatch
@@ -10,26 +11,31 @@ from pymochow.model.table import Table
 from requests.adapters import HTTPAdapter
 
 
+class AttrDict(dict):
+    def __getattr__(self, item):
+        return self.get(item)
+
+
 class MockBaiduVectorDBClass:
     def mock_vector_db_client(
         self,
         config=None,
         adapter: HTTPAdapter = None,
     ):
-        self._conn = None
-        self._config = None
+        self.conn = MagicMock()
+        self._config = MagicMock()
 
     def list_databases(self, config=None) -> list[Database]:
         return [
             Database(
-                conn=self._conn,
+                conn=self.conn,
                 database_name="dify",
                 config=self._config,
             )
         ]
 
     def create_database(self, database_name: str, config=None) -> Database:
-        return Database(conn=self._conn, database_name=database_name, config=config)
+        return Database(conn=self.conn, database_name=database_name, config=config)
 
     def list_table(self, config=None) -> list[Table]:
         return []
@@ -88,16 +94,18 @@ class MockBaiduVectorDBClass:
         read_consistency=ReadConsistency.EVENTUAL,
         config=None,
     ):
-        return {
-            "row": {
-                "id": "doc_id_001",
-                "vector": [0.23432432, 0.8923744, 0.89238432],
-                "text": "text",
-                "metadata": {"doc_id": "doc_id_001"},
-            },
-            "code": 0,
-            "msg": "Success",
-        }
+        return AttrDict(
+            {
+                "row": {
+                    "id": primary_key.get("id"),
+                    "vector": [0.23432432, 0.8923744, 0.89238432],
+                    "text": "text",
+                    "metadata": '{"doc_id": "doc_id_001"}',
+                },
+                "code": 0,
+                "msg": "Success",
+            }
+        )
 
     def delete(self, primary_key=None, partition_key=None, filter=None, config=None):
         return {"code": 0, "msg": "Success"}
@@ -111,22 +119,24 @@ class MockBaiduVectorDBClass:
         read_consistency=ReadConsistency.EVENTUAL,
         config=None,
     ):
-        return {
-            "rows": [
-                {
-                    "row": {
-                        "id": "doc_id_001",
-                        "vector": [0.23432432, 0.8923744, 0.89238432],
-                        "text": "text",
-                        "metadata": {"doc_id": "doc_id_001"},
-                    },
-                    "distance": 0.1,
-                    "score": 0.5,
-                }
-            ],
-            "code": 0,
-            "msg": "Success",
-        }
+        return AttrDict(
+            {
+                "rows": [
+                    {
+                        "row": {
+                            "id": "doc_id_001",
+                            "vector": [0.23432432, 0.8923744, 0.89238432],
+                            "text": "text",
+                            "metadata": '{"doc_id": "doc_id_001"}',
+                        },
+                        "distance": 0.1,
+                        "score": 0.5,
+                    }
+                ],
+                "code": 0,
+                "msg": "Success",
+            }
+        )
 
 
 MOCK = os.getenv("MOCK_SWITCH", "false").lower() == "true"
@@ -146,6 +156,7 @@ def setup_baiduvectordb_mock(request, monkeypatch: MonkeyPatch):
         monkeypatch.setattr(Table, "rebuild_index", MockBaiduVectorDBClass.rebuild_index)
         monkeypatch.setattr(Table, "describe_index", MockBaiduVectorDBClass.describe_index)
         monkeypatch.setattr(Table, "delete", MockBaiduVectorDBClass.delete)
+        monkeypatch.setattr(Table, "query", MockBaiduVectorDBClass.query)
         monkeypatch.setattr(Table, "search", MockBaiduVectorDBClass.search)
 
     yield

api/tests/integration_tests/vdb/baidu/test_baidu.py

@@ -4,9 +4,6 @@ from core.rag.datasource.vdb.baidu.baidu_vector import BaiduConfig, BaiduVector
 from tests.integration_tests.vdb.__mock.baiduvectordb import setup_baiduvectordb_mock
 from tests.integration_tests.vdb.test_vector_store import AbstractVectorTest, get_example_text, setup_mock_redis
 
-mock_client = MagicMock()
-mock_client.list_databases.return_value = [{"name": "test"}]
-
 
 class BaiduVectorTest(AbstractVectorTest):
     def __init__(self):

dev/pytest/pytest_vdb.sh

@@ -8,4 +8,5 @@ pytest api/tests/integration_tests/vdb/chroma \
   api/tests/integration_tests/vdb/qdrant \
   api/tests/integration_tests/vdb/weaviate \
   api/tests/integration_tests/vdb/elasticsearch \
-  api/tests/integration_tests/vdb/vikingdb
+  api/tests/integration_tests/vdb/vikingdb \
+  api/tests/integration_tests/vdb/baidu

docker/.env.example

@@ -58,7 +58,7 @@ DEBUG=false
 # which is convenient for debugging.
 FLASK_DEBUG=false
 
-# A secretkey that is used for securely signing the session cookie 
+# A secretkey that is used for securely signing the session cookie
 # and encrypting sensitive information on the database.
 # You can generate a strong key using `openssl rand -base64 42`.
 SECRET_KEY=sk-9f73s3ljTXVcMT3Blb3ljTqtsKiGHXVcMT3BlbkFJLK7U
@@ -366,8 +366,8 @@ SUPABASE_URL=your-server-url
 # Vector Database Configuration
 # ------------------------------
 
-# The type of vector store to use. 
-# Supported values are `weaviate`, `qdrant`, `milvus`, `myscale`, `relyt`, `pgvector`, `pgvecto-rs`, ``chroma`, `opensearch`, `tidb_vector`, `oracle`, `tencent`, `elasticsearch`, `analyticdb`.
+# The type of vector store to use.
+# Supported values are `weaviate`, `qdrant`, `milvus`, `myscale`, `relyt`, `pgvector`, `pgvecto-rs`, `chroma`, `opensearch`, `tidb_vector`, `oracle`, `tencent`, `elasticsearch`, `analyticdb`, `vikingdb`.
 VECTOR_STORE=weaviate
 
 # The Weaviate endpoint URL. Only available when VECTOR_STORE is `weaviate`.
@@ -492,6 +492,15 @@ BAIDU_VECTOR_DB_DATABASE=dify
 BAIDU_VECTOR_DB_SHARD=1
 BAIDU_VECTOR_DB_REPLICAS=3
 
+# VikingDB configurations, only available when VECTOR_STORE is `vikingdb`
+VIKINGDB_ACCESS_KEY=your-ak
+VIKINGDB_SECRET_KEY=your-sk
+VIKINGDB_REGION=cn-shanghai
+VIKINGDB_HOST=api-vikingdb.xxx.volces.com
+VIKINGDB_SCHEMA=http
+VIKINGDB_CONNECTION_TIMEOUT=30
+VIKINGDB_SOCKET_TIMEOUT=30
+
 # ------------------------------
 # Knowledge Configuration
 # ------------------------------
@@ -668,7 +677,7 @@ SANDBOX_PORT=8194
 # Environment Variables for weaviate Service
 # (only used when VECTOR_STORE is weaviate)
 # ------------------------------
-WEAVIATE_PERSISTENCE_DATA_PATH='/var/lib/weaviate'
+WEAVIATE_PERSISTENCE_DATA_PATH=/var/lib/weaviate
 WEAVIATE_QUERY_DEFAULTS_LIMIT=25
 WEAVIATE_AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED=true
 WEAVIATE_DEFAULT_VECTORIZER_MODULE=none
@@ -829,4 +838,4 @@ POSITION_PROVIDER_PINS=
 POSITION_PROVIDER_INCLUDES=
 POSITION_PROVIDER_EXCLUDES=
 # CSP https://developer.mozilla.org/en-US/docs/Web/HTTP/CSP
-CSP_WHITELIST=
+CSP_WHITELIST=

sdks/python-client/dify_client/__init__.py

@@ -1 +1 @@
-from dify_client.client import ChatClient, CompletionClient, DifyClient
+from dify_client.client import ChatClient, CompletionClient, DifyClient

sdks/python-client/dify_client/client.py

@@ -9,10 +9,15 @@ class DifyClient:
         self.base_url = base_url
 
     def _send_request(self, method, endpoint, json=None, params=None, stream=False):
-        headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json",
+        }
 
         url = f"{self.base_url}{endpoint}"
-        response = requests.request(method, url, json=json, params=params, headers=headers, stream=stream)
+        response = requests.request(
+            method, url, json=json, params=params, headers=headers, stream=stream
+        )
 
         return response
 
@@ -20,7 +25,9 @@ class DifyClient:
         headers = {"Authorization": f"Bearer {self.api_key}"}
 
         url = f"{self.base_url}{endpoint}"
-        response = requests.request(method, url, data=data, headers=headers, files=files)
+        response = requests.request(
+            method, url, data=data, headers=headers, files=files
+        )
 
         return response
 
@@ -34,7 +41,9 @@ class DifyClient:
 
     def file_upload(self, user, files):
         data = {"user": user}
-        return self._send_request_with_files("POST", "/files/upload", data=data, files=files)
+        return self._send_request_with_files(
+            "POST", "/files/upload", data=data, files=files
+        )
 
     def text_to_audio(self, text: str, user: str, streaming: bool = False):
         data = {"text": text, "user": user, "streaming": streaming}
@@ -47,25 +56,52 @@ class DifyClient:
 
 class CompletionClient(DifyClient):
     def create_completion_message(self, inputs, response_mode, user, files=None):
-        data = {"inputs": inputs, "response_mode": response_mode, "user": user, "files": files}
+        data = {
+            "inputs": inputs,
+            "response_mode": response_mode,
+            "user": user,
+            "files": files,
+        }
         return self._send_request(
-            "POST", "/completion-messages", data, stream=True if response_mode == "streaming" else False
+            "POST",
+            "/completion-messages",
+            data,
+            stream=True if response_mode == "streaming" else False,
         )
 
 
 class ChatClient(DifyClient):
-    def create_chat_message(self, inputs, query, user, response_mode="blocking", conversation_id=None, files=None):
-        data = {"inputs": inputs, "query": query, "user": user, "response_mode": response_mode, "files": files}
+    def create_chat_message(
+        self,
+        inputs,
+        query,
+        user,
+        response_mode="blocking",
+        conversation_id=None,
+        files=None,
+    ):
+        data = {
+            "inputs": inputs,
+            "query": query,
+            "user": user,
+            "response_mode": response_mode,
+            "files": files,
+        }
         if conversation_id:
             data["conversation_id"] = conversation_id
 
         return self._send_request(
-            "POST", "/chat-messages", data, stream=True if response_mode == "streaming" else False
+            "POST",
+            "/chat-messages",
+            data,
+            stream=True if response_mode == "streaming" else False,
         )
 
     def get_suggested(self, message_id, user: str):
         params = {"user": user}
-        return self._send_request("GET", f"/messages/{message_id}/suggested", params=params)
+        return self._send_request(
+            "GET", f"/messages/{message_id}/suggested", params=params
+        )
 
     def stop_message(self, task_id, user):
         data = {"user": user}
@@ -75,7 +111,9 @@ class ChatClient(DifyClient):
         params = {"user": user, "last_id": last_id, "limit": limit, "pinned": pinned}
         return self._send_request("GET", "/conversations", params=params)
 
-    def get_conversation_messages(self, user, conversation_id=None, first_id=None, limit=None):
+    def get_conversation_messages(
+        self, user, conversation_id=None, first_id=None, limit=None
+    ):
         params = {"user": user}
 
         if conversation_id:
@@ -87,9 +125,13 @@ class ChatClient(DifyClient):
 
         return self._send_request("GET", "/messages", params=params)
 
-    def rename_conversation(self, conversation_id, name, auto_generate: bool, user: str):
+    def rename_conversation(
+        self, conversation_id, name, auto_generate: bool, user: str
+    ):
         data = {"name": name, "auto_generate": auto_generate, "user": user}
-        return self._send_request("POST", f"/conversations/{conversation_id}/name", data)
+        return self._send_request(
+            "POST", f"/conversations/{conversation_id}/name", data
+        )
 
     def delete_conversation(self, conversation_id, user):
         data = {"user": user}
@@ -102,7 +144,9 @@ class ChatClient(DifyClient):
 
 
 class WorkflowClient(DifyClient):
-    def run(self, inputs: dict, response_mode: str = "streaming", user: str = "abc-123"):
+    def run(
+        self, inputs: dict, response_mode: str = "streaming", user: str = "abc-123"
+    ):
         data = {"inputs": inputs, "response_mode": response_mode, "user": user}
         return self._send_request("POST", "/workflows/run", data)
 
@@ -115,7 +159,9 @@ class WorkflowClient(DifyClient):
 
 
 class KnowledgeBaseClient(DifyClient):
-    def __init__(self, api_key, base_url: str = "https://api.dify.ai/v1", dataset_id: str = None):
+    def __init__(
+        self, api_key, base_url: str = "https://api.dify.ai/v1", dataset_id: str = None
+    ):
         """
         Construct a KnowledgeBaseClient object.
 
@@ -137,7 +183,9 @@ class KnowledgeBaseClient(DifyClient):
         return self._send_request("POST", "/datasets", {"name": name}, **kwargs)
 
     def list_datasets(self, page: int = 1, page_size: int = 20, **kwargs):
-        return self._send_request("GET", f"/datasets?page={page}&limit={page_size}", **kwargs)
+        return self._send_request(
+            "GET", f"/datasets?page={page}&limit={page_size}", **kwargs
+        )
 
     def create_document_by_text(self, name, text, extra_params: dict = None, **kwargs):
         """
@@ -165,13 +213,20 @@ class KnowledgeBaseClient(DifyClient):
         }
         :return: Response from the API
         """
-        data = {"indexing_technique": "high_quality", "process_rule": {"mode": "automatic"}, "name": name, "text": text}
+        data = {
+            "indexing_technique": "high_quality",
+            "process_rule": {"mode": "automatic"},
+            "name": name,
+            "text": text,
+        }
         if extra_params is not None and isinstance(extra_params, dict):
             data.update(extra_params)
         url = f"/datasets/{self._get_dataset_id()}/document/create_by_text"
         return self._send_request("POST", url, json=data, **kwargs)
 
-    def update_document_by_text(self, document_id, name, text, extra_params: dict = None, **kwargs):
+    def update_document_by_text(
+        self, document_id, name, text, extra_params: dict = None, **kwargs
+    ):
         """
         Update a document by text.
 
@@ -201,10 +256,14 @@ class KnowledgeBaseClient(DifyClient):
         data = {"name": name, "text": text}
         if extra_params is not None and isinstance(extra_params, dict):
             data.update(extra_params)
-        url = f"/datasets/{self._get_dataset_id()}/documents/{document_id}/update_by_text"
+        url = (
+            f"/datasets/{self._get_dataset_id()}/documents/{document_id}/update_by_text"
+        )
         return self._send_request("POST", url, json=data, **kwargs)
 
-    def create_document_by_file(self, file_path, original_document_id=None, extra_params: dict = None):
+    def create_document_by_file(
+        self, file_path, original_document_id=None, extra_params: dict = None
+    ):
         """
         Create a document by file.
 
@@ -231,15 +290,22 @@ class KnowledgeBaseClient(DifyClient):
         :return: Response from the API
         """
         files = {"file": open(file_path, "rb")}
-        data = {"process_rule": {"mode": "automatic"}, "indexing_technique": "high_quality"}
+        data = {
+            "process_rule": {"mode": "automatic"},
+            "indexing_technique": "high_quality",
+        }
         if extra_params is not None and isinstance(extra_params, dict):
             data.update(extra_params)
         if original_document_id is not None:
             data["original_document_id"] = original_document_id
         url = f"/datasets/{self._get_dataset_id()}/document/create_by_file"
-        return self._send_request_with_files("POST", url, {"data": json.dumps(data)}, files)
+        return self._send_request_with_files(
+            "POST", url, {"data": json.dumps(data)}, files
+        )
 
-    def update_document_by_file(self, document_id, file_path, extra_params: dict = None):
+    def update_document_by_file(
+        self, document_id, file_path, extra_params: dict = None
+    ):
         """
         Update a document by file.
 
@@ -269,8 +335,12 @@ class KnowledgeBaseClient(DifyClient):
         data = {}
         if extra_params is not None and isinstance(extra_params, dict):
             data.update(extra_params)
-        url = f"/datasets/{self._get_dataset_id()}/documents/{document_id}/update_by_file"
-        return self._send_request_with_files("POST", url, {"data": json.dumps(data)}, files)
+        url = (
+            f"/datasets/{self._get_dataset_id()}/documents/{document_id}/update_by_file"
+        )
+        return self._send_request_with_files(
+            "POST", url, {"data": json.dumps(data)}, files
+        )
 
     def batch_indexing_status(self, batch_id: str, **kwargs):
         """
@@ -301,7 +371,9 @@ class KnowledgeBaseClient(DifyClient):
         url = f"/datasets/{self._get_dataset_id()}/documents/{document_id}"
         return self._send_request("DELETE", url)
 
-    def list_documents(self, page: int = None, page_size: int = None, keyword: str = None, **kwargs):
+    def list_documents(
+        self, page: int = None, page_size: int = None, keyword: str = None, **kwargs
+    ):
         """
         Get a list of documents in this dataset.
 
@@ -329,7 +401,9 @@ class KnowledgeBaseClient(DifyClient):
         url = f"/datasets/{self._get_dataset_id()}/documents/{document_id}/segments"
         return self._send_request("POST", url, json=data, **kwargs)
 
-    def query_segments(self, document_id, keyword: str = None, status: str = None, **kwargs):
+    def query_segments(
+        self, document_id, keyword: str = None, status: str = None, **kwargs
+    ):
         """
         Query segments in this document.
 

sdks/python-client/setup.py

@@ -12,17 +12,15 @@ setup(
     long_description=long_description,
     long_description_content_type="text/markdown",
     url="https://github.com/langgenius/dify",
-    license='MIT',
-    packages=['dify_client'],
+    license="MIT",
+    packages=["dify_client"],
     classifiers=[
         "Programming Language :: Python :: 3",
         "License :: OSI Approved :: MIT License",
         "Operating System :: OS Independent",
     ],
     python_requires=">=3.6",
-    install_requires=[
-        "requests"
-    ],
-    keywords='dify nlp ai language-processing',
+    install_requires=["requests"],
+    keywords="dify nlp ai language-processing",
     include_package_data=True,
 )

sdks/python-client/tests/test_client.py

@@ -2,7 +2,12 @@ import os
 import time
 import unittest
 
-from dify_client.client import ChatClient, CompletionClient, DifyClient, KnowledgeBaseClient
+from dify_client.client import (
+    ChatClient,
+    CompletionClient,
+    DifyClient,
+    KnowledgeBaseClient,
+)
 
 API_KEY = os.environ.get("API_KEY")
 APP_ID = os.environ.get("APP_ID")
@@ -13,7 +18,9 @@ FILE_PATH_BASE = os.path.dirname(__file__)
 class TestKnowledgeBaseClient(unittest.TestCase):
     def setUp(self):
         self.knowledge_base_client = KnowledgeBaseClient(API_KEY, base_url=API_BASE_URL)
-        self.README_FILE_PATH = os.path.abspath(os.path.join(FILE_PATH_BASE, "../README.md"))
+        self.README_FILE_PATH = os.path.abspath(
+            os.path.join(FILE_PATH_BASE, "../README.md")
+        )
         self.dataset_id = None
         self.document_id = None
         self.segment_id = None
@@ -21,7 +28,9 @@ class TestKnowledgeBaseClient(unittest.TestCase):
 
     def _get_dataset_kb_client(self):
         self.assertIsNotNone(self.dataset_id)
-        return KnowledgeBaseClient(API_KEY, base_url=API_BASE_URL, dataset_id=self.dataset_id)
+        return KnowledgeBaseClient(
+            API_KEY, base_url=API_BASE_URL, dataset_id=self.dataset_id
+        )
 
     def test_001_create_dataset(self):
         response = self.knowledge_base_client.create_dataset(name="test_dataset")
@@ -67,7 +76,9 @@ class TestKnowledgeBaseClient(unittest.TestCase):
     def _test_004_update_document_by_text(self):
         client = self._get_dataset_kb_client()
         self.assertIsNotNone(self.document_id)
-        response = client.update_document_by_text(self.document_id, "test_document_updated", "test_text_updated")
+        response = client.update_document_by_text(
+            self.document_id, "test_document_updated", "test_text_updated"
+        )
         data = response.json()
         self.assertIn("document", data)
         self.assertIn("batch", data)
@@ -82,7 +93,9 @@ class TestKnowledgeBaseClient(unittest.TestCase):
     def _test_006_update_document_by_file(self):
         client = self._get_dataset_kb_client()
         self.assertIsNotNone(self.document_id)
-        response = client.update_document_by_file(self.document_id, self.README_FILE_PATH)
+        response = client.update_document_by_file(
+            self.document_id, self.README_FILE_PATH
+        )
         data = response.json()
         self.assertIn("document", data)
         self.assertIn("batch", data)
@@ -112,9 +125,9 @@ class TestKnowledgeBaseClient(unittest.TestCase):
 
     def _test_010_add_segments(self):
         client = self._get_dataset_kb_client()
-        response = client.add_segments(self.document_id, [
-            {"content": "test text segment 1"}
-        ])
+        response = client.add_segments(
+            self.document_id, [{"content": "test text segment 1"}]
+        )
         data = response.json()
         self.assertIn("data", data)
         self.assertGreater(len(data["data"]), 0)
@@ -131,9 +144,11 @@ class TestKnowledgeBaseClient(unittest.TestCase):
     def _test_012_update_document_segment(self):
         client = self._get_dataset_kb_client()
         self.assertIsNotNone(self.segment_id)
-        response = client.update_document_segment(self.document_id, self.segment_id,
-                                                  {"content": "test text segment 1 updated"}
-                                                  )
+        response = client.update_document_segment(
+            self.document_id,
+            self.segment_id,
+            {"content": "test text segment 1 updated"},
+        )
         data = response.json()
         self.assertIn("data", data)
         self.assertGreater(len(data["data"]), 0)
@@ -159,29 +174,37 @@ class TestChatClient(unittest.TestCase):
         self.chat_client = ChatClient(API_KEY)
 
     def test_create_chat_message(self):
-        response = self.chat_client.create_chat_message({}, "Hello, World!", "test_user")
+        response = self.chat_client.create_chat_message(
+            {}, "Hello, World!", "test_user"
+        )
         self.assertIn("answer", response.text)
 
     def test_create_chat_message_with_vision_model_by_remote_url(self):
-        files = [{
-            "type": "image",
-            "transfer_method": "remote_url",
-            "url": "your_image_url"
-        }]
-        response = self.chat_client.create_chat_message({}, "Describe the picture.", "test_user", files=files)
+        files = [
+            {"type": "image", "transfer_method": "remote_url", "url": "your_image_url"}
+        ]
+        response = self.chat_client.create_chat_message(
+            {}, "Describe the picture.", "test_user", files=files
+        )
         self.assertIn("answer", response.text)
 
     def test_create_chat_message_with_vision_model_by_local_file(self):
-        files = [{
-            "type": "image",
-            "transfer_method": "local_file",
-            "upload_file_id": "your_file_id"
-        }]
-        response = self.chat_client.create_chat_message({}, "Describe the picture.", "test_user", files=files)
+        files = [
+            {
+                "type": "image",
+                "transfer_method": "local_file",
+                "upload_file_id": "your_file_id",
+            }
+        ]
+        response = self.chat_client.create_chat_message(
+            {}, "Describe the picture.", "test_user", files=files
+        )
         self.assertIn("answer", response.text)
 
     def test_get_conversation_messages(self):
-        response = self.chat_client.get_conversation_messages("test_user", "your_conversation_id")
+        response = self.chat_client.get_conversation_messages(
+            "test_user", "your_conversation_id"
+        )
         self.assertIn("answer", response.text)
 
     def test_get_conversations(self):
@@ -194,28 +217,31 @@ class TestCompletionClient(unittest.TestCase):
         self.completion_client = CompletionClient(API_KEY)
 
     def test_create_completion_message(self):
-        response = self.completion_client.create_completion_message({"query": "What's the weather like today?"},
-                                                                    "blocking", "test_user")
+        response = self.completion_client.create_completion_message(
+            {"query": "What's the weather like today?"}, "blocking", "test_user"
+        )
         self.assertIn("answer", response.text)
 
     def test_create_completion_message_with_vision_model_by_remote_url(self):
-        files = [{
-            "type": "image",
-            "transfer_method": "remote_url",
-            "url": "your_image_url"
-        }]
+        files = [
+            {"type": "image", "transfer_method": "remote_url", "url": "your_image_url"}
+        ]
         response = self.completion_client.create_completion_message(
-            {"query": "Describe the picture."}, "blocking", "test_user", files)
+            {"query": "Describe the picture."}, "blocking", "test_user", files
+        )
         self.assertIn("answer", response.text)
 
     def test_create_completion_message_with_vision_model_by_local_file(self):
-        files = [{
-            "type": "image",
-            "transfer_method": "local_file",
-            "upload_file_id": "your_file_id"
-        }]
+        files = [
+            {
+                "type": "image",
+                "transfer_method": "local_file",
+                "upload_file_id": "your_file_id",
+            }
+        ]
         response = self.completion_client.create_completion_message(
-            {"query": "Describe the picture."}, "blocking", "test_user", files)
+            {"query": "Describe the picture."}, "blocking", "test_user", files
+        )
         self.assertIn("answer", response.text)
 
 
@@ -224,7 +250,9 @@ class TestDifyClient(unittest.TestCase):
         self.dify_client = DifyClient(API_KEY)
 
     def test_message_feedback(self):
-        response = self.dify_client.message_feedback("your_message_id", 'like', "test_user")
+        response = self.dify_client.message_feedback(
+            "your_message_id", "like", "test_user"
+        )
         self.assertIn("success", response.text)
 
     def test_get_application_parameters(self):
@@ -237,9 +265,7 @@ class TestDifyClient(unittest.TestCase):
         mime_type = "image/jpeg"
 
         with open(file_path, "rb") as file:
-            files = {
-                "file": (file_name, file, mime_type)
-            }
+            files = {"file": (file_name, file, mime_type)}
             response = self.dify_client.file_upload("test_user", files)
             self.assertIn("name", response.text)
 

web/.eslintrc.json

@@ -1,7 +1,8 @@
 {
   "extends": [
     "next",
-    "@antfu"
+    "@antfu",
+    "plugin:storybook/recommended"
   ],
   "rules": {
     "@typescript-eslint/consistent-type-definitions": [

web/.gitignore

@@ -49,4 +49,5 @@ package-lock.json
 # pmpm
 pnpm-lock.yaml
 
-.favorites.json
+.favorites.json
+*storybook.log

web/.storybook/main.ts

@@ -0,0 +1,19 @@
+import type { StorybookConfig } from '@storybook/nextjs'
+
+const config: StorybookConfig = {
+    // stories: ['../stories/**/*.mdx', '../stories/**/*.stories.@(js|jsx|mjs|ts|tsx)'],
+    stories: ['../app/components/**/*.stories.@(js|jsx|mjs|ts|tsx)'],
+    addons: [
+        '@storybook/addon-onboarding',
+        '@storybook/addon-links',
+        '@storybook/addon-essentials',
+        '@chromatic-com/storybook',
+        '@storybook/addon-interactions',
+    ],
+    framework: {
+        name: '@storybook/nextjs',
+        options: {},
+    },
+    staticDirs: ['../public'],
+}
+export default config

web/.storybook/preview.tsx

@@ -0,0 +1,37 @@
+import React from 'react'
+import type { Preview } from '@storybook/react'
+import { withThemeByDataAttribute } from '@storybook/addon-themes';
+import I18nServer from '../app/components/i18n-server'
+
+import '../app/styles/globals.css'
+import '../app/styles/markdown.scss'
+import './storybook.css'
+
+export const decorators = [
+    withThemeByDataAttribute({
+      themes: {
+        light: 'light',
+        dark: 'dark',
+      },
+      defaultTheme: 'light',
+      attributeName: 'data-theme',
+    }),
+    Story => {
+      return <I18nServer>
+        <Story />
+      </I18nServer>
+    }
+  ];
+
+const preview: Preview = {
+  parameters: {
+        controls: {
+            matchers: {
+                color: /(background|color)$/i,
+                date: /Date$/i,
+            },
+        },
+    },
+}
+
+export default preview

web/.storybook/storybook.css

@@ -0,0 +1,6 @@
+html,
+body {
+  max-width: unset;
+  overflow: auto;
+  user-select: text;
+}

web/README.md

@@ -74,6 +74,18 @@ If you want to customize the host and port:
 npm run start --port=3001 --host=0.0.0.0
 ```
 
+## Storybook
+
+This project uses [Storybook](https://storybook.js.org/) for UI component development.
+
+To start the storybook server, run:
+
+```bash
+yarn storybook
+```
+
+Open [http://localhost:6006](http://localhost:6006) with your browser to see the result.
+
 ## Lint Code
 
 If your IDE is VSCode, rename `web/.vscode/settings.example.json` to `web/.vscode/settings.json` for lint code setting.

web/app/(commonLayout)/datasets/template/template.en.mdx

@@ -1050,6 +1050,151 @@ import { Row, Col, Properties, Property, Heading, SubProperty, Paragraph } from
 
 ---
 
+<Heading
+  url='/datasets/{dataset_id}/hit_testing'
+  method='POST'
+  title='Dataset hit testing'
+  name='#dataset_hit_testing'
+/>
+<Row>
+  <Col>
+    ### Path
+    <Properties>
+      <Property name='dataset_id' type='string' key='dataset_id'>
+        Dataset ID
+      </Property>
+    </Properties>
+
+    ### Request Body
+    <Properties>
+      <Property name='query' type='string' key='query'>
+        retrieval keywordc
+      </Property>
+      <Property name='retrieval_model' type='object' key='retrieval_model'>
+        retrieval keyword(Optional, if not filled, it will be recalled according to the default method)
+        - <code>search_method</code> (text) Search method: One of the following four keywords is required
+          - <code>keyword_search</code> Keyword search
+          - <code>semantic_search</code> Semantic search
+          - <code>full_text_search</code> Full-text search
+          - <code>hybrid_search</code> Hybrid search
+        - <code>reranking_enable</code> (bool) Whether to enable reranking, optional, required if the search mode is semantic_search or hybrid_search
+        - <code>reranking_mode</code> (object) Rerank model configuration, optional, required if reranking is enabled
+            - <code>reranking_provider_name</code> (string) Rerank model provider
+            - <code>reranking_model_name</code> (string) Rerank model name
+        - <code>weights</code> (double) Semantic search weight setting in hybrid search mode
+        - <code>top_k</code> (integer) Number of results to return, optional
+        - <code>score_threshold_enabled</code> (bool) Whether to enable score threshold
+        - <code>score_threshold</code> (double) Score threshold
+      </Property>
+      <Property name='external_retrieval_model' type='object' key='external_retrieval_model'>
+          Unused field
+      </Property>
+    </Properties>
+  </Col>
+  <Col sticky>
+    <CodeGroup
+      title="Request"
+      tag="POST"
+      label="/datasets/{dataset_id}/hit_testing"
+      targetCode={`curl --location --request GET '${props.apiBaseUrl}/datasets/{dataset_id}/hit_testing' \\\n--header 'Authorization: Bearer {api_key}'\\\n--header 'Content-Type: application/json'\\\n--data-raw '{
+        "query": "test",
+        "retrieval_model": {
+            "search_method": "keyword_search",
+            "reranking_enable": false,
+            "reranking_mode": null,
+            "reranking_model": {
+                "reranking_provider_name": "",
+                "reranking_model_name": ""
+            },
+            "weights": null,
+            "top_k": 1,
+            "score_threshold_enabled": false,
+            "score_threshold": null
+        }
+    }'`}
+    >
+    ```bash {{ title: 'cURL' }}
+    curl --location --request POST '${props.apiBaseUrl}/datasets/{dataset_id}/hit_testing' \
+    --header 'Authorization: Bearer {api_key}' \
+    --header 'Content-Type: application/json' \
+    --data-raw '{
+        "query": "test",
+        "retrieval_model": {
+            "search_method": "keyword_search",
+            "reranking_enable": false,
+            "reranking_mode": null,
+            "reranking_model": {
+                "reranking_provider_name": "",
+                "reranking_model_name": ""
+            },
+            "weights": null,
+            "top_k": 2,
+            "score_threshold_enabled": false,
+            "score_threshold": null
+        }
+    }'
+    ```
+    </CodeGroup>
+    <CodeGroup title="Response">
+    ```json {{ title: 'Response' }}
+    {
+      "query": {
+        "content": "test"
+      },
+      "records": [
+        {
+          "segment": {
+            "id": "7fa6f24f-8679-48b3-bc9d-bdf28d73f218",
+            "position": 1,
+            "document_id": "a8c6c36f-9f5d-4d7a-8472-f5d7b75d71d2",
+            "content": "Operation guide",
+            "answer": null,
+            "word_count": 847,
+            "tokens": 280,
+            "keywords": [
+              "install",
+              "java",
+              "base",
+              "scripts",
+              "jdk",
+              "manual",
+              "internal",
+              "opens",
+              "add",
+              "vmoptions"
+            ],
+            "index_node_id": "39dd8443-d960-45a8-bb46-7275ad7fbc8e",
+            "index_node_hash": "0189157697b3c6a418ccf8264a09699f25858975578f3467c76d6bfc94df1d73",
+            "hit_count": 0,
+            "enabled": true,
+            "disabled_at": null,
+            "disabled_by": null,
+            "status": "completed",
+            "created_by": "dbcb1ab5-90c8-41a7-8b78-73b235eb6f6f",
+            "created_at": 1728734540,
+            "indexing_at": 1728734552,
+            "completed_at": 1728734584,
+            "error": null,
+            "stopped_at": null,
+            "document": {
+              "id": "a8c6c36f-9f5d-4d7a-8472-f5d7b75d71d2",
+              "data_source_type": "upload_file",
+              "name": "readme.txt",
+              "doc_type": null
+            }
+          },
+          "score": 3.730463140527718e-05,
+          "tsne_position": null
+        }
+      ]
+    }
+    ```
+    </CodeGroup>
+  </Col>
+</Row>
+
+---
+
 <Row>
   <Col>
     ### Error message

web/app/(commonLayout)/datasets/template/template.zh.mdx

@@ -1049,6 +1049,152 @@ import { Row, Col, Properties, Property, Heading, SubProperty, Paragraph } from
   </Col>
 </Row>
 
+---
+
+<Heading
+  url='/datasets/{dataset_id}/hit_testing'
+  method='POST'
+  title='知识库召回测试'
+  name='#dataset_hit_testing'
+/>
+<Row>
+  <Col>
+    ### Path
+    <Properties>
+      <Property name='dataset_id' type='string' key='dataset_id'>
+        知识库 ID
+      </Property>
+    </Properties>
+
+    ### Request Body
+    <Properties>
+      <Property name='query' type='string' key='query'>
+        召回关键词
+      </Property>
+      <Property name='retrieval_model' type='object' key='retrieval_model'>
+        召回参数(选填，如不填，按照默认方式召回)
+        - <code>search_method</code> (text) 检索方法：以下三个关键字之一，必填
+          - <code>keyword_search</code> 关键字检索
+          - <code>semantic_search</code> 语义检索
+          - <code>full_text_search</code> 全文检索
+          - <code>hybrid_search</code> 混合检索
+        - <code>reranking_enable</code> (bool) 是否启用 Reranking，非必填，如果检索模式为semantic_search模式或者hybrid_search则传值
+        - <code>reranking_mode</code> (object) Rerank模型配置，非必填，如果启用了 reranking 则传值
+            - <code>reranking_provider_name</code> (string) Rerank 模型提供商
+            - <code>reranking_model_name</code> (string) Rerank 模型名称
+        - <code>weights</code> (double) 混合检索模式下语意检索的权重设置
+        - <code>top_k</code> (integer) 返回结果数量，非必填
+        - <code>score_threshold_enabled</code> (bool) 是否开启Score阈值
+        - <code>score_threshold</code> (double) Score阈值
+      </Property>
+      <Property name='external_retrieval_model' type='object' key='external_retrieval_model'>
+          未启用字段
+      </Property>
+    </Properties>
+  </Col>
+  <Col sticky>
+    <CodeGroup
+      title="Request"
+      tag="POST"
+      label="/datasets/{dataset_id}/hit_testing"
+      targetCode={`curl --location --request GET '${props.apiBaseUrl}/datasets/{dataset_id}/hit_testing' \\\n--header 'Authorization: Bearer {api_key}'\\\n--header 'Content-Type: application/json'\\\n--data-raw '{
+        "query": "test",
+        "retrieval_model": {
+            "search_method": "keyword_search",
+            "reranking_enable": false,
+            "reranking_mode": null,
+            "reranking_model": {
+                "reranking_provider_name": "",
+                "reranking_model_name": ""
+            },
+            "weights": null,
+            "top_k": 1,
+            "score_threshold_enabled": false,
+            "score_threshold": null
+        }
+    }'`}
+    >
+    ```bash {{ title: 'cURL' }}
+    curl --location --request POST '${props.apiBaseUrl}/datasets/{dataset_id}/hit_testing' \
+    --header 'Authorization: Bearer {api_key}' \
+    --header 'Content-Type: application/json' \
+    --data-raw '{
+        "query": "test",
+        "retrieval_model": {
+            "search_method": "keyword_search",
+            "reranking_enable": false,
+            "reranking_mode": null,
+            "reranking_model": {
+                "reranking_provider_name": "",
+                "reranking_model_name": ""
+            },
+            "weights": null,
+            "top_k": 2,
+            "score_threshold_enabled": false,
+            "score_threshold": null
+        }
+    }'
+    ```
+    </CodeGroup>
+    <CodeGroup title="Response">
+    ```json {{ title: 'Response' }}
+    {
+      "query": {
+        "content": "test"
+      },
+      "records": [
+        {
+          "segment": {
+            "id": "7fa6f24f-8679-48b3-bc9d-bdf28d73f218",
+            "position": 1,
+            "document_id": "a8c6c36f-9f5d-4d7a-8472-f5d7b75d71d2",
+            "content": "Operation guide",
+            "answer": null,
+            "word_count": 847,
+            "tokens": 280,
+            "keywords": [
+              "install",
+              "java",
+              "base",
+              "scripts",
+              "jdk",
+              "manual",
+              "internal",
+              "opens",
+              "add",
+              "vmoptions"
+            ],
+            "index_node_id": "39dd8443-d960-45a8-bb46-7275ad7fbc8e",
+            "index_node_hash": "0189157697b3c6a418ccf8264a09699f25858975578f3467c76d6bfc94df1d73",
+            "hit_count": 0,
+            "enabled": true,
+            "disabled_at": null,
+            "disabled_by": null,
+            "status": "completed",
+            "created_by": "dbcb1ab5-90c8-41a7-8b78-73b235eb6f6f",
+            "created_at": 1728734540,
+            "indexing_at": 1728734552,
+            "completed_at": 1728734584,
+            "error": null,
+            "stopped_at": null,
+            "document": {
+              "id": "a8c6c36f-9f5d-4d7a-8472-f5d7b75d71d2",
+              "data_source_type": "upload_file",
+              "name": "readme.txt",
+              "doc_type": null
+            }
+          },
+          "score": 3.730463140527718e-05,
+          "tsne_position": null
+        }
+      ]
+    }
+    ```
+    </CodeGroup>
+  </Col>
+</Row>
+
+
 ---
 
 <Row>

web/app/components/base/button/index.stories.tsx

@@ -0,0 +1,107 @@
+import type { Meta, StoryObj } from '@storybook/react'
+import { fn } from '@storybook/test'
+
+import { RocketLaunchIcon } from '@heroicons/react/20/solid'
+import { Button } from '.'
+
+const meta = {
+  title: 'Base/Button',
+  component: Button,
+  parameters: {
+    layout: 'centered',
+  },
+  tags: ['autodocs'],
+  argTypes: {
+    loading: { control: 'boolean' },
+    variant: {
+      control: 'select',
+      options: ['primary', 'warning', 'secondary', 'secondary-accent', 'ghost', 'ghost-accent', 'tertiary'],
+    },
+  },
+  args: {
+    variant: 'ghost',
+    onClick: fn(),
+    children: 'adsf',
+  },
+} satisfies Meta<typeof Button>
+
+export default meta
+type Story = StoryObj<typeof meta>
+
+export const Default: Story = {
+  args: {
+    variant: 'primary',
+    loading: false,
+    children: 'Primary Button',
+  },
+}
+
+export const Secondary: Story = {
+  args: {
+    variant: 'secondary',
+    children: 'Secondary Button',
+  },
+}
+
+export const SecondaryAccent: Story = {
+  args: {
+    variant: 'secondary-accent',
+    children: 'Secondary Accent Button',
+  },
+}
+
+export const Ghost: Story = {
+  args: {
+    variant: 'ghost',
+    children: 'Ghost Button',
+  },
+}
+
+export const GhostAccent: Story = {
+  args: {
+    variant: 'ghost-accent',
+    children: 'Ghost Accent Button',
+  },
+}
+
+export const Tertiary: Story = {
+  args: {
+    variant: 'tertiary',
+    children: 'Tertiary Button',
+  },
+}
+
+export const Warning: Story = {
+  args: {
+    variant: 'warning',
+    children: 'Warning Button',
+  },
+}
+
+export const Disabled: Story = {
+  args: {
+    variant: 'primary',
+    disabled: true,
+    children: 'Disabled Button',
+  },
+}
+
+export const Loading: Story = {
+  args: {
+    variant: 'primary',
+    loading: true,
+    children: 'Loading Button',
+  },
+}
+
+export const WithIcon: Story = {
+  args: {
+    variant: 'primary',
+    children: (
+      <>
+        <RocketLaunchIcon className="h-4 w-4 mr-1.5 stroke-[1.8px]" />
+        Launch
+      </>
+    ),
+  },
+}

web/app/components/base/chat/chat-with-history/header-in-mobile.tsx

@@ -28,6 +28,8 @@ const HeaderInMobile = () => {
             className='mr-2'
             size='tiny'
             icon={appData?.site.icon}
+            iconType={appData?.site.icon_type}
+            imageUrl={appData?.site.icon_url}
             background={appData?.site.icon_background}
           />
           <div className='py-1 text-base font-semibold text-gray-800 truncate'>