chore: apply ruff's pyupgrade linter rules to modernize Python code with targeted version (#2419)

api/app.py

-# -*- coding:utf-8 -*-
 import os
 
 from werkzeug.exceptions import Unauthorized

api/config.py

-# -*- coding:utf-8 -*-
 import os
 
 import dotenv

api/controllers/console/app/app.py

-# -*- coding:utf-8 -*-
 import json
 import logging
 from datetime import datetime

api/controllers/console/app/audio.py

-# -*- coding:utf-8 -*-
 import logging
 
 from flask import request

api/controllers/console/app/completion.py

-# -*- coding:utf-8 -*-
 import json
 import logging
-from typing import Generator, Union
+from collections.abc import Generator
+from typing import Union
 
 import flask_login
 from flask import Response, stream_with_context
@@ -169,8 +169,7 @@ def compact_response(response: Union[dict, Generator]) -> Response:
         return Response(response=json.dumps(response), status=200, mimetype='application/json')
     else:
         def generate() -> Generator:
-            for chunk in response:
-                yield chunk
+            yield from response
 
         return Response(stream_with_context(generate()), status=200,
                         mimetype='text/event-stream')

api/controllers/console/app/message.py

 import json
 import logging
-from typing import Generator, Union
+from collections.abc import Generator
+from typing import Union
 
 from flask import Response, stream_with_context
 from flask_login import current_user
@@ -246,8 +247,7 @@ def compact_response(response: Union[dict, Generator]) -> Response:
         return Response(response=json.dumps(response), status=200, mimetype='application/json')
     else:
         def generate() -> Generator:
-            for chunk in response:
-                yield chunk
+            yield from response
 
         return Response(stream_with_context(generate()), status=200,
                         mimetype='text/event-stream')

api/controllers/console/app/model_config.py

-# -*- coding:utf-8 -*-
 
 from flask import request
 from flask_login import current_user

api/controllers/console/app/site.py

-# -*- coding:utf-8 -*-
 from flask_login import current_user
 from flask_restful import Resource, marshal_with, reqparse
 from werkzeug.exceptions import Forbidden, NotFound

api/controllers/console/app/statistic.py

-# -*- coding:utf-8 -*-
 from datetime import datetime
 from decimal import Decimal
 

api/controllers/console/auth/login.py

-# -*- coding:utf-8 -*-
 import flask_login
 from flask import current_app, request
 from flask_restful import Resource, reqparse

api/controllers/console/datasets/datasets.py

-# -*- coding:utf-8 -*-
 import flask_restful
 from flask import current_app, request
 from flask_login import current_user

api/controllers/console/datasets/datasets_document.py

-# -*- coding:utf-8 -*-
 from datetime import datetime
-from typing import List
 
 from flask import request
 from flask_login import current_user
@@ -71,7 +69,7 @@ class DocumentResource(Resource):
 
         return document
 
-    def get_batch_documents(self, dataset_id: str, batch: str) -> List[Document]:
+    def get_batch_documents(self, dataset_id: str, batch: str) -> list[Document]:
         dataset = DatasetService.get_dataset(dataset_id)
         if not dataset:
             raise NotFound('Dataset not found.')

api/controllers/console/datasets/datasets_segments.py

-# -*- coding:utf-8 -*-
 import uuid
 from datetime import datetime
 

api/controllers/console/explore/audio.py

-# -*- coding:utf-8 -*-
 import logging
 
 from flask import request

api/controllers/console/explore/completion.py

-# -*- coding:utf-8 -*-
 import json
 import logging
+from collections.abc import Generator
 from datetime import datetime
-from typing import Generator, Union
+from typing import Union
 
 from flask import Response, stream_with_context
 from flask_login import current_user
@@ -164,8 +164,7 @@ def compact_response(response: Union[dict, Generator]) -> Response:
         return Response(response=json.dumps(response), status=200, mimetype='application/json')
     else:
         def generate() -> Generator:
-            for chunk in response:
-                yield chunk
+            yield from response
 
         return Response(stream_with_context(generate()), status=200,
                         mimetype='text/event-stream')

api/controllers/console/explore/conversation.py

-# -*- coding:utf-8 -*-
 from flask_login import current_user
 from flask_restful import marshal_with, reqparse
 from flask_restful.inputs import int_range

api/controllers/console/explore/error.py

-# -*- coding:utf-8 -*-
 from libs.exception import BaseHTTPException
 
 

api/controllers/console/explore/installed_app.py

-# -*- coding:utf-8 -*-
 from datetime import datetime
 
 from flask_login import current_user

api/controllers/console/explore/message.py

-# -*- coding:utf-8 -*-
 import json
 import logging
-from typing import Generator, Union
+from collections.abc import Generator
+from typing import Union
 
 from flask import Response, stream_with_context
 from flask_login import current_user
@@ -123,8 +123,7 @@ def compact_response(response: Union[dict, Generator]) -> Response:
         return Response(response=json.dumps(response), status=200, mimetype='application/json')
     else:
         def generate() -> Generator:
-            for chunk in response:
-                yield chunk
+            yield from response
 
         return Response(stream_with_context(generate()), status=200,
                         mimetype='text/event-stream')

api/controllers/console/explore/parameter.py

-# -*- coding:utf-8 -*-
 import json
 
 from flask import current_app

api/controllers/console/explore/recommended_app.py

-# -*- coding:utf-8 -*-
 from flask_login import current_user
 from flask_restful import Resource, fields, marshal_with
 from sqlalchemy import and_

api/controllers/console/setup.py

-# -*- coding:utf-8 -*-
 from functools import wraps
 
 from flask import current_app, request

api/controllers/console/version.py

-# -*- coding:utf-8 -*-
 
 import json
 import logging

api/controllers/console/workspace/account.py

-# -*- coding:utf-8 -*-
 from datetime import datetime
 
 import pytz

api/controllers/console/workspace/members.py

-# -*- coding:utf-8 -*-
 from flask import current_app
 from flask_login import current_user
 from flask_restful import Resource, abort, fields, marshal_with, reqparse

api/controllers/console/workspace/workspace.py

-# -*- coding:utf-8 -*-
 import logging
 
 from flask import request

api/controllers/console/wraps.py

-# -*- coding:utf-8 -*-
 import json
 from functools import wraps
 

api/controllers/service_api/app/app.py

-# -*- coding:utf-8 -*-
 import json
 
 from flask import current_app

api/controllers/service_api/app/completion.py

 import json
 import logging
-from typing import Generator, Union
+from collections.abc import Generator
+from typing import Union
 
 from flask import Response, stream_with_context
 from flask_restful import reqparse
@@ -182,8 +183,7 @@ def compact_response(response: Union[dict, Generator]) -> Response:
         return Response(response=json.dumps(response), status=200, mimetype='application/json')
     else:
         def generate() -> Generator:
-            for chunk in response:
-                yield chunk
+            yield from response
 
         return Response(stream_with_context(generate()), status=200,
                         mimetype='text/event-stream')

api/controllers/service_api/app/conversation.py

-# -*- coding:utf-8 -*-
 from flask import request
 from flask_restful import marshal_with, reqparse
 from flask_restful.inputs import int_range

api/controllers/service_api/app/error.py

-# -*- coding:utf-8 -*-
 from libs.exception import BaseHTTPException
 
 

api/controllers/service_api/app/message.py

-# -*- coding:utf-8 -*-
 from flask_restful import fields, marshal_with, reqparse
 from flask_restful.inputs import int_range
 from werkzeug.exceptions import NotFound

api/controllers/service_api/wraps.py

-# -*- coding:utf-8 -*-
 from datetime import datetime
 from functools import wraps
 

api/controllers/web/app.py

-# -*- coding:utf-8 -*-
 import json
 
 from flask import current_app

api/controllers/web/audio.py

-# -*- coding:utf-8 -*-
 import logging
 
 from flask import request

api/controllers/web/completion.py

-# -*- coding:utf-8 -*-
 import json
 import logging
-from typing import Generator, Union
+from collections.abc import Generator
+from typing import Union
 
 from flask import Response, stream_with_context
 from flask_restful import reqparse
@@ -154,8 +154,7 @@ def compact_response(response: Union[dict, Generator]) -> Response:
         return Response(response=json.dumps(response), status=200, mimetype='application/json')
     else:
         def generate() -> Generator:
-            for chunk in response:
-                yield chunk
+            yield from response
 
         return Response(stream_with_context(generate()), status=200,
                         mimetype='text/event-stream')

api/controllers/web/conversation.py

-# -*- coding:utf-8 -*-
 from flask_restful import marshal_with, reqparse
 from flask_restful.inputs import int_range
 from werkzeug.exceptions import NotFound

api/controllers/web/error.py

-# -*- coding:utf-8 -*-
 from libs.exception import BaseHTTPException
 
 

api/controllers/web/message.py

-# -*- coding:utf-8 -*-
 import json
 import logging
-from typing import Generator, Union
+from collections.abc import Generator
+from typing import Union
 
 from flask import Response, stream_with_context
 from flask_restful import fields, marshal_with, reqparse
@@ -160,8 +160,7 @@ def compact_response(response: Union[dict, Generator]) -> Response:
         return Response(response=json.dumps(response), status=200, mimetype='application/json')
     else:
         def generate() -> Generator:
-            for chunk in response:
-                yield chunk
+            yield from response
 
         return Response(stream_with_context(generate()), status=200,
                         mimetype='text/event-stream')

api/controllers/web/passport.py

-# -*- coding:utf-8 -*-
 import uuid
 
 from flask import request

api/controllers/web/site.py

-# -*- coding:utf-8 -*-
 
 from flask import current_app
 from flask_restful import fields, marshal_with

api/controllers/web/wraps.py

-# -*- coding:utf-8 -*-
 from functools import wraps
 
 from flask import request

api/core/agent/agent/agent_llm_callback.py

 import logging
-from typing import List, Optional
+from typing import Optional
 
 from core.callback_handler.agent_loop_gather_callback_handler import AgentLoopGatherCallbackHandler
 from core.model_runtime.callbacks.base_callback import Callback
@@ -17,7 +17,7 @@ class AgentLLMCallback(Callback):
 
     def on_before_invoke(self, llm_instance: AIModel, model: str, credentials: dict,
                          prompt_messages: list[PromptMessage], model_parameters: dict,
-                         tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None,
+                         tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
                          stream: bool = True, user: Optional[str] = None) -> None:
         """
         Before invoke callback
@@ -38,7 +38,7 @@ class AgentLLMCallback(Callback):
 
     def on_new_chunk(self, llm_instance: AIModel, chunk: LLMResultChunk, model: str, credentials: dict,
                      prompt_messages: list[PromptMessage], model_parameters: dict,
-                     tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None,
+                     tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
                      stream: bool = True, user: Optional[str] = None):
         """
         On new chunk callback
@@ -58,7 +58,7 @@ class AgentLLMCallback(Callback):
 
     def on_after_invoke(self, llm_instance: AIModel, result: LLMResult, model: str, credentials: dict,
                         prompt_messages: list[PromptMessage], model_parameters: dict,
-                        tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None,
+                        tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
                         stream: bool = True, user: Optional[str] = None) -> None:
         """
         After invoke callback
@@ -80,7 +80,7 @@ class AgentLLMCallback(Callback):
 
     def on_invoke_error(self, llm_instance: AIModel, ex: Exception, model: str, credentials: dict,
                         prompt_messages: list[PromptMessage], model_parameters: dict,
-                        tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None,
+                        tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
                         stream: bool = True, user: Optional[str] = None) -> None:
         """
         Invoke error callback

api/core/agent/agent/calc_token_mixin.py

-from typing import List, cast
+from typing import cast
 
 from core.entities.application_entities import ModelConfigEntity
 from core.model_runtime.entities.message_entities import PromptMessage
@@ -8,7 +8,7 @@ from core.model_runtime.model_providers.__base.large_language_model import Large
 
 class CalcTokenMixin:
 
-    def get_message_rest_tokens(self, model_config: ModelConfigEntity, messages: List[PromptMessage], **kwargs) -> int:
+    def get_message_rest_tokens(self, model_config: ModelConfigEntity, messages: list[PromptMessage], **kwargs) -> int:
         """
         Got the rest tokens available for the model after excluding messages tokens and completion max tokens
 

api/core/agent/agent/multi_dataset_router_agent.py

-from typing import Any, List, Optional, Sequence, Tuple, Union
+from collections.abc import Sequence
+from typing import Any, Optional, Union
 
 from langchain.agents import BaseSingleActionAgent, OpenAIFunctionsAgent
 from langchain.agents.openai_functions_agent.base import _format_intermediate_steps, _parse_ai_message
@@ -42,7 +43,7 @@ class MultiDatasetRouterAgent(OpenAIFunctionsAgent):
 
     def plan(
         self,
-        intermediate_steps: List[Tuple[AgentAction, str]],
+        intermediate_steps: list[tuple[AgentAction, str]],
         callbacks: Callbacks = None,
         **kwargs: Any,
     ) -> Union[AgentAction, AgentFinish]:
@@ -85,7 +86,7 @@ class MultiDatasetRouterAgent(OpenAIFunctionsAgent):
 
     def real_plan(
         self,
-        intermediate_steps: List[Tuple[AgentAction, str]],
+        intermediate_steps: list[tuple[AgentAction, str]],
         callbacks: Callbacks = None,
         **kwargs: Any,
     ) -> Union[AgentAction, AgentFinish]:
@@ -146,7 +147,7 @@ class MultiDatasetRouterAgent(OpenAIFunctionsAgent):
 
     async def aplan(
             self,
-            intermediate_steps: List[Tuple[AgentAction, str]],
+            intermediate_steps: list[tuple[AgentAction, str]],
             callbacks: Callbacks = None,
             **kwargs: Any,
     ) -> Union[AgentAction, AgentFinish]:
@@ -158,7 +159,7 @@ class MultiDatasetRouterAgent(OpenAIFunctionsAgent):
             model_config: ModelConfigEntity,
             tools: Sequence[BaseTool],
             callback_manager: Optional[BaseCallbackManager] = None,
-            extra_prompt_messages: Optional[List[BaseMessagePromptTemplate]] = None,
+            extra_prompt_messages: Optional[list[BaseMessagePromptTemplate]] = None,
             system_message: Optional[SystemMessage] = SystemMessage(
                 content="You are a helpful AI assistant."
             ),

api/core/agent/agent/openai_function_call.py

-from typing import Any, List, Optional, Sequence, Tuple, Union
+from collections.abc import Sequence
+from typing import Any, Optional, Union
 
 from langchain.agents import BaseSingleActionAgent, OpenAIFunctionsAgent
 from langchain.agents.openai_functions_agent.base import _format_intermediate_steps, _parse_ai_message
@@ -51,7 +52,7 @@ class AutoSummarizingOpenAIFunctionCallAgent(OpenAIFunctionsAgent, CalcTokenMixi
             model_config: ModelConfigEntity,
             tools: Sequence[BaseTool],
             callback_manager: Optional[BaseCallbackManager] = None,
-            extra_prompt_messages: Optional[List[BaseMessagePromptTemplate]] = None,
+            extra_prompt_messages: Optional[list[BaseMessagePromptTemplate]] = None,
             system_message: Optional[SystemMessage] = SystemMessage(
                 content="You are a helpful AI assistant."
             ),
@@ -125,7 +126,7 @@ class AutoSummarizingOpenAIFunctionCallAgent(OpenAIFunctionsAgent, CalcTokenMixi
 
     def plan(
             self,
-            intermediate_steps: List[Tuple[AgentAction, str]],
+            intermediate_steps: list[tuple[AgentAction, str]],
             callbacks: Callbacks = None,
             **kwargs: Any,
     ) -> Union[AgentAction, AgentFinish]:
@@ -207,7 +208,7 @@ class AutoSummarizingOpenAIFunctionCallAgent(OpenAIFunctionsAgent, CalcTokenMixi
     def return_stopped_response(
             self,
             early_stopping_method: str,
-            intermediate_steps: List[Tuple[AgentAction, str]],
+            intermediate_steps: list[tuple[AgentAction, str]],
             **kwargs: Any,
     ) -> AgentFinish:
         try:
@@ -215,7 +216,7 @@ class AutoSummarizingOpenAIFunctionCallAgent(OpenAIFunctionsAgent, CalcTokenMixi
         except ValueError:
             return AgentFinish({"output": "I'm sorry, I don't know how to respond to that."}, "")
 
-    def summarize_messages_if_needed(self, messages: List[PromptMessage], **kwargs) -> List[PromptMessage]:
+    def summarize_messages_if_needed(self, messages: list[PromptMessage], **kwargs) -> list[PromptMessage]:
         # calculate rest tokens and summarize previous function observation messages if rest_tokens < 0
         rest_tokens = self.get_message_rest_tokens(
             self.model_config,
@@ -264,7 +265,7 @@ class AutoSummarizingOpenAIFunctionCallAgent(OpenAIFunctionsAgent, CalcTokenMixi
         return new_messages
 
     def predict_new_summary(
-        self, messages: List[BaseMessage], existing_summary: str
+        self, messages: list[BaseMessage], existing_summary: str
     ) -> str:
         new_lines = get_buffer_string(
             messages,
@@ -275,7 +276,7 @@ class AutoSummarizingOpenAIFunctionCallAgent(OpenAIFunctionsAgent, CalcTokenMixi
         chain = LLMChain(model_config=self.summary_model_config, prompt=SUMMARY_PROMPT)
         return chain.predict(summary=existing_summary, new_lines=new_lines)
 
-    def get_num_tokens_from_messages(self, model_config: ModelConfigEntity, messages: List[BaseMessage], **kwargs) -> int:
+    def get_num_tokens_from_messages(self, model_config: ModelConfigEntity, messages: list[BaseMessage], **kwargs) -> int:
         """Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
 
         Official documentation: https://github.com/openai/openai-cookbook/blob/

api/core/agent/agent/structed_multi_dataset_router_agent.py

 import re
-from typing import Any, List, Optional, Sequence, Tuple, Union, cast
+from collections.abc import Sequence
+from typing import Any, Optional, Union, cast
 
 from langchain import BasePromptTemplate, PromptTemplate
 from langchain.agents import Agent, AgentOutputParser, StructuredChatAgent
@@ -68,7 +69,7 @@ class StructuredMultiDatasetRouterAgent(StructuredChatAgent):
 
     def plan(
             self,
-            intermediate_steps: List[Tuple[AgentAction, str]],
+            intermediate_steps: list[tuple[AgentAction, str]],
             callbacks: Callbacks = None,
             **kwargs: Any,
     ) -> Union[AgentAction, AgentFinish]:
@@ -125,8 +126,8 @@ class StructuredMultiDatasetRouterAgent(StructuredChatAgent):
             suffix: str = SUFFIX,
             human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
             format_instructions: str = FORMAT_INSTRUCTIONS,
-            input_variables: Optional[List[str]] = None,
-            memory_prompts: Optional[List[BasePromptTemplate]] = None,
+            input_variables: Optional[list[str]] = None,
+            memory_prompts: Optional[list[BasePromptTemplate]] = None,
     ) -> BasePromptTemplate:
         tool_strings = []
         for tool in tools:
@@ -153,7 +154,7 @@ class StructuredMultiDatasetRouterAgent(StructuredChatAgent):
             tools: Sequence[BaseTool],
             prefix: str = PREFIX,
             format_instructions: str = FORMAT_INSTRUCTIONS,
-            input_variables: Optional[List[str]] = None,
+            input_variables: Optional[list[str]] = None,
     ) -> PromptTemplate:
         """Create prompt in the style of the zero shot agent.
 
@@ -180,7 +181,7 @@ Thought: {agent_scratchpad}
         return PromptTemplate(template=template, input_variables=input_variables)
 
     def _construct_scratchpad(
-            self, intermediate_steps: List[Tuple[AgentAction, str]]
+            self, intermediate_steps: list[tuple[AgentAction, str]]
     ) -> str:
         agent_scratchpad = ""
         for action, observation in intermediate_steps:
@@ -213,8 +214,8 @@ Thought: {agent_scratchpad}
             suffix: str = SUFFIX,
             human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
             format_instructions: str = FORMAT_INSTRUCTIONS,
-            input_variables: Optional[List[str]] = None,
-            memory_prompts: Optional[List[BasePromptTemplate]] = None,
+            input_variables: Optional[list[str]] = None,
+            memory_prompts: Optional[list[BasePromptTemplate]] = None,
             **kwargs: Any,
     ) -> Agent:
         """Construct an agent from an LLM and tools."""

api/core/agent/agent/structured_chat.py

 import re
-from typing import Any, List, Optional, Sequence, Tuple, Union, cast
+from collections.abc import Sequence
+from typing import Any, Optional, Union, cast
 
 from langchain import BasePromptTemplate, PromptTemplate
 from langchain.agents import Agent, AgentOutputParser, StructuredChatAgent
@@ -82,7 +83,7 @@ class AutoSummarizingStructuredChatAgent(StructuredChatAgent, CalcTokenMixin):
 
     def plan(
         self,
-        intermediate_steps: List[Tuple[AgentAction, str]],
+        intermediate_steps: list[tuple[AgentAction, str]],
         callbacks: Callbacks = None,
         **kwargs: Any,
     ) -> Union[AgentAction, AgentFinish]:
@@ -127,7 +128,7 @@ class AutoSummarizingStructuredChatAgent(StructuredChatAgent, CalcTokenMixin):
             return AgentFinish({"output": "I'm sorry, the answer of model is invalid, "
                                           "I don't know how to respond to that."}, "")
 
-    def summarize_messages(self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs):
+    def summarize_messages(self, intermediate_steps: list[tuple[AgentAction, str]], **kwargs):
         if len(intermediate_steps) >= 2 and self.summary_model_config:
             should_summary_intermediate_steps = intermediate_steps[self.moving_summary_index:-1]
             should_summary_messages = [AIMessage(content=observation)
@@ -154,7 +155,7 @@ class AutoSummarizingStructuredChatAgent(StructuredChatAgent, CalcTokenMixin):
         return self.get_full_inputs([intermediate_steps[-1]], **kwargs)
 
     def predict_new_summary(
-        self, messages: List[BaseMessage], existing_summary: str
+        self, messages: list[BaseMessage], existing_summary: str
     ) -> str:
         new_lines = get_buffer_string(
             messages,
@@ -173,8 +174,8 @@ class AutoSummarizingStructuredChatAgent(StructuredChatAgent, CalcTokenMixin):
             suffix: str = SUFFIX,
             human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
             format_instructions: str = FORMAT_INSTRUCTIONS,
-            input_variables: Optional[List[str]] = None,
-            memory_prompts: Optional[List[BasePromptTemplate]] = None,
+            input_variables: Optional[list[str]] = None,
+            memory_prompts: Optional[list[BasePromptTemplate]] = None,
     ) -> BasePromptTemplate:
         tool_strings = []
         for tool in tools:
@@ -200,7 +201,7 @@ class AutoSummarizingStructuredChatAgent(StructuredChatAgent, CalcTokenMixin):
             tools: Sequence[BaseTool],
             prefix: str = PREFIX,
             format_instructions: str = FORMAT_INSTRUCTIONS,
-            input_variables: Optional[List[str]] = None,
+            input_variables: Optional[list[str]] = None,
     ) -> PromptTemplate:
         """Create prompt in the style of the zero shot agent.
 
@@ -227,7 +228,7 @@ Thought: {agent_scratchpad}
         return PromptTemplate(template=template, input_variables=input_variables)
 
     def _construct_scratchpad(
-        self, intermediate_steps: List[Tuple[AgentAction, str]]
+        self, intermediate_steps: list[tuple[AgentAction, str]]
     ) -> str:
         agent_scratchpad = ""
         for action, observation in intermediate_steps:
@@ -260,8 +261,8 @@ Thought: {agent_scratchpad}
             suffix: str = SUFFIX,
             human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
             format_instructions: str = FORMAT_INSTRUCTIONS,
-            input_variables: Optional[List[str]] = None,
-            memory_prompts: Optional[List[BasePromptTemplate]] = None,
+            input_variables: Optional[list[str]] = None,
+            memory_prompts: Optional[list[BasePromptTemplate]] = None,
             agent_llm_callback: Optional[AgentLLMCallback] = None,
             **kwargs: Any,
     ) -> Agent:

api/core/app_runner/app_runner.py

 import time
-from typing import Generator, List, Optional, Tuple, Union, cast
+from collections.abc import Generator
+from typing import Optional, Union, cast
 
 from core.application_queue_manager import ApplicationQueueManager, PublishFrom
 from core.entities.application_entities import (
@@ -84,7 +85,7 @@ class AppRunner:
         return rest_tokens
 
     def recale_llm_max_tokens(self, model_config: ModelConfigEntity,
-                              prompt_messages: List[PromptMessage]):
+                              prompt_messages: list[PromptMessage]):
         # recalc max_tokens if sum(prompt_token +  max_tokens) over model token limit
         model_type_instance = model_config.provider_model_bundle.model_type_instance
         model_type_instance = cast(LargeLanguageModel, model_type_instance)
@@ -126,7 +127,7 @@ class AppRunner:
                                  query: Optional[str] = None,
                                  context: Optional[str] = None,
                                  memory: Optional[TokenBufferMemory] = None) \
-            -> Tuple[List[PromptMessage], Optional[List[str]]]:
+            -> tuple[list[PromptMessage], Optional[list[str]]]:
         """
         Organize prompt messages
         :param context:
@@ -295,7 +296,7 @@ class AppRunner:
                               tenant_id: str,
                               app_orchestration_config_entity: AppOrchestrationConfigEntity,
                               inputs: dict,
-                              query: str) -> Tuple[bool, dict, str]:
+                              query: str) -> tuple[bool, dict, str]:
         """
         Process sensitive_word_avoidance.
         :param app_id: app id

api/core/app_runner/generate_task_pipeline.py

 import json
 import logging
 import time
-from typing import Generator, Optional, Union, cast
+from collections.abc import Generator
+from typing import Optional, Union, cast
 
 from pydantic import BaseModel
 
@@ -118,7 +119,7 @@ class GenerateTaskPipeline:
                     }
 
                     self._task_state.llm_result.message.content = annotation.content
-            elif isinstance(event, (QueueStopEvent, QueueMessageEndEvent)):
+            elif isinstance(event, QueueStopEvent | QueueMessageEndEvent):
                 if isinstance(event, QueueMessageEndEvent):
                     self._task_state.llm_result = event.llm_result
                 else:
@@ -201,7 +202,7 @@ class GenerateTaskPipeline:
                 data = self._error_to_stream_response_data(self._handle_error(event))
                 yield self._yield_response(data)
                 break
-            elif isinstance(event, (QueueStopEvent, QueueMessageEndEvent)):
+            elif isinstance(event, QueueStopEvent | QueueMessageEndEvent):
                 if isinstance(event, QueueMessageEndEvent):
                     self._task_state.llm_result = event.llm_result
                 else:
@@ -353,7 +354,7 @@ class GenerateTaskPipeline:
 
                     yield self._yield_response(response)
 
-            elif isinstance(event, (QueueMessageEvent, QueueAgentMessageEvent)):
+            elif isinstance(event, QueueMessageEvent | QueueAgentMessageEvent):
                 chunk = event.chunk
                 delta_text = chunk.delta.message.content
                 if delta_text is None:

api/core/app_runner/moderation_handler.py

 import logging
 import threading
 import time
-from typing import Any, Dict, Optional
+from typing import Any, Optional
 
 from flask import Flask, current_app
 from pydantic import BaseModel
@@ -15,7 +15,7 @@ logger = logging.getLogger(__name__)
 
 class ModerationRule(BaseModel):
     type: str
-    config: Dict[str, Any]
+    config: dict[str, Any]
 
 
 class OutputModerationHandler(BaseModel):

api/core/application_manager.py

@@ -2,7 +2,8 @@ import json
 import logging
 import threading
 import uuid
-from typing import Any, Generator, Optional, Tuple, Union, cast
+from collections.abc import Generator
+from typing import Any, Optional, Union, cast
 
 from flask import Flask, current_app
 from pydantic import ValidationError
@@ -585,7 +586,7 @@ class ApplicationManager:
         return AppOrchestrationConfigEntity(**properties)
 
     def _init_generate_records(self, application_generate_entity: ApplicationGenerateEntity) \
-            -> Tuple[Conversation, Message]:
+            -> tuple[Conversation, Message]:
         """
         Initialize generate records
         :param application_generate_entity: application generate entity

api/core/application_queue_manager.py

 import queue
 import time
+from collections.abc import Generator
 from enum import Enum
-from typing import Any, Generator
+from typing import Any
 
 from sqlalchemy.orm import DeclarativeMeta
 

api/core/callback_handler/agent_loop_gather_callback_handler.py

 import json
 import logging
 import time
-from typing import Any, Dict, List, Optional, Union, cast
+from typing import Any, Optional, Union, cast
 
 from langchain.agents import openai_functions_agent, openai_functions_multi_agent
 from langchain.callbacks.base import BaseCallbackHandler
@@ -37,7 +37,7 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
         self._message_agent_thought = None
 
     @property
-    def agent_loops(self) -> List[AgentLoop]:
+    def agent_loops(self) -> list[AgentLoop]:
         return self._agent_loops
 
     def clear_agent_loops(self) -> None:
@@ -95,14 +95,14 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
 
     def on_chat_model_start(
             self,
-            serialized: Dict[str, Any],
-            messages: List[List[BaseMessage]],
+            serialized: dict[str, Any],
+            messages: list[list[BaseMessage]],
             **kwargs: Any
     ) -> Any:
         pass
 
     def on_llm_start(
-        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
+        self, serialized: dict[str, Any], prompts: list[str], **kwargs: Any
     ) -> None:
         pass
 
@@ -120,7 +120,7 @@ class AgentLoopGatherCallbackHandler(BaseCallbackHandler):
 
     def on_tool_start(
         self,
-        serialized: Dict[str, Any],
+        serialized: dict[str, Any],
         input_str: str,
         **kwargs: Any,
     ) -> None:

api/core/callback_handler/agent_tool_callback_handler.py

 import os
-from typing import Any, Dict, Optional, Union
+from typing import Any, Optional, Union
 
 from langchain.callbacks.base import BaseCallbackHandler
 from langchain.input import print_text
@@ -21,7 +21,7 @@ class DifyAgentCallbackHandler(BaseCallbackHandler, BaseModel):
     def on_tool_start(
         self,
         tool_name: str,
-        tool_inputs: Dict[str, Any],
+        tool_inputs: dict[str, Any],
     ) -> None:
         """Do nothing."""
         print_text("\n[on_tool_start] ToolCall:" + tool_name + "\n" + str(tool_inputs) + "\n", color=self.color)
@@ -29,7 +29,7 @@ class DifyAgentCallbackHandler(BaseCallbackHandler, BaseModel):
     def on_tool_end(
         self,
         tool_name: str,
-        tool_inputs: Dict[str, Any],
+        tool_inputs: dict[str, Any],
         tool_outputs: str,
     ) -> None:
         """If not the final action, print out observation."""

api/core/callback_handler/index_tool_callback_handler.py

-from typing import List
 
 from langchain.schema import Document
 
@@ -40,7 +39,7 @@ class DatasetIndexToolCallbackHandler:
         db.session.add(dataset_query)
         db.session.commit()
 
-    def on_tool_end(self, documents: List[Document]) -> None:
+    def on_tool_end(self, documents: list[Document]) -> None:
         """Handle tool end."""
         for document in documents:
             doc_id = document.metadata['doc_id']
@@ -55,7 +54,7 @@ class DatasetIndexToolCallbackHandler:
 
             db.session.commit()
 
-    def return_retriever_resource_info(self, resource: List):
+    def return_retriever_resource_info(self, resource: list):
         """Handle return_retriever_resource_info."""
         if resource and len(resource) > 0:
             for item in resource:

api/core/callback_handler/std_out_callback_handler.py

 import os
 import sys
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Optional, Union
 
 from langchain.callbacks.base import BaseCallbackHandler
 from langchain.input import print_text
@@ -16,8 +16,8 @@ class DifyStdOutCallbackHandler(BaseCallbackHandler):
 
     def on_chat_model_start(
             self,
-            serialized: Dict[str, Any],
-            messages: List[List[BaseMessage]],
+            serialized: dict[str, Any],
+            messages: list[list[BaseMessage]],
             **kwargs: Any
     ) -> Any:
         print_text("\n[on_chat_model_start]\n", color='blue')
@@ -26,7 +26,7 @@ class DifyStdOutCallbackHandler(BaseCallbackHandler):
                 print_text(str(sub_message) + "\n", color='blue')
 
     def on_llm_start(
-        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
+        self, serialized: dict[str, Any], prompts: list[str], **kwargs: Any
     ) -> None:
         """Print out the prompts."""
         print_text("\n[on_llm_start]\n", color='blue')
@@ -48,13 +48,13 @@ class DifyStdOutCallbackHandler(BaseCallbackHandler):
         print_text("\n[on_llm_error]\nError: " + str(error) + "\n", color='blue')
 
     def on_chain_start(
-        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
+        self, serialized: dict[str, Any], inputs: dict[str, Any], **kwargs: Any
     ) -> None:
         """Print out that we are entering a chain."""
         chain_type = serialized['id'][-1]
         print_text("\n[on_chain_start]\nChain: " + chain_type + "\nInputs: " + str(inputs) + "\n", color='pink')
 
-    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
+    def on_chain_end(self, outputs: dict[str, Any], **kwargs: Any) -> None:
         """Print out that we finished a chain."""
         print_text("\n[on_chain_end]\nOutputs: " + str(outputs) + "\n", color='pink')
 
@@ -66,7 +66,7 @@ class DifyStdOutCallbackHandler(BaseCallbackHandler):
 
     def on_tool_start(
         self,
-        serialized: Dict[str, Any],
+        serialized: dict[str, Any],
         input_str: str,
         **kwargs: Any,
     ) -> None:

api/core/chain/llm_chain.py

-from typing import Any, Dict, List, Optional
+from typing import Any, Optional
 
 from langchain import LLMChain as LCLLMChain
 from langchain.callbacks.manager import CallbackManagerForChainRun
@@ -16,12 +16,12 @@ class LLMChain(LCLLMChain):
     model_config: ModelConfigEntity
     """The language model instance to use."""
     llm: BaseLanguageModel = FakeLLM(response="")
-    parameters: Dict[str, Any] = {}
+    parameters: dict[str, Any] = {}
     agent_llm_callback: Optional[AgentLLMCallback] = None
 
     def generate(
         self,
-        input_list: List[Dict[str, Any]],
+        input_list: list[dict[str, Any]],
         run_manager: Optional[CallbackManagerForChainRun] = None,
     ) -> LLMResult:
         """Generate LLM result from inputs."""

api/core/data_loader/file_extractor.py

 import tempfile
 from pathlib import Path
-from typing import List, Optional, Union
+from typing import Optional, Union
 
 import requests
 from flask import current_app
@@ -28,7 +28,7 @@ USER_AGENT = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTM
 
 class FileExtractor:
     @classmethod
-    def load(cls, upload_file: UploadFile, return_text: bool = False, is_automatic: bool = False) -> Union[List[Document], str]:
+    def load(cls, upload_file: UploadFile, return_text: bool = False, is_automatic: bool = False) -> Union[list[Document], str]:
         with tempfile.TemporaryDirectory() as temp_dir:
             suffix = Path(upload_file.key).suffix
             file_path = f"{temp_dir}/{next(tempfile._get_candidate_names())}{suffix}"
@@ -37,7 +37,7 @@ class FileExtractor:
             return cls.load_from_file(file_path, return_text, upload_file, is_automatic)
 
     @classmethod
-    def load_from_url(cls, url: str, return_text: bool = False) -> Union[List[Document], str]:
+    def load_from_url(cls, url: str, return_text: bool = False) -> Union[list[Document], str]:
         response = requests.get(url, headers={
             "User-Agent": USER_AGENT
         })
@@ -53,7 +53,7 @@ class FileExtractor:
     @classmethod
     def load_from_file(cls, file_path: str, return_text: bool = False,
                        upload_file: Optional[UploadFile] = None,
-                       is_automatic: bool = False) -> Union[List[Document], str]:
+                       is_automatic: bool = False) -> Union[list[Document], str]:
         input_file = Path(file_path)
         delimiter = '\n'
         file_extension = input_file.suffix.lower()

api/core/data_loader/loader/csv_loader.py

 import csv
 import logging
-from typing import Dict, List, Optional
+from typing import Optional
 
 from langchain.document_loaders import CSVLoader as LCCSVLoader
 from langchain.document_loaders.helpers import detect_file_encodings
@@ -14,7 +14,7 @@ class CSVLoader(LCCSVLoader):
             self,
             file_path: str,
             source_column: Optional[str] = None,
-            csv_args: Optional[Dict] = None,
+            csv_args: Optional[dict] = None,
             encoding: Optional[str] = None,
             autodetect_encoding: bool = True,
     ):
@@ -24,7 +24,7 @@ class CSVLoader(LCCSVLoader):
         self.csv_args = csv_args or {}
         self.autodetect_encoding = autodetect_encoding
 
-    def load(self) -> List[Document]:
+    def load(self) -> list[Document]:
         """Load data into document objects."""
         try:
             with open(self.file_path, newline="", encoding=self.encoding) as csvfile:

api/core/data_loader/loader/excel.py

 import logging
-from typing import List
 
 from langchain.document_loaders.base import BaseLoader
 from langchain.schema import Document
@@ -23,7 +22,7 @@ class ExcelLoader(BaseLoader):
         """Initialize with file path."""
         self._file_path = file_path
 
-    def load(self) -> List[Document]:
+    def load(self) -> list[Document]:
         data = []
         keys = []
         wb = load_workbook(filename=self._file_path, read_only=True)

api/core/data_loader/loader/html.py

 import logging
-from typing import List
 
 from bs4 import BeautifulSoup
 from langchain.document_loaders.base import BaseLoader
@@ -23,7 +22,7 @@ class HTMLLoader(BaseLoader):
         """Initialize with file path."""
         self._file_path = file_path
 
-    def load(self) -> List[Document]:
+    def load(self) -> list[Document]:
         return [Document(page_content=self._load_as_text())]
 
     def _load_as_text(self) -> str:

api/core/data_loader/loader/markdown.py

 import logging
 import re
-from typing import List, Optional, Tuple, cast
+from typing import Optional, cast
 
 from langchain.document_loaders.base import BaseLoader
 from langchain.document_loaders.helpers import detect_file_encodings
@@ -42,7 +42,7 @@ class MarkdownLoader(BaseLoader):
         self._encoding = encoding
         self._autodetect_encoding = autodetect_encoding
 
-    def load(self) -> List[Document]:
+    def load(self) -> list[Document]:
         tups = self.parse_tups(self._file_path)
         documents = []
         for header, value in tups:
@@ -54,13 +54,13 @@ class MarkdownLoader(BaseLoader):
 
         return documents
 
-    def markdown_to_tups(self, markdown_text: str) -> List[Tuple[Optional[str], str]]:
+    def markdown_to_tups(self, markdown_text: str) -> list[tuple[Optional[str], str]]:
         """Convert a markdown file to a dictionary.
 
         The keys are the headers and the values are the text under each header.
 
         """
-        markdown_tups: List[Tuple[Optional[str], str]] = []
+        markdown_tups: list[tuple[Optional[str], str]] = []
         lines = markdown_text.split("\n")
 
         current_header = None
@@ -103,11 +103,11 @@ class MarkdownLoader(BaseLoader):
         content = re.sub(pattern, r"\1", content)
         return content
 
-    def parse_tups(self, filepath: str) -> List[Tuple[Optional[str], str]]:
+    def parse_tups(self, filepath: str) -> list[tuple[Optional[str], str]]:
         """Parse file into tuples."""
         content = ""
         try:
-            with open(filepath, "r", encoding=self._encoding) as f:
+            with open(filepath, encoding=self._encoding) as f:
                 content = f.read()
         except UnicodeDecodeError as e:
             if self._autodetect_encoding:

api/core/data_loader/loader/notion.py

 import json
 import logging
-from typing import Any, Dict, List, Optional
+from typing import Any, Optional
 
 import requests
 from flask import current_app
@@ -67,7 +67,7 @@ class NotionLoader(BaseLoader):
             document_model=document_model
         )
 
-    def load(self) -> List[Document]:
+    def load(self) -> list[Document]:
         self.update_last_edited_time(
             self._document_model
         )
@@ -78,7 +78,7 @@ class NotionLoader(BaseLoader):
 
     def _load_data_as_documents(
             self, notion_obj_id: str, notion_page_type: str
-    ) -> List[Document]:
+    ) -> list[Document]:
         docs = []
         if notion_page_type == 'database':
             # get all the pages in the database
@@ -94,8 +94,8 @@ class NotionLoader(BaseLoader):
         return docs
 
     def _get_notion_database_data(
-            self, database_id: str, query_dict: Dict[str, Any] = {}
-    ) -> List[Document]:
+            self, database_id: str, query_dict: dict[str, Any] = {}
+    ) -> list[Document]:
         """Get all the pages from a Notion database."""
         res = requests.post(
             DATABASE_URL_TMPL.format(database_id=database_id),
@@ -149,12 +149,12 @@ class NotionLoader(BaseLoader):
 
         return database_content_list
 
-    def _get_notion_block_data(self, page_id: str) -> List[str]:
+    def _get_notion_block_data(self, page_id: str) -> list[str]:
         result_lines_arr = []
         cur_block_id = page_id
         while True:
             block_url = BLOCK_CHILD_URL_TMPL.format(block_id=cur_block_id)
-            query_dict: Dict[str, Any] = {}
+            query_dict: dict[str, Any] = {}
 
             res = requests.request(
                 "GET",
@@ -216,7 +216,7 @@ class NotionLoader(BaseLoader):
         cur_block_id = block_id
         while True:
             block_url = BLOCK_CHILD_URL_TMPL.format(block_id=cur_block_id)
-            query_dict: Dict[str, Any] = {}
+            query_dict: dict[str, Any] = {}
 
             res = requests.request(
                 "GET",
@@ -280,7 +280,7 @@ class NotionLoader(BaseLoader):
         cur_block_id = block_id
         while not done:
             block_url = BLOCK_CHILD_URL_TMPL.format(block_id=cur_block_id)
-            query_dict: Dict[str, Any] = {}
+            query_dict: dict[str, Any] = {}
 
             res = requests.request(
                 "GET",
@@ -346,7 +346,7 @@ class NotionLoader(BaseLoader):
         else:
             retrieve_page_url = RETRIEVE_PAGE_URL_TMPL.format(page_id=obj_id)
 
-        query_dict: Dict[str, Any] = {}
+        query_dict: dict[str, Any] = {}
 
         res = requests.request(
             "GET",

api/core/data_loader/loader/pdf.py

 import logging
-from typing import List, Optional
+from typing import Optional
 
 from langchain.document_loaders import PyPDFium2Loader
 from langchain.document_loaders.base import BaseLoader
@@ -28,7 +28,7 @@ class PdfLoader(BaseLoader):
         self._file_path = file_path
         self._upload_file = upload_file
 
-    def load(self) -> List[Document]:
+    def load(self) -> list[Document]:
         plaintext_file_key = ''
         plaintext_file_exists = False
         if self._upload_file:

api/core/data_loader/loader/unstructured/unstructured_eml.py

 import base64
 import logging
-from typing import List
 
 from bs4 import BeautifulSoup
 from langchain.document_loaders.base import BaseLoader
@@ -24,7 +23,7 @@ class UnstructuredEmailLoader(BaseLoader):
         self._file_path = file_path
         self._api_url = api_url
 
-    def load(self) -> List[Document]:
+    def load(self) -> list[Document]:
         from unstructured.partition.email import partition_email
         elements = partition_email(filename=self._file_path, api_url=self._api_url)
 

api/core/data_loader/loader/unstructured/unstructured_markdown.py

 import logging
-from typing import List
 
 from langchain.document_loaders.base import BaseLoader
 from langchain.schema import Document
@@ -34,7 +33,7 @@ class UnstructuredMarkdownLoader(BaseLoader):
         self._file_path = file_path
         self._api_url = api_url
 
-    def load(self) -> List[Document]:
+    def load(self) -> list[Document]:
         from unstructured.partition.md import partition_md
 
         elements = partition_md(filename=self._file_path, api_url=self._api_url)

api/core/data_loader/loader/unstructured/unstructured_msg.py

 import logging
-from typing import List
 
 from langchain.document_loaders.base import BaseLoader
 from langchain.schema import Document
@@ -24,7 +23,7 @@ class UnstructuredMsgLoader(BaseLoader):
         self._file_path = file_path
         self._api_url = api_url
 
-    def load(self) -> List[Document]:
+    def load(self) -> list[Document]:
         from unstructured.partition.msg import partition_msg
 
         elements = partition_msg(filename=self._file_path, api_url=self._api_url)

api/core/data_loader/loader/unstructured/unstructured_ppt.py

 import logging
-from typing import List
 
 from langchain.document_loaders.base import BaseLoader
 from langchain.schema import Document
@@ -23,7 +22,7 @@ class UnstructuredPPTLoader(BaseLoader):
         self._file_path = file_path
         self._api_url = api_url
 
-    def load(self) -> List[Document]:
+    def load(self) -> list[Document]:
         from unstructured.partition.ppt import partition_ppt
 
         elements = partition_ppt(filename=self._file_path, api_url=self._api_url)

api/core/data_loader/loader/unstructured/unstructured_pptx.py

 import logging
-from typing import List
 
 from langchain.document_loaders.base import BaseLoader
 from langchain.schema import Document
@@ -22,7 +21,7 @@ class UnstructuredPPTXLoader(BaseLoader):
         self._file_path = file_path
         self._api_url = api_url
 
-    def load(self) -> List[Document]:
+    def load(self) -> list[Document]:
         from unstructured.partition.pptx import partition_pptx
 
         elements = partition_pptx(filename=self._file_path, api_url=self._api_url)

api/core/data_loader/loader/unstructured/unstructured_text.py

 import logging
-from typing import List
 
 from langchain.document_loaders.base import BaseLoader
 from langchain.schema import Document
@@ -24,7 +23,7 @@ class UnstructuredTextLoader(BaseLoader):
         self._file_path = file_path
         self._api_url = api_url
 
-    def load(self) -> List[Document]:
+    def load(self) -> list[Document]:
         from unstructured.partition.text import partition_text
 
         elements = partition_text(filename=self._file_path, api_url=self._api_url)

api/core/data_loader/loader/unstructured/unstructured_xml.py

 import logging
-from typing import List
 
 from langchain.document_loaders.base import BaseLoader
 from langchain.schema import Document
@@ -24,7 +23,7 @@ class UnstructuredXmlLoader(BaseLoader):
         self._file_path = file_path
         self._api_url = api_url
 
-    def load(self) -> List[Document]:
+    def load(self) -> list[Document]:
         from unstructured.partition.xml import partition_xml
 
         elements = partition_xml(filename=self._file_path, xml_keep_tags=True, api_url=self._api_url)

api/core/docstore/dataset_docstore.py

-from typing import Any, Dict, Optional, Sequence, cast
+from collections.abc import Sequence
+from typing import Any, Optional, cast
 
 from langchain.schema import Document
 from sqlalchemy import func
@@ -22,10 +23,10 @@ class DatasetDocumentStore:
         self._document_id = document_id
 
     @classmethod
-    def from_dict(cls, config_dict: Dict[str, Any]) -> "DatasetDocumentStore":
+    def from_dict(cls, config_dict: dict[str, Any]) -> "DatasetDocumentStore":
         return cls(**config_dict)
 
-    def to_dict(self) -> Dict[str, Any]:
+    def to_dict(self) -> dict[str, Any]:
         """Serialize to dict."""
         return {
             "dataset_id": self._dataset.id,
@@ -40,7 +41,7 @@ class DatasetDocumentStore:
         return self._user_id
 
     @property
-    def docs(self) -> Dict[str, Document]:
+    def docs(self) -> dict[str, Document]:
         document_segments = db.session.query(DocumentSegment).filter(
             DocumentSegment.dataset_id == self._dataset.id
         ).all()

api/core/embedding/cached_embedding.py

 import base64
 import logging
-from typing import List, Optional, cast
+from typing import Optional, cast
 
 import numpy as np
 from langchain.embeddings.base import Embeddings
@@ -21,7 +21,7 @@ class CacheEmbedding(Embeddings):
         self._model_instance = model_instance
         self._user = user
 
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
         """Embed search docs in batches of 10."""
         text_embeddings = []
         try:
@@ -52,7 +52,7 @@ class CacheEmbedding(Embeddings):
 
         return text_embeddings
 
-    def embed_query(self, text: str) -> List[float]:
+    def embed_query(self, text: str) -> list[float]:
         """Embed query text."""
         # use doc embedding cache or store if not exists
         hash = helper.generate_text_hash(text)

api/core/entities/provider_configuration.py

 import datetime
 import json
 import logging
+from collections.abc import Iterator
 from json import JSONDecodeError
-from typing import Dict, Iterator, List, Optional, Tuple
+from typing import Optional
 
 from pydantic import BaseModel
 
@@ -135,7 +136,7 @@ class ProviderConfiguration(BaseModel):
             if self.provider.provider_credential_schema else []
         )
 
-    def custom_credentials_validate(self, credentials: dict) -> Tuple[Provider, dict]:
+    def custom_credentials_validate(self, credentials: dict) -> tuple[Provider, dict]:
         """
         Validate custom credentials.
         :param credentials: provider credentials
@@ -282,7 +283,7 @@ class ProviderConfiguration(BaseModel):
         return None
 
     def custom_model_credentials_validate(self, model_type: ModelType, model: str, credentials: dict) \
-            -> Tuple[ProviderModel, dict]:
+            -> tuple[ProviderModel, dict]:
         """
         Validate custom model credentials.
 
@@ -711,7 +712,7 @@ class ProviderConfigurations(BaseModel):
     Model class for provider configuration dict.
     """
     tenant_id: str
-    configurations: Dict[str, ProviderConfiguration] = {}
+    configurations: dict[str, ProviderConfiguration] = {}
 
     def __init__(self, tenant_id: str):
         super().__init__(tenant_id=tenant_id)
@@ -759,7 +760,7 @@ class ProviderConfigurations(BaseModel):
 
         return all_models
 
-    def to_list(self) -> List[ProviderConfiguration]:
+    def to_list(self) -> list[ProviderConfiguration]:
         """
         Convert to list.
 

api/core/extension/extensible.py

@@ -61,7 +61,7 @@ class Extensible:
 
                     builtin_file_path = os.path.join(subdir_path, '__builtin__')
                     if os.path.exists(builtin_file_path):
-                        with open(builtin_file_path, 'r', encoding='utf-8') as f:
+                        with open(builtin_file_path, encoding='utf-8') as f:
                             position = int(f.read().strip())
 
                 if (extension_name + '.py') not in file_names:
@@ -93,7 +93,7 @@ class Extensible:
                     json_path = os.path.join(subdir_path, 'schema.json')
                     json_data = {}
                     if os.path.exists(json_path):
-                        with open(json_path, 'r', encoding='utf-8') as f:
+                        with open(json_path, encoding='utf-8') as f:
                             json_data = json.load(f)
 
                 extensions[extension_name] = ModuleExtension(

api/core/features/assistant_base_runner.py

@@ -2,7 +2,7 @@ import json
 import logging
 from datetime import datetime
 from mimetypes import guess_extension
-from typing import List, Optional, Tuple, Union, cast
+from typing import Optional, Union, cast
 
 from core.app_runner.app_runner import AppRunner
 from core.application_queue_manager import ApplicationQueueManager
@@ -50,7 +50,7 @@ class BaseAssistantApplicationRunner(AppRunner):
                  message: Message,
                  user_id: str,
                  memory: Optional[TokenBufferMemory] = None,
-                 prompt_messages: Optional[List[PromptMessage]] = None,
+                 prompt_messages: Optional[list[PromptMessage]] = None,
                  variables_pool: Optional[ToolRuntimeVariablePool] = None,
                  db_variables: Optional[ToolConversationVariables] = None,
                  model_instance: ModelInstance = None
@@ -122,7 +122,7 @@ class BaseAssistantApplicationRunner(AppRunner):
 
         return app_orchestration_config
 
-    def _convert_tool_response_to_str(self, tool_response: List[ToolInvokeMessage]) -> str:
+    def _convert_tool_response_to_str(self, tool_response: list[ToolInvokeMessage]) -> str:
         """
         Handle tool response
         """
@@ -140,7 +140,7 @@ class BaseAssistantApplicationRunner(AppRunner):
 
         return result
     
-    def _convert_tool_to_prompt_message_tool(self, tool: AgentToolEntity) -> Tuple[PromptMessageTool, Tool]:
+    def _convert_tool_to_prompt_message_tool(self, tool: AgentToolEntity) -> tuple[PromptMessageTool, Tool]:
         """
             convert tool to prompt message tool
         """
@@ -325,7 +325,7 @@ class BaseAssistantApplicationRunner(AppRunner):
 
         return prompt_tool
     
-    def extract_tool_response_binary(self, tool_response: List[ToolInvokeMessage]) -> List[ToolInvokeMessageBinary]:
+    def extract_tool_response_binary(self, tool_response: list[ToolInvokeMessage]) -> list[ToolInvokeMessageBinary]:
         """
         Extract tool response binary
         """
@@ -356,7 +356,7 @@ class BaseAssistantApplicationRunner(AppRunner):
 
         return result
     
-    def create_message_files(self, messages: List[ToolInvokeMessageBinary]) -> List[Tuple[MessageFile, bool]]:
+    def create_message_files(self, messages: list[ToolInvokeMessageBinary]) -> list[tuple[MessageFile, bool]]:
         """
         Create message file
 
@@ -404,7 +404,7 @@ class BaseAssistantApplicationRunner(AppRunner):
         return result
         
     def create_agent_thought(self, message_id: str, message: str, 
-                             tool_name: str, tool_input: str, messages_ids: List[str]
+                             tool_name: str, tool_input: str, messages_ids: list[str]
                              ) -> MessageAgentThought:
         """
         Create agent thought
@@ -449,7 +449,7 @@ class BaseAssistantApplicationRunner(AppRunner):
                            thought: str, 
                            observation: str, 
                            answer: str,
-                           messages_ids: List[str],
+                           messages_ids: list[str],
                            llm_usage: LLMUsage = None) -> MessageAgentThought:
         """
         Save agent thought
@@ -505,7 +505,7 @@ class BaseAssistantApplicationRunner(AppRunner):
 
         db.session.commit()
 
-    def get_history_prompt_messages(self) -> List[PromptMessage]:
+    def get_history_prompt_messages(self) -> list[PromptMessage]:
         """
         Get history prompt messages
         """
@@ -516,7 +516,7 @@ class BaseAssistantApplicationRunner(AppRunner):
 
         return self.history_prompt_messages
     
-    def transform_tool_invoke_messages(self, messages: List[ToolInvokeMessage]) -> List[ToolInvokeMessage]:
+    def transform_tool_invoke_messages(self, messages: list[ToolInvokeMessage]) -> list[ToolInvokeMessage]:
         """
         Transform tool message into agent thought
         """

api/core/features/assistant_cot_runner.py

 import json
 import re
-from typing import Dict, Generator, List, Literal, Union
+from collections.abc import Generator
+from typing import Literal, Union
 
 from core.application_queue_manager import PublishFrom
 from core.entities.application_entities import AgentPromptEntity, AgentScratchpadUnit
@@ -29,7 +30,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
     def run(self, conversation: Conversation,
         message: Message,
         query: str,
-        inputs: Dict[str, str],
+        inputs: dict[str, str],
     ) -> Union[Generator, LLMResult]:
         """
         Run Cot agent application
@@ -37,7 +38,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
         app_orchestration_config = self.app_orchestration_config
         self._repack_app_orchestration_config(app_orchestration_config)
 
-        agent_scratchpad: List[AgentScratchpadUnit] = []
+        agent_scratchpad: list[AgentScratchpadUnit] = []
 
         # check model mode
         if self.app_orchestration_config.model_config.mode == "completion":
@@ -56,7 +57,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
         prompt_messages = self.history_prompt_messages
 
         # convert tools into ModelRuntime Tool format
-        prompt_messages_tools: List[PromptMessageTool] = []
+        prompt_messages_tools: list[PromptMessageTool] = []
         tool_instances = {}
         for tool in self.app_orchestration_config.agent.tools if self.app_orchestration_config.agent else []:
             try:
@@ -83,7 +84,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
         }
         final_answer = ''
 
-        def increase_usage(final_llm_usage_dict: Dict[str, LLMUsage], usage: LLMUsage):
+        def increase_usage(final_llm_usage_dict: dict[str, LLMUsage], usage: LLMUsage):
             if not final_llm_usage_dict['usage']:
                 final_llm_usage_dict['usage'] = usage
             else:
@@ -493,7 +494,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
             if not next_iteration.find("{{observation}}") >= 0:
                 raise ValueError("{{observation}} is required in next_iteration")
             
-    def _convert_scratchpad_list_to_str(self, agent_scratchpad: List[AgentScratchpadUnit]) -> str:
+    def _convert_scratchpad_list_to_str(self, agent_scratchpad: list[AgentScratchpadUnit]) -> str:
         """
             convert agent scratchpad list to str
         """
@@ -506,13 +507,13 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
         return result
     
     def _organize_cot_prompt_messages(self, mode: Literal["completion", "chat"],
-                                      prompt_messages: List[PromptMessage],
-                                      tools: List[PromptMessageTool], 
-                                      agent_scratchpad: List[AgentScratchpadUnit],
+                                      prompt_messages: list[PromptMessage],
+                                      tools: list[PromptMessageTool], 
+                                      agent_scratchpad: list[AgentScratchpadUnit],
                                       agent_prompt_message: AgentPromptEntity,
                                       instruction: str,
                                       input: str,
-        ) -> List[PromptMessage]:
+        ) -> list[PromptMessage]:
         """
             organize chain of thought prompt messages, a standard prompt message is like:
                 Respond to the human as helpfully and accurately as possible. 

api/core/features/assistant_fc_runner.py

 import json
 import logging
-from typing import Any, Dict, Generator, List, Tuple, Union
+from collections.abc import Generator
+from typing import Any, Union
 
 from core.application_queue_manager import PublishFrom
 from core.features.assistant_base_runner import BaseAssistantApplicationRunner
@@ -44,7 +45,7 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
         )
 
         # convert tools into ModelRuntime Tool format
-        prompt_messages_tools: List[PromptMessageTool] = []
+        prompt_messages_tools: list[PromptMessageTool] = []
         tool_instances = {}
         for tool in self.app_orchestration_config.agent.tools if self.app_orchestration_config.agent else []:
             try:
@@ -70,13 +71,13 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
 
         # continue to run until there is not any tool call
         function_call_state = True
-        agent_thoughts: List[MessageAgentThought] = []
+        agent_thoughts: list[MessageAgentThought] = []
         llm_usage = {
             'usage': None
         }
         final_answer = ''
 
-        def increase_usage(final_llm_usage_dict: Dict[str, LLMUsage], usage: LLMUsage):
+        def increase_usage(final_llm_usage_dict: dict[str, LLMUsage], usage: LLMUsage):
             if not final_llm_usage_dict['usage']:
                 final_llm_usage_dict['usage'] = usage
             else:
@@ -117,7 +118,7 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
                 callbacks=[],
             )
 
-            tool_calls: List[Tuple[str, str, Dict[str, Any]]] = []
+            tool_calls: list[tuple[str, str, dict[str, Any]]] = []
 
             # save full response
             response = ''
@@ -364,7 +365,7 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
             return True
         return False
 
-    def extract_tool_calls(self, llm_result_chunk: LLMResultChunk) -> Union[None, List[Tuple[str, str, Dict[str, Any]]]]:
+    def extract_tool_calls(self, llm_result_chunk: LLMResultChunk) -> Union[None, list[tuple[str, str, dict[str, Any]]]]:
         """
         Extract tool calls from llm result chunk
 
@@ -381,7 +382,7 @@ class AssistantFunctionCallApplicationRunner(BaseAssistantApplicationRunner):
 
         return tool_calls
     
-    def extract_blocking_tool_calls(self, llm_result: LLMResult) -> Union[None, List[Tuple[str, str, Dict[str, Any]]]]:
+    def extract_blocking_tool_calls(self, llm_result: LLMResult) -> Union[None, list[tuple[str, str, dict[str, Any]]]]:
         """
         Extract blocking tool calls from llm result
 

api/core/features/dataset_retrieval.py

-from typing import List, Optional, cast
+from typing import Optional, cast
 
 from langchain.tools import BaseTool
 
@@ -96,7 +96,7 @@ class DatasetRetrievalFeature:
                                   return_resource: bool,
                                   invoke_from: InvokeFrom,
                                   hit_callback: DatasetIndexToolCallbackHandler) \
-            -> Optional[List[BaseTool]]:
+            -> Optional[list[BaseTool]]:
         """
         A dataset tool is a tool that can be used to retrieve information from a dataset
         :param tenant_id: tenant id

api/core/features/external_data_fetch.py

@@ -2,7 +2,7 @@ import concurrent
 import json
 import logging
 from concurrent.futures import ThreadPoolExecutor
-from typing import Optional, Tuple
+from typing import Optional
 
 from flask import Flask, current_app
 
@@ -62,7 +62,7 @@ class ExternalDataFetchFeature:
                                   app_id: str,
                                   external_data_tool: ExternalDataVariableEntity,
                                   inputs: dict,
-                                  query: str) -> Tuple[Optional[str], Optional[str]]:
+                                  query: str) -> tuple[Optional[str], Optional[str]]:
         """
         Query external data tool.
         :param flask_app: flask app

api/core/features/moderation.py

 import logging
-from typing import Tuple
 
 from core.entities.application_entities import AppOrchestrationConfigEntity
 from core.moderation.base import ModerationAction, ModerationException
@@ -13,7 +12,7 @@ class ModerationFeature:
               tenant_id: str,
               app_orchestration_config_entity: AppOrchestrationConfigEntity,
               inputs: dict,
-              query: str) -> Tuple[bool, dict, str]:
+              query: str) -> tuple[bool, dict, str]:
         """
         Process sensitive_word_avoidance.
         :param app_id: app id

api/core/file/message_file_parser.py

-from typing import Dict, List, Optional, Union
+from typing import Optional, Union
 
 import requests
 
@@ -15,8 +15,8 @@ class MessageFileParser:
         self.tenant_id = tenant_id
         self.app_id = app_id
 
-    def validate_and_transform_files_arg(self, files: List[dict], app_model_config: AppModelConfig,
-                                         user: Union[Account, EndUser]) -> List[FileObj]:
+    def validate_and_transform_files_arg(self, files: list[dict], app_model_config: AppModelConfig,
+                                         user: Union[Account, EndUser]) -> list[FileObj]:
         """
         validate and transform files arg
 
@@ -96,7 +96,7 @@ class MessageFileParser:
         # return all file objs
         return new_files
 
-    def transform_message_files(self, files: List[MessageFile], app_model_config: Optional[AppModelConfig]) -> List[FileObj]:
+    def transform_message_files(self, files: list[MessageFile], app_model_config: Optional[AppModelConfig]) -> list[FileObj]:
         """
         transform message files
 
@@ -110,8 +110,8 @@ class MessageFileParser:
         # return all file objs
         return [file_obj for file_objs in type_file_objs.values() for file_obj in file_objs]
 
-    def _to_file_objs(self, files: List[Union[Dict, MessageFile]],
-                      file_upload_config: dict) -> Dict[FileType, List[FileObj]]:
+    def _to_file_objs(self, files: list[Union[dict, MessageFile]],
+                      file_upload_config: dict) -> dict[FileType, list[FileObj]]:
         """
         transform files to file objs
 
@@ -119,7 +119,7 @@ class MessageFileParser:
         :param file_upload_config:
         :return:
         """
-        type_file_objs: Dict[FileType, List[FileObj]] = {
+        type_file_objs: dict[FileType, list[FileObj]] = {
             # Currently only support image
             FileType.IMAGE: []
         }

api/core/index/base.py

 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from typing import Any, List
+from typing import Any
 
 from langchain.schema import BaseRetriever, Document
 
@@ -53,7 +53,7 @@ class BaseIndex(ABC):
     def search(
             self, query: str,
             **kwargs: Any
-    ) -> List[Document]:
+    ) -> list[Document]:
         raise NotImplementedError
 
     def delete(self) -> None:

api/core/index/keyword_table_index/jieba_keyword_table_handler.py

 import re
-from typing import Set
 
 import jieba
 from jieba.analyse import default_tfidf
@@ -12,7 +11,7 @@ class JiebaKeywordTableHandler:
     def __init__(self):
         default_tfidf.stop_words = STOPWORDS
 
-    def extract_keywords(self, text: str, max_keywords_per_chunk: int = 10) -> Set[str]:
+    def extract_keywords(self, text: str, max_keywords_per_chunk: int = 10) -> set[str]:
         """Extract keywords with JIEBA tfidf."""
         keywords = jieba.analyse.extract_tags(
             sentence=text,
@@ -21,7 +20,7 @@ class JiebaKeywordTableHandler:
 
         return set(self._expand_tokens_with_subtokens(keywords))
 
-    def _expand_tokens_with_subtokens(self, tokens: Set[str]) -> Set[str]:
+    def _expand_tokens_with_subtokens(self, tokens: set[str]) -> set[str]:
         """Get subtokens from a list of tokens., filtering for stopwords."""
         results = set()
         for token in tokens:

api/core/index/keyword_table_index/keyword_table_index.py

 import json
 from collections import defaultdict
-from typing import Any, Dict, List, Optional
+from typing import Any, Optional
 
 from langchain.schema import BaseRetriever, Document
 from pydantic import BaseModel, Extra, Field
@@ -116,7 +116,7 @@ class KeywordTableIndex(BaseIndex):
     def search(
             self, query: str,
             **kwargs: Any
-    ) -> List[Document]:
+    ) -> list[Document]:
         keyword_table = self._get_dataset_keyword_table()
 
         search_kwargs = kwargs.get('search_kwargs') if kwargs.get('search_kwargs') else {}
@@ -221,7 +221,7 @@ class KeywordTableIndex(BaseIndex):
         keywords = keyword_table_handler.extract_keywords(query)
 
         # go through text chunks in order of most matching keywords
-        chunk_indices_count: Dict[str, int] = defaultdict(int)
+        chunk_indices_count: dict[str, int] = defaultdict(int)
         keywords = [keyword for keyword in keywords if keyword in set(keyword_table.keys())]
         for keyword in keywords:
             for node_id in keyword_table[keyword]:
@@ -235,7 +235,7 @@ class KeywordTableIndex(BaseIndex):
 
         return sorted_chunk_indices[: k]
 
-    def _update_segment_keywords(self, dataset_id: str, node_id: str, keywords: List[str]):
+    def _update_segment_keywords(self, dataset_id: str, node_id: str, keywords: list[str]):
         document_segment = db.session.query(DocumentSegment).filter(
             DocumentSegment.dataset_id == dataset_id,
             DocumentSegment.index_node_id == node_id
@@ -244,7 +244,7 @@ class KeywordTableIndex(BaseIndex):
             document_segment.keywords = keywords
             db.session.commit()
 
-    def create_segment_keywords(self, node_id: str, keywords: List[str]):
+    def create_segment_keywords(self, node_id: str, keywords: list[str]):
         keyword_table = self._get_dataset_keyword_table()
         self._update_segment_keywords(self.dataset.id, node_id, keywords)
         keyword_table = self._add_text_to_keyword_table(keyword_table, node_id, keywords)
@@ -266,7 +266,7 @@ class KeywordTableIndex(BaseIndex):
                 keyword_table = self._add_text_to_keyword_table(keyword_table, segment.index_node_id, list(keywords))
         self._save_dataset_keyword_table(keyword_table)
 
-    def update_segment_keywords_index(self, node_id: str, keywords: List[str]):
+    def update_segment_keywords_index(self, node_id: str, keywords: list[str]):
         keyword_table = self._get_dataset_keyword_table()
         keyword_table = self._add_text_to_keyword_table(keyword_table, node_id, keywords)
         self._save_dataset_keyword_table(keyword_table)
@@ -282,7 +282,7 @@ class KeywordTableRetriever(BaseRetriever, BaseModel):
         extra = Extra.forbid
         arbitrary_types_allowed = True
 
-    def get_relevant_documents(self, query: str) -> List[Document]:
+    def get_relevant_documents(self, query: str) -> list[Document]:
         """Get documents relevant for a query.
 
         Args:
@@ -293,7 +293,7 @@ class KeywordTableRetriever(BaseRetriever, BaseModel):
         """
         return self.index.search(query, **self.search_kwargs)
 
-    async def aget_relevant_documents(self, query: str) -> List[Document]:
+    async def aget_relevant_documents(self, query: str) -> list[Document]:
         raise NotImplementedError("KeywordTableRetriever does not support async")
 
 

api/core/index/vector_index/base.py

 import json
 import logging
 from abc import abstractmethod
-from typing import Any, List, cast
+from typing import Any, cast
 
 from langchain.embeddings.base import Embeddings
 from langchain.schema import BaseRetriever, Document
@@ -43,13 +43,13 @@ class BaseVectorIndex(BaseIndex):
     def search_by_full_text_index(
             self, query: str,
             **kwargs: Any
-    ) -> List[Document]:
+    ) -> list[Document]:
         raise NotImplementedError
 
     def search(
             self, query: str,
             **kwargs: Any
-    ) -> List[Document]:
+    ) -> list[Document]:
         vector_store = self._get_vector_store()
         vector_store = cast(self._get_vector_store_class(), vector_store)
 

api/core/index/vector_index/milvus_vector_index.py

-from typing import Any, List, cast
+from typing import Any, cast
 
 from langchain.embeddings.base import Embeddings
 from langchain.schema import Document
@@ -160,6 +160,6 @@ class MilvusVectorIndex(BaseVectorIndex):
             ],
         ))
 
-    def search_by_full_text_index(self, query: str, **kwargs: Any) -> List[Document]:
+    def search_by_full_text_index(self, query: str, **kwargs: Any) -> list[Document]:
         # milvus/zilliz doesn't support bm25 search
         return []

api/core/index/vector_index/qdrant_vector_index.py

 import os
-from typing import Any, List, Optional, cast
+from typing import Any, Optional, cast
 
 import qdrant_client
 from langchain.embeddings.base import Embeddings
@@ -210,7 +210,7 @@ class QdrantVectorIndex(BaseVectorIndex):
 
         return False
 
-    def search_by_full_text_index(self, query: str, **kwargs: Any) -> List[Document]:
+    def search_by_full_text_index(self, query: str, **kwargs: Any) -> list[Document]:
         vector_store = self._get_vector_store()
         vector_store = cast(self._get_vector_store_class(), vector_store)
 

api/core/index/vector_index/weaviate_vector_index.py

-from typing import Any, List, Optional, cast
+from typing import Any, Optional, cast
 
 import requests
 import weaviate
@@ -172,7 +172,7 @@ class WeaviateVectorIndex(BaseVectorIndex):
 
         return False
 
-    def search_by_full_text_index(self, query: str, **kwargs: Any) -> List[Document]:
+    def search_by_full_text_index(self, query: str, **kwargs: Any) -> list[Document]:
         vector_store = self._get_vector_store()
         vector_store = cast(self._get_vector_store_class(), vector_store)
         return vector_store.similarity_search_by_bm25(query, kwargs.get('top_k', 2), **kwargs)

api/core/indexing_runner.py

@@ -5,7 +5,7 @@ import re
 import threading
 import time
 import uuid
-from typing import List, Optional, cast
+from typing import Optional, cast
 
 from flask import Flask, current_app
 from flask_login import current_user
@@ -40,7 +40,7 @@ class IndexingRunner:
         self.storage = storage
         self.model_manager = ModelManager()
 
-    def run(self, dataset_documents: List[DatasetDocument]):
+    def run(self, dataset_documents: list[DatasetDocument]):
         """Run the indexing process."""
         for dataset_document in dataset_documents:
             try:
@@ -238,7 +238,7 @@ class IndexingRunner:
             dataset_document.stopped_at = datetime.datetime.utcnow()
             db.session.commit()
 
-    def file_indexing_estimate(self, tenant_id: str, file_details: List[UploadFile], tmp_processing_rule: dict,
+    def file_indexing_estimate(self, tenant_id: str, file_details: list[UploadFile], tmp_processing_rule: dict,
                                doc_form: str = None, doc_language: str = 'English', dataset_id: str = None,
                                indexing_technique: str = 'economy') -> dict:
         """
@@ -494,7 +494,7 @@ class IndexingRunner:
             "preview": preview_texts
         }
 
-    def _load_data(self, dataset_document: DatasetDocument, automatic: bool = False) -> List[Document]:
+    def _load_data(self, dataset_document: DatasetDocument, automatic: bool = False) -> list[Document]:
         # load file
         if dataset_document.data_source_type not in ["upload_file", "notion_import"]:
             return []
@@ -526,7 +526,7 @@ class IndexingRunner:
         )
 
         # replace doc id to document model id
-        text_docs = cast(List[Document], text_docs)
+        text_docs = cast(list[Document], text_docs)
         for text_doc in text_docs:
             # remove invalid symbol
             text_doc.page_content = self.filter_string(text_doc.page_content)
@@ -540,7 +540,7 @@ class IndexingRunner:
         text = re.sub(r'\|>', '>', text)
         text = re.sub(r'[\x00-\x08\x0B\x0C\x0E-\x1F\x7F\xEF\xBF\xBE]', '', text)
         # Unicode  U+FFFE
-        text = re.sub(u'\uFFFE', '', text)
+        text = re.sub('\uFFFE', '', text)
         return text
 
     def _get_splitter(self, processing_rule: DatasetProcessRule,
@@ -577,9 +577,9 @@ class IndexingRunner:
 
         return character_splitter
 
-    def _step_split(self, text_docs: List[Document], splitter: TextSplitter,
+    def _step_split(self, text_docs: list[Document], splitter: TextSplitter,
                     dataset: Dataset, dataset_document: DatasetDocument, processing_rule: DatasetProcessRule) \
-            -> List[Document]:
+            -> list[Document]:
         """
         Split the text documents into documents and save them to the document segment.
         """
@@ -624,9 +624,9 @@ class IndexingRunner:
 
         return documents
 
-    def _split_to_documents(self, text_docs: List[Document], splitter: TextSplitter,
+    def _split_to_documents(self, text_docs: list[Document], splitter: TextSplitter,
                             processing_rule: DatasetProcessRule, tenant_id: str,
-                            document_form: str, document_language: str) -> List[Document]:
+                            document_form: str, document_language: str) -> list[Document]:
         """
         Split the text documents into nodes.
         """
@@ -699,8 +699,8 @@ class IndexingRunner:
 
             all_qa_documents.extend(format_documents)
 
-    def _split_to_documents_for_estimate(self, text_docs: List[Document], splitter: TextSplitter,
-                                         processing_rule: DatasetProcessRule) -> List[Document]:
+    def _split_to_documents_for_estimate(self, text_docs: list[Document], splitter: TextSplitter,
+                                         processing_rule: DatasetProcessRule) -> list[Document]:
         """
         Split the text documents into nodes.
         """
@@ -770,7 +770,7 @@ class IndexingRunner:
             for q, a in matches if q and a
         ]
 
-    def _build_index(self, dataset: Dataset, dataset_document: DatasetDocument, documents: List[Document]) -> None:
+    def _build_index(self, dataset: Dataset, dataset_document: DatasetDocument, documents: list[Document]) -> None:
         """
         Build the index for the document.
         """
@@ -877,7 +877,7 @@ class IndexingRunner:
         DocumentSegment.query.filter_by(document_id=dataset_document_id).update(update_params)
         db.session.commit()
 
-    def batch_add_segments(self, segments: List[DocumentSegment], dataset: Dataset):
+    def batch_add_segments(self, segments: list[DocumentSegment], dataset: Dataset):
         """
         Batch add segments index processing
         """

api/core/model_manager.py

-from typing import IO, Generator, List, Optional, Union, cast
+from collections.abc import Generator
+from typing import IO, Optional, Union, cast
 
 from core.entities.provider_configuration import ProviderModelBundle
 from core.errors.error import ProviderTokenNotInitError
@@ -47,7 +48,7 @@ class ModelInstance:
         return credentials
 
     def invoke_llm(self, prompt_messages: list[PromptMessage], model_parameters: Optional[dict] = None,
-                   tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None,
+                   tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
                    stream: bool = True, user: Optional[str] = None, callbacks: list[Callback] = None) \
             -> Union[LLMResult, Generator]:
         """

api/core/model_runtime/callbacks/base_callback.py

 from abc import ABC
-from typing import List, Optional
+from typing import Optional
 
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
 from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool
@@ -23,7 +23,7 @@ class Callback(ABC):
 
     def on_before_invoke(self, llm_instance: AIModel, model: str, credentials: dict,
                          prompt_messages: list[PromptMessage], model_parameters: dict,
-                         tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None,
+                         tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
                          stream: bool = True, user: Optional[str] = None) -> None:
         """
         Before invoke callback
@@ -42,7 +42,7 @@ class Callback(ABC):
 
     def on_new_chunk(self, llm_instance: AIModel, chunk: LLMResultChunk, model: str, credentials: dict,
                      prompt_messages: list[PromptMessage], model_parameters: dict,
-                     tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None,
+                     tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
                      stream: bool = True, user: Optional[str] = None):
         """
         On new chunk callback
@@ -62,7 +62,7 @@ class Callback(ABC):
 
     def on_after_invoke(self, llm_instance: AIModel, result: LLMResult, model: str, credentials: dict,
                         prompt_messages: list[PromptMessage], model_parameters: dict,
-                        tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None,
+                        tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
                         stream: bool = True, user: Optional[str] = None) -> None:
         """
         After invoke callback
@@ -82,7 +82,7 @@ class Callback(ABC):
 
     def on_invoke_error(self, llm_instance: AIModel, ex: Exception, model: str, credentials: dict,
                         prompt_messages: list[PromptMessage], model_parameters: dict,
-                        tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None,
+                        tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
                         stream: bool = True, user: Optional[str] = None) -> None:
         """
         Invoke error callback

api/core/model_runtime/callbacks/logging_callback.py

 import json
 import logging
 import sys
-from typing import List, Optional
+from typing import Optional
 
 from core.model_runtime.callbacks.base_callback import Callback
 from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
@@ -13,7 +13,7 @@ logger = logging.getLogger(__name__)
 class LoggingCallback(Callback):
     def on_before_invoke(self, llm_instance: AIModel, model: str, credentials: dict,
                          prompt_messages: list[PromptMessage], model_parameters: dict,
-                         tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None,
+                         tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
                          stream: bool = True, user: Optional[str] = None) -> None:
         """
         Before invoke callback
@@ -60,7 +60,7 @@ class LoggingCallback(Callback):
 
     def on_new_chunk(self, llm_instance: AIModel, chunk: LLMResultChunk, model: str, credentials: dict,
                      prompt_messages: list[PromptMessage], model_parameters: dict,
-                     tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None,
+                     tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
                      stream: bool = True, user: Optional[str] = None):
         """
         On new chunk callback
@@ -81,7 +81,7 @@ class LoggingCallback(Callback):
 
     def on_after_invoke(self, llm_instance: AIModel, result: LLMResult, model: str, credentials: dict,
                         prompt_messages: list[PromptMessage], model_parameters: dict,
-                        tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None,
+                        tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
                         stream: bool = True, user: Optional[str] = None) -> None:
         """
         After invoke callback
@@ -113,7 +113,7 @@ class LoggingCallback(Callback):
 
     def on_invoke_error(self, llm_instance: AIModel, ex: Exception, model: str, credentials: dict,
                         prompt_messages: list[PromptMessage], model_parameters: dict,
-                        tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None,
+                        tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
                         stream: bool = True, user: Optional[str] = None) -> None:
         """
         Invoke error callback

api/core/model_runtime/entities/defaults.py

-from typing import Dict
 
 from core.model_runtime.entities.model_entities import DefaultParameterName
 
-PARAMETER_RULE_TEMPLATE: Dict[DefaultParameterName, dict] = {
+PARAMETER_RULE_TEMPLATE: dict[DefaultParameterName, dict] = {
     DefaultParameterName.TEMPERATURE: {
         'label': {
             'en_US': 'Temperature',

api/core/model_runtime/model_providers/__base/ai_model.py

@@ -153,7 +153,7 @@ class AIModel(ABC):
         # read _position.yaml file
         position_map = {}
         if os.path.exists(position_file_path):
-            with open(position_file_path, 'r', encoding='utf-8') as f:
+            with open(position_file_path, encoding='utf-8') as f:
                 positions = yaml.safe_load(f)
                 # convert list to dict with key as model provider name, value as index
                 position_map = {position: index for index, position in enumerate(positions)}
@@ -161,7 +161,7 @@ class AIModel(ABC):
         # traverse all model_schema_yaml_paths
         for model_schema_yaml_path in model_schema_yaml_paths:
             # read yaml data from yaml file
-            with open(model_schema_yaml_path, 'r', encoding='utf-8') as f:
+            with open(model_schema_yaml_path, encoding='utf-8') as f:
                 yaml_data = yaml.safe_load(f)
 
             new_parameter_rules = []

api/core/model_runtime/model_providers/__base/large_language_model.py

@@ -3,7 +3,8 @@ import os
 import re
 import time
 from abc import abstractmethod
-from typing import Generator, List, Optional, Union
+from collections.abc import Generator
+from typing import Optional, Union
 
 from core.model_runtime.callbacks.base_callback import Callback
 from core.model_runtime.callbacks.logging_callback import LoggingCallback
@@ -29,7 +30,7 @@ class LargeLanguageModel(AIModel):
 
     def invoke(self, model: str, credentials: dict,
                prompt_messages: list[PromptMessage], model_parameters: Optional[dict] = None,
-               tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None,
+               tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
                stream: bool = True, user: Optional[str] = None, callbacks: list[Callback] = None) \
             -> Union[LLMResult, Generator]:
         """
@@ -122,7 +123,7 @@ class LargeLanguageModel(AIModel):
     def _invoke_result_generator(self, model: str, result: Generator, credentials: dict,
                                  prompt_messages: list[PromptMessage], model_parameters: dict,
                                  tools: Optional[list[PromptMessageTool]] = None,
-                                 stop: Optional[List[str]] = None, stream: bool = True,
+                                 stop: Optional[list[str]] = None, stream: bool = True,
                                  user: Optional[str] = None, callbacks: list[Callback] = None) -> Generator:
         """
         Invoke result generator
@@ -186,7 +187,7 @@ class LargeLanguageModel(AIModel):
     @abstractmethod
     def _invoke(self, model: str, credentials: dict,
                 prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None,
+                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
                 stream: bool = True, user: Optional[str] = None) \
             -> Union[LLMResult, Generator]:
         """
@@ -218,7 +219,7 @@ class LargeLanguageModel(AIModel):
         """
         raise NotImplementedError
 
-    def enforce_stop_tokens(self, text: str, stop: List[str]) -> str:
+    def enforce_stop_tokens(self, text: str, stop: list[str]) -> str:
         """Cut off the text as soon as any stop words occur."""
         return re.split("|".join(stop), text, maxsplit=1)[0]
 
@@ -329,7 +330,7 @@ class LargeLanguageModel(AIModel):
     def _trigger_before_invoke_callbacks(self, model: str, credentials: dict,
                                          prompt_messages: list[PromptMessage], model_parameters: dict,
                                          tools: Optional[list[PromptMessageTool]] = None,
-                                         stop: Optional[List[str]] = None, stream: bool = True,
+                                         stop: Optional[list[str]] = None, stream: bool = True,
                                          user: Optional[str] = None, callbacks: list[Callback] = None) -> None:
         """
         Trigger before invoke callbacks
@@ -367,7 +368,7 @@ class LargeLanguageModel(AIModel):
     def _trigger_new_chunk_callbacks(self, chunk: LLMResultChunk, model: str, credentials: dict,
                                      prompt_messages: list[PromptMessage], model_parameters: dict,
                                      tools: Optional[list[PromptMessageTool]] = None,
-                                     stop: Optional[List[str]] = None, stream: bool = True,
+                                     stop: Optional[list[str]] = None, stream: bool = True,
                                      user: Optional[str] = None, callbacks: list[Callback] = None) -> None:
         """
         Trigger new chunk callbacks
@@ -406,7 +407,7 @@ class LargeLanguageModel(AIModel):
     def _trigger_after_invoke_callbacks(self, model: str, result: LLMResult, credentials: dict,
                                         prompt_messages: list[PromptMessage], model_parameters: dict,
                                         tools: Optional[list[PromptMessageTool]] = None,
-                                        stop: Optional[List[str]] = None, stream: bool = True,
+                                        stop: Optional[list[str]] = None, stream: bool = True,
                                         user: Optional[str] = None, callbacks: list[Callback] = None) -> None:
         """
         Trigger after invoke callbacks
@@ -446,7 +447,7 @@ class LargeLanguageModel(AIModel):
     def _trigger_invoke_error_callbacks(self, model: str, ex: Exception, credentials: dict,
                                         prompt_messages: list[PromptMessage], model_parameters: dict,
                                         tools: Optional[list[PromptMessageTool]] = None,
-                                        stop: Optional[List[str]] = None, stream: bool = True,
+                                        stop: Optional[list[str]] = None, stream: bool = True,
                                         user: Optional[str] = None, callbacks: list[Callback] = None) -> None:
         """
         Trigger invoke error callbacks
@@ -527,7 +528,7 @@ class LargeLanguageModel(AIModel):
                     raise ValueError(
                         f"Model Parameter {parameter_name} should be less than or equal to {parameter_rule.max}.")
             elif parameter_rule.type == ParameterType.FLOAT:
-                if not isinstance(parameter_value, (float, int)):
+                if not isinstance(parameter_value, float | int):
                     raise ValueError(f"Model Parameter {parameter_name} should be float.")
 
                 # validate parameter value precision

api/core/model_runtime/model_providers/__base/model_provider.py

 import importlib
 import os
 from abc import ABC, abstractmethod
-from typing import Dict
 
 import yaml
 
@@ -12,7 +11,7 @@ from core.model_runtime.model_providers.__base.ai_model import AIModel
 
 class ModelProvider(ABC):
     provider_schema: ProviderEntity = None
-    model_instance_map: Dict[str, AIModel] = {}
+    model_instance_map: dict[str, AIModel] = {}
 
     @abstractmethod
     def validate_provider_credentials(self, credentials: dict) -> None:
@@ -47,7 +46,7 @@ class ModelProvider(ABC):
         yaml_path = os.path.join(current_path, f'{provider_name}.yaml')
         yaml_data = {}
         if os.path.exists(yaml_path):
-            with open(yaml_path, 'r', encoding='utf-8') as f:
+            with open(yaml_path, encoding='utf-8') as f:
                 yaml_data = yaml.safe_load(f)
 
         try:

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

-from typing import Generator, List, Optional, Union
+from collections.abc import Generator
+from typing import Optional, Union
 
 import anthropic
 from anthropic import Anthropic, Stream
@@ -29,7 +30,7 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
 
     def _invoke(self, model: str, credentials: dict,
                 prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None,
+                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
                 stream: bool = True, user: Optional[str] = None) \
             -> Union[LLMResult, Generator]:
         """
@@ -90,7 +91,7 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
 
     def _generate(self, model: str, credentials: dict,
                   prompt_messages: list[PromptMessage], model_parameters: dict,
-                  stop: Optional[List[str]] = None, stream: bool = True,
+                  stop: Optional[list[str]] = None, stream: bool = True,
                   user: Optional[str] = None) -> Union[LLMResult, Generator]:
         """
         Invoke large language model
@@ -255,7 +256,7 @@ class AnthropicLargeLanguageModel(LargeLanguageModel):
 
         return message_text
 
-    def _convert_messages_to_prompt_anthropic(self, messages: List[PromptMessage]) -> str:
+    def _convert_messages_to_prompt_anthropic(self, messages: list[PromptMessage]) -> str:
         """
         Format a list of messages into a full prompt for the Anthropic model
 

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

 import copy
 import logging
-from typing import Generator, List, Optional, Union, cast
+from collections.abc import Generator
+from typing import Optional, Union, cast
 
 import tiktoken
 from openai import AzureOpenAI, Stream
@@ -34,7 +35,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
 
     def _invoke(self, model: str, credentials: dict,
                 prompt_messages: list[PromptMessage], model_parameters: dict,
-                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None,
+                tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
                 stream: bool = True, user: Optional[str] = None) \
             -> Union[LLMResult, Generator]:
 
@@ -121,7 +122,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
         return ai_model_entity.entity if ai_model_entity else None
 
     def _generate(self, model: str, credentials: dict,
-                  prompt_messages: list[PromptMessage], model_parameters: dict, stop: Optional[List[str]] = None,
+                  prompt_messages: list[PromptMessage], model_parameters: dict, stop: Optional[list[str]] = None,
                   stream: bool = True, user: Optional[str] = None) -> Union[LLMResult, Generator]:
 
         client = AzureOpenAI(**self._to_credential_kwargs(credentials))
@@ -239,7 +240,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
 
     def _chat_generate(self, model: str, credentials: dict,
                        prompt_messages: list[PromptMessage], model_parameters: dict,
-                       tools: Optional[list[PromptMessageTool]] = None, stop: Optional[List[str]] = None,
+                       tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
                        stream: bool = True, user: Optional[str] = None) -> Union[LLMResult, Generator]:
 
         client = AzureOpenAI(**self._to_credential_kwargs(credentials))
@@ -537,7 +538,7 @@ class AzureOpenAILargeLanguageModel(_CommonAzureOpenAI, LargeLanguageModel):
 
         return num_tokens
 
-    def _num_tokens_from_messages(self, credentials: dict, messages: List[PromptMessage],
+    def _num_tokens_from_messages(self, credentials: dict, messages: list[PromptMessage],
                                   tools: Optional[list[PromptMessageTool]] = None) -> int:
         """Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.