enhancement: introduce Ruff for Python linter for reordering and removing unused imports with automated pre-commit and sytle check (#2366)

api/core/agent/agent/calc_token_mixin.py

@@ -1,11 +1,9 @@
 from typing import List, cast
 
 from core.entities.application_entities import ModelConfigEntity
-from core.entities.message_entities import lc_messages_to_prompt_messages
 from core.model_runtime.entities.message_entities import PromptMessage
 from core.model_runtime.entities.model_entities import ModelPropertyKey
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
-from langchain.schema import BaseMessage
 
 
 class CalcTokenMixin:

api/core/agent/agent/multi_dataset_router_agent.py

@@ -1,10 +1,5 @@
-from typing import Any, List, Optional, Sequence, Tuple, Union, cast
+from typing import Any, List, Optional, Sequence, Tuple, Union
 
-from core.entities.application_entities import ModelConfigEntity
-from core.entities.message_entities import lc_messages_to_prompt_messages
-from core.model_manager import ModelInstance
-from core.model_runtime.entities.message_entities import PromptMessageTool
-from core.third_party.langchain.llms.fake import FakeLLM
 from langchain.agents import BaseSingleActionAgent, OpenAIFunctionsAgent
 from langchain.agents.openai_functions_agent.base import _format_intermediate_steps, _parse_ai_message
 from langchain.callbacks.base import BaseCallbackManager
@@ -14,6 +9,12 @@ from langchain.schema import AgentAction, AgentFinish, AIMessage, SystemMessage
 from langchain.tools import BaseTool
 from pydantic import root_validator
 
+from core.entities.application_entities import ModelConfigEntity
+from core.entities.message_entities import lc_messages_to_prompt_messages
+from core.model_manager import ModelInstance
+from core.model_runtime.entities.message_entities import PromptMessageTool
+from core.third_party.langchain.llms.fake import FakeLLM
+
 
 class MultiDatasetRouterAgent(OpenAIFunctionsAgent):
     """

api/core/agent/agent/openai_function_call.py

@@ -1,4 +1,23 @@
-from typing import Any, List, Optional, Sequence, Tuple, Union, cast
+from typing import Any, List, Optional, Sequence, Tuple, Union
+
+from langchain.agents import BaseSingleActionAgent, OpenAIFunctionsAgent
+from langchain.agents.openai_functions_agent.base import _format_intermediate_steps, _parse_ai_message
+from langchain.callbacks.base import BaseCallbackManager
+from langchain.callbacks.manager import Callbacks
+from langchain.chat_models.openai import _convert_message_to_dict, _import_tiktoken
+from langchain.memory.prompt import SUMMARY_PROMPT
+from langchain.prompts.chat import BaseMessagePromptTemplate
+from langchain.schema import (
+    AgentAction,
+    AgentFinish,
+    AIMessage,
+    BaseMessage,
+    HumanMessage,
+    SystemMessage,
+    get_buffer_string,
+)
+from langchain.tools import BaseTool
+from pydantic import root_validator
 
 from core.agent.agent.agent_llm_callback import AgentLLMCallback
 from core.agent.agent.calc_token_mixin import CalcTokenMixin, ExceededLLMTokensLimitError
@@ -7,19 +26,7 @@ from core.entities.application_entities import ModelConfigEntity
 from core.entities.message_entities import lc_messages_to_prompt_messages
 from core.model_manager import ModelInstance
 from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool
-from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.third_party.langchain.llms.fake import FakeLLM
-from langchain.agents import BaseSingleActionAgent, OpenAIFunctionsAgent
-from langchain.agents.openai_functions_agent.base import _format_intermediate_steps, _parse_ai_message
-from langchain.callbacks.base import BaseCallbackManager
-from langchain.callbacks.manager import Callbacks
-from langchain.chat_models.openai import _convert_message_to_dict, _import_tiktoken
-from langchain.memory.prompt import SUMMARY_PROMPT
-from langchain.prompts.chat import BaseMessagePromptTemplate
-from langchain.schema import (AgentAction, AgentFinish, AIMessage, BaseMessage, HumanMessage, SystemMessage,
-                              get_buffer_string)
-from langchain.tools import BaseTool
-from pydantic import root_validator
 
 
 class AutoSummarizingOpenAIFunctionCallAgent(OpenAIFunctionsAgent, CalcTokenMixin):

api/core/agent/agent/structed_multi_dataset_router_agent.py

@@ -1,8 +1,6 @@
 import re
 from typing import Any, List, Optional, Sequence, Tuple, Union, cast
 
-from core.chain.llm_chain import LLMChain
-from core.entities.application_entities import ModelConfigEntity
 from langchain import BasePromptTemplate, PromptTemplate
 from langchain.agents import Agent, AgentOutputParser, StructuredChatAgent
 from langchain.agents.structured_chat.base import HUMAN_MESSAGE_TEMPLATE
@@ -13,6 +11,9 @@ from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, Sy
 from langchain.schema import AgentAction, AgentFinish, OutputParserException
 from langchain.tools import BaseTool
 
+from core.chain.llm_chain import LLMChain
+from core.entities.application_entities import ModelConfigEntity
+
 FORMAT_INSTRUCTIONS = """Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
 The nouns in the format of "Thought", "Action", "Action Input", "Final Answer" must be expressed in English.
 Valid "action" values: "Final Answer" or {tool_names}

api/core/agent/agent/structured_chat.py

@@ -1,11 +1,6 @@
 import re
 from typing import Any, List, Optional, Sequence, Tuple, Union, cast
 
-from core.agent.agent.agent_llm_callback import AgentLLMCallback
-from core.agent.agent.calc_token_mixin import CalcTokenMixin, ExceededLLMTokensLimitError
-from core.chain.llm_chain import LLMChain
-from core.entities.application_entities import ModelConfigEntity
-from core.entities.message_entities import lc_messages_to_prompt_messages
 from langchain import BasePromptTemplate, PromptTemplate
 from langchain.agents import Agent, AgentOutputParser, StructuredChatAgent
 from langchain.agents.structured_chat.base import HUMAN_MESSAGE_TEMPLATE
@@ -14,10 +9,23 @@ from langchain.callbacks.base import BaseCallbackManager
 from langchain.callbacks.manager import Callbacks
 from langchain.memory.prompt import SUMMARY_PROMPT
 from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate
-from langchain.schema import (AgentAction, AgentFinish, AIMessage, BaseMessage, HumanMessage, OutputParserException,
-                              get_buffer_string)
+from langchain.schema import (
+    AgentAction,
+    AgentFinish,
+    AIMessage,
+    BaseMessage,
+    HumanMessage,
+    OutputParserException,
+    get_buffer_string,
+)
 from langchain.tools import BaseTool
 
+from core.agent.agent.agent_llm_callback import AgentLLMCallback
+from core.agent.agent.calc_token_mixin import CalcTokenMixin, ExceededLLMTokensLimitError
+from core.chain.llm_chain import LLMChain
+from core.entities.application_entities import ModelConfigEntity
+from core.entities.message_entities import lc_messages_to_prompt_messages
+
 FORMAT_INSTRUCTIONS = """Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
 The nouns in the format of "Thought", "Action", "Action Input", "Final Answer" must be expressed in English.
 Valid "action" values: "Final Answer" or {tool_names}

api/core/agent/agent_executor.py

@@ -2,6 +2,12 @@ import enum
 import logging
 from typing import Optional, Union
 
+from langchain.agents import AgentExecutor as LCAgentExecutor
+from langchain.agents import BaseMultiActionAgent, BaseSingleActionAgent
+from langchain.callbacks.manager import Callbacks
+from langchain.tools import BaseTool
+from pydantic import BaseModel, Extra
+
 from core.agent.agent.agent_llm_callback import AgentLLMCallback
 from core.agent.agent.multi_dataset_router_agent import MultiDatasetRouterAgent
 from core.agent.agent.openai_function_call import AutoSummarizingOpenAIFunctionCallAgent
@@ -15,11 +21,6 @@ from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_runtime.errors.invoke import InvokeError
 from core.tools.tool.dataset_retriever.dataset_multi_retriever_tool import DatasetMultiRetrieverTool
 from core.tools.tool.dataset_retriever.dataset_retriever_tool import DatasetRetrieverTool
-from langchain.agents import AgentExecutor as LCAgentExecutor
-from langchain.agents import BaseMultiActionAgent, BaseSingleActionAgent
-from langchain.callbacks.manager import Callbacks
-from langchain.tools import BaseTool
-from pydantic import BaseModel, Extra
 
 
 class PlanningStrategy(str, enum.Enum):