improve: introduce isort for linting Python imports (#1983)

api/core/agent/agent/agent_llm_callback.py

@@ -1,10 +1,10 @@
 import logging
-from typing import Optional, List
+from typing import List, Optional
 
 from core.callback_handler.agent_loop_gather_callback_handler import AgentLoopGatherCallbackHandler
 from core.model_runtime.callbacks.base_callback import Callback
-from core.model_runtime.entities.llm_entities import LLMResultChunk, LLMResult
-from core.model_runtime.entities.message_entities import PromptMessageTool, PromptMessage
+from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk
+from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool
 from core.model_runtime.model_providers.__base.ai_model import AIModel
 
 logger = logging.getLogger(__name__)

api/core/agent/agent/calc_token_mixin.py

@@ -1,12 +1,11 @@
 from typing import List, cast
 
-from langchain.schema import BaseMessage
-
 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

@@ -1,20 +1,19 @@
-from typing import Tuple, List, Any, Union, Sequence, Optional, cast
+from typing import Any, List, Optional, Sequence, Tuple, Union, cast
 
-from langchain.agents import OpenAIFunctionsAgent, BaseSingleActionAgent
+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
 from langchain.callbacks.manager import Callbacks
 from langchain.prompts.chat import BaseMessagePromptTemplate
-from langchain.schema import AgentAction, AgentFinish, SystemMessage, AIMessage
+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.model_manager import ModelInstance
-from core.entities.message_entities import lc_messages_to_prompt_messages
-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

@@ -1,28 +1,26 @@
-from typing import List, Tuple, Any, Union, Sequence, Optional, cast
+from typing import Any, List, Optional, Sequence, Tuple, Union, cast
 
-from langchain.agents import OpenAIFunctionsAgent, BaseSingleActionAgent
-from langchain.agents.openai_functions_agent.base import _parse_ai_message, \
-    _format_intermediate_steps
+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 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, SystemMessage, AIMessage, HumanMessage, BaseMessage, \
-    get_buffer_string
+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 ExceededLLMTokensLimitError, CalcTokenMixin
-from core.chain.llm_chain import LLMChain
-from core.entities.application_entities import ModelConfigEntity
-from core.model_manager import ModelInstance
-from core.entities.message_entities import lc_messages_to_prompt_messages
-from core.model_runtime.entities.message_entities import PromptMessageTool, PromptMessage
-from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
-from core.third_party.langchain.llms.fake import FakeLLM
-
 
 class AutoSummarizingOpenAIFunctionCallAgent(OpenAIFunctionsAgent, CalcTokenMixin):
     moving_summary_buffer: str = ""

api/core/agent/agent/output_parser/structured_chat.py

@@ -2,8 +2,8 @@ import json
 import re
 from typing import Union
 
-from langchain.agents.structured_chat.output_parser import StructuredChatOutputParser as LCStructuredChatOutputParser, \
-    logger
+from langchain.agents.structured_chat.output_parser import StructuredChatOutputParser as LCStructuredChatOutputParser
+from langchain.agents.structured_chat.output_parser import logger
 from langchain.schema import AgentAction, AgentFinish, OutputParserException
 
 

api/core/agent/agent/structed_multi_dataset_router_agent.py

@@ -1,18 +1,17 @@
 import re
-from typing import List, Tuple, Any, Union, Sequence, Optional, cast
-
-from langchain import BasePromptTemplate, PromptTemplate
-from langchain.agents import StructuredChatAgent, AgentOutputParser, Agent
-from langchain.agents.structured_chat.base import HUMAN_MESSAGE_TEMPLATE
-from langchain.callbacks.base import BaseCallbackManager
-from langchain.callbacks.manager import Callbacks
-from langchain.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate
-from langchain.schema import AgentAction, AgentFinish, OutputParserException
-from langchain.tools import BaseTool
-from langchain.agents.structured_chat.prompt import PREFIX, SUFFIX
+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
+from langchain.agents.structured_chat.prompt import PREFIX, SUFFIX
+from langchain.callbacks.base import BaseCallbackManager
+from langchain.callbacks.manager import Callbacks
+from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate
+from langchain.schema import AgentAction, AgentFinish, OutputParserException
+from langchain.tools import BaseTool
 
 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.

api/core/agent/agent/structured_chat.py

@@ -1,23 +1,22 @@
 import re
-from typing import List, Tuple, Any, Union, Sequence, Optional, cast
-
-from langchain import BasePromptTemplate, PromptTemplate
-from langchain.agents import StructuredChatAgent, AgentOutputParser, Agent
-from langchain.agents.structured_chat.base import HUMAN_MESSAGE_TEMPLATE
-from langchain.callbacks.base import BaseCallbackManager
-from langchain.callbacks.manager import Callbacks
-from langchain.memory.prompt import SUMMARY_PROMPT
-from langchain.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate
-from langchain.schema import AgentAction, AgentFinish, AIMessage, HumanMessage, OutputParserException, BaseMessage, \
-    get_buffer_string
-from langchain.tools import BaseTool
-from langchain.agents.structured_chat.prompt import PREFIX, SUFFIX
+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
+from langchain.agents.structured_chat.prompt import PREFIX, SUFFIX
+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.tools import BaseTool
 
 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.

api/core/agent/agent_executor.py

@@ -1,11 +1,6 @@
 import enum
 import logging
-from typing import Union, Optional
-
-from langchain.agents import BaseSingleActionAgent, BaseMultiActionAgent
-from langchain.callbacks.manager import Callbacks
-from langchain.tools import BaseTool
-from pydantic import BaseModel, Extra
+from typing import Optional, Union
 
 from core.agent.agent.agent_llm_callback import AgentLLMCallback
 from core.agent.agent.multi_dataset_router_agent import MultiDatasetRouterAgent
@@ -13,8 +8,6 @@ from core.agent.agent.openai_function_call import AutoSummarizingOpenAIFunctionC
 from core.agent.agent.output_parser.structured_chat import StructuredChatOutputParser
 from core.agent.agent.structed_multi_dataset_router_agent import StructuredMultiDatasetRouterAgent
 from core.agent.agent.structured_chat import AutoSummarizingStructuredChatAgent
-from langchain.agents import AgentExecutor as LCAgentExecutor
-
 from core.entities.application_entities import ModelConfigEntity
 from core.entities.message_entities import prompt_messages_to_lc_messages
 from core.helper import moderation
@@ -22,6 +15,11 @@ from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_runtime.errors.invoke import InvokeError
 from core.tool.dataset_multi_retriever_tool import DatasetMultiRetrieverTool
 from core.tool.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):