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

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This commit is contained in:
Bowen Liang
2024-02-09 15:21:33 +08:00
committed by GitHub
parent 589099a005
commit 063191889d
246 changed files with 912 additions and 937 deletions
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20
api/core/features/assistant_base_runner.py
View File

@@ -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
"""

21
api/core/features/assistant_cot_runner.py
View File

@@ -1,6 +1,7 @@
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.

15
api/core/features/assistant_fc_runner.py
View File

@@ -1,6 +1,7 @@
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

4
api/core/features/dataset_retrieval.py
View File

@@ -1,4 +1,4 @@
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

4
api/core/features/external_data_fetch.py
View File

@@ -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

3
api/core/features/moderation.py
View File

@@ -1,5 +1,4 @@
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