Fix/typos (#2308)
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@@ -12,7 +12,7 @@ from core.model_runtime.entities.llm_entities import LLMResult, LLMUsage, LLMRes
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from core.model_manager import ModelInstance
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from core.tools.errors import ToolInvokeError, ToolNotFoundError, \
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ToolNotSupportedError, ToolProviderNotFoundError, ToolParamterValidationError, \
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ToolNotSupportedError, ToolProviderNotFoundError, ToolParameterValidationError, \
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ToolProviderCredentialValidationError
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from core.features.assistant_base_runner import BaseAssistantApplicationRunner
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@@ -28,7 +28,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
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Run Cot agent application
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"""
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app_orchestration_config = self.app_orchestration_config
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self._repacket_app_orchestration_config(app_orchestration_config)
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self._repack_app_orchestration_config(app_orchestration_config)
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agent_scratchpad: List[AgentScratchpadUnit] = []
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@@ -71,7 +71,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
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}
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final_answer = ''
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def increse_usage(final_llm_usage_dict: Dict[str, LLMUsage], usage: LLMUsage):
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def increase_usage(final_llm_usage_dict: Dict[str, LLMUsage], usage: LLMUsage):
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if not final_llm_usage_dict['usage']:
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final_llm_usage_dict['usage'] = usage
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else:
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@@ -105,7 +105,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
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self.queue_manager.publish_agent_thought(agent_thought, PublishFrom.APPLICATION_MANAGER)
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# update prompt messages
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prompt_messages = self._originze_cot_prompt_messages(
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prompt_messages = self._organize_cot_prompt_messages(
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mode=app_orchestration_config.model_config.mode,
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prompt_messages=prompt_messages,
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tools=prompt_messages_tools,
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@@ -138,7 +138,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
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# get llm usage
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if llm_result.usage:
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increse_usage(llm_usage, llm_result.usage)
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increase_usage(llm_usage, llm_result.usage)
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# publish agent thought if it's first iteration
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if iteration_step == 1:
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@@ -208,7 +208,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
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try:
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tool_response = tool_instance.invoke(
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user_id=self.user_id,
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tool_paramters=tool_call_args if isinstance(tool_call_args, dict) else json.loads(tool_call_args)
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tool_parameters=tool_call_args if isinstance(tool_call_args, dict) else json.loads(tool_call_args)
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)
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# transform tool response to llm friendly response
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tool_response = self.transform_tool_invoke_messages(tool_response)
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@@ -226,15 +226,15 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
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message_file_ids = [message_file.id for message_file, _ in message_files]
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except ToolProviderCredentialValidationError as e:
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error_response = f"Plese check your tool provider credentials"
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error_response = f"Please check your tool provider credentials"
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except (
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ToolNotFoundError, ToolNotSupportedError, ToolProviderNotFoundError
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) as e:
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error_response = f"there is not a tool named {tool_call_name}"
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except (
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ToolParamterValidationError
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ToolParameterValidationError
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) as e:
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error_response = f"tool paramters validation error: {e}, please check your tool paramters"
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error_response = f"tool parameters validation error: {e}, please check your tool parameters"
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except ToolInvokeError as e:
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error_response = f"tool invoke error: {e}"
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except Exception as e:
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@@ -469,7 +469,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
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if not next_iteration.find("{{observation}}") >= 0:
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raise ValueError("{{observation}} is required in next_iteration")
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def _convert_strachpad_list_to_str(self, agent_scratchpad: List[AgentScratchpadUnit]) -> str:
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def _convert_scratchpad_list_to_str(self, agent_scratchpad: List[AgentScratchpadUnit]) -> str:
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"""
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convert agent scratchpad list to str
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"""
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@@ -481,7 +481,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
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return result
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def _originze_cot_prompt_messages(self, mode: Literal["completion", "chat"],
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def _organize_cot_prompt_messages(self, mode: Literal["completion", "chat"],
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prompt_messages: List[PromptMessage],
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tools: List[PromptMessageTool],
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agent_scratchpad: List[AgentScratchpadUnit],
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@@ -490,7 +490,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
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input: str,
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) -> List[PromptMessage]:
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"""
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originze chain of thought prompt messages, a standard prompt message is like:
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organize chain of thought prompt messages, a standard prompt message is like:
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Respond to the human as helpfully and accurately as possible.
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{{instruction}}
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@@ -528,7 +528,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
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.replace("{{tools}}", tools_str) \
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.replace("{{tool_names}}", tool_names)
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# originze prompt messages
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# organize prompt messages
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if mode == "chat":
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# override system message
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overrided = False
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@@ -559,7 +559,7 @@ class AssistantCotApplicationRunner(BaseAssistantApplicationRunner):
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return prompt_messages
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elif mode == "completion":
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# parse agent scratchpad
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agent_scratchpad_str = self._convert_strachpad_list_to_str(agent_scratchpad)
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agent_scratchpad_str = self._convert_scratchpad_list_to_str(agent_scratchpad)
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# parse prompt messages
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return [UserPromptMessage(
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content=first_prompt.replace("{{instruction}}", instruction)
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