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fix: got unknown type of prompt message in multi-round ReAct agent chat (#5245)
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sino
GitHub
parent
54756cd3b2
commit
edffa5666d
@ -32,9 +32,9 @@ class CotAgentRunner(BaseAgentRunner, ABC):
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_prompt_messages_tools: list[PromptMessage] = None
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def run(self, message: Message,
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query: str,
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inputs: dict[str, str],
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) -> Union[Generator, LLMResult]:
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query: str,
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inputs: dict[str, str],
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) -> Union[Generator, LLMResult]:
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"""
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Run Cot agent application
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"""
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@ -52,7 +52,8 @@ class CotAgentRunner(BaseAgentRunner, ABC):
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# init instruction
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inputs = inputs or {}
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instruction = app_config.prompt_template.simple_prompt_template
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self._instruction = self._fill_in_inputs_from_external_data_tools(instruction, inputs)
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self._instruction = self._fill_in_inputs_from_external_data_tools(
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instruction, inputs)
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iteration_step = 1
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max_iteration_steps = min(app_config.agent.max_iteration, 5) + 1
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@ -61,7 +62,7 @@ class CotAgentRunner(BaseAgentRunner, ABC):
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tool_instances, self._prompt_messages_tools = self._init_prompt_tools()
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prompt_messages = self._organize_prompt_messages()
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function_call_state = True
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llm_usage = {
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'usage': None
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@ -120,9 +121,10 @@ class CotAgentRunner(BaseAgentRunner, ABC):
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# check llm result
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if not chunks:
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raise ValueError("failed to invoke llm")
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usage_dict = {}
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react_chunks = CotAgentOutputParser.handle_react_stream_output(chunks, usage_dict)
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react_chunks = CotAgentOutputParser.handle_react_stream_output(
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chunks, usage_dict)
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scratchpad = AgentScratchpadUnit(
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agent_response='',
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thought='',
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@ -160,15 +162,16 @@ class CotAgentRunner(BaseAgentRunner, ABC):
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)
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)
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scratchpad.thought = scratchpad.thought.strip() or 'I am thinking about how to help you'
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scratchpad.thought = scratchpad.thought.strip(
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) or 'I am thinking about how to help you'
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self._agent_scratchpad.append(scratchpad)
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# get llm usage
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if 'usage' in usage_dict:
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increase_usage(llm_usage, usage_dict['usage'])
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else:
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usage_dict['usage'] = LLMUsage.empty_usage()
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self.save_agent_thought(
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agent_thought=agent_thought,
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tool_name=scratchpad.action.action_name if scratchpad.action else '',
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@ -182,7 +185,7 @@ class CotAgentRunner(BaseAgentRunner, ABC):
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messages_ids=[],
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llm_usage=usage_dict['usage']
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)
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if not scratchpad.is_final():
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self.queue_manager.publish(QueueAgentThoughtEvent(
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agent_thought_id=agent_thought.id
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@ -196,7 +199,8 @@ class CotAgentRunner(BaseAgentRunner, ABC):
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# action is final answer, return final answer directly
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try:
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if isinstance(scratchpad.action.action_input, dict):
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final_answer = json.dumps(scratchpad.action.action_input)
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final_answer = json.dumps(
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scratchpad.action.action_input)
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elif isinstance(scratchpad.action.action_input, str):
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final_answer = scratchpad.action.action_input
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else:
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@ -207,7 +211,7 @@ class CotAgentRunner(BaseAgentRunner, ABC):
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function_call_state = True
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# action is tool call, invoke tool
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tool_invoke_response, tool_invoke_meta = self._handle_invoke_action(
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action=scratchpad.action,
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action=scratchpad.action,
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tool_instances=tool_instances,
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message_file_ids=message_file_ids
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)
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@ -217,10 +221,13 @@ class CotAgentRunner(BaseAgentRunner, ABC):
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self.save_agent_thought(
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agent_thought=agent_thought,
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tool_name=scratchpad.action.action_name,
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tool_input={scratchpad.action.action_name: scratchpad.action.action_input},
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tool_input={
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scratchpad.action.action_name: scratchpad.action.action_input},
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thought=scratchpad.thought,
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observation={scratchpad.action.action_name: tool_invoke_response},
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tool_invoke_meta={scratchpad.action.action_name: tool_invoke_meta.to_dict()},
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observation={
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scratchpad.action.action_name: tool_invoke_response},
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tool_invoke_meta={
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scratchpad.action.action_name: tool_invoke_meta.to_dict()},
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answer=scratchpad.agent_response,
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messages_ids=message_file_ids,
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llm_usage=usage_dict['usage']
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@ -232,7 +239,8 @@ class CotAgentRunner(BaseAgentRunner, ABC):
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# update prompt tool message
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for prompt_tool in self._prompt_messages_tools:
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self.update_prompt_message_tool(tool_instances[prompt_tool.name], prompt_tool)
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self.update_prompt_message_tool(
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tool_instances[prompt_tool.name], prompt_tool)
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iteration_step += 1
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@ -251,12 +259,12 @@ class CotAgentRunner(BaseAgentRunner, ABC):
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# save agent thought
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self.save_agent_thought(
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agent_thought=agent_thought,
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agent_thought=agent_thought,
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tool_name='',
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tool_input={},
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tool_invoke_meta={},
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thought=final_answer,
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observation={},
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observation={},
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answer=final_answer,
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messages_ids=[]
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)
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@ -269,11 +277,12 @@ class CotAgentRunner(BaseAgentRunner, ABC):
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message=AssistantPromptMessage(
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content=final_answer
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),
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usage=llm_usage['usage'] if llm_usage['usage'] else LLMUsage.empty_usage(),
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usage=llm_usage['usage'] if llm_usage['usage'] else LLMUsage.empty_usage(
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),
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system_fingerprint=''
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)), PublishFrom.APPLICATION_MANAGER)
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def _handle_invoke_action(self, action: AgentScratchpadUnit.Action,
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def _handle_invoke_action(self, action: AgentScratchpadUnit.Action,
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tool_instances: dict[str, Tool],
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message_file_ids: list[str]) -> tuple[str, ToolInvokeMeta]:
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"""
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@ -290,7 +299,7 @@ class CotAgentRunner(BaseAgentRunner, ABC):
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if not tool_instance:
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answer = f"there is not a tool named {tool_call_name}"
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return answer, ToolInvokeMeta.error_instance(answer)
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if isinstance(tool_call_args, str):
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try:
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tool_call_args = json.loads(tool_call_args)
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@ -311,7 +320,8 @@ class CotAgentRunner(BaseAgentRunner, ABC):
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# publish files
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for message_file, save_as in message_files:
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if save_as:
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self.variables_pool.set_file(tool_name=tool_call_name, value=message_file.id, name=save_as)
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self.variables_pool.set_file(
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tool_name=tool_call_name, value=message_file.id, name=save_as)
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# publish message file
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self.queue_manager.publish(QueueMessageFileEvent(
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@ -342,7 +352,7 @@ class CotAgentRunner(BaseAgentRunner, ABC):
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continue
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return instruction
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def _init_react_state(self, query) -> None:
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"""
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init agent scratchpad
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@ -350,7 +360,7 @@ class CotAgentRunner(BaseAgentRunner, ABC):
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self._query = query
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self._agent_scratchpad = []
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self._historic_prompt_messages = self._organize_historic_prompt_messages()
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@abstractmethod
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def _organize_prompt_messages(self) -> list[PromptMessage]:
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"""
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@ -382,13 +392,6 @@ class CotAgentRunner(BaseAgentRunner, ABC):
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scratchpads: list[AgentScratchpadUnit] = []
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current_scratchpad: AgentScratchpadUnit = None
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self.history_prompt_messages = AgentHistoryPromptTransform(
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model_config=self.model_config,
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prompt_messages=current_session_messages or [],
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history_messages=self.history_prompt_messages,
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memory=self.memory
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).get_prompt()
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for message in self.history_prompt_messages:
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if isinstance(message, AssistantPromptMessage):
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if not current_scratchpad:
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@ -404,7 +407,8 @@ class CotAgentRunner(BaseAgentRunner, ABC):
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try:
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current_scratchpad.action = AgentScratchpadUnit.Action(
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action_name=message.tool_calls[0].function.name,
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action_input=json.loads(message.tool_calls[0].function.arguments)
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action_input=json.loads(
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message.tool_calls[0].function.arguments)
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)
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current_scratchpad.action_str = json.dumps(
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current_scratchpad.action.to_dict()
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@ -424,10 +428,15 @@ class CotAgentRunner(BaseAgentRunner, ABC):
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result.append(message)
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if scratchpads:
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result.append(AssistantPromptMessage(
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content=self._format_assistant_message(scratchpads)
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))
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return result
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historic_prompts = AgentHistoryPromptTransform(
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model_config=self.model_config,
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prompt_messages=current_session_messages or [],
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history_messages=result,
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memory=self.memory
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).get_prompt()
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return historic_prompts
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