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@ -375,6 +375,59 @@ class AgentExecutor(Chain, BaseModel):
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final_output["intermediate_steps"] = intermediate_steps
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return final_output
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def _take_next_step(
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self,
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name_to_tool_map: Dict[str, Tool],
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color_mapping: Dict[str, str],
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inputs: Dict[str, str],
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intermediate_steps: List[Tuple[AgentAction, str]],
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) -> Union[AgentFinish, Tuple[AgentAction, str]]:
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"""Take a single step in the thought-action-observation loop.
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Override this to take control of how the agent makes and acts on choices.
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"""
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# Call the LLM to see what to do.
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output = self.agent.plan(intermediate_steps, **inputs)
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# If the tool chosen is the finishing tool, then we end and return.
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if isinstance(output, AgentFinish):
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return output
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# Otherwise we lookup the tool
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if output.tool in name_to_tool_map:
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tool = name_to_tool_map[output.tool]
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self.callback_manager.on_tool_start(
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{"name": str(tool.func)[:60] + "..."},
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output,
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color="green",
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verbose=self.verbose,
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)
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try:
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# We then call the tool on the tool input to get an observation
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observation = tool.func(output.tool_input)
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color = color_mapping[output.tool]
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return_direct = tool.return_direct
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except (KeyboardInterrupt, Exception) as e:
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self.callback_manager.on_tool_error(e, verbose=self.verbose)
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raise e
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else:
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self.callback_manager.on_tool_start(
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{"name": "N/A"}, output, color="green", verbose=self.verbose
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)
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observation = f"{output.tool} is not a valid tool, try another one."
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color = None
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return_direct = False
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llm_prefix = "" if return_direct else self.agent.llm_prefix
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self.callback_manager.on_tool_end(
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observation,
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color=color,
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observation_prefix=self.agent.observation_prefix,
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llm_prefix=llm_prefix,
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verbose=self.verbose,
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)
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if return_direct:
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# Set the log to "" because we do not want to log it.
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return AgentFinish({self.agent.return_values[0]: observation}, "")
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return output, observation
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def _call(self, inputs: Dict[str, str]) -> Dict[str, Any]:
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"""Run text through and get agent response."""
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# Make sure that every tool is synchronous (not a coroutine)
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@ -398,49 +451,13 @@ class AgentExecutor(Chain, BaseModel):
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iterations = 0
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# We now enter the agent loop (until it returns something).
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while self._should_continue(iterations):
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# Call the LLM to see what to do.
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output = self.agent.plan(intermediate_steps, **inputs)
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# If the tool chosen is the finishing tool, then we end and return.
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if isinstance(output, AgentFinish):
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return self._return(output, intermediate_steps)
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# Otherwise we lookup the tool
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if output.tool in name_to_tool_map:
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tool = name_to_tool_map[output.tool]
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self.callback_manager.on_tool_start(
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{"name": str(tool.func)[:60] + "..."},
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output,
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color="green",
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verbose=self.verbose,
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)
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try:
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# We then call the tool on the tool input to get an observation
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observation = tool.func(output.tool_input)
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color = color_mapping[output.tool]
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return_direct = tool.return_direct
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except (KeyboardInterrupt, Exception) as e:
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self.callback_manager.on_tool_error(e, verbose=self.verbose)
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raise e
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else:
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self.callback_manager.on_tool_start(
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{"name": "N/A"}, output, color="green", verbose=self.verbose
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)
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observation = f"{output.tool} is not a valid tool, try another one."
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color = None
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return_direct = False
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llm_prefix = "" if return_direct else self.agent.llm_prefix
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self.callback_manager.on_tool_end(
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observation,
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color=color,
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observation_prefix=self.agent.observation_prefix,
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llm_prefix=llm_prefix,
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verbose=self.verbose,
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next_step_output = self._take_next_step(
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name_to_tool_map, color_mapping, inputs, intermediate_steps
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)
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intermediate_steps.append((output, observation))
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if return_direct:
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# Set the log to "" because we do not want to log it.
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output = AgentFinish({self.agent.return_values[0]: observation}, "")
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return self._return(output, intermediate_steps)
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if isinstance(next_step_output, AgentFinish):
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return self._return(next_step_output, intermediate_steps)
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intermediate_steps.append(next_step_output)
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iterations += 1
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output = self.agent.return_stopped_response(
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self.early_stopping_method, intermediate_steps, **inputs
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