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langchain[patch]: Extract _aperform_agent_action from _aiter_next_step from AgentExecutor (#15707)
- **Description:** extreact the _aperform_agent_action in the AgentExecutor class to allow for easier overriding. Extracted logic from _iter_next_step into a new method _perform_agent_action for consistency and easier overriding. - **Issue:** #15706 Closes #15706
This commit is contained in:
Gianfranco Demarco
GitHub
parent
95ee69a301
commit
c69f599594
@ -1179,37 +1179,48 @@ class AgentExecutor(Chain):
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for agent_action in actions:
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yield agent_action
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for agent_action in actions:
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if run_manager:
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run_manager.on_agent_action(agent_action, color="green")
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# Otherwise we lookup the tool
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if agent_action.tool in name_to_tool_map:
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tool = name_to_tool_map[agent_action.tool]
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return_direct = tool.return_direct
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color = color_mapping[agent_action.tool]
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tool_run_kwargs = self.agent.tool_run_logging_kwargs()
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if return_direct:
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tool_run_kwargs["llm_prefix"] = ""
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# We then call the tool on the tool input to get an observation
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observation = tool.run(
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agent_action.tool_input,
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verbose=self.verbose,
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color=color,
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callbacks=run_manager.get_child() if run_manager else None,
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**tool_run_kwargs,
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)
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else:
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tool_run_kwargs = self.agent.tool_run_logging_kwargs()
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observation = InvalidTool().run(
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{
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"requested_tool_name": agent_action.tool,
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"available_tool_names": list(name_to_tool_map.keys()),
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},
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verbose=self.verbose,
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color=None,
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callbacks=run_manager.get_child() if run_manager else None,
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**tool_run_kwargs,
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)
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yield AgentStep(action=agent_action, observation=observation)
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yield self._perform_agent_action(
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name_to_tool_map, color_mapping, agent_action, run_manager
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)
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def _perform_agent_action(
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self,
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name_to_tool_map: Dict[str, BaseTool],
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color_mapping: Dict[str, str],
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agent_action: AgentAction,
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> AgentStep:
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if run_manager:
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run_manager.on_agent_action(agent_action, color="green")
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# Otherwise we lookup the tool
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if agent_action.tool in name_to_tool_map:
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tool = name_to_tool_map[agent_action.tool]
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return_direct = tool.return_direct
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color = color_mapping[agent_action.tool]
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tool_run_kwargs = self.agent.tool_run_logging_kwargs()
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if return_direct:
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tool_run_kwargs["llm_prefix"] = ""
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# We then call the tool on the tool input to get an observation
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observation = tool.run(
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agent_action.tool_input,
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verbose=self.verbose,
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color=color,
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callbacks=run_manager.get_child() if run_manager else None,
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**tool_run_kwargs,
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)
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else:
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tool_run_kwargs = self.agent.tool_run_logging_kwargs()
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observation = InvalidTool().run(
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{
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"requested_tool_name": agent_action.tool,
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"available_tool_names": list(name_to_tool_map.keys()),
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},
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verbose=self.verbose,
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color=None,
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callbacks=run_manager.get_child() if run_manager else None,
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**tool_run_kwargs,
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)
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return AgentStep(action=agent_action, observation=observation)
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async def _atake_next_step(
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self,
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@ -1303,52 +1314,61 @@ class AgentExecutor(Chain):
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for agent_action in actions:
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yield agent_action
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async def _aperform_agent_action(
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agent_action: AgentAction,
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) -> AgentStep:
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if run_manager:
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await run_manager.on_agent_action(
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agent_action, verbose=self.verbose, color="green"
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)
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# Otherwise we lookup the tool
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if agent_action.tool in name_to_tool_map:
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tool = name_to_tool_map[agent_action.tool]
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return_direct = tool.return_direct
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color = color_mapping[agent_action.tool]
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tool_run_kwargs = self.agent.tool_run_logging_kwargs()
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if return_direct:
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tool_run_kwargs["llm_prefix"] = ""
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# We then call the tool on the tool input to get an observation
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observation = await tool.arun(
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agent_action.tool_input,
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verbose=self.verbose,
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color=color,
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callbacks=run_manager.get_child() if run_manager else None,
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**tool_run_kwargs,
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)
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else:
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tool_run_kwargs = self.agent.tool_run_logging_kwargs()
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observation = await InvalidTool().arun(
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{
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"requested_tool_name": agent_action.tool,
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"available_tool_names": list(name_to_tool_map.keys()),
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},
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verbose=self.verbose,
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color=None,
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callbacks=run_manager.get_child() if run_manager else None,
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**tool_run_kwargs,
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)
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return AgentStep(action=agent_action, observation=observation)
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# Use asyncio.gather to run multiple tool.arun() calls concurrently
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result = await asyncio.gather(
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*[_aperform_agent_action(agent_action) for agent_action in actions]
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*[
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self._aperform_agent_action(
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name_to_tool_map, color_mapping, agent_action, run_manager
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)
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for agent_action in actions
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],
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)
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# TODO This could yield each result as it becomes available
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for chunk in result:
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yield chunk
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async def _aperform_agent_action(
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self,
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name_to_tool_map: Dict[str, BaseTool],
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color_mapping: Dict[str, str],
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agent_action: AgentAction,
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run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
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) -> AgentStep:
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if run_manager:
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await run_manager.on_agent_action(
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agent_action, verbose=self.verbose, color="green"
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)
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# Otherwise we lookup the tool
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if agent_action.tool in name_to_tool_map:
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tool = name_to_tool_map[agent_action.tool]
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return_direct = tool.return_direct
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color = color_mapping[agent_action.tool]
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tool_run_kwargs = self.agent.tool_run_logging_kwargs()
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if return_direct:
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tool_run_kwargs["llm_prefix"] = ""
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# We then call the tool on the tool input to get an observation
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observation = await tool.arun(
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agent_action.tool_input,
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verbose=self.verbose,
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color=color,
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callbacks=run_manager.get_child() if run_manager else None,
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**tool_run_kwargs,
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)
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else:
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tool_run_kwargs = self.agent.tool_run_logging_kwargs()
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observation = await InvalidTool().arun(
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{
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"requested_tool_name": agent_action.tool,
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"available_tool_names": list(name_to_tool_map.keys()),
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},
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verbose=self.verbose,
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color=None,
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callbacks=run_manager.get_child() if run_manager else None,
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**tool_run_kwargs,
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)
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return AgentStep(action=agent_action, observation=observation)
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def _call(
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self,
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inputs: Dict[str, str],
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