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Harrison/refactor agent (#781)

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Co-authored-by: Amos Ng <me@amos.ng>
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Harrison Chase 2023-01-28 08:24:13 -08:00 committed by GitHub
parent 5bb2952860
commit 7198a1cb22
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1 changed files with 29 additions and 16 deletions
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45
langchain/agents/agent.py
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@ -48,6 +48,29 @@ class Agent(BaseModel):
def _stop(self) -> List[str]: def _stop(self) -> List[str]:
return [f"\n{self.observation_prefix}"] return [f"\n{self.observation_prefix}"]
def _construct_scratchpad(
self, intermediate_steps: List[Tuple[AgentAction, str]]
) -> str:
"""Construct the scratchpad that lets the agent continue its thought process."""
thoughts = ""
for action, observation in intermediate_steps:
thoughts += action.log
thoughts += f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
return thoughts
def _get_next_action(self, full_inputs: Dict[str, str]) -> AgentAction:
full_output = self.llm_chain.predict(**full_inputs)
parsed_output = self._extract_tool_and_input(full_output)
while parsed_output is None:
full_output = self._fix_text(full_output)
full_inputs["agent_scratchpad"] += full_output
output = self.llm_chain.predict(**full_inputs)
full_output += output
parsed_output = self._extract_tool_and_input(full_output)
return AgentAction(
tool=parsed_output[0], tool_input=parsed_output[1], log=full_output
)
def plan( def plan(
self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
) -> Union[AgentAction, AgentFinish]: ) -> Union[AgentAction, AgentFinish]:
@ -61,24 +84,14 @@ class Agent(BaseModel):
Returns: Returns:
Action specifying what tool to use. Action specifying what tool to use.
""" """
thoughts = "" thoughts = self._construct_scratchpad(intermediate_steps)
for action, observation in intermediate_steps:
thoughts += action.log
thoughts += f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
new_inputs = {"agent_scratchpad": thoughts, "stop": self._stop} new_inputs = {"agent_scratchpad": thoughts, "stop": self._stop}
full_inputs = {**kwargs, **new_inputs} full_inputs = {**kwargs, **new_inputs}
full_output = self.llm_chain.predict(**full_inputs)
parsed_output = self._extract_tool_and_input(full_output) action = self._get_next_action(full_inputs)
while parsed_output is None: if action.tool == self.finish_tool_name:
full_output = self._fix_text(full_output) return AgentFinish({"output": action.tool_input}, action.log)
full_inputs["agent_scratchpad"] += full_output return action
output = self.llm_chain.predict(**full_inputs)
full_output += output
parsed_output = self._extract_tool_and_input(full_output)
tool, tool_input = parsed_output
if tool == self.finish_tool_name:
return AgentFinish({"output": tool_input}, full_output)
return AgentAction(tool, tool_input, full_output)
def prepare_for_new_call(self) -> None: def prepare_for_new_call(self) -> None:
"""Prepare the agent for new call, if needed.""" """Prepare the agent for new call, if needed."""