Harrison/refactor agent (#781)

Co-authored-by: Amos Ng <me@amos.ng>

langchain/agents/agent.py

@@ -48,6 +48,29 @@ class Agent(BaseModel):
     def _stop(self) -> List[str]:
         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(
         self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
     ) -> Union[AgentAction, AgentFinish]:
@@ -61,24 +84,14 @@ class Agent(BaseModel):
         Returns:
             Action specifying what tool to use.
         """
-        thoughts = ""
-        for action, observation in intermediate_steps:
-            thoughts += action.log
-            thoughts += f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
+        thoughts = self._construct_scratchpad(intermediate_steps)
         new_inputs = {"agent_scratchpad": thoughts, "stop": self._stop}
         full_inputs = {**kwargs, **new_inputs}
-        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)
-        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)
+
+        action = self._get_next_action(full_inputs)
+        if action.tool == self.finish_tool_name:
+            return AgentFinish({"output": action.tool_input}, action.log)
+        return action
 
     def prepare_for_new_call(self) -> None:
         """Prepare the agent for new call, if needed."""