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51 lines
1.8 KiB
Python
51 lines
1.8 KiB
Python
from typing import List
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from .prompt import get_prompt, parse_response
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from opendevin.agent import Agent
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from opendevin.action import AgentFinishAction
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from opendevin.llm.llm import LLM
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from opendevin.state import State
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from opendevin.action import Action
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class PlannerAgent(Agent):
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"""
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The planner agent utilizes a special prompting strategy to create long term plans for solving problems.
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The agent is given its previous action-observation pairs, current task, and hint based on last action taken at every step.
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"""
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def __init__(self, llm: LLM):
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"""
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Initialize the Planner Agent with an LLM
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Parameters:
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- llm (LLM): The llm to be used by this agent
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"""
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super().__init__(llm)
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def step(self, state: State) -> Action:
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"""
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Checks to see if current step is completed, returns AgentFinishAction if True.
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Otherwise, creates a plan prompt and sends to model for inference, returning the result as the next action.
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Parameters:
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- state (State): The current state given the previous actions and observations
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Returns:
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- AgentFinishAction: If the last state was 'completed', 'verified', or 'abandoned'
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- Action: The next action to take based on llm response
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"""
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if state.plan.task.state in ['completed', 'verified', 'abandoned']:
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return AgentFinishAction()
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prompt = get_prompt(state.plan, state.history)
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messages = [{'content': prompt, 'role': 'user'}]
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resp = self.llm.completion(messages=messages)
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action_resp = resp['choices'][0]['message']['content']
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state.num_of_chars += len(prompt) + len(action_resp)
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action = parse_response(action_resp)
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return action
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def search_memory(self, query: str) -> List[str]:
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return []
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