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01ae22ef57
* Replace OpenDevin with OpenHands * Update CONTRIBUTING.md * Update README.md * Update README.md * update poetry lock; move opendevin folder to openhands * fix env var * revert image references in docs * revert permissions * revert permissions --------- Co-authored-by: Xingyao Wang <xingyao6@illinois.edu>
55 lines
2.1 KiB
Python
55 lines
2.1 KiB
Python
from agenthub.planner_agent.response_parser import PlannerResponseParser
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from openhands.controller.agent import Agent
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from openhands.controller.state.state import State
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from openhands.core.config import AgentConfig
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from openhands.core.message import ImageContent, Message, TextContent
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from openhands.events.action import Action, AgentFinishAction
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from openhands.llm.llm import LLM
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from .prompt import get_prompt_and_images
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class PlannerAgent(Agent):
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VERSION = '1.0'
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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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response_parser = PlannerResponseParser()
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def __init__(self, llm: LLM, config: AgentConfig):
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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, config)
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def step(self, state: State) -> Action:
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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.root_task.state in [
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'completed',
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'verified',
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'abandoned',
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]:
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return AgentFinishAction()
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prompt, image_urls = get_prompt_and_images(
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state, self.llm.config.max_message_chars
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)
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content = [TextContent(text=prompt)]
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if image_urls:
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content.append(ImageContent(image_urls=image_urls))
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message = Message(role='user', content=content)
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resp = self.llm.completion(messages=[message.model_dump()])
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return self.response_parser.parse(resp)
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