wehub-resource-sync
255 lines
10 KiB
Python
255 lines
10 KiB
Python
import logging
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import re
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import textwrap
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from typing import Dict, List, Tuple
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import platform
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from gui_agents.s2.agents.grounding import ACI
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from gui_agents.s2.core.module import BaseModule
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from gui_agents.s2.core.knowledge import KnowledgeBase
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from gui_agents.s2.memory.procedural_memory import PROCEDURAL_MEMORY
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from gui_agents.s2.utils.common_utils import (
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Node,
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calculate_tokens,
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call_llm_safe,
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parse_single_code_from_string,
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sanitize_code,
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extract_first_agent_function,
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)
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logger = logging.getLogger("desktopenv.agent")
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class Worker(BaseModule):
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def __init__(
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self,
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engine_params: Dict,
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grounding_agent: ACI,
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local_kb_path: str,
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embedding_engine,
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platform: str = platform.system().lower(),
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enable_reflection: bool = True,
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use_subtask_experience: bool = True,
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):
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"""
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Worker receives a subtask list and active subtask and generates the next action for the to execute.
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Args:
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engine_params: Dict
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Parameters for the multimodal engine
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grounding_agent: Agent
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The grounding agent to use
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local_kb_path: str
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Path to knowledge base
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platform: str
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OS platform the agent runs on (darwin, linux, windows)
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enable_reflection: bool
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Whether to enable reflection
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use_subtask_experience: bool
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Whether to use subtask experience
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"""
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super().__init__(engine_params, platform)
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self.grounding_agent = grounding_agent
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self.local_kb_path = local_kb_path
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self.embedding_engine = embedding_engine
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self.enable_reflection = enable_reflection
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self.use_subtask_experience = use_subtask_experience
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self.reset()
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def reset(self):
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if self.platform != "linux":
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skipped_actions = ["set_cell_values"]
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else:
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skipped_actions = []
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sys_prompt = PROCEDURAL_MEMORY.construct_worker_procedural_memory(
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type(self.grounding_agent), skipped_actions=skipped_actions
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).replace("CURRENT_OS", self.platform)
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self.generator_agent = self._create_agent(sys_prompt)
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self.reflection_agent = self._create_agent(
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PROCEDURAL_MEMORY.REFLECTION_ON_TRAJECTORY
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)
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self.knowledge_base = KnowledgeBase(
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embedding_engine=self.embedding_engine,
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local_kb_path=self.local_kb_path,
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platform=self.platform,
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engine_params=self.engine_params,
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)
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self.turn_count = 0
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self.worker_history = []
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self.reflections = []
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self.cost_this_turn = 0
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self.screenshot_inputs = []
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self.planner_history = []
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self.max_trajector_length = 8
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def flush_messages(self):
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# generator msgs are alternating [user, assistant], so 2 per round
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if len(self.generator_agent.messages) > 2 * self.max_trajector_length + 1:
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self.generator_agent.remove_message_at(1)
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self.generator_agent.remove_message_at(1)
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# reflector msgs are all [(user text, user image)], so 1 per round
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if len(self.reflection_agent.messages) > self.max_trajector_length + 1:
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self.reflection_agent.remove_message_at(1)
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def generate_next_action(
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self,
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instruction: str,
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search_query: str,
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subtask: str,
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subtask_info: Dict,
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future_tasks: List[Node],
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done_task: List[Node],
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obs: Dict,
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) -> Tuple[Dict, List]:
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"""
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Predict the next action(s) based on the current observation.
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"""
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# Provide the top_app to the Grounding Agent to remove all other applications from the tree. At t=0, top_app is None
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agent = self.grounding_agent
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# Get RAG knowledge, only update system message at t=0
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if self.turn_count == 0:
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if self.use_subtask_experience:
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subtask_query_key = (
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"Task:\n"
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+ search_query
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+ "\n\nSubtask: "
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+ subtask
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+ "\nSubtask Instruction: "
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+ subtask_info
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)
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retrieved_similar_subtask, retrieved_subtask_experience = (
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self.knowledge_base.retrieve_episodic_experience(subtask_query_key)
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)
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# Dirty fix to replace id with element description during subtask retrieval
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pattern = r"\(\d+"
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retrieved_subtask_experience = re.sub(
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pattern, "(element_description", retrieved_subtask_experience
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)
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retrieved_subtask_experience = retrieved_subtask_experience.replace(
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"_id", "_description"
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)
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logger.info(
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"SIMILAR SUBTASK EXPERIENCE: %s",
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retrieved_similar_subtask
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+ "\n"
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+ retrieved_subtask_experience.strip(),
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)
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instruction += "\nYou may refer to some similar subtask experience if you think they are useful. {}".format(
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retrieved_similar_subtask + "\n" + retrieved_subtask_experience
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)
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self.generator_agent.add_system_prompt(
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self.generator_agent.system_prompt.replace(
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"SUBTASK_DESCRIPTION", subtask
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)
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.replace("TASK_DESCRIPTION", instruction)
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.replace("FUTURE_TASKS", ", ".join([f.name for f in future_tasks]))
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.replace("DONE_TASKS", ",".join(d.name for d in done_task))
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)
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# Reflection generation does not add its own response, it only gets the trajectory
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reflection = None
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if self.enable_reflection:
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# Load the initial subtask info
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if self.turn_count == 0:
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text_content = textwrap.dedent(f"""
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Subtask Description: {subtask}
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Subtask Information: {subtask_info}
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Current Trajectory below:
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""")
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updated_sys_prompt = (
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self.reflection_agent.system_prompt + "\n" + text_content
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)
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self.reflection_agent.add_system_prompt(updated_sys_prompt)
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self.reflection_agent.add_message(
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text_content="The initial screen is provided. No action has been taken yet.",
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image_content=obs["screenshot"],
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role="user",
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)
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# Load the latest action
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else:
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text_content = self.clean_worker_generation_for_reflection(
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self.planner_history[-1]
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)
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self.reflection_agent.add_message(
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text_content=text_content,
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image_content=obs["screenshot"],
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role="user",
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)
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reflection = call_llm_safe(self.reflection_agent)
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self.reflections.append(reflection)
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logger.info("REFLECTION: %s", reflection)
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generator_message = (
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f"\nYou may use this reflection on the previous action and overall trajectory: {reflection}\n"
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if reflection and self.turn_count > 0
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else ""
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) + f"Text Buffer = [{','.join(agent.notes)}]."
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# Only provide subinfo in the very first message to avoid over influence and redundancy
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if self.turn_count == 0:
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generator_message += f"Remember only complete the subtask: {subtask}\n"
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generator_message += f"You can use this extra information for completing the current subtask: {subtask_info}.\n"
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# logger.info("GENERATOR MESSAGE: %s", generator_message)
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self.generator_agent.add_message(
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generator_message, image_content=obs["screenshot"], role="user"
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)
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plan = call_llm_safe(self.generator_agent)
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self.planner_history.append(plan)
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logger.info("PLAN: %s", plan)
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self.generator_agent.add_message(plan, role="assistant")
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# Calculate input/output tokens and gpt-4o cost
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input_tokens, output_tokens = calculate_tokens(self.generator_agent.messages)
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cost = input_tokens * (0.0050 / 1000) + output_tokens * (0.0150 / 1000)
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self.cost_this_turn += cost
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logger.info("EXECTUOR COST: %s", self.cost_this_turn)
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# Use the DescriptionBasedACI to convert agent_action("desc") into agent_action([x, y])
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try:
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agent.assign_coordinates(plan, obs)
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plan_code = parse_single_code_from_string(plan.split("Grounded Action")[-1])
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plan_code = sanitize_code(plan_code)
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plan_code = extract_first_agent_function(plan_code)
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exec_code = eval(plan_code)
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except Exception as e:
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logger.error("Error in parsing plan code: %s", e)
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plan_code = "agent.wait(1.0)"
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exec_code = eval(plan_code)
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executor_info = {
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"current_subtask": subtask,
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"current_subtask_info": subtask_info,
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"executor_plan": plan,
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"plan_code": plan_code,
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"reflection": reflection,
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"num_input_tokens_executor": input_tokens,
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"num_output_tokens_executor": output_tokens,
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}
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self.turn_count += 1
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self.screenshot_inputs.append(obs["screenshot"])
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self.flush_messages()
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return executor_info, [exec_code]
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# Removes the previous action verification, and removes any extraneous grounded actions
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def clean_worker_generation_for_reflection(self, worker_generation: str) -> str:
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# Remove the previous action verification
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res = worker_generation[worker_generation.find("(Screenshot Analysis)") :]
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action = extract_first_agent_function(worker_generation)
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# Cut off extra grounded actions
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res = res[: res.find("(Grounded Action)")]
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res += f"(Grounded Action)\n```python\n{action}\n```\n"
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return res
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