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fd9e598136
* add options for controlling memory * Update agenthub/monologue_agent/utils/memory.py Co-authored-by: Jim Su <jimsu@protonmail.com> * move memory initialization switch back * fix lint * fix type --------- Co-authored-by: Jim Su <jimsu@protonmail.com>
255 lines
10 KiB
Python
255 lines
10 KiB
Python
from typing import List
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from opendevin.agent import Agent
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from opendevin.state import State
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from opendevin.llm.llm import LLM
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from opendevin.schema import ActionType, ObservationType
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from opendevin.exceptions import AgentNoInstructionError
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from opendevin.schema.config import ConfigType
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from opendevin import config
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from opendevin.action import (
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Action,
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NullAction,
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CmdRunAction,
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FileWriteAction,
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FileReadAction,
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AgentRecallAction,
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BrowseURLAction,
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AgentThinkAction,
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)
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from opendevin.observation import (
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Observation,
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NullObservation,
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CmdOutputObservation,
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FileReadObservation,
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AgentRecallObservation,
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BrowserOutputObservation,
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)
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import agenthub.monologue_agent.utils.prompts as prompts
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from agenthub.monologue_agent.utils.monologue import Monologue
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if config.get(ConfigType.AGENT_MEMORY_ENABLED):
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from agenthub.monologue_agent.utils.memory import LongTermMemory
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MAX_MONOLOGUE_LENGTH = 20000
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MAX_OUTPUT_LENGTH = 5000
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INITIAL_THOUGHTS = [
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'I exist!',
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'Hmm...looks like I can type in a command line prompt',
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'Looks like I have a web browser too!',
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"Here's what I want to do: $TASK",
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'How am I going to get there though?',
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'It seems like I have some kind of short term memory.',
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'Each of my thoughts seems to be stored in a JSON array.',
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'It seems whatever I say next will be added as an object to the list.',
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'But no one has perfect short-term memory. My list of thoughts will be summarized and condensed over time, losing information in the process.',
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'Fortunately I have long term memory!',
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'I can just perform a recall action, followed by the thing I want to remember. And then related thoughts just spill out!',
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"Sometimes they're random thoughts that don't really have to do with what I wanted to remember. But usually they're exactly what I need!",
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"Let's try it out!",
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'RECALL what it is I want to do',
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"Here's what I want to do: $TASK",
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'How am I going to get there though?',
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"Neat! And it looks like it's easy for me to use the command line too! I just have to perform a run action and include the command I want to run in the command argument. The command output just jumps into my head!",
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'RUN echo "hello world"',
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'hello world',
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'Cool! I bet I can write files too using the write action.',
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"WRITE echo \"console.log('hello world')\" > test.js",
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'',
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"I just created test.js. I'll try and run it now.",
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'RUN node test.js',
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'hello world',
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'It works!',
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"I'm going to try reading it now using the read action.",
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'READ test.js',
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"console.log('hello world')",
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'Nice! I can read files too!',
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'And if I want to use the browser, I just need to use the browse action and include the url I want to visit in the url argument',
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"Let's try that...",
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'BROWSE google.com',
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'<form><input type="text"></input><button type="submit"></button></form>',
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'I can browse the web too!',
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'And once I have completed my task, I can use the finish action to stop working.',
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"But I should only use the finish action when I'm absolutely certain that I've completed my task and have tested my work.",
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'Very cool. Now to accomplish my task.',
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"I'll need a strategy. And as I make progress, I'll need to keep refining that strategy. I'll need to set goals, and break them into sub-goals.",
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'In between actions, I must always take some time to think, strategize, and set new goals. I should never take two actions in a row.',
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"OK so my task is to $TASK. I haven't made any progress yet. Where should I start?",
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"It seems like there might be an existing project here. I should probably start by running `ls` to see what's here.",
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]
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class MonologueAgent(Agent):
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"""
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The Monologue Agent utilizes long and short term memory to complete tasks.
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Long term memory is stored as a LongTermMemory object and the model uses it to search for examples from the past.
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Short term memory is stored as a Monologue object and the model can condense it as necessary.
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"""
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_initialized = False
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monologue: Monologue
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memory: 'LongTermMemory | None'
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def __init__(self, llm: LLM):
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"""
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Initializes the Monologue Agent with an llm, monologue, and memory.
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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 _add_event(self, event: dict):
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"""
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Adds a new event to the agent's monologue and memory.
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Monologue automatically condenses when it gets too large.
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Parameters:
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- event (dict): The event that will be added to monologue and memory
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"""
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if 'extras' in event and 'screenshot' in event['extras']:
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del event['extras']['screenshot']
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if (
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'args' in event
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and 'output' in event['args']
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and len(event['args']['output']) > MAX_OUTPUT_LENGTH
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):
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event['args']['output'] = (
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event['args']['output'][:MAX_OUTPUT_LENGTH] + '...'
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)
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self.monologue.add_event(event)
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if self.memory is not None:
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self.memory.add_event(event)
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if self.monologue.get_total_length() > MAX_MONOLOGUE_LENGTH:
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self.monologue.condense(self.llm)
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def _initialize(self, task: str):
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"""
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Utilizes the INITIAL_THOUGHTS list to give the agent a context for it's capabilities
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and how to navigate the WORKSPACE_MOUNT_PATH_IN_SANDBOX in `config` (e.g., /workspace by default).
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Short circuited to return when already initialized.
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Parameters:
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- task (str): The initial goal statement provided by the user
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Raises:
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- AgentNoInstructionError: If task is not provided
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"""
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if self._initialized:
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return
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if task is None or task == '':
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raise AgentNoInstructionError()
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self.monologue = Monologue()
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if config.get(ConfigType.AGENT_MEMORY_ENABLED):
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self.memory = LongTermMemory()
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else:
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self.memory = None
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output_type = ''
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for thought in INITIAL_THOUGHTS:
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thought = thought.replace('$TASK', task)
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if output_type != '':
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observation: Observation = NullObservation(content='')
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if output_type == ObservationType.RUN:
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observation = CmdOutputObservation(
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content=thought, command_id=0, command=''
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)
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elif output_type == ObservationType.READ:
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observation = FileReadObservation(content=thought, path='')
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elif output_type == ObservationType.RECALL:
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observation = AgentRecallObservation(
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content=thought, memories=[])
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elif output_type == ObservationType.BROWSE:
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observation = BrowserOutputObservation(
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content=thought, url='', screenshot=''
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)
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self._add_event(observation.to_memory())
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output_type = ''
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else:
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action: Action = NullAction()
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if thought.startswith('RUN'):
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command = thought.split('RUN ')[1]
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action = CmdRunAction(command)
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output_type = ActionType.RUN
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elif thought.startswith('WRITE'):
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parts = thought.split('WRITE ')[1].split(' > ')
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path = parts[1]
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content = parts[0]
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action = FileWriteAction(path=path, content=content)
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elif thought.startswith('READ'):
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path = thought.split('READ ')[1]
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action = FileReadAction(path=path)
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output_type = ActionType.READ
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elif thought.startswith('RECALL'):
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query = thought.split('RECALL ')[1]
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action = AgentRecallAction(query=query)
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output_type = ActionType.RECALL
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elif thought.startswith('BROWSE'):
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url = thought.split('BROWSE ')[1]
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action = BrowseURLAction(url=url)
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output_type = ActionType.BROWSE
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else:
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action = AgentThinkAction(thought=thought)
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self._add_event(action.to_memory())
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self._initialized = True
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def step(self, state: State) -> Action:
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"""
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Modifies the current state by adding the most recent actions and observations, then prompts the model to think about it's next action to take using monologue, memory, and hint.
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Parameters:
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- state (State): The current state based on previous steps taken
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Returns:
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- Action: The next action to take based on LLM response
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"""
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self._initialize(state.plan.main_goal)
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for prev_action, obs in state.updated_info:
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self._add_event(prev_action.to_memory())
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self._add_event(obs.to_memory())
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state.updated_info = []
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prompt = prompts.get_request_action_prompt(
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state.plan.main_goal,
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self.monologue.get_thoughts(),
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state.background_commands_obs,
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)
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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 = prompts.parse_action_response(action_resp)
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self.latest_action = action
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return action
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def search_memory(self, query: str) -> List[str]:
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"""
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Uses VectorIndexRetriever to find related memories within the long term memory.
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Uses search to produce top 10 results.
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Parameters:
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- query (str): The query that we want to find related memories for
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Returns:
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- List[str]: A list of top 10 text results that matched the query
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"""
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if self.memory is None:
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return []
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return self.memory.search(query)
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def reset(self) -> None:
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super().reset()
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self.monologue = Monologue()
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if config.get(ConfigType.AGENT_MEMORY_ENABLED):
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self.memory = LongTermMemory()
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else:
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self.memory = None
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