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https://github.com/All-Hands-AI/OpenHands.git
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c68478f470
Currently, OpenDevin uses a global singleton LLM config and a global singleton agent config. This PR allows customers to configure an LLM config for each agent. A hypothetically useful scenario is to use a cheaper LLM for repo exploration / code search, and a more powerful LLM to actually do the problem solving (CodeActAgent). Partially solves #2075 (web GUI improvement is not the goal of this PR)
85 lines
2.6 KiB
Python
85 lines
2.6 KiB
Python
from jinja2 import BaseLoader, Environment
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from opendevin.controller.agent import Agent
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from opendevin.controller.state.state import State
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from opendevin.core.config import config
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from opendevin.core.utils import json
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from opendevin.events.action import Action
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from opendevin.events.serialization.action import action_from_dict
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from opendevin.events.serialization.event import event_to_memory
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from opendevin.llm.llm import LLM
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from opendevin.memory.history import ShortTermHistory
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from .instructions import instructions
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from .registry import all_microagents
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def parse_response(orig_response: str) -> Action:
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# attempt to load the JSON dict from the response
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action_dict = json.loads(orig_response)
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# load the action from the dict
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return action_from_dict(action_dict)
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def to_json(obj, **kwargs):
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"""
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Serialize an object to str format
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"""
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return json.dumps(obj, **kwargs)
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def history_to_json(history: ShortTermHistory, max_events=20, **kwargs):
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"""
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Serialize and simplify history to str format
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"""
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# TODO: get agent specific llm config
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llm_config = config.get_llm_config()
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max_message_chars = llm_config.max_message_chars
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processed_history = []
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event_count = 0
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for event in history.get_events(reverse=True):
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if event_count >= max_events:
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break
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processed_history.append(event_to_memory(event, max_message_chars))
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event_count += 1
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# history is in reverse order, let's fix it
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processed_history.reverse()
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return json.dumps(processed_history, **kwargs)
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class MicroAgent(Agent):
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VERSION = '1.0'
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prompt = ''
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agent_definition: dict = {}
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def __init__(self, llm: LLM):
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super().__init__(llm)
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if 'name' not in self.agent_definition:
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raise ValueError('Agent definition must contain a name')
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self.prompt_template = Environment(loader=BaseLoader).from_string(self.prompt)
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self.delegates = all_microagents.copy()
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del self.delegates[self.agent_definition['name']]
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def step(self, state: State) -> Action:
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prompt = self.prompt_template.render(
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state=state,
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instructions=instructions,
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to_json=to_json,
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history_to_json=history_to_json,
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delegates=self.delegates,
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latest_user_message=state.get_current_user_intent(),
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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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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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