Files
OpenHands/agenthub/micro/agent.py
Jirka Borovec 0c2ebfd6e1 Ruff: use I rule for isort (#1410)
Ruff: use I rule for isort
2024-04-29 15:41:58 -07:00

78 lines
2.3 KiB
Python

import json
from typing import Dict, List
from jinja2 import BaseLoader, Environment
from opendevin.action import Action, action_from_dict
from opendevin.agent import Agent
from opendevin.exceptions import LLMOutputError
from opendevin.llm.llm import LLM
from opendevin.state import State
from .instructions import instructions
from .registry import all_microagents
def parse_response(orig_response: str) -> Action:
json_start = orig_response.find('{')
json_end = orig_response.rfind('}') + 1
response = orig_response[json_start:json_end]
try:
action_dict = json.loads(response)
except json.JSONDecodeError as e:
raise LLMOutputError(
'Invalid JSON in response. Please make sure the response is a valid JSON object'
) from e
action = action_from_dict(action_dict)
return action
def my_encoder(obj):
"""
Encodes objects as dictionaries
Parameters:
- obj (Object): An object that will be converted
Returns:
- dict: If the object can be converted it is returned in dict format
"""
if hasattr(obj, 'to_dict'):
return obj.to_dict()
def to_json(obj, **kwargs):
"""
Serialize an object to str format
"""
return json.dumps(obj, default=my_encoder, **kwargs)
class MicroAgent(Agent):
prompt = ''
agent_definition: Dict = {}
def __init__(self, llm: LLM):
super().__init__(llm)
if 'name' not in self.agent_definition:
raise ValueError('Agent definition must contain a name')
self.prompt_template = Environment(loader=BaseLoader).from_string(self.prompt)
self.delegates = all_microagents.copy()
del self.delegates[self.agent_definition['name']]
def step(self, state: State) -> Action:
prompt = self.prompt_template.render(
state=state,
instructions=instructions,
to_json=to_json,
delegates=self.delegates)
messages = [{'content': prompt, 'role': 'user'}]
resp = self.llm.completion(messages=messages)
action_resp = resp['choices'][0]['message']['content']
state.num_of_chars += len(prompt) + len(action_resp)
action = parse_response(action_resp)
return action
def search_memory(self, query: str) -> List[str]:
return []