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https://github.com/All-Hands-AI/OpenHands.git
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26dcf4fd7c
* remove screenshot in browser observation * refactor utils * allow only dict * fix screenshot not showing up in frontend --------- Co-authored-by: Robert Brennan <accounts@rbren.io>
79 lines
2.3 KiB
Python
79 lines
2.3 KiB
Python
import json
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from typing import Dict, List
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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.exceptions import LLMOutputError
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from opendevin.events.action import Action, action_from_dict
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from opendevin.llm.llm import LLM
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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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json_start = orig_response.find('{')
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json_end = orig_response.rfind('}') + 1
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response = orig_response[json_start:json_end]
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try:
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action_dict = json.loads(response)
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except json.JSONDecodeError as e:
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raise LLMOutputError(
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'Invalid JSON in response. Please make sure the response is a valid JSON object'
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) from e
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action = action_from_dict(action_dict)
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return action
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def my_encoder(obj):
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"""
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Encodes objects as dictionaries
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Parameters:
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- obj (Object): An object that will be converted
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Returns:
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- dict: If the object can be converted it is returned in dict format
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"""
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if hasattr(obj, 'to_dict'):
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return obj.to_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, default=my_encoder, **kwargs)
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class MicroAgent(Agent):
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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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delegates=self.delegates,
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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 = 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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