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https://github.com/All-Hands-AI/OpenHands.git
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dffbeec45a
* Add SANDBOX_WORKSPACE_DIR into config * Add SANDBOX_WORKSPACE_DIR into config * fix occurence of /workspace
132 lines
3.9 KiB
Python
132 lines
3.9 KiB
Python
import os
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import argparse
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import toml
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from dotenv import load_dotenv
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from opendevin.schema import ConfigType
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load_dotenv()
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DEFAULT_CONFIG: dict = {
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ConfigType.LLM_API_KEY: None,
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ConfigType.LLM_BASE_URL: None,
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ConfigType.WORKSPACE_BASE: os.getcwd(),
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ConfigType.WORKSPACE_MOUNT_PATH: None,
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ConfigType.WORKSPACE_MOUNT_PATH_IN_SANDBOX: '/workspace',
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ConfigType.WORKSPACE_MOUNT_REWRITE: None,
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ConfigType.LLM_MODEL: 'gpt-3.5-turbo-1106',
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ConfigType.SANDBOX_CONTAINER_IMAGE: 'ghcr.io/opendevin/sandbox',
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ConfigType.RUN_AS_DEVIN: 'true',
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ConfigType.LLM_EMBEDDING_MODEL: 'local',
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ConfigType.LLM_EMBEDDING_DEPLOYMENT_NAME: None,
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ConfigType.LLM_API_VERSION: None,
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ConfigType.LLM_NUM_RETRIES: 1,
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ConfigType.LLM_COOLDOWN_TIME: 1,
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ConfigType.MAX_ITERATIONS: 100,
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# GPT-4 pricing is $10 per 1M input tokens. Since tokenization happens on LLM side,
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# we cannot easily count number of tokens, but we can count characters.
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# Assuming 5 characters per token, 5 million is a reasonable default limit.
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ConfigType.MAX_CHARS: 5_000_000,
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ConfigType.AGENT: 'MonologueAgent',
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ConfigType.E2B_API_KEY: '',
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ConfigType.SANDBOX_TYPE: 'ssh', # Can be 'ssh', 'exec', or 'e2b'
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ConfigType.USE_HOST_NETWORK: 'false',
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ConfigType.SSH_HOSTNAME: 'localhost',
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ConfigType.DISABLE_COLOR: 'false',
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}
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config_str = ''
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if os.path.exists('config.toml'):
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with open('config.toml', 'rb') as f:
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config_str = f.read().decode('utf-8')
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tomlConfig = toml.loads(config_str)
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config = DEFAULT_CONFIG.copy()
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for k, v in config.items():
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if k in os.environ:
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config[k] = os.environ[k]
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elif k in tomlConfig:
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config[k] = tomlConfig[k]
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def get_parser():
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parser = argparse.ArgumentParser(
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description='Run an agent with a specific task')
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parser.add_argument(
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'-d',
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'--directory',
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type=str,
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help='The working directory for the agent',
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)
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parser.add_argument(
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'-t', '--task', type=str, default='', help='The task for the agent to perform'
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)
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parser.add_argument(
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'-f',
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'--file',
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type=str,
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help='Path to a file containing the task. Overrides -t if both are provided.',
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)
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parser.add_argument(
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'-c',
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'--agent-cls',
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default='MonologueAgent',
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type=str,
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help='The agent class to use',
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)
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parser.add_argument(
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'-m',
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'--model-name',
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default=config.get(ConfigType.LLM_MODEL),
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type=str,
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help='The (litellm) model name to use',
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)
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parser.add_argument(
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'-i',
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'--max-iterations',
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default=config.get(ConfigType.MAX_ITERATIONS),
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type=int,
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help='The maximum number of iterations to run the agent',
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)
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parser.add_argument(
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'-n',
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'--max-chars',
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default=config.get(ConfigType.MAX_CHARS),
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type=int,
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help='The maximum number of characters to send to and receive from LLM per task',
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)
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return parser
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def parse_arguments():
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parser = get_parser()
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args, _ = parser.parse_known_args()
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if args.directory:
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config[ConfigType.WORKSPACE_BASE] = os.path.abspath(args.directory)
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print(f'Setting workspace base to {config[ConfigType.WORKSPACE_BASE]}')
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return args
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args = parse_arguments()
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def finalize_config():
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if config.get(ConfigType.WORKSPACE_MOUNT_REWRITE) and not config.get(ConfigType.WORKSPACE_MOUNT_PATH):
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base = config.get(ConfigType.WORKSPACE_BASE) or os.getcwd()
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parts = config[ConfigType.WORKSPACE_MOUNT_REWRITE].split(':')
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config[ConfigType.WORKSPACE_MOUNT_PATH] = base.replace(parts[0], parts[1])
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finalize_config()
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def get(key: str, required: bool = False):
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"""
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Get a key from the environment variables or config.toml or default configs.
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"""
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value = config.get(key)
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if not value and required:
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raise KeyError(f"Please set '{key}' in `config.toml` or `.env`.")
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return value
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