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from dataclasses import asdict
from datetime import datetime
from enum import Enum
from typing import Any
from pydantic import BaseModel
from openhands.events import Event, EventSource
from openhands.events.serialization.action import action_from_dict
from openhands.events.serialization.observation import observation_from_dict
from openhands.events.serialization.utils import remove_fields
from openhands.events.tool import ToolCallMetadata
from openhands.llm.metrics import Cost, Metrics, ResponseLatency, TokenUsage
# TODO: move `content` into `extras`
TOP_KEYS = [
'id',
'timestamp',
'source',
'message',
'cause',
'action',
'observation',
'tool_call_metadata',
'llm_metrics',
]
UNDERSCORE_KEYS = [
'id',
'timestamp',
'source',
'cause',
'tool_call_metadata',
'llm_metrics',
]
DELETE_FROM_TRAJECTORY_EXTRAS = {
'dom_object',
'axtree_object',
'active_page_index',
'last_browser_action',
'last_browser_action_error',
'focused_element_bid',
'extra_element_properties',
}
DELETE_FROM_TRAJECTORY_EXTRAS_AND_SCREENSHOTS = DELETE_FROM_TRAJECTORY_EXTRAS | {
'screenshot',
'set_of_marks',
}
def event_from_dict(data: dict[str, Any]) -> 'Event':
evt: Event
if 'action' in data:
evt = action_from_dict(data)
elif 'observation' in data:
evt = observation_from_dict(data)
else:
raise ValueError(f'Unknown event type: {data}')
for key in UNDERSCORE_KEYS:
if key in data:
value = data[key]
if key == 'timestamp' and isinstance(value, datetime):
value = value.isoformat()
if key == 'source':
value = EventSource(value)
if key == 'tool_call_metadata':
value = ToolCallMetadata(**value)
if key == 'llm_metrics':
metrics = Metrics()
if isinstance(value, dict):
metrics.accumulated_cost = value.get('accumulated_cost', 0.0)
for cost in value.get('costs', []):
metrics._costs.append(Cost(**cost))
metrics.response_latencies = [
ResponseLatency(**latency)
for latency in value.get('response_latencies', [])
]
metrics.token_usages = [
TokenUsage(**usage) for usage in value.get('token_usages', [])
]
# Set accumulated token usage if available
if 'accumulated_token_usage' in value:
metrics._accumulated_token_usage = TokenUsage(
**value.get('accumulated_token_usage', {})
)
value = metrics
setattr(evt, '_' + key, value)
return evt
def _convert_pydantic_to_dict(obj: BaseModel | dict) -> dict:
if isinstance(obj, BaseModel):
return obj.model_dump()
return obj
def event_to_dict(event: 'Event') -> dict:
props = asdict(event)
d = {}
for key in TOP_KEYS:
if hasattr(event, key) and getattr(event, key) is not None:
d[key] = getattr(event, key)
elif hasattr(event, f'_{key}') and getattr(event, f'_{key}') is not None:
d[key] = getattr(event, f'_{key}')
if key == 'id' and d.get('id') == -1:
d.pop('id', None)
if key == 'timestamp' and 'timestamp' in d:
if isinstance(d['timestamp'], datetime):
d['timestamp'] = d['timestamp'].isoformat()
if key == 'source' and 'source' in d:
d['source'] = d['source'].value
if key == 'recall_type' and 'recall_type' in d:
d['recall_type'] = d['recall_type'].value
if key == 'tool_call_metadata' and 'tool_call_metadata' in d:
d['tool_call_metadata'] = d['tool_call_metadata'].model_dump()
if key == 'llm_metrics' and 'llm_metrics' in d:
d['llm_metrics'] = d['llm_metrics'].get()
props.pop(key, None)
if 'security_risk' in props and props['security_risk'] is None:
props.pop('security_risk')
if 'action' in d:
d['args'] = props
if event.timeout is not None:
d['timeout'] = event.timeout
elif 'observation' in d:
d['content'] = props.pop('content', '')
# props is a dict whose values can include a complex object like an instance of a BaseModel subclass
# such as CmdOutputMetadata
# we serialize it along with the rest
# we also handle the Enum conversion for RecallObservation
d['extras'] = {
k: (v.value if isinstance(v, Enum) else _convert_pydantic_to_dict(v))
for k, v in props.items()
}
# Include success field for CmdOutputObservation
if hasattr(event, 'success'):
d['success'] = event.success
else:
raise ValueError(f'Event must be either action or observation. has: {event}')
return d
def event_to_trajectory(event: 'Event', include_screenshots: bool = False) -> dict:
d = event_to_dict(event)
if 'extras' in d:
remove_fields(
d['extras'],
DELETE_FROM_TRAJECTORY_EXTRAS
if include_screenshots
else DELETE_FROM_TRAJECTORY_EXTRAS_AND_SCREENSHOTS,
)
return d
def truncate_content(content: str, max_chars: int | None = None) -> str:
"""Truncate the middle of the observation content if it is too long."""
if max_chars is None or len(content) <= max_chars or max_chars < 0:
return content
# truncate the middle and include a message to the LLM about it
half = max_chars // 2
return (
content[:half]
+ '\n[... Observation truncated due to length ...]\n'
+ content[-half:]
)
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