OpenHands/openhands/events/serialization/event.py

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)
                    # Set max_budget_per_task if available
                    metrics.max_budget_per_task = value.get('max_budget_per_task')
                    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')

    # Remove task_completed from serialization when it's None (backward compatibility)
    if 'task_completed' in props and props['task_completed'] is None:
        props.pop('task_completed')
    if 'action' in d:
        # Handle security_risk for actions - include it in args
        if 'security_risk' in props:
            props['security_risk'] = props['security_risk'].value
        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:]
    )