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ab65628 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 | """Observation model for the RL environment."""
from datetime import datetime, timezone
from typing import Any
from pydantic import BaseModel, ConfigDict, Field
class ToolSnapshot(BaseModel):
"""Snapshot of a tool from the registry."""
name: str
description: str
parameters: list[dict[str, Any]]
enabled: bool = True
cost_estimate: float = 0.0
class MemoryContext(BaseModel):
"""Context from memory systems."""
short_term: list[dict[str, Any]] = Field(default_factory=list)
working: list[dict[str, Any]] = Field(default_factory=list)
long_term_relevant: list[dict[str, Any]] = Field(default_factory=list)
shared: dict[str, Any] = Field(default_factory=dict)
class PageElement(BaseModel):
"""A significant element on the page."""
selector: str
tag: str
text: str | None = None
attributes: dict[str, str] = Field(default_factory=dict)
is_interactive: bool = False
is_visible: bool = True
bounding_box: dict[str, float] | None = None
class ExtractedField(BaseModel):
"""A field that has been extracted."""
field_name: str
value: Any
confidence: float = 1.0
source_selector: str | None = None
extraction_step: int = 0
verified: bool = False
class AvailableAction(BaseModel):
"""An action that is currently available."""
action_type: str
description: str
parameters: dict[str, Any] = Field(default_factory=dict)
estimated_reward: float | None = None
risk_level: str = "low"
class TaskContext(BaseModel):
"""Context about the current task."""
task_id: str
task_name: str
task_type: str
target_fields: list[str]
required_fields: list[str]
hints: list[str] = Field(default_factory=list)
success_criteria: dict[str, Any] = Field(default_factory=dict)
class Observation(BaseModel):
"""
Complete observation provided to the agent after each step.
Contains all information the agent needs to make decisions:
- Episode and task context
- Current page state
- Extracted data so far
- Memory context
- Available tools and actions
"""
# Episode identification
episode_id: str = Field(..., description="Unique episode identifier")
task_id: str = Field(..., description="Task being executed")
step_number: int = Field(..., description="Current step in the episode")
# Timing
timestamp: str = Field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
elapsed_seconds: float = Field(default=0.0, description="Time elapsed in episode")
# Page state
current_url: str | None = Field(default=None, description="Current page URL")
page_title: str | None = Field(default=None, description="Current page title")
page_html: str | None = Field(default=None, description="Full HTML of current page")
page_html_chunked: list[str] = Field(
default_factory=list,
description="HTML split into semantic chunks",
)
page_text: str | None = Field(default=None, description="Visible text content")
page_elements: list[PageElement] = Field(
default_factory=list,
description="Significant page elements",
)
# Navigation state
navigation_history: list[str] = Field(
default_factory=list,
description="URLs visited in this episode",
)
can_go_back: bool = Field(default=False)
can_go_forward: bool = Field(default=False)
# Task context
task_context: TaskContext | None = Field(
default=None,
description="Information about the current task",
)
# Extraction state
extracted_so_far: list[ExtractedField] = Field(
default_factory=list,
description="Fields extracted so far",
)
extraction_progress: float = Field(
default=0.0,
description="Progress towards task completion (0-1)",
)
fields_remaining: list[str] = Field(
default_factory=list,
description="Fields still to be extracted",
)
# Memory context
memory_context: MemoryContext = Field(
default_factory=MemoryContext,
description="Relevant memories from all layers",
)
# Tool registry snapshot
tool_registry_snapshot: list[ToolSnapshot] = Field(
default_factory=list,
description="Available tools and their state",
)
# Available actions
available_actions: list[AvailableAction] = Field(
default_factory=list,
description="Actions available in current state",
)
# Agent coordination
pending_messages: list[dict[str, Any]] = Field(
default_factory=list,
description="Messages from other agents",
)
active_plan: dict[str, Any] | None = Field(
default=None,
description="Current execution plan if any",
)
current_plan_step: int | None = Field(
default=None,
description="Current step in the plan",
)
# Error state
last_action_error: str | None = Field(
default=None,
description="Error from last action if any",
)
consecutive_errors: int = Field(
default=0,
description="Number of consecutive action errors",
)
# Cost tracking
tokens_used: int = Field(default=0, description="LLM tokens used so far")
api_calls_made: int = Field(default=0, description="API calls made")
estimated_cost_usd: float = Field(default=0.0, description="Estimated cost so far")
# Hints and guidance
system_hints: list[str] = Field(
default_factory=list,
description="Hints from the environment or previous steps",
)
model_config = ConfigDict(
json_schema_extra={
"example": {
"episode_id": "ep_abc123",
"task_id": "task_001",
"step_number": 5,
"current_url": "https://example.com/product/123",
"page_title": "Product Details - Example Store",
"extracted_so_far": [
{
"field_name": "product_name",
"value": "Example Product",
"confidence": 0.95,
}
],
"extraction_progress": 0.33,
"fields_remaining": ["price", "description"],
}
}
)
def get_extraction_dict(self) -> dict[str, Any]:
"""Get extracted fields as a dictionary."""
return {field.field_name: field.value for field in self.extracted_so_far}
def is_field_extracted(self, field_name: str) -> bool:
"""Check if a field has been extracted."""
return any(f.field_name == field_name for f in self.extracted_so_far)
def get_context_summary(self) -> str:
"""Get a summary of the current context for LLM prompts."""
parts = [
f"Step {self.step_number}",
f"URL: {self.current_url or 'None'}",
f"Progress: {self.extraction_progress:.0%}",
f"Extracted: {len(self.extracted_so_far)}/{len(self.extracted_so_far) + len(self.fields_remaining)} fields",
]
if self.last_action_error:
parts.append(f"Last error: {self.last_action_error}")
return " | ".join(parts)
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