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db4fa53 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 | from __future__ import annotations
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any
from osint_env.data.generator import DatasetGenerator
from osint_env.domain.models import Action, ActionType, Edge, EnvironmentConfig, Observation, TaskInstance
from osint_env.env.openenv_compat import Env
from osint_env.env.reward import (
build_reward_model,
compute_answer_reward,
compute_edge_reward,
compute_graph_f1,
)
from osint_env.memory.store import MemoryGraph, SemanticMemory
from osint_env.platforms.tools import ToolRegistry
if TYPE_CHECKING:
from osint_env.llm.interface import LLMClient
@dataclass(slots=True)
class EpisodeState:
task: TaskInstance
task_index: int = 0
difficulty: str = "hard"
step_count: int = 0
done: bool = False
total_reward: float = 0.0
tool_calls: int = 0
redundant_tool_calls: int = 0
action_history: list[dict[str, Any]] = field(default_factory=list)
tool_outputs: list[dict[str, Any]] = field(default_factory=list)
answer: str | None = None
call_fingerprints: set[str] = field(default_factory=set)
reward_components: dict[str, float] = field(default_factory=dict)
class OSINTEnvironment(Env):
def __init__(self, config: EnvironmentConfig, llm: "LLMClient | None" = None):
super().__init__(
name="OSINTEnvironment",
state_space="json-observation",
action_space=["CALL_TOOL", "ADD_EDGE", "ANSWER"],
episode_max_length=config.max_steps,
)
self.config = config
self.generator = DatasetGenerator(config, llm=llm)
self.graph = self.generator.build_canonical_graph()
self.views = self.generator.build_platform_views(self.graph)
self.tasks = self.generator.generate_tasks(self.graph, self.views, count=24)
self.reward_model = build_reward_model(self.graph)
self.tools = ToolRegistry(self.views)
self.memory_graph = MemoryGraph()
self.semantic_memory = SemanticMemory()
self._task_idx = 0
self.state: EpisodeState | None = None
@staticmethod
def _normalize_difficulty(value: str) -> str:
token = str(value or "").strip().lower()
if token in {"easy", "e"}:
return "easy"
if token in {"mid", "medium", "m"}:
return "medium"
if token in {"high", "hard", "h"}:
return "hard"
return "hard"
def _resolve_task_difficulty(self, task: TaskInstance, task_index: int) -> str:
metadata = dict(task.metadata or {})
if "difficulty" in metadata:
return self._normalize_difficulty(str(metadata.get("difficulty", "")))
if task_index < 10:
return "easy"
if task_index < 20:
return "medium"
return "hard"
def reset(self) -> Observation:
task_index = self._task_idx % len(self.tasks)
task = self.tasks[task_index]
self._task_idx += 1
self.state = EpisodeState(
task=task,
task_index=task_index,
difficulty=self._resolve_task_difficulty(task, task_index),
)
self.memory_graph = MemoryGraph()
self.semantic_memory = SemanticMemory()
return self._observation()
def step(self, action: Action) -> tuple[Observation, float, bool, dict[str, Any]]:
if self.state is None:
raise RuntimeError("Call reset() before step().")
if self.state.done:
return self._observation(), 0.0, True, self._info()
self.state.step_count += 1
reward = 0.0
if action.action_type == ActionType.CALL_TOOL:
reward += self._handle_tool(action.payload)
elif action.action_type == ActionType.ADD_EDGE:
reward += self._handle_add_edge(action.payload)
elif action.action_type == ActionType.ANSWER:
reward += self._handle_answer(action.payload)
else:
reward -= 0.5
if self.state.step_count >= self.config.max_steps and not self.state.done:
self.state.done = True
reward -= 0.3
self.state.total_reward += reward
self.state.action_history.append({"type": action.action_type.value, "payload": action.payload, "reward": reward})
return self._observation(), reward, self.state.done, self._info()
def _handle_tool(self, payload: dict[str, Any]) -> float:
if self.state is None:
return 0.0
tool_name = payload["tool_name"]
args = payload.get("args", {})
fp = f"{tool_name}:{sorted(args.items())}"
self.state.tool_calls += 1
if fp in self.state.call_fingerprints:
self.state.redundant_tool_calls += 1
penalty = -0.2
else:
penalty = 0.05
self.state.call_fingerprints.add(fp)
invalid_tool_penalty = 0.0
try:
if tool_name == "search_memory":
query = str(args.get("query", "")).strip()
top_k = int(args.get("k", 5)) if str(args.get("k", "")).strip() else 5
results = self.semantic_memory.search(query=query, k=max(1, top_k)) if query else []
output = {"results": results, "count": len(results)}
else:
output = self.tools.call(tool_name, args)
except Exception as exc:
output = {"error": str(exc)}
invalid_tool_penalty = -0.25
self.state.tool_outputs.append({"tool": tool_name, "args": args, "output": output})
self.semantic_memory.add(f"{tool_name} {args} {output}", {"tool": tool_name})
relevance_bonus = 0.08 * self._tool_relevance(self.state.task, output)
total = penalty + relevance_bonus + invalid_tool_penalty
self._accumulate_reward_components(
{
"tool_novelty": penalty,
"tool_relevance": relevance_bonus,
"invalid_tool_penalty": invalid_tool_penalty,
}
)
return total
def _handle_add_edge(self, payload: dict[str, Any]) -> float:
if self.state is None:
return 0.0
edge = Edge(payload["src"], payload["rel"], payload["dst"], float(payload.get("confidence", 1.0)))
existing_edges = list(self.memory_graph.edges)
added = self.memory_graph.add_edge(edge)
if not added:
self._accumulate_reward_components({"duplicate_edge_penalty": -0.15})
return -0.15
breakdown = compute_edge_reward(
edge=edge,
task=self.state.task,
existing_edges=existing_edges,
step_count=self.state.step_count,
model=self.reward_model,
graph=self.graph,
difficulty=self.state.difficulty,
)
self._accumulate_reward_components(breakdown.to_dict())
return breakdown.total
def _handle_answer(self, payload: dict[str, Any]) -> float:
if self.state is None:
return 0.0
proposed = str(payload.get("answer", "")).strip()
self.state.answer = proposed
self.state.done = True
breakdown = compute_answer_reward(
proposed_answer=proposed,
task=self.state.task,
pred_edges=self.memory_graph.edges,
tool_outputs=self.state.tool_outputs,
step_count=self.state.step_count,
model=self.reward_model,
difficulty=self.state.difficulty,
)
self._accumulate_reward_components(breakdown.to_dict())
return breakdown.total
def _tool_relevance(self, task: TaskInstance, output: dict[str, Any]) -> float:
haystack = str(output).lower()
clues = {task.answer.lower()}
for edge in task.supporting_edges:
clues.add(edge.src.lower())
clues.add(edge.dst.lower())
clues.add(edge.rel.lower())
if not clues:
return 0.0
matches = sum(1 for token in clues if token in haystack)
return matches / len(clues)
def _accumulate_reward_components(self, values: dict[str, float]) -> None:
if self.state is None:
return
for key, value in values.items():
self.state.reward_components[key] = self.state.reward_components.get(key, 0.0) + float(value)
def _observation(self) -> Observation:
if self.state is None:
raise RuntimeError("State is not initialized.")
metadata = dict(self.state.task.metadata or {})
grader = metadata.get("grader") if isinstance(metadata.get("grader"), dict) else None
task_payload = {
"task_id": self.state.task.task_id,
"task_type": self.state.task.task_type,
"question": self.state.task.question,
"difficulty": self.state.difficulty,
"grader": (
dict(grader)
if grader is not None
else {
"type": "difficulty_exact_match",
"answer_type": "node_id",
"case_sensitive": True,
"reward_profile": self.state.difficulty,
}
),
}
if "scenario" in metadata:
task_payload["scenario"] = str(metadata.get("scenario", ""))
return Observation(
tool_outputs=self.state.tool_outputs[-5:],
graph_snapshot=self.memory_graph.to_snapshot(),
action_history=self.state.action_history[-10:],
task=task_payload,
)
def _info(self) -> dict[str, Any]:
if self.state is None:
return {}
return {
"step_count": self.state.step_count,
"difficulty": self.state.difficulty,
"task_index": self.state.task_index,
"total_reward": self.state.total_reward,
"tool_calls": self.state.tool_calls,
"redundant_tool_calls": self.state.redundant_tool_calls,
"task_answer": self.state.task.answer,
"agent_answer": self.state.answer,
"graph_f1": compute_graph_f1(self.memory_graph.edges, self.state.task.supporting_edges),
"reward_components": dict(self.state.reward_components),
}
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