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5e0b58b | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 | """Read-only research registry and report source for the dashboard.
Exposes the Brain-5D Scientific Evidence Framework (B5D-SEF) artifacts
located under ``research/`` to the operator dashboard. The source is
strictly read-only and resolves paths defensively so a missing or
partially populated research tree never crashes the dashboard.
"""
from __future__ import annotations
import json
from dataclasses import dataclass
from pathlib import Path
from typing import Any, cast
from .models import JSONValue
@dataclass(frozen=True)
class ResearchDocument:
"""One research artifact exposed to the dashboard."""
name: str
path: str
kind: str
size_bytes: int
category: str
def classify_ai_operation(manifest: dict[str, Any]) -> str:
"""Classify AI execution provenance for read-only dashboard display."""
explicit = manifest.get("ai_operation_mode", manifest.get("operation_mode"))
if isinstance(explicit, str) and explicit in {
"REPLAY",
"LIVE_FROZEN_MODEL",
"LIVE_EXTERNAL_API",
"NONE",
"UNKNOWN",
}:
return explicit
interactions_value: object = manifest.get("ai_interactions", [])
interactions: list[Any] = []
if isinstance(interactions_value, list):
interactions.extend(
interactions_value # pyright: ignore[reportUnknownArgumentType]
)
if not interactions and isinstance(manifest.get("ai_model_provenance"), dict):
interactions = [{"model_provenance": manifest["ai_model_provenance"]}]
if not interactions:
return "NONE"
network_mode = str(manifest.get("network_mode", "")).upper()
for interaction in interactions:
if not isinstance(interaction, dict):
continue
interaction_data = cast(dict[str, Any], interaction)
provenance_value: Any = interaction_data.get("model_provenance", {})
provenance = (
cast(dict[str, Any], provenance_value)
if isinstance(provenance_value, dict)
else {}
)
if provenance:
provider = str(
provenance.get("provider", provenance.get("backend", ""))
).lower()
if "replay" in provider or provenance.get("replay") is True:
return "REPLAY"
if network_mode in {"OFFLINE", "FROZEN_CORPUS"} and (
provider in {"ollama", "local", "frozen_model"} or "frozen" in provider
):
return "LIVE_FROZEN_MODEL"
if network_mode == "LIVE_NETWORK" or (
provider and provider not in {"unknown", "not_reported"}
):
return "LIVE_EXTERNAL_API"
return "UNKNOWN"
def classify_research_operation_status(manifest: dict[str, Any]) -> str:
"""Classify the scientific AI operating status for read-only display."""
exposure = str(manifest.get("ai_exposure", "")).lower()
taint = str(manifest.get("causal_taint", "")).upper()
if exposure == "none" and taint == "PURE":
return "PURE EXPERIMENT"
if taint == "AI_INFLUENCED" or exposure in {
"bounded_controller",
"adaptive_controller",
}:
return "AI CAUSALLY ACTIVE"
if taint == "PROPOSED" or exposure == "advisor":
return "AI PROPOSING"
if taint == "OBSERVED" or exposure in {"observer_only", "semantic_interface"}:
return "AI OBSERVING"
return "UNKNOWN"
class ResearchSource:
"""Read-only view over the ``research/`` directory tree.
Categories:
registry — YAML registry files (questions, hypotheses, ...)
generated — auto-generated markdown reports
experiments — experiment manifests
literature — BibTeX databases
schemas — JSON schemas
"""
_CATEGORIES: tuple[str, ...] = (
"registry",
"generated",
"experiments",
"reports",
"analysis",
"benchmarks",
"literature",
"schemas",
)
def __init__(self, research_root: Path) -> None:
self._root = research_root.resolve()
# ------------------------------------------------------------------
# Public API
# ------------------------------------------------------------------
def is_available(self) -> bool:
"""Return True when the research root exists and is a directory."""
return self._root.is_dir()
def root(self) -> Path:
return self._root
def list_documents(self) -> list[ResearchDocument]:
"""List all research artifacts grouped by category."""
if not self.is_available():
return []
documents: list[ResearchDocument] = []
for category in self._CATEGORIES:
directory = self._root / category
if not directory.is_dir():
continue
for entry in sorted(directory.rglob("*")):
if not entry.is_file():
continue
rel = entry.relative_to(self._root)
documents.append(
ResearchDocument(
name=entry.name,
path=str(rel).replace("\\", "/"),
kind=entry.suffix.lstrip(".") or "file",
size_bytes=entry.stat().st_size,
category=category,
)
)
return documents
def read_content(self, relative_path: str) -> str:
"""Read a research file as UTF-8 text with path-traversal protection."""
resolved = self._safe_resolve(relative_path)
if resolved is None:
raise FileNotFoundError(f"Research document not found: {relative_path}")
return resolved.read_text(encoding="utf-8")
def read_bytes(self, relative_path: str) -> bytes:
resolved = self._safe_resolve(relative_path)
if resolved is None:
raise FileNotFoundError(f"Research document not found: {relative_path}")
return resolved.read_bytes()
def registry_summary(self) -> dict[str, JSONValue]:
"""Return a compact summary of registry counts for the dashboard."""
summary: dict[str, JSONValue] = {
"available": self.is_available(),
"root": str(self._root),
"categories": {},
}
if not self.is_available():
return summary
counts: dict[str, JSONValue] = {}
for category in self._CATEGORIES:
directory = self._root / category
if directory.is_dir():
counts[category] = sum(
1 for entry in directory.rglob("*") if entry.is_file()
)
else:
counts[category] = 0
summary["categories"] = counts
return summary
def generated_reports(self) -> list[dict[str, JSONValue]]:
"""List auto-generated markdown reports with metadata."""
directory = self._root / "generated"
if not directory.is_dir():
return []
reports: list[dict[str, JSONValue]] = []
for entry in sorted(directory.glob("*.md")):
reports.append(
{
"name": entry.stem,
"path": str(entry.relative_to(self._root)).replace("\\", "/"),
"size_bytes": entry.stat().st_size,
}
)
return reports
def ai_reports(
self, experiment_id: str | None = None
) -> list[dict[str, JSONValue]]:
"""List canonical AIRR JSON/Markdown pairs without exposing write access."""
reports: list[dict[str, JSONValue]] = []
directories = [
directory
for directory in (self._root / "experiments").glob("*/reports")
if directory.is_dir()
]
legacy_directory = self._root / "reports"
if legacy_directory.is_dir():
directories.append(legacy_directory)
for directory in directories:
experiment_id = directory.parent.name
for entry in sorted(directory.glob("AIRR-*.json")):
if entry.name.endswith(".review.json"):
continue
current_experiment = experiment_id
reports.append(
{
"report_id": entry.stem,
"experiment_id": current_experiment,
"json_path": str(entry.relative_to(self._root)).replace(
"\\", "/"
),
"markdown_path": str(
entry.with_suffix(".md").relative_to(self._root)
).replace("\\", "/"),
"size_bytes": entry.stat().st_size,
}
)
if experiment_id is not None:
reports = [
report for report in reports if report["experiment_id"] == experiment_id
]
return reports
def experiment_manifest(self, experiment_id: str) -> dict[str, JSONValue] | None:
"""Load one experiment manifest.json if present."""
candidate = self._root / "experiments" / experiment_id / "manifest.json"
if not candidate.is_file():
return None
try:
data: Any = json.loads(candidate.read_text(encoding="utf-8"))
except (OSError, json.JSONDecodeError):
return None
return data # type: ignore[no-any-return]
def list_experiments(self) -> list[dict[str, JSONValue]]:
"""List experiment directories that contain a manifest."""
directory = self._root / "experiments"
if not directory.is_dir():
return []
experiments: list[dict[str, JSONValue]] = []
for entry in directory.iterdir():
manifest = entry / "manifest.json"
if not manifest.is_file():
continue
try:
data: Any = json.loads(manifest.read_text(encoding="utf-8"))
except (OSError, json.JSONDecodeError):
continue
manifest_data = (
cast(dict[str, Any], data) if isinstance(data, dict) else None
)
created_at = (
str(manifest_data.get("created_at") or manifest_data.get("timestamp"))
if manifest_data is not None
else None
)
experiments.append(
{
"ai_operation_mode": (
classify_ai_operation(manifest_data)
if manifest_data is not None
else "NONE"
),
"research_operation_status": (
classify_research_operation_status(manifest_data)
if manifest_data is not None
else "UNKNOWN"
),
"id": entry.name,
"created_at": created_at,
"path": str(entry.relative_to(self._root)).replace("\\", "/"),
"manifest": data,
}
)
experiments.sort(
key=lambda item: str(item.get("created_at") or ""), reverse=True
)
return experiments
# ------------------------------------------------------------------
# Internals
# ------------------------------------------------------------------
def _safe_resolve(self, relative_path: str) -> Path | None:
if not relative_path or ".." in relative_path:
return None
candidate = (self._root / relative_path).resolve()
try:
candidate.relative_to(self._root)
except ValueError:
return None
if not candidate.is_file():
return None
return candidate
def create_research_source(research_root: Path | None) -> ResearchSource | None:
"""Create a ResearchSource if the root exists, else None."""
if research_root is None or not research_root.exists():
return None
return ResearchSource(research_root)
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