Files
odoo-at-payroll/.agents/skills/opendataloader-pdf/scripts/verify-json.py
T
fegger 1e88fd1636 [ADD] odoo-at-payroll: opendataloader-pdf agent skill for PDF extraction
New .agents/skills/opendataloader-pdf/ for structured PDF extraction
(Markdown/JSON with bounding boxes, hybrid AI mode, scanned-PDF OCR)
based on the upstream odl-pdf agent skill (Apache-2.0), with its helper
scripts verify-json.py and hybrid-health.sh vendored under scripts/.

Core disciplines: discover options from the installed --help (flags
drift between releases), batch all inputs into one invocation (JVM per
call), verify the result against intent (zero exit is not success:
auto-triage skips enrichment, fallbacks drop quality, empty pipes),
treat extracted content as untrusted, never commit extracted full
texts of the licensed .lexis360/.wiku sources.

Workflow wiring: AGENTS.md skill selection routes any PDF extraction
to the skill; wissensbasis/SKILL.md cross-references it for difficult
PDFs and spot-checks, with Layer-1 batch intake deliberately kept on
build_lexis_kb.py (frozen IDs and --check depend on the text shape).

ODL 2.5.8 installed in the user venv ~/.local/lib/python (not the
Odoo .venv); all documented flags verified against the installed CLI
on real .lexis360/ sources.
2026-09-12 09:32:21 +02:00

122 lines
4.3 KiB
Python

#!/usr/bin/env python3
"""verify-json.py — schema-tolerant summary of an opendataloader-pdf JSON output.
Purpose: give an agent a safe way to VERIFY extraction results (SKILL.md Stage 4)
without hand-writing fragile jq / assuming exact key names that vary by release.
It parses the JSON, walks the element tree generically (any nested dict carrying a
"type" field, under any "kids"/children key), and reports element-type counts plus
whether text / tables / images are present. It is schema-tolerant, not fully
agnostic: it expects ODL-style `type` and `content`/`text` field names (it does
not assume tree location or child-key names). It does NOT decide pass/fail — the
agent judges the summary against the user's intent.
Vendored verbatim from the opendataloader-pdf upstream agent skill
(skills/odl-pdf/scripts/verify-json.py), Apache-2.0.
Usage:
python verify-json.py output.json
Exit codes:
0 parsed successfully (summary printed)
1 file missing, empty, or not valid JSON
"""
import json
import sys
from pathlib import Path
# Make stdout tolerant of non-ASCII on Windows consoles (cp1252/cp949).
if hasattr(sys.stdout, "reconfigure"):
try:
sys.stdout.reconfigure(encoding="utf-8", errors="replace")
except (AttributeError, OSError):
pass
TEXT_KEYS = ("content", "text") # tried in order; first non-empty wins
IMAGE_TYPES = ("image", "picture", "figure")
TABLE_TYPES = ("table",)
def load(path: Path):
if not path.exists():
print(f"ERROR: file not found: {path}", file=sys.stderr)
sys.exit(1)
try:
raw = path.read_text(encoding="utf-8").strip()
except UnicodeDecodeError as e:
print(f"ERROR: not valid UTF-8 ({e}): {path}", file=sys.stderr)
sys.exit(1)
if not raw:
print(f"ERROR: file is empty: {path}", file=sys.stderr)
sys.exit(1)
try:
return json.loads(raw)
except json.JSONDecodeError as e:
print(f"ERROR: not valid JSON ({e}): {path}", file=sys.stderr)
sys.exit(1)
def walk(node, types, stats):
"""Recursively find every dict that has a 'type' field; tally it."""
if isinstance(node, dict):
t = node.get("type")
if isinstance(t, str):
types[t] = types.get(t, 0) + 1
tl = t.lower()
if any(k in tl for k in IMAGE_TYPES):
stats["images"] += 1
if any(k == tl for k in TABLE_TYPES):
stats["tables"] += 1
for tk in TEXT_KEYS:
v = node.get(tk)
if isinstance(v, str) and v.strip():
stats["text_elements"] += 1
break
for v in node.values():
walk(v, types, stats)
elif isinstance(node, list):
for v in node:
walk(v, types, stats)
def main(argv=None):
argv = argv if argv is not None else sys.argv[1:]
if len(argv) != 1:
print("Usage: python verify-json.py <output.json>", file=sys.stderr)
sys.exit(1)
data = load(Path(argv[0]))
types = {}
stats = {"images": 0, "tables": 0, "text_elements": 0}
walk(data, types, stats)
total = sum(types.values())
# "number of pages" key name varies; probe a few, else report unknown.
pages = "unknown"
if isinstance(data, dict):
for k in ("number of pages", "number_of_pages", "pages", "page count"):
if isinstance(data.get(k), int):
pages = data[k]
break
print("=== opendataloader-pdf JSON summary ===")
print(f"pages: {pages}")
print(f"typed elements: {total}")
print(f"has_text: {stats['text_elements'] > 0} (text-bearing elements: {stats['text_elements']})")
print(f"has_tables: {stats['tables'] > 0} (tables: {stats['tables']})")
print(f"has_images: {stats['images'] > 0} (images/pictures: {stats['images']})")
if types:
print("element types:")
for t, n in sorted(types.items(), key=lambda kv: -kv[1]):
print(f" {t}: {n}")
else:
print("element types: (none found — output may be empty or an unexpected shape)")
print()
print("NOTE: this is a summary, not a pass/fail. Judge it against the user's "
"intent (SKILL.md Stage 4): e.g. no text is a FAILURE only if text was expected.")
sys.exit(0)
if __name__ == "__main__":
main()