Add training corpus UI with Nevo proofreading pipeline
- New proofreader service strips Nevo editorial additions (front matter,
postamble, page headers, watermarks, inline codes) from DOCX/PDF/MD
- PDF pages use Google Vision OCR for clean Hebrew RTL extraction
- New training page at #/training with drag-and-drop upload, automatic
metadata extraction (decision number, date, categories), reviewable
preview, and style pattern report grouped by type
- API endpoints: /api/training/{analyze,upload,corpus,patterns,
analyze-style,analyze-style/status}
- Fix claude_session.query to pipe prompt via stdin, avoiding ARG_MAX
overflow when analyzing 900K+ char corpus
- CLI scripts for batch proofreading and corpus upload
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
349
scripts/batch_upload_training.py
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349
scripts/batch_upload_training.py
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"""Batch upload proofread training corpus to style DB.
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Two-phase workflow:
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--preview Extract metadata from all .md files, print review table, don't upload
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--upload Actually upload all files (with optional --only FILE to run one)
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Metadata extraction:
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* decision_number: from filename (ARAR-YY-NNNN / ערר NNNN-YY) or decision date year
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* decision_date: from "ניתנה ... <day> ב<Hebrew month> <YYYY>" near end of text
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* categories: keyword heuristics on body text
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"""
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from __future__ import annotations
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import argparse
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import asyncio
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import os
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import re
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import sys
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from pathlib import Path
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PROOFREAD_DIR = Path("/home/chaim/legal-ai/data/training/proofread")
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# Manual metadata overrides for files where auto-extraction can't determine values.
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METADATA_OVERRIDES: dict[str, dict] = {
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"ARAR-25-1067 - יחיעם יפה ואח׳.md": {
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"decision_date": "2025-11-27", # no "ניתנה" signature in file; user-provided
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},
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}
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# Files to skip — already in style_corpus from legacy ingestion
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# (verified by exact character-count match with existing DB rows).
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SKIP_FILES = {
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"תמא 38-בית הכרם-1126+1141-החלטה.md", # → corpus: 1126/1141
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"היתר בניה-בית שמש-1180+1181-החלטה.md", # → corpus: 1180/1181
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"היתר בניה-הראל-1043+1054-החלטה.md", # → corpus: 1043/1054
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"היתר בניה-הראל-1071+1077-החלטה.md", # → corpus: 1071/1077
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}
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# Load env vars needed by mcp-server
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ENV_FILE = Path.home() / ".env"
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if ENV_FILE.exists():
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for line in ENV_FILE.read_text().splitlines():
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if "=" in line and not line.startswith("#"):
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k, v = line.split("=", 1)
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os.environ.setdefault(k.strip(), v.strip().strip('"').strip("'"))
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# Make mcp-server package importable
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sys.path.insert(0, "/home/chaim/legal-ai/mcp-server/src")
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# ── Decision number extraction ───────────────────────────────────
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FILENAME_NUMBER_PATTERNS = [
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# ARAR-YY-NNNN[-X] - title.md
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re.compile(r"^ARAR-(\d{2})-(\d{3,4})"),
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# ערר NNNN-YY title.md or ערר NNNN-YY title
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re.compile(r"^ערר\s+(\d{3,4})-(\d{2})"),
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# ערר NNNN - title (no year in filename — needs date lookup)
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re.compile(r"^ערר\s+(\d{3,4})\s*-"),
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]
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LEGACY_MULTI_PATTERN = re.compile(r"(\d{3,4})\+(\d{3,4})")
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def decision_number_from_filename(stem: str) -> tuple[str | None, str | None]:
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"""Return (number, year_short) or (multi_number, None) or (None, None).
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year_short is YY (last 2 digits) if extractable from filename.
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For legacy files with 'NNNN+NNNN' or no year, returns partial info
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that must be completed from decision date.
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"""
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# ARAR-YY-NNNN
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m = FILENAME_NUMBER_PATTERNS[0].match(stem)
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if m:
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year, num = m.group(1), m.group(2)
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return f"{num}/{year}", year
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# ערר NNNN-YY
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m = FILENAME_NUMBER_PATTERNS[1].match(stem)
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if m:
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num, year = m.group(1), m.group(2)
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return f"{num}/{year}", year
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# ערר NNNN - title (no year)
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m = FILENAME_NUMBER_PATTERNS[2].match(stem)
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if m:
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num = m.group(1)
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return f"{num}/??", None
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# Legacy: "NNNN+NNNN" merged decisions
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m = LEGACY_MULTI_PATTERN.search(stem)
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if m:
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return f"{m.group(1)}+{m.group(2)}/??", None
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return None, None
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# ── Decision date extraction ─────────────────────────────────────
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HEBREW_MONTHS = {
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"ינואר": 1, "בינואר": 1,
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"פברואר": 2, "בפברואר": 2,
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"מרץ": 3, "מרס": 3, "במרץ": 3, "במרס": 3,
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"אפריל": 4, "באפריל": 4,
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"מאי": 5, "במאי": 5,
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"יוני": 6, "ביוני": 6,
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"יולי": 7, "ביולי": 7,
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"אוגוסט": 8, "באוגוסט": 8,
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"ספטמבר": 9, "בספטמבר": 9,
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"אוקטובר": 10, "באוקטובר": 10,
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"נובמבר": 11, "בנובמבר": 11,
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"דצמבר": 12, "בדצמבר": 12,
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}
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# Matches "<day> ב<month>, <year>" or "<day> <month>, <year>" (with optional commas)
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DATE_RE = re.compile(
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r"(\d{1,2})\s+(ב?(?:ינואר|פברואר|מרץ|מרס|אפריל|מאי|יוני|יולי|אוגוסט|ספטמבר|אוקטובר|נובמבר|דצמבר))\s*[,.]?\s*(\d{4})"
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)
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NITNA_RE = re.compile(r"ניתנ[הו]?\s+(?:פה\s+אחד|בדעת\s+רוב|היום)?")
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def decision_date_from_text(text: str) -> str | None:
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"""Extract decision date in YYYY-MM-DD format from 'ניתנה... DATE' section.
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Searches the last ~2000 chars where the signing block lives.
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"""
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tail = text[-2500:] if len(text) > 2500 else text
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# Prefer dates near "ניתנה" marker
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nitna_match = NITNA_RE.search(tail)
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search_text = tail[nitna_match.start():] if nitna_match else tail
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m = DATE_RE.search(search_text)
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if not m:
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# Fall back: search whole tail
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m = DATE_RE.search(tail)
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if not m:
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return None
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day = int(m.group(1))
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month = HEBREW_MONTHS.get(m.group(2))
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year = int(m.group(3))
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if not month:
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return None
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try:
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from datetime import date
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return date(year, month, day).isoformat()
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except ValueError:
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return None
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# ── Subject category extraction ──────────────────────────────────
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# Categories as defined in the tool signature.
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ALL_CATEGORIES = [
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"בנייה", "שימוש חורג", "תכנית", "היתר", "הקלה",
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"חלוקה", 'תמ"א 38', "היטל השבחה", "פיצויים 197",
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]
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def categorize(text: str) -> list[str]:
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"""Heuristic category detection based on subject matter, not incidental mentions.
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Strategy: the real subject is established in the opening 2000 chars
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(first decision-opening paragraph). Secondary signal is repetition count
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— casual mentions in law citations don't repeat.
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"""
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opening = text[:2000] # subject is stated up front
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t = text
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cats: list[str] = []
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# תמ"א 38 — very specific marker, single mention is fine
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if re.search(r'תמ[״"\']?א\s*38|תמא\s*38', t):
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cats.append('תמ"א 38')
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# היטל השבחה — require real engagement: must appear in opening OR 3+ times
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hsbacha_count = len(re.findall(r"היטל(?:י)?\s+השבחה", t))
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if hsbacha_count >= 3 or re.search(r"היטל(?:י)?\s+השבחה", opening):
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cats.append("היטל השבחה")
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# פיצויים 197 — require multiple mentions OR in opening
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p197_re = r"פיצויים\s+לפי\s+(?:ס(?:עיף|')\s*)?197|סעיף\s*197|ס['\"]?\s*197"
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p197_count = len(re.findall(p197_re, t))
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if p197_count >= 2 or re.search(p197_re, opening):
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cats.append("פיצויים 197")
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# שימוש חורג — must appear in opening OR 3+ times (avoids law-quote false positives)
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shimush_count = t.count("שימוש חורג")
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if shimush_count >= 3 or "שימוש חורג" in opening:
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cats.append("שימוש חורג")
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# הקלה — real subject if 3+ mentions AND appears in opening
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hakala_count = len(re.findall(r"\bהקלה\b|\bהקלות\b", t))
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if hakala_count >= 3 and re.search(r"\bהקלה\b|\bהקלות\b", opening):
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cats.append("הקלה")
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# חלוקה — "איחוד וחלוקה" or "חלוקה חדשה" (specific phrases)
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if re.search(r"איחוד\s+וחלוקה|חלוקה\s+חדשה|תכנית\s+לחלוקה", t):
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cats.append("חלוקה")
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# תכנית — plan-level appeal (primary subject). Allow ה/ב/ל prefixes on תכנית.
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tochnit_opening = bool(re.search(
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r"הפקדת\s+ה?תכנית|"
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r"אישור\s+ה?תכנית|"
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r"המלצה\s+להפקיד|"
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r"להפקיד\s+את\s+ה?תכנית|"
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r"לדון\s+בתכנית|"
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r"דנה\s+בתכנית|"
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r"החלטה\s+לאשר\s+ה?תכנית",
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opening,
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))
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if tochnit_opening:
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cats.append("תכנית")
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# היתר — "בקשה להיתר" or "היתר בניה" as subject in opening
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if re.search(r"בקשה\s+להיתר|היתר\s+בני(?:י)?ה", opening):
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cats.append("היתר")
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# בנייה — default/fallback for building-permit cases
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# (not for plan-level תכנית-only cases)
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has_permit_subject = "היתר" in cats or "הקלה" in cats or 'תמ"א 38' in cats
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if has_permit_subject and "בנייה" not in cats:
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cats.append("בנייה")
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# If nothing matched, default to בנייה
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return cats or ["בנייה"]
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# ── Year fallback from date ──────────────────────────────────────
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def finalize_decision_number(number: str | None, date_iso: str | None) -> str:
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"""If filename number is missing year, fill it from decision date."""
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if not number:
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if date_iso:
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# Extract last 2 digits of Hebrew year via Gregorian year
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return f"??/{date_iso[2:4]}"
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return ""
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if number.endswith("/??"):
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if date_iso:
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yy = date_iso[2:4]
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return number.replace("/??", f"/{yy}")
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return number.replace("/??", "")
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return number
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# ── Main metadata extraction ─────────────────────────────────────
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def extract_metadata(path: Path) -> dict:
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text = path.read_text(encoding="utf-8")
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num_from_name, _ = decision_number_from_filename(path.stem)
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date_iso = decision_date_from_text(text)
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decision_number = finalize_decision_number(num_from_name, date_iso)
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cats = categorize(text)
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meta = {
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"file": path.name,
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"decision_number": decision_number,
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"decision_date": date_iso or "??",
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"categories": cats,
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"chars": len(text),
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}
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# Apply manual overrides
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if path.name in METADATA_OVERRIDES:
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meta.update(METADATA_OVERRIDES[path.name])
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return meta
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def print_preview(results: list[dict]) -> None:
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"""Print review table of metadata for all files."""
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print(f"\n{'#':<3} {'FILE':<55} {'NUMBER':<15} {'DATE':<12} {'CATEGORIES'}")
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print("-" * 130)
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for i, r in enumerate(results, 1):
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file_short = r["file"] if len(r["file"]) <= 53 else r["file"][:50] + "..."
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cats = ", ".join(r["categories"])
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print(f"{i:<3} {file_short:<55} {r['decision_number']:<15} {r['decision_date']:<12} {cats}")
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print()
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# Highlight issues
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issues = [r for r in results if r["decision_date"] == "??" or not r["decision_number"] or "??" in r["decision_number"]]
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if issues:
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print(f"⚠️ {len(issues)} files with incomplete metadata:")
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for r in issues:
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print(f" - {r['file']} → number={r['decision_number']!r} date={r['decision_date']!r}")
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# ── Upload ───────────────────────────────────────────────────────
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async def upload_one(meta: dict) -> dict:
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from legal_mcp.tools.documents import document_upload_training
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path = PROOFREAD_DIR / meta["file"]
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result = await document_upload_training(
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file_path=str(path),
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decision_number=meta["decision_number"],
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decision_date=meta["decision_date"] if meta["decision_date"] != "??" else "",
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subject_categories=meta["categories"],
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title=path.stem,
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)
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return {"file": meta["file"], "result": result}
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async def upload_all(results: list[dict]) -> None:
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for i, meta in enumerate(results, 1):
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try:
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r = await upload_one(meta)
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print(f"[{i}/{len(results)}] ✓ {meta['file']}")
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print(f" {r['result'][:200]}")
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except Exception as e:
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print(f"[{i}/{len(results)}] ✗ {meta['file']}: {e}")
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# ── CLI ──────────────────────────────────────────────────────────
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def main() -> int:
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ap = argparse.ArgumentParser()
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ap.add_argument("--preview", action="store_true", help="Show metadata table without uploading")
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ap.add_argument("--upload", action="store_true", help="Upload all files to style corpus")
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ap.add_argument("--only", help="Only process this specific filename")
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args = ap.parse_args()
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files = sorted(PROOFREAD_DIR.glob("*.md"))
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files = [f for f in files if f.name not in SKIP_FILES]
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if args.only:
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files = [f for f in files if f.name == args.only]
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if not files:
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print(f"File not found: {args.only}")
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return 1
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results = [extract_metadata(f) for f in files]
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if args.preview or not args.upload:
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print_preview(results)
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if not args.upload:
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return 0
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if args.upload:
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print(f"\n>>> Uploading {len(results)} files to style corpus...\n")
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asyncio.run(upload_all(results))
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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Reference in New Issue
Block a user