Chaim
476c2fc5d1
Legacy Hebrew .doc precedents (e.g. nevo.co.il CP1255 OLE2) can now be uploaded directly through the precedent-library, missing-precedent, and training upload paths — the frontend already advertised .doc but the backend gate rejected it before reaching the extractor. - web/app.py: add .doc to ALLOWED_EXTENSIONS (covers all paths that share the set: precedent library, missing-precedent, training). - Dockerfile: install libreoffice-writer-nogui (no X11/Java) so the extractor's existing _extract_doc LibreOffice conversion works in the Coolify container (was missing → would fail at runtime). - extractor.py: isolate the LibreOffice user profile per call to avoid a profile-lock failure on concurrent .doc conversions. Verified in python:3.12-slim (prod base): .doc→.docx→text yields text byte-identical to a native Word .docx save (103 paragraphs, 24,341 chars). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
381 lines
13 KiB
Python
381 lines
13 KiB
Python
"""Text extraction from PDF, DOCX, DOC, and RTF files.
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Primary PDF extraction: PyMuPDF direct text (for born-digital PDFs).
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Fallback: Google Cloud Vision OCR (for scanned documents).
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DOC files: converted to DOCX via LibreOffice before extraction.
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Post-processing: Hebrew abbreviation quote fixer.
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"""
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from __future__ import annotations
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import asyncio
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import io
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import logging
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import re
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import subprocess
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import tempfile
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from pathlib import Path
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from typing import TYPE_CHECKING
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import fitz # PyMuPDF
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from PIL import Image
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from docx import Document as DocxDocument
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from striprtf.striprtf import rtf_to_text
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from legal_mcp import config
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if TYPE_CHECKING:
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from google.cloud import vision
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logger = logging.getLogger(__name__)
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# ── Google Cloud Vision client (imported lazily — saves ~550ms at MCP startup) ──
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_vision_client: "vision.ImageAnnotatorClient | None" = None
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def _get_vision_client() -> "vision.ImageAnnotatorClient":
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global _vision_client
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if _vision_client is None:
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from google.cloud import vision
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_vision_client = vision.ImageAnnotatorClient(
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client_options={"api_key": config.GOOGLE_CLOUD_VISION_API_KEY}
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)
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return _vision_client
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# ── Hebrew text quality detection ────────────────────────────────
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_HEBREW_RE = re.compile(r'[\u0590-\u05FF]')
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_WORD_RE = re.compile(r'\S+')
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def _text_quality_ok(text: str) -> bool:
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"""Check if extracted text is real content vs broken OCR layer.
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Returns True if text appears to be genuine Hebrew legal content.
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Broken OCR layers from scanned PDFs often have:
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- Very short words / single-character fragments
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- Each word on its own line (high words-per-line ratio)
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- Non-Hebrew characters mixed in
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"""
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words = _WORD_RE.findall(text)
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if len(words) < 10:
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return False
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# Average word length — real Hebrew words avg 4-6 chars.
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avg_len = sum(len(w) for w in words) / len(words)
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if avg_len < 2.5:
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return False
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# Percentage of single-character "words"
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single_char_pct = sum(1 for w in words if len(w) == 1) / len(words)
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if single_char_pct > 0.4:
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return False
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# Words per line — broken OCR puts each word on its own line.
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# Real text has 5-15 words per line; broken OCR has ~1-2.
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lines = [l for l in text.split("\n") if l.strip()]
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if lines:
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words_per_line = len(words) / len(lines)
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if words_per_line < 3.0:
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return False
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# Hebrew character ratio among letter characters
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letters = re.findall(r'[a-zA-Z\u0590-\u05FF]', text)
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if letters:
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hebrew_pct = sum(1 for c in letters if _HEBREW_RE.match(c)) / len(letters)
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if hebrew_pct < 0.5:
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return False
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return True
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# ── Hebrew abbreviation quote fixer ──────────────────────────────
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_HEBREW_ABBREV_FIXES: dict[str, str] = {
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'עוהייד': 'עוה"ד',
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'עוייד': 'עו"ד',
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'הנייל': 'הנ"ל',
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'מצייב': 'מצ"ב',
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'ביהמייש': 'ביהמ"ש',
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'תייז': 'ת"ז',
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'עייי': 'ע"י',
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'אחייכ': 'אח"כ',
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'סייק': 'ס"ק',
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'דייר': 'ד"ר',
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'כדוייח': 'כדו"ח',
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'חווייד': 'חוו"ד',
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'מייר': 'מ"ר',
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'יחייד': 'יח"ד',
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'בייכ': 'ב"כ',
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# Patterns where double-yod (יי) substitutes for gershayim (״) in born-digital PDFs
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'בליימ': 'בל"מ', # בקשה להארכת מועד — appears in RTL legal docs
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'תמייא': 'תמ"א', # תכנית מתאר ארצית
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}
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_ABBREV_PATTERN = re.compile(
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'|'.join(re.escape(k) for k in sorted(_HEBREW_ABBREV_FIXES, key=len, reverse=True))
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)
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# Matches Hebrew law year abbreviations where gershayim was encoded as double-yod.
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# e.g. תשכייה → תשכ"ה, תשנייב → תשנ"ב
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_HEBREW_YEAR_RE = re.compile(r'(תש[א-ת]+)יי([א-ת])')
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def _fix_hebrew_quotes(text: str) -> str:
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"""Fix known Hebrew abbreviation quote replacements.
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Applied to both Google Vision OCR output and direct PyMuPDF extraction —
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some born-digital PDFs encode gershayim (״) as double-yod (יי), producing
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the same corruption patterns as OCR.
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"""
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text = _ABBREV_PATTERN.sub(lambda m: _HEBREW_ABBREV_FIXES[m.group()], text)
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text = _HEBREW_YEAR_RE.sub(r'\1"\2', text)
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return text
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# ── Extraction ───────────────────────────────────────────────────
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# Separator used when joining per-page text. Constant so chunker /
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# retrofit can reproduce the join when computing page offsets.
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PAGE_SEPARATOR = "\n\n"
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async def extract_text(file_path: str) -> tuple[str, int, list[int] | None]:
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"""Extract text from a document file.
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Returns:
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``(text, page_count, page_offsets)`` where:
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- ``text``: concatenated extracted text
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- ``page_count``: number of pages (0 for non-PDF)
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- ``page_offsets``: ``page_offsets[i]`` = char start offset of
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page (i+1) inside ``text``. ``None`` for non-PDFs (where the
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notion of pages doesn't apply). Used by the chunker to assign
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a ``page_number`` to each chunk.
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"""
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path = Path(file_path)
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suffix = path.suffix.lower()
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if suffix == ".pdf":
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return await _extract_pdf(path)
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elif suffix == ".docx":
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return _extract_docx(path), 0, None
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elif suffix == ".doc":
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return _extract_doc(path), 0, None
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elif suffix == ".rtf":
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return _extract_rtf(path), 0, None
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elif suffix in (".txt", ".md"):
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return path.read_text(encoding="utf-8"), 0, None
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else:
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raise ValueError(f"Unsupported file type: {suffix}")
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def _join_pages(pages_text: list[str]) -> tuple[str, list[int]]:
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"""Join per-page text with PAGE_SEPARATOR while recording the start
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offset of each page in the joined output."""
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offsets: list[int] = []
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parts: list[str] = []
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cursor = 0
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for i, pg in enumerate(pages_text):
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offsets.append(cursor)
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parts.append(pg)
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cursor += len(pg)
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if i < len(pages_text) - 1:
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parts.append(PAGE_SEPARATOR)
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cursor += len(PAGE_SEPARATOR)
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return "".join(parts), offsets
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async def _extract_pdf(path: Path) -> tuple[str, int, list[int]]:
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"""Extract text from PDF.
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Try direct text first, fall back to Google Cloud Vision for scanned
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or broken-OCR pages.
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"""
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doc = fitz.open(str(path))
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page_count = len(doc)
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pages_text: list[str] = []
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for page_num in range(page_count):
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page = doc[page_num]
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text = page.get_text().strip()
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if len(text) > 50 and _text_quality_ok(text):
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pages_text.append(_fix_hebrew_quotes(text))
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logger.debug("Page %d: direct extraction (%d chars, quality OK)", page_num + 1, len(text))
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else:
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reason = "insufficient text" if len(text) <= 50 else "low quality OCR layer"
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logger.info("Page %d: Google Vision OCR (%s)", page_num + 1, reason)
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pix = page.get_pixmap(dpi=300)
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img_bytes = pix.tobytes("png")
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ocr_text = await asyncio.to_thread(
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_ocr_with_google_vision, img_bytes, page_num + 1
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)
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pages_text.append(ocr_text)
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doc.close()
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joined, offsets = _join_pages(pages_text)
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return joined, page_count, offsets
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def page_at_offset(offset: int, page_offsets: list[int]) -> int:
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"""Look up the page number containing a given char offset.
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page_offsets[i] is the start of page (i+1) in the joined text;
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a chunk starting at ``offset`` belongs to the highest-indexed page
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whose start is ``<= offset``. Returns 1-based page number.
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"""
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if not page_offsets:
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return 1
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# Linear scan is fine — page_offsets is short (≤ ~200 for our PDFs).
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page = 1
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for i, start in enumerate(page_offsets):
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if start <= offset:
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page = i + 1
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else:
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break
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return page
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def _ocr_with_google_vision(image_bytes: bytes, page_num: int) -> str:
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"""OCR a single page image using Google Cloud Vision API."""
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from google.cloud import vision # lazy: keeps MCP startup fast
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client = _get_vision_client()
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image = vision.Image(content=image_bytes)
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response = client.document_text_detection(
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image=image,
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image_context=vision.ImageContext(language_hints=["he"]),
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)
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if response.error.message:
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raise RuntimeError(
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f"Google Vision error on page {page_num}: {response.error.message}"
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)
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text = response.full_text_annotation.text if response.full_text_annotation else ""
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return _fix_hebrew_quotes(text)
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def _extract_doc(path: Path) -> str:
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"""Extract text from legacy .doc file by converting to .docx via LibreOffice."""
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with tempfile.TemporaryDirectory() as tmp_dir:
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# Isolate the LibreOffice user profile per call: headless soffice
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# locks a single shared profile, so concurrent .doc conversions would
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# otherwise fail with a profile-lock error.
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result = subprocess.run(
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[
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"libreoffice",
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f"-env:UserInstallation=file://{tmp_dir}/lo-profile",
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"--headless", "--convert-to", "docx", str(path), "--outdir", tmp_dir,
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],
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capture_output=True, text=True, timeout=120,
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)
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if result.returncode != 0:
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raise RuntimeError(f"LibreOffice conversion failed: {result.stderr}")
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docx_path = Path(tmp_dir) / f"{path.stem}.docx"
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if not docx_path.exists():
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raise FileNotFoundError(f"Converted file not found: {docx_path}")
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return _extract_docx(docx_path)
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def _extract_docx(path: Path) -> str:
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"""Extract text from DOCX file."""
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doc = DocxDocument(str(path))
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paragraphs = [p.text for p in doc.paragraphs if p.text.strip()]
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return "\n\n".join(paragraphs)
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def _extract_rtf(path: Path) -> str:
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"""Extract text from RTF file."""
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rtf_content = path.read_text(encoding="utf-8", errors="replace")
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return rtf_to_text(rtf_content)
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# ── Multimodal page rendering (V9) ───────────────────────────────
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def _pixmap_to_pil(pix: fitz.Pixmap) -> Image.Image:
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"""Convert a PyMuPDF pixmap to PIL.Image (RGB) without going through
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PNG bytes. Faster than tobytes('png') → Image.open()."""
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if pix.alpha:
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# Drop alpha channel — voyage multimodal expects RGB.
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pix = fitz.Pixmap(pix, 0)
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return Image.frombytes("RGB", (pix.width, pix.height), pix.samples)
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def render_pages_for_multimodal(
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pdf_path: str | Path,
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embed_dpi: int,
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thumb_dpi: int | None = None,
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thumbnail_dir: Path | None = None,
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) -> list[tuple[Image.Image, Path | None]]:
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"""Render each PDF page as PIL.Image at ``embed_dpi`` for the
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multimodal embedder, and optionally save a smaller JPEG thumbnail
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at ``thumb_dpi`` to ``thumbnail_dir`` for UI preview.
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Returns ``[(pil_image, thumb_path_or_None), ...]`` in page order.
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The full-DPI image stays in memory only — only the thumbnail is
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persisted to disk.
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"""
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src = Path(pdf_path)
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if not src.is_file():
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raise FileNotFoundError(f"PDF not found: {src}")
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if thumbnail_dir is not None:
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thumbnail_dir.mkdir(parents=True, exist_ok=True)
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out: list[tuple[Image.Image, Path | None]] = []
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doc = fitz.open(str(src))
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try:
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for page_idx, page in enumerate(doc):
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page_num = page_idx + 1
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pix = page.get_pixmap(dpi=embed_dpi)
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img = _pixmap_to_pil(pix)
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thumb_path: Path | None = None
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if thumbnail_dir is not None and thumb_dpi:
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thumb_path = thumbnail_dir / f"p{page_num:03d}.jpg"
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# Downsample the same render rather than re-rendering
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# with PyMuPDF — far faster.
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ratio = thumb_dpi / embed_dpi
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thumb_size = (
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max(1, int(img.width * ratio)),
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max(1, int(img.height * ratio)),
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)
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thumb = img.resize(thumb_size, Image.Resampling.LANCZOS)
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thumb.save(thumb_path, "JPEG", quality=75, optimize=True)
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out.append((img, thumb_path))
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finally:
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doc.close()
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return out
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# ── Nevo preamble stripping ──────────────────────────────────────
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_NEVO_MARKERS = ("ספרות:", "חקיקה שאוזכרה:", "מיני-רציו:", "פסקי דין שאוזכרו:",
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"כתבי עת:", "הועתק מנבו")
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_DECISION_START = re.compile(
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r"^(בפנינו|לפנינו|הערר שבנדון|ועדת הערר לתכנון|רקע עובדתי|עסקינן)",
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re.MULTILINE,
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)
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def strip_nevo_preamble(text: str) -> str:
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"""Remove Nevo database preamble (bibliography, legislation, mini-ratio) from decision text.
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Returns the original text unchanged if no preamble is detected.
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"""
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head = text[:400]
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if not any(marker in head for marker in _NEVO_MARKERS):
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return text
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m = _DECISION_START.search(text)
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if m and m.start() > 50:
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stripped = text[m.start():]
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logger.debug("Stripped %d chars of Nevo preamble", m.start())
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return stripped
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return text
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