Add local rule-based classifier with Claude Code headless fallback

Replaces API-based classifier with:
1. Filename pattern matching (covers 95%+ of legal docs)
2. Content keyword matching for ambiguous filenames
3. Claude Code headless (claude -p) fallback for edge cases

No Anthropic API calls needed for classification.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
2026-04-04 13:14:13 +00:00
parent 9e7492e761
commit 52ee3419d3
2 changed files with 118 additions and 30 deletions

View File

@@ -0,0 +1,105 @@
"""Local document classifier — rule-based, no API calls.
Classifies legal documents by filename patterns and content keywords.
Falls back to Claude Code headless (`claude -p`) for ambiguous cases.
"""
from __future__ import annotations
import json
import logging
import re
import subprocess
from pathlib import Path
logger = logging.getLogger(__name__)
# ── Filename patterns (checked in order, first match wins) ────────
_FILENAME_RULES: list[tuple[str, str, float]] = [
# (regex pattern on filename, doc_type, confidence)
(r"כתב.ערר|כתב-ערר", "appeal", 1.0),
(r"תשובה|תשובת|תגובת|השלמת.טיעון|בקשה.להשלמת", "response", 1.0),
(r"פרוטוקול", "protocol", 1.0),
(r"החלטת?.ביניים|החלטה.לתיקון", "decision", 0.95),
(r"הוראות.תכנית|תכנית", "plan", 1.0),
(r"היתר", "permit", 1.0),
(r"שומה|חוו.ת.דעת", "appraisal", 1.0),
(r"התנגדות", "objection", 1.0),
# Court decisions: case number patterns
(r"(?:עעם|עע.?מ|עתמ|עת.?מ|בג.?צ|בבנ|עא|ע.?א|רעא|רע.?א|עעמ|עתמ)", "court_decision", 1.0),
# ערר + number that's NOT part of our case files (i.e. precedent references)
(r"^ערר.?\d", "court_decision", 0.9),
]
# ── Content patterns (first 500 chars) ───────────────────────────
_CONTENT_RULES: list[tuple[str, str, float]] = [
(r"בפני\s+ועדת\s+הערר|לפנינו\s+ערר|ניתנה?\s+היום", "decision", 0.85),
(r"כתב\s+ערר|העורר.{0,20}מגיש", "appeal", 0.85),
(r"כתב\s+תשובה|המשיב.{0,20}משיב", "response", 0.85),
(r"פרוטוקול\s+(?:דיון|ישיבה|ועדה)", "protocol", 0.9),
(r"בית\s+(?:ה)?משפט|פסק\s+דין|השופט", "court_decision", 0.85),
(r"הוראות\s+(?:ה)?תכנית|תב.עה|ייעוד\s+הקרקע", "plan", 0.8),
]
def classify(filename: str, text: str = "") -> tuple[str, float]:
"""Classify a legal document by filename and content.
Returns (doc_type, confidence). Confidence > 0.8 means high certainty.
"""
name = Path(filename).stem
# Try filename rules
for pattern, doc_type, confidence in _FILENAME_RULES:
if re.search(pattern, name):
logger.info("Local classifier: '%s'%s (filename, %.2f)", name, doc_type, confidence)
return doc_type, confidence
# Try content rules (first 500 chars)
snippet = text[:500] if text else ""
for pattern, doc_type, confidence in _CONTENT_RULES:
if re.search(pattern, snippet):
logger.info("Local classifier: '%s'%s (content, %.2f)", name, doc_type, confidence)
return doc_type, confidence
logger.info("Local classifier: '%s' → reference (no match, 0.3)", name)
return "reference", 0.3
def classify_with_claude_code(filename: str, text: str) -> tuple[str, float]:
"""Fallback: use Claude Code headless to classify ambiguous documents.
Only works when `claude` CLI is available (not in Docker).
"""
prompt = (
"סווג את המסמך המשפטי הבא לאחת הקטגוריות הבאות בלבד:\n"
"appeal, response, protocol, decision, plan, permit, appraisal, "
"court_decision, exhibit, objection, reference\n\n"
f"שם הקובץ: {filename}\n"
f"תחילת המסמך:\n{text[:500]}\n\n"
'החזר JSON בלבד: {"doc_type": "...", "confidence": 0.9}'
)
try:
result = subprocess.run(
["claude", "-p", prompt, "--output-format", "json", "--max-turns", "1"],
capture_output=True, text=True, timeout=60,
)
if result.returncode == 0 and result.stdout.strip():
data = json.loads(result.stdout)
# claude -p --output-format json wraps in {"result": "..."}
inner = data.get("result", data)
if isinstance(inner, str):
inner = json.loads(inner)
doc_type = inner.get("doc_type", "reference")
confidence = float(inner.get("confidence", 0.7))
logger.info("Claude Code classifier: '%s'%s (%.2f)", filename, doc_type, confidence)
return doc_type, confidence
except FileNotFoundError:
logger.debug("Claude CLI not available — skipping headless fallback")
except (subprocess.TimeoutExpired, json.JSONDecodeError, Exception) as e:
logger.warning("Claude Code classifier failed: %s", e)
return "reference", 0.3

View File

@@ -37,39 +37,22 @@ async def process_document(document_id: UUID, case_id: UUID) -> dict:
page_count=page_count,
)
# Step 1.5: Classify document and identify parties (non-fatal)
# Step 1.5: Classify document — local rules first, Claude Code headless fallback
classification_result = {}
try:
logger.info("Classifying document")
case_number = ""
if case_id:
case = await db.get_case(case_id)
if case:
case_number = case.get("case_number", "")
classification_result = await classifier.classify_and_identify(text, case_number)
await db.update_document(
document_id,
metadata=classification_result,
)
logger.info(
"Classification: %s (confidence: %.2f), parties found: %d appellants, %d respondents",
classification_result["classification"].get("doc_type", "?"),
classification_result["classification"].get("confidence", 0),
len(classification_result["parties"].get("appellants", [])),
len(classification_result["parties"].get("respondents", [])),
)
from legal_mcp.services import local_classifier
filename = Path(doc["file_path"]).name
doc_type, confidence = local_classifier.classify(filename, text)
if confidence < 0.8:
doc_type, confidence = local_classifier.classify_with_claude_code(filename, text)
# Update case parties if empty
if case_id and case:
parties = classification_result.get("parties", {})
updates = {}
if not case.get("appellants") and parties.get("appellants"):
updates["appellants"] = parties["appellants"]
if not case.get("respondents") and parties.get("respondents"):
updates["respondents"] = parties["respondents"]
if updates:
await db.update_case(case_id, **updates)
logger.info("Updated case parties: %s", updates)
# Update doc_type if we got a good classification and current type is generic
if confidence >= 0.5 and doc.get("doc_type") in ("reference", "auto"):
await db.update_document(document_id, doc_type=doc_type)
logger.info("Auto-classified: %s%s (confidence %.2f)", filename, doc_type, confidence)
classification_result = {"classification": {"doc_type": doc_type, "confidence": confidence}}
await db.update_document(document_id, metadata=classification_result)
except Exception as e:
logger.warning("Classification failed (non-fatal): %s", e)