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>
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mcp-server/src/legal_mcp/services/local_classifier.py
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mcp-server/src/legal_mcp/services/local_classifier.py
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"""Local document classifier — rule-based, no API calls.
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Classifies legal documents by filename patterns and content keywords.
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Falls back to Claude Code headless (`claude -p`) for ambiguous cases.
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"""
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from __future__ import annotations
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import json
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import logging
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import re
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import subprocess
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from pathlib import Path
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logger = logging.getLogger(__name__)
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# ── Filename patterns (checked in order, first match wins) ────────
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_FILENAME_RULES: list[tuple[str, str, float]] = [
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# (regex pattern on filename, doc_type, confidence)
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(r"כתב.ערר|כתב-ערר", "appeal", 1.0),
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(r"תשובה|תשובת|תגובת|השלמת.טיעון|בקשה.להשלמת", "response", 1.0),
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(r"פרוטוקול", "protocol", 1.0),
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(r"החלטת?.ביניים|החלטה.לתיקון", "decision", 0.95),
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(r"הוראות.תכנית|תכנית", "plan", 1.0),
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(r"היתר", "permit", 1.0),
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(r"שומה|חוו.ת.דעת", "appraisal", 1.0),
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(r"התנגדות", "objection", 1.0),
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# Court decisions: case number patterns
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(r"(?:עעם|עע.?מ|עתמ|עת.?מ|בג.?צ|בבנ|עא|ע.?א|רעא|רע.?א|עעמ|עתמ)", "court_decision", 1.0),
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# ערר + number that's NOT part of our case files (i.e. precedent references)
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(r"^ערר.?\d", "court_decision", 0.9),
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]
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# ── Content patterns (first 500 chars) ───────────────────────────
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_CONTENT_RULES: list[tuple[str, str, float]] = [
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(r"בפני\s+ועדת\s+הערר|לפנינו\s+ערר|ניתנה?\s+היום", "decision", 0.85),
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(r"כתב\s+ערר|העורר.{0,20}מגיש", "appeal", 0.85),
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(r"כתב\s+תשובה|המשיב.{0,20}משיב", "response", 0.85),
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(r"פרוטוקול\s+(?:דיון|ישיבה|ועדה)", "protocol", 0.9),
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(r"בית\s+(?:ה)?משפט|פסק\s+דין|השופט", "court_decision", 0.85),
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(r"הוראות\s+(?:ה)?תכנית|תב.עה|ייעוד\s+הקרקע", "plan", 0.8),
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]
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def classify(filename: str, text: str = "") -> tuple[str, float]:
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"""Classify a legal document by filename and content.
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Returns (doc_type, confidence). Confidence > 0.8 means high certainty.
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"""
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name = Path(filename).stem
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# Try filename rules
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for pattern, doc_type, confidence in _FILENAME_RULES:
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if re.search(pattern, name):
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logger.info("Local classifier: '%s' → %s (filename, %.2f)", name, doc_type, confidence)
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return doc_type, confidence
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# Try content rules (first 500 chars)
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snippet = text[:500] if text else ""
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for pattern, doc_type, confidence in _CONTENT_RULES:
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if re.search(pattern, snippet):
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logger.info("Local classifier: '%s' → %s (content, %.2f)", name, doc_type, confidence)
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return doc_type, confidence
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logger.info("Local classifier: '%s' → reference (no match, 0.3)", name)
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return "reference", 0.3
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def classify_with_claude_code(filename: str, text: str) -> tuple[str, float]:
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"""Fallback: use Claude Code headless to classify ambiguous documents.
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Only works when `claude` CLI is available (not in Docker).
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"""
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prompt = (
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"סווג את המסמך המשפטי הבא לאחת הקטגוריות הבאות בלבד:\n"
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"appeal, response, protocol, decision, plan, permit, appraisal, "
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"court_decision, exhibit, objection, reference\n\n"
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f"שם הקובץ: {filename}\n"
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f"תחילת המסמך:\n{text[:500]}\n\n"
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'החזר JSON בלבד: {"doc_type": "...", "confidence": 0.9}'
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)
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try:
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result = subprocess.run(
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["claude", "-p", prompt, "--output-format", "json", "--max-turns", "1"],
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capture_output=True, text=True, timeout=60,
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)
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if result.returncode == 0 and result.stdout.strip():
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data = json.loads(result.stdout)
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# claude -p --output-format json wraps in {"result": "..."}
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inner = data.get("result", data)
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if isinstance(inner, str):
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inner = json.loads(inner)
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doc_type = inner.get("doc_type", "reference")
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confidence = float(inner.get("confidence", 0.7))
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logger.info("Claude Code classifier: '%s' → %s (%.2f)", filename, doc_type, confidence)
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return doc_type, confidence
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except FileNotFoundError:
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logger.debug("Claude CLI not available — skipping headless fallback")
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except (subprocess.TimeoutExpired, json.JSONDecodeError, Exception) as e:
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logger.warning("Claude Code classifier failed: %s", e)
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return "reference", 0.3
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@@ -37,39 +37,22 @@ async def process_document(document_id: UUID, case_id: UUID) -> dict:
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page_count=page_count,
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)
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# Step 1.5: Classify document and identify parties (non-fatal)
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# Step 1.5: Classify document — local rules first, Claude Code headless fallback
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classification_result = {}
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try:
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logger.info("Classifying document")
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case_number = ""
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if case_id:
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case = await db.get_case(case_id)
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if case:
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case_number = case.get("case_number", "")
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classification_result = await classifier.classify_and_identify(text, case_number)
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await db.update_document(
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document_id,
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metadata=classification_result,
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)
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logger.info(
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"Classification: %s (confidence: %.2f), parties found: %d appellants, %d respondents",
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classification_result["classification"].get("doc_type", "?"),
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classification_result["classification"].get("confidence", 0),
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len(classification_result["parties"].get("appellants", [])),
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len(classification_result["parties"].get("respondents", [])),
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)
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from legal_mcp.services import local_classifier
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filename = Path(doc["file_path"]).name
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doc_type, confidence = local_classifier.classify(filename, text)
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if confidence < 0.8:
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doc_type, confidence = local_classifier.classify_with_claude_code(filename, text)
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# Update case parties if empty
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if case_id and case:
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parties = classification_result.get("parties", {})
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updates = {}
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if not case.get("appellants") and parties.get("appellants"):
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updates["appellants"] = parties["appellants"]
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if not case.get("respondents") and parties.get("respondents"):
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updates["respondents"] = parties["respondents"]
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if updates:
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await db.update_case(case_id, **updates)
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logger.info("Updated case parties: %s", updates)
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# Update doc_type if we got a good classification and current type is generic
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if confidence >= 0.5 and doc.get("doc_type") in ("reference", "auto"):
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await db.update_document(document_id, doc_type=doc_type)
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logger.info("Auto-classified: %s → %s (confidence %.2f)", filename, doc_type, confidence)
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classification_result = {"classification": {"doc_type": doc_type, "confidence": confidence}}
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await db.update_document(document_id, metadata=classification_result)
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except Exception as e:
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logger.warning("Classification failed (non-fatal): %s", e)
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