Practice area separation: multi-tenant axis across DB, RAG, and UI

Adds two orthogonal columns — practice_area (top-level legal domain:
appeals_committee / national_insurance / labor_law) and appeal_subtype
(building_permit / betterment_levy / compensation_197) — denormalized
into cases, documents, document_chunks, decisions, and style_corpus so
vector searches can filter without JOINs.

Why: the system handles two unrelated sub-domains under the same
appeals committee (1xxx building permits and 8xxx/9xxx betterment/197),
with different rules and writing style. Without a separation axis,
search_similar() and the block-writer's precedent lookup were free to
surface betterment-levy paragraphs while drafting a building-permit
decision — a real risk of cross-domain contamination. The same axis
also lets future domains (national insurance, labor law) coexist
without separate schemas.

Schema (V4 migration in db.py):
- ALTER ... ADD COLUMN IF NOT EXISTS on all five tables + composite
  indexes (practice_area first).
- Idempotent backfill: case_number ~ '^1' → building_permit, '^8' →
  betterment_levy, '^9' → compensation_197; propagated to documents,
  chunks, and decisions via case_id; training-corpus rows (case_id NULL)
  default to appeals_committee.

Code:
- New services/practice_area.py with derive_subtype, validate, and
  is_override + enum constants.
- db.create_case / create_document / store_chunks / create_decision
  inherit practice_area from the parent case (or take an explicit
  override for the case_id=None training corpus).
- db.search_similar and search_similar_paragraphs accept practice_area
  + appeal_subtype filters using the denormalized columns.
- tools/search.py auto-resolves the filter from case_number when given.
- block_writer._build_precedents_context now passes the active case's
  practice_area to search_similar_paragraphs — closes the contamination
  hole for the discussion-block precedent fetch.
- tools/cases.case_create auto-derives subtype from case_number; an
  explicit override that disagrees writes a case_subtype_override entry
  to audit_log so we can spot bad classifications later.
- tools/documents.document_upload_training tags new training material
  with practice_area + subtype end-to-end (corpus, document, chunks).

UI (web/static/index.html + web/app.py):
- New-case wizard gets a practice_area dropdown (others disabled until
  national_insurance / labor_law arrive) and an appeal_subtype dropdown
  with JS auto-fill from the case-number prefix; manual edits stick.
- Case header shows a blue badge with practice_area · subtype.
- CaseCreateRequest plumbs both fields through to cases_tools.case_create.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

mcp-server/src/legal_mcp/tools/search.py

@@ -3,28 +3,52 @@
 from __future__ import annotations
 
 import json
+import logging
 from uuid import UUID
 
 from legal_mcp.services import db, embeddings
 
+logger = logging.getLogger(__name__)
+
 
 async def search_decisions(
     query: str,
     limit: int = 10,
     section_type: str = "",
+    practice_area: str = "",
+    appeal_subtype: str = "",
+    case_number: str = "",
 ) -> str:
-    """חיפוש סמנטי בהחלטות קודמות ובמסמכים.
+    """חיפוש סמנטי בהחלטות קודמות ובמסמכים — מסונן לפי תחום משפטי.
 
     Args:
-        query: שאילתת חיפוש בעברית (לדוגמה: "שימוש חורג למסחר באזור מגורים")
+        query: שאילתת חיפוש בעברית
         limit: מספר תוצאות מקסימלי
-        section_type: סינון לפי סוג סעיף (facts, legal_analysis, conclusion, ruling, וכו'). ריק = הכל
+        section_type: סינון לפי סוג סעיף (facts, legal_analysis, ...)
+        practice_area: תחום משפטי לסינון (appeals_committee/national_insurance/...)
+        appeal_subtype: סוג ערר לסינון (building_permit/betterment_levy/compensation_197)
+        case_number: אם סופק, ה-practice_area/subtype יוסקו אוטומטית מהתיק
     """
+    # Auto-resolve practice_area from case_number if available
+    if case_number and not practice_area:
+        case = await db.get_case_by_number(case_number)
+        if case:
+            practice_area = case.get("practice_area") or ""
+            appeal_subtype = appeal_subtype or (case.get("appeal_subtype") or "")
+
+    if not practice_area:
+        logger.warning(
+            "search_decisions called without practice_area filter — "
+            "results may mix legal domains"
+        )
+
     query_emb = await embeddings.embed_query(query)
     results = await db.search_similar(
         query_embedding=query_emb,
         limit=limit,
         section_type=section_type or None,
+        practice_area=practice_area or None,
+        appeal_subtype=appeal_subtype or None,
     )
 
     if not results:
@@ -61,6 +85,7 @@ async def search_case_documents(
         return f"תיק {case_number} לא נמצא."
 
     query_emb = await embeddings.embed_query(query)
+    # Restricted to case_id — practice_area filter would be redundant.
     results = await db.search_similar(
         query_embedding=query_emb,
         limit=limit,
@@ -86,17 +111,37 @@ async def search_case_documents(
 async def find_similar_cases(
     description: str,
     limit: int = 5,
+    practice_area: str = "",
+    appeal_subtype: str = "",
+    case_number: str = "",
 ) -> str:
-    """מציאת תיקים דומים על בסיס תיאור.
+    """מציאת תיקים דומים על בסיס תיאור — מסונן לפי תחום משפטי.
 
     Args:
-        description: תיאור התיק או הנושא (לדוגמה: "ערר על סירוב להיתר בנייה לתוספת קומה")
+        description: תיאור התיק או הנושא
         limit: מספר תוצאות מקסימלי
+        practice_area: תחום משפטי לסינון
+        appeal_subtype: סוג ערר לסינון
+        case_number: אם סופק, ה-practice_area/subtype יוסקו אוטומטית מהתיק
     """
+    if case_number and not practice_area:
+        case = await db.get_case_by_number(case_number)
+        if case:
+            practice_area = case.get("practice_area") or ""
+            appeal_subtype = appeal_subtype or (case.get("appeal_subtype") or "")
+
+    if not practice_area:
+        logger.warning(
+            "find_similar_cases called without practice_area filter — "
+            "results may mix legal domains"
+        )
+
     query_emb = await embeddings.embed_query(description)
     results = await db.search_similar(
         query_embedding=query_emb,
         limit=limit * 3,  # Get more to deduplicate by case
+        practice_area=practice_area or None,
+        appeal_subtype=appeal_subtype or None,
     )
 
     if not results: