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/services/block_writer.py

@@ -476,12 +476,17 @@ async def _build_precedents_context(case_id: UUID, block_id: str) -> str:
         case = await db.get_case(case_id)
         case_number = case.get("case_number", "") if case else ""
         subject = case.get("subject", "") if case else ""
+        practice_area = case.get("practice_area") if case else None
+        appeal_subtype = case.get("appeal_subtype") if case else None
         query = f"דיון משפטי בנושא {subject}" if subject else "דיון משפטי ועדת ערר"
         query_emb = await embeddings.embed_query(query)
 
-        # Search 1: paragraph_embeddings (from other decisions by Dafna)
+        # Search 1: paragraph_embeddings (from other decisions by Dafna).
+        # Filter by practice_area + appeal_subtype so we don't pull a
+        # betterment-levy paragraph when writing a building-permit decision.
         para_results = await db.search_similar_paragraphs(
             query_embedding=query_emb, limit=10, block_type="block-yod",
+            practice_area=practice_area, appeal_subtype=appeal_subtype,
         )
         # Filter out same case
         para_results = [r for r in para_results if r.get("case_number", "") != case_number]

mcp-server/src/legal_mcp/services/db.py

@@ -200,6 +200,76 @@ CREATE TABLE IF NOT EXISTS appeal_type_rules (
 ALTER TABLE decision_blocks ADD COLUMN IF NOT EXISTS image_placeholders JSONB DEFAULT '[]';
 """
 
+# ── Phase 4: Practice area separation (multi-tenant axis) ──────────
+
+SCHEMA_V4_SQL = """
+-- ═══════════════════════════════════════════════════════════════════
+-- practice_area  = top-level legal domain (multi-tenant axis):
+--   appeals_committee | national_insurance | labor_law | ...
+-- appeal_subtype = refines within practice_area:
+--   building_permit | betterment_levy | compensation_197 | unknown
+-- Both columns are denormalized to documents/chunks/decisions/style_corpus
+-- so vector searches can filter without expensive JOINs.
+-- ═══════════════════════════════════════════════════════════════════
+
+ALTER TABLE cases          ADD COLUMN IF NOT EXISTS practice_area  TEXT;
+ALTER TABLE cases          ADD COLUMN IF NOT EXISTS appeal_subtype TEXT;
+ALTER TABLE documents      ADD COLUMN IF NOT EXISTS practice_area  TEXT;
+ALTER TABLE documents      ADD COLUMN IF NOT EXISTS appeal_subtype TEXT;
+ALTER TABLE document_chunks ADD COLUMN IF NOT EXISTS practice_area  TEXT;
+ALTER TABLE document_chunks ADD COLUMN IF NOT EXISTS appeal_subtype TEXT;
+ALTER TABLE decisions      ADD COLUMN IF NOT EXISTS practice_area  TEXT;
+ALTER TABLE decisions      ADD COLUMN IF NOT EXISTS appeal_subtype TEXT;
+ALTER TABLE style_corpus   ADD COLUMN IF NOT EXISTS practice_area  TEXT;
+ALTER TABLE style_corpus   ADD COLUMN IF NOT EXISTS appeal_subtype TEXT;
+
+CREATE INDEX IF NOT EXISTS idx_cases_practice
+    ON cases(practice_area, appeal_subtype);
+CREATE INDEX IF NOT EXISTS idx_chunks_practice
+    ON document_chunks(practice_area);
+CREATE INDEX IF NOT EXISTS idx_corpus_practice
+    ON style_corpus(practice_area, appeal_subtype);
+CREATE INDEX IF NOT EXISTS idx_decisions_practice
+    ON decisions(practice_area);
+
+-- Backfill (idempotent — only fills NULLs)
+UPDATE cases SET practice_area = 'appeals_committee' WHERE practice_area IS NULL;
+UPDATE cases SET appeal_subtype = CASE
+    WHEN case_number ~ '^1[0-9]{3}' THEN 'building_permit'
+    WHEN case_number ~ '^8[0-9]{3}' THEN 'betterment_levy'
+    WHEN case_number ~ '^9[0-9]{3}' THEN 'compensation_197'
+    ELSE 'unknown'
+END WHERE appeal_subtype IS NULL;
+
+UPDATE documents d
+   SET practice_area = c.practice_area, appeal_subtype = c.appeal_subtype
+  FROM cases c
+ WHERE d.case_id = c.id AND d.practice_area IS NULL;
+
+UPDATE document_chunks dc
+   SET practice_area = c.practice_area, appeal_subtype = c.appeal_subtype
+  FROM cases c
+ WHERE dc.case_id = c.id AND dc.practice_area IS NULL;
+
+UPDATE decisions de
+   SET practice_area = c.practice_area, appeal_subtype = c.appeal_subtype
+  FROM cases c
+ WHERE de.case_id = c.id AND de.practice_area IS NULL;
+
+-- All existing style_corpus entries are דפנה's appeals-committee decisions
+UPDATE style_corpus SET practice_area = 'appeals_committee' WHERE practice_area IS NULL;
+
+-- Training corpus documents/chunks have case_id = NULL. All historical
+-- training material is from דפנה's appeals committee, so default them.
+UPDATE documents SET practice_area = 'appeals_committee'
+ WHERE case_id IS NULL AND practice_area IS NULL;
+
+UPDATE document_chunks dc
+   SET practice_area = d.practice_area, appeal_subtype = d.appeal_subtype
+  FROM documents d
+ WHERE dc.document_id = d.id AND dc.practice_area IS NULL;
+"""
+
 # ── Phase 2: Decision + Knowledge + RAG layers ────────────────────
 
 SCHEMA_V2_SQL = """
@@ -388,7 +458,8 @@ async def init_schema() -> None:
         await conn.execute(MIGRATIONS_SQL)
         await conn.execute(SCHEMA_V2_SQL)
         await conn.execute(SCHEMA_V3_SQL)
-    logger.info("Database schema initialized (v1 + v2 + v3)")
+        await conn.execute(SCHEMA_V4_SQL)
+    logger.info("Database schema initialized (v1 + v2 + v3 + v4)")
 
 
 # ── Case CRUD ───────────────────────────────────────────────────────
@@ -405,6 +476,8 @@ async def create_case(
     hearing_date: date | None = None,
     notes: str = "",
     expected_outcome: str = "",
+    practice_area: str = "appeals_committee",
+    appeal_subtype: str | None = None,
 ) -> dict:
     pool = await get_pool()
     case_id = uuid4()
@@ -412,17 +485,43 @@ async def create_case(
         await conn.execute(
             """INSERT INTO cases (id, case_number, title, appellants, respondents,
                subject, property_address, permit_number, committee_type,
-               hearing_date, notes, expected_outcome)
+               hearing_date, notes, expected_outcome,
-               VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, $11, $12)""",
+               practice_area, appeal_subtype)
+               VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, $11, $12, $13, $14)""",
             case_id, case_number, title,
             json.dumps(appellants or []),
             json.dumps(respondents or []),
             subject, property_address, permit_number, committee_type,
             hearing_date, notes, expected_outcome,
+            practice_area, appeal_subtype,
         )
     return await get_case(case_id)
 
 
+async def get_case_practice_area(case_id: UUID) -> tuple[str | None, str | None]:
+    """Return (practice_area, appeal_subtype) for a case, or (None, None) if missing."""
+    pool = await get_pool()
+    async with pool.acquire() as conn:
+        row = await conn.fetchrow(
+            "SELECT practice_area, appeal_subtype FROM cases WHERE id = $1", case_id
+        )
+    if row is None:
+        return None, None
+    return row["practice_area"], row["appeal_subtype"]
+
+
+async def get_case_practice_area_by_number(case_number: str) -> tuple[str | None, str | None]:
+    pool = await get_pool()
+    async with pool.acquire() as conn:
+        row = await conn.fetchrow(
+            "SELECT practice_area, appeal_subtype FROM cases WHERE case_number = $1",
+            case_number,
+        )
+    if row is None:
+        return None, None
+    return row["practice_area"], row["appeal_subtype"]
+
+
 async def get_case(case_id: UUID) -> dict | None:
     pool = await get_pool()
     async with pool.acquire() as conn:
@@ -488,19 +587,34 @@ def _row_to_case(row: asyncpg.Record) -> dict:
 # ── Document CRUD ───────────────────────────────────────────────────
 
 async def create_document(
-    case_id: UUID,
+    case_id: UUID | None,
     doc_type: str,
     title: str,
     file_path: str,
     page_count: int | None = None,
+    practice_area: str | None = None,
+    appeal_subtype: str | None = None,
 ) -> dict:
     pool = await get_pool()
     doc_id = uuid4()
     async with pool.acquire() as conn:
+        # If practice_area not explicitly given, inherit from the parent case
+        # (for case-bound documents). Training corpus passes case_id=None and
+        # provides the practice_area directly.
+        if practice_area is None and case_id is not None:
+            case_row = await conn.fetchrow(
+                "SELECT practice_area, appeal_subtype FROM cases WHERE id = $1",
+                case_id,
+            )
+            if case_row:
+                practice_area = case_row["practice_area"]
+                appeal_subtype = case_row["appeal_subtype"]
         await conn.execute(
-            """INSERT INTO documents (id, case_id, doc_type, title, file_path, page_count)
+            """INSERT INTO documents (id, case_id, doc_type, title, file_path,
-               VALUES ($1, $2, $3, $4, $5, $6)""",
+                                     page_count, practice_area, appeal_subtype)
+               VALUES ($1, $2, $3, $4, $5, $6, $7, $8)""",
             doc_id, case_id, doc_type, title, file_path, page_count,
+            practice_area, appeal_subtype,
         )
         row = await conn.fetchrow("SELECT * FROM documents WHERE id = $1", doc_id)
     return _row_to_doc(row)
@@ -622,12 +736,20 @@ async def create_decision(
         )
         version = (existing["version"] + 1) if existing else 1
 
+        case_row = await conn.fetchrow(
+            "SELECT practice_area, appeal_subtype FROM cases WHERE id = $1", case_id
+        )
+        practice_area = case_row["practice_area"] if case_row else None
+        appeal_subtype = case_row["appeal_subtype"] if case_row else None
+
         await conn.execute(
             """INSERT INTO decisions (id, case_id, version, outcome, outcome_summary,
-                                     outcome_reasoning, direction_doc)
+                                     outcome_reasoning, direction_doc,
-               VALUES ($1, $2, $3, $4, $5, $6, $7)""",
+                                     practice_area, appeal_subtype)
+               VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9)""",
             decision_id, case_id, version, outcome, outcome_summary,
             outcome_reasoning, json.dumps(direction_doc) if direction_doc else None,
+            practice_area, appeal_subtype,
         )
     return await get_decision(decision_id)
 
@@ -701,12 +823,37 @@ async def store_chunks(
     document_id: UUID,
     case_id: UUID | None,
     chunks: list[dict],
+    practice_area: str | None = None,
+    appeal_subtype: str | None = None,
 ) -> int:
     """Store document chunks with embeddings. Each chunk dict has:
-    content, section_type, embedding (list[float]), page_number, chunk_index
+    content, section_type, embedding (list[float]), page_number, chunk_index.
+
+    practice_area defaults to the parent case's value, or — when case_id is
+    None (training corpus) — falls back to the parent document's value so
+    vector search can still filter cleanly.
     """
     pool = await get_pool()
     async with pool.acquire() as conn:
+        # Resolve practice_area in priority order: explicit > case > document.
+        if practice_area is None:
+            if case_id is not None:
+                case_row = await conn.fetchrow(
+                    "SELECT practice_area, appeal_subtype FROM cases WHERE id = $1",
+                    case_id,
+                )
+                if case_row:
+                    practice_area = case_row["practice_area"]
+                    appeal_subtype = case_row["appeal_subtype"]
+            if practice_area is None:
+                doc_row = await conn.fetchrow(
+                    "SELECT practice_area, appeal_subtype FROM documents WHERE id = $1",
+                    document_id,
+                )
+                if doc_row:
+                    practice_area = doc_row["practice_area"]
+                    appeal_subtype = doc_row["appeal_subtype"]
+
         # Delete existing chunks for this document
         await conn.execute(
             "DELETE FROM document_chunks WHERE document_id = $1", document_id
@@ -714,14 +861,16 @@ async def store_chunks(
         for chunk in chunks:
             await conn.execute(
                 """INSERT INTO document_chunks
-                   (document_id, case_id, chunk_index, content, section_type, embedding, page_number)
+                   (document_id, case_id, chunk_index, content, section_type,
-                   VALUES ($1, $2, $3, $4, $5, $6, $7)""",
+                    embedding, page_number, practice_area, appeal_subtype)
+                   VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9)""",
                 document_id, case_id,
                 chunk["chunk_index"],
                 chunk["content"],
                 chunk.get("section_type", "other"),
                 chunk["embedding"],
                 chunk.get("page_number"),
+                practice_area, appeal_subtype,
             )
     return len(chunks)
 
@@ -731,8 +880,15 @@ async def search_similar(
     limit: int = 10,
     case_id: UUID | None = None,
     section_type: str | None = None,
+    practice_area: str | None = None,
+    appeal_subtype: str | None = None,
 ) -> list[dict]:
-    """Cosine similarity search on document chunks."""
+    """Cosine similarity search on document chunks.
+
+    Filter by practice_area to keep precedents from the same legal domain
+    (e.g. don't surface betterment-levy chunks when working on building
+    permits). Uses the denormalized column on document_chunks — no JOIN.
+    """
     pool = await get_pool()
     conditions = []
     params: list = [query_embedding, limit]
@@ -746,6 +902,14 @@ async def search_similar(
         conditions.append(f"dc.section_type = ${param_idx}")
         params.append(section_type)
         param_idx += 1
+    if practice_area:
+        conditions.append(f"dc.practice_area = ${param_idx}")
+        params.append(practice_area)
+        param_idx += 1
+    if appeal_subtype:
+        conditions.append(f"dc.appeal_subtype = ${param_idx}")
+        params.append(appeal_subtype)
+        param_idx += 1
 
     where = f"WHERE {' AND '.join(conditions)}" if conditions else ""
 
@@ -778,6 +942,8 @@ async def add_to_style_corpus(
     summary: str = "",
     outcome: str = "",
     key_principles: list[str] | None = None,
+    practice_area: str = "appeals_committee",
+    appeal_subtype: str | None = None,
 ) -> UUID:
     pool = await get_pool()
     corpus_id = uuid4()
@@ -785,11 +951,13 @@ async def add_to_style_corpus(
         await conn.execute(
             """INSERT INTO style_corpus
                (id, document_id, decision_number, decision_date,
-                subject_categories, full_text, summary, outcome, key_principles)
+                subject_categories, full_text, summary, outcome, key_principles,
-               VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9)""",
+                practice_area, appeal_subtype)
+               VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10, $11)""",
             corpus_id, document_id, decision_number, decision_date,
             json.dumps(subject_categories), full_text, summary, outcome,
             json.dumps(key_principles or []),
+            practice_area, appeal_subtype,
         )
     return corpus_id
 
@@ -893,8 +1061,15 @@ async def search_similar_paragraphs(
     query_embedding: list[float],
     limit: int = 10,
     block_type: str | None = None,
+    practice_area: str | None = None,
+    appeal_subtype: str | None = None,
 ) -> list[dict]:
-    """Search decision paragraphs by semantic similarity."""
+    """Search decision paragraphs by semantic similarity.
+
+    Filtering by practice_area uses the denormalized column on `decisions`
+    so we don't pull, e.g., betterment-levy paragraphs when writing a
+    building-permit decision.
+    """
     pool = await get_pool()
     conditions = []
     params: list = [query_embedding, limit]
@@ -904,6 +1079,14 @@ async def search_similar_paragraphs(
         conditions.append(f"db.block_id = ${param_idx}")
         params.append(block_type)
         param_idx += 1
+    if practice_area:
+        conditions.append(f"d.practice_area = ${param_idx}")
+        params.append(practice_area)
+        param_idx += 1
+    if appeal_subtype:
+        conditions.append(f"d.appeal_subtype = ${param_idx}")
+        params.append(appeal_subtype)
+        param_idx += 1
 
     where = f"WHERE {' AND '.join(conditions)}" if conditions else ""
 

mcp-server/src/legal_mcp/services/practice_area.py

@@ -0,0 +1,96 @@
+"""Practice area + appeal subtype: derivation, validation, constants.
+
+Two orthogonal axes used to separate legal domains across the system:
+
+  practice_area  — top-level domain (multi-tenant axis). Examples:
+                   appeals_committee, national_insurance, labor_law.
+  appeal_subtype — refines within a domain. For appeals_committee:
+                   building_permit (1xxx), betterment_levy (8xxx),
+                   compensation_197 (9xxx), unknown.
+
+Both columns are denormalized into documents/chunks/decisions/style_corpus
+so vector searches can filter cheaply.
+"""
+
+from __future__ import annotations
+
+import re
+
+# ── Enums ──────────────────────────────────────────────────────────
+
+PRACTICE_AREAS: set[str] = {
+    "appeals_committee",
+    "national_insurance",
+    "labor_law",
+}
+
+APPEALS_COMMITTEE_SUBTYPES: set[str] = {
+    "building_permit",
+    "betterment_levy",
+    "compensation_197",
+    "unknown",
+}
+
+DEFAULT_PRACTICE_AREA = "appeals_committee"
+
+# Subtypes per practice_area (extend when adding domains)
+SUBTYPES_BY_AREA: dict[str, set[str]] = {
+    "appeals_committee": APPEALS_COMMITTEE_SUBTYPES,
+    "national_insurance": {"unknown"},
+    "labor_law": {"unknown"},
+}
+
+
+# ── Derivation ─────────────────────────────────────────────────────
+
+_FIRST_DIGIT = re.compile(r"^\s*(\d)")
+
+_APPEALS_COMMITTEE_DIGIT_TO_SUBTYPE = {
+    "1": "building_permit",
+    "8": "betterment_levy",
+    "9": "compensation_197",
+}
+
+
+def derive_subtype(case_number: str, practice_area: str = DEFAULT_PRACTICE_AREA) -> str:
+    """Infer the appeal_subtype from case_number.
+
+    For appeals_committee, the convention is:
+      1xxx → building_permit, 8xxx → betterment_levy, 9xxx → compensation_197.
+
+    For other practice areas there is no public numbering convention yet,
+    so we return 'unknown' until a real rule is defined.
+    """
+    if practice_area != "appeals_committee":
+        return "unknown"
+    m = _FIRST_DIGIT.match(case_number or "")
+    if not m:
+        return "unknown"
+    return _APPEALS_COMMITTEE_DIGIT_TO_SUBTYPE.get(m.group(1), "unknown")
+
+
+# ── Validation ─────────────────────────────────────────────────────
+
+
+def validate(practice_area: str, appeal_subtype: str | None) -> None:
+    """Raise ValueError on unknown values. appeal_subtype=None is allowed."""
+    if practice_area not in PRACTICE_AREAS:
+        raise ValueError(
+            f"unknown practice_area: {practice_area!r}. "
+            f"expected one of {sorted(PRACTICE_AREAS)}"
+        )
+    if appeal_subtype is None:
+        return
+    allowed = SUBTYPES_BY_AREA.get(practice_area, {"unknown"})
+    if appeal_subtype not in allowed:
+        raise ValueError(
+            f"unknown appeal_subtype {appeal_subtype!r} for practice_area "
+            f"{practice_area!r}. expected one of {sorted(allowed)}"
+        )
+
+
+def is_override(case_number: str, practice_area: str, appeal_subtype: str) -> bool:
+    """True iff the user-supplied subtype disagrees with what derive_subtype
+    would have produced (and the derived value is not 'unknown')."""
+    derived = derive_subtype(case_number, practice_area)
+    return derived != "unknown" and derived != appeal_subtype

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

@@ -8,7 +8,7 @@ from pathlib import Path
 from uuid import UUID
 
 from legal_mcp import config
-from legal_mcp.services import db
+from legal_mcp.services import audit, db, practice_area as pa
 
 
 async def case_create(
@@ -23,6 +23,8 @@ async def case_create(
     hearing_date: str = "",
     notes: str = "",
     expected_outcome: str = "",
+    practice_area: str = "appeals_committee",
+    appeal_subtype: str = "",
 ) -> str:
     """יצירת תיק ערר חדש.
 
@@ -38,6 +40,9 @@ async def case_create(
         hearing_date: תאריך דיון (YYYY-MM-DD)
         notes: הערות
         expected_outcome: תוצאה צפויה (rejection/partial_acceptance/full_acceptance/betterment_levy)
+        practice_area: תחום משפטי (appeals_committee / national_insurance / labor_law)
+        appeal_subtype: סוג ערר (building_permit / betterment_levy / compensation_197).
+                        ריק = יוסק אוטומטית ממספר התיק
     """
     from datetime import date as date_type
 
@@ -45,6 +50,12 @@ async def case_create(
     if hearing_date:
         h_date = date_type.fromisoformat(hearing_date)
 
+    # Resolve appeal_subtype: explicit override > auto-derive > 'unknown'
+    derived_subtype = pa.derive_subtype(case_number, practice_area)
+    if not appeal_subtype:
+        appeal_subtype = derived_subtype
+    pa.validate(practice_area, appeal_subtype)
+
     case = await db.create_case(
         case_number=case_number,
         title=title,
@@ -57,8 +68,24 @@ async def case_create(
         hearing_date=h_date,
         notes=notes,
         expected_outcome=expected_outcome,
+        practice_area=practice_area,
+        appeal_subtype=appeal_subtype,
     )
 
+    # If the user overrode the case-number convention (e.g. case 8500 marked
+    # as building_permit), record it so we can audit later.
+    if pa.is_override(case_number, practice_area, appeal_subtype):
+        await audit.log_action(
+            action="case_subtype_override",
+            case_id=UUID(case["id"]),
+            details={
+                "case_number": case_number,
+                "derived_subtype": derived_subtype,
+                "chosen_subtype": appeal_subtype,
+                "practice_area": practice_area,
+            },
+        )
+
     # Initialize git repo for the case
     case_dir = config.find_case_dir(case_number)
     case_dir.mkdir(parents=True, exist_ok=True)

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

@@ -105,6 +105,8 @@ async def document_upload_training(
     decision_date: str = "",
     subject_categories: list[str] | None = None,
     title: str = "",
+    practice_area: str = "appeals_committee",
+    appeal_subtype: str = "",
 ) -> str:
     """העלאת החלטה קודמת של דפנה לקורפוס הסגנון (training).
 
@@ -114,10 +116,13 @@ async def document_upload_training(
         decision_date: תאריך ההחלטה (YYYY-MM-DD)
         subject_categories: קטגוריות - אפשר לבחור כמה (בנייה, שימוש חורג, תכנית, היתר, הקלה, חלוקה, תמ"א 38, היטל השבחה, פיצויים 197)
         title: שם המסמך
+        practice_area: תחום משפטי (appeals_committee / national_insurance / labor_law)
+        appeal_subtype: סוג ערר (building_permit / betterment_levy / compensation_197).
+                        ריק = יוסק אוטומטית ממספר ההחלטה
     """
     from datetime import date as date_type
 
-    from legal_mcp.services import extractor, embeddings, chunker
+    from legal_mcp.services import chunker, embeddings, extractor, practice_area as pa
 
     source = Path(file_path)
     if not source.exists():
@@ -126,6 +131,11 @@ async def document_upload_training(
     if not title:
         title = source.stem
 
+    # Resolve subtype: explicit > derived from decision_number > 'unknown'
+    if not appeal_subtype:
+        appeal_subtype = pa.derive_subtype(decision_number, practice_area)
+    pa.validate(practice_area, appeal_subtype)
+
     # Copy to training directory (skip if already there)
     config.TRAINING_DIR.mkdir(parents=True, exist_ok=True)
     dest = config.TRAINING_DIR / source.name
@@ -140,25 +150,29 @@ async def document_upload_training(
     if decision_date:
         d_date = date_type.fromisoformat(decision_date)
 
-    # Add to style corpus
+    # Add to style corpus (tagged by domain so block-writer can filter)
     corpus_id = await db.add_to_style_corpus(
         document_id=None,
         decision_number=decision_number,
         decision_date=d_date,
         subject_categories=subject_categories or [],
         full_text=text,
+        practice_area=practice_area,
+        appeal_subtype=appeal_subtype,
     )
 
     # Chunk and embed for RAG search over training corpus
     chunks = chunker.chunk_document(text)
     if chunks:
-        # Create a document record (no case association)
+        # Create a document record (no case association — tag explicitly)
         doc = await db.create_document(
             case_id=None,
             doc_type="decision",
             title=f"[קורפוס] {title}",
             file_path=str(dest),
             page_count=page_count,
+            practice_area=practice_area,
+            appeal_subtype=appeal_subtype,
         )
         doc_id = UUID(doc["id"])
         await db.update_document(doc_id, extracted_text=text, extraction_status="completed")
@@ -176,7 +190,10 @@ async def document_upload_training(
             }
             for c, emb in zip(chunks, embs)
         ]
-        await db.store_chunks(doc_id, None, chunk_dicts)
+        await db.store_chunks(
+            doc_id, None, chunk_dicts,
+            practice_area=practice_area, appeal_subtype=appeal_subtype,
+        )
 
     return json.dumps({
         "corpus_id": str(corpus_id),

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:

web/app.py

@@ -1069,6 +1069,8 @@ class CaseCreateRequest(BaseModel):
     hearing_date: str = ""
     notes: str = ""
     expected_outcome: str = ""
+    practice_area: str = "appeals_committee"
+    appeal_subtype: str = ""
 
 
 class CaseUpdateRequest(BaseModel):
@@ -1097,6 +1099,8 @@ async def api_case_create(req: CaseCreateRequest):
         hearing_date=req.hearing_date,
         notes=req.notes,
         expected_outcome=req.expected_outcome,
+        practice_area=req.practice_area,
+        appeal_subtype=req.appeal_subtype,
     )
     return json.loads(result)
 

web/static/index.html

@@ -1964,14 +1964,26 @@ kbd {
           </div>
         </div>
         <div class="form-row">
-          <div class="form-group">
+          <div class="form-group" style="max-width:200px">
-            <label>סוג ערר</label>
+            <label>תחום משפטי</label>
-            <select id="wiz-committee-type">
+            <select id="wiz-practice-area">
-              <option value="רישוי">רישוי ובניה</option>
+              <option value="appeals_committee">ועדת ערר</option>
-              <option value="היטל השבחה">היטל השבחה</option>
+              <option value="national_insurance" disabled>ביטוח לאומי (בקרוב)</option>
-              <option value="פיצויים">פיצויים (ס' 197)</option>
+              <option value="labor_law" disabled>דיני עבודה (בקרוב)</option>
             </select>
           </div>
+          <div class="form-group">
+            <label>סוג ערר <span style="color:#888;font-size:0.85em">(מוסק אוטומטית ממספר התיק)</span></label>
+            <select id="wiz-appeal-subtype">
+              <option value="building_permit">רישוי ובנייה</option>
+              <option value="betterment_levy">היטל השבחה</option>
+              <option value="compensation_197">פיצויים (ס' 197)</option>
+              <option value="unknown">לא ידוע</option>
+            </select>
+          </div>
+          <input type="hidden" id="wiz-committee-type" value="רישוי">
+        </div>
+        <div class="form-row">
           <div class="form-group">
             <label>כתובת נכס</label>
             <input type="text" id="wiz-address" placeholder="רח' אבינדב 23, קריית יערים">
@@ -2730,11 +2742,14 @@ function getListValues(listId) {
 function buildSummary() {
   const data = getWizardData();
   const OUTCOME_LABELS = { rejection: 'דחייה', partial_acceptance: 'קבלה חלקית', full_acceptance: 'קבלה מלאה', betterment_levy: 'היטל השבחה' };
+  const PRACTICE_AREA_LABELS = { appeals_committee: 'ועדת ערר', national_insurance: 'ביטוח לאומי', labor_law: 'דיני עבודה' };
+  const SUBTYPE_LABELS = { building_permit: 'רישוי ובנייה', betterment_levy: 'היטל השבחה', compensation_197: "פיצויים (ס' 197)", unknown: 'לא ידוע' };
   document.getElementById('wizSummary').innerHTML = `
     <table style="width:100%;font-size:0.88em;border-collapse:collapse">
       <tr><td style="padding:6px;color:#888;width:120px">מספר תיק</td><td style="padding:6px;font-weight:600">${esc(data.case_number)}</td></tr>
       <tr><td style="padding:6px;color:#888">כותרת</td><td style="padding:6px">${esc(data.title)}</td></tr>
-      <tr><td style="padding:6px;color:#888">סוג</td><td style="padding:6px">${esc(data.committee_type)}</td></tr>
+      <tr><td style="padding:6px;color:#888">תחום</td><td style="padding:6px">${esc(PRACTICE_AREA_LABELS[data.practice_area] || data.practice_area)}</td></tr>
+      <tr><td style="padding:6px;color:#888">סוג ערר</td><td style="padding:6px">${esc(SUBTYPE_LABELS[data.appeal_subtype] || data.appeal_subtype)}</td></tr>
       <tr><td style="padding:6px;color:#888">כתובת</td><td style="padding:6px">${esc(data.property_address || '—')}</td></tr>
       <tr><td style="padding:6px;color:#888">עוררים</td><td style="padding:6px">${data.appellants.length ? data.appellants.map(esc).join(', ') : '—'}</td></tr>
       <tr><td style="padding:6px;color:#888">משיבים</td><td style="padding:6px">${data.respondents.length ? data.respondents.map(esc).join(', ') : '—'}</td></tr>
@@ -2743,11 +2758,44 @@ function buildSummary() {
   `;
 }
 
+// 1xxx → building_permit, 8xxx → betterment_levy, 9xxx → compensation_197
+function deriveSubtypeFromCaseNumber(caseNumber) {
+  const m = (caseNumber || '').trim().match(/^(\d)/);
+  if (!m) return 'unknown';
+  return ({1: 'building_permit', 8: 'betterment_levy', 9: 'compensation_197'})[m[1]] || 'unknown';
+}
+
+// Auto-fill subtype + committee_type when the user types/edits the case number.
+// User can override the dropdown manually afterwards.
+function wireSubtypeAutofill() {
+  const cnInput = document.getElementById('wiz-case-number');
+  const subtypeSel = document.getElementById('wiz-appeal-subtype');
+  const committeeHidden = document.getElementById('wiz-committee-type');
+  if (!cnInput || !subtypeSel) return;
+  const SUBTYPE_TO_COMMITTEE = {
+    building_permit: 'רישוי',
+    betterment_levy: 'היטל השבחה',
+    compensation_197: 'פיצויים',
+    unknown: 'רישוי',
+  };
+  let userOverrode = false;
+  subtypeSel.addEventListener('change', () => { userOverrode = true; });
+  cnInput.addEventListener('input', () => {
+    if (userOverrode) return;
+    const derived = deriveSubtypeFromCaseNumber(cnInput.value);
+    subtypeSel.value = derived;
+    if (committeeHidden) committeeHidden.value = SUBTYPE_TO_COMMITTEE[derived];
+  });
+}
+document.addEventListener('DOMContentLoaded', wireSubtypeAutofill);
+
 function getWizardData() {
   return {
     case_number: document.getElementById('wiz-case-number').value.trim(),
     title: document.getElementById('wiz-title').value.trim(),
     committee_type: document.getElementById('wiz-committee-type').value,
+    practice_area: document.getElementById('wiz-practice-area').value,
+    appeal_subtype: document.getElementById('wiz-appeal-subtype').value,
     property_address: document.getElementById('wiz-address').value.trim(),
     permit_number: document.getElementById('wiz-permit').value.trim(),
     appellants: getListValues('appellantsList'),
@@ -2847,9 +2895,18 @@ async function loadCaseView(caseNumber) {
       new: 'חדש', in_progress: 'בתהליך', documents_ready: 'מסמכים מוכנים',
       drafted: 'טיוטה', final: 'סופי',
     };
+    const PRACTICE_AREA_LABELS = { appeals_committee: 'ועדת ערר', national_insurance: 'ביטוח לאומי', labor_law: 'דיני עבודה' };
+    const SUBTYPE_LABELS = { building_permit: 'רישוי ובנייה', betterment_levy: 'היטל השבחה', compensation_197: "פיצויים (ס' 197)", unknown: 'לא ידוע' };
     const meta = [];
     meta.push(`<span class="badge ${data.status}">${STATUS_LABELS[data.status] || data.status}</span>`);
-    if (data.committee_type) meta.push(data.committee_type);
+    if (data.practice_area || data.appeal_subtype) {
+      const parts = [];
+      if (data.practice_area) parts.push(PRACTICE_AREA_LABELS[data.practice_area] || data.practice_area);
+      if (data.appeal_subtype) parts.push(SUBTYPE_LABELS[data.appeal_subtype] || data.appeal_subtype);
+      meta.push(`<span class="badge" style="background:#e8f0fe;color:#1a56db" title="תחום משפטי / סוג ערר">${parts.join(' · ')}</span>`);
+    } else if (data.committee_type) {
+      meta.push(data.committee_type);
+    }
     if (data.property_address) meta.push(data.property_address);
     if (data.appellants?.length) meta.push('עוררים: ' + data.appellants.join(', '));
     document.getElementById('caseViewMeta').innerHTML = meta.map(m => `<span>${m}</span>`).join('');