feat(bulletins): catalog monthly "עו"ד על נדל"ן" bulletins into the radar (X12)

עלון חודשי רב-נושאי (פרסום נפרד מהיומון היומי) → מתפצל ל-N שורות digest באותה
טבלה (publication='עו"ד על נדל"ן', לא קורפוס מקביל — G2):
- bulletin_splitter (LLM local-only, tools=""): מפצל ל-cases[]+articles[];
  עדכוני-חקיקה מדולגים (החלטת יו"ר).
- bulletin_library.ingest_bulletin: כל מצביע-פסיקה → digest_kind='decision'
  + embedding + autolink (כולל X13 court-fetch); כל מאמר → digest_kind='article'
  (טקסט-מלא + embedding, רקע בלבד — INV-DIG1 חל).
- content_hash per-item הוא מפתח-הדדאפ (yomon_number ריק) → אידמפוטנטי.
- db.create_digest: פרמטר digest_kind (זורם ל-INSERT + upsert).
- scripts/ingest_bulletins.py (host, venv) לעיבוד הארכיון.
- spec X12 §2.1.

אומת (dry-run, ללא DB): עלון 180 → 4 cases+1 article · עלון 201 → 4 cases
(כולל ערר-197) +1 article. עדכוני-חקיקה דולגו. claude_session נשאר local-only.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

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

@@ -0,0 +1,121 @@
+"""Ingest a monthly "עו"ד על נדל"ן" bulletin into the digests radar (X12).
+
+A bulletin PDF is multi-topic: it EXPLODES into several digest rows — one per
+case-law pointer (digest_kind='decision') and one per article (digest_kind=
+'article'), all tagged publication='עו"ד על נדל"ן' to distinguish them from the
+daily "כל יום" issues. This reuses the existing radar (no parallel corpus — G2):
+the case pointers join search_digests / the /digests page and autolink to the
+underlying ruling exactly like a daily digest; articles are deep-context only.
+
+LOCAL-ONLY (LLM split + embedding) — host scripts/MCP, never the container path.
+Idempotent: each item's content_hash (hash of its analysis_text) is the dedup
+key, so re-running a bulletin skips already-ingested items.
+"""
+from __future__ import annotations
+
+import logging
+from pathlib import Path
+
+from legal_mcp.services import db, embeddings, extractor
+from legal_mcp.services import bulletin_splitter, digest_library
+
+logger = logging.getLogger(__name__)
+
+PUBLICATION = 'עו"ד על נדל"ן'
+SOURCE_FIRM = "צבי שוב + רונית אלפר, עורכי דין"
+
+
+async def _store_and_embed(digest_row: dict) -> None:
+    """Compute + store the single radar embedding for a freshly created item."""
+    emb_text = digest_library._embedding_text(digest_row)
+    if not emb_text:
+        return
+    try:
+        vecs = await embeddings.embed_texts([emb_text], input_type="document")
+        if vecs:
+            await db.store_digest_embedding(digest_row["id"], vecs[0])
+    except Exception as e:  # §6 — surfaced, not swallowed
+        logger.warning("bulletin item embedding failed for %s: %s", digest_row.get("id"), e)
+
+
+async def _create_item(*, analysis_text: str, kind: str, concept_tag: str,
+                       headline: str, summary: str, citation: str, court: str,
+                       practice_area: str, subject_tags: list[str], src: str) -> dict | None:
+    """Create one digest row from a bulletin item. Returns the row, or None if it
+    already exists (idempotent skip) or the insert raced on content_hash."""
+    content_hash = db._content_hash(analysis_text)
+    if await db.get_digest_by_content_hash(content_hash):
+        return None
+    try:
+        return await db.create_digest(
+            analysis_text=analysis_text,
+            publication=PUBLICATION,
+            source_firm=SOURCE_FIRM,
+            concept_tag=concept_tag,
+            headline_holding=headline,
+            summary=summary,
+            underlying_citation=citation,
+            underlying_court=court,
+            practice_area=practice_area,
+            subject_tags=subject_tags,
+            source_document_path=src,
+            extraction_status="completed",
+            digest_kind=kind,
+        )
+    except Exception as e:
+        # uq_digests_content_hash race (concurrent run) → treat as already-present.
+        if "uq_digests_content_hash" in str(e):
+            return None
+        raise
+
+
+async def ingest_bulletin(file_path: str, model: str | None = None) -> dict:
+    """Split a bulletin PDF into digest rows (case pointers + articles).
+
+    Returns counts: {cases, articles, created, skipped, linked}. Idempotent.
+    """
+    path = str(file_path)
+    raw_text, _pages, _meta = await extractor.extract_text(path)
+    split = await bulletin_splitter.split(raw_text, model=model)
+    cases, articles = split.get("cases", []), split.get("articles", [])
+
+    out = {"file": Path(path).name, "cases": len(cases), "articles": len(articles),
+           "created": 0, "skipped": 0, "linked": 0}
+
+    for c in cases:
+        # analysis_text bundles the pointer's substance → stable per-item hash.
+        atext = "\n".join(p for p in (
+            c["concept_tag"], c["headline_holding"], c["summary"], c["underlying_citation"]
+        ) if p).strip()
+        row = await _create_item(
+            analysis_text=atext, kind="decision", concept_tag=c["concept_tag"],
+            headline=c["headline_holding"], summary=c["summary"],
+            citation=c["underlying_citation"], court=c["underlying_court"],
+            practice_area=c["practice_area"], subject_tags=c["subject_tags"], src=path,
+        )
+        if row is None:
+            out["skipped"] += 1
+            continue
+        out["created"] += 1
+        await _store_and_embed(row)
+        linked = await digest_library.try_autolink(row["id"], c["underlying_citation"])
+        if linked:
+            out["linked"] += 1
+
+    for a in articles:
+        # The article body is the substance; prefix authors into the summary.
+        body = a["body"] or a["summary"]
+        summary = (f"מאת {a['authors']}. " if a["authors"] else "") + (a["summary"] or "")
+        atext = "\n".join(p for p in (a["title"], summary, body) if p).strip()
+        row = await _create_item(
+            analysis_text=atext, kind="article", concept_tag=a["title"],
+            headline=a["title"], summary=summary, citation="", court="",
+            practice_area=a["practice_area"], subject_tags=a["subject_tags"], src=path,
+        )
+        if row is None:
+            out["skipped"] += 1
+            continue
+        out["created"] += 1
+        await _store_and_embed(row)
+
+    return out