Merge pull request 'feat(bulletins): catalog monthly "עו"ד על נדל"ן" bulletins into the radar (X12)' (#154) from worktree-bulletins-catalog into main

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

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

@@ -0,0 +1,147 @@
+"""Split a monthly "עו"ד על נדל"ן" bulletin into typed radar items (X12).
+
+The monthly bulletin (a SEPARATE publication from the daily "כל יום" digest) is
+multi-topic: it bundles a featured ARTICLE, a list of legislative updates, and a
+set of CASE-LAW pointers grouped by topic. The chair chose to catalog the
+**case-law pointers** (each → a digest, like the daily issue) and the
+**articles** (deep-context background) — legislative updates are skipped.
+
+This module is the LLM splitter only. ``bulletin_library.ingest_bulletin`` turns
+its output into digest rows. Like the daily extractor it is LOCAL-ONLY (claude
+CLI) and MUST NOT be imported from the FastAPI container path.
+"""
+from __future__ import annotations
+
+import logging
+
+from legal_mcp import config
+
+logger = logging.getLogger(__name__)
+
+_VALID_PRACTICE_AREAS = {"rishuy_uvniya", "betterment_levy", "compensation_197"}
+
+BULLETIN_SPLIT_PROMPT = """\
+אתה מקבל טקסט מלא של **עלון חודשי "עו"ד על נדל"ן"** (פרסום מקצועי רב-נושאי בתחום
+תכנון ובנייה, מקרקעין, היטל השבחה, פיצויים והתחדשות עירונית). פצל אותו לפריטים.
+
+העלון בנוי משלושה חלקים: (א) **מאמר** מקצועי ארוך אחד או יותר; (ב) **עדכוני חקיקה**
+(תיקוני-חוק, אישורי-תכניות, חוזרים) — **התעלם מהם, אל תחלץ**; (ג) **עדכוני פסיקה**
+מקובצים לפי נושא — כל פריט = מראה-מקום של פסק דין/החלטה + שורת-תקציר.
+
+**אל תמציא** — חלץ רק מה שמופיע בטקסט. שדה חסר → מחרוזת ריקה.
+
+## פלט נדרש
+החזר JSON אחד (object), ללא markdown:
+
+{
+  "cases": [
+    {
+      "underlying_citation": "מראה-המקום המלא של הפסק כפי שמופיע, מילה במילה (למשל 'ערר 8018-02-22 הועדה המקומית בת ים נ' קבוצת מזרחי ובניו השקעות בע\\"מ'). השדה הקריטי.",
+      "concept_tag": "הנושא/הכותרת שתחתיה מופיע הפריט (למשל 'היטל השבחה', 'הפקעות', 'פירוק שיתוף').",
+      "headline_holding": "שורת-התקציר/הכותרת של הפריט — מה נקבע/השאלה (למשל 'חוסר וודאות בין תכנית קודמת לבין ההקלה').",
+      "summary": "תקציר ניטרלי קצר אם יש פירוט נוסף בגוף; אחרת חזור על headline_holding.",
+      "underlying_court": "הערכאה אם מצוינת (למשל 'בית המשפט המחוזי', 'ועדת ערר').",
+      "practice_area": "אחד מ: 'rishuy_uvniya' / 'betterment_levy' / 'compensation_197' — אם ברור מהנושא; אחרת ריק.",
+      "subject_tags": ["2-5 תגיות snake_case בעברית"]
+    }
+  ],
+  "articles": [
+    {
+      "title": "כותרת המאמר (למשל 'הפקעת קרקעות כיום - על המחוקק לתקן את העיוות שנוצר').",
+      "authors": "שמות המחברים (למשל 'עו\\"ד צבי שוב, עו\\"ד רונית אלפר').",
+      "summary": "2-4 משפטים: על מה המאמר ומה הטענה המרכזית.",
+      "body": "הטקסט המלא של המאמר (כל הפסקאות), לצורך embedding וחיפוש-עומק.",
+      "practice_area": "אחד מ-3 אם ברור; אחרת ריק.",
+      "subject_tags": ["2-5 תגיות snake_case"]
+    }
+  ]
+}
+
+## כללים
+1. **underlying_citation** — חלץ במלואו ובדיוק; הוא הגשר לפסק. פריט-פסיקה בלי מראה-מקום ברור → דלג עליו.
+2. **cases** — כל מצביעי-הפסיקה בעלון, גם אם תחת נושאים שונים. אל תאחד פריטים נפרדים.
+3. **articles** — רק מאמרי-עומק (לא רשימת עדכונים). body = הטקסט המלא.
+4. **עדכוני חקיקה/אישורי-תכניות/חוזרים — לא לחלץ כלל.**
+5. אם אין מאמר או אין פסיקה — החזר מערך ריק לאותו מפתח.
+"""
+
+
+def _norm_str(d: dict, key: str) -> str:
+    v = d.get(key)
+    return v.strip() if isinstance(v, str) else ""
+
+
+def _norm_tags(d: dict) -> list[str]:
+    tags = d.get("subject_tags")
+    if not isinstance(tags, list):
+        return []
+    return [str(t).strip() for t in tags if str(t).strip()][:8]
+
+
+def _norm_pa(d: dict) -> str:
+    pa = _norm_str(d, "practice_area")
+    return pa if pa in _VALID_PRACTICE_AREAS else ""
+
+
+async def split(raw_text: str, model: str | None = None) -> dict:
+    """Return ``{"cases": [...], "articles": [...]}`` extracted from a bulletin.
+
+    Empty lists on any failure (surfaced as a warning, never raised) so the
+    batch keeps going. Each item is type-normalized; malformed items are dropped.
+    """
+    from legal_mcp.services import claude_session
+
+    text = (raw_text or "").strip()
+    if not text:
+        return {"cases": [], "articles": []}
+
+    try:
+        result = await claude_session.query_json(
+            text,
+            system=BULLETIN_SPLIT_PROMPT,
+            model=(model or config.DIGEST_EXTRACT_MODEL or None),
+            tools="",  # pure text→JSON; disable tools (avoids error_max_turns)
+        )
+    except Exception as e:  # §6 — surfaced, not swallowed
+        logger.warning("bulletin_splitter: query failed: %s", e)
+        return {"cases": [], "articles": []}
+
+    if not isinstance(result, dict):
+        logger.warning("bulletin_splitter: expected dict, got %s", type(result).__name__)
+        return {"cases": [], "articles": []}
+
+    cases: list[dict] = []
+    for c in result.get("cases") or []:
+        if not isinstance(c, dict):
+            continue
+        citation = _norm_str(c, "underlying_citation")
+        if not citation:  # rule 1: no anchor → skip
+            continue
+        cases.append({
+            "underlying_citation": citation,
+            "concept_tag": _norm_str(c, "concept_tag"),
+            "headline_holding": _norm_str(c, "headline_holding"),
+            "summary": _norm_str(c, "summary") or _norm_str(c, "headline_holding"),
+            "underlying_court": _norm_str(c, "underlying_court"),
+            "practice_area": _norm_pa(c),
+            "subject_tags": _norm_tags(c),
+        })
+
+    articles: list[dict] = []
+    for a in result.get("articles") or []:
+        if not isinstance(a, dict):
+            continue
+        title = _norm_str(a, "title")
+        body = _norm_str(a, "body")
+        if not (title or body):
+            continue
+        articles.append({
+            "title": title,
+            "authors": _norm_str(a, "authors"),
+            "summary": _norm_str(a, "summary"),
+            "body": body,
+            "practice_area": _norm_pa(a),
+            "subject_tags": _norm_tags(a),
+        })
+
+    return {"cases": cases, "articles": articles}

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

@@ -3667,10 +3667,12 @@ async def create_digest(
     subject_tags: list[str] | None = None,
     source_document_path: str = "",
     extraction_status: str = "processing",
+    digest_kind: str = "",
 ) -> dict:
     """Upsert a digest (X12). Idempotent on yomon_number (INV-G3): a repeat
     upload of the same yomon updates in place. content_hash is the secondary
-    dedup key for digests whose number couldn't be parsed."""
+    dedup key for digests whose number couldn't be parsed (and the primary key
+    for bulletin items, which carry no yomon_number — see uq_digests_content_hash)."""
     pool = await get_pool()
     content_hash = _content_hash(analysis_text)
     async with pool.acquire() as conn:
@@ -3684,10 +3686,10 @@ async def create_digest(
                 headline_holding, analysis_text, summary, underlying_citation,
                 underlying_court, underlying_date, underlying_judge, practice_area,
                 appeal_subtype, subject_tags, source_document_path,
-                content_hash, extraction_status
+                content_hash, extraction_status, digest_kind
             ) VALUES (
                 $1, $2, $3, $4, $5, $6, $7, $8, $9, $10, $11, $12, $13,
-                $14, $15, $16, $17, $18
+                $14, $15, $16, $17, $18, $19
             )
             ON CONFLICT (yomon_number) WHERE yomon_number <> ''
             DO UPDATE SET
@@ -3708,6 +3710,7 @@ async def create_digest(
                 source_document_path = COALESCE(NULLIF(EXCLUDED.source_document_path, ''), digests.source_document_path),
                 content_hash = EXCLUDED.content_hash,
                 extraction_status = EXCLUDED.extraction_status,
+                digest_kind = COALESCE(NULLIF(EXCLUDED.digest_kind, ''), digests.digest_kind),
                 updated_at = now()
             RETURNING {_DIGEST_COLS}
             """,
@@ -3715,7 +3718,7 @@ async def create_digest(
             headline_holding, analysis_text, summary, underlying_citation,
             underlying_court, underlying_date, underlying_judge, practice_area,
             appeal_subtype, list(subject_tags or []), source_document_path,
-            content_hash, extraction_status,
+            content_hash, extraction_status, digest_kind,
         )
     return _row_to_digest(row)