Chaim
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Six-phase upgrade of /training from a read-only dashboard into a full Style Studio for managing Daphna's style corpus. - Upload Sheet on /training: file → proofread preview → commit (no more CLI-only `upload-training` skill). - Rich corpus metadata: GET /api/training/corpus returns summary, outcome, key_principles, page_count, parties (regex), legal_citation, lessons_count. PATCH endpoint for chair edits. CorpusDetailDrawer with 4 tabs (details /content/lessons/patterns) replaces the bare table row. - LLM metadata enrichment: style_metadata_extractor + MCP tools (style_corpus_enrich, style_corpus_pending_enrichment) fill summary /outcome/key_principles via claude_session (free, host-side). - Per-decision lessons: new decision_lessons table + 4 REST endpoints + LessonsTab in drawer; hermes-curator now auto-posts findings as decision_lessons(source=curator). - Curator Portrait tab: prompt rendered with link to Gitea, recent curator findings, style_analyzer training prompts, propose-change form that writes proposals to data/curator-proposals/ for manual chair review (no auto-mutation of the agent file). - Style chat tab: SSE-streamed conversations with the style agent. New host-side pm2 service (legal-chat-service, port 8770) wraps claude CLI with stream-json + --resume continuation; FastAPI proxies via host.docker.internal. Zero API cost — uses chaim's claude.ai subscription. chat_conversations + chat_messages persist history. Architecture: keeps the existing rule that claude_session only runs on the host (not the container). The new legal-chat-service is the canonical bridge between the container and the local CLI for the chat feature; everything else (upload, metadata, lessons) stays within the container's existing capabilities. Audit script (scripts/audit_training_corpus.py) included for verifying which corpus rows still need enrichment. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
86 lines
3.1 KiB
Python
86 lines
3.1 KiB
Python
"""MCP tool wrappers for the style_corpus metadata-enrichment flow.
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The actual extractor lives in
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``legal_mcp.services.style_metadata_extractor``; this module just exposes
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it as MCP tools that the chair (or a future automation) can call from
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Claude Code.
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Why these tools matter: the upload pipeline (`/api/training/upload` →
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`_process_proofread_training`) inserts a style_corpus row with
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``summary=''``, ``outcome=''``, ``key_principles=[]`` because LLM
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extraction can't run from the FastAPI container (no claude CLI there).
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This module fills that gap — call it from the host, where ``claude``
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CLI is available, and the row gets enriched.
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"""
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from __future__ import annotations
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import json
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from uuid import UUID
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from legal_mcp.services import db, style_metadata_extractor
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def _ok(payload) -> str:
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return json.dumps({"ok": True, **payload}, ensure_ascii=False, default=str)
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def _err(msg: str) -> str:
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return json.dumps({"ok": False, "error": msg}, ensure_ascii=False)
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async def extract_decision_metadata(corpus_id: str, overwrite: bool = False) -> str:
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"""חילוץ מטא-דאטה (summary, outcome, key_principles, appeal_subtype) להחלטה בקורפוס הסגנון.
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ברירת מחדל ``overwrite=False`` ממלא רק שדות ריקים. הזן ``overwrite=true``
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כדי לרענן ערכים שכבר נכתבו.
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"""
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try:
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cid = UUID(corpus_id)
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except ValueError:
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return _err("corpus_id לא תקין")
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try:
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result = await style_metadata_extractor.extract_and_apply(cid, overwrite=overwrite)
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except Exception as e:
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return _err(str(e))
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return _ok(result)
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async def list_corpus_pending_enrichment(limit: int = 50) -> str:
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"""רשימת רשומות style_corpus שחסר להן summary/outcome/key_principles — מועמדות להעשרה."""
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pool = await db.get_pool()
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async with pool.acquire() as conn:
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rows = await conn.fetch(
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"""
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SELECT id, decision_number, decision_date,
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length(full_text) AS chars,
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coalesce(summary, '') = '' AS missing_summary,
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coalesce(outcome, '') = '' AS missing_outcome,
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coalesce(jsonb_array_length(key_principles), 0) = 0 AS missing_principles
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FROM style_corpus
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WHERE coalesce(summary, '') = ''
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OR coalesce(outcome, '') = ''
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OR coalesce(jsonb_array_length(key_principles), 0) = 0
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ORDER BY decision_date NULLS LAST
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LIMIT $1
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""",
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limit,
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)
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items = [
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{
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"corpus_id": str(r["id"]),
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"decision_number": r["decision_number"] or "",
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"decision_date": str(r["decision_date"]) if r["decision_date"] else "",
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"chars": r["chars"],
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"missing": [
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f for f, v in (
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("summary", r["missing_summary"]),
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("outcome", r["missing_outcome"]),
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("key_principles", r["missing_principles"]),
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) if v
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],
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}
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for r in rows
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]
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return _ok({"count": len(items), "items": items})
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