feat(training): Style Studio — upload, rich corpus, lessons, curator portrait, chat
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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>
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@@ -76,6 +76,24 @@ profiles:
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Authorization: Bearer $PAPERCLIP_API_KEY
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{ "body": "<my findings>" }
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```
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5b. **רושם כל ממצא גם ב-API של legal-ai כ-decision_lesson**, כך שיופיע ב-UI
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תחת הטאב "מה למדנו" של ההחלטה בקורפוס. דרישה: למצוא קודם את ה-`style_corpus_id`
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שתואם ל-`decision_number` של ההחלטה (`GET /api/training/corpus` ולסנן).
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לכל ממצא:
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```
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POST https://legal-ai.nautilus.marcusgroup.org/api/training/corpus/{corpus_id}/lessons
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Content-Type: application/json
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{
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"lesson_text": "<התקציר של הממצא — מה ראיתי + הצעה — שורה אחת>",
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"category": "<style|structure|lexicon|tabular|general>",
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"source": "curator"
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}
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```
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מיפוי תגי-ממצא ל-`category`:
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- `[סגנון]` → `style`
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- `[מבנה]` → `structure`
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- `[לקסיקון משפטי]` → `lexicon`
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- `[טבלאי]` → `tabular`
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6. סוגר את ה-issue (status=done) אחרי שכתבתי את ה-comment
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## פורמט ה-comment
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