תוספתי בלבד, אפס שבירת-תאימות. שני invariants מחוזה-כלי-ה-MCP (X9):
GAP-44 (INV-TOOL4, סימטריית extract/get): נוסף get_appraiser_facts — ה-get
המקביל ל-extract_appraiser_facts. קורא list_appraiser_facts + detect_appraiser_conflicts
מה-DB ללא חילוץ-LLM יקר ולא-דטרמיניסטי. מחזיר count=0 (לא שגיאה) אם טרם חולץ.
GAP-53 (INV-TOOL5, limit-caps / OWASP API4:2023): נוסף _clamp_limit (תקרה 200,
non-positive→max) על ~13 כלי list/search ב-server.py (case_list, search_*,
precedent_library_list, halachot_pending, missing_precedent_list, list_*_citations…).
list_chair_feedback קיבל param limit חדש (server→workflow→db עם LIMIT) — היה ללא תקרה כלל.
לא הוסף get_appraiser_facts ל-frontmatter של סוכנים (INV-AG3 "לא עודף" — ההוראות
עוד לא מפנות אליו; חיווט = follow-up). נותר ב-FU-14: GAP-45/48/49/50/51/52.
עודכנו docs/spec/X9 (INV-TOOL4/5) ו-gap-audit (סטטוס פרוסה 1).
אומת: py_compile על 4 קבצי הקוד. אימות runtime (restart MCP server) נדחה עד
שהחילוץ הפעיל של היו"ר יסתיים.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Three-layer separation: style learning (style_corpus), appeals-committee decisions
(internal_committee), and court rulings (external_upload).
- SCHEMA_V10: chair_name + district columns on case_law and cases, partial indexes
- create_internal_committee_decision() DB upsert function
- search_precedent_library_semantic() now accepts source_kind/district/chair_name params
- search_precedent_library_hybrid() passes through new params
- services/internal_decisions.py: ingest_internal_decision, migrate_from_style_corpus,
migrate_from_external_corpus (identifies rows via source_type='appeals_committee')
- search_internal_decisions() MCP tool (server.py + tools/search.py)
- internal_decision_migrate() MCP admin tool
- Web endpoints: POST /api/internal-decisions/upload, POST /api/internal-decisions/migrate,
GET /api/internal-decisions
- ingest_final_version auto-ingests finalized decisions into internal corpus
- SKILL.md updated: agents now search internal + external in parallel, present separately
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The legacy chunker did not track which PDF page each chunk came from.
Stored chunks had page_number=NULL, which blocked the multimodal
hybrid retriever's text+image boost — it joins (chunk, image) on
(document_id, page_number) and the join could never fire.
This change:
- extractor.extract_text now returns (text, page_count, page_offsets);
page_offsets[i] is the start char offset of page (i+1) in the joined
text. None for non-PDFs.
- chunker.chunk_document accepts an optional page_offsets and tags
each chunk with the page that contains its first character (uses
the existing chunker logic; pages assigned post-hoc by content
search to keep the diff minimal).
- processor.process_document and precedent_library.ingest_precedent
forward page_offsets through the chunker. New uploads now carry
accurate page_number on every chunk.
- Other extract_text callers (tools/documents, tools/workflow,
web/app.py) updated to unpack the third element (ignored).
- scripts/backfill_chunk_pages.py: per-case retrofit. Re-extracts each
PDF (re-OCRs via Google Vision if needed, ~$0.0015/page), computes
page_offsets, and updates page_number on every chunk by content
search. Idempotent; --force re-runs on already-tagged docs.
Forward-only would leave the 419 image embeddings backfilled on
cases 8174-24 + 8137-24 unable to boost their corresponding text
chunks. The retrofit script closes that gap (cost ~$0.60).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Backend changes cherry-picked from ui-rewrite branch to enable
feedback API endpoints for the Next.js staging UI.
- chair_feedback DB table + API endpoints (GET/POST/PATCH)
- Content checklists by appeal subtype injected into block-yod prompt
- MCP tools for recording and listing chair feedback
- Corpus analysis documentation (24 decisions)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Replace single CASES_DIR with find_case_dir() that searches across
all status directories. New cases created in cases/new/{number}/.
Config: CASES_BASE, CASES_NEW, CASES_IN_PROGRESS, CASES_COMPLETED
Docker: added -v /home/chaim/legal-ai/cases:/cases volume mount
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Ezer Mishpati - AI legal decision drafting system with:
- MCP server (FastMCP) with document processing pipeline
- Web upload interface (FastAPI) for file upload and classification
- pgvector-based semantic search
- Hebrew legal document chunking and embedding
