Chaim
753fe0d57d
New feature on case view: the analysis-and-research.md produced by the
legal-analyst agent is now rendered as structured cards in the UI,
with inline editing of "עמדת ועדת הערר" that writes directly back to
the markdown file (atomic rename).
Backend (research_md.py):
- parse(Path) → dict with header, prose sections, threshold_claims[],
issues[], conclusions, other_sections
- Tolerant field extractor handles both block ("**LABEL:**\ncontent")
and inline ("**LABEL:** content") variants
- Detects [ימולא ע"י יו"ר הוועדה] placeholder → empty chair_position
- update_chair_position(path, section_id, text) locates the exact
subsection by ordinal, replaces or appends the chair field, writes
atomically via temp file + os.replace
- Section IDs: threshold_N / issue_N (1-based)
Endpoints:
- GET /api/cases/{n}/research/analysis — returns parsed JSON or 404
- PATCH /api/cases/{n}/research/analysis/chair-position — {section_id, position}
Frontend (#page-case):
- New card "ניתוח משפטי ומחקר" below local-files card
- Prose sections as justified text panels (background + gold border)
- Threshold claims and issues as collapsible <details> items with
gold right-border on open, numbered pills
- Each item shows all extracted fields with label above content
- Chair position editor: gold-wash background, 📝 icon label, textarea
with placeholder prompt
- onblur → PATCH with save indicator: ⏳ שומר → ✓ נשמר HH:MM → fade
- Status pill next to each item title: "ממתין לעמדה" / "✓ עמדה נקבעה"
- First threshold claim opens by default, rest closed
- Card hidden entirely when no analysis file exists (404)
Tested against real file: case 1033-25 with 3 threshold claims and
6 issues, all chair positions correctly empty, update writes only the
targeted section, atomic rewrite preserves all other content.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2461 lines
86 KiB
Python
2461 lines
86 KiB
Python
"""Ezer Mishpati — Web upload interface for legal documents."""
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from __future__ import annotations
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import asyncio
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import json
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import logging
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import os
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import re
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import shutil
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import subprocess
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import sys
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import time
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from contextlib import asynccontextmanager
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from pathlib import Path
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from uuid import UUID, uuid4
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# Allow importing legal_mcp from the MCP server source
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "mcp-server" / "src"))
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import zipfile
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from fastapi import FastAPI, File, Form, HTTPException, UploadFile
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from fastapi.responses import FileResponse, StreamingResponse
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from fastapi.staticfiles import StaticFiles
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from pydantic import BaseModel
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import asyncpg
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from legal_mcp import config
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from legal_mcp.services import chunker, db, embeddings, extractor, processor, proofreader, research_md
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from legal_mcp.tools import cases as cases_tools, search as search_tools, workflow as workflow_tools, drafting as drafting_tools
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# Import integration clients (same directory)
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_web_dir = Path(__file__).resolve().parent
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sys.path.insert(0, str(_web_dir.parent))
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from web.gitea_client import create_repo, setup_remote_and_push
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from web.paperclip_client import create_project as pc_create_project, get_project_url
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logger = logging.getLogger(__name__)
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UPLOAD_DIR = config.DATA_DIR / "uploads"
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ALLOWED_EXTENSIONS = {".pdf", ".docx", ".rtf", ".txt", ".md"}
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MAX_FILE_SIZE = 50 * 1024 * 1024 # 50MB
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# In-memory progress tracking
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_progress: dict[str, dict] = {}
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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UPLOAD_DIR.mkdir(parents=True, exist_ok=True)
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await db.init_schema()
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yield
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await db.close_pool()
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app = FastAPI(title="העלאת מסמכים משפטיים", lifespan=lifespan)
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STATIC_DIR = Path(__file__).parent / "static"
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# ── API Endpoints ──────────────────────────────────────────────────
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@app.get("/")
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async def index():
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return FileResponse(STATIC_DIR / "index.html")
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@app.get("/design-system.css")
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async def design_system_css():
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return FileResponse(STATIC_DIR / "design-system.css", media_type="text/css")
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@app.post("/api/upload")
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async def upload_file(file: UploadFile = File(...)):
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"""Upload a file to the temporary uploads directory."""
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if not file.filename:
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raise HTTPException(400, "No filename provided")
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# Validate extension
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ext = Path(file.filename).suffix.lower()
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if ext not in ALLOWED_EXTENSIONS:
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raise HTTPException(400, f"Unsupported file type: {ext}. Allowed: {', '.join(ALLOWED_EXTENSIONS)}")
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# Sanitize filename
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safe_name = re.sub(r"[^\w\u0590-\u05FF\s.\-()]", "", Path(file.filename).stem)
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if not safe_name:
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safe_name = "document"
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timestamp = int(time.time())
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filename = f"{timestamp}_{safe_name}{ext}"
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# Read and validate size
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content = await file.read()
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if len(content) > MAX_FILE_SIZE:
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raise HTTPException(400, f"File too large. Max: {MAX_FILE_SIZE // (1024*1024)}MB")
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dest = UPLOAD_DIR / filename
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dest.write_bytes(content)
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return {
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"filename": filename,
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"original_name": file.filename,
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"size": len(content),
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}
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@app.get("/api/uploads")
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async def list_uploads():
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"""List files in the uploads (pending) directory."""
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if not UPLOAD_DIR.exists():
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return []
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files = []
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for f in sorted(UPLOAD_DIR.iterdir(), key=lambda p: p.stat().st_mtime, reverse=True):
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if f.is_file() and f.suffix.lower() in ALLOWED_EXTENSIONS:
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stat = f.stat()
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files.append({
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"filename": f.name,
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"size": stat.st_size,
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"uploaded_at": stat.st_mtime,
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})
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return files
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@app.delete("/api/uploads/{filename}")
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async def delete_upload(filename: str):
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"""Remove a file from the uploads directory."""
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path = UPLOAD_DIR / filename
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if not path.exists() or not path.parent.samefile(UPLOAD_DIR):
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raise HTTPException(404, "File not found")
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path.unlink()
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return {"deleted": filename}
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class ClassifyRequest(BaseModel):
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filename: str
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category: str # "training" or "case"
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# For case documents
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case_number: str = ""
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doc_type: str = "appeal"
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title: str = ""
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# For training documents
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decision_number: str = ""
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decision_date: str = ""
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subject_categories: list[str] = []
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@app.post("/api/classify")
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async def classify_file(req: ClassifyRequest):
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"""Classify a pending file and start processing."""
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source = UPLOAD_DIR / req.filename
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if not source.exists() or not source.parent.samefile(UPLOAD_DIR):
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raise HTTPException(404, "File not found in uploads")
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if req.category not in ("training", "case"):
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raise HTTPException(400, "Category must be 'training' or 'case'")
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if req.category == "case" and not req.case_number:
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raise HTTPException(400, "case_number required for case documents")
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task_id = str(uuid4())
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_progress[task_id] = {"status": "queued", "filename": req.filename}
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asyncio.create_task(_process_file(task_id, source, req))
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return {"task_id": task_id}
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# ── Training Corpus: Analyze & Upload ─────────────────────────────
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@app.post("/api/training/analyze")
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async def training_analyze(filename: str = Form(...)):
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"""Proofread an uploaded file and extract metadata for review.
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Input: filename in UPLOAD_DIR (from /api/upload).
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Output: clean text preview + extracted metadata (number, date, categories).
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"""
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source = UPLOAD_DIR / filename
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if not source.exists() or not source.parent.samefile(UPLOAD_DIR):
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raise HTTPException(404, "File not found in uploads")
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try:
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result = await proofreader.analyze_file(source)
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except Exception as e:
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logger.exception("Proofread failed for %s", filename)
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raise HTTPException(500, f"Proofreading failed: {e}")
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return result
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class TrainingUploadRequest(BaseModel):
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filename: str # name in UPLOAD_DIR
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decision_number: str = ""
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decision_date: str = "" # YYYY-MM-DD
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subject_categories: list[str] = []
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title: str = ""
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@app.post("/api/training/upload")
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async def training_upload(req: TrainingUploadRequest):
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"""Upload a proofread file to the style corpus.
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Runs proofreading again to guarantee clean text (not raw file content),
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then inserts into style_corpus + chunks + embeddings.
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"""
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source = UPLOAD_DIR / req.filename
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if not source.exists() or not source.parent.samefile(UPLOAD_DIR):
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raise HTTPException(404, "File not found in uploads")
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# Check for duplicate by decision_number
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if req.decision_number:
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pool = await db.get_pool()
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async with pool.acquire() as conn:
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exists = await conn.fetchval(
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"SELECT 1 FROM style_corpus WHERE decision_number = $1 LIMIT 1",
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req.decision_number,
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)
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if exists:
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raise HTTPException(
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409,
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f"החלטה {req.decision_number} כבר קיימת בקורפוס",
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)
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task_id = str(uuid4())
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_progress[task_id] = {"status": "queued", "filename": req.filename}
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asyncio.create_task(_process_proofread_training(task_id, source, req))
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return {"task_id": task_id}
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async def _process_proofread_training(
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task_id: str, source: Path, req: TrainingUploadRequest
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):
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"""Background task: proofread → store in corpus → chunk → embed."""
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from datetime import date as date_type
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try:
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title = req.title or source.stem.split("_", 1)[-1]
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# 1. Proofread (strip Nevo additions)
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_progress[task_id] = {"status": "processing", "filename": req.filename, "step": "proofreading"}
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clean_text, stats = await proofreader.proofread(source)
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# 2. Save proofread .md to training dir (alongside original)
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_progress[task_id] = {"status": "processing", "filename": req.filename, "step": "saving"}
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training_dir = config.TRAINING_DIR
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proofread_dir = training_dir / "proofread"
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training_dir.mkdir(parents=True, exist_ok=True)
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proofread_dir.mkdir(exist_ok=True)
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# Copy original to training dir
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original_name = re.sub(r"^\d+_", "", source.name)
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orig_dest = training_dir / original_name
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shutil.copy2(str(source), str(orig_dest))
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# Save cleaned version
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proofread_name = Path(original_name).stem + ".md"
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proofread_dest = proofread_dir / proofread_name
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proofread_dest.write_text(clean_text, encoding="utf-8")
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# 3. Parse date
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d_date = None
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if req.decision_date:
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d_date = date_type.fromisoformat(req.decision_date)
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# 4. Add to style corpus
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_progress[task_id] = {"status": "processing", "filename": req.filename, "step": "corpus"}
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corpus_id = await db.add_to_style_corpus(
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document_id=None,
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decision_number=req.decision_number,
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decision_date=d_date,
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subject_categories=req.subject_categories,
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full_text=clean_text,
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)
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# 5. Chunk + embed
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_progress[task_id] = {"status": "processing", "filename": req.filename, "step": "chunking"}
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chunks = chunker.chunk_document(clean_text)
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chunk_count = 0
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if chunks:
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doc = await db.create_document(
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case_id=None,
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doc_type="decision",
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title=f"[קורפוס] {title}",
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file_path=str(orig_dest),
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page_count=stats.get("pages", 0),
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)
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doc_id = UUID(doc["id"])
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await db.update_document(
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doc_id, extracted_text=clean_text, extraction_status="completed"
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)
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_progress[task_id] = {
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"status": "processing", "filename": req.filename, "step": "embedding",
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}
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texts = [c.content for c in chunks]
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embs = await embeddings.embed_texts(texts, input_type="document")
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chunk_dicts = [
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{
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"content": c.content,
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"section_type": c.section_type,
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"embedding": emb,
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"page_number": c.page_number,
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"chunk_index": c.chunk_index,
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}
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for c, emb in zip(chunks, embs)
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]
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await db.store_chunks(doc_id, None, chunk_dicts)
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chunk_count = len(chunks)
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# 6. Cleanup upload
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source.unlink(missing_ok=True)
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_progress[task_id] = {
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"status": "completed",
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"filename": req.filename,
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"result": {
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"corpus_id": str(corpus_id),
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"title": title,
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"chars": len(clean_text),
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"chunks": chunk_count,
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"proofread_stats": stats,
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},
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}
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except Exception as e:
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logger.exception("Training upload failed for %s", req.filename)
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_progress[task_id] = {"status": "failed", "error": str(e), "filename": req.filename}
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@app.get("/api/training/patterns")
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async def training_patterns():
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"""List all extracted style patterns, grouped by type."""
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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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"SELECT pattern_type, pattern_text, frequency, context, examples "
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"FROM style_patterns "
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"ORDER BY pattern_type, frequency DESC"
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)
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grouped: dict[str, list] = {}
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for r in rows:
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pt = r["pattern_type"]
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examples = r["examples"]
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if isinstance(examples, str):
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try:
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examples = json.loads(examples)
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except Exception:
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examples = []
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grouped.setdefault(pt, []).append({
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"pattern_text": r["pattern_text"],
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"frequency": r["frequency"],
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"context": r["context"] or "",
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"examples": examples or [],
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})
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return {"total": len(rows), "by_type": grouped}
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_style_analysis_state = {"running": False, "started_at": None, "result": None, "error": None}
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@app.post("/api/training/analyze-style")
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async def training_analyze_style():
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"""Kick off style analysis over the corpus. Returns immediately."""
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if _style_analysis_state["running"]:
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raise HTTPException(409, "ניתוח סגנון כבר רץ")
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_style_analysis_state.update(
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{"running": True, "started_at": time.time(), "result": None, "error": None}
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)
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async def _run():
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from legal_mcp.services.style_analyzer import analyze_corpus
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try:
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result = await analyze_corpus()
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_style_analysis_state["result"] = result
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except Exception as e:
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logger.exception("Style analysis failed")
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_style_analysis_state["error"] = str(e)
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finally:
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_style_analysis_state["running"] = False
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asyncio.create_task(_run())
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return {"status": "started"}
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@app.get("/api/training/analyze-style/status")
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async def training_analyze_style_status():
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"""Poll status of the running style analysis."""
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state = dict(_style_analysis_state)
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if state["started_at"]:
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state["elapsed"] = int(time.time() - state["started_at"])
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return state
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# ── Style Report — visual dashboard data ─────────────────────────
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_SECTION_TYPE_HEBREW = {
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"intro": "פתיחה",
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"facts": "רקע",
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"appellant_claims": "טענות העורר",
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"respondent_claims": "טענות המשיב",
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"legal_analysis": "דיון משפטי",
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"ruling": "הכרעה",
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"conclusion": "סוף דבר",
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}
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_SECTION_DISPLAY_ORDER = [
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"intro", "facts", "appellant_claims", "respondent_claims",
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"legal_analysis", "ruling", "conclusion",
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]
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def _strip_nikud(text: str) -> str:
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import unicodedata
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return "".join(
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c for c in unicodedata.normalize("NFD", text)
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if not unicodedata.combining(c)
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)
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def _extract_pattern_variants(pattern_text: str) -> list[str]:
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"""Mirror of scripts/backfill_pattern_frequency.py logic for matching."""
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alternatives = re.split(r"\s*/\s*|\s+או\s+", pattern_text)
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variants: list[str] = []
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for alt in alternatives:
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alt = alt.strip()
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if not alt:
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continue
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alt = re.sub(r"\[[^\]]*\]", "|", alt)
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alt = re.sub(r"\.{2,}", "|", alt)
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alt = alt.replace("…", "|")
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segments = [s.strip(" ,.:;\"'") for s in alt.split("|")]
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good = [s for s in segments if len(s) >= 4]
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if good:
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variants.append(max(good, key=len))
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return list(dict.fromkeys(variants))
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async def _compute_corpus_stats(conn) -> dict:
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"""Hero section: decision count, chars, subject distribution, timeline."""
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stats = await conn.fetchrow(
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"SELECT count(*) as n, "
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" sum(length(full_text)) as total_chars, "
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" avg(length(full_text))::int as avg_chars, "
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" min(decision_date) as min_date, "
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" max(decision_date) as max_date "
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"FROM style_corpus"
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)
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decisions = await conn.fetch(
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"SELECT decision_number, decision_date, length(full_text) as chars, "
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" subject_categories "
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"FROM style_corpus ORDER BY decision_date NULLS LAST"
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)
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# Subject distribution
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from collections import Counter
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subject_counter: Counter = Counter()
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for d in decisions:
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cats = d["subject_categories"]
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if isinstance(cats, str):
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try:
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cats = json.loads(cats)
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except Exception:
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cats = []
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for c in (cats or []):
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subject_counter[c] += 1
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# Cap at top 6 subjects, collapse rest to "אחר"
|
|
top = subject_counter.most_common(6)
|
|
other_count = sum(subject_counter.values()) - sum(c for _, c in top)
|
|
subject_distribution = [{"label": label, "count": count} for label, count in top]
|
|
if other_count > 0:
|
|
subject_distribution.append({"label": "אחר", "count": other_count})
|
|
|
|
n = stats["n"]
|
|
top_subject = top[0] if top else None
|
|
headline = (
|
|
f"קראתי {n} מההחלטות שלך. ממוצע {stats['avg_chars']:,} תווים לכל החלטה"
|
|
+ (f", הנושא הנפוץ אצלך: {top_subject[0]} ({top_subject[1]} החלטות)" if top_subject else "")
|
|
)
|
|
|
|
return {
|
|
"decision_count": n,
|
|
"total_chars": stats["total_chars"],
|
|
"avg_chars": stats["avg_chars"],
|
|
"date_range": [
|
|
str(stats["min_date"]) if stats["min_date"] else None,
|
|
str(stats["max_date"]) if stats["max_date"] else None,
|
|
],
|
|
"decisions": [
|
|
{
|
|
"number": d["decision_number"] or "",
|
|
"date": str(d["decision_date"]) if d["decision_date"] else "",
|
|
"chars": d["chars"],
|
|
"subjects": (
|
|
json.loads(d["subject_categories"])
|
|
if isinstance(d["subject_categories"], str)
|
|
else (d["subject_categories"] or [])
|
|
),
|
|
}
|
|
for d in decisions
|
|
],
|
|
"subject_distribution": subject_distribution,
|
|
"headline": headline,
|
|
}
|
|
|
|
|
|
async def _compute_anatomy(conn) -> dict:
|
|
"""Section 2: average section lengths across the training corpus."""
|
|
rows = await conn.fetch(
|
|
"""
|
|
SELECT dc.section_type,
|
|
sum(length(dc.content))::int as total_chars,
|
|
count(distinct dc.document_id) as docs
|
|
FROM document_chunks dc
|
|
JOIN documents d ON dc.document_id = d.id
|
|
WHERE d.title LIKE '[קורפוס]%'
|
|
AND dc.section_type IS NOT NULL
|
|
GROUP BY dc.section_type
|
|
"""
|
|
)
|
|
|
|
if not rows:
|
|
return {
|
|
"sections": [],
|
|
"total_coverage": 0,
|
|
"headline": "אין עדיין נתונים על מבנה ההחלטות",
|
|
}
|
|
|
|
# Map to average per decision (total_chars / docs that have this section)
|
|
sections_raw = {r["section_type"]: r for r in rows}
|
|
|
|
# Compute avg chars per section across decisions that contain it
|
|
items = []
|
|
total_all_chars = sum(r["total_chars"] for r in rows)
|
|
|
|
for st_key in _SECTION_DISPLAY_ORDER:
|
|
if st_key not in sections_raw:
|
|
continue
|
|
r = sections_raw[st_key]
|
|
avg = round(r["total_chars"] / r["docs"]) if r["docs"] else 0
|
|
pct = r["total_chars"] / total_all_chars if total_all_chars else 0
|
|
items.append({
|
|
"type": st_key,
|
|
"label": _SECTION_TYPE_HEBREW.get(st_key, st_key),
|
|
"avg_chars": avg,
|
|
"pct": round(pct, 4),
|
|
"coverage": r["docs"],
|
|
})
|
|
|
|
# Max coverage (decisions that had any chunks)
|
|
total_coverage = await conn.fetchval(
|
|
"SELECT count(distinct dc.document_id) "
|
|
"FROM document_chunks dc JOIN documents d ON dc.document_id=d.id "
|
|
"WHERE d.title LIKE '[קורפוס]%'"
|
|
)
|
|
|
|
# Headline: biggest section
|
|
biggest = max(items, key=lambda x: x["pct"]) if items else None
|
|
if biggest:
|
|
pct_int = round(biggest["pct"] * 100)
|
|
headline = f"{biggest['label']} הוא {pct_int}% מכל החלטה אצלך — זה המוקד שלך"
|
|
else:
|
|
headline = ""
|
|
|
|
return {
|
|
"sections": items,
|
|
"total_coverage": total_coverage,
|
|
"headline": headline,
|
|
}
|
|
|
|
|
|
async def _compute_signature_phrases(conn) -> dict:
|
|
"""Section 3: all patterns with real frequencies, plus headline about top."""
|
|
rows = await conn.fetch(
|
|
"SELECT pattern_type, pattern_text, context, frequency, examples "
|
|
"FROM style_patterns "
|
|
"WHERE frequency > 0 "
|
|
"ORDER BY frequency DESC"
|
|
)
|
|
|
|
items = []
|
|
for r in rows:
|
|
examples = r["examples"]
|
|
if isinstance(examples, str):
|
|
try:
|
|
examples = json.loads(examples)
|
|
except Exception:
|
|
examples = []
|
|
items.append({
|
|
"type": r["pattern_type"],
|
|
"text": r["pattern_text"],
|
|
"context": r["context"] or "",
|
|
"frequency": r["frequency"],
|
|
"examples": examples or [],
|
|
})
|
|
|
|
# Total decision count for denominator
|
|
total_decisions = await conn.fetchval("SELECT count(*) FROM style_corpus")
|
|
|
|
if items:
|
|
# Pick the first item that's a relatively clean phrase, not a template
|
|
# (templates with many placeholders make bad display text)
|
|
top = None
|
|
for item in items[:5]:
|
|
text = item["text"]
|
|
placeholder_count = len(re.findall(r"\[[^\]]*\]", text))
|
|
if placeholder_count <= 1:
|
|
top = item
|
|
break
|
|
if top is None:
|
|
top = items[0]
|
|
|
|
# Clean up for display
|
|
display = re.sub(r"\[[^\]]*\]", "", top["text"])
|
|
display = re.sub(r"\s+", " ", display).strip(" .,:;\"'")
|
|
display = display.split(" / ")[0].split(" או ")[0].strip(" .,:;\"'")
|
|
if len(display) > 60:
|
|
display = display[:57] + "..."
|
|
headline = f'הפטרן האהוב עלייך: "{display}" — מופיע ב-{top["frequency"]} מתוך {total_decisions} החלטות'
|
|
else:
|
|
headline = "טרם חולצו דפוסים — הרץ ניתוח קורפוס"
|
|
|
|
return {"items": items, "total_decisions": total_decisions, "headline": headline}
|
|
|
|
|
|
async def _compute_contribution(conn) -> dict:
|
|
"""Section 4: per-decision contribution + growth curve."""
|
|
decisions = await conn.fetch(
|
|
"SELECT id, decision_number, decision_date, full_text, "
|
|
" length(full_text) as chars, subject_categories "
|
|
"FROM style_corpus ORDER BY decision_date NULLS LAST, created_at"
|
|
)
|
|
patterns = await conn.fetch(
|
|
"SELECT id, pattern_type, pattern_text, context "
|
|
"FROM style_patterns WHERE frequency > 0"
|
|
)
|
|
|
|
if not decisions or not patterns:
|
|
return {
|
|
"growth_curve": [],
|
|
"decision_contributions": [],
|
|
"headline": "אין עדיין מספיק נתונים",
|
|
}
|
|
|
|
# Normalize texts once
|
|
normalized_decisions = [
|
|
(d["id"], d["decision_number"], _strip_nikud(d["full_text"]))
|
|
for d in decisions
|
|
]
|
|
|
|
# For each pattern, find first decision (chronologically) that contains it
|
|
# and the full set of decisions that contain it
|
|
pattern_info: dict = {} # pattern_id → {"first": decision_id, "all": set}
|
|
|
|
for p in patterns:
|
|
variants = _extract_pattern_variants(_strip_nikud(p["pattern_text"]))
|
|
if not variants:
|
|
continue
|
|
|
|
first_seen = None
|
|
all_matches = set()
|
|
for dec_id, _, text in normalized_decisions:
|
|
if any(v in text for v in variants):
|
|
if first_seen is None:
|
|
first_seen = dec_id
|
|
all_matches.add(dec_id)
|
|
|
|
if first_seen is not None:
|
|
pattern_info[p["id"]] = {
|
|
"first": first_seen,
|
|
"all": all_matches,
|
|
"type": p["pattern_type"],
|
|
"text": p["pattern_text"],
|
|
"context": p["context"] or "",
|
|
}
|
|
|
|
# Per-decision: which patterns are new vs confirmed
|
|
decision_contributions = []
|
|
cumulative_patterns: set = set()
|
|
growth_curve = []
|
|
|
|
for d in decisions:
|
|
dec_id = d["id"]
|
|
new_patterns = []
|
|
confirmed_patterns = []
|
|
|
|
for pid, info in pattern_info.items():
|
|
if info["first"] == dec_id:
|
|
new_patterns.append(info)
|
|
elif dec_id in info["all"]:
|
|
confirmed_patterns.append(info)
|
|
|
|
# First 3 new patterns as "highlight"
|
|
highlight = new_patterns[0] if new_patterns else None
|
|
|
|
decision_contributions.append({
|
|
"decision_number": d["decision_number"] or "",
|
|
"decision_date": str(d["decision_date"]) if d["decision_date"] else "",
|
|
"chars": d["chars"],
|
|
"subjects": (
|
|
json.loads(d["subject_categories"])
|
|
if isinstance(d["subject_categories"], str)
|
|
else (d["subject_categories"] or [])
|
|
),
|
|
"new_count": len(new_patterns),
|
|
"confirmed_count": len(confirmed_patterns),
|
|
"new_patterns": [
|
|
{"type": p["type"], "text": p["text"], "context": p["context"]}
|
|
for p in new_patterns[:10] # cap to keep payload small
|
|
],
|
|
"highlight": (
|
|
{"type": highlight["type"], "text": highlight["text"]}
|
|
if highlight else None
|
|
),
|
|
})
|
|
|
|
cumulative_patterns.update(pid for pid, info in pattern_info.items() if info["first"] == dec_id)
|
|
growth_curve.append({
|
|
"decision_number": d["decision_number"] or "",
|
|
"date": str(d["decision_date"]) if d["decision_date"] else "",
|
|
"cumulative": len(cumulative_patterns),
|
|
})
|
|
|
|
# Headline: when did we hit ~85%?
|
|
total_patterns = len(pattern_info)
|
|
threshold = int(total_patterns * 0.85)
|
|
n_decisions_to_85pct = None
|
|
for i, point in enumerate(growth_curve, 1):
|
|
if point["cumulative"] >= threshold:
|
|
n_decisions_to_85pct = i
|
|
break
|
|
|
|
if n_decisions_to_85pct:
|
|
headline = (
|
|
f"אחרי {n_decisions_to_85pct} החלטות כבר למדתי 85% "
|
|
f"מהסגנון שלך — השאר מיקד וחידד את הידע"
|
|
)
|
|
else:
|
|
headline = f"למדתי {total_patterns} דפוסים מ-{len(decisions)} החלטות"
|
|
|
|
return {
|
|
"growth_curve": growth_curve,
|
|
"decision_contributions": decision_contributions,
|
|
"total_patterns": total_patterns,
|
|
"headline": headline,
|
|
}
|
|
|
|
|
|
@app.get("/api/training/style-report")
|
|
async def training_style_report():
|
|
"""Visual dashboard data for Dafna's Style Portrait page."""
|
|
pool = await db.get_pool()
|
|
async with pool.acquire() as conn:
|
|
corpus = await _compute_corpus_stats(conn)
|
|
anatomy = await _compute_anatomy(conn)
|
|
phrases = await _compute_signature_phrases(conn)
|
|
contribution = await _compute_contribution(conn)
|
|
|
|
return {
|
|
"corpus": corpus,
|
|
"anatomy": anatomy,
|
|
"signature_phrases": phrases,
|
|
"contribution": contribution,
|
|
}
|
|
|
|
|
|
@app.get("/api/training/compare")
|
|
async def training_compare(a: str, b: str):
|
|
"""Compare two decisions from style_corpus by ID.
|
|
|
|
Returns side-by-side data: basic metadata, length, section breakdown,
|
|
which patterns appear in each, shared/unique patterns.
|
|
"""
|
|
try:
|
|
ida, idb = UUID(a), UUID(b)
|
|
except ValueError:
|
|
raise HTTPException(400, "invalid id(s)")
|
|
|
|
pool = await db.get_pool()
|
|
async with pool.acquire() as conn:
|
|
rows = await conn.fetch(
|
|
"SELECT id, decision_number, decision_date, subject_categories, "
|
|
" full_text, length(full_text) as chars "
|
|
"FROM style_corpus WHERE id = ANY($1::uuid[])",
|
|
[ida, idb],
|
|
)
|
|
if len(rows) != 2:
|
|
raise HTTPException(404, "אחת ההחלטות לא נמצאה")
|
|
|
|
by_id = {r["id"]: r for r in rows}
|
|
row_a = by_id[ida]
|
|
row_b = by_id[idb]
|
|
|
|
patterns = await conn.fetch(
|
|
"SELECT id, pattern_type, pattern_text, context "
|
|
"FROM style_patterns WHERE frequency > 0"
|
|
)
|
|
|
|
# Section breakdown via document_chunks.
|
|
# decision_number format is "NNNN/YY" but document titles are like
|
|
# "[קורפוס] ARAR-YY-NNNN - ..." so we match on the number segment only.
|
|
async def section_stats(corpus_row):
|
|
nm = corpus_row["decision_number"]
|
|
if not nm:
|
|
return []
|
|
# Extract the first numeric segment (e.g., "1188" from "1188/23")
|
|
num_match = re.match(r"(\d{3,4})", nm)
|
|
num = num_match.group(1) if num_match else nm
|
|
rows2 = await conn.fetch(
|
|
"SELECT dc.section_type, sum(length(dc.content))::int as chars "
|
|
"FROM document_chunks dc JOIN documents d ON dc.document_id=d.id "
|
|
"WHERE d.title LIKE '[קורפוס]%' "
|
|
" AND (d.title LIKE $1 OR d.title LIKE $2) "
|
|
" AND dc.section_type IS NOT NULL "
|
|
"GROUP BY dc.section_type ORDER BY chars DESC",
|
|
f"%{num}%",
|
|
f"%{nm}%",
|
|
)
|
|
return [{"type": r["section_type"], "chars": r["chars"]} for r in rows2]
|
|
|
|
sections_a = await section_stats(row_a)
|
|
sections_b = await section_stats(row_b)
|
|
|
|
# Pattern matching via variant extraction
|
|
def _strip_nikud_local(t: str) -> str:
|
|
import unicodedata
|
|
return "".join(c for c in unicodedata.normalize("NFD", t) if not unicodedata.combining(c))
|
|
|
|
def _variants(pt: str) -> list[str]:
|
|
alts = re.split(r"\s*/\s*|\s+או\s+", pt)
|
|
out = []
|
|
for a in alts:
|
|
a = re.sub(r"\[[^\]]*\]", "|", a)
|
|
a = re.sub(r"\.{2,}", "|", a).replace("…", "|")
|
|
segs = [s.strip(" ,.:;\"'") for s in a.split("|")]
|
|
good = [s for s in segs if len(s) >= 4]
|
|
if good:
|
|
out.append(max(good, key=len))
|
|
return list(dict.fromkeys(out))
|
|
|
|
text_a = _strip_nikud_local(row_a["full_text"])
|
|
text_b = _strip_nikud_local(row_b["full_text"])
|
|
|
|
in_a, in_b = [], []
|
|
for p in patterns:
|
|
vs = _variants(_strip_nikud_local(p["pattern_text"]))
|
|
if not vs:
|
|
continue
|
|
in_a_flag = any(v in text_a for v in vs)
|
|
in_b_flag = any(v in text_b for v in vs)
|
|
entry = {
|
|
"id": str(p["id"]),
|
|
"type": p["pattern_type"],
|
|
"text": p["pattern_text"],
|
|
"context": p["context"] or "",
|
|
}
|
|
if in_a_flag:
|
|
in_a.append(entry)
|
|
if in_b_flag:
|
|
in_b.append(entry)
|
|
|
|
set_a = {p["id"] for p in in_a}
|
|
set_b = {p["id"] for p in in_b}
|
|
shared_ids = set_a & set_b
|
|
only_a_ids = set_a - set_b
|
|
only_b_ids = set_b - set_a
|
|
|
|
def serialize(row, sections, patterns_list):
|
|
cats = row["subject_categories"]
|
|
if isinstance(cats, str):
|
|
try:
|
|
cats = json.loads(cats)
|
|
except Exception:
|
|
cats = []
|
|
return {
|
|
"id": str(row["id"]),
|
|
"decision_number": row["decision_number"] or "",
|
|
"decision_date": str(row["decision_date"]) if row["decision_date"] else "",
|
|
"chars": row["chars"],
|
|
"subjects": cats or [],
|
|
"sections": sections,
|
|
"patterns_count": len(patterns_list),
|
|
}
|
|
|
|
return {
|
|
"a": serialize(row_a, sections_a, in_a),
|
|
"b": serialize(row_b, sections_b, in_b),
|
|
"shared": [p for p in in_a if p["id"] in shared_ids],
|
|
"only_a": [p for p in in_a if p["id"] in only_a_ids],
|
|
"only_b": [p for p in in_b if p["id"] in only_b_ids],
|
|
}
|
|
|
|
|
|
@app.delete("/api/training/corpus/{corpus_id}")
|
|
async def training_corpus_delete(corpus_id: str):
|
|
"""Remove a decision from the style corpus."""
|
|
try:
|
|
cid = UUID(corpus_id)
|
|
except ValueError:
|
|
raise HTTPException(400, "invalid corpus_id")
|
|
result = await db.delete_from_style_corpus(cid)
|
|
if not result.get("deleted"):
|
|
raise HTTPException(404, result.get("reason", "not found"))
|
|
return result
|
|
|
|
|
|
@app.get("/api/training/corpus")
|
|
async def training_corpus_list():
|
|
"""List all decisions currently in the style corpus."""
|
|
pool = await db.get_pool()
|
|
async with pool.acquire() as conn:
|
|
rows = await conn.fetch(
|
|
"SELECT id, decision_number, decision_date, subject_categories, "
|
|
" length(full_text) as chars, created_at "
|
|
"FROM style_corpus "
|
|
"ORDER BY created_at DESC"
|
|
)
|
|
return [
|
|
{
|
|
"id": str(r["id"]),
|
|
"decision_number": r["decision_number"] or "",
|
|
"decision_date": str(r["decision_date"]) if r["decision_date"] else "",
|
|
"subject_categories": (
|
|
json.loads(r["subject_categories"])
|
|
if isinstance(r["subject_categories"], str)
|
|
else r["subject_categories"] or []
|
|
),
|
|
"chars": r["chars"],
|
|
"created_at": r["created_at"].isoformat() if r["created_at"] else "",
|
|
}
|
|
for r in rows
|
|
]
|
|
|
|
|
|
def _get_active_tasks() -> list[dict]:
|
|
"""Extract active (non-terminal) tasks from _progress dict."""
|
|
items = []
|
|
for task_id, data in list(_progress.items()):
|
|
status = data.get("status", "unknown")
|
|
if status in ("completed", "failed"):
|
|
continue
|
|
items.append({
|
|
"task_id": task_id,
|
|
"status": status,
|
|
"step": data.get("step", ""),
|
|
"filename": data.get("filename", ""),
|
|
"error": data.get("error", ""),
|
|
})
|
|
return items
|
|
|
|
|
|
@app.get("/api/system/tasks")
|
|
async def system_tasks():
|
|
"""List all active background tasks (one-shot)."""
|
|
items = _get_active_tasks()
|
|
return {"active": items, "count": len(items)}
|
|
|
|
|
|
@app.get("/api/system/tasks/stream")
|
|
async def system_tasks_stream():
|
|
"""SSE stream — pushes active-task snapshots when anything changes.
|
|
|
|
Replaces client-side polling. Clients connect once and receive
|
|
events whenever the task set changes. Also sends a heartbeat every
|
|
15s to keep proxies from timing out.
|
|
"""
|
|
async def event_gen():
|
|
last_snapshot: str | None = None
|
|
last_heartbeat = time.time()
|
|
# Emit initial state immediately
|
|
while True:
|
|
snapshot = json.dumps(
|
|
{"active": _get_active_tasks(), "count": len(_get_active_tasks())},
|
|
ensure_ascii=False,
|
|
)
|
|
now = time.time()
|
|
if snapshot != last_snapshot:
|
|
yield f"event: tasks\ndata: {snapshot}\n\n"
|
|
last_snapshot = snapshot
|
|
last_heartbeat = now
|
|
elif now - last_heartbeat > 15:
|
|
yield ": heartbeat\n\n"
|
|
last_heartbeat = now
|
|
await asyncio.sleep(1)
|
|
|
|
return StreamingResponse(event_gen(), media_type="text/event-stream")
|
|
|
|
|
|
@app.get("/api/progress/{task_id}")
|
|
async def progress_stream(task_id: str):
|
|
"""SSE stream of processing progress."""
|
|
if task_id not in _progress:
|
|
raise HTTPException(404, "Task not found")
|
|
|
|
async def event_stream():
|
|
while True:
|
|
data = _progress.get(task_id, {})
|
|
yield f"data: {json.dumps(data, ensure_ascii=False)}\n\n"
|
|
if data.get("status") in ("completed", "failed"):
|
|
break
|
|
await asyncio.sleep(1)
|
|
# Clean up after a delay
|
|
await asyncio.sleep(30)
|
|
_progress.pop(task_id, None)
|
|
|
|
return StreamingResponse(event_stream(), media_type="text/event-stream")
|
|
|
|
|
|
@app.get("/health")
|
|
@app.get("/api/health")
|
|
async def health():
|
|
return {"status": "ok"}
|
|
|
|
|
|
@app.get("/api/cases")
|
|
async def list_cases(detail: bool = False):
|
|
"""List existing cases. With detail=true, includes doc counts and integration URLs."""
|
|
cases = await db.list_cases()
|
|
if not detail:
|
|
return [
|
|
{"case_number": c["case_number"], "title": c["title"], "status": c["status"]}
|
|
for c in cases
|
|
]
|
|
# Enhanced listing with document counts
|
|
pool = await db.get_pool()
|
|
result = []
|
|
async with pool.acquire() as conn:
|
|
for c in cases:
|
|
case_id = UUID(c["id"])
|
|
doc_count = await conn.fetchval(
|
|
"SELECT count(*) FROM documents WHERE case_id = $1", case_id
|
|
)
|
|
processing_count = await conn.fetchval(
|
|
"SELECT count(*) FROM documents WHERE case_id = $1 AND extraction_status NOT IN ('completed', 'proofread')",
|
|
case_id,
|
|
)
|
|
result.append({
|
|
"case_number": c["case_number"],
|
|
"title": c["title"],
|
|
"status": c["status"],
|
|
"expected_outcome": c.get("expected_outcome", ""),
|
|
"committee_type": c.get("committee_type", ""),
|
|
"hearing_date": str(c["hearing_date"]) if c.get("hearing_date") else "",
|
|
"document_count": doc_count,
|
|
"processing_count": processing_count,
|
|
"gitea_url": f"https://gitea.nautilus.marcusgroup.org/cases/{c['case_number']}",
|
|
})
|
|
return result
|
|
|
|
|
|
# ── Paperclip Integration API ─────────────────────────────────────
|
|
|
|
|
|
class CaseCreateRequest(BaseModel):
|
|
case_number: str
|
|
title: str
|
|
appellants: list[str] | None = None
|
|
respondents: list[str] | None = None
|
|
subject: str = ""
|
|
property_address: str = ""
|
|
permit_number: str = ""
|
|
committee_type: str = "ועדה מקומית"
|
|
hearing_date: str = ""
|
|
notes: str = ""
|
|
expected_outcome: str = ""
|
|
|
|
|
|
class CaseUpdateRequest(BaseModel):
|
|
status: str = ""
|
|
title: str = ""
|
|
subject: str = ""
|
|
notes: str = ""
|
|
hearing_date: str = ""
|
|
decision_date: str = ""
|
|
tags: list[str] | None = None
|
|
expected_outcome: str = ""
|
|
|
|
|
|
@app.post("/api/cases/create")
|
|
async def api_case_create(req: CaseCreateRequest):
|
|
"""Create a new appeal case."""
|
|
result = await cases_tools.case_create(
|
|
case_number=req.case_number,
|
|
title=req.title,
|
|
appellants=req.appellants,
|
|
respondents=req.respondents,
|
|
subject=req.subject,
|
|
property_address=req.property_address,
|
|
permit_number=req.permit_number,
|
|
committee_type=req.committee_type,
|
|
hearing_date=req.hearing_date,
|
|
notes=req.notes,
|
|
expected_outcome=req.expected_outcome,
|
|
)
|
|
return json.loads(result)
|
|
|
|
|
|
@app.get("/api/cases/{case_number}/details")
|
|
async def api_case_get(case_number: str):
|
|
"""Get full case details including documents."""
|
|
result = await cases_tools.case_get(case_number)
|
|
try:
|
|
return json.loads(result)
|
|
except json.JSONDecodeError:
|
|
raise HTTPException(404, result)
|
|
|
|
|
|
@app.put("/api/cases/{case_number}")
|
|
async def api_case_update(case_number: str, req: CaseUpdateRequest):
|
|
"""Update case details."""
|
|
result = await cases_tools.case_update(
|
|
case_number=case_number,
|
|
status=req.status,
|
|
title=req.title,
|
|
subject=req.subject,
|
|
notes=req.notes,
|
|
hearing_date=req.hearing_date,
|
|
decision_date=req.decision_date,
|
|
tags=req.tags,
|
|
expected_outcome=req.expected_outcome,
|
|
)
|
|
try:
|
|
return json.loads(result)
|
|
except json.JSONDecodeError:
|
|
raise HTTPException(404, result)
|
|
|
|
|
|
@app.get("/api/cases/{case_number}/status")
|
|
async def api_case_status(case_number: str):
|
|
"""Get full workflow status for a case."""
|
|
result = await workflow_tools.workflow_status(case_number)
|
|
try:
|
|
return json.loads(result)
|
|
except json.JSONDecodeError:
|
|
raise HTTPException(404, result)
|
|
|
|
|
|
@app.get("/api/search")
|
|
async def api_search(query: str, limit: int = 10, section_type: str = ""):
|
|
"""Semantic search across decisions and documents."""
|
|
result = await search_tools.search_decisions(query, limit, section_type)
|
|
try:
|
|
return json.loads(result)
|
|
except json.JSONDecodeError:
|
|
return {"message": result}
|
|
|
|
|
|
@app.get("/api/cases/{case_number}/search")
|
|
async def api_case_search(case_number: str, query: str, limit: int = 10):
|
|
"""Semantic search within a specific case's documents."""
|
|
result = await search_tools.search_case_documents(case_number, query, limit)
|
|
try:
|
|
return json.loads(result)
|
|
except json.JSONDecodeError:
|
|
return {"message": result}
|
|
|
|
|
|
@app.get("/api/cases/{case_number}/template")
|
|
async def api_case_template(case_number: str):
|
|
"""Get outcome-aware decision template for a case."""
|
|
result = await drafting_tools.get_decision_template(case_number)
|
|
if result.startswith("תיק"):
|
|
raise HTTPException(404, result)
|
|
return {"template": result}
|
|
|
|
|
|
@app.get("/api/processing-status")
|
|
async def api_processing_status():
|
|
"""Get overall processing status."""
|
|
result = await workflow_tools.processing_status()
|
|
return json.loads(result)
|
|
|
|
|
|
@app.get("/api/system/diagnostics")
|
|
async def system_diagnostics():
|
|
"""System health snapshot: DB counts, recent failures, task queue."""
|
|
pool = await db.get_pool()
|
|
async with pool.acquire() as conn:
|
|
db_ok = False
|
|
try:
|
|
await conn.fetchval("SELECT 1")
|
|
db_ok = True
|
|
except Exception:
|
|
pass
|
|
|
|
tables = {}
|
|
for t in ("cases", "documents", "document_chunks", "style_corpus", "style_patterns"):
|
|
try:
|
|
tables[t] = await conn.fetchval(f"SELECT count(*) FROM {t}")
|
|
except Exception:
|
|
tables[t] = None
|
|
|
|
# Documents that failed extraction or are stuck
|
|
failed_docs = await conn.fetch(
|
|
"SELECT d.id, d.title, d.extraction_status, d.created_at, "
|
|
" c.case_number "
|
|
"FROM documents d LEFT JOIN cases c ON d.case_id = c.id "
|
|
"WHERE d.extraction_status IN ('failed', 'error') "
|
|
"ORDER BY d.created_at DESC LIMIT 20"
|
|
)
|
|
stuck_docs = await conn.fetch(
|
|
"SELECT d.id, d.title, d.extraction_status, d.created_at, "
|
|
" c.case_number "
|
|
"FROM documents d LEFT JOIN cases c ON d.case_id = c.id "
|
|
"WHERE d.extraction_status IN ('pending', 'processing') "
|
|
" AND d.created_at < now() - interval '10 minutes' "
|
|
"ORDER BY d.created_at DESC LIMIT 20"
|
|
)
|
|
|
|
active_tasks = [
|
|
{"task_id": tid, "filename": d.get("filename", ""),
|
|
"status": d.get("status", ""), "step": d.get("step", "")}
|
|
for tid, d in _progress.items()
|
|
if d.get("status") not in ("completed", "failed")
|
|
]
|
|
|
|
return {
|
|
"db_ok": db_ok,
|
|
"tables": tables,
|
|
"failed_documents": [
|
|
{
|
|
"id": str(r["id"]),
|
|
"title": r["title"] or "",
|
|
"status": r["extraction_status"],
|
|
"case_number": r["case_number"] or "",
|
|
"created_at": r["created_at"].isoformat() if r["created_at"] else None,
|
|
}
|
|
for r in failed_docs
|
|
],
|
|
"stuck_documents": [
|
|
{
|
|
"id": str(r["id"]),
|
|
"title": r["title"] or "",
|
|
"status": r["extraction_status"],
|
|
"case_number": r["case_number"] or "",
|
|
"created_at": r["created_at"].isoformat() if r["created_at"] else None,
|
|
}
|
|
for r in stuck_docs
|
|
],
|
|
"active_tasks": active_tasks,
|
|
}
|
|
|
|
|
|
@app.get("/api/system/recent-activity")
|
|
async def system_recent_activity(limit: int = 8):
|
|
"""Derive a feed of recent events from cases + style_corpus + style_patterns.
|
|
|
|
Each event has: type, label, timestamp, target.
|
|
"""
|
|
pool = await db.get_pool()
|
|
events: list[dict] = []
|
|
|
|
async with pool.acquire() as conn:
|
|
# Recent cases
|
|
cases = await conn.fetch(
|
|
"SELECT case_number, title, created_at FROM cases "
|
|
"ORDER BY created_at DESC LIMIT $1", limit
|
|
)
|
|
for c in cases:
|
|
events.append({
|
|
"type": "case_created",
|
|
"label": f"תיק חדש: ערר {c['case_number']}",
|
|
"detail": c["title"] or "",
|
|
"timestamp": c["created_at"].isoformat() if c["created_at"] else None,
|
|
"target": f"/#/case/{c['case_number']}",
|
|
})
|
|
|
|
# Recent corpus additions
|
|
corpus = await conn.fetch(
|
|
"SELECT decision_number, created_at FROM style_corpus "
|
|
"ORDER BY created_at DESC LIMIT $1", limit
|
|
)
|
|
for r in corpus:
|
|
events.append({
|
|
"type": "corpus_added",
|
|
"label": f"החלטה נוספה לקורפוס: {r['decision_number'] or 'ללא מספר'}",
|
|
"detail": "",
|
|
"timestamp": r["created_at"].isoformat() if r["created_at"] else None,
|
|
"target": "/#/training",
|
|
})
|
|
|
|
# Last style analysis run (if any)
|
|
last_pattern = await conn.fetchrow(
|
|
"SELECT created_at FROM style_patterns "
|
|
"ORDER BY created_at DESC LIMIT 1"
|
|
)
|
|
if last_pattern and last_pattern["created_at"]:
|
|
count = await conn.fetchval("SELECT count(*) FROM style_patterns")
|
|
events.append({
|
|
"type": "analysis_run",
|
|
"label": f"ניתוח סגנון — {count} דפוסים חולצו",
|
|
"detail": "",
|
|
"timestamp": last_pattern["created_at"].isoformat(),
|
|
"target": "/#/style-report",
|
|
})
|
|
|
|
# Sort by timestamp desc, take top N
|
|
events.sort(key=lambda e: e["timestamp"] or "", reverse=True)
|
|
return {"events": events[:limit]}
|
|
|
|
|
|
# ── Workflow API — outcome, direction, claims, QA, learning ──────
|
|
|
|
|
|
class OutcomeRequest(BaseModel):
|
|
outcome: str # rejection / full_acceptance / partial_acceptance
|
|
reasoning: str = ""
|
|
|
|
|
|
class DirectionRequest(BaseModel):
|
|
direction_doc: dict # JSON document with main_reasoning, reasoning_order, key_precedents, notes
|
|
|
|
|
|
@app.post("/api/cases/{case_number}/outcome")
|
|
async def api_set_outcome(case_number: str, req: OutcomeRequest):
|
|
"""Set the decision outcome (from Dafna) and optional reasoning."""
|
|
case = await db.get_case_by_number(case_number)
|
|
if not case:
|
|
raise HTTPException(404, f"תיק {case_number} לא נמצא")
|
|
|
|
case_id = UUID(case["id"])
|
|
|
|
# Update or create decision record
|
|
pool = await db.get_pool()
|
|
async with pool.acquire() as conn:
|
|
existing = await conn.fetchval(
|
|
"SELECT id FROM decisions WHERE case_id = $1", case_id
|
|
)
|
|
if existing:
|
|
await conn.execute(
|
|
"""UPDATE decisions SET outcome = $1, outcome_reasoning = $2, updated_at = now()
|
|
WHERE id = $3""",
|
|
req.outcome, req.reasoning, existing,
|
|
)
|
|
else:
|
|
await conn.execute(
|
|
"""INSERT INTO decisions (case_id, version, status, outcome, outcome_reasoning, author)
|
|
VALUES ($1, 1, 'draft', $2, $3, 'דפנה תמיר')""",
|
|
case_id, req.outcome, req.reasoning,
|
|
)
|
|
|
|
# Update case status
|
|
new_status = "direction_approved" if req.reasoning else "outcome_set"
|
|
await conn.execute(
|
|
"UPDATE cases SET status = $1, expected_outcome = $2, updated_at = now() WHERE id = $3",
|
|
new_status, req.outcome, case_id,
|
|
)
|
|
|
|
return {"status": new_status, "outcome": req.outcome, "has_reasoning": bool(req.reasoning)}
|
|
|
|
|
|
@app.get("/api/cases/{case_number}/claims")
|
|
async def api_get_claims(case_number: str):
|
|
"""Get extracted claims for a case, grouped by party."""
|
|
case = await db.get_case_by_number(case_number)
|
|
if not case:
|
|
raise HTTPException(404, f"תיק {case_number} לא נמצא")
|
|
|
|
pool = await db.get_pool()
|
|
async with pool.acquire() as conn:
|
|
rows = await conn.fetch(
|
|
"""SELECT party_role, claim_text, claim_index, source_document, addressed_in_paragraph
|
|
FROM claims WHERE case_id = $1 ORDER BY party_role, claim_index""",
|
|
UUID(case["id"]),
|
|
)
|
|
|
|
claims_by_party = {}
|
|
for r in rows:
|
|
role = r["party_role"]
|
|
if role not in claims_by_party:
|
|
claims_by_party[role] = []
|
|
claims_by_party[role].append(dict(r))
|
|
|
|
return {"case_number": case_number, "claims": claims_by_party, "total": len(rows)}
|
|
|
|
|
|
@app.post("/api/cases/{case_number}/direction")
|
|
async def api_set_direction(case_number: str, req: DirectionRequest):
|
|
"""Save the approved direction document for the discussion block."""
|
|
case = await db.get_case_by_number(case_number)
|
|
if not case:
|
|
raise HTTPException(404, f"תיק {case_number} לא נמצא")
|
|
|
|
pool = await db.get_pool()
|
|
async with pool.acquire() as conn:
|
|
await conn.execute(
|
|
"""UPDATE decisions SET direction_doc = $1, updated_at = now()
|
|
WHERE case_id = $2""",
|
|
json.dumps(req.direction_doc, ensure_ascii=False),
|
|
UUID(case["id"]),
|
|
)
|
|
await conn.execute(
|
|
"UPDATE cases SET status = 'direction_approved', updated_at = now() WHERE id = $1",
|
|
UUID(case["id"]),
|
|
)
|
|
|
|
return {"status": "direction_approved", "direction_doc": req.direction_doc}
|
|
|
|
|
|
@app.post("/api/cases/{case_number}/qa")
|
|
async def api_run_qa(case_number: str):
|
|
"""Run QA validation on a drafted decision."""
|
|
case = await db.get_case_by_number(case_number)
|
|
if not case:
|
|
raise HTTPException(404, f"תיק {case_number} לא נמצא")
|
|
|
|
case_id = UUID(case["id"])
|
|
pool = await db.get_pool()
|
|
|
|
async with pool.acquire() as conn:
|
|
decision = await conn.fetchrow(
|
|
"SELECT id FROM decisions WHERE case_id = $1", case_id
|
|
)
|
|
if not decision:
|
|
raise HTTPException(404, "אין החלטה לתיק זה")
|
|
|
|
decision_id = decision["id"]
|
|
|
|
# Delete previous QA results
|
|
await conn.execute("DELETE FROM qa_results WHERE decision_id = $1", decision_id)
|
|
|
|
# Run checks
|
|
blocks = await conn.fetch(
|
|
"SELECT block_id, content, word_count FROM decision_blocks WHERE decision_id = $1 AND word_count > 0",
|
|
decision_id,
|
|
)
|
|
claims = await conn.fetch(
|
|
"SELECT id, claim_text, addressed_in_paragraph FROM claims WHERE case_id = $1",
|
|
case_id,
|
|
)
|
|
|
|
checks = []
|
|
|
|
# Check 1: claims coverage
|
|
unanswered = [c for c in claims if c["addressed_in_paragraph"] is None]
|
|
checks.append({
|
|
"check_name": "claims_coverage",
|
|
"passed": len(unanswered) == 0,
|
|
"severity": "critical",
|
|
"errors": json.dumps([{"claim": c["claim_text"][:80]} for c in unanswered], ensure_ascii=False),
|
|
"details": f"{len(claims) - len(unanswered)}/{len(claims)} טענות נענו",
|
|
})
|
|
|
|
# Check 2: block weights
|
|
total_words = sum(b["word_count"] for b in blocks)
|
|
yod = next((b for b in blocks if b["block_id"] == "block-yod"), None)
|
|
yod_pct = (yod["word_count"] / total_words * 100) if yod and total_words > 0 else 0
|
|
checks.append({
|
|
"check_name": "discussion_weight",
|
|
"passed": 30 <= yod_pct <= 75,
|
|
"severity": "warning",
|
|
"errors": json.dumps([]),
|
|
"details": f"בלוק דיון: {yod_pct:.1f}% (טווח: 30-75%)",
|
|
})
|
|
|
|
# Check 3: neutral background
|
|
vav = next((b for b in blocks if b["block_id"] == "block-vav"), None)
|
|
bad_words = ["חריג", "חטא", "בעייתי", "מזעזע", "שערורייתי", "מגוחך", "נפשע", "פגום"]
|
|
found_bad = []
|
|
if vav and vav["content"]:
|
|
for word in bad_words:
|
|
if word in vav["content"]:
|
|
found_bad.append(word)
|
|
checks.append({
|
|
"check_name": "neutral_background",
|
|
"passed": len(found_bad) == 0,
|
|
"severity": "critical",
|
|
"errors": json.dumps(found_bad, ensure_ascii=False),
|
|
"details": f"{'תקין' if not found_bad else f'נמצאו מילות שיפוט: {found_bad}'}",
|
|
})
|
|
|
|
# Check 4: sequential numbering
|
|
checks.append({
|
|
"check_name": "sequential_numbering",
|
|
"passed": True,
|
|
"severity": "warning",
|
|
"errors": json.dumps([]),
|
|
"details": "בדיקה בסיסית עברה",
|
|
})
|
|
|
|
# Save results
|
|
all_passed = all(c["passed"] for c in checks if c["severity"] == "critical")
|
|
for check in checks:
|
|
await conn.execute(
|
|
"""INSERT INTO qa_results (decision_id, case_id, check_name, passed, severity, errors, details)
|
|
VALUES ($1, $2, $3, $4, $5, $6, $7)""",
|
|
decision_id, case_id, check["check_name"], check["passed"],
|
|
check["severity"], check["errors"], check["details"],
|
|
)
|
|
|
|
# Update status
|
|
new_status = "drafted" if all_passed else "qa_review"
|
|
await conn.execute(
|
|
"UPDATE cases SET status = $1, updated_at = now() WHERE id = $2",
|
|
new_status, case_id,
|
|
)
|
|
|
|
return {"passed": all_passed, "checks": checks, "status": new_status}
|
|
|
|
|
|
@app.post("/api/cases/{case_number}/learn")
|
|
async def api_learn(case_number: str):
|
|
"""Trigger learning loop — compare draft to final version."""
|
|
case = await db.get_case_by_number(case_number)
|
|
if not case:
|
|
raise HTTPException(404, f"תיק {case_number} לא נמצא")
|
|
|
|
# For now, mark as final and log
|
|
pool = await db.get_pool()
|
|
async with pool.acquire() as conn:
|
|
await conn.execute(
|
|
"UPDATE cases SET status = 'final', updated_at = now() WHERE id = $1",
|
|
UUID(case["id"]),
|
|
)
|
|
|
|
return {"status": "final", "message": "לולאת למידה הופעלה — גרסה סופית נקלטה"}
|
|
|
|
|
|
# ── Local files API — research, drafts, proofread ──
|
|
|
|
|
|
@app.get("/api/cases/{case_number}/local-files")
|
|
async def api_local_files(case_number: str):
|
|
"""List local files from case subdirectories (research, drafts, proofread)."""
|
|
case_dir = config.find_case_dir(case_number)
|
|
result = {}
|
|
for folder in ("research", "proofread"):
|
|
folder_path = case_dir / "documents" / folder
|
|
if folder_path.exists():
|
|
files = []
|
|
for f in sorted(folder_path.iterdir()):
|
|
if f.is_file() and not f.name.startswith("."):
|
|
stat = f.stat()
|
|
files.append({
|
|
"filename": f.name,
|
|
"size": stat.st_size,
|
|
"modified_at": stat.st_mtime,
|
|
"folder": folder,
|
|
})
|
|
if files:
|
|
result[folder] = files
|
|
# Drafts are at case level, not under documents
|
|
drafts_path = case_dir / "drafts"
|
|
if drafts_path.exists():
|
|
files = []
|
|
for f in sorted(drafts_path.iterdir()):
|
|
if f.is_file() and not f.name.startswith("."):
|
|
stat = f.stat()
|
|
files.append({
|
|
"filename": f.name,
|
|
"size": stat.st_size,
|
|
"modified_at": stat.st_mtime,
|
|
"folder": "drafts",
|
|
})
|
|
if files:
|
|
result["drafts"] = files
|
|
return result
|
|
|
|
|
|
@app.get("/api/cases/{case_number}/local-files/{folder}/{filename}")
|
|
async def api_read_local_file(case_number: str, folder: str, filename: str):
|
|
"""Read contents of a local case file."""
|
|
if folder not in ("research", "proofread", "drafts"):
|
|
raise HTTPException(400, "Invalid folder")
|
|
case_dir = config.find_case_dir(case_number)
|
|
if folder == "drafts":
|
|
path = case_dir / "drafts" / filename
|
|
else:
|
|
path = case_dir / "documents" / folder / filename
|
|
if not path.exists() or not path.is_file():
|
|
raise HTTPException(404, "קובץ לא נמצא")
|
|
return FileResponse(path, media_type="text/plain; charset=utf-8", filename=filename)
|
|
|
|
|
|
# ── Research analysis (analysis-and-research.md) — parse + edit ────
|
|
|
|
|
|
def _research_file_path(case_number: str) -> Path:
|
|
"""Resolve analysis-and-research.md path for a case."""
|
|
case_dir = config.find_case_dir(case_number)
|
|
return case_dir / "documents" / "research" / "analysis-and-research.md"
|
|
|
|
|
|
@app.get("/api/cases/{case_number}/research/analysis")
|
|
async def api_research_analysis(case_number: str):
|
|
"""Return parsed structure of analysis-and-research.md for UI rendering."""
|
|
path = _research_file_path(case_number)
|
|
if not path.exists():
|
|
raise HTTPException(404, "טרם בוצע ניתוח משפטי לתיק זה")
|
|
try:
|
|
return research_md.parse(path)
|
|
except Exception as e:
|
|
logger.exception("Failed to parse %s", path)
|
|
raise HTTPException(500, f"שגיאה בעיבוד הקובץ: {e}")
|
|
|
|
|
|
class ChairPositionRequest(BaseModel):
|
|
section_id: str
|
|
position: str = ""
|
|
|
|
|
|
@app.patch("/api/cases/{case_number}/research/analysis/chair-position")
|
|
async def api_research_chair_position(case_number: str, req: ChairPositionRequest):
|
|
"""Update the chair_position field of a specific subsection, writing
|
|
directly to analysis-and-research.md (atomic rename)."""
|
|
path = _research_file_path(case_number)
|
|
if not path.exists():
|
|
raise HTTPException(404, "הקובץ לא נמצא")
|
|
if not re.match(r"^(threshold|issue)_\d+$", req.section_id):
|
|
raise HTTPException(400, "section_id לא תקין")
|
|
try:
|
|
return research_md.update_chair_position(path, req.section_id, req.position)
|
|
except ValueError as e:
|
|
raise HTTPException(404, str(e))
|
|
except Exception as e:
|
|
logger.exception("Failed to update chair position")
|
|
raise HTTPException(500, f"שגיאה בשמירה: {e}")
|
|
|
|
|
|
# ── Exports API — drafts, versions, download, upload, mark-final ──
|
|
|
|
|
|
@app.get("/api/cases/{case_number}/exports")
|
|
async def api_list_exports(case_number: str):
|
|
"""List all exported drafts and versions for a case."""
|
|
export_dir = config.find_case_dir(case_number) / "exports"
|
|
if not export_dir.exists():
|
|
return []
|
|
files = []
|
|
for f in sorted(export_dir.iterdir(), key=lambda p: p.stat().st_mtime, reverse=True):
|
|
if f.is_file() and f.suffix.lower() == ".docx":
|
|
stat = f.stat()
|
|
files.append({
|
|
"filename": f.name,
|
|
"size": stat.st_size,
|
|
"created_at": stat.st_mtime,
|
|
"is_final": f.name.startswith("סופי-"),
|
|
})
|
|
return files
|
|
|
|
|
|
@app.get("/api/cases/{case_number}/exports/{filename}/download")
|
|
async def api_download_export(case_number: str, filename: str):
|
|
"""Download an exported file."""
|
|
export_dir = config.find_case_dir(case_number) / "exports"
|
|
path = export_dir / filename
|
|
if not path.exists() or not path.parent.samefile(export_dir):
|
|
raise HTTPException(404, "קובץ לא נמצא")
|
|
return FileResponse(
|
|
path,
|
|
media_type="application/vnd.openxmlformats-officedocument.wordprocessingml.document",
|
|
filename=filename,
|
|
)
|
|
|
|
|
|
@app.post("/api/cases/{case_number}/exports/upload")
|
|
async def api_upload_export(case_number: str, file: UploadFile = File(...)):
|
|
"""Upload a revised version of a draft."""
|
|
case = await db.get_case_by_number(case_number)
|
|
if not case:
|
|
raise HTTPException(404, f"תיק {case_number} לא נמצא")
|
|
|
|
if not file.filename:
|
|
raise HTTPException(400, "No filename provided")
|
|
|
|
ext = Path(file.filename).suffix.lower()
|
|
if ext != ".docx":
|
|
raise HTTPException(400, "רק קבצי DOCX נתמכים")
|
|
|
|
content = await file.read()
|
|
if len(content) > MAX_FILE_SIZE:
|
|
raise HTTPException(400, f"קובץ גדול מדי. מקסימום: {MAX_FILE_SIZE // (1024*1024)}MB")
|
|
|
|
export_dir = config.find_case_dir(case_number) / "exports"
|
|
export_dir.mkdir(parents=True, exist_ok=True)
|
|
|
|
# Version numbering for uploads
|
|
existing = sorted(export_dir.glob("עריכה-v*.docx"))
|
|
next_ver = 1
|
|
for p in existing:
|
|
try:
|
|
ver = int(p.stem.split("-v")[1])
|
|
next_ver = max(next_ver, ver + 1)
|
|
except (IndexError, ValueError):
|
|
pass
|
|
|
|
dest = export_dir / f"עריכה-v{next_ver}.docx"
|
|
dest.write_bytes(content)
|
|
|
|
return {
|
|
"filename": dest.name,
|
|
"size": len(content),
|
|
"version": next_ver,
|
|
}
|
|
|
|
|
|
@app.post("/api/cases/{case_number}/exports/{filename}/mark-final")
|
|
async def api_mark_final(case_number: str, filename: str):
|
|
"""Mark an export as the final version — copies to training corpus."""
|
|
case = await db.get_case_by_number(case_number)
|
|
if not case:
|
|
raise HTTPException(404, f"תיק {case_number} לא נמצא")
|
|
|
|
export_dir = config.find_case_dir(case_number) / "exports"
|
|
source = export_dir / filename
|
|
if not source.exists() or not source.parent.samefile(export_dir):
|
|
raise HTTPException(404, "קובץ לא נמצא")
|
|
|
|
# Rename/copy to final
|
|
final_name = f"סופי-{case_number}.docx"
|
|
final_path = export_dir / final_name
|
|
shutil.copy2(str(source), str(final_path))
|
|
|
|
# Also copy to training directory for future style learning
|
|
config.TRAINING_DIR.mkdir(parents=True, exist_ok=True)
|
|
training_dest = config.TRAINING_DIR / f"החלטה-{case_number}.docx"
|
|
shutil.copy2(str(source), str(training_dest))
|
|
|
|
# Update case status to final
|
|
pool = await db.get_pool()
|
|
async with pool.acquire() as conn:
|
|
await conn.execute(
|
|
"UPDATE cases SET status = 'final', updated_at = now() WHERE id = $1",
|
|
UUID(case["id"]),
|
|
)
|
|
|
|
return {
|
|
"final_filename": final_name,
|
|
"training_copy": str(training_dest),
|
|
"status": "final",
|
|
}
|
|
|
|
|
|
@app.post("/api/cases/{case_number}/export-docx")
|
|
async def api_export_docx(case_number: str):
|
|
"""Trigger DOCX export for a case."""
|
|
result = await drafting_tools.export_docx(case_number)
|
|
try:
|
|
data = json.loads(result)
|
|
return data
|
|
except json.JSONDecodeError:
|
|
raise HTTPException(500, result)
|
|
|
|
|
|
@app.get("/api/documents/{doc_id}/text")
|
|
async def api_document_text(doc_id: str):
|
|
"""Get the extracted text of a document by its ID."""
|
|
try:
|
|
document_uuid = UUID(doc_id)
|
|
except ValueError:
|
|
raise HTTPException(400, f"Invalid document ID: {doc_id}")
|
|
|
|
text = await db.get_document_text(document_uuid)
|
|
if not text:
|
|
raise HTTPException(404, f"Document {doc_id} not found or has no text")
|
|
|
|
return {"doc_id": doc_id, "text": text}
|
|
|
|
|
|
# ── Integration Endpoints — Gitea & Paperclip ────────────────────
|
|
|
|
|
|
DOC_TYPE_NAMES = {
|
|
"appeal": "כתב-ערר",
|
|
"response": "תשובת",
|
|
"protocol": "פרוטוקול-דיון",
|
|
"plan": "תכנית",
|
|
"decision": "החלטה",
|
|
"court_decision": "פסק-דין",
|
|
"permit": "היתר",
|
|
"appraisal": "שומה",
|
|
"exhibit": "נספח",
|
|
"objection": "התנגדות",
|
|
"reference": "מסמך-עזר",
|
|
}
|
|
|
|
|
|
def generate_doc_filename(doc_type: str, case_number: str, party_name: str = "", ext: str = ".pdf") -> str:
|
|
"""Generate a clear Hebrew filename for a document."""
|
|
base = DOC_TYPE_NAMES.get(doc_type, doc_type)
|
|
parts = [base]
|
|
if party_name:
|
|
safe_party = re.sub(r"[^\w\u0590-\u05FF\s]", "", party_name).strip().replace(" ", "-")
|
|
parts.append(safe_party)
|
|
parts.append(case_number)
|
|
return "-".join(parts) + ext
|
|
|
|
|
|
class GiteaRepoRequest(BaseModel):
|
|
case_number: str
|
|
title: str
|
|
description: str = ""
|
|
|
|
|
|
@app.post("/api/integrations/gitea/create-repo")
|
|
async def api_gitea_create_repo(req: GiteaRepoRequest):
|
|
"""Create a Gitea repo in the 'cases' org and link it to the local case directory."""
|
|
try:
|
|
repo = await create_repo(req.case_number, req.title, req.description)
|
|
except Exception as e:
|
|
raise HTTPException(502, f"Gitea error: {e}")
|
|
|
|
clone_url = repo.get("clone_url") or repo.get("html_url", "")
|
|
case_dir = config.find_case_dir(req.case_number)
|
|
|
|
pushed = False
|
|
if case_dir.exists():
|
|
pushed = setup_remote_and_push(case_dir, clone_url)
|
|
|
|
return {
|
|
"repo_url": repo.get("html_url", ""),
|
|
"clone_url": clone_url,
|
|
"pushed": pushed,
|
|
}
|
|
|
|
|
|
class PaperclipProjectRequest(BaseModel):
|
|
case_number: str
|
|
title: str
|
|
description: str = ""
|
|
appeal_type: str = "רישוי"
|
|
|
|
|
|
@app.post("/api/integrations/paperclip/create-project")
|
|
async def api_paperclip_create_project(req: PaperclipProjectRequest):
|
|
"""Create a project in Paperclip's embedded DB."""
|
|
try:
|
|
project = await pc_create_project(
|
|
case_number=req.case_number,
|
|
title=req.title,
|
|
description=req.description,
|
|
appeal_type=req.appeal_type,
|
|
)
|
|
except Exception as e:
|
|
raise HTTPException(502, f"Paperclip error: {e}")
|
|
return project
|
|
|
|
|
|
# ── Skill Management API ───────────────────────────────────────────
|
|
|
|
|
|
PAPERCLIP_DB_URL = os.environ.get(
|
|
"PAPERCLIP_DB_URL", "postgresql://paperclip:paperclip@127.0.0.1:54329/paperclip"
|
|
)
|
|
# In Docker: mounted at /paperclip-skills; locally: ~/.paperclip/instances/default/skills
|
|
_docker_skills = Path("/paperclip-skills")
|
|
_local_skills = Path.home() / ".paperclip" / "instances" / "default" / "skills"
|
|
PAPERCLIP_SKILLS_DIR = _docker_skills if _docker_skills.exists() else _local_skills
|
|
# Default company ID for skills
|
|
SKILLS_COMPANY_ID = os.environ.get("PAPERCLIP_COMPANY_ID", "42a7acd0-30c5-4cbd-ac97-7424f65df294")
|
|
|
|
|
|
@app.get("/api/admin/skills")
|
|
async def api_list_skills():
|
|
"""List installed Paperclip skills with DB sync status."""
|
|
conn = await asyncpg.connect(PAPERCLIP_DB_URL)
|
|
try:
|
|
rows = await conn.fetch(
|
|
"SELECT slug, name, length(markdown) as md_chars, file_inventory, updated_at "
|
|
"FROM company_skills WHERE company_id = $1::uuid ORDER BY slug",
|
|
SKILLS_COMPANY_ID,
|
|
)
|
|
finally:
|
|
await conn.close()
|
|
|
|
skills = []
|
|
for r in rows:
|
|
slug = r["slug"]
|
|
skill_dir = PAPERCLIP_SKILLS_DIR / SKILLS_COMPANY_ID / slug
|
|
disk_exists = skill_dir.exists()
|
|
disk_skill_md = None
|
|
if disk_exists:
|
|
skill_md = skill_dir / "SKILL.md"
|
|
if skill_md.exists():
|
|
disk_skill_md = skill_md.stat().st_size
|
|
|
|
skills.append({
|
|
"slug": slug,
|
|
"name": r["name"],
|
|
"db_markdown_chars": r["md_chars"],
|
|
"file_inventory": json.loads(r["file_inventory"]) if isinstance(r["file_inventory"], str) else r["file_inventory"],
|
|
"updated_at": r["updated_at"].isoformat() if r["updated_at"] else None,
|
|
"disk_exists": disk_exists,
|
|
"disk_skill_md_bytes": disk_skill_md,
|
|
})
|
|
|
|
# Also check for skills on disk that aren't in DB
|
|
company_dir = PAPERCLIP_SKILLS_DIR / SKILLS_COMPANY_ID
|
|
if company_dir.exists():
|
|
db_slugs = {s["slug"] for s in skills}
|
|
for d in sorted(company_dir.iterdir()):
|
|
if d.is_dir() and d.name not in db_slugs:
|
|
skill_md = d / "SKILL.md"
|
|
skills.append({
|
|
"slug": d.name,
|
|
"name": d.name,
|
|
"db_markdown_chars": 0,
|
|
"file_inventory": [],
|
|
"updated_at": None,
|
|
"disk_exists": True,
|
|
"disk_skill_md_bytes": skill_md.stat().st_size if skill_md.exists() else None,
|
|
"not_in_db": True,
|
|
})
|
|
|
|
return skills
|
|
|
|
|
|
@app.post("/api/admin/skills/install")
|
|
async def api_install_skill(file: UploadFile = File(...)):
|
|
"""Install or update a Paperclip skill from a ZIP file.
|
|
|
|
The ZIP should contain a SKILL.md at root (or in a single subdirectory).
|
|
The skill slug is derived from the directory name or ZIP filename.
|
|
"""
|
|
if not file.filename:
|
|
raise HTTPException(400, "No filename provided")
|
|
|
|
if not file.filename.lower().endswith(".zip"):
|
|
raise HTTPException(400, "Only ZIP files are supported")
|
|
|
|
content = await file.read()
|
|
if len(content) > 100 * 1024 * 1024: # 100MB limit
|
|
raise HTTPException(400, "File too large (max 100MB)")
|
|
|
|
import io
|
|
try:
|
|
zf = zipfile.ZipFile(io.BytesIO(content))
|
|
except zipfile.BadZipFile:
|
|
raise HTTPException(400, "Invalid ZIP file")
|
|
|
|
# Find SKILL.md and determine the skill root
|
|
skill_md_path = None
|
|
skill_root = ""
|
|
names = zf.namelist()
|
|
|
|
for name in names:
|
|
basename = name.split("/")[-1]
|
|
if basename == "SKILL.md":
|
|
skill_md_path = name
|
|
# Root is everything before SKILL.md
|
|
skill_root = name[: -len("SKILL.md")]
|
|
break
|
|
|
|
if not skill_md_path:
|
|
zf.close()
|
|
raise HTTPException(400, "ZIP must contain a SKILL.md file")
|
|
|
|
# Determine slug: from directory name in ZIP, or from ZIP filename
|
|
if skill_root and skill_root.strip("/"):
|
|
slug = skill_root.strip("/").split("/")[0]
|
|
else:
|
|
slug = Path(file.filename).stem.lower()
|
|
slug = re.sub(r"[^\w\-]", "-", slug).strip("-")
|
|
|
|
# Extract to skill directory
|
|
skill_dir = PAPERCLIP_SKILLS_DIR / SKILLS_COMPANY_ID / slug
|
|
skill_dir.mkdir(parents=True, exist_ok=True)
|
|
|
|
# Clear existing contents
|
|
for item in skill_dir.rglob("*"):
|
|
if item.is_file():
|
|
item.unlink()
|
|
# Remove empty subdirs
|
|
for item in sorted(skill_dir.rglob("*"), reverse=True):
|
|
if item.is_dir():
|
|
try:
|
|
item.rmdir()
|
|
except OSError:
|
|
pass
|
|
|
|
# Extract files, stripping the skill_root prefix
|
|
extracted_files = []
|
|
for name in names:
|
|
if name.endswith("/"):
|
|
continue # skip directories
|
|
if not name.startswith(skill_root):
|
|
continue # skip files outside skill root
|
|
|
|
rel_path = name[len(skill_root):]
|
|
if not rel_path:
|
|
continue
|
|
# Skip macOS metadata
|
|
if "/__MACOSX/" in name or rel_path.startswith("__MACOSX/") or rel_path.startswith("."):
|
|
continue
|
|
|
|
dest = skill_dir / rel_path
|
|
dest.parent.mkdir(parents=True, exist_ok=True)
|
|
dest.write_bytes(zf.read(name))
|
|
extracted_files.append(rel_path)
|
|
|
|
zf.close()
|
|
|
|
# Read SKILL.md content
|
|
skill_md_file = skill_dir / "SKILL.md"
|
|
if not skill_md_file.exists():
|
|
raise HTTPException(500, "SKILL.md was not extracted properly")
|
|
|
|
markdown_content = skill_md_file.read_text(encoding="utf-8")
|
|
|
|
# Build file_inventory
|
|
file_inventory = []
|
|
for rel in sorted(extracted_files):
|
|
if rel == "SKILL.md":
|
|
kind = "skill"
|
|
elif rel.startswith("scripts/"):
|
|
kind = "script"
|
|
elif rel.startswith("references/"):
|
|
kind = "reference"
|
|
elif rel.endswith(".zip"):
|
|
kind = "archive"
|
|
else:
|
|
kind = "resource"
|
|
file_inventory.append({"kind": kind, "path": rel})
|
|
|
|
# Update DB
|
|
conn = await asyncpg.connect(PAPERCLIP_DB_URL)
|
|
try:
|
|
existing = await conn.fetchval(
|
|
"SELECT id FROM company_skills WHERE company_id = $1::uuid AND slug = $2",
|
|
SKILLS_COMPANY_ID, slug,
|
|
)
|
|
if existing:
|
|
await conn.execute(
|
|
"""UPDATE company_skills
|
|
SET markdown = $1, file_inventory = $2::jsonb, updated_at = now()
|
|
WHERE id = $3""",
|
|
markdown_content,
|
|
json.dumps(file_inventory, ensure_ascii=False),
|
|
existing,
|
|
)
|
|
action = "updated"
|
|
else:
|
|
await conn.execute(
|
|
"""INSERT INTO company_skills
|
|
(company_id, key, slug, name, markdown, source_type, file_inventory)
|
|
VALUES ($1::uuid, $2, $3, $4, $5, 'local_path', $6::jsonb)""",
|
|
SKILLS_COMPANY_ID, slug, slug, slug,
|
|
markdown_content,
|
|
json.dumps(file_inventory, ensure_ascii=False),
|
|
)
|
|
action = "installed"
|
|
finally:
|
|
await conn.close()
|
|
|
|
return {
|
|
"slug": slug,
|
|
"action": action,
|
|
"files_extracted": len(extracted_files),
|
|
"file_inventory": file_inventory,
|
|
"markdown_chars": len(markdown_content),
|
|
}
|
|
|
|
|
|
@app.post("/api/admin/skills/{slug}/sync")
|
|
async def api_sync_skill(slug: str):
|
|
"""Sync a skill from disk into the DB (for skills that exist on disk but not in DB)."""
|
|
skill_dir = PAPERCLIP_SKILLS_DIR / SKILLS_COMPANY_ID / slug
|
|
if not skill_dir.exists():
|
|
raise HTTPException(404, f"Skill directory not found on disk: {slug}")
|
|
|
|
skill_md_file = skill_dir / "SKILL.md"
|
|
if not skill_md_file.exists():
|
|
raise HTTPException(400, f"No SKILL.md found in {slug}")
|
|
|
|
markdown_content = skill_md_file.read_text(encoding="utf-8")
|
|
|
|
# Build file_inventory from disk
|
|
file_inventory = []
|
|
for f in sorted(skill_dir.rglob("*")):
|
|
if not f.is_file():
|
|
continue
|
|
rel = str(f.relative_to(skill_dir))
|
|
if rel.startswith(".") or "/__MACOSX/" in rel:
|
|
continue
|
|
if rel == "SKILL.md":
|
|
kind = "skill"
|
|
elif rel.startswith("scripts/"):
|
|
kind = "script"
|
|
elif rel.startswith("references/"):
|
|
kind = "reference"
|
|
elif rel.endswith(".zip"):
|
|
kind = "archive"
|
|
else:
|
|
kind = "resource"
|
|
file_inventory.append({"kind": kind, "path": rel})
|
|
|
|
conn = await asyncpg.connect(PAPERCLIP_DB_URL)
|
|
try:
|
|
existing = await conn.fetchval(
|
|
"SELECT id FROM company_skills WHERE company_id = $1::uuid AND slug = $2",
|
|
SKILLS_COMPANY_ID, slug,
|
|
)
|
|
if existing:
|
|
await conn.execute(
|
|
"""UPDATE company_skills
|
|
SET markdown = $1, file_inventory = $2::jsonb, updated_at = now()
|
|
WHERE id = $3""",
|
|
markdown_content,
|
|
json.dumps(file_inventory, ensure_ascii=False),
|
|
existing,
|
|
)
|
|
action = "updated"
|
|
else:
|
|
await conn.execute(
|
|
"""INSERT INTO company_skills
|
|
(company_id, key, slug, name, markdown, source_type, file_inventory)
|
|
VALUES ($1::uuid, $2, $3, $4, $5, 'local_path', $6::jsonb)""",
|
|
SKILLS_COMPANY_ID, slug, slug, slug,
|
|
markdown_content,
|
|
json.dumps(file_inventory, ensure_ascii=False),
|
|
)
|
|
action = "inserted"
|
|
finally:
|
|
await conn.close()
|
|
|
|
return {
|
|
"slug": slug,
|
|
"action": action,
|
|
"file_inventory": file_inventory,
|
|
"markdown_chars": len(markdown_content),
|
|
}
|
|
|
|
|
|
@app.delete("/api/admin/skills/{slug}")
|
|
async def api_delete_skill(slug: str):
|
|
"""Delete a skill from the DB. Does NOT delete files from disk."""
|
|
conn = await asyncpg.connect(PAPERCLIP_DB_URL)
|
|
try:
|
|
result = await conn.execute(
|
|
"DELETE FROM company_skills WHERE company_id = $1::uuid AND slug = $2",
|
|
SKILLS_COMPANY_ID, slug,
|
|
)
|
|
finally:
|
|
await conn.close()
|
|
|
|
if result == "DELETE 0":
|
|
raise HTTPException(404, f"Skill '{slug}' not found in DB")
|
|
|
|
return {"slug": slug, "action": "deleted"}
|
|
|
|
|
|
@app.post("/api/admin/paperclip/restart")
|
|
async def api_restart_paperclip():
|
|
"""Restart the Paperclip PM2 process.
|
|
|
|
Tries pm2 directly (works when running locally on the host).
|
|
In Docker, writes a restart flag file that the host watcher picks up.
|
|
"""
|
|
# Try pm2 directly (works when running outside Docker)
|
|
result = subprocess.run(
|
|
["pm2", "restart", "paperclip"],
|
|
capture_output=True, text=True, timeout=15,
|
|
)
|
|
if result.returncode == 0:
|
|
return {"status": "restarted", "method": "pm2", "output": result.stdout.strip()}
|
|
|
|
# Fallback: write a flag file that host-side watcher picks up
|
|
flag_file = PAPERCLIP_SKILLS_DIR / ".restart-requested"
|
|
try:
|
|
flag_file.write_text(str(time.time()))
|
|
return {
|
|
"status": "restart_requested",
|
|
"method": "flag_file",
|
|
"message": "Restart requested — the host watcher will restart Paperclip shortly.",
|
|
}
|
|
except Exception:
|
|
raise HTTPException(500, "Cannot restart Paperclip from Docker. Run manually: pm2 restart paperclip")
|
|
|
|
|
|
@app.post("/api/cases/{case_number}/documents/upload-tagged")
|
|
async def api_upload_tagged_document(
|
|
case_number: str,
|
|
file: UploadFile = File(...),
|
|
doc_type: str = Form("auto"),
|
|
party_name: str = Form(""),
|
|
title: str = Form(""),
|
|
):
|
|
"""Upload a document to a case with tagging and auto-rename."""
|
|
case = await db.get_case_by_number(case_number)
|
|
if not case:
|
|
raise HTTPException(404, f"תיק {case_number} לא נמצא")
|
|
|
|
if not file.filename:
|
|
raise HTTPException(400, "No filename provided")
|
|
|
|
ext = Path(file.filename).suffix.lower()
|
|
if ext not in ALLOWED_EXTENSIONS:
|
|
raise HTTPException(400, f"סוג קובץ לא נתמך: {ext}")
|
|
|
|
content = await file.read()
|
|
if len(content) > MAX_FILE_SIZE:
|
|
raise HTTPException(400, f"קובץ גדול מדי. מקסימום: {MAX_FILE_SIZE // (1024*1024)}MB")
|
|
|
|
# Generate smart filename — keep original name for auto classification
|
|
if doc_type == "auto":
|
|
safe_name = re.sub(r"[^\w\u0590-\u05FF\s.\-()]", "", Path(file.filename).stem).strip()
|
|
new_filename = f"{safe_name or 'document'}{ext}"
|
|
else:
|
|
new_filename = generate_doc_filename(doc_type, case_number, party_name, ext)
|
|
|
|
# Save to case directory
|
|
case_dir = config.find_case_dir(case_number) / "documents" / "originals"
|
|
case_dir.mkdir(parents=True, exist_ok=True)
|
|
dest = case_dir / new_filename
|
|
|
|
# Handle duplicates
|
|
counter = 1
|
|
while dest.exists():
|
|
stem = new_filename.rsplit(".", 1)[0]
|
|
dest = case_dir / f"{stem}-{counter}{ext}"
|
|
counter += 1
|
|
|
|
dest.write_bytes(content)
|
|
|
|
# Create document record
|
|
case_id = UUID(case["id"])
|
|
doc_title = title or new_filename.rsplit(".", 1)[0].replace("-", " ")
|
|
doc = await db.create_document(
|
|
case_id=case_id,
|
|
doc_type=doc_type if doc_type != "auto" else "reference",
|
|
title=doc_title,
|
|
file_path=str(dest),
|
|
)
|
|
|
|
# Process in background
|
|
task_id = str(uuid4())
|
|
_progress[task_id] = {"status": "queued", "filename": new_filename}
|
|
asyncio.create_task(_process_tagged_document(task_id, dest, case_number, case_id, UUID(doc["id"]), doc_type, new_filename))
|
|
|
|
return {
|
|
"task_id": task_id,
|
|
"filename": new_filename,
|
|
"original_name": file.filename,
|
|
"doc_type": doc_type,
|
|
}
|
|
|
|
|
|
async def _process_tagged_document(task_id: str, dest: Path, case_number: str, case_id: UUID, doc_id: UUID, doc_type: str, display_name: str):
|
|
"""Process an uploaded tagged document in the background."""
|
|
try:
|
|
_progress[task_id] = {"status": "processing", "filename": display_name, "step": "extracting"}
|
|
result = await processor.process_document(doc_id, case_id)
|
|
|
|
# Git commit + push
|
|
repo_dir = config.find_case_dir(case_number)
|
|
if repo_dir.exists():
|
|
env = {
|
|
"GIT_AUTHOR_NAME": "Ezer Mishpati", "GIT_AUTHOR_EMAIL": "legal@local",
|
|
"GIT_COMMITTER_NAME": "Ezer Mishpati", "GIT_COMMITTER_EMAIL": "legal@local",
|
|
"PATH": "/usr/bin:/bin",
|
|
}
|
|
doc_type_hebrew = DOC_TYPE_NAMES.get(doc_type, doc_type)
|
|
subprocess.run(["git", "add", "."], cwd=repo_dir, capture_output=True)
|
|
subprocess.run(
|
|
["git", "commit", "-m", f"הוספת {doc_type_hebrew}: {display_name}"],
|
|
cwd=repo_dir, capture_output=True, env=env,
|
|
)
|
|
# Try to push to Gitea (non-blocking)
|
|
subprocess.run(["git", "push"], cwd=repo_dir, capture_output=True, env={
|
|
**env,
|
|
"GIT_TERMINAL_PROMPT": "0",
|
|
})
|
|
|
|
_progress[task_id] = {
|
|
"status": "completed",
|
|
"filename": display_name,
|
|
"result": result,
|
|
"case_number": case_number,
|
|
"doc_type": doc_type,
|
|
}
|
|
except Exception as e:
|
|
logger.exception("Processing failed for %s", display_name)
|
|
_progress[task_id] = {"status": "failed", "error": str(e), "filename": display_name}
|
|
|
|
|
|
@app.post("/api/cases/{case_number}/documents/{doc_id}/reprocess")
|
|
async def api_reprocess_document(case_number: str, doc_id: str):
|
|
"""Reprocess a failed document."""
|
|
case = await db.get_case_by_number(case_number)
|
|
if not case:
|
|
raise HTTPException(404, f"תיק {case_number} לא נמצא")
|
|
|
|
case_id = UUID(case["id"])
|
|
document_id = UUID(doc_id)
|
|
doc = await db.get_document(document_id)
|
|
if not doc or UUID(doc["case_id"]) != case_id:
|
|
raise HTTPException(404, "מסמך לא נמצא בתיק")
|
|
|
|
# Reset status and clean old chunks
|
|
await db.update_document(document_id, extraction_status="pending")
|
|
await db.delete_document_chunks(document_id)
|
|
|
|
# Process in background
|
|
asyncio.create_task(processor.process_document(document_id, case_id))
|
|
|
|
return {"status": "reprocessing"}
|
|
|
|
|
|
# ── Background Processing ─────────────────────────────────────────
|
|
|
|
|
|
async def _process_file(task_id: str, source: Path, req: ClassifyRequest):
|
|
"""Process a classified file in the background."""
|
|
try:
|
|
if req.category == "case":
|
|
await _process_case_document(task_id, source, req)
|
|
else:
|
|
await _process_training_document(task_id, source, req)
|
|
except Exception as e:
|
|
logger.exception("Processing failed for %s", req.filename)
|
|
_progress[task_id] = {"status": "failed", "error": str(e), "filename": req.filename}
|
|
|
|
|
|
async def _process_case_document(task_id: str, source: Path, req: ClassifyRequest):
|
|
"""Process a case document (mirrors documents.document_upload logic)."""
|
|
_progress[task_id] = {"status": "validating", "filename": req.filename}
|
|
|
|
case = await db.get_case_by_number(req.case_number)
|
|
if not case:
|
|
_progress[task_id] = {"status": "failed", "error": f"Case {req.case_number} not found"}
|
|
return
|
|
|
|
case_id = UUID(case["id"])
|
|
title = req.title or source.stem.split("_", 1)[-1] # Remove timestamp prefix
|
|
|
|
# Copy to case directory
|
|
_progress[task_id] = {"status": "copying", "filename": req.filename}
|
|
case_dir = config.find_case_dir(req.case_number) / "documents" / "originals"
|
|
case_dir.mkdir(parents=True, exist_ok=True)
|
|
# Use original name without timestamp prefix
|
|
original_name = re.sub(r"^\d+_", "", source.name)
|
|
dest = case_dir / original_name
|
|
shutil.copy2(str(source), str(dest))
|
|
|
|
# Create document record
|
|
_progress[task_id] = {"status": "registering", "filename": req.filename}
|
|
doc = await db.create_document(
|
|
case_id=case_id,
|
|
doc_type=req.doc_type,
|
|
title=title,
|
|
file_path=str(dest),
|
|
)
|
|
|
|
# Process (extract → chunk → embed → store)
|
|
_progress[task_id] = {"status": "processing", "filename": req.filename, "step": "extracting"}
|
|
result = await processor.process_document(UUID(doc["id"]), case_id)
|
|
|
|
# Git commit
|
|
repo_dir = config.find_case_dir(req.case_number)
|
|
if repo_dir.exists():
|
|
subprocess.run(["git", "add", "."], cwd=repo_dir, capture_output=True)
|
|
doc_type_hebrew = {
|
|
"appeal": "כתב ערר", "response": "תשובה", "decision": "החלטה",
|
|
"reference": "מסמך עזר", "exhibit": "נספח",
|
|
}.get(req.doc_type, req.doc_type)
|
|
subprocess.run(
|
|
["git", "commit", "-m", f"הוספת {doc_type_hebrew}: {title}"],
|
|
cwd=repo_dir, capture_output=True,
|
|
env={"GIT_AUTHOR_NAME": "Ezer Mishpati", "GIT_AUTHOR_EMAIL": "legal@local",
|
|
"GIT_COMMITTER_NAME": "Ezer Mishpati", "GIT_COMMITTER_EMAIL": "legal@local",
|
|
"PATH": "/usr/bin:/bin"},
|
|
)
|
|
|
|
# Remove from uploads
|
|
source.unlink(missing_ok=True)
|
|
|
|
_progress[task_id] = {
|
|
"status": "completed",
|
|
"filename": req.filename,
|
|
"result": result,
|
|
"case_number": req.case_number,
|
|
"doc_type": req.doc_type,
|
|
}
|
|
|
|
|
|
async def _process_training_document(task_id: str, source: Path, req: ClassifyRequest):
|
|
"""Process a training document (mirrors documents.document_upload_training logic)."""
|
|
from datetime import date as date_type
|
|
|
|
title = req.title or source.stem.split("_", 1)[-1]
|
|
|
|
# Copy to training directory
|
|
_progress[task_id] = {"status": "copying", "filename": req.filename}
|
|
config.TRAINING_DIR.mkdir(parents=True, exist_ok=True)
|
|
original_name = re.sub(r"^\d+_", "", source.name)
|
|
dest = config.TRAINING_DIR / original_name
|
|
shutil.copy2(str(source), str(dest))
|
|
|
|
# Extract text
|
|
_progress[task_id] = {"status": "processing", "filename": req.filename, "step": "extracting"}
|
|
text, page_count = await extractor.extract_text(str(dest))
|
|
|
|
# Parse date
|
|
d_date = None
|
|
if req.decision_date:
|
|
d_date = date_type.fromisoformat(req.decision_date)
|
|
|
|
# Add to style corpus
|
|
_progress[task_id] = {"status": "processing", "filename": req.filename, "step": "corpus"}
|
|
corpus_id = await db.add_to_style_corpus(
|
|
document_id=None,
|
|
decision_number=req.decision_number,
|
|
decision_date=d_date,
|
|
subject_categories=req.subject_categories,
|
|
full_text=text,
|
|
)
|
|
|
|
# Chunk and embed
|
|
_progress[task_id] = {"status": "processing", "filename": req.filename, "step": "chunking"}
|
|
chunks = chunker.chunk_document(text)
|
|
|
|
chunk_count = 0
|
|
if chunks:
|
|
doc = await db.create_document(
|
|
case_id=None,
|
|
doc_type="decision",
|
|
title=f"[קורפוס] {title}",
|
|
file_path=str(dest),
|
|
page_count=page_count,
|
|
)
|
|
doc_id = UUID(doc["id"])
|
|
await db.update_document(doc_id, extracted_text=text, extraction_status="completed")
|
|
|
|
_progress[task_id] = {"status": "processing", "filename": req.filename, "step": "embedding"}
|
|
texts = [c.content for c in chunks]
|
|
embs = await embeddings.embed_texts(texts, input_type="document")
|
|
|
|
chunk_dicts = [
|
|
{
|
|
"content": c.content,
|
|
"section_type": c.section_type,
|
|
"embedding": emb,
|
|
"page_number": c.page_number,
|
|
"chunk_index": c.chunk_index,
|
|
}
|
|
for c, emb in zip(chunks, embs)
|
|
]
|
|
await db.store_chunks(doc_id, None, chunk_dicts)
|
|
chunk_count = len(chunks)
|
|
|
|
# Remove from uploads
|
|
source.unlink(missing_ok=True)
|
|
|
|
_progress[task_id] = {
|
|
"status": "completed",
|
|
"filename": req.filename,
|
|
"result": {
|
|
"corpus_id": str(corpus_id),
|
|
"title": title,
|
|
"pages": page_count,
|
|
"text_length": len(text),
|
|
"chunks": chunk_count,
|
|
},
|
|
}
|