Includes: - docs/: architecture, block-schema, migration-plan, product-specification - scripts/: bidi_table, decompose-decisions, extract-claims, seed-knowledge, etc. - skill-legal-decision/: SKILL.md + references + block-schema - skill-legal-assistant/: SKILL.md - skill-legal-docx/: SKILL.md + references - .claude/commands/: bidi-table skill - .taskmaster/: task config + PRDs - .gitignore: exclude legacy/, kiryat-yearim/, node_modules/, memory/ Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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4.1 KiB
System Architecture — Legal Decision Assistant
Components
┌─────────────────────────────────────────────────────┐
│ Nautilus Server │
│ 158.178.131.193 │
│ │
│ ┌──────────┐ ┌──────────┐ ┌──────────────────┐ │
│ │ Coolify │ │ Traefik │ │ ezer-mishpati-web│ │
│ │ (manage) │ │ (proxy) │ │ (upload UI) │ │
│ └──────────┘ └──────────┘ └──────────────────┘ │
│ │
│ ┌──────────────────┐ ┌──────────────────────────┐ │
│ │ PostgreSQL │ │ Redis │ │
│ │ + pgvector │ │ (task queue) │ │
│ │ (legal-ai-postgres│ │ (legal-ai-redis) │ │
│ └──────────────────┘ └──────────────────────────┘ │
│ │
│ ┌──────────┐ ┌──────────┐ │
│ │ Gitea │ │ n8n │ │
│ │ (code) │ │ (automate│ │
│ └──────────┘ └──────────┘ │
│ │
│ ┌──────────────────────────────────────────────┐ │
│ │ Claude Code (via SSH or API) │ │
│ │ — Skills: legal-decision, legal-docx │ │
│ │ — MCP: postgres, n8n, cloudflare, chrome │ │
│ └──────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────┘
External:
← Claude API (embeddings, analysis)
← Cloudflare DNS (*.nautilus.marcusgroup.org)
← User (Putty SSH / Browser)
Data Flow
1. Document Upload
User → ezer-mishpati-web → file storage → n8n trigger
→ classify document → store metadata in PostgreSQL
→ generate embeddings → store in pgvector
2. Decision Writing
Claude Code → read source materials from DB
→ generate structure DOCX (12 blocks)
→ write each block with appropriate model/parameters
→ validate against block-schema
→ export final DOCX
3. Precedent Search (RAG)
Query → generate embedding → pgvector similarity search
→ return relevant paragraphs/decisions
→ Claude analyzes relevance → present to user
Database Schema — 4 Layers
Layer 1: Core
appeals, parties, panels, documents
Layer 2: Decision
decisions, decision_blocks, decision_paragraphs, claims
Layer 3: Legal Knowledge
case_law, case_law_citations, statutory_provisions, transition_phrases, lessons_learned
Layer 4: Semantic Search (RAG)
document_embeddings, paragraph_embeddings, case_law_embeddings (all using pgvector vector(1536) columns)
Technology Choices
- Database: PostgreSQL 15 + pgvector 0.8.1
- Embedding model: TBD (Claude/OpenAI ada-002/local)
- Automation: n8n (workflow engine)
- Code repository: Gitea (self-hosted)
- Deployment: Coolify (Docker management)
- Proxy: Traefik v3.6 (auto-SSL)
- Frontend: ezer-mishpati-web (static HTML + API)