Material Kai Vision Platform - Documentation

AI-Powered Material Intelligence System

Production-grade platform serving 5,000+ users with 99.5%+ uptime. Transforms material catalog PDFs into searchable knowledge using 12 AI models across a 14-stage processing pipeline.

📚 Documentation

🎯 Start Here

INDEX.md - Complete documentation index with learning paths BLOG-POST-OVERVIEW.md - Comprehensive blog post overview (6,000+ words)

📖 Core Documentation

overview.md - Complete platform overview

Executive summary with key metrics
Architecture overview
AI models integration (12 models)
14-stage PDF processing pipeline
Multi-modal search capabilities
Database architecture
Production metrics

system-architecture.md - System architecture & design

Three-tier architecture
Hybrid architecture pattern
Technology stack
Authentication & security
API endpoints (113)
Scalability & monitoring

ai-models-guide.md - AI models reference

12 AI models overview
Claude Sonnet 4.5 & Haiku 4.5
GPT-4o & embeddings
Llama 4 Scout 17B Vision
OpenAI CLIP (5 types)
Model usage by stage
Cost optimization

pdf-processing-pipeline.md - PDF processing pipeline

14-stage pipeline breakdown
Products + Metadata extraction (inseparable)
Document entities (certificates, logos, specs)
Stage-by-stage details
Checkpoint recovery (9 checkpoints)
Performance metrics
API endpoint

api-endpoints.md - API reference

113 endpoints across 14 categories
RAG Routes (25)
Admin Routes (18)
Search Routes (18)
Documents Routes (11)
AI Services Routes (10)
Images Routes (5)
Document Entities Routes (5) ✨ NEW
PDF Routes (4)
Products Routes (3)
Embeddings Routes (3)
Together AI Routes (3)
Anthropic Routes (3)
Monitoring Routes (3)
AI Metrics Routes (2)

database-schema-complete.md - Database schema

Core tables (products, chunks, images, document_entities)
Products + Metadata architecture (JSONB)
Document entities (certificates, logos, specifications)
Product-document relationships
Relationship tables with relevance scores
Row-Level Security (RLS)
Indexes & performance
Storage capacity
Backup & recovery

job-queue-system.md - Job queue & async processing

Supabase-native job queue
Checkpoint-based recovery
Auto-recovery for stuck jobs
Real-time progress tracking
Priority queuing
Health monitoring

features-guide.md - Platform features

Intelligent PDF processing
Multi-modal search
Materials catalog
Product management
Admin dashboard
RAG system
Real-time monitoring
Metadata management
Image management
Workspace isolation
Batch processing
Security features

deployment-guide.md - Production deployment

Deployment architecture
Frontend (Vercel)
Backend (Self-hosted)
Database (Supabase)
CI/CD pipeline
Secrets management
Monitoring & alerts
Rollback procedures

troubleshooting-guide.md - Common issues & solutions

Critical issues (API down, database, OOM)
Common issues (PDF processing, search, latency, auth)
Performance optimization
Support resources

product-discovery-architecture.md - Product discovery system

Products + Metadata architecture (inseparable)
Document entities (certificates, logos, specifications)
Factory/group identification for agentic queries
Product-document relationships
API endpoints for entity management
Future extensibility (marketing, bank statements)

🎓 Learning Paths

For New Developers

overview.md - Understand the platform
system-architecture.md - Learn the architecture
pdf-processing-pipeline.md - Understand the pipeline
job-queue-system.md - Learn async job processing
api-endpoints.md - Learn the API
deployment-guide.md - Understand deployment

For API Integration

api-endpoints.md - All endpoints
ai-models-guide.md - AI models used
database-schema-complete.md - Data structure
system-architecture.md - Authentication

For Operations

deployment-guide.md - Deployment process
job-queue-system.md - Job monitoring & recovery
troubleshooting-guide.md - Common issues
system-architecture.md - Monitoring
database-schema-complete.md - Backup strategy

For Product Managers

overview.md - Platform overview
features-guide.md - All features
pdf-processing-pipeline.md - Processing pipeline
ai-models-guide.md - AI capabilities

📊 Quick Reference

Key Numbers

5,000+ users in production
99.5%+ uptime SLA
12 AI models integrated
14 processing pipeline stages
74+ API endpoints
9 API categories
6 embedding types
200+ metafield types
95%+ product detection accuracy
85%+ search relevance
90%+ material recognition accuracy

Technology Stack

Frontend: React 18, TypeScript, Vite, Shadcn/ui, Vercel
Backend: FastAPI, Python 3.11, Uvicorn, self-hosted
Database: PostgreSQL 15, pgvector, Supabase
AI: Claude, GPT-4o, Llama, CLIP, LlamaIndex

🔗 External Resources

API Documentation:

Swagger UI: /docs
ReDoc: /redoc
OpenAPI Schema: /openapi.json

Dashboards:

Vercel: https://vercel.com/dashboard
Supabase: https://app.supabase.com
Server: SSH to v1api.materialshub.gr

Repositories:

Frontend: https://github.com/creativeghq/material-kai-vision-platform
Backend: https://github.com/creativeghq/mivaa-pdf-extractor

📝 Documentation Standards

All documentation follows these standards:

✅ Clear, concise language
✅ Code examples where applicable
✅ Structured with headers
✅ Links to related docs
✅ No task lists or planning documents
✅ Production-focused content
✅ Updated regularly

� Support

For questions or issues:

Check troubleshooting-guide.md
Review api-endpoints.md
Contact: support@materialkaivision.com
GitHub Issues: https://github.com/creativeghq/material-kai-vision-platform/issues

Last Updated: October 31, 2025 Version: 1.0.1 Status: Production Total Documentation: 11 comprehensive guides Total Lines: 6,000+ Coverage: 100% of platform features