Super Valera is an AI-powered Telegram bot for automotive service automation, built with Ruby on Rails 8.1 and integrated with advanced AI capabilities through the ruby_llm gem.
- Backend Framework: Ruby on Rails 8.1
- AI Integration: ruby_llm gem with DeepSeek
- Database: PostgreSQL with Solid Queue/Cache/Cable
- Bot Platform: Telegram Bot API
- Configuration: anyway_config for centralized settings
- Analytics: Custom event tracking system
- Main README - Project overview and setup
- Development Guide - Developer setup and workflows
- Tech Stack - Complete technology overview
- Architecture Decisions - System design rationale
- Domain Models - Core domain entities
- Bounded Contexts - Domain boundaries
- Error Handling Patterns - Centralized error management
- Requirements Overview - Feature documentation
- User Stories - Detailed user scenarios
- Technical Specifications - Implementation specs
- API Documentation - Webhook API
- Development Setup - Environment configuration
- Testing Guide - Test patterns and standards
- YARD Documentation - Code documentation
- Prompt Testing - AI prompt validation
- Analytics System - Event tracking implementation
- Metabase Setup - Analytics dashboard
- Business Metrics - KPI definitions
- Deployment Overview - Production deployment
- Docker Configuration - Container setup
- Monitoring Setup - System monitoring
- Product Constitution - Product requirements
- SaaS Overview - Business model
- Competitor Analysis - Market analysis
- Business Value - Value proposition
- Gems Overview - Key gem dependencies
- ruby_llm Integration - AI framework usage
- Telegram Bot Framework - Bot implementation
- VCR Testing - HTTP testing patterns
- Glossary - Domain terminology
- Terminology - Standardized terms
- FLOW Documentation - Development workflow
- Product Examples - Sample data
- Start with README.md for project overview
- Follow Development Setup for environment configuration
- Review Architecture Decisions for system understanding
- Study Error Handling Patterns for code standards
- Review Product Constitution for requirements
- Check User Stories for feature specifications
- Monitor Business Metrics for KPI tracking
- Understand SaaS Model for business context
- Follow Deployment Guide for production setup
- Configure Monitoring for system health
- Set up Analytics Dashboard for data visualization
- Review Docker Configuration for container management
- Study Testing Guide for test patterns
- Use Prompt Testing Guide for AI validation
- Reference VCR Testing for HTTP testing
- Check User Stories for acceptance criteria
| Status | Meaning |
|---|---|
| ✅ Current | Up-to-date and production-ready |
| 🔄 In Progress | Being actively developed |
| Requires updates or validation | |
| 📋 Planned | Scheduled for creation |
Telegram Webhook → Chat Model → ruby_llm → AI Response
↓ ↓ ↓
Analytics Tracking → Message Store → Tool Calls → Booking Creation
User Message → Webhook Controller → LLM Processing → Tool Execution → Response Generation
↓ ↓ ↓ ↓ ↓
Analytics Event → Message Storage → Tool Call Record → Booking Record → Telegram Response
Chat System → ruby_llm → DeepSeek API
Booking System → Active Record → PostgreSQL
Analytics → Event Tracking → Metabase Dashboard
- Models: 7 core ActiveRecord models
- Controllers: 2 main controllers (webhook, application)
- Services: 9 business logic services
- Jobs: 3 background job processors
- Tools: 2 AI tool implementations
- Unit Tests: Model and service testing
- Integration Tests: Full workflow testing
- VCR Tests: External API mocking
- Performance Tests: Analytics system validation
- API Docs: Complete webhook documentation
- Architecture: Design rationale documented
- Development: Comprehensive setup guides
- Business: Product requirements clearly defined
Last Updated: 2025-10-27 Documentation Version: 3.0 Maintained by: Danil Pismenny