Architecture Overview

BotServer follows a modular architecture designed for scalability, maintainability, and extensibility. Each module handles specific responsibilities and communicates through well-defined interfaces. This chapter provides a comprehensive tour of the system architecture and how components work together.

Core Architecture

The architecture diagrams below illustrate the major components and their relationships.

Data Flow Architecture

System Architecture

Module Dependency Graph

Module Organization

The codebase is organized into modules that group related functionality together. Each module has clear responsibilities and well-defined interfaces with other modules.

Data Flow Through Modules

Core Modules

The auth/ module handles authentication and authorization throughout the system. It manages user accounts and group memberships, implements role-based access control (RBAC), handles JWT token generation and validation, provides OAuth integration for external identity providers, and supports two-factor authentication for enhanced security.

The automation/ module provides the workflow automation engine. It handles process automation for complex multi-step operations, manages scheduled tasks that run at specified intervals, enables event-driven automation that responds to system events, orchestrates workflows across multiple services, and integrates with external systems for extended capabilities.

The basic/ module implements the BASIC dialect interpreter and runtime environment. It provides keyword implementations for all BASIC commands, handles script compilation from source to executable form, manages variables and their scopes, implements flow control structures like loops and conditionals, integrates with external tools the LLM can invoke, and provides comprehensive error handling with helpful messages.

The bootstrap/ module handles system initialization and startup procedures. It verifies all required components are available, sequences service startup in the correct order, runs database migrations to update schema, deploys default templates for new installations, performs health checks to ensure system readiness, and loads configuration from files and environment variables.

The bot/ module manages bot instances and their interactions. It handles the bot lifecycle including creation, mounting, and unmounting. It processes conversations between users and bots, handles user input and routes it appropriately, coordinates response generation from various sources, manages multi-bot deployments on a single server, and ensures session isolation between different users and bots.

Communication Modules

The channels/ module provides multi-channel messaging adapters that allow bots to communicate across different platforms. Supported channels include the web interface for browser-based chat, WhatsApp Business API for messaging app integration, Microsoft Teams for enterprise collaboration, Slack for team communication, Instagram for social media engagement, SMS for text messaging, and voice for telephone interactions.

The meet/ module enables real-time communication features. It provides video conferencing capabilities for face-to-face meetings, voice calling for audio-only communication, screen sharing for presentations and collaboration, recording functionality for meeting archives, transcription services for accessibility, and meeting scheduling integration with calendars.

The web_server/ module implements the HTTP server and web interface. It serves static files for the UI, handles WebSocket connections for real-time chat, routes REST API requests to appropriate handlers, manages CORS policies for browser security, and processes requests and responses throughout the system.

AI and Knowledge Modules

The llm/ module provides large language model integration. It handles model selection based on configuration and requirements, formats prompts according to model expectations, manages token counting and context limits, streams responses for real-time display, tracks API costs for budgeting, and implements model fallbacks when primary providers are unavailable.

The llm_models/ module contains specific implementations for different model providers. OpenAI integration supports GPT-5 and o3 models. Anthropic integration provides access to Claude Sonnet 4.5 and Opus 4.5 models. Google integration enables Gemini model usage. Meta integration supports Llama models for local deployment. Local model support allows self-hosted inference. Custom model implementations can be added for specialized providers.

The prompt_manager/ module provides centralized prompt management capabilities. It maintains prompt templates for consistent interactions, handles variable substitution in prompts, optimizes prompts for specific models, supports version control of prompt changes, enables A/B testing of different approaches, and tracks prompt performance metrics.

The context/ module manages conversation context throughout interactions. It optimizes the context window to fit within model limits, manages conversation history retention, compresses context when necessary to preserve information, filters context for relevance to current queries, and tracks multi-turn conversations across messages.

Storage and Data Modules

The drive/ module handles file and document management. It supports file upload and download operations, processes documents for indexing and search, maintains version control of files, manages sharing permissions between users, enforces quota limits on storage usage, and indexes content for search functionality.

The drive_monitor/ module provides storage monitoring and synchronization. It detects changes to files for automatic processing, synchronizes content across storage locations, resolves conflicts when multiple changes occur, manages backups of important data, and provides analytics on storage usage patterns.

The package_manager/ module handles bot package management. It loads packages from storage into the runtime, resolves dependencies between packages, manages package versions and updates, supports hot reload of changed packages without restart, and validates packages before deployment.

Processing Modules

The engines/ module contains various processing engines for different tasks. The rule engine evaluates business rules and conditions. The workflow engine orchestrates complex processes. The event processor handles system and external events. The message queue manages asynchronous communication. The job scheduler executes background tasks.

The calendar_engine/ module provides calendar and scheduling functionality. It manages events and appointments, checks availability for scheduling, coordinates meetings between participants, sends reminders for upcoming events, and handles timezone conversions correctly.

The task_engine/ module implements the task management system. It creates tasks from user requests or automation, assigns tasks to appropriate parties, tracks task status through completion, manages dependencies between tasks, and sends notifications about task updates.

The email/ module provides email integration capabilities. It sends email via SMTP protocols, receives email via IMAP connections, manages email templates for consistent formatting, tracks email delivery and opens, and handles bounced emails appropriately.

Utility Modules

The session/ module manages user sessions throughout their interactions. It creates sessions for new users, persists session state to storage, enforces session timeouts for security, handles concurrent sessions from the same user, and recovers sessions after server restarts.

The config/ module handles configuration management. It loads configuration from files and databases, reads environment variables for deployment settings, supports hot reload of configuration changes, validates configuration values, and provides sensible defaults for optional settings.

The shared/ module contains shared utilities and models used across the system. It defines database models for persistence, provides common types used throughout the codebase, implements helper functions for repeated tasks, centralizes constants and magic values, and defines error types for consistent error handling.

The compliance/ module implements regulatory compliance features. It ensures GDPR compliance for data protection, enforces data retention policies, maintains comprehensive audit logging, provides privacy controls for sensitive data, and manages user consent records.

The nvidia/ module provides GPU acceleration support for local model inference. It integrates with CUDA for GPU computation, runs model inference on GPU hardware, batches requests for efficient processing, and optimizes performance for available hardware.

The ui_tree/ module manages UI component trees for the interface. It maintains a virtual DOM for efficient updates, manages component lifecycles, handles state across components, processes events from user interactions, and optimizes rendering performance.

The web_automation/ module provides web scraping and automation capabilities. It automates browser interactions for data gathering, extracts content from web pages, fills forms programmatically, captures screenshots for documentation, and monitors pages for changes.

Data Flow

Request Processing Pipeline

When a user sends a message, it flows through several processing stages. First, the Channel Adapter receives the user input from the appropriate platform. The Session Manager then identifies the existing session or creates a new one. The Context Manager loads conversation history and relevant context. The BASIC Interpreter executes the dialog script that handles the message. If needed, LLM Integration processes natural language to understand intent. The Knowledge Base provides relevant information from loaded documents. The Response Generator formats the output for the user. Finally, the Channel Adapter delivers the response back through the original platform.

Storage Architecture

The primary database uses PostgreSQL to store structured data including user accounts, bot configurations, session data, conversation history, and system metadata. The Diesel ORM provides type-safe database access.

Object storage using Drive provides S3-compatible storage for files including user uploads, processed documents, media files, system backups, and application logs.

The cache layer provides fast access to frequently needed data. It stores session information for quick retrieval, caches commonly accessed data, implements rate limiting counters, holds temporary processing data, and supports pub/sub messaging between components.

The vector database uses Qdrant to store document embeddings for semantic search. It maintains the semantic search index, stores knowledge base vectors, and performs similarity matching for relevant content retrieval.

Security Architecture

Authentication Flow

The authentication process follows a secure sequence. Users provide credentials through the login interface. The auth module validates credentials against stored records. Upon successful validation, a JWT token is issued. Each subsequent request includes this token for verification. A session is established to maintain state. Permissions are checked before any operation is performed.

Data Protection

Data protection operates at multiple layers. Encryption at rest protects data stored in the database and files. Encryption in transit using TLS/SSL protects data during transmission. Sensitive data masking prevents exposure in logs and displays. PII detection identifies and protects personal information. Secure key management protects cryptographic keys from exposure.

Access Control

Access control mechanisms ensure appropriate authorization. Role-based permissions determine what actions users can perform. Resource-level authorization controls access to specific objects. API rate limiting prevents abuse and ensures fair usage. IP allowlisting restricts access to known addresses when configured. Comprehensive audit logging records all significant actions.

Deployment Architecture

Container Structure

Production deployments typically use containers for isolation and portability. The main application container runs the BotServer binary. PostgreSQL runs in a separate database container. Drive storage uses an S3-compatible container like MinIO. The cache layer uses Valkey in its own container. Qdrant provides vector database functionality in another container. Nginx serves as a reverse proxy for external traffic.

Scaling Strategy

The system scales to handle increased load through several mechanisms. Horizontal scaling adds more web server instances behind a load balancer. Read replicas for the database handle query load. Distributed cache spreads session data across nodes. Load balancing distributes requests across available instances. Auto-scaling policies adjust capacity based on demand.

High Availability

High availability configurations ensure continuous operation. Multi-zone deployment protects against facility failures. Database replication maintains copies of data. Storage redundancy prevents data loss. Health monitoring detects problems quickly. Automatic failover redirects traffic when components fail.

Performance Optimization

Caching Strategy

Caching improves response times throughout the system. Response caching stores generated responses for reuse. Query result caching avoids repeated database queries. Static asset caching serves files directly from cache. API response caching stores external API results. Knowledge base caching keeps frequently accessed content in memory.

Async Processing

Asynchronous processing improves throughput and responsiveness. Background jobs handle long-running tasks without blocking. Message queues decouple producers from consumers. Event-driven architecture responds to changes efficiently. Non-blocking I/O maximizes resource utilization. Worker pools distribute processing across threads.

Resource Management

Careful resource management ensures efficient operation. Connection pooling reuses database connections. Memory management prevents leaks and excessive usage. Token optimization minimizes LLM API costs. Query optimization reduces database load. Lazy loading defers work until necessary.

Monitoring and Observability

Metrics Collection

Comprehensive metrics provide visibility into system behavior. System metrics track CPU, memory, and disk usage. Application metrics measure request rates and latencies. Business metrics track user engagement and outcomes. User analytics show usage patterns. Performance tracking identifies bottlenecks.

Logging

Structured logging supports debugging and analysis. All logs use consistent structured formats. Log aggregation collects logs from all components. Error tracking captures and groups exceptions. Audit trails record security-relevant events. Debug logging provides detailed information when needed.

Health Checks

Health checks ensure system availability and readiness. Liveness probes confirm the application is running. Readiness probes verify the application can serve requests. Dependency checks validate external services are available. Performance monitoring tracks response times. The alert system notifies operators of problems.

Extension Points

Plugin System

The system provides extension points for customization. Custom keywords extend the BASIC language with new capabilities. External tools integrate third-party services. API integrations connect to external systems. Custom channels add support for new platforms. Model providers integrate additional LLM services.

Webhook Support

Webhooks enable event-driven integrations. Incoming webhooks accept notifications from external systems. Outgoing webhooks notify external systems of events. Event subscriptions define what events trigger webhooks. Callback handling processes webhook responses. Retry mechanisms ensure delivery despite transient failures.

API Integration

Multiple API protocols support different integration needs. The REST API provides standard HTTP access. GraphQL support is planned for flexible queries. WebSocket connections enable real-time bidirectional communication. gRPC support is planned for high-performance integrations. OpenAPI specifications document all endpoints.

Development Workflow

Local Development

Setting up a local development environment follows a straightforward process. First, clone the repository to your machine. Install required dependencies using Cargo and system packages. Configure environment variables for local services. Run database migrations to set up the schema. Start the required services like PostgreSQL and cache. Load default templates for testing.

Testing Strategy

Testing ensures code quality at multiple levels. Unit tests verify individual functions and methods. Integration tests check interactions between components. End-to-end tests validate complete user workflows. Load testing measures performance under stress. Security testing identifies vulnerabilities.

CI/CD Pipeline

Continuous integration and deployment automates quality assurance. Automated testing runs on every commit. Code quality checks enforce standards. Security scanning identifies known vulnerabilities. The build process produces deployable artifacts. Deployment automation pushes releases to environments.

Future Architecture Plans

Planned Enhancements

Future development will expand system capabilities. Microservices migration will enable independent scaling of components. Kubernetes native deployment will simplify orchestration. Multi-region support will improve global performance. Edge deployment will reduce latency for distributed users. Serverless functions will enable elastic scaling for specific workloads.

Performance Goals

Performance targets guide optimization efforts. Response times should be sub-100ms for typical requests. The system should support 10,000 or more concurrent users. Uptime should reach 99.99% for production deployments. Elastic scaling should handle traffic spikes automatically. Global CDN integration should improve worldwide access times.