move rhailib to herolib

rhailib/_archive/worker/docs/ARCHITECTURE.md

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+# Architecture of the `rhailib_worker` Crate
+
+The `rhailib_worker` crate implements a distributed task execution system for Rhai scripts, providing scalable, reliable script processing through Redis-based task queues. Workers are decoupled from contexts, allowing a single worker to process tasks for multiple contexts (circles).
+
+## Core Architecture
+
+```mermaid
+graph TD
+    A[Worker Process] --> B[Task Queue Processing]
+    A --> C[Script Execution Engine]
+    A --> D[Result Management]
+    
+    B --> B1[Redis Queue Monitoring]
+    B --> B2[Task Deserialization]
+    B --> B3[Priority Handling]
+    
+    C --> C1[Rhai Engine Integration]
+    C --> C2[Context Management]
+    C --> C3[Error Handling]
+    
+    D --> D1[Result Serialization]
+    D --> D2[Reply Queue Management]
+    D --> D3[Status Updates]
+```
+
+## Key Components
+
+### Task Processing Pipeline
+- **Queue Monitoring**: Continuous Redis queue polling for new tasks
+- **Task Execution**: Secure Rhai script execution with proper context
+- **Result Handling**: Comprehensive result and error management
+
+### Engine Integration
+- **Rhailib Engine**: Full integration with rhailib_engine for DSL access
+- **Context Injection**: Proper authentication and database context setup
+- **Security**: Isolated execution environment with access controls
+
+### Scalability Features
+- **Horizontal Scaling**: Multiple worker instances for load distribution
+- **Queue-based Architecture**: Reliable task distribution via Redis
+- **Fault Tolerance**: Robust error handling and recovery mechanisms
+
+## Dependencies
+
+- **Redis Integration**: Task queue management and communication
+- **Rhai Engine**: Script execution with full DSL capabilities
+- **Client Integration**: Shared data structures with rhai_dispatcher
+- **Heromodels**: Database and business logic integration
+- **Async Runtime**: Tokio for high-performance concurrent processing
+
+## Deployment Patterns
+
+Workers can be deployed as standalone processes, containerized services, or embedded components, providing flexibility for various deployment scenarios from development to production.