Rust & Golang Integration

Why Rust/Golang?

Why Rust/Golang?

• Rust brings safety, concurrency without data races, and performance. It's ideal for the core engine where reliability is crucial.

• Golang complements with its simplicity, excellent for building networking components and managing concurrent operations.

Dual Language Setup

• Compilation: Thor uses Cargo for Rust and Go's build system for Golang, with a custom build script to manage the integration.

• Interoperability: We utilize cgo for Golang to call Rust functions and the cbindgen tool to generate C headers from Rust for Golang to use.

Cross-compilation Instructions

• Setup for cross-compilation environments for both languages.

• Use of Docker containers or build systems like cross for Rust to ensure consistent builds across different architectures.

Core Modules

Core Engine

• Processing Pipeline: Describes how data flows from tingestion to processing, through various AI models, to output.

• Memory Management: Techniques for efficient use of memory, especially when dealing with large datasets or complex state management.

llmprovider.rsCopy

‌type Provider interface {
    GenerateCompletion(context.Context, CompletionRequest) (string, error)
    GenerateJSON(context.Context, JSONRequest, interface{}) error
    EmbedText(context.Context, string) ([]float32, error)
}

Copy

managers.rs
type Manager interface {
    GetID() ManagerID
    GetDependencies() []ManagerID
    Process(state *state.State) error
    PostProcess(state *state.State) error
    Context(state *state.State) ([]state.StateData, error)
    Store(fragment *db.Fragment) error
    StartBackgroundProcesses()
    StopBackgroundProcesses()
    RegisterEventHandler(callback EventCallbackFunc)
    triggerEvent(eventData EventData)
}

API Layer

• RESTful Services: Define endpoints for API interaction, authentication, and rate limiting.

• gRPC Integration: For scenarios requiring high performance and streaming capabilities.

Golang API Example

func (s *Server) ProcessData(ctx context.Context, req *pb.Request) (*pb.Response, error) {
    // Implement the business logic here
    return &pb.Response{/*...*/}, nil
}

Data Models

• Schema Definitions: How data is structured for both internal use and API communication.

• Serialization/Deserialization: Efficient methods for converting data between formats for processing and storage.

Building Your First AgentAgent Lifecycle

• Initialization: Setting up the agent with necessary configurations and models.

• Operation: How the agent interacts, learns, and responds to stimuli.

• Termination: Graceful shutdown and state preservation.

Example Walkthrough

• Agent Definition: Define what the agent does, possibly focusing on interaction with Twitter.

• Setup: Guide through the setup, including environment variables, API keys, etc.

• Implementation: Code snippets showing how to use Thor's components to create a simple Twitter bot for sentiment analysis.

Copy

thor.rs
use thor_framework::agent::Agent;

struct MyAgent;

impl Agent for MyAgent {
    fn init(&self) {
        // Initialization logic
    }

    fn run(&self) -> Result<(), Box<dyn std::error::Error>> {
        // Main execution loop
        Ok(())
    }
}

Customization

• Discuss how developers can tweak or extend the agent's behavior, perhaps adding new Twitter API calls or integrating with other data sources.

By understanding and leveraging the Thor Framework, developers can craft AI agents that are not only functional but also scalable and secure. Thor's architecture is designed to support a diverse range of applications, from simple social media bots to complex, multi-modal AI systems that can transform how we interact with digital environments.