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ForgeFluid

Backend

The backend architecture of the ForgeFluid Platform is built as a microservices-based system, providing scalability, flexibility, and maintainability. Each service is designed to handle a specific function, allowing the system to grow and adapt without impacting other components.

Key Features

  • Microservices Architecture:

    • Each service operates independently, communicating via APIs or messaging queues.

    • Services can be developed in different programming languages or frameworks, depending on the task.

  • Primary Framework:

    • Node.js: Used for core services requiring robust modularity and TypeScript support.

    • Python: Employed for AI-heavy operations or data processing services.

  • Communication:

    • RESTful APIs: For synchronous communication between services and external clients.

    • Message Queues: Asynchronous communication via Redis or RabbitMQ for decoupling tasks and ensuring resilience.

Services

  1. AI Orchestration Service:

  • Manages interactions with local AI models or third-party AI providers (OpenAI, Anthropic, etc).

  • Handles task preprocessing, dispatching, and result aggregation.

  1. User Management Service:

  • Manages user authentication, including traditional credentials and account abstraction for Web3 wallets.

  • Tracks user tiers, permissions, and $FORGE token holdings.

  1. Payment Service:

  • Integrates fiat payments (e.g., Stripe) and Web3 payments for seamless transactions.

  • Handles $FORGE token utilities like staking, task prioritization, and fee reductions.

  1. Task Queueing Service:

  • Prioritizes tasks based on user tier and system load.

  • Integrates closely with premium features to offer reduced latency for high-tier users.

  1. Project Management Service:

  • Manages project metadata, including file paths, versions, and sandbox status.

  • Interfaces with the storage layer to ensure reliable persistence and retrieval.

Database Systems

  • Relational Database:

    • PostgreSQL serves as the primary data store for structured information like user profiles, transaction logs, and project metadata.

  • In-Memory Data Store:

    • Redis supports fast data access for session management, caching, and queueing metadata.

Key Advantages

  • Flexibility: Services can evolve independently, incorporating the best-suited frameworks or languages for their purposes.

  • Resilience: Failures in one service do not cascade across the system, improving reliability.

  • Scalability: Each service can be scaled individually to meet demand, optimizing resource utilization.