Architecture - BigBossBoolingB/DashAIBrowser GitHub Wiki

Architecture 🏗️ Core Browser & AI Integration Architecture - The Quantum-Cognitive Foundation DashAIBrowser's foundation is a robust, performant browser core with a seamless, secure, and flexible integration layer for diverse AI APIs, making AI a first-class citizen of the browsing experience. This architecture is designed for Systematize for Scalability and Synchronize for Synergy, ensuring every component works in harmony. 1.1. Browser Core Architecture: The Intelligent Canvas

  • What: DashAIBrowser is built upon a Chromium/Blink fork, providing a battle-tested rendering engine, broad web compatibility, and extensive existing infrastructure. Our adaptation involves a modular design with enhanced Mojo IPC (Inter-Process Communication) interfaces. These provide secure, high-throughput communication channels between the browser process (handling UI, networking) and renderer processes (displaying web content). AI hooks are meticulously designed to provide granular, secure access to the Document Object Model (DOM) structure, network requests, and JavaScript execution contexts.
  • Why: Chromium's robust foundation ensures performance and compatibility. Deep integration via Mojo IPC provides the necessary secure and efficient pathways for AI to "see" and "act" on web content in real-time without compromising browser stability or security. This embodies "Know Your Core, Keep it Clear" by building on a solid, extensible foundation.
  • Synergies: This core architecture directly leverages V-Architect's virtualization concepts, enabling secure sandboxing of AI-driven browser processes or even isolated AI model execution within the browser environment. 1.2. AI Services Orchestration Layer (ASOL): The Central Intelligence Hub
  • What: ASOL is a secure, efficient, and flexible layer for integrating multiple external AI APIs, augmented with internal intelligence and privacy controls. It acts as the brain's executive function for AI interactions.
    • API Gateway: A central component, conceptually managed by an EchoSphere AI-vCPU Control_Core, responsible for managing API keys, enforcing rate limits, and intelligently routing AI requests to various providers.
    • Provider Adapters: Modular, pluggable interfaces for diverse external AI APIs (e.g., Google Gemini for high-quality text/multimodal, OpenAI API for general text generation, Anthropic Claude API for robust long-context summarization, Hugging Face Inference API for specialized domain models, Google Vision AI for image analysis, ElevenLabs Text-to-Speech for voice synthesis).
    • Data Minimization & Privacy-Preserving Proxy (DMP): The EchoSphere Minimizer Engine rigorously filters, anonymizes, or redacts Personally Identifiable Information (PII) from all data based on user consent (leveraging Privacy Protocol's core engine). This critical component acts as a GIGO Antidote for privacy, ensuring sensitive data never reaches external AI models.
    • EchoSphere Behavioral Orchestrator (EBO): This is the "Cognitive Decision Matrix" of the ASOL. It translates the internal state of the EchoSphere AI-vCPU and nuanced user input into precise action_request (what the LLM should do), interaction_goal (the desired outcome), and context_modifiers (tone, style) for the LLM.
    • EchoSphere Enrichment Engine: Takes the abstract, privacy-minimized prompt from the EBO and infuses it with relevant context, persona details, and knowledge. It draws this knowledge from the EchoSphere AI-vCPU's caches (e.g., Holographic Memory, L3 Cache), sculpting the LLM's final directive to be rich and accurate.
    • Intelligent AI Model Selection/Routing: An EchoSphere AI-vCPU Logic_Processor or Control_Core dynamically selects the optimal AI model/API for a given task based on real-time criteria like cost, speed, specific capability, and user preferences. This ensures the "highest statistically positive variable of best likely outcomes" for every AI query.
  • Why: This layered approach ensures seamless synergies across diverse AI capabilities, optimizing resource usage (cost and speed), and fundamentally enhancing security and privacy (the unseen code of trust). It's a direct application of "Systematize for Scalability, Synchronize for Synergy" for managing complex AI interactions.
  • How (Implementation Strategy): ASOL is implemented as a hybrid model: a core C++ component residing in the privileged browser process (for security and direct access to browser internals), communicating with potentially client-side WebAssembly/Rust modules for performance-critical or privacy-sensitive preprocessing (e.g., initial PII detection, leveraging Prometheus Protocol's integrity checks for data validation). Internal communication standardizes on Protobuf over gRPC for efficiency and strict typing. Next: AI-Powered Features