Core Architecture The system is built on three primary layers: Distributed Intelligence Layer Implements triple redundancy using three independent LLM nodes Each node runs a quantized, space-optimized language model Independent RAG (Retrieval Augmented Generation) modules per node Isolated memory and processing resources Individual vector databases for context retrieval Knowledge Management Layer Consensus Layer Advanced NLP-based response similarity analysis Majority voting with semantic understanding Automatic anomaly detection and filtering Graceful degradation under node failures Key Innovations Semantic Consensus Protocol Novel approach to comparing LLM outputs Handles natural language variance Maintains reliability under partial failures Lightweight but capable inference engine Distributed RAG Implementation Synchronized vector databases Consistent knowledge access Redundant information retrieval Failure Recovery Automatic node health monitoring Self-healing capabilities Graceful performance degradation Zero-downtime recovery Implementation Details Docker-based containerization for isolation gRPC for high-performance inter-node communication FAISS for efficient vector similarity search Sentence-BERT for response embedding Custom consensus protocols for LLM output validation The system is specifically designed to operate in space environments where traditional AI systems would fail due to radiation effects, resource constraints, or hardware failures. It provides mission-critical reliability while maintaining the advanced capabilities of modern LLMs.