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Standout features that elevate RASS beyond a well-engineered RAG system into a genuinely novel, technically impressive platform. These items are high-effort, high-reward additions that demonstrate frontier engineering: adaptive retrieval, local model support, multi-modal understanding, and real-time collaboration.
No due date•5/5 issues closedTransform RASS from an engineering demonstration into a compelling, interview-ready knowledge discovery platform. This phase focuses on demo UX quality, comprehensive documentation, one-click setup, and the narrative that makes RASS stand out as a portfolio piece and enterprise showcase.
No due date•5/5 issues closedMake the performance, reliability, and quality of RASS fully visible and provable. This phase introduces distributed tracing, a metrics dashboard, an automated evaluation discipline, and a compelling performance story backed by real numbers. Engineers should be able to identify bottlenecks, prove retrieval quality, and demonstrate system health at a glance.
No due date•5/5 issues closedTransform RASS from a personal-use RAG tool into an enterprise-capable knowledge management platform. This phase introduces multi-tenant knowledge bases with workspace isolation, a proper RBAC permission model, data retention and compliance policies, and production-grade authentication options (SSO/SAML).
No due date•5/5 issues closedElevate RASS from a functional RAG proof-of-concept to a retrieval system with measurable, state-of-the-art quality. Introduce a modular retrieval pipeline, cross-encoder reranking, HyDE query expansion, structured citations, and an automated evaluation harness with tracked metrics.
No due date•5/5 issues closedTransform RASS into a fully async, ETL-provenance-aware ingestion platform with a proper document registry and the foundation for Bring-Your-Own Knowledge Base (BYO KB). These are the structural changes needed before adding higher-order retrieval or enterprise features.
No due date•5/5 issues closed