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rag-governance

Skill2 starsChanged 5 months agoMITView on GitHub

Implement access-aware RAG in Enterprise RAG or VAP: hybrid retrieval, rerank, citations, AegisAI HITL for sensitive chunks. Use when tuning retrieval, adding Qdrant adapter, or wiring enterprise_rag_platform.

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SKILL.md

RAG Governance

Principles

  1. Access before ranking — filter by tenant/role before vector search
  2. Citations required — every answer cites chunk IDs
  3. HITL for sensitive — low confidence or PII-tagged chunks → gateway pause

Hybrid retrieval

query → embed → vector + BM25 → merge (hybrid_alpha) → rerank → top_k

VAP strategy adapter

  • enterprise_rag strategy in venkat-ai-platform delegates to Enterprise RAG API
  • Env: ENTERPRISE_RAG_API_URL

LoopForge tuning (ODAEU)

  • top_k, hybrid_alpha, rerank_threshold versioned in RAG config tree
  • Lessons stored when eval fails

Reference

  • enterprise_rag_platform — Qdrant adapter, OTLP export
  • loop-engine-agent-platform/src/loop_engine/rag/

Tests

  • Golden queries with expected chunk IDs
  • Access denial: user without role gets empty retrieval, not leaked chunks

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