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You are viewing v0.1.3 documentation. This is the current stable release.

Welcome to RAG Control

rag_control is an enterprise-grade governance, security, execution control, and observability layer for Retrieval-Augmented Generation (RAG) systems.

What is rag_control?​

RAG systems combine powerful retrieval and generation capabilities but introduce governance, security, and compliance challenges. rag_control addresses these with:

  • Policy-Based Generation: Define and enforce generation policies (temperature, output length, citation requirements, external knowledge restrictions)
  • Runtime Enforcement: Validate responses against policies before returning them to users
  • Governance & Security: Apply organization-level rules, role-based access control, and data classification filters
  • Comprehensive Audit Logging: Track all requests, decisions, and denials for compliance
  • Distributed Tracing: Understand execution flow and identify performance bottlenecks
  • Metrics & Observability: 18+ metrics covering throughput, latency, quality, costs, and errors

Key Features​

🛡️ Policy Enforcement​

  • Define multiple policies with different strictness levels
  • Control temperature, max output tokens, reasoning depth
  • Enforce citation requirements and validation
  • Prevent external knowledge generation
  • Apply context-aware fallback strategies

🔐 Governance & Security​

  • Organization-level access control
  • Retrieval filtering by data classification and metadata
  • User context validation
  • Policy resolution based on org rules and data sensitivity

📊 Observability​

  • Audit Logging: Full request/response lifecycle tracking
  • Distributed Tracing: OpenTelemetry integration for flow analysis
  • Metrics: Token usage, latency, error rates, policy decisions

🚀 Production Ready​

  • Exception-swallowing pattern ensures governance failures never break request flow
  • Comprehensive error handling with custom exception types
  • Type-safe with mypy strict mode compliance
  • 100% code coverage with extensive test suite

Support & Community​

Support the Project​

If you find rag_control useful, please consider ⭐ starring the repository on GitHub to show your support! Your star helps us reach more developers and organizations building secure RAG systems.

Built by RetrievalLabs — Enterprise AI Governance and Security