Open Arsenal

RAG infrastructure with enforced schemas.

RAGBase is a schema-enforced infrastructure framework for building Retrieval-Augmented Generation (RAG) systems with strict modular boundaries and predictable behavior. It is designed to eliminate the common instability found in traditional RAG pipelines by enforcing explicit contracts between every stage of the system, from data ingestion to final generation. Instead of relying on loosely connected components, RAGBase treats every module as a formally defined unit with clear inputs, outputs, and behavioral constraints.

At its core, RAGBase introduces a spec hygiene layer that validates every module against its declared schema before and during execution. This prevents silent failures, schema drift, and incompatible pipeline configurations. Each component must explicitly define its expected data structures, versioning, and operational constraints, ensuring that changes are intentional, traceable, and safe to deploy. This makes the system especially suited for production environments where reliability and reproducibility are critical.

RAGBase is fully modular and supports interchangeable components across the entire RAG pipeline. Developers can swap chunking strategies, embedding models, vector databases, retrieval methods, and LLM providers without breaking system integrity, as long as schema compatibility is preserved. This plugin-based design allows teams to iterate quickly while maintaining structural guarantees across the system.

Beyond modularity, RAGBase also includes drift detection and compatibility checking systems that monitor runtime behavior against declared specifications. This ensures that performance degradation, unexpected output shifts, or structural inconsistencies are identified early. Combined with versioned modules and strict interface definitions, RAGBase provides a foundation for building reliable, auditable, and scalable AI retrieval systems.