Knowledge Base Refresher

Software componentKnowledge & DataKnowledge & Dataarc:KnowledgeBaseRefresher

An update pipeline that ingests new or changed publications from authoritative sources into the knowledge base when they are released, keeping retrieval sources current.

Responsibility. Keeps knowledge base content temporally fresh.

Also known as: Automated update pipeline, Temporal freshness pipeline, Incremental indexer

Direct neighbourhood (hover for relationship types)

Relationships

invokes dependency

reads dependency

Knowledge Source System Ch5.8

writes dependency

Vector Index Store abstract Ch3.10 Ch5.8

is triggered by dynamic

Design guidance

SHOULD refresh content promptly after authoritative publication (e.g., earnings within hours, regulatory changes on publication, news the same business day).
SHOULD be triggered by detected hallucinations about specific entities so the underlying data gap is fixed.
SHOULD re-embed and re-insert only the chunks of changed documents rather than regenerating the entire index.
SHOULD apply knowledge graph updates with transactional semantics and consistency validation, since edge changes can cascade.
SHOULD follow an explicit update strategy that keeps RAG knowledge current (Ref6.01).

Quantitative guidance

As stated by the sources; verify before use.

E-commerce feedback loop achieved ongoing ~5% monthly hallucination reduction via catalog integration improvements (Ch3.10).

Classification

Patterns: Event-driven knowledge refreshDetection-to-update feedback loopIncremental indexing of changed documents onlyTransactional update semantics
Quality attributes: Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)Performance efficiency (ISO/IEC 25010)
Risks mitigated: Temporal misinformationKnowledge base staleness

Sources

Ch3.10: T. Nguyen, "Efficiency Metrics," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.10. ISBN: 9798244538229.
Ch5.8: T. Nguyen, "Semantic Memory," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.8. ISBN: 9798244538229.
Ref6.01: S. Schürch, "How to Make Your LLM More Accurate with RAG & Fine-Tuning," Towards Data Science, Mar. 11, 2025. [Online]. Available: https://towardsdatascience.com/how-to-make-your-llm-more-accurate-with-rag-fine-tuning/