GenAI - RAG part Reliable and Incremental RAG Ingestion Pipelines for Enterprise Knowledge Systems

Abstract

In creating reliable and context-proper retrieval mechanism from dynamically changing organization repositories, Enterprise Retrieval-Augmented Generation (RAG) systems rely increasingly on mechanisms for ongoing ingestion of knowledge. This is often the case with the existing ingestion pipelines, which are semantically inconsistent, repeatedly regenerating vectors, leniently looking up vectors, and complexifying synchronizations when your repository is updated incrementally. The restrictions lower the likelihood of retrievals being accurate and impact the contextual accuracy of enterprise generative AI systems. This paper presents a new trustworthy and incremental enterprise knowledge ingestion framework called Evidence-Coupled Knowledge Resonance Architecture (EKRA) to tackle these issues. The proposed EKRA framework provides a semantic stabilization environment that can guarantee the continuation of context in the repository during the changes of the same. The framework incorporates semantic validation procedures, evidence retention, prioritized dependency reassessment and synchronization that allows for the retrieval consistency without the need to recreate full vector repositories. Moreover, the proposed architecture clearly has the property of fewest semantic disruptions, by maintaining a structure of relationships between knowledge that reflect their meanings, while preserving the evidence links, they carry, on incremental additions to the knowledge. Another framework enhancement is adaptive synchronization of impacted semantic areas within enterprise retrieval structures, which enhances the continuity of retrieval. Through experiments, it has been shown that the proposed EKRA framework is significantly more successful in achieving retrieval reliability, less synchronization difference, better semantic preservation, and can also better ensure incremental ingestion stability than current enterprise RAG ingestion methods. The proposed architecture will offer a flexible and scalable solution for dynamic enterprise knowledge space. The suggested EKRA framework obtained a total enterprise retrieval accuracy of 96.8% in incremental RAG knowledge ingestion procedures.