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June 3, 2026Open Access

Vector Annotation Databases: An Architecture for Auditable Semantic Retrieval

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Authors

ITIan Tepoot

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Overview

Proposed architecture improves data retrieval and auditability through deterministic semantic metadata.

Key Points

  • This research aims to address the limitations of current vector databases by introducing the Vector Annotation Database (VAD) architecture.
  • Proposes a two-stage retrieval pattern using Boolean narrowing and embedding-based similarity refinement.
  • Describes the audit block for documentation and regulatory compliance in retrieval processes.
  • Surveys existing vector database products and identifies the gap in current technologies' treatment of embeddings.
  • VAD architecture enables stable re-indexing with persistent relational mappings.
  • Facilitates multi-embedding concurrency and operational fallback.
  • Independently functions the retrieval surface from the embedding layer, enhancing auditability.

Cite This Study

Ian Tepoot (2026) studied this question.

synapsesocial.com/papers/6a1fc756dee9eb8c0dce8379https://doi.org/10.5281/zenodo.20498724
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