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| Main Authors: | , |
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| Formato: | Recurso digital |
| Idioma: | |
| Publicado em: |
Zenodo
2025
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| Acesso em linha: | https://doi.org/10.5281/zenodo.17823264 |
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Sumário:
- The rapid proliferation of unstructured data has accentuated the need for advanced information retrieval systems capable of understanding and leveraging semantic relationships. Traditional vector databases, while effective for similarity search, often lack the dynamic adaptability and deep contextual understanding required for truly intelligent semantic retrieval. This paper introduces a novel framework for Intelligent Vector Databases (IVDBs) designed to be self-optimizing and context-aware. Our proposed framework integrates advanced natural language understanding (NLU), machine learning agents, and continuous feedback loops to dynamically refine vector embeddings, optimize indexing strategies, and enhance retrieval relevance based on evolving user intent and data context. We detail the architectural components, including a multi-modal embedding generation module, a contextualization engine that infers implicit user and data context, and a self-optimization agent that employs reinforcement learning principles to adapt the database's internal parameters and retrieval algorithms. The framework moves beyond static similarity search, offering a proactive approach to information access that improves accuracy and efficiency over time. We discuss the theoretical underpinnings, potential challenges, and future directions for IVDBs, positing them as a crucial component for next-generation AI applications requiring sophisticated semantic understanding and adaptive intelligence.