THE ROLE OF VECTOR DATABASES IN THE ERA OF GENERATIVE AI
Merita KASA HALILI, Festim HALILI, Sarah KADRIU, Viola MAZLAMI
Abstract
The rise of modern technologies has created a need for systems that manage high-dimensional data efficiently. In this context, vector databases have emerged as a key solution, especially in applications involving generative artificial intelligence (GenAI). These databases allow fast and intelligent retrieval of relevant information by storing data as numerical vectors. However, deploying these models in real-world business contexts reveals critical limitations, notably a lack of long-term memory and reliance on a static knowledge base. To address these challenges, we explore the role of vector databases in augmenting LLMs via high-dimensional semantic vector search techniques. In particular, we posit that vector databases can act as external memory and dynamic knowledge bases for LLMs, allowing chatbots to retrieve, in real time, relevant historical interactions and domain-specific information on demand. This capability ensures that the model’s responses remain contextually aware and factually grounded. The motivation for this work stems from the need for enhanced customer service, personalized interactions, and efficient knowledge management processes in enterprise AI deployments. The proposed framework integrates LLM-driven text generation with real-time vector database queries (a form of retrieval-augmented generation) to ground outputs in relevant data and maintain an extended conversational context. We will evaluate this approach through case studies in customer support and organizational knowledge management systems, assessing improvements in response accuracy, contextual coherence, and user satisfaction. By clearly articulating the synergy between vector databases and generative AI, this research aims to contribute a conceptual framework and empirical insights, as well as practical guidance for enterprise deployment at scale. We expect to demonstrate that such integration can significantly improve chatbot performance, enabling more reliable, context-aware, and tailored interactions, and thereby advancing the state of the art in AI-driven business communication.
Pages: 174 - 183