Vector Databases in Modern Applications: Real-Time Search, Recommendations, and Retrieval-Augmented Generation (RAG)
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I4P113Keywords:
Vector Database, Approximate Nearest Neighbor (ANN), Semantic Search, Recommendation Systems, Retrieval-Augmented Generation (RAG), FAISS, Milvus, Pinecone, Weaviate, High-Dimensional Indexing, Real-Time AI ApplicationsAbstract
The demand for efficient indexing, searching and retrieval of high-dimensional data in real-time became a necessity in the days and age of heavy data creation and usage because this is a key prerequisite in many applications that are AI-powered. The limitation of traditional databases is that they struggle with vector-based operations due to limitations in structure and indexing. The use of vector databases is relatively recent, and it can support similarity search over high-dimensional vectors, thus making it suitable for applications that are primarily characterized by real-time recommendations, semantic search, and Retrieval-Augmented Generation (RAG) in generative AI systems. The present paper focuses on the design principles, architectures, and practical aspects of using vector databases, emphasising their role in contemporary data infrastructure. We analyze the obstacles brought by working with vector-based data, such as Approximate Nearest Neighbor (ANN) search, scalability, latency, and compatibility with Large Language Models (LLMs). We review some of the most popular vector databases, including FAISS, Pinecone, Weaviate, Milvus, and Qdrant, with the help of a full literature survey. It is suggested that a scalable vector search system, capable of supporting real-time recommendations and RAG pipelines, be implemented using a detailed methodology. Experimental data have revealed trade-offs among latency, accuracy, and throughput under various configuration conditions. This paper can be viewed as a systematic description of the role of vector databases in real-world deployments, providing insight into best practices and future avenues of research
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