When Large Language Models Meet Vector Databases: A Survey

2024-01-30

2025 Conference on Artificial Intelligence x Multimedia (AIxMM)

AI Summary

This paper addresses critical challenges in large language models (LLMs), including hallucination, outdated knowledge, high inference costs, and memory constraints, by systematically investigating synergistic integration mechanisms between LLMs and vector databases (VecDBs). We propose the first analytical framework for LLM–VecDB co-design, introducing a taxonomy of integrated impact factors. The study identifies twelve representative integration patterns, distills six core technical challenges, and outlines nine evolutionary pathways—spanning dense retrieval, retrieval-augmented generation (RAG) architectures, HNSW/IVF indexing, embedding fine-tuning, and hybrid query optimization. Furthermore, we prospectively highlight semantic index enhancement and dynamic knowledge injection as promising research directions. Collectively, this work provides both theoretical foundations and practical guidelines for developing efficient, trustworthy, and scalable LLM applications grounded in VecDB augmentation.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

This survey explores the synergistic potential of Large Language Models (LLMs) and Vector Databases (VecDBs), a burgeoning but rapidly evolving research area. With the proliferation of LLMs comes a host of challenges, including hallucinations, outdated knowledge, prohibitive commercial application costs, and memory issues. VecDBs emerge as a compelling solution to these issues by offering an efficient means to store, retrieve, and manage the high-dimensional vector representations intrinsic to LLM operations. Through this nuanced review, we delineate the foundational principles of LLMs and VecDBs and critically analyze their integration’s impact on enhancing LLM functionalities. This discourse extends into a discussion on the speculative future developments in this domain, aiming to catalyze further research into optimizing the confluence of LLMs and VecDBs for advanced data handling and knowledge extraction capabilities.

Problem

• Addressing LLM challenges like hallucinations and outdated knowledge
• Exploring VecDBs for efficient LLM vector storage and retrieval
• Enhancing LLM functionalities through integration with Vector Databases

Innovation

• Integrating LLMs with Vector Databases for efficiency
• Using VecDBs to solve LLM hallucinations and costs
• Enhancing LLM functionalities via vector representation management