1 Introduction    
2 Background  
2.1 Large Language Models  
2.2 Vector Databases, the V-factor  
3 Synergizing LLMs with VecDBs: Enhancements and Innovations  
3.1 VecDB as an External Knowledge Base: Retrieval-Augmented Generation (RAG)  
3.2 VecDB as a Cost-effective Semantic Cache    
3.3 VecDB as a Reliable Memory of GPTs  
4 Expanding Horizons: RAG Advancements  
4.1 Multi-modality of RAG  
4.2 Retrieval Optimizations of RAG  
5 Discussion: Challenges and Future Work  
6 Conclusion