Summary

In this chapter, you explored the pivotal role of vector search in enhancing AI-powered systems. The key takeaway is that vector search plays a vital role in AI applications, addressing the challenge of efficient search as unstructured and multimodal datasets expand. It benefits image recognition, NLP, and recommendation systems.

MongoDB Atlas is used to demonstrate vector search implementation using its flexible schema and vector indexing capabilities. You were able to build a RAG framework for solving QA use cases that combines retrieval and generation models, with a simple RAG system utilizing pre-trained language models and embedding models from OpenAI. You also learned how to build an advanced RAG system that employs iterative refinement and sophisticated retrieval algorithms with the help of LLMs for building a recommendation system for the fashion industry. With these insights, you can now build efficient AI applications for any domain or industry.

In the next...