Summary

RAG for generative AI relies on two main components: a retriever and a generator. The retriever processes data and defines a search method, such as fetching labeled documents with keywords—the generator’s input, an LLM, benefits from augmented information when producing sequences. We went through the three main configurations of the RAG framework: naïve RAG, which accesses datasets through keywords and other entry-level search methods; advanced RAG, which introduces embeddings and indexes to improve the search methods; and modular RAG, which can combine naïve and advanced RAG as well as other ML methods.

The RAG framework relies on datasets that can contain dynamic data. A generative AI model relies on parametric data through its weights. These two approaches are not mutually exclusive. If the RAG datasets become too cumbersome, fine-tuning can prove useful. When fine-tuned models cannot respond to everyday information, RAG can come in handy. RAG frameworks also rely heavily on the ecosystem that provides the critical functionality to make the systems work. We went through the main components of the RAG ecosystem, from the retriever to the generator, for which the trainer is necessary, and the evaluator. Finally, we built an entry-level naïve, advanced, and modular RAG program in Python, leveraging keyword matching, vector search, and index-based retrieval, augmenting the input of GPT-4o.

Our next step in Chapter 2, RAG Embedding Vector Stores with Deep Lake and OpenAI, is to embed data in vectors. We will store the vectors in vector stores to enhance the speed and precision of the retrieval functions of a RAG ecosystem.