From embeddings to search
As mentioned, a RAG system comprises a retriever that finds relevant information, an augmentation mechanism that integrates this information, and a generator that produces the final output. When building AI applications with LLMs, we often focus on the exciting parts – prompts, chains, and model outputs. However, the foundation of any robust RAG system lies in how we store and retrieve our vector embeddings. Think of it like building a library – before we can efficiently find books (vector search), we need both a building to store them (vector storage) and an organization system to find them (vector indexing). In this section, we introduce the core components of a RAG system: vector embeddings, vector stores, and indexing strategies to optimize retrieval.
To make RAG work, we first need to solve a fundamental challenge: how do we help computers understand the meaning of text so they can find relevant information? This is where embeddings come...
