RAG Embedding Vector Stores with Deep Lake and OpenAI

There will come a point in the execution of your project where complexity is unavoidable when implementing RAG-driven generative AI. Embeddings transform bulky structured or unstructured texts into compact, high-dimensional vectors that capture their semantic essence, enabling faster and more efficient information retrieval. However, we will inevitably be faced with a storage issue as the creation and storage of document embeddings become necessary when managing increasingly large datasets. You could ask the question at this point, why not use keywords instead of embeddings? And the answer is simple: although embeddings require more storage space, they capture the deeper semantic meanings of texts, with more nuanced and context-aware retrieval compared to the rigid and often-matched keywords. This results in better, more pertinent retrievals. Hence, our option is to turn to vector stores in which embeddings are organized and rapidly...