Google Cloud published an article outlining how to build an advanced Retrieval Augmented Generation (RAG) application on Google Cloud using LlamaIndex. What I found particularly interesting was the emphasis on flexibility and experimentation in building RAG solutions, as there is no one-size-fits-all solution.
I appreciated how the article broke down the RAG workflow using LlamaIndex, from indexing and storing data to retrieving, ranking, and synthesizing information into a final response.
One notable aspect was the use of Google Cloud tools like the Document AI Layout Parser to analyze documents and understand their content hierarchically, improving retrieval accuracy.
I was also intrigued by the use of advanced techniques like Hypothetical Document Embedding (HyDE) and LLM-based node re-ranking to enhance the quality of results.
Finally, the article provided practical examples of using RAGAS to evaluate the performance of the RAG pipeline, making it easier for developers to fine-tune their solutions.
Overall, I believe this article offers a comprehensive and practical guide to building effective RAG applications on Google Cloud.
