@llama_index
Deploying advanced RAG is challenging. We make it a simple 3-step process: 1. Write your advanced RAG workflow in Python 2. Deploy it as API services with persistence and message queues through llama_deploy 3. Run it! @pavan_mantha1 has an excellent tutorial showing you how to build a RAG pipeline with in-built reflection/filtering/retries, and then deploy them as services through llama_deploy. It’s great weekend reading if you’re looking to not only code a workflow in a notebook, but put it behind an API server https://t.co/XoNHRr4cZb