Build Multi-Index Advanced RAG Apps

Lesson 12 of the LLM Twin course focuses on implementing multi-index structures for advanced Retrieval-Augmented Generation (RAG) systems using Superlinked, a vector compute engine optimized for vector data operations. The lesson guides learners through designing a powerful RAG server with Superlinked, demonstrating how to define data schemas, create embedding spaces, and construct multi-index queries that incorporate metadata filters for efficient data retrieval. It covers the use of Superlinked to optimize various advanced RAG methods such as query expansion, self-query, filtered vector search, and rerank, although it notes that Superlinked currently lacks support for reranking with cross-encoder models. The lesson builds on previous course content by integrating real-time data ingestion pipelines with Superlinked, ultimately showcasing the potential of this young Python framework to enhance vector-based applications through scalable and flexible data retrieval solutions.

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