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The Role of Re-ranking The re-ranking process acts as an intelligent filter. When a retriever fetches multiple context chunks from a vector store, they possess varying degrees of relevance to the user's query. Some chunks may contain the exact answer required, while others might be semantically similar but lack the specific details needed. The ...

Advanced RAG Techniques Overview Recent developments in retrieval-augmented generation have led to three distinct paradigms: Naive RAG Advenced RAG Modular RAG This article explores these approaches and demonstrates how to implement an advanced RAG pipeline using LlamaIndex with Python. We'll cover three key optimization techniques: Pre-retr ...

Rendering a LlamaIndex Workflow as an Interactive Diagram The last missing piece in our exploration of LlamaIndex’s execution engine is the single line that turns the abstract workflow into a browsable picture. The helper draw_all_possible_flows consumes a workflow class and produces an HTML file that can be opened in any modern browser. from l ...

Current RAG Landscape Retrieval-Augmented Generation has evolved far beyond simple vector search. The latest survey "Retrieval-Augmented Generation for Large Language Models" highlights three active areas of innovation: Query-side augmentation (query transformation) Agentic orchestration of retrieval Post-retrieval refinement Self-R ...