Prototyping a Retrieval-Augmented Generation (RAG) application is relatively straightforward, but the challenge lies in optimizing it for performance, robustness, and scalability across vast knowledge repositories. This guide aims to provide insights, strategies, and implementations leveraging LlamaIndex to enhance the efficiency of your RAG pipeline, catering to complex datasets and ensuring accurate query responses without hallucinations.
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Here we chain together a full RAG pipeline consisting of query rewriting, retrieval, reranking, and response synthesis.
Here we can’t use chain syntax because certain modules depend on multiple inputs (for instance, response synthesis expects both the retrieved nodes and the original question). Instead we’ll construct a DAG explicitly, through add_modules and then add_link.
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In this article, we’ll introduce you to the innovative world of Autogen, an AI agent that’s revolutionizing how we fine-tune and customize large multimodal models. Autogen takes the complexity out of the equation by automating and simplifying the fine-tuning process, making it accessible to developers and researchers alike. We’ll explore how Autogen collaborates seamlessly with models like LLaVA, streamlining AI agent development and opening the doors to more efficient and precise AI-driven solutions.
Here we will explore how to scrape YouTube video transcripts into a knowledge graph for Retrieval Augmented Generation (RAG) applications. We will use Google Cloud Platform to store our initial transcripts, LangChain to create documents from the transcripts and a Neo4j graph database to store the resulting documents. In this example we will be creating a knowledge graph containing objective musical facts spoken by Anthony Fantano himself on a select few music genres.
