Retrieval Augmented Generation with Citations

This tutorial explains how to implement retrieval augmented generation (RAG) with citations using LlamaIndex and Milvus. RAG is a technique used in large language model (LLM) applications to supplement their knowledge, addressing the lack of up-to-date or domain-specific information. The process involves using a vector database like Milvus to inject knowledge into an app. Citations and attributions are crucial for determining trustworthy answers as more data is added. LlamaIndex and Milvus can be used together to create a citation query engine, allowing users to retrieve information with citations or attributions. The tutorial demonstrates this process using Python libraries and provides code examples for scraping data from Wikipedia, setting up the vector store in LlamaIndex, and querying the engine with citations.

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