Evaluations for Retrieval Augmented Generation: TruLens + Milvus

This article discusses the use of vector search technologies, such as Milvus and Zilliz Cloud, in building retrieval augmented generation (RAG) applications. RAGs are question-answering applications that allow large language models (LLMs) to access a verified knowledge base for context. The article highlights various configuration choices that can affect the quality of retrieval, including data selection, embedding model, index type, amount of context retrieved, and chunk size. It also introduces TruLens, an open-source library for evaluating and tracking the performance of LLM applications like RAGs. By using TruLens to evaluate different configurations and parameters, developers can identify failure modes and find the most performant combination for their specific use case.