RAG2Rich
Building an RAG app is easy. However, optimizing its different settings, such as chunk size and context, is necessarily not. With RAG2Rich, one can identify the optimal parameters/configurations based on an answer "richness" score. Answer richness, or the "Rich" score, is a composite metric, based on the context relevance, answer relevance, and groundedness measures computed by TruLens. The average richness score is computed by transforming a relevant vector measure into a scalar using a weighted logistic function. Subsequently, the optimal parameters are chosen based on the optimal value of the average richness score. In other words, RAG2Rich offers a scientific approach to optimizing RAG-based systems. RAG2Rich is built with several technologies, such as Vertex AI/PaLM 2, LlamaIndex, Chainlit, Milvus/Zilliz, TruLens, and Cohere. We demonstrate RAG2Rich with a use case from Substation Automation Systems.
Category tags:
"Great use of the technology to find the optimal configuration through the RICH score."
Josh Reini
DevRel
For Innovators & Creators