Unlocking Advanced Retrieval Capabilities: LLM and Deep Memory for RAG Applications

Building a robust RAG (Retrieval-Augmented Generation) system that incorporates Large Language Model (LLM) and is performant is not easy, which is why ActiveLoop’s Deep Memory comes to our aid as by significantly improving the quality of retrieval of useful information in the dataset. In this blog post, we delve into the process of building this system and evaluate the improvements on three distinct datasets comparing the quality of responses with and without the Deep Memory feature. Deep Memory is a technique developed by Activeloop that enables optimizing vector stores for specific use cases to achieve higher accuracy in LLM applications. Some key points about Deep Memory: - Deep Memory significantly enhances Deep Lake’s vector search accuracy by up to 22%, achieved through learning an index from labeled queries customized for your application, with no impact on search time. This significantly improves the user experience of LLM applications. - Deep Memory can also reduce costs by decreasing the amount of context (k) that needs to be injected into the LLM prompt to achieve a given accuracy, thereby reducing token usage. In summary, Activeloop’s Deep Memory is a powerful tool that significantly enhances retrieval accuracy in LLM applications in a cost-effective manner by optimizing vector stores for specific use cases.