Demonstrating CAESURA: Language Models as Multi-Modal Query Planners

In many domains, multi-modal data takes an important role and modern question-answering systems based on LLMs allow users to query this data using simple natural language queries. Retrieval Augmented Generation (RAG) is a recent approach that extends Large Language Models (LLM) with database technology to enable such multi-modal QA systems. In RAG, relevant data is first retrieved from a vector database and then fed into an LLM that computes the query result. However, RAG-based approaches have severe issues, such as regarding efficiency and scalability, since LLMs have high inference costs and can only process limited amounts of data. Therefore, in this demo paper, we propose CAESURA, a database-first approach that extends databases with LLMs. The main idea is that CAESURA utilizes the reasoning capabilities of LLMs to translate natural language queries into execution plans. Using such execution plans allows CAESURA to process multi-modal data outside the LLM using query operators and optimization strategies that are footed in scalable query execution strategies of databases. Our demo allows users to experience CAESURA on two example datasets containing tables, texts, and images1.