Talking to Your Graph Database with LLMs Using GraphChat

In a recent Memgraph Community Call, Katarina from the Dev Experience team and Toni Lastre, Head of Platform at Memgraph, introduced GraphChat, a feature in Memgraph Lab that allows users to interact with graph databases using natural language queries powered by large language models (LLMs). This tool simplifies the process by converting plain English questions into Cypher queries, making database interaction as intuitive as chatting with a friend. The session highlighted challenges faced by LLMs, such as handling proprietary datasets, and discussed approaches like Retrieval-Augmented Generation (RAG) to address these issues. A live demo showcased GraphChat's capabilities, including handling follow-up queries and its application on datasets like the Pandora Papers. Upcoming features aim to enhance context integration and error recovery, and the tool's role as a GraphRAG system was emphasized for its ability to bridge LLMs and knowledge graphs for improved data retrieval and reasoning. The call concluded with insights into how Memgraph complements vector databases, offering a hybrid workflow for AI-powered search systems, and encouraged participants to explore the full webinar for a deeper understanding.