Building knowledge graph agents with LlamaIndex Workflows
Retrieval-augmented generation (RAG) is here to stay, and for good reason. It’s a powerful framework that blends advanced language models with targeted information retrieval techniques, enabling quicker access to relevant data and producing more accurate, context-aware responses. While RAG applications often focus on unstructured data, I’m a big fan of integrating structured data into the mix, a vital yet frequently overlooked approach. One of my favorite ways to do this is by leveraging graph databases like Neo4j.
Often, the go-to approach for retrieving data from a graph is text2cypher, where natural language queries are automatically converted into Cypher statements to query the graph database. This technique relies on a language model (or rule-based system) that interprets user queries, infers their underlying intent, and translates them into valid Cypher queries, enabling RAG applications to retrieve the relevant information from the knowledge graph and produce accurate answers.
Text2cypher offers remarkable flexibility because it allows users to formulate questions in natural language without having to know the underlying graph schema or Cypher syntax. However, due to the nuances of language interpretation and the need for precise schema-specific details, its accuracy can still be lacking as shown in this text2cypher article.
