Beyond Text Embeddings: Addressing the Gaps in RAG Applications for Structured Data Queries

Text embedding models excel at encoding unstructured text but struggle with structured data operations. A study using the MovieLens dataset demonstrates this limitation when answering questions about highest-rated movies or counting films by year. To address these issues, the article proposes combining text embeddings for semantic search with specialized functions for structured data operations. This hybrid approach uses an LLM agent to interpret user questions and call appropriate tools, resulting in more accurate and versatile responses for both structured and unstructured data queries in RAG applications.