How do you handle structured data in RAG systems?
RAG systems typically focus on unstructured text, but integrating structured data requires specific approaches. Text-to-SQL converts natural language queries to SQL for relational databases. Table serialization converts database rows and spreadsheet data into text chunks that can be embedded and retrieved. Knowledge graphs provide entity-relationship structured data that can augment retrieved text context. For mixed data sources, a routing layer determines whether to query the vector store for text content, a SQL database for structured data, or a knowledge graph for relationship queries. LlamaIndex provides table and SQL integrations. When embedding structured data, preserve the schema context including column names and relationships so the LLM can properly interpret retrieved data. For time-series and numerical data, consider generating natural language descriptions of the data patterns for better semantic matching.