Yes, LlamaIndex can handle structured data, though it is primarily designed to work with unstructured text. The framework provides tools to integrate structured data sources like SQL databases, spreadsheets, or CSV files into its indexing and retrieval workflows. For example, LlamaIndex can connect to a SQL database using a SQLAlchemy wrapper, allowing developers to query structured tables alongside unstructured documents. This flexibility makes it possible to combine structured data with text-based retrieval systems, such as in applications requiring hybrid search across both formats.
Developers can use LlamaIndex’s built-in connectors to process structured data in familiar formats. For instance, a pandas DataFrame can be loaded as a set of “documents” where each row is treated as a text snippet (e.g., “Product ID: 123, Price: $20, Category: Electronics”). This approach lets structured data be indexed similarly to text, enabling semantic search over tabular data. Additionally, LlamaIndex supports translating natural language queries into SQL for databases. A user might ask, “Which products under $50 sold the most last month?” and LlamaIndex could generate a SQL query to fetch the results, then combine them with context from unstructured data like product descriptions.
However, structured data handling in LlamaIndex has limitations. It doesn’t natively support complex joins, aggregations, or database-specific optimizations as a dedicated SQL engine would. Developers often need to preprocess structured data into a text-friendly format or use external tools for advanced operations. A practical use case might involve a retail app that indexes product databases (structured) and customer reviews (unstructured). LlamaIndex can unify these sources, allowing queries like “Find budget laptops with positive reviews” by blending SQL-like filters and semantic search. While not a full database replacement, its structured data integration is sufficient for scenarios requiring lightweight hybrid retrieval.