Yes, LlamaIndex can handle both structured and unstructured data, making it a flexible tool for developers working with diverse data sources. It provides a unified framework to process, index, and query data regardless of its format. This capability is particularly useful for applications that need to combine insights from databases, spreadsheets, documents, or other sources into a single workflow.
For structured data, such as SQL databases or CSV files, LlamaIndex offers connectors to ingest tabular data and integrate it with language models (LLMs). For example, it can use SQLAlchemy to query a PostgreSQL database, then format the results into natural language for LLM processing. Developers can also define schemas or metadata to guide how structured data is interpreted. A common use case is converting database rows into text descriptions (e.g., “User John Doe purchased 3 items on July 5”) or embedding structured fields like timestamps or categories for hybrid search. Tools like PandasQueryEngine enable querying structured datasets using natural language, such as asking, “What was the total sales in Q2?” directly against a DataFrame.
For unstructured data, like text documents, PDFs, or emails, LlamaIndex provides tools to split, embed, and index content for semantic search. It supports document loaders (e.g., SimpleDirectoryReader for local files or integrations with cloud storage) and preprocessing steps like chunking text into manageable segments. These chunks are stored in vector databases (e.g., Pinecone, FAISS) to enable similarity-based retrieval. For instance, a support chatbot could index thousands of support tickets (unstructured text) and retrieve relevant answers using semantic matching. LlamaIndex also handles metadata extraction, allowing developers to link unstructured data to structured context, such as associating a user manual PDF with a product ID from a database.
LlamaIndex excels in hybrid use cases where structured and unstructured data are combined. For example, a retail app might use structured product databases to filter items by price or category, then use unstructured customer reviews to answer detailed questions about product quality. Developers can build pipelines that first query a SQL database for user order history (structured) and then search unstructured support chats to resolve order-related issues. The framework’s VectorStoreIndex and SQLStructStoreIndex can be used together, enabling queries like, “Summarize complaints about Product X from users in New York,” which requires joining location data (structured) with customer feedback (unstructured). This flexibility makes LlamaIndex adaptable to scenarios requiring multi-modal data integration.