How do I handle multiple indexing sources with LlamaIndex?

Handling multiple indexing sources with LlamaIndex involves creating and managing separate indices for different data sources, then combining them to enable unified querying. Start by defining distinct indices for each data type (e.g., documents, databases, APIs). LlamaIndex provides tools like SimpleDirectoryReader to load files from folders, or custom connectors for databases and web APIs. For example, you might create one index for PDF reports using a PDF loader, another for SQL query results via a database connector, and a third for webpage content scraped with an HTML parser. Each index is built independently, allowing you to optimize parameters like chunk size or embedding models based on the data type.

Once indices are created, use LlamaIndex’s composability features to merge them. The ComposableGraph class lets you link multiple indices into a hierarchical structure. For instance, you could combine a product documentation index with a customer support ticket index, enabling queries to pull context from both. When querying, the graph routes the request through relevant indices. To improve accuracy, define metadata filters (e.g., source type, date ranges) or use routing logic to prioritize specific indices. For example, a query like “List recent bug reports” might first check the support ticket index, then fall back to a general documentation index if no matches are found.

Key challenges include ensuring data consistency and avoiding redundancy. Preprocess all sources to standardize formats (e.g., converting HTML to plain text) and deduplicate content. Use LlamaIndex’s NodeParser to split data into uniform chunks across sources, ensuring compatibility during retrieval. For performance, cache frequently accessed indices or use incremental updates (via insert and delete methods) to avoid rebuilding entire indices when sources change. Tools like the RouterQueryEngine can automate query routing based on metadata, while SummaryQueryEngine can generate unified summaries from multiple indices. Testing with real-world queries is critical to refine routing rules and balance speed versus comprehensiveness.