How do I create custom index structures using LlamaIndex?

Creating custom index structures in LlamaIndex involves extending its core classes to tailor data organization and retrieval to your specific needs. LlamaIndex provides a flexible framework that allows you to define how data is stored, indexed, and queried. To start, you’ll typically subclass existing index classes (like BaseIndex) and override methods responsible for building the index structure and processing queries. This approach lets you combine LlamaIndex’s built-in components (e.g., node parsers, retrievers) with custom logic, such as adding metadata filters, hybrid search strategies, or domain-specific optimizations.

For example, suppose you want an index that prioritizes hierarchical data relationships. You might create a HierarchicalIndex class that groups nodes by categories during indexing. This could involve overriding the _build method to parse data into parent-child nodes and store them in a graph structure. During querying, your custom _query method might traverse the hierarchy to retrieve contextually relevant nodes. To implement this, you’d define how nodes are connected, how relationships are stored (e.g., in a graph database), and how the query engine navigates these connections. LlamaIndex’s Node and BaseRetriever classes can be adapted to handle these relationships, while its query pipelines let you chain retrieval and post-processing steps.

A practical implementation might look like this:

1. Subclass BaseIndex and define a _build method that organizes nodes into a tree structure.
2. Create a custom HierarchicalRetriever that starts at a root node and expands to child nodes based on query relevance.
3. Use LlamaIndex’s ServiceContext to integrate your retriever with LLM calls for response synthesis. Testing is critical—validate that your index performs better than flat structures for hierarchical data. You can save and load custom indices using LlamaIndex’s storage utilities, ensuring compatibility with existing workflows. This approach is useful for applications like document taxonomies or knowledge graphs, where data relationships are as important as content. By focusing on specific use cases, custom indices can improve retrieval accuracy while leveraging LlamaIndex’s infrastructure for scalability.