How do I build custom indices in LlamaIndex?

Building custom indices in LlamaIndex involves creating structured representations of your data to optimize retrieval and querying for large language models (LLMs). The process starts with understanding the components: documents (raw data), nodes (chunks of processed data), and indices (data structures that organize nodes for efficient access). Customization occurs by modifying how these components are created, linked, or prioritized. For example, you might design an index that prioritizes recent data or integrates domain-specific metadata for better context.

To implement a custom index, first define how your data is processed. Use LlamaIndex’s SimpleDirectoryReader or custom parsers to load data into documents, then split them into nodes using text splitters. For instance, a medical document index might split text by sections like “Diagnosis” and “Treatment” instead of generic paragraphs. Next, choose or extend an existing index type (e.g., VectorStoreIndex for semantic search or TreeIndex for hierarchical data). If the default options don’t fit, create a subclass of BaseIndex and override methods like build or query to implement logic like filtering nodes by metadata or combining multiple retrieval strategies. For example, a hybrid index could combine keyword-based retrieval with vector similarity scores.

Advanced customization often involves modifying retrievers or node postprocessors. A retriever determines which nodes are fetched during a query, while postprocessors refine results (e.g., reranking). To build a custom retriever, subclass BaseRetriever and implement a _retrieve method that uses your logic, such as querying a SQL database alongside vector stores. For example, a product support index might retrieve nodes based on both user intent (vector similarity) and product version (metadata filtering). Testing is critical: validate retrieval accuracy and latency with real-world queries. By tailoring these components, you can create indices that align with specific use cases while leveraging LlamaIndex’s infrastructure for LLM integration.