Summary

In this chapter, we explored various indexing strategies and architectures within LlamaIndex. Indexes provide essential capabilities for building performant RAG systems.

Throughout the chapter, we looked at the VectorStoreIndex, which is the most commonly used Index type. We also gained an understanding of embeddings, vector stores, similarity search, and storage contexts. These are key concepts related to the VectorStoreIndex.

We also covered other Index types such as SummaryIndex for simple linear scans, KeywordTableIndex for keyword search, TreeIndex for hierarchical data, and KnowledgeGraphIndex for relationship-based queries. ComposableGraph was introduced as a tool for building multi-level Indexes, and cost estimation techniques were discussed together with best practices.

Overall, this chapter provided an overview of indexing capabilities in LlamaIndex, laying the foundation for building sophisticated and efficient RAG applications.

See you in Chapter 6,...