What is the role of LlamaIndex in building RAG applications?

LlamaIndex is a comprehensive framework for building RAG applications that provides data connectors, indexing strategies, retrieval engines, and response synthesis in a unified API. It supports 160+ data connectors for ingesting from various sources including databases, APIs, file systems, and SaaS tools. LlamaIndex provides multiple index types including vector store index for semantic search, summary index for document-level queries, tree index for hierarchical retrieval, and knowledge graph index for entity-relationship queries. Advanced features include sub-question query decomposition, recursive retrieval across multiple index types, auto-merging retriever for parent-child chunking, and routing query engines for multi-source retrieval. It integrates with all major vector databases, embedding models, and LLM providers. For production use, LlamaIndex provides evaluation modules, observability integrations via callbacks, and optimization tools for chunk size and retrieval parameters.