LlamaIndex

Why It Matters

LlamaIndex focuses specifically on the data layer of LLM applications. While LangChain aims to be a general-purpose framework, LlamaIndex excels at one thing: connecting LLMs to your custom data. This specialization makes it particularly effective for RAG systems, document Q&A, and enterprise knowledge bases.

The framework handles the entire data pipeline: loading documents from various sources, chunking them intelligently, creating embeddings, storing them in vector databases, and retrieving relevant context at query time. These aren’t trivial problems. Chunk size, overlap, embedding models, and retrieval strategies all significantly impact answer quality.

For AI engineers building production RAG systems, LlamaIndex provides battle-tested solutions for common challenges. Advanced features like recursive retrieval, query routing, and evaluation tools help you move beyond basic implementations to systems that perform well on real-world data.

Implementation Basics

LlamaIndex is built around a few core abstractions:

Nodes and Documents represent your data. Documents are the raw files; nodes are the chunks that get embedded and stored. LlamaIndex provides sophisticated chunking strategies including sentence-based, semantic, and hierarchical approaches.

Indices organize your data for retrieval. The most common is the Vector Store Index, but LlamaIndex also offers keyword indices, tree indices (for hierarchical data), and knowledge graphs.

Query Engines handle the retrieval and generation pipeline. They take a question, retrieve relevant nodes, and synthesize an answer. You can customize every step: retrieval strategy, reranking, response synthesis.

Data Connectors load data from various sources: PDFs, web pages, databases, Notion, Slack, and many more. This saves significant time compared to writing custom loaders.

Start with a simple VectorStoreIndex for your first implementation. Use the built-in evaluation tools to measure retrieval quality. Add complexity (query routing, reranking, hierarchical retrieval) only when basic retrieval isn’t finding the right context.