LlamaIndex RAG Guide: Build Powerful Retrieval Systems in 2026
Master retrieval-augmented generation with LlamaIndex. Complete guide to building production RAG systems with indexing, retrieval, and synthesis.
LlamaIndex is the leading framework for building retrieval-augmented generation (RAG) systems. It provides the data infrastructure to connect LLMs with your private data, enabling AI applications that are accurate, up-to-date, and grounded in your knowledge base.
Overview
LlamaIndex handles the entire RAG pipeline: data ingestion, indexing, storage, retrieval, and response synthesis. It supports 160+ data sources, multiple vector stores, and advanced retrieval strategies like hybrid search and re-ranking.
Key Features
- 160+ Data Connectors — Ingest from PDFs, databases, APIs, Slack, Notion, and more
- Advanced Indexing — Vector, keyword, knowledge graph, and tree indices
- Retrieval Strategies — Hybrid search, re-ranking, recursive retrieval, and auto-merging
- Response Synthesis — Multiple synthesis modes including refine, tree summarize, and compact
- Agents — Build agents that can query multiple data sources and tools
- Evaluation — Built-in RAG evaluation metrics for quality measurement
Getting Started
pip install llama-index
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What is the main topic?")Use Cases
- Enterprise Knowledge Base — Search across company documents with natural language
- Legal Document Analysis — Query contracts, regulations, and case law
- Technical Documentation — Build intelligent docs search for developer tools
- Financial Research — Analyze earnings reports, filings, and market data
Best Practices
- Chunk wisely — Experiment with chunk sizes (256-1024 tokens) for your specific data
- Use hybrid retrieval — Combine vector and keyword search for better results
- Evaluate continuously — Use built-in metrics to measure and improve RAG quality
- Implement caching — Cache embeddings and frequent queries for performance
Frequently Asked Questions
LlamaIndex vs LangChain for RAG?
LlamaIndex is purpose-built for RAG with more advanced retrieval features. LangChain is more general-purpose. For pure RAG, LlamaIndex is usually the better choice.
What vector database should I use?
For small projects: ChromaDB. For production: Pinecone, Weaviate, or Qdrant. See our vector DB comparison.
Can LlamaIndex work offline?
Yes, with local embeddings (HuggingFace) and local LLMs (Ollama), LlamaIndex works fully offline.
Conclusion
Stay ahead of the curve by exploring our comprehensive directories. Browse the AI Agent directory with 400+ agents and the MCP Server directory with 2,300+ servers to find the perfect tools for your workflow.