Getting Started with LlamaIndex: Build Your First RAG Application in 10 Minutes

Quick Start: RAG in Minutes

LlamaIndex makes building RAG applications incredibly simple. In this tutorial, you will build a document Q&A system in under 10 minutes using Python. No prior experience with RAG needed.

Step 1: Install LlamaIndex

pip install llama-index-core llama-index-readers-file llama-index-embeddings-openai llama-index-llms-openai

Or install everything at once: pip install llama-index. For local models, you can use Ollama: pip install llama-index-llms-ollama llama-index-embeddings-ollama.

Step 2: Set Up Your API Key

export OPENAI_API_KEY=sk-your-key-here

If using Ollama, no API key is needed. Just make sure Ollama is running locally.

Step 3: Index Your Documents

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader('data').load_data()
index = VectorStoreIndex.from_documents(documents)

That is it. LlamaIndex reads all documents from the data/ folder (PDFs, text files, Markdown, images, etc.), chunks them, generates embeddings, and builds a searchable index.

Step 4: Query Your Data

query_engine = index.as_query_engine()
response = query_engine.query('What is the main topic of the document?')
print(response)

LlamaIndex retrieves the most relevant chunks from your documents, passes them to the LLM as context, and returns a grounded answer. No hallucination, no guesswork.

Advanced: Customize Your Pipeline

LlamaIndex is designed for customization at every level:

Chunking: Adjust chunk size, overlap, and splitting strategy
Embeddings: Use OpenAI, HuggingFace, Cohere, or local models
Retrieval: Hybrid search, auto-merging, or recursive retrieval
LLM: OpenAI, Anthropic, Ollama, vLLM, or any LangChain model
Storage: In-memory, Chroma, Weaviate, Milvus, or Postgres/pgvector

Why Use LlamaIndex?

LlamaIndex is not just a simple wrapper. It is a production-grade framework that handles the complexities of RAG: document parsing, chunking, embedding management, retrieval strategies, query transformation, response synthesis, and observability. It has been battle-tested by thousands of companies and is regularly updated with the latest research on RAG best practices.

Next Steps

Ready to go deeper? Check out the official documentation at docs.llamaindex.ai for advanced topics like agents, multi-modal RAG, knowledge graphs, and structured data extraction. LlamaIndex is free and open-source under the MIT license.