LlamaIndex Has a Free Data Framework for Building RAG Applications With LLMs

LlamaIndex is the leading data framework for building RAG (Retrieval-Augmented Generation) applications. It connects your data to LLMs with minimal code.

What You Get for Free

• Data connectors — 160+ sources (PDF, SQL, APIs, Notion, Slack)
• Indexing — automatic chunking, embedding, and storage
• Query engine — natural language over your data
• Agents — data-aware autonomous agents
• Workflows — event-driven orchestration
• Evaluation — built-in RAG evaluation metrics
• LlamaParse — best PDF/document parsing

Simple RAG

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 our refund policy?')
print(response)

3 lines to build a RAG app over your documents.

LlamaIndex vs LangChain

Feature	LlamaIndex	LangChain
Focus	RAG/data	General LLM
Data connectors	160+	Fewer
Simplicity	Higher for RAG	More flexible

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Need RAG development? Check my work on GitHub or email [e-mail] for consulting.