What is LlamaIndex and how does it compare to LangChain for RAG use cases?

LlamaIndex (formerly GPT Index) is a data framework specialised for connecting LLMs to diverse data sources. While LangChain is a general-purpose composable LLM framework covering agents, chains, memory, and RAG, LlamaIndex focuses almost exclusively on the data ingestion and indexing layer — providing more sophisticated out-of-the-box RAG patterns like query routing, recursive retrieval, and knowledge graphs.

# pip install llama-index llama-index-llms-openai llama-index-embeddings-openai
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding

# ── Configure global settings
Settings.llm       = OpenAI(model='gpt-4o-mini', temperature=0)
Settings.embed_model = OpenAIEmbedding(model='text-embedding-3-small')
Settings.chunk_size = 1024

# ── Load and index documents in 3 lines
docs    = SimpleDirectoryReader('./docs').load_data()
index   = VectorStoreIndex.from_documents(docs)    # embeds and indexes
engine  = index.as_query_engine()                  # wraps retriever + LLM

response = engine.query('What are the key conclusions of the report?')
print(response.response)
print(response.source_nodes[0].text[:200])  # retrieved passage

# ── Persist index to disk and reload
index.storage_context.persist('./index_store')

from llama_index.core import StorageContext, load_index_from_storage
storage = StorageContext.from_defaults(persist_dir='./index_store')
index2  = load_index_from_storage(storage)

# ── Advanced: Sub-question engine (breaks complex queries into sub-queries)
from llama_index.core.query_engine import SubQuestionQueryEngine
from llama_index.core.tools import QueryEngineTool

q_tool = QueryEngineTool.from_defaults(query_engine=engine,
                                        description='Annual report 2024')
sub_engine = SubQuestionQueryEngine.from_defaults(query_engine_tools=[q_tool])
resp = sub_engine.query('Compare revenue and profit growth, then summarise trends.')
print(resp.response)