LlamaIndex RAG¶
Alternative RAG Framework¶
LlamaIndex (formerly GPT Index) specializes in data indexing!
Key Features¶
Data connectors (100+ sources)
Index structures (vector, keyword, graph)
Query engines
Response synthesis
import numpy as np
import pandas as pd
from typing import List, Dict, Tuple
import json
import os
from pathlib import Path
from llama_index import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms import OpenAI
Quick Example¶
From Directory to Answers in Three Steps¶
LlamaIndex takes a data-first approach to RAG. SimpleDirectoryReader loads all files from a directory (PDF, TXT, DOCX, and more), VectorStoreIndex.from_documents chunks the text, generates embeddings, and builds an in-memory vector index in one call, and index.as_query_engine() returns a query interface that handles retrieval and generation. Under the hood, LlamaIndex manages chunk sizes, embedding batching, prompt templates, and response synthesis. This makes it exceptionally fast to prototype RAG applications – you can go from raw documents to a working question-answering system in under ten lines of code.
# Load documents
documents = SimpleDirectoryReader("./data").load_data()
# Create index
index = VectorStoreIndex.from_documents(documents)
# Query
query_engine = index.as_query_engine()
response = query_engine.query("Explain RAG")
print(response)