Complete LlamaIndex Tutorial: Building RAG Applications with LLMs

LlamaIndex is a powerful data framework for building LLM-powered applications. It provides tools to ingest, structure, and access private or domain-specific data, making it perfect for building Retrieval-Augmented Generation (RAG) systems.

Why LlamaIndex?

• Easy data ingestion: Connect to 100+ data sources
• Flexible indexing: Multiple index types for different use cases
• Query engines: Natural language querying over your data
• Agent capabilities: Build autonomous LLM agents
• Production ready: Scalable and observable

• Question answering over documents
• Chatbots with knowledge bases
• Document summarization
• Semantic search
• Data analysis agents

Installation

pip install llama-index

With OpenAI
pip install llama-index-llms-openai llama-index-embeddings-openai

With local models
pip install llama-index-llms-ollama llama-index-embeddings-huggingface

Verify
python -c "import llamaindex; print(llamaindex.version)"

Quick Start

1. Basic RAG Pipeline

from llamaindex.core import VectorStoreIndex, SimpleDirectoryReader
from llamaindex.llms.openai import OpenAI
import os

os.environ["OPENAIAPIKEY"] = "your-api-key"

Load documents
documents = SimpleDirectoryReader("./data").loaddata()


Create index
index = VectorStoreIndex.fromdocuments(documents)

Query
queryengine = index.asqueryengine()

response = queryengine.query("What is the main topic of these documents?")
print(response)

2. With Custom LLM

from llamaindex.core import VectorStoreIndex, SimpleDirectoryReader, Settings
from llamaindex.llms.openai import OpenAI
from llamaindex.embeddings.openai import OpenAIEmbedding


Configure settings
Settings.llm = OpenAI(model="gpt-4", temperature=0.1)
Settings.embedmodel = OpenAIEmbedding(model="text-embedding-3-small")

Load and index
documents = SimpleDirectoryReader("./data").loaddata()

index = VectorStoreIndex.fromdocuments(documents)

Query with streaming
queryengine = index.asqueryengine(streaming=True)

response = queryengine.query("Summarize the key points")

for token in response.responsegen:

    print(token, end="", flush=True)

from llamaindex.core import VectorStoreIndex, SimpleDirectoryReader, Settings
from llamaindex.llms.ollama import Ollama

from llamaindex.embeddings.huggingface import HuggingFaceEmbedding

Use local models
Settings.llm = Ollama(model="llama2", requesttimeout=300.0)

Settings.embedmodel = HuggingFaceEmbedding(modelname="BAAI/bge-small-en-v1.5")


Build RAG
documents = SimpleDirectoryReader("./data").loaddata()
index = VectorStoreIndex.fromdocuments(documents)

queryengine = index.asqueryengine()

response = queryengine.query("What does this document discuss?")

print(response)

from llamaindex.core import SimpleDirectoryReader

Load from directory
documents = SimpleDirectoryReader(
    inputdir="./data",

    recursive=True,
    requiredexts=[".pdf", ".txt", ".md"]
).loaddata()


Load specific files
documents = SimpleDirectoryReader(
    inputfiles=["doc1.pdf", "doc2.txt"]
).loaddata()


With metadata
documents = SimpleDirectoryReader(
    "./data",
    filemetadata=lambda filename: {"source": filename}
).loaddata()

from llamaindex.readers.web import SimpleWebPageReader, BeautifulSoupWebReader

Simple web reader
reader = SimpleWebPageReader()
documents = reader.loaddata(["https://example.com/page1", "https://example.com/page2"])


BeautifulSoup reader
reader = BeautifulSoupWebReader()
documents = reader.loaddata(
    urls=["https://example.com"],