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How to build advanced RAG with LlamaIndex

Q: How to build advanced RAG with LlamaIndex

Use LlamaIndex to create an advanced Retrieval-Augmented Generation (RAG) pipeline by combining document ingestion, vector indexing, and a large language model like gpt-4o. This involves loading documents, building a vector index with LlamaIndex, and querying it with context-aware prompts to generate accurate, context-rich responses.

Quick answer

Use LlamaIndex to create an advanced Retrieval-Augmented Generation (RAG) pipeline by combining document ingestion, vector indexing, and a large language model like gpt-4o. This involves loading documents, building a vector index with LlamaIndex, and querying it with context-aware prompts to generate accurate, context-rich responses.

PREREQUISITES

Python 3.8+
OpenAI API key (free tier works)
pip install llama-index openai faiss-cpu

Setup

Install the required packages and set your environment variables for API keys.

bash

pip install llama-index openai faiss-cpu

Step by step

This example demonstrates building an advanced RAG system using LlamaIndex with OpenAI's gpt-4o model and FAISS vector store for efficient retrieval.

python

import os
from llama_index import (SimpleDirectoryReader, GPTVectorStoreIndex, ServiceContext, StorageContext, load_index_from_storage)
from openai import OpenAI
import faiss

# Set your OpenAI API key in environment variable
# export OPENAI_API_KEY=os.environ["ANTHROPIC_API_KEY"]

# Initialize OpenAI client
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

# Load documents from local directory
documents = SimpleDirectoryReader("./docs").load_data()

# Create a service context with OpenAI client and model
service_context = ServiceContext.from_defaults(
    llm=client,
    llm_model="gpt-4o"
)

# Build vector store index with FAISS
index = GPTVectorStoreIndex.from_documents(
    documents,
    service_context=service_context
)

# Save index to disk for persistence
index.storage_context.persist(persist_dir="./storage")

# Later, load the index from storage
storage_context = StorageContext.from_defaults(persist_dir="./storage")
index = load_index_from_storage(storage_context, service_context=service_context)

# Query the index with a question
query = "Explain the main benefits of Retrieval-Augmented Generation."
response = index.query(query)

print("Response:", response.response)

output

Response: Retrieval-Augmented Generation (RAG) enhances language models by integrating external knowledge through retrieval, improving accuracy, relevance, and reducing hallucinations.

Common variations

Use asynchronous calls with asyncio for scalable querying.
Switch to other vector stores like Chroma or Weaviate by adapting the storage context.
Use different LLMs such as claude-3-5-sonnet-20241022 by changing the llm in ServiceContext.

python

import asyncio

async def async_query(index, query_text):
    response = await index.aquery(query_text)
    print("Async response:", response.response)

# Example usage
# asyncio.run(async_query(index, "What is RAG?"))

output

Async response: Retrieval-Augmented Generation (RAG) combines retrieval of relevant documents with generation to improve response quality.

Troubleshooting

If you see API key missing, ensure OPENAI_API_KEY is set in your environment.
If vector index loading fails, verify the persist_dir path and that the index was saved correctly.
For slow queries, consider reducing max_tokens or using a smaller model like gpt-4o-mini.

✅

Key Takeaways

Use LlamaIndex with vector stores like FAISS to build scalable RAG systems.
Persist and reload indexes to optimize retrieval performance and reduce costs.
Customize ServiceContext to switch LLMs or vector backends easily.

Verified 2026-04 · gpt-4o, gpt-4o-mini, claude-3-5-sonnet-20241022

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