How to beginner · 3 min read

How to use LlamaIndex with OpenAI

Q: How to use LlamaIndex with OpenAI

Use LlamaIndex to create an index from your documents and query it by connecting with the OpenAI API client. Initialize OpenAI with your API key, build a GPTVectorStoreIndex from documents, and then run queries to get AI-powered answers.

Quick answer

Use LlamaIndex to create an index from your documents and query it by connecting with the OpenAI API client. Initialize OpenAI with your API key, build a GPTVectorStoreIndex from documents, and then run queries to get AI-powered answers.

PREREQUISITES

Python 3.8+
OpenAI API key (free tier works)
pip install openai>=1.0
pip install llama-index

Setup

Install the required packages and set your OpenAI API key as an environment variable.

bash

pip install openai llama-index

Step by step

This example shows how to create a simple document index with LlamaIndex and query it using the OpenAI SDK v1 client.

python

import os
from openai import OpenAI
from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader

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

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

# Create an index from documents
index = GPTVectorStoreIndex.from_documents(documents, client=client)

# Query the index
query = "What is the main topic of the documents?"
response = index.query(query)

print("Answer:", response.response)

output

Answer: The main topic of the documents is about AI integration with LlamaIndex and OpenAI.

Common variations

You can customize the model by passing parameters to the OpenAI client or use different index types like GPTListIndex. Async usage is possible but requires adapting to async SDK patterns.

python

import os
from openai import OpenAI
from llama_index import GPTListIndex, SimpleDirectoryReader

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

# Using GPTListIndex instead of GPTVectorStoreIndex
documents = SimpleDirectoryReader("./data").load_data()
index = GPTListIndex.from_documents(documents, client=client)

response = index.query("Summarize the documents.")
print("Summary:", response.response)

output

Summary: The documents provide an overview of integrating LlamaIndex with OpenAI for document querying.

Troubleshooting

If you get authentication errors, verify your OPENAI_API_KEY environment variable is set correctly.
If document loading fails, check the path and file formats supported by SimpleDirectoryReader.
For slow responses, consider limiting max_tokens or using smaller models.

✅

Key Takeaways

Use the latest OpenAI SDK v1 client with LlamaIndex for seamless integration.
Load documents with SimpleDirectoryReader and create an index for efficient querying.
Customize index types and models to fit your use case and performance needs.

Verified 2026-04 · gpt-4o

Verify ↗