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How to build conversational RAG with LlamaIndex

Q: How to build conversational RAG with LlamaIndex

Use LlamaIndex to create an index over your documents, then integrate it with a chat model like gpt-4o to build a conversational Retrieval-Augmented Generation (RAG) system. Query the index with user inputs and generate responses that combine retrieved knowledge and language model generation.

Quick answer

Use LlamaIndex to create an index over your documents, then integrate it with a chat model like gpt-4o to build a conversational Retrieval-Augmented Generation (RAG) system. Query the index with user inputs and generate responses that combine retrieved knowledge and language model generation.

PREREQUISITES

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

Setup

Install llama-index and openai Python packages, and set your OpenAI API key as an environment variable.

bash

pip install llama-index openai

Step by step

This example shows how to build a conversational RAG system using LlamaIndex with OpenAI's gpt-4o model. It loads documents, creates an index, and then runs a chat loop that queries the index and generates conversational answers.

python

import os
from llama_index import GPTSimpleVectorIndex, SimpleDirectoryReader, LLMPredictor, PromptHelper
from openai import OpenAI

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

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

# Configure LLM predictor using OpenAI's gpt-4o
llm_predictor = LLMPredictor(
    llm=lambda prompt: client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}]
    ).choices[0].message.content
)

# Create the vector index
index = GPTSimpleVectorIndex(documents, llm_predictor=llm_predictor)

# Conversational query loop
print("Start chatting with your documents (type 'exit' to quit):")
while True:
    query = input("You: ")
    if query.lower() == "exit":
        break
    response = index.query(query)
    print(f"Bot: {response.response}")

output

Start chatting with your documents (type 'exit' to quit):
You: What is LlamaIndex?
Bot: LlamaIndex is a Python library that helps you build retrieval-augmented generation systems by creating indices over your documents and querying them with language models.
You: exit

Common variations

Use asynchronous calls with OpenAI SDK for better performance in web apps.
Switch to other LlamaIndex index types like GPTTreeIndex or GPTKeywordTableIndex for different retrieval strategies.
Use other chat models such as claude-3-5-sonnet-20241022 by adapting the LLM predictor accordingly.

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 queries, consider limiting max_tokens or using smaller models like gpt-4o-mini.

✅

Key Takeaways

Use LlamaIndex to build a vector index over your documents for retrieval.
Integrate the index with OpenAI's gpt-4o model to generate conversational answers.
Customize index types and models to optimize retrieval and generation for your use case.

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

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