Basic RAG example with LlamaIndex

This example demonstrates a basic Retrieval-Augmented Generation (RAG) setup using LlamaIndex. It loads text from a local file, creates a vector store index, and then queries the index to retrieve relevant information.

Requirements

• Python 3.10+
• An OpenAI API key set as an environment variable (OPENAI_API_KEY)

Steps for running this example in the shell

1. Install dependencies:

python -m venv .venv

# macOS/Linux:
source .venv/bin/activate

# Windows Command Prompt:
.venv\Scripts\activate.bat

# Windows PowerShell:
.venv\Scripts\Activate.ps1

pip install -r requirements.txt

2. Export your API key as an environment variable:

export OPENAI_API_KEY="sk-..." # Windows cmd: set OPENAI_API_KEY="sk-..." # Windows PowerShell: $env:OPENAI_API_KEY="sk-..."

3. Run the script:

python rag_basic.py

Output

The script will print the answer to the query "What is LlamaIndex?" based on the content of the data.txt file.