RAG made easy using LLama

# use virtual environment to install python and packages

uv init ai-app2

cd ai-app2

pip install llama-index

# download training data

mkdir data

cd data

wget https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt

cd ..

# start python prompt

python

import os

os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("data").load_data()

index = VectorStoreIndex.from_documents(documents)

query_engine = index.as_query_engine()

response = query_engine.query("What the author do growing up?")

print(response)