samples/llamaindex_1 — awesome-ai-stack

Example

LlamaIndex — implementation sample

A real, runnable mini-project. Download it and run with Docker.

View samples/llamaindex_1 on GitHub ↗

June 28, 2026

LlamaIndex — implementation sample

A tiny LlamaIndex sample: it indexes two documents and answers a question grounded in them (OpenAI embeddings + LLM).

Configure

cd samples/llamaindex_1
cp .env.sample .env
# edit .env: set OPENAI_API_KEY (used for embeddings + the LLM)

Run with Docker

cd samples/llamaindex_1
docker build -t aas-llamaindex .
docker run --rm --env-file .env aas-llamaindex "What language is Qdrant written in?"

Run with Docker (in a devcontainer with DooD)

The foreground docker run may print nothing under Docker-outside-of-Docker — run detached and follow the logs:

docker logs -f "$(docker run -d --env-file .env aas-llamaindex "What language is Qdrant written in?")"

Run locally

cd samples/llamaindex_1
pip install -r requirements.txt
python app.py "What language is Qdrant written in?"

Example run

Output varies by model and run — LLMs are non-deterministic. One run with gpt-4o-mini:

Qdrant is written in Rust.

.env.sample

# Copy to `.env` and add your OpenAI API key (used for embeddings + the LLM).
#   cp .env.sample .env
OPENAI_API_KEY=

MODEL=gpt-4o-mini

app.py

"""LlamaIndex: index a few documents and answer a question grounded in them.

Builds an in-memory vector index over two documents (OpenAI embeddings), then
runs a query engine (OpenAI LLM) and prints the grounded answer.

Run:
    docker build -t aas-llamaindex .
    docker run --rm --env-file .env aas-llamaindex "What language is Qdrant written in?"
"""

import os
import sys

from dotenv import load_dotenv
from llama_index.core import Document, VectorStoreIndex
from llama_index.llms.openai import OpenAI

load_dotenv()

MODEL = os.environ.get("MODEL", "gpt-4o-mini")


def main() -> None:
    question = " ".join(sys.argv[1:]) or "What language is Qdrant written in?"
    docs = [
        Document(text="Qdrant is a vector database written in Rust."),
        Document(text="LlamaIndex builds RAG pipelines over your own data."),
    ]
    index = VectorStoreIndex.from_documents(docs)
    engine = index.as_query_engine(llm=OpenAI(model=MODEL))
    print(engine.query(question))


if __name__ == "__main__":
    main()

Dockerfile

FROM python:3.13-slim

ENV PYTHONUNBUFFERED=1

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY app.py .

ENTRYPOINT ["python", "app.py"]