samples/llamaindex_1 — awesome-ai-stack

예제

LlamaIndex — 구현 샘플

실제로 실행 가능한 미니 프로젝트입니다. 받아서 Docker로 실행해 보세요.

GitHub에서 samples/llamaindex_1 보기 ↗

2026년 6월 28일

LlamaIndex — 구현 샘플

아주 작은 LlamaIndex 샘플입니다. 문서 둘을 색인해 그에 근거한 답을 돌려줍니다(OpenAI 임베딩 + LLM).

설정

cd samples/llamaindex_1
cp .env.sample .env
# .env 편집: OPENAI_API_KEY 설정(임베딩과 LLM에 사용)

Docker로 실행

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

Docker로 실행 (DooD를 쓰는 devcontainer에서)

Docker-outside-of-Docker에서는 포그라운드 docker run이 아무것도 출력하지 않을 수 있습니다 — detached로 실행하고 로그를 따라가세요:

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

로컬에서 실행

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

실행 결과

모델과 실행마다 결과가 달라집니다 — LLM은 비결정적입니다. gpt-4o-mini로 실행한 한 예입니다:

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"]