llama-stack-mirror/docs/source/getting_started/index.md

Quickstart

Get started with Llama Stack in minutes!

Llama Stack is a stateful service with REST APIs to support the seamless transition of AI applications across different environments. You can build and test using a local server first and deploy to a hosted endpoint for production.

In this guide, we'll walk through how to build a RAG application locally using Llama Stack with Ollama as the inference provider for a Llama Model.

Step 1. Install and Setup

Install uv, setup your virtual environment, and run inference on a Llama model with Ollama.

uv pip install llama-stack aiosqlite faiss-cpu ollama openai datasets opentelemetry-exporter-otlp-proto-http mcp autoevals
source .venv/bin/activate
export INFERENCE_MODEL="llama3.2:3b"
ollama run llama3.2:3b --keepalive 60m

Step 2: Run the Llama Stack Server

INFERENCE_MODEL=llama3.2:3b llama stack build --template ollama --image-type venv --run

Step 3: Run the Demo

Now open up a new terminal using the same virtual environment and you can run this demo as a script using uv run demo_script.py or in an interactive shell.

from termcolor import cprint
from llama_stack_client.types import Document
from llama_stack_client import LlamaStackClient


vector_db = "faiss"
vector_db_id = "test-vector-db"
model_id = "llama3.2:3b-instruct-fp16"
query = "Can you give me the arxiv link for Lora Fine Tuning in Pytorch?"
documents = [
    Document(
        document_id="document_1",
        content=f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/lora_finetune.rst",
        mime_type="text/plain",
        metadata={},
    )
]

client = LlamaStackClient(base_url="http://localhost:8321")
client.vector_dbs.register(
    provider_id=vector_db,
    vector_db_id=vector_db_id,
    embedding_model="all-MiniLM-L6-v2",
    embedding_dimension=384,
)

client.tool_runtime.rag_tool.insert(
    documents=documents,
    vector_db_id=vector_db_id,
    chunk_size_in_tokens=50,
)

response = client.tool_runtime.rag_tool.query(
    vector_db_ids=[vector_db_id],
    content=query,
)

cprint("" + "-" * 50, "yellow")
cprint(f"Query> {query}", "red")
cprint("" + "-" * 50, "yellow")
for chunk in response.content:
    cprint(f"Chunk ID> {chunk.text}", "green")
    cprint("" + "-" * 50, "yellow")

And you should see output like below.

--------------------------------------------------
Query> Can you give me the arxiv link for Lora Fine Tuning in Pytorch?
--------------------------------------------------
Chunk ID> knowledge_search tool found 5 chunks:
BEGIN of knowledge_search tool results.

--------------------------------------------------
Chunk ID> Result 1:
Document_id:docum
Content: .. _lora_finetune_label:

============================
Fine-Tuning Llama2 with LoRA
============================

This guide will teach you about `LoRA <https://arxiv.org/abs/2106.09685>`_, a

--------------------------------------------------

Congratulations! You've successfully built your first RAG application using Llama Stack! 🎉🥳

Next Steps

Now you're ready to dive deeper into Llama Stack!

• Explore the Detailed Tutorial.
• Try the Getting Started Notebook.
• Browse more Notebooks on GitHub.
• Learn about Llama Stack Concepts.
• Discover how to Build Llama Stacks.
• Refer to our References for details on the Llama CLI and Python SDK.
• Check out the llama-stack-apps repository for example applications and tutorials.

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