Getting Started

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.

💡 Notebook Version: You can also follow this quickstart guide in a Jupyter notebook format: quick_start.ipynb

Step 1: Install and setup

1. Install uv

2. Run inference on a Llama model with Ollama

ollama run llama3.2:3b --keepalive 60m

Step 2: Run the Llama Stack server

We will use uv to run the Llama Stack server.

OLLAMA_URL=http://localhost:11434 \
  uv run --with llama-stack llama stack build --distro starter --image-type venv --run

Step 3: Run the demo

Now open up a new terminal and copy the following script into a file named demo_script.py.

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.

from llama_stack_client import Agent, AgentEventLogger, RAGDocument, LlamaStackClient

vector_db_id = "my_demo_vector_db"
client = LlamaStackClient(base_url="http://localhost:8321")

models = client.models.list()

# Select the first LLM and first embedding models
model_id = next(m for m in models if m.model_type == "llm").identifier
embedding_model_id = (
    em := next(m for m in models if m.model_type == "embedding")
).identifier
embedding_dimension = em.metadata["embedding_dimension"]

vector_db = client.vector_dbs.register(
    vector_db_id=vector_db_id,
    embedding_model=embedding_model_id,
    embedding_dimension=embedding_dimension,
    provider_id="faiss",
)
vector_db_id = vector_db.identifier
source = "https://www.paulgraham.com/greatwork.html"
print("rag_tool> Ingesting document:", source)
document = RAGDocument(
    document_id="document_1",
    content=source,
    mime_type="text/html",
    metadata={},
)
client.tool_runtime.rag_tool.insert(
    documents=[document],
    vector_db_id=vector_db_id,
    chunk_size_in_tokens=100,
)
agent = Agent(
    client,
    model=model_id,
    instructions="You are a helpful assistant",
    tools=[
        {
            "name": "builtin::rag/knowledge_search",
            "args": {"vector_db_ids": [vector_db_id]},
        }
    ],
)

prompt = "How do you do great work?"
print("prompt>", prompt)

use_stream = True
response = agent.create_turn(
    messages=[{"role": "user", "content": prompt}],
    session_id=agent.create_session("rag_session"),
    stream=use_stream,
)

# Only call `AgentEventLogger().log(response)` for streaming responses.
if use_stream:
    for log in AgentEventLogger().log(response):
        log.print()
else:
    print(response)

We will use uv to run the script

uv run --with llama-stack-client,fire,requests demo_script.py

And you should see output like below.

rag_tool> Ingesting document: https://www.paulgraham.com/greatwork.html

prompt> How do you do great work?

inference> [knowledge_search(query="What is the key to doing great work")]

tool_execution> Tool:knowledge_search Args:{'query': 'What is the key to doing great work'}

tool_execution> Tool:knowledge_search Response:[TextContentItem(text='knowledge_search tool found 5 chunks:\nBEGIN of knowledge_search tool results.\n', type='text'), TextContentItem(text="Result 1:\nDocument_id:docum\nContent:  work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text="Result 2:\nDocument_id:docum\nContent:  work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text="Result 3:\nDocument_id:docum\nContent:  work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text="Result 4:\nDocument_id:docum\nContent:  work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text="Result 5:\nDocument_id:docum\nContent:  work. Doing great work means doing something important\nso well that you expand people's ideas of what's possible. But\nthere's no threshold for importance. It's a matter of degree, and\noften hard to judge at the time anyway.\n", type='text'), TextContentItem(text='END of knowledge_search tool results.\n', type='text')]

inference> Based on the search results, it seems that doing great work means doing something important so well that you expand people's ideas of what's possible. However, there is no clear threshold for importance, and it can be difficult to judge at the time.

To further clarify, I would suggest that doing great work involves:

* Completing tasks with high quality and attention to detail
* Expanding on existing knowledge or ideas
* Making a positive impact on others through your work
* Striving for excellence and continuous improvement

Ultimately, great work is about making a meaningful contribution and leaving a lasting impression.

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

HuggingFace access

If you are getting a 401 Client Error from HuggingFace for the all-MiniLM-L6-v2 model, try setting HF_TOKEN to a valid HuggingFace token in your environment

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.

Libraries (SDKs)

We have a number of client-side SDKs available for different languages.

Language	Client SDK	Package
Python	llama-stack-client-python
Swift	llama-stack-client-swift
Node	llama-stack-client-node
Kotlin	llama-stack-client-kotlin

Detailed Tutorial

In this guide, we’ll walk through how you can use the Llama Stack (server and client SDK) to test a simple agent. A Llama Stack agent is a simple integrated system that can perform tasks by combining a Llama model for reasoning with tools (e.g., RAG, web search, code execution, etc.) for taking actions. In Llama Stack, we provide a server exposing multiple APIs. These APIs are backed by implementations from different providers.

Llama Stack is a stateful service with REST APIs to support seamless transition of AI applications across different environments. The server can be run in a variety of ways, including as a standalone binary, Docker container, or hosted service. 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 agent locally using Llama Stack with Ollama as the inference provider for a Llama Model.

Step 1: Installation and Setup

Install Ollama by following the instructions on the Ollama website, then download Llama 3.2 3B model, and then start the Ollama service.

ollama pull llama3.2:3b
ollama run llama3.2:3b --keepalive 60m

Install uv to setup your virtual environment

macOS and Linux

Use curl to download the script and execute it with sh:

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows

Use irm to download the script and execute it with iex:

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Setup your virtual environment.

uv sync --python 3.12
source .venv/bin/activate

Step 2: Run Llama Stack

Llama Stack is a server that exposes multiple APIs, you connect with it using the Llama Stack client SDK.

Using venv

You can use Python to build and run the Llama Stack server, which is useful for testing and development.

Llama Stack uses a YAML configuration file to specify the stack setup, which defines the providers and their settings. The generated configuration serves as a starting point that you can customize for your specific needs. Now let’s build and run the Llama Stack config for Ollama. We use starter as template. By default all providers are disabled, this requires enable ollama by passing environment variables.

llama stack build --distro starter --image-type venv --run

Using venv

You can use Python to build and run the Llama Stack server, which is useful for testing and development.

Llama Stack uses a YAML configuration file to specify the stack setup, which defines the providers and their settings. Now let’s build and run the Llama Stack config for Ollama.

llama stack build --distro starter --image-type venv --run

Using a Container

You can use a container image to run the Llama Stack server. We provide several container images for the server component that works with different inference providers out of the box. For this guide, we will use llamastack/distribution-starter as the container image. If you’d like to build your own image or customize the configurations, please check out this guide. First lets setup some environment variables and create a local directory to mount into the container’s file system.

export LLAMA_STACK_PORT=8321
mkdir -p ~/.llama

Then start the server using the container tool of your choice. For example, if you are running Docker you can use the following command:

docker run -it \
  --pull always \
  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
  -v ~/.llama:/root/.llama \
  llamastack/distribution-starter \
  --port $LLAMA_STACK_PORT \
  --env OLLAMA_URL=http://host.docker.internal:11434

Note to start the container with Podman, you can do the same but replace docker at the start of the command with podman. If you are using podman older than 4.7.0, please also replace host.docker.internal in the OLLAMA_URL with host.containers.internal.

The configuration YAML for the Ollama distribution is available at distributions/ollama/run.yaml.

Tip

Docker containers run in their own isolated network namespaces on Linux. To allow the container to communicate with services running on the host via localhost, you need --network=host. This makes the container use the host’s network directly so it can connect to Ollama running on localhost:11434.

Linux users having issues running the above command should instead try the following:

docker run -it \
  --pull always \
  -p $LLAMA_STACK_PORT:$LLAMA_STACK_PORT \
  -v ~/.llama:/root/.llama \
  --network=host \
  llamastack/distribution-starter \
  --port $LLAMA_STACK_PORT \
  --env OLLAMA_URL=http://localhost:11434

You will see output like below:

INFO:     Application startup complete.
INFO:     Uvicorn running on http://['::', '0.0.0.0']:8321 (Press CTRL+C to quit)

Now you can use the Llama Stack client to run inference and build agents!

You can reuse the server setup or use the Llama Stack Client. Note that the client package is already included in the llama-stack package.

Step 3: Run Client CLI

Open a new terminal and navigate to the same directory you started the server from. Then set up a new or activate your existing server virtual environment.

Reuse Server venv

# The client is included in the llama-stack package so we just activate the server venv
source .venv/bin/activate

Install with venv

uv venv client --python 3.12
source client/bin/activate
pip install llama-stack-client

Now let’s use the llama-stack-client CLI to check the connectivity to the server.

llama-stack-client configure --endpoint http://localhost:8321 --api-key none

You will see the below:

Done! You can now use the Llama Stack Client CLI with endpoint http://localhost:8321

List the models

llama-stack-client models list
Available Models

┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━┓
┃ model_type      ┃ identifier                          ┃ provider_resource_id                ┃ metadata                                  ┃ provider_id           ┃
┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━┩
│ embedding       │ ollama/all-minilm:l6-v2             │ all-minilm:l6-v2                    │ {'embedding_dimension': 384.0}            │ ollama                │
├─────────────────┼─────────────────────────────────────┼─────────────────────────────────────┼───────────────────────────────────────────┼───────────────────────┤
│ ...             │ ...                                 │ ...                                 │                                           │ ...                   │
├─────────────────┼─────────────────────────────────────┼─────────────────────────────────────┼───────────────────────────────────────────┼───────────────────────┤
│ llm             │ ollama/Llama-3.2:3b                 │ llama3.2:3b                         │                                           │ ollama                │
└─────────────────┴─────────────────────────────────────┴─────────────────────────────────────┴───────────────────────────────────────────┴───────────────────────┘

You can test basic Llama inference completion using the CLI.

llama-stack-client inference chat-completion --model-id "ollama/llama3.2:3b" --message "tell me a joke"

Sample output:

OpenAIChatCompletion(
    id="chatcmpl-08d7b2be-40f3-47ed-8f16-a6f29f2436af",
    choices=[
        OpenAIChatCompletionChoice(
            finish_reason="stop",
            index=0,
            message=OpenAIChatCompletionChoiceMessageOpenAIAssistantMessageParam(
                role="assistant",
                content="Why couldn't the bicycle stand up by itself?\n\nBecause it was two-tired.",
                name=None,
                tool_calls=None,
                refusal=None,
                annotations=None,
                audio=None,
                function_call=None,
            ),
            logprobs=None,
        )
    ],
    created=1751725254,
    model="llama3.2:3b",
    object="chat.completion",
    service_tier=None,
    system_fingerprint="fp_ollama",
    usage={
        "completion_tokens": 18,
        "prompt_tokens": 29,
        "total_tokens": 47,
        "completion_tokens_details": None,
        "prompt_tokens_details": None,
    },
)

Step 4: Run the Demos

Note that these demos show the Python Client SDK. Other SDKs are also available, please refer to the Client SDK list for the complete options.

Basic Inference

Now you can run inference using the Llama Stack client SDK.

i. Create the Script

Create a file inference.py and add the following code:

from llama_stack_client import LlamaStackClient

client = LlamaStackClient(base_url="http://localhost:8321")

# List available models
models = client.models.list()

# Select the first LLM
llm = next(m for m in models if m.model_type == "llm" and m.provider_id == "ollama")
model_id = llm.identifier

print("Model:", model_id)

response = client.chat.completions.create(
    model=model_id,
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Write a haiku about coding"},
    ],
)
print(response)

ii. Run the Script

Let’s run the script using uv

uv run python inference.py

Which will output:

Model: ollama/llama3.2:3b
OpenAIChatCompletion(id='chatcmpl-30cd0f28-a2ad-4b6d-934b-13707fc60ebf', choices=[OpenAIChatCompletionChoice(finish_reason='stop', index=0, message=OpenAIChatCompletionChoiceMessageOpenAIAssistantMessageParam(role='assistant', content="Lines of code unfold\nAlgorithms dance with ease\nLogic's gentle kiss", name=None, tool_calls=None, refusal=None, annotations=None, audio=None, function_call=None), logprobs=None)], created=1751732480, model='llama3.2:3b', object='chat.completion', service_tier=None, system_fingerprint='fp_ollama', usage={'completion_tokens': 16, 'prompt_tokens': 37, 'total_tokens': 53, 'completion_tokens_details': None, 'prompt_tokens_details': None})

Build a Simple Agent

Next we can move beyond simple inference and build an agent that can perform tasks using the Llama Stack server.

i. Create the Script

Create a file agent.py and add the following code:

from llama_stack_client import LlamaStackClient
from llama_stack_client import Agent, AgentEventLogger
from rich.pretty import pprint
import uuid

client = LlamaStackClient(base_url=f"http://localhost:8321")

models = client.models.list()
llm = next(m for m in models if m.model_type == "llm" and m.provider_id == "ollama")
model_id = llm.identifier

agent = Agent(client, model=model_id, instructions="You are a helpful assistant.")

s_id = agent.create_session(session_name=f"s{uuid.uuid4().hex}")

print("Non-streaming ...")
response = agent.create_turn(
    messages=[{"role": "user", "content": "Who are you?"}],
    session_id=s_id,
    stream=False,
)
print("agent>", response.output_message.content)

print("Streaming ...")
stream = agent.create_turn(
    messages=[{"role": "user", "content": "Who are you?"}], session_id=s_id, stream=True
)
for event in stream:
    pprint(event)

print("Streaming with print helper...")
stream = agent.create_turn(
    messages=[{"role": "user", "content": "Who are you?"}], session_id=s_id, stream=True
)
for event in AgentEventLogger().log(stream):
    event.print()

ii. Run the Script

Let’s run the script using uv

uv run python agent.py

👋 Click here to see the sample output

Non-streaming ...
agent> I'm an artificial intelligence designed to assist and communicate with users like you. I don't have a personal identity, but I can provide information, answer questions, and help with tasks to the best of my abilities.

I'm a large language model, which means I've been trained on a massive dataset of text from various sources, allowing me to understand and respond to a wide range of topics and questions. My purpose is to provide helpful and accurate information, and I'm constantly learning and improving my responses based on the interactions I have with users like you.

I can help with:

* Answering questions on various subjects
* Providing definitions and explanations
* Offering suggestions and ideas
* Assisting with language-related tasks, such as proofreading and editing
* Generating text and content
* And more!

Feel free to ask me anything, and I'll do my best to help!
Streaming ...
AgentTurnResponseStreamChunk(
│   event=TurnResponseEvent(
│   │   payload=AgentTurnResponseStepStartPayload(
│   │   │   event_type='step_start',
│   │   │   step_id='69831607-fa75-424a-949b-e2049e3129d1',
│   │   │   step_type='inference',
│   │   │   metadata={}
│   │   )
│   )
)
AgentTurnResponseStreamChunk(
│   event=TurnResponseEvent(
│   │   payload=AgentTurnResponseStepProgressPayload(
│   │   │   delta=TextDelta(text='As', type='text'),
│   │   │   event_type='step_progress',
│   │   │   step_id='69831607-fa75-424a-949b-e2049e3129d1',
│   │   │   step_type='inference'
│   │   )
│   )
)
AgentTurnResponseStreamChunk(
│   event=TurnResponseEvent(
│   │   payload=AgentTurnResponseStepProgressPayload(
│   │   │   delta=TextDelta(text=' a', type='text'),
│   │   │   event_type='step_progress',
│   │   │   step_id='69831607-fa75-424a-949b-e2049e3129d1',
│   │   │   step_type='inference'
│   │   )
│   )
)
...
AgentTurnResponseStreamChunk(
│   event=TurnResponseEvent(
│   │   payload=AgentTurnResponseStepCompletePayload(
│   │   │   event_type='step_complete',
│   │   │   step_details=InferenceStep(
│   │   │   │   api_model_response=CompletionMessage(
│   │   │   │   │   content='As a conversational AI, I don\'t have a personal identity in the classical sense. I exist as a program running on computer servers, designed to process and respond to text-based inputs.\n\nI\'m an instance of a type of artificial intelligence called a "language model," which is trained on vast amounts of text data to generate human-like responses. My primary function is to understand and respond to natural language inputs, like our conversation right now.\n\nThink of me as a virtual assistant, a chatbot, or a conversational interface – I\'m here to provide information, answer questions, and engage in conversation to the best of my abilities. I don\'t have feelings, emotions, or consciousness like humans do, but I\'m designed to simulate human-like interactions to make our conversations feel more natural and helpful.\n\nSo, that\'s me in a nutshell! What can I help you with today?',
│   │   │   │   │   role='assistant',
│   │   │   │   │   stop_reason='end_of_turn',
│   │   │   │   │   tool_calls=[]
│   │   │   │   ),
│   │   │   │   step_id='69831607-fa75-424a-949b-e2049e3129d1',
│   │   │   │   step_type='inference',
│   │   │   │   turn_id='8b360202-f7cb-4786-baa9-166a1b46e2ca',
│   │   │   │   completed_at=datetime.datetime(2025, 4, 3, 1, 15, 21, 716174, tzinfo=TzInfo(UTC)),
│   │   │   │   started_at=datetime.datetime(2025, 4, 3, 1, 15, 14, 28823, tzinfo=TzInfo(UTC))
│   │   │   ),
│   │   │   step_id='69831607-fa75-424a-949b-e2049e3129d1',
│   │   │   step_type='inference'
│   │   )
│   )
)
AgentTurnResponseStreamChunk(
│   event=TurnResponseEvent(
│   │   payload=AgentTurnResponseTurnCompletePayload(
│   │   │   event_type='turn_complete',
│   │   │   turn=Turn(
│   │   │   │   input_messages=[UserMessage(content='Who are you?', role='user', context=None)],
│   │   │   │   output_message=CompletionMessage(
│   │   │   │   │   content='As a conversational AI, I don\'t have a personal identity in the classical sense. I exist as a program running on computer servers, designed to process and respond to text-based inputs.\n\nI\'m an instance of a type of artificial intelligence called a "language model," which is trained on vast amounts of text data to generate human-like responses. My primary function is to understand and respond to natural language inputs, like our conversation right now.\n\nThink of me as a virtual assistant, a chatbot, or a conversational interface – I\'m here to provide information, answer questions, and engage in conversation to the best of my abilities. I don\'t have feelings, emotions, or consciousness like humans do, but I\'m designed to simulate human-like interactions to make our conversations feel more natural and helpful.\n\nSo, that\'s me in a nutshell! What can I help you with today?',
│   │   │   │   │   role='assistant',
│   │   │   │   │   stop_reason='end_of_turn',
│   │   │   │   │   tool_calls=[]
│   │   │   │   ),
│   │   │   │   session_id='abd4afea-4324-43f4-9513-cfe3970d92e8',
│   │   │   │   started_at=datetime.datetime(2025, 4, 3, 1, 15, 14, 28722, tzinfo=TzInfo(UTC)),
│   │   │   │   steps=[
│   │   │   │   │   InferenceStep(
│   │   │   │   │   │   api_model_response=CompletionMessage(
│   │   │   │   │   │   │   content='As a conversational AI, I don\'t have a personal identity in the classical sense. I exist as a program running on computer servers, designed to process and respond to text-based inputs.\n\nI\'m an instance of a type of artificial intelligence called a "language model," which is trained on vast amounts of text data to generate human-like responses. My primary function is to understand and respond to natural language inputs, like our conversation right now.\n\nThink of me as a virtual assistant, a chatbot, or a conversational interface – I\'m here to provide information, answer questions, and engage in conversation to the best of my abilities. I don\'t have feelings, emotions, or consciousness like humans do, but I\'m designed to simulate human-like interactions to make our conversations feel more natural and helpful.\n\nSo, that\'s me in a nutshell! What can I help you with today?',
│   │   │   │   │   │   │   role='assistant',
│   │   │   │   │   │   │   stop_reason='end_of_turn',
│   │   │   │   │   │   │   tool_calls=[]
│   │   │   │   │   │   ),
│   │   │   │   │   │   step_id='69831607-fa75-424a-949b-e2049e3129d1',
│   │   │   │   │   │   step_type='inference',
│   │   │   │   │   │   turn_id='8b360202-f7cb-4786-baa9-166a1b46e2ca',
│   │   │   │   │   │   completed_at=datetime.datetime(2025, 4, 3, 1, 15, 21, 716174, tzinfo=TzInfo(UTC)),
│   │   │   │   │   │   started_at=datetime.datetime(2025, 4, 3, 1, 15, 14, 28823, tzinfo=TzInfo(UTC))
│   │   │   │   │   )
│   │   │   │   ],
│   │   │   │   turn_id='8b360202-f7cb-4786-baa9-166a1b46e2ca',
│   │   │   │   completed_at=datetime.datetime(2025, 4, 3, 1, 15, 21, 727364, tzinfo=TzInfo(UTC)),
│   │   │   │   output_attachments=[]
│   │   │   )
│   │   )
│   )
)


Streaming with print helper...
inference> Déjà vu! You're asking me again!

As I mentioned earlier, I'm a computer program designed to simulate conversation and answer questions. I don't have a personal identity or consciousness like a human would. I exist solely as a digital entity, running on computer servers and responding to inputs from users like you.

I'm a type of artificial intelligence (AI) called a large language model, which means I've been trained on a massive dataset of text from various sources. This training allows me to understand and respond to a wide range of questions and topics.

My purpose is to provide helpful and accurate information, answer questions, and assist users like you with tasks and conversations. I don't have personal preferences, emotions, or opinions like humans do. My goal is to be informative, neutral, and respectful in my responses.

So, that's me in a nutshell!

Build a RAG Agent

For our last demo, we can build a RAG agent that can answer questions about the Torchtune project using the documents in a vector database.

i. Create the Script

Create a file rag_agent.py and add the following code:

from llama_stack_client import LlamaStackClient
from llama_stack_client import Agent, AgentEventLogger
from llama_stack_client.types import Document
import uuid

client = LlamaStackClient(base_url="http://localhost:8321")

# Create a vector database instance
embed_lm = next(m for m in client.models.list() if m.model_type == "embedding")
embedding_model = embed_lm.identifier
vector_db_id = f"v{uuid.uuid4().hex}"
client.vector_dbs.register(
    vector_db_id=vector_db_id,
    embedding_model=embedding_model,
)

# Create Documents
urls = [
    "memory_optimizations.rst",
    "chat.rst",
    "llama3.rst",
    "qat_finetune.rst",
    "lora_finetune.rst",
]
documents = [
    Document(
        document_id=f"num-{i}",
        content=f"https://raw.githubusercontent.com/pytorch/torchtune/main/docs/source/tutorials/{url}",
        mime_type="text/plain",
        metadata={},
    )
    for i, url in enumerate(urls)
]

# Insert documents
client.tool_runtime.rag_tool.insert(
    documents=documents,
    vector_db_id=vector_db_id,
    chunk_size_in_tokens=512,
)

# Get the model being served
llm = next(
    m
    for m in client.models.list()
    if m.model_type == "llm" and m.provider_id == "ollama"
)
model = llm.identifier

# Create the RAG agent
rag_agent = Agent(
    client,
    model=model,
    instructions="You are a helpful assistant. Use the RAG tool to answer questions as needed.",
    tools=[
        {
            "name": "builtin::rag/knowledge_search",
            "args": {"vector_db_ids": [vector_db_id]},
        }
    ],
)

session_id = rag_agent.create_session(session_name=f"s{uuid.uuid4().hex}")

turns = ["what is torchtune", "tell me about dora"]

for t in turns:
    print("user>", t)
    stream = rag_agent.create_turn(
        messages=[{"role": "user", "content": t}], session_id=session_id, stream=True
    )
    for event in AgentEventLogger().log(stream):
        event.print()

ii. Run the Script

Let’s run the script using uv

uv run python rag_agent.py

👋 Click here to see the sample output

user> what is torchtune
inference> [knowledge_search(query='TorchTune')]
tool_execution> Tool:knowledge_search Args:{'query': 'TorchTune'}
tool_execution> Tool:knowledge_search Response:[TextContentItem(text='knowledge_search tool found 5 chunks:\nBEGIN of knowledge_search tool results.\n', type='text'), TextContentItem(text='Result 1:\nDocument_id:num-1\nContent:  conversational data, :func:`~torchtune.datasets.chat_dataset` seems to be a good fit. ..., type='text'), TextContentItem(text='END of knowledge_search tool results.\n', type='text')]
inference> Here is a high-level overview of the text:

**LoRA Finetuning with PyTorch Tune**

PyTorch Tune provides a recipe for LoRA (Low-Rank Adaptation) finetuning, which is a technique to adapt pre-trained models to new tasks. The recipe uses the `lora_finetune_distributed` command.
...
Overall, DORA is a powerful reinforcement learning algorithm that can learn complex tasks from human demonstrations. However, it requires careful consideration of the challenges and limitations to achieve optimal results.

You’re Ready to Build Your Own Apps!

Congrats! 🥳 Now you’re ready to build your own Llama Stack applications! 🚀