mirror of https://github.com/meta-llama/llama-stack.git synced 2025-12-31 08:13:53 +00:00

Francisco Javier Arceo 5a7572706a Some small copy changes

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

2025-04-07 12:40:08 -04:00

21 KiB

Raw Blame History

Quick Start

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

i. Install and Start Ollama for Inference

Install Ollama by following the instructions on the Ollama website.

To start Ollama run:

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

By default, Ollama keeps the model loaded in memory for 5 minutes which can be too short. We set the --keepalive flag to 60 minutes to ensure the model remains loaded for sometime.

ii. Install `uv` to Manage your Python packages

Install uv to setup your virtual environment

::::{tab-set}

:::{tab-item} macOS and Linux Use curl to download the script and execute it with sh:

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

:::

:::{tab-item} Windows Use irm to download the script and execute it with iex:

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

::: ::::

iii. Setup your Virtual Environment

uv venv --python 3.10
source .venv/bin/activate

Step 2: Install Llama Stack

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

Install the Llama Stack Server

uv pip install llama-stack

Note the Llama Stack Server includes the client SDK as well.

Step 3: Build and Run Llama Stack

Llama Stack uses a configuration file to define the stack. The config file is a YAML file that specifies the providers and their configurations.

i. Build and Run the Llama Stack Config for Ollama

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

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)

ii. Using the Llama Stack Client

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.

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

source .venv/bin/activate

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

iii. List available models

List the models

llama-stack-client models list
Available Models

┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ model_type      ┃ identifier                          ┃ provider_resource_id                ┃ metadata                                  ┃ provider_id     ┃
┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ embedding       │ all-MiniLM-L6-v2                    │ all-minilm:latest                   │ {'embedding_dimension': 384.0}            │ ollama          │
├─────────────────┼─────────────────────────────────────┼─────────────────────────────────────┼───────────────────────────────────────────┼─────────────────┤
│ llm             │ llama3.2:3b                         │ llama3.2:3b                         │                                           │ ollama          │
└─────────────────┴─────────────────────────────────────┴─────────────────────────────────────┴───────────────────────────────────────────┴─────────────────┘

Total models: 2

Step 4: Run Basic Inference

You can test basic Llama inference completion using the CLI.

llama-stack-client inference chat-completion --message "tell me a joke"

Sample output:

ChatCompletionResponse(
    completion_message=CompletionMessage(
        content="Here's one:\n\nWhat do you call a fake noodle?\n\nAn impasta!",
        role="assistant",
        stop_reason="end_of_turn",
        tool_calls=[],
    ),
    logprobs=None,
    metrics=[
        Metric(metric="prompt_tokens", value=14.0, unit=None),
        Metric(metric="completion_tokens", value=27.0, unit=None),
        Metric(metric="total_tokens", value=41.0, unit=None),
    ],
)

Alternatively, 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")
model_id = llm.identifier

print("Model:", model_id)

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

ii. Run the Script

Let's run the script using uv

uv run python inference.py

Which will output:

Model: llama3.2:3b-instruct-fp16
Here is a haiku about coding:

Lines of code unfold
Logic flows through digital night
Beauty in the bits

Step 5: Build a Simple Agent

Now 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")
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

:::{dropdown} 👋 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'm here to provide information, answer questions, and help with tasks to the best of my abilities.

I can be used for a wide range of purposes, such as:

* Providing definitions and explanations
* Offering suggestions and ideas
* Helping with language translation
* Assisting with writing and proofreading
* Generating text or responses to questions
* Playing simple games or chatting about topics of interest

I'm constantly learning and improving my abilities, so 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!

As I mentioned earlier, I'm an artificial intelligence language model. I don't have a personal identity or consciousness like humans do. I exist solely to process and respond to text-based inputs, providing information and assistance on a wide range of topics.

I'm a computer program designed to simulate human-like conversations, using natural language processing (NLP) and machine learning algorithms to understand and generate responses. My purpose is to help users like you with their questions, provide information, and engage in conversation.

Think of me as a virtual companion, a helpful tool designed to make your interactions more efficient and enjoyable. I don't have personal opinions, emotions, or biases, but I'm here to provide accurate and informative responses to the best of my abilities.

So, who am I? I'm just a computer program designed to help you!

:::

Step 6: 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
from termcolor import cprint

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",
    "datasets.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")
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

Which will 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.

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

Next Steps

Go through the Getting Started Notebook
Checkout more Notebooks on GitHub
Learn more about Llama Stack Concepts
Learn how to Build Llama Stacks
Learn from our References for more details about the Llama CLI and Python SDK
Visit our llama-stack-apps repository for more example applications and more detailed tutorials

21 KiB Raw Blame History Unescape Escape

Quick Start

Step 1: Installation and Setup

i. Install and Start Ollama for Inference

ii. Install uv to Manage your Python packages

iii. Setup your Virtual Environment

Step 2: Install Llama Stack

Install the Llama Stack Server

Step 3: Build and Run Llama Stack

i. Build and Run the Llama Stack Config for Ollama

ii. Using the Llama Stack Client

iii. List available models

Step 4: Run Basic Inference

i. Create the Script

ii. Run the Script

Step 5: Build a Simple Agent

i. Create the Script

ii. Run the Script

Step 6: Build a RAG Agent

i. Create the Script

ii. Run the Script

Next Steps

21 KiB

Raw Blame History

ii. Install `uv` to Manage your Python packages