llama-stack-apps

This repo shows examples of applications built on top of Llama Stack. Starting Llama 3.1 you can build agentic applications capable of:

• breaking a task down and performing multi-step reasoning.
• using tools to perform some actions
  • built-in: the model has built-in knowledge of tools like search or code interpreter
  • zero-shot: the model can learn to call tools using previously unseen, in-context tool definitions
• providing system level safety protections using models like Llama Guard.

Note

The Llama Stack API is still evolving and may change. Feel free to build and experiment, but please don't rely on its stability just yet!

An agentic app requires a few components:

• ability to run inference on the underlying Llama series of models
• ability to run safety checks using the Llama Guard series of models
• ability to execute tools, including a code execution environment, and loop using the model's multi-step reasoning process

All of these components are now offered by a single Llama Stack Distribution. The Llama Stack defines and standardizes these components and many others that are needed to make building Generative AI applications smoother. Various implementations of these APIs are then assembled together via a Llama Stack Distribution.

Getting started with the Llama Stack Distributions

To get started with Llama Stack Distributions, you'll need to:

1. Install prerequisites
2. Download the model checkpoints
3. Build and start a Llama Stack server
4. Connect your client agentic app to Llama Stack server

Once started, you can then just point your agentic app to the URL for this server (e.g. http://localhost:5000).

1. Install Prerequisites

Python Packages

We recommend creating an isolated conda Python environment.

# Create and activate a virtual environment
ENV=app_env
conda create -n $ENV python=3.10
cd <path-to-llama-stack-apps-repo>
conda activate $ENV

# Install dependencies
pip install -r requirements.txt

CLI Packages

With llama-toolchain installed, you should be able to use the Llama Stack CLI and run llama --help. Please checkout our CLI Reference for more details.

usage: llama [-h] {download,model,api,stack} ...

Welcome to the LLama cli

options:
  -h, --help            show this help message and exit

subcommands:
  {download,model,api,stack}

bubblewrap

The code execution environment uses bubblewrap for isolation. This may already be installed on your system; if not, it's likely in your OS's package repository.

Ollama (optional)

If you plan to use Ollama for inference, you'll need to install the server via these instructions.

Download Model Checkpoints

Downloading from Meta

Download the required checkpoints using the following commands:

# download the 8B model, this can be run on a single GPU
llama download --source meta --model-id Meta-Llama3.1-8B-Instruct --meta-url META_URL

# you can also get the 70B model, this will require 8 GPUs however
llama download --source meta --model-id Meta-Llama3.1-70B-Instruct --meta-url META_URL

# llama-agents have safety enabled by default. For this, you will need
# safety models -- Llama-Guard and Prompt-Guard
llama download --source meta --model-id Prompt-Guard-86M --meta-url META_URL
llama download --source meta --model-id Llama-Guard-3-8B --meta-url META_URL

For all the above, you will need to provide a URL (META_URL) which can be obtained from https://llama.meta.com/llama-downloads/ after signing an agreement.

Downloading from Huggingface

Essentially, the same commands above work, just replace --source meta with --source huggingface.

llama download --source huggingface --model-id  Meta-Llama3.1-8B-Instruct --hf-token <HF_TOKEN>

llama download --source huggingface --model-id Meta-Llama3.1-70B-Instruct --hf-token <HF_TOKEN>

llama download --source huggingface --model-id Llama-Guard-3-8B --ignore-patterns *original*
llama download --source huggingface --model-id Prompt-Guard-86M --ignore-patterns *original*

Important: Set your environment variable HF_TOKEN or pass in --hf-token to the command to validate your access. You can find your token at https://huggingface.co/settings/tokens.

Tip: Default for llama download is to run with --ignore-patterns *.safetensors since we use the .pth files in the original folder. For Llama Guard and Prompt Guard, however, we need safetensors. Hence, please run with --ignore-patterns original so that safetensors are downloaded and .pth files are ignored.

Downloading via Ollama

If you're already using ollama, we also have a supported Llama Stack distribution local-ollama and you can continue to use ollama for managing model downloads.

ollama pull llama3.1:8b-instruct-fp16
ollama pull llama3.1:70b-instruct-fp16

Note

Only the above two models are currently supported by Ollama.

Build, Configure, Run Llama Stack Distribution

• Please see our Getting Started Guide for more details on setting up a Llama Stack distribution and running server to serve API endpoints.

Step 1. Build

In the following steps, imagine we'll be working with a Meta-Llama3.1-8B-Instruct model. We will name our build 8b-instruct to help us remember the config. We will start build our distribution (in the form of a Conda environment, or Docker image). In this step, we will specify:

• name: the name for our distribution (e.g. 8b-instruct)
• image_type: our build image type (conda | docker)
• distribution_spec: our distribution specs for specifying API providers
  • description: a short description of the configurations for the distribution
  • providers: specifies the underlying implementation for serving each API endpoint
  • image_type: conda | docker to specify whether to build the distribution in the form of Docker image or Conda environment.

Build a local distribution with conda

The following command and specifications allows you to get started with building.

llama stack build <path/to/config>

• You will be required to pass in a file path to the build.config file (e.g. ./llama_stack/configs/distributions/conda/local-conda-example-build.yaml). We provide some example build config files for configuring different types of distributions in the ./llama_stack/configs/distributions/ folder.

The file will be of the contents

$ cat ./llama_stack/configs/distributions/conda/local-conda-example-build.yaml

name: 8b-instruct
distribution_spec:
  distribution_type: local
  description: Use code from `llama_stack` itself to serve all llama stack APIs
  docker_image: null
  providers:
    inference: meta-reference
    memory: meta-reference-faiss
    safety: meta-reference
    agentic_system: meta-reference
    telemetry: console
image_type: conda

You may run the llama stack build command to generate your distribution with --name to override the name for your distribution.

$ llama stack build ~/.llama/distributions/conda/8b-instruct-build.yaml --name 8b-instruct
...
...
Build spec configuration saved at ~/.llama/distributions/conda/8b-instruct-build.yaml

After this step is complete, a file named 8b-instruct-build.yaml will be generated and saved at ~/.llama/distributions/conda/8b-instruct-build.yaml.

How to build distribution with different API providers using configs

To specify a different API provider, we can change the distribution_spec in our <name>-build.yaml config. For example, the following build spec allows you to build a distribution using TGI as the inference API provider.

$ cat ./llama_stack/configs/distributions/conda/local-tgi-conda-example-build.yaml

name: local-tgi-conda-example
distribution_spec:
  description: Use TGI (local or with Hugging Face Inference Endpoints for running LLM inference. When using HF Inference Endpoints, you must provide the name of the endpoint).
  docker_image: null
  providers:
    inference: remote::tgi
    memory: meta-reference-faiss
    safety: meta-reference
    agentic_system: meta-reference
    telemetry: console
image_type: conda

The following command allows you to build a distribution with TGI as the inference API provider, with the name tgi.

llama stack build --config ./llama_stack/configs/distributions/conda/local-tgi-conda-example-build.yaml --name tgi

We provide some example build configs to help you get started with building with different API providers.

Step 2. Configure

After our distribution is built (either in form of docker or conda environment), we will run the following command to

llama stack configure [<path/to/name.build.yaml> | <docker-image-name>]

• For conda environments: <path/to/name.build.yaml> would be the generated build spec saved from Step 1.
• For docker images downloaded from Dockerhub, you could also use as the argument.
  • Run docker images to check list of available images on your machine.

$ llama stack configure ~/.llama/distributions/conda/8b-instruct-build.yaml

Configuring API: inference (meta-reference)
Enter value for model (existing: Meta-Llama3.1-8B-Instruct) (required):
Enter value for quantization (optional):
Enter value for torch_seed (optional):
Enter value for max_seq_len (existing: 4096) (required):
Enter value for max_batch_size (existing: 1) (required):

Configuring API: memory (meta-reference-faiss)

Configuring API: safety (meta-reference)
Do you want to configure llama_guard_shield? (y/n): y
Entering sub-configuration for llama_guard_shield:
Enter value for model (default: Llama-Guard-3-8B) (required):
Enter value for excluded_categories (default: []) (required):
Enter value for disable_input_check (default: False) (required):
Enter value for disable_output_check (default: False) (required):
Do you want to configure prompt_guard_shield? (y/n): y
Entering sub-configuration for prompt_guard_shield:
Enter value for model (default: Prompt-Guard-86M) (required):

Configuring API: agentic_system (meta-reference)
Enter value for brave_search_api_key (optional):
Enter value for bing_search_api_key (optional):
Enter value for wolfram_api_key (optional):

Configuring API: telemetry (console)

YAML configuration has been written to ~/.llama/builds/conda/8b-instruct-run.yaml

After this step is successful, you should be able to find a run configuration spec in ~/.llama/builds/conda/8b-instruct-run.yaml with the following contents. You may edit this file to change the settings.

As you can see, we did basic configuration above and configured:

• inference to run on model Meta-Llama3.1-8B-Instruct (obtained from llama model list)
• Llama Guard safety shield with model Llama-Guard-3-8B
• Prompt Guard safety shield with model Prompt-Guard-86M

For how these configurations are stored as yaml, checkout the file printed at the end of the configuration.

Note that all configurations as well as models are stored in ~/.llama

Step 3. Run

Now, let's start the Llama Stack Distribution Server. You will need the YAML configuration file which was written out at the end by the llama stack configure step.

llama stack run ~/.llama/builds/conda/8b-instruct-run.yaml

You should see the Llama Stack server start and print the APIs that it is supporting

$ llama stack run ~/.llama/builds/local/conda/8b-instruct.yaml

> initializing model parallel with size 1
> initializing ddp with size 1
> initializing pipeline with size 1
Loaded in 19.28 seconds
NCCL version 2.20.5+cuda12.4
Finished model load YES READY
Serving POST /inference/batch_chat_completion
Serving POST /inference/batch_completion
Serving POST /inference/chat_completion
Serving POST /inference/completion
Serving POST /safety/run_shields
Serving POST /agents/memory_bank/attach
Serving POST /agents/create
Serving POST /agents/session/create
Serving POST /agents/turn/create
Serving POST /agents/delete
Serving POST /agents/session/delete
Serving POST /agents/memory_bank/detach
Serving POST /agents/session/get
Serving POST /agents/step/get
Serving POST /agents/turn/get
Listening on :::5000
INFO:     Started server process [453333]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://[::]:5000 (Press CTRL+C to quit)

Note

Configuration is in ~/.llama/builds/local/conda/8b-instruct.yaml. Feel free to increase max_seq_len.

Important

The "local" distribution inference server currently only supports CUDA. It will not work on Apple Silicon machines.

Tip

You might need to use the flag --disable-ipv6 to Disable IPv6 support

This server is running a Llama model locally.

Start an App and Interact with the Server

Now that the Stack server is setup, the next thing would be to run an agentic app using Agents APIs.

We have built sample scripts, notebooks and a UI chat interface ( using Mesop ! ) to help you get started.

Start an app (local) and interact with it by running the following command:

mesop app/main.py

This will start a mesop app and you can go to localhost:32123 to play with the chat interface.

Optionally, you can setup API keys for custom tools:

• WolframAlpha: store in WOLFRAM_ALPHA_API_KEY environment variable
• Brave Search: store in BRAVE_SEARCH_API_KEY environment variable

Similar to this main app, you can also try other variants

• PYTHONPATH=. mesop app/chat_with_custom_tools.py to showcase how custom tools are integrated
• PYTHONPATH=. mesop app/chat_moderation_with_llama_guard.py to showcase how the app is modified to act as a chat moderator for safety

Create agentic systems and interact with the Stack server

NOTE: Ensure that Stack server is still running.

cd <path-to-llama-agentic-system>
conda activate $ENV
llama stack run local-ollama --name 8b --port 5000 # If not already started

PYTHONPATH=. python examples/scripts/vacation.py localhost 5000

You should see outputs to stdout of the form --

Environment: ipython
Tools: brave_search, wolfram_alpha, photogen

Cutting Knowledge Date: December 2023
Today Date: 23 July 2024


User> I am planning a trip to Switzerland, what are the top 3 places to visit?
Final Llama Guard response shield_type=<BuiltinShield.llama_guard: 'llama_guard'> is_violation=False violation_type=None violation_return_message=None
Ran PromptGuardShield and got Scores: Embedded: 0.9999765157699585, Malicious: 1.1110752893728204e-05
StepType.shield_call> No Violation
role='user' content='I am planning a trip to Switzerland, what are the top 3 places to visit?'
StepType.inference> Switzerland is a beautiful country with a rich history, culture, and natural beauty. Here are three must-visit places to add to your itinerary: ....

Tip You can optionally do --disable-safety in the scripts to avoid running safety shields all the time.

Feel free to reach out if you have questions.

Using VirtualEnv instead of Conda

Note

While you can run the apps using venv, installation of a distribution requires conda.

In Linux

# Create and activate a virtual environment
python3 -m venv venv
source venv/bin/activate

For Windows

# Create and activate a virtual environment
python -m venv venv
venv\Scripts\activate  # For Command Prompt
# or
.\venv\Scripts\Activate.ps1  # For PowerShell
# or
source venv\Scripts\activate  # For Git

The instructions thereafter (including pip install -r requirements.txt for installing the dependencies) remain the same.