llama-stack-provider-trustyai-fms 0.3.2

Remote safety provider for Llama Stack integrating FMS Guardrails Orchestrator and community detectors

trustyai_fms: out-of-tree remote safety provider for llama stack

This repo implements FMS Guardrails Orchestrator together with community detectors:

• regex detectors
• Hugging Face content detectors
• vllm detector adapter

as an out-of-tree remote safety provider for llama stack based on this guideline

Folder structure

.
├── llama_stack_provider_trustyai_fms
│   ├── __init__.py
│   ├── config.py
│   ├── detectors
│   │   ├── base.py
│   │   ├── chat.py
│   │   └── content.py
│   └── provider.py
├── notebooks
│   ├── trustyai-fms-detector-api.ipynb
│   └── trustyai-fms-orchestrator-api.ipynb
├── providers.d
│   └── remote
│       └── safety
│           └── trustyai_fms.yaml
├── pyproject.toml
├── README.md
└── runtime_configurations
    ├── detector_api.yaml
    └── orchestrator_api.yaml

• llama_stack_provider_trustyai_fms/ -- the main package with the implementation of the remote safety provider
• notebooks/ -- jupyter notebooks with examples of running shields once they are configured
• providers.d -- directory containing external provider specifications
• runtime_configurations/ -- examples of YAML file to configure the stack with the provider either using the orchestrator API or the detector API

Running demos

To run the demos in full, there is a need to deploy the orchestrator and detectors on Openshift, unless you have access to the necessary routes of the deployed services. If you do not have access to these routes, follow Part A below to set them up.

Subsequently, to create a local llama stack distribution, follow Part B below

Part A. Openshift setup for the orchestrator and detectors

The demos require deploying the orchestrator and detectors on Openshift.

The following operators are required in the Openshift cluster:

GPU -- follow this guide and install:

• Node Feature Discovery Operator (4.17.0-202505061137 provided by Red Hat):
  • ensure to create an instance of NodeFeatureDiscovery using the NodeFeatureDiscovery tab
• NVIDIA GPU Operator (25.3.0 provided by NVIDIA Corporation)
  • ensure to create an instance of ClusterPolicy using the ClusterPolicy tab

Model Serving:

• Red Hat OpenShift Service Mesh 2 (2.6.7-0 provided by Red Hat, Inc.)
• Red Hat OpenShift Serverless (1.35.1 provided by Red Hat) Authentication:
• Red Hat - Authorino Operator (1.2.1 provided by Red Hat)

AI Platform:

• Red Hat OpenShift AI (2.20.0 provided by Red Hat, Inc.):
  • in the DataScienceInitialization resource, set the value of managementState for the serviceMesh component to Removed
  • in the default-dsc, ensure:
    1. trustyai managementState is set to Managed
    2. kserve is set to:

       kserve:
           defaultDeploymentMode: RawDeployment
           managementState: Managed
           serving:
               managementState: Removed
               name: knative-serving
       

Once the above steps are completed,

1. Create a new project

oc new-project test

2. Apply the manifests in the openshift-manifests/ directory to deploy the orchestrator and detectors.

oc apply -k openshift-manifests/

Part B. Setup to create a local llama stack distribution with external trustyai_fms remote safety provider

1. Clone the repo

git clone https://github.com/m-misiura/llama-stack-provider-trustyai-fms.git

2. Change directory to the cloned repo

cd llama-stack-provider-trustyai-fms

3. Create a virtual environment

python3 -m venv .venv

4. Activate the virtual environment

source .venv/bin/activate

5. Install the requirements

pip install -e .

6. Pick a runtime configuration file from runtime_configurations/ and run the stack:

   a. for the orchestrator API:

   llama stack run runtime_configurations/orchestrator_api.yaml --image-type=venv
   

   Note that you might need to export the following environment variables:

   export FMS_ORCHESTRATOR_URL="https://$(oc get routes guardrails-orchestrator-http -o jsonpath='{.spec.host}')"
   

   b. for the detector API:

   llama stack run runtime_configurations/detector_api.yaml --image-type=venv
   

   Not that you might need to export the following environment variables:

   export FMS_CHAT_URL="http://$(oc get routes granite-2b-detector-route -o jsonpath='{.spec.host}')"
   export FMS_REGEX_URL="http://$(oc get routes pii-detector-route   -o jsonpath='{.spec.host}')"
   export FMS_HAP_URL="http://$(oc get routes hap-detector-route  -o jsonpath='{.spec.host}')"
   

7. Go through the notebook to see how to use the stack, e.g. in an other terminal open:

   • for for the orchestrator API:

   jupyter notebook notebooks/trustyai-fms-orchestrator-api.ipynb
   

   • for for the detector API:

   jupyter notebook noteboooks/trustyai-fms-detector-api.ipynb