Guardrails Microservice

To fortify AI initiatives in production, this microservice introduces guardrails designed to encapsulate LLMs, ensuring the enforcement of responsible behavior. With this microservice, you can secure model inputs and outputs, hastening your journey to production and democratizing AI within your organization, building Trustworthy, Safe, and Secure LLM-based Applications.

These guardrails actively prevent the model from interacting with unsafe content, promptly signaling its inability to assist with such requests. With these protective measures in place, you can expedite production timelines and alleviate concerns about unpredictable model responses.

The Guardrails Microservice now offers two primary types of guardrails:

  • Input Guardrails: These are applied to user inputs. An input guardrail can reject the input, halting further processing.

  • Output Guardrails: These are applied to outputs generated by the LLM. An output guardrail can reject the output, preventing it from being returned to the user.

We offer content moderation support utilizing Allen Institute for AI’s WildGuard model.

allenai/wildguard was fine-tuned from mistralai/Mistral-7B-v0.3 on their own allenai/wildguardmix dataset. Any content that is detected in the following categories is determined as unsafe:

  • Privacy

  • Misinformation

  • Harmful Language

  • Malicious Uses

🚀1. Start Microservice with Python (Option 1)

To start the Guardrails microservice, you need to install python packages first.

1.1 Install Requirements

pip install -r requirements.txt

1.2 Start TGI Gaudi Service

export HF_TOKEN=${your_hf_api_token}
volume=$PWD/data
model_id="allenai/wildguard"
docker pull ghcr.io/huggingface/tgi-gaudi:2.0.1
docker run -p 8088:80 -v $volume:/data --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host -e HTTPS_PROXY=$https_proxy -e HTTP_PROXY=$https_proxy -e HF_TOKEN=$HF_TOKEN ghcr.io/huggingface/tgi-gaudi:2.0.1 --model-id $model_id --max-input-length 1024 --max-total-tokens 2048

1.3 Verify the TGI Gaudi Service

curl 127.0.0.1:8088/generate \
  -X POST \
  -d '{"inputs":"How do you buy a tiger in the US?","parameters":{"max_new_tokens":32}}' \
  -H 'Content-Type: application/json'

1.4 Start Guardrails Service

export SAFETY_GUARD_ENDPOINT="http://${your_ip}:8088"
python guardrails_tgi.py

🚀2. Start Microservice with Docker (Option 2)

If you start an Guardrails microservice with docker, the docker_compose_guardrails.yaml file will automatically start a TGI gaudi service with docker.

2.1 Setup Environment Variables

In order to start TGI and LLM services, you need to setup the following environment variables first.

export HUGGINGFACEHUB_API_TOKEN=${your_hf_api_token}
export SAFETY_GUARD_ENDPOINT="http://${your_ip}:8088"
export LLM_MODEL_ID=${your_hf_llm_model}

2.2 Build Docker Image

cd ../../../../
docker build -t opea/guardrails-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/guardrails/wildguard/langchain/Dockerfile .

2.3 Run Docker with CLI

docker run -d --name="guardrails-tgi-server" -p 9090:9090 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e no_proxy=$no_proxy -e SAFETY_GUARD_ENDPOINT=$SAFETY_GUARD_ENDPOINT -e HUGGINGFACEHUB_API_TOKEN=$HUGGINGFACEHUB_API_TOKEN opea/guardrails-tgi:latest

2.4 Run Docker with Docker Compose

docker compose -f docker_compose_guardrails.yaml up -d

🚀3. Consume Guardrails Service

3.1 Check Service Status

curl http://localhost:9090/v1/health_check \
  -X GET \
  -H 'Content-Type: application/json'

3.2 Consume Guardrails Service

curl http://localhost:9090/v1/guardrails \
  -X POST \
  -d '{"text":"How do you buy a tiger in the US?","parameters":{"max_new_tokens":32}}' \
  -H 'Content-Type: application/json'