tei¶

Helm chart for deploying Hugging Face Text Generation Inference service.

Installing the Chart¶

To install the chart, run the following:

cd ${GenAIInfro_repo}/helm-charts/common
export MODELDIR=/mnt/opea-models
export MODELNAME="BAAI/bge-base-en-v1.5"
helm install tei tei --set global.modelUseHostPath=${MODELDIR} --set EMBEDDING_MODEL_ID=${MODELNAME}

By default, the tei service will downloading the “BAAI/bge-base-en-v1.5” which is about 1.1GB.

If you already cached the model locally, you can pass it to container like this example:

MODELDIR=/mnt/opea-models

MODELNAME=”/data/BAAI/bge-base-en-v1.5”

Verify¶

To verify the installation, run the command kubectl get pod to make sure all pods are runinng.

Then run the command kubectl port-forward svc/tei 2081:80 to expose the tei service for access.

Open another terminal and run the following command to verify the service if working:

curl http://localhost:2081/embed -X POST -d '{"inputs":"What is Deep Learning?"}' -H 'Content-Type: application/json'

Values¶

Key	Type	Default	Description
EMBEDDING_MODEL_ID	string	`"BAAI/bge-base-en-v1.5"`	Models id from https://huggingface.co/, or predownloaded model directory
global.modelUseHostPath	string	`"/mnt/opea-models"`	Cached models directory, tei will not download if the model is cached here. The host path “modelUseHostPath” will be mounted to container as /data directory. Set this to null/empty will force it to download model.
image.repository	string	`"ghcr.io/huggingface/text-embeddings-inference"`
image.tag	string	`"cpu-1.5"`
autoscaling.enabled	bool	`false`	Enable HPA autoscaling for the service deployment based on metrics it provides. See HPA instructions before enabling!
global.monitoring	bool	`false`	Enable usage metrics for the service. Required for HPA. See monitoring instructions before enabling!