llm-uservice¶

Helm chart for deploying LLM microservice.

llm-uservice depends on TGI, you should set TGI_LLM_ENDPOINT as tgi endpoint.

(Option1): Installing the chart separately¶

First, you need to install the tgi chart, please refer to the tgi chart for more information.

After you’ve deployted the tgi chart successfully, please run kubectl get svc to get the tgi service endpoint, i.e. http://tgi.

To install the chart, run the following:

cd GenAIInfra/helm-charts/common/llm-uservice
export HFTOKEN="insert-your-huggingface-token-here"
export TGI_LLM_ENDPOINT="http://tgi"
helm dependency update
helm install llm-uservice . --set global.HUGGINGFACEHUB_API_TOKEN=${HFTOKEN} --set TGI_LLM_ENDPOINT=${TGI_LLM_ENDPOINT} --wait

(Option2): Installing the chart with dependencies automatically¶

cd GenAIInfra/helm-charts/common/llm-uservice
export HFTOKEN="insert-your-huggingface-token-here"
helm dependency update
helm install llm-uservice . --set global.HUGGINGFACEHUB_API_TOKEN=${HFTOKEN} --set tgi.enabled=true --wait

Verify¶

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

Then run the command kubectl port-forward svc/llm-uservice 9000:9000 to expose the llm-uservice service for access.

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

curl http://localhost:9000/v1/chat/completions \
    -X POST \
    -d '{"query":"What is Deep Learning?","max_tokens":17,"top_k":10,"top_p":0.95,"typical_p":0.95,"temperature":0.01,"repetition_penalty":1.03,"streaming":true}' \
    -H 'Content-Type: application/json'

Values¶

Key	Type	Default	Description
global.HUGGINGFACEHUB_API_TOKEN	string	`""`	Your own Hugging Face API token
image.repository	string	`"opea/llm-tgi"`
service.port	string	`"9000"`
TGI_LLM_ENDPOINT	string	`""`	LLM endpoint
global.monitoring	bool	`false`	Service usage metrics