llm-uservice

Helm chart for deploying OPEA LLM microservices.

Installing the chart

llm-uservice depends on one of the following inference backend services:

• TGI: please refer to tgi chart for more information

• vLLM: please refer to vllm chart for more information

First, you need to install one of the dependent chart, i.e. tgi or vllm helm chart.

After you’ve deployed the dependent chart successfully, please run kubectl get svc to get the backend inference service endpoint, e.g. http://tgi, http://vllm.

To install the llm-uservice chart, run the following:

cd GenAIInfra/helm-charts/common/llm-uservice
helm dependency update
export HFTOKEN="insert-your-huggingface-token-here"
# set backend inferene service endpoint URL
# for tgi
export LLM_ENDPOINT="http://tgi"
# for vllm
# export LLM_ENDPOINT="http://vllm"

# set the same model used by the backend inference service
export LLM_MODEL_ID="Intel/neural-chat-7b-v3-3"

# install llm-textgen with TGI backend
helm install llm-uservice . --set TEXTGEN_BACKEND="TGI" --set LLM_ENDPOINT=${LLM_ENDPOINT} --set LLM_MODEL_ID=${LLM_MODEL_ID} --set global.HUGGINGFACEHUB_API_TOKEN=${HFTOKEN} --wait

# install llm-textgen with vLLM backend
# helm install llm-uservice . --set TEXTGEN_BACKEND="vLLM" --set LLM_ENDPOINT=${LLM_ENDPOINT} --set LLM_MODEL_ID=${LLM_MODEL_ID} --set global.HUGGINGFACEHUB_API_TOKEN=${HFTOKEN} --wait

# install llm-docsum with TGI backend
# helm install llm-uservice . --set image.repository="opea/llm-docsum" --set DOCSUM_BACKEND="TGI" --set LLM_ENDPOINT=${LLM_ENDPOINT} --set LLM_MODEL_ID=${LLM_MODEL_ID} --set MAX_INPUT_TOKENS=2048 --set MAX_TOTAL_TOKENS=4096 --set global.HUGGINGFACEHUB_API_TOKEN=${HFTOKEN} --wait

# install llm-docsum with vLLM backend
# helm install llm-uservice . --set image.repository="opea/llm-docsum" --set DOCSUM_BACKEND="vLLM" --set LLM_ENDPOINT=${LLM_ENDPOINT} --set LLM_MODEL_ID=${LLM_MODEL_ID} --set MAX_INPUT_TOKENS=2048 --set MAX_TOTAL_TOKENS=4096 --set global.HUGGINGFACEHUB_API_TOKEN=${HFTOKEN} --wait

# install llm-faqgen with TGI backend
# helm install llm-uservice . --set image.repository="opea/llm-faqgen" --set FAQGEN_BACKEND="TGI" --set LLM_ENDPOINT=${LLM_ENDPOINT} --set LLM_MODEL_ID=${LLM_MODEL_ID} --set global.HUGGINGFACEHUB_API_TOKEN=${HFTOKEN} --wait

# install llm-faqgen with vLLM backend
# helm install llm-uservice . --set image.repository="opea/llm-faqgen" --set FAQGEN_BACKEND="vLLM" --set LLM_ENDPOINT=${LLM_ENDPOINT} --set LLM_MODEL_ID=${LLM_MODEL_ID} --set global.HUGGINGFACEHUB_API_TOKEN=${HFTOKEN} --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 service for access.

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

# for llm-textgen service
curl http://localhost:9000/v1/chat/completions \
  -X POST \
  -d d '{"model": "${LLM_MODEL_ID}", "messages": "What is Deep Learning?", "max_tokens":17}' \
  -H 'Content-Type: application/json'

# for llm-docsum service
curl http://localhost:9000/v1/docsum \
  -X POST \
  -d '{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5.", "max_tokens":32, "language":"en"}' \
  -H 'Content-Type: application/json'

# for llm-faqgen service
curl http://localhost:9000/v1/faqgen \
  -X POST \
  -d '{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5.","max_tokens": 128}' \
  -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-textgen"	one of “opea/llm-textgen”, “opea/llm-docsum”, “opea/llm-faqgen”
LLM_ENDPOINT	string	""	backend inference service endpoint
LLM_MODEL_ID	string	"Intel/neural-chat-7b-v3-3"	model used by the inference backend
TEXTGEN_BACKEND	string	"TGI"	backend inference engine, only valid for llm-textgen image, one of “TGI”, “vLLM”
DOCSUM_BACKEND	string	"TGI"	backend inference engine, only valid for llm-docsum image, one of “TGI”, “vLLM”
FAQGEN_BACKEND	string	"TGI"	backend inference engine, only valid for llm-faqgen image, one of “TGi”, “vLLM”
global.monitoring	bool	false	Service usage metrics