vLLM FAQGen LLM Microservice¶

This microservice interacts with the vLLM server to generate FAQs from Input Text.vLLM is a fast and easy-to-use library for LLM inference and serving, it delivers state-of-the-art serving throughput with a set of advanced features such as PagedAttention, Continuous batching and etc.. Besides GPUs, vLLM already supported Intel CPUs and Gaudi accelerators.

🚀1. Start Microservice with Docker¶

If you start an LLM microservice with docker, the docker_compose_llm.yaml file will automatically start a VLLM service with docker.

To setup or build the vLLM image follow the instructions provided in vLLM Gaudi

1.1 Setup Environment Variables¶

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

export HF_TOKEN=${your_hf_api_token}
export vLLM_ENDPOINT="http://${your_ip}:8008"
export LLM_MODEL_ID=${your_hf_llm_model}

1.3 Build Docker Image¶

cd ../../../../../
docker build -t opea/llm-faqgen-vllm:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/faq-generation/vllm/langchain/Dockerfile .

To start a docker container, you have two options:

A. Run Docker with CLI
B. Run Docker with Docker Compose

You can choose one as needed.

1.3 Run Docker with CLI (Option A)¶

docker run -d -p 8008:80 -v ./data:/data --name vllm-service --shm-size 1g opea/vllm:hpu --model-id ${LLM_MODEL_ID}

docker run -d --name="llm-faqgen-server" -p 9000:9000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e vLLM_ENDPOINT=$vLLM_ENDPOINT -e HUGGINGFACEHUB_API_TOKEN=$HF_TOKEN opea/llm-faqgen-vllm:latest

1.4 Run Docker with Docker Compose (Option B)¶

docker compose -f docker_compose_llm.yaml up -d

🚀3. Consume LLM Service¶

3.1 Check Service Status¶

curl http://${your_ip}:9000/v1/health_check\
  -X GET \
  -H 'Content-Type: application/json'

3.2 Consume FAQGen LLM Service¶

# Streaming Response
# Set streaming to True. Default will be True.
curl http://${your_ip}: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."}' \
  -H 'Content-Type: application/json'

# Non-Streaming Response
# Set streaming to False.
curl http://${your_ip}: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.", "streaming":false}' \
  -H 'Content-Type: application/json'