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LLM Microservice

This microservice, designed for Language Model Inference (LLM), processes input consisting of a query string and associated reranked documents. It constructs a prompt based on the query and documents, which is then used to perform inference with a large language model. The service delivers the inference results as output.

A prerequisite for using this microservice is that users must have a LLM text generation service (etc., TGI, vLLM and Ray) already running. Users need to set the LLM service's endpoint into an environment variable. The microservice utilizes this endpoint to create an LLM object, enabling it to communicate with the LLM service for executing language model operations.

Overall, this microservice offers a streamlined way to integrate large language model inference into applications, requiring minimal setup from the user beyond initiating a TGI/vLLM/Ray service and configuring the necessary environment variables. This allows for the seamless processing of queries and documents to generate intelligent, context-aware responses.

Validated LLM Models

Model TGI-Gaudi vLLM-CPU vLLM-Gaudi Ray
Intel/neural-chat-7b-v3-3 ✓ ✓ ✓ ✓
Llama-2-7b-chat-hf ✓ ✓ ✓ ✓
Llama-2-70b-chat-hf ✓ - ✓ x
Meta-Llama-3-8B-Instruct ✓ ✓ ✓ ✓
Meta-Llama-3-70B-Instruct ✓ - ✓ x
Phi-3 x Limit 4K Limit 4K ✓

Clone OPEA GenAIComps

Clone this repository at your desired location and set an environment variable for easy setup and usage throughout the instructions.

git clone https://github.com/opea-project/GenAIComps.git

export OPEA_GENAICOMPS_ROOT=$(pwd)/GenAIComps

🚀1. Start Microservice with Python (Option 1)

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

1.1 Install Requirements

pip install opea-comps
pip install -r ${OPEA_GENAICOMPS_ROOT}/comps/llms/requirements.txt

# Install requirements of your choice of microservice in the text-generation folder (tgi, vllm, vllm-ray, etc.)
export MICROSERVICE_DIR=your_chosen_microservice

pip install -r ${OPEA_GENAICOMPS_ROOT}/comps/llms/text-generation/${MICROSERVICE_DIR}/requirements.txt

Set an environment variable your_ip to the IP address of the machine where you would like to consume the microservice.

# For example, this command would set the IP address of your currently logged-in machine.
export your_ip=$(hostname -I | awk '{print $1}')

1.2 Start LLM Service with Python Script

1.2.1 Start the TGI Service

export TGI_LLM_ENDPOINT="http://${your_ip}:8008"
python ${OPEA_GENAICOMPS_ROOT}/comps/llms/text-generation/tgi/llm.py
python ${OPEA_GENAICOMPS_ROOT}/comps/llms/text-generation/tgi/llm.py

1.2.2 Start the vLLM Service

export vLLM_LLM_ENDPOINT="http://${your_ip}:8008"
python ${OPEA_GENAICOMPS_ROOT}/comps/llms/text-generation/vllm/llm.py
python ${OPEA_GENAICOMPS_ROOT}/comps/llms/text-generation/vllm/llm.py

1.2.3 Start the Ray Service

export RAY_Serve_ENDPOINT="http://${your_ip}:8008"
python ${OPEA_GENAICOMPS_ROOT}/comps/llms/text-generation/ray_serve/llm.py
python ${OPEA_GENAICOMPS_ROOT}/comps/llms/text-generation/ray_serve/llm.py

🚀2. Start Microservice with Docker (Option 2)

You can use either a published docker image or build your own docker image with the respective microservice Dockerfile of your choice. You must create a user account with HuggingFace and obtain permission to use the restricted LLM models by adhering to the guidelines provided on the respective model's webpage.

2.1 Start LLM Service with published image

2.1.1 Start TGI Service

export HF_LLM_MODEL=${your_hf_llm_model}
export HF_TOKEN=${your_hf_api_token}

docker run \
  -p 8008:80 \
  -e HF_TOKEN=${HF_TOKEN} \
  -v ./data:/data \
  --name tgi_service \
  --shm-size 1g \
  ghcr.io/huggingface/text-generation-inference:1.4 \
  --model-id ${HF_LLM_MODEL}

2.1.2 Start vLLM Service

# Use the script to build the docker image as opea/vllm:cpu
bash ${OPEA_GENAICOMPS_ROOT}/comps/llms/text-generation/vllm/build_docker_vllm.sh cpu

export HF_LLM_MODEL=${your_hf_llm_model}
export HF_TOKEN=${your_hf_api_token}

docker run -it \
  --name vllm_service \
  -p 8008:80 \
  -e HF_TOKEN=${HF_TOKEN} \
  -e VLLM_CPU_KVCACHE_SPACE=40 \
  -v ./data:/data \
  opea/vllm:cpu \
  --model ${HF_LLM_MODEL}
  --port 80

2.1.3 Start Ray Service

export HF_LLM_MODEL=${your_hf_llm_model}
export HF_CHAT_PROCESSOR=${your_hf_chatprocessor}
export HF_TOKEN=${your_hf_api_token}
export TRUST_REMOTE_CODE=True

docker run -it \
  --runtime=habana \
  --name ray_serve_service \
  -e OMPI_MCA_btl_vader_single_copy_mechanism=none \
  --cap-add=sys_nice \
  --ipc=host \
  -p 8008:80 \
  -e HF_TOKEN=$HF_TOKEN \
  -e TRUST_REMOTE_CODE=$TRUST_REMOTE_CODE \
  opea/llm-ray:latest \
  /bin/bash -c " \
    ray start --head && \
    python api_server_openai.py \
      --port_number 80 \
      --model_id_or_path ${HF_LLM_MODEL} \
      --chat_processor ${HF_CHAT_PROCESSOR}"

2.2 Start LLM Service with image built from source

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

2.2.1 Setup Environment Variables

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

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

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

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

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

export HF_TOKEN=${your_hf_api_token}
export RAY_Serve_ENDPOINT="http://${your_ip}:8008"
export LLM_MODEL=${your_hf_llm_model}
export CHAT_PROCESSOR="ChatModelLlama"

2.2 Build Docker Image

2.2.1 TGI

cd ${OPEA_GENAICOMPS_ROOT}

docker build \
  -t opea/llm-tgi:latest \
  --build-arg https_proxy=$https_proxy \
  --build-arg http_proxy=$http_proxy \
  -f comps/llms/text-generation/tgi/Dockerfile .

2.2.2 vLLM

Build vllm docker.

bash ${OPEA_GENAICOMPS_ROOT}/comps/llms/text-generation/vllm/langchain/dependency/build_docker_vllm.sh

Build microservice docker.

cd ${OPEA_GENAICOMPS_ROOT}

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

2.2.3 Ray Serve

Build Ray Serve docker.

bash ${OPEA_GENAICOMPS_ROOT}/comps/llms/text-generation/vllm/ray/dependency/build_docker_vllmray.sh

Build microservice docker.

cd ${OPEA_GENAICOMPS_ROOT}

docker build \
  -t opea/llm-ray:latest \
  --build-arg https_proxy=$https_proxy \
  --build-arg http_proxy=$http_proxy \
  -f comps/llms/text-generation/vllm/ray/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.

2.3 Run Docker with CLI (Option A)

2.3.1 TGI

docker run -d \
  --name="llm-tgi-server" \
  -p 9000:9000 \
  --ipc=host \
  -e http_proxy=$http_proxy \
  -e https_proxy=$https_proxy \
  -e TGI_LLM_ENDPOINT=$TGI_LLM_ENDPOINT \
  -e HF_TOKEN=$HF_TOKEN \
  opea/llm-tgi:latest

2.3.2 vLLM

Start vllm endpoint.

bash ${OPEA_GENAICOMPS_ROOT}/comps/llms/text-generation/vllm/langchain/dependency/launch_vllm_service.sh

Start vllm microservice.

docker run \
  --name="llm-vllm-server" \
  -p 9000:9000 \
  --ipc=host \
  -e http_proxy=$http_proxy \
  -e https_proxy=$https_proxy \
  -e no_proxy=${no_proxy} \
  -e vLLM_LLM_ENDPOINT=$vLLM_LLM_ENDPOINT \
  -e HF_TOKEN=$HF_TOKEN \
  -e LLM_MODEL_ID=$LLM_MODEL_ID \
  opea/llm-vllm:latest

2.3.3 Ray Serve

Start Ray Serve endpoint.

bash ${OPEA_GENAICOMPS_ROOT}/comps/llms/text-generation/vllm/ray/dependency/launch_vllmray.sh

Start Ray Serve microservice.

docker run -d \
  --name="llm-ray-server" \
  -p 9000:9000 \
  --ipc=host \
  -e http_proxy=$http_proxy \
  -e https_proxy=$https_proxy \
  -e RAY_Serve_ENDPOINT=$RAY_Serve_ENDPOINT \
  -e HF_TOKEN=$HF_TOKEN \
  -e LLM_MODEL=$LLM_MODEL \
  opea/llm-ray:latest

2.4 Run Docker with Docker Compose (Option B)

2.4.1 TGI

cd ${OPEA_GENAICOMPS_ROOT}/comps/llms/text-generation/tgi
docker compose -f docker_compose_llm.yaml up -d

2.4.2 vLLM

cd ${OPEA_GENAICOMPS_ROOT}/comps/llms/text-generation/vllm/langchain
docker compose -f docker_compose_llm.yaml up -d

2.4.3 Ray Serve

cd ${OPEA_GENAICOMPS_ROOT}/comps/llms/text-generation/vllm/ray
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 Verify the LLM Service

3.2.1 Verify the TGI Service

curl http://${your_ip}:8008/generate \
  -X POST \
  -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
  -H 'Content-Type: application/json'

3.2.2 Verify the vLLM Service

curl http://${your_ip}:8008/v1/completions \
  -H "Content-Type: application/json" \
  -d '{
  "model": ${your_hf_llm_model},
  "prompt": "What is Deep Learning?",
  "max_tokens": 32,
  "temperature": 0
  }'

3.2.3 Verify the Ray Service

curl http://${your_ip}:8008/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
  "model": ${your_hf_llm_model},
  "messages": [
        {"role": "assistant", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is Deep Learning?"},
    ],
  "max_tokens": 32,
  "stream": True
  }'

3.3 Consume LLM Service

You can set the following model parameters according to your actual needs, such as max_tokens, streaming.

The streaming parameter determines the format of the data returned by the API. It will return text string with streaming=false, return text streaming flow with streaming=true.

# non-streaming mode
curl http://${your_ip}:9000/v1/chat/completions \
  -X POST \
  -H 'Content-Type: application/json' \
  -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":false
  }'


# streaming mode
curl http://${your_ip}:9000/v1/chat/completions \
  -X POST \
  -H 'Content-Type: application/json' \
  -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
  }'