Build MegaService of ChatQnA on NVIDIA GPU

This document outlines the deployment process for a ChatQnA application utilizing the GenAIComps microservice pipeline on NVIDIA GPU platform. The steps include Docker image creation, container deployment via Docker Compose, and service execution to integrate microservices such as embedding, retriever, rerank, and llm. We will publish the Docker images to Docker Hub, it will simplify the deployment process for this service.

Quick Start Deployment Steps:

1. Set up the environment variables.

2. Modify the TEI Docker Image for Reranking

3. Run Docker Compose.

4. Consume the ChatQnA Service.

Quick Start: 1.Setup Environment Variable

To set up environment variables for deploying ChatQnA services, follow these steps:

1. Set the required environment variables:

   # Example: host_ip="192.168.1.1"
   export host_ip="External_Public_IP"
   export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"
   

2. If you are in a proxy environment, also set the proxy-related environment variables:

   export http_proxy="Your_HTTP_Proxy"
   export https_proxy="Your_HTTPs_Proxy"
   # Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1"
   export no_proxy="Your_No_Proxy",chatqna-ui-server,chatqna-backend-server,dataprep-redis-service,tei-embedding-service,retriever,tei-reranking-service,tgi-service
   

3. Set up other environment variables:

   source ./set_env.sh
   

Quick Start: 2.Modify the TEI Docker Image for Reranking

Note: The default Docker image for the tei-reranking-service in compose.yaml is built for A100 and A30 backend with compute capacity 8.0. If you are using A100/A30, skip this step. For other GPU architectures, please modify the image with specific tag for tei-reranking-service based on the following table with target CUDA compute capacity.

GPU Arch	GPU	Compute Capacity	Image
Volta	V100	7.0	NOT SUPPORTED
Turing	T4, GeForce RTX 2000 Series	7.5	ghcr.io/huggingface/text-embeddings-inference:turing-1.5
Ampere 80	A100, A30	8.0	ghcr.io/huggingface/text-embeddings-inference:1.5
Ampere 86	A40, A10, A16, A2, GeForce RTX 3000 Series	8.6	ghcr.io/huggingface/text-embeddings-inference:86-1.5
Ada Lovelace	L40S, L40, L4, GeForce RTX 4000 Series	8.9	ghcr.io/huggingface/text-embeddings-inference:89-1.5
Hopper	H100	9.0	ghcr.io/huggingface/text-embeddings-inference:hopper-1.5

For instance, if Hopper arch GPU (such as H100/H100 NVL) is the target backend:

# vim compose.yaml
tei-reranking-service:
  #image: ghcr.io/huggingface/text-embeddings-inference:1.5
  image: ghcr.io/huggingface/text-embeddings-inference:hopper-1.5

Quick Start: 3.Run Docker Compose

docker compose up -d

It will automatically download the docker image on docker hub:

docker pull opea/chatqna:latest
docker pull opea/chatqna-ui:latest

In following cases, you could build docker image from source by yourself.

• Failed to download the docker image.

• If you want to use a specific version of Docker image.

Please refer to ‘Build Docker Images’ in below.

QuickStart: 4.Consume the ChatQnA Service

curl http://${host_ip}:8888/v1/chatqna \
    -H "Content-Type: application/json" \
    -d '{
        "messages": "What is the revenue of Nike in 2023?"
    }'

🚀 Build Docker Images

First of all, you need to build Docker Images locally. This step can be ignored after the Docker images published to Docker hub.

1. Source Code install GenAIComps

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

2. Build Retriever Image

docker build --no-cache -t opea/retriever-redis:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/redis/langchain/Dockerfile .

3. Build Dataprep Image

docker build --no-cache -t opea/dataprep-redis:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/redis/langchain/Dockerfile .

4. Build MegaService Docker Image

To construct the Mega Service, we utilize the GenAIComps microservice pipeline within the chatqna.py Python script. Build the MegaService Docker image using the command below:

git clone https://github.com/opea-project/GenAIExamples.git
cd GenAIExamples/ChatQnA
docker build --no-cache -t opea/chatqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
cd ../..

5. Build UI Docker Image

Construct the frontend Docker image using the command below:

cd GenAIExamples/ChatQnA/ui
docker build --no-cache -t opea/chatqna-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .
cd ../../../

6. Build React UI Docker Image (Optional)

Construct the frontend Docker image using the command below:

cd GenAIExamples/ChatQnA/ui
docker build --no-cache -t opea/chatqna-react-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile.react .
cd ../../..

7. Build Nginx Docker Image

cd GenAIComps
docker build -t opea/nginx:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/nginx/Dockerfile .

Then run the command docker images, you will have the following 5 Docker Images:

1. opea/retriever-redis:latest

2. opea/dataprep-redis:latest

3. opea/chatqna:latest

4. opea/chatqna-ui:latest or opea/chatqna-react-ui:latest

5. opea/nginx:latest

🚀 Start MicroServices and MegaService

Required Models

By default, the embedding, reranking and LLM models are set to a default value as listed below:

Service	Model
Embedding	BAAI/bge-base-en-v1.5
Reranking	BAAI/bge-reranker-base
LLM	Intel/neural-chat-7b-v3-3

Change the xxx_MODEL_ID below for your needs.

Setup Environment Variables

1. Set the required environment variables:

   # Example: host_ip="192.168.1.1"
   export host_ip="External_Public_IP"
   export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"
   # Example: NGINX_PORT=80
   export NGINX_PORT=${your_nginx_port}
   

2. If you are in a proxy environment, also set the proxy-related environment variables:

   export http_proxy="Your_HTTP_Proxy"
   export https_proxy="Your_HTTPs_Proxy"
   # Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1"
   export no_proxy="Your_No_Proxy",chatqna-ui-server,chatqna-backend-server,dataprep-redis-service,tei-embedding-service,retriever,tei-reranking-service,tgi-service
   

3. Set up other environment variables:

   source ./set_env.sh
   

Modify the TEI Docker Image for Reranking

Note: The default Docker image for the tei-reranking-service in compose.yaml is built for A100 and A30 backend with compute capacity 8.0. If you are using A100/A30, skip this step. For other GPU architectures, please modify the image with specific tag for tei-reranking-service based on the following table with target CUDA compute capacity.

GPU Arch	GPU	Compute Capacity	Image
Volta	V100	7.0	NOT SUPPORTED
Turing	T4, GeForce RTX 2000 Series	7.5	ghcr.io/huggingface/text-embeddings-inference:turing-1.5
Ampere 80	A100, A30	8.0	ghcr.io/huggingface/text-embeddings-inference:1.5
Ampere 86	A40, A10, A16, A2, GeForce RTX 3000 Series	8.6	ghcr.io/huggingface/text-embeddings-inference:86-1.5
Ada Lovelace	L40S, L40, L4, GeForce RTX 4000 Series	8.9	ghcr.io/huggingface/text-embeddings-inference:89-1.5
Hopper	H100	9.0	ghcr.io/huggingface/text-embeddings-inference:hopper-1.5

For instance, if Hopper arch GPU (such as H100/H100 NVL) is the target backend:

# vim compose.yaml
tei-reranking-service:
  #image: ghcr.io/huggingface/text-embeddings-inference:1.5
  image: ghcr.io/huggingface/text-embeddings-inference:hopper-1.5

Start all the services Docker Containers

cd GenAIExamples/ChatQnA/docker_compose/nvidia/gpu/
docker compose up -d

Validate MicroServices and MegaService

1. TEI Embedding Service

   curl ${host_ip}:8090/embed \
       -X POST \
       -d '{"inputs":"What is Deep Learning?"}' \
       -H 'Content-Type: application/json'
   

2. Retriever Microservice

   To consume the retriever microservice, you need to generate a mock embedding vector by Python script. The length of embedding vector is determined by the embedding model. Here we use the model EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5", which vector size is 768.

   Check the vecotor dimension of your embedding model, set your_embedding dimension equals to it.

   export your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
   curl http://${host_ip}:7000/v1/retrieval \
     -X POST \
     -d "{\"text\":\"test\",\"embedding\":${your_embedding}}" \
     -H 'Content-Type: application/json'
   

3. TEI Reranking Service

   curl http://${host_ip}:8808/rerank \
       -X POST \
       -d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
       -H 'Content-Type: application/json'
   

4. TGI Service

   In first startup, this service will take more time to download the model files. After it’s finished, the service will be ready.

   Try the command below to check whether the TGI service is ready.

   docker logs ${CONTAINER_ID} | grep Connected
   

   If the service is ready, you will get the response like below.

   2024-09-03T02:47:53.402023Z  INFO text_generation_router::server: router/src/server.rs:2311: Connected
   

   Then try the cURL command below to validate TGI.

   curl http://${host_ip}:9009/v1/chat/completions \
     -X POST \
     -d '{"model": "Intel/neural-chat-7b-v3-3", "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens":17}' \
     -H 'Content-Type: application/json'
   

5. MegaService

   curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
        "messages": "What is the revenue of Nike in 2023?"
        }'
   

6. Nginx Service

   curl http://${host_ip}:${NGINX_PORT}/v1/chatqna \
       -H "Content-Type: application/json" \
       -d '{"messages": "What is the revenue of Nike in 2023?"}'
   

7. Dataprep Microservice（Optional）

If you want to update the default knowledge base, you can use the following commands:

Update Knowledge Base via Local File Upload:

curl -X POST "http://${host_ip}:6007/v1/dataprep" \
     -H "Content-Type: multipart/form-data" \
     -F "files=@./nke-10k-2023.pdf"

This command updates a knowledge base by uploading a local file for processing. Update the file path according to your environment.

Add Knowledge Base via HTTP Links:

curl -X POST "http://${host_ip}:6007/v1/dataprep" \
     -H "Content-Type: multipart/form-data" \
     -F 'link_list=["https://opea.dev"]'

This command updates a knowledge base by submitting a list of HTTP links for processing.

Also, you are able to get the file list that you uploaded:

curl -X POST "http://${host_ip}:6007/v1/dataprep/get_file" \
     -H "Content-Type: application/json"

To delete the file/link you uploaded:

# delete link
curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
     -d '{"file_path": "https://opea.dev"}' \
     -H "Content-Type: application/json"

# delete file
curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
     -d '{"file_path": "nke-10k-2023.pdf"}' \
     -H "Content-Type: application/json"

# delete all uploaded files and links
curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
     -d '{"file_path": "all"}' \
     -H "Content-Type: application/json"

🚀 Launch the UI

Launch with origin port

To access the frontend, open the following URL in your browser: http://{host_ip}:5173. By default, the UI runs on port 5173 internally. If you prefer to use a different host port to access the frontend, you can modify the port mapping in the compose.yaml file as shown below:

  chaqna-ui-server:
    image: opea/chatqna-ui:latest
    ...
    ports:
      - "80:5173"

Launch with Nginx

If you want to launch the UI using Nginx, open this URL: http://${host_ip}:${NGINX_PORT} in your browser to access the frontend.

🚀 Launch the Conversational UI (Optional)

To access the Conversational UI (react based) frontend, modify the UI service in the compose.yaml file. Replace chaqna-ui-server service with the chatqna-react-ui-server service as per the config below:

chatqna-react-ui-server:
  image: opea/chatqna-react-ui:latest
  container_name: chatqna-react-ui-server
  environment:
    - APP_BACKEND_SERVICE_ENDPOINT=${BACKEND_SERVICE_ENDPOINT}
    - APP_DATA_PREP_SERVICE_URL=${DATAPREP_SERVICE_ENDPOINT}
  ports:
    - "5174:80"
  depends_on:
    - chaqna-backend-server
  ipc: host
  restart: always

Once the services are up, open the following URL in your browser: http://{host_ip}:5174. By default, the UI runs on port 80 internally. If you prefer to use a different host port to access the frontend, you can modify the port mapping in the compose.yaml file as shown below:

  chaqna-react-ui-server:
    image: opea/chatqna-react-ui:latest
    ...
    ports:
      - "80:80"

Here is an example of running ChatQnA:

Here is an example of running ChatQnA with Conversational UI (React):