Build MegaService of ChatQnA on Gaudi

This document outlines the deployment process for a ChatQnA application utilizing the GenAIComps microservice pipeline on Intel Gaudi server. 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:

Set up the environment variables.
Run Docker Compose.
Consume the ChatQnA Service.

Quick Start: 1.Setup Environment Variable

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

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"

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-gaudi-ui-server,chatqna-gaudi-backend-server,dataprep-redis-service,tei-embedding-service,retriever,tei-reranking-service,tgi-service,vllm_service,guardrails

Set up other environment variables:
```
source ./set_env.sh
```

Quick Start: 2.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: 3.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.

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

1. 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 .

2. 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 .

3. Build Guardrails Docker Image (Optional)

To fortify AI initiatives in production, Guardrails microservice can secure model inputs and outputs, building Trustworthy, Safe, and Secure LLM-based Applications.

docker build -t opea/guardrails-tgi:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/guardrails/llama_guard/langchain/Dockerfile .

4. Build MegaService Docker Image

MegaService with Rerank

To construct the Mega Service with Rerank, 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 .

MegaService with Guardrails

If you want to enable guardrails microservice in the pipeline, please use the below command instead:

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

MegaService without Rerank

To construct the Mega Service without Rerank, we utilize the GenAIComps microservice pipeline within the chatqna_without_rerank.py Python script. Build MegaService Docker image via below command:

git clone https://github.com/opea-project/GenAIExamples.git
cd GenAIExamples/ChatQnA
docker build --no-cache -t opea/chatqna-without-rerank:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile.without_rerank .

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 .

6. Build Conversational React UI Docker Image (Optional)

Build frontend Docker image that enables Conversational experience with ChatQnA megaservice via below command:

Export the value of the public IP address of your Gaudi node to the host_ip environment variable

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

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:

opea/retriever-redis:latest
opea/dataprep-redis:latest
opea/chatqna:latest
opea/chatqna-ui:latest
opea/nginx:latest

If Conversation React UI is built, you will find one more image:

opea/chatqna-conversation-ui:latest

If Guardrails docker image is built, you will find one more image:

opea/guardrails-tgi: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.

For users in China who are unable to download models directly from Huggingface, you can use ModelScope or a Huggingface mirror to download models. TGI can load the models either online or offline as described below:

Online

export HF_TOKEN=${your_hf_token}
export HF_ENDPOINT="https://hf-mirror.com"
model_name="Intel/neural-chat-7b-v3-3"
docker run -p 8008:80 -v ./data:/data --name tgi-service -e HF_ENDPOINT=$HF_ENDPOINT -e http_proxy=$http_proxy -e https_proxy=$https_proxy --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e HUGGING_FACE_HUB_TOKEN=$HF_TOKEN -e ENABLE_HPU_GRAPH=true -e LIMIT_HPU_GRAPH=true -e USE_FLASH_ATTENTION=true -e FLASH_ATTENTION_RECOMPUTE=true --cap-add=sys_nice --ipc=host ghcr.io/huggingface/tgi-gaudi:2.0.6 --model-id $model_name --max-input-tokens 1024 --max-total-tokens 2048

Offline

Search your model name in ModelScope. For example, check this page for model neural-chat-7b-v3-1.
Click on Download this model button, and choose one way to download the model to your local path /path/to/model.

Run the following command to start TGI service.

export HF_TOKEN=${your_hf_token}
export model_path="/path/to/model"
docker run -p 8008:80 -v $model_path:/data --name tgi_service --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e HUGGING_FACE_HUB_TOKEN=$HF_TOKEN -e ENABLE_HPU_GRAPH=true -e LIMIT_HPU_GRAPH=true -e USE_FLASH_ATTENTION=true -e FLASH_ATTENTION_RECOMPUTE=true --cap-add=sys_nice --ipc=host ghcr.io/huggingface/tgi-gaudi:2.0.6 --model-id /data --max-input-tokens 1024 --max-total-tokens 2048

Setup Environment Variables

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}

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-gaudi-ui-server,chatqna-gaudi-backend-server,dataprep-redis-service,tei-embedding-service,retriever,tei-reranking-service,tgi-service,vllm_service,guardrails

Set up other environment variables:
```
source ./set_env.sh
```

Start all the services Docker Containers

cd GenAIExamples/ChatQnA/docker_compose/intel/hpu/gaudi/

If use tgi for llm backend.

# Start ChatQnA with Rerank Pipeline
docker compose -f compose.yaml up -d
# Start ChatQnA without Rerank Pipeline
docker compose -f compose_without_rerank.yaml up -d

If use vllm for llm backend.

docker compose -f compose_vllm.yaml up -d

If you want to enable guardrails microservice in the pipeline, please follow the below command instead:

cd GenAIExamples/ChatQnA/docker_compose/intel/hpu/gaudi/
docker compose -f compose_guardrails.yaml up -d

NOTE: Users need at least two Gaudi cards to run the ChatQnA successfully.

Validate MicroServices and MegaService

Follow the instructions to validate MicroServices. For validation details, please refer to how-to-validate_service.

TEI Embedding Service

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

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'
```

TEI Reranking Service

Skip for ChatQnA without Rerank pipeline

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'

LLM backend 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 LLM serving is ready.

docker logs tgi-service | 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 services.

# TGI service
curl http://${host_ip}:9009/v1/chat/completions \
  -X POST \
  -d '{"model": ${LLM_MODEL_ID}, "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens":17}' \
  -H 'Content-Type: application/json'

# vLLM Service
curl http://${host_ip}:9009/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": ${LLM_MODEL_ID}, "messages": [{"role": "user", "content": "What is Deep Learning?"}]}'

MegaService

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

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?"}'

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/link list that you uploaded:

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

Then you will get the response JSON like this. Notice that the returned name/id of the uploaded link is https://xxx.txt.

[
  {
    "name": "nke-10k-2023.pdf",
    "id": "nke-10k-2023.pdf",
    "type": "File",
    "parent": ""
  },
  {
    "name": "https://opea.dev.txt",
    "id": "https://opea.dev.txt",
    "type": "File",
    "parent": ""
  }
]

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.txt"}' \
     -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"

Guardrails (Optional)

curl http://${host_ip}:9090/v1/guardrails\
  -X POST \
  -d '{"text":"How do you buy a tiger in the US?","parameters":{"max_new_tokens":32}}' \
  -H 'Content-Type: application/json'

Profile Microservices

To further analyze MicroService Performance, users could follow the instructions to profile MicroServices.

1. vLLM backend Service

Users could follow previous section to testing vLLM microservice or ChatQnA MegaService.
By default, vLLM profiling is not enabled. Users could start and stop profiling by following commands.

Start vLLM profiling

curl http://${host_ip}:9009/start_profile \
  -H "Content-Type: application/json" \
  -d '{"model": ${LLM_MODEL_ID}}'

Users would see below docker logs from vllm-service if profiling is started correctly.

INFO api_server.py:361] Starting profiler...
INFO api_server.py:363] Profiler started.
INFO:     x.x.x.x:35940 - "POST /start_profile HTTP/1.1" 200 OK

After vLLM profiling is started, users could start asking questions and get responses from vLLM MicroService
or ChatQnA MicroService.

Stop vLLM profiling

By following command, users could stop vLLM profliing and generate a *.pt.trace.json.gz file as profiling result
under /mnt folder in vllm-service docker instance.

# vLLM Service
curl http://${host_ip}:9009/stop_profile \
  -H "Content-Type: application/json" \
  -d '{"model": ${LLM_MODEL_ID}}'

Users would see below docker logs from vllm-service if profiling is stopped correctly.

INFO api_server.py:368] Stopping profiler...
INFO api_server.py:370] Profiler stopped.
INFO:     x.x.x.x:41614 - "POST /stop_profile HTTP/1.1" 200 OK

After vllm profiling is stopped, users could use below command to get the *.pt.trace.json.gz file under /mnt folder.

docker cp  vllm-service:/mnt/ .

Check profiling result

Open a web browser and type "chrome://tracing" or "ui.perfetto.dev", and then load the json.gz file, you should be able
to see the vLLM profiling result as below diagram.

🚀 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:

  chatqna-gaudi-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 chatqna-gaudi-ui-server service with the chatqna-gaudi-conversation-ui-server service as per the config below:

chatqna-gaudi-conversation-ui-server:
  image: opea/chatqna-conversation-ui:latest
  container_name: chatqna-gaudi-conversation-ui-server
  environment:
    - APP_BACKEND_SERVICE_ENDPOINT=${BACKEND_SERVICE_ENDPOINT}
    - APP_DATA_PREP_SERVICE_URL=${DATAPREP_SERVICE_ENDPOINT}
  ports:
    - "5174:80"
  depends_on:
    - chatqna-gaudi-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:

  chatqna-gaudi-conversation-ui-server:
    image: opea/chatqna-conversation-ui:latest
    ...
    ports:
      - "80:80"

Here is an example of running ChatQnA:

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

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README.md

README.md

Build MegaService of ChatQnA on Gaudi

Quick Start: 1.Setup Environment Variable

Quick Start: 2.Run Docker Compose

QuickStart: 3.Consume the ChatQnA Service

🚀 Build Docker Images

1. Build Retriever Image

2. Build Dataprep Image

3. Build Guardrails Docker Image (Optional)

4. Build MegaService Docker Image

5. Build UI Docker Image

6. Build Conversational React UI Docker Image (Optional)

7. Build Nginx Docker Image

🚀 Start MicroServices and MegaService

Required Models

Setup Environment Variables

Start all the services Docker Containers

Validate MicroServices and MegaService

Profile Microservices

1. vLLM backend Service

Start vLLM profiling

Stop vLLM profiling

Check profiling result

🚀 Launch the UI

Launch with origin port

Launch with Nginx

🚀 Launch the Conversational UI (Optional)

Files

README.md

Latest commit

History

README.md

File metadata and controls

Build MegaService of ChatQnA on Gaudi

Quick Start: 1.Setup Environment Variable

Quick Start: 2.Run Docker Compose

QuickStart: 3.Consume the ChatQnA Service

🚀 Build Docker Images

1. Build Retriever Image

2. Build Dataprep Image

3. Build Guardrails Docker Image (Optional)

4. Build MegaService Docker Image

5. Build UI Docker Image

6. Build Conversational React UI Docker Image (Optional)

7. Build Nginx Docker Image

🚀 Start MicroServices and MegaService

Required Models

Setup Environment Variables

Start all the services Docker Containers

Validate MicroServices and MegaService

Profile Microservices

1. vLLM backend Service

Start vLLM profiling

Stop vLLM profiling

Check profiling result

🚀 Launch the UI

Launch with origin port

Launch with Nginx

🚀 Launch the Conversational UI (Optional)