Build Mega Service of AgentQnA on AMD ROCm GPU¶

Build Docker Images¶

1. Build Docker Image¶

Create application install directory and go to it:

mkdir ~/agentqna-install && cd agentqna-install

Clone the repository GenAIExamples (the default repository branch “main” is used here):
```
git clone https://github.com/opea-project/GenAIExamples.git
```
If you need to use a specific branch/tag of the GenAIExamples repository, then (v1.3 replace with its own value):
```
git clone https://github.com/opea-project/GenAIExamples.git && cd GenAIExamples && git checkout v1.3
```
We remind you that when using a specific version of the code, you need to use the README from this version:

Go to build directory:

cd ~/agentqna-install/GenAIExamples/AgentQnA/docker_image_build

Cleaning up the GenAIComps repository if it was previously cloned in this directory. This is necessary if the build was performed earlier and the GenAIComps folder exists and is not empty:
```
echo Y | rm -R GenAIComps
```
Clone the repository GenAIComps (the default repository branch “main” is used here):

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

We remind you that when using a specific version of the code, you need to use the README from this version.

Setting the list of images for the build (from the build file.yaml)

If you want to deploy a vLLM-based or TGI-based application, then the set of services is installed as follows:

vLLM-based application
```
service_list="vllm-rocm agent agent-ui"
```
TGI-based application
```
service_list="agent agent-ui"
```

Optional. Pull TGI Docker Image (Do this if you want to use TGI)

docker pull ghcr.io/huggingface/text-generation-inference:2.3.1-rocm

Build Docker Images

docker compose -f build.yaml build ${service_list} --no-cache

Build DocIndexRetriever Docker Images

cd ~/agentqna-install/GenAIExamples/DocIndexRetriever/docker_image_build/
git clone https://github.com/opea-project/GenAIComps.git
service_list="doc-index-retriever dataprep embedding retriever reranking"
docker compose -f build.yaml build ${service_list} --no-cache

Pull DocIndexRetriever Docker Images
```
docker pull redis/redis-stack:7.2.0-v9
docker pull ghcr.io/huggingface/text-embeddings-inference:cpu-1.7
```
After the build, we check the list of images with the command:
```
docker image ls
```
The list of images should include:

vLLM-based application:
- opea/vllm-rocm:latest
- opea/agent:latest
- redis/redis-stack:7.2.0-v9
- ghcr.io/huggingface/text-embeddings-inference:cpu-1.7
- opea/embedding:latest
- opea/retriever:latest
- opea/reranking:latest
- opea/doc-index-retriever:latest
TGI-based application:
- ghcr.io/huggingface/text-generation-inference:2.3.1-rocm
- opea/agent:latest
- redis/redis-stack:7.2.0-v9
- ghcr.io/huggingface/text-embeddings-inference:cpu-1.7
- opea/embedding:latest
- opea/retriever:latest
- opea/reranking:latest
- opea/doc-index-retriever:latest

Deploy the AgentQnA Application¶

Docker Compose Configuration for AMD GPUs¶

To enable GPU support for AMD GPUs, the following configuration is added to the Docker Compose file:

compose_vllm.yaml - for vLLM-based application
compose.yaml - for TGI-based

shm_size: 1g
devices:
  - /dev/kfd:/dev/kfd
  - /dev/dri:/dev/dri
cap_add:
  - SYS_PTRACE
group_add:
  - video
security_opt:
  - seccomp:unconfined

This configuration forwards all available GPUs to the container. To use a specific GPU, specify its cardN and renderN device IDs. For example:

shm_size: 1g
devices:
  - /dev/kfd:/dev/kfd
  - /dev/dri/card0:/dev/dri/card0
  - /dev/dri/render128:/dev/dri/render128
cap_add:
  - SYS_PTRACE
group_add:
  - video
security_opt:
  - seccomp:unconfined

How to Identify GPU Device IDs: Use AMD GPU driver utilities to determine the correct cardN and renderN IDs for your GPU.

Set deploy environment variables¶

Setting variables in the operating system environment:¶

### Replace the string 'server_address' with your local server IP address
export host_ip='server_address'
### Replace the string 'your_huggingfacehub_token' with your HuggingFacehub repository access token.
export HF_TOKEN='your_huggingfacehub_token'
### Replace the string 'your_langchain_api_key' with your LANGCHAIN API KEY.
export LANGCHAIN_API_KEY='your_langchain_api_key'
export LANGCHAIN_TRACING_V2=""

Start the services:¶

If you use vLLM¶

cd ~/agentqna-install/GenAIExamples/AgentQnA/docker_compose/amd/gpu/rocm
bash launch_agent_service_vllm_rocm.sh

If you use TGI¶

cd ~/agentqna-install/GenAIExamples/AgentQnA/docker_compose/amd/gpu/rocm
bash launch_agent_service_tgi_rocm.sh

All containers should be running and should not restart:

If you use vLLM:¶

dataprep-redis-server
doc-index-retriever-server
embedding-server
rag-agent-endpoint
react-agent-endpoint
redis-vector-db
reranking-tei-xeon-server
retriever-redis-server
sql-agent-endpoint
tei-embedding-server
tei-reranking-server
vllm-service

If you use TGI:¶

dataprep-redis-server
doc-index-retriever-server
embedding-server
rag-agent-endpoint
react-agent-endpoint
redis-vector-db
reranking-tei-xeon-server
retriever-redis-server
sql-agent-endpoint
tei-embedding-server
tei-reranking-server
tgi-service

Validate the Services¶

1. Validate the vLLM/TGI Service¶

If you use vLLM:¶

DATA='{"model": "Intel/neural-chat-7b-v3-3t", '\
'"messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens": 256}'

curl http://${HOST_IP}:${VLLM_SERVICE_PORT}/v1/chat/completions \
  -X POST \
  -d "$DATA" \
  -H 'Content-Type: application/json'

Checking the response from the service. The response should be similar to JSON:

{
  "id": "chatcmpl-142f34ef35b64a8db3deedd170fed951",
  "object": "chat.completion",
  "created": 1742270316,
  "model": "Intel/neural-chat-7b-v3-3",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "",
        "tool_calls": []
      },
      "logprobs": null,
      "finish_reason": "length",
      "stop_reason": null
    }
  ],
  "usage": { "prompt_tokens": 66, "total_tokens": 322, "completion_tokens": 256, "prompt_tokens_details": null },
  "prompt_logprobs": null
}

If the service response has a meaningful response in the value of the “choices.message.content” key, then we consider the vLLM service to be successfully launched

If you use TGI:¶

DATA='{"inputs":"What is Deep Learning?",'\
'"parameters":{"max_new_tokens":256,"do_sample": true}}'

curl http://${HOST_IP}:${TGI_SERVICE_PORT}/generate \
  -X POST \
  -d "$DATA" \
  -H 'Content-Type: application/json'

Checking the response from the service. The response should be similar to JSON:

{
  "generated_text": " "
}

If the service response has a meaningful response in the value of the “generated_text” key, then we consider the TGI service to be successfully launched

2. Validate Agent Services¶

Validate Rag Agent Service¶

export agent_port=${WORKER_RAG_AGENT_PORT}
prompt="Tell me about Michael Jackson song Thriller"
python3 ~/agentqna-install/GenAIExamples/AgentQnA/tests/test.py --prompt "$prompt" --agent_role "worker" --ext_port $agent_port

The response must contain the meaningful text of the response to the request from the “prompt” variable

Validate Sql Agent Service¶

export agent_port=${WORKER_SQL_AGENT_PORT}
prompt="How many employees are there in the company?"
python3 ~/agentqna-install/GenAIExamples/AgentQnA/tests/test.py --prompt "$prompt" --agent_role "worker" --ext_port $agent_port

The answer should make sense - “8 employees in the company”

Validate React (Supervisor) Agent Service¶

export agent_port=${SUPERVISOR_REACT_AGENT_PORT}
python3 ~/agentqna-install/GenAIExamples/AgentQnA/tests/test.py --agent_role "supervisor" --ext_port $agent_port --stream

The response should contain “Iron Maiden”

3. Stop application¶

If you use vLLM¶

cd ~/agentqna-install/GenAIExamples/AgentQnA/docker_compose/amd/gpu/rocm
bash stop_agent_service_vllm_rocm.sh

If you use TGI¶

cd ~/agentqna-install/GenAIExamples/AgentQnA/docker_compose/amd/gpu/rocm
bash stop_agent_service_tgi_rocm.sh

Build Mega Service of AgentQnA on AMD ROCm GPU¶

Build Docker Images¶

1. Build Docker Image¶

Create application install directory and go to it:

Clone the repository GenAIExamples (the default repository branch “main” is used here):

Go to build directory:

Clone the repository GenAIComps (the default repository branch “main” is used here):

Setting the list of images for the build (from the build file.yaml)

vLLM-based application

TGI-based application

Optional. Pull TGI Docker Image (Do this if you want to use TGI)

Build Docker Images

Build DocIndexRetriever Docker Images

Pull DocIndexRetriever Docker Images

vLLM-based application:

TGI-based application:

Deploy the AgentQnA Application¶

Docker Compose Configuration for AMD GPUs¶

Set deploy environment variables¶

Setting variables in the operating system environment:¶

Start the services:¶

If you use vLLM¶

If you use TGI¶

If you use vLLM:¶

If you use TGI:¶

Validate the Services¶

1. Validate the vLLM/TGI Service¶

If you use vLLM:¶

If you use TGI:¶

2. Validate Agent Services¶

Validate Rag Agent Service¶

Validate Sql Agent Service¶

Validate React (Supervisor) Agent Service¶

3. Stop application¶

If you use vLLM¶

If you use TGI¶