Deploying SearchQnA on AMD ROCm Platform

This document outlines the single node deployment process for a SearchQnA application utilizing the GenAIComps microservices on AMD ROCm Platform.

Table of Contents

1. SearchQnA Quick Start Deployment

2. SearchQnA Docker Compose Files

3. Validate Microservices

4. Launch the UI: Guideline for UI usage

5. Conclusion

SearchQnA Quick Start Deployment

This section describes how to quickly deploy and test the SearchQnA service manually on an AMD ROCm Platform. The basic steps are:

1. Access the Code

2. Configure the Deployment Environment

3. Deploy the Services Using Docker Compose

4. Check the Deployment Status

5. Validate the Pipeline

6. Cleanup the Deployment

Access the Code

Clone the GenAIExample repository and access the SearchQnA AMD ROCm Platform Docker Compose files and supporting scripts:

git clone https://github.com/opea-project/GenAIExamples.git
cd GenAIExamples/SearchQnA/docker_compose/amd/gpu/rocm

Checkout a released version, such as v1.2:

git checkout v1.2

Generate a HuggingFace Access Token

Some HuggingFace resources require an access token. Developers can create one by first signing up on HuggingFace and then generating a user access token.

Configure the Deployment Environment

To set up environment variables for deploying SearchQnA services, set up some parameters specific to the deployment environment and source the set_env.sh script in this directory:

For vLLM inference type deployment (default)

export host_ip="External_Public_IP"           # ip address of the node
export GOOGLE_CSE_ID="your cse id"
export GOOGLE_API_KEY="your google api key"
export HUGGINGFACEHUB_API_TOKEN="Your_HuggingFace_API_Token"
export http_proxy="Your_HTTP_Proxy"           # http proxy if any
export https_proxy="Your_HTTPs_Proxy"         # https proxy if any
export no_proxy=localhost,127.0.0.1,$host_ip  # additional no proxies if needed
export NGINX_PORT=${your_nginx_port}          # your usable port for nginx, 80 for example
source ./set_env_vllm.sh

For TGI inference type deployment

export host_ip="External_Public_IP"           # ip address of the node
export GOOGLE_CSE_ID="your cse id"
export GOOGLE_API_KEY="your google api key"
export HUGGINGFACEHUB_API_TOKEN="Your_HuggingFace_API_Token"
export http_proxy="Your_HTTP_Proxy"           # http proxy if any
export https_proxy="Your_HTTPs_Proxy"         # https proxy if any
export no_proxy=localhost,127.0.0.1,$host_ip  # additional no proxies if needed
export NGINX_PORT=${your_nginx_port}          # your usable port for nginx, 80 for example
source ./set_env.sh

Consult the section on SearchQnA Service configuration for information on how service specific configuration parameters affect deployments.

Deploy the Services Using Docker Compose

To deploy the SearchQnA services, execute the docker compose up command with the appropriate arguments. For a default deployment, execute:

For vLLM inference type deployment (default)

//with VLLM:
docker compose -f compose_vllm.yaml up -d

For TGI inference type deployment

//with TGI:
docker compose -f compose.yaml up -d

Note: developers should build docker image from source when:

• Developing off the git main branch (as the container’s ports in the repo may be different from the published docker image).

• Unable to download the docker image.

• Use a specific version of Docker image.

Please refer to the table below to build different microservices from source:

Microservice	Deployment Guide
Reranking	whisper build guide
vLLM	vLLM build guide
LLM-TextGen	LLM-TextGen build guide
Web-Retriever	Web-Retriever build guide
Embedding	Embedding build guide
MegaService	MegaService build guide
UI	Basic UI build guide

Check the Deployment Status

After running Docker Compose, the list of images can be checked using the following command:

docker ps -a

For the default deployment, the following containers should have started

For vLLM inference type deployment (default)

CONTAINER ID   IMAGE                                                   COMMAND                  CREATED              STATUS              PORTS                                           NAMES
50e5f4a00fcc   opea/searchqna-ui:latest                                "docker-entrypoint.s…"   About a minute ago   Up About a minute   0.0.0.0:18143->5173/tcp, [::]:18143->5173/tcp   search-ui-server
a8f030d17e40   opea/searchqna:latest                                   "python searchqna.py"    About a minute ago   Up About a minute   0.0.0.0:18142->8888/tcp, [::]:18142->8888/tcp   search-backend-server
916c5db048a2   opea/llm-textgen:latest                                 "bash entrypoint.sh"     About a minute ago   Up About a minute   0.0.0.0:3007->9000/tcp, [::]:3007->9000/tcp     search-llm-server
bb46cdaf1794   opea/reranking:latest                                   "python opea_reranki…"   About a minute ago   Up About a minute   0.0.0.0:3005->8000/tcp, [::]:3005->8000/tcp     search-reranking-server
d89ab0ef3f41   opea/embedding:latest                                   "sh -c 'python $( [ …"   About a minute ago   Up About a minute   0.0.0.0:3002->6000/tcp, [::]:3002->6000/tcp     search-embedding-server
b248e55dd20f   opea/vllm-rocm:latest                                   "python3 /workspace/…"   About a minute ago   Up About a minute   0.0.0.0:3080->8011/tcp, [::]:3080->8011/tcp     search-vllm-service
c3800753fac5   opea/web-retriever:latest                               "python opea_web_ret…"   About a minute ago   Up About a minute   0.0.0.0:3003->7077/tcp, [::]:3003->7077/tcp     search-web-retriever-server
0db8af486bd0   ghcr.io/huggingface/text-embeddings-inference:cpu-1.5   "text-embeddings-rou…"   About a minute ago   Up About a minute   0.0.0.0:3001->80/tcp, [::]:3001->80/tcp         search-tei-embedding-server
3125915447ef   ghcr.io/huggingface/text-embeddings-inference:cpu-1.5   "text-embeddings-rou…"   About a minute ago   Up About a minute   0.0.0.0:3004->80/tcp, [::]:3004->80/tcp         search-tei-reranking-server

For TGI inference type deployment

CONTAINER ID   IMAGE                                                      COMMAND                  CREATED              STATUS              PORTS                                           NAMES
67cc886949a3   opea/searchqna-ui:latest                                   "docker-entrypoint.s…"   About a minute ago   Up About a minute   0.0.0.0:18143->5173/tcp, [::]:18143->5173/tcp   search-ui-server
6547aca0d5fd   opea/searchqna:latest                                      "python searchqna.py"    About a minute ago   Up About a minute   0.0.0.0:18142->8888/tcp, [::]:18142->8888/tcp   search-backend-server
213b5d4d5fa5   opea/embedding:latest                                      "sh -c 'python $( [ …"   About a minute ago   Up About a minute   0.0.0.0:3002->6000/tcp, [::]:3002->6000/tcp     search-embedding-server
6b90d16100b2   opea/reranking:latest                                      "python opea_reranki…"   About a minute ago   Up About a minute   0.0.0.0:3005->8000/tcp, [::]:3005->8000/tcp     search-reranking-server
3266fd85207e   opea/llm-textgen:latest                                    "bash entrypoint.sh"     About a minute ago   Up About a minute   0.0.0.0:3007->9000/tcp, [::]:3007->9000/tcp     search-llm-server
d7322b70c15d   ghcr.io/huggingface/text-generation-inference:2.4.1-rocm   "/tgi-entrypoint.sh …"   About a minute ago   Up About a minute   0.0.0.0:3006->80/tcp, [::]:3006->80/tcp         search-tgi-service
a703b91b28ed   ghcr.io/huggingface/text-embeddings-inference:cpu-1.6      "text-embeddings-rou…"   About a minute ago   Up About a minute   0.0.0.0:3001->80/tcp, [::]:3001->80/tcp         search-tei-embedding-server
22098a5eaf59   ghcr.io/huggingface/text-embeddings-inference:cpu-1.6      "text-embeddings-rou…"   About a minute ago   Up About a minute   0.0.0.0:3004->80/tcp, [::]:3004->80/tcp         search-tei-reranking-server
830fe84c971d   opea/web-retriever:latest                                  "python opea_web_ret…"   About a minute ago   Up About a minute   0.0.0.0:3003->7077/tcp, [::]:3003->7077/tcp     search-web-retriever-server

If any issues are encountered during deployment, refer to the Troubleshooting section.

Validate the Pipeline

Once the SearchQnA services are running, test the pipeline using the following command:

    DATA='{"messages": "What is the latest news from the AI world? '\
    'Give me a summary.","stream": "True"}'

    curl http://${host_ip}:3008/v1/searchqna \
    -H "Content-Type: application/json" \
    -d "$DATA"

Note The value of host_ip was set using the set_env.sh script and can be found in the .env file.

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

data: {"id":"cmpl-f095893d094a4e9989423c2364f00bc1","choices":[{"finish_reason":null,"index":0,"logprobs":null,"text":",","stop_reason":null}],"created":1742960360,"model":"Intel/neural-chat-7b-v3-3","object":"text_completion","system_fingerprint":null,"usage":null}
data: {"id":"cmpl-f095893d094a4e9989423c2364f00bc1","choices":[{"finish_reason":null,"index":0,"logprobs":null,"text":" with","stop_reason":null}],"created":1742960360,"model":"Intel/neural-chat-7b-v3-3","object":"text_completion","system_fingerprint":null,"usage":null}
data: {"id":"cmpl-f095893d094a4e9989423c2364f00bc1","choices":[{"finish_reason":null,"index":0,"logprobs":null,"text":" calls","stop_reason":null}],"created":1742960360,"model":"Intel/neural-chat-7b-v3-3","object":"text_completion","system_fingerprint":null,"usage":null}
data: {"id":"cmpl-f095893d094a4e9989423c2364f00bc1","choices":[{"finish_reason":null,"index":0,"logprobs":null,"text":" for","stop_reason":null}],"created":1742960360,"model":"Intel/neural-chat-7b-v3-3","object":"text_completion","system_fingerprint":null,"usage":null}
data: {"id":"cmpl-f095893d094a4e9989423c2364f00bc1","choices":[{"finish_reason":null,"index":0,"logprobs":null,"text":" more","stop_reason":null}],"created":1742960360,"model":"Intel/neural-chat-7b-v3-3","object":"text_completion","system_fingerprint":null,"usage":null}
data: {"id":"cmpl-f095893d094a4e9989423c2364f00bc1","choices":[{"finish_reason":null,"index":0,"logprobs":null,"text":" regulation","stop_reason":null}],"created":1742960360,"model":"Intel/neural-chat-7b-v3-3","object":"text_completion","system_fingerprint":null,"usage":null}
data: {"id":"cmpl-f095893d094a4e9989423c2364f00bc1","choices":[{"finish_reason":null,"index":0,"logprobs":null,"text":" and","stop_reason":null}],"created":1742960360,"model":"Intel/neural-chat-7b-v3-3","object":"text_completion","system_fingerprint":null,"usage":null}
data: {"id":"cmpl-f095893d094a4e9989423c2364f00bc1","choices":[{"finish_reason":null,"index":0,"logprobs":null,"text":" trans","stop_reason":null}],"created":1742960360,"model":"Intel/neural-chat-7b-v3-3","object":"text_completion","system_fingerprint":null,"usage":null}
data: {"id":"cmpl-f095893d094a4e9989423c2364f00bc1","choices":[{"finish_reason":null,"index":0,"logprobs":null,"text":"parency","stop_reason":null}],"created":1742960360,"model":"Intel/neural-chat-7b-v3-3","object":"text_completion","system_fingerprint":null,"usage":null}
data: {"id":"cmpl-f095893d094a4e9989423c2364f00bc1","choices":[{"finish_reason":null,"index":0,"logprobs":null,"text":".","stop_reason":null}],"created":1742960360,"model":"Intel/neural-chat-7b-v3-3","object":"text_completion","system_fingerprint":null,"usage":null}
data: {"id":"cmpl-f095893d094a4e9989423c2364f00bc1","choices":[{"finish_reason":"stop","index":0,"logprobs":null,"text":"","stop_reason":null}],"created":1742960360,"model":"Intel/neural-chat-7b-v3-3","object":"text_completion","system_fingerprint":null,"usage":null}
data: [DONE]

A response text similar to the one above indicates that the service verification was successful.

Note : Access the SearchQnA UI by web browser through this URL: http://${host_ip}:80. Please confirm the 80 port is opened in the firewall. To validate each microservice used in the pipeline refer to the Validate Microservices section.

Cleanup the Deployment

To stop the containers associated with the deployment, execute the following command:

For vLLM inference type deployment (default)

//with VLLM:
docker compose -f compose_vllm.yaml down

For TGI inference type deployment

//with TGI:
docker compose -f compose.yaml down

All the SearchQnA containers will be stopped and then removed on completion of the “down” command.

SearchQnA Docker Compose Files

When deploying a SearchQnA pipeline on an AMD GPUs (ROCm), different large language model serving frameworks can be selected. The table below outlines the available configurations included in the application. These configurations can serve as templates and be extended to other components available in GenAIComps.

File	Description
compose.yaml	Default compose file using tgi as serving framework
compose_vllm.yaml	The LLM serving framework is vLLM. All other configurations remain the same as the default

Validate Microservices

1. Embedding backend Service

    curl http://${host_ip}:3001/embed \
        -X POST \
        -d '{"inputs":"What is Deep Learning?"}' \
        -H 'Content-Type: application/json'
   

2. Embedding Microservice

    curl http://${host_ip}:3002/v1/embeddings\
      -X POST \
      -d '{"text":"hello"}' \
      -H 'Content-Type: application/json'
   

3. Web Retriever Microservice

    export your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
    curl http://${host_ip}:3003/v1/web_retrieval \
      -X POST \
      -d "{\"text\":\"What is the 2024 holiday schedule?\",\"embedding\":${your_embedding}}" \
      -H 'Content-Type: application/json'
   

4. Reranking backend Service

 # TEI Reranking service
  curl http://${host_ip}:3004/rerank \
      -X POST \
      -d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
      -H 'Content-Type: application/json'

5. Reranking Microservice

  curl http://${host_ip}:3005/v1/reranking\
    -X POST \
    -d '{"initial_query":"What is Deep Learning?", "retrieved_docs": [{"text":"Deep Learning is not..."}, {"text":"Deep learning is..."}]}' \
    -H 'Content-Type: application/json'

6. LLM backend Service

 # TGI service
  curl http://${host_ip}:3006/generate \
    -X POST \
    -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
    -H 'Content-Type: application/json'

7. LLM Microservice

    curl http://${host_ip}:3007/v1/chat/completions\
      -X POST \
      -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,"stream":true}' \
      -H 'Content-Type: application/json'
   

8. MegaService

    curl http://${host_ip}:3008/v1/searchqna -H "Content-Type: application/json" -d '{
        "messages": "What is the latest news? Give me also the source link.",
        "stream": "true"
        }'
   

9. Nginx Service

   curl http://${host_ip}:${NGINX_PORT}/v1/searchqna \
       -H "Content-Type: application/json" \
       -d '{
        "messages": "What is the latest news? Give me also the source link.",
        "stream": "true"
        }'
   

Launch the UI

Access the UI at http://${EXTERNAL_HOST_IP}:${SEARCH_FRONTEND_SERVICE_PORT}. A page should open when navigating to this address.

The appearance of such a page indicates that the service is operational and responsive, allowing functional UI testing to proceed.

Let’s enter the task for the service in the “Enter prompt here” field. For example, “What is DeepLearning?” and press Enter. After that, a page with the result of the task should open:

A correct result displayed on the page indicates that the UI service has been successfully verified.

Conclusion

This guide should enable developers to deploy the default configuration or any of the other compose yaml files for different configurations. It also highlights the configurable parameters that can be set before deployment.