236 lines
8.3 KiB
Bash
236 lines
8.3 KiB
Bash
#!/bin/bash
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# Copyright (C) 2024 Advanced Micro Devices, Inc.
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# SPDX-License-Identifier: Apache-2.0
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set -xe
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IMAGE_REPO=${IMAGE_REPO:-"opea"}
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IMAGE_TAG=${IMAGE_TAG:-"latest"}
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echo "REGISTRY=IMAGE_REPO=${IMAGE_REPO}"
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echo "TAG=IMAGE_TAG=${IMAGE_TAG}"
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export REGISTRY=${IMAGE_REPO}
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export TAG=${IMAGE_TAG}
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export MODEL_CACHE=${model_cache:-"./data"}
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WORKPATH=$(dirname "$PWD")
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LOG_PATH="$WORKPATH/tests"
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ip_address=$(hostname -I | awk '{print $1}')
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source $WORKPATH/docker_compose/amd/gpu/rocm/set_env_faqgen.sh
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export PATH="~/miniconda3/bin:$PATH"
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function build_docker_images() {
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opea_branch=${opea_branch:-"main"}
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cd $WORKPATH/docker_image_build
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git clone --depth 1 --branch ${opea_branch} https://github.com/opea-project/GenAIComps.git
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pushd GenAIComps
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echo "GenAIComps test commit is $(git rev-parse HEAD)"
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docker build --no-cache -t ${REGISTRY}/comps-base:${TAG} --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
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popd && sleep 1s
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echo "Build all the images with --no-cache, check docker_image_build.log for details..."
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service_list="chatqna chatqna-ui dataprep retriever llm-faqgen nginx"
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docker compose -f build.yaml build ${service_list} --no-cache > "${LOG_PATH}"/docker_image_build.log
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docker images && sleep 1s
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}
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function start_services() {
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cd "$WORKPATH"/docker_compose/amd/gpu/rocm
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# Start Docker Containers
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docker compose -f compose_faqgen.yaml up -d > "${LOG_PATH}"/start_services_with_compose.log
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n=0
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until [[ "$n" -ge 160 ]]; do
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docker logs chatqna-tgi-service > "${LOG_PATH}"/tgi_service_start.log
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if grep -q Connected "${LOG_PATH}"/tgi_service_start.log; then
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break
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fi
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sleep 5s
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n=$((n+1))
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done
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echo "all containers start!"
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}
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function validate_service() {
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local URL="$1"
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local EXPECTED_RESULT="$2"
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local SERVICE_NAME="$3"
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local DOCKER_NAME="$4"
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local INPUT_DATA="$5"
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if [[ $SERVICE_NAME == *"dataprep_upload_file"* ]]; then
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cd "$LOG_PATH"
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HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -F 'files=@./dataprep_file.txt' -H 'Content-Type: multipart/form-data' "$URL")
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elif [[ $SERVICE_NAME == *"dataprep_upload_link"* ]]; then
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HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -F 'link_list=["https://www.ces.tech/"]' "$URL")
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elif [[ $SERVICE_NAME == *"dataprep_get"* ]]; then
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HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -H 'Content-Type: application/json' "$URL")
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elif [[ $SERVICE_NAME == *"dataprep_del"* ]]; then
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HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -d '{"file_path": "all"}' -H 'Content-Type: application/json' "$URL")
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else
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HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -d "$INPUT_DATA" -H 'Content-Type: application/json' "$URL")
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fi
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HTTP_STATUS=$(echo "$HTTP_RESPONSE" | tr -d '\n' | sed -e 's/.*HTTPSTATUS://')
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RESPONSE_BODY=$(echo "$HTTP_RESPONSE" | sed -e 's/HTTPSTATUS\:.*//g')
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docker logs "${DOCKER_NAME}" >> "${LOG_PATH}"/"${SERVICE_NAME}".log
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# check response status
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if [ "$HTTP_STATUS" -ne "200" ]; then
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echo "[ $SERVICE_NAME ] HTTP status is not 200. Received status was $HTTP_STATUS"
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exit 1
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else
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echo "[ $SERVICE_NAME ] HTTP status is 200. Checking content..."
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fi
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# check response body
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if [[ "$RESPONSE_BODY" != *"$EXPECTED_RESULT"* ]]; then
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echo "[ $SERVICE_NAME ] Content does not match the expected result: $RESPONSE_BODY"
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exit 1
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else
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echo "[ $SERVICE_NAME ] Content is as expected."
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fi
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sleep 1s
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}
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function validate_microservices() {
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# Check if the microservices are running correctly.
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# tei for embedding service
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validate_service \
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"${ip_address}:${CHATQNA_TEI_EMBEDDING_PORT}/embed" \
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"[[" \
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"tei-embedding" \
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"chatqna-tei-embedding-service" \
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'{"inputs":"What is Deep Learning?"}'
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sleep 1m # retrieval can't curl as expected, try to wait for more time
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# retrieval microservice
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test_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
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validate_service \
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"${ip_address}:${CHATQNA_REDIS_RETRIEVER_PORT}/v1/retrieval" \
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" " \
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"retrieval-microservice" \
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"chatqna-retriever" \
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"{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${test_embedding}}"
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# tei for rerank microservice
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validate_service \
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"${ip_address}:${CHATQNA_TEI_RERANKING_PORT}/rerank" \
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'{"index":1,"score":' \
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"tei-rerank" \
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"chatqna-tei-reranking-service" \
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'{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}'
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# tgi for llm service
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validate_service \
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"${ip_address}:${CHATQNA_TGI_SERVICE_PORT}/generate" \
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"generated_text" \
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"tgi-llm" \
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"chatqna-tgi-service" \
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'{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}'
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# faqgen llm microservice
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echo "validate llm-faqgen..."
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validate_service \
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"${ip_address}:${CHATQNA_LLM_FAQGEN_PORT}/v1/faqgen" \
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"text" \
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"llm" \
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"chatqna-llm-faqgen" \
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'{"messages":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}'
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}
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function validate_megaservice() {
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# Curl the Mega Service
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validate_service \
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"${ip_address}:${CHATQNA_BACKEND_SERVICE_PORT}/v1/chatqna" \
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"Embed" \
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"chatqna-megaservice" \
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"chatqna-backend-server" \
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'{"messages": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5.","max_tokens":32}'
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validate_service \
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"${ip_address}:${CHATQNA_BACKEND_SERVICE_PORT}/v1/chatqna" \
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"Embed" \
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"chatqna-megaservice" \
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"chatqna-backend-server" \
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'{"messages": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5.","max_tokens":32,"stream":false}'
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}
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function validate_frontend() {
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echo "[ TEST INFO ]: --------- frontend test started ---------"
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cd "$WORKPATH"/ui/svelte
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local conda_env_name="OPEA_e2e"
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export PATH=${HOME}/miniconda3/bin/:$PATH
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if conda info --envs | grep -q "$conda_env_name"; then
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echo "$conda_env_name exist!"
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else
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conda create -n ${conda_env_name} python=3.12 -y
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fi
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source activate ${conda_env_name}
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echo "[ TEST INFO ]: --------- conda env activated ---------"
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sed -i "s/localhost/$ip_address/g" playwright.config.ts
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conda install -c conda-forge nodejs=22.6.0 -y
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npm install && npm ci && npx playwright install --with-deps
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node -v && npm -v && pip list
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exit_status=0
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npx playwright test || exit_status=$?
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if [ $exit_status -ne 0 ]; then
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echo "[TEST INFO]: ---------frontend test failed---------"
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exit $exit_status
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else
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echo "[TEST INFO]: ---------frontend test passed---------"
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fi
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}
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function stop_docker() {
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cd "$WORKPATH"/docker_compose/amd/gpu/rocm
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docker compose -f compose_faqgen.yaml stop && docker compose rm -f
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}
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function main() {
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echo "::group::stop_docker"
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stop_docker
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echo "::endgroup::"
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echo "::group::build_docker_images"
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if [[ "$IMAGE_REPO" == "opea" ]]; then build_docker_images; fi
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echo "::endgroup::"
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echo "::group::start_services"
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start_services
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echo "::endgroup::"
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echo "::group::validate_microservices"
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validate_microservices
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echo "::endgroup::"
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echo "::group::validate_megaservice"
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validate_megaservice
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echo "::endgroup::"
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echo "::group::validate_frontend"
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validate_frontend
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echo "::endgroup::"
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echo "::group::stop_docker"
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stop_docker
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echo "::endgroup::"
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docker system prune -f
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}
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main
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