d73129cbf0
Signed-off-by: Xinyao Wang <xinyao.wang@intel.com> Signed-off-by: chensuyue <suyue.chen@intel.com>
241 lines
8.1 KiB
Bash
241 lines
8.1 KiB
Bash
#!/bin/bash
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# Copyright (C) 2024 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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set -e
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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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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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function build_docker_images() {
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cd $WORKPATH/docker_image_build
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git clone https://github.com/opea-project/GenAIComps.git && cd GenAIComps && git checkout "${opea_branch:-"main"}" && cd ../
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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-qdrant embedding-tei retriever-qdrant reranking-tei llm-tgi"
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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 $WORKPATH/docker_compose/intel/cpu/xeon
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export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
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export RERANK_MODEL_ID="BAAI/bge-reranker-base"
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export LLM_MODEL_ID="Intel/neural-chat-7b-v3-3"
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export TEI_EMBEDDING_ENDPOINT="http://${ip_address}:6040"
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export TEI_RERANKING_ENDPOINT="http://${ip_address}:6041"
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export TGI_LLM_ENDPOINT="http://${ip_address}:6042"
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export QDRANT_HOST=${ip_address}
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export QDRANT_PORT=6333
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export INDEX_NAME="rag-qdrant"
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export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
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export MEGA_SERVICE_HOST_IP=${ip_address}
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export EMBEDDING_SERVICE_HOST_IP=${ip_address}
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export RETRIEVER_SERVICE_HOST_IP=${ip_address}
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export RERANK_SERVICE_HOST_IP=${ip_address}
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export LLM_SERVICE_HOST_IP=${ip_address}
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export EMBEDDING_SERVICE_PORT=6044
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export RETRIEVER_SERVICE_PORT=6045
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export RERANK_SERVICE_PORT=6046
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export LLM_SERVICE_PORT=6047
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export BACKEND_SERVICE_ENDPOINT="http://${ip_address}:8912/v1/chatqna"
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export DATAPREP_SERVICE_ENDPOINT="http://${ip_address}:6043/v1/dataprep"
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sed -i "s/backend_address/$ip_address/g" $WORKPATH/ui/svelte/.env
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# Start Docker Containers
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docker compose -f compose_qdrant.yaml up -d > ${LOG_PATH}/start_services_with_compose.log
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n=0
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until [[ "$n" -ge 100 ]]; do
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docker logs tgi-service > tgi_service_start.log
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if grep -q Connected 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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}
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function validate_services() {
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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/"]' "$URL")
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else
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HTTP_RESPONSE=$(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 "[ $SERVICE_NAME ] Content is as expected."
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fi
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sleep 1s
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}
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function validate_microservices() {
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# Check if the microservices are running correctly.
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# tei for embedding service
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validate_services \
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"${ip_address}:6040/embed" \
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"[[" \
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"tei-embedding" \
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"tei-embedding-server" \
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'{"inputs":"What is Deep Learning?"}'
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# embedding microservice
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validate_services \
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"${ip_address}:6044/v1/embeddings" \
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'"text":"What is Deep Learning?","embedding":[' \
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"embedding" \
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"embedding-tei-server" \
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'{"text":"What is Deep Learning?"}'
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# test /v1/dataprep upload file
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echo "Deep learning is a subset of machine learning that utilizes neural networks with multiple layers to analyze various levels of abstract data representations. It enables computers to identify patterns and make decisions with minimal human intervention by learning from large amounts of data." > $LOG_PATH/dataprep_file.txt
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validate_services \
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"${ip_address}:6043/v1/dataprep" \
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"Data preparation succeeded" \
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"dataprep_upload_file" \
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"dataprep-qdrant-server"
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# test upload link
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validate_services \
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"${ip_address}:6043/v1/dataprep" \
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"Data preparation succeeded" \
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"dataprep_upload_link" \
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"dataprep-qdrant-server"
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# retrieval microservice
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test_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
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validate_services \
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"${ip_address}:6045/v1/retrieval" \
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"retrieved_docs" \
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"retrieval" \
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"retriever-qdrant-server" \
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"{\"text\":\"What is Deep Learning?\",\"embedding\":${test_embedding}}"
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# tei for rerank microservice
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validate_services \
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"${ip_address}:6041/rerank" \
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'{"index":1,"score":' \
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"tei-rerank" \
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"tei-reranking-server" \
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'{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}'
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# rerank microservice
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validate_services \
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"${ip_address}:6046/v1/reranking" \
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"Deep learning is..." \
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"rerank" \
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"reranking-tei-xeon-server" \
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'{"initial_query":"What is Deep Learning?", "retrieved_docs": [{"text":"Deep Learning is not..."}, {"text":"Deep learning is..."}]}'
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# tgi for llm service
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validate_services \
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"${ip_address}:6042/generate" \
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"generated_text" \
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"tgi-llm" \
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"tgi-service" \
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'{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}'
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# llm microservice
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validate_services \
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"${ip_address}:6047/v1/chat/completions" \
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"data: " \
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"llm" \
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"llm-tgi-server" \
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'{"query":"Deep Learning"}'
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}
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function validate_megaservice() {
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# Curl the Mega Service
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validate_services \
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"${ip_address}:8912/v1/chatqna" \
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"data: " \
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"mega-chatqna" \
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"chatqna-xeon-backend-server" \
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'{"messages": "What is the revenue of Nike in 2023?"}'
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}
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function validate_frontend() {
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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}/miniforge3/bin/:$PATH
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source activate ${conda_env_name}
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sed -i "s/localhost/$ip_address/g" playwright.config.ts
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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 $WORKPATH/docker_compose/intel/cpu/xeon
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docker compose -f compose_qdrant.yaml stop && docker compose -f compose_qdrant.yaml rm -f
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}
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function main() {
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stop_docker
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if [[ "$IMAGE_REPO" == "opea" ]]; then build_docker_images; fi
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start_time=$(date +%s)
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start_services
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end_time=$(date +%s)
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duration=$((end_time-start_time))
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echo "Mega service start duration is $duration s" && sleep 1s
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validate_microservices
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validate_megaservice
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validate_frontend
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stop_docker
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echo y | docker system prune
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}
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main
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