256 lines
9.1 KiB
Bash
256 lines
9.1 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 -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=${model_cache:-"/data/cache"}
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WORKPATH=$(dirname "$PWD")
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LOG_PATH="$WORKPATH/tests"
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ip_address=$(hostname -I | awk '{print $1}')
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function build_docker_images() {
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opea_branch=${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/intel/hpu/gaudi
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export FAQGen_COMPONENT_NAME="OpeaFaqGenTgi"
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source set_env_faqgen.sh
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# Start Docker Containers
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docker compose -f compose_faqgen_tgi.yaml up -d > ${LOG_PATH}/start_services_with_compose.log
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sleep 30s
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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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echo "Response"
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echo $RESPONSE_BODY
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echo "Expected Result"
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echo $EXPECTED_RESULT
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# check response body
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if [[ "$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}:8090/embed" \
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"[[" \
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"tei-embedding" \
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"tei-embedding-gaudi-server" \
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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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# 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." > $LOG_PATH/dataprep_file.txt
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validate_service \
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"http://${ip_address}:6007/v1/dataprep/ingest" \
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"Data preparation succeeded" \
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"dataprep_upload_file" \
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"dataprep-redis-server"
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# test /v1/dataprep upload link
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validate_service \
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"http://${ip_address}:6007/v1/dataprep/ingest" \
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"Data preparation succeeded" \
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"dataprep_upload_link" \
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"dataprep-redis-server"
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# test /v1/dataprep/get_file
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validate_service \
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"http://${ip_address}:6007/v1/dataprep/get" \
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'{"name":' \
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"dataprep_get" \
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"dataprep-redis-server"
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# test /v1/dataprep/delete_file
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validate_service \
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"http://${ip_address}:6007/v1/dataprep/delete" \
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'{"status":true}' \
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"dataprep_del" \
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"dataprep-redis-server"
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# retrieval microservice
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test_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
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validate_service \
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"${ip_address}:7000/v1/retrieval" \
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" " \
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"retrieval" \
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"retriever-redis-server" \
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"{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${test_embedding}}"
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# tei for rerank microservice
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echo "validate tei..."
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validate_service \
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"${ip_address}:8808/rerank" \
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'{"index":1,"score":' \
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"tei-rerank" \
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"tei-reranking-gaudi-server" \
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'{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}'
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# tgi for llm service
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echo "validate tgi..."
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validate_service \
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"${ip_address}:${LLM_ENDPOINT_PORT}/v1/chat/completions" \
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"content" \
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"tgi-llm" \
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"tgi-gaudi-server" \
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'{"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens":17}'
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# faqgen llm microservice
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echo "validate llm-faqgen..."
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validate_service \
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"${ip_address}:${LLM_SERVER_PORT}/v1/faqgen" \
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"text" \
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"llm" \
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"llm-faqgen-server" \
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'{"messages":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}'
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}
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function validate_megaservice() {
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# Curl the Mega Service
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validate_service \
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"${ip_address}:${CHATQNA_BACKEND_PORT}/v1/chatqna" \
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"Embed" \
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"chatqna-megaservice" \
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"chatqna-gaudi-backend-server" \
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'{"messages": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5.","max_tokens":32}'
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validate_service \
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"${ip_address}:${CHATQNA_BACKEND_PORT}/v1/chatqna" \
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"Embed" \
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"chatqna-megaservice" \
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"chatqna-gaudi-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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cd $WORKPATH/ui/svelte
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local conda_env_name="OPEA_e2e"
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export PATH=${HOME}/miniforge3/bin/:$PATH
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if conda info --envs | grep -q "$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 ${conda_env_name}
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sed -i "s/localhost/$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 $WORKPATH/docker_compose/intel/hpu/gaudi
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docker compose -f compose_faqgen_tgi.yaml down
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}
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function main() {
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echo "::group::stop_docker"
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stop_docker
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echo "::endgroup::"
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echo "::group::build_docker_images"
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if [[ "$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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