3fb60608b3
Signed-off-by: lvliang-intel <liang1.lv@intel.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
283 lines
10 KiB
Bash
283 lines
10 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 chatqna-conversation-ui dataprep-redis embedding-tei retriever-redis reranking-tei llm-tgi 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 pull ghcr.io/huggingface/tgi-gaudi:2.0.5
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docker pull ghcr.io/huggingface/text-embeddings-inference:cpu-1.5
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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}:6006"
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export TEI_RERANKING_ENDPOINT="http://${ip_address}:8808"
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export TGI_LLM_ENDPOINT="http://${ip_address}:9009"
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export REDIS_URL="redis://${ip_address}:6379"
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export REDIS_HOST=${ip_address}
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export INDEX_NAME="rag-redis"
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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 BACKEND_SERVICE_ENDPOINT="http://${ip_address}:8888/v1/chatqna"
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export DATAPREP_SERVICE_ENDPOINT="http://${ip_address}:6007/v1/dataprep"
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export DATAPREP_GET_FILE_ENDPOINT="http://${ip_address}:6007/v1/dataprep/get_file"
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export DATAPREP_DELETE_FILE_ENDPOINT="http://${ip_address}:6007/v1/dataprep/delete_file"
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export FRONTEND_SERVICE_IP=${host_ip}
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export FRONTEND_SERVICE_PORT=5173
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export BACKEND_SERVICE_NAME=chatqna
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export BACKEND_SERVICE_IP=${host_ip}
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export BACKEND_SERVICE_PORT=8888
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export NGINX_PORT=80
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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 up -d > ${LOG_PATH}/start_services_with_compose.log
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n=0
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until [[ "$n" -ge 500 ]]; do
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docker logs tgi-service > ${LOG_PATH}/tgi_service_start.log
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if grep -q Connected ${LOG_PATH}/tgi_service_start.log; then
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break
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fi
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sleep 1s
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n=$((n+1))
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done
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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}:6006/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_service \
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"${ip_address}:6000/v1/embeddings" \
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'"text":"What is Deep Learning?","embedding":[' \
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"embedding-microservice" \
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"embedding-tei-server" \
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'{"text":"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" \
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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://${ip_address}:6007/v1/dataprep" \
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"Data preparation succeeded" \
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"dataprep_upload_link" \
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"dataprep-redis-server"
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# test /v1/dataprep/get_file
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validate_service \
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"http://${ip_address}:6007/v1/dataprep/get_file" \
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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://${ip_address}:6007/v1/dataprep/delete_file" \
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'{"status":true}' \
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"dataprep_del" \
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"dataprep-redis-server"
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# retrieval microservice
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test_embedding=$(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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"${ip_address}:7000/v1/retrieval" \
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"retrieved_docs" \
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"retrieval-microservice" \
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"retriever-redis-server" \
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"{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${test_embedding}}"
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# tei for rerank microservice
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validate_service \
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"${ip_address}:8808/rerank" \
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'{"index":1,"score":' \
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"tei-rerank" \
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"tei-reranking-server" \
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'{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}'
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# rerank microservice
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validate_service \
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"${ip_address}:8000/v1/reranking" \
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"Deep learning is..." \
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"rerank-microservice" \
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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_service \
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"${ip_address}:9009/generate" \
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"generated_text" \
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"tgi-llm" \
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"tgi-service" \
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'{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}'
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# llm microservice
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validate_service \
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"${ip_address}:9000/v1/chat/completions" \
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"data: " \
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"llm-microservice" \
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"llm-tgi-server" \
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'{"query":"What is 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_service \
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"${ip_address}:8888/v1/chatqna" \
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"data: " \
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"chatqna-megaservice" \
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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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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}/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 ${conda_env_name}
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echo "[ TEST INFO ]: --------- conda env activated ---------"
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sed -i "s/localhost/$ip_address/g" playwright.config.ts
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conda install -c conda-forge nodejs -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 $WORKPATH/docker_compose/intel/cpu/xeon/
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docker compose stop && docker compose rm -f
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}
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function main() {
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stop_docker
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if [[ "$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 $duration s" && sleep 1s
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if [ "${mode}" == "perf" ]; then
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python3 $WORKPATH/tests/chatqna_benchmark.py
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elif [ "${mode}" == "" ]; then
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validate_microservices
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echo "==== microservices validated ===="
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validate_megaservice
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echo "==== megaservice validated ===="
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validate_frontend
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echo "==== frontend validated ===="
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fi
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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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