229 lines
7.3 KiB
Bash
229 lines
7.3 KiB
Bash
#!/bin/bash
|
|
# Copyright (C) 2024 Intel Corporation
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
set -xe
|
|
IMAGE_REPO=${IMAGE_REPO:-"opea"}
|
|
IMAGE_TAG=${IMAGE_TAG:-"latest"}
|
|
echo "REGISTRY=IMAGE_REPO=${IMAGE_REPO}"
|
|
echo "TAG=IMAGE_TAG=${IMAGE_TAG}"
|
|
export REGISTRY=${IMAGE_REPO}
|
|
export TAG=${IMAGE_TAG}
|
|
export MODEL_CACHE=${model_cache:-"./data"}
|
|
|
|
WORKPATH=$(dirname "$PWD")
|
|
LOG_PATH="$WORKPATH/tests"
|
|
ip_address=$(hostname -I | awk '{print $1}')
|
|
|
|
function build_docker_images() {
|
|
opea_branch=${opea_branch:-"main"}
|
|
cd $WORKPATH/docker_image_build
|
|
git clone --depth 1 --branch ${opea_branch} https://github.com/opea-project/GenAIComps.git
|
|
pushd GenAIComps
|
|
echo "GenAIComps test commit is $(git rev-parse HEAD)"
|
|
docker build --no-cache -t ${REGISTRY}/comps-base:${TAG} --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
|
|
popd && sleep 1s
|
|
git clone https://github.com/vllm-project/vllm.git && cd vllm
|
|
VLLM_VER="v0.8.3"
|
|
echo "Check out vLLM tag ${VLLM_VER}"
|
|
git checkout ${VLLM_VER} &> /dev/null
|
|
# Not change the pwd
|
|
cd ../
|
|
|
|
echo "Build all the images with --no-cache, check docker_image_build.log for details..."
|
|
service_list="chatqna chatqna-ui dataprep retriever vllm nginx"
|
|
docker compose -f build.yaml build ${service_list} --no-cache > ${LOG_PATH}/docker_image_build.log
|
|
|
|
docker images && sleep 1s
|
|
}
|
|
|
|
function start_services() {
|
|
cd $WORKPATH/docker_compose/intel/cpu/xeon
|
|
|
|
export INDEX_NAME="rag-qdrant"
|
|
source set_env.sh
|
|
|
|
sed -i "s/backend_address/$ip_address/g" $WORKPATH/ui/svelte/.env
|
|
|
|
# Start Docker Containers
|
|
docker compose -f compose_qdrant.yaml up -d > ${LOG_PATH}/start_services_with_compose.log
|
|
n=0
|
|
until [[ "$n" -ge 100 ]]; do
|
|
docker logs vllm-service > ${LOG_PATH}/vllm_service_start.log 2>&1
|
|
if grep -q complete ${LOG_PATH}/vllm_service_start.log; then
|
|
break
|
|
fi
|
|
sleep 5s
|
|
n=$((n+1))
|
|
done
|
|
}
|
|
|
|
function validate_service() {
|
|
local URL="$1"
|
|
local EXPECTED_RESULT="$2"
|
|
local SERVICE_NAME="$3"
|
|
local DOCKER_NAME="$4"
|
|
local INPUT_DATA="$5"
|
|
|
|
if [[ $SERVICE_NAME == *"dataprep_upload_file"* ]]; then
|
|
cd $LOG_PATH
|
|
HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -F 'files=@./dataprep_file.txt' -H 'Content-Type: multipart/form-data' "$URL")
|
|
elif [[ $SERVICE_NAME == *"dataprep_upload_link"* ]]; then
|
|
HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -F 'link_list=["https://www.ces.tech/"]' "$URL")
|
|
else
|
|
HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -d "$INPUT_DATA" -H 'Content-Type: application/json' "$URL")
|
|
fi
|
|
HTTP_STATUS=$(echo $HTTP_RESPONSE | tr -d '\n' | sed -e 's/.*HTTPSTATUS://')
|
|
RESPONSE_BODY=$(echo $HTTP_RESPONSE | sed -e 's/HTTPSTATUS\:.*//g')
|
|
|
|
docker logs ${DOCKER_NAME} >> ${LOG_PATH}/${SERVICE_NAME}.log
|
|
|
|
# check response status
|
|
if [ "$HTTP_STATUS" -ne "200" ]; then
|
|
echo "[ $SERVICE_NAME ] HTTP status is not 200. Received status was $HTTP_STATUS"
|
|
exit 1
|
|
else
|
|
echo "[ $SERVICE_NAME ] HTTP status is 200. Checking content..."
|
|
fi
|
|
# check response body
|
|
if [[ "$RESPONSE_BODY" != *"$EXPECTED_RESULT"* ]]; then
|
|
echo "[ $SERVICE_NAME ] Content does not match the expected result: $RESPONSE_BODY"
|
|
exit 1
|
|
else
|
|
echo "[ $SERVICE_NAME ] Content is as expected."
|
|
fi
|
|
|
|
sleep 1s
|
|
}
|
|
|
|
function validate_microservices() {
|
|
# Check if the microservices are running correctly.
|
|
|
|
# tei for embedding service
|
|
validate_service \
|
|
"${ip_address}:6040/embed" \
|
|
"[[" \
|
|
"tei-embedding" \
|
|
"tei-embedding-server" \
|
|
'{"inputs":"What is Deep Learning?"}'
|
|
|
|
# test /v1/dataprep/ingest upload file
|
|
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." > $LOG_PATH/dataprep_file.txt
|
|
validate_service \
|
|
"${ip_address}:6043/v1/dataprep/ingest" \
|
|
"Data preparation succeeded" \
|
|
"dataprep_upload_file" \
|
|
"dataprep-qdrant-server"
|
|
|
|
# test upload link
|
|
validate_service \
|
|
"${ip_address}:6043/v1/dataprep/ingest" \
|
|
"Data preparation succeeded" \
|
|
"dataprep_upload_link" \
|
|
"dataprep-qdrant-server"
|
|
|
|
# retrieval microservice
|
|
test_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
|
|
validate_service \
|
|
"${ip_address}:6045/v1/retrieval" \
|
|
"retrieved_docs" \
|
|
"retrieval" \
|
|
"retriever-qdrant-server" \
|
|
"{\"text\":\"What is Deep Learning?\",\"embedding\":${test_embedding}}"
|
|
|
|
# tei for rerank microservice
|
|
validate_service \
|
|
"${ip_address}:6041/rerank" \
|
|
'{"index":1,"score":' \
|
|
"tei-rerank" \
|
|
"tei-reranking-server" \
|
|
'{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}'
|
|
|
|
# vllm for llm service
|
|
validate_service \
|
|
"${ip_address}:6042/v1/chat/completions" \
|
|
"content" \
|
|
"vllm-llm" \
|
|
"vllm-service" \
|
|
'{"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens": 17}'
|
|
}
|
|
|
|
function validate_megaservice() {
|
|
# Curl the Mega Service
|
|
validate_service \
|
|
"${ip_address}:8912/v1/chatqna" \
|
|
"Nike" \
|
|
"mega-chatqna" \
|
|
"chatqna-xeon-backend-server" \
|
|
'{"messages": "What is the revenue of Nike in 2023?"}'
|
|
|
|
}
|
|
|
|
function validate_frontend() {
|
|
cd $WORKPATH/ui/svelte
|
|
local conda_env_name="OPEA_e2e"
|
|
export PATH=${HOME}/miniforge3/bin/:$PATH
|
|
if conda info --envs | grep -q "$conda_env_name"; then
|
|
echo "$conda_env_name exist!"
|
|
else
|
|
conda create -n ${conda_env_name} python=3.12 -y
|
|
fi
|
|
source activate ${conda_env_name}
|
|
|
|
sed -i "s/localhost/$ip_address/g" playwright.config.ts
|
|
|
|
npm install && npm ci && npx playwright install --with-deps
|
|
node -v && npm -v && pip list
|
|
|
|
exit_status=0
|
|
npx playwright test || exit_status=$?
|
|
|
|
if [ $exit_status -ne 0 ]; then
|
|
echo "[TEST INFO]: ---------frontend test failed---------"
|
|
exit $exit_status
|
|
else
|
|
echo "[TEST INFO]: ---------frontend test passed---------"
|
|
fi
|
|
}
|
|
|
|
function stop_docker() {
|
|
cd $WORKPATH/docker_compose/intel/cpu/xeon
|
|
docker compose -f compose_qdrant.yaml down
|
|
}
|
|
|
|
function main() {
|
|
|
|
echo "::group::stop_docker"
|
|
stop_docker
|
|
echo "::endgroup::"
|
|
|
|
echo "::group::build_docker_images"
|
|
if [[ "$IMAGE_REPO" == "opea" ]]; then build_docker_images; fi
|
|
echo "::endgroup::"
|
|
|
|
echo "::group::start_services"
|
|
start_services
|
|
echo "::endgroup::"
|
|
|
|
echo "::group::validate_microservices"
|
|
validate_microservices
|
|
echo "::endgroup::"
|
|
|
|
echo "::group::validate_megaservice"
|
|
validate_megaservice
|
|
echo "::endgroup::"
|
|
|
|
echo "::group::validate_frontend"
|
|
validate_frontend
|
|
echo "::endgroup::"
|
|
|
|
echo "::group::stop_docker"
|
|
stop_docker
|
|
echo "::endgroup::"
|
|
|
|
docker system prune -f
|
|
|
|
}
|
|
|
|
main
|