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GenAIExamples/ChatQnA/tests/test_compose_tgi_on_gaudi.sh

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#!/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
echo "Build all the images with --no-cache, check docker_image_build.log for details..."
service_list="chatqna chatqna-ui dataprep retriever 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 $WORKPATH/docker_compose/intel/hpu/gaudi
export NON_INTERACTIVE=true
export host_ip=${ip_address}
export telemetry=yes
source set_env.sh
# Start Docker Containers
docker compose -f compose_tgi.yaml -f compose_tgi.telemetry.yaml up -d > ${LOG_PATH}/start_services_with_compose.log
n=0
until [[ "$n" -ge 500 ]]; do
docker logs tgi-gaudi-server > ${LOG_PATH}/tgi_service_start.log
if grep -q Connected ${LOG_PATH}/tgi_service_start.log; then
break
fi
sleep 1s
n=$((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")
elif [[ $SERVICE_NAME == *"dataprep_get"* ]]; then
HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -H 'Content-Type: application/json' "$URL")
elif [[ $SERVICE_NAME == *"dataprep_del"* ]]; then
HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -d '{"file_path": "all"}' -H 'Content-Type: application/json' "$URL")
else
HTTP_RESPONSE=$(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}:8090/embed" \
"[[" \
"tei-embedding" \
"tei-embedding-gaudi-server" \
'{"inputs":"What is Deep Learning?"}'
sleep 1m # retrieval can't curl as expected, try to wait for more time
# test /v1/dataprep 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." > $LOG_PATH/dataprep_file.txt
validate_service \
"http://${ip_address}:6007/v1/dataprep/ingest" \
"Data preparation succeeded" \
"dataprep_upload_file" \
"dataprep-redis-server"
# test /v1/dataprep upload link
validate_service \
"http://${ip_address}:6007/v1/dataprep/ingest" \
"Data preparation succeeded" \
"dataprep_upload_link" \
"dataprep-redis-server"
# test /v1/dataprep/get_file
validate_service \
"http://${ip_address}:6007/v1/dataprep/get" \
'{"name":' \
"dataprep_get" \
"dataprep-redis-server"
# test /v1/dataprep/delete_file
validate_service \
"http://${ip_address}:6007/v1/dataprep/delete" \
'{"status":true}' \
"dataprep_del" \
"dataprep-redis-server"
# retrieval microservice
test_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
validate_service \
"${ip_address}:7000/v1/retrieval" \
"retrieved_docs" \
"retrieval-microservice" \
"retriever-redis-server" \
"{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${test_embedding}}"
# tei for rerank microservice
validate_service \
"${ip_address}:8808/rerank" \
'{"index":1,"score":' \
"tei-rerank" \
"tei-reranking-gaudi-server" \
'{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}'
# tgi for llm service
validate_service \
"${ip_address}:8005/v1/chat/completions" \
"content" \
"tgi-llm" \
"tgi-gaudi-server" \
'{"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}:8888/v1/chatqna" \
"Nike" \
"chatqna-megaservice" \
"chatqna-gaudi-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
conda install -c conda-forge nodejs=22.6.0 -y
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 $WORKPATH/docker_compose/intel/hpu/gaudi
docker compose -f compose_tgi.yaml -f compose_tgi.telemetry.yaml down
}
function main() {
echo "::group::stop_docker"
stop_docker
echo "::endgroup::"
echo "::group::build_docker_images"
if [[ "$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
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