386 lines
14 KiB
Bash
Executable File
386 lines
14 KiB
Bash
Executable File
#!/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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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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docker compose -f build.yaml build --no-cache > ${LOG_PATH}/docker_image_build.log
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docker pull ghcr.io/huggingface/text-embeddings-inference:cpu-1.5
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docker pull ghcr.io/huggingface/text-generation-inference:2.1.0
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docker images && sleep 1s
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}
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function start_services() {
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cd $WORKPATH/docker_compose/intel/cpu/xeon/
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export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
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export RERANK_MODEL_ID="BAAI/bge-reranker-base"
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export LLM_MODEL_ID="Intel/neural-chat-7b-v3-3"
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export LLM_MODEL_ID_CODEGEN="Intel/neural-chat-7b-v3-3"
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export TEI_EMBEDDING_ENDPOINT="http://${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 LLM_SERVICE_HOST_IP_DOCSUM=${ip_address}
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export LLM_SERVICE_HOST_IP_FAQGEN=${ip_address}
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export LLM_SERVICE_HOST_IP_CODEGEN=${ip_address}
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export LLM_SERVICE_HOST_IP_CHATQNA=${ip_address}
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export TGI_LLM_ENDPOINT_CHATQNA="http://${ip_address}:9009"
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export TGI_LLM_ENDPOINT_CODEGEN="http://${ip_address}:8028"
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export TGI_LLM_ENDPOINT_FAQGEN="http://${ip_address}:9009"
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export TGI_LLM_ENDPOINT_DOCSUM="http://${ip_address}:9009"
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export BACKEND_SERVICE_ENDPOINT_CHATQNA="http://${ip_address}:8888/v1/chatqna"
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export BACKEND_SERVICE_ENDPOINT_FAQGEN="http://${ip_address}:8889/v1/faqgen"
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export DATAPREP_DELETE_FILE_ENDPOINT="http://${ip_address}:6009/v1/dataprep/delete_file"
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export BACKEND_SERVICE_ENDPOINT_CODEGEN="http://${ip_address}:7778/v1/codegen"
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export BACKEND_SERVICE_ENDPOINT_DOCSUM="http://${ip_address}:8890/v1/docsum"
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export DATAPREP_SERVICE_ENDPOINT="http://${ip_address}:6007/v1/dataprep"
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export DATAPREP_GET_FILE_ENDPOINT="http://${ip_address}:6008/v1/dataprep/get_file"
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export CHAT_HISTORY_CREATE_ENDPOINT="http://${ip_address}:6012/v1/chathistory/create"
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export CHAT_HISTORY_CREATE_ENDPOINT="http://${ip_address}:6012/v1/chathistory/create"
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export CHAT_HISTORY_DELETE_ENDPOINT="http://${ip_address}:6012/v1/chathistory/delete"
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export CHAT_HISTORY_GET_ENDPOINT="http://${ip_address}:6012/v1/chathistory/get"
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export PROMPT_SERVICE_GET_ENDPOINT="http://${ip_address}:6015/v1/prompt/get"
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export PROMPT_SERVICE_CREATE_ENDPOINT="http://${ip_address}:6015/v1/prompt/create"
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export KEYCLOAK_SERVICE_ENDPOINT="http://${ip_address}:8080"
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export MONGO_HOST=${ip_address}
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export MONGO_PORT=27017
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export DB_NAME="opea"
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export COLLECTION_NAME="Conversations"
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export LLM_SERVICE_HOST_PORT_FAQGEN=9002
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export LLM_SERVICE_HOST_PORT_CODEGEN=9001
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export LLM_SERVICE_HOST_PORT_DOCSUM=9003
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export PROMPT_COLLECTION_NAME="prompt"
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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 100 ]]; do
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docker logs tgi-service > ${LOG_PATH}/tgi_service_start.log
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if grep -q Connected ${LOG_PATH}/tgi_service_start.log; then
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echo "ChatQnA TGI Service Connected"
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break
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fi
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sleep 5s
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n=$((n+1))
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done
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n=0
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until [[ "$n" -ge 100 ]]; do
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docker logs tgi_service_codegen > ${LOG_PATH}/tgi_service_codegen_start.log
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if grep -q Connected ${LOG_PATH}/tgi_service_codegen_start.log; then
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echo "CodeGen TGI Service Connected"
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break
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fi
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sleep 5s
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n=$((n+1))
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done
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}
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function validate_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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"${ip_address}:9009/generate" \
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"generated_text" \
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"tgi-llm" \
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"tgi-service" \
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'{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}'
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# ChatQnA llm microservice
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validate_service \
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"${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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# FAQGen llm microservice
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validate_service \
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"${ip_address}:9002/v1/faqgen" \
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"data: " \
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"llm_faqgen" \
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"llm-faqgen-server" \
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'{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}'
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# Docsum llm microservice
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validate_service \
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"${ip_address}:9003/v1/chat/docsum" \
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"data: " \
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"llm_docsum" \
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"llm-docsum-server" \
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'{"query":"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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# CodeGen llm microservice
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validate_service \
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"${ip_address}:9001/v1/chat/completions" \
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"data: " \
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"llm_codegen" \
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"llm-tgi-server-codegen" \
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'{"query":"def print_hello_world():"}'
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result=$(curl -X 'POST' \
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http://${ip_address}:6012/v1/chathistory/create \
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-H 'accept: application/json' \
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-H 'Content-Type: application/json' \
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-d '{
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"data": {
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"messages": "test Messages", "user": "test"
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}
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}')
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echo $result
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if [[ ${#result} -eq 26 ]]; then
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echo "Correct result."
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else
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echo "Incorrect result."
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exit 1
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fi
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result=$(curl -X 'POST' \
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http://$ip_address:6015/v1/prompt/create \
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-H 'accept: application/json' \
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-H 'Content-Type: application/json' \
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-d '{
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"prompt_text": "test prompt", "user": "test"
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}')
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echo $result
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if [[ ${#result} -eq 26 ]]; then
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echo "Correct result."
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else
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echo "Incorrect result."
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exit 1
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fi
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}
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function validate_megaservice() {
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# Curl the ChatQnAMega Service
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validate_service \
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"${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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# Curl the FAQGen Service
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validate_service \
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"${ip_address}:8889/v1/faqgen" \
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"Text Embeddings Inference" \
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"faqgen-xeon-backend-server" \
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"faqgen-xeon-backend-server" \
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'{"messages": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}'\
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# Curl the DocSum Mega Service
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validate_service \
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"${ip_address}:8890/v1/docsum" \
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"toolkit" \
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"docsum-xeon-backend-server" \
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"docsum-xeon-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."}'
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# Curl the CodeGen Mega Service
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validate_service \
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"${ip_address}:7778/v1/codegen" \
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"print" \
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"codegen-xeon-backend-server" \
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"codegen-xeon-backend-server" \
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'{"messages": "def print_hello_world():"}'
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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 $WORKPATH/ui/react
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local conda_env_name="OPEA_e2e"
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export PATH=${HOME}/miniforge3/bin/:$PATH
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# conda remove -n ${conda_env_name} --all -y
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# conda create -n ${conda_env_name} python=3.12 -y
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source activate ${conda_env_name}
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echo "[ TEST INFO ]: --------- conda env activated ---------"
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# conda install -c conda-forge nodejs -y
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npm install && npm ci
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node -v && npm -v && pip list
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exit_status=0
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npm run test || exit_status=$?
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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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validate_microservices
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echo "==== microservices validated ===="
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validate_megaservice
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echo "==== megaservices validated ===="
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validate_frontend
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echo "==== frontend validated ===="
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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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