305 lines
11 KiB
Bash
Executable File
305 lines
11 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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export MODEL_CACHE=${model_cache:-"./data"}
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WORKPATH=$(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 --depth 1 --branch ${opea_branch:-"main"} https://github.com/opea-project/GenAIComps.git
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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-1.6
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docker pull ghcr.io/huggingface/text-generation-inference:2.4.0-intel-cpu
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docker images && sleep 1s
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}
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function start_services() {
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cd $WORKPATH/docker_compose/intel/cpu/xeon/
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export DB_NAME="opea"
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export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
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export RERANK_MODEL_ID="BAAI/bge-reranker-base"
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export LLM_MODEL_ID="Intel/neural-chat-7b-v3-3"
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export LLM_MODEL_ID_CODEGEN="Intel/neural-chat-7b-v3-3"
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export INDEX_NAME="rag-redis"
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export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN}
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export BACKEND_SERVICE_ENDPOINT_CHATQNA="http://${ip_address}:8888/v1/chatqna"
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export DATAPREP_DELETE_FILE_ENDPOINT="http://${ip_address}:6007/v1/dataprep/delete"
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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://${ip_address}:6007/v1/dataprep/ingest"
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export DATAPREP_GET_FILE_ENDPOINT="http://${ip_address}:6007/v1/dataprep/get"
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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://${ip_address}:6018/v1/prompt/get"
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export PROMPT_SERVICE_CREATE_ENDPOINT="http://${ip_address}:6018/v1/prompt/create"
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export PROMPT_SERVICE_DELETE_ENDPOINT="http://${ip_address}:6018/v1/prompt/delete"
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export KEYCLOAK_SERVICE_ENDPOINT="http://${ip_address}:8080"
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export DocSum_COMPONENT_NAME="OpeaDocSumTgi"
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export host_ip=${ip_address}
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export LOGFLAG=True
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export no_proxy="$no_proxy,tgi_service_codegen,llm_codegen,tei-embedding-service,tei-reranking-service,chatqna-xeon-backend-server,retriever,tgi-service,redis-vector-db,whisper,llm-docsum-tgi,docsum-xeon-backend-server,mongo,codegen"
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# Start Docker Containers
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docker compose up -d > ${LOG_PATH}/start_services_with_compose.log
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sleep 30s
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n=0
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until [[ "$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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sleep 1m # retrieval can't curl as expected, try to wait for more time
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# test /v1/dataprep/delete
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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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# test /v1/dataprep/ingest 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://${ip_address}:6007/v1/dataprep/ingest" \
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"Data preparation succeeded" \
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"dataprep_upload_link" \
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"dataprep-redis-server"
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# test /v1/dataprep/get
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validate_service \
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"http://${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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# 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}:7001/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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# tgi for llm service
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validate_service \
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"${ip_address}:9009/generate" \
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"generated_text" \
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"tgi-llm" \
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"tgi-service" \
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'{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}'
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# CodeGen llm microservice
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validate_service \
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"${ip_address}:9001/v1/chat/completions" \
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"data: " \
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"llm_codegen" \
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"llm-textgen-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:6018/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 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/:$PATH
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if conda info --envs | grep -q "^${conda_env_name}[[:space:]]"; then
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echo "[ TEST INFO ]: Conda environment '${conda_env_name}' exists. Activating..."
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else
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echo "[ TEST INFO ]: Conda environment '${conda_env_name}' not found. Creating..."
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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 ${conda_env_name}
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echo "[ TEST INFO ]: --------- conda env activated ---------"
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conda install -c conda-forge nodejs=22.6.0 -y
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npm install && npm ci
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node -v && npm -v && pip list
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exit_status=0
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npm run test || exit_status=$?
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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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