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>
256 lines
8.3 KiB
Bash
256 lines
8.3 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-guardrails chatqna-ui dataprep-redis embedding-tei retriever-redis reranking-tei llm-tgi guardrails-tgi"
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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 pull ghcr.io/huggingface/tei-gaudi:latest
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docker images && sleep 1s
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}
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function start_services() {
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cd $WORKPATH/docker_compose/intel/hpu/gaudi
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export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
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export RERANK_MODEL_ID="BAAI/bge-reranker-base"
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export LLM_MODEL_ID="Intel/neural-chat-7b-v3-3"
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export TEI_EMBEDDING_ENDPOINT="http://${ip_address}:8090"
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export TEI_RERANKING_ENDPOINT="http://${ip_address}:8808"
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export TGI_LLM_ENDPOINT="http://${ip_address}:8008"
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export REDIS_URL="redis://${ip_address}:6379"
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export INDEX_NAME="rag-redis"
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export HUGGINGFACEHUB_API_TOKEN=${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=${ip_address}
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export GUARDRAIL_SERVICE_HOST_IP=${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 GURADRAILS_MODEL_ID="meta-llama/Meta-Llama-Guard-2-8B"
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export SAFETY_GUARD_MODEL_ID="meta-llama/Meta-Llama-Guard-2-8B"
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export SAFETY_GUARD_ENDPOINT="http://${ip_address}:8088"
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sed -i "s/backend_address/$ip_address/g" $WORKPATH/ui/svelte/.env
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# Start Docker Containers
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docker compose -f compose_guardrails.yaml 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-gaudi-server > tgi_service_start.log
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if grep -q Connected tgi_service_start.log; then
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break
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fi
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sleep 5s
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n=$((n+1))
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done
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# Make sure tgi guardrails service is ready
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n=0
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until [[ "$n" -ge 100 ]]; do
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docker logs tgi-guardrails-server > tgi_guardrails_service_start.log
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if grep -q Connected tgi_guardrails_service_start.log; then
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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_services() {
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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="$5"
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HTTP_RESPONSE=$(curl --silent --write-out "HTTPSTATUS:%{http_code}" -X POST -d "$INPUT_DATA" -H 'Content-Type: application/json' "$URL")
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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 "[ $SERVICE_NAME ] Content is as expected."
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fi
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sleep 1s
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}
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function validate_microservices() {
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# Check if the microservices are running correctly.
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# tei for embedding service
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validate_services \
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"${ip_address}:8090/embed" \
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"[[" \
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"tei-embedding" \
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"tei-embedding-gaudi-server" \
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'{"inputs":"What is Deep Learning?"}'
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# embedding microservice
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validate_services \
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"${ip_address}:6000/v1/embeddings" \
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'"text":"What is Deep Learning?","embedding":[' \
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"embedding" \
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"embedding-tei-server" \
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'{"text":"What is Deep Learning?"}'
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sleep 1m # retrieval can't curl as expected, try to wait for more time
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# retrieval microservice
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test_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
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validate_services \
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"${ip_address}:7000/v1/retrieval" \
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"retrieved_docs" \
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"retrieval" \
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"retriever-redis-server" \
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"{\"text\":\"What is the revenue of Nike in 2023?\",\"embedding\":${test_embedding}}"
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# tei for rerank microservice
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validate_services \
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"${ip_address}:8808/rerank" \
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'{"index":1,"score":' \
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"tei-rerank" \
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"tei-reranking-gaudi-server" \
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'{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}'
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# rerank microservice
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validate_services \
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"${ip_address}:8000/v1/reranking" \
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"Deep learning is..." \
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"rerank" \
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"reranking-tei-gaudi-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_services \
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"${ip_address}:8008/generate" \
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"generated_text" \
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"tgi-llm" \
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"tgi-gaudi-server" \
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'{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}'
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# llm microservice
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validate_services \
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"${ip_address}:9000/v1/chat/completions" \
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"data: " \
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"llm" \
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"llm-tgi-gaudi-server" \
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'{"query":"What is Deep Learning?"}'
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# tgi for guardrails service
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validate_services \
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"${ip_address}:8088/generate" \
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"generated_text" \
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"tgi-guardrails" \
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"tgi-guardrails-server" \
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'{"inputs":"How do you buy a tiger in the US?","parameters":{"max_new_tokens":32}}'
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# guardrails microservice
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validate_services \
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"${ip_address}:9090/v1/guardrails" \
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"Violated policies" \
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"guardrails" \
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"guardrails-tgi-gaudi-server" \
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'{"text":"How do you buy a tiger in the US?"}'
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}
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function validate_megaservice() {
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# Curl the Mega Service
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validate_services \
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"${ip_address}:8888/v1/chatqna" \
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"billion" \
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"mega-chatqna" \
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"chatqna-gaudi-guardrails-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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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 ${conda_env_name}
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sed -i "s/localhost/$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 $WORKPATH/docker_compose/intel/hpu/gaudi
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docker compose -f compose_guardrails.yaml down
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}
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function main() {
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stop_docker
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if [[ "$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"
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validate_microservices
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validate_megaservice
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# validate_frontend
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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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