diff --git a/tests/test_llm_summarization_tgi.sh b/tests/test_llm_summarization_tgi.sh
new file mode 100644
index 000000000..adffb3b28
--- /dev/null
+++ b/tests/test_llm_summarization_tgi.sh
@@ -0,0 +1,91 @@
+#!/bin/bash
+# Copyright (C) 2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+set -xe
+
+WORKPATH=$(dirname "$PWD")
+ip_address=$(hostname -I | awk '{print $1}')
+LOG_PATH="$WORKPATH/tests"
+
+function build_docker_images() {
+    cd $WORKPATH
+    docker build --no-cache -t opea/llm-tgi:comps --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/llms/summarization/tgi/Dockerfile .
+}
+
+function start_service() {
+    tgi_endpoint_port=5014
+    export your_hf_llm_model="Intel/neural-chat-7b-v3-3"
+    # Remember to set HF_TOKEN before invoking this test!
+    export HF_TOKEN=${HF_TOKEN}
+    docker run -d --name="test-comps-llm-tgi-endpoint" -p $tgi_endpoint_port:80 -v ./data:/data -e http_proxy=$http_proxy -e https_proxy=$https_proxy --shm-size 1g ghcr.io/huggingface/text-generation-inference:1.4 --model-id ${your_hf_llm_model}
+    export TGI_LLM_ENDPOINT="http://${ip_address}:${tgi_endpoint_port}"
+
+    tei_service_port=5015
+    docker run -d --name="test-comps-llm-tgi-server" -p ${tei_service_port}:9000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e TGI_LLM_ENDPOINT=$TGI_LLM_ENDPOINT -e HUGGINGFACEHUB_API_TOKEN=$HF_TOKEN opea/llm-tgi:comps
+
+    # check whether tgi is fully ready
+    n=0
+    until [[ "$n" -ge 100 ]] || [[ $ready == true ]]; do
+        docker logs test-comps-llm-tgi-endpoint > ${WORKPATH}/tests/test-comps-llm-tgi-endpoint.log
+        n=$((n+1))
+        if grep -q Connected ${WORKPATH}/tests/test-comps-llm-tgi-endpoint.log; then
+            break
+        fi
+        sleep 5s
+    done
+    sleep 5s
+}
+
+function validate_microservice() {
+    tei_service_port=5015
+    http_proxy="" curl http://${ip_address}:${tei_service_port}/v1/chat/docsum \
+        -X POST \
+        -d '{"query":"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."}' \
+        -H 'Content-Type: application/json'
+    docker logs test-comps-llm-tgi-endpoint
+    docker logs test-comps-llm-tgi-server
+
+    cd $LOG_PATH
+    tei_service_port=5015
+    URL="http://${ip_address}:$tei_service_port/v1/chat/docsum"
+    HTTP_STATUS=$(curl -s -o /dev/null -w "%{http_code}" -X POST -d '{"query": "What is Deep Learning?"}' -H 'Content-Type: application/json' "$URL")
+    if [ "$HTTP_STATUS" -eq 200 ]; then
+        echo "[ llm - summarization ] HTTP status is 200. Checking content..."
+        local CONTENT=$(curl -s -X POST -d '{"query": "What is Deep Learning?"}' -H 'Content-Type: application/json' "$URL" | tee ${LOG_PATH}/llm_summarization.log)
+
+        if echo 'text: ' | grep -q "$EXPECTED_RESULT"; then
+            echo "[ llm - summarization ] Content is as expected."
+            docker logs test-comps-llm-tgi-server >> ${LOG_PATH}/llm_summarization.log
+        else
+            echo "[ llm - summarization ] Content does not match the expected result: $CONTENT"
+            docker logs test-comps-llm-tgi-server >> ${LOG_PATH}/llm_summarization.log
+            exit 1
+        fi
+    else
+        echo "[ llm - summarization ] HTTP status is not 200. Received status was $HTTP_STATUS"
+        docker logs test-comps-llm-tgi-server >> ${LOG_PATH}/llm_summarization.log
+        exit 1
+    fi
+}
+
+function stop_docker() {
+    cid=$(docker ps -aq --filter "name=test-comps-llm-*")
+    if [[ ! -z "$cid" ]]; then docker stop $cid && docker rm $cid && sleep 1s; fi
+}
+
+function main() {
+
+    stop_docker
+
+    build_docker_images
+    start_service
+
+    validate_microservice
+
+    stop_docker
+    echo y | docker system prune
+
+}
+
+main