diff --git a/tests/integration/kubernetes/k8s-nvidia-nim.bats b/tests/integration/kubernetes/k8s-nvidia-nim.bats
new file mode 100644
index 0000000000..aba47ec553
--- /dev/null
+++ b/tests/integration/kubernetes/k8s-nvidia-nim.bats
@@ -0,0 +1,308 @@
+#!/usr/bin/env bats
+#
+# Copyright (c) 2025 NVIDIA Corporation
+#
+# SPDX-License-Identifier: Apache-2.0
+#
+
+load "${BATS_TEST_DIRNAME}/../../common.bash"
+load "${BATS_TEST_DIRNAME}/tests_common.sh"
+
+export POD_NAME_INSTRUCT="nvidia-nim-llama-3-1-8b-instruct"
+export POD_NAME_EMBEDQA="nvidia-nim-llama-3-2-nv-embedqa-1b-v2"
+
+export POD_SECRET_INSTRUCT="ngc-secret-instruct"
+
+export DOCKER_CONFIG_JSON=$(
+    echo -n "{\"auths\":{\"nvcr.io\":{\"username\":\"\$oauthtoken\",\"password\":\"${NGC_API_KEY}\",\"auth\":\"$(echo -n "\$oauthtoken:${NGC_API_KEY}" | base64 -w0)\"}}}" |
+        base64 -w0
+)
+
+setup_file() {
+    dpkg -s jq 2>&1 >/dev/null || sudo apt -y install jq
+
+    export PYENV_ROOT="$HOME/.pyenv"
+    [[ -d $PYENV_ROOT/bin ]] && export PATH="$PYENV_ROOT/bin:$PATH"
+    eval "$(pyenv init - bash)"
+
+    python3 -m venv ${HOME}/.cicd/venv
+
+    get_pod_config_dir
+
+    pod_instruct_yaml_in="${pod_config_dir}/${POD_NAME_INSTRUCT}.yaml.in"
+    pod_instruct_yaml="${pod_config_dir}/${POD_NAME_INSTRUCT}.yaml"
+
+    envsubst <"${pod_instruct_yaml_in}" >"${pod_instruct_yaml}"
+
+    export POD_INSTRUCT_YAML="${pod_instruct_yaml}"
+}
+
+@test "NVIDIA NIM Llama 3.1-8b Instruct" {
+    kubectl apply -f "${POD_INSTRUCT_YAML}"
+    kubectl wait --for=condition=Ready --timeout=500s pod "${POD_NAME_INSTRUCT}"
+    POD_IP_INSTRUCT=$(kubectl get pod "${POD_NAME_INSTRUCT}" -o jsonpath='{.status.podIP}')
+    [ -n "${POD_IP_INSTRUCT}" ]
+
+    echo POD_IP_INSTRUCT=${POD_IP_INSTRUCT} >"$BATS_SUITE_TMPDIR/env"
+    echo "# POD_IP_INSTRUCT=${POD_IP_INSTRUCT}" >&3
+}
+
+@test "NVIDIA NIM Llama 3.2 Embedqa" {
+    kubectl wait --for=condition=Ready --timeout=500s pod "${POD_NAME_EMBEDQA}" -n nim-embedqa
+    POD_IP_EMBEDQA=$(kubectl get pod "${POD_NAME_EMBEDQA}" -o jsonpath='{.status.podIP}' -n nim-embedqa)
+    [ -n "${POD_IP_EMBEDQA}" ]
+
+    echo POD_IP_EMBEDQA=${POD_IP_EMBEDQA} >>"$BATS_SUITE_TMPDIR/env"
+    echo "# POD_IP_EMBEDQA=${POD_IP_EMBEDQA}" >&3
+}
+
+@test "List of models available for inference" {
+    source "$BATS_SUITE_TMPDIR/env"
+    [ -n "${POD_IP_INSTRUCT}" ]
+
+    run curl -sX GET "http://${POD_IP_INSTRUCT}:8000/v1/models"
+    [ "$status" -eq 0 ]
+
+    export MODEL_NAME=$(echo "${output}" | jq '.data[0].id' | tr -d '"')
+    [ -n "${MODEL_NAME}" ]
+    echo MODEL_NAME=${MODEL_NAME} >>"$BATS_SUITE_TMPDIR/env"
+    echo "# MODEL_NAME=${MODEL_NAME}" >&3
+
+}
+
+@test "Simple OpenAI completion request" {
+    source "$BATS_SUITE_TMPDIR/env"
+    [ -n ${POD_IP_INSTRUCT} ]
+    [ -n ${MODEL_NAME} ]
+
+    QUESTION="What are Kata Containers?"
+
+    run curl -sX 'POST' \
+        "http://${POD_IP_INSTRUCT}:8000/v1/completions" \
+        -H "accept: application/json" \
+        -H "Content-Type: application/json" \
+        -d "{\"model\": \"${MODEL_NAME}\", \"prompt\": \"${QUESTION}\", \"max_tokens\": 64}"
+
+    ANWSER=$(echo ${output} | jq '.choices[0].text')
+    [ -n "${ANWSER}" ]
+
+    echo "# QUESTION: ${QUESTION}" >&3
+    echo "# ANWSER: ${ANWSER}" >&3
+}
+
+@test "Setup the LangChain flow" {
+    source ${HOME}/.cicd/venv/bin/activate
+
+    pip install --upgrade pip
+    [[ "$(pip show langchain 2>/dev/null | awk '/^Version:/{print $2}')" = "0.2.5" ]] || pip install langchain==0.2.5
+    [[ "$(pip show langchain-nvidia-ai-endpoints 2>/dev/null | awk '/^Version:/{print $2}')" = "0.1.2" ]] || pip install langchain-nvidia-ai-endpoints==0.1.2
+    [[ "$(pip show faiss-gpu 2>/dev/null | awk '/^Version:/{print $2}')" = "1.7.2" ]] || pip install faiss-gpu==1.7.2
+    [[ "$(pip show langchain-community 2>/dev/null | awk '/^Version:/{print $2}')" = "0.2.5" ]] || pip install langchain-community==0.2.5
+    [[ "$(pip show beautifulsoup4 2>/dev/null | awk '/^Version:/{print $2}')" = "4.13.4" ]] || pip install beautifulsoup4==4.13.4
+}
+
+@test "LangChain NVIDIA AI Endpoints" {
+    source "$BATS_SUITE_TMPDIR/env"
+    [ -n ${POD_IP_INSTRUCT} ]
+    [ -n ${MODEL_NAME} ]
+
+    QUESTION="What is the capital of France?"
+    ANWSER="The capital of France is Paris."
+
+    source ${HOME}/.cicd/venv/bin/activate
+    cat <<-EOF >${HOME}/.cicd/venv/langchain_nim.py
+                from langchain_nvidia_ai_endpoints import ChatNVIDIA
+
+                llm = ChatNVIDIA(base_url="http://${POD_IP_INSTRUCT}:8000/v1", model="${MODEL_NAME}", temperature=0.1, max_tokens=1000, top_p=1.0)
+
+                result = llm.invoke("${QUESTION}")
+                print(result.content)
+        EOF
+        run python3 ${HOME}/.cicd/venv/langchain_nim.py
+
+        [ "${status}" -eq 0 ]
+        [ "${output}" = "${ANWSER}" ]
+EOF
+}
+
+@test "Kata Documentation RAG" {
+    source "$BATS_SUITE_TMPDIR/env"
+    [ -n ${POD_IP_EMBEDQA} ]
+    [ -n ${POD_IP_INSTRUCT} ]
+
+    source ${HOME}/.cicd/venv/bin/activate
+    cat <<EOF >${HOME}/.cicd/venv/langchain_nim_kata_rag.py
+import os
+from langchain.chains import ConversationalRetrievalChain, LLMChain
+from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT, QA_PROMPT
+from langchain.chains.question_answering import load_qa_chain
+from langchain.memory import ConversationBufferMemory
+from langchain_community.vectorstores import FAISS
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_nvidia_ai_endpoints import ChatNVIDIA
+from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings
+EOF
+
+    cat <<EOF >>${HOME}/.cicd/venv/langchain_nim_kata_rag.py
+import re
+from typing import List, Union
+
+import requests
+from bs4 import BeautifulSoup
+
+def html_document_loader(url: Union[str, bytes]) -> str:
+    """
+    Loads the HTML content of a document from a given URL and return it's content.
+
+    Args:
+        url: The URL of the document.
+
+    Returns:
+        The content of the document.
+
+    Raises:
+        Exception: If there is an error while making the HTTP request.
+
+    """
+    try:
+        response = requests.get(url)
+        html_content = response.text
+    except Exception as e:
+        print(f"Failed to load {url} due to exception {e}")
+        return ""
+
+    try:
+        # Create a Beautiful Soup object to parse html
+        soup = BeautifulSoup(html_content, "html.parser")
+
+        # Remove script and style tags
+        for script in soup(["script", "style"]):
+            script.extract()
+
+        # Get the plain text from the HTML document
+        text = soup.get_text()
+
+        # Remove excess whitespace and newlines
+        text = re.sub("\s+", " ", text).strip()
+
+        return text
+    except Exception as e:
+        print(f"Exception {e} while loading document")
+        return ""
+
+EOF
+
+    cat <<EOF >>${HOME}/.cicd/venv/langchain_nim_kata_rag.py
+def create_embeddings(embedding_path: str = "./data/nv_embedding"):
+
+    embedding_path = "./data/nv_embedding"
+    print(f"Storing embeddings to {embedding_path}")
+
+    # List of web pages containing Kata technical documentation
+    urls = [
+        "https://katacontainers.io/",
+        "https://katacontainers.io/learn",
+        "https://github.com/kata-containers/kata-containers/releases",
+    ]
+
+    documents = []
+    for url in urls:
+        document = html_document_loader(url)
+        documents.append(document)
+
+
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1000,
+        chunk_overlap=0,
+        length_function=len,
+    )
+    texts = text_splitter.create_documents(documents)
+    index_docs(url, text_splitter, texts, embedding_path)
+    print("Generated embedding successfully")
+EOF
+
+    cat <<EOF >>${HOME}/.cicd/venv/langchain_nim_kata_rag.py
+def index_docs(url: Union[str, bytes], splitter, documents: List[str], dest_embed_dir) -> None:
+    """
+    Split the document into chunks and create embeddings for the document
+
+    Args:
+        url: Source url for the document.
+        splitter: Splitter used to split the document
+        documents: list of documents whose embeddings needs to be created
+        dest_embed_dir: destination directory for embeddings
+
+    Returns:
+        None
+    """
+    embeddings = NVIDIAEmbeddings(base_url="http://${POD_IP_EMBEDQA}:8000/v1", model="nvidia/llama-3.2-nv-embedqa-1b-v2")
+
+    for document in documents:
+        texts = splitter.split_text(document.page_content)
+
+        # metadata to attach to document
+        metadatas = [document.metadata]
+
+        # create embeddings and add to vector store
+        if os.path.exists(dest_embed_dir):
+            update = FAISS.load_local(folder_path=dest_embed_dir, embeddings=embeddings, allow_dangerous_deserialization=True)
+            update.add_texts(texts, metadatas=metadatas)
+            update.save_local(folder_path=dest_embed_dir)
+        else:
+            docsearch = FAISS.from_texts(texts, embedding=embeddings, metadatas=metadatas)
+            docsearch.save_local(folder_path=dest_embed_dir)
+EOF
+
+    cat <<EOF >>${HOME}/.cicd/venv/langchain_nim_kata_rag.py
+create_embeddings()
+
+embedding_model = NVIDIAEmbeddings(base_url="http://${POD_IP_EMBEDQA}:8000/v1", model="nvidia/llama-3.2-nv-embedqa-1b-v2")
+EOF
+
+    cat <<EOF >>${HOME}/.cicd/venv/langchain_nim_kata_rag.py
+# Embed documents
+embedding_path = "./data/nv_embedding"
+docsearch = FAISS.load_local(folder_path=embedding_path, embeddings=embedding_model, allow_dangerous_deserialization=True)
+EOF
+
+    cat <<EOF >>${HOME}/.cicd/venv/langchain_nim_kata_rag.py
+llm = ChatNVIDIA(base_url="http://${POD_IP_INSTRUCT}:8000/v1", model="meta/llama3-8b-instruct", temperature=0.1, max_tokens=1000, top_p=1.0)
+
+memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+
+qa_prompt=QA_PROMPT
+
+doc_chain = load_qa_chain(llm, chain_type="stuff", prompt=QA_PROMPT)
+
+qa = ConversationalRetrievalChain.from_llm(
+    llm=llm,
+    retriever=docsearch.as_retriever(),
+    chain_type="stuff",
+    memory=memory,
+    combine_docs_chain_kwargs={'prompt': qa_prompt},
+)
+
+EOF
+
+    QUESTION="What is the latest Kata Containers release?"
+
+    cat <<EOF >>${HOME}/.cicd/venv/langchain_nim_kata_rag.py
+query = "${QUESTION}"
+result = qa({"question": query})
+print("#"+ result.get("answer"))
+
+EOF
+
+    run python3 ${HOME}/.cicd/venv/langchain_nim_kata_rag.py
+    [ "$status" -eq 0 ]
+
+    ANWSER=$(echo ${output} | cut -d '#' -f2)
+    [ -n "${ANWSER}" ]
+
+    echo "# QUESTION: ${QUESTION}" >&3
+    echo "# ANWSER: ${ANWSER}" >&3
+}
+
+teardown_file() {
+        kubectl delete -f "${POD_INSTRUCT_YAML}"
+}