chatbot-rag-app: adds Kubernetes manifest and instructions

Signed-off-by: Adrian Cole <[email protected]>

docker/README.md

@@ -9,8 +9,11 @@ Note: If you haven't checked out this repository, all you need is one file:
 wget https://raw.githubusercontent.com/elastic/elasticsearch-labs/refs/heads/main/docker/docker-compose-elastic.yml
 ```
 
-Use docker compose to run Elastic stack in the background:
+Before you begin, ensure you have free CPU and memory on your Docker host. If
+you plan to use ELSER, assume a minimum of 8 cpus and 6GB memory for the
+containers in this compose file.
 
+First, start this Elastic Stack in the background:
 ```bash
 docker compose -f docker-compose-elastic.yml up --force-recreate --wait -d
 ```
@@ -20,7 +23,6 @@ Then, you can view Kibana at http://localhost:5601/app/home#/
 If asked for a username and password, use username: elastic and password: elastic.
 
 Clean up when finished, like this:
-
 ```bash
 docker compose -f docker-compose-elastic.yml down
 ```

example-apps/chatbot-rag-app/Dockerfile

@@ -5,9 +5,7 @@ COPY frontend ./frontend
 RUN cd frontend && yarn install
 RUN cd frontend && REACT_APP_API_HOST=/api yarn build
 
-# langchain and vertexai depend on a large number of system packages including
-# linux-headers, g++, geos, geos-dev, rust and cargo. These are already present
-# on -slim and adding them to -alpine results in a larger image than -slim.
+# Use glibc-based image to get pre-compiled wheels for grpcio and tiktoken
 FROM python:3.12-slim
 
 WORKDIR /app

example-apps/chatbot-rag-app/README.md

@@ -22,8 +22,8 @@ Copy [env.example](env.example) to `.env` and fill in values noted inside.
 ## Installing and connecting to Elasticsearch
 
 There are a number of ways to install Elasticsearch. Cloud is best for most
-use-cases. We also have [docker-compose-elastic.yml](../../docker), that starts
-Elasticsearch, Kibana, and APM Server on your laptop with one command.
+use-cases. We also have [docker-compose-elastic.yml][docker-compose-elastic],
+that starts Elasticsearch, Kibana, and APM Server on your laptop in one step.
 
 Once you decided your approach, edit your `.env` file accordingly.
 
@@ -71,6 +71,62 @@ Clean up when finished, like this:
 docker compose down
 ```
 
+### Run with Kubernetes
+
+Kubernetes is more complicated than Docker, but closer to the production
+experience for many users. [k8s-manifest.yml](k8s-manifest.yml) creates the
+same services, but needs additional configuration first.
+
+First step is to setup your environment. [env.example](env.example) must be
+copied to a file name `.env` and updated with `ELASTICSEARCH_URL` and
+`OTEL_EXPORTER_OTLP_ENDPOINT` values visible to you Kubernetes deployment.
+
+For example, if you started your Elastic Stack with [k8s-manifest-elastic.yml][k8s-manifest-elastic],
+you would update these values:
+```
+ELASTICSEARCH_URL=http://elasticsearch:9200
+OTEL_EXPORTER_OTLP_ENDPOINT=http://apm-server:8200
+```
+
+Then, import your `.env` file as a configmap like this:
+```bash
+kubectl create configmap chatbot-rag-app-env --from-env-file=.env
+```
+
+If you are using Vertex AI, make a secret for authentication:
+```bash
+kubectl create secret generic gcloud-credentials \
+  --from-file=application_default_credentials.json=$HOME/.config/gcloud/application_default_credentials.json
+```
+
+Now that your configuration is applied, create the chatbot-rag-app deployment
+and service by applying this manifest:
+```bash
+kubectl apply -f k8s-manifest.yml
+```
+
+Next, block until chatbot-rag-app is available.
+```bash
+kubectl wait --for=condition=available --timeout=20m  deployment/chatbot-rag-app
+```
+
+*Note*: The first run may take several minutes to become available. Here's how
+to follow logs on this stage:
+```bash
+kubectl logs deployment.apps/chatbot-rag-app -c create-index -f
+```
+
+Next, forward the kibana port:
+```bash
+kubectl port-forward service/kibana 5601:5601 &
+```
+
+Clean up when finished, like this:
+
+```bash
+kubectl delete -f k8s-manifest.yml
+```
+
 ### Run with Python
 
 If you want to run this example with Python, you need to do a few things listed
@@ -196,3 +252,5 @@ docker compose up --build --force-recreate
 ---
 [loader-docs]: https://python.langchain.com/docs/how_to/#document-loaders
 [install-es]: https://www.elastic.co/search-labs/tutorials/install-elasticsearch
+[docker-compose-elastic]: ../../docker/docker-compose-elastic.yml
+[k8s-manifest-elastic]: ../../k8s/k8s-manifest-elastic.yml

example-apps/chatbot-rag-app/env.example

@@ -6,6 +6,8 @@ FLASK_APP=api/app.py
 PYTHONUNBUFFERED=1
 
 # How you connect to Elasticsearch: change details to your instance
+# This defaults to a Elastic Stack accessible via localhost. When this is
+# running inside Kubernetes, update to http://elasticsearch:9200 or similar.
 ELASTICSEARCH_URL=http://localhost:9200
 ELASTICSEARCH_USER=elastic
 ELASTICSEARCH_PASSWORD=elastic
@@ -68,7 +70,9 @@ OTEL_SDK_DISABLED=true
 # Assign the service name that shows up in Kibana
 OTEL_SERVICE_NAME=chatbot-rag-app
 
-# Default to send traces to the Elastic APM server
+# Default to send logs, traces and metrics to an Elastic APM server accessible
+# via localhost. If using running inside k8s, update to http://apm-server:8200
+# or similar.
 OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:8200
 OTEL_EXPORTER_OTLP_PROTOCOL=http/protobuf
 

example-apps/chatbot-rag-app/k8s-manifest.yml

@@ -0,0 +1,57 @@
+---
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: chatbot-rag-app
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app: chatbot-rag-app
+  template:
+    metadata:
+      labels:
+        app: chatbot-rag-app
+    spec:
+      # The below will recreate your secret based on the gcloud credentials file
+      # kubectl create secret generic gcloud-credentials \
+      #  --from-file=application_default_credentials.json=$HOME/.config/gcloud/application_default_credentials.json
+      volumes:
+        - name: gcloud-credentials
+          secret:
+            secretName: gcloud-credentials
+      initContainers:
+      - name: create-index
+        image: &image ghcr.io/elastic/elasticsearch-labs/chatbot-rag-app:latest
+        args: ["flask", "create-index"]
+        env:
+          - name: FLASK_APP
+            value: api/app.py
+        # This recreates your configmap based on your .env file:
+        # kubectl create configmap chatbot-rag-app-env --from-env-file=.env
+        envFrom: &envFrom
+          - configMapRef:
+              name: chatbot-rag-app-env
+        volumeMounts: &volumeMounts
+          - name: gcloud-credentials
+            mountPath: /root/.config/application_default_credentials.json
+            readOnly: true
+      containers:
+      - name: api-frontend
+        image: *image
+        ports:
+          - containerPort: 4000
+        envFrom: *envFrom
+        volumeMounts: *volumeMounts
+---
+apiVersion: v1
+kind: Service
+metadata:
+  name: api
+spec:
+  selector:
+    app: chatbot-rag-app
+  ports:
+    - protocol: TCP
+      port: 4000
+      targetPort: 4000

k8s/README.md

@@ -0,0 +1,47 @@
+# Running your own Elastic Stack with Kubernetes
+
+If you'd like to start Elastic with Kubernetes, you can use the provided
+[manifest-elastic.yml](manifest-elastic.yml) file. This starts
+Elasticsearch, Kibana, and APM Server in an existing Kubernetes cluster.
+
+Note: If you haven't checked out this repository, all you need is one file:
+```bash
+wget https://raw.githubusercontent.com/elastic/elasticsearch-labs/refs/heads/main/docker/docker-compose-elastic.yml
+```
+
+Before you begin, ensure you have free CPU and memory in your cluster. If you
+plan to use ELSER, assume a minimum of 8 cpus and 6GB memory for the containers
+in this manifest.
+
+First, start this Elastic Stack in the background:
+```bash
+kubectl apply -f k8s-manifest-elastic.yml
+```
+
+**Note**: For simplicity, this adds an Elastic Stack to the default namespace.
+Commands after here are simpler due to this. If you want to choose a different
+one, use `kubectl`'s `--namespace` flag!
+
+Next, block until the whole stack is available. First install or changing the
+Elastic Stack version can take a long time due to image pulling.
+```bash
+kubectl wait --for=condition=available --timeout=10m \
+  deployment/elasticsearch \
+  deployment/kibana \
+  deployment/apm-server
+```
+
+Next, forward the kibana port:
+```bash
+kubectl port-forward service/kibana 5601:5601 &
+```
+
+Finally, you can view Kibana at http://localhost:5601/app/home#/
+
+If asked for a username and password, use username: elastic and password: elastic.
+
+Clean up when finished, like this:
+
+```bash
+kubectl delete -f k8s-manifest-elastic.yml
+```