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chatbot-rag-app: adds Kubernetes manifest and instructions
Signed-off-by: Adrian Cole <[email protected]>
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‎example-apps/chatbot-rag-app/README.md‎

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@@ -22,8 +22,8 @@ Copy [env.example](env.example) to `.env` and fill in values noted inside.
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## Installing and connecting to Elasticsearch
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There are a number of ways to install Elasticsearch. Cloud is best for most
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use-cases. We also have [docker-compose-elastic.yml](../../docker), that starts
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Elasticsearch, Kibana, and APM Server on your laptop with one command.
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use-cases. We also have [docker-compose-elastic.yml][docker-compose-elastic],
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that starts Elasticsearch, Kibana, and APM Server on your laptop in one step.
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Once you decided your approach, edit your `.env` file accordingly.
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docker compose down
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```
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### Run with Kubernetes
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Kubernetes is more complicated than Docker, but closer to the production
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experience for many users. [k8s-manifest.yml](k8s-manifest.yml) creates the
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same services, but needs additional configuration first.
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First step is to setup your environment. [env.example](env.example) must be
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copied to a file name `.env` and updated with `ELASTICSEARCH_URL` and
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`OTEL_EXPORTER_OTLP_ENDPOINT` values visible to you Kubernetes deployment.
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For example, if you started your Elastic Stack with [k8s-manifest-elastic.yml][k8s-manifest-elastic],
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you would update these values:
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```
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ELASTICSEARCH_URL=http://elasticsearch:9200
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OTEL_EXPORTER_OTLP_ENDPOINT=http://apm-server:8200
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```
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Then, import your `.env` file as a configmap like this:
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```bash
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kubectl create configmap chatbot-rag-app-env --from-env-file=.env
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```
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If you are using Vertex AI, make a secret for authentication:
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```bash
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kubectl create secret generic gcloud-credentials \
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--from-file=application_default_credentials.json=$HOME/.config/gcloud/application_default_credentials.json
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```
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Now that your configuration is applied, create the chatbot-rag-app deployment
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and service by applying this manifest:
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```bash
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kubectl apply -f k8s-manifest.yml
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```
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Next, block until chatbot-rag-app is available.
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```bash
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kubectl wait --for=condition=available --timeout=10m deployment/chatbot-rag-app
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```
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*Note*: The first run may take several minutes to become available.
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Next, forward the kibana port:
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```bash
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kubectl port-forward service/kibana 5601:5601 &
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```
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Clean up when finished, like this:
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```bash
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kubectl delete -f k8s-manifest.yml
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```
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### Run with Python
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If you want to run this example with Python, you need to do a few things listed
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---
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[loader-docs]: https://python.langchain.com/docs/how_to/#document-loaders
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[install-es]: https://www.elastic.co/search-labs/tutorials/install-elasticsearch
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[docker-compose-elastic]: ../../docker/docker-compose-elastic.yml
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[k8s-manifest-elastic]: ../../k8s/k8s-manifest-elastic.yml

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

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

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

‎k8s/README.md‎

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# Running your own Elastic Stack with Kubernetes
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If you'd like to start Elastic with Kubernetes, you can use the provided
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[manifest-elastic.yml](manifest-elastic.yml) file. This starts
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Elasticsearch, Kibana, and APM Server and only requires Docker installed.
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First, configure the Elastic stack:
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```bash
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kubectl apply -f manifest-elastic.yml
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```
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**Note**: For simplicity, this adds an Elastic Stack to the default namespace.
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Commands after here are simpler due to this. If you want to choose a different
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one, use `kubectl`'s `--namespace` flag!
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Next, block until the whole stack is available. First install or changing the
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Elastic Stack version can take a long time due to image pulling.
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```bash
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kubectl wait --for=condition=available --timeout=10m \
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deployment/elasticsearch \
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deployment/kibana \
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deployment/apm-server
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```
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Next, forward the kibana port:
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```bash
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kubectl port-forward service/kibana 5601:5601 &
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```
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Finally, you can view Kibana at http://localhost:5601/app/home#/
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If asked for a username and password, use username: elastic and password: elastic.
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Clean up when finished, like this:
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```bash
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kubectl delete -f manifest-elastic.yml
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```

‎k8s/k8s-manifest-elastic.yml‎

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apiVersion: v1
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kind: Service
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metadata:
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name: elasticsearch
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spec:
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ports:
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- port: 9200
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targetPort: 9200
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selector:
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app: elasticsearch
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---
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apiVersion: apps/v1
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kind: Deployment
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metadata:
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name: elasticsearch
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spec:
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replicas: 1
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selector:
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matchLabels:
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app: elasticsearch
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template:
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metadata:
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labels:
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app: elasticsearch
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spec:
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containers:
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- name: elasticsearch
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image: docker.elastic.co/elasticsearch/elasticsearch:8.17.2
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ports:
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- containerPort: 9200
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env:
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- name: node.name
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value: elasticsearch
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- name: cluster.name
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value: docker-cluster
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- name: discovery.type
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value: single-node
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- name: ELASTIC_PASSWORD
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value: elastic
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- name: bootstrap.memory_lock
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value: "true"
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- name: xpack.security.enabled
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value: "true"
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- name: xpack.security.http.ssl.enabled
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value: "false"
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- name: xpack.security.transport.ssl.enabled
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value: "false"
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- name: xpack.license.self_generated.type
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value: trial
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# Note that ELSER is recommended to have 2GB, but it is JNI (PyTorch).
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# So, ELSER's memory is in addition to the heap and other overhead.
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- name: ES_JAVA_OPTS
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value: "-Xms2g -Xmx2g"
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resources:
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limits:
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memory: "4.5Gi" # max heap plus overhead and 2GB for ELSER
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securityContext:
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capabilities:
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add: ["CHOWN", "DAC_OVERRIDE", "SETGID", "SETUID"]
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drop: ["ALL"]
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readinessProbe:
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exec:
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command: ["sh", "-c", "curl -s http://localhost:9200 | grep -q 'missing authentication credentials'"]
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initialDelaySeconds: 5
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periodSeconds: 1
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timeoutSeconds: 10
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failureThreshold: 120
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---
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apiVersion: v1
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kind: Service
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metadata:
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name: kibana
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spec:
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ports:
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- port: 5601
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targetPort: 5601
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selector:
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app: kibana
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---
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apiVersion: apps/v1
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kind: Deployment
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metadata:
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name: kibana
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spec:
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replicas: 1
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selector:
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matchLabels:
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app: kibana
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template:
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metadata:
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labels:
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app: kibana
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spec:
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initContainers:
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- name: setup-kibana-system-user
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image: docker.elastic.co/elasticsearch/elasticsearch:8.17.2
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command:
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- bash
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- -xc
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- |
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echo "Setup the kibana_system password";
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until curl -vs -u "elastic:elastic" -X POST http://elasticsearch:9200/_security/user/kibana_system/_password -d '{"password":"elastic"}' -H "Content-Type: application/json" | grep -q "^{}"; do sleep 5; done;
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containers:
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- name: kibana
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image: docker.elastic.co/kibana/kibana:8.17.2
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ports:
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- containerPort: 5601
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env:
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- name: SERVERNAME
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value: kibana
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- name: ELASTICSEARCH_HOSTS
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value: http://elasticsearch:9200
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- name: ELASTICSEARCH_USERNAME
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value: kibana_system
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- name: ELASTICSEARCH_PASSWORD
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value: elastic
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- name: MONITORING_UI_CONTAINER_ELASTICSEARCH_ENABLED
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value: "true"
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- name: XPACK_SECURITY_ENCRYPTIONKEY
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value: fhjskloppd678ehkdfdlliverpoolfcr
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- name: XPACK_ENCRYPTEDSAVEDOBJECTS_ENCRYPTIONKEY
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value: fhjskloppd678ehkdfdlliverpoolfcr
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- name: SERVER_PUBLICBASEURL
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value: http://127.0.0.1:5601
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readinessProbe:
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httpGet:
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path: /api/status
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port: 5601
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initialDelaySeconds: 10
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periodSeconds: 1
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failureThreshold: 300
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---
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apiVersion: v1
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kind: Service
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metadata:
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name: apm-server
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spec:
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ports:
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- port: 8200
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targetPort: 8200
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selector:
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app: apm-server
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---
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apiVersion: apps/v1
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kind: Deployment
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metadata:
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name: apm-server
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spec:
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replicas: 1
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selector:
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matchLabels:
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app: apm-server
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template:
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metadata:
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labels:
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app: apm-server
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spec:
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containers:
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- name: apm-server
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image: docker.elastic.co/apm/apm-server:8.17.2
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ports:
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- containerPort: 8200
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env:
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- name: apm-server.kibana.enabled
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value: "true"
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- name: apm-server.kibana.host
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value: http://kibana:5601
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- name: apm-server.kibana.username
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value: elastic
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- name: apm-server.kibana.password
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value: elastic
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- name: output.elasticsearch.hosts
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value: http://elasticsearch:9200
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- name: output.elasticsearch.username
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value: elastic
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- name: output.elasticsearch.password
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value: elastic
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readinessProbe:
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tcpSocket:
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port: 8200
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initialDelaySeconds: 10
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periodSeconds: 1
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failureThreshold: 300

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