chore: import upstream snapshot with attribution

examples/tutorials/flow-deploy/docker/README.md

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+---
+resources: examples/connections/azure_openai.yml, examples/flows/standard/web-classification
+category: deployment
+weight: 40
+---
+
+# Deploy a flow using Docker
+
+This example demos how to deploy flow as a docker app.
+We will use [web-classification](../../../flows/standard/web-classification/README.md) as example in this tutorial.
+
+## Build a flow as docker format app
+
+Note that all dependent connections must be created before building as docker.
+```bash
+# create connection if not created before
+pf connection create --file ../../../connections/azure_openai.yml --set api_key=<your_api_key> api_base=<your_api_base> --name open_ai_connection
+```
+
+Use the command below to build a flow as docker format app:
+
+```bash
+pf flow build --source ../../../flows/standard/web-classification --output dist --format docker
+```
+
+## Deploy with Docker
+### Build Docker image
+
+Like other Dockerfile, you need to build the image first. You can tag the image with any name you want. In this example, we use `promptflow-serve`.
+
+Run the command below to build image:
+
+```shell
+docker build dist -t web-classification-serve
+```
+
+### Run Docker image
+
+Run the docker image will start a service to serve the flow inside the container.
+
+#### Connections
+If the service involves connections, all related connections will be exported as yaml files and recreated in containers.
+Secrets in connections won't be exported directly. Instead, we will export them as a reference to environment variables:
+```yaml
+$schema: https://azuremlschemas.azureedge.net/promptflow/latest/OpenAIConnection.schema.json
+type: open_ai
+name: open_ai_connection
+module: promptflow.connections
+api_key: ${env:OPEN_AI_CONNECTION_API_KEY} # env reference
+```
+You'll need to set up the environment variables in the container to make the connections work.
+
+### Run with `docker run`
+
+
+You can run the docker image directly set via below commands:
+```shell
+# The started service will listen on port 8080.You can map the port to any port on the host machine as you want.
+docker run -p 8080:8080 -e OPEN_AI_CONNECTION_API_KEY=<secret-value> web-classification-serve
+```
+
+### Test the endpoint
+After start the service, you can use curl to test it:
+
+```shell
+curl http://localhost:8080/score --data '{"url":"https://play.google.com/store/apps/details?id=com.twitter.android"}' -X POST  -H "Content-Type: application/json"
+```