create connection with wrong settings

.github/workflows/bootstrapping-resources.yml

@@ -162,6 +162,17 @@ jobs:
       working-directory: cli/jobs/pipelines-with-components/basics/1b_e2e_registered_components
       timeout-minutes: 30
       continue-on-error: true
+    - name: create asset for cli/jobs/pipelines-with-components/pipeline_job_with_flow_as_component
+      if: ${{ always() }}
+      run: |
+        source "${{ github.workspace }}/infra/bootstrapping/sdk_helpers.sh";
+        source "${{ github.workspace }}/infra/bootstrapping/init_environment.sh";
+        set -x
+        pip install promptflow
+        pf connection create -f connection.yaml --set api_key="${{ github.workspace }}" --set api_base="${{ github.workspace }}"
+      working-directory: cli/jobs/pipelines-with-components/basics/pipeline_job_with_flow_as_component
+      timeout-minutes: 30
+      continue-on-error: true
     - name: setup env for cli/jobs/pipelines-with-components/rai_pipeline_adult_analyse/
       if: ${{ always() }}
       run: |

cli/jobs/pipelines-with-components/pipeline_job_with_flow_as_component/README.md

@@ -1,8 +1,11 @@
 This is a dummy pipeline job with anonymous reference of a flow as a component. Flow directory is copied from [sample in promptflow repository](https://github.com/microsoft/promptflow/tree/main/examples/flows/standard/web-classification).
 
-Note that `.promptflow/flow.tools.json` in the flow directory is required to use a flow as a component. Usually you can use `pf flow validate` or `pf run validate` to generate it.
+Prerequirements:
+1. `.promptflow/flow.tools.json` in the flow directory is required to use a flow as a component. Usually you can use `pf flow validate` or `pf run validate` to generate it.
+2. You should either update connection name in `flow.dag.yaml` or update `connection.yaml` with your own api information and use `pf connection create --file connection.yaml` to create a workspace connection.
+3. You need to either edit the compute cluster in `pipeline.yml` or create a compute cluster named `cpu-cluster` in your workspace.
 
-After that, You need to edit the compute cluster in the defaults section and run `az ml job create --file pipeline.yml` to submit the pipeline job.
+After that, you can run `az ml job create --file pipeline.yml` to submit the pipeline job.
 
 References:
 - [microsoft/promptflow: Build high-quality LLM apps](https://github.com/microsoft/promptflow)

cli/jobs/pipelines-with-components/pipeline_job_with_flow_as_component/connection.yaml

@@ -0,0 +1,7 @@
+$schema: https://azuremlschemas.azureedge.net/promptflow/latest/AzureOpenAIConnection.schema.json
+name: azure_open_ai_connection
+type: azure_open_ai  # snake case
+api_key: "<to-be-replaced>"
+api_base: "aoai-api-endpoint"
+api_type: "azure"
+api_version: "2023-07-01-preview"

sdk/python/jobs/pipelines/1l_flow_in_pipeline/README.md

@@ -1,7 +1,5 @@
 This is a dummy pipeline job with anonymous reference of a flow as a component. This example has reused the flow in corresponding CLI example, which is copied from [sample in promptflow repository](https://github.com/microsoft/promptflow/tree/main/examples/flows/standard/web-classification). Please check [this path](../../../../../cli/jobs/pipelines-with-components/pipeline_job_with_flow_as_component/) for dependent resources.
 
-Note that `.promptflow/flow.tools.json` in the flow directory is required to use a flow as a component. Usually you can use `pf flow validate` or `pf run validate` to generate it.
-
 References:
 - [microsoft/promptflow: Build high-quality LLM apps](https://github.com/microsoft/promptflow)
 - [Reference - Prompt flow docuentation](https://microsoft.github.io/promptflow/reference/index.html)

sdk/python/jobs/pipelines/1l_flow_in_pipeline/flow_in_pipeline.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Use Flow as Component in Pipeline\n",
+    "# Use Flow as Component in Pipeline Job\n",
     "\n",
     "**Requirements** - In order to benefit from this tutorial, you will need:\n",
     "- A basic understanding of Machine Learning\n",
@@ -79,7 +79,9 @@
    "source": [
     "## 1.3 Get a handle to the workspace\n",
     "\n",
-    "We use config file to connect to a workspace. The Azure ML workspace should be configured with computer cluster. [Check this notebook for configure a workspace](../../configuration.ipynb)"
+    "We use config file to connect to a workspace. The Azure ML workspace should be configured with computer cluster. [Check this notebook for configure a workspace](../../configuration.ipynb).\n",
+    "\n",
+    "As we'll use flow with connection, the workspace should also be configured with a workspace connection. [Check this notebook for configure a connection](../../../resources/connections/connections.ipynb)"
    ]
   },
   {
@@ -92,8 +94,13 @@
     "ml_client = MLClient.from_config(credential=credential)\n",
     "\n",
     "# Retrieve an already attached Azure Machine Learning Compute.\n",
-    "cluster_name = \"gpu-cluster\"\n",
-    "print(ml_client.compute.get(cluster_name))"
+    "cluster_name = \"cpu-cluster\"\n",
+    "print(ml_client.compute.get(cluster_name))\n",
+    "\n",
+    "# note that you'll need to install package promptflow to check connections, but it's not required if you want to submit the pipeline job only\n",
+    "from promptflow import PFClient\n",
+    "pf = PFClient(ml_client)\n",
+    "print(pf.connections.get(\"azure_open_ai_connection\"))"
    ]
   },
   {
@@ -102,9 +109,12 @@
    "source": [
     "# 2. Load flow as component\n",
     "\n",
-    "We suppose that there has already been a flow authored with Promptflow SDK/CLI/portal. Then we can load its flow dag yaml as a component like regular component specs.\n",
+    "We suppose that there has already been a flow authored with Promptflow SDK/CLI/portal and `.promptflow/flow.tools.json` is already generated in the flow directory:\n",
+    "\n",
+    "- For more information about how to author a flow, please check [official doc site](https://microsoft.github.io/promptflow/).\n",
+    "- If `.promptflow/flow.tools.json` is not generated yet or is not up-to-date, you may use `pf flow validate` or `pf run validate` to generate it - [reference doc](https://microsoft.github.io/promptflow/reference/pf-command-reference.html).\n",
     "\n",
-    "Here we reuse [the flow definition yaml in CLI examples](../../../../../cli/jobs/pipelines-with-components/pipeline_job_with_flow_as_component/web_classification/flow.dag.yaml)."
+    "Then we can load its flow dag yaml as a component like regular component specs. Here we reused [the flow definition yaml in CLI examples](../../../../../cli/jobs/pipelines-with-components/pipeline_job_with_flow_as_component/web_classification/flow.dag.yaml)."
    ]
   },
   {

sdk/python/setup.sh

@@ -20,4 +20,8 @@ pip install azure-ai-ml
 # https://docsupport.blob.core.windows.net/ml-sample-submissions/1905732/azure_ai_ml-1.0.0-py3-none-any.whl
 # </az_ml_sdk_test_install>
 
+# <connection_manage_install>
+pip install promptflow
+# </connection_manage_install>
+
 pip list