[Example] Add example flow for activate config

examples/flows/standard/conditional-flow-for-if-else/README.md

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+# Conditional flow for if-else scenario
+
+This example is a conditonal flow for if-else scenario.
+
+In this flow, it checks if an input query passes content safety check. If it's denied, we'll return a default response; otherwise, we'll call LLM to get a response and then summarize the final results.
+
+:::{admonition} Notice
+The 'content_safety_check' and 'llm_result' node in this flow are dummy nodes that do not actually use connections of `Azure Content Safty` and `Azure Open AI`. You can replace them with the real ones. Learn more: [Manage connections](../../../connections/connection.ipynb)
+:::
+
+By following this example, you will learn how to create a conditional flow using the `activate config`.
+
+## Prerequisites
+
+Install promptflow sdk and other dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+## Run flow
+
+- Test flow/node
+```bash
+# test with default input value in flow.dag.yaml
+pf flow test --flow .
+
+# test with flow inputs
+pf flow test --flow . --inputs query="What is Prompt flow?"
+```
+
+- List and show run meta
+```bash
+# list created run
+pf run list
+
+# get a sample run name
+name=$(pf run list -r 10 | jq '.[] | select(.name | contains("basic_default")) | .name'| head -n 1 | tr -d '"')
+
+# show specific run detail
+pf run show --name $name
+
+# show output
+pf run show-details --name $name
+
+# visualize run in browser
+pf run visualize --name $name
+```

examples/flows/standard/conditional-flow-for-if-else/content_safety_check.py

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+from promptflow import tool
+import random
+
+
+@tool
+def content_safety_check(text: str) -> str:
+    return random.choice([True, False])

examples/flows/standard/conditional-flow-for-if-else/data.jsonl

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+{"query": "What is Prompt flow?"}
+{"query": "What is ChatGPT?"}

examples/flows/standard/conditional-flow-for-if-else/default_result.py

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+from promptflow import tool
+
+
+@tool
+def default_result(request: str) -> str:
+    return f"I'm not familiar with your query: {request}."

examples/flows/standard/conditional-flow-for-if-else/flow.dag.yaml

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+inputs:
+  query:
+    type: string
+    default: What is Prompt flow?
+outputs:
+  answer:
+    type: string
+    reference: ${generate_result.output}
+nodes:
+- name: content_safety_check
+  type: python
+  source:
+    type: code
+    path: content_safety_check.py
+  inputs:
+    text: ${inputs.query}
+- name: llm_result
+  type: python
+  source:
+    type: code
+    path: llm_result.py
+  inputs:
+    request: ${inputs.query}
+  activate:
+    when: ${content_safety_check.output}
+    is: true
+- name: default_result
+  type: python
+  source:
+    type: code
+    path: default_result.py
+  inputs:
+    request: ${inputs.query}
+  activate:
+    when: ${content_safety_check.output}
+    is: false
+- name: generate_result
+  type: python
+  source:
+    type: code
+    path: generate_result.py
+  inputs:
+    llm_result: ${llm_result.output}
+    default_result: ${default_result.output}

examples/flows/standard/conditional-flow-for-if-else/generate_result.py

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+from promptflow import tool
+
+
+@tool
+def generate_result(llm_result="", default_result="") -> str:
+    if llm_result:
+        return llm_result
+    else:
+        return default_result

examples/flows/standard/conditional-flow-for-if-else/llm_result.py

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+from promptflow import tool
+
+
+@tool
+def llm_result(request: str) -> str:
+    return (
+        "Prompt flow is a suite of development tools designed to streamline "
+        "the end-to-end development cycle of LLM-based AI applications."
+    )

examples/flows/standard/conditional-flow-for-if-else/requirements.txt

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+promptflow
+promptflow-tools