chore: import upstream snapshot with attribution

examples/flows/evaluation/eval-perceived-intelligence/README.md

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+# Perceived Intelligence Evaluation
+
+This is a flow leverage llm to eval perceived intelligence.
+Perceived intelligence is the degree to which a bot can impress the user with its responses, by showing originality, insight, creativity, knowledge, and adaptability.
+
+Tools used in this flow：
+- `python` tool
+- built-in `llm` tool
+
+### 0. Setup connection
+
+Prepare your Azure OpenAI resource follow this [instruction](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal) and get your `api_key` if you don't have one.
+
+```bash
+# Override keys with --set to avoid yaml file changes
+pf connection create --file ../../../connections/azure_openai.yml --set api_key=<your_api_key> api_base=<your_api_base>
+```
+
+### 1. Test flow/node
+
+```bash
+# test with default input value in flow.dag.yaml
+pf flow test --flow .
+```
+
+### 2. create flow run with multi line data
+
+```bash
+pf run create --flow . --data ./data.jsonl --column-mapping question='${data.question}' answer='${data.answer}' context='${data.context}' --stream
+```
+
+You can also skip providing `column-mapping` if provided data has same column name as the flow.
+Reference [here](https://aka.ms/pf/column-mapping) for default behavior when `column-mapping` not provided in CLI.