chore: import upstream snapshot with attribution

examples/flows/evaluation/eval-basic/README.md

@@ -0,0 +1,43 @@
+# Basic Eval
+This example shows how to create a basic evaluation flow. 
+
+Tools used in this flow：
+- `python` tool
+
+## Prerequisites
+
+Install promptflow sdk and other dependencies in this folder:
+```bash
+pip install -r requirements.txt
+```
+
+## What you will learn
+
+In this flow, you will learn
+- how to compose a point based evaluation flow, where you can calculate point-wise metrics.
+- the way to log metrics. use `from promptflow.core import log_metric`
+    - see file [aggregate](aggregate.py).
+
+### 1. Test flow with single line data
+
+Testing 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 groundtruth=ABC prediction=ABC
+
+# test node with inputs
+pf flow test --flow . --node line_process --inputs groundtruth=ABC prediction=ABC
+```
+
+### 2. create flow run with multi line data
+There are two ways to evaluate an classification flow.
+
+```bash
+pf run create --flow . --data ./data.jsonl --column-mapping groundtruth='${data.groundtruth}' prediction='${data.prediction}' --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.