[Samples]Add column mapping in CLI sample

docs/how-to-guides/manage-runs.md

@@ -45,7 +45,7 @@ column_mapping:
 run: <existing-flow-run-name>
 ```
 
-Reference [here](./run-and-evaluate-a-flow/use-column-mapping.md) for detailed information for column mapping.
+Reference [here](https://aka.ms/pf/column-mapping) for detailed information for column mapping.
 You can find additional information about flow yaml schema in [Run YAML Schema](../reference/run-yaml-schema-reference.md).
 
 After preparing the yaml file, use the CLI command below to create them:

docs/how-to-guides/run-and-evaluate-a-flow/run-and-evaluate-a-flow.md

@@ -28,7 +28,7 @@ Create the run with flow and data, can add `--stream` to stream the run.
 pf run create --flow standard/web-classification --data standard/web-classification/data.jsonl --column-mapping url='${data.url}' --stream 
 ```
 
-Note `column-mapping` is a mapping from flow input name to specified values, see more details in [Use column mapping](./use-column-mapping.md).
+Note `column-mapping` is a mapping from flow input name to specified values, see more details in [Use column mapping](https://aka.ms/pf/column-mapping).
 
 You can also name the run by specifying `--name my_first_run` in above command, otherwise the run name will be generated in a certain pattern which has timestamp inside.
 
@@ -118,10 +118,10 @@ In this guide, we use [eval-classification-accuracy](https://github.com/microsof
 
 After the run is finished, you can evaluate the run with below command, compared with the normal run create command, note there are two extra arguments:
 
-- `column-mapping`: A mapping from flow input name to specified data values. Reference [here](./use-column-mapping.md) for detailed information.
+- `column-mapping`: A mapping from flow input name to specified data values. Reference [here](https://aka.ms/pf/column-mapping) for detailed information.
 - `run`: The run name of the flow run to be evaluated.
 
-More details can be found in [Use column mapping](./use-column-mapping.md).
+More details can be found in [Use column mapping](https://aka.ms/pf/column-mapping).
 
 ```sh
 pf run create --flow evaluation/eval-classification-accuracy --data standard/web-classification/data.jsonl --column-mapping groundtruth='${data.answer}' prediction='${run.outputs.category}' --run my_first_run --stream
@@ -161,7 +161,7 @@ After the run is finished, you can evaluate the run with below command, compared
   - If the data column is from your flow output, then it is specified as `${run.outputs.<output_name>}`.
 - `run`: The run name or run instance of the flow run to be evaluated.
 
-More details can be found in [Use column mapping](./use-column-mapping.md).
+More details can be found in [Use column mapping](https://aka.ms/pf/column-mapping).
 
 ```python
 # set eval flow path

docs/how-to-guides/tune-prompts-with-variants.md

@@ -71,7 +71,7 @@ Assuming you are in working directory `<path-to-the-sample-repo>/examples/flows/
 Note we pass `--variant` to specify which variant of the node should be running.
 
 ```sh
-pf run create --flow web-classification --data web-classification/data.jsonl --variant '${summarize_text_content.variant_1}' --stream --name my_first_variant_run
+pf run create --flow web-classification --data web-classification/data.jsonl --variant '${summarize_text_content.variant_1}' --column-mapping url='${data.url}' --stream --name my_first_variant_run
 ```
 
 :::
@@ -91,6 +91,7 @@ variant_run = pf.run(
     flow=flow,
     data=data,
     variant="${summarize_text_content.variant_1}",  # use variant 1.
+    column_mapping={"url": "${data.url}"},
 )
 
 pf.stream(variant_run)

docs/reference/pfazure-command-reference.md

@@ -64,7 +64,7 @@ Inputs column mapping, use `${data.xx}` to refer to data file columns, use `${ru
 
 `--run`
 
-Referenced flow run name. For example, you can run an evaluation flow against an existing run. For example, "pfazure run create --flow evaluation_flow_dir --run existing_bulk_run".
+Referenced flow run name. For example, you can run an evaluation flow against an existing run. For example, "pfazure run create --flow evaluation_flow_dir --run existing_bulk_run --column-mapping url='${data.url}'".
 
 `--variant`
 

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

@@ -36,5 +36,8 @@ pf flow test --flow . --node line_process --inputs groundtruth=ABC prediction=AB
 There are two ways to evaluate an classification flow.
 
 ```bash
-pf run create --flow . --data ./data.jsonl --stream
-```
+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.

examples/flows/evaluation/eval-classification-accuracy/README.md

@@ -38,9 +38,12 @@ pf flow test --flow . --node grade --inputs groundtruth=groundtruth prediction=p
 There are two ways to evaluate an classification flow.
 
 ```bash
-pf run create --flow . --data ./data.jsonl --stream
+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.
+
 ### 3. create run against other flow run
 
 Learn more in [web-classification](../../standard/web-classification/README.md)

examples/flows/evaluation/eval-entity-match-rate/README.md

@@ -22,6 +22,8 @@ pf flow test --flow .
 ### 2. create flow run with multi line data
 
 ```bash
-pf run create --flow . --data ./data.jsonl --stream
+pf run create --flow . --data ./data.jsonl --column-mapping ground_truth='${data.ground_truth}' entities='${data.entities}' --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.

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

@@ -25,6 +25,8 @@ pf flow test --flow .
 ### 2. create flow run with multi line data
 
 ```bash
-pf run create --flow . --data ./data.jsonl --stream
+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.

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

@@ -26,6 +26,8 @@ pf flow test --flow .
 ### 2. create flow run with multi line data
 
 ```bash
-pf run create --flow . --data ./data.jsonl --stream
+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.

examples/flows/standard/autonomous-agent/README.md

@@ -56,8 +56,11 @@ pf flow test --flow .
 
 ```bash
 # create run using command line args
-pf run create --flow . --data ./data.jsonl --stream
+pf run create --flow . --data ./data.jsonl --column-mapping name='${data.name}' role='${data.role}' goals='${data.goals}' --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.
+
 ## Disclaimer
 LLM systems are susceptible to prompt injection, and you can gain a deeper understanding of this issue in the [technical blog](https://developer.nvidia.com/blog/securing-llm-systems-against-prompt-injection/). As an illustration, the PythonREPL function might execute harmful code if provided with a malicious prompt within the provided sample. Furthermore, we cannot guarantee that implementing AST validations solely within the PythonREPL function will reliably elevate the sample's security to an enterprise level. We kindly remind you to refrain from utilizing this in a production environment.

examples/flows/standard/basic-with-builtin-llm/README.md

@@ -48,9 +48,12 @@ pf flow test --flow . --inputs text="Python Hello World!"
 
 - create run
 ```bash
-pf run create --flow . --data ./data.jsonl --stream
+pf run create --flow . --data ./data.jsonl --column-mapping text='${data.text}' --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.
+
 - list and show run meta
 ```bash
 # list created run

examples/flows/standard/basic-with-connection/README.md

@@ -49,9 +49,12 @@ pf flow test --flow . --node llm --inputs prompt="Write a simple Hello World! pr
 
 - create run
 ```bash
-pf run create --flow . --data ./data.jsonl --stream
+pf run create --flow . --data ./data.jsonl --column-mapping text='${data.text}' --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.
+
 - list and show run meta
 ```bash
 # list created run
@@ -87,7 +90,7 @@ pf connection create --file ../../../connections/azure_openai.yml --set api_key=
 Run flow with newly created connection.
 
 ```bash
-pf run create --flow . --data ./data.jsonl --connections llm.connection=open_ai_connection --stream
+pf run create --flow . --data ./data.jsonl --connections llm.connection=open_ai_connection --column-mapping text='${data.text}' --stream
 ```
 
 ### Run in cloud with connection override
@@ -101,5 +104,5 @@ Run flow with connection `open_ai_connection`.
 az account set -s <your_subscription_id>
 az configure --defaults group=<your_resource_group_name> workspace=<your_workspace_name>
 
-pfazure run create --flow . --data ./data.jsonl --connections llm.connection=open_ai_connection --stream --runtime example-runtime-ci
+pfazure run create --flow . --data ./data.jsonl --connections llm.connection=open_ai_connection --column-mapping text='${data.text}' --stream --runtime example-runtime-ci
 ```

examples/flows/standard/basic/README.md

@@ -42,9 +42,12 @@ pf flow test --flow . --node llm --inputs prompt="Write a simple Hello World pro
 - Create run with multiple lines data
 ```bash
 # using environment from .env file (loaded in user code: hello.py)
-pf run create --flow . --data ./data.jsonl --stream
+pf run create --flow . --data ./data.jsonl --column-mapping text='${data.text}' --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.
+
 - List and show run meta
 ```bash
 # list created run
@@ -87,7 +90,7 @@ pf flow test --flow . --environment-variables AZURE_OPENAI_API_KEY='${open_ai_co
 - Create run using connection secret binding specified in environment variables, see [run.yml](run.yml)
 ```bash
 # create run
-pf run create --flow . --data ./data.jsonl --stream --environment-variables AZURE_OPENAI_API_KEY='${open_ai_connection.api_key}' AZURE_OPENAI_API_BASE='${open_ai_connection.api_base}'
+pf run create --flow . --data ./data.jsonl --stream --environment-variables AZURE_OPENAI_API_KEY='${open_ai_connection.api_key}' AZURE_OPENAI_API_BASE='${open_ai_connection.api_base}' --column-mapping text='${data.text}'
 # create run using yaml file
 pf run create --file run.yml --stream
 
@@ -107,7 +110,7 @@ az configure --defaults group=<your_resource_group_name> workspace=<your_workspa
 - Create run
 ```bash
 # run with environment variable reference connection in azureml workspace 
-pfazure run create --flow . --data ./data.jsonl --environment-variables AZURE_OPENAI_API_KEY='${open_ai_connection.api_key}' AZURE_OPENAI_API_BASE='${open_ai_connection.api_base}' --stream --runtime example-runtime-ci
+pfazure run create --flow . --data ./data.jsonl --environment-variables AZURE_OPENAI_API_KEY='${open_ai_connection.api_key}' AZURE_OPENAI_API_BASE='${open_ai_connection.api_base}' --column-mapping text='${data.text}' --stream --runtime example-runtime-ci
 # run using yaml file
 pfazure run create --file run.yml --stream --runtime example-runtime-ci
 ```

examples/flows/standard/basic/run.yml

@@ -5,4 +5,6 @@ environment_variables:
   # environment variables from connection
   AZURE_OPENAI_API_KEY: ${open_ai_connection.api_key}
   AZURE_OPENAI_API_BASE: ${open_ai_connection.api_base}
-  AZURE_OPENAI_API_TYPE: azure
+  AZURE_OPENAI_API_TYPE: azure
+column_mapping:
+  text: ${data.text}

examples/flows/standard/customer-intent-extraction/README.md

@@ -42,9 +42,12 @@ pf flow test --flow . --input ./data/denormalized-flat.jsonl
 
 4. run with multiple lines input
 ```bash
-pf run create --flow . --data ./data
+pf run create --flow . --data ./data --column-mapping history='${data.history}' customer_info='${data.customer_info}'
 ```
 
+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.
+
 5. list/show 
 
 ```bash

examples/flows/standard/flow-with-additional-includes/README.md

@@ -56,11 +56,14 @@ pf flow test --flow . --inputs url='https://www.microsoft.com/en-us/d/xbox-wirel
 
 ```bash
 # create run using command line args
-pf run create --flow . --data ./data.jsonl --stream
+pf run create --flow . --data ./data.jsonl --column-mapping url='${data.url}' --stream
 # create run using yaml file
 pf run create --file run.yml --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.
+
 #### Submit run to cloud
 
 Assume we already have a connection named `open_ai_connection` in workspace.
@@ -73,9 +76,9 @@ az configure --defaults group=<your_resource_group_name> workspace=<your_workspa
 
 ``` bash
 # create run
-pfazure run create --flow . --data ./data.jsonl --stream --runtime example-runtime-ci 
-# pfazure run create --flow . --data ./data.jsonl --stream # automatic runtime
-pfazure run create --file run.yml --runtime example-runtime-ci
+pfazure run create --flow . --data ./data.jsonl --column-mapping url='${data.url}' --stream --runtime example-runtime-ci 
+# pfazure run create --flow . --data ./data.jsonl --column-mapping url='${data.url}' --stream # automatic runtime
+pfazure run create --file run.yml --runtime example-runtime-ci 
 # pfazure run create --file run.yml --stream # automatic runtime
 ```
 

examples/flows/standard/flow-with-additional-includes/run.yml

@@ -1,4 +1,6 @@
 $schema: https://azuremlschemas.azureedge.net/promptflow/latest/Run.schema.json
 flow: .
 data: data.jsonl
-variant: ${summarize_text_content.variant_1}
+variant: ${summarize_text_content.variant_1}
+column_mapping:
+   url: ${data.url}

examples/flows/standard/flow-with-symlinks/README.md

@@ -65,18 +65,21 @@ pf flow test --flow . --node convert_to_dict --inputs classify_with_llm.output='
 
 ```bash
 # create run using command line args
-pf run create --flow . --data ./data.jsonl --stream
+pf run create --flow . --data ./data.jsonl --column-mapping url='${data.url}' --stream
 # create run using yaml file
 pf run create --file run.yml --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.
+
 
 #### Submit run to cloud
 
 ``` bash
 # create run
-pfazure run create --flow . --data ./data.jsonl --stream --runtime example-runtime-ci --subscription <your_subscription_id> -g <your_resource_group_name> -w <your_workspace_name>
-# pfazure run create --flow . --data ./data.jsonl --stream # automatic runtime
+pfazure run create --flow . --data ./data.jsonl --column-mapping url='${data.url}' --stream --runtime example-runtime-ci --subscription <your_subscription_id> -g <your_resource_group_name> -w <your_workspace_name>
+# pfazure run create --flow . --data ./data.jsonl --column-mapping url='${data.url}' --stream # automatic runtime
 
 # set default workspace
 az account set -s <your_subscription_id>

examples/flows/standard/flow-with-symlinks/run.yml

@@ -0,0 +1,6 @@
+$schema: https://azuremlschemas.azureedge.net/promptflow/latest/Run.schema.json
+flow: .
+data: data.jsonl
+variant: ${summarize_text_content.variant_1}
+column_mapping:
+   url: ${data.url}

examples/flows/standard/gen-docstring/README.md

@@ -54,8 +54,9 @@ pf flow test --flow . --inputs source="./azure_open_ai.py"
 
 ```bash
 # run flow with batch data
-pf run create --flow . --data ./data.jsonl --name auto_generate_docstring
+pf run create --flow . --data ./data.jsonl --name auto_generate_docstring --column-mapping source='${data.source}'
 ```
 Output the code after add the docstring.
 
-
+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.

examples/flows/standard/named-entity-recognition/README.md

@@ -52,7 +52,8 @@ pf flow test --flow . --inputs text='The phone number (321) 654-0987 is no longe
 
 - create run
 ```bash
-pf run create --flow . --data ./data.jsonl --stream
+pf run create --flow . --data ./data.jsonl --column-mapping entity_type='${data.entity_type}' text='${data.text}' --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.

examples/flows/standard/web-classification/README.md

@@ -52,14 +52,17 @@ pf flow test --flow . --inputs url='https://www.youtube.com/watch?v=kYqRtjDBci8'
 
 ```bash
 # create run using command line args
-pf run create --flow . --data ./data.jsonl --stream
+pf run create --flow . --data ./data.jsonl --column-mapping url='${data.url}' --stream
 
 # (Optional) create a random run name
 run_name="web_classification_"$(openssl rand -hex 12)
 # create run using yaml file, run_name will be used in following contents, --name is optional
 pf run create --file run.yml --stream --name $run_name
 ```
 
+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.
+
 ```bash
 # list run
 pf run list
@@ -96,8 +99,8 @@ az account set -s <your_subscription_id>
 az configure --defaults group=<your_resource_group_name> workspace=<your_workspace_name>
 
 # create run
-pfazure run create --flow . --data ./data.jsonl --stream --runtime example-runtime-ci
-# pfazure run create --flow . --data ./data.jsonl --stream # automatic runtime
+pfazure run create --flow . --data ./data.jsonl --column-mapping url='${data.url}' --stream --runtime example-runtime-ci 
+# pfazure run create --flow . --data ./data.jsonl --column-mapping url='${data.url}' --stream # automatic runtime
 
 # (Optional) create a new random run name for further use
 run_name="web_classification_"$(openssl rand -hex 12)

examples/flows/standard/web-classification/run.yml

@@ -1,4 +1,6 @@
 $schema: https://azuremlschemas.azureedge.net/promptflow/latest/Run.schema.json
 flow: .
 data: data.jsonl
-variant: ${summarize_text_content.variant_1}
+variant: ${summarize_text_content.variant_1}
+column_mapping:
+   url: ${data.url}

examples/tutorials/e2e-development/chat-with-pdf.md

@@ -245,6 +245,7 @@ The output will include something like below:
 }
 ```
 
+Reference [here](https://aka.ms/pf/column-mapping) for default behavior when `column-mapping` not provided in CLI.
 And we developed two evaluation flows one for "[groundedness](../../flows/evaluation/eval-groundedness/)" and one for "[perceived intelligence](../../flows/evaluation/eval-perceived-intelligence/)". These two flows are using GPT models (ChatGPT or GPT4) to "grade" the answers. Reading the prompts will give you better idea what are these two metrics:
 - [groundedness prompt](../../flows/evaluation/eval-groundedness/gpt_groundedness.md)
 - [perceived intelligence prompt](../../flows/evaluation/eval-perceived-intelligence/gpt_perceived_intelligence.md)