[Samples]Add column mapping in CLI sample

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](../../../../docs/how-to-guides/use-column-mapping.md) 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](../../../../docs/how-to-guides/use-column-mapping.md) 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 groundtruth='${data.groundtruth}' entities='${data.entities}' --stream
 ```
 
+You can also skip providing `column-mapping` if provided data has same column name as the flow.
+Reference [here](../../../../docs/how-to-guides/use-column-mapping.md) 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](../../../../docs/how-to-guides/use-column-mapping.md) 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](../../../../docs/how-to-guides/use-column-mapping.md) 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](../../../../docs/how-to-guides/use-column-mapping.md) 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](../../../../docs/how-to-guides/use-column-mapping.md) 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](../../../../docs/how-to-guides/use-column-mapping.md) 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 demo-mir
+pfazure run create --flow . --data ./data.jsonl --connections llm.connection=open_ai_connection --column-mapping text='${data.text}' --stream --runtime demo-mir
 ```

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](../../../../docs/how-to-guides/use-column-mapping.md) 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 demo-mir
+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 demo-mir
 # run using yaml file
 pfazure run create --file run.yml --stream --runtime demo-mir
 ```

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](../../../../docs/how-to-guides/use-column-mapping.md) 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](../../../../docs/how-to-guides/use-column-mapping.md) 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.
@@ -73,8 +76,8 @@ az configure --defaults group=<your_resource_group_name> workspace=<your_workspa
 
 ``` bash
 # create run
-pfazure run create --flow . --data ./data.jsonl --stream --runtime demo-mir 
-# pfazure run create --flow . --data ./data.jsonl --stream # automatic runtime
+pfazure run create --flow . --data ./data.jsonl --column-mapping url='${data.url}' --stream --runtime demo-mir 
+# pfazure run create --flow . --data ./data.jsonl --column-mapping url='${data.url}' --stream # automatic runtime
 pfazure run create --file run.yml --runtime demo-mir
 # 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](../../../../docs/how-to-guides/use-column-mapping.md) 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 demo-mir --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='${data.url}' --stream --runtime demo-mir --subscription <your_subscription_id> -g <your_resource_group_name> -w <your_workspace_name>
+# pfazure run create --flow . --data ./data.jsonl --column-mapping url='${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](../../../../docs/how-to-guides/use-column-mapping.md) 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](../../../../docs/how-to-guides/use-column-mapping.md) 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](../../../../docs/how-to-guides/use-column-mapping.md) 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 demo-mir
-# pfazure run create --flow . --data ./data.jsonl --stream # automatic runtime
+pfazure run create --flow . --data ./data.jsonl --column-mapping url='${data.url}' --stream --runtime demo-mir
+# pfazure run create --flow . --data ./data.jsonl --column-mapping url='${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](../../../docs/how-to-guides/use-column-mapping.md) 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)

src/promptflow/CHANGELOG.md

@@ -34,7 +34,7 @@
 - **pf flow validate**: support validate flow
 - **pf config set**: support set user-level promptflow config.
   - Support workspace connection provider, usage: `pf config set connection.provider=azureml:/subscriptions/<subscription_id>/resourceGroups/<resource_group>/providers/Microsoft.MachineLearningServices/workspaces/<workspace_name>`
-- Support override openai connection's model when submitting a flow. For example: `pf run create --flow ./ --data ./data.jsonl --connection llm.model=xxx`
+- Support override openai connection's model when submitting a flow. For example: `pf run create --flow ./ --data ./data.jsonl --connection llm.model=xxx --column-mapping url='${data.url}'`
 
 ### Bugs Fixed
 - [Flow build] Fix flow build file name and environment variable name when connection name contains space.