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106 lines
2.8 KiB
Markdown
106 lines
2.8 KiB
Markdown
# Basic standard flow
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A basic standard flow define using function entry that calls Azure OpenAI with connection info stored in environment variables.
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## Prerequisites
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Install promptflow sdk and other dependencies:
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```bash
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pip install -r requirements.txt
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```
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## Run flow
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- 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.
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- Setup environment variables
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Ensure you have put your azure OpenAI endpoint key in [.env](../.env) file. You can create one refer to this [example file](../.env.example).
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```bash
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cat ../.env
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```
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- Run/Debug as normal Python file
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```bash
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python programmer.py
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```
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- Test with flow entry
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```bash
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pf flow test --flow programmer:write_simple_program --inputs text="Java Hello World!"
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```
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- Test with flow yaml
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```bash
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# test with sample input value in flow.flex.yaml
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pf flow test --flow .
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```
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```shell
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# test with UI
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pf flow test --flow . --ui
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```
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- Create run with multiple lines data
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```bash
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# using environment from .env file (loaded in user code: hello.py)
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pf run create --flow . --data ./data.jsonl --column-mapping text='${data.text}' --stream
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```
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You can also skip providing `column-mapping` if provided data has same column name as the flow.
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Reference [here](https://aka.ms/pf/column-mapping) for default behavior when `column-mapping` not provided in CLI.
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- List and show run meta
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```bash
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# list created run
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pf run list
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# get a sample run name
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name=$(pf run list -r 10 | jq '.[] | select(.name | contains("basic_")) | .name'| head -n 1 | tr -d '"')
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# show specific run detail
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pf run show --name $name
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# show output
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pf run show-details --name $name
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# visualize run in browser
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pf run visualize --name $name
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```
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## Run flow in cloud with connection
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- Assume we already have a connection named `open_ai_connection` in workspace.
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```bash
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# set default workspace
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az account set -s <your_subscription_id>
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az configure --defaults group=<your_resource_group_name> workspace=<your_workspace_name>
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```
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- Create run
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```bash
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# run with environment variable reference connection in azureml workspace
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pfazure run create --flow . --data ./data.jsonl --column-mapping text='${data.text}' --environment-variables AZURE_OPENAI_API_KEY='${open_ai_connection.api_key}' AZURE_OPENAI_ENDPOINT='${open_ai_connection.api_base}' --stream
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# run using yaml file
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pfazure run create --file run.yml --stream
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```
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- List and show run meta
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```bash
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# list created run
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pfazure run list -r 3
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# get a sample run name
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name=$(pfazure run list -r 100 | jq '.[] | select(.name | contains("basic_")) | .name'| head -n 1 | tr -d '"')
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# show specific run detail
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pfazure run show --name $name
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# show output
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pfazure run show-details --name $name
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# visualize run in browser
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pfazure run visualize --name $name
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```
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