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134 lines
4.4 KiB
Markdown
134 lines
4.4 KiB
Markdown
# Basic standard flow
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A basic standard flow using custom python tool that calls Azure OpenAI with connection info stored in environment variables.
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Tools used in this flow：
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- `prompt` tool
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- custom `python` Tool
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Connections used in this flow:
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- None
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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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- Test flow/node
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```bash
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# test with default input value in flow.dag.yaml
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pf flow test --flow .
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# test with flow inputs
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pf flow test --flow . --inputs text="Java Hello World!"
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# test node with inputs
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pf flow test --flow . --node llm --inputs prompt="Write a simple Hello World program that displays the greeting message."
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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_variant_0")) | .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 $name
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```
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## Run flow with connection
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Storing connection info in .env with plaintext is not safe. We recommend to use `pf connection` to guard secrets like `api_key` from leak.
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- Show or create `open_ai_connection`
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```bash
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# create connection from `azure_openai.yml` file
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# Override keys with --set to avoid yaml file changes
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pf connection create --file ../../../connections/azure_openai.yml --set api_key=<your_api_key> api_base=<your_api_base>
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# check if connection exists
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pf connection show -n open_ai_connection
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```
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- Test using connection secret specified in environment variables
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**Note**: we used `'` to wrap value since it supports raw value without escape in powershell & bash. For windows command prompt, you may remove the `'` to avoid it become part of the value.
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```bash
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# test with default input value in flow.dag.yaml
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pf flow test --flow . --environment-variables AZURE_OPENAI_API_KEY='${open_ai_connection.api_key}' AZURE_OPENAI_API_BASE='${open_ai_connection.api_base}'
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```
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- Create run using connection secret binding specified in environment variables, see [run.yml](run.yml)
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```bash
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# create run
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pf run create --flow . --data ./data.jsonl --stream --environment-variables AZURE_OPENAI_API_KEY='${open_ai_connection.api_key}' AZURE_OPENAI_API_BASE='${open_ai_connection.api_base}' --column-mapping text='${data.text}'
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# create run using yaml file
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pf run create --file run.yml --stream
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# show outputs
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name=$(pf run list -r 10 | jq '.[] | select(.name | contains("basic_variant_0")) | .name'| head -n 1 | tr -d '"')
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pf run show-details --name $name
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```
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## Run flow in cloud with connection
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- Assume we already have a connection named `open_ai_connection` in workspace.
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```bash
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# set default workspace
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az account set -s <your_subscription_id>
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az configure --defaults group=<your_resource_group_name> workspace=<your_workspace_name>
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```
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- Create run
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```bash
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# run with environment variable reference connection in azureml workspace
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pfazure run create --flow . --data ./data.jsonl --environment-variables AZURE_OPENAI_API_KEY='${open_ai_connection.api_key}' AZURE_OPENAI_API_BASE='${open_ai_connection.api_base}' --column-mapping text='${data.text}' --stream
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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_variant_0")) | .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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``` |