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91 lines
2.4 KiB
Markdown
91 lines
2.4 KiB
Markdown
# Eval Code Quality
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A example flow defined using class based entry which leverages model config to evaluate the quality of code snippet.
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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 connection
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Go to "Prompt flow" "Connections" tab. Click on "Create" button, select one of LLM tool supported connection types and fill in the configurations.
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Or use CLI to create connection:
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```bash
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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> --name open_ai_connection
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```
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Note in [flow.flex.yaml](flow.flex.yaml) we are using connection named `open_ai_connection`.
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```bash
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# show registered connection
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pf connection show --name open_ai_connection
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```
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- Run as normal Python file
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```bash
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python code_quality.py
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```
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- Test flow
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```bash
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# correct
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pf flow test --flow . --inputs code='print(\"Hello, world!\")' --init init.json
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# incorrect
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pf flow test --flow . --inputs code='printf("Hello, world!")' --init init.json
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```
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- Create run with multiple lines data
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```bash
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pf run create --flow . --init init.json --data ./data.jsonl --stream
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```
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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("eval_code_quality_")) | .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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# show metrics
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pf run show-metrics --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
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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 . --init init.json --data ./data.jsonl --stream
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