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93 lines
2.9 KiB
Markdown
93 lines
2.9 KiB
Markdown
# Prompty output format
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A few examples that demos different prompty response format like text, json_object, and how to enable stream output.
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## Prerequisites
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Install `promptflow-devkit`:
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```bash
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pip install promptflow-devkit
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```
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## What you will learn
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In this flow, you will learn
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- Understand how to handle output format of prompty like: text, json_object.
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- Understand how to consume stream output of prompty
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## Getting started
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### Create connection for prompty to use
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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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Currently, there are two connection types supported by LLM tool: "AzureOpenAI" and "OpenAI". If you want to use "AzureOpenAI" connection type, you need to create an Azure OpenAI service first. Please refer to [Azure OpenAI Service](https://azure.microsoft.com/en-us/products/cognitive-services/openai-service/) for more details. If you want to use "OpenAI" connection type, you need to create an OpenAI account first. Please refer to [OpenAI](https://platform.openai.com/) for more details.
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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>
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```
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Note 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 prompty
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- [text format output](./text_format.prompty)
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```bash
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pf flow test --flow text_format.prompty
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```
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- [json format output](./json_format.prompty)
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```bash
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pf flow test --flow json_format.prompty
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```
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- [all response](./all_response.prompty)
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```bash
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# TODO
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# pf flow test --flow all_response.prompty
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```
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- [stream output](./stream_output.prompty)
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```bash
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pf flow test --flow stream_output.prompty
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```
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- Test flow: multi turn
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```powershell
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# start test in chat ui
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pf flow test --flow stream_output.prompty --ui
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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 json_format.prompty --data ./data.jsonl --column-mapping question='${data.question}' --stream
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pf run create --flow text_format.prompty --data ./data.jsonl --column-mapping question='${data.question}' --stream
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pf run create --flow stream_output.prompty --data ./data.jsonl --column-mapping question='${data.question}' --stream
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# TODO
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# pf run create --flow all_response.prompty --data ./data.jsonl --column-mapping question='${data.question}' --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("format_output_")) | .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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``` |