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94 lines
3.0 KiB
Markdown
94 lines
3.0 KiB
Markdown
# Basic chat
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A prompt that uses the chat API to answer questions with chat history, leveraging promptflow connection.
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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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- how to compose a chat flow.
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- prompt template format of chat api. Message delimiter is a separate line containing role name and colon: "system:", "user:", "assistant:".
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See <a href="https://platform.openai.com/docs/api-reference/chat/create#chat/create-role" target="_blank">OpenAI Chat</a> for more about message role.
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```jinja
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system:
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You are a chatbot having a conversation with a human.
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user:
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{{question}}
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```
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- how to consume chat history in prompt.
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```jinja
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{% for item in chat_history %}
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{{item.role}}:
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{{item.content}}
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{% endfor %}
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```
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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 in [chat.prompty](chat.prompty) 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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- Test flow: single turn
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```bash
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# run chat flow with default question in flow.flex.yaml
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pf flow test --flow chat.prompty
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# run chat flow with new question
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pf flow test --flow chat.prompty --inputs question="What's Azure Machine Learning?"
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# run chat flow with sample.json
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pf flow test --flow chat.prompty --inputs sample.json
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```
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- Test flow: multi turn
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```shell
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# start test in chat ui
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pf flow test --flow chat.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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# using environment from .env file (loaded in user code: hello.py)
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pf run create --flow chat.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("chat_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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``` |