# Basic chat
A basic chat flow defined using class entry. It demonstrates how to create a chatbot that can remember previous interactions and use the conversation history to generate next message.

## Prerequisites

Install promptflow sdk and other dependencies in this folder:
```bash
pip install -r requirements.txt
```

## What you will learn

In this flow, you will learn
- how to compose a chat flow.
- prompt template format of LLM tool chat api. Message delimiter is a separate line containing role name and colon: "system:", "user:", "assistant:".
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.
    ```jinja
    system:
    You are a chatbot having a conversation with a human.

    user:
    {{question}}
    ```
- how to consume chat history in prompt.
    ```jinja
    {% for item in chat_history %}
    {{item.role}}:
    {{item.content}}
    {% endfor %}
    ```

## Run flow

- 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.

- Setup connection

Go to "Prompt flow" "Connections" tab. Click on "Create" button, select one of prompty supported connection types and fill in the configurations.

Or use CLI to create connection:

```bash
# Override keys with --set to avoid yaml file changes
pf connection create --file ../../connections/azure_openai.yml --set api_key=<your_api_key> api_base=<your_api_base> --name open_ai_connection
```

Note in [flow.flex.yaml](flow.flex.yaml) we are using connection named `open_ai_connection`.
```bash
# show registered connection
pf connection show --name open_ai_connection
```

- Run as normal Python file

```bash
python flow.py
```
- Test flow

```bash
pf flow test --flow flow:ChatFlow --init init.json --inputs question="What's Azure Machine Learning?"
```

- Test flow with yaml
You'll need to write flow entry `flow.flex.yaml` to test with prompt flow.

```bash
# run chat flow with default question in flow.flex.yaml
pf flow test --flow . 
# run chat flow with new question
pf flow test --flow . --inputs question="What is ChatGPT? Please explain with consise statement."
# run chat flow with specific init and inputs
pf flow test --flow . --init init.json --inputs sample.json
```

- Test flow: multi turn
```shell
# start test in chat UI
pf flow test --flow . --ui --init init.json
```

- Create run with multiple lines data

```bash
pf run create --flow . --init init.json --data ./data.jsonl --column-mapping question='${data.question}' --stream
```

You can also skip providing `column-mapping` if provided data has same column name as the flow.
Reference [here](https://aka.ms/pf/column-mapping) for default behavior when `column-mapping` not provided in CLI.

- List and show run meta
```bash
# list created run
pf run list

# get a sample run name

name=$(pf run list -r 10 | jq '.[] | select(.name | contains("chat_basic_")) | .name'| head -n 1 | tr -d '"')
# show specific run detail
pf run show --name $name

# show output
pf run show-details --name $name

# visualize run in browser
pf run visualize --name $name
```

## Run flow in cloud

- Assume we already have a connection named `open_ai_connection` in workspace.

```bash
# set default workspace
az account set -s <your_subscription_id>
az configure --defaults group=<your_resource_group_name> workspace=<your_workspace_name>
```

- Create run

```bash
# run with environment variable reference connection in azureml workspace
pfazure run create --flow . --init init.json --data ./data.jsonl --column-mapping question='${data.question}' --stream
# run using yaml file
pfazure run create --file run.yml --init init.json --stream