chore: import upstream snapshot with attribution

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+# Basic Chat
+This example shows how to create a basic chat flow. It demonstrates how to create a chatbot that can remember previous interactions and use the conversation history to generate next message.
+
+Tools used in this flow：
+- `llm` tool
+
+## 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 %}
+    # user:
+    {{item.inputs.question}}
+    # assistant:
+    {{item.outputs.answer}}
+    {% endfor %}
+    ```
+
+## Getting started
+
+### 1 Create connection for LLM tool to use
+Go to "Prompt flow" "Connections" tab. Click on "Create" button, select one of LLM tool supported connection types and fill in the configurations.
+
+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.
+
+```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.dag.yaml](flow.dag.yaml) we are using connection named `open_ai_connection`.
+```bash
+# show registered connection
+pf connection show --name open_ai_connection
+```
+
+### 2 Start chatting
+
+```bash
+# run chat flow with default question in flow.dag.yaml
+pf flow test --flow .
+
+# run chat flow with new question
+pf flow test --flow . --inputs question="What's Azure Machine Learning?"
+
+# start a interactive chat session in CLI
+pf flow test --flow . --interactive
+
+# start a interactive chat session in CLI with verbose info
+pf flow test --flow . --interactive --verbose
+```
+
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+# system:
+You are a helpful assistant.
+
+{% for item in chat_history %}
+# user:
+{{item.inputs.question}}
+# assistant:
+{{item.outputs.answer}}
+{% endfor %}
+
+# user:
+{{question}}
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+$schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
+inputs:
+  chat_history:
+    type: list
+    default: []
+  question:
+    type: string
+    is_chat_input: true
+    default: What is ChatGPT?
+outputs:
+  answer:
+    type: string
+    reference: ${chat.output}
+    is_chat_output: true
+nodes:
+- inputs:
+    # This is to easily switch between openai and azure openai.
+    # deployment_name is required by azure openai, model is required by openai.
+    deployment_name: gpt-35-turbo
+    model: gpt-3.5-turbo
+    max_tokens: "256"
+    temperature: "0.7"
+    chat_history: ${inputs.chat_history}
+    question: ${inputs.question}
+  name: chat
+  type: llm
+  source:
+    type: code
+    path: chat.jinja2
+  api: chat
+  connection: open_ai_connection
+node_variants: {}
+environment:
+    python_requirements_txt: requirements.txt
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+promptflow
+promptflow-tools