add Prompt flow samples

README.md

@@ -40,6 +40,7 @@ Here are the preferred tools for development.
 | [8_streamlit_azure_openai_batch](./apps/8_streamlit_azure_openai_batch/README.md)                   | Call Azure OpenAI Batch API with Streamlit              | ![8_streamlit_azure_openai_batch](./docs/images/8_streamlit_azure_openai_batch.main.png)                   |
 | [9_streamlit_azure_document_intelligence](./apps/9_streamlit_azure_document_intelligence/README.md) | Call Azure AI Document Intelligence API with Streamlit  | ![9_streamlit_azure_document_intelligence](./docs/images/9_streamlit_azure_document_intelligence.main.png) |
 | [10_streamlit_batch_transcription](./apps/10_streamlit_batch_transcription/README.md)               | Call Batch Transcription API with Streamlit             | ![10_streamlit_batch_transcription](./docs/images/10_streamlit_batch_transcription.main.png)               |
+| [11_promptflow](./apps/11_promptflow/README.md)                                                     | Get started with Prompt flow                            | No Image                                                                                                   |
 | [99_streamlit_examples](./apps/99_streamlit_examples/README.md)                                     | Code samples for Streamlit                              | ![99_streamlit_examples](./docs/images/99_streamlit_examples.explaindata.png)                              |
 
 ## How to run

apps/11_promptflow/.gitignore

@@ -0,0 +1 @@
+**/.promptflow/*

apps/11_promptflow/README.md

@@ -0,0 +1,183 @@
+# Getting Started with Prompt flow
+
+This application explains how to get started with [Prompt flow](https://github.com/microsoft/promptflow), a Python library that provides a simple and easy way to build conversational AI applications.
+
+## Prerequisites
+
+- Python 3.10 or later
+- Azure OpenAI Service
+
+## Overview
+
+Prompt flow is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.
+
+## Usage
+
+1. Get the API key for Azure OpenAI Service
+1. Copy [.env.template](../../.env.template) to `.env` in the same directory
+1. Set credentials in `.env`
+1. Run scripts in the [apps/11_promptflow](./) directory
+
+Set up the environment and install dependencies:
+
+```shell
+# Create a virtual environment
+$ python -m venv .venv
+
+# Activate the virtual environment
+$ source .venv/bin/activate
+
+# Install dependencies
+$ pip install -r requirements.txt
+```
+
+## Examples
+
+[Prompt flow > Quick start](https://microsoft.github.io/promptflow/how-to-guides/quick-start.html) provides a quick start guide to Prompt flow.
+
+### [chat_minimal](https://github.com/microsoft/promptflow/tree/main/examples/flex-flows/chat-minimal)
+
+**Run as normal Python script**
+
+```shell
+$ python apps/11_promptflow/chat_minimal/main.py
+```
+
+**Run from CLI**
+
+```shell
+$ cd apps/11_promptflow/chat_minimal
+
+# Test flow
+$ pf flow test \
+    --flow main:chat \
+    --inputs question="What's the capital of France?"
+
+# Test flow: multi turn, access to http://localhost:{EPHEMERAL_PORT}
+$ pf flow test \
+    --flow main:chat \
+    --ui
+
+# Create run with multiple lines data
+$ pf run create \
+    --flow main:chat \
+    --data ./data.jsonl \
+    --column-mapping question='${data.question}' \
+    --stream
+```
+
+### playground_chat
+
+```shell
+cd apps/11_promptflow
+
+# Initialize a new flow
+$ pf flow init \
+    --flow playground_chat \
+    --type chat
+
+$ cd playground_chat
+
+# Set parameters
+$ CONNECTION_NAME=open_ai_connection
+$ AZURE_OPENAI_KEY=<your_api_key>
+$ AZURE_OPENAI_ENDPOINT=<your_api_endpoint>
+
+# List connections
+$ pf connection list
+
+
+# Delete connection (if needed)
+$ pf connection delete \
+    --name $CONNECTION_NAME
+
+# Create connection
+$ pf connection create \
+    --file azure_openai.yaml \
+    --set api_key=$AZURE_OPENAI_KEY \
+    --set api_base=$AZURE_OPENAI_ENDPOINT \
+    --name $CONNECTION_NAME
+
+# Show connection
+$ pf connection show \
+    --name $CONNECTION_NAME
+
+# Interact with chat flow
+$ pf flow test \
+    --flow . \
+    --interactive
+
+# Test flow
+$ pf flow test \
+    --flow . \
+    --inputs question="What's the capital of France?"
+
+# Create run with multiple lines data
+$ RUN_NAME=playground_chat-$(date +%s)
+$ pf run create \
+    --name $RUN_NAME \
+    --flow . \
+    --data ./data.jsonl \
+    --column-mapping question='${data.question}' \
+    --stream
+
+# Show run details
+$ pf run show-details --name $RUN_NAME
+```
+
+### playground_evaluation
+
+```shell
+cd apps/11_promptflow
+
+# Initialize a new flow
+$ pf flow init \
+    --flow playground_evaluation \
+    --type evaluation
+
+$ cd playground_evaluation
+
+# Create run with multiple lines data
+$ RUN_NAME=playground_evaluation-$(date +%s)
+$ pf run create \
+    --name $RUN_NAME \
+    --flow . \
+    --data ./data.jsonl \
+    --column-mapping \
+        groundtruth='${data.groundtruth}' \
+        prediction='${data.prediction}' \
+    --stream
+
+# Show run details
+$ pf run show-details --name $RUN_NAME
+```
+
+### playground_standard
+
+```shell
+cd apps/11_promptflow
+
+# Initialize a new flow
+$ pf flow init \
+    --flow playground_standard \
+    --type standard
+
+$ cd playground_standard
+
+# Create run with multiple lines data
+$ RUN_NAME=playground_standard-$(date +%s)
+$ pf run create \
+    --name $RUN_NAME \
+    --flow . \
+    --data ./data.jsonl \
+    --column-mapping text='${data.text}' \
+    --stream
+
+# Show run details
+$ pf run show-details --name $RUN_NAME
+```
+
+## References
+
+- [Prompt flow > repos](https://github.com/microsoft/promptflow)
+- [Prompt flow > documents](https://microsoft.github.io/promptflow/)

apps/11_promptflow/chat_minimal/chat.prompty

@@ -0,0 +1,22 @@
+---
+name: Minimal Chat
+model:
+  api: chat
+  configuration:
+    type: azure_openai
+    azure_deployment: gpt-4o
+  parameters:
+    temperature: 0.2
+    max_tokens: 1024
+inputs:
+  question:
+    type: string
+sample:
+  question: "What is Prompt flow?"
+---
+
+system:
+You are a helpful assistant.
+
+user:
+{{question}}

apps/11_promptflow/chat_minimal/data.jsonl

@@ -0,0 +1,2 @@
+{"question": "What is Prompt flow?"}
+{"question": "What is ChatGPT? Please explain with consise statement"}

apps/11_promptflow/chat_minimal/main.py

@@ -0,0 +1,21 @@
+from pathlib import Path
+
+from dotenv import load_dotenv
+from promptflow.core import Prompty
+from promptflow.tracing import start_trace, trace
+
+BASE_DIR = Path(__file__).absolute().parent
+
+
+@trace
+def chat(question: str) -> str:
+    load_dotenv()
+    prompty = Prompty.load(source=f"{BASE_DIR}/chat.prompty")
+    return prompty(question=question)
+
+
+if __name__ == "__main__":
+    start_trace()
+
+    result = chat("What's the capital of France?")
+    print(result)

apps/11_promptflow/playground_chat/.gitignore

@@ -0,0 +1,5 @@
+.env
+__pycache__/
+.promptflow/*
+!.promptflow/flow.tools.json
+.runs/

apps/11_promptflow/playground_chat/.promptflow/flow.tools.json

@@ -0,0 +1,21 @@
+{
+    "package": {},
+    "code": {
+        "chat.jinja2": {
+            "type": "llm",
+            "inputs": {
+                "question": {
+                    "type": [
+                        "string"
+                    ]
+                },
+                "chat_history": {
+                    "type": [
+                        "string"
+                    ]
+                }
+            },
+            "description": "Chat with Chatbot"
+        }
+    }
+}

apps/11_promptflow/playground_chat/README.md

@@ -0,0 +1,86 @@
+# Chat flow
+Chat flow is designed for conversational application development, building upon the capabilities of standard flow and providing enhanced support for chat inputs/outputs and chat history management. With chat flow, you can easily create a chatbot that handles chat input and output.
+
+## Create connection for LLM tool to use
+You can follow these steps to create a connection required by a LLM tool.
+
+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
+# Create OpenAI connection
+pf connection create --file openai.yaml --set api_key=<your_api_key> --name open_ai_connection
+
+# Create azure OpenAI connection
+# pf connection create --file azure_openai.yaml --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
+```
+Please refer to connections [document](https://promptflow.azurewebsites.net/community/local/manage-connections.html) and [example](https://github.com/microsoft/promptflow/tree/main/examples/connections) for more details.
+
+## Develop a chat flow
+
+The most important elements that differentiate a chat flow from a standard flow are **Chat Input**, **Chat History**, and **Chat Output**.
+
+- **Chat Input**: Chat input refers to the messages or queries submitted by users to the chatbot. Effectively handling chat input is crucial for a successful conversation, as it involves understanding user intentions, extracting relevant information, and triggering appropriate responses.
+
+- **Chat History**: Chat history is the record of all interactions between the user and the chatbot, including both user inputs and AI-generated outputs. Maintaining chat history is essential for keeping track of the conversation context and ensuring the AI can generate contextually relevant responses. Chat History is a special type of chat flow input, that stores chat messages in a structured format.
+
+- **Chat Output**: Chat output refers to the AI-generated messages that are sent to the user in response to their inputs. Generating contextually appropriate and engaging chat outputs is vital for a positive user experience.
+
+A chat flow can have multiple inputs, but Chat History and Chat Input are required inputs in chat flow.
+
+## Interact with chat flow
+
+Promptflow CLI provides a way to start an interactive chat session for chat flow. Customer can use below command to start an interactive chat session:
+
+```
+pf flow test --flow <flow_folder> --interactive
+```
+
+After executing this command, customer can interact with the chat flow in the terminal. Customer can press **Enter** to send the message to chat flow. And customer can quit with **ctrl+C**.
+Promptflow CLI will distinguish the output of different roles by color, <span style="color:Green">User input</span>, <span style="color:Gold">Bot output</span>, <span style="color:Blue">Flow script output</span>, <span style="color:Cyan">Node output</span>.
+
+> =========================================<br>
+> Welcome to chat flow, <You-flow-name>.<br>
+> Press Enter to send your message.<br>
+> You can quit with ctrl+C.<br>
+> =========================================<br>
+> <span style="color:Green">User:</span> What types of container software there are<br>
+> <span style="color:Gold">Bot:</span> There are several types of container software available, including:<br>
+> 1. Docker: This is one of the most popular containerization software that allows developers to package their applications into containers and deploy them across different environments.<br>
+> 2. Kubernetes: This is an open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications.<br>
+>
+> <span style="color:Green">User:</span> What's the different between them<br>
+> <span style="color:Gold">Bot:</span> The main difference between the various container software systems is their functionality and purpose. Here are some key differences between them:<br>
+> 1. Docker is more focused on container packaging and deployment, while Kubernetes is more focused on container orchestration and management.<br>
+> 2. Kubernetes: Kubernetes is a container orchestration tool that helps manage and deploy containers at scale. It automates the deployment, scaling, and management of containerized applications across multiple hosts.<br>
+
+If customer adds "--verbose" in the pf command, the output of each step will be displayed.
+
+> =========================================<br>
+> Welcome to chat flow, Template Chat Flow.<br>
+> Press Enter to send your message.<br>
+> You can quit with ctrl+C.<br>
+> =========================================<br>
+> <span style="color:Green">User:</span> What types of container software there are<br>
+> <span style="color:Cyan">chat:</span> There are several types of container software available, including:<br>
+> 1. Docker: A popular container platform that is widely used in the industry.<br>
+> 2. Kubernetes: A container orchestration tool that helps manage and deploy containers at scale.<br>
+>
+> <span style="color:Gold">Bot:</span> There are several types of container software available, including:<br>
+> 1. Docker: A popular container platform that is widely used in the industry.<br>
+> 2. Kubernetes: A container orchestration tool that helps manage and deploy containers at scale.<br>
+>
+> <span style="color:Green">User:</span> What's the different between them<br>
+> <span style="color:Cyan">chat:</span> The main differences between container software are in their architecture, feature sets, and use cases. Here are some brief explanations of the differences between the examples I listed:<br>
+> 1. Docker: Docker is a container platform that is widely used for building, packaging, and deploying containerized applications. It is known for its ease of use, portability, and large ecosystem of tools and services.<br>
+> 2. Kubernetes: Kubernetes is a container orchestration tool that helps manage and deploy containers at scale. It automates the deployment, scaling, and management of containerized applications across multiple hosts.<br>
+>
+> <span style="color:Gold">Bot:</span> The main differences between container software are in their architecture, feature sets, and use cases. Here are some brief explanations of the differences between the examples I listed:<br>
+> 1. Docker: Docker is a container platform that is widely used for building, packaging, and deploying containerized applications. It is known for its ease of use, portability, and large ecosystem of tools and services.<br>
+> 2. Kubernetes: Kubernetes is a container orchestration tool that helps manage and deploy containers at scale. It automates the deployment, scaling, and management of containerized applications across multiple hosts.<br>

apps/11_promptflow/playground_chat/azure_openai.yaml

@@ -0,0 +1,7 @@
+$schema: https://azuremlschemas.azureedge.net/promptflow/latest/AzureOpenAIConnection.schema.json
+name: open_ai_connection
+type: azure_open_ai
+api_key: "<user-input>"
+api_base: "<user-input>"
+api_type: "azure"
+api_version: "2024-07-01-preview"

apps/11_promptflow/playground_chat/chat.jinja2

@@ -0,0 +1,12 @@
+system:
+You are a helpful assistant.
+
+{% for item in chat_history %}
+user:
+{{item.inputs.question}}
+assistant:
+{{item.outputs.answer}}
+{% endfor %}
+
+user:
+{{question}}

apps/11_promptflow/playground_chat/data.jsonl

@@ -0,0 +1,3 @@
+{"question": "What's the capital of France?"}
+{"question": "What's the capital of Japan?"}
+{"question": "What's the capital of China?"}