Promptflow examples

Get started

Install dependencies

Bootstrap your python environment.
- e.g: create a new conda environment. conda create -n pf-examples python=3.9.
- install required packages in python environment : pip install -r requirements.txt
  - show installed sdk: pip show promptflow

Quick start

path	status	description
quickstart.ipynb		A quickstart tutorial to run a flow and evaluate it.
quickstart-azure.ipynb		A quickstart tutorial to run a flow in Azure AI and evaluate it.

CLI examples

Tutorials (tutorials)

path	status	description
chat-with-pdf		Retrieval Augmented Generation (or RAG) has become a prevalent pattern to build intelligent application with Large Language Models (or LLMs) since it can infuse external knowledge into the model, which is not trained with those up-to-date or proprietary information
azure-app-service		This example demos how to deploy a flow using Azure App Service
create-service-with-flow		This example shows how to create a simple service with flow
distribute-flow-as-executable-app		This example demos how to package flow as a executable app
docker		This example demos how to deploy flow as a docker app
kubernetes		This example demos how to deploy flow as a Kubernetes app
promptflow-quality-improvement		This tutorial is designed to enhance your understanding of improving flow quality through prompt tuning and evaluation
tracing		Prompt flow provides the tracing feature to capture and visualize the internal execution details for all flows

Prompty (prompty)

path	status	description
basic		A basic prompt that uses the chat API to answer questions, with connection configured using environment variables
chat-basic		A prompt that uses the chat API to answer questions with chat history, leveraging promptflow connection
eval-apology		A prompt that determines whether a chat conversation contains an apology from the assistant
eval-basic		Basic evaluator prompt for QA scenario
format-output		A few examples that demos different prompty response format like text, json_object, and how to enable stream output

Flex Flows (flex-flows)

path	status	description
basic		A basic standard flow define using function entry that calls Azure OpenAI with connection info stored in environment variables
chat-async-stream		A chat flow defined using async class entry that return output in stream mode
chat-basic		A basic chat flow defined using class entry
chat-minimal		A chat flow defined using function with minimal code
chat-stream		A chat flow defined using class entry that return output in stream mode
chat-with-functions		This flow covers how to use the LLM chat API in combination with external functions to extend the capabilities of GPT models
eval-checklist		A example flow defined using class entry which demos how to evaluate the answer pass user specified check list
eval-code-quality		A example flow defined using class based entry which leverages model config to evaluate the quality of code snippet
eval-criteria-with-langchain		A example flow of converting LangChain criteria evaluator application to flex flow

Flows (flows)

Standard flows

path	status	description
autonomous-agent		This is a flow showcasing how to construct a AutoGPT agent with promptflow to autonomously figures out how to apply the given functions to solve the goal, which is film trivia that provides accurate and up-to-date information about movies, directors, actors, and more in this sample
basic		A basic standard flow using custom python tool that calls Azure OpenAI with connection info stored in environment variables
basic-with-builtin-llm		A basic standard flow that calls Azure OpenAI with builtin llm tool
basic-with-connection		A basic standard flow that using custom python tool calls Azure OpenAI with connection info stored in custom connection
conditional-flow-for-if-else		This example is a conditional flow for if-else scenario
conditional-flow-for-switch		This example is a conditional flow for switch scenario
customer-intent-extraction		This sample is using OpenAI chat model(ChatGPT/GPT4) to identify customer intent from customer's question
describe-image		A flow that take image input, flip it horizontally and uses OpenAI GPT-4V tool to describe it
flow-with-additional-includes		User sometimes need to reference some common files or folders, this sample demos how to solve the problem using additional_includes
flow-with-symlinks		User sometimes need to reference some common files or folders, this sample demos how to solve the problem using symlinks
gen-docstring		This example can help you automatically generate Python code's docstring and return the modified code
maths-to-code		Math to Code is a project that utilizes the power of the chatGPT model to generate code that models math questions and then executes the generated code to obtain the final numerical answer
named-entity-recognition		A flow that perform named entity recognition task
question-simulation		This question simulation flow is used to generate suggestions for the next question based on the previous chat history
web-classification		This is a flow demonstrating multi-class classification with LLM

Evaluation flows

path	status	description
eval-basic		This example shows how to create a basic evaluation flow
eval-chat-math		This example shows how to evaluate the answer of math questions, which can compare the output results with the standard answers numerically
eval-classification-accuracy		This is a flow illustrating how to evaluate the performance of a classification system
eval-entity-match-rate		This is a flow evaluates: entity match rate
eval-groundedness		This is a flow leverage llm to eval groundedness: whether answer is stating facts that are all present in the given context
eval-multi-turn-metrics		This evaluation flow will evaluate a conversation by using Large Language Models (LLM) to measure the quality of the responses
eval-perceived-intelligence		This is a flow leverage llm to eval perceived intelligence
eval-qna-non-rag		This is a flow evaluating the Q&A systems by leveraging Large Language Models (LLM) to measure the quality and safety of responses
eval-qna-rag-metrics		This is a flow evaluating the Q&A RAG (Retrieval Augmented Generation) systems by leveraging the state-of-the-art Large Language Models (LLM) to measure the quality and safety of responses
eval-single-turn-metrics		This evaluation flow will evaluate a question and answer pair by using Large Language Models (LLM) to measure the quality of the answer
eval-summarization		This flow implements a reference-free automatic abstractive summarization evaluation across four dimensions: fluency, coherence, consistency, relevance

Chat flows

path	status	description
chat-basic		This example shows how to create a basic chat flow
chat-math-variant		This is a prompt tuning case with 3 prompt variants for math question answering
chat-with-image		This flow demonstrates how to create a chatbot that can take image and text as input
chat-with-pdf		This is a simple flow that allow you to ask questions about the content of a PDF file and get answers
chat-with-wikipedia		This flow demonstrates how to create a chatbot that can remember previous interactions and use the conversation history to generate next message
use_functions_with_chat_models		This flow covers how to use the LLM tool chat API in combination with external functions to extend the capabilities of GPT models

Tool Use Cases (Tool Use Cases)

path	status	description
cascading-inputs-tool-showcase		This is a flow demonstrating the use of a tool with cascading inputs which frequently used in situations where the selection in one input field determines what subsequent inputs should be shown, and it helps in creating a more efficient, user-friendly, and error-free input process
custom-strong-type-connection-package-tool-showcase		This is a flow demonstrating the use of a package tool with custom string type connection which provides a secure way to manage credentials for external APIs and data sources, and it offers an improved user-friendly and intellisense experience compared to custom connections
custom-strong-type-connection-script-tool-showcase		This is a flow demonstrating the use of a script tool with custom string type connection which provides a secure way to manage credentials for external APIs and data sources, and it offers an improved user-friendly and intellisense experience compared to custom connections
custom_llm_tool_showcase		This is a flow demonstrating how to use a `custom_llm` tool, which enables users to seamlessly connect to a large language model with prompt tuning experience using a `PromptTemplate`
dynamic-list-input-tool-showcase		This is a flow demonstrating how to use a tool with a dynamic list input

Connections (connections)

path	status	description
connections		This folder contains example `YAML` files for creating `connection` using `pf` cli

SDK examples

path	status	description
quickstart.ipynb		A quickstart tutorial to run a flow and evaluate it.
quickstart-azure.ipynb		A quickstart tutorial to run a flow in Azure AI and evaluate it.
flow-as-function.ipynb		This guide will walk you through the main scenarios of executing flow as a function.
pipeline.ipynb		Create pipeline using components to run a distributed job with tensorflow
cloud-run-management.ipynb		Flow run management in Azure AI
run-management.ipynb		Flow run management
trace-autogen-groupchat.ipynb		Tracing LLM calls in autogen group chat application
otlp-trace-collector.ipynb		A tutorial on how to levarage custom OTLP collector.
trace-langchain.ipynb		Tracing LLM calls in langchain application
trace-llm.ipynb		Tracing LLM application
connection.ipynb		Manage various types of connections using sdk
flex-flow-quickstart-azure.ipynb		A quickstart tutorial to run a flex flow and evaluate it in azure.
flex-flow-quickstart.ipynb		A quickstart tutorial to run a flex flow and evaluate it.
chat-stream-with-async-flex-flow.ipynb		A quickstart tutorial to run a class based flex flow in stream mode and evaluate it.
chat-with-class-based-flow-azure.ipynb		A quickstart tutorial to run a class based flex flow and evaluate it in azure.
chat-with-class-based-flow.ipynb		A quickstart tutorial to run a class based flex flow and evaluate it.
chat-stream-with-flex-flow.ipynb		A quickstart tutorial to run a class based flex flow in stream mode and evaluate it.
langchain-eval.ipynb		A tutorial to converting LangChain criteria evaluator application to flex flow.
prompty-quickstart.ipynb		A quickstart tutorial to run a prompty and evaluate it.
chat-with-prompty.ipynb		A quickstart tutorial to run a chat prompty and evaluate it.
prompty-output-format.ipynb
chat-with-pdf-azure.ipynb		A tutorial of chat-with-pdf flow that executes in Azure AI
chat-with-pdf.ipynb		A tutorial of chat-with-pdf flow that allows user ask questions about the content of a PDF file and get answers