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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
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status |
description |
chat-with-pdf |
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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 |
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This example demos how to deploy a flow using Azure App Service |
distribute-flow-as-executable-app |
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This example demos how to package flow as a executable app |
docker |
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This example demos how to deploy flow as a docker app |
kubernetes |
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This example demos how to deploy flow as a Kubernetes app |
promptflow-quality-improvement |
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This tutorial is designed to enhance your understanding of improving flow quality through prompt tuning and evaluation |
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status |
description |
autonomous-agent |
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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 |
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A basic standard flow using custom python tool that calls Azure OpenAI with connection info stored in environment variables |
basic-with-builtin-llm |
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A basic standard flow that calls Azure OpenAI with builtin llm tool |
basic-with-connection |
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A basic standard flow that using custom python tool calls Azure OpenAI with connection info stored in custom connection |
conditional-flow-for-if-else |
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This example is a conditional flow for if-else scenario |
conditional-flow-for-switch |
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This example is a conditional flow for switch scenario |
customer-intent-extraction |
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This sample is using OpenAI chat model(ChatGPT/GPT4) to identify customer intent from customer's question |
flow-with-additional-includes |
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User sometimes need to reference some common files or folders, this sample demos how to solve the problem using additional_includes |
flow-with-symlinks |
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User sometimes need to reference some common files or folders, this sample demos how to solve the problem using symlinks |
gen-docstring |
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This example can help you automatically generate Python code's docstring and return the modified code |
maths-to-code |
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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 |
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A flow that perform named entity recognition task |
web-classification |
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This is a flow demonstrating multi-class classification with LLM |
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description |
eval-basic |
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This example shows how to create a basic evaluation flow |
eval-chat-math |
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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 |
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This is a flow illustrating how to evaluate the performance of a classification system |
eval-entity-match-rate |
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This is a flow evaluates: entity match rate |
eval-groundedness |
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This is a flow leverage llm to eval groundedness: whether answer is stating facts that are all present in the given context |
eval-perceived-intelligence |
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This is a flow leverage llm to eval perceived intelligence |
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description |
basic-chat |
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This example shows how to create a basic chat flow |
chat-math-variant |
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This is a prompt tuning case with 3 prompt variants for math question answering |
chat-with-pdf |
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This is a simple flow that allow you to ask questions about the content of a PDF file and get answers |
chat-with-wikipedia |
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This flow demonstrates how to create a chatbot that can remember previous interactions and use the conversation history to generate next message |
path |
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description |
cascading-inputs-tool-showcase |
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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 |
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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 |
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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 |
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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 |
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This is a flow demonstrating how to use a tool with a dynamic list input |
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description |
connections |
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This folder contains example YAML files for creating connection using pf cli |
We welcome contributions and suggestions! Please see the contributing guidelines for details.
This project has adopted the Microsoft Open Source Code of Conduct. Please see the code of conduct for details.