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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
path |
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 |
docker |
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This example demos how to deploy flow as a docker app |
path |
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 functionsto 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 |
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 |
path |
status |
description |
basic-chat |
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This example shows how to create a basic chat flow |
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 |
status |
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.