semvec/pypi-packages huggingface dataset:
promptflow-azure 1.18.2
Prompt flow azure
License: MIT
Requires Python: !=2.7.*,!=3.0.*,!=3.1.*,!=3.14.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*,>=3.9
Keywords: azure

# Prompt flow azure

[![Python package](https://img.shields.io/pypi/v/promptflow-azure)](https://pypi.org/project/promptflow-azure/)
[![Python](https://img.shields.io/pypi/pyversions/promptflow.svg?maxAge=2592000)](https://pypi.python.org/pypi/promptflow-azure/)
[![PyPI - Downloads](https://img.shields.io/pypi/dm/promptflow-azure)](https://pypi.org/project/promptflow-azure/)
[![CLI](https://img.shields.io/badge/CLI-reference-blue)](https://microsoft.github.io/promptflow/reference/pfazure-command-reference.html)
[![SDK](https://img.shields.io/badge/SDK-reference-blue)](https://microsoft.github.io/promptflow/reference/python-library-reference/promptflow-azure/promptflow.azure.html)

## Introduction

Azure Machine Learning prompt flow is a development tool designed to streamline the entire development cycle of AI applications powered by Large Language Models (LLMs). As the momentum for LLM-based AI applications continues to grow across the globe, Azure Machine Learning prompt flow provides a comprehensive solution that simplifies the process of prototyping, experimenting, iterating, and deploying your AI applications.

The `promptflow-azure` package help user to leverage the cloud version of [prompt flow in Azure AI](https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/overview-what-is-prompt-flow?view=azureml-api-2), which provides below features:

- Create executable flows that link LLMs, prompts, and Python tools through a visualized graph or flex flow code.
- Debug, share, and iterate your flows with ease through team collaboration.
- Evaluate their performance through large-scale batch run.


## ChangeLog

Reach the full change log [here](https://microsoft.github.io/promptflow/reference/changelog/promptflow-azure.html).
promptflow-core 1.18.2
Prompt flow core
License: MIT
Requires Python: <4.0,>=3.9
Keywords: core

# Prompt flow core

[![Python package](https://img.shields.io/pypi/v/promptflow-core)](https://pypi.org/project/promptflow-core/)
[![Python](https://img.shields.io/pypi/pyversions/promptflow.svg?maxAge=2592000)](https://pypi.python.org/pypi/promptflow-core/)
[![SDK](https://img.shields.io/badge/SDK-reference-blue)](https://microsoft.github.io/promptflow/reference/python-library-reference/promptflow-core/promptflow.html)
[![PyPI - Downloads](https://img.shields.io/pypi/dm/promptflow-core)](https://pypi.org/project/promptflow-core/)

## Introduction

The `promptflow-core` package provides the essential features needed to execute a `flow` in prompt flow.

For those seeking a **minimal** dependency to execute a flow in serving or cloud run scenarios, installing this package alone will suffice.

However, if you require the full capabilities of the `devkit` to simplify the usage of flow, connection, and run in local development lifecycle, please install the complete package using `pip install promptflow`.

## ChangeLog

Reach the full change log [here](https://microsoft.github.io/promptflow/reference/changelog/promptflow-core.html).
promptflow-tracing 1.18.2
Prompt flow tracing
License: MIT
Requires Python: <4.0,>=3.9
Keywords: telemetry

# Prompt flow tracing

[![Python package](https://img.shields.io/pypi/v/promptflow-tracing)](https://pypi.org/project/promptflow-tracing/)
[![SDK](https://img.shields.io/badge/SDK-reference-blue)](https://microsoft.github.io/promptflow/reference/python-library-reference/promptflow-tracing/promptflow.tracing.html)
[![License: MIT](https://img.shields.io/github/license/microsoft/promptflow)](https://github.com/microsoft/promptflow/blob/main/LICENSE)

## Introduction

The `promptflow-tracing` package offers tracing capabilities to capture and illustrate the internal execution processes of both DAG flow and Flex flow. It is designed to be compatible with [OpenTelemetry](https://opentelemetry.io/). This makes it a valuable tool for Flex flow developers who use various frameworks (such as langchain, semantic kernel, OpenAI, and various agents) to build LLM-based applications. By using promptflow-tracing, developers can achieve comprehensive observability of their LLM applications.

## ChangeLog

Reach the full change log [here](https://microsoft.github.io/promptflow/reference/changelog/promptflow-tracing.html).