This template can be used for both Azure AI Studio and Azure Machine Learning.It can be used for both AZURE and LOCAL execution.It supports all types of flow - python Class flows, Function flows and YAML flows.It supports Github, Azure DevOps and Jenkins CI/CD orchestration.It supports pure python based Evaluation as well using promptflow-evals package.It should be used for INNER-LOOP Experimentation and Evaluation.It should be used for OUTER-LOOP Deployment and Inferencing.
NOTE: A new FAQ section is added to help Engineers, Data Scientist and developers find answers to general questions on configuring and using this template.
Large Language Model Operations, or LLMOps, has become the cornerstone of efficient prompt engineering and LLM induced application development and deployment. As the demand for LLM induced applications continues to soar, organizations find themselves in need of a cohesive and streamlined process to manage their end-to-end lifecycle.
The rise of AI and large language models (LLMs) has transformed various industries, enabling the development of innovative applications with human-like text understanding and generation capabilities. This revolution has opened up new possibilities across fields such as customer service, content creation, and data analysis.
As LLMs rapidly evolve, the importance of Prompt Engineering becomes increasingly evident. Prompt Engineering plays a crucial role in harnessing the full potential of LLMs by creating effective prompts that cater to specific business scenarios. This process enables developers to create tailored AI solutions, making AI more accessible and useful to a broader audience.
It is an experimentation and evaluation framework for Prompt Flow. It is just not CI/CD pipelines for Prompt Flow, although it supports it. It has rich set of features for experimentation, evaluation, deployment and monitoring of Prompt Flow. It is a complete end-to-end solution for Prompt Flow operationalization.
The template supports both Azure AI Studio as well as Azure Machine Learning. Depending on the configuration, the template can be used for both Azure AI Studio and Azure Machine Learning. It provides a seamless migration experience for experimentation, evaluation and deployment of Prompt Flow across services.
This template supports different types of flows, allowing you to define and execute workflows based on your specific requirements. The two main flow types supported are:
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Flexible Flows
- Function based flows (python functions)
- Class based flows (python classes)
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Directed Acyclic Graph (DAG) Flows
- YAML based flows
One of the powerful features of this project is its ability to automatically detect the flow type and execute the flow accordingly. This allows you to experiment with different flow types and choose the one that best suits your needs.
This template supports:
- Local invocation and execution of flows for experimentation and evaluation locally.
- Local invocation but execution of flows for experimentation and evaluation on Azure.
- Pipeline based invocation but execution of flows for experimentation and evaluation on Azure.
- Pipeline based invocation and execution of flows for experimentation and evaluation on Build Server.
- Deployment of all types of flows to Kubernetes, Azure Web Apps and AML/AI Studio Managed compute.
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Managing Large Language based flows, from local experimentation to production deployment, has been far from straightforward and isn't a one-size-fits-all task.
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Each flow has its unique lifecycle, from initial experimentation to deployment, and each stage presents its own set of challenges.
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Organizations often deal with multiple flows concurrently, each with its objectives, requirements, and complexities. This can quickly become overwhelming without proper management tools.
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It involves handling multiple flows, their unique lifecycles, experimentation with various configurations, and ensuring smooth deployments.
- LLM-infused applications are designed to understand and generate human-like text based on the input they receive. They comprise of prompts that need engineering cadence and rigour.
- Prompt flow is a powerful feature that simplifies and streamlines the Prompt Engineering process for LLM-infused applications. It enables users to create, evaluate, and deploy high-quality flows with ease and efficiency.
- How do we best augment LLM-infused applications with LLMOps and engineering rigour? This template aims to assist in the development of those types of applications using Prompt flow and LLMOps.
- Bringing discipline to the data preparation for LLM app development by following DataOps best practices.
That's where LLMOps with Prompt flow comes in. LLMOps with Prompt flow is a "LLMOps template and guidance" to help you build LLM-infused apps using Prompt flow. It provides the following features:
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Centralized Code Hosting: This repo supports hosting code for multiple flows based on prompt flow, providing a single repository for all your flows. Think of this platform as a single repository where all your prompt flow code resides. It's like a library for your flows, making it easy to find, access, and collaborate on different projects.
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Lifecycle Management: Each flow enjoys its own lifecycle, allowing for smooth transitions from local experimentation to production deployment.
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Variant and Hyperparameter Experimentation: Experiment with multiple variants and hyperparameters, evaluating flow variants with ease. Variants and hyperparameters are like ingredients in a recipe. This platform allows you to experiment with different combinations of variants across multiple nodes in a flow.
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A/B Deployment: Seamlessly implement A/B deployments, enabling you to compare different flow versions effortlessly. Just as in traditional A/B testing for websites, this platform facilitates A/B deployment for prompt flow flows. This means you can effortlessly compare different versions of a flow in a real-world setting to determine which performs best.
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Many-to-many dataset/flow relationships: Accommodate multiple datasets for each standard and evaluation flow, ensuring versatility in flow test and evaluation. The platform is designed to accommodate multiple datasets for each flow.
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Multiple Deployment Targets: The repo supports deployment of flows to Kubernetes and Azure Managed computes driven through configuration ensuring that your flows can scale as needed.
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Comprehensive Reporting: Generate detailed reports for each variant configuration, allowing you to make informed decisions. Provides detailed metric collection for all variant bulk runs and experiments, enabling data-driven decisions in csv as well as HTML files.
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Offers BYOF (bring-your-own-flows). A complete platform for developing multiple use-cases related to LLM-infused applications.
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Offers configuration based development. No need to write extensive boiler-plate code.
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Provides execution of both prompt experimentation and evaluation locally as well on cloud.
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Provides notebooks for local evaluation of the prompts. Provides library of functions for local experimentation.
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Endpoint testing within pipeline after deployment to check its availability and readiness.
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Provides optional Human-in-loop to validate prompt metrics before deployment.
LLMOps with Prompt flow provides capabilities for both simple as well as complex LLM-infused apps. It is completely customizable to the needs of the application.
Each use case (set of Prompt flow standard and evaluation flows) should follow the folder structure as shown here:
- .azure-pipelines : It contains the CI and PR related pipelines for Azure DevOps and specific to a use-case
- configs : It contains the configuration files for the use-case deployment
- data : This folder contains data files related to Prompt flow standard and evaluation flow
- environment : It contains a dockerfile used for running containers with flows for inferencing on Azure webapps and env.yml for declaring app specific environment variables.
- flows : It should contain minimally two folder - one for standard Prompt flow related files and another for Evaluation flow related file. There can be multiple evaluation flow related folders.
- tests : contains unit tests for the flows
- data-pipelines : It contains the data pipelines to generate the datasets (experimentation, evaluation etc.) necessary for the flows. This folder will have sub-folders specific to the data engineering tool - Microsoft Fabric, Azure ML etc.
Additionally, there is a experiment.yaml file that configures the use-case (see file description and specs for more details). There is also a sample-request.json file containing test data for testing endpoints after deployment.
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The '.azure-pipelines' folder contains the common Azure DevOps pipelines for the platform and any changes to them will impact execution of all the flows.
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The '.github' folder contains the Github workflows for the platform as well as the use-cases. This is bit different than Azure DevOps because all Github workflows should be within this single folder for execution.
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The '.jenkins' folder contains the Jenkins declarative pipelines for the platform as well as the use-cases and individual jobs.
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The 'docs' folder contains documentation for step-by-step guides for both Azure DevOps, Github Workflow and Jenkins related configuration.
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The 'llmops' folder contains all the code related to flow execution, evaluation and deployment.
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The 'dataops' folder contains all the code related to data pipeline deployment.
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The 'local_execution' folder contains python scripts for executing both the standard and evaluation flow locally.
The project includes 6 examples demonstrating different scenarios:
- Web Classification (YAML-based)
Location: ./web_classification Importance: Demonstrates website content summarization with multiple variants, showcasing the flexibility and customization options available in the template.
- Named Entity Recognition (YAML-based)
Location: ./named_entity_recognition Importance: Showcases the extraction of named entities from text, which is valuable for various natural language processing tasks and information extraction.
- Math Coding (YAML-based)
Location: ./math_coding Importance: Showcases the ability to perform mathematical calculations and generate code snippets, highlighting the versatility of the template in handling computational tasks.
- Chat with PDF (YAML-based, RAG-based)
Location: ./chat_with_pdf Importance: Demonstrates a conversational interface for interacting with PDF documents, leveraging the power of retrieval-augmented generation (RAG) to provide accurate and relevant responses.
- Code Generation (Function-based flows)
Location: ./function_flows Importance: Demonstrates the generation of code snippets based on user prompts, showcasing the potential for automating code generation tasks.
- Chat Application (Class-based flows)
Location: ./class_flows Importance: Showcases a chat application built using class-based flows, illustrating the structuring and organization of more complex conversational interfaces.
- Full documentation on using this repo using Azure DevOps can be found here
- Full documentation on using this repo using Github Workflows can be found here
- Full documentation on using this repo using Jenkins can be found here
- Documentation about adding a new flow is available here
- New experiment.yaml configuration here
The repo helps in deploying to Kubernetes, Kubernetes ARC , Azure Web Apps and AzureML Managed compute along with A/B deployment for AzureML Managed compute.
The pipeline execution consists of multiple stages and jobs in each stage:
The repo generates multiple reports (experiments-runs and metrics examples are shown) :
To harness the capabilities of the local execution, follow these installation steps:
- Clone the Repository: Begin by cloning the template's repository from its GitHub repository.
git clone https://github.com/microsoft/llmops-promptflow-template.git- setup env file: create .env file at top folder level and provide information for items mentioned. Add as many connection names as needed. All the flow examples in this repo uses AzureOpenAI connection named
aoai. Add a lineaoai={"api_key": "","api_base": "","api_type": "azure","api_version": "2023-03-15-preview"}with updated values for api_key and api_base. If additional connections with different names are used in your flows, they should be added accordingly. Currently, flow with AzureOpenAI as provider as supported.
experiment_name=
connection_name_1={ "api_key": "","api_base": "","api_type": "azure","api_version": "2023-03-15-preview"}
connection_name_2={ "api_key": "","api_base": "","api_type": "azure","api_version": "2023-03-15-preview"}- Prepare the local conda or virtual environment to install the dependencies.
python -m pip install promptflow promptflow-tools promptflow-sdk jinja2 promptflow[azure] openai promptflow-sdk[builtins] python-dotenv
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Bring or write your flows into the template based on documentation here.
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Write python scripts similar to the provided examples in local_execution folder.
DataOps combines aspects of DevOps, agile methodologies, and data management practices to streamline the process of collecting, processing, and analyzing data. DataOps can help to bring discipline in building the datasets (training, experimentation, evaluation etc.) necessary for LLM app development.
The data pipelines are kept seperate from the prompt engineering flows. Data pipelines create the datasets and the datasets are registered as data assets in Azure ML for the flows to consume. This approach helps to scale and troubleshoot independently different parts of the system.
For details on how to get started with DataOps, please follow this document - How to Configure DataOps.
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.
Copyright (c) Microsoft Corporation. All rights reserved.
Licensed under the MIT license.