1. 🧠 AI Gateway
2. 🧪 Labs
3. 🚀 Getting started
4. 🏛️ Well Architected Framework
5. 🪞 Mock Server
6. 🎒 Show and tell
7. 🥇 Other Resources

The rapid pace of AI advances demands experimentation-driven approaches for organizations to remain at the forefront of the industry. With AI steadily becoming a game-changer for an array of sectors, maintaining a fast-paced innovation trajectory is crucial for businesses aiming to leverage its full potential.

AI services are predominantly accessed via APIs, underscoring the essential need for a robust and efficient API management strategy. This strategy is instrumental for maintaining control and governance over the consumption of AI services.

With the expanding horizons of AI services and their seamless integration with APIs, there is a considerable demand for a comprehensive AI Gateway pattern, which broadens the core principles of API management. Aiming to accelerate the experimentation of advanced use cases and pave the road for further innovation in this rapidly evolving field. The well-architected principles of the AI Gateway provides a framework for the confident deployment of Intelligent Apps into production..

This repo explores the AI Gateway pattern through a series of experimental labs. Azure API Management plays a crucial role within these labs, handling AI services APIs, with security, reliability, performance, overall operational efficiency and cost controls. The primary focus is on Azure OpenAI, which sets the standard reference for Large Language Models (LLM). However, the same principles and design patterns could potentially be applied to any LLM.

Acknowledging the rising dominance of Python, particularly in the realm of AI, along with the powerful experimental capabilities of Jupyter notebooks, the following labs are structured around Jupyter notebooks, with step-by-step instructions with Python scripts, Bicep files and APIM policies:

Request forwarding		Playground to try forwarding requests to either an Azure OpenAI endpoint or a mock server. APIM uses the system managed identity to authenticate into the Azure OpenAI service.	💬
Backend circuit breaking		Playground to try the built-in backend circuit breaker functionality of APIM to either an Azure OpenAI endpoints or a mock server.	💬
Backend pool load balancing		Playground to try the built-in load balancing backend pool functionality of APIM to either a list of Azure OpenAI endpoints or mock servers.	💬
Advanced load balancing		Playground to try the advanced load balancing (based on a custom APIM policy) to either a list of Azure OpenAI endpoints or mock servers.	💬
Response streaming		Playground to try response streaming with APIM and Azure OpenAI endpoints to explore the advantages and shortcomings associated with streaming.	💬
Vector searching		Playground to try the Retrieval Augmented Generation (RAG) pattern with Azure AI Search, Azure OpenAI embeddings and Azure OpenAI completions. All the endpoints are managed via APIM.	💬
Built-in logging		Playground to try the buil-in logging capabilities of API Management. The requests are logged into Application Insights and it's easy to track request/response details and token usage with provided notebook.	💬
SLM self-hosting		Playground to try the self-hosted phy-3 Small Language Model (SLM) trough the APIM self-hosted gateway with OpenAI API compatibility.	💬
Access controlling		Playground to try the OAuth 2.0 authorization feature using identity provider to enable more fine-grained access to OpenAPI APIs by particular users or client.	💬
Token rate limiting		Playground to try the token rate limiting policy to either a list of Azure OpenAI endpoints or mock servers.	💬
Semantic caching		Playground to try the sementic caching policy.	💬
Token metrics emitting		Playground to try the emit token metric policy. The policy sends metrics to Application Insights about consumption of large language model tokens through Azure OpenAI Service APIs.	💬
GPT-4o inferencing		Playground to try the new GPT-4o model. GPT-4o ("o" for "omni") is designed to handle a combination of text, audio, and video inputs, and can generate outputs in text, audio, and image formats.	💬

• Developer tooling
• App building
• Token counting
• Function calling
• Assistants load balancing
• Semantic Kernel plugin
• Cost tracking
• Content filtering
• PII handling
• Prompt storing
• Prompt guarding
• Prompt model routing
• Llama inferencing

• Python 3.8 or later version installed
• VS Code installed with the Jupyter notebook extension enabled
• Azure CLI installed
• An Azure Subscription with Contributor permissions
• Access granted to Azure OpenAI or just enable the mock service
• Sign in to Azure with Azure CLI

1. Clone this repo and configure your local machine with the prerequisites. Or just create a GitHub Codespace and run it on the browser or in VS Code.
2. Navigate through the available labs and select one that best suits your needs. For starters we recommend the request forwarding with just the Azure CLI or the backend pool load balancing with Bicep.
3. Open the notebook and run the provided steps.
4. Tailor the experiment according to your requirements. If you wish to contribute to our collective work, we would appreciate your submission of a pull request.

The Azure Well-Architected Framework is a design framework that can improve the quality of a workload. The following table maps labs with the Well-Architected Framework pillars to set you up for success through architectural experimentation.

The AI-Gateway Mock server is designed to mimic the behavior and responses of the OpenAI API, thereby creating an efficient simulation environment suitable for testing and development purposes on the integration with APIM and other use cases. The app.py can be customized to tailor the Mock server to specific use cases.

• Run locally or deploy to Azure

Numerous reference architectures, best practices and starter kits are available on this topic. Please refer to the resources provided if you need comprehensive solutions or a landing zone to initiate your project. We suggest leveraging the AI-Gateway labs to discover additional capabilities that can be integrated into the reference architectures.

• AI Hub Gateway Landing Zone
• Designing and implementing a gateway solution with Azure OpenAI resources
• Azure OpenAI Using PTUs/TPMs With API Management - Using the Scaling Special Sauce
• Manage Azure OpenAI using APIM
• Setting up Azure OpenAI as a central capability with Azure API Management
• Introduction to Building AI Apps

We believe that there may be valuable content that we are currently unaware of. We would greatly appreciate any suggestions or recommendations to enhance this list.