Understanding API Management and Its Role in Generative AI

In this article, we’ll explore how API management (APIM) interacts with generative AI and how it enhances large language model interactions. Whether you're a developer or someone interested in optimizing applications with AI, this guide covers the key concepts and features that API management brings to the table.

Overview of API Management and Its Benefits

API management provides a middle layer between your application and its target endpoint, such as an AI model. It offers several advantages, including:

• Governance: Adds control over the data and operations going through the API.
• Security: Enhances protection by applying additional layers of security.
• Visibility: Allows monitoring and analyzing API interactions.
• Rate limiting and load balancing: Ensures smooth API operations under different loads.

The great thing about API management is that it’s designed to be transparent to the developer. This means you don’t need to make significant changes to the application’s code to benefit from it. For instance, if your application is interacting with a large language model, the only adjustment required is to change the API endpoint. Instead of directly connecting to the AI model’s endpoint, the API management system is placed in between, allowing for additional features without disrupting the original setup.

Subscription Management in API Management

One of the most important concepts in API management is subscriptions. This isn’t to be confused with cloud service subscriptions like Azure. In this case, subscriptions allow different applications or business units to access APIs with unique credentials, often a subscription key. This enables the API manager to:

• Assign different limits to different users or applications.
• Track usage and charge based on consumption.
• Control access to specific APIs depending on the requirements.

For example, when interacting with large language models (LLMs), each application uses a key to authenticate its requests. With API management, this key can become a subscription key, allowing developers to better track and manage usage across different applications.

Authentication in API Management

When an application communicates with an API, the API management system can authenticate requests through several methods:

• Managed Identity: A preferred method that removes the need to manage secrets like keys.
• API Keys: Developers can still use keys to authenticate requests, but managed identity is generally recommended as it offers enhanced security by eliminating the need to store or manage secrets.

By using API management, you can abstract away the complexity of dealing with multiple AI models and authentication mechanisms.

Integrating API Management with Generative AI

Let’s explore how API management interacts with generative AI models, particularly Azure’s OpenAI services. By incorporating Azure API management, developers can add large language models as APIs, exposing them to the management layer and taking advantage of the various features offered, such as:

1. Token Management: Generative AI models, particularly those running on OpenAI, process text inputs as tokens. Each token represents a fragment of the input text. The number of tokens affects pricing and usage limits. Through API management, developers can set token limits and quotas to manage costs effectively. For example, you can limit the number of tokens per minute per application or user.
2. Flexible Usage Limits: With subscription keys, API management can apply different quotas for different users or applications, providing more granular control. This flexibility is especially useful for companies working with multiple departments or clients who need varying levels of access to AI models.
3. Policy Creation: One of the major strengths of API management is its policy engine. Through policies, developers can apply additional controls like rate limiting, token usage tracking, and authentication rules. For example, when adding a large language model as an API, developers can define policies to limit the number of tokens an application can send within a certain time frame.

Onboarding Experience in Azure OpenAI Services

Azure offers a seamless onboarding experience when adding generative AI services to API management. The onboarding process allows developers to:

• Add AI models as APIs.
• Define policies for managing token consumption.
• Track token usage through metrics emitted to App Insights.

By adding an AI model to API management, developers can ensure that their applications can access these powerful models while maintaining control over how the API is consumed.

Advanced Features in API Management for Generative AI

Some advanced capabilities that API management offers when working with generative AI include:

• Retry Logic: In case of token overuse or errors, API management can automatically handle retries based on predefined policies.
• Dynamic Policy Expressions: API management allows for custom policy expressions, enabling developers to implement complex logic for managing API interactions. For example, usage limits can be dynamically adjusted based on user behavior or other factors.

So, to enhance API management (APIM) when integrating with large language models (LLMs) and generative AI, we can implement various strategies for governance, security, and optimization.

Key Takeaways:

1. Transparent Integration: One goal is to make APIM integration transparent for developers and applications. The only necessary change would be the API endpoint, while using subscription keys to differentiate between various apps or business units for better control and tracking.
2. APIM for Large Language Models: APIM can serve as a middle layer between applications and the LLM, adding visibility, governance, and policies for token limits or quotas. It allows setting quotas on the model’s usage, which can be segmented by different keys (e.g., subscription key, IP address). This segmentation ensures that multiple applications consuming a model can have their own set of quotas without impacting the overall usage.
3. Token Limits and Monitoring: Defining token limits per minute is crucial to managing costs since billing is often based on the number of tokens processed. APIM can emit metrics to App Insights for detailed monitoring of token usage, allowing developers to track metrics like user ID, API ID, and client IP.
4. Resiliency with Load Balancing: Resiliency can be added by balancing requests between multiple backend instances of the LLM (e.g., a primary instance with Provisioned Throughput Units (PTU) and a secondary pay-as-you-go instance). A circuit breaker can prevent continuous retries when token limits are exceeded and can help switch the load balancer to the secondary instance until the PTU is available again.
5. Diagnostics and Logging: APIM can enable diagnostic settings to log prompts and inference responses, although logging is limited if you’re using streaming mode, where tokens are sent one at a time as the model predicts them. Non-streaming mode logs can still provide insights into prompt and response behavior for applications that aren’t interactive.
6. Caching for Common Queries: Frequently, users send similar or identical queries. To avoid sending redundant requests to the LLM, you can implement a caching mechanism via APIM. The caching could store previously generated responses, reducing token consumption, cost, and latency for common or repetitive queries.

Enhancing Efficiency:

This method allows developers to have better control over LLM usage while keeping costs in check. By maximizing PTU usage, minimizing unnecessary requests, and introducing load balancing and caching, you can create a more resilient, efficient system without burdening developers with too many changes.

You're dealing with an API management (APIM) integration for optimizing the consumption of large language models (LLMs) like OpenAI. This setup is designed to improve performance, reduce costs, and streamline processes for applications making frequent or similar requests. Here’s a summary of the process:

1. Token and Metric Management: You can set up token limits and manage usage efficiently. The circuit breaker mechanism is used to handle 429 (rate-limited) errors by switching between different instances of the LLM, such as provisioned throughput units (PTU) and pay-as-you-go models. You can monitor metrics and usage, even though streamed responses (where tokens appear in real-time) can’t be logged.
2. Embedding and Caching: A Redis cache (specifically Azure Redis Cache Enterprise) is used to store the results of frequent API requests to prevent repeated calls to the LLM. This process is enhanced by an embedding model, which generates high-dimensional vectors representing the semantic meaning of each request. When a similar request is received, the cache can return a pre-computed result instead of querying the LLM, reducing both cost (by saving tokens) and latency.
3. Semantic Caching Workflow: The incoming request is converted into an embedding vector. A lookup is performed in the Redis cache for a matching result within a defined similarity threshold (e.g., 0.4). If a match is found, the cached response is returned without querying the LLM. If no match is found, the request is sent to the LLM, and the result is then cached for future similar requests.
4. Enhanced Performance with Load Balancing: The system allows load balancing across multiple backends, ensuring that if one service reaches its limit or becomes unhealthy (e.g., PTU runs out), requests are automatically redirected to another service (e.g., pay-as-you-go model).
5. Flexibility in APIM Policies: You can use policy expressions in APIM to manage more advanced workflows like different rate limits for various users or chaining models to first preprocess the request with a smaller model before passing it to a larger one. There's also potential for integrating Azure AI Search for more robust search functionality.
6. Example Use Case: A help desk app frequently receives similar questions (e.g., "What is the time-off policy?"). Instead of repeatedly querying the LLM, the APIM uses the embedding model to compare the incoming question to cached responses, returning answers directly from the cache if the similarity is high enough.

This entire setup is aimed at improving the scalability, efficiency, and cost-effectiveness of working with large language models while making the infrastructure and optimizations invisible to the end application.