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Enable semantic caching for Azure OpenAI APIs in Azure API Management

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APPLIES TO: All API Management tiers

Enable semantic caching of responses to Azure OpenAI API requests to reduce bandwidth and processing requirements imposed on the backend APIs and lower latency perceived by API consumers. With semantic caching, you can return cached responses for identical prompts and also for prompts that are similar in meaning, even if the text isn't the same. For background, see Tutorial: Use Azure Cache for Redis as a semantic cache.

Note

The configuration steps in this article enable semantic caching for Azure OpenAI APIs. These steps can be generalized to enable semantic caching for corresponding large language model (LLM) APIs available through the Azure AI Model Inference API.

Prerequisites

Test Chat API deployment

First, test the Azure OpenAI deployment to ensure that the Chat Completion API or Chat API is working as expected. For steps, see Import an Azure OpenAI API to Azure API Management.

For example, test the Azure OpenAI Chat API by sending a POST request to the API endpoint with a prompt in the request body. The response should include the completion of the prompt. Example request:

POST https://my-api-management.azure-api.net/my-api/openai/deployments/chat-deployment/chat/completions?api-version=2024-02-01

with request body:

{"messages":[{"role":"user","content":"Hello"}]}

When the request succeeds, the response includes a completion for the chat message.

Create a backend for embeddings API

Configure a backend resource for the embeddings API deployment with the following settings:

  • Name - A name of your choice, such as embeddings-backend. You use this name to reference the backend in policies.
  • Type - Select Custom URL.
  • Runtime URL - The URL of the embeddings API deployment in the Azure OpenAI Service, similar to: https://my-aoai.openai.azure.com/openai/deployments/embeddings-deployment/embeddings

Test backend

To test the backend, create an API operation for your Azure OpenAI Service API:

  1. On the Design tab of your API, select + Add operation.
  2. Enter a Display name and optionally a Name for the operation.
  3. In the Frontend section, in URL, select POST and enter the path /.
  4. On the Headers tab, add a required header with the name Content-Type and value application/json.
  5. Select Save

Configure the following policies in the Inbound processing section of the API operation. In the set-backend-service policy, substitute the name of the backend you created.

<policies>
    <inbound>
        <set-backend-service backend-id="embeddings-backend" />
        <authentication-managed-identity resource="https://cognitiveservices.azure.com/" />
        [...]
    </inbound>
    [...]
</policies>

On the Test tab, test the operation by adding an api-version query parameter with value such as 2024-02-01. Provide a valid request body. For example:

{"input":"Hello"}

If the request is successful, the response includes a vector representation of the input text:

{
    "object": "list",
    "data": [{
        "object": "embedding",
        "index": 0,
        "embedding": [
            -0.021829502,
            -0.007157768,
            -0.028619017,
            [...]
        ]
    }]
}

Configure semantic caching policies

Configure the following policies to enable semantic caching for Azure OpenAI APIs in Azure API Management:

  • In the Inbound processing section for the API, add the azure-openai-semantic-cache-lookup policy. In the embeddings-backend-id attribute, specify the Embeddings API backend you created.

    Note

    When enabling semantic caching for other large language model APIs, use the llm-semantic-cache-lookup policy instead.

    Example:

    <azure-openai-semantic-cache-lookup
    score-threshold="0.8"
    embeddings-backend-id="embeddings-deployment"
    embeddings-backend-auth="system-assigned"
    ignore-system-messages="true"
    max-message-count="10">
    <vary-by>@(context.Subscription.Id)</vary-by>
</azure-openai-semantic-cache-lookup>

  • In the Outbound processing section for the API, add the azure-openai-semantic-cache-store policy.

    Note

    When enabling semantic caching for other large language model APIs, use the llm-semantic-cache-store policy instead.

    Example:

    <azure-openai-semantic-cache-store duration="60" />

Confirm caching

To confirm that semantic caching is working as expected, trace a test Completion or Chat Completion operation using the test console in the portal. Confirm that the cache was used on subsequent tries by inspecting the trace. Learn more about tracing API calls in Azure API Management.

For example, if the cache was used, the Output section includes entries similar to ones in the following screenshot:

Screenshot of request trace in the Azure portal.

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