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AI models, like OpenAI ChatGPT, are often packaged as APIs. Through these APIs, developers can trigger the AI, sending prompts as input allowing developers to integrate AI into their applications seamlessly. A commonly used architecture for building a new AI app also utilizes two OpenAI API endpoints like Vector Embeddings and Chat Completion as you see in the diagram below:
The output from an AI model in response to a prompt is typically textual. To translate these "ideas" into actionable outcomes, AI services need to call APIs. These APIs can initiate actions in the real or digital world, such as making payments, booking an appointment, sending messages, or adjusting room temperatures. In essence, APIs act as the hands of an AI service, enabling it to interact with its environment. A good example of it can be ChatGPT custom plugins. This article explains how to build a custom plugin for API Gateway using APISIX. Because APISIX can be in the front of APIs to route AI requests to the intended backend API services. We can implement easily security measures like authentication, authorization, and rate limiting or cache similar responses from APIs and it allows us to gather valuable insights about API usage, performance, and potential issues.