Public API Endpoint For AI Chat Models: A Feature Request

Hey everyone! Today, we're diving deep into a feature request that aims to make our AI chat models more accessible and user-friendly for developers. This is all about adding a public endpoint to list the available AI chat models. Let's break down why this is important, what it means, and how it'll make your lives easier.

Motivation: Why a Public Endpoint for AI Models?

The motivation behind this feature is to drastically improve the developer experience. Think about it: when you're building an application that uses AI chat models, you need to know what models are available before you can even start making requests. Currently, this information isn't readily accessible without authentication, which can be a roadblock.

Imagine you're trying out a new platform. Wouldn't it be great to quickly see a catalog of available AI models, just like you would on other AI platforms? This is precisely what we're aiming for. By exposing our model catalog through a public endpoint, we're making it easier for developers to discover and integrate our AI chat models into their applications. This enhancement streamlines the development process, allowing developers to explore options and choose the best fit for their needs without unnecessary hurdles. We want to ensure that accessing information about our models is as straightforward and efficient as possible, mirroring the user-friendly approach adopted by leading AI platforms in the industry. This proactive step not only simplifies initial integration but also fosters a more transparent and collaborative environment for our developer community. By reducing friction and providing clear visibility, we empower developers to innovate and create compelling applications leveraging our AI capabilities.

Current Behavior: The Challenge We Face

So, what's the current behavior? Right now, the Puter backend doesn't have any public endpoints that you can use to query available AI chat models without authentication. Our AIChatService keeps track of these models internally, but you can only get to them through authenticated driver interfaces. This means that if you're not authenticated, you're essentially in the dark about what models are available.

Let's walk through the steps to reproduce this. First, you'd start the Puter backend server. Then, you'd try to make an unauthenticated GET request to /puterai/chat/models. Guess what? You'll get a 404 Not Found error because the endpoint simply doesn't exist. If you dig into the codebase, you'll confirm that there are no public endpoints that list AI models in the current setup. This lack of accessibility can slow down the development process, making it harder for developers to integrate our AI solutions efficiently. The absence of a readily available, unauthenticated endpoint forces developers to navigate additional layers of authentication before they can even begin to explore the range of models on offer. This initial barrier can be a significant impediment, especially for new users or those prototyping applications. Addressing this limitation is crucial to fostering a more streamlined, user-friendly experience for our developer community, ensuring they have the necessary visibility and access to our AI chat models without unnecessary complications.

Expected Behavior: What We Want to Achieve

Now, let's talk about the expected behavior. We envision a system where anyone can query the available AI chat models through public, unauthenticated REST API endpoints. We're thinking of two main endpoints: one that gives you a simple list of models and another that provides detailed information about each model, including its capabilities and cost data. This level of transparency and accessibility is key to empowering developers and fostering innovation.

Imagine being able to quickly retrieve a list of models and then dive deeper into the specifics of each one, all without needing to authenticate first. This is the experience we want to create. By implementing these public endpoints, we're not just making our models more accessible; we're also providing developers with the information they need to make informed decisions about which models to use. The inclusion of detailed information, such as cost and capability data, ensures that developers can align their choices with both their technical requirements and budgetary constraints. This holistic approach to providing information fosters a more efficient and effective development process, allowing users to optimize their applications from the outset. Our goal is to create an environment where developers feel empowered with the knowledge they need, facilitating seamless integration and innovation within our AI ecosystem.

Acceptance Criteria: How We'll Know We've Succeeded

To make sure we hit the mark, we've set some acceptance criteria. These are the benchmarks we'll use to measure our success.

Here’s the checklist:

• [ ] We'll create a new service specifically for handling public chat-related API endpoints.
• [ ] A GET endpoint at /puterai/chat/models will return a simple list of available AI models without needing authentication.
• [ ] A GET endpoint at /puterai/chat/models/details will return detailed information about the models, including cost and capability data.
• [ ] This new service will be properly registered in the CoreModule.
• [ ] We'll write unit tests to verify that both endpoints return the correct data from the AIChatService.
• [ ] We'll make sure the endpoints use the right privilege escalation to access system-level model information.

These criteria ensure that we not only deliver the functionality but also do it in a robust and secure way. Each point addresses a critical aspect of the implementation, from creating a dedicated service to ensuring data accuracy and security. By adhering to these standards, we can be confident that the public API endpoints will meet the needs of our developers while maintaining the integrity of our system. The comprehensive nature of these criteria reflects our commitment to quality and our dedication to providing a reliable and user-friendly experience for everyone interacting with our AI chat models.

Steps to Test: Let's Put It to the Test!

Alright, guys, let's get our hands dirty and talk about steps to test this new feature. Testing is super crucial to make sure everything works as expected. Here’s how we’re going to do it:

1. First, fire up the Puter backend server in development mode. This is where the magic happens!
2. Next, we’re going to make an unauthenticated GET request to http://localhost:4100/puterai/chat/models. Think of it as knocking on the door to see who’s home.
3. We need to verify that the response we get back contains a JSON object. This object should have a models array that lists all the available AI models. It’s like checking the guest list to see who’s at the party.
4. Now, let’s get into the nitty-gritty. We’ll make another unauthenticated GET request, this time to http://localhost:4100/puterai/chat/models/details. This is where we get the VIP treatment and see all the juicy details.
5. We’ll verify that this response contains detailed model information. This includes things like cost and capability data. It’s like reading the fine print to know what each model can do and how much it costs.
6. Last but not least, we’re going to run the test suite. This ensures that all the new tests we’ve added pass with flying colors. It’s like the final exam to make sure we’ve learned everything we need to know.

By following these steps, we can thoroughly test the new public endpoints and ensure they’re working correctly. This multi-faceted approach, from simple GET requests to comprehensive test suites, guarantees that we’ve covered all bases. We want to make sure that when developers use these endpoints, they have a smooth and reliable experience. Testing isn't just a formality; it’s our way of guaranteeing the quality and stability of the features we release.

Submission: Show Us What You've Got!

Finally, let's talk about submission. To make the review process as smooth as possible, we're asking everyone to record their screen while testing this feature. You can use a tool like cap.so (Studio mode is your friend here!). Export the recording as an MP4 and simply drag and drop it into an issue comment below. This helps us see exactly what you're doing and makes it easier to spot any potential issues.

And hey, if you're new to pull requests, don't sweat it! We've got a handy guide for you here. It'll walk you through the process step by step. We believe in making it as easy as possible for everyone to contribute. Providing a screen recording is a fantastic way to give us a clear picture of your testing process, ensuring we can address any concerns efficiently. The guide to submitting pull requests is designed to help you navigate the contribution process smoothly, regardless of your experience level. We're committed to fostering an inclusive environment where everyone feels empowered to contribute and improve our platform.

So, there you have it! We're excited about this new feature and how it's going to make life easier for developers. Let's get testing and make it happen! By implementing these enhancements, we’re not just adding functionality; we’re building a more accessible and user-friendly platform for everyone. This proactive approach to developer experience will undoubtedly foster innovation and collaboration within our community.