AI-gateway

Summary

The AI-gateway is a standalone-service that will give access to AI features to all users of GitLab, no matter which instance they are using: self-managed, dedicated or GitLab.com.

Initially, all AI-gateway deployments will be managed by GitLab (the organization), and GitLab.com and all GitLab self-managed instances will use the same gateway. However, in the future we could also deploy regional gateways, or even customer-specific gateways if the need arises.

The AI-Gateway is an API-Gateway that takes traffic from clients, in this case GitLab installations, and directing it to different services, in this case AI-providers and their models. This North/South traffic pattern allows us to control what requests go where and to translate the content of the redirected request where needed.

Diagram source

By using a hosted service under the control of GitLab we can ensure that we provide all GitLab instances with AI features in a scalable way. It is easier to scale this small stateless service, than scaling GitLab-rails with it's dependencies (database, Redis).

It allows users of self-managed installations to have access to features using AI without them having to host their own models or connect to 3rd party providers.

Language: Python

The AI-Gateway was originally started as the "model-gateway" that handled requests from IDEs to provide Code Suggestions. It was written in Python.

Python is an object oriented language that is familiar enough for Rubyists to pick up through in the younger codebase that is the AI-gateway. It also makes it easy for data- and ML-engineers that already have Python experience to contribute.

API

Basic stable API for the AI-gateway

Because the API of the AI-gateway will be consumed by a wide variety of GitLab instances, it is important that we design a stable, yet flexible API.

To do this, we can implement an API-endpoint per use-case we build. This means that the interface between GitLab and the AI-gateway is one that we build and own. This ensures future scalability, composability and security.

The API is not versioned, but is backward compatible. See cross version compatibility for details. The AI-gateway will support the last 2 major versions. For example when working on GitLab 17.2, we would support both GitLab 17 and GitLab 16.

We can add common functionality like rate-limiting, circuit-breakers and secret redaction at this level of the stack as well as in GitLab-rails.

Protocol

We're choosing to use a simple JSON API for the AI-gateway service. This allows us to re-use a lot of what is already in place in the current model-gateway. It also allows us to make the endpoints version agnostic. We could have an API that expects only a rudimentary envelope that can contain dynamic information. We should make sure that we make these APIs compatible with multiple versions of GitLab, or other clients that use the gateway through GitLab. This means that all client versions talk to the same API endpoint, the AI-gateway needs to support this, but we don't need to support different endpoints per version.

We also considered gRPC as a the protocol for communication between GitLab instances, they differ on these items:

Discussion: Because we chose REST+JSON in this iteration to port features that already partially exist does not mean we need to exclude new features using gRPC or Websockets. For example: Chat features might be better served by streaming requests and responses. Since we are suggesting an endpoint per use-case, different features could also opt for different protocols, as long as we keep cross-version compatibility in mind.

Single purpose endpoints

For features using AI, we prefer building a single purpose endpoint with a stable API over the provider API we expose as a direct proxy.

Some features will have specific endpoints, while others can share endpoints. For example, Code Suggestions or chat could have their own endpoint, while several features that summarize issues or merge requests could use the same endpoint but make the distinction on what information is provided in the payload.

The end goal is to build an API that exposes AI for building features without having to touch the AI-gateway. This is analogous to how we built Gitaly, adding features to Gitaly where it was needed, and reusing existing endpoints when that was possible. We had some cost to pay up-front in the case where we needed to implement a new endpoint (RPC), but pays off in the long run when most of the required functionality is implemented.

This does not mean that prompts need to be built inside the AI-gateway. But if prompts are part of the payload to a single purpose endpoint, the payload needs to specify which model they were built for along with other metadata about the prompts. By doing this, we can gracefully degrade or otherwise try to support the request if one of the prompt payloads is no longer supported by the AI gateway. It allows us to potentially avoid breaking features in older GitLab installations as the AI landscape changes.

Cross version compatibility

When building single purpose endpoints, we should be mindful that these endpoints will be used by different GitLab instances indirectly by external clients. To achieve this, we have a very simple envelope to provide information. It has to have a series of prompt_components that contain information the AI-gateway can use to build prompts and query the model of it selects.

Each prompt component contains 3 elements:

• type: This is the kind of information that is being presented in payload. The AI-gateway should ignore any types it does not know about.
• payload: The actual information that can be used by the AI-gateway to build the payload that is going to go out to AI providers. The payload will be different depending on the type, and the version of the client providing the payload. This means that the AI-gateway needs to consider all fields optional.
• metadata: Information about the client that built this part of the prompt. This may, or may not be used by GitLab for telemetry. Nothing inside this field should be required.

The only component in there that is likely to change often is the payload one. There we need to make sure that all fields are optional, and avoid renaming, removing or repurposing fields.

When this is needed, we need to build support for the old versions of a field in the gateway, and keep them around for at least 2 major versions of GitLab. For example, we could consider adding 2 versions of a prompt to the prompt_components payload:

{
  prompt_components: [
    {
      "type": "prompt",
      "metadata": {
        "source": "GitLab EE",
        "version": "16.3",
      },
      "payload": {
        "content": "You can fetch information about a resource called an issue...",
        "params": {
          "temperature": 0.2,
          "maxOutputTokens": 1024
        },
        "model": "text-bison",
        "provider": "vertex-ai"
      }
    },
    {
      "type": "prompt",
      "metadata": {
        "source": "GitLab EE",
        "version": "16.3",
      },
      "payload": {
        "content": "System: You can fetch information about a resource called an issue...\n\nHuman:",
        "params": {
          "temperature": 0.2,
        },
        "model": "claude-2",
        "provider": "anthropic"
      }
    }

  ]
}

Allowing the API to direct the prompt to either provider, based on what is in the payload.

To document and validate the content of payload we can specify their format using JSON-schema.

Example feature: Code Suggestions

For example, a rough Code Suggestions service could look like this:

POST /internal/code-suggestions/completions

{
  "prompt_components": [
    {
      "type": "prompt",
      "metadata": {
        "source": "GitLab EE",
        "version": "16.3",
      },
      "payload": {
        "content": "...",
        "params": {
          "temperature": 0.2,
          "maxOutputTokens": 1024
        },
        "model": "code-gecko",
        "provider": "vertex-ai"
      }
    },
    {
      "type": "editor_content",
      "metadata": {
        "source": "vscode",
        "version": "1.1.1"
      },
       "payload": {
        "filename": "application.rb",
        "before_cursor": "require 'active_record/railtie'",
        "after_cursor": "\nrequire 'action_controller/railtie'",
        "open_files": [
          {
            "filename": "app/controllers/application_controller.rb",
            "content": "class ApplicationController < ActionController::Base..."
          }
        ]
      }
    }
  ]
}

A response could look like this:

{
  "response": "require 'something/else'",
  "metadata": {
    "identifier": "deadbeef",
    "model": "code-gecko",
    "timestamp": 1688118443
  }
}

The metadata field contains information that could be used in a telemetry endpoint on the AI-gateway where we could count suggestion-acceptance rates among other things.

The way we will receive telemetry for Code Suggestions is being discussed in #415745. We will try to come up with an architecture for all AI-related features.

Exposing AI providers

A lot of AI functionality has already been built into GitLab-Rails that currently builds prompts and submits this directly to different AI providers. At the time of writing, GitLab has API-clients for the following providers:

• Anthropic
• Vertex
• OpenAI

To make these features available to self-managed instances, we should provide endpoints for each of these that GitLab.com, self-managed or dedicated installations can use to give these customers to these features.

In a first iteration we could build endpoints that proxy the request to the AI provider. This should make it easier to migrate to routing these requests through the AI-Gateway. As an example, the endpoint for Anthropic could look like this:

POST /internal/proxy/anthropic/(*endpoint)

The *endpoint means that the client specifies what is going to be called, for example /v1/complete. The request body is entirely forwarded to the AI provider. The AI-gateway makes sure the request is correctly authenticated.

Having the proxy in between GitLab and the AI provider means that we still have control over what goes through to the AI provider and if the need arises, we can manipulate or reroute the request to a different provider. Doing this means that we could keep supporting the features of older GitLab installations even if the provider's API changes or we decide not to work with a certain provider anymore.

I think there is value in moving features that use API providers directly to a feature-specific purpose built API. Doing this means that we can improve these features as AI providers evolve by changing the AI-gateway that is under our control. Customers using self-managed or dedicated installations could then start getting better AI-supported features without having to upgrade their GitLab instance.

Features that are currently experimental can use these generic APIs, but we should aim to convert to a single purpose API endpoint before we make the feature generally available for self-managed installations. This makes it easier for us to support features long-term even if the landscape of AI providers change.

The Experimental REST API available to GitLab team members should also use this proxy in the short term. In the longer term, we should provide developers access to a separate proxy that allows them to use GitLab owned authentication to several AI providers for experimentation. This will separate the traffic from developers trying out new things from the fleet that is serving paying customers.

API in GitLab instances

This is the API that external clients can consume on their local GitLab instance. For example VSCode that talks to a self-managed instance.

These versions could also widely defer: it could be that the VSCode extension is kept up-to-date by developers. But the GitLab instance they use for work is kept a minor version behind. So the same requirements in terms of stability and flexibility apply for the clients as for the AI gateway.

In a first iteration we could consider keeping the current REST payloads that the VSCode extension and the Web-IDE send, but direct it to the appropriate GitLab installation. GitLab-rails can wrap the payload in an envelope for the AI-gateway without having to interpret it.

When we do this then the GitLab-instance that receives the request from the extension doesn't need to understand it to enrich it and pass it on to the AI-Gateway. GitLab can add information to the prompt_components and pass everything that was already there straight through to the AI-gateway.

If a request is initiated from another client (for example VSCode), GitLab-rails needs to forward the entire payload in addition to any other enhancements and prompts. This is required so we can potentially support changes from a newer version of the client, traveling through an outdated GitLab installation to a recent AI-gateway.

Discussion: This first iteration is also using a REST+JSON approach. This is how the VSCode extension is currently communicating with the model gateway. This means that it's a smaller iteration to go from that, to wrapping that existing payload into an envelope. With the added advantage of cross version compatibility. But it does not mean that future iterations also need to use REST+JSON. As each feature would have it's own endpoint, the protocol could also be different.

Authentication & Authorization

GitLab provides the first layer of authorization: It authenticates the user and checks if the license allows using the feature the user is trying to use. This can be done using the authentication, policy and license checks that are already built into GitLab.

Authenticating the GitLab-instance on the AI-gateway was discussed in:

• Issue 177
• Epic 10808

The specific mechanism by which trust is delegated between end-users, GitLab instances, and the AI-gateway is covered in the AI gateway access token validation documentation.

Embeddings

Embeddings can be requested for all features in a single endpoint, for example through a request like this:

POST /internal/embeddings

{
  "content": "The lazy fox and the jumping dog",
  "content_type": "issue_title",
  "metadata": {
    "source": "GitLab EE",
    "version": "16.3"
  }
}

The content_type and properties content could in the future be used to create embeddings from different models based on what is appropriate.

The response will include the embedding vector besides the used provider and model. For example:

{
  "response": [0.2, -1, ...],
  "metadata": {
    "identifier": "8badf00d",
    "model": "text-embedding-ada-002",
    "provider": "open_ai",
  }
}

When storing the embedding, we should make sure we include the model and provider data. When embeddings are used to generate a prompt, we could include that metadata in the payload so we can judge the quality of the embedding.

Deployment

Currently, the model-gateway that will become the AI-gateway is being deployed using from the project repository in gitlab-org/modelops/applied-ml/code-suggestions/ai-assist.

It is deployed to a Kubernetes cluster in it's own project. There is a staging environment that is currently used directly by engineers for testing.

In the future, this will be deloyed using Runway. At that time, there will be a production and staging deployment. The staging deployment can be used for automated QA-runs that will have the potential to stop a deployment from reaching production.

Further testing strategy is being discussed in &10563.

Alternative solutions

Alternative solutions were discussed in applied-ml/code-suggestions/ai-assist#161.