AI features based on 3rd-party integrations

Instructions for setting up GitLab Duo features in the local development environment

Required: Install AI Gateway

Why: Certain AI operations are provided by AI Gateway only, such as text completion, embedding and semantic search.

How: Follow these instructions to install the AI Gateway with GDK.

Required: Setup Google Cloud Platform in AI Gateway

Why: You may not be able to boot AI Gateway if Google Cloud Platform credentials isn’t correctly setup because AI Gateway checks the access at boot time.

How:

  1. Set up a Google Cloud project
    1. Option 1 (recommended for GitLab team members): use the existing ai-enablement-dev-69497ba7) Google Cloud project. Using the existing project is recommended because this project has Vertex APIs and Vertex AI Search already enabled. Also, all GitLab team members should already have access to this project. Visit the Google Cloud console to confirm that you already have access.
    2. Option 2: Create a sandbox Google Cloud project by following the instructions in the handbook. If you are using an individual Google Cloud project, you may also need to enable the Vertex AI API:
      1. Visit welcome page, choose your project (for example: jdoe-5d23dpe).
      2. Go to APIs & Services > Enabled APIs & services.
      3. Select Enable APIs and Services.
      4. Search for Vertex AI API.
      5. Select Vertex AI API, then select Enable.
  2. Install the gcloud CLI
    1. If you already use asdf for runtime version management, you can install gcloud with the asdf gcloud plugin

  3. Authenticate locally with Google Cloud using the gcloud auth application-default login command.

    note
    This command tries to find a quota project from gcloud’s context and write it to ADC(Application Default Credentials) so that Google client libraries can use it for billing and quota. To be able to use a project as the quota project, the account in ADC must have the serviceusage.services.use permission on the project. If you don’t have a project with this permission and you always want to bill the project owning the resources, you can disable the quota project and authenticate using gcloud auth application-default login --disable-quota-project command.

  4. Update the application settings file in AI Gateway: 

    # <GDK-root>/gitlab-ai-gateway/.env

# PROJECT_ID = "ai-enablement-dev-69497ba7" for GitLab team members with access
# to the shared project

# PROJECT_ID = "your-google-cloud-project-name" for those with their own sandbox
# Google Cloud project.

AIGW_GOOGLE_CLOUD_PLATFORM__PROJECT='PROJECT_ID'

Required: Setup Anthropic in the AI Gateway

Why: some GitLab Duo features use Anthropic models.

How: After filling out an access request, you can sign up for an Anthropic account and create an API key.

Update the application settings file in AI Gateway: 

# <GDK-root>/gitlab-ai-gateway/.env

ANTHROPIC_API_KEY='<your-anthropic-api-key>'

Required: Setup AI Gateway endpoint in GitLab-Rails

Why: Your need to tell your local GitLab instance to talk to your local AI Gateway. Otherwise, it tries to talk to the production AI Gateway (cloud.gitlab.com), which results in an authentication error.

How:

Update following variable in the env.runit file in your GDK root: 

# <GDK-root>/env.runit

export AI_GATEWAY_URL=http://0.0.0.0:5052

By default, the above URL works as-is.

You can also change it to a different URL by updating the application settings file in AI Gateway: 

# <GDK-root>/gitlab-ai-gateway/.env

AIGW_FASTAPI__API_HOST=0.0.0.0
AIGW_FASTAPI__API_PORT=5052

To check if your monolith is using correct AI Gateway, please visit http://<your-gdk-url>/help/instance_configuration#ai_gateway_url page.

Required: Setup Licenses in GitLab-Rails

Why: GitLab Duo is available to Premium and Ultimate customers only. You likely want an Ultimate license for your GDK. Ultimate gets you access to all GitLab Duo features.

How:

Follow the process to obtain an EE license for your local instance and upload the license.

To verify that the license is applied, go to Admin area > Subscription and check the subscription plan.

Required: Enable feature flags in GitLab-Rails

Why: some GitLab Duo functionality is behind a feature flag.

How:

Enable all feature flags maintained by “group::ai framework” by running this command in your /gitlab directory: 

bundle exec rake gitlab:duo:enable_feature_flags

This Rake task ensures that the local environment is ready to run GitLab Duo. The task can be run in either SaaS or Self-Managed modes, depending on which installation you currently imitate in GDK.

If you currently run you local GDK as SaaS (imitating GitLab.com), you need to provide the argument to the task: GITLAB_SIMULATE_SAAS=1 bundle exec 'rake gitlab:duo:setup[<test-group-name>]'

Replace <test-group-name> with the name of any top-level group. Duo will be configured for that group. If the group doesn’t exist, it creates a new one. Make sure the script succeeded. It prints error messages with links on how to resolve the error. You can re-run the script until it succeeds.

If you currently run you local GDK as Self-Managed (default for GDK), no arguments for Rake task are expected: GITLAB_SIMULATE_SAAS=0 bundle exec 'rake gitlab:duo:setup'

It’s recommended to run gdk restart after the task succeeded.

If you need to use evaluation framework (as described here) you can run special Rake task: GITLAB_SIMULATE_SAAS=1 bundle exec 'rake gitlab:duo:setup_evaluation[<test-group-name>]'. It repeats steps from original setup Rake task, and also imports specially prepared groups and projects. Since we use Setup class (under ee/lib/gitlab/duo/developments/setup.rb) that requires “saas” mode to create a group (necessary for importing subgroups), you need to set GITLAB_SIMULATE_SAAS=1 if it’s currently GITLAB_SIMULATE_SAAS=0. This is just to complete the import successfully, and then you can switch back to GITLAB_SIMULATE_SAAS=0. To run this task, your GDK server must be running. After running this Rake task, import process will be in progress for said groups and projects.

If you plan to run you local GDK as Self-Managed (for GDK), it is recommended to set this environment variable. It has no effect if you run you local GDK as SaaS, so you can always keep it set.

Setting this environment variable will allow the local GL instance to issue tokens itself, without syncing with CustomersDot first. This is similar how GitLab.com operates, and we allow it for development purposes to simplify the setup. With it you can skip the CustomersDot setup. This can done by either:

  • setting it in the env.runit file in your GDK root
  • executing export CLOUD_CONNECTOR_SELF_SIGN_TOKENS=1 in your shell (but you need to repeat it for every new session)

You need to restart GDK to apply the change.

If you plan to use local CustomersDot or test cross-service integration, you may want to unset this variable.

Option A: Run GDK in SaaS mode and enable AI features for a test group

How: the following should be set in the env.runit file in your GDK root: 

# <GDK-root>/env.runit

export GITLAB_SIMULATE_SAAS=1

or, just for a current session: 

export CLOUD_CONNECTOR_SELF_SIGN_TOKENS=1 && gdk restart

Make sure you run gitlab:duo:setup Rake task /gitlab directory: 

GITLAB_SIMULATE_SAAS=1 RAILS_ENV=development bundle exec rake 'gitlab:duo:setup[<test-group-name>]'

Membership to a group with Duo features enabled is what enables many AI features. To enable AI feature access locally, make sure that your test user is a member of the group with Duo features enabled (<test-group-name>) and (for some features) have a seat assigned.

Finally, you must clear the GitLab-Rails Redis cache. User access to GitLab Duo features in SaaS mode is cached in Redis. This cache expires every 60 minutes. A manual cache-clearing ensures that you can use Duo features immediately: 

bundle exec rake cache:clear

Troubleshooting: If you have problems with your setup at this point, double-check your admin settings. When GDK is running, go to admin settings (Navigation -> Admin), then go to general settings (Settings -> General), and expand the “Account and limit” section. Scroll to the bottom of this section to make sure the setting “Allow use of licensed EE features” is toggled on.

Option B: Run GDK in Self-Managed mode and enable AI features for the instance

How: This is the default for GDK. To set it explicitly, the following should be set in the env.runit file in your GDK root: 

# <GDK-root>/env.runit

export GITLAB_SIMULATE_SAAS=0

or, just for a current session: 

export CLOUD_CONNECTOR_SELF_SIGN_TOKENS=0 && gdk restart

Make sure you executed gitlab:duo:setup Rake task in /gitlab directory: 

GITLAB_SIMULATE_SAAS=0 RAILS_ENV=development bundle exec rake 'gitlab:duo:setup'

and it succeeded.

Some AI features requires a seat to be assigned to a user to have access. If you use CLOUD_CONNECTOR_SELF_SIGN_TOKENS=1 you need to assign root/admin user to a seat, to receive a “Code completion test was successful” notification from the health check on the http://localhost:3000/admin/code_suggestions page.

Our customers (production environment) do not need to do that to run a Code Suggestions health check.

Why: you’ve completed all of the setup steps, now it’s time to confirm that GitLab Duo is actually working.

How:

After the setup is complete, you can test clients in GitLab-Rails to see if it can correctly reach to AI Gateway:

  1. Run gdk start.
  2. Login to Rails console with gdk rails console.
  3. Talk to a model: 
  # Talk to Anthropic model
  Gitlab::Llm::Anthropic::Client.new(User.first, unit_primitive: 'duo_chat').complete(prompt: "\n\nHuman: Hi, How are you?\n\nAssistant:")

  # Talk to Vertex AI model
  Gitlab::Llm::VertexAi::Client.new(User.first, unit_primitive: 'documentation_search').text_embeddings(content: "How can I create an issue?")

  # Test `/v1/chat/agent` endpoint
  Gitlab::Llm::Chain::Requests::AiGateway.new(User.first).request(prompt: [{role: "user", content: "Hi, how are you?"}])
note
See this doc for registering unit primitives in Cloud Connector.

Optional: Enable logging in AI Gateway

Why: Logging makes it easier to debug any issues with GitLab Duo requests.

How:

Update the application settings file in AI Gateway: 

# <GDK-root>/gitlab-ai-gateway/.env

AIGW_LOGGING__LEVEL=debug
AIGW_LOGGING__FORMAT_JSON=false
AIGW_LOGGING__TO_FILE='./ai-gateway.log'

For example, you can watch the log file with the following command when in the gitlab-ai-gateway directory: 

tail -f ai-gateway.log | fblog -a prefix -a suffix -a current_file_name -a suggestion -a language -a input -a parameters -a score -a exception

Optional: Enable authentication and authorization in AI Gateway

Why: The AI Gateway has authentication and authorization flow to verify if clients have permission to access the features. Auth is enforced in any live environments hosted by GitLab infra team. You may want to test this flow in your local development environment.

note
In development environments (for example: GDK), this process is disabled by default.

To enable authorization checks, set AIGW_AUTH__BYPASS_EXTERNAL to false in the application setting file (<GDK-root>/gitlab-ai-gateway/.env) in AI Gateway.

Option 1: Use your GitLab instance as a provider

Why: this is the simplest method of testing authentication and reflects our setup on GitLab.com.

How: Assuming that you are running the AI Gateway with GDK, apply the following configuration to GDK: 

# <GDK-root>/env.runit

export GITLAB_SIMULATE_SAAS=1

Update the application settings file in AI Gateway: 

# <GDK-root>/gitlab-ai-gateway/.env

AIGW_AUTH__BYPASS_EXTERNAL=false
AIGW_GITLAB_URL=<your-gdk-url>

and gdk restart.

Option 2: Use your customersDot instance as a provider

Why: CustomersDot setup is required when you want to test or update functionality related to cloud licensing or if you are running GDK in non-SaaS mode.

note
This setup is challenging. There is an issue for discussing how to make it easier to test the customersDot integration locally. Until that is addressed, this setup process is time consuming and should be avoided if possible.

If you need to get customersDot working for your local GitLab Rails instance for any reason, reach out to #s_fulfillment_engineering in Slack. For questions around the integration of CDot with other systems to deliver AI use cases, reach out to #g_cloud_connector. assistance.

Help

Tips for local development

  1. When responses are taking too long to appear in the user interface, consider restarting Sidekiq by running gdk restart rails-background-jobs. If that doesn’t work, try gdk kill and then gdk start.
  2. Alternatively, bypass Sidekiq entirely and run the service synchronously. This can help with debugging errors as GraphQL errors are now available in the network inspector instead of the Sidekiq logs. To do that, temporarily alter the perform_for method in Llm::CompletionWorker class by changing perform_async to perform_inline.

Feature development (Abstraction Layer)

Feature flags

Apply the following feature flags to any AI feature work:

  • A general flag (ai_duo_chat_switch) that applies to all GitLab Duo Chat features. It’s enabled by default.
  • A general flag (ai_global_switch) that applies to all other AI features. It’s enabled by default.
  • A flag specific to that feature. The feature flag name must be different than the licensed feature name.

See the feature flag tracker epic for the list of all feature flags and how to use them.

GraphQL API

To connect to the AI provider API using the Abstraction Layer, use an extendable GraphQL API called aiAction. The input accepts key/value pairs, where the key is the action that needs to be performed. We only allow one AI action per mutation request.

Example of a mutation: 

mutation {
  aiAction(input: {summarizeComments: {resourceId: "gid://gitlab/Issue/52"}}) {
    clientMutationId
  }
}

As an example, assume we want to build an “explain code” action. To do this, we extend the input with a new key, explainCode. The mutation would look like this: 

mutation {
  aiAction(
    input: {
      explainCode: { resourceId: "gid://gitlab/MergeRequest/52", code: "foo() { console.log() }" }
    }
  ) {
    clientMutationId
  }
}

The GraphQL API then uses the Anthropic Client to send the response.

How to receive a response

The API requests to AI providers are handled in a background job. We therefore do not keep the request alive and the Frontend needs to match the request to the response from the subscription.

caution
Determining the right response to a request can cause problems when only userId and resourceId are used. For example, when two AI features use the same userId and resourceId both subscriptions will receive the response from each other. To prevent this interference, we introduced the clientSubscriptionId.

To match a response on the aiCompletionResponse subscription, you can provide a clientSubscriptionId to the aiAction mutation.

  • The clientSubscriptionId should be unique per feature and within a page to not interfere with other AI features. We recommend to use a UUID.
  • Only when the clientSubscriptionId is provided as part of the aiAction mutation, it will be used for broadcasting the aiCompletionResponse.
  • If the clientSubscriptionId is not provided, only userId and resourceId are used for the aiCompletionResponse.

As an example mutation for summarizing comments, we provide a randomId as part of the mutation: 

mutation {
  aiAction(
    input: {
      summarizeComments: { resourceId: "gid://gitlab/Issue/52" }
      clientSubscriptionId: "randomId"
    }
  ) {
    clientMutationId
  }
}

In our component, we then listen on the aiCompletionResponse using the userId, resourceId and clientSubscriptionId ("randomId"): 

subscription aiCompletionResponse(
  $userId: UserID
  $resourceId: AiModelID
  $clientSubscriptionId: String
) {
  aiCompletionResponse(
    userId: $userId
    resourceId: $resourceId
    clientSubscriptionId: $clientSubscriptionId
  ) {
    content
    errors
  }
}

The subscription for chat behaves differently.

To not have many concurrent subscriptions, you should also only subscribe to the subscription once the mutation is sent by using skip().

Current abstraction layer flow

The following graph uses VertexAI as an example. You can use different providers.

flowchart TD A[GitLab frontend] -->B[AiAction GraphQL mutation] B --> C[Llm::ExecuteMethodService] C --> D[One of services, for example: Llm::GenerateSummaryService] D -->|scheduled| E[AI worker:Llm::CompletionWorker] E -->F[::Gitlab::Llm::Completions::Factory] F -->G[`::Gitlab::Llm::VertexAi::Completions::...` class using `::Gitlab::Llm::Templates::...` class] G -->|calling| H[Gitlab::Llm::VertexAi::Client] H --> |response| I[::Gitlab::Llm::GraphqlSubscriptionResponseService] I --> J[GraphqlTriggers.ai_completion_response] J --> K[::GitlabSchema.subscriptions.trigger]

How to implement a new action

Implementing a new AI action will require changes in the GitLab monolith as well as in the AI Gateway. We’ll use the example of wanting to implement an action that allows users to rewrite issue descriptions according to a given prompt.

1. Add your action to the Cloud Connector feature list

The Cloud Connector configuration stores the permissions needed to access your service, as well as additional metadata. For more information, see Cloud Connector: Configuration. 

# ee/config/cloud_connector/access_data.yml

services:
  # ...
  rewrite_description:
    backend: 'gitlab-ai-gateway'
    bundled_with:
      duo_enterprise:
        unit_primitives:
          - rewrite_issue_description

2. Create an Agent definition in the AI Gateway

In the AI Gateway project, create a new agent definition under ai_gateway/agents/definitions. Create a new subfolder corresponding to the name of your AI action, and a new YAML file for your agent. Specify the model and provider you wish to use, and the prompts that will be fed to the model. You can specify inputs to be plugged into the prompt by using {}. 

# ai_gateway/agents/definitions/rewrite_description/base.yml

name: Description rewriter
model:
  name: claude-3-sonnet-20240229
  params:
    model_class_provider: anthropic
prompt_template:
  system: |
    You are a helpful assistant that rewrites the description of resources. You'll be given the current description, and a prompt on how you should rewrite it. Reply only with your rewritten description.

    <description>{description}</description>

    <prompt>{prompt}</prompt>

If your AI action is part of a broader feature, the definitions can be organized in a tree structure: 

# ai_gateway/agents/definitions/code_suggestions/generations/base.yml

name: Code generations
model:
  name: claude-3-sonnet-20240229
  params:
    model_class_provider: anthropic
...

To specify prompts for multiple models, use the name of the model as the filename for the definition: 

# ai_gateway/agents/definitions/code_suggestions/generations/mistral.yml

name: Code generations
model:
  name: mistral
  params:
    model_class_provider: litellm
...

3. Create a Completion class

  1. Create a new completion under ee/lib/gitlab/llm/ai_gateway/completions/ and inherit it from the Base AI Gateway Completion. 
# ee/lib/gitlab/llm/ai_gateway/completions/rewrite_description.rb

module Gitlab
  module Llm
    module AiGateway
      module Completions
        class RewriteDescription < Base
          def agent_name
            'base' # Must match the name of the agent you defined on the AI Gateway
          end

          def inputs
            { description: resource.description, prompt: prompt_message.content }
          end
        end
      end
    end
  end
end

4. Create a Service

  1. Create a new service under ee/app/services/llm/ and inherit it from the BaseService.
  2. The resource is the object we want to act on. It can be any object that includes the Ai::Model concern. For example it could be a Project, MergeRequest, or Issue. 
# ee/app/services/llm/rewrite_description_service.rb

module Llm
  class RewriteDescriptionService < BaseService
    extend ::Gitlab::Utils::Override

    override :valid
    def valid?
      super &&
        # You can restrict which type of resources your service applies to
        resource.to_ability_name == "issue" &&
        # Always check that the user is allowed to perform this action on the resource
        Ability.allowed?(user, :rewrite_description, resource)
    end

    private

    def perform
      schedule_completion_worker
    end
  end
end

5. Register the feature in the catalogue

Go to Gitlab::Llm::Utils::AiFeaturesCatalogue and add a new entry for your AI action. 

class AiFeaturesCatalogue
  LIST = {
    # ...
    rewrite_description: {
      service_class: ::Gitlab::Llm::AiGateway::Completions::RewriteDescription,
      feature_category: :ai_abstraction_layer,
      execute_method: ::Llm::RewriteDescriptionService,
      maturity: :experimental,
      self_managed: false,
      internal: false
    }
  }.freeze

How to migrate an existing action to the AI Gateway

AI actions were initially implemented inside the GitLab monolith. As part of our AI Gateway as the Sole Access Point for Monolith to Access Models Epic we’re migrating prompts, model selection and model parameters into the AI Gateway. This will increase the speed at which we can deliver improvements to self-managed users, by decoupling prompt and model changes from monolith releases. To migrate an existing action:

  1. Follow steps 1 through 3 on How to implement a new action.
  2. Modify the entry for your AI action in the catalogue to list the new completion class as the aigw_service_class. 
class AiFeaturesCatalogue
  LIST = {
    # ...
    generate_description: {
      service_class: ::Gitlab::Llm::Anthropic::Completions::GenerateDescription,
      aigw_service_class: ::Gitlab::Llm::AiGateway::Completions::GenerateDescription,
      prompt_class: ::Gitlab::Llm::Templates::GenerateDescription,
      feature_category: :ai_abstraction_layer,
      execute_method: ::Llm::GenerateDescriptionService,
      maturity: :experimental,
      self_managed: false,
      internal: false
    },
    # ...
  }.freeze

When the feature flag ai_gateway_agents is enabled, the aigw_service_class will be used to process the AI action. Once you’ve validated the correct functioning of your action, you can remove the aigw_service_class key and replace the service_class with the new AiGateway::Completions class to make it the permanent provider.

For a complete example of the changes needed to migrate an AI action, see the following MRs:

Authorization in GitLab-Rails

We recommend to use policies to deal with authorization for a feature. Currently we need to make sure to cover the following checks:

  1. For GitLab Duo Chat feature, ai_duo_chat_switch is enabled.
  2. For other general AI features, ai_global_switch is enabled.
  3. Feature specific feature flag is enabled.
  4. The namespace has the required license for the feature.
  5. User is a member of the group/project.
  6. experiment_features_enabled settings are set on the Namespace.

For our example, we need to implement the allowed?(:rewrite_description) call. As an example, you can look at the Issue Policy for the summarize comments feature. In our example case, we want to implement the feature for Issues as well: 

# ee/app/policies/ee/issue_policy.rb

module EE
  module IssuePolicy
    extend ActiveSupport::Concern
    prepended do
      with_scope :global
      condition(:ai_available) do
        ::Feature.enabled?(:ai_global_switch, type: :ops)
      end

      with_scope :subject
      condition(:rewrite_description_enabled) do
        ::Feature.enabled?(:rewrite_description, subject_container) &&
          subject_container.licensed_feature_available?(:rewrite_description)
      end

      rule do
        ai_available & rewrite_description_enabled & is_project_member
      end.enable :rewrite_description
    end
  end
end
note
See this section about authentication and authorization in AI Gateway.

Pairing requests with responses

Because multiple users’ requests can be processed in parallel, when receiving responses, it can be difficult to pair a response with its original request. The requestId field can be used for this purpose, because both the request and response are assured to have the same requestId UUID.

Caching

AI requests and responses can be cached. Cached conversation is being used to display user interaction with AI features. In the current implementation, this cache is not used to skip consecutive calls to the AI service when a user repeats their requests. 

query {
  aiMessages {
    nodes {
      id
      requestId
      content
      role
      errors
      timestamp
    }
  }
}

This cache is especially useful for chat functionality. For other services, caching is disabled. You can enable this for a service by using the cache_response: true option.

Caching has following limitations:

  • Messages are stored in Redis stream.
  • There is a single stream of messages per user. This means that all services currently share the same cache. If needed, this could be extended to multiple streams per user (after checking with the infrastructure team that Redis can handle the estimated amount of messages).
  • Only the last 50 messages (requests + responses) are kept.
  • Expiration time of the stream is 3 days since adding last message.
  • User can access only their own messages. There is no authorization on the caching level, and any authorization (if accessed by not current user) is expected on the service layer.

Check if feature is allowed for this resource based on namespace settings

There is one setting allowed on root namespace level that restrict the use of AI features:

  • experiment_features_enabled

To check if that feature is allowed for a given namespace, call: 

Gitlab::Llm::StageCheck.available?(namespace, :name_of_the_feature)

Add the name of the feature to the Gitlab::Llm::StageCheck class. There are arrays there that differentiate between experimental and beta features.

This way we are ready for the following different cases:

  • If the feature is not in any array, the check will return true. For example, the feature was moved to GA.

To move the feature from the experimental phase to the beta phase, move the name of the feature from the EXPERIMENTAL_FEATURES array to the BETA_FEATURES array.

Implement calls to AI APIs and the prompts

The CompletionWorker will call the Completions::Factory which will initialize the Service and execute the actual call to the API. In our example, we will use VertexAI and implement two new classes: 

# /ee/lib/gitlab/llm/vertex_ai/completions/rewrite_description.rb

module Gitlab
  module Llm
    module VertexAi
      module Completions
        class AmazingNewAiFeature < Gitlab::Llm::Completions::Base
          def execute
            prompt = ai_prompt_class.new(options[:user_input]).to_prompt

            response = Gitlab::Llm::VertexAi::Client.new(user, unit_primitive: 'amazing_feature').text(content: prompt)

            response_modifier = ::Gitlab::Llm::VertexAi::ResponseModifiers::Predictions.new(response)

            ::Gitlab::Llm::GraphqlSubscriptionResponseService.new(
              user, nil, response_modifier, options: response_options
            ).execute
          end
        end
      end
    end
  end
end

# /ee/lib/gitlab/llm/vertex_ai/templates/rewrite_description.rb

module Gitlab
  module Llm
    module VertexAi
      module Templates
        class AmazingNewAiFeature
          def initialize(user_input)
            @user_input = user_input
          end

          def to_prompt
            <<~PROMPT
            You are an assistant that writes code for the following context:

            context: #{user_input}
            PROMPT
          end
        end
      end
    end
  end
end

Because we support multiple AI providers, you may also use those providers for the same example: 

Gitlab::Llm::VertexAi::Client.new(user, unit_primitive: 'your_feature')
Gitlab::Llm::Anthropic::Client.new(user, unit_primitive: 'your_feature')

Add AI Action to GraphQL

TODO

Monitoring

Security

Refer to the secure coding guidelines for Artificial Intelligence (AI) features.