GitLab Duo Chat

Set up GitLab Duo Chat

NOTE: Use this snippet for help automating the following section.

1. Enable Anthropic API features.
2. Ensure the embedding database is configured.
3. Ensure that your current branch is up-to-date with master.
4. Enable the feature in Rails console: Feature.enable(:tanuki_bot_breadcrumbs_entry_point)

Working with GitLab Duo Chat

Prompts are the most vital part of GitLab Duo Chat system. Prompts are the instructions sent to the Large Language Model to perform certain tasks.

The state of the prompts is the result of weeks of iteration. If you want to change any prompt in the current tool, you must put it behind a feature flag.

If you have any new or updated prompts, ask members of AI Framework team to review, because they have significant experience with them.

Contributing to GitLab Duo Chat

The Chat feature uses a zero-shot agent that includes a system prompt explaining how the large language model should interpret the question and provide an answer. The system prompt defines available tools that can be used to gather information to answer the user's question.

The zero-shot agent receives the user's question and decides which tools to use to gather information to answer it. It then makes a request to the large language model, which decides if it can answer directly or if it needs to use one of the defined tools.

The tools each have their own prompt that provides instructions to the large language model on how to use that tool to gather information. The tools are designed to be self-sufficient and avoid multiple requests back and forth to the large language model.

After the tools have gathered the required information, it is returned to the zero-shot agent, which asks the large language model if enough information has been gathered to provide the final answer to the user's question.

Adding a new tool

To add a new tool:

1. Create files for the tool in the ee/lib/gitlab/llm/chain/tools/ folder. Use existing tools like issue_identifier or resource_reader as a template.

2. Write a class for the tool that includes:

   • Name and description of what the tool does
   • Example questions that would use this tool
   • Instructions for the large language model on how to use the tool to gather information - so the main prompts that this tool is using.

3. Test and iterate on the prompt using RSpec tests that make real requests to the large language model.

   • Prompts require trial and error, the non-deterministic nature of working with LLM can be surprising.
   • Anthropic provides good guide on working on prompts.
   • GitLab guide on working with prompts.

4. Implement code in the tool to parse the response from the large language model and return it to the zero-shot agent.

5. Add the new tool name to the tools array in ee/lib/gitlab/llm/completions/chat.rb so the zero-shot agent knows about it.

6. Add tests by adding questions to the test-suite for which the new tool should respond to. Iterate on the prompts as needed.

The key things to keep in mind are properly instructing the large language model through prompts and tool descriptions, keeping tools self-sufficient, and returning responses to the zero-shot agent. With some trial and error on prompts, adding new tools can expand the capabilities of the Chat feature.

There are available short videos covering this topic.

Debugging

To gather more insights about the full request, use the Gitlab::Llm::Logger file to debug logs. The default logging level on production is INFO and must not be used to log any data that could contain personal identifying information.

To follow the debugging messages related to the AI requests on the abstraction layer, you can use:

export LLM_DEBUG=1
gdk start
tail -f log/llm.log

Testing GitLab Duo Chat against real LLMs locally

Because success of answers to user questions in GitLab Duo Chat heavily depends on toolchain and prompts of each tool, it's common that even a minor change in a prompt or a tool impacts processing of some questions.

To make sure that a change in the toolchain doesn't break existing functionality, you can use the following RSpec tests to validate answers to some predefined questions when using real LLMs:

export VERTEX_AI_EMBEDDINGS='true' # if using Vertex embeddings
export ANTHROPIC_API_KEY='<key>' # can use dev value of Gitlab::CurrentSettings
export VERTEX_AI_CREDENTIALS='<vertex-ai-credentials>' # can set as dev value of Gitlab::CurrentSettings.vertex_ai_credentials
export VERTEX_AI_PROJECT='<vertex-project-name>' # can use dev value of Gitlab::CurrentSettings.vertex_ai_project

REAL_AI_REQUEST=1 bundle exec rspec ee/spec/lib/gitlab/llm/completions/chat_real_requests_spec.rb

When you need to update the test questions that require documentation embeddings, make sure a new fixture is generated and committed together with the change.

Running the rspecs tagged with real_ai_request

The following CI jobs for GitLab project run the rspecs tagged with real_ai_request:

• rspec-ee unit gitlab-duo-chat-zeroshot: the job runs ee/spec/lib/gitlab/llm/completions/chat_real_requests_spec.rb. The job is optionally triggered and allowed to fail.

• rspec-ee unit gitlab-duo-chat-qa: The job runs the QA evaluation tests in ee/spec/lib/gitlab/llm/chain/agents/zero_shot/qa_evaluation_spec.rb. The job is optionally triggered and allowed to fail. Read about GitLab Duo Chat QA Evaluation Test.

• rspec-ee unit gitlab-duo-chat-qa-fast: The job runs a single QA evaluation test from ee/spec/lib/gitlab/llm/chain/agents/zero_shot/qa_evaluation_spec.rb. The job is always run and not allowed to fail. Although there's a chance that the QA test still might fail, it is cheap and fast to run and intended to prevent a regression in the QA test helpers.

• rspec-ee unit gitlab-duo pg14: This job runs tests to ensure that the GitLab Duo features are functional without running into system errors. The job is always run and not allowed to fail. This job does NOT conduct evaluations. The quality of the feature is tested in the other jobs such as QA jobs.

Management of credentials and API keys for CI jobs

All API keys required to run the rspecs should be masked

The exception is GCP credentials as they contain characters that prevent them from being masked. Because the CI jobs need to run on MR branches, GCP credentials cannot be added as a protected variable and must be added as a regular CI variable. For security, the GCP credentials and the associated project added to GitLab project's CI must not be able to access any production infrastructure and sandboxed.

GitLab Duo Chat QA Evaluation Test

Evaluation of a natural language generation (NLG) system such as GitLab Duo Chat is a rapidly evolving area with many unanswered questions and ambiguities.

A practical working assumption is LLMs can generate a reasonable answer when given a clear question and a context. With the assumption, we are exploring using LLMs as evaluators to determine the correctness of a sample of questions to track the overall accuracy of GitLab Duo Chat's responses and detect regressions in the feature.

For the discussions related to the topic, see the merge request and the issue.

The current QA evaluation test consists of the following components.

Epic and issue fixtures

The fixtures are the replicas of the public issues and epics from projects and groups owned by GitLab. The internal notes were excluded when they were sampled. The fixtures have been commited into the canonical gitlab repository. See the snippet used to create the fixtures.

RSpec and helpers

1. The RSpec file and the included helpers invoke the Chat service, an internal interface with the question.

2. After collecting the Chat service's answer, the answer is injected into a prompt, also known as an "evaluation prompt", that instructs a LLM to grade the correctness of the answer based on the question and a context. The context is simply a JSON serialization of the issue or epic being asked about in each question.

3. The evaluation prompt is sent to two LLMs, Claude and Vertex.

4. The evaluation responses of the LLMs are saved as JSON files.

5. For each question, RSpec will regex-match for CORRECT or INCORRECT.

Collection and tracking of QA evaluations via CI/CD automation

The gitlab project's CI configurations have been setup to run the RSpec, collect the evaluation response as artifacts and execute a reporter script that automates collection and tracking of evaluations.

When rspec-ee unit gitlab-duo-chat-qa job runs in a pipeline for a merge request, the reporter script uses the evaluations saved as CI artifacts to generate a Markdown report and posts it as a note in the merge request.

When rspec-ee unit gitlab-duo-chat-qa is run in a pipeline for a commit on master branch, the reporter script instead posts the generated report as an issue, saves the evaluations artfacts as a snippet, and updates the tracking issue in gitlab-org/ai-powered/ai-framework/qa-evaluation#1 in the project gitlab-org/ai-powered/ai-framework/qa-evaluation.

GraphQL Subscription

The GraphQL Subscription for Chat behaves slightly different because it's user-centric. A user could have Chat open on multiple browser tabs, or also on their IDE. We therefore need to broadcast messages to multiple clients to keep them in sync. The aiAction mutation with the chat action behaves the following:

1. All complete Chat messages (including messages from the user) are broadcasted with the userId, aiAction: "chat" as identifier.
2. Chunks from streamed Chat messages and currently used tools are broadcasted with the userId, resourceId, and the clientSubscriptionId from the mutation as identifier.

Note that we still broadcast chat messages and currently used tools using the userId and resourceId as identifier. However, this is deprecated and should no longer be used. We want to remove resourceId on the subscription as part of this issue.

Testing GitLab Duo Chat in production-like environments

GitLab Duo Chat is enabled in the Staging and Staging Ref GitLab environments.

Because GitLab Duo Chat is currently only available to members of groups in the Premium and Ultimate tiers, Staging Ref may be an easier place to test changes as a GitLab team member because you can make yourself an instance Admin in Staging Ref and, as an Admin, easily create licensed groups for testing.

Product Analysis

To better understand how the feature is used, each production user input message is analyzed using LLM and Ruby, and the analysis is tracked as a Snowplow event.

The analysis can contain any of the attributes defined in the latest iglu schema.

• All possible "category" and "detailed_category" are listed here.
• The following are yet to be implemented:
  • "is_proper_sentence"
  • "asked_on_the_same_page"
• The following are deprecated:
  • "number_of_questions_in_history"
  • "length_of_questions_in_history"
  • "time_since_first_question"

Dashboards can be created to visualize the collected data.

How access_duo_chat policy works

In the table below I present what requirements must be fulfilled so the access_duo_chat policy would return true in different contexts.