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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. To access the GitLab Duo Chat interface, in the lower-left corner of any page, select Help and Ask GitLab Duo Chat.

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

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/chain/agents/zero_shot/executor_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 rspecs tagged with the metadata real_ai_request can be run in GitLab project's CI by triggering rspec-ee unit gitlab-duo-chat. The former runs with Vertex APIs enabled. The CI jobs are optional and allowed to fail to account for the non-deterministic nature of LLM responses.

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 rspec-ee unit gitlab-duo-chat needs 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.

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.