Charis
794e45378c
## I have read the [CONTRIBUTING.md](https://github.com/supabase/supabase/blob/master/CONTRIBUTING.md) file. YES ## What kind of change does this PR introduce? Feature (test coverage) — second PR of the notebooks-evals plan, covering FE-4088 (Evals: Assistant can read notebook). Follows #49010 (FE-4086, list_notebooks). ## What is the current behavior? `dataset.ts` has no coverage for `get_notebook`. Separately, `NOTEBOOKS_PROMPT` only covers *choosing* between `create_notebook`, `update_notebook`, and `execute_sql` — it says nothing about reading or describing an existing notebook. ## What is the new behavior? **Eval cases** (`evals/dataset.ts`), three of them: - Resolve a notebook by name via `list_notebooks`, then `get_notebook`, and report the queries it actually contains. - Summarize a smaller, single-log-cell notebook as a baseline. - Report a nonexistent notebook id as not found instead of hallucinating contents. The mock's `execute` throws, which the AI SDK surfaces to the model as a `tool-error` part rather than failing the eval task. No new scorers or tool changes — `toolUsageScorer` and `correctnessScorer` already cover these, and `get_notebook` has unit coverage in `notebook-tools.test.ts`. **Prompt change** (`lib/ai/prompts.ts`) — please review this one separately, it's the only production behavior change here: Running the first case surfaced a real gap. The assistant transcribed each cell's SQL correctly but was inconsistent about the configuration that changes what a cell returns — dropping the log cell's time range in some runs, miscounting markdown cells as queries in others. Adds one bullet to `NOTEBOOKS_PROMPT` telling it to report a query cell's configuration and not count markdown cells as queries. Gated behind the Explorer flag, same as the rest of that prompt. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Added notebook evaluation scenarios for database and log queries, including concise summaries and nonexistent notebook handling. * Improved notebook descriptions by reporting query configurations that affect returned results. * Markdown cells are now excluded from query counts. <!-- end of auto-generated comment: release notes by coderabbit.ai -->
Studio Assistant Evals
We use Braintrust to evaluate Assistant behaviors against a tracked dataset (offline evals) and against live traces (online evals).
Offline Evals
Add offline eval test cases to dataset.ts. If needed, add new scorers (see below) for the specific dimension you wish to test. Expect to update and run offline evals when adding new Assistant behaviors
You may wish to run offline evals when:
- You updated the eval suite with a new test case or scorer
- You changed Assistant's behavior and want to check for improvements/regressions
Running Offline Evals in CI
Add the run-evals label on a PR to the repo and Braintrust's GitHub Action will run evals and post a summary comment (example).
You can find detailed results in the "Experiments" tab of the "Assistant" project on Braintrust.
Running Offline Evals in Local Dev
Within apps/studio
# To set up WASM files
pnpm evals:setup
# Run all evals and upload results to Braintrust
pnpm evals:upload
# Run all evals without uploading results
pnpm evals:run
# Run an upload single test case
pnpm braintrust eval evals/assistant.eval.ts --filter "input.prompt=How many projects"
Upload results when you want to inspect Experiments or Logs in the Braintrust dashboard or API. You can use developer tools like Braintrust MCP or bt CLI to analyze results with an agent.
Scorers
Scorers look at a thread or task output and assign a score deterministically or via LLM-as-a-judge. Optionally they can consider expected values.
Define scorers in scorer.ts and include them in assistant.eval.ts to run them in offline evals.
Updating Online Scorers
Online scorers run as serverless functions on Braintrust infrastructure. They're deployed from the scorer-online.ts script. Since these scoring against production traces, they can't rely on ground truth expected values. Structure scoring logic and LLM prompts accordingly. Not every scorer needs to be an online scorer.
To opt-in to online scoring, add the scorer to scorer-online-manifest.json and add a corresponding handler in scorer-online.ts
Testing & Deploying Online Scorers
Add the preview-scorers label to a PR to deploy branch-prefixed scorers to the "Assistant (Staging Scorers)" Braintrust project (example). From that project dashboard, you can manually test the scorer against a trace from any project.
After merge to master, preview scorers automatically clean up and deploy to the production in the "Assistant" Braintrust project. Update the "Online Scoring" automation in the Logs page to include the new scorer function.