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## Motivation When Assistant runs a potentially destructive tool like `execute_sql`, it stops the LLM request and prompts for client-side approval and execution of the tool. After approval, a second request kicks off under a separate trace. This has made scoring and [Topics](https://www.braintrust.dev/blog/topics) classification challenging, as the generated `output` is split across stateless requests. The [span-level scoring](https://www.braintrust.dev/docs/evaluate/custom-code#score-spans) approach we've used thusfar (after the LLM call, we massage the result into an `output` payload that's stuck onto the root span) has been cumbersome and led to invalid scores / topics where only part of the assistant response is considered. It's also inefficient, as we're duplicating potentially large info (like the `search_docs` output) that already exists within the trace. An alternative to scoring spans is to [score traces](https://www.braintrust.dev/docs/evaluate/custom-code#score-traces). Braintrust [best practices](https://www.braintrust.dev/docs/evaluate/score-online#best-practices) advise: > Use span scope for evaluating individual operations or outputs. Use trace scope for evaluating multi-turn conversations, overall workflow completion, or when your scorer needs access to the full execution context. We've also received [direct guidance](https://supabase.slack.com/archives/C05QYJBLX89/p1777925770927149?thread_ts=1777905716.911979&cid=C05QYJBLX89) from their team to use this approach. ## Changes Migrates eval scorers from custom `AssistantEvalOutput` shape to trace-level scoring via `trace.getThread()` / `trace.getSpans()`, with thread parsing that scores the full latest Assistant turn and passes prior conversation separately where relevant. Moves `execute_sql` and `deploy_edge_function` from client-side execution after approval to AI SDK `needsApproval` + server-side `execute()`. SQL results returned to the model are gated by AI opt-in level, so row data is only included with `schema_and_log_and_data`; otherwise the tool returns the no-data-permissions sentinel. Adds `metadata.isFinalStep` to disambiguate multiple LLM requests within an "assistant" turn due to tool call requests/responses. For online evals, this means we should configure automations to only score traces with `metadata.isFinalStep = true` to ensure we're judging the complete generated response. Other minor kaizen changes: - Renamed `promptProviderOptions` to `systemProviderOptions` to clarify that this is associated with the "system" message and disambiguate from the root `providerOptions` - Adds `evals/trace-utils.ts` to handle Zod validation of the `unknown` span shapes from Braintrust, to more easily access typed inputs/output on tool spans. - Bumps AI SDK floor version `^6.0.116` → `^6.0.174` - Tweaked the "Conciseness" scorer to not unfairly dock points for the new `[called tool_name]` labels in serialized assistant response ## Verification In the studio staging build, I asked Assistant to create a todos table with 3 sample todos. I manually approved the `execute_sql` call and saw Assistant generate text before & after the call. In Braintrust I verified two traces were produced (see [filtered logs](https://www.braintrust.dev/app/supabase.io/p/Assistant/logs?v=Staging&tvt=trace&search={%22filter%22:[{%22text%22:%22metadata.environment%2520%253D%2520%27staging%27%22,%22label%22:%22metadata.environment%2520%253D%2520%27staging%27%22,%22originType%22:%22btql%22},{%22text%22:%22%2560Chat%2520ID%2560%2520%253D%2520%25221cb2ac45-e5e7-458c-9da4-3bf6863b8842%2522%22,%22label%22:%22Chat%2520ID%2520equals%25201cb2ac45-e5e7-458c-9da4-3bf6863b8842%22,%22originType%22:%22form%22}]})), the first with `metadata.isFinalStep = false` and the second with `metadata.isFinalStep = true`. In the Braintrust staging scorers, I ran the preview Completeness scorer on the second trace and verified it sees the complete Assistant response including markers for tool calls ([link to trace](https://www.braintrust.dev/app/supabase.io/p/Assistant%20(Staging%20Scorers)/trace?object_type=project_logs&object_id=b5214b62-ad1e-4929-9d5b-40b1daebe948&r=0ed0a4f8-8aff-4a34-bb1d-1df1d88a5070&s=ff9015f8-6bf7-4ab3-83a9-ca4e69e27e82)) <img width="1193" height="960" alt="CleanShot 2026-05-07 at 11 27 10@2x" src="https://github.com/user-attachments/assets/509d4858-c3a1-4068-986d-3aa4d5617d1a" /> I also tested the `deploy_edge_function` workflow and verified it still prompts for permission and warns on deployment of existing functions. **References** - https://www.braintrust.dev/docs/evaluate/custom-code#score-traces - https://ai-sdk.dev/docs/ai-sdk-core/tools-and-tool-calling#tool-execution-approval Supercedes https://github.com/supabase/supabase/pull/45556 and https://github.com/supabase/supabase/pull/45339 Closes AI-473 <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Tool actions (SQL execution, edge-function deploy) now require explicit user Approve/Deny before proceeding. * **Improvements** * Assistant pauses for approval responses before sending follow-ups, giving clearer control over risky actions. * Deploy/replace flows show confirmation and clearer replace warnings. * Evaluation/scoring updated to use richer trace data for more accurate assistant performance signals. <!-- end of auto-generated comment: release notes by coderabbit.ai -->
101 lines
3.2 KiB
TypeScript
101 lines
3.2 KiB
TypeScript
import { openai } from '@ai-sdk/openai'
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import { afterEach, beforeEach, describe, expect, it, vi } from 'vitest'
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import * as bedrockModule from './bedrock'
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import { getModel } from './model'
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import { DEFAULT_COMPLETION_MODEL, openaiModelEntry } from './model.utils'
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vi.mock('@ai-sdk/openai', () => ({
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openai: vi.fn(() => 'openai-model'),
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}))
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vi.mock('./bedrock', async () => ({
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...(await vi.importActual('./bedrock')),
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createRoutedBedrock: vi.fn(() => async (_modelId: string) => 'bedrock-model'),
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checkAwsCredentials: vi.fn(),
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}))
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describe('getModel', () => {
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const originalEnv = { ...process.env }
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beforeEach(() => {
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vi.resetAllMocks()
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})
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afterEach(() => {
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process.env = { ...originalEnv }
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})
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it('returns bedrock model without systemProviderOptions', async () => {
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vi.mocked(bedrockModule.checkAwsCredentials).mockResolvedValue(true)
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vi.stubEnv('AWS_BEDROCK_ROLE_ARN', 'test')
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const { modelParams, error, systemProviderOptions } = await getModel({
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provider: 'bedrock',
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routingKey: 'test',
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})
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expect(modelParams?.model).toEqual('bedrock-model')
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expect(systemProviderOptions).toBeUndefined()
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expect(error).toBeUndefined()
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})
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it('returns error when bedrock credentials are not available', async () => {
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vi.mocked(bedrockModule.checkAwsCredentials).mockResolvedValue(false)
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const { error } = await getModel({ provider: 'bedrock', routingKey: 'test' })
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expect(error).toBeDefined()
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})
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it('returns openai model with default model', async () => {
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vi.stubEnv('OPENAI_API_KEY', 'test-key')
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const { modelParams, systemProviderOptions } = await getModel({
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provider: 'openai',
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modelEntry: openaiModelEntry({ id: 'gpt-5.4-nano' }),
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})
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expect(modelParams?.model).toEqual('openai-model')
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expect(openai).toHaveBeenCalledWith('gpt-5.4-nano')
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expect(systemProviderOptions).toBeUndefined()
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})
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it('returns error when OPENAI_API_KEY is not available', async () => {
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vi.stubEnv('OPENAI_API_KEY', '')
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const { error } = await getModel({
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provider: 'openai',
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modelEntry: openaiModelEntry({ id: 'gpt-5.4-nano' }),
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})
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expect(error).toEqual(new Error('OPENAI_API_KEY not available'))
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})
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it('returns openai gpt-5.3-codex when hasAccessToAdvanceModel and not throttled', async () => {
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vi.stubEnv('OPENAI_API_KEY', 'test-key')
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vi.stubEnv('IS_THROTTLED', 'false')
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const { modelParams, error } = await getModel({
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provider: 'openai',
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modelEntry: openaiModelEntry({ id: 'gpt-5.3-codex', reasoningEffort: 'low' }),
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})
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expect(error).toBeUndefined()
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expect(modelParams?.model).toEqual('openai-model')
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expect(openai).toHaveBeenCalledWith('gpt-5.3-codex')
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expect(modelParams?.providerOptions?.openai?.reasoningEffort).toBe('low')
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})
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it('applies reasoningEffort from DEFAULT_COMPLETION_MODEL', async () => {
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vi.stubEnv('OPENAI_API_KEY', 'test-key')
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const { modelParams, error } = await getModel({
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provider: 'openai',
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modelEntry: DEFAULT_COMPLETION_MODEL,
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})
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expect(error).toBeUndefined()
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expect(openai).toHaveBeenCalledWith('gpt-5.4-nano')
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expect(modelParams?.providerOptions?.openai?.reasoningEffort).toBe('none')
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})
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})
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