Files
supabase/apps/studio/lib/ai/model.utils.ts
Matt Rossman d143571586 feat(assistant): trace-level scorers + server-side tool execution with needsApproval (#45654)
## 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 -->
2026-05-12 15:24:21 -04:00

168 lines
5.3 KiB
TypeScript

export type ProviderName = 'bedrock' | 'openai'
export type BedrockModel = 'anthropic.claude-3-7-sonnet-20250219-v1:0' | 'openai.gpt-oss-120b-1:0'
export type OpenAIModelId = 'gpt-5.4-nano' | 'gpt-5.3-codex'
// Source: https://developers.openai.com/api/docs/guides/reasoning + per-model pages
export type ReasoningEffort = 'none' | 'minimal' | 'low' | 'medium' | 'high' | 'xhigh'
// Per-model reasoning effort compatibility.
// Sources: https://developers.openai.com/api/docs/models/gpt-5.4-nano
// https://developers.openai.com/api/docs/models/gpt-5.3-codex
type ModelReasoningSupport = {
'gpt-5.4-nano': 'none' | 'low' | 'medium' | 'high' | 'xhigh'
'gpt-5.3-codex': 'low' | 'medium' | 'high' | 'xhigh'
}
type ReasoningEffortFor<ModelId extends OpenAIModelId> = ModelId extends keyof ModelReasoningSupport
? ModelReasoningSupport[ModelId]
: never
/** Type-safe factory for configuring OpenAI models with compatible reasoning efforts. */
export function openaiModelEntry<
ModelId extends OpenAIModelId,
RequiresAdvance extends boolean = false,
>(config: {
id: ModelId
/** When true, the model requires the `assistant.advance_model` entitlement (paid plans). Defaults to false. */
requiresAdvanceModelEntitlement?: RequiresAdvance
/**
* When omitted, OpenAI applies its own default reasoning effort for the model,
* which may not be zero. Use an explicit level to control cost and latency.
*/
reasoningEffort?: ReasoningEffortFor<ModelId>
}): {
id: ModelId
requiresAdvanceModelEntitlement: RequiresAdvance
reasoningEffort?: ReasoningEffortFor<ModelId>
} {
return {
requiresAdvanceModelEntitlement: false as RequiresAdvance,
...config,
}
}
export type OpenAIModelEntry = ReturnType<typeof openaiModelEntry>
/** Default model entry for simple completion endpoints where latency is more important than reasoning. */
export const DEFAULT_COMPLETION_MODEL = openaiModelEntry({
id: 'gpt-5.4-nano',
reasoningEffort: 'none',
})
// Single source of truth for all Assistant chat model variants and their reasoning levels.
// Models with requiresAdvanceModelEntitlement false are available to all users; true requires the assistant.advance_model entitlement.
export const ASSISTANT_MODELS = [
openaiModelEntry({
id: 'gpt-5.4-nano',
requiresAdvanceModelEntitlement: false,
reasoningEffort: 'low',
}),
openaiModelEntry({
id: 'gpt-5.3-codex',
requiresAdvanceModelEntitlement: true,
reasoningEffort: 'low',
}),
] as const
export type AssistantBaseModelId = Extract<
(typeof ASSISTANT_MODELS)[number],
{ requiresAdvanceModelEntitlement: false }
>['id']
export type AssistantModelId = (typeof ASSISTANT_MODELS)[number]['id']
const ASSISTANT_MODELS_MAP = Object.fromEntries(ASSISTANT_MODELS.map((m) => [m.id, m])) as Record<
AssistantModelId,
(typeof ASSISTANT_MODELS)[number]
>
export const DEFAULT_ASSISTANT_BASE_MODEL_ID = 'gpt-5.4-nano' satisfies AssistantBaseModelId
export const DEFAULT_ASSISTANT_ADVANCE_MODEL_ID = 'gpt-5.3-codex' satisfies AssistantModelId
export function defaultAssistantModelId(hasAccessToAdvanceModel: boolean): AssistantModelId {
return hasAccessToAdvanceModel
? DEFAULT_ASSISTANT_ADVANCE_MODEL_ID
: DEFAULT_ASSISTANT_BASE_MODEL_ID
}
export function isKnownAssistantModelId(id: string): id is AssistantModelId {
return Object.hasOwn(ASSISTANT_MODELS_MAP, id)
}
export function isAssistantBaseModelId(id: string): id is AssistantBaseModelId {
return (
id in ASSISTANT_MODELS_MAP &&
!ASSISTANT_MODELS_MAP[id as AssistantModelId].requiresAdvanceModelEntitlement
)
}
export function isAdvanceOnlyModelId(id: string): boolean {
return (
id in ASSISTANT_MODELS_MAP &&
ASSISTANT_MODELS_MAP[id as AssistantModelId].requiresAdvanceModelEntitlement
)
}
export function getAssistantModelEntry(id: AssistantModelId): (typeof ASSISTANT_MODELS)[number] {
return ASSISTANT_MODELS_MAP[id]
}
export type Model = BedrockModel | OpenAIModelId
export type ProviderModelConfig = {
/** Optional providerOptions to attach to the system message for this model */
systemProviderOptions?: Record<string, any>
/** The default model for this provider (used when limited or no preferred specified) */
default: boolean
}
export type ProviderRegistry = {
bedrock: {
models: Record<BedrockModel, ProviderModelConfig>
providerOptions?: Record<string, any>
}
openai: {
models: Record<OpenAIModelId, ProviderModelConfig>
providerOptions?: Record<string, any>
}
}
export const PROVIDERS: ProviderRegistry = {
bedrock: {
models: {
'anthropic.claude-3-7-sonnet-20250219-v1:0': {
systemProviderOptions: {
bedrock: {
// Always cache the system prompt (must not contain dynamic content)
cachePoint: { type: 'default' },
},
},
default: false,
},
'openai.gpt-oss-120b-1:0': {
default: true,
},
},
},
openai: {
models: {
'gpt-5.3-codex': { default: false },
'gpt-5.4-nano': { default: true },
},
providerOptions: {
openai: {
store: false,
},
},
},
}
export function getDefaultModelForProvider(provider: ProviderName): Model | undefined {
const models = PROVIDERS[provider]?.models as Record<Model, ProviderModelConfig>
if (!models) return undefined
return Object.keys(models).find((id) => models[id as Model]?.default) as Model | undefined
}