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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 -->
119 lines
4.1 KiB
TypeScript
119 lines
4.1 KiB
TypeScript
import { tool } from 'ai'
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import { z } from 'zod'
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import { deployEdgeFunction } from '@/data/edge-functions/edge-functions-deploy-mutation'
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import { executeSql } from '@/data/sql/execute-sql-query'
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import type { AiOptInLevel } from '@/hooks/misc/useOrgOptedIntoAi'
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import {
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EDGE_FUNCTION_PROMPT,
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PG_BEST_PRACTICES,
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REALTIME_PROMPT,
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RLS_PROMPT,
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} from '@/lib/ai/prompts'
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import { NO_DATA_PERMISSIONS } from '@/lib/ai/tools/tool-sanitizer'
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import { fixSqlBackslashEscapes } from '@/lib/ai/util'
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const KNOWLEDGE = {
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pg_best_practices: PG_BEST_PRACTICES,
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rls: RLS_PROMPT,
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edge_functions: EDGE_FUNCTION_PROMPT,
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realtime: REALTIME_PROMPT,
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} as const
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type KnowledgeName = keyof typeof KNOWLEDGE
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export const executeSqlInputSchema = z.object({
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// Transform at parse time so the corrected SQL is what gets stored in
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// toolCall.input — ensuring evals and logs reflect what actually runs.
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sql: z.string().describe('The SQL statement to execute.').transform(fixSqlBackslashEscapes),
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label: z.string().describe('A short 2-4 word label for the SQL statement.'),
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chartConfig: z
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.object({
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view: z.enum(['table', 'chart']).describe('How to render the results after execution'),
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xAxis: z.string().optional().describe('The column to use for the x-axis of the chart.'),
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yAxis: z.string().optional().describe('The column to use for the y-axis of the chart.'),
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})
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.describe('Chart configuration for rendering the results'),
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isWriteQuery: z
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.boolean()
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.default(false)
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.describe(
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'Whether the SQL statement performs a write operation of any kind instead of a read operation'
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),
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})
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export const loadKnowledgeInputSchema = z.object({
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name: z
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.enum(Object.keys(KNOWLEDGE) as [KnowledgeName, ...KnowledgeName[]])
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.describe('The knowledge to load'),
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})
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export type StudioToolsContext = {
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projectRef?: string
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connectionString?: string
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authorization?: string
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aiOptInLevel?: AiOptInLevel
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}
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export const getStudioTools = (ctx: StudioToolsContext = {}) => {
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const { projectRef, connectionString, authorization, aiOptInLevel = 'schema' } = ctx
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const authHeaders = authorization
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? { 'Content-Type': 'application/json', Authorization: authorization }
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: undefined
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return {
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execute_sql: tool({
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description:
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'Asks the user to execute a SQL statement and return the results. Requires user approval before executing.',
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inputSchema: executeSqlInputSchema,
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needsApproval: true,
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execute: async ({ sql }) => {
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const { result } = await executeSql(
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{ projectRef, connectionString, sql },
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undefined,
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authHeaders
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)
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return aiOptInLevel === 'schema_and_log_and_data' ? result : NO_DATA_PERMISSIONS
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},
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}),
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deploy_edge_function: tool({
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description:
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'Asks the user to deploy a Supabase Edge Function from provided code. Requires user approval before deploying.',
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inputSchema: z.object({
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name: z.string().describe('The URL-friendly name/slug of the Edge Function.'),
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code: z.string().describe('The TypeScript code for the Edge Function.'),
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}),
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needsApproval: true,
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execute: async ({ name, code }) => {
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await deployEdgeFunction({
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projectRef: projectRef ?? '',
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slug: name,
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metadata: {
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entrypoint_path: 'index.ts',
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name,
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verify_jwt: true,
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},
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files: [{ name: 'index.ts', content: code }],
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authorization,
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})
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return { success: true }
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},
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}),
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rename_chat: tool({
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description: `Rename the current chat session when the current chat name doesn't describe the conversation topic.`,
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inputSchema: z.object({
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newName: z.string().describe('The new name for the chat session. Five words or less.'),
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}),
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execute: async () => {
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return { status: 'Chat request sent to client' }
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},
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}),
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load_knowledge: tool({
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description:
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'Load detailed knowledge about a Supabase topic before answering questions about it.',
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inputSchema: loadKnowledgeInputSchema,
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execute: ({ name }) => KNOWLEDGE[name],
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}),
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}
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}
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