mirror of
https://github.com/supabase/supabase.git
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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 -->
256 lines
7.2 KiB
TypeScript
256 lines
7.2 KiB
TypeScript
import pgMeta from '@supabase/pg-meta'
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import type { JwtPayload } from '@supabase/supabase-js'
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import { safeValidateUIMessages } from 'ai'
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import { IS_PLATFORM } from 'common'
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import type { NextApiRequest, NextApiResponse } from 'next'
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import z from 'zod'
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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 { getOrgAIDetails, getProjectAIDetails } from '@/lib/ai/ai-details'
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import { isTracingAllowed } from '@/lib/ai/braintrust-logger'
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import { generateAssistantResponse } from '@/lib/ai/generate-assistant-response'
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import { getModel } from '@/lib/ai/model'
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import {
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DEFAULT_ASSISTANT_ADVANCE_MODEL_ID,
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DEFAULT_ASSISTANT_BASE_MODEL_ID,
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getAssistantModelEntry,
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isAssistantBaseModelId,
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isKnownAssistantModelId,
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type AssistantModelId,
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} from '@/lib/ai/model.utils'
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import { getTools } from '@/lib/ai/tools'
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import apiWrapper from '@/lib/api/apiWrapper'
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import { executeQuery } from '@/lib/api/self-hosted/query'
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import { getURL } from '@/lib/helpers'
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export const maxDuration = 120
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export const config = {
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api: {
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bodyParser: {
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sizeLimit: '5mb',
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},
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},
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}
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async function handler(req: NextApiRequest, res: NextApiResponse, claims?: JwtPayload) {
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const { method } = req
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switch (method) {
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case 'POST':
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return handlePost(req, res, claims)
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default:
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res.setHeader('Allow', ['POST'])
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res.status(405).json({
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data: null,
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error: { message: `Method ${method} Not Allowed` },
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})
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}
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}
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const wrapper = (req: NextApiRequest, res: NextApiResponse) =>
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apiWrapper(req, res, handler, { withAuth: true })
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export default wrapper
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const requestBodySchema = z.object({
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messages: z.array(z.any()),
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projectRef: z.string(),
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connectionString: z.string(),
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schema: z.string().optional(),
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table: z.string().optional(),
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chatId: z.string().optional(),
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chatName: z.string().optional(),
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orgSlug: z.string().optional(),
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model: z.string().optional(),
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})
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async function handlePost(req: NextApiRequest, res: NextApiResponse, claims?: JwtPayload) {
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const authorization = req.headers.authorization
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const accessToken = authorization?.replace('Bearer ', '')
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if (IS_PLATFORM && !accessToken) {
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return res.status(401).json({ error: 'Authorization token is required' })
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}
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const userId = claims?.sub
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const body = typeof req.body === 'string' ? JSON.parse(req.body) : req.body
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const { data, error: parseError } = requestBodySchema.safeParse(body)
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if (parseError) {
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return res.status(400).json({ error: 'Invalid request body', issues: parseError.issues })
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}
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const {
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messages: rawMessages,
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projectRef,
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connectionString,
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orgSlug,
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chatId,
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chatName,
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model: rawRequestedModel,
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} = data
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const requestedModel: AssistantModelId | undefined =
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rawRequestedModel && isKnownAssistantModelId(rawRequestedModel) ? rawRequestedModel : undefined
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const messagesValidation = await safeValidateUIMessages({
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messages: rawMessages,
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})
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if (!messagesValidation.success) {
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return res.status(400).json({
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error: 'Invalid request body',
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message: messagesValidation.error.message,
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})
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}
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const messages = messagesValidation.data
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let aiOptInLevel: AiOptInLevel = 'disabled'
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let hasAccessToAdvanceModel = false
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let orgHasHipaaAddon: boolean | undefined
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let projectIsSensitive: boolean | undefined
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let projectRegion: string | undefined
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let orgId: number | undefined
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let planId: string | undefined
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if (!IS_PLATFORM) {
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aiOptInLevel = 'schema'
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hasAccessToAdvanceModel = true
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}
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if (IS_PLATFORM && orgSlug && authorization && projectRef) {
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try {
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const [orgDetails, projectDetails] = await Promise.all([
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getOrgAIDetails({ orgSlug, authorization }),
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getProjectAIDetails({ projectRef, authorization }),
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])
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aiOptInLevel = orgDetails.aiOptInLevel
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hasAccessToAdvanceModel = orgDetails.hasAccessToAdvanceModel
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orgHasHipaaAddon = orgDetails.hasHipaaAddon
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orgId = orgDetails.orgId
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planId = orgDetails.planId
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projectIsSensitive = projectDetails.isSensitive
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projectRegion = projectDetails.region
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} catch (error) {
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return res.status(400).json({
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error: 'There was an error fetching your organization details',
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})
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}
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}
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const envThrottled = process.env.IS_THROTTLED !== 'false'
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let effectiveModel: AssistantModelId = requestedModel ?? DEFAULT_ASSISTANT_ADVANCE_MODEL_ID
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if (!hasAccessToAdvanceModel || (envThrottled && !isAssistantBaseModelId(effectiveModel))) {
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effectiveModel = DEFAULT_ASSISTANT_BASE_MODEL_ID
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}
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const {
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modelParams,
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error: modelError,
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systemProviderOptions,
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} = await getModel({
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provider: 'openai',
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modelEntry: getAssistantModelEntry(effectiveModel),
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})
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if (modelError) {
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return res.status(500).json({ error: modelError.message })
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}
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try {
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const abortController = new AbortController()
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req.on('close', () => abortController.abort())
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req.on('aborted', () => abortController.abort())
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const tools = await getTools({
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projectRef,
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connectionString,
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authorization,
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aiOptInLevel,
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accessToken,
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baseUrl: getURL(),
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})
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// Get a list of all schemas to add to context
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const getSchemas = async (): Promise<string> => {
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const pgMetaSchemasList = pgMeta.schemas.list()
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type Schemas = z.infer<(typeof pgMetaSchemasList)['zod']>
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const { result: schemas } = await executeSql<Schemas>(
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{
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projectRef,
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connectionString,
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sql: pgMetaSchemasList.sql,
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},
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undefined,
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{
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'Content-Type': 'application/json',
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...(authorization && { Authorization: authorization }),
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},
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IS_PLATFORM ? undefined : executeQuery
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)
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return schemas?.length > 0
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? `The available database schema names are: ${JSON.stringify(schemas)}`
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: "You don't have access to any schemas."
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}
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const result = await generateAssistantResponse({
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messages,
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...modelParams,
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tools,
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aiOptInLevel,
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getSchemas: aiOptInLevel !== 'disabled' ? getSchemas : undefined,
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projectRef,
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chatId,
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chatName,
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allowTracing: isTracingAllowed({
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orgHasHipaaAddon,
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projectIsSensitive,
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projectRegion,
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}),
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userId,
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orgId,
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planId,
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requestedModel,
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systemProviderOptions,
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abortSignal: abortController.signal,
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onSpanCreated: (spanId) => {
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res.setHeader('x-braintrust-span-id', spanId)
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},
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})
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result.pipeUIMessageStreamToResponse(res, {
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sendReasoning: true,
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headers: { 'Content-Encoding': 'none' },
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onError: (error) => {
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console.error('Assistant stream error:', error)
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if (error == null) {
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return 'unknown error'
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}
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if (typeof error === 'string') {
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return error
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}
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if (error instanceof Error) {
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return error.message
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}
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return JSON.stringify(error)
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},
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})
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} catch (error) {
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console.error('Error in handlePost:', error)
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if (error instanceof Error) {
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return res.status(500).json({ message: error.message })
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}
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return res.status(500).json({ message: 'An unexpected error occurred.' })
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}
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}
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