Files
simstudioai--sim/apps/sim/app/api/v1/knowledge/search/route.ts
T
wehub-resource-sync d25d482dc2
CI / Migrate Dev DB (push) Has been skipped
CI / Detect Version (push) Has been cancelled
CI / Migrate DB (push) Has been cancelled
CI / Build Dev ECR (./docker/app.Dockerfile, ECR_APP) (push) Has been cancelled
CI / Build Dev ECR (./docker/db.Dockerfile, ECR_MIGRATIONS) (push) Has been cancelled
CI / Build Dev ECR (./docker/pii.Dockerfile, ECR_PII) (push) Has been cancelled
CI / Build Dev ECR (./docker/realtime.Dockerfile, ECR_REALTIME) (push) Has been cancelled
CI / Deploy Trigger.dev (Dev) (push) Has been cancelled
CI / Build AMD64 (./docker/app.Dockerfile, ECR_APP, ghcr.io/simstudioai/simstudio) (push) Has been cancelled
CI / Build AMD64 (./docker/db.Dockerfile, ECR_MIGRATIONS, ghcr.io/simstudioai/migrations) (push) Has been cancelled
CI / Build AMD64 (./docker/pii.Dockerfile, ECR_PII, ghcr.io/simstudioai/pii) (push) Has been cancelled
CI / Build AMD64 (./docker/realtime.Dockerfile, ECR_REALTIME, ghcr.io/simstudioai/realtime) (push) Has been cancelled
CI / Build ARM64 (GHCR Only) (./docker/app.Dockerfile, ghcr.io/simstudioai/simstudio) (push) Has been cancelled
CI / Build ARM64 (GHCR Only) (./docker/db.Dockerfile, ghcr.io/simstudioai/migrations) (push) Has been cancelled
CI / Build ARM64 (GHCR Only) (./docker/pii.Dockerfile, ghcr.io/simstudioai/pii) (push) Has been cancelled
CI / Build ARM64 (GHCR Only) (./docker/realtime.Dockerfile, ghcr.io/simstudioai/realtime) (push) Has been cancelled
CI / Create GHCR Manifests (ghcr.io/simstudioai/migrations) (push) Has been cancelled
CI / Create GHCR Manifests (ghcr.io/simstudioai/pii) (push) Has been cancelled
CI / Create GHCR Manifests (ghcr.io/simstudioai/realtime) (push) Has been cancelled
CI / Create GHCR Manifests (ghcr.io/simstudioai/simstudio) (push) Has been cancelled
CI / Check Docs Changes (push) Has been cancelled
CI / Process Docs (push) Has been cancelled
CI / Create GitHub Release (push) Has been cancelled
CI / Test and Build (push) Has been cancelled
Publish CLI Package / publish-npm (push) Has been cancelled
Publish Python SDK / publish-pypi (push) Has been cancelled
Publish TypeScript SDK / publish-npm (push) Has been cancelled
chore: import upstream snapshot with attribution
2026-07-13 13:20:55 +08:00

280 lines
9.8 KiB
TypeScript

import { type NextRequest, NextResponse } from 'next/server'
import { v1KnowledgeSearchContract } from '@/lib/api/contracts/v1/knowledge'
import { parseRequest } from '@/lib/api/server'
import { checkActorUsageLimits } from '@/lib/billing/calculations/usage-monitor'
import { withRouteHandler } from '@/lib/core/utils/with-route-handler'
import { ALL_TAG_SLOTS } from '@/lib/knowledge/constants'
import { recordSearchEmbeddingUsage } from '@/lib/knowledge/embeddings'
import { getDocumentTagDefinitions } from '@/lib/knowledge/tags/service'
import { buildUndefinedTagsError, validateTagValue } from '@/lib/knowledge/tags/utils'
import type { StructuredFilter } from '@/lib/knowledge/types'
import {
generateSearchEmbedding,
getDocumentMetadataByIds,
getQueryStrategy,
handleTagAndVectorSearch,
handleTagOnlySearch,
handleVectorOnlySearch,
type SearchResult,
} from '@/app/api/knowledge/search/utils'
import { checkKnowledgeBaseAccess, type KnowledgeBaseAccessResult } from '@/app/api/knowledge/utils'
import { handleError } from '@/app/api/v1/knowledge/utils'
import { authenticateRequest, validateWorkspaceAccess } from '@/app/api/v1/middleware'
export const dynamic = 'force-dynamic'
export const revalidate = 0
/** POST /api/v1/knowledge/search — Vector search across knowledge bases. */
export const POST = withRouteHandler(async (request: NextRequest) => {
const auth = await authenticateRequest(request, 'knowledge-search')
if (auth instanceof NextResponse) return auth
const { requestId, userId, rateLimit } = auth
try {
const parsed = await parseRequest(v1KnowledgeSearchContract, request, {})
if (!parsed.success) return parsed.response
const { workspaceId, topK, query, tagFilters } = parsed.data.body
const accessError = await validateWorkspaceAccess(rateLimit, userId, workspaceId)
if (accessError) return accessError
// A query incurs hosted embedding (+ optional rerank) cost — gate the actor's
// usage and frozen status before spending. Tag-only search is free, so skip it.
if (query && query.trim().length > 0) {
const usage = await checkActorUsageLimits(userId, workspaceId)
if (usage.isExceeded) {
return NextResponse.json(
{ error: usage.message || 'Usage limit exceeded. Please upgrade your plan to continue.' },
{ status: 402 }
)
}
}
const knowledgeBaseIds = Array.isArray(parsed.data.body.knowledgeBaseIds)
? parsed.data.body.knowledgeBaseIds
: [parsed.data.body.knowledgeBaseIds]
const accessChecks = await Promise.all(
knowledgeBaseIds.map((kbId) => checkKnowledgeBaseAccess(kbId, userId))
)
const accessibleKbs = accessChecks
.filter(
(ac): ac is KnowledgeBaseAccessResult =>
ac.hasAccess === true && ac.knowledgeBase.workspaceId === workspaceId
)
.map((ac) => ac.knowledgeBase)
const accessibleKbIds = accessibleKbs.map((kb) => kb.id)
if (accessibleKbIds.length === 0) {
return NextResponse.json(
{ error: 'Knowledge base not found or access denied' },
{ status: 404 }
)
}
const inaccessibleKbIds = knowledgeBaseIds.filter((id) => !accessibleKbIds.includes(id))
if (inaccessibleKbIds.length > 0) {
return NextResponse.json(
{ error: `Knowledge bases not found or access denied: ${inaccessibleKbIds.join(', ')}` },
{ status: 404 }
)
}
let structuredFilters: StructuredFilter[] = []
const tagDefsCache = new Map<string, Awaited<ReturnType<typeof getDocumentTagDefinitions>>>()
if (tagFilters && tagFilters.length > 0 && accessibleKbIds.length > 1) {
return NextResponse.json(
{ error: 'Tag filters are only supported when searching a single knowledge base' },
{ status: 400 }
)
}
if (tagFilters && tagFilters.length > 0 && accessibleKbIds.length > 0) {
const kbId = accessibleKbIds[0]
const tagDefs = await getDocumentTagDefinitions(kbId)
tagDefsCache.set(kbId, tagDefs)
const displayNameToTagDef: Record<string, { tagSlot: string; fieldType: string }> = {}
tagDefs.forEach((def) => {
displayNameToTagDef[def.displayName] = {
tagSlot: def.tagSlot,
fieldType: def.fieldType,
}
})
const undefinedTags: string[] = []
const typeErrors: string[] = []
for (const filter of tagFilters) {
const tagDef = displayNameToTagDef[filter.tagName]
if (!tagDef) {
undefinedTags.push(filter.tagName)
continue
}
const validationError = validateTagValue(
filter.tagName,
String(filter.value),
tagDef.fieldType
)
if (validationError) {
typeErrors.push(validationError)
}
}
if (undefinedTags.length > 0 || typeErrors.length > 0) {
const errorParts: string[] = []
if (undefinedTags.length > 0) {
errorParts.push(buildUndefinedTagsError(undefinedTags))
}
if (typeErrors.length > 0) {
errorParts.push(...typeErrors)
}
return NextResponse.json({ error: errorParts.join('\n') }, { status: 400 })
}
structuredFilters = tagFilters.map((filter) => {
const tagDef = displayNameToTagDef[filter.tagName]!
return {
tagSlot: tagDef.tagSlot,
fieldType: tagDef.fieldType,
operator: filter.operator,
value: filter.value,
valueTo: filter.valueTo,
}
})
}
const hasQuery = query && query.trim().length > 0
const hasFilters = structuredFilters.length > 0
const embeddingModels = Array.from(new Set(accessibleKbs.map((kb) => kb.embeddingModel)))
if (hasQuery && embeddingModels.length > 1) {
return NextResponse.json(
{
error:
'Selected knowledge bases use different embedding models and cannot be searched together. Search them separately.',
},
{ status: 400 }
)
}
const queryEmbeddingModel = embeddingModels[0]
let results: SearchResult[]
let queryEmbeddingIsBYOK: boolean | null = null
if (!hasQuery && hasFilters) {
results = await handleTagOnlySearch({
knowledgeBaseIds: accessibleKbIds,
topK,
structuredFilters,
})
} else if (hasQuery && hasFilters) {
const strategy = getQueryStrategy(accessibleKbIds.length, topK)
const queryEmbeddingResult = await generateSearchEmbedding(
query!,
queryEmbeddingModel,
workspaceId
)
queryEmbeddingIsBYOK = queryEmbeddingResult.isBYOK
const queryVector = JSON.stringify(queryEmbeddingResult.embedding)
results = await handleTagAndVectorSearch({
knowledgeBaseIds: accessibleKbIds,
topK,
structuredFilters,
queryVector,
distanceThreshold: strategy.distanceThreshold,
})
} else if (hasQuery) {
const strategy = getQueryStrategy(accessibleKbIds.length, topK)
const queryEmbeddingResult = await generateSearchEmbedding(
query!,
queryEmbeddingModel,
workspaceId
)
queryEmbeddingIsBYOK = queryEmbeddingResult.isBYOK
const queryVector = JSON.stringify(queryEmbeddingResult.embedding)
results = await handleVectorOnlySearch({
knowledgeBaseIds: accessibleKbIds,
topK,
queryVector,
distanceThreshold: strategy.distanceThreshold,
})
} else {
return NextResponse.json(
{ error: 'Either query or tagFilters must be provided' },
{ status: 400 }
)
}
if (queryEmbeddingIsBYOK !== null) {
await recordSearchEmbeddingUsage({
userId,
workspaceId,
embeddingModel: queryEmbeddingModel,
query: query!,
isBYOK: queryEmbeddingIsBYOK,
sourceReference: `v1-kb-search:${requestId}`,
})
}
const tagDefsResults = await Promise.all(
accessibleKbIds.map(async (kbId) => {
try {
const tagDefs = tagDefsCache.get(kbId) ?? (await getDocumentTagDefinitions(kbId))
const map: Record<string, string> = {}
tagDefs.forEach((def) => {
map[def.tagSlot] = def.displayName
})
return { kbId, map }
} catch {
return { kbId, map: {} as Record<string, string> }
}
})
)
const tagDefinitionsMap: Record<string, Record<string, string>> = {}
tagDefsResults.forEach(({ kbId, map }) => {
tagDefinitionsMap[kbId] = map
})
const documentIds = results.map((r) => r.documentId)
const documentMetadataMap = await getDocumentMetadataByIds(documentIds)
return NextResponse.json({
success: true,
data: {
results: results.map((result) => {
const kbTagMap = tagDefinitionsMap[result.knowledgeBaseId] || {}
const tags: Record<string, string | number | boolean | Date | null> = {}
ALL_TAG_SLOTS.forEach((slot) => {
const tagValue = result[slot as keyof SearchResult]
if (tagValue !== null && tagValue !== undefined) {
const displayName = kbTagMap[slot] || slot
tags[displayName] = tagValue as string | number | boolean | Date | null
}
})
const docMeta = documentMetadataMap[result.documentId]
return {
documentId: result.documentId,
documentName: docMeta?.filename || undefined,
sourceUrl: docMeta?.sourceUrl ?? null,
content: result.content,
chunkIndex: result.chunkIndex,
metadata: tags,
similarity: hasQuery ? 1 - result.distance : 1,
}
}),
query: query || '',
knowledgeBaseIds: accessibleKbIds,
topK,
totalResults: results.length,
},
})
} catch (error) {
return handleError(requestId, error, 'Failed to perform search')
}
})