Function GET

apps/docs/app/api/search/route.ts:36–239

(request: NextRequest)

 * - Other languages: Keyword search only
 */
export async function GET(request: NextRequest) {
  try {
    const { query, locale, limit } = getSearchParams(request)

    if (!query || query.trim().length === 0) {
      return NextResponse.json([])
    }

    const candidateLimit = limit * 3
    const similarityThreshold = 0.6

    const localeMap: Record<string, string> = {
      en: 'english',
      es: 'spanish',
      fr: 'french',
      de: 'german',
      ja: 'simple', // PostgreSQL doesn't have Japanese support, use simple
      zh: 'simple', // PostgreSQL doesn't have Chinese support, use simple
    }
    const tsConfig = localeMap[locale] || 'simple'

    const useVectorSearch = locale === 'en'
    let vectorResults: Array<{
      chunkId: string
      chunkText: string
      sourceDocument: string
      sourceLink: string
      headerText: string
      headerLevel: number
      similarity: number
      searchType: string
    }> = []

    if (useVectorSearch) {
      const queryEmbedding = await generateSearchEmbedding(query)
      vectorResults = await db
        .select({
          chunkId: docsEmbeddings.chunkId,
          chunkText: docsEmbeddings.chunkText,
          sourceDocument: docsEmbeddings.sourceDocument,
          sourceLink: docsEmbeddings.sourceLink,
          headerText: docsEmbeddings.headerText,
          headerLevel: docsEmbeddings.headerLevel,
          similarity: sql<number>`1 - (${docsEmbeddings.embedding} <=> ${JSON.stringify(queryEmbedding)}::vector)`,
          searchType: sql<string>`'vector'`,
        })
        .from(docsEmbeddings)
        .where(
          sql`1 - (${docsEmbeddings.embedding} <=> ${JSON.stringify(queryEmbedding)}::vector) >= ${similarityThreshold}`
        )
        .orderBy(sql`${docsEmbeddings.embedding} <=> ${JSON.stringify(queryEmbedding)}::vector`)
        .limit(candidateLimit)
    }

    const keywordResults = await db
      .select({
        chunkId: docsEmbeddings.chunkId,
        chunkText: docsEmbeddings.chunkText,