It had automatic data validation, data serialization and OpenAPI schema generation based on the same type hints in several places.

      - name: Install Dependencies
        run: uv pip install -r requirements-tests.txt
      - name: Install Pydantic v2
        run: uv pip install --upgrade "pydantic>=2.0.2,<3.0.0"
      - name: Lint
        run: bash scripts/lint.sh

  test:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        python-version:
          - "3.13"
          - "3.12"
          - "3.11"
          - "3.10"
          - "3.9"

        searchHits.forEach(hit -> {
            final Map<String, Object> source = hit.getSourceAsMap();
            final RESULT entity = createEntity(source, entityType);
            final DocMeta docMeta = ((EsAbstractEntity) entity).asDocMeta();
            docMeta.id(hit.getId());
            docMeta.version(hit.getVersion());
            docMeta.seqNo(hit.getSeqNo());

// RegEx matches only numeric values
var numericPlaceholderRe = regexp.MustCompile(`\$\d+\$`)

func isNumeric(k reflect.Kind) bool {
	switch k {
	case reflect.Int, reflect.Int8, reflect.Int16, reflect.Int32, reflect.Int64:
		return true
	case reflect.Uint, reflect.Uint8, reflect.Uint16, reflect.Uint32, reflect.Uint64:
		return true
	case reflect.Float32, reflect.Float64:
		return true
	default:
		return false
	}
}

    when {
      // Server sent a 100-continue even though we did not request one. Try again to read the
      // actual response status.
      code == 100 -> true

      // Handle Processing (102) & Early Hints (103) and any new codes without failing
      // 100 and 101 are the exceptions with different meanings
      // But Early Hints not currently exposed

C'était l'une des premières implémentations d'un framework utilisant les type hints Python pour déclarer les paramètres
et les requêtes que j'ai vues (avant NestJS et Molten). Je l'ai trouvé plus ou moins en même temps que Hug. Mais APIStar utilisait le standard OpenAPI.

Il disposait de la validation automatique, sérialisation des données et d'une génération de schéma OpenAPI basée sur les mêmes type hints à plusieurs endroits.

	lockSource := getSource(2)
	start := UTCNow()
	const readLock = false
	success := make([]int, len(li.paths))
	for i, path := range li.paths {
		if !li.ns.lock(ctx, li.volume, path, lockSource, li.opsID, readLock, timeout.Timeout()) {
			timeout.LogFailure()
			for si, sint := range success {
				if sint == 1 {
					li.ns.unlock(li.volume, li.paths[si], readLock)
				}
			}

	tests := []struct {
		name     string
		input    []int
		trimLen  int
		expected []int
	}{
		{
			name:     "Trim two elements from end",
			input:    []int{1, 2, 3, 4, 5},
			trimLen:  2,
			expected: []int{1, 2, 3},
		},
		{
			name:     "Trim entire slice",
			input:    []int{1, 2, 3},
			trimLen:  3,
			expected: []int{},
		},
		{

pkg math/rand/v2, func NormFloat64() float64 #61716
pkg math/rand/v2, func Perm(int) []int #61716
pkg math/rand/v2, func Shuffle(int, func(int, int)) #61716
pkg math/rand/v2, func Uint32() uint32 #61716
pkg math/rand/v2, func Uint32N(uint32) uint32 #61716
pkg math/rand/v2, func Uint64() uint64 #61716
pkg math/rand/v2, func Uint64N(uint64) uint64 #61716
pkg math/rand/v2, func UintN(uint) uint #61716

	if a == nil {
		return
	}
	a.Curr = globalReplicationPool.Get().ActiveWorkers()
	a.hist.Update(int64(a.Curr))
	a.Avg = float32(a.hist.Mean())
	a.Max = int(a.hist.Max())
}

func (a *ActiveWorkerStat) get() ActiveWorkerStat {
	w := ActiveWorkerStat{
		Curr: a.Curr,
		Avg:  a.Avg,
		Max:  a.Max,
	}
	return w
}

// QStat holds queue stats for replication