suggester.indexer().index(item2);
        suggester.refresh();

        // Search still sees old data (search alias points to old index)
        assertEquals("Should still see 1 word from old index", 1, suggester.getAllWordsNum());

        // Switch search to new index
        suggester.switchIndex();
        suggester.refresh();

            V1Field["Pydantic v1 Model"]
        end
        subgraph V1["Pydantic v1 Model"]
            V2Field["Pydantic v2 Model"]
        end
    end

    style V2 fill:#f9fff3
    style V1 fill:#fff6f0
    style V1Field fill:#fff6f0
    style V2Field fill:#f9fff3
```

...mas, você pode ter modelos separados usando Pydantic v1 e v2 na mesma aplicação.

```mermaid
graph TB
    subgraph "✅ Supported"

            V1Field["Pydantic v1 Model"]
        end
        subgraph V1["Pydantic v1 Model"]
            V2Field["Pydantic v2 Model"]
        end
    end

    style V2 fill:#f9fff3
    style V1 fill:#fff6f0
    style V1Field fill:#fff6f0
    style V2Field fill:#f9fff3
```

... mais vous pouvez avoir des modèles séparés utilisant Pydantic v1 et v2 dans la même application.

```mermaid
graph TB
    subgraph "✅ Supported"

            V1Field["Pydantic v1 Model"]
        end
        subgraph V1["Pydantic v1 Model"]
            V2Field["Pydantic v2 Model"]
        end
    end

    style V2 fill:#f9fff3
    style V1 fill:#fff6f0
    style V1Field fill:#fff6f0
    style V2Field fill:#f9fff3
```

...但是，你可以在同一个应用中分别使用 Pydantic v1 和 v2 的独立模型。

```mermaid
graph TB
    subgraph "✅ Supported"
        direction TB

   * size.
   *
   * @param chunkSize the number of bytes available per {@link #process(ByteBuffer)} invocation;
   *     must be at least 4
   */
  protected AbstractStreamingHasher(int chunkSize) {
    this(chunkSize, chunkSize);
  }

  /**
   * Constructor for use by subclasses. This hasher instance will process chunks of the specified

    void testDecodeValidResumeKey() throws SMBProtocolDecodingException {
        // Prepare test data - 24 bytes for resume key + 4 bytes for context length
        byte[] buffer = new byte[28];

        // Fill resume key with test pattern
        for (int i = 0; i < 24; i++) {
            buffer[i] = (byte) (i + 1);
        }

        // Add context length (reserved) - 4 bytes
        buffer[24] = 0x00;

          projects:
            - name: elastic+elasticsearch+%BRANCH%+multijob+packaging-tests-unix
              kill-phase-on: NEVER
              current-parameters: true
              git-revision: true
            - name: elastic+elasticsearch+%BRANCH%+multijob+packaging-tests-windows
              kill-phase-on: NEVER
              current-parameters: true
              git-revision: true

  @Override
  public void clear() {
    Arrays.fill(keys, 0, size, null);
    Arrays.fill(values, 0, size, null);
    Arrays.fill(hashTableKToV, ABSENT);
    Arrays.fill(hashTableVToK, ABSENT);
    Arrays.fill(nextInBucketKToV, 0, size, ABSENT);
    Arrays.fill(nextInBucketVToK, 0, size, ABSENT);
    Arrays.fill(prevInInsertionOrder, 0, size, ABSENT);
    Arrays.fill(nextInInsertionOrder, 0, size, ABSENT);
    size = 0;

            int offset = 10;
            byte[] buffer = new byte[64];
            byte[] fileId = createTestData(16);
            byte oplockLevel = 0x02;

            // Fill some random data before offset
            Arrays.fill(buffer, 0, offset, (byte) 0xAB);

            // Write valid data at offset
            SMBUtil.writeInt2(24, buffer, offset);
            buffer[offset + 2] = oplockLevel;

You can also use `dataclasses` in the `response_model` parameter:

{* ../../docs_src/dataclasses_/tutorial002_py310.py hl[1,6:12,18] *}

The dataclass will be automatically converted to a Pydantic dataclass.

This way, its schema will show up in the API docs user interface:

<img src="/img/tutorial/dataclasses/image01.png">

## Dataclasses in Nested Data Structures { #dataclasses-in-nested-data-structures }