## ネストされたモデル { #nested-models }

Pydanticモデルの各属性には型があります。

しかし、その型はそれ自体が別のPydanticモデルである可能性があります。

そのため、特定の属性名、型、バリデーションを指定して、深くネストしたJSON「オブジェクト」を宣言することができます。

すべては、任意のネストにされています。

### サブモデルの定義 { #define-a-submodel }

例えば、`Image`モデルを定義することができます:

{* ../../docs_src/body_nested_models/tutorial004_py310.py hl[7:9] *}

### サブモデルを型として使用 { #use-the-submodel-as-a-type }

そして、それを属性の型として使用することができます:

Elasticsearch builds using Java 16. When importing into IntelliJ you will need
to define an appropriate SDK. The convention is that **this SDK should be named
"16"** so that the project import will detect it automatically. For more details
on defining an SDK in IntelliJ please refer to [their documentation](https://www.jetbrains.com/help/idea/sdk.html#define-sdk).
SDK definitions are global, so you can add the JDK from any project, or after

                        logger.info("[RAG] Regenerated query. newQuery={}", newQuery);
                        callback.onPhaseStart(ChatPhaseCallback.PHASE_SEARCH, "Searching with refined query...", newQuery);
                        final ChatSearchResult fallbackResult = searchWithQueryAndMetadata(newQuery, safeFields, safeExtraQueries);
                        searchResults = fallbackResult.getDocuments();

            }
        }
        return defaultValue;
    }

    /**
     * Gets the maximum characters for context building for a specific prompt type.
     * Each LlmClient implementation defines per-prompt-type defaults appropriate
     * for its target model.
     *
     * @param promptType the prompt type (e.g., "answer", "summary", "faq")
     * @return the maximum characters
     */

						t = star.X
					}
					if sel, ok := unparen(t).(*ast.SelectorExpr); ok {
						var buf strings.Builder
						format.Node(&buf, fset, recvType)
						error_(sel.Pos(), `cannot define new methods on non-local type %s`, &buf)
					}
				}
			}
		}

	}
	if !sawC {
		error_(ast1.Package, `cannot find import "C"`)
	}

	// In ast2, strip the import "C" line.
	if *godefs {

  }

 private:
  bool first_call_ = true;
};

// After the graph pass is registered, it takes effect globally and can affect
// other test cases. Define a static variable to switch it on and off.
bool GraphErrorInjectionPass::enabled_ = false;

// Test to ensure that a registered graph optimization pass is only executed

Usamos `int | None` para el campo de clave primaria para que en el código Python podamos *crear un objeto sin un `id`* (`id=None`), asumiendo que la base de datos lo *generará al guardar*. SQLModel entiende que la base de datos proporcionará el `id` y *define la columna como un `INTEGER` no nulo* en el esquema de la base de datos. Consulta la [documentación de SQLModel sobre claves primarias](https://sqlmodel.tiangolo.com/tutorial/create-db-and-table/#primary-key-id) para más detalles.

* `Field(index=True)`...

* **Cloud services** that handle this for you
    * The cloud service will probably **handle replication for you**. It would possibly let you define **a process to run**, or a **container image** to use, in any case, it would most probably be **a single Uvicorn process**, and the cloud service would be in charge of replicating it.

/// tip

  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
  */

   * separators).
   *
   * @return a splitter with the desired configuration
   */
  public Splitter omitEmptyStrings() {
    return new Splitter(strategy, true, trimmer, limit);
  }

  /**
   * Returns a splitter that behaves equivalently to {@code this} splitter but stops splitting after
   * it reaches the limit. The limit defines the maximum number of items returned by the iterator,