}
    }

    @Test
    public void test_execute_withVeryLargeBoost() {
        // Test executing BoostQuery with very large boost value
        Term term = new Term("field", "large");
        TermQuery termQuery = new TermQuery(term);
        BoostQuery boostQuery = new BoostQuery(termQuery, Float.MAX_VALUE);

        QueryContext context = new QueryContext("test", false);

          .build(),
      )
    call1.execute().use { response -> response.body.string() }

    listener.shouldClose = true
    // Large headers are a likely reason the servers would cut off the connection before it completes sending
    // request headers.
    // 431 "Request Header Fields Too Large"
    val largeHeaders =
      Headers
        .Builder()
        .apply {
          repeat(32) {

FastAPI version 0.100.0 prenait en charge soit Pydantic v1 soit v2. Il utilisait celle que vous aviez installée.

FastAPI version 0.119.0 a introduit une prise en charge partielle de Pydantic v1 depuis l'intérieur de Pydantic v2 (comme `pydantic.v1`), pour faciliter la migration vers v2.

FastAPI 0.126.0 a supprimé la prise en charge de Pydantic v1, tout en continuant à prendre en charge `pydantic.v1` pendant un certain temps.

from fastapi import FastAPI
from fastapi.responses import StreamingResponse

some_file_path = "large-video-file.mp4"
app = FastAPI()


@app.get("/")
def main():
    def iterfile():  # (1)
        with open(some_file_path, mode="rb") as file_like:  # (2)
            yield from file_like  # (3)

        // When - initially should not need rotation
        assertFalse(context.needsKeyRotation(), "Should not need rotation initially");

        // Note: We can't directly test the byte limit without encrypting large amounts of data,
        // but we can verify the method exists and returns proper boolean
    }

    @Test
    @DisplayName("Should reset key rotation tracking")
    void testResetKeyRotationTracking() {

	opReadRune4 readOp = 4  // Read rune of size 4.
)

// ErrTooLarge is passed to panic if memory cannot be allocated to store data in a buffer.
var ErrTooLarge = errors.New("bytes.Buffer: too large")
var errNegativeRead = errors.New("bytes.Buffer: reader returned negative count from Read")

const maxInt = int(^uint(0) >> 1)

// Bytes returns a slice of length b.Len() holding the unread portion of the buffer.

        String result3 = aggregateLogJob.execute();
        assertEquals("", result3);
    }

    @Test
    public void test_execute_performanceWithLargeErrorMessage() {
        // Test with very large error message
        final StringBuilder largeMessage = new StringBuilder();
        for (int i = 0; i < 1000; i++) {
            largeMessage.append("Error detail ").append(i).append(" ");
        }

* It uses a "spooled" file:
    * A file stored in memory up to a maximum size limit, and after passing this limit it will be stored in disk.
* This means that it will work well for large files like images, videos, large binaries, etc. without consuming all the memory.
* You can get metadata from the uploaded file.
* It has a [file-like](https://docs.python.org/3/glossary.html#term-file-like-object) `async` interface.

Starten Sie Ihre Anwendung und gehen Sie auf [http://127.0.0.1:8000/static/redoc.standalone.js](http://127.0.0.1:8000/static/redoc.standalone.js).

Sie sollten eine sehr lange JavaScript-Datei für **ReDoc** sehen.

Sie könnte beginnen mit etwas wie:

```JavaScript
/*! For license information please see redoc.standalone.js.LICENSE.txt */

from fastapi import FastAPI
from fastapi.responses import FileResponse

some_file_path = "large-video-file.mp4"
app = FastAPI()


@app.get("/")
async def main():