searchBody.put("size", 100);
        searchBody.put("q", NAME_PREFIX + "large_*");

        String response = checkMethodBase(searchBody).get(SEARCHLIST_API_PATH + "/docs").asString();
        List<Map<String, Object>> docs = JsonPath.from(response).getList("response.docs");

        logger.info("Created large set of documents: {} documents", docs.size());

            if (fileSize > maxSize) {
                throw new IORuntimeException(new IOException(
                        "File too large: " + fileSize + " bytes (max: " + maxSize + " bytes). Use streaming APIs for large files."));
            }

            final ByteBuffer buffer = ByteBuffer.allocate((int) fileSize);
            ChannelUtil.read(channel, buffer);

## Why this policy exists

There is a fundamental **asymmetry** between submitting a contribution and reviewing one: AI tools make it easy to generate and submit large amounts of code, but they do not make it equally faster for us to review it. 
This policy protects our limited review budget, allowing us to invest our attention wisely.

And multiple processes normally **don't share any memory**. This means that each running process has its own things, variables, and memory. And if you are consuming a large amount of memory in your code, **each process** will consume an equivalent amount of memory.

from fastapi.testclient import TestClient

from docs_src.advanced_middleware.tutorial003_py310 import app


@app.get("/large")
async def large():
    return PlainTextResponse("x" * 4000, status_code=200)


client = TestClient(app)


def test_middleware():
    response = client.get("/large", headers={"accept-encoding": "gzip"})
    assert response.status_code == 200, response.text
    assert response.text == "x" * 4000

	if len(b) == 0 {
		return []byte{}
	}

	// Past a certain chunk size it is counterproductive to use
	// larger chunks as the source of the write, as when the source
	// is too large we are basically just thrashing the CPU D-cache.
	// So if the result length is larger than an empirically-found
	// limit (8KB), we stop growing the source string once the limit

### `examples` spécifiques à OpenAPI { #openapi-specific-examples }

Avant que **JSON Schema** ne prenne en charge `examples`, OpenAPI prenait déjà en charge un autre champ également appelé `examples`.

}

const minReadBufferSize = 16
const maxConsecutiveEmptyReads = 100

// NewReaderSize returns a new [Reader] whose buffer has at least the specified
// size. If the argument io.Reader is already a [Reader] with large enough
// size, it returns the underlying [Reader].
func NewReaderSize(rd io.Reader, size int) *Reader {
	// Is it already a Reader?
	b, ok := rd.(*Reader)
	if ok && len(b.buf) >= size {
		return b
	}

        }
    }

    @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);

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.