### Tech Stack

- **HTTP**: Apache HttpComponents 4.5+
- **Extraction**: Apache Tika 3.0+, POI 5.3+, PDFBox 3.0+
- **Testing**: JUnit 4, UTFlute, Mockito 5.7.0
- **Storage**: In-memory (default), OpenSearch (optional)

### Protocols

- **HTTP/HTTPS**: Full crawling, cookies, auth, robots.txt
- **File**: Local/network file systems
- **FTP**: With authentication

        connection.connect();
        return connection;
    }

    @Override
    public RdmaMemoryRegion registerMemory(ByteBuffer buffer, EnumSet<RdmaAccess> access) throws IOException {
        // TCP doesn't need real memory registration
        return new TcpMemoryRegion(buffer, access);
    }

    @Override
    public String getProviderName() {
        return "TCP Fallback";
    }

    @Override

     */
    public void setReplaceDuplication(final boolean replaceDuplication) {
        this.replaceDuplication = replaceDuplication;
    }

    /**
     * Sets the memory size.
     * @param memorySize The memory size.
     */
    public void setMemorySize(final int memorySize) {
        this.memorySize = memorySize;
    }

    /**
     * Sets the maximum size of an alpha-numeric term.

### Memoria por Proceso { #memory-per-process }

Ahora, cuando el programa carga cosas en memoria, por ejemplo, un modelo de Machine Learning en una variable, o el contenido de un archivo grande en una variable, todo eso **consume un poco de la memoria (RAM)** del servidor.

        EnumSet<RdmaAccess> access = EnumSet.of(RdmaAccess.LOCAL_READ, RdmaAccess.LOCAL_WRITE);

        RdmaMemoryRegion region = provider.registerMemory(buffer, access);
        assertNotNull(region, "Memory region should not be null");
        assertEquals(buffer, region.getBuffer(), "Buffer should match");
        assertEquals(1024, region.getSize(), "Size should match buffer size");

    false

-   type: input id: Cuda attributes: label: CUDA/cuDNN version description:
    placeholder: validations: required: false

-   type: input id: Gpu attributes: label: GPU model and memory description: if
    compiling from source placeholder: validations: required: false

-   type: textarea id: what-happened attributes: label: Current Behaviour?

        /** File system errors (not found, access denied, etc.) */
        FILE_SYSTEM,
        /** Protocol errors (invalid message, unsupported operation, etc.) */
        PROTOCOL,
        /** Resource errors (out of memory, disk space, etc.) */
        RESOURCE,
        /** Configuration errors */
        CONFIGURATION,
        /** Transient errors that may succeed on retry */
        TRANSIENT,
        /** Unknown or unclassified errors */

   * {@snippet :
   * Stream.of("foo", "quux", "banana", "elephant")
   *     .collect(least(2, comparingInt(String::length)))
   * // returns {"foo", "quux"}
   * }
   *
   * <p>This {@code Collector} uses O(k) memory and takes expected time O(n) (worst-case O(n log
   * k)), as opposed to e.g. {@code Stream.sorted(comparator).limit(k)}, which currently takes O(n
   * log n) time and O(n) space.
   *

models or graphs is equivalent to running untrusted code.

If you need to run untrusted models, execute them inside a
[**sandbox**](https://developers.google.com/code-sandboxing). Memory corruptions
in TensorFlow ops can be recognized as security issues only if they are
reachable and exploitable through production-grade, benign models.

### Compilation

 * java.util.LinkedHashMap}. Generally speaking, this class reduces object allocation and memory
 * consumption at the price of moderately increased constant factors of CPU. Only use this class
 * when there is a specific reason to prioritize memory over CPU.
 *
 * @author Louis Wasserman
 */
@J2ktIncompatible // no support for access-order mode in LinkedHashMap delegate