FastContentParse 0.1.0 — Content parsing for FastJava

Lightweight Java parser and normalizer for building retrieval pipelines.

FastContentParse provides a pure-Java content parser, text normalization, and a simple character-based chunker for embedding pipelines. Optionally integrate the native FastContentChunk tokenizer (separate repo) for higher-performance tokenization.

// Quick Start — Example
import fastcontentparse.FastContentParse;

public class Demo {
    public static void main(String[] args) {
        FastContentParse p = new FastContentParse();
        var doc = p.parseString("Hello world", "example.txt");
        System.out.println(doc.getText());
    }
}

Table of Contents

• Key Features
• Performance
• API Quick Reference
• Installation
• Technical Examples & Hero Demos
• Documentation
• Platform Support
• License

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Key Features

• 🚀 Native Performance — Direct Win32/DirectX access via JNI.
• ⚡ Zero Overhead — No polling, purely event-driven callbacks.
• 📦 Zero Dependencies — Just requires Java 17+ and Windows.

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📊 Performance

FastXXX is significantly faster than standard Java alternatives:

Operation	Standard Java	FastXXX Native	Speedup
Action A	50 ms	5 ms	10x
Action B	120 ms	12 ms	10x

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API Quick Reference

Method	Description	Path
actionA(...)	Brief description of action A.	Reference →
actionB(...)	Brief description of action B.	Reference →

Tip

See REFERENCE.md for full JNI contracts and fallback rules.

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📥 Installation

FastJava modules are available via JitPack. Depending on the module type (Pure-Java or JNI-Native), select the appropriate integration:

• Pure-Java Modules: Only require the main module dependency.
• JNI-Native Modules: Require two dependencies: the module itself and FastCore (the mandatory native DLL loader).

Option 1: Maven (JitPack)

Add the JitPack repository and the dependencies to your pom.xml:

<repositories>
    <repository>
        <id>jitpack.io</id>
        <url>https://jitpack.io</url>
    </repository>
</repositories>

<dependencies>
    <!-- 1. The main Module -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>fastXXX</artifactId>
        <version>v0.1.0</version>
    </dependency>
    
    <!-- 2. FastCore (Required ONLY for JNI-Native Modules) -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>fastcore</artifactId>
        <version>v1.0.0</version>
    </dependency>
</dependencies>

Option 2: Gradle (JitPack)

Add this to your build.gradle file:

repositories {
    maven { url 'https://jitpack.io' }
}

dependencies {
    implementation 'com.github.andrestubbe:fastXXX:v0.1.0'
    implementation 'com.github.andrestubbe:fastcore:v1.0.0' // Required ONLY for JNI-Native Modules
}

Option 3: Direct Download (No Build Tool)

Download the latest pre-compiled JARs directly to add them to your project's classpath:

1. 📦 fastXXX-v0.1.0.jar (The Core Library)
2. ⚙️ fastcore-v1.0.0.jar (The Mandatory JNI Loader — ONLY for JNI-Native Modules)

Important

Both JARs must be present in your classpath for FastXXX's native functions to operate correctly.

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Technical Examples & Hero Demos

See the examples/ directory for technical implementations and high-speed races:

Case	Java Example	Performance Race / Demo	JMH Benchmark
Feature A	ExampleA.java	“Hero Demo A”	JMH_A.java
Feature B	ExampleB.java	—	—

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Documentation

• REFERENCE.md: Full technical specification and JNI contracts.
• PHILOSOPHY.md: The "Native-First" philosophy.
• CHANGELOG.md: Project history.
• ROADMAP.md: Future development and milestones.

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Platform Support

Platform	Status
Windows 10/11 (x64)	✅ Fully Supported
Linux	🚧 Planned
macOS	🚧 Planned

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License

MIT License — See LICENSE file for details.

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Related Projects

• FastCore — Native Library Loader for Java
• FastAudioPlayer — Native Windows WASAPI Audio Playback for Java
• FastTTS — High-Performance Native Windows TTS API for Java
• FastSTT — Ultra-Fast Native Speech-to-Text for Java
• FastWakeWord

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Part of the FastJava Ecosystem — Making the JVM faster. Small package. Maximum speed. Zero bloat. 🚀📋