chunkr is a high-performance document chunking library built in Rust with first-class Python native bindings for Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) applications. It delivers throughput up to hundreds of MB/sec with zero superfluous heap allocations, advanced structure awareness, OpenAI BPE tokenization, semantic clustering, proposition decomposition, query-adaptive sizing, agentic topic segmentation, and multi-core parallel processing.

📦 Installation

Rust

Add chunkr to your Cargo.toml:

[dependencies]
chunkr = "1.0"

Python

Install chunkr-rs via pip:

pip install chunkr-rs

Or build from source with maturin:

maturin develop --release

🚀 Supported Chunking Strategies (12 Strategies)

Python Quickstart

import chunkr

sample_text = (
    "Convolutional neural networks specialize in visual imagery. "
    "Recurrent networks process sequential text.\n\n"
    "In conclusion, deep learning powers modern vision systems."
)

# 1. Recursive Character Chunking
recursive_chunker = chunkr.RecursiveChunker(chunk_size=500, overlap=50)
docs = recursive_chunker.chunk(sample_text)
for doc in docs:
    print(doc.content, doc.metadata)

# 2. Token-Based Chunking (OpenAI cl100k_base / GPT-4)
token_chunker = chunkr.TokenChunker(chunk_size=100, overlap=20, encoding="cl100k_base")
token_docs = token_chunker.chunk(sample_text)

# 3. Query-Aware Adaptive Chunking
query_chunker = chunkr.QueryAwareChunker(query="neural networks", hotspot_sentences=1, context_sentences=2)
query_docs = query_chunker.chunk(sample_text)

# 4. Agentic Topic Chunking
agentic_chunker = chunkr.AgenticChunker(min_chars=100, max_chars=1000)
agentic_docs = agentic_chunker.chunk(sample_text)

# 5. Markdown Structure Chunking (with header breadcrumbs)
md_chunker = chunkr.MarkdownChunker(chunk_size=1000, overlap=100)
md_docs = md_chunker.chunk("# Title\n## Section\nContent...")

# 6. PDF Document Loading & Chunking
loader = chunkr.PDFLoader()
pages = loader.load_pages("path/to/document.pdf")
pdf_chunks = recursive_chunker.chunk(pages[0].content)

Rust Quickstart

use chunkr::prelude::*;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let text = "Convolutional neural networks specialize in visual imagery. Recurrent networks process sequential text.\n\nIn conclusion, deep learning powers modern vision.";

    // 1. Recursive Chunker
    let recursive_chunker = RecursiveChunker::new()
        .with_chunk_size(500)
        .with_overlap(50);
    let chunks = recursive_chunker.chunk(text)?;

    // 2. Query-Aware Adaptive Chunker
    let query_chunker = QueryAwareChunker::new("convolutional neural networks")
        .with_hotspot_sizing(1, 0)
        .with_context_sizing(3, 1);
    let query_chunks = query_chunker.chunk(text)?;

    // 3. Hierarchical Parent-Child Tree Chunker
    let hier_chunker = HierarchicalChunker::with_sizes(150, 20, 50, 10)?;
    let tree = hier_chunker.chunk_tree(text)?;

    // 4. PDF Document Loading & Chunking
    let loader = PDFLoader::new();
    let pdf_pages = loader.load_pages_from_file("tests/test_files/sample_doc.pdf")?;
    let pdf_chunks = recursive_chunker.chunk_documents(&pdf_pages)?;

    Ok(())
}

📊 In-Memory Speed Benchmark: Chunkr (Rust/Python) vs. LangChain

Pure in-memory Python runtime comparison (import chunkr vs. langchain-text-splitters):

💡 Contributing

Contributions are welcome! Please check out the Contribution Guide to get started.

📝 License

Licensed under the MIT License - see the LICENSE file for details.