feyninc/chonkie

Tired of making your gazillionth chunker? Sick of the overhead of large libraries? Want to chunk your texts quickly and efficiently? Chonkie the mighty hippo is here to help!

🚀 Feature-rich: All the CHONKs you’d ever need 🔄 End-to-end: Fetch, CHONK, refine, embed and ship straight to your vector DB! ✨ Easy to use: Install, Import, CHONK ⚡ Fast: CHONK at the speed of light! zooooom 🪶 Light-weight: No bloat, just CHONK 🔌 32+ integrations: Works with your favorite tools and vector DBs out of the box! 💬 ️Multilingual: Out-of-the-box support for 56 languages ☁️ Cloud-Friendly: CHONK locally or in the Cloud 🦛 Cute CHONK mascot: psst it’s a pygmy hippo btw ❤️ Moto Moto’s favorite python library

Chonkie is a chunking library that “just works” ✨

📦 Installation

Basic Installation

Using pip:

pip install chonkie

Or using uv (faster):

uv pip install chonkie

Full Installation

Chonkie follows the rule of minimum installs. Have a favorite chunker? Read our docs to install only what you need. Don’t want to think about it? Simply install all (Not recommended for production environments).

Using pip:

pip install "chonkie[all]"

Or using uv:

uv pip install "chonkie[all]"

🚀 Usage

Basic Usage

Here’s a basic example to get you started:

# First import the chunker you want from Chonkie
from chonkie import RecursiveChunker

# Initialize the chunker
chunker = RecursiveChunker()

# Chunk some text
chunks = chunker("Chonkie is the goodest boi! My favorite chunking hippo hehe.")

# Access chunks
for chunk in chunks:
    print(f"Chunk: {chunk.text}")
    print(f"Tokens: {chunk.token_count}")

Pipeline Usage

You can also use the chonkie.Pipeline to chain components together and handle complex workflows. Read more about pipelines in the docs!

from chonkie import Pipeline

# Create a pipeline with multiple chunking and refinement steps
pipe = (
    Pipeline()
    .chunk_with("recursive", tokenizer="gpt2", chunk_size=2048, recipe="markdown")
    .chunk_with("semantic", chunk_size=512)
    .refine_with("overlap", context_size=128)
    .refine_with("embeddings", embedding_model="sentence-transformers/all-MiniLM-L6-v2")
)

# CHONK some Texts!
doc = pipe.run(texts="Chonkie is the goodest boi! My favorite chunking hippo hehe.")

# Access the processed chunks in the `doc` object
for chunk in doc.chunks:
    print(chunk.text)

# Run asynchronously for high-throughput applications
import asyncio

async def main():
    doc = await pipe.arun(texts="Chonkie runs fast!")
    print(len(doc.chunks))

asyncio.run(main())

Check out more usage examples in the docs!

🌐 API Server

Run Chonkie as a self-hosted REST API for easy integration into any application:

# Install with API dependencies (includes catsu for multi-provider embeddings)
pip install "chonkie[api,semantic,code,catsu]"

# Start the server using the CLI
chonkie serve

# Or with custom options
chonkie serve --port 3000 --reload --log-level debug

# Or directly with uvicorn
uvicorn chonkie.api.main:app --host 0.0.0.0 --port 8000

Or use Docker:

docker compose up

The API provides endpoints for all chunkers, refineries, and pipelines — reusable workflow configurations stored in a local SQLite database.

# Create a reusable pipeline
curl -X POST http://localhost:8000/v1/pipelines \
  -H "Content-Type: application/json" \
  -d '{
    "name": "rag-chunker",
    "steps": [
      {"type": "chunk", "chunker": "semantic", "config": {"chunk_size": 512}},
      {"type": "refine", "refinery": "embeddings", "config": {"embedding_model": "text-embedding-3-small"}}
    ]
  }'

# List your pipelines
curl http://localhost:8000/v1/pipelines

Interactive documentation is available at /docs when the server is running.

✂️ Chunkers

Chonkie provides several chunkers to help you split your text efficiently for RAG applications. Here’s a quick overview of the available chunkers:

More on these methods and the approaches taken inside the docs

🔌 Integrations

Chonkie boasts 45+ integrations across tokenizers, embedding providers, LLMs, refineries, porters, vector databases, and utilities, ensuring it fits seamlessly into your existing workflow.

With Chonkie’s wide range of integrations, you can easily plug it into your existing infrastructure and start CHONKING!

🤖 AI Agent Skills & Plugins

Chonkie provides an official skill and plugin for AI coding agents, giving them deep knowledge of Chonkie’s API, chunking strategies, and pipeline patterns — so they can help you build RAG pipelines faster.

Supported agents: Claude Code, Cursor, Gemini CLI, and more.

# Via skills.sh (works with Claude Code, Cursor, Copilot, and 20+ agents)
npx skills add chonkie-inc/skills

# Claude Code only
/plugin marketplace add chonkie-inc/skills

Once installed, your agent gains knowledge of all chunkers, the Pipeline API, tokenizer selection, embeddings refineries, vector DB handshakes, the REST API server, recipes, and async/batch processing patterns.

Learn more at github.com/chonkie-inc/skills.

📊 Benchmarks

“I may be smol hippo, but I pack a big punch!” 🦛

Chonkie is not just cute, it’s also fast and efficient! Here’s how it stacks up against the competition:

Size📦

• Wheel Size: 505KB (vs 1-12MB for alternatives)
• Installed Size: 49MB (vs 80-171MB for alternatives)
• With Semantic: Still 10x lighter than the closest competition!

Speed⚡

• Token Chunking: 33x faster than the slowest alternative
• Sentence Chunking: Almost 2x faster than competitors
• Semantic Chunking: Up to 2.5x faster than others

Check out our detailed benchmarks to see how Chonkie races past the competition! 🏃‍♂️💨

🤝 Contributing

Want to help grow Chonkie? Check out CONTRIBUTING.md to get started! Whether you’re fixing bugs, adding features, or improving docs, every contribution helps make Chonkie a better CHONK for everyone.

Remember: No contribution is too small for this tiny hippo! 🦛

🙏 Acknowledgements

Chonkie would like to CHONK its way through a special thanks to all the users and contributors who have helped make this library what it is today! Your feedback, issue reports, and improvements have helped make Chonkie the CHONKIEST it can be.

And of course, special thanks to Moto Moto for endorsing Chonkie with his famous quote:

“I like them big, I like them chonkie.” ~ Moto Moto

📝 Citation

If you use Chonkie in your research, please cite it as follows:

@software{chonkie2025,
  author = {Minhas, Bhavnick AND Nigam, Shreyash},
  title = {Chonkie: The lightweight ingestion library for fast, efficient and robust RAG pipelines},
  year = {2025},
  publisher = {GitHub},
  howpublished = {\url{https://github.com/chonkie-inc/chonkie}},
}