chunkrank 0.2.2

Model-aware text chunking and answer re-ranking for LLM pipelines. Automatically adapts chunk size to tokenizer and context window, then consolidates and ranks answers across chunks.

ChunkRank: Model-Aware Chunking + Answer Ranking

Used internally for long-document QA and evaluation pipelines handling 1,000+ PDFs.

ChunkRank is a lightweight Python library that automatically chunks 
text based on an LLM’s tokenizer and context window, then consolidates
and ranks answers across chunks. In short ChunkRank is a model-aware text 
chunking and answer re-ranking library for LLM pipelines.

🔗 PyPI : https://pypi.org/project/chunkrank/

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Why ChunkRank?

When working with LLMs, long documents must be split into chunks, but:

• Every model has different tokenizers and context limits
• Chunk sizes are usually hard-coded and error-prone
• Answer quality drops when responses come from multiple chunks
• Existing RAG frameworks are heavy when you only need chunking + ranking

ChunkRank solves this gap.

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What It Does

✅Model-aware chunking

• Pass a model name (gpt-4o-mini, claude-3.5-sonnet, Llama-3.1-8B etc.)
• ChunkRank automatically:
  • Selects the correct tokenizer
  • Applies the correct context window
  • Reserves token space for prompts and responses

No manual token math. No trial-and-error.

✅Answer consolidation & ranking

• Query runs across multiple chunks
• Multiple candidate answers are produced
• ChunkRank re-ranks them to return the best answer Works standalone — no full RAG stack required.

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Installation

pip install chunkrank

or for development:

poetry install

Quick Example

from chunkrank import ChunkRankPipeline

text = open("document.txt").read()

pipe = ChunkRankPipeline(model="gpt-4o-mini")

answer = pipe.process(
    question="What is the main topic of this document?",
    text=text
)

print(answer)

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Core API

chunks = chunkrank.split(text, model="gpt-4o-mini")

answers = chunkrank.answer(question, chunks)

best_answer = chunkrank.rank(answers)

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Supported Capabilities

• Automatic model → tokenizer → context resolution
• Token, sentence, and paragraph chunking strategies
• Cross-encoder based answer re-ranking
• Works with OpenAI, Anthropic, HF, Llama-based models
• Drop-in utility for QA, summarization, extraction

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How It Fits

Tool	What it does
LangChain / LlamaIndex	Full RAG pipelines
Haystack	End-to-end retrieval frameworks
ChunkRank	Focused, model-aware chunking + answer ranking

ChunkRank complements RAG frameworks — it doesn’t replace them.

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Roadmap

1. Build the model registry (model → context window + tokenizer).
2. Implement chunking strategies (tokens, sentences, paragraphs).
3. Integrate a re-ranking engine (start with Hugging Face cross-encoder).
4. Package and release to PyPI with a simple API.

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Community

• Contributors
• Maintainers

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