chunkrank 1.1.3

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 text chunking and answer re-ranking for LLM pipelines

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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.

🔗 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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Installation

pip install chunkrank

With semantic chunking + cross-encoder reranking:

pip install chunkrank[semantic]

With all optional backends:

pip install chunkrank[all]

For development:

poetry install --with dev

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Quick Example

import chunkrank

text = open("document.txt").read()
question = "What is the main topic of this document?"

chunks = chunkrank.split(text, model="gpt-4o-mini")
answers = chunkrank.answer(question, chunks)
best = chunkrank.rank(answers)

print(best)

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Core API

import chunkrank

# 1. Split text into model-aware chunks
chunks = chunkrank.split(text, model="gpt-4o-mini")

# 2. Answer the question across all chunks
#    Default: local extractive (no API key required)
answers = chunkrank.answer(question, chunks)

#    With OpenAI:
answers = chunkrank.answer(question, chunks, provider="openai", api_key="sk-...")

#    With Anthropic:
answers = chunkrank.answer(question, chunks, provider="anthropic", api_key="sk-ant-...")

# 3. Rank and return the best answer
best_answer = chunkrank.rank(answers)

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Pipeline API

from chunkrank import ChunkRankPipeline

# Local (no LLM required)
pipe = ChunkRankPipeline(model="gpt-4o-mini")

# With OpenAI
pipe = ChunkRankPipeline(model="gpt-4o-mini", provider="openai", api_key="sk-...")

# With Anthropic
pipe = ChunkRankPipeline(model="gpt-4o-mini", provider="anthropic", api_key="sk-ant-...")

# Process — returns best answer
answer = pipe.process(question="What is the main topic?", text=text)

# Stream — yields answers progressively as each chunk is processed
for partial in pipe.stream(question="What is the main topic?", text=text):
    print(partial)

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Async API

from chunkrank import AsyncChunkRankPipeline

pipe = AsyncChunkRankPipeline(model="gpt-4o-mini", provider="openai", api_key="sk-...")

# Parallel chunk answering via asyncio.gather
answer = await pipe.process(question, text)

# Async streaming
async for partial in pipe.stream(question, text):
    print(partial)

Module-level async functions:

import chunkrank

chunks = await chunkrank.async_split(text, model="gpt-4o-mini")
answers = await chunkrank.async_answer(question, chunks)   # parallel LLM calls
best = await chunkrank.async_rank(answers)

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Ranking Methods

Method	Description	Extra dep
bm25 (default)	BM25 lexical ranking	none
tfidf	TF-IDF cosine similarity	none
embedding	Dense vector similarity	[semantic] or openai-embed
cross-encoder	Semantic cross-encoder (most accurate)	[semantic]

from chunkrank import Ranker

ranker = Ranker(method="cross-encoder")
ranked = ranker.rank(question, answers)

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Chunking Strategies

# Token-budget sliding window (default)
chunks = chunkrank.split(text, model="gpt-4o-mini", strategy="tokens", overlap_tokens=64)

# Semantic — splits on embedding similarity drops between sentences
chunks = chunkrank.split(text, model="gpt-4o-mini", strategy="semantic", similarity_threshold=0.5)

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Retrieve-then-Answer (top-K)

Rank chunks first, answer only the top-K — reduces LLM calls on large documents:

pipe = ChunkRankPipeline(model="gpt-4o-mini", retrieval_top_k=3)
answer = pipe.process(question, text)

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Disk Cache

Avoid re-chunking the same document on repeated runs:

from chunkrank import ChunkCache, Chunker, ChunkerConfig

cache = ChunkCache(".chunkrank_cache")
chunks = cache.get(text, model="gpt-4o-mini")
if chunks is None:
    chunks = Chunker(ChunkerConfig(model="gpt-4o-mini")).split(text)
    cache.set(text, model="gpt-4o-mini", chunks=chunks)

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Runtime Model Registration

Register new models without editing the registry JSON:

import chunkrank

chunkrank.register_model("my-custom-model", max_context=200_000)

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Supported Models

54 models in the built-in registry, including:

Provider	Models
OpenAI	gpt-4o, gpt-4o-mini, gpt-4-turbo, gpt-4, gpt-3.5-turbo, o1, o3, o3-mini, o4-mini
Anthropic	claude-3-opus/sonnet/haiku, claude-3-5-sonnet/haiku, claude-sonnet-4-6, claude-opus-4-6
Google	gemini-1.5-pro, gemini-1.5-flash, gemini-2.0-flash, gemini-2.5-pro
Meta	Llama-3.1/3.2/3.3, Llama-4-Scout (10M ctx), Llama-4-Maverick
Mistral	mistral-7b, mixtral-8x7b, mistral-large, codestral
Cohere	command-r, command-r-plus, command-r7b
DeepSeek	deepseek-v3, deepseek-r1
Qwen	qwen2.5-72b-instruct, qwen2.5-coder-32b-instruct

Unknown models fall back to 128k context with tiktoken (o200k_base).

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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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Requirements

• Python 3.10+
• numpy, scikit-learn, rank-bm25

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License

Apache 2.0 — see LICENCE.

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Community

• Contributors
• Changelog
• Issues: https://github.com/AmitoVrito/chunkrank/issues