chunklabel 0.1.7

Split text into semantically coherent, LLM-categorized chunks

chunklabel

A Python library for splitting text into categorized chunks using an LLM.

Overview

chunklabel segments text into semantically coherent spans, assigning a free-form category to each. Categories are named by the LLM without a predefined schema. Each chunk's quote is a verbatim excerpt from the source text, aligned back to the original after LLM output.

from chunklabel import ChunkLabeler

labeler = ChunkLabeler()
chunks = labeler.split(
    "The project kicked off in January with a small team. "
    "Budget constraints forced a scope reduction in March. "
    "Despite the setbacks, the product launched successfully in June."
)

# [
#   Chunk(category="initiation", quote="The project kicked off in January with a small team", start=0,   end=51),
#   Chunk(category="obstacle",   quote="Budget constraints forced a scope reduction in March", start=53,  end=104),
#   Chunk(category="outcome",    quote="the product launched successfully in June", start=120, end=160),
# ]

Installation

pip install chunklabel

Data structures

The LLM returns raw chunks without span information. Alignment is performed as a separate step, producing the final Chunk with character-level positions.

# Intermediate: LLM output
@dataclass
class RawChunk:
    category: str   # Free-form category name assigned by the LLM
    quote: str      # Verbatim excerpt (may contain minor transcription noise)

# Final: after alignment
@dataclass
class Chunk:
    category: str   # Same as RawChunk
    quote: str      # Excerpt aligned to source text
    start: int      # Start index in source text
    end: int        # End index in source text

Pipeline

Input text
     │
     ▼
LLM  →  [{category, quote}, ...]   (RawChunk list)
     │
     ▼
rapidfuzz alignment  →  (start, end) resolved per chunk
     │
     ▼
Span post-processing  (lenient mode)
     │  gap-filling / overlap resolution
     ▼
Chunk list

Lenient mode

• Gaps: unassigned spans between chunks are filled automatically as category="uncategorized"
• Overlaps: the earlier chunk takes priority; the later chunk's start is pushed forward

Category normalization (offline)

After processing multiple texts, category names can drift across runs. A dedicated normalization step lets the LLM consolidate them in batch.

from chunklabel import Normalizer

normalizer = Normalizer()
normalizer.build_mapping(all_chunks)
# {"kick-off": "initiation", "project start": "initiation", "blocker": "obstacle", ...}

normalized_chunks = normalizer.apply(all_chunks)

The mapping is stored internally after build_mapping, so it can be passed to apply implicitly. To reuse the mapping across runs without calling the LLM again:

# Save after building
normalizer.save("mapping.json")

# Restore later
normalizer = Normalizer.load("mapping.json")
normalized_chunks = normalizer.apply(all_chunks)

Normalization runs offline over the full category inventory, so the LLM can make globally consistent decisions rather than local ones.

Configuration

labeler = ChunkLabeler(
    model="gpt-4o",          # LLM model to use
    fuzzy_threshold=80,      # Match threshold for rapidfuzz alignment (0–100)
)

Using local LLMs

chunklabel uses LangChain's BaseChatModel interface internally, so any compatible model can be passed via the llm parameter.

Ollama

from langchain_ollama import ChatOllama
from chunklabel import ChunkLabeler

labeler = ChunkLabeler(llm=ChatOllama(model="llama3"))

llama.cpp (OpenAI-compatible server)

from langchain_openai import ChatOpenAI
from chunklabel import ChunkLabeler

labeler = ChunkLabeler(llm=ChatOpenAI(
    model="llama3",
    base_url="http://localhost:8080/v1",
    api_key="not-used",
))

Note: local models must support structured output (JSON mode). If with_structured_output is not reliable, wrap the model with a JSON-enforcing layer before passing it in.

Downstream use cases

The Chunk list produced by chunklabel is designed as input for further analysis:

• NLI: score the relationship between hypotheses and chunk categories
• NER: analyze co-occurrence between entity labels and categories
• Relation extraction: map entity-pair relations to chunk categories
• Conditional generation: use category as a conditioning signal for language models

License

MIT