cognigraph-chunker 2.2.4

Fast text chunking toolkit: fixed-size, delimiter, semantic, cognition-aware, intent-driven, topology-aware, enriched, and adaptive strategies

cognigraph-chunker

Fast text chunking toolkit for RAG pipelines, written in Rust with Python bindings.

Eight chunking strategies — fixed-size, delimiter, semantic, cognition-aware, intent-driven, topology-aware, enriched, and adaptive — plus token-aware merging, signal-processing primitives, and five intrinsic quality metrics.

Install

pip install cognigraph-chunker

Quick start

from cognigraph_chunker import Chunker, OllamaProvider, SemanticConfig, semantic_chunk

# Fixed-size chunking — no external services needed
for chunk in Chunker("Your long document text here...", size=1024):
    print(chunk)

# Semantic chunking with a local Ollama embedding model
provider = OllamaProvider(model="nomic-embed-text")
result = semantic_chunk(open("document.md").read(), provider, SemanticConfig())
for text, offset in result.chunks:
    print(f"[{offset}] {text[:80]}")

Cognition-aware chunking (entity/discourse-preserving boundaries with quality metrics), intent-driven, topology-aware, enriched, and adaptive strategies are exposed as cognitive_chunk, intent_chunk, topo_chunk, enriched_chunk, and adaptive_chunk, alongside evaluate_chunks for standalone quality scoring.

Full documentation, the CLI, and the REST API live in the project repository.

MIT licensed.