lasr 0.1.0

LASER (Least Action Semantic Router) — globally optimal text chunking for RAG

LASR — Least Action Semantic Router

Pronounced "laser"

Globally optimal text chunking for RAG pipelines. LASR treats chunking as a physics-inspired optimization problem — it considers every possible way to partition a document and selects the one that minimizes a global objective balancing semantic cohesion against boundary cost.

No heuristics. No greedy local decisions. Just dynamic programming that finds the mathematically optimal partition.

Install

pip install lasr
python -m spacy download en_core_web_sm

Quick Start

from lasr import chunk

chunks = chunk(open("document.txt").read())

for c in chunks:
    print(f"[{c.start_char}:{c.end_char}] ({c.num_sentences} sentences)")
    print(c.text)
    print("---")

Control Granularity

from lasr import chunk

# Fewer, larger chunks (higher alpha = more expensive boundaries)
chunks = chunk(document, alpha=3.0)

# More, smaller chunks
chunks = chunk(document, alpha=1.5)

# Adjust sentence constraints
chunks = chunk(document, min_sentences=3, max_sentences=20)

Power-User API

For full control, use LaserPipeline and LaserConfig directly:

from lasr import LaserPipeline, LaserConfig

config = LaserConfig(
    alpha_base=2.5,     # boundary cost
    rho=1.0,            # tension coefficient
    l_min=5,            # min sentences per chunk
    l_max=30,           # max sentences per chunk
    model_name="all-MiniLM-L6-v2",
)

pipeline = LaserPipeline(config)
chunks = pipeline.chunk(text)

# Each chunk has context bleed for richer retrieval
for c in chunks:
    print(c.text)               # core DP-optimal text
    print(c.text_with_context)  # with 1-sentence bleed from neighbors

CLI

lasr chunk document.txt --alpha 2.5 --format json
lasr chunk document.txt --format text --output chunks.txt
lasr chunk document.txt --encoder openai --model text-embedding-3-large

Parameters

Parameter	Default	Effect
alpha / alpha_base	2.5	Boundary cost. Higher = fewer, larger chunks.
rho	1.0	Tension coefficient (anchor parameter).
min_sentences / l_min	5	Minimum sentences per chunk.
max_sentences / l_max	30	Maximum sentences per chunk.
w_struct	0.25	Structural discount (headers, double newlines).
w_bind	1.0	Coreference binding penalty (pronouns).
w_disc	0.3	Discourse connective penalty.

Benchmark Highlights

All results use all-MiniLM-L6-v2 (22M parameters, 384 dimensions) with alpha=2.5.

Dataset	Domain	LASR Recall@5	Next Best	Margin
MSMARCO	Web passages	0.999	0.985	+0.014
HotpotQA	Multi-hop QA	0.974	0.972	+0.002
FinanceBench	SEC filings	0.930	0.629	+0.301
CUAD	Legal contracts	0.826	0.775	+0.051

LASR places first on every retrieval benchmark tested. On FinanceBench, the margin over the next best method is 30 percentage points.

How It Works

LASR models each document as a chain of semantic units (sentences) and finds the partition that minimizes:

Action = Tension + Boundary Cost

• Tension measures semantic dispersion inside each chunk (cosine distance to centroid via prefix sums)
• Boundary Cost (alpha) penalizes each split, preventing over-fragmentation

The optimization is solved exactly via dynamic programming in O(T * L_max) time, where T is the number of sentences. No approximations, no sampling — the same input always produces the same output.

Development

git clone https://github.com/lasr-chunker/lasr
cd lasr
pip install -e ".[dev]"
python -m spacy download en_core_web_sm
pytest

License

MIT