rt-seg 1.1.0

rt_seg is a Python 3.12.x package for segmenting reasoning traces into coherent chunks and (optionally) assigning a label to each chunk.

RT-SEG — Reasoning Trace Segmentation

rt_seg is a Python 3.12.x package for segmenting reasoning traces into coherent chunks and (optionally) assigning a label to each chunk.

The main entry point is:

RTSeg

(from rt_segmentation.seg_factory)

It orchestrates one or more segmentation engines and — if multiple engines are used — an offset aligner that fuses their boundaries into a single segmentation.

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https://github.com/user-attachments/assets/d38d8e4f-ab49-4c16-a9eb-ab7d789e64ce

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Installation

Install from PyPI (once published)

pip install rt-seg

Development Install (repo checkout)

python3.12 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Install TUI APP with Docker/Podman

Note: requires NVIDIA GPU (12.4.1+).

docker build -f docker/Dockerfile -t mytui:gpu .
docker run -it --rm --gpus all mytui:gpu

# podman build -f docker/Dockerfile -t mytui:gpu .
# podman run -it --rm --device nvidia.com/gpu=all mytui:gpu

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Core Concepts

What RTSeg Returns

RTSeg(trace) produces:

• offsets: list[tuple[int, int]] — character offsets into the trace
• labels: list[str] — one label per segment

You can reconstruct segments via:

segments = [trace[s:e] for (s, e) in offsets]

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Segmentation Base Unit (seg_base_unit)

Most engines operate on a base segmentation first:

• "clause" (default) → finer granularity
• "sent" → coarser segmentation

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Quickstart — Single Engine

from rt_seg import RTSeg
from rt_seg import RTRuleRegex

trace = "First step... Then second step... Finally conclude."

segmentor = RTSeg(
    engines=RTRuleRegex,
    seg_base_unit="clause",
)

offsets, labels = segmentor(trace)

for (s, e), label in zip(offsets, labels):
    print(label, "=>", trace[s:e])

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Multiple Engines + Late Fusion

If you pass multiple engines, you must provide an aligner.

from rt_seg import RTSeg
from rt_seg import RTRuleRegex
from rt_seg import RTBERTopicSegmentation
from rt_seg import OffsetFusionGraph

segmentor = RTSeg(
    engines=[RTRuleRegex, RTBERTopicSegmentation],
    aligner=OffsetFusionGraph,
    label_fusion_type="concat",  # or "majority"
    seg_base_unit="clause",
)

offsets, labels = segmentor(trace)

Label Fusion Modes

• "majority" — choose most frequent label
• "concat" — concatenate labels (useful for debugging)

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Available Engines

Rule-Based

• RTRuleRegex
• RTNewLine

Probabilistic

• RTLLMForcedDecoderBased
• RTLLMSurprisal
• RTLLMEntropy
• RTLLMTopKShift
• RTLLMFlatnessBreak

LLM Discourse / Reasoning Schemas

• RTLLMThoughtAnchor
• RTLLMReasoningFlow
• RTLLMArgument

LLM

• RTLLMOffsetBased
• RTLLMSegUnitBased

PRM-Based

• RTPRMBase

Topic / Semantic / NLI

• RTBERTopicSegmentation
• RTEmbeddingBasedSemanticShift
• RTEntailmentBasedSegmentation
• RTZeroShotSeqClassification
• RTZeroShotSeqClassificationRF
• RTZeroShotSeqClassificationTA

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Engine Configuration

You can override engine parameters at call time:

offsets, labels = segmentor(
    trace,
    model_name="Qwen/Qwen2.5-7B-Instruct",
    chunk_size=200,
)

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Available Aligners

• OffsetFusionGraph
• OffsetFusionFuzzy
• OffsetFusionIntersect
• OffsetFusionMerge
• OffsetFusionVoting

Strategy	Behavior
Intersect	Conservative
Merge	Permissive
Voting / Graph / Fuzzy	Balanced (recommended)

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Implementing a Custom Engine

from typing import Tuple, List
from rt_seg import SegBase

class MyEngine(SegBase):
    @staticmethod
    def _segment(trace: str, **kwargs) -> Tuple[List[tuple[int, int]], List[str]]:
        offsets = [(0, len(trace))]
        labels = ["UNK"]
        return offsets, labels

Using Base Offsets

base_offsets = SegBase.get_base_offsets(trace, seg_base_unit="clause")

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Implementing a Custom Aligner

from typing import List, Tuple

class MyOffsetFusion:
    @staticmethod
    def fuse(engine_offsets: List[List[Tuple[int, int]]], **kwargs):
        return engine_offsets[0]

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Running the TUI (Without Docker)

python3.12 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python -m tui

If needed:

python src/tui.py

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SurrealDB (Optional — Reproducible Experiments)

Required only for full experiment pipeline.

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1️⃣ Start SurrealDB (Docker Recommended)

docker run --rm -it \
  -p 8000:8000 \
  -v "$(pwd)/data:/data" \
  surrealdb/surrealdb:latest \
  start --user root --pass root file:/data/surreal.db

Endpoints:

• WebSocket: ws://127.0.0.1:8000/rpc
• HTTP: http://127.0.0.1:8000

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2️⃣ Import Database Snapshot

surreal import \
  --endpoint ws://127.0.0.1:8000/rpc \
  --username root \
  --password root \
  --namespace NR \
  --database RT \
  ./data/YOUR_EXPORT_FILE.surql

⚠️ Make sure namespace/database match your config.

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3️⃣ Configure data/sdb_login.json

{
  "user": "root",
  "pwd": "root",
  "ns": "NR",
  "db": "RT",
  "url": "ws://127.0.0.1:8000/rpc"
}

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4️⃣ Run Experiment Scripts

python src/eval_main.py
python src/evo.py

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Docker + GPU Setup

Requirements

• Linux
• NVIDIA GPU
• NVIDIA driver
• Docker
• NVIDIA Container Toolkit

Verify:

nvidia-smi
docker run --rm --gpus all nvidia/cuda:12.4.1-base-ubuntu22.04 nvidia-smi

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CUDA Compatibility Rule

Host driver CUDA ≥ Container CUDA

Host	Container	Result
12.8	12.4	✅
12.8	13.1	❌
13.x	12.4	✅

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Build Image

docker build -f docker/Dockerfile -t rt-seg:gpu .

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Run

./run_tui_app_docker.sh

Internally:

docker run -it --rm --gpus all rt-seg:gpu

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Summary

RT-SEG provides:

• Modular segmentation engines
• Late fusion strategies
• LLM-based reasoning segmentation
• Reproducible DB-backed experiments
• GPU Docker deployment

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