rMatch

Automated matching of recall segments to story segments.

rMatch matches each segment of a participant's recall to the corresponding segment(s) in the original story, using a large language model. Runs fully locally with google/gemma-4-31B-it, and with slightly higher performance in the cloud with Claude:

Model	short text (N=21)	long text (N=19)	movie transcripts (N=138)
google/gemma-4-31B-it	0.84	0.78	0.67
Claude Opus 4.6	0.87	0.8	0.7

Pearson r with human ratings.

Table of contents

• Table of contents
• Quickstart
• Installation
• Matcher object
  • MatcherCuda
  • MatcherAnthropic / MatcherOpenai
  • MatcherMac
  • MatcherHuggingface
• API keys
• Prompts
• Batch matching from files directly
• Benchmark results
• CLI

Quickstart

Local:

from rmatch import MatcherCuda

# MatcherCuda requires nvidia GPUs
matcher = MatcherCuda()
matches = matcher.match(
    story_segments=["The cat sat on the mat.", "It purred softly."],
    recall_segments=["A cat was on a mat."],
)
# [(0, [0])]  — recall segment 0 matched story segment 0

Installation

pip install rmatch              # API matchers + huggingface fallback
pip install rmatch[cuda]        # NVIDIA GPU (vLLM)
pip install rmatch[mac]         # Apple Silicon (MLX)

Requires Python 3.12 or 3.13.

Matcher object

The Matcher object is the main tool to do the matching:

Matcher	When to use
MatcherCuda	Run locally on NVIDIA GPUs with vLLM (pip install rmatch[cuda])
MatcherAnthropic	Best performance; Needs an Anthropic API key (default model: claude-opus-4-6)
MatcherOpenai	Cloud alternative; Needs an OpenAI API key (default: gpt-4.1)
MatcherMac	Run locally on Apple Silicon (pip install rmatch[mac])
MatcherHuggingface	Local fallback; works on any local archtiecture, but is not resource efficient

Each matcher implements a function matcher.match(story_segments, recall_segments) that expects:

• story_segments — ordered list of story segments.
• recall_segments — ordered list of one participant's recall segment strings.

The function returns one entry per recall segment in the format of (recall_index, [story_indices]) tuples (0-based):

[
    (0, [2, 5]),   # recall segment 0 -> matches story segments 2 and 5
    (1, []),       # recall segment 1 -> no match
    (2, [0]),      # recall segment 2 -> matches story segment 0
]

MatcherCuda

The main matcher to run locally - if you have nvidia gpus available. 94GB of VRAM (across multiple gpus) was enough to run gemma-4-31B-it without quantization. See here for quantized versions.

from rmatch import MatcherCuda

# install rmatch with `pip install rmatch[cuda]`
matcher = MatcherCuda()
matches = matcher.match(
    story_segments=["The cat sat on the mat.", "It purred softly."],
    recall_segments=["A cat was on a mat."],
)
# [(0, [0])]  — recall segment 0 matched story segment 0

Argument	Default	Notes
model_name	"gemma-4-31B-it"	Override the default model
prompt	"primary"	See Prompts
max_retries	10	Retries when the model output cannot be parsed
api_key	from .env / env	See API keys
window_size	5	Recall context window
max_new_tokens	1024	Max tokens generated per segment
max_model_len	90000	Max sequence length (prompt + generation); lower to save GPU memory
tensor_parallel_size	auto	Number of GPUs to use. Auto will use all of them.
gpu_memory_utilization	0.90	Fraction of GPU memory to use, see vLLM
verbose_errors	False	Log raw output on parse failures

MatcherAnthropic / MatcherOpenai

Use a cloud provider to do the matching. It's pretty inexpensive, and --dry_run will give you an approximate estimate of the cost. Usually a single recall doesn't cost more than 0.5$, even on frontier models.

from rmatch import MatcherAnthropic

# install rmatch with `pip install rmatch`
matcher = MatcherAnthropic(api_key="your_api_key")
matches = matcher.match(
    story_segments=["The cat sat on the mat.", "It purred softly."],
    recall_segments=["A cat was on a mat."],
)
# [(0, [0])]  — recall segment 0 matched story segment 0

You can also put your API key in a .env file instead of passing it directly (see API keys).

Argument	Default	Notes
model_name	"claude-opus-4-6" / "gpt-4.1"	Override the default model
prompt	"primary"	See Prompts
max_retries	10	Retries when the model output cannot be parsed
api_key	from .env / env	See API keys
window_size	5	Recall context window
dry_run	False	Estimate API cost without calling the API

MatcherMac

You can try running the matching on your mac with apple silicon! You'll probably need a lot of unified memory, or use a quantized model. The standard unsloth/gemma-4-E4B-it-MLX-8bit model should run on a mac with 24GB of unified memory.

from rmatch import MatcherMac

# install rmatch with `pip install rmatch[mac]`
matcher = MatcherMac()
matches = matcher.match(
    story_segments=["The cat sat on the mat.", "It purred softly."],
    recall_segments=["A cat was on a mat."],
)
# [(0, [0])]  — recall segment 0 matched story segment 0

Argument	Default	Notes
model_name	"unsloth/gemma-4-E4B-it-MLX-8bit"	Override the default model
prompt	"primary"	See Prompts
max_retries	10	Retries when the model output cannot be parsed
api_key	from .env / env	See API keys
window_size	5	Recall context window
max_new_tokens	300	Max tokens generated per segment
verbose_errors	False	Log raw output on parse failures

MatcherHuggingface

The fallback matcher that should work on any platform. Can run the same models as the Cuda/Mac matcher, and will achieve the same matching performance, but will require considerably more computing resources and be a lot slower.

from rmatch import MatcherHuggingface

# install rmatch with `pip install rmatch`
matcher = MatcherHuggingface()
matches = matcher.match(
    story_segments=["The cat sat on the mat.", "It purred softly."],
    recall_segments=["A cat was on a mat."],
)
# [(0, [0])]  — recall segment 0 matched story segment 0

Argument	Default	Notes
model_name	matcher-specific	Override the default model
prompt	"primary"	See Prompts
max_retries	10	Retries when the model output cannot be parsed
api_key	from .env / env	See API keys
window_size	5	Recall context window
quantization	none	"4bit" or "8bit" to reduce memory
batch_size	64	Inference batch size
max_new_tokens	300	Max tokens generated per segment
verbose_errors	False	Log raw output on parse failures

API keys

For MatcheAnthropic/MatcherOpenai you need the API key to access the models. For all local matchers, you made need the API key in form of the HF_token - to allow you access to download a large language model from the hub.

rMatch will look for the API key in this order (first match wins):

1. api_key argument in Python
2. .env file in the working directory
3. Environment variables in your shell

ANTHROPIC_API_KEY="your_api_key"   # anthropic
OPENAI_API_KEY="your_api_key"      # openai
HF_TOKEN="your_hf_token"           # huggingface, mac, cuda (model download)

Prompts

All matchers share the same prompt templates, but you can change it. Pass prompt="primary_no_story". Default is primary.

Prompt	Full story	Segmented story	Chain of thought	Notes
primary	yes	yes	yes	Default; most complete prompt
primary_no_story	no	yes	yes	For long stories that exceed the context window
primary_no_cot	yes	yes	no	Ablation: no chain-of-thought
primary_no_story_no_cot	no	yes	no	Minimal prompt
secondary	yes	yes	yes	Alternative wording with XML output

Batch matching from files directly

If your story and recalls are saved as .txt or .json files, you can match them as a batch with match().

from pathlib import Path
from rmatch import match, MatcherCuda


matcher_gemma = MatcherCuda()

results = match(
    matcher=matcher_gemma,
    story_file=Path("story.txt"),
    recall_file=Path("recalls/"),   # file or directory of subject files
)

This loads all subjects, runs matching, and writes a JSON results file next to your recall data with the following format:

{
  "matcher_name": "anthropic",
  "story_name": "story",
  "story_segmentation": "lines",
  "recall_segmentation": "lines",
  "matches": {
    "sub-001": [[0, [3, 7]], [1, [12]]],
    "sub-002": [[0, [1]], [1, [5, 6]]]
  }
}

Each subject maps to a list of [recall_segment_id, [matched_story_segment_ids...]] pairs.

Benchmark results

Matcher	Testset	F1	Precision	Recall	Pearson R
MatcherCuda:google/gemma-4-31B-it	alice	.84	.8	.89	.83
MatcherCuda:google/gemma-4-31B-it	monthiversary	.79	.72	.86	.78
MatcherCuda:google/gemma-4-31B-it	memsearch	.67	.56	.84	.64

• alice (medium text) are 21 recalls with a length of ~200 words, and a story length of ~700 words.
• monthiversary (long text) are 19 recalls with a length of ~1000 words, and a story length of ~4700.
• memsearch (short movie transcripts) are 138 recalls with a length of ~140 words, and a story transcript length of ~240 words.

Replicate results by downloading rBench:

git clone git@github.com:GabrielKP/rBench.git

Add to .env or environment:

BENCHMARK_ROOT="path/to/rBench"

Run:

uv run src/rmatch/evaluate.py {alice,monthiversary,memsearch}

CLI

If you prefer the command line over a Python script:

rmatch story.txt recalls/ --matcher anthropic

See rmatch --help for all options (model, prompt, window size, etc.).