ragaroo 0.1.1

A lightweight retrieval benchmarking toolkit for experimenting with retrieval pipelines.

Ragaroo: A Lightweight Retrieval Benchmarking Library for Custom Datasets

Ragaroo is a lightweight Python library for benchmarking retrieval pipelines on custom datasets. It is designed for practical RAG/retrieval experiments: load a dataset, define pipelines, run an experiment, compare quality and latency, and save reproducible artifacts.

Features

• Strict dataset loading from corpus.jsonl, queries.jsonl, and qrels.tsv
• BM25 retrieval with bm25s
• Dense retrieval with Sentence Transformers and FAISS
• Sparse retrieval with Sentence Transformers sparse encoders
• Hybrid retrieval with reciprocal-rank fusion or average-score fusion
• Cross-encoder reranking and retriever-as-reranker workflows
• LLM-based query augmentation: HyDE, spelling correction, intent clarification
• Ranking metrics, latency metrics, CSV/JSON exports, plots, and manifests
• Local index caching for repeatable experiments

Installation

For most users, installation should be simple:

pip install ragaroo

OR,

If you are working directly from the GitHub repository:

git clone https://github.com/marcharaoui/ragaroo.git
cd ragaroo

For development:

uv sync --extra dev

From a local checkout:

pip install -e .

Ragaroo requires Python 3.10+.

Dataset Format

Each dataset folder must contain:

my_dataset/
  corpus.jsonl
  queries.jsonl
  qrels.tsv

corpus.jsonl:

{"id":"d1","text":"Paris is the capital of France.","metadata":{"source":"wiki"}}
{"id":"d2","text":"Berlin is the capital of Germany."}

queries.jsonl:

{"id":"q1","text":"capital of France"}
{"id":"q2","text":"capital of Germany"}

qrels.tsv:

query-id	corpus-id	score
q1	d1	1
q2	d2	1

id and _id are both accepted. Ragaroo validates missing files, malformed JSON, duplicate ids, empty text, and qrels that reference unknown queries or documents.

Quickstart

import ragaroo as roo

dataset = roo.Dataset.from_folder("data/your_custom_dataset")
embedder = roo.SentenceTransformerEmbedder("intfloat/e5-small-v2")

pipelines = [
    roo.Pipeline(
        name="bm25",
        retriever=roo.BM25Retriever(top_k=10),
    ),
    roo.Pipeline(
        name="dense_hnsw",
        retriever=roo.DenseRetriever(
            embedder=embedder,
            top_k=10,
            index_technique="hnsw",
            distance_metric="cosine",
        ),
    ),
    roo.Pipeline(
        name="hybrid_rrf",
        retriever=roo.HybridRetriever(
            retriever_1=roo.DenseRetriever(embedder=embedder, top_k=10),
            retriever_2=roo.BM25Retriever(top_k=10),
            top_k=10,
        ),
    ),
]

report = roo.Experiment(
    dataset=dataset,
    pipelines=pipelines,
    show_progress=True,
).run()

report.summary(sort_by="mrr@10")

Common Patterns

Reranking with a cross-encoder:

roo.Pipeline(
    name="dense_rerank",
    retriever=roo.DenseRetriever(embedder=embedder, top_k=50),
    reranker=roo.CrossEncoderReranker(
        model_name="cross-encoder/ms-marco-MiniLM-L-6-v2",
        top_k=10,
    ),
)

Using a retriever as a reranker:

roo.Pipeline(
    name="bm25_then_dense",
    retriever=roo.BM25Retriever(top_k=50),
    reranker=roo.DenseRetriever(embedder=embedder, top_k=10),
)

HyDE query augmentation:

import os

provider = roo.OpenRouterProvider(
    api_key=os.environ["OPENROUTER_API_KEY"],
    model=os.environ.get("OPENROUTER_MODEL", "openai/gpt-4o-mini"),
)

roo.Pipeline(
    name="dense_hyde",
    retriever=roo.DenseRetriever(embedder=embedder, top_k=10),
    query_augmentation=[
        roo.HyDEQueryTransform(
            provider=provider,
            user_prompt="Write a concise support-style passage for this query.",
            system_prompt="Return only the passage.",
            temperature=0.3,
            max_tokens=220,
        )
    ],
)

If user_prompt, system_prompt, or temperature is omitted, the transform uses its default. Custom user_prompt values are prepended to the dataset query.

By default, experiments evaluate all queries. While prototyping, pass query_limit=50 or any other integer directly to Experiment.

Metrics

Supported quality metrics:

• recall@k
• precision@k
• mrr@k
• map@k
• hit_rate@k
• ndcg@k

Supported latency metrics:

• latency_ms
• query_augmentation_latency_ms
• retrieval_latency_ms
• rerank_latency_ms
• p50_latency_ms
• p95_latency_ms
• total_time_s

recall, precision, mrr, map, and hit_rate treat qrel scores greater than zero as relevant. ndcg uses graded qrel scores.

Experiment Outputs

Each experiment saves:

• report.json
• report.csv
• manifest.json
• config.json
• plots under plots/

With store_query_results=True, Ragaroo also saves per-query metrics and retrieved ids.

The manifest records dataset hashes, pipeline configs, pipeline hashes, dependency versions, platform metadata, git metadata when available, notes, tags, and random seed.

Examples

See examples/README.md.

Included examples:

• examples/compare_retrievers.py
• examples/compare_topk.py
• examples/compare_models.py
• examples/compare_rerank.py
• examples/compare_hyde.py
• examples/compare_multiple_datasets.py

Most examples default to data/your_custom_dataset. Change the constants at the top of each script to use another dataset, model, cache folder, or query limit.

Model Access

Use HF_TOKEN for gated Hugging Face models or higher Hub rate limits. Use OPENROUTER_API_KEY only for LLM-based query augmentation. Dataset paths, model choices, model cache folders, and query limits are regular Python arguments in scripts and examples, not environment variables.

To keep model downloads in a project-local folder:

import ragaroo as roo

roo.store_models("./models")

Limitations

• Ragaroo evaluates retrieval, not answer generation.
• It assumes the benchmark target is retrieving the right passages for each query.
• It currently loads dataset files into memory.
• Generated query augmentation can affect reproducibility unless the provider/model is controlled.

Development Note

Ragaroo was built with human engineering work and assistance from LLM technologies. The code is tested, but LLM-assisted projects can still contain mistakes. Please validate benchmark setup, metrics, and outputs before relying on them for important decisions.

Author

Ragaroo is created and maintained by Marc Haraoui.

Citation

@software{haraoui_ragaroo,
  author = {Marc Haraoui},
  title = {Ragaroo: A Lightweight Retrieval Benchmarking Library for Custom Datasets},
  year = {2026},
  url = {https://github.com/marcharaoui/ragaroo}
}