🎯 Evret

A focused, lightweight retriever evaluation framework with standard Information Retrieval metrics

Evret brings standard Information Retrieval metrics to your recommendation, RAG and search systems. Evaluate retrievers with Hit Rate, Recall, Precision, MRR, NDCG, and Average Precision in just a few lines of code. Built for simplicity, extensibility, and seamless integration with vector databases and AI frameworks.

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🌟 Overview

Evret is a modern Python framework designed for evaluating retrieval systems in Information Retrieval pipelines and search applications. It provides:

• Standard IR Metrics: Hit Rate, Recall, Precision, MRR, NDCG, and Average Precision
• Judge-Based Matching: Token overlap, semantic, and LLM judges for text relevance
• Vector Database Support: Native adapters for Qdrant and other vector databases
• Framework Integration: Adapters for LangChain and LlamaIndex

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🚀 Quick Start

Installation

pip install evret

For optional integrations:

pip install evret[all]

# Install specific integrations
pip install "evret[qdrant]"
pip install "evret[langchain]"
pip install "evret[semantic]"

5-Minute Evaluation

from evret import EvaluationDataset, Evaluator, HitRate, MRR, NDCG, TokenOverlapJudge

dataset = EvaluationDataset.from_json("eval_data.json")

evaluator = Evaluator(
    retriever=my_retriever,
    metrics=[HitRate(k=4), MRR(k=4), NDCG(k=4)],
    judge=TokenOverlapJudge(min_tokens=2, overlap_ratio=0.6),
)

results = evaluator.evaluate(dataset)
print(results.summary())

# Optional: Export results
results.to_json("results.json")
results.to_csv("results.csv")

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🛠 Local Development Setup

Use uv for local development:

uv venv
source .venv/bin/activate
uv pip install -e .

Install all optional integrations:

uv pip install -e ".[all]"

Run the default test suite:

pytest

See tests/README.md for coverage areas, optional integration tests, and test setup notes.

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📊 Metrics

Evret supports all standard Information Retrieval metrics:

Metric	Description	Use Case
Hit Rate@k	% of queries with at least one relevant doc in top-k	Binary relevance, recall-focused
Recall@k	% of relevant docs found in top-k	Comprehensive retrieval
Precision@k	% of top-k results that are relevant	Precision-focused systems
MRR@k	Mean Reciprocal Rank of first relevant doc	Single-answer retrieval
NDCG@k	Normalized Discounted Cumulative Gain	Rank-aware binary relevance quality
Average Precision@k	Area under precision-recall curve	Overall ranking quality

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📋 Evaluation Datasets

Create evaluation datasets with queries and expected answers for judge-based evaluation:

from evret import EvaluationDataset, QueryExample, DocumentExample

dataset = EvaluationDataset(
    documents=[
        DocumentExample(doc_id="doc_1", text="Python uses pip for packages."),
        DocumentExample(doc_id="doc_2", text="Virtual environments isolate dependencies."),
    ],
    queries=[
        QueryExample(
            query_id="q1",
            query_text="How to install Python packages?",
            expected_answers=["pip install"]  # Judge matches this against retrieved text
        )
    ]
)

Load datasets from JSON or CSV files:

dataset = EvaluationDataset.from_json("eval_data.json")
dataset = EvaluationDataset.from_csv("eval_data.csv")

For detailed dataset format documentation, classic IR evaluation with document IDs, and more examples, see the Dataset Format Guide

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⚖️ Judges

Judges decide whether retrieved text matches the expected text in your evaluation dataset. Evaluator uses TokenOverlapJudge() by default, and you can pass judge= when you want explicit matching behavior.

from evret import Evaluator, HitRate, Recall
from evret.judges import TokenOverlapJudge

evaluator = Evaluator(
    retriever=my_retriever,
    metrics=[HitRate(k=4), Recall(k=4)],
    judge=TokenOverlapJudge(min_tokens=2, overlap_ratio=0.6),
)

Use SemanticJudge for embedding similarity and LLMJudge for LLM-provider based judgment.

from evret.judges import LLMJudge, SemanticJudge

semantic_judge = SemanticJudge(threshold=0.75)
llm_judge = LLMJudge(provider="openai", model="gpt-4o-mini")

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🔌 Integrations

Qdrant Vector Database

from evret.retrievers import QdrantRetriever

retriever = QdrantRetriever(
    collection_name="docs",
    query_encoder=embed_query,
    url="http://localhost:6333",
    id_field="doc_id",
)

LangChain Integration

from evret.integrations import LangChainRetrieverAdapter

# Wrap any Evret retriever for use in LangChain
lc_retriever = LangChainRetrieverAdapter(evret_retriever=retriever, k=5)
docs = lc_retriever.invoke("what is information retrieval?")

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🧪 Testing

Run the default suite:

pytest

Run Docker-backed integration tests:

EVRET_RUN_INTEGRATION=1 pytest -m integration

More details are in tests/README.md.

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📚 Documentation

Evret docs use MkDocs with Material theme.

Install docs dependencies:

uv pip install -e ".[docs]"

Run docs locally:

mkdocs serve

Build static docs:

mkdocs build

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📄 License

MIT License - see LICENSE for details.

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🤝 Contributing

Contributions are welcome! Please read our Contributing Guide for details on our code of conduct and the process for submitting pull requests.

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📚 Citation

If you use Evret in your research, please cite:

@software{evret2026,
  title={Evret: A Focused Retriever Evaluation Framework},
  author={lucifertrj},
  year={2026},
}

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**Built with ❤️ for the Information Retrieval community**