bharatrag 0.1.9

RAG evaluation library for Indian languages (Hindi, Marathi)

BharatRAG 🇮🇳

RAG Evaluation Library for Indian Languages

BharatRAG is the first open-source RAG evaluation library built specifically for Indian languages (Hindi, Marathi, Tamil, Bengali, Telugu, Gujarati).

Existing tools like RAGAS are built and tested on English data. BharatRAG fills the gap — giving developers a reliable way to measure RAG quality in Indic languages, with no API key and no cost.

BharatRAG was created and is maintained by Pradnya Gundu — original author and project lead. First released July 2026.

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The Problem

RAG (Retrieval Augmented Generation) systems are being deployed across India for:

• Government scheme chatbots (PM Kisan, Ayushman Bharat)
• Health information systems in regional languages
• EdTech platforms for vernacular learners
• Banking and insurance customer support

But there is no standard way to evaluate whether these systems are actually working correctly in Hindi, Marathi, or other Indian languages. RAGAS — the most popular RAG evaluation tool — uses English-first embedding models that produce unreliable scores for Indic text.

BharatRAG solves this.

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What it measures

BharatRAG computes the RAG Triad in Indian languages:

Metric	Question it answers
Context Relevance	Did we retrieve the right documents?
Groundedness	Is the answer based on the context, or hallucinated?
Answer Relevance	Does the answer actually address the question?

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Installation

pip install bharatrag

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Quick Start

from bharatrag import evaluate

results = evaluate(
    questions=["पीएम किसान योजना में कितने रुपये मिलते हैं?"],
    contexts=[[
        "पीएम किसान सम्मान निधि योजना के तहत किसानों को",
        "प्रति वर्ष 6000 रुपये तीन किश्तों में मिलते हैं।"
    ]],
    answers=["पीएम किसान योजना में किसानों को 6000 रुपये मिलते हैं।"],
    language="hindi"
)

print(results)
# {
#   'context_relevance': 0.72,
#   'groundedness': 1.0,
#   'answer_relevance': 0.66,
#   'overall': 0.79,
#   'language': 'hindi',
#   'num_questions': 1
# }

Marathi

results = evaluate(
    questions=["पीएम किसान योजनेत किती रुपये मिळतात?"],
    contexts=[["पीएम किसान सन्मान निधी योजनेंतर्गत शेतकऱ्यांना दरवर्षी 6000 रुपये मिळतात."]],
    answers=["पीएम किसान योजनेत 6000 रुपये मिळतात."],
    language="marathi"
)

Tamil

results = evaluate(
    questions=["பிஎம் கிசான் திட்டத்தில் எவ்வளவு பணம் கிடைக்கிறது?"],
    contexts=[["பிஎம் கிசான் திட்டத்தின் கீழ் விவசாயிகளுக்கு ஆண்டுக்கு 6000 ரூபாய் கிடைக்கிறது."]],
    answers=["பிஎம் கிசான் திட்டத்தில் 6000 ரூபாய் கிடைக்கிறது."],
    language="tamil"
)

Telugu

results = evaluate(
    questions=["పీఎం కిసాన్ పథకంలో రైతులకు ఎంత డబ్బు లభిస్తుంది?"],
    contexts=[["ప్రధానమంత్రి కిసాన్ సమ్మాన్ నిధి పథకం కింద రైతులకు సంవత్సరానికి 6000 రూపాయలు లభిస్తుంది."]],
    answers=["పీఎం కిసాన్ పథకంలో 6000 రూపాయలు లభిస్తాయి."],
    language="telugu"
)

Individual metrics

from bharatrag.metrics.context_relevance import ContextRelevance

cr = ContextRelevance(language="hindi")
score = cr.score(
    question="भारत की राजधानी क्या है?",
    contexts=["भारत की राजधानी नई दिल्ली है।"]
)
print(score)  # 0.61

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Framework Integrations

BharatRAG plugs directly into LangChain and LlamaIndex — evaluate Indic RAG systems inside your existing pipelines.

LangChain

pip install bharatrag[langchain]

from bharatrag.integrations import BharatRAGLangChainEvaluator

evaluator = BharatRAGLangChainEvaluator(metric="groundedness", language="hindi")

result = evaluator.evaluate_strings(
    prediction="पीएम किसान योजना में 6000 रुपये मिलते हैं।",
    reference="प्रधानमंत्री किसान सम्मान निधि योजना के तहत किसानों को 6000 रुपये मिलते हैं।",
    input="पीएम किसान योजना में कितने रुपये मिलते हैं?"
)
print(result)  # {'score': 1.0}

LlamaIndex

pip install bharatrag[llamaindex]

from bharatrag.integrations import BharatRAGLlamaIndexEvaluator

evaluator = BharatRAGLlamaIndexEvaluator(metric="overall", language="hindi")

result = evaluator.evaluate(
    query="पीएम किसान योजना में कितने रुपये मिलते हैं?",
    contexts=["प्रधानमंत्री किसान सम्मान निधि योजना के तहत किसानों को 6000 रुपये मिलते हैं।"],
    response="पीएम किसान योजना में 6000 रुपये मिलते हैं।"
)
print(result.score)

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Supported Languages

Language	Embedding Model
Hindi	sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
Marathi	l3cube-pune/marathi-sentence-bert-nli
Tamil	l3cube-pune/tamil-sentence-bert-nli
Bengali	l3cube-pune/bengali-sentence-bert-nli
Telugu	l3cube-pune/telugu-sentence-bert-nli
Gujarati	l3cube-pune/gujarati-sentence-bert-nli
English	sentence-transformers/all-MiniLM-L6-v2

More languages coming soon — Bengali, Gujarati, Punjabi.

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Benchmark Dataset

BharatRAG ships with a hand-curated benchmark dataset of 70 QA pairs across Hindi, Marathi, and Tamil, spanning:

• Government schemes (PM Kisan, Ayushman Bharat, Jan Dhan, Ujjwala)
• Agriculture (crop insurance, drip irrigation, organic farming)
• Health (diabetes, TB, anaemia, sanitation)
• Education (Mid Day Meal, Beti Bachao, NEP 2020)
• Banking & Finance (UPI, KYC, net banking)

Each example includes a correct answer and a hallucinated answer for evaluation testing.

• Location in repo: data/benchmark.json
• On HuggingFace: PradnyaGundu/bharatrag-benchmark

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Why BharatRAG?

Feature	RAGAS	BharatRAG
English RAG evaluation	✅	✅
Hindi RAG evaluation	❌ Unreliable	✅
Marathi / Tamil evaluation	❌ Not supported	✅
Indic benchmark dataset	❌	✅
LangChain / LlamaIndex integration	✅	✅
Free, no API key needed	❌ (needs LLM judge)	✅ Fully offline

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Running Tests

pip install -e ".[dev]"
pytest tests/ -v

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Roadmap

• Hindi support
• Marathi support
• Tamil support
• 70-example benchmark dataset
• LangChain integration
• LlamaIndex integration
• Bengali, Gujarati, Punjabi support
• Streamlit UI for interactive evaluation
• Hinglish / code-switching support
• Benchmarking vs RAGAS / DeepEval
• Expand benchmark dataset to 500+ examples

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Contributors

Huge thanks to the community contributors who've helped shape BharatRAG:

• @rishabh-108272 — LangChain & LlamaIndex integrations, groundedness bug fix
• @AshayK003 — CI improvements, model caching, logging, dependency cleanup
• @Yashwanth-Kumar-Kotla — language-agnostic benchmark runner

Contributions welcome! See CONTRIBUTING.md.

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Author

Pradnya Gundu B.E. Artificial Intelligence & Data Science, APCOER Pune

• GitHub: @pradnyagundu
• LinkedIn: pradnya-gundu

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License

MIT License — free to use, modify, and distribute.