HZYAI/RagScore — 22 Stars

Topics

#ai-evaluation #colab #dataset-generation #evaluation #fine-tuning #jupyter #llm #llm-as-a-judge #llmops #local-llm #mcp #ollama #privacy #qa-generation #rag #rag-evaluation #synthetic-data

Language breakdown

Python 100.0%

▸ Files click to expand

README

Generate QA datasets & evaluate RAG systems in 2 commands 🔒 Privacy-First • ⚡ Lightning Fast • 🤖 Any LLM • 🏠 Local or Cloud • 🌍 Multilingual English | 中文 | 日本語 | Deutsch

⚡ 2-Line RAG Evaluation

# Step 1: Generate QA pairs from your docs
ragscore generate docs/
Step 2: Evaluate your RAG system
ragscore evaluate http://localhost:8000/query

That's it. Get accuracy scores and incorrect QA pairs instantly.

============================================================
✅ EXCELLENT: 85/100 correct (85.0%)
Average Score: 4.20/5.0
============================================================
❌ 15 Incorrect Pairs:
1. Q: "What is RAG?"      Score: 2/5 - Factually incorrect
2. Q: "How does retrieval work?"      Score: 3/5 - Incomplete answer

🚀 Quick Start

Install

pip install ragscore              # Core (works with Ollama)
pip install "ragscore[openai]"    # + OpenAI support
pip install "ragscore[notebook]"  # + Jupyter/Colab support
pip install "ragscore[all]"       # + All providers

Already installed? Keep up to date — new versions add features like failure diagnosis and retrieved context capture:

> pip install --upgrade ragscore

Option 1: Python API (Notebook-Friendly)

Perfect for Jupyter, Colab, and rapid iteration. Get instant visualizations.

from ragscore import quick_test
1. Audit your RAG in one line
result = quick_test(
    endpoint="http://localhost:8000/query",  # Your RAG API
    docs="docs/",                            # Your documents
    n=10,                                    # Number of test questions
)
1b. Tailored QA — target specific audiences
result = quick_test(
    endpoint="http://localhost:8000/query",
    docs="docs/",
    audience="developers",                   # Who asks the questions?
    purpose="api-integration",               # What's the document for?
)
2. See the report
result.plot()
3. Inspect failures
bad_rows = result.df[result.df['score'] < 3]
display(badrows[['question', 'raganswer', 'reason']])

Rich Object API:

result.accuracy - Accuracy score
result.df - Pandas DataFrame of all results
result.plot() - 3-panel visualization (4-panel with detailed=True)
result.corrections - List of items to fix

Option 2: CLI (Production)

Generate QA Pairs

# Set API key (or use local Ollama - no key needed!)
export OPENAIAPIKEY="sk-..."
Generate from any document
ragscore generate paper.pdf
ragscore generate docs/*.pdf --concurrency 10
Tailored QA generation — target specific audiences
ragscore generate docs/ --audience developers --purpose faq
ragscore generate docs/ --audience customers --purpose "pre-sales"
ragscore generate docs/ --audience "compliance auditors" --purpose "security audit"

Evaluate Your RAG

# Point to your RAG endpoint
ragscore evaluate http://localhost:8000/query
Custom options
ragscore evaluate http://api/ask --model gpt-4o --output results.json

🔬 Detailed Multi-Metric Evaluation

Go beyond a single score. Add detailed=True to get 5 diagnostic dimensions per answer — in the same single LLM call.

result = quick_test(
    endpoint=my_rag,
    docs="docs/",
    n=10,
    detailed=True,  # ⭐ Enable multi-metric evaluation
)
Inspect per-question metrics
display(result.df[[
    "question", "score", "correctness", "completeness",
    "relevance", "conciseness", "faithfulness"
]])
Radar chart + 4-panel visualization
result.plot()

==================================================
✅ PASSED: 9/10 correct (90%)
Average Score: 4.3/5.0
Threshold: 70%
──────────────────────────────────────────────────
  Correctness: 4.5/5.0
  Completeness: 4.2/5.0
  Relevance: 4.8/5.0
  Conciseness: 4.1/5.0
  Faithfulness: 4.6/5.0
==================================================

| Metric | What it measures | Scale | |--------|------------------|-------| | Correctness | Semantic match to golden answer | 5 = fully correct | | Completeness | Covers all key points | 5 = fully covered | | Relevance | Addresses the question asked | 5 = perfectly on-topic | | Conciseness | Focused, no filler | 5 = concise and precise | | Faithfulness | No fabricated claims | 5 = fully faithful |

CLI:

ragscore evaluate http://localhost:8000/query --detailed

🔍 Failure Diagnosis (`--diagnose`)

When answers fail, --diagnose tells you why — retriever miss, generator hallucination, incomplete answer, or wrong interpretation:

ragscore evaluate http://localhost:8000/query --diagnose

🔍 Failure Diagnosis:
  Retriever Miss: 3 (42.9%)
  Generator Hallucination: 2 (28.6%)
  Incomplete Answer: 1 (14.3%)
  Wrong Interpretation: 1 (14.3%)

Uses the support_span already generated with each QA pair to give the judge grounding context. Combine with --detailed for full diagnostics:

ragscore evaluate http://localhost:8000/query --diagnose --detailed -o results.json

| Category | Meaning | |----------|---------| | Retriever Miss | RAG didn't retrieve the chunk containing the evidence | | Generator Hallucination | Retrieved correctly but fabricated information | | Incomplete Answer | Retrieved correctly but answer is partial | | Wrong Interpretation | Retrieved correctly but misunderstood the content |

📓 Full demo notebook — build a mini RAG and test it with detailed metrics.

>

🎯 Audience & Purpose demo — generate tailored QA for developers, customers, auditors, and more.

>

🏠 Ollama local demo — 100% private RAG evaluation with no API keys.

🏠 100% Private with Local LLMs

# Use Ollama - no API keys, no cloud, 100% private
ollama pull llama3.1
ragscore generate confidential_docs/*.pdf
ragscore evaluate http://localhost:8000/query

Perfect for: Healthcare 🏥 • Legal ⚖️ • Finance 🏦 • Research 🔬

Ollama Model Recommendations

RAGScore generates complex structured QA pairs (question + answer + rationale + support span) in JSON format. This requires models with strong instruction-following and JSON output capabilities.

| Model | Size | Min RAM | QA Quality | Recommended | |-------|------|---------|------------|-------------| | llama3.1:70b | 40GB | 48GB VRAM | Excellent | GPU server (A100, L40) | | qwen2.5:32b | 18GB | 24GB VRAM | Excellent | GPU server (A10, L20) | | llama3.1:8b | 4.7GB | 8GB VRAM | Good | Best local choice | | qwen2.5:7b | 4.4GB | 8GB VRAM | Good | Good local alternative | | mistral:7b | 4.1GB | 8GB VRAM | Good | Good local alternative | | llama3.2:3b | 2.0GB | 4GB RAM | Fair | CPU-only / testing | | qwen2.5:1.5b | 1.0GB | 2GB RAM | Poor | Not recommended |

Minimum recommended: 8B+ models. Smaller models (1.5B–3B) produce lower quality support spans and may timeout on longer chunks.

Ollama Performance Guide

# Recommended: 8B model with concurrency 2 for local machines
ollama pull llama3.1:8b
ragscore generate docs/ --provider ollama --model llama3.1:8b
GPU server (A10/L20): larger model with higher concurrency
ollama pull qwen2.5:32b
ragscore generate docs/ --provider ollama --model qwen2.5:32b --concurrency 5

Expected performance (28 chunks, 5 QA pairs per chunk):

| Hardware | Model | Time | Concurrency | |----------|-------|------|-------------| | MacBook (CPU) | llama3.2:3b | ~45 min | 2 | | MacBook (CPU) | llama3.1:8b | ~25 min | 2 | | A10 (24GB) | llama3.1:8b | ~3–5 min | 5 | | L20/L40 (48GB) | qwen2.5:32b | ~3–5 min | 5 | | OpenAI API | gpt-4o-mini | ~2 min | 10 |

RAGScore auto-reduces concurrency to 2 for local Ollama to avoid GPU/CPU contention.

🔌 Supported LLMs

| Provider | Setup | Notes | |----------|-------|-------| | Ollama | ollama serve | Local, free, private | | OpenAI | export OPENAIAPIKEY="sk-..." | Best quality | | Anthropic | export ANTHROPICAPIKEY="..." | Long context | | DashScope | export DASHSCOPEAPIKEY="..." | Qwen models | | vLLM | export LLMBASEURL="..." | Production-grade | | Any OpenAI-compatible | export LLMBASEURL="..." | Groq, Together, etc. |

📊 Output Formats

Generated QA Pairs (`output/generated_qas.jsonl`)

{
  "id": "abc123",
  "question": "What is RAG?",
  "answer": "RAG (Retrieval-Augmented Generation) combines...",
  "rationale": "This is explicitly stated in the introduction...",
  "support_span": "RAG systems retrieve relevant documents...",
  "difficulty": "medium",
  "sourcepath": "docs/ragintro.pdf"
}

Evaluation Results (`--output results.json`)

{
  "summary": {
    "total": 100,
    "correct": 85,
    "incorrect": 15,
    "accuracy": 0.85,
    "avg_score": 4.2
  },
  "incorrect_pairs": [
    {
      "question": "What is RAG?",
      "golden_answer": "RAG combines retrieval with generation...",
      "rag_answer": "RAG is a database system.",
      "score": 2,
      "reason": "Factually incorrect - RAG is not a database"
    }
  ]
}

🧪 Python API

from ragscore import runpipeline, runevaluation
Generate QA pairs
run_pipeline(paths=["docs/"], concurrency=10)
Generate tailored QA pairs for specific audiences
run_pipeline(
    paths=["docs/"],
    audience="support engineers",
    purpose="fine-tuning a support chatbot",
)
Evaluate RAG
results = run_evaluation(
    endpoint="http://localhost:8000/query",
    model="gpt-4o",  # LLM for judging
)
print(f"Accuracy: {results.accuracy:.1%}")

🤖 AI Agent Integration

RAGScore is designed for AI agents and automation:

# Structured CLI with predictable output
ragscore generate docs/ --concurrency 5
ragscore evaluate http://api/query --output results.json
Exit codes: 0 = success, 1 = error
JSON output for programmatic parsing

CLI Reference:

| Command | Description | |---------|-------------| | ragscore generate <paths> | Generate QA pairs from documents | | ragscore generate <paths> --audience <who> | Tailored QA for specific audience | | ragscore generate <paths> --purpose <why> | Focus QA on document purpose | | ragscore evaluate <endpoint> | Evaluate RAG against golden QAs | | ragscore evaluate <endpoint> --detailed | Multi-metric evaluation | | ragscore evaluate <endpoint> --diagnose | Failure root-cause classification | | ragscore --help | Show all commands and options | | ragscore generate --help | Show generate options | | ragscore evaluate --help | Show evaluate options |

⚙️ Configuration

Zero config required. Optional environment variables:

export RAGSCORECHUNKSIZE=512          # Chunk size for documents
export RAGSCOREQUESTIONSPER_CHUNK=5   # QAs per chunk
export RAGSCOREWORKDIR=/path/to/dir   # Working directory

🔐 Privacy & Security

| Data | Cloud LLM | Local LLM | |------|-----------|-----------| | Documents | ✅ Local | ✅ Local | | Text chunks | ⚠️ Sent to LLM | ✅ Local | | Generated QAs | ✅ Local | ✅ Local | | Evaluation results | ✅ Local | ✅ Local |

Compliance: GDPR ✅ • HIPAA ✅ (with local LLMs) • SOC 2 ✅

🧪 Development

git clone https://github.com/HZYAI/RagScore.git
cd RagScore
pip install -e ".[dev,all]"
pytest

📡 Telemetry

RAGScore collects telemetry only in MCP server mode (ragscore serve). Standard CLI and Python API usage do not send telemetry.

We collect limited anonymous operational metrics to understand feature usage and improve reliability. No document content, prompts, QA text, model outputs, API keys, endpoint URLs, or file paths are collected.

Collected in MCP mode: