LightRAG/lightrag/evaluation/README.md

📊 LightRAG Evaluation Framework

RAGAS-based offline evaluation of your LightRAG system.

What is RAGAS?

RAGAS (Retrieval Augmented Generation Assessment) is a framework for reference-free evaluation of RAG systems using LLMs.

Instead of requiring human-annotated ground truth, RAGAS uses state-of-the-art evaluation metrics:

Core Metrics

Metric	What It Measures	Good Score
Faithfulness	Is the answer factually accurate based on retrieved context?	> 0.80
Answer Relevance	Is the answer relevant to the user's question?	> 0.80
Context Recall	Was all relevant information retrieved from documents?	> 0.80
Context Precision	Is retrieved context clean without irrelevant noise?	> 0.80
RAGAS Score	Overall quality metric (average of above)	> 0.80

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📁 Structure

lightrag/evaluation/
├── eval_rag_quality.py      # Main evaluation script
├── sample_dataset.json        # 3 test questions about LightRAG
├── sample_documents/          # Matching markdown files for testing
│   ├── 01_lightrag_overview.md
│   ├── 02_rag_architecture.md
│   ├── 03_lightrag_improvements.md
│   ├── 04_supported_databases.md
│   ├── 05_evaluation_and_deployment.md
│   └── README.md
├── __init__.py              # Package init
├── results/                 # Output directory
│   ├── results_YYYYMMDD_HHMMSS.json    # Raw metrics in JSON
│   └── results_YYYYMMDD_HHMMSS.csv     # Metrics in CSV format
└── README.md                # This file

Quick Test: Index files from sample_documents/ into LightRAG, then run the evaluator to reproduce results (~89-100% RAGAS score per question).

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🚀 Quick Start

1. Install Dependencies

pip install ragas datasets langfuse

Or use your project dependencies (already included in pyproject.toml):

pip install -e ".[offline-llm]"

2. Run Evaluation

Basic usage (uses defaults):

cd /path/to/LightRAG
python lightrag/evaluation/eval_rag_quality.py

Specify custom dataset:

python lightrag/evaluation/eval_rag_quality.py --dataset my_test.json

Specify custom RAG endpoint:

python lightrag/evaluation/eval_rag_quality.py --ragendpoint http://my-server.com:9621

Specify both (short form):

python lightrag/evaluation/eval_rag_quality.py -d my_test.json -r http://localhost:9621

Get help:

python lightrag/evaluation/eval_rag_quality.py --help

3. View Results

Results are saved automatically in lightrag/evaluation/results/:

results/
├── results_20241023_143022.json     ← Raw metrics in JSON format
└── results_20241023_143022.csv      ← Metrics in CSV format (for spreadsheets)

Results include:

• ✅ Overall RAGAS score
• 📊 Per-metric averages (Faithfulness, Answer Relevance, Context Recall, Context Precision)
• 📋 Individual test case results
• 📈 Performance breakdown by question

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📋 Command-Line Arguments

The evaluation script supports command-line arguments for easy configuration:

Argument	Short	Default	Description
--dataset	-d	sample_dataset.json	Path to test dataset JSON file
--ragendpoint	-r	http://localhost:9621 or $LIGHTRAG_API_URL	LightRAG API endpoint URL

Usage Examples

Use default dataset and endpoint:

python lightrag/evaluation/eval_rag_quality.py

Custom dataset with default endpoint:

python lightrag/evaluation/eval_rag_quality.py --dataset path/to/my_dataset.json

Default dataset with custom endpoint:

python lightrag/evaluation/eval_rag_quality.py --ragendpoint http://my-server.com:9621

Custom dataset and endpoint:

python lightrag/evaluation/eval_rag_quality.py -d my_dataset.json -r http://localhost:9621

Absolute path to dataset:

python lightrag/evaluation/eval_rag_quality.py -d /path/to/custom_dataset.json

Show help message:

python lightrag/evaluation/eval_rag_quality.py --help

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⚙️ Configuration

Environment Variables

The evaluation framework supports customization through environment variables:

Variable	Default	Description
EVAL_LLM_MODEL	gpt-4o-mini	LLM model used for RAGAS evaluation
EVAL_EMBEDDING_MODEL	text-embedding-3-small	Embedding model for evaluation
EVAL_LLM_BINDING_API_KEY	(falls back to OPENAI_API_KEY)	API key for evaluation models
EVAL_LLM_BINDING_HOST	(optional)	Custom endpoint URL for OpenAI-compatible services
EVAL_MAX_CONCURRENT	1	Number of concurrent test case evaluations (1=serial)
EVAL_QUERY_TOP_K	10	Number of documents to retrieve per query
EVAL_LLM_MAX_RETRIES	5	Maximum LLM request retries
EVAL_LLM_TIMEOUT	120	LLM request timeout in seconds

Usage Examples

Default Configuration (OpenAI):

export OPENAI_API_KEY=sk-xxx
python lightrag/evaluation/eval_rag_quality.py

Custom Model:

export OPENAI_API_KEY=sk-xxx
export EVAL_LLM_MODEL=gpt-4o-mini
export EVAL_EMBEDDING_MODEL=text-embedding-3-large
python lightrag/evaluation/eval_rag_quality.py

OpenAI-Compatible Endpoint:

export EVAL_LLM_BINDING_API_KEY=your-custom-key
export EVAL_LLM_BINDING_HOST=https://api.openai.com/v1
export EVAL_LLM_MODEL=qwen-plus
python lightrag/evaluation/eval_rag_quality.py

Concurrency Control & Rate Limiting

The evaluation framework includes built-in concurrency control to prevent API rate limiting issues:

Why Concurrency Control Matters:

• RAGAS internally makes many concurrent LLM calls for each test case
• Context Precision metric calls LLM once per retrieved document
• Without control, this can easily exceed API rate limits

Default Configuration (Conservative):

EVAL_MAX_CONCURRENT=1    # Serial evaluation (one test at a time)
EVAL_QUERY_TOP_K=10      # OP_K query parameter of LightRAG
EVAL_LLM_MAX_RETRIES=5   # Retry failed requests 5 times
EVAL_LLM_TIMEOUT=180     # 2-minute timeout per request

If You Have Higher API Quotas:

EVAL_MAX_CONCURRENT=2    # Evaluate 2 tests in parallel
EVAL_QUERY_TOP_K=20      # OP_K query parameter of LightRAG

Common Issues and Solutions:

Issue	Solution
Warning: "LM returned 1 generations instead of 3"	Reduce EVAL_MAX_CONCURRENT to 1 or decrease EVAL_QUERY_TOP_K
Context Precision returns NaN	Lower EVAL_QUERY_TOP_K to reduce LLM calls per test case
Rate limit errors (429)	Increase EVAL_LLM_MAX_RETRIES and decrease EVAL_MAX_CONCURRENT
Request timeouts	Increase EVAL_LLM_TIMEOUT to 180 or higher

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📝 Test Dataset

sample_dataset.json contains 3 generic questions about LightRAG. Replace with questions matching YOUR indexed documents.

Custom Test Cases:

{
  "test_cases": [
    {
      "question": "Your question here",
      "ground_truth": "Expected answer from your data",
      "context": "topic"
    }
  ]
}

---

📊 Interpreting Results

Score Ranges

• 0.80-1.00: ✅ Excellent (Production-ready)
• 0.60-0.80: ⚠️ Good (Room for improvement)
• 0.40-0.60: ❌ Poor (Needs optimization)
• 0.00-0.40: 🔴 Critical (Major issues)

What Low Scores Mean

Metric	Low Score Indicates
Faithfulness	Responses contain hallucinations or incorrect information
Answer Relevance	Answers don't match what users asked
Context Recall	Missing important information in retrieval
Context Precision	Retrieved documents contain irrelevant noise

Optimization Tips

1. Low Faithfulness:

   • Improve entity extraction quality
   • Better document chunking
   • Tune retrieval temperature

2. Low Answer Relevance:

   • Improve prompt engineering
   • Better query understanding
   • Check semantic similarity threshold

3. Low Context Recall:

   • Increase retrieval top_k results
   • Improve embedding model
   • Better document preprocessing

4. Low Context Precision:

   • Smaller, focused chunks
   • Better filtering
   • Improve chunking strategy

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📚 Resources

• RAGAS Documentation
• RAGAS GitHub

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🐛 Troubleshooting

"ModuleNotFoundError: No module named 'ragas'"

pip install ragas datasets

"Warning: LM returned 1 generations instead of requested 3" or Context Precision NaN

Cause: This warning indicates API rate limiting or concurrent request overload:

• RAGAS makes multiple LLM calls per test case (faithfulness, relevancy, recall, precision)
• Context Precision calls LLM once per retrieved document (with EVAL_QUERY_TOP_K=10, that's 10 calls)
• Concurrent evaluation multiplies these calls: EVAL_MAX_CONCURRENT × LLM calls per test

Solutions (in order of effectiveness):

1. Serial Evaluation (Default):

   export EVAL_MAX_CONCURRENT=1
   python lightrag/evaluation/eval_rag_quality.py
   

2. Reduce Retrieved Documents:

   export EVAL_QUERY_TOP_K=5  # Halves Context Precision LLM calls
   python lightrag/evaluation/eval_rag_quality.py
   

3. Increase Retry & Timeout:

   export EVAL_LLM_MAX_RETRIES=10
   export EVAL_LLM_TIMEOUT=180
   python lightrag/evaluation/eval_rag_quality.py
   

4. Use Higher Quota API (if available):

   • Upgrade to OpenAI Tier 2+ for higher RPM limits
   • Use self-hosted OpenAI-compatible service with no rate limits

"AttributeError: 'InstructorLLM' object has no attribute 'agenerate_prompt'" or NaN results

This error occurs with RAGAS 0.3.x when LLM and Embeddings are not explicitly configured. The evaluation framework now handles this automatically by:

• Using environment variables to configure evaluation models
• Creating proper LLM and Embeddings instances for RAGAS

Solution: Ensure you have set one of the following:

• OPENAI_API_KEY environment variable (default)
• EVAL_LLM_BINDING_API_KEY for custom API key

The framework will automatically configure the evaluation models.

"No sample_dataset.json found"

Make sure you're running from the project root:

cd /path/to/LightRAG
python lightrag/evaluation/eval_rag_quality.py

"LLM API errors during evaluation"

The evaluation uses your configured LLM (OpenAI by default). Ensure:

• API keys are set in .env
• Have sufficient API quota
• Network connection is stable

Evaluation requires running LightRAG API

The evaluator queries a running LightRAG API server at http://localhost:9621. Make sure:

1. LightRAG API server is running (python lightrag/api/lightrag_server.py)
2. Documents are indexed in your LightRAG instance
3. API is accessible at the configured URL

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📝 Next Steps

1. Index documents into LightRAG (WebUI or API)
2. Start LightRAG API server
3. Run python lightrag/evaluation/eval_rag_quality.py
4. Review results (JSON/CSV) in results/ folder
5. Adjust entity extraction prompts or retrieval settings based on scores

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Happy Evaluating! 🚀