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

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).

pip install ragas datasets langfuse

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

pip install -e ".[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

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

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

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

The evaluation framework supports customization through environment variables:

⚠️ IMPORTANT: Both LLM and Embedding endpoints MUST be OpenAI-compatible

• The RAGAS framework requires OpenAI-compatible API interfaces
• Custom endpoints must implement the OpenAI API format (e.g., vLLM, SGLang, LocalAI)
• Non-compatible endpoints will cause evaluation failures

Variable	Default	Description
LLM Configuration
EVAL_LLM_MODEL	gpt-4o-mini	LLM model used for RAGAS evaluation
EVAL_LLM_BINDING_API_KEY	falls back to OPENAI_API_KEY	API key for LLM evaluation
EVAL_LLM_BINDING_HOST	(optional)	Custom OpenAI-compatible endpoint URL for LLM
Embedding Configuration
EVAL_EMBEDDING_MODEL	text-embedding-3-large	Embedding model for evaluation
EVAL_EMBEDDING_BINDING_API_KEY	falls back to EVAL_LLM_BINDING_API_KEY → OPENAI_API_KEY	API key for embeddings
EVAL_EMBEDDING_BINDING_HOST	falls back to EVAL_LLM_BINDING_HOST	Custom OpenAI-compatible endpoint URL for embeddings
Performance Tuning
EVAL_MAX_CONCURRENT	2	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	180	LLM request timeout in seconds

Example 1: Default Configuration (OpenAI Official API)

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

Both LLM and embeddings use OpenAI's official API with default models.

Example 2: Custom Models on OpenAI

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

Example 3: Same Custom OpenAI-Compatible Endpoint for Both

# Both LLM and embeddings use the same custom endpoint
export EVAL_LLM_BINDING_API_KEY=your-custom-key
export EVAL_LLM_BINDING_HOST=http://localhost:8000/v1
export EVAL_LLM_MODEL=qwen-plus
export EVAL_EMBEDDING_MODEL=BAAI/bge-m3
python lightrag/evaluation/eval_rag_quality.py

Embeddings automatically inherit LLM endpoint configuration.

Example 4: Separate Endpoints (Cost Optimization)

# Use OpenAI for LLM (high quality)
export EVAL_LLM_BINDING_API_KEY=sk-openai-key
export EVAL_LLM_MODEL=gpt-4o-mini
# No EVAL_LLM_BINDING_HOST means use OpenAI official API

# Use local vLLM for embeddings (cost-effective)
export EVAL_EMBEDDING_BINDING_API_KEY=local-key
export EVAL_EMBEDDING_BINDING_HOST=http://localhost:8001/v1
export EVAL_EMBEDDING_MODEL=BAAI/bge-m3

python lightrag/evaluation/eval_rag_quality.py

LLM uses OpenAI official API, embeddings use local custom endpoint.

Example 5: Different Custom Endpoints for LLM and Embeddings

# LLM on one OpenAI-compatible server
export EVAL_LLM_BINDING_API_KEY=key1
export EVAL_LLM_BINDING_HOST=http://llm-server:8000/v1
export EVAL_LLM_MODEL=custom-llm

# Embeddings on another OpenAI-compatible server
export EVAL_EMBEDDING_BINDING_API_KEY=key2
export EVAL_EMBEDDING_BINDING_HOST=http://embedding-server:8001/v1
export EVAL_EMBEDDING_MODEL=custom-embedding

python lightrag/evaluation/eval_rag_quality.py

Both use different custom OpenAI-compatible endpoints.

Example 6: Using Environment Variables from .env File

# Create .env file in project root
cat > .env << EOF
EVAL_LLM_BINDING_API_KEY=your-key
EVAL_LLM_BINDING_HOST=http://localhost:8000/v1
EVAL_LLM_MODEL=qwen-plus
EVAL_EMBEDDING_MODEL=BAAI/bge-m3
EOF

# Run evaluation (automatically loads .env)
python lightrag/evaluation/eval_rag_quality.py

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=2    # 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     # 3-minute timeout per request

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

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",
      "project": "evaluation_project_name"
    }
  ]
}

---

• 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)

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

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

---

• RAGAS Documentation
• RAGAS GitHub

---

pip install ragas datasets

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

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.

Make sure you're running from the project root:

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

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

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

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

1. Start LightRAG API server
2. Upload sample documents into LightRAG throught WebUI
3. Run python lightrag/evaluation/eval_rag_quality.py
4. Review results (JSON/CSV) in results/ folder

Evaluation Result Sample:

INFO: ======================================================================
INFO: 🔍 RAGAS Evaluation - Using Real LightRAG API
INFO: ======================================================================
INFO: Evaluation Models:
INFO:   • LLM Model:            gpt-4.1
INFO:   • Embedding Model:      text-embedding-3-large
INFO:   • Endpoint:             OpenAI Official API
INFO: Concurrency & Rate Limiting:
INFO:   • Query Top-K:          10 Entities/Relations
INFO:   • LLM Max Retries:      5
INFO:   • LLM Timeout:          180 seconds
INFO: Test Configuration:
INFO:   • Total Test Cases:     6
INFO:   • Test Dataset:         sample_dataset.json
INFO:   • LightRAG API:         http://localhost:9621
INFO:   • Results Directory:    results
INFO: ======================================================================
INFO: 🚀 Starting RAGAS Evaluation of LightRAG System
INFO: 🔧 RAGAS Evaluation (Stage 2): 2 concurrent
INFO: ======================================================================
INFO:
INFO: ===================================================================================================================
INFO: 📊 EVALUATION RESULTS SUMMARY
INFO: ===================================================================================================================
INFO: #    | Question                                           |  Faith | AnswRel | CtxRec | CtxPrec |  RAGAS | Status
INFO: -------------------------------------------------------------------------------------------------------------------
INFO: 1    | How does LightRAG solve the hallucination probl... | 1.0000 |  1.0000 | 1.0000 |  1.0000 | 1.0000 |      ✓
INFO: 2    | What are the three main components required in ... | 0.8500 |  0.5790 | 1.0000 |  1.0000 | 0.8573 |      ✓
INFO: 3    | How does LightRAG's retrieval performance compa... | 0.8056 |  1.0000 | 1.0000 |  1.0000 | 0.9514 |      ✓
INFO: 4    | What vector databases does LightRAG support and... | 0.8182 |  0.9807 | 1.0000 |  1.0000 | 0.9497 |      ✓
INFO: 5    | What are the four key metrics for evaluating RA... | 1.0000 |  0.7452 | 1.0000 |  1.0000 | 0.9363 |      ✓
INFO: 6    | What are the core benefits of LightRAG and how ... | 0.9583 |  0.8829 | 1.0000 |  1.0000 | 0.9603 |      ✓
INFO: ===================================================================================================================
INFO:
INFO: ======================================================================
INFO: 📊 EVALUATION COMPLETE
INFO: ======================================================================
INFO: Total Tests:    6
INFO: Successful:     6
INFO: Failed:         0
INFO: Success Rate:   100.00%
INFO: Elapsed Time:   161.10 seconds
INFO: Avg Time/Test:  26.85 seconds
INFO:
INFO: ======================================================================
INFO: 📈 BENCHMARK RESULTS (Average)
INFO: ======================================================================
INFO: Average Faithfulness:      0.9053
INFO: Average Answer Relevance:  0.8646
INFO: Average Context Recall:    1.0000
INFO: Average Context Precision: 1.0000
INFO: Average RAGAS Score:       0.9425
INFO: ----------------------------------------------------------------------
INFO: Min RAGAS Score:           0.8573
INFO: Max RAGAS Score:           1.0000

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Happy Evaluating! 🚀