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graphrag-evaluation

graphrag-evaluation

github.com/lyndonklTesting & quality18777 installsUpdated 9w ago
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About graphrag-evaluation

A skill for evaluating GraphRAG systems across graph quality, retrieval, answer correctness, hallucinations, and reasoning depth. It helps teams select metrics, design test sets, compare baselines, and produce an evaluation report.

Key features

  1. 01Define evaluation scope
  2. 02Select metrics for system goals
  3. 03Design varied test queries
  4. 04Assess reasoning and hallucinations
  5. 05Compare baseline systems
  6. 06Generate evaluation reports

Use cases

  1. 01Evaluate a GraphRAG pipeline or component
  2. 02Measure grounding and unsupported claims
  3. 03Test multi-hop and temporal reasoning
  4. 04Compare graph retrieval with baseline systems

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/lyndonkl/claude --skill graphrag-evaluation

After installing, type/graphrag-evaluationin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

resources/
methodology.md
reasoning-patterns.md
resources/evaluators/
rubric_evaluation.json

Details

From the project README

Table of Contents

GraphRAG Evaluation

Workflow

Copy this checklist and work through each step:

  • Step 1. Identify Evaluation Scope
  • Step 2. Select Metrics
  • Step 3. Design Test Protocol
  • Step 4. Test Reasoning Capabilities
  • Step 5. Measure Hallucination Rate
  • Step 6. Compare Against Baselines
  • Step 7. Produce Evaluation Report
Step 1. Identify Evaluation Scope

Define what aspects of your GraphRAG system you need to evaluate and why. Determine whether you are evaluating the full pipeline or specific components (KG construction, retrieval, generation). Clarify the use case context: domain, query complexity, expected reasoning depth.

See methodology.md for the full evaluation dimensions framework.

Step 2. Select Metrics

Choose metrics appropriate to your evaluation scope. Not every evaluation requires every metric. Match metrics to your system's maturity and the questions you need answered.

See the Metric Selection Guide below and methodology.md for detailed metric definitions.

Step 3. Design Test Protocol

Build test sets that cover your evaluation dimensions. Include single-hop factual queries, multi-hop reasoning queries, constraint satisfaction queries, temporal reasoning queries, comparative queries, and negative queries (questions the system should not answer).

See methodology.md for baseline comparison approaches and statistical significance testing.

Step 4. Test Reasoning Capabilities

Evaluate how well your system handles multi-step reasoning. Verify that each reasoning step is grounded in retrieved KG evidence. Check for error propagation where an incorrect intermediate step leads to wrong conclusions.

See reasoning-patterns.md for chain validation, pattern matching, hypothesis verification, and causal reasoning evaluation.

Step 5. Measure Hallucination Rate

Quantify both intrinsic hallucination (contradicts retrieved evidence) and extrinsic hallucination (claims not supported by any retrieved source). Measure the KG grounding rate: what percentage of generated claims are traceable to knowledge graph entities and relations.

See methodology.md for hallucination detection approaches and comparison protocols.

Step 6. Compare Against Baselines

Run identical test sets against baseline systems: pure vector RAG, LLM-only (no retrieval), and alternative graph configurations. Use controlled ablation studies to isolate the contribution of each component.

See methodology.md for baseline comparison and ablation study design.

Step 7. Produce Evaluation Report

Compile findings into the structured output template below. Include metric values, baseline comparisons, identified weaknesses, and prioritized recommendations.

See rubric_evaluation.json for the scoring rubric (minimum passing score: 3.0).

Evaluation Dimensions

DimensionWhat It MeasuresKey MetricsPriority
KG QualityCompleteness and accuracy of the knowledge graphEntity coverage, relation completeness, schema consistencyHigh
Retrieval QualityEffectiveness of graph-based retrievalContext recall (C-Rec), context precision, multi-hop coverageHigh
Answer CorrectnessAccuracy and completeness of generated answersFactual accuracy, answer completeness, citation accuracyCritical
Hallucination RateFrequency of unsupported or contradicted claimsIntrinsic hallucination rate, extrinsic hallucination rate, KG grounding rateCritical
Reasoning DepthAbility to perform multi-step reasoning correctlyMulti-hop accuracy, stepwise verification score, error propagation rateMedium-High

Metric Selection Guide

Choose metrics based on your evaluation goals:

Quick Health Check (minimal effort):

  • Answer correctness on a curated test set (20-50 questions)
  • KG grounding rate (sample 20 responses)
  • Single baseline comparison (pure vector RAG)

Standard Evaluation (recommended):

  • All five dimensions with standardized test sets
  • Context recall and context precision
  • Multi-hop reasoning tests
  • Hallucination rate measurement
  • Two or more baseline comparisons

Comprehensive Benchmark (production readiness):

  • Full metrics suite across all dimensions
  • Statistical significance testing with confidence intervals
  • Controlled ablation study
  • Process-oriented reasoning evaluation (stepwise correctness)
  • Automated evaluation pipeline for reproducibility

Output Template

# GraphRAG Evaluation Report

## 1. System Under Evaluation
- System name and version:
- Domain:
- KG size (entities/relations):
- Evaluation date:

## 2. Evaluation Scope
- Dimensions evaluated:
- Test set size and composition:
- Baseline systems:

## 3. KG Quality Results
- Entity coverage: ____%
- Relation completeness: ____%
- Schema consistency score: ____
- Notable gaps:

## 4. Retrieval Quality Results
- Context recall (C-Rec): ____
- Context precision: ____
- Multi-hop coverage: ____%
- Latency (p50/p95/p99): ____

## 5. Answer Correctness Results
- Factual accuracy: ____%
- Answer completeness: ____%
- Citation accuracy: ____%

## 6. Hallucination Analysis
- Intrinsic hallucination rate: ____%
- Extrinsic hallucination rate: ____%
- KG grounding rate: ____%
- Comparison with/without graph augmentation:

## 7. Reasoning Depth Results
- Single-hop accuracy: ____%
- Multi-hop accuracy: ____%
- Stepwise reasoning correctness: ____%
- Error propagation incidents: ____

## 8. Baseline Comparison
| Metric | GraphRAG | Pure Vector RAG | LLM Only |
|--------|----------|-----------------|----------|
| Answer correctness | | | |
| Hallucination rate | | | |
| Multi-hop accuracy | | | |

## 9. Statistical Significance
- Test used:
- Confidence level:
- Significant improvements:
- Non-significant differences:

## 10. Identified Weaknesses
1.
2.
3.

## 11. Recommendations
| Priority | Recommendation | Expected Impact | Effort |
|----------|---------------|-----------------|--------|
| | | | |

## 12. Rubric Score
- Metric Coverage: __ / 5
- Measurement Rigor: __ / 5
- Baseline Comparison: __ / 5
- Reasoning Depth: __ / 5
- Actionable Recommendations: __ / 5
- **Weighted Total: __ / 5.0** (minimum passing: 3.0)

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