gen-eval 0.0.2

Project GenEval: A Unified Evaluation Framework for Generative AI Applications

GenEval

A unified evaluation framework for Gen AI applications. GenEval provides a standardized interface for evaluating generative AI models using multiple evaluation frameworks including RAGAS and DeepEval, with profile-driven evaluation for reproducible, reusable quality gates.

Overview

GenEval solves the problem of fragmented evaluation approaches in the RAG ecosystem. Instead of learning different APIs and managing separate evaluation workflows, GenEval provides a single interface that works with multiple evaluation frameworks.

Key benefits:

• Profile-driven evaluation with reusable profiles, weighted scoring, and pass/fail verdicts
• Automatic adapter routing -- metrics resolve to the best available framework (RAGAS or DeepEval) without manual tool selection
• CI/CD integration -- exit codes, JSON output, and policy overrides for pipeline gates
• Unified API for RAGAS and DeepEval metrics
• Consistent output format across all frameworks
• Support for 8 unique evaluation metrics
• Config-driven LLM management supporting OpenAI, Anthropic, Google Gemini, Ollama, DeepSeek, Amazon Bedrock, Azure OpenAI, and vLLM

Supported Metrics

GenEval supports 8 unique metrics through its metric registry. Each metric automatically resolves to the best available adapter:

Metric	RAGAS	DeepEval
faithfulness	1st	2nd
answer_relevancy	1st	2nd
context_recall	1st	2nd
context_precision	1st	2nd
context_precision_with_reference	yes	--
context_entity_recall	yes	--
noise_sensitivity	yes	--
context_relevance	--	yes

When a metric is available in both frameworks, the registry tries the higher-priority adapter first and falls back to the other if it fails.

Installation

From Source

git clone https://github.com/savitharaghunathan/gen-eval.git
cd gen-eval
uv sync

# Set your API keys
export OPENAI_API_KEY="your-openai-api-key"
export ANTHROPIC_API_KEY="your-anthropic-api-key"
export GOOGLE_API_KEY="your-google-api-key"
export DEEPSEEK_API_KEY="your-deepseek-api-key"

# For vLLM servers
export VLLM_BASE_URL="https://your-vllm-server.com"
export VLLM_API_PATH="/v1"

# For AWS Bedrock (optional)
export AWS_ACCESS_KEY_ID="your-aws-access-key"
export AWS_SECRET_ACCESS_KEY="your-aws-secret-key"

Development Setup

uv sync --dev --all-extras
uv run pre-commit install
make lint
make test

Configuration

GenEval requires an LLM configuration file. API keys are read from environment variables -- never hardcoded.

Create llm_config.yaml:

providers:
  openai:
    enabled: true
    default: true
    api_key_env: "OPENAI_API_KEY"
    model: "gpt-4o-mini"

  anthropic:
    enabled: true
    default: false
    api_key_env: "ANTHROPIC_API_KEY"
    model: "claude-3-5-haiku-20241022"

  gemini:
    enabled: true
    default: false
    api_key_env: "GOOGLE_API_KEY"
    model: "gemini-1.5-flash"

  ollama:
    enabled: true
    default: false
    base_url: "http://localhost:11434"
    model: "llama3.2"

  deepseek:
    enabled: true
    default: false
    api_key_env: "DEEPSEEK_API_KEY"
    model: "deepseek-chat"

  vllm:
    enabled: true
    default: false
    base_url_env: "VLLM_BASE_URL"
    api_path_env: "VLLM_API_PATH"
    api_key_env: "OPENAI_API_KEY"
    model: "your-model-name"
    ssl_verify: false

  amazon_bedrock:
    enabled: true
    default: false
    model: "anthropic.claude-3-sonnet-20240229-v1:0"
    region_name: "us-east-1"

  azure_openai:
    enabled: true
    default: false
    model: "gpt-4"
    deployment_name: "your-deployment-name"
    azure_openai_api_key: "your-azure-openai-api-key"
    openai_api_version: "2025-01-01-preview"
    azure_endpoint: "https://your-resource.openai.azure.com/"

settings:
  temperature: 0.1
  max_tokens: 1000
  timeout: 30

Important: Only one provider should have default: true.

A sample config is available at config/llm_config.yaml.

Profile-Driven Evaluation

Profiles let you define evaluation standards once and reuse them across teams, workflows, and CI pipelines.

What's in a Profile

A profile specifies:

• Metrics -- which metrics to evaluate
• Weights -- how much each metric contributes to the composite score (must sum to 1.0)
• Criteria -- minimum threshold for each individual metric
• Composite threshold -- minimum weighted composite score to pass

Built-in Profiles

GenEval ships with three profiles:

Profile	Metrics	Composite Threshold	Use Case
rag_default	faithfulness, answer_relevancy, context_recall	0.70	General RAG evaluation
strict	faithfulness, answer_relevancy, context_recall, context_precision	0.85	Production deployments
smoke_test	faithfulness, answer_relevancy	0.50	Quick sanity checks

Custom Profiles

Create a profiles.yaml file:

profiles:
  medical_rag:
    description: "High-accuracy medical Q&A evaluation"
    metrics:
      - faithfulness
      - answer_relevancy
      - context_recall
    weights:
      faithfulness: 0.5
      answer_relevancy: 0.3
      context_recall: 0.2
    criteria:
      faithfulness: 0.95
      answer_relevancy: 0.85
      context_recall: 0.80
    composite_threshold: 0.90

Policies

Policies let you create runtime variations of a profile without redefining metrics or weights. Only criteria and composite_threshold can be overridden:

profiles:
  rag_default:
    metrics: [faithfulness, answer_relevancy, context_recall]
    weights: {faithfulness: 0.4, answer_relevancy: 0.3, context_recall: 0.3}
    criteria: {faithfulness: 0.7, answer_relevancy: 0.7, context_recall: 0.7}
    composite_threshold: 0.7

policies:
  ci_gate:
    profile: rag_default
    overrides:
      criteria:
        faithfulness: 0.9
      composite_threshold: 0.85

  nightly:
    profile: rag_default

Pass/Fail Logic

A test case passes when both conditions are met:

1. Every metric score meets its individual criteria threshold
2. The weighted composite score meets the composite threshold

CLI Usage

Run Evaluation

# Evaluate with a profile
geneval evaluate --profile rag_default --data test_data.yaml --config llm_config.yaml

# Evaluate with a policy
geneval evaluate --policy ci_gate --data test_data.yaml --profiles profiles.yaml

# Output as table
geneval evaluate --profile strict --data test_data.yaml --format table

# Write results to file
geneval evaluate --profile rag_default --data test_data.yaml --output results.json

The CLI exits with code 0 if all test cases pass, code 1 if any fail.

Manage Profiles

# List all available profiles
geneval profiles list

# List with custom profiles
geneval profiles list --profiles profiles.yaml

# Show profile details
geneval profiles show rag_default

# Validate a profiles file
geneval profiles validate profiles.yaml

Test Data Format

test_cases:
  - id: "test_001"
    user_input: "What is the capital of France?"
    retrieved_contexts: "France is a country in Europe. Its capital city is Paris."
    response: "Paris is the capital of France."
    reference: "Paris"

  - id: "test_002"
    user_input: "What is 2+2?"
    retrieved_contexts: "Basic arithmetic: 2+2 equals 4."
    response: "2+2 equals 4."
    reference: "4"

Python API

Profile-Based Evaluation

from geneval import GenEvalFramework

framework = GenEvalFramework(config_path="llm_config.yaml")

# Single test case
result = framework.evaluate_profile(
    profile="rag_default",
    question="What is the capital of France?",
    response="Paris is the capital of France.",
    reference="Paris",
    retrieval_context="France is a country in Europe. Its capital city is Paris.",
)

print(f"Passed: {result.overall_passed}")
print(f"Composite score: {result.composite_score:.2f}")
for mr in result.metric_results:
    print(f"  {mr.name}: {mr.score:.2f} (threshold: {mr.threshold}, passed: {mr.passed})")

# Batch evaluation
batch = framework.evaluate_profile_batch(
    data_path="test_data.yaml",
    profile="strict",
)

print(f"Pass rate: {batch.pass_rate:.0%}")
print(f"Overall: {'PASSED' if batch.overall_passed else 'FAILED'}")

Direct Metric Evaluation

from geneval import GenEvalFramework

framework = GenEvalFramework(config_path="llm_config.yaml")

# Evaluate with explicit metrics (runs on all adapters that support them)
results = framework.evaluate(
    question="What is the capital of France?",
    response="Paris is the capital of France.",
    reference="Paris",
    retrieval_context="France is a country in Europe. Its capital city is Paris.",
    metrics=["faithfulness", "answer_relevancy", "context_precision"]
)
print(results)

CI/CD Integration

GenEval is designed for pipeline integration. Use profiles to set quality gates:

# .github/workflows/eval.yml
- name: Run evaluation
  run: |
    geneval evaluate \
      --profile strict \
      --data tests/eval_data.yaml \
      --config config/llm_config.yaml \
      --output eval_results.json

The exit code (0 = pass, 1 = fail) integrates directly with CI systems.

Use policies to vary thresholds per environment without changing the profile:

# Strict gate for production
geneval evaluate --policy prod_gate --data test_data.yaml

# Relaxed gate for development
geneval evaluate --policy dev_gate --data test_data.yaml

Output Format

Profile Evaluation (JSON)

{
  "profile_name": "rag_default",
  "policy_name": null,
  "overall_passed": true,
  "composite_score": 0.87,
  "composite_threshold": 0.7,
  "composite_passed": true,
  "metric_results": [
    {
      "name": "faithfulness",
      "score": 0.95,
      "threshold": 0.7,
      "passed": true,
      "weight": 0.4,
      "weighted_score": 0.38,
      "adapter": "ragas",
      "details": "High faithfulness"
    }
  ],
  "metadata": {
    "timestamp": "2026-05-01T12:00:00+00:00"
  }
}

Table Output

Profile: rag_default
Pass Rate: 100% (5/5)

Metric                         Mean Score   Threshold
------------------------------------------------------
faithfulness                   0.9200       0.70
answer_relevancy               0.8800       0.70
context_recall                 0.8500       0.70

Overall: PASSED

Project Structure

gen-eval/
├── pyproject.toml
├── README.md
├── Makefile
├── config/
│   └── llm_config.yaml
├── geneval/
│   ├── __init__.py
│   ├── schemas.py              # Pydantic models (Input, Output, ProfileResult, BatchResult)
│   ├── framework.py            # Main evaluation framework
│   ├── llm_manager.py          # LLM provider management
│   ├── profile_manager.py      # Profile/policy loading, validation, scoring
│   ├── metric_registry.py      # Metric-to-adapter resolution
│   ├── exceptions.py           # ProfileValidationError, UnknownMetricError, ProfileNotFoundError
│   ├── cli.py                  # Click CLI (evaluate, profiles list/show/validate)
│   ├── normalization.py        # Score normalization utilities
│   ├── profiles/
│   │   └── default_profiles.yaml  # Built-in profiles (rag_default, strict, smoke_test)
│   └── adapters/
│       ├── ragas_adapter.py    # RAGAS integration
│       └── deepeval_adapter.py # DeepEval integration
├── tests/
│   ├── test_framework.py
│   ├── test_ragas_adapter.py
│   ├── test_deepeval_adapter.py
│   ├── test_llm_manager.py
│   ├── test_schemas.py
│   ├── test_schemas_profile.py
│   ├── test_profile_manager.py
│   ├── test_metric_registry.py
│   ├── test_cli.py
│   ├── test_data.yaml
│   └── test_data_clean.yaml
└── uv.lock

Development

Code Quality

• Black -- Code formatting
• isort -- Import sorting
• Ruff -- Fast linting
• pre-commit -- Git hooks for automated checks

Available Commands

make install       # Install dependencies
make dev           # Install pre-commit hooks
make format        # Format code
make lint          # Run linting
make test          # Run tests with coverage
make test-ci       # Run tests without coverage (faster)
make pre-commit    # Run all hooks on all files
make pre-push      # Format + lint before pushing
make build         # Build the package
make security      # Run security checks
make clean         # Clean generated files

Running Tests

uv run pytest tests/ -v
uv run pytest tests/ --cov=geneval --cov-report=term-missing
uv run pytest tests/test_profile_manager.py -v
uv run pytest tests/test_ragas_adapter.py -v

Contributing

1. Fork and clone the repo
2. Set up dev environment: make install && make dev
3. Create a feature branch: git checkout -b feature/your-feature
4. Make changes, add tests, verify: make test && make lint
5. Push and create a pull request

Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions