Skip to main content
Version: 2.0

Vectara Open Eval Framework

The Vectara Open RAG Eval framework is an open-source Python toolkit that helps developers evaluate and optimize their Retrieval-Augmented Generation (RAG) pipelines. This framework addresses a critical need for systematic RAG evaluation and optimization, enabling you to find the best model configurations and settings for your specific use cases.

Why RAG evaluation is important​

Traditional approaches to RAG optimization leave developers guessing about which models, parameters, and configurations work best for their applications. The Open Eval framework provides data-driven insights to help you:

  • Optimize model selection for specific use cases and datasets
  • Fine-tune RAG parameters based on measurable performance metrics
  • Compare different configurations systematically
  • Validate improvements before deploying to production

Here are the key features of the Vectara Open RAG Eval framework:

Flexible evaluation metrics​

  • Standard metrics from TREC-RAG benchmark
  • No requirement for "golden chunks" or "golden answers"
  • Research-backed techniques from University of Waterloo
  • Support for custom metric integration

Multi-platform support​

  • Vectara integration (native support)
  • LlamaIndex compatibility
  • LangChain compatibility
  • Extensible architecture for other RAG platforms

Comprehensive analysis​

  • Per-query scoring and detailed analysis
  • Multiple evaluation metrics including:
    • UMBRELA
    • AutoNuggetizer
    • BERT Score
    • ROUGE-L
    • Consistency evaluation across generations

Visualization tools​

  • Web-based evaluation viewer at openevaluation.ai
  • Command-line plotting capabilities
  • Streamlit local viewer for interactive analysis

Solve model selection challenges​

The framework directly addresses the question: "How do I know which model and settings work best for my use case?"

Before Open Eval: trial and error​

  • Guessing optimal model configurations
  • No systematic comparison method
  • Difficulty validating improvements
  • Black box optimization

With Open Eval: data-driven optimization​

  • Systematic evaluation across models and parameters
  • Quantifiable performance metrics
  • Clear comparison between configurations
  • Evidence-based decision making

Best Practices​

Dataset creation​

  • Include representative queries from your actual use case
  • Cover edge cases and common scenarios
  • Ensure diversity in query complexity and topics
  • Validate dataset quality before evaluation

Configuration testing​

  • Test systematically, changing one parameter at a time
  • Include baseline configurations for comparison
  • Document all tested configurations
  • Consider cost vs. performance trade-offs

Results analysis​

  • Look beyond single metrics - use multiple evaluation criteria
  • Consider statistical significance of results
  • Validate findings with real user feedback when possible
  • Monitor production performance after optimization

Continuous improvement​

  • Re-evaluate periodically as your dataset evolves
  • Test new models and presets as they become available
  • Update evaluation queries based on user feedback
  • Maintain evaluation history for trend analysis

The Open Eval framework transforms RAG optimization from guesswork into a systematic, data-driven process. By providing quantifiable metrics and comparison capabilities, it enables you to confidently select the best model configurations for your specific applications and use cases.