Title: Introducing Open RAG Eval: A Scalable Solution for Retrieval and Generation Metrics without Golden Answers

Join Offer from Victara as he introduces Open Rag Eval, an open-source project aimed at solving the scalability issues of rag evaluation by providing a research-backed solution without requiring golden answers.

1. Offer from Victara discussing Open RAG Eval, a new open-source project for quick and scalable Retrieval-Augmented Generation (RAG) evaluation.
2. Aiming to solve the problem of requiring "golden answers" or "golden chunks," which is not scalable.
3. Research-backed collaboration with the University of Waterloo Jimmy Lynn lab.
4. Users start with a set of queries, collected for their RAG system.
5. A RAG Connector collects actual information and answers from various pipelines such as vector, LangChain, Lama Index, etc.
6. Evaluation runs metrics, grouped into evaluators to assess the quality of RAG outputs.
7. Metrics include: Umbrella, AutoNuggetizer, Citation Faithfulness, and Hallucination Detection.
8. Umbrella is a retrieval metric that scores chunks based on their relevance to queries (0-3).
9. Umbrella research from the University of Waterloo shows strong correlation with human judgment.
10. AutoNuggetizer generates atomic units and assigns vitality or OK ratings to each nugget.
11. An LLM judge analyzes the response to determine if selected nuggets are supported by the answer.
12. Citation Faithfulness measures citation fidelity (fully supported, partially supported, no support).
13. Hallucination Detection checks if the entire response aligns with retrieved content.
14. A user interface is available at openevaluation.ai for easy result visualization and comparison.
15. Users can compare queries, retrieval scores, and different generation scores.
16. Open RAG Eval promotes transparency in metrics used for assessment.
17. Includes connectors for popular RAG pipelines like vector, LangChain, Lama Index.
18. Contributions of other issues or PRs for additional connectors are welcome.
19. Project encourages community involvement and collaboration.
20. Open RAG Eval helps optimize and tune RAG pipelines.
21. Users can understand the clear metrics and inner workings by examining open-source code.
22. Open RAG Eval fosters innovation in the field of information retrieval and AI.
23. Offer encourages viewers to explore the powerful package and its potential benefits for their RAG pipelines.
24. Invites listeners with questions or suggestions regarding Open RAG Eval to reach out.

Source: AI Engineer via YouTube

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