diff --git a/docs/community/index.md b/docs/community/index.md index 4b5db4069..7bb4cceb4 100644 --- a/docs/community/index.md +++ b/docs/community/index.md @@ -17,8 +17,8 @@ Meet some of our outstanding members who made significant contributions ! Explore insightful articles, tutorials, and stories written by and for our community members. - [Luka Panić](https://www.linkedin.com/in/luka-pani%C4%87-20b671277/) shares his work on - - [Ragas Evaluation: In-Depth Insights | PIXION Blog](https://pixion.co/blog/ragas-evaluation-in-depth-insights): A detailed explanation of the metrics and how they are calculated. - - [RAG in practice - Test Set Generation | PIXION Blog](https://pixion.co/blog/rag-in-practice-test-set-generation): A tutorial on how to generate a test set using Ragas. + - [Ragas Evaluation: In-Depth Insights | PIXION Blog](https://pixion.co/blog/ragas-evaluation-in-depth-insights): A detailed explanation of the metrics and how they are calculated. + - [RAG in practice - Test Set Generation | PIXION Blog](https://pixion.co/blog/rag-in-practice-test-set-generation): A tutorial on how to generate a test set using Ragas. - [Shanthi Vardhan](https://www.linkedin.com/in/shanthivardhan/) shares how his team at [Atomicwork uses ragas](https://www.atomicwork.com/blog/ragas-improving-atom-accuracy) to improve their AI system's ability to accurately identify and retrieve more precise information for enhanced service management. - [Pinecone's](https://pinecone.io/blog) study on how RAGs can enhance capabilities of LLMs in ["RAG makes LLMs better and equal"](https://www.pinecone.io/blog/rag-study/) uses ragas to proves context retrieval makes LLMs provide significantly better results, even when increasing the data size to 1 billion. - [Aishwarya Prabhat](https://www.linkedin.com/in/aishwaryaprabhat/) shares her expertise on advanced RAG techniques in her comprehensive guide, ["Performing, Evaluating & Tracking Advanced RAG (ft. AzureML, LlamaIndex & Ragas)"](https://www.linkedin.com/pulse/performing-evaluating-tracking-advanced-rag-ft-azureml-prabhat-i1rkc/). @@ -34,13 +34,16 @@ Explore insightful articles, tutorials, and stories written by and for our commu - [RAG Evaluation : Computational Metrics in RAG and Calculation Methods in Ragas](https://tech.beatrust.com/entry/2024/05/02/RAG_Evaluation_%3A_Computational_Metrics_in_RAG_and_Calculation_Methods_in_Ragas) - [RAG Evaluation: Assessing the Usefulness of Ragas](https://tech.beatrust.com/entry/2024/05/02/RAG_Evaluation%3A_Assessing_the_Usefulness_of_Ragas) - [Atita Arora](https://www.linkedin.com/in/atitaarora/) writes about [Evaluating Retrieval Augmented Generation using RAGAS](https://superlinked.com/vectorhub/articles/retrieval-augmented-generation-eval-qdrant-ragas), an end-to-end tutorial on building RAG using [Qdrant](https://qdrant.tech/) and [Langchain](https://www.langchain.com/) and evaluating it with RAGAS. - - *Bonus content* : Learn how to create an evaluation dataset that serves as a reference point for evaluating our RAG pipeline, Understand the RAGAS evaluation metrics and how to make sense of them and putting them in action to test a Naive RAG pipeline and measure its performance using RAGAS metrics. - - *Code walkthrough* : https://github.com/qdrant/qdrant-rag-eval/tree/master/workshop-rag-eval-qdrant-ragas - - *Code walkthrough using [Deepset Haystack](https://haystack.deepset.ai/) and [Mixedbread.ai](https://www.mixedbread.ai/)* : https://github.com/qdrant/qdrant-rag-eval/tree/master/workshop-rag-eval-qdrant-ragas-haystack + - *Bonus content* : Learn how to create an evaluation dataset that serves as a reference point for evaluating our RAG pipeline, Understand the RAGAS evaluation metrics and how to make sense of them and putting them in action to test a Naive RAG pipeline and measure its performance using RAGAS metrics. + - *Code walkthrough* : https://github.com/qdrant/qdrant-rag-eval/tree/master/workshop-rag-eval-qdrant-ragas + - *Code walkthrough using [Deepset Haystack](https://haystack.deepset.ai/) and [Mixedbread.ai](https://www.mixedbread.ai/)* : https://github.com/qdrant/qdrant-rag-eval/tree/master/workshop-rag-eval-qdrant-ragas-haystack - [Minoru Onda](https://x.com/minorun365) writes for beginners about how to start Ragas v0.2 evaluation with Amazon Bedrock, and integrate with Langfuse. - - [RAG精度評価の定番ツール「Ragas」にAWSのBedrockで入門しよう！（v0.2対応） - Qiita](https://qiita.com/minorun365/items/2f4e238f8bbc6e393ba5) - - [生成AIアプリの出力をRagasで評価して、LangfuseでGUI監視しよう！ - Qiita](https://qiita.com/minorun365/items/70ad2f5a0afaac6e5cb9) - + - [RAG精度評価の定番ツール「Ragas」にAWSのBedrockで入門しよう！（v0.2対応） - Qiita](https://qiita.com/minorun365/items/2f4e238f8bbc6e393ba5) + - [生成AIアプリの出力をRagasで評価して、LangfuseでGUI監視しよう！ - Qiita](https://qiita.com/minorun365/items/70ad2f5a0afaac6e5cb9) +- [Yunnglin](https://github.com/Yunnglin) has penned a guide on integrating Ragas v0.2 into [EvalScope](https://github.com/modelscope/eval-scope) (an evaluation framework for large models), thereby utilizing the [ModelScope](https://github.com/modelscope/modelscope) ecosystem. + - Tutorial: [Using Ragas with EvalScope](https://evalscope.readthedocs.io/en/latest/user_guides/backend/rageval_backend/ragas.html) + - 教程: [在EvalScope中使用Ragas一键发起RAG评估](https://evalscope.readthedocs.io/zh-cn/latest/user_guides/backend/rageval_backend/ragas.html) + - 最佳实践: [打破文本边界：如何进行多模态RAG评估](https://evalscope.readthedocs.io/zh-cn/latest/blog/RAG/multimodal_RAG.html#multimodal-rag) ## **📅 Events**