From a6653ad3c58f987857dd76a61ca5f2de8b1cfdba Mon Sep 17 00:00:00 2001 From: Jael Gu Date: Wed, 18 Sep 2024 13:28:54 +0800 Subject: [PATCH] Add more resources Signed-off-by: Jael Gu --- README.md | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/README.md b/README.md index 7b55a4c..b915a00 100644 --- a/README.md +++ b/README.md @@ -130,3 +130,15 @@ Path or url of the document to be loaded. **Returns:** MutationResult A MutationResult after inserting Milvus. + + +# More Resources + +- [Enhancing RAG with Knowledge Graphs - Zilliz blog](https://zilliz.com/blog/enhance-rag-with-knowledge-graphs): Knowledge Graphs (KGs) store and link data based on their relationships. KG-enhanced RAG can significantly improve retrieval capabilities and answer quality. +- [Metrics-Driven Development of RAGs - Zilliz blog](https://zilliz.com/blog/metrics-driven-development-of-rags): Evaluating and improving Retrieval-Augmented Generation (RAG) systems is a nuanced but essential task in the realm of AI-driven information retrieval. By leveraging a metrics-driven approach, as demonstrated by Jithin James and Shahul Es, you can systematically refine your RAG systems to ensure they deliver accurate, relevant, and trustworthy information. +- [How to Evaluate RAG Applications - Zilliz blog](https://zilliz.com/learn/How-To-Evaluate-RAG-Applications): Effective Evaluation strategies for your RAG Application +- [Building an Intelligent QA System with NLP and Milvus - Zilliz blog](https://zilliz.com/blog/building-intelligent-chatbot-with-nlp-and-milvus): The Next-Gen QA Bot is here +- [Using Voyage AI's embedding models in Zilliz Cloud Pipelines - Zilliz blog](https://zilliz.com/blog/craft-superior-rag-for-code-intensive-texts-with-zcp-and-voyage): Assess the effectiveness of a RAG system implemented with various embedding models for code-related tasks. +- [How to Build Retrieval Augmented Generation (RAG) with Milvus Lite, Llama3 and LlamaIndex - Zilliz blog](https://zilliz.com/learn/build-rag-with-milvus-lite-llama3-and-llamaindex): Retrieval Augmented Generation (RAG) is a method for mitigating LLM hallucinations. Learn how to build a chatbot RAG with Milvus, Llama3, and LlamaIndex. +- [Safeguarding Data Integrity: On-Prem RAG Deployment](https://zilliz.com/event/ai-bloks-safeguarding-data-integrity/success): Register for a free webinar exploring how you can deploy RAG applications on-prem using open source tools such as LLMWare and Milvus. +- [Safeguarding Data Integrity: On-Prem RAG Deployment](https://zilliz.com/event/ai-bloks-safeguarding-data-integrity): Register for a free webinar exploring how you can deploy RAG applications on-prem using open source tools such as LLMWare and Milvus. \ No newline at end of file