From 0ffe4ec19652169f5ae994785774e5b99f1d0d71 Mon Sep 17 00:00:00 2001 From: Jael Gu Date: Wed, 18 Sep 2024 13:26:32 +0800 Subject: [PATCH] Add more resources Signed-off-by: Jael Gu --- README.md | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 3cdd209..b115098 100644 --- a/README.md +++ b/README.md @@ -80,4 +80,12 @@ This operator is used to sort the documents of the query content and return the **Return**: List[str], List[float] -The list of documents after rerank and the list of corresponding scores. \ No newline at end of file +The list of documents after rerank and the list of corresponding scores. + +# More Resources + +- [The guide to rerank-english-v3.0 | Cohere](https://zilliz.com/ai-models/rerank-english-v3.0): rerank-english-v3.0: a reranking model for English documents and semi-structured data (JSON); context length: 4096 tokens. +- [Optimizing RAG with Rerankers: The Role and Trade-offs - Zilliz blog](https://zilliz.com/learn/optimize-rag-with-rerankers-the-role-and-tradeoffs): Rerankers can enhance the accuracy and relevance of answers in RAG systems, but these benefits come with increased latency and computational costs. +- [What Are Rerankers and How They Enhance Information Retrieval - Zilliz blog](https://zilliz.com/learn/what-are-rerankers-enhance-information-retrieval): Rerankers are specialized components in information retrieval systems that perform a crucial second-stage evaluation of search results. +- [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 +- [The guide to rerank-english-v2.0 | Cohere](https://zilliz.com/ai-models/rerank-english-v2.0): rerank-english-v2.0: a reranking model for English language documents with a context length of 512 tokens. \ No newline at end of file