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@@ -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.
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+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.
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