@ -80,4 +80,12 @@ This operator is used to sort the documents of the query content and return the
**Return**: List[str], List[float]
**Return**: List[str], List[float]
The list of documents after rerank and the list of corresponding scores.
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.
