# Rerank QA Content ## Description The Rerank operator is used to reorder the list of relevant documents for a query. It uses the [MS MARCO Cross-Encoders](https://www.sbert.net/docs/pretrained_cross-encoders.html#ms-marco) model to get the relevant scores and then reorders the documents.
## Code Example - Run with ops ```Python from towhee import ops op = ops.rerank(threshold=0) res = op('What is Towhee?', ['Towhee is Towhee is a cutting-edge framework to deal with unstructure data.', 'I do not know about towhee', 'Towhee has many powerful operators.', 'The weather is good' ]) ``` - Run a pipeline ```python from towhee import ops, pipe, DataCollection p = (pipe.input('query', 'doc') .map(('query', 'doc'), ('doc', 'score'), ops.rerank(threshold=0)) .flat_map(('doc', 'score'), ('doc', 'score'), lambda x, y: [(i, j) for i, j in zip(x, y)]) .output('query', 'doc', 'score') ) DataCollection(p('What is Towhee?', ['Towhee is Towhee is a cutting-edge framework to deal with unstructure data.', 'I do not know about towhee', 'Towhee has many powerful operators.', 'The weather is good' ]) ).show() ```
## Factory Constructor Create the operator via the following factory method ***towhee.rerank(model_name: str = 'cross-encoder/ms-marco-MiniLM-L-12-v2')*** **Parameters:** ***model_name***: str ​ The model name of CrossEncoder, you can set it according to the [Model List](https://www.sbert.net/docs/pretrained-models/ce-msmarco.html#models-performance). ​ ***threshold***: float ​ The threshold for filtering with score ***device***: str
## Interface This operator is used to sort the documents of the query content and return the score, and can also set a threshold to filter the results. **Parameters:** ***query***: str The query content. ​ ***docs***: list A list of sentences to check the correlation with the query content.
**Return**: List[str], List[float] 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.