> For the complete documentation index, see [llms.txt](https://nomadamas.gitbook.io/ragchain-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://nomadamas.gitbook.io/ragchain-docs/ragchain-structure/reranker.md). # Reranker ## Overview The reranker is a part of the retrieval process in information retrieval systems. It's a system that reorders the list of [`Passage`](https://nomadamas.github.io/RAGchain/build/html/RAGchain.schema.html#module-RAGchain.schema.passage)s initially retrieved by a base retrieval model, usually with the goal of improving precision at top ranks. This approach is particularly useful when dealing with large document collections where exhaustive computation for all documents is not feasible or efficient. ## Supporting Reranker ### 1. UPR reranker It uses a language model to generate a question based on the passage and reranks the passages by the likelihood of the question. ### 2. TART reranker It is designed to rerank passages using specific instructions. ### 3. MonoT5 reranker The `MonoT5Reranker` class is a reranker that uses the MonoT5 model. This model rerank passages based on their relevance to a given query. ### 4. LLM reranker LLM uses pre-trained language models like GPT or Llama can be used to rank passages based on their likelihood of being relevant to user queries. ### 5. BM25 reranker The `BM25Reranker` is a class that leverages the BM25 ranking function to rerank a list of passages based on their relevance to a given query. ## Roles of the Reranker in the Framework The primary role of a reranker in RAGchain: 1. **Improving Precision**: By reordering retrieved results using more sophisticated models than initial retrieval mechanisms. 2. **Diversity**: By providing an opportunity to introduce diversity into search results by using different ranking algorithms. 3. **Scalability**: When dealing with large-scale data sets, it might not be feasible to apply complex machine learning algorithms on all data points due to computational limitations. ## Advantages of Using A Reranker 1. **Improved Performance**: Re-ranking can often improve performance over using single retrieval alone. 2. **Ensemble Learning Benefits**: Combining different types/models can lead to better generalization and improved performance. 3. **Computational Efficiency**: Instead of applying expensive computations across all data points initially retrieved, rerankers focus on top-k results which saves resources while still improving accuracy. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://nomadamas.gitbook.io/ragchain-docs/ragchain-structure/reranker.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.