内容来源：https://blog.n8n.io/implementing-rerankers-in-your-ai-workflows/

内容总结：

在人工智能技术快速发展的当下，检索增强生成（RAG）系统中的重排序技术正成为提升信息检索质量的关键环节。传统向量搜索虽能快速获取候选文档，但结果缺乏精准排序，可能导致相关度较低的文档被优先采用。

为解决这一问题，重排序模型通过二次筛选机制，对初步检索结果进行语义层面的精细分析，其工作原理可类比搜索引擎的结果排序——用户总期望最相关的内容出现在首页。该技术能显著提升检索质量，因其在子文档和子查询层面运作，采用更精准的转换器模型，同步解析查询与文档的语义关联。

目前业界提供三种主流部署方案：通过Cohere、Jina等商业方案的API服务可快速集成；云平台部署兼顾性能与扩展性；本地化部署则满足数据安全与定制化需求。开源领域涌现出ColBERT、FlashRank、RankZephyr等工具，它们通过令牌级嵌入矩阵和零样本重排序等技术，在保证效率的同时提升检索准确率。

商业服务方面，Cohere重排序器支持百种语言及复杂文档格式处理，Jina则具备代码搜索和表格数据专项优化能力。技术实现上，业界通常结合双编码器的高效初筛与交叉编码器的精准重排序，大型语言模型虽能提升相关性判断质量，但需权衡其延迟与成本。

值得注意的是，该技术已实现开箱即用的产品化集成。以n8n平台为例，通过Cohere重排序节点可快速构建智能问答系统，验证了该技术在优化RAG pipeline实践中的成熟度与实用性。

中文翻译：

在检索增强生成（RAG）流程中，检索器首先通过向量相似性搜索进行粗筛，获取一组候选文档。这些文档在检索时并未排序，可能导致相关性较低的文档被优先使用。

此时就需要引入重排序模型——它们对检索过程进行二次处理，根据文档与用户查询的语义相关性对检索文本进行排序。通过搜索引擎可以直观理解重排序的作用：在谷歌搜索时，我们期望首屏显示最相关结果，随着页码增加，结果与查询的相关性会逐渐递减。

重排序如何提升检索质量
重排序器能显著改善搜索质量，因为它能在子文档和子查询层面运作。该模型采用更精确的算法（如可同步解析查询与文档的Transformer架构）来精细评估候选文本。其工作流程清晰简明：

• 检索：搜索系统基于用户查询，通过向量嵌入获取潜在相关文档集合
• 分析：重排序器结合查询术语与文档内容的细微关联，解析结果的语义内涵
• 重排：模型根据深度分析重新排列搜索结果，将判定最相关的内容置顶

AI生态系统中存在多种开源与商业工具可助力重排序优化RAG流程。接下来我们将介绍重排序的部署方案，并列举相关工具。

重排序部署方案
重排序器作为独立模型需单独运行，您可选择直接部署模型或采用服务化模式。具体有三种方案：

• 通过API调用服务化方案（-aaS）
• 云托管部署
• 本地自托管部署

方案一：服务化模式（aaS）
API模式是实施重排序最便捷的途径。Cohere、Jina等商业解决方案均提供该服务，开发者能以最小基础设施开销将重排序功能集成至RAG流程。这些服务通过API端点接收查询与文档列表，返回按语义相关性降序排列的结果。其底层架构通过文档分块处理并计算各段落相关性得分，最终以最高分段落确定文档排序。

方案二：云托管部署
通过主流云服务商的AI平台部署重排序模型，既能享受商业模型的性能优势，又兼具云基础设施的稳健性与扩展性。该方案特别适合需要稳定性能、自动扩缩容以及与现有云数据管道无缝集成的组织。虽然便利性不及API模式，但云托管能降低对第三方供应商的依赖，满足安全合规与服务等级协议要求。

方案三：自托管部署
自托管方案使企业能在自有基础设施内运行重排序模型，确保数据完全由内部处理。该方案支持自定义部署配置、硬件专项优化与企业系统深度集成。虽然需要更多技术储备和基础设施管理投入，但能实现毫秒级实时重排序，同时全面掌控数据隐私与安全协议。

开源重排序工具
值得关注的开源工具包括：

• ColBERT：基于细粒度上下文延迟交互的快速检索模型，通过令牌级嵌入矩阵实现毫秒级大规模文本检索，利用可扩展向量相似度运算符进行上下文匹配
• FlashRank：集成Pairwise/Listwise重排序器的Python库，可为现有搜索检索流程增强排序功能
• RankZephyr：基于70亿参数Zephyr-β模型的开源列表式零样本重排序大模型，通过指令微调从RankGPT系列蒸馏重排序能力，无需人工标注相关度标签

商业重排序服务提供商
典型商业服务包括Cohere与Jina：

• Cohere重排序模型采用交叉注意力机制实现细粒度排序，直接进行查询-文档比对，显著提升复杂查询与模糊查询的结果质量。支持超100种语言的多语言检索，能精准处理邮件、表格、JSON及代码等半结构化企业数据，提供虚拟私有云或本地化部署方案
• Jina重排序器具备超100种语言的跨语言检索能力，集成专用函数调用支持与高级代码搜索特性，可基于自然语言查询对代码片段排序。第二代版本强化了对表格与结构化数据的处理，能在SQL生成前通过自然语言查询排序最相关表结构

重排序技术原理
自然语言处理领域采用双编码器与交叉编码器架构处理文本相似度任务：

• 交叉编码器适用于预定义句对评分场景，例如为100组句对计算相似度得分
• 双编码器适用于需要句向量嵌入的高效比对场景，如信息检索/语义搜索或聚类任务

交叉编码器性能更优但扩展性受限：对万级语句聚类需计算约5000万组句对相似度（耗时约65小时），而双编码器仅需5秒即可完成所有语句嵌入计算。实际应用中可组合使用两种架构：先通过高效双编码器检索Top100相似句，再采用交叉编码器对100组（查询,结果）组合进行重排序。

基于大语言模型的重排序
与评估RAG系统类似，大语言模型也可用于重排序检索文档。虽然LLM驱动的检索能返回比嵌入检索更相关的结果，但需以更高延迟和成本为代价。该方法的核心是让LLM判定文档与查询的相关度：输入提示包含候选文档集合，LLM负责筛选相关文档集合并通过内部度量标准评分。LlamaIndex用户可通过框架内置的LLMRerank模块（NodePostprocessor抽象组件）实现该功能。

n8n平台的重排序实践
在n8n中可通过Cohere重排序节点快速实现功能：该节点可连接向量数据库，对检索获得的文本块按查询相关度降序重排。"智能AI聊天机器人+RAG+Cohere重排序"工作流模板即采用该方案，通过融合RAG与重排序技术确保优先呈现最相关信息。

英文来源：

In a Retrieval-Augmented Generation (RAG) pipeline, a retriever fetches a set of candidate documents using a coarse filter via a vector similarity search. When these documents are retrieved, they are not ordered, so a less relevant document may be used before a more relevant one.
This is where we apply reranking models, which offer a second pass at the retrieval process to sort the retrieved text by semantic relevance with respect to the user’s query.
You can get an intuitive understanding of rerankers by thinking of search engines. When searching for something on Google, we expect that the most relevant results will be displayed on the first page. As we go through the following pages, the relevance of the results with respect to the query would decrease.
How rerankers improve retrieval quality
The reranker can significantly improve the search quality because it operates at a sub-document and sub-query level. It uses a more precise model, such as a transformer that jointly reads the query and the document to scrutinize the candidate texts.
The reranking process is straightforward:

• Retrieve: First, a search system uses vector embeddings to find a broad set of potentially relevant documents based on the user's query.
• Analyze: Then, the reranker takes these results and analyzes their semantic content considering the nuances of how the query terms interact with the document content.
• Reorder: Lastly, the model reorders the search results, placing the ones it deems most relevant at the top, based on this deeper analysis.
  The AI ecosystem offers a range of open-source and commercial tools that can help improve RAG pipelines using rerankers. In the following section, we will present some options for deploying rerankers, followed by open-source and commercial tools.
  How to deploy rerankers
  Rerankers are standalone models that must be run independently. You can either choose to deploy and run the model directly or consume it in an as-a-service fashion. As such, you have three options:
• An -aaS delivery via an API
• A cloud-hosted option, or
• A self-hosted option for local deployments.
  Option 1: as-a-Service (aaS)
  The API model is perhaps the most straightforward approach to implementing rerankers. These are available from commercial solutions such as Cohere and Jina. This method allows developers to integrate reranking capabilities into their RAG pipelines with minimal infrastructure overhead. The commercial solutions expose API endpoints where users submit a query along with a list of retrieved documents, and the service returns these documents reordered from most to least semantically relevant. The underlying architecture typically processes user input by chunking documents and computing relevance scores for each segment, with the final document score determined by the highest-scoring chunk.
  Option 2: Cloud-hosted deployments
  Cloud-hosted deployment involves deploying reranker models through major cloud providers' AI platforms, combining the robustness and scalability of cloud infrastructure with the performance of commercial reranking models. This deployment method is particularly beneficial for organizations that require consistent performance, automatic scaling, and integration with existing cloud-based data pipelines. While not as convenient as the API option, hosting the model in your cloud minimizes dependency on the third-party vendor and can deliver on any security mandates, compliance certifications, and service level agreements.
  Option 3: Self-hosted deployments
  Self-hosted deployments allow enterprises to run reranker models within their own infrastructure, such that no data has to be processed by a third party. It also offers flexibility to customize deployment configurations, optimize for specific hardware, and integrate with existing enterprise systems. While this approach requires more technical expertise and infrastructure management, it delivers the benefits of real-time reranking with minimal latency while maintaining full control over data privacy and security protocols.
  Open source reranking tools
  Some of the most notable open source tools for reranking include the following:
  ColBERT is a fast and accurate retrieval model, enabling scalable BERT-based search over large text collections in tens of milliseconds. It relies on fine-grained contextual late interaction, encoding each passage into a matrix of token-level embeddings. At search time, it embeds every query into another matrix and efficiently finds passages that contextually match the query using scalable vector-similarity (MaxSim) operators.
  FlashRank uses Pairwise or Listwise rerankers. It’s a Python library that adds re-ranking to your existing search & retrieval pipelines.
  RankZephyr is an open-source large language model (LLM) for listwise zero-shot reranking. It’s built on the 7-billion parameter Zephyr-β model (based on Mistral), and uses instruction fine-tuning to distill reranking capabilities from both RankGPT-3.5 and RankGPT-4 without requiring human-annotated query-passage relevance pairs.
  Commercial reranking providers
  Some examples of commercial rerankers include Cohere and Jina.
  Cohere's reranker model employs cross-attention mechanisms for fine-grained ranking, enabling direct query-document comparison that significantly improves result quality for complex and under-specified queries. The model offers multilingual capabilities, supporting over 100 languages and delivering accurate retrieval across language boundaries from international and multilingual datasets. It can handle complex enterprise data formats, ranking multi-aspect and semi-structured documents—including emails, tables, JSON, and code—with the same precision as traditional long-form text. The solution is designed for enterprise deployment flexibility, offering private deployment options in virtual private clouds or on-premises environments for maximum data privacy and security control, while also being available through Cohere's platform and trusted cloud providers.
  Jina Reranker offers comprehensive multilingual retrieval capabilities across over 100 languages, enabling effective document retrieval regardless of the query language used. The model features specialized function-calling support and advanced code search capabilities, allowing it to rank code snippets and function signatures based on natural language queries, making it particularly well-suited for Agentic RAG applications. Additionally, Jina Reranker v2 provides robust tabular and structured data support, effectively ranking the most relevant tables based on natural language queries and helping to sort different table schemas to identify the most appropriate one before SQL query generation, making it a versatile solution for enterprise environments with diverse data formats and multilingual requirements.
  How reranking works
  Bi-encoders and cross-encoders are two architectures used in natural language processing (NLP) for tasks like text similarity, retrieval, or ranking. Cross-Encoders can be used whenever you have a pre-defined set of sentence pairs you want to score. For example, you have 100 sentence pairs and you want to get similarity scores for these 100 pairs.
  Bi-Encoders are used whenever you need a sentence embedding in a vector space for efficient comparison. Applications are for example, Information Retrieval / Semantic Search or Clustering.
  Cross-Encoder achieve higher performance than Bi-Encoders, however, they do not scale well for large datasets. Clustering 10,000 sentences with CrossEncoders would require computing similarity scores for about 50 million sentence combinations, which takes about 65 hours. With a Bi-Encoder, you compute the embedding for each sentence, which takes only 5 seconds.
  You can combine Cross- and Bi-Encoders. For example, you can use an efficient Bi-Encoder to retrieve the top-100 most similar sentences for a query, then a Cross-Encoder to re-rank these 100 hits by computing the score for every (query, hit) combination.
  LLM-based reranking
  Just as when evaluating RAG, you can use an LLM to rerank retrieved documents. LLM-powered retrieval can return more relevant documents than embedding-based retrieval, with the tradeoff being much higher latency and cost. At a high-level, this approach uses the LLM to decide which documents and chunks are relevant to the given query. The input prompt would consist of a set of candidate documents, and the LLM is tasked with selecting the relevant set of documents as well as scoring their relevance with an internal metric.
  If you are using LlamaInde, the LLMRerank is a module baked framework as part of the NodePostprocessor abstraction.
  Reranking in n8n
  You can easily implement reranking in n8n using the Reranker Cohere node. It allows users to rerank the resulting chunks from a vector store. You can connect this node to a vector store. The reranker reorders the list of documents retrieved from a vector store for a given query in order of descending relevance.
  This Intelligent AI chatbot with RAG and Cohere Reranker workflow template creates an intelligent AI assistant that combines RAG with Cohere's reranking technology to ensure the most relevant information is prioritized.