RAG技术原理是什么？2026年深度解析检索增强生成

引言

随着人工智能技术的飞速发展，信息处理和知识利用的效率与精度成为了科研与产业界关注的焦点。在这一背景下，RAG（Retrieval-Augmented Generation，检索增强生成）技术应运而生。RAG 结合了检索（Retrieval）与生成（Generation）两大模块，为解决复杂信息处理和知识密集型任务提供了新的思路。本文将深入解析 RAG 的技术原理及关键要点，以帮助读者全面理解这一前沿技术。

With the rapid development of artificial intelligence technology, the efficiency and accuracy of information processing and knowledge utilization have become focal points for both academia and industry. In this context, RAG (Retrieval-Augmented Generation) technology has emerged. By integrating retrieval and generation modules, RAG provides a novel approach to tackling complex information processing and knowledge-intensive tasks. This article will delve into the technical principles and key aspects of RAG to help readers gain a comprehensive understanding of this cutting-edge technology.

RAG 技术的诞生背景

在大数据时代的浪潮中，信息如潮水般涌来，企业和个人面临着如何高效处理、理解和利用这些信息的巨大挑战。传统的人工智能生成模型，如基于规则的专家系统以及简单的统计语言模型，在面对复杂多变、知识更新迅速的现实任务时，逐渐暴露出诸多局限性。一方面，规则系统高度依赖人工设定的规则，缺乏灵活性与适应性，难以应对未预料到的输入情况，且规则的维护与更新成本极高；另一方面，统计语言模型虽然能依据历史数据生成具有一定连贯性的文本，但往往局限于自身训练时所吸收的固定知识范围，对于新出现的事件、概念或特定领域的深层次专业知识，很难做出准确且有价值的回应。在这样的背景下，研究者们迫切需要一种能够结合人类智慧与机器智能优势，既具备广泛知识覆盖又能在特定任务中精准响应的技术方案。RAG 应运而生，它巧妙地将信息检索系统与先进的生成模型相融合，通过这种协同工作模式突破传统技术瓶颈，满足日益增长的信息处理需求。

In the wave of the big data era, information floods in like a tide, posing significant challenges for businesses and individuals on how to efficiently process, understand, and utilize this information. Traditional AI generation models, such as rule-based expert systems and simple statistical language models, have gradually revealed numerous limitations when facing complex, rapidly evolving real-world tasks. On one hand, rule-based systems heavily rely on manually defined rules, lacking flexibility and adaptability, struggling to handle unforeseen inputs, and incurring high costs for rule maintenance and updates. On the other hand, while statistical language models can generate somewhat coherent text based on historical data, they are often confined to the fixed knowledge scope absorbed during their training. They find it difficult to provide accurate and valuable responses to newly emerging events, concepts, or deep domain-specific expertise. Against this backdrop, researchers urgently needed a technical solution that could combine the strengths of human intelligence and machine intelligence, offering both broad knowledge coverage and precise responses in specific tasks. RAG emerged as a solution, ingeniously fusing information retrieval systems with advanced generation models. This collaborative working mode breaks through traditional technical bottlenecks and meets the growing demands for information processing.

RAG 的工作原理

RAG 的工作流程可以清晰地划分为两个核心阶段：检索（Retrieval）和生成（Generation）。

The workflow of RAG can be clearly divided into two core stages: Retrieval and Generation.

检索阶段（Retrieval）

检索阶段的目标是从一个庞大的知识库中，快速、准确地找到与用户查询最相关的信息片段。

The goal of the retrieval stage is to quickly and accurately find the information snippets most relevant to a user's query from a vast knowledge base.

1. 查询向量化：当接收到用户输入的问题或任务请求时，RAG 系统首先利用检索模型对问题进行语义分析。该模型基于向量相似性原理，将问题文本转化为一个高维向量表示（即嵌入向量）。同时，知识库中的所有文档内容也预先进行了相同的向量化处理。常用的向量化技术将文本转换为高维向量表示的技术，如使用BERT、Word2Vec等预训练模型生成语义向量，用于相似性计算和检索。包括预训练的词嵌入模型（如 Word2Vec、GloVe）或更先进的上下文感知型预训练语言模型（如 BERT、Sentence-BERT）。

   Query Vectorization: Upon receiving a user's input question or task request, the RAG system first uses a retrieval model to perform semantic analysis on the question. Based on the principle of vector similarity, this model converts the question text into a high-dimensional vector representation (i.e., an embedding vector). Simultaneously, all document content in the knowledge base has been pre-processed with the same vectorization. Common vectorization techniques include pre-trained word embedding models (e.g., Word2Vec, GloVe) or more advanced context-aware pre-trained language models (e.g., BERT, Sentence-BERT).

2. 相似度计算与检索：系统将问题向量与知识库中所有文档向量进行高效比对，计算它们之间的余弦相似度或其他距离度量。目标是找出与问题向量最相似的若干条文档片段作为候选检索结果。

   Similarity Calculation and Retrieval: The system efficiently compares the question vector with all document vectors in the knowledge base, calculating the cosine similarity or other distance metrics between them. The goal is to identify several document snippets whose vectors are most similar to the question vector as candidate retrieval results.

3. 结果优化：为了提高检索效率和质量，系统通常会采用优化的数据结构和算法。例如，使用倒排索引快速定位包含特定关键词的文档集合，或采用近似最近邻搜索（ANN）算法（如 HNSW, IVF）在保证一定精度的前提下，大幅缩短在高维向量空间中的搜索时间，这对于处理大规模数据集至关重要。检索结果的排序也可能结合额外的语义分析、文档权威性、时效性等因素进行优化。

   Result Optimization: To improve retrieval efficiency and quality, the system typically employs optimized data structures and algorithms. For example, using an inverted index to quickly locate document sets containing specific keywords, or employing Approximate Nearest Neighbor (ANN) search algorithms (e.g., HNSW, IVF) to significantly reduce search time in high-dimensional vector spaces while maintaining acceptable accuracy, which is crucial for handling large-scale datasets. The ranking of retrieval results may also be optimized by incorporating additional factors such as semantic analysis, document authority, and timeliness.

生成阶段（Generation）

生成阶段的任务是综合利用原始查询和检索到的相关知识，生成连贯、准确且符合用户意图的最终输出。

The task of the generation stage is to comprehensively utilize the original query and the retrieved relevant knowledge to generate a coherent, accurate, and user-intent-aligned final output.

1. 上下文构建：获取到一组与问题相关的检索结果后，这些结果（通常以文本片段形式）与原始问题一同被组合成一个增强的上下文提示（Prompt），并输入到生成模型中。

   Context Construction: After obtaining a set of retrieval results relevant to the question, these results (typically in the form of text snippets) are combined with the original question to form an augmented context prompt, which is then fed into the generation model.

2. 知识融合与文本生成：生成模型（通常是一个基于Transformer架构的大语言模型，如GPT系列、LLaMA等）接收这个增强的上下文。模型通过其内部的注意力机制神经网络中的一种技术，使模型能够根据输入的不同部分分配不同的重要性权重，从而更好地处理序列数据。和多层神经网络，对输入的序列进行深度理解，将检索到的外部知识与问题的语义信息进行深度融合。它基于整个输入序列的上下文关系、词汇语义关联以及语法结构，逐步推理并生成出最终的回答或内容。

   Knowledge Fusion and Text Generation: The generation model (typically a large language model based on the Transformer architecture, such as the GPT series, LLaMA, etc.) receives this augmented context. Through its internal attention mechanisms and multi-layer neural networks, the model deeply comprehends the input sequence, seamlessly integrating the retrieved external knowledge with the semantic information of the question. Based on the contextual relationships, lexical semantic associations, and grammatical structure of the entire input sequence, it progressively reasons and generates the final answer or content.

3. 生成策略调控：生成过程可以通过调节超参数来优化结果质量。例如，“温度”（Temperature）参数控制输出词的概率分布的随机性，影响生成文本的创造性与确定性；不同的解码策略（如贪心解码、束搜索）则会影响生成过程的效率和文本的多样性。

   Generation Strategy Control: The generation process can be optimized by tuning hyperparameters. For example, the "Temperature" parameter controls the randomness of the output word probability distribution, affecting the creativity and determinism of the generated text; different decoding strategies (e.g., greedy decoding, beam search) influence the efficiency of the generation process and the diversity of the text.

RAG 的关键技术组件

一个高效的 RAG 系统依赖于几个核心组件的紧密协作。

An efficient RAG system relies on the close collaboration of several core components.

检索模型 (Retrieval Model)

检索模型负责将文本转换为有意义的向量表示，并高效执行相似性搜索。

The retrieval model is responsible for converting text into meaningful vector representations and efficiently performing similarity searches.

1. 语义向量化技术将文本转换为高维向量表示的技术，如使用BERT、Word2Vec等预训练模型生成语义向量，用于相似性计算和检索。：这是检索模型的核心。基于预训练语言模型（如BERT、RoBERTa、E5）的语义向量化技术将文本转换为高维向量表示的技术，如使用BERT、Word2Vec等预训练模型生成语义向量，用于相似性计算和检索。已成为主流。这些模型通过在大规模语料上进行无监督或自监督训练，学习到了丰富的语言知识和上下文语义信息，能够生成区分度高、语义一致性强的文本向量，从而大幅提升检索的准确性。

   Semantic Vectorization Technology: This is the core of the retrieval model. Semantic vectorization techniques based on pre-trained language models (e.g., BERT, RoBERTa, E5) have become mainstream. These models learn rich linguistic knowledge and contextual semantic information through unsupervised or self-supervised training on large-scale corpora, enabling them to generate text vectors with high discriminative power and strong semantic consistency, thereby significantly improving retrieval accuracy.

2. 索引与存储结构：为了管理海量向量数据并支持快速查询，需要专门的向量数据库A database system designed to store and perform high-dimensional semantic similarity searches on vector embeddings of data.或索引库。例如 Facebook AI Similarity Search (Faiss)、Milvus、Pinecone、Weaviate 等。这些系统采用了分区、量化、图索引（如HNSW）等多种优化技术，能够在毫秒级时间内从数百万甚至数十亿的向量中完成近似最近邻搜索。

   Indexing and Storage Structures: To manage massive vector data and support fast queries, specialized vector databases or index libraries are required, such as Facebook AI Similarity Search (Faiss), Milvus, Pinecone, Weaviate, etc. These systems employ various optimization techniques like partitioning, quantization, and graph indexing (e.g., HNSW), enabling approximate nearest neighbor searches across millions or even billions of vectors within milliseconds.

生成模型 (Generation Model)

生成模型是内容的创造者，其性能直接决定最终输出的质量。

The generation model is the creator of content, and its performance directly determines the quality of the final output.

1. 预训练与微调：生成模型通常是基于海量通用文本预训练的大语言模型（LLM）。为了使其更好地适应 RAG 任务（如根据给定上下文回答问题），通常需要在特定领域或任务的数据集上进行有监督微调（SFT），让模型学会如何有效利用提供的检索上下文。

   Pre-training and Fine-tuning: Generation models are typically large language models (LLMs) pre-trained on massive amounts of general text. To better adapt them to RAG tasks (e.g., answering questions based on given context), supervised fine-tuning (SFT) on domain-specific or task-specific datasets is usually required, teaching the model how to effectively utilize the provided retrieval context.

2. 注意力机制神经网络中的一种技术，使模型能够根据输入的不同部分分配不同的重要性权重，从而更好地处理序列数据。：Transformer架构中的自注意力机制神经网络中的一种技术，使模型能够根据输入的不同部分分配不同的重要性权重，从而更好地处理序列数据。和交叉注意力机制神经网络中的一种技术，使模型能够根据输入的不同部分分配不同的重要性权重，从而更好地处理序列数据。是模型理解长序列和融合多源信息的关键。它允许模型在生成每个新词时，动态地关注输入序列（包括问题和检索文档）中最相关的部分。

   Attention Mechanism: The self-attention and cross-attention mechanisms within the Transformer architecture are key for the model to understand long sequences and fuse multi-source information. They allow the model to dynamically focus on the most relevant parts of the input sequence (including the question and retrieved documents) when generating each new word.

检索与生成的融合机制 (Fusion Mechanism)

如何将检索到的信息有效地“喂给”生成模型，是实现高效协同的关键。

How to effectively "feed" the retrieved information to the generation model is key to achieving efficient collaboration.

1. 信息传递方式：
   • 拼接（Concatenation）：最简单的方法是将检索到的文档文本直接拼接在用户问题之后，作为模型的完整输入。[问题] + [文档1] + [文档2] + ...
   • 提示工程（Prompt Engineering）：设计结构化的提示模板，明确指示模型使用提供的上下文。例如：“基于以下信息回答问题：[检索文档]。问题：[用户问题]”。
   • 适配器与重排序（Adapter & Reranking）：更高级的方法包括使用专门的适配器网络对检索结果进行特征提取和融合，或在检索后引入一个重排序模型，对初步检索结果进行二次评分和筛选，只将最相关的少量片段传递给生成模型，以节省上下文窗口并提升质量。

     Information Transfer Methods:

     • Concatenation: The simplest method is to directly concatenate the retrieved document text after the user's question, serving as the complete input for the model. [Question] + [Doc1] + [Doc2] + ...
     • Prompt Engineering: Design structured prompt templates that explicitly instruct the model to use the provided context. For example: "Answer the question based on the following information: [Retrieved Docs]. Question: [User Question]".
     • Adapter & Reranking: More advanced methods include using specialized adapter networks for feature extraction and fusion of retrieval results, or introducing a reranking model after retrieval to perform secondary scoring and filtering of the initial results, passing only the most relevant few snippets to the generation model to save context window space and improve quality.

2. 联合训练与优化：为了使检索器和生成器更好地配合，可以采用端到端的训练方式。例如，通过梯度传播让检索模型学习检索那些能帮助生成模型产生更佳答案的文档，而不仅仅是表面相似的文档。

   Joint Training and Optimization: To enable better coordination between the retriever and the generator, end-to-end training can be employed. For instance, through gradient propagation, the retrieval model can learn to retrieve documents that help the generation model produce better answers, rather than just superficially similar ones.

(Note: Due to the length of the original content, the subsequent sections on "Spring AI一个基于Spring框架的AI开发工具包，提供了实现RAG等AI功能的组件和API，简化集成过程。 Implementation Example," "Advantages and Challenges," and the conclusion will be summarized concisely in the following section.)

技术实践概览：基于 Spring AI一个基于Spring框架的AI开发工具包，提供了实现RAG等AI功能的组件和API，简化集成过程。 的 RAG 实现

原文章提供了一个使用 Spring AI一个基于Spring框架的AI开发工具包，提供了实现RAG等AI功能的组件和API，简化集成过程。 框架构建 RAG 系统的详细代码示例。其核心流程包括：

The original article provides a detailed code example of building a RAG system using the Spring AI一个基于Spring框架的AI开发工具包，提供了实现RAG等AI功能的组件和API，简化集成过程。 framework. Its core workflow includes:

1. 文档处理与嵌入：使用 PagePdfDocumentReader 解析 PDF 文档，通过 TokenTextSplitter 将文档分割成适合处理的片段（块），然后利用嵌入模型将这些文本块转换为向量，并存储到向量数据库A database system designed to store and perform high-dimensional semantic similarity searches on vector embeddings of data.（如 Milvus）中。

   Document Processing and Embedding: Use PagePdfDocumentReader to parse PDF documents, split them into manageable chunks using TokenTextSplitter, then convert these text chunks into vectors using an embedding model, and store them in a vector database (e.g., Milvus).

2. 检索增强生成：在查询时，系统通过 RetrievalRerankAdvisor 这一组件协调工作。其 before 方法执行向量相似度搜索，doRerank 方法可对初步结果进行重排序和过滤。最终，检索到的相关文档上下文被注入到精心设计的提示模板中，与大语言模型（如通过 ChatModel 接口接入的模型）交互，生成最终回答。该框架支持同步（JSON）和流式（Server-Sent Events）两种响应方式。

   Retrieval-Augmented Generation: During a query, the system coordinates work through the RetrievalRerankAdvisor component. Its before method performs vector similarity search, and the doRerank method can rerank and filter the initial results. Finally, the retrieved relevant document context is injected into a carefully designed prompt template to interact with a large language model (e.g., a model accessed via the ChatModel interface) to generate the final answer. The framework supports both synchronous (JSON) and streaming (Server-Sent Events) response modes.

RAG 的优势与挑战

核心优势

1. 突破模型知识局限：RAG 使生成模型能够访问并利用训练数据之外的最新、特定领域或专有知识，有效缓解了大模型的“幻觉”问题和知识陈旧性限制。

   Overcoming Model Knowledge Limitations: RAG enables generation models to access and utilize the latest, domain-specific, or proprietary knowledge beyond their training data, effectively mitigating the "hallucination" problem and knowledge obsolescence limitations of large models.

2. 提升结果可信度：生成内容基于检索到的可验证信息源，提高了输出的准确性、事实性和可追溯性。

   Enhancing Result Credibility: Generated content is based on retrieved, verifiable information sources, improving the accuracy, factuality, and traceability of the output.

3. 模块化与可更新性：知识库与生成模型分离，允许独立更新知识库内容而无需重新训练昂贵的大模型，维护成本更低，灵活性更高。

   Modularity and Updatability: The separation of the knowledge base and the generation model allows for independent updates to the knowledge base content without retraining expensive large models, resulting in lower maintenance costs and higher flexibility.

面临的主要挑战

1. 检索质量依赖：系统整体性能高度依赖于检索阶段返回片段的相关性和质量。“垃圾进，垃圾出”，不相关的检索结果会导致生成答案偏离正轨。

   Dependence on Retrieval Quality: The overall system performance is highly dependent on the relevance and quality of the snippets returned in the retrieval stage. "Garbage in, garbage out" – irrelevant retrieval results can lead the generated answer astray.

2. 上下文窗口限制：生成模型有固定的上下文长度限制。当检索到的相关文档过多或过长时，需要进行有效的筛选、压缩或摘要，以确保所有关键信息能被纳入。

   Context Window Limitations: Generation models have fixed context length limits. When there are too many or too long relevant documents retrieved, effective filtering, compression, or summarization is needed to ensure all key information can be included.

3. 系统延迟与成本：检索和生成两个步骤都会引入计算开销。特别是使用大型生成模型和搜索海量向量库时，需要在响应速度、结果质量和计算成本之间做出权衡。

   System Latency and Cost: Both retrieval and generation steps introduce computational overhead. Especially when using large generation models and searching massive vector libraries, trade-offs must be made between response speed, result quality, and computational cost.

结论

RAG

常见问题（FAQ）

RAG技术具体是如何工作的？

RAG工作流程分为检索和生成两阶段。检索阶段从知识库中查找与查询最相关的信息片段；生成阶段则基于检索到的信息，由生成模型生成准确、上下文感知的最终回答。

RAG与传统生成模型相比有什么优势？

传统生成模型受限于训练时的固定知识，难以应对新事件或专业领域问题。RAG通过结合检索系统，能动态获取最新外部知识，提供更准确、信息丰富的回答，突破了传统模型的局限性。

RAG的检索阶段是如何找到相关信息的？

检索阶段首先将用户查询和知识库文档都转化为高维向量（向量化），然后通过计算向量间的相似度（如余弦相似度），快速找出与查询最匹配的文档片段作为候选结果。