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<h1 class="title-article" id="articleContentId">大模型入门到进阶：什么是 RAG？为什么需要 RAG？RAG 的流程</h1>
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<div class="up-time"><span>于 2024-08-14 09:49:08 首次发布</span></div>
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</svg>
<h2><a id="_RAG_0"></a>一、什么是 RAG？</h2>
<p>RAG 全称 Retrieval-Augmented Generation，翻译成中文是检索增强生成。检索指的是检索外部知识库，增强生成指的是将检索到的知识送给大语言模型以此来优化大模型的生成结果，使得大模型在生成更精确、更贴合上下文答案的同时，也能有效减少产生误导性信息的可能。</p>
<h2><a id="_RAG_3"></a>二、为什么需要 RAG？</h2>
<p>之所以需要 RAG，是因为大语言模型本身存在一些局限性。</p>
<h4><a id="1_6"></a>1.时效性</h4>
<p>模型的训练是基于截至某一时间点之前的数据集完成的。这意味着在该时间点之后发生的任何事件、新发现、新趋势或数据更新都不会反映在模型的知识库中。例如，我的训练数据在 2023 年底截止，之后发生的事情我都无法了解。另外，大型模型的训练涉及巨大的计算资源和时间。这导致频繁更新模型以包括最新信息是不现实的，尤其是在资源有限的情况下。</p>
<h4><a id="2_9"></a>2.覆盖性</h4>
<p>虽然大模型的训练数据集非常庞大，但仍可能无法涵盖所有领域的知识或特定领域的深度信息。例如，某些专业的医学、法律或技术问题可能只在特定的文献中被详细讨论，而这些文献可能未被包括在模型的训练数据中。另外，对于一些私有数据集，也是没有被包含在训练数据中的。当我们问的问题的答案没有包含在大模型的训练数据集中时，这时候大模型在回答问题时便会出现幻觉，答案也就缺乏可信度。</p>
<p>由于以上的一些局限性，大模型可能会生成虚假信息。为了解决这个问题，需要给大模型外挂一个知识库，这样大模型在回答问题时便可以参考外挂知识库中的知识，也就是 RAG 要做的事情。</p>
<h2><a id="RAG__14"></a>三、RAG 的流程</h2>
<p>RAG 的中文名称是检索增强生成，从字面意思来理解，包含三个检索、增强和生成三个过程。</p>
<ul><li><strong>检索</strong>：根据用户的查询内容，从外挂知识库获取相关信息。具体来说，就是将用户的查询通过嵌入模型转换成向量，以便与向量数据库中存储的知识相关的向量进行比对。通过相似性搜索，从向量数据库中找出最匹配的前 K 个数据。</li><li><strong>增强</strong>：将用户的查询内容和检索到的相关知识一起嵌入到一个预设的提示词模板中。</li><li><strong>生成</strong>：将经过检索增强的提示词内容输入到大语言模型（LLM）中，以此生成所需的输出。 流程图如下所示：<br /> <img src="https://i-blog.csdnimg.cn/direct/0d5e44402f0a40c88c7d1c1f63ef00c3.png#pic_center" alt="在这里插入图片描述" /></li></ul>
<h4><a id="_22"></a>简单示例</h4>
<p>在下面这个简单示例中，我们使用 Elasticsearch 作为存储向量的数据库。使用讯飞的星火大模型来进行问题的回答。具体代码如下：连接本地的 Elasticsearch 数据库</p>
<pre><code class="prism language-python"><span class="token keyword">import</span> os
<span class="token keyword">from</span> elasticsearch <span class="token keyword">import</span> Elasticsearch
<span class="token keyword">from</span> dotenv <span class="token keyword">import</span> load_dotenv
load_dotenv<span class="token punctuation">(</span><span class="token punctuation">)</span>
client <span class="token operator">=</span> Elasticsearch<span class="token punctuation">(</span>
<span class="token string">"https://localhost:9200"</span><span class="token punctuation">,</span>
ssl_assert_fingerprint<span class="token operator">=</span>os<span class="token punctuation">.</span>environ<span class="token punctuation">[</span><span class="token string">"CERT_FINGERPRINT"</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
basic_auth<span class="token operator">=</span><span class="token punctuation">(</span><span class="token string">"elastic"</span><span class="token punctuation">,</span> os<span class="token punctuation">.</span>environ<span class="token punctuation">[</span><span class="token string">"ELASTIC_PASSWORD"</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
<span class="token punctuation">)</span>
</code></pre>
<p><strong>加载向量模型</strong></p>
<pre><code class="prism language-python"><span class="token keyword">from</span> sentence_transformers <span class="token keyword">import</span> SentenceTransformer
model <span class="token operator">=</span> SentenceTransformer<span class="token punctuation">(</span><span class="token string">"./stella-base-zh-v3-1792d"</span><span class="token punctuation">)</span>
</code></pre>
<p><strong>创建索引</strong></p>
<pre><code class="prism language-python">client<span class="token punctuation">.</span>indices<span class="token punctuation">.</span>create<span class="token punctuation">(</span>index <span class="token operator">=</span> <span class="token string">"rag_tutorial_vector_index"</span><span class="token punctuation">,</span>
mappings <span class="token operator">=</span> <span class="token punctuation">{<!-- --></span>
<span class="token string">'properties'</span><span class="token punctuation">:</span> <span class="token punctuation">{<!-- --></span>
<span class="token string">'embedding'</span><span class="token punctuation">:</span> <span class="token punctuation">{<!-- --></span>
<span class="token string">'type'</span><span class="token punctuation">:</span> <span class="token string">'dense_vector'</span>
<span class="token punctuation">}</span>
<span class="token punctuation">}</span>
<span class="token punctuation">}</span><span class="token punctuation">)</span>
</code></pre>
<p><strong>读取本地的文本文件并进行切分，然后向索引中添加数据</strong></p>
<pre><code class="prism language-python"><span class="token keyword">from</span> langchain<span class="token punctuation">.</span>text_splitter <span class="token keyword">import</span> RecursiveCharacterTextSplitter
<span class="token keyword">with</span> <span class="token builtin">open</span><span class="token punctuation">(</span><span class="token string">"patent.txt"</span><span class="token punctuation">,</span> <span class="token string">"r"</span><span class="token punctuation">)</span> <span class="token keyword">as</span> fp<span class="token punctuation">:</span>
text <span class="token operator">=</span> fp<span class="token punctuation">.</span>read<span class="token punctuation">(</span><span class="token punctuation">)</span>
text_splitter <span class="token operator">=</span> RecursiveCharacterTextSplitter<span class="token punctuation">(</span>chunk_size <span class="token operator">=</span> <span class="token number">200</span><span class="token punctuation">,</span> chunk_overlap<span class="token operator">=</span><span class="token number">50</span><span class="token punctuation">)</span>
chunks <span class="token operator">=</span> text_splitter<span class="token punctuation">.</span>create_documents<span class="token punctuation">(</span><span class="token punctuation">[</span>text<span class="token punctuation">]</span><span class="token punctuation">)</span>
doc <span class="token operator">=</span> <span class="token punctuation">{<!-- --></span><span class="token punctuation">}</span>
<span class="token keyword">for</span> doc_id<span class="token punctuation">,</span> chunk <span class="token keyword">in</span> <span class="token builtin">enumerate</span><span class="token punctuation">(</span>chunks<span class="token punctuation">)</span><span class="token punctuation">:</span>
chunk_embedding <span class="token operator">=</span> model<span class="token punctuation">.</span>encode<span class="token punctuation">(</span>chunk<span class="token punctuation">.</span>page_content<span class="token punctuation">,</span> normalize_embeddings<span class="token operator">=</span><span class="token boolean">True</span><span class="token punctuation">)</span>
doc<span class="token punctuation">[</span><span class="token string">'text'</span><span class="token punctuation">]</span> <span class="token operator">=</span> chunk<span class="token punctuation">.</span>page_content
client<span class="token punctuation">.</span>index<span class="token punctuation">(</span>
index <span class="token operator">=</span> <span class="token string">"rag_tutorial_vector_index"</span><span class="token punctuation">,</span>
<span class="token builtin">id</span> <span class="token operator">=</span> doc_id<span class="token punctuation">,</span>
document <span class="token operator">=</span> <span class="token punctuation">{<!-- --></span>
<span class="token operator">**</span>doc<span class="token punctuation">,</span>
<span class="token string">"embedding"</span><span class="token punctuation">:</span> chunk_embedding
<span class="token punctuation">}</span>
<span class="token punctuation">)</span>
</code></pre>
<p><strong>使用查询从向量数据库中检索数据</strong></p>
<pre><code class="prism language-python">query <span class="token operator">=</span> <span class="token string">"本次专利法修改对于更好地鼓励医药产业创新，有哪些新规定？"</span>
query_embedding <span class="token operator">=</span> model<span class="token punctuation">.</span>encode<span class="token punctuation">(</span>query<span class="token punctuation">,</span> normalize_embeddings<span class="token operator">=</span><span class="token boolean">True</span><span class="token punctuation">)</span>
resp_vector <span class="token operator">=</span> client<span class="token punctuation">.</span>search<span class="token punctuation">(</span>
knn <span class="token operator">=</span> <span class="token punctuation">{<!-- --></span>
<span class="token string">'field'</span><span class="token punctuation">:</span> <span class="token string">'embedding'</span><span class="token punctuation">,</span>
<span class="token string">'query_vector'</span><span class="token punctuation">:</span> query_embedding<span class="token punctuation">,</span>
<span class="token string">'num_candidates'</span><span class="token punctuation">:</span> <span class="token number">10</span><span class="token punctuation">,</span>
<span class="token string">'k'</span><span class="token punctuation">:</span> <span class="token number">3</span>
<span class="token punctuation">}</span><span class="token punctuation">,</span>
min_score <span class="token operator">=</span> <span class="token number">0.8</span><span class="token punctuation">,</span>
size <span class="token operator">=</span> <span class="token number">3</span><span class="token punctuation">,</span>
index <span class="token operator">=</span> <span class="token string">'rag_tutorial_vector_index'</span>
<span class="token punctuation">)</span>
</code></pre>
<p><strong>将检索的结果进行汇总</strong></p>
<pre><code class="prism language-python">retrieval_content <span class="token operator">=</span> <span class="token string">""</span>
<span class="token keyword">for</span> hit <span class="token keyword">in</span> resp_vector<span class="token punctuation">[</span><span class="token string">'hits'</span><span class="token punctuation">]</span><span class="token punctuation">[</span><span class="token string">'hits'</span><span class="token punctuation">]</span><span class="token punctuation">:</span>
retrieval_content <span class="token operator">+=</span> hit<span class="token punctuation">[</span><span class="token string">'_source'</span><span class="token punctuation">]</span><span class="token punctuation">[</span><span class="token string">'text'</span><span class="token punctuation">]</span>
</code></pre>
<p>将检索到的结果送到星火大模型，并输出结果</p>
<pre><code class="prism language-python"><span class="token keyword">import</span> os
<span class="token keyword">from</span> sparkai<span class="token punctuation">.</span>llm<span class="token punctuation">.</span>llm <span class="token keyword">import</span> ChatSparkLLM<span class="token punctuation">,</span> ChunkPrintHandler
<span class="token keyword">from</span> sparkai<span class="token punctuation">.</span>core<span class="token punctuation">.</span>messages <span class="token keyword">import</span> ChatMessage
<span class="token keyword">from</span> dotenv <span class="token keyword">import</span> load_dotenv
load_dotenv<span class="token punctuation">(</span><span class="token punctuation">)</span>
<span class="token keyword">if</span> __name__ <span class="token operator">==</span> <span class="token string">'__main__'</span><span class="token punctuation">:</span>
spark <span class="token operator">=</span> ChatSparkLLM<span class="token punctuation">(</span>
spark_api_url<span class="token operator">=</span>os<span class="token punctuation">.</span>environ<span class="token punctuation">[</span><span class="token string">"SPARKAI_URL"</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
spark_app_id<span class="token operator">=</span>os<span class="token punctuation">.</span>environ<span class="token punctuation">[</span><span class="token string">"SPARKAI_APP_ID"</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
spark_api_key<span class="token operator">=</span>os<span class="token punctuation">.</span>environ<span class="token punctuation">[</span><span class="token string">"SPARKAI_API_KEY"</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
spark_api_secret<span class="token operator">=</span>os<span class="token punctuation">.</span>environ<span class="token punctuation">[</span><span class="token string">"SPARKAI_API_SECRET"</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
spark_llm_domain<span class="token operator">=</span>os<span class="token punctuation">.</span>environ<span class="token punctuation">[</span><span class="token string">"SPARKAI_DOMAIN"</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
streaming<span class="token operator">=</span><span class="token boolean">False</span><span class="token punctuation">,</span>
<span class="token punctuation">)</span>
prompt <span class="token operator">=</span> <span class="token string-interpolation"><span class="token string">f"""
你是一个善于回答问题的助手。请使用以下提供的检索内容和自身知识来回答问题。如果你不知道答案，请直接说不知道，不要杜撰答案。请用三句话以内回答，保持简洁。
问题：</span><span class="token interpolation"><span class="token punctuation">{<!-- --></span>query<span class="token punctuation">}</span></span><span class="token string">
检索内容：</span><span class="token interpolation"><span class="token punctuation">{<!-- --></span>retrieval_content<span class="token punctuation">}</span></span><span class="token string">
"""</span></span>
messages <span class="token operator">=</span> <span class="token punctuation">[</span>ChatMessage<span class="token punctuation">(</span>
role <span class="token operator">=</span> <span class="token string">"user"</span><span class="token punctuation">,</span>
content <span class="token operator">=</span> prompt
<span class="token punctuation">)</span><span class="token punctuation">]</span>
handler <span class="token operator">=</span> ChunkPrintHandler<span class="token punctuation">(</span><span class="token punctuation">)</span>
a <span class="token operator">=</span> spark<span class="token punctuation">.</span>generate<span class="token punctuation">(</span><span class="token punctuation">[</span>messages<span class="token punctuation">]</span><span class="token punctuation">,</span> callbacks<span class="token operator">=</span><span class="token punctuation">[</span>handler<span class="token punctuation">]</span><span class="token punctuation">)</span>
<span class="token keyword">print</span><span class="token punctuation">(</span>a<span class="token punctuation">.</span>generations<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">.</span>text<span class="token punctuation">)</span>
</code></pre>
<blockquote>
<p>本文从大模型的局限性切入，探讨了检索增强生成架构的核心机制及其带来的好处。RAG 通过结合检索和生成能力，能从外部知识库搜索相关信息，生成更准确的回应，有效克服了大模型在知识更新上的短板。</p>
</blockquote>
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<a href="https://blog.csdn.net/weixin_43679037" target="_blank"><span class="blog-title">谢白羽</span></a>
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<span class="info-block time">03-04</span>
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<div class="desc ellipsis-online ellipsis-online-1">1、LLM 的知识不是实时的2、LLM 可能不知道你私有的领域/业务知识搭建过程：1、文档加载，并按一定条件切割成片段2、将切割的文本片段灌入检索引擎3、封装检索接口4、构建调用<em>流程</em>：Query -> 检索 -> Prompt -> LLM -> 回复1、文档加载2、文档切分3、向量化4、灌入向量数据库。</div>
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<div class="desc ellipsis-online ellipsis-online-1"><em>RAG</em> 是2023年最流行的基于 LLM 的应用系统架构。有许多产品几乎完全建立在 <em>RAG</em> 之上，覆盖了结合网络搜索引擎和 LLM 的问答服务，到成千上万个数据聊天的应用程序。很多人将<em>RAG</em>和Agent 作为大<em>模型</em>应用的两种主流架构，但什么是<em>RAG</em>呢？<em>RAG</em>又涉及了哪些具体的技术呢？1. 什么是<em>RAG</em><em>RAG</em>即检索增强生成，为 LLM 提供了从某些数据源检索到的信息，并基于此修正生成的答案。<em>RAG</em> ...</div>
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<div class="left ellipsis-online ellipsis-online-1">大<em>模型</em>技术知识点：<em>RAG</em></div>
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<div class="desc ellipsis-online ellipsis-online-1"><em>RAG</em>的核心思想是让语言<em>模型</em>在生成回答或文本时能够动态地从外部知识库中检索相关信息。这种方法能够提高<em>模型</em>生成内容的准确性、可靠性和透明度，同时减少“幻觉”（即<em>模型</em>生成看似合理但实际上错误的信息）。</div>
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<div class="left ellipsis-online ellipsis-online-1">一文读懂：大<em>模型</em><em>RAG</em>（检索增强生成）</div>
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<div class="desc ellipsis-online ellipsis-online-1">本文概述 <em>RAG</em> 的核心算法，并举例说明其中的一些方法。<em>RAG</em>融合是一个强大的功能，能够提高<em>RAG</em>应用的语义搜索效率。通过使用语言<em>模型</em>生成多个查询并对搜索结果进行重新排序，<em>RAG</em>融合可以呈现更丰富多样的内容，并提供了一个额外的层次，用于调整应用。此外，<em>RAG</em>融合还可以实现自动纠正、节省成本以及增加内容多样性。但是，<em>需要</em>注意一些权衡，比如潜在的延迟问题、自动纠正的挑战以及成本影响。对于依赖常见概念但可能出现内部行话或重叠词汇的应用来说，<em>RAG</em>融合尤其有用。</div>
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<div class="desc ellipsis-online ellipsis-online-1"><em>RAG</em>的全称是Retrieval-Augmented Generation，中文翻译为检索增强生成。它是一个为大<em>模型</em>提供外部知识源的概念，这使它们能够生成准确且符合上下文的答案，同时能够减少<em>模型</em>幻觉。本文介绍了 <em>RAG</em> 的概念及其背后的一些理论，本文通过Python、LangCh<em>ai</em>n将其实现，在此过程中使用了 Open<em>AI</em>的ChatGPT接口（可以自己搭建chatGLM3）、Weaviate矢量数据库（可以自己搭建Milvus ）、Open<em>AI</em> 嵌入<em>模型</em>实现了 <em>RAG</em> 管道。</div>
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<div class="desc ellipsis-online ellipsis-online-1">2、生成阶段（Generation）： 在这一阶段，<em>RAG</em><em>模型</em>使用生成<em>模型</em>来基于检索到的信息和给定的上下文生成文本。生成<em>模型</em>根据检索到的信息和上下文生成连贯、相关的文本。它的工作原理是结合了检索<em>模型</em>和生成<em>模型</em>的优点，以解决文本生成中的一些挑战和问题。通过将检索和生成两种技术结合在一起，<em>RAG</em><em>模型</em>能够利用大规模语料库中的信息来指导文本生成过程，从而提高生成文本的质量、相关性和多样性。1、检索阶段（Retrieval）： 在这一阶段，<em>RAG</em><em>模型</em>使用检索技术从大型文本语料库中检索与给定上下文相关的信息或段落。</div>
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<div class="desc ellipsis-online ellipsis-online-1"><em>RAG</em>（Retrieval-Augmented Generation，检索增强生成） 是一种结合了信息检索技术与语言生成<em>模型</em>的<em>人工智能</em>技术。该技术通过从外部知识库中检索相关信息，并将其作为提示（Prompt）输入给大型语言<em>模型</em>（LLMs），以增强<em>模型</em>处理知识密集型任务的能力，如问答、文本摘要、内容生成等。<em>RAG</em><em>模型</em>由Facebook <em>AI</em> Research（F<em>AI</em>R）团队于2020年首次提出，并迅速成为大<em>模型</em>应用中的热门方案。</div>
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<div class="left ellipsis-online ellipsis-online-1">大<em>模型</em><em>RAG</em>从<em>入门</em>到精通（非常详细）看这篇就够了，草履虫都能轻松看懂！！！</div>
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<div class="desc ellipsis-online ellipsis-online-1">在<em>RAG</em>系统的实际应用中，<em>需要</em>工程和算法等的多方参与和努力，理论上有很多方法，在实践的过程中我觉得还<em>需要</em>大量的实验对比，不断验证和优化，也可能会遇到许多细节问题，比如可想到的异构数据源的加载和处理啊，知识的展示形态(文本、图片、表格)等是否能一起回答，提升下用户体验，以及建立一套自动化的评估机制，当然还有<em>模型</em>的持续迭代和大小<em>模型</em>的训练支持。作为一名热心肠的互联网老兵，我决定把宝贵的<em>AI</em>知识分享给大家。具体来说，设计了一个轻量级的检索评估器来评估检索到的文档的整体质量，并基于评估结果触发不同的知识检索操作。</div>
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<div class="desc ellipsis-online ellipsis-online-1">在大<em>模型</em>的应用中，尤其是行业大<em>模型</em>的实现方式中，<em>RAG</em>扮演着重要的角色。：<em>RAG</em>通过检索机制，能够灵活适应各种行业和场景的需求，尤其是对于那些<em>需要</em>大量专业知识和实时信息的应用场景，如金融分析、法律咨询等。：<em>RAG</em>能够根据不同的查询检索到不同的信息，为生成的内容提供个性化的背景和依据，促进内容创新和个性化。：<em>RAG</em>在生成响应前会先检索相关信息，这一过程为<em>模型</em>的决策提供了可追溯的依据，增强了<em>模型</em>的可解释性。：通过检索到的相关信息直接辅助生成，<em>RAG</em>可以减少<em>模型</em><em>需要</em>生成的内容量，提高处理效率和响应速度。</div>
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<a class="title text-truncate" target="_blank" href="https://blog.csdn.net/star_nwe/article/details/133703900#comments_35666765" data-report-click='{"mod":"popu_542","spm":"1001.2101.3001.4231","dest":"https://blog.csdn.net/star_nwe/article/details/133703900#comments_35666765","ab":"new"}' data-report-view='{"mod":"popu_542","spm":"1001.2101.3001.4231","dest":"https://blog.csdn.net/star_nwe/article/details/133703900#comments_35666765","ab":"new"}'>Android Compose之mutableStateListOf</a>
<p class="comment ellipsis">
<a href="https://blog.csdn.net/qq_34400960" class="user-name" target="_blank">杨文浩android: </a>
<span class="code-comments">那岂不是list改变一个数据，整个列表都重组了？如何只刷新单条item呢，最近正好在看compose,这里感觉很奇怪</span>
</p>
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<a class="title text-truncate" target="_blank" href="https://blog.csdn.net/star_nwe/article/details/143590176#comments_35429265" data-report-click='{"mod":"popu_542","spm":"1001.2101.3001.4231","dest":"https://blog.csdn.net/star_nwe/article/details/143590176#comments_35429265","ab":"new"}' data-report-view='{"mod":"popu_542","spm":"1001.2101.3001.4231","dest":"https://blog.csdn.net/star_nwe/article/details/143590176#comments_35429265","ab":"new"}'>一文彻底搞懂深度学习：注意力机制（Attention Mechanism）</a>
<p class="comment ellipsis">
<a href="https://blog.csdn.net/l202205761" class="user-name" target="_blank">l202205761: </a>
<span class="code-comments">这讲了啥</span>
</p>
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<a class="title text-truncate" target="_blank" href="https://blog.csdn.net/star_nwe/article/details/142926448#comments_35402777" data-report-click='{"mod":"popu_542","spm":"1001.2101.3001.4231","dest":"https://blog.csdn.net/star_nwe/article/details/142926448#comments_35402777","ab":"new"}' data-report-view='{"mod":"popu_542","spm":"1001.2101.3001.4231","dest":"https://blog.csdn.net/star_nwe/article/details/142926448#comments_35402777","ab":"new"}'>AI大模型 | MemoRAG重磅登场：彻底革新AI问答的长期记忆功能！</a>
<p class="comment ellipsis">
<a href="https://blog.csdn.net/comrade1059" class="user-name" target="_blank">comrade1059: </a>
<span class="code-comments">基本上就是把github库里的readme翻译过来了</span>
</p>
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<a class="title text-truncate" target="_blank" href="https://blog.csdn.net/star_nwe/article/details/143703686#comments_35352634" data-report-click='{"mod":"popu_542","spm":"1001.2101.3001.4231","dest":"https://blog.csdn.net/star_nwe/article/details/143703686#comments_35352634","ab":"new"}' data-report-view='{"mod":"popu_542","spm":"1001.2101.3001.4231","dest":"https://blog.csdn.net/star_nwe/article/details/143703686#comments_35352634","ab":"new"}'>RAGFlow安装教程 | 带你一步步搭建RAGFlow【干货分享】</a>
<p class="comment ellipsis">
<a href="https://blog.csdn.net/m0_54360533" class="user-name" target="_blank">没有小蛋糕: </a>
<span class="code-comments">为什么docker compose是2.30，仍然报错呢</span>
</p>
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<a class="title text-truncate" target="_blank" href="https://blog.csdn.net/star_nwe/article/details/143590176#comments_35178484" data-report-click='{"mod":"popu_542","spm":"1001.2101.3001.4231","dest":"https://blog.csdn.net/star_nwe/article/details/143590176#comments_35178484","ab":"new"}' data-report-view='{"mod":"popu_542","spm":"1001.2101.3001.4231","dest":"https://blog.csdn.net/star_nwe/article/details/143590176#comments_35178484","ab":"new"}'>一文彻底搞懂深度学习：注意力机制（Attention Mechanism）</a>
<p class="comment ellipsis">
<a href="https://blog.csdn.net/weixin_56911377" class="user-name" target="_blank">小白一只~: </a>
<span class="code-comments">讲了和没讲一样，最后一看是是宣传广告的</span>
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