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  <h1>1.1 项目架构</h1>

<h2 id="rag">一、RAG原理<a class="headerlink" href="#rag" title="Permanent link">¶</a></h2>
<p>RAG的基本原理：</p>
<div align="center"><img src="../img/6-9.png" style="zoom:80%"><img></div>

<hr>
<h2 id="_1">二、项目流程<a class="headerlink" href="#_1" title="Permanent link">¶</a></h2>
<div align="center"><img src="../img/8-2-3.png" style="zoom:100%"><img></div>

<p>（AI）知识域智能问答系统RAG主要步骤：</p>
<ul>
<li>第一步：将现有后台搜集的FQA数据集存储到Mysql数据库中</li>
<li>第二步：基于query实现Mysql数据库检索：将query和现有问题匹配（做相似度计算），如果阈值&gt;=0.85，就认为问题比较明确，直接返回对应的答案；否则，进入RAG检索系统</li>
<li>第三步：搭建本地知识库：对本地文档加载读取；进行文档分割；文档向量化；存储向量数据库（Milvus）</li>
<li>第四步：基于query实现Milvus数据库检索：将query进行向量表示，并从Milvus数据库中检索出相似的top-k个文本段。</li>
<li>第五步：将query和检索出的top-k文本段拼接，送入大模型，实现预测。</li>
</ul>
<h2 id="_2">三、项目结构<a class="headerlink" href="#_2" title="Permanent link">¶</a></h2>
<div class="highlight"><pre><span></span><code>integrated_qa_system/
├── config.ini                 # 配置文件，包含所有模块的配置
├── base/
│   ├── config.py              # 配置管理，加载 config.ini
│   ├── logger.py              # 日志设置
├── rag_qa/
│   ├── core/
│   │   ├── prompts.py         # RAG 提示模板
│   │   ├── query_classifier.py # 查询分类器
│   │   ├── strategy_selector.py # 检索策略选择器
│   │   ├── vector_store.py    # 向量存储与检索
│   │   ├── rag_system.py      # RAG 系统核心逻辑
│   ├── main.py                # RAG 系统独立入口，支持存储和查询
├── mysql_qa/
│   ├── db/
│   │   ├── mysql_client.py    # MySQL 数据库操作
│   ├── cache/
│   │   ├── redis_client.py    # Redis 缓存操作
│   ├── retrieval/
│   │   ├── bm25_search.py     # BM25 搜索
│   ├── utils/
│   │   ├── preprocess.py      # 文本预处理
│   ├── main.py                # MySQL 系统独立入口，支持查询
├── main.py                    # 集成系统入口，结合 RAG 和 MySQL
├── requirements.txt           # 依赖文件
└── logs/
    └── app.log                # 日志文件
</code></pre></div>


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