feat:增加RAG文档

docs/.vitepress/theme/router/index.ts

@@ -26,7 +26,7 @@ export const routers = [
           { text: '启动流程', link: '/Web/SpringBoot/SpringBoot-Start-Process' },
           { text: 'Starter原理', link: '/Web/SpringBoot/SpringBoot-Starter' },
           { text: 'Bean简介', link: '/Web/SpringBoot/SpringBoot-Bean' },
-          { text: '注解', link: '/Web/SpringBoot/SpringBoot-Annotation' },
+          { text: 'SpingBoot注解', link: '/Web/SpringBoot/SpringBoot-Annotation' },
           { text: 'SpringBoot3原生镜像', link: '/Web/SpringBoot/SpringBoot3-GraalVM' },
           { text: 'SpringBoot技巧', link: '/Web/SpringBoot/SpringBoot-Skills' },
           { text: 'RestClient简介', link: '/Web/SpringBoot/SpringBoot-RestClient' },
@@ -57,8 +57,9 @@ export const routers = [
       { 
         text: '🤖 AI',
         items: [
-          { text: '基于Langchain的Agent开发', link: '/Web/AI/Langchain' },    
+          { text: 'Agent开发', link: '/Web/AI/Langchain' },    
           { text: 'Text To SQL开发', link: '/Web/AI/TextToSQL' },    
+          { text: 'RAG开发', link: '/Web/AI/RAG' }, 
         ]
       },
       { 

docs/Web/AI/RAG.md

@@ -0,0 +1,211 @@
+---
+  title: RAG开发
+  date: 2026-06-245
+---
+
+# 一、工作流程
+```text
+原始文档 / 数据
+        ↓
+【文档收集与清洗】
+        ↓
+【文档切分（Chunking）】
+        ↓
+【向量化（Embedding）】
+        ↓
+【向量数据库存储】
+        ↓
+用户自然语言问题
+        ↓
+【检索系统】召回相关知识片段
+        ↓
+【上下文组装】构建增强 Prompt
+        ↓
+【大模型】基于知识生成回答
+        ↓
+【引用 / 校验】
+        ↓
+最终返回给用户（答案 + 来源）
+``` 
+
+# 二、实战
+## 2.1 资料处理
+### 2.1.1 pdf处理
+  [PaddleOCR](https://aistudio.baidu.com/paddleocr)是百度飞桨（PaddlePaddle）团队开源的产业级 OCR（光学字符识别）与文档智能开发套件。   
+```python
+JOB_URL = "https://paddleocr.aistudio-app.com/api/v2/ocr/jobs"
+TOKEN = ""
+MODEL = "PP-OCRv5"
+
+FILE_URL = ""
+
+HEADERS = {
+    "Authorization": f"bearer {TOKEN}",
+    "Content-Type": "application/json",
+}
+
+# ========== 提交 OCR 任务 ==========
+payload = {
+    "fileUrl": FILE_URL,
+    "model": MODEL,
+    "optionalPayload": {
+        "useDocOrientationClassify": False,
+        "useDocUnwarping": False,
+        "useChartRecognition": False,
+    },
+}
+
+print("🚀 Submitting OCR job...")
+resp = requests.post(JOB_URL, json=payload, headers=HEADERS)
+resp.raise_for_status()
+
+job_id = resp.json()["data"]["jobId"]
+print(f"✅ Job submitted, jobId={job_id}")
+
+# ========== 轮询任务状态 ==========
+while True:
+    r = requests.get(f"{JOB_URL}/{job_id}", headers=HEADERS)
+    r.raise_for_status()
+
+    data = r.json()["data"]
+    state = data["state"]
+
+    if state == "pending":
+        print("⏳ Job status: pending")
+    elif state == "running":
+        prog = data.get("extractProgress", {})
+        total = prog.get("totalPages")
+        done = prog.get("extractedPages")
+        if total and done:
+            print(f"📄 Processing: {done}/{total} pages")
+        else:
+            print("📄 Processing...")
+    elif state == "done":
+        prog = data["extractProgress"]
+        print(
+            f"✅ Job finished | Pages: {prog['extractedPages']} | "
+            f"Start: {prog['startTime']} | End: {prog['endTime']}"
+        )
+        break
+    elif state == "failed":
+        print("❌ Job failed:", data.get("errorMsg", "Unknown error"))
+        sys.exit(1)
+
+    time.sleep(5)
+
+# ========== 获取 OCR 结果 ==========
+jsonl_url = data["resultUrl"]["jsonUrl"]
+print(f"📥 Fetching result: {jsonl_url}")
+
+resp = requests.get(jsonl_url)
+resp.raise_for_status()
+```
+
+  这里只处理了url形式的文档，实际开发中，可以搭建一个管理页面集中处理文档资料。    
+  获取到的结果是按照区域划分的，还需要进一步的处理：   
+```python
+pages = []
+
+for line in resp.text.strip().splitlines():
+    if not line.strip():
+        continue
+
+    obj = json.loads(line)
+
+    for page in obj.get("result", {}).get("ocrResults", []):
+        pruned = page.get("prunedResult", {})
+        texts = pruned.get("rec_texts", [])
+
+        # 一页一段
+        page_text = "\n".join(texts)
+        pages.append(page_text)
+
+# ✅ 最终文档
+full_doc = "\n\n".join(pages)
+```
+
+## 2.2 向量化
+  使用在线或者离线向量化模型处理：   
+```python
+text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size=500,      # 每个 chunk 最大长度
+    chunk_overlap=50,    # 重叠，防止切坏语义
+    separators=["\n\n", "\n", "。", "！", "？", "；", "，", " ", ""]
+)
+
+chunks = text_splitter.split_text(full_doc)
+print(f"✅ 切分成 {len(chunks)} 个 chunk")
+
+for i, chunk in enumerate(chunks):
+    print(f"\n===== Chunk {i} =====")
+    print(chunk)
+
+model = SentenceTransformer("BAAI/bge-base-zh", cache_folder="./models")
+
+embeddings = model.encode(
+    chunks,
+    normalize_embeddings=True
+)
+
+print(f"✅ 向量维度: {embeddings.shape}")
+
+client = chromadb.PersistentClient(path="./vector_db/train_docs")
+collection = client.get_or_create_collection(name="train_docs")
+
+for i, (chunk, emb) in enumerate(zip(chunks, embeddings)):
+    collection.add(
+        ids=[str(i)],
+        documents=[chunk],
+        embeddings=[emb.tolist()]
+    )
+
+print("✅ 向量库写入完成")
+```
+
+  这里使用的是`BAAI/bge-base-zh`模型，第一次会将模型下载到本地。   
+  将处理结果存储到chroma向量数据库中。   
+
+## 2.3 检索系统
+```python
+model_path = "./models/models--BAAI--bge-base-zh/snapshots/0e5f83d4895db7955e4cb9ed37ab73f7ded339b6"
+model = SentenceTransformer(model_path, local_files_only=True)
+
+client = chromadb.PersistentClient(path="./vector_db/train_docs")
+collection = client.get_or_create_collection(name="train_docs")
+
+q = "培训计划怎么制定？"
+q_emb = model.encode([q], normalize_embeddings=True)
+
+res = collection.query(
+    query_embeddings=q_emb.tolist(),
+    n_results=3
+)
+
+print("\n\n".join(res["documents"][0]))
+```
+
+::: tip
+注意这里使用离线模型时的路径
+:::
+
+## 2.4 结合大模型
+  常用提示词：  
+```python
+你是专业的知识问答助手。
+请严格基于下方提供的参考资料回答问题。
+如果参考资料中不包含答案，请明确说明“当前资料无法回答该问题”。
+不要编造、推测或引入外部知识。
+
+参考资料：
+"""
+{{context}}
+"""
+
+用户问题：
+{{question}}
+
+请按以下要求回答：
+1. 回答简洁、准确、有条理
+2. 必要时使用列表或分点说明
+3. 避免冗余描述
+```

docs/Web/AI/TextToSql.md

@@ -4,7 +4,7 @@
 ---
 
 # 一、工作流程
-```plain
+```text
 用户自然语言问题
         ↓
 【大模型】生成 SQL