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