init: RAG 知识库服务初始提交

- 后端 API（Flask + Gunicorn）
- RAG 引擎（混合检索 + 云端 Reranker + 引用溯源）
- 文档解析（MinerU + 多格式支持）
- Docker 生产部署配置
- 排除前端项目、敏感配置、模型文件

scripts/test_rag_image_recall.py

@@ -0,0 +1,139 @@
+# -*- coding: utf-8 -*-
+"""
+RAG 接口测试：验证图片召回功能
+
+运行方式：
+.\venv\Scripts\python.exe scripts/test_rag_image_recall.py
+"""
+
+import sys
+import os
+import json
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from knowledge.manager import get_kb_manager
+from core.engine import get_engine
+
+
+def test_rag_image_recall():
+    """测试 RAG 检索的图片召回"""
+    print("=" * 60)
+    print("RAG 图片召回测试")
+    print("=" * 60)
+
+    # 初始化引擎
+    engine = get_engine()
+    if not engine._initialized:
+        engine.initialize()
+
+    # 测试查询
+    test_queries = [
+        "文档中有哪些图片？",
+        "有哪些图表？",
+        "表格内容是什么？",
+    ]
+
+    for query in test_queries:
+        print(f"\n🔍 查询: {query}")
+        print("-" * 40)
+
+        # 执行检索
+        result = engine.search_knowledge(
+            query=query,
+            collection_name='public_kb',
+            top_k=10
+        )
+
+        if not result or not result.get('ids'):
+            print("  ❌ 无检索结果")
+            continue
+
+        print(f"  📊 检索到 {len(result['ids'])} 个切片")
+
+        # 检查图片信息
+        images_found = []
+        for i, meta in enumerate(result.get('metadatas', [])):
+            images_json = meta.get('images_json')
+            image_path = meta.get('image_path')
+            chunk_type = meta.get('chunk_type')
+
+            if images_json:
+                try:
+                    imgs = json.loads(images_json)
+                    images_found.extend(imgs)
+                    print(f"  [{i+1}] chunk_type={chunk_type}, images_json={len(imgs)}张图片")
+                except:
+                    pass
+
+            if image_path:
+                images_found.append({'id': image_path, 'type': 'image_path'})
+                print(f"  [{i+1}] chunk_type={chunk_type}, image_path={image_path}")
+
+        if images_found:
+            print(f"\n  ✅ 共召回 {len(images_found)} 张图片")
+        else:
+            print("\n  ⚠️ 未召回任何图片")
+
+
+def test_extract_rich_media():
+    """测试 _extract_rich_media 函数"""
+    print("\n" + "=" * 60)
+    print("_extract_rich_media 函数测试")
+    print("=" * 60)
+
+    from core.agentic import AgenticRAG
+
+    # 创建 AgenticRAG 实例
+    rag = AgenticRAG()
+
+    # 模拟检索结果
+    mock_contexts = [
+        {
+            'doc': '这是一段文本',
+            'meta': {
+                'chunk_type': 'text',
+                'source': 'test.pdf',
+                'page': 1,
+            }
+        },
+        {
+            'doc': '这是一张图片',
+            'meta': {
+                'chunk_type': 'image',
+                'source': 'test.pdf',
+                'page': 2,
+                'image_path': 'abc123.jpg',
+            }
+        },
+        {
+            'doc': '这是带关联图片的文本',
+            'meta': {
+                'chunk_type': 'text',
+                'source': 'test.pdf',
+                'page': 3,
+                'images_json': '[{"id": "img1.jpg", "order": 1}, {"id": "img2.jpg", "order": 2}]',
+            }
+        },
+    ]
+
+    # 调用 _extract_rich_media
+    rich_media = rag._extract_rich_media(mock_contexts)
+
+    print(f"\n📊 提取结果:")
+    print(f"  - 图片数量: {len(rich_media.get('images', []))}")
+    print(f"  - 表格数量: {len(rich_media.get('tables', []))}")
+
+    for img in rich_media.get('images', []):
+        print(f"  📷 {img}")
+
+    if not rich_media.get('images'):
+        print("  ⚠️ 未提取到图片，检查 _extract_rich_media 逻辑")
+
+
+if __name__ == "__main__":
+    if sys.platform == 'win32':
+        sys.stdout.reconfigure(encoding='utf-8')
+
+    test_rag_image_recall()
+    test_extract_rich_media()