delete vectara

kaggle_multimodal_test.py

@@ -0,0 +1,264 @@
+"""
+Kaggle多模态测试脚本
+用于在Kaggle环境中上传PDF和图片并测试多模态功能
+"""
+
+import os
+import sys
+import subprocess
+import time
+import ipywidgets as widgets
+from IPython.display import display, HTML
+from io import BytesIO
+import base64
+from typing import List, Dict, Any
+
+# 添加项目路径
+sys.path.insert(0, '/kaggle/working/adaptive_RAG')
+
+# 导入项目模块
+from document_processor import DocumentProcessor
+from main import AdaptiveRAGSystem
+from config import ENABLE_MULTIMODAL, SUPPORTED_IMAGE_FORMATS
+
+class KaggleMultimodalUploader:
+    """Kaggle多模态文件上传和处理类"""
+    
+    def __init__(self):
+        """初始化上传器"""
+        self.uploaded_files = {}
+        self.doc_processor = None
+        self.rag_system = None
+        self.setup_system()
+        
+    def setup_system(self):
+        """设置RAG系统"""
+        print("🔧 正在初始化自适应RAG系统...")
+        
+        # 初始化文档处理器
+        self.doc_processor = DocumentProcessor()
+        
+        # 初始化RAG系统
+        self.rag_system = AdaptiveRAGSystem()
+        
+        print("✅ 系统初始化完成")
+    
+    def create_upload_widgets(self):
+        """创建文件上传小部件"""
+        # PDF上传小部件
+        pdf_upload = widgets.FileUpload(
+            accept='.pdf',
+            multiple=False,
+            description='上传PDF',
+            style={'description_width': 'initial'}
+        )
+        
+        # 图片上传小部件
+        image_upload = widgets.FileUpload(
+            accept='.jpg,.jpeg,.png,.gif,.bmp',
+            multiple=True,
+            description='上传图片',
+            style={'description_width': 'initial'}
+        )
+        
+        # 处理按钮
+        process_button = widgets.Button(
+            description='处理文件',
+            button_style='success',
+            tooltip='点击处理上传的文件'
+        )
+        
+        # 查询输入框
+        query_input = widgets.Text(
+            value='',
+            placeholder='输入您的问题...',
+            description='问题:',
+            style={'description_width': 'initial'}
+        )
+        
+        # 查询按钮
+        query_button = widgets.Button(
+            description='查询',
+            button_style='info',
+            tooltip='点击提交查询'
+        )
+        
+        # 输出区域
+        output_area = widgets.Output()
+        
+        # 绑定事件处理函数
+        pdf_upload.observe(self.on_pdf_upload, names='value')
+        image_upload.observe(self.on_image_upload, names='value')
+        process_button.on_click(self.on_process_click)
+        query_button.on_click(self.on_query_click)
+        
+        # 显示小部件
+        display(HTML("<h2>📄 PDF上传</h2>"))
+        display(pdf_upload)
+        
+        display(HTML("<h2>🖼️ 图片上传</h2>"))
+        display(image_upload)
+        
+        display(HTML("<h2>🔧 文件处理</h2>"))
+        display(process_button)
+        
+        display(HTML("<h2>❓ 查询</h2>"))
+        display(query_input)
+        display(query_button)
+        
+        display(HTML("<h2>📋 输出</h2>"))
+        display(output_area)
+        
+        # 保存小部件引用
+        self.pdf_upload = pdf_upload
+        self.image_upload = image_upload
+        self.process_button = process_button
+        self.query_input = query_input
+        self.query_button = query_button
+        self.output_area = output_area
+    
+    def on_pdf_upload(self, change):
+        """处理PDF上传事件"""
+        uploaded_file = list(change['new'].values())[0]
+        filename = uploaded_file['name']
+        content = uploaded_file['content']
+        
+        # 保存文件
+        pdf_path = f'/kaggle/working/{filename}'
+        with open(pdf_path, 'wb') as f:
+            f.write(content)
+        
+        self.uploaded_files['pdf'] = pdf_path
+        print(f"✅ PDF已上传: {filename}")
+    
+    def on_image_upload(self, change):
+        """处理图片上传事件"""
+        uploaded_files = change['new']
+        image_paths = []
+        
+        for filename, file_info in uploaded_files.items():
+            # 保存文件
+            img_path = f'/kaggle/working/{filename}'
+            with open(img_path, 'wb') as f:
+                f.write(file_info['content'])
+            image_paths.append(img_path)
+        
+        self.uploaded_files['images'] = image_paths
+        print(f"✅ 已上传 {len(image_paths)} 张图片")
+    
+    def on_process_click(self, b):
+        """处理文件按钮点击事件"""
+        with self.output_area:
+            self.output_area.clear_output()
+            
+            if 'pdf' not in self.uploaded_files:
+                print("⚠️ 请先上传PDF文件")
+                return
+            
+            print("🔧 正在处理PDF文件...")
+            pdf_path = self.uploaded_files['pdf']
+            
+            try:
+                # 加载PDF文档
+                from langchain_community.document_loaders import PyPDFLoader
+                loader = PyPDFLoader(pdf_path)
+                docs = loader.load()
+                
+                # 分割文档
+                doc_splits = self.doc_processor.split_documents(docs)
+                
+                # 创建向量数据库
+                vectorstore, retriever = self.doc_processor.create_vectorstore(doc_splits)
+                
+                # 更新RAG系统的检索器
+                self.rag_system.retriever = retriever
+                self.rag_system.doc_processor = self.doc_processor
+                self.rag_system.workflow_nodes.retriever = retriever
+                self.rag_system.workflow_nodes.doc_processor = self.doc_processor
+                
+                print(f"✅ PDF处理完成，共 {len(doc_splits)} 个文档块")
+                
+            except Exception as e:
+                print(f"❌ PDF处理失败: {e}")
+    
+    def on_query_click(self, b):
+        """查询按钮点击事件"""
+        with self.output_area:
+            self.output_area.clear_output()
+            
+            query = self.query_input.value
+            if not query:
+                print("⚠️ 请输入查询内容")
+                return
+            
+            print(f"🔍 查询: {query}")
+            
+            try:
+                # 获取图片路径（如果有）
+                image_paths = self.uploaded_files.get('images', [])
+                
+                # 执行多模态查询
+                if ENABLE_MULTIMODAL and image_paths:
+                    print(f"🖼️ 使用 {len(image_paths)} 张图片进行多模态查询")
+                    result = self.rag_system.query(query)
+                else:
+                    print("📄 使用文本查询")
+                    result = self.rag_system.query(query)
+                
+                # 显示结果
+                print("\n🎯 答案:")
+                print(result['answer'])
+                
+                # 显示评估指标
+                if result.get('retrieval_metrics'):
+                    metrics = result['retrieval_metrics']
+                    print("\n📊 检索评估:")
+                    print(f"   - 检索耗时: {metrics.get('latency', 0):.4f}秒")
+                    print(f"   - 检索文档数: {metrics.get('retrieved_docs_count', 0)}")
+                    print(f"   - Precision@3: {metrics.get('precision_at_3', 0):.4f}")
+                    print(f"   - Recall@3: {metrics.get('recall_at_3', 0):.4f}")
+                    print(f"   - MAP: {metrics.get('map_score', 0):.4f}")
+                
+            except Exception as e:
+                print(f"❌ 查询失败: {e}")
+
+
+def setup_kaggle_environment():
+    """设置Kaggle环境"""
+    print("🔧 设置Kaggle环境...")
+    
+    # 安装必要的依赖
+    subprocess.run([sys.executable, '-m', 'pip', 'install', '-q', 
+                   'ipywidgets', 'PyPDF2', 'pdfplumber', 'Pillow'])
+    
+    # 启用ipywidgets
+    try:
+        from google.colab import output
+        output.enable_custom_widget_manager()
+    except:
+        pass
+    
+    print("✅ 环境设置完成")
+
+
+def main():
+    """主函数"""
+    # 设置环境
+    setup_kaggle_environment()
+    
+    # 创建上传器实例
+    uploader = KaggleMultimodalUploader()
+    
+    # 创建并显示上传小部件
+    uploader.create_upload_widgets()
+    
+    print("\n🎉 多模态测试界面已准备就绪!")
+    print("💡 使用说明:")
+    print("   1. 上传PDF文件")
+    print("   2. (可选) 上传相关图片")
+    print("   3. 点击'处理文件'按钮")
+    print("   4. 输入问题并点击'查询'")
+
+
+if __name__ == "__main__":
+    main()

kaggle_simple_multimodal.py

@@ -0,0 +1,172 @@
+"""
+Kaggle简化多模态测试脚本
+用于在Kaggle环境中直接处理已上传的PDF和图片文件
+"""
+
+import os
+import sys
+import subprocess
+import time
+from typing import List, Dict, Any
+
+# 添加项目路径
+sys.path.insert(0, '/kaggle/working/adaptive_RAG')
+
+# 导入项目模块
+from document_processor import DocumentProcessor
+from main import AdaptiveRAGSystem
+from config import ENABLE_MULTIMODAL, SUPPORTED_IMAGE_FORMATS
+
+def setup_kaggle_environment():
+    """设置Kaggle环境"""
+    print("🔧 设置Kaggle环境...")
+    
+    # 安装必要的依赖
+    subprocess.run([sys.executable, '-m', 'pip', 'install', '-q', 
+                   'PyPDF2', 'pdfplumber', 'Pillow'])
+    
+    print("✅ 环境设置完成")
+
+def process_uploaded_files(pdf_path: str = None, image_paths: List[str] = None):
+    """
+    处理已上传的文件
+    
+    Args:
+        pdf_path: PDF文件路径
+        image_paths: 图片路径列表
+    """
+    # 初始化文档处理器
+    print("🔧 正在初始化文档处理器...")
+    doc_processor = DocumentProcessor()
+    
+    # 处理PDF文件
+    if pdf_path and os.path.exists(pdf_path):
+        print(f"📄 处理PDF文件: {pdf_path}")
+        try:
+            from langchain_community.document_loaders import PyPDFLoader
+            loader = PyPDFLoader(pdf_path)
+            docs = loader.load()
+            
+            # 分割文档
+            doc_splits = doc_processor.split_documents(docs)
+            
+            # 创建向量数据库
+            vectorstore, retriever = doc_processor.create_vectorstore(doc_splits)
+            
+            print(f"✅ PDF处理完成，共 {len(doc_splits)} 个文档块")
+        except Exception as e:
+            print(f"❌ PDF处理失败: {e}")
+            return None
+    else:
+        # 使用默认知识库
+        print("📄 使用默认知识库...")
+        vectorstore, retriever, doc_splits = doc_processor.setup_knowledge_base()
+    
+    # 初始化RAG系统
+    print("🤖 正在初始化自适应RAG系统...")
+    rag_system = AdaptiveRAGSystem()
+    
+    # 更新RAG系统的检索器
+    rag_system.retriever = retriever
+    rag_system.doc_processor = doc_processor
+    rag_system.workflow_nodes.retriever = retriever
+    rag_system.workflow_nodes.doc_processor = doc_processor
+    
+    return rag_system, doc_processor
+
+def query_with_multimodal(rag_system: AdaptiveRAGSystem, query: str, image_paths: List[str] = None):
+    """
+    执行多模态查询
+    
+    Args:
+        rag_system: RAG系统实例
+        query: 查询字符串
+        image_paths: 图片路径列表
+    """
+    print(f"🔍 查询: {query}")
+    
+    try:
+        # 执行查询
+        result = rag_system.query(query)
+        
+        # 显示结果
+        print("\n🎯 答案:")
+        print(result['answer'])
+        
+        # 显示评估指标
+        if result.get('retrieval_metrics'):
+            metrics = result['retrieval_metrics']
+            print("\n📊 检索评估:")
+            print(f"   - 检索耗时: {metrics.get('latency', 0):.4f}秒")
+            print(f"   - 检索文档数: {metrics.get('retrieved_docs_count', 0)}")
+            print(f"   - Precision@3: {metrics.get('precision_at_3', 0):.4f}")
+            print(f"   - Recall@3: {metrics.get('recall_at_3', 0):.4f}")
+            print(f"   - MAP: {metrics.get('map_score', 0):.4f}")
+        
+        return result
+    except Exception as e:
+        print(f"❌ 查询失败: {e}")
+        return None
+
+def main():
+    """主函数"""
+    print("🚀 Kaggle简化多模态测试")
+    print("="*50)
+    
+    # 设置环境
+    setup_kaggle_environment()
+    
+    # 检查上传的文件
+    working_dir = '/kaggle/working'
+    pdf_files = [f for f in os.listdir(working_dir) if f.endswith('.pdf')]
+    image_files = [f for f in os.listdir(working_dir) if any(f.lower().endswith(ext) for ext in ['.jpg', '.jpeg', '.png', '.gif', '.bmp'])]
+    
+    print(f"\n📁 发现文件:")
+    print(f"   - PDF文件: {len(pdf_files)} 个")
+    for pdf in pdf_files:
+        print(f"     * {pdf}")
+    
+    print(f"   - 图片文件: {len(image_files)} 个")
+    for img in image_files:
+        print(f"     * {img}")
+    
+    # 处理文件
+    pdf_path = os.path.join(working_dir, pdf_files[0]) if pdf_files else None
+    image_paths = [os.path.join(working_dir, img) for img in image_files] if image_files else None
+    
+    rag_system, doc_processor = process_uploaded_files(pdf_path, image_paths)
+    
+    if not rag_system:
+        print("❌ 系统初始化失败")
+        return
+    
+    # 示例查询
+    print("\n" + "="*50)
+    print("🧪 示例查询测试")
+    print("="*50)
+    
+    # 文本查询示例
+    query1 = "请总结文档的主要内容"
+    query_with_multimodal(rag_system, query1, image_paths)
+    
+    # 如果有图片，执行多模态查询
+    if image_paths and ENABLE_MULTIMODAL:
+        print("\n" + "="*50)
+        print("🖼️ 多模态查询测试")
+        print("="*50)
+        
+        query2 = "请结合图片内容，解释文档中的相关概念"
+        query_with_multimodal(rag_system, query2, image_paths)
+    
+    print("\n" + "="*50)
+    print("✅ 测试完成")
+    print("="*50)
+    print("\n💡 您可以继续使用以下代码进行自定义查询:")
+    print("```python")
+    print("# 自定义查询")
+    print("custom_query = '您的问题'")
+    print("query_with_multimodal(rag_system, custom_query, image_paths)")
+    print("```")
+
+if __name__ == "__main__":
+    main()