delete urls

KAGGLE_FIX_OLLAMA_CONNECTION.py

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+#!/usr/bin/env python3
+"""
+Kaggle Ollama 连接问题诊断和修复脚本
+解决 GraphRAG 异步处理时的连接错误
+"""
+
+import subprocess
+import time
+import requests
+import os
+
+def check_ollama_service():
+    """检查 Ollama 服务状态"""
+    print("="*70)
+    print("🔍 Ollama 服务诊断")
+    print("="*70)
+    
+    # 1. 检查进程
+    print("\n1️⃣ 检查 Ollama 进程...")
+    ps_check = subprocess.run(['pgrep', '-f', 'ollama serve'], capture_output=True)
+    
+    if ps_check.returncode == 0:
+        print("   ✅ Ollama 进程正在运行")
+        pids = ps_check.stdout.decode().strip().split('\n')
+        print(f"   📊 进程 PID: {', '.join(pids)}")
+    else:
+        print("   ❌ Ollama 进程未运行")
+        return False
+    
+    # 2. 检查端口
+    print("\n2️⃣ 检查端口 11434...")
+    port_check = subprocess.run(
+        ['netstat', '-tuln'], 
+        capture_output=True, 
+        text=True
+    )
+    
+    if '11434' in port_check.stdout:
+        print("   ✅ 端口 11434 已监听")
+    else:
+        print("   ❌ 端口 11434 未监听")
+        return False
+    
+    # 3. 测试 API 连接
+    print("\n3️⃣ 测试 API 连接...")
+    try:
+        response = requests.get('http://localhost:11434/api/tags', timeout=5)
+        if response.status_code == 200:
+            print("   ✅ API 连接正常")
+            models = response.json().get('models', [])
+            print(f"   📦 可用模型: {len(models)}")
+            for model in models:
+                print(f"      • {model.get('name', 'unknown')}")
+            return True
+        else:
+            print(f"   ❌ API 返回错误: {response.status_code}")
+            return False
+    except Exception as e:
+        print(f"   ❌ API 连接失败: {e}")
+        return False
+
+def start_ollama_service():
+    """启动 Ollama 服务"""
+    print("\n"+"="*70)
+    print("🚀 启动 Ollama 服务")
+    print("="*70)
+    
+    # 先杀死可能存在的僵尸进程
+    print("\n1️⃣ 清理旧进程...")
+    subprocess.run(['pkill', '-9', 'ollama'], capture_output=True)
+    time.sleep(2)
+    
+    # 启动服务
+    print("\n2️⃣ 启动新服务...")
+    process = subprocess.Popen(
+        ['ollama', 'serve'],
+        stdout=subprocess.PIPE,
+        stderr=subprocess.PIPE,
+        env=os.environ.copy()
+    )
+    
+    print(f"   ✅ 服务进程已启动 (PID: {process.pid})")
+    
+    # 等待服务就绪
+    print("\n3️⃣ 等待服务就绪...")
+    max_wait = 30
+    for i in range(max_wait):
+        try:
+            response = requests.get('http://localhost:11434/api/tags', timeout=2)
+            if response.status_code == 200:
+                print(f"   ✅ 服务就绪！(耗时 {i+1} 秒)")
+                return True
+        except:
+            pass
+        
+        if i < max_wait - 1:
+            print(f"   ⏳ 等待中... ({i+1}/{max_wait})", end='\r')
+            time.sleep(1)
+    
+    print(f"\n   ⚠️ 服务启动超时，但可能仍在初始化中")
+    return False
+
+def test_generation():
+    """测试生成功能"""
+    print("\n"+"="*70)
+    print("🧪 测试文本生成")
+    print("="*70)
+    
+    try:
+        response = requests.post(
+            'http://localhost:11434/api/generate',
+            json={
+                "model": "mistral",
+                "prompt": "Say 'Hello' in one word",
+                "stream": False
+            },
+            timeout=30
+        )
+        
+        if response.status_code == 200:
+            result = response.json()
+            print(f"   ✅ 生成成功")
+            print(f"   📝 响应: {result.get('response', '')[:100]}")
+            return True
+        else:
+            print(f"   ❌ 生成失败: {response.status_code}")
+            return False
+    except Exception as e:
+        print(f"   ❌ 生成错误: {e}")
+        return False
+
+def main():
+    """主函数"""
+    print("\n" + "="*70)
+    print("🔧 Kaggle Ollama 连接问题修复工具")
+    print("="*70)
+    print("\n解决问题: Cannot connect to host localhost:11434")
+    print("场景: GraphRAG 异步批处理时")
+    
+    # 检查服务
+    is_running = check_ollama_service()
+    
+    if not is_running:
+        print("\n⚠️ Ollama 服务未正常运行，正在修复...")
+        start_ollama_service()
+        
+        # 再次检查
+        print("\n"+"="*70)
+        print("🔍 验证修复结果")
+        print("="*70)
+        is_running = check_ollama_service()
+    
+    # 测试生成
+    if is_running:
+        test_generation()
+    
+    # 输出建议
+    print("\n"+"="*70)
+    print("💡 使用建议")
+    print("="*70)
+    
+    if is_running:
+        print("""
+✅ Ollama 服务正常！现在可以运行 GraphRAG 了
+
+📝 在 Kaggle Notebook 中运行:
+
+from document_processor import DocumentProcessor
+from graph_indexer import GraphRAGIndexer
+
+# 初始化
+processor = DocumentProcessor()
+vectorstore, retriever, doc_splits = processor.setup_knowledge_base(
+    enable_graphrag=True
+)
+
+# GraphRAG 索引（异步处理）
+indexer = GraphRAGIndexer(
+    enable_async=True,      # 启用异步
+    async_batch_size=8      # 并发处理 8 个文档
+)
+
+graph = indexer.index_documents(doc_splits)
+        """)
+    else:
+        print("""
+❌ Ollama 服务仍然异常
+
+🔧 手动修复步骤:
+
+1. 在 Kaggle Notebook 新单元格运行:
+   !pkill -9 ollama
+   !ollama serve &
+   
+2. 等待 15 秒后，运行:
+   !curl http://localhost:11434/api/tags
+   
+3. 如果成功，重新运行此脚本验证
+
+4. 如果失败，检查 Ollama 是否正确安装:
+   !which ollama
+   !ollama --version
+        """)
+    
+    print("="*70)
+
+if __name__ == "__main__":
+    main()