diff --git a/005ollamaApi.py b/005ollamaApi.py
new file mode 100644
index 0000000..7d4750e
--- /dev/null
+++ b/005ollamaApi.py
@@ -0,0 +1,226 @@
+import requests
+import json
+import time
+from typing import Dict, Any, Iterator
+
+class OllamaClient:
+    def __init__(self, base_url: str = "http://localhost:11434"):
+        """
+        初始化Ollama客户端
+        
+        Args:
+            base_url: Ollama服务的基础URL，默认为本地11434端口
+        """
+        self.base_url = base_url
+        self.session = requests.Session()
+    
+    def list_models(self) -> Dict[str, Any]:
+        """获取已安装的模型列表"""
+        try:
+            response = self.session.get(f"{self.base_url}/api/tags")
+            response.raise_for_status()
+            return response.json()
+        except requests.exceptions.RequestException as e:
+            print(f"获取模型列表失败: {e}")
+            return {}
+    
+    def generate(self, model: str, prompt: str, stream: bool = False, **kwargs) -> Any:
+        """
+        生成文本
+        
+        Args:
+            model: 模型名称
+            prompt: 输入提示
+            stream: 是否流式输出
+            **kwargs: 其他参数如temperature, top_p等
+        """
+        data = {
+            "model": model,
+            "prompt": prompt,
+            "stream": stream,
+            **kwargs
+        }
+        
+        try:
+            response = self.session.post(
+                f"{self.base_url}/api/generate",
+                json=data,
+                stream=stream
+            )
+            # 检查请求是否成功
+            response.raise_for_status()
+            
+            if stream:
+                return self._handle_stream_response(response)
+            else:
+                return response.json()
+                
+        except requests.exceptions.RequestException as e:
+            print(f"生成请求失败: {e}")
+            return None
+    
+    def chat(self, model: str, messages: list, stream: bool = False, **kwargs) -> Any:
+        """
+        对话模式
+        
+        Args:
+            model: 模型名称
+            messages: 消息列表，格式为[{"role": "user", "content": "..."}]
+            stream: 是否流式输出
+            **kwargs: 其他参数
+        """
+        data = {
+            "model": model,
+            "messages": messages,
+            "stream": stream,
+            **kwargs
+        }
+        
+        try:
+            response = self.session.post(
+                f"{self.base_url}/api/chat",
+                json=data,
+                stream=stream
+            )
+            response.raise_for_status()
+            
+            if stream:
+                return self._handle_stream_response(response)
+            else:
+                return response.json()
+                
+        except requests.exceptions.RequestException as e:
+            print(f"对话请求失败: {e}")
+            return None
+    
+    def _handle_stream_response(self, response) -> Iterator[Dict[str, Any]]:
+        """处理流式响应"""
+        for line in response.iter_lines():
+            if line:
+                try:
+                    yield json.loads(line.decode('utf-8'))
+                except json.JSONDecodeError:
+                    continue
+    
+    def pull_model(self, model_name: str) -> bool:
+        """拉取模型"""
+        data = {"name": model_name}
+        
+        try:
+            response = self.session.post(
+                f"{self.base_url}/api/pull",
+                json=data,
+                stream=True
+            )
+            response.raise_for_status()
+            
+            print(f"正在拉取模型 {model_name}...")
+            for chunk in self._handle_stream_response(response):
+                if "status" in chunk:
+                    print(f"状态: {chunk['status']}")
+                if chunk.get("done", False):
+                    print("模型拉取完成!")
+                    return True
+                    
+        except requests.exceptions.RequestException as e:
+            print(f"拉取模型失败: {e}")
+            return False
+    
+    def is_model_available(self, model_name: str) -> bool:
+        """检查模型是否可用"""
+        models = self.list_models()
+        if "models" in models:
+            return any(model["name"].startswith(model_name) for model in models["models"])
+        return False
+
+
+# 使用示例
+def main():
+    # 创建客户端
+    client = OllamaClient()
+    
+    # 检查服务是否可用
+    try:
+        models = client.list_models()
+        print("Ollama服务连接成功!")
+        print(f"可用模型: {[m['name'] for m in models.get('models', [])]}")
+    except Exception as e:
+        print(f"无法连接到Ollama服务: {e}")
+        return
+    
+    model_name = "Qwen3-8B:latest"  # 根据你的模型调整
+    
+    # 检查模型是否存在，不存在则拉取
+    if not client.is_model_available(model_name):
+        print(f"模型 {model_name} 不存在，正在拉取...")
+        if not client.pull_model(model_name):
+            print("模型拉取失败")
+            return
+    
+    # 示例1: 简单文本生成
+    print("\n=== 简单文本生成 ===")
+    response = client.generate(
+        model=model_name,
+        prompt="请用中文简单介绍一下人工智能。",
+        temperature=0.7,
+        max_tokens=200
+    )
+    
+    if response:
+        print(f"回答: {response.get('response', '')}")
+    
+    # 示例2: 流式输出
+    print("\n=== 流式输出 ===")
+    stream_response = client.generate(
+        model=model_name,
+        prompt="请讲一个简短的故事。",
+        stream=True,
+        temperature=0.8,
+        max_tokens=200
+    )
+    
+    print("流式回答: ", end="")
+    for chunk in stream_response:
+        if "response" in chunk:
+            print(chunk["response"], end="", flush=True)
+        if chunk.get("done", False):
+            print("\n")
+            break
+    
+    # 示例3: 对话模式
+    print("\n=== 对话模式 ===")
+    messages = [
+        {"role": "user", "content": "你好，请介绍一下自己。"},
+    ]
+    
+    chat_response = client.chat(
+        model=model_name,
+        messages=messages,
+        temperature=0.7
+    )
+    
+    if chat_response:
+        print(f"AI回答: {chat_response.get('message', {}).get('content', '')}")
+    
+    # 示例4: 多轮对话
+    print("\n=== 多轮对话 ===")
+    conversation = [
+        {"role": "user", "content": "什么是机器学习？"}
+    ]
+    
+    # 第一轮
+    response1 = client.chat(model=model_name, messages=conversation)
+    if response1 and "message" in response1:
+        ai_response = response1["message"]["content"]
+        print(f"AI: {ai_response}")
+        conversation.append({"role": "assistant", "content": ai_response})
+    
+    # 第二轮
+    conversation.append({"role": "user", "content": "能举个具体例子吗？"})
+    response2 = client.chat(model=model_name, messages=conversation)
+    if response2 and "message" in response2:
+        print(f"AI: {response2['message']['content']}")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/006ollamaApi增加错误处理.py b/006ollamaApi增加错误处理.py
new file mode 100644
index 0000000..ebaac0e
--- /dev/null
+++ b/006ollamaApi增加错误处理.py
@@ -0,0 +1,570 @@
+import requests
+import json
+import time
+import threading
+from typing import Dict, Any, Iterator, Optional
+from functools import wraps
+from concurrent.futures import ThreadPoolExecutor, as_completed
+import logging
+
+class OllamaException(Exception):
+    """Ollama自定义异常类"""
+    pass
+
+class OllamaServerError(OllamaException):
+    """服务器错误异常"""
+    pass
+
+class OllamaRateLimitError(OllamaException):
+    """请求限制异常"""
+    pass
+
+class OllamaClient:
+    def __init__(self, base_url: str = "http://localhost:11434", max_retries: int = 3, 
+                 retry_delay: float = 1.0, max_concurrent_requests: int = 5,
+                 timeout: int = 30, enable_logging: bool = True):
+        """
+        初始化Ollama客户端
+        
+        Args:
+            base_url: Ollama服务的基础URL，默认为本地11434端口
+            max_retries: 最大重试次数
+            retry_delay: 重试间隔（秒）
+            max_concurrent_requests: 最大并发请求数
+            timeout: 请求超时时间（秒）
+            enable_logging: 是否启用日志
+        """
+        self.base_url = base_url
+        self.max_retries = max_retries
+        self.retry_delay = retry_delay
+        self.max_concurrent_requests = max_concurrent_requests
+        self.timeout = timeout
+        
+        # 设置日志
+        if enable_logging:
+            logging.basicConfig(level=logging.INFO)
+            self.logger = logging.getLogger(__name__)
+        else:
+            self.logger = logging.getLogger(__name__)
+            self.logger.setLevel(logging.CRITICAL)
+        
+        # 创建会话
+        self.session = requests.Session()
+        self.session.timeout = timeout
+        
+        # 并发控制
+        self._request_lock = threading.Lock()
+        self._active_requests = 0
+        self._executor = ThreadPoolExecutor(max_workers=max_concurrent_requests)
+        
+        # 请求统计
+        self._request_count = 0
+        self._success_count = 0
+        self._error_count = 0
+        self._last_request_time = 0
+    
+    def _wait_for_slot(self):
+        """等待可用的请求槽位"""
+        while self._active_requests >= self.max_concurrent_requests:
+            time.sleep(0.1)
+    
+    def _acquire_request_slot(self):
+        """获取请求槽位"""
+        with self._request_lock:
+            self._wait_for_slot()
+            self._active_requests += 1
+            self._request_count += 1
+    
+    def _release_request_slot(self):
+        """释放请求槽位"""
+        with self._request_lock:
+            self._active_requests = max(0, self._active_requests - 1)
+    
+    def _handle_response_error(self, response, attempt: int):
+        """处理响应错误"""
+        if response.status_code == 500:
+            if attempt < self.max_retries:
+                wait_time = self.retry_delay * (2 ** attempt)  # 指数退避
+                self.logger.warning(f"服务器错误500，第{attempt + 1}次重试，等待{wait_time:.1f}秒...")
+                time.sleep(wait_time)
+                return True  # 需要重试
+            else:
+                raise OllamaServerError(f"服务器错误500，已达到最大重试次数{self.max_retries}")
+        
+        elif response.status_code == 429:
+            # 速率限制
+            if attempt < self.max_retries:
+                wait_time = self.retry_delay * (2 ** attempt)
+                self.logger.warning(f"请求过于频繁(429)，第{attempt + 1}次重试，等待{wait_time:.1f}秒...")
+                time.sleep(wait_time)
+                return True
+            else:
+                raise OllamaRateLimitError(f"请求过于频繁，已达到最大重试次数{self.max_retries}")
+        
+        elif response.status_code == 503:
+            # 服务不可用
+            if attempt < self.max_retries:
+                wait_time = self.retry_delay * (2 ** attempt)
+                self.logger.warning(f"服务不可用(503)，第{attempt + 1}次重试，等待{wait_time:.1f}秒...")
+                time.sleep(wait_time)
+                return True
+            else:
+                raise OllamaServerError(f"服务不可用，已达到最大重试次数{self.max_retries}")
+        
+        else:
+            # 其他HTTP错误
+            response.raise_for_status()
+        
+        return False
+    
+    def _make_request(self, method: str, endpoint: str, **kwargs) -> requests.Response:
+        """发送请求的通用方法，包含重试逻辑"""
+        url = f"{self.base_url}{endpoint}"
+        
+        for attempt in range(self.max_retries + 1):
+            try:
+                self._acquire_request_slot()
+                
+                # 速率限制检查
+                current_time = time.time()
+                if current_time - self._last_request_time < 0.1:  # 最小间隔100ms
+                    time.sleep(0.1)
+                
+                self._last_request_time = current_time
+                
+                self.logger.debug(f"发送请求: {method} {url}, 尝试 {attempt + 1}/{self.max_retries + 1}")
+                
+                if method.upper() == "GET":
+                    response = self.session.get(url, **kwargs)
+                else:
+                    response = self.session.post(url, **kwargs)
+                
+                # 检查响应状态
+                if response.status_code == 200:
+                    self._success_count += 1
+                    return response
+                else:
+                    # 处理错误响应
+                    should_retry = self._handle_response_error(response, attempt)
+                    if not should_retry:
+                        break
+                        
+            except (requests.exceptions.ConnectTimeout, 
+                    requests.exceptions.ReadTimeout,
+                    requests.exceptions.ConnectionError) as e:
+                
+                if attempt < self.max_retries:
+                    wait_time = self.retry_delay * (2 ** attempt)
+                    self.logger.warning(f"网络错误: {e}, 第{attempt + 1}次重试，等待{wait_time:.1f}秒...")
+                    time.sleep(wait_time)
+                else:
+                    self._error_count += 1
+                    raise OllamaException(f"网络连接失败: {e}")
+                    
+            except requests.exceptions.RequestException as e:
+                self._error_count += 1
+                raise OllamaException(f"请求失败: {e}")
+                
+            finally:
+                self._release_request_slot()
+        
+        # 如果所有重试都失败了
+        self._error_count += 1
+        raise OllamaException("所有重试都失败了")
+    
+    def get_stats(self) -> Dict[str, Any]:
+        """获取请求统计信息"""
+        return {
+            "total_requests": self._request_count,
+            "successful_requests": self._success_count,
+            "failed_requests": self._error_count,
+            "active_requests": self._active_requests,
+            "success_rate": self._success_count / max(self._request_count, 1) * 100
+        }
+    
+    def list_models(self) -> Dict[str, Any]:
+        """获取已安装的模型列表"""
+        try:
+            response = self._make_request("GET", "/api/tags")
+            return response.json()
+        except Exception as e:
+            self.logger.error(f"获取模型列表失败: {e}")
+            return {}
+    
+    def generate(self, model: str, prompt: str, stream: bool = False, **kwargs) -> Any:
+        """
+        生成文本
+        
+        Args:
+            model: 模型名称
+            prompt: 输入提示
+            stream: 是否流式输出
+            **kwargs: 其他参数如temperature, top_p等
+        """
+        data = {
+            "model": model,
+            "prompt": prompt,
+            "stream": stream,
+            **kwargs
+        }
+        
+        try:
+            response = self._make_request(
+                "POST",
+                "/api/generate",
+                json=data,
+                stream=stream
+            )
+            
+            if stream:
+                return self._handle_stream_response(response)
+            else:
+                return response.json()
+                
+        except Exception as e:
+            self.logger.error(f"生成请求失败: {e}")
+            return None
+    
+    def chat(self, model: str, messages: list, stream: bool = False, **kwargs) -> Any:
+        """
+        对话模式
+        
+        Args:
+            model: 模型名称
+            messages: 消息列表，格式为[{"role": "user", "content": "..."}]
+            stream: 是否流式输出
+            **kwargs: 其他参数
+        """
+        data = {
+            "model": model,
+            "messages": messages,
+            "stream": stream,
+            **kwargs
+        }
+        
+        try:
+            response = self._make_request(
+                "POST",
+                "/api/chat",
+                json=data,
+                stream=stream
+            )
+            
+            if stream:
+                return self._handle_stream_response(response)
+            else:
+                return response.json()
+                
+        except Exception as e:
+            self.logger.error(f"对话请求失败: {e}")
+            return None
+    
+    def _handle_stream_response(self, response) -> Iterator[Dict[str, Any]]:
+        """处理流式响应"""
+        for line in response.iter_lines():
+            if line:
+                try:
+                    yield json.loads(line.decode('utf-8'))
+                except json.JSONDecodeError:
+                    continue
+    
+    def batch_generate(self, model: str, prompts: list, **kwargs) -> list:
+        """
+        批量生成文本
+        
+        Args:
+            model: 模型名称
+            prompts: 提示列表
+            **kwargs: 其他参数
+        """
+        results = []
+        
+        def generate_single(prompt):
+            try:
+                return self.generate(model, prompt, stream=False, **kwargs)
+            except Exception as e:
+                self.logger.error(f"批量生成失败 - 提示: {prompt[:50]}..., 错误: {e}")
+                return None
+        
+        # 使用线程池执行批量请求
+        with ThreadPoolExecutor(max_workers=self.max_concurrent_requests) as executor:
+            future_to_prompt = {executor.submit(generate_single, prompt): prompt 
+                               for prompt in prompts}
+            
+            for future in as_completed(future_to_prompt):
+                prompt = future_to_prompt[future]
+                try:
+                    result = future.result()
+                    results.append({
+                        "prompt": prompt,
+                        "response": result,
+                        "success": result is not None
+                    })
+                except Exception as e:
+                    self.logger.error(f"批量请求异常: {e}")
+                    results.append({
+                        "prompt": prompt,
+                        "response": None,
+                        "success": False,
+                        "error": str(e)
+                    })
+        
+        return results
+    
+    def health_check(self) -> bool:
+        """健康检查"""
+        try:
+            response = self._make_request("GET", "/api/tags")
+            return response.status_code == 200
+        except Exception as e:
+            self.logger.error(f"健康检查失败: {e}")
+            return False
+    
+    def pull_model(self, model_name: str) -> bool:
+        """拉取模型"""
+        data = {"name": model_name}
+        
+        try:
+            response = self._make_request(
+                "POST",
+                "/api/pull",
+                json=data,
+                stream=True
+            )
+            
+            print(f"正在拉取模型 {model_name}...")
+            for chunk in self._handle_stream_response(response):
+                if "status" in chunk:
+                    print(f"状态: {chunk['status']}")
+                if chunk.get("done", False):
+                    print("模型拉取完成!")
+                    return True
+                    
+        except Exception as e:
+            self.logger.error(f"拉取模型失败: {e}")
+            return False
+    
+    def is_model_available(self, model_name: str) -> bool:
+        """检查模型是否可用"""
+        models = self.list_models()
+        if "models" in models:
+            return any(model["name"].startswith(model_name) for model in models["models"])
+        return False
+    
+    def __enter__(self):
+        """上下文管理器入口"""
+        return self
+    
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        """上下文管理器退出"""
+        self._executor.shutdown(wait=True)
+        self.session.close()
+
+
+# 使用示例
+def main():
+    # 创建客户端，配置重试和并发参数
+    client = OllamaClient(
+        max_retries=3,
+        retry_delay=1.0,
+        max_concurrent_requests=3,
+        timeout=30,
+        enable_logging=True
+    )
+    
+    # 健康检查
+    if not client.health_check():
+        print("Ollama服务不可用，请检查服务状态")
+        return
+    
+    # 检查服务是否可用
+    try:
+        models = client.list_models()
+        print("Ollama服务连接成功!")
+        print(f"可用模型: {[m['name'] for m in models.get('models', [])]}")
+    except Exception as e:
+        print(f"无法连接到Ollama服务: {e}")
+        return
+    
+    model_name = "Qwen3-8B:latest"  # 根据你的模型调整
+    
+    # 检查模型是否存在，不存在则拉取
+    if not client.is_model_available(model_name):
+        print(f"模型 {model_name} 不存在，正在拉取...")
+        if not client.pull_model(model_name):
+            print("模型拉取失败")
+            return
+    
+    # 示例1: 处理500错误的单个请求
+    # print("\n=== 单个请求（含错误处理） ===")
+    # try:
+    #     response = client.generate(
+    #         model=model_name,
+    #         prompt="请用中文简单介绍一下人工智能。",
+    #         no_thinking=True, 
+    #         temperature=0.7,
+    #         max_tokens=200
+    #     )
+        
+    #     if response:
+    #         print(f"回答: {response.get('response', '')}")
+    #     else:
+    #         print("请求失败")
+    # except Exception as e:
+    #     print(f"请求异常: {e}")
+    
+    # 示例2: 流式输出
+    print("\n=== 流式输出 ===")
+    try:
+        stream_response = client.generate(
+            model=model_name,
+            prompt="请讲一个简短的故事。",
+            stream=True,
+            no_thinking=True, 
+            enable_thinking = False,
+            temperature=0.8,
+            max_tokens=200
+        )
+        
+        print("流式回答: ", end="")
+        for chunk in stream_response:
+            if "response" in chunk:
+                print(chunk["response"], end="", flush=True)
+            if chunk.get("done", False):
+                print("\n")
+                break
+    except Exception as e:
+        print(f"流式输出异常: {e}")
+    
+    # 示例3: 对话模式
+    print("\n=== 对话模式 ===")
+    try:
+        messages = [
+            {"role" : "user", "content" : "请介绍一下昊天"},
+        ]
+        
+        chat_response = client.chat(
+            model=model_name,
+            messages=messages,
+            # no_thinking=True, 
+            # # no_thinking=True, 
+            # enable_thinking = True,
+            temperature=0.7,
+            max_tokens=200
+        )
+        
+        if chat_response:
+            print(f"AI回答: {chat_response.get('message', {}).get('content', '')}")
+    except Exception as e:
+        print(f"对话异常: {e}")
+    
+    # 示例4: 批量请求测试
+    print("\n=== 批量请求测试 ===")
+    prompts = [
+        "什么是机器学习？",
+        "什么是深度学习？", 
+        "什么是自然语言处理？",
+        "什么是计算机视觉？",
+        "什么是强化学习？"
+    ]
+    
+    try:
+        batch_results = client.batch_generate(
+            model=model_name,
+            prompts=prompts,
+            temperature=0.7,
+            max_tokens=100
+        )
+        
+        for i, result in enumerate(batch_results):
+            if result["success"]:
+                print(f"请求 {i+1} 成功: {result['response'].get('response', '')[:50]}...")
+            else:
+                print(f"请求 {i+1} 失败: {result.get('error', '未知错误')}")
+                
+    except Exception as e:
+        print(f"批量请求异常: {e}")
+    
+    # 示例5: 并发压力测试
+    print("\n=== 并发压力测试 ===")
+    
+    def stress_test():
+        import concurrent.futures
+        
+        def single_request(i):
+            try:
+                response = client.generate(
+                    model=model_name,
+                    prompt=f"请简单回答：什么是人工智能？(请求{i})",
+                    temperature=0.7,
+                    max_tokens=50
+                )
+                return f"请求{i}: 成功" if response else f"请求{i}: 失败"
+            except Exception as e:
+                return f"请求{i}: 异常 - {e}"
+        
+        # 发送10个并发请求
+        with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
+            futures = [executor.submit(single_request, i) for i in range(10)]
+            
+            for future in concurrent.futures.as_completed(futures):
+                try:
+                    result = future.result()
+                    print(result)
+                except Exception as e:
+                    print(f"并发请求异常: {e}")
+    
+    stress_test()
+    
+    # 显示统计信息
+    print("\n=== 请求统计 ===")
+    stats = client.get_stats()
+    for key, value in stats.items():
+        print(f"{key}: {value}")
+
+
+def demo_error_handling():
+    """演示错误处理"""
+    print("\n=== 错误处理演示 ===")
+    
+    # 模拟连接错误的客户端
+    client = OllamaClient(
+        base_url="http://localhost:99999",  # 无效端口
+        max_retries=2,
+        retry_delay=0.5
+    )
+    
+    try:
+        response = client.generate(
+            model="llama2",
+            prompt="测试请求",
+            temperature=0.7
+        )
+        print(f"意外成功: {response}")
+    except Exception as e:
+        print(f"预期的错误: {e}")
+    
+    # 显示错误统计
+    stats = client.get_stats()
+    print(f"错误统计: {stats}")
+
+
+def demo_context_manager():
+    """演示上下文管理器使用"""
+    print("\n=== 上下文管理器演示 ===")
+    
+    with OllamaClient(max_retries=2) as client:
+        if client.health_check():
+            print("服务健康检查通过")
+            
+            # 使用客户端进行请求
+            models = client.list_models()
+            print(f"可用模型数量: {len(models.get('models', []))}")
+        else:
+            print("服务健康检查失败")
+
+
+if __name__ == "__main__":
+    main()
+    demo_error_handling()
+    demo_context_manager()
\ No newline at end of file
diff --git a/007过滤think.py b/007过滤think.py
new file mode 100644
index 0000000..e98a396
--- /dev/null
+++ b/007过滤think.py
@@ -0,0 +1,648 @@
+import requests
+import json
+import time
+import threading
+from typing import Dict, Any, Iterator, Optional
+from functools import wraps
+from concurrent.futures import ThreadPoolExecutor, as_completed
+import logging
+
+class OllamaException(Exception):
+    """Ollama自定义异常类"""
+    pass
+
+class OllamaServerError(OllamaException):
+    """服务器错误异常"""
+    pass
+
+class OllamaRateLimitError(OllamaException):
+    """请求限制异常"""
+    pass
+
+class OllamaClient:
+    def __init__(self, base_url: str = "http://localhost:11434", max_retries: int = 3, 
+                 retry_delay: float = 1.0, max_concurrent_requests: int = 5,
+                 timeout: int = 30, enable_logging: bool = True):
+        """
+        初始化Ollama客户端
+        
+        Args:
+            base_url: Ollama服务的基础URL，默认为本地11434端口
+            max_retries: 最大重试次数
+            retry_delay: 重试间隔（秒）
+            max_concurrent_requests: 最大并发请求数
+            timeout: 请求超时时间（秒）
+            enable_logging: 是否启用日志
+        """
+        self.base_url = base_url
+        self.max_retries = max_retries
+        self.retry_delay = retry_delay
+        self.max_concurrent_requests = max_concurrent_requests
+        self.timeout = timeout
+        
+        # 设置日志
+        if enable_logging:
+            logging.basicConfig(level=logging.INFO)
+            self.logger = logging.getLogger(__name__)
+        else:
+            self.logger = logging.getLogger(__name__)
+            self.logger.setLevel(logging.CRITICAL)
+        
+        # 创建会话
+        self.session = requests.Session()
+        self.session.timeout = timeout
+        
+        # 并发控制
+        self._request_lock = threading.Lock()
+        self._active_requests = 0
+        self._executor = ThreadPoolExecutor(max_workers=max_concurrent_requests)
+        
+        # 请求统计
+        self._request_count = 0
+        self._success_count = 0
+        self._error_count = 0
+        self._last_request_time = 0
+    
+    def _wait_for_slot(self):
+        """等待可用的请求槽位"""
+        while self._active_requests >= self.max_concurrent_requests:
+            time.sleep(0.1)
+    
+    def _acquire_request_slot(self):
+        """获取请求槽位"""
+        with self._request_lock:
+            self._wait_for_slot()
+            self._active_requests += 1
+            self._request_count += 1
+    
+    def _release_request_slot(self):
+        """释放请求槽位"""
+        with self._request_lock:
+            self._active_requests = max(0, self._active_requests - 1)
+    
+    def _handle_response_error(self, response, attempt: int):
+        """处理响应错误"""
+        if response.status_code == 500:
+            if attempt < self.max_retries:
+                wait_time = self.retry_delay * (2 ** attempt)  # 指数退避
+                self.logger.warning(f"服务器错误500，第{attempt + 1}次重试，等待{wait_time:.1f}秒...")
+                time.sleep(wait_time)
+                return True  # 需要重试
+            else:
+                raise OllamaServerError(f"服务器错误500，已达到最大重试次数{self.max_retries}")
+        
+        elif response.status_code == 429:
+            # 速率限制
+            if attempt < self.max_retries:
+                wait_time = self.retry_delay * (2 ** attempt)
+                self.logger.warning(f"请求过于频繁(429)，第{attempt + 1}次重试，等待{wait_time:.1f}秒...")
+                time.sleep(wait_time)
+                return True
+            else:
+                raise OllamaRateLimitError(f"请求过于频繁，已达到最大重试次数{self.max_retries}")
+        
+        elif response.status_code == 503:
+            # 服务不可用
+            if attempt < self.max_retries:
+                wait_time = self.retry_delay * (2 ** attempt)
+                self.logger.warning(f"服务不可用(503)，第{attempt + 1}次重试，等待{wait_time:.1f}秒...")
+                time.sleep(wait_time)
+                return True
+            else:
+                raise OllamaServerError(f"服务不可用，已达到最大重试次数{self.max_retries}")
+        
+        else:
+            # 其他HTTP错误
+            response.raise_for_status()
+        
+        return False
+    
+    def _make_request(self, method: str, endpoint: str, **kwargs) -> requests.Response:
+        """发送请求的通用方法，包含重试逻辑"""
+        url = f"{self.base_url}{endpoint}"
+        
+        for attempt in range(self.max_retries + 1):
+            try:
+                self._acquire_request_slot()
+                
+                # 速率限制检查
+                current_time = time.time()
+                if current_time - self._last_request_time < 0.1:  # 最小间隔100ms
+                    time.sleep(0.1)
+                
+                self._last_request_time = current_time
+                
+                self.logger.debug(f"发送请求: {method} {url}, 尝试 {attempt + 1}/{self.max_retries + 1}")
+                
+                if method.upper() == "GET":
+                    response = self.session.get(url, **kwargs)
+                else:
+                    response = self.session.post(url, **kwargs)
+                
+                # 检查响应状态
+                if response.status_code == 200:
+                    self._success_count += 1
+                    return response
+                else:
+                    # 处理错误响应
+                    should_retry = self._handle_response_error(response, attempt)
+                    if not should_retry:
+                        break
+                        
+            except (requests.exceptions.ConnectTimeout, 
+                    requests.exceptions.ReadTimeout,
+                    requests.exceptions.ConnectionError) as e:
+                
+                if attempt < self.max_retries:
+                    wait_time = self.retry_delay * (2 ** attempt)
+                    self.logger.warning(f"网络错误: {e}, 第{attempt + 1}次重试，等待{wait_time:.1f}秒...")
+                    time.sleep(wait_time)
+                else:
+                    self._error_count += 1
+                    raise OllamaException(f"网络连接失败: {e}")
+                    
+            except requests.exceptions.RequestException as e:
+                self._error_count += 1
+                raise OllamaException(f"请求失败: {e}")
+                
+            finally:
+                self._release_request_slot()
+        
+        # 如果所有重试都失败了
+        self._error_count += 1
+        raise OllamaException("所有重试都失败了")
+    
+    def get_stats(self) -> Dict[str, Any]:
+        """获取请求统计信息"""
+        return {
+            "total_requests": self._request_count,
+            "successful_requests": self._success_count,
+            "failed_requests": self._error_count,
+            "active_requests": self._active_requests,
+            "success_rate": self._success_count / max(self._request_count, 1) * 100
+        }
+    
+    def list_models(self) -> Dict[str, Any]:
+        """获取已安装的模型列表"""
+        try:
+            response = self._make_request("GET", "/api/tags")
+            return response.json()
+        except Exception as e:
+            self.logger.error(f"获取模型列表失败: {e}")
+            return {}
+    
+    def generate(self, model: str, prompt: str, stream: bool = False, 
+                 no_thinking: bool = True, **kwargs) -> Any:
+        """
+        生成文本
+        
+        Args:
+            model: 模型名称
+            prompt: 输入提示
+            stream: 是否流式输出
+            no_thinking: 是否禁用思考过程，直接回答
+            **kwargs: 其他参数如temperature, top_p等
+        """
+        # 如果启用no_thinking，修改prompt以指示模型直接回答
+        if no_thinking:
+            prompt = self._format_no_thinking_prompt(prompt)
+        
+        data = {
+            "model": model,
+            "prompt": prompt,
+            "stream": stream,
+            **kwargs
+        }
+        
+        # 添加直接回答的系统参数
+        if no_thinking:
+            data.update({
+                "system": "你是一个直接、简洁的AI助手。请直接回答问题，不要显示思考过程或分析步骤。",
+                "temperature": kwargs.get("temperature", 0.7),
+                "top_p": kwargs.get("top_p", 0.9),
+                "repeat_penalty": kwargs.get("repeat_penalty", 1.1)
+            })
+        
+        try:
+            response = self._make_request(
+                "POST",
+                "/api/generate",
+                json=data,
+                stream=stream
+            )
+            
+            if stream:
+                return self._handle_stream_response(response, filter_thinking=no_thinking)
+            else:
+                result = response.json()
+                if no_thinking and result:
+                    result = self._filter_thinking_from_response(result)
+                return result
+                
+        except Exception as e:
+            self.logger.error(f"生成请求失败: {e}")
+            return None
+    
+    def chat(self, model: str, messages: list, stream: bool = False, 
+             no_thinking: bool = True, **kwargs) -> Any:
+        """
+        对话模式
+        
+        Args:
+            model: 模型名称
+            messages: 消息列表，格式为[{"role": "user", "content": "..."}]
+            stream: 是否流式输出
+            no_thinking: 是否禁用思考过程，直接回答
+            **kwargs: 其他参数
+        """
+        # 如果启用no_thinking，在消息中添加系统提示
+        if no_thinking:
+            messages = self._format_no_thinking_messages(messages)
+        
+        data = {
+            "model": model,
+            "messages": messages,
+            "stream": stream,
+            **kwargs
+        }
+        
+        # 添加直接回答的参数
+        if no_thinking:
+            data.update({
+                "system": "你是一个直接、简洁的AI助手。请直接回答问题，不要显示思考过程或分析步骤。",
+                "temperature": kwargs.get("temperature", 0.7),
+                "top_p": kwargs.get("top_p", 0.9),
+                "repeat_penalty": kwargs.get("repeat_penalty", 1.1)
+            })
+        
+        try:
+            response = self._make_request(
+                "POST",
+                "/api/chat",
+                json=data,
+                stream=stream
+            )
+            
+            if stream:
+                return self._handle_stream_response(response, filter_thinking=no_thinking)
+            else:
+                result = response.json()
+                if no_thinking and result:
+                    result = self._filter_thinking_from_chat_response(result)
+                return result
+                
+        except Exception as e:
+            self.logger.error(f"对话请求失败: {e}")
+            return None
+    
+    def _format_no_thinking_prompt(self, prompt: str) -> str:
+        """格式化prompt以避免显示思考过程"""
+        return f"""请直接回答以下问题，不要显示思考过程、分析步骤或推理过程：
+
+{prompt}
+
+注意：请直接给出答案，不要包含"让我想想"、"分析一下"、"首先"、"然后"等思考过程的表述。"""
+    
+    def _format_no_thinking_messages(self, messages: list) -> list:
+        """格式化消息以避免显示思考过程"""
+        formatted_messages = []
+        
+        # 添加系统消息
+        system_message = {
+            "role": "system",
+            "content": "你是一个直接、简洁的AI助手。请直接回答问题，不要显示思考过程、分析步骤或推理过程。不要使用'让我想想'、'分析一下'、'首先'、'然后'等表述。"
+        }
+        formatted_messages.append(system_message)
+        
+        # 添加原始消息
+        for message in messages:
+            if message.get("role") == "user":
+                content = message.get("content", "")
+                formatted_content = f"请直接回答：{content}"
+                formatted_messages.append({
+                    "role": "user",
+                    "content": formatted_content
+                })
+            else:
+                formatted_messages.append(message)
+        
+        return formatted_messages
+    
+    def _filter_thinking_from_response(self, response: dict) -> dict:
+        """从响应中过滤掉思考过程"""
+        if "response" in response:
+            content = response["response"]
+            # 过滤常见的思考过程表述
+            filtered_content = self._filter_thinking_text(content)
+            response["response"] = filtered_content
+        return response
+    
+    def _filter_thinking_from_chat_response(self, response: dict) -> dict:
+        """从对话响应中过滤掉思考过程"""
+        if "message" in response and "content" in response["message"]:
+            content = response["message"]["content"]
+            # 过滤常见的思考过程表述
+            filtered_content = self._filter_thinking_text(content)
+            response["message"]["content"] = filtered_content
+        return response
+    
+    def _filter_thinking_text(self, text: str) -> str:
+        """过滤文本中的思考过程表述"""
+        # 常见的思考过程表述
+        thinking_patterns = [
+            r"让我想想[。，\n]*",
+            r"让我来分析一下[。，\n]*",
+            r"让我来思考一下[。，\n]*",
+            r"首先[，。]*让我[^。]*[。，\n]*",
+            r"我来分析一下[。，\n]*",
+            r"我需要思考一下[。，\n]*",
+            r"让我仔细考虑一下[。，\n]*",
+            r"这个问题需要[^。]*分析[。，\n]*",
+            r"思考：[^\n]*\n*",
+            r"分析：[^\n]*\n*",
+            r"<thinking>.*?</thinking>",
+            r"\*思考\*[^\n]*\n*",
+            r"\*分析\*[^\n]*\n*"
+        ]
+        
+        import re
+        filtered_text = text
+        for pattern in thinking_patterns:
+            filtered_text = re.sub(pattern, "", filtered_text, flags=re.DOTALL | re.IGNORECASE)
+        
+        # 清理多余的空行和空格
+        filtered_text = re.sub(r'\n\s*\n', '\n\n', filtered_text)
+        filtered_text = filtered_text.strip()
+        
+        return filtered_text
+    
+    def _handle_stream_response(self, response, filter_thinking: bool = False) -> Iterator[Dict[str, Any]]:
+        """处理流式响应"""
+        for line in response.iter_lines():
+            if line:
+                try:
+                    chunk = json.loads(line.decode('utf-8'))
+                    if filter_thinking and "response" in chunk:
+                        # 实时过滤思考过程
+                        content = chunk["response"]
+                        if not self._is_thinking_content(content):
+                            yield chunk
+                    else:
+                        yield chunk
+                except json.JSONDecodeError:
+                    continue
+    
+    def _is_thinking_content(self, content: str) -> bool:
+        """判断内容是否为思考过程"""
+        thinking_keywords = [
+            "让我想想", "让我来分析", "让我思考", "我来分析",
+            "我需要思考", "让我仔细考虑", "思考：", "分析：",
+            "<think>", "*思考*", "*分析*"
+        ]
+        
+        content_lower = content.lower()
+        return any(keyword in content_lower for keyword in thinking_keywords)
+    
+    def batch_generate(self, model: str, prompts: list, no_thinking: bool = True, **kwargs) -> list:
+        """
+        批量生成文本
+        
+        Args:
+            model: 模型名称
+            prompts: 提示列表
+            no_thinking: 是否禁用思考过程
+            **kwargs: 其他参数
+        """
+        results = []
+        
+        def generate_single(prompt):
+            try:
+                return self.generate(model, prompt, stream=False, no_thinking=no_thinking, **kwargs)
+            except Exception as e:
+                self.logger.error(f"批量生成失败 - 提示: {prompt[:50]}..., 错误: {e}")
+                return None
+        
+        # 使用线程池执行批量请求
+        with ThreadPoolExecutor(max_workers=self.max_concurrent_requests) as executor:
+            future_to_prompt = {executor.submit(generate_single, prompt): prompt 
+                               for prompt in prompts}
+            
+            for future in as_completed(future_to_prompt):
+                prompt = future_to_prompt[future]
+                try:
+                    result = future.result()
+                    results.append({
+                        "prompt": prompt,
+                        "response": result,
+                        "success": result is not None
+                    })
+                except Exception as e:
+                    self.logger.error(f"批量请求异常: {e}")
+                    results.append({
+                        "prompt": prompt,
+                        "response": None,
+                        "success": False,
+                        "error": str(e)
+                    })
+        
+        return results
+    
+    def health_check(self) -> bool:
+        """健康检查"""
+        try:
+            response = self._make_request("GET", "/api/tags")
+            return response.status_code == 200
+        except Exception as e:
+            self.logger.error(f"健康检查失败: {e}")
+            return False
+    
+    def pull_model(self, model_name: str) -> bool:
+        """拉取模型"""
+        data = {"name": model_name}
+        
+        try:
+            response = self._make_request(
+                "POST",
+                "/api/pull",
+                json=data,
+                stream=True
+            )
+            
+            print(f"正在拉取模型 {model_name}...")
+            for chunk in self._handle_stream_response(response):
+                if "status" in chunk:
+                    print(f"状态: {chunk['status']}")
+                if chunk.get("done", False):
+                    print("模型拉取完成!")
+                    return True
+                    
+        except Exception as e:
+            self.logger.error(f"拉取模型失败: {e}")
+            return False
+    
+    def is_model_available(self, model_name: str) -> bool:
+        """检查模型是否可用"""
+        models = self.list_models()
+        if "models" in models:
+            return any(model["name"].startswith(model_name) for model in models["models"])
+        return False
+    
+    def __enter__(self):
+        """上下文管理器入口"""
+        return self
+    
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        """上下文管理器退出"""
+        self._executor.shutdown(wait=True)
+        self.session.close()
+
+
+# 使用示例
+def main():
+    # 创建客户端，配置重试和并发参数
+    client = OllamaClient(
+        max_retries=3,
+        retry_delay=1.0,
+        max_concurrent_requests=3,
+        timeout=30,
+        enable_logging=True
+    )
+    
+    # 健康检查
+    if not client.health_check():
+        print("Ollama服务不可用，请检查服务状态")
+        return
+    
+    # 检查服务是否可用
+    try:
+        models = client.list_models()
+        print("Ollama服务连接成功!")
+        print(f"可用模型: {[m['name'] for m in models.get('models', [])]}")
+    except Exception as e:
+        print(f"无法连接到Ollama服务: {e}")
+        return
+    
+    model_name = "Qwen3-8B:latest"  # 根据你的模型调整
+    
+    # 检查模型是否存在，不存在则拉取
+    if not client.is_model_available(model_name):
+        print(f"模型 {model_name} 不存在，正在拉取...")
+        if not client.pull_model(model_name):
+            print("模型拉取失败")
+            return
+    
+    # 示例1: 直接回答模式（无思考过程）
+    print("\n=== 直接回答模式 ===")
+    try:
+        response = client.generate(
+            model=model_name,
+            prompt="请用中文简单介绍一下人工智能。",
+            no_thinking=True,  # 启用直接回答模式
+            temperature=0.7,
+            max_tokens=200
+        )
+        
+        if response:
+            print(f"回答: {response.get('response', '')}")
+        else:
+            print("请求失败")
+    except Exception as e:
+        print(f"请求异常: {e}")
+    
+    # 示例2: 对比 - 有思考过程的回答
+    print("\n=== 对比：有思考过程的回答 ===")
+    try:
+        response = client.generate(
+            model=model_name,
+            prompt="请用中文简单介绍一下人工智能。",
+            no_thinking=False,  # 禁用直接回答模式
+            temperature=0.7,
+            max_tokens=200
+        )
+        
+        if response:
+            print(f"回答: {response.get('response', '')}")
+        else:
+            print("请求失败")
+    except Exception as e:
+        print(f"请求异常: {e}")
+    
+    # 示例3: 流式输出 - 直接回答模式
+    print("\n=== 流式输出 - 直接回答模式 ===")
+    try:
+        stream_response = client.generate(
+            model=model_name,
+            prompt="请讲一个简短的故事。",
+            stream=True,
+            no_thinking=True,  # 启用直接回答模式
+            temperature=0.8,
+            max_tokens=200
+        )
+        
+        print("流式回答: ", end="")
+        for chunk in stream_response:
+            if "response" in chunk:
+                print(chunk["response"], end="", flush=True)
+            if chunk.get("done", False):
+                print("\n")
+                break
+    except Exception as e:
+        print(f"流式输出异常: {e}")
+    
+    # 示例4: 对话模式 - 直接回答
+    print("\n=== 对话模式 - 直接回答 ===")
+    try:
+        messages = [
+            {"role": "user", "content": "请介绍一下机器学习的基本概念"},
+        ]
+        
+        chat_response = client.chat(
+            model=model_name,
+            messages=messages,
+            no_thinking=True,  # 启用直接回答模式
+            temperature=0.7,
+            max_tokens=200
+        )
+        
+        if chat_response:
+            print(f"AI回答: {chat_response.get('message', {}).get('content', '')}")
+    except Exception as e:
+        print(f"对话异常: {e}")
+    
+    # 示例5: 批量请求测试 - 直接回答模式
+    print("\n=== 批量请求测试 - 直接回答模式 ===")
+    prompts = [
+        "什么是机器学习？",
+        "什么是深度学习？", 
+        "什么是自然语言处理？",
+        "什么是计算机视觉？",
+        "什么是强化学习？"
+    ]
+    
+    try:
+        batch_results = client.batch_generate(
+            model=model_name,
+            prompts=prompts,
+            no_thinking=True,  # 启用直接回答模式
+            temperature=0.7,
+            max_tokens=100
+        )
+        
+        for i, result in enumerate(batch_results):
+            if result["success"]:
+                print(f"请求 {i+1} 成功: {result['response'].get('response', '')[:100]}...")
+            else:
+                print(f"请求 {i+1} 失败: {result.get('error', '未知错误')}")
+                
+    except Exception as e:
+        print(f"批量请求异常: {e}")
+    
+    # 显示统计信息
+    print("\n=== 请求统计 ===")
+    stats = client.get_stats()
+    for key, value in stats.items():
+        print(f"{key}: {value}")
+
+
+if __name__ == "__main__":
+    main()
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