109 lines
4.2 KiB
Python
109 lines
4.2 KiB
Python
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import requests
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import pyperclip
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import math
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import os
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import json
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# --- 配置 ---
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CHUNK_SIZE = 5000 # 每次处理文本长度 (字符)
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MODEL_NAME = "grok-3" # 使用的 AI 模型
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API_URL = "https://aizex.top/v1/chat/completions" # API 地址 (请确认这是正确的聊天补全端点)
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# !! 安全警告: 请不要直接将 API 密钥硬编码在脚本中 !!
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# 建议使用环境变量 (os.environ.get) 或配置文件读取
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API_KEY = os.environ.get("AIZEX_API_KEY", "YOUR_API_KEY_HERE") # 从环境变量读取或替换为你的密钥
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# --- AI 调用函数 ---
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def call_ai_api(prompt, api_key, model, api_url):
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"""调用 AI API 并返回结果"""
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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}
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# 根据常见的 API 结构构建 payload，可能需要根据 aizex.top 的具体要求调整
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payload = {
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"model": model,
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"messages": [{"role": "user", "content": prompt}],
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# 可以添加其他参数如 temperature, max_tokens 等
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# "temperature": 0.7,
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}
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try:
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response = requests.post(api_url, headers=headers, json=payload, timeout=180) # 增加超时时间
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response.raise_for_status() # 如果状态码不是 2xx，则抛出异常
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result = response.json()
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# 假设 API 返回格式类似 OpenAI，提取内容
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if "choices" in result and len(result["choices"]) > 0:
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content = result["choices"][0].get("message", {}).get("content", "")
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return content.strip(), True
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else:
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print(f"警告: API 响应格式不符合预期: {result}")
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return f"API 响应格式错误: {result}", False
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except requests.exceptions.RequestException as e:
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print(f"错误: 调用 AI API 时出错: {e}")
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return f"API 调用失败: {e}", False
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except json.JSONDecodeError:
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print(f"错误: 解析 API 响应 JSON 时出错。响应内容: {response.text}")
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return f"API 响应解析失败: {response.text}", False
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# --- 主处理函数 ---
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def process_text_in_batches():
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"""主函数：获取剪贴板文本，分批处理并调用 AI"""
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if API_KEY == "YOUR_API_KEY_HERE":
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print("错误: 请在脚本中或环境变量 AIZEX_API_KEY 中设置您的 API 密钥。")
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return
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try:
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original_text = pyperclip.paste()
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if not original_text:
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print("错误: 剪贴板中没有文本。")
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return
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except Exception as e:
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print(f"错误: 无法从剪贴板读取文本: {e}")
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print("请确保已安装 pyperclip 库并且有所需的权限。")
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return
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text_length = len(original_text)
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num_chunks = math.ceil(text_length / CHUNK_SIZE)
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print(f"总字数: {text_length}")
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print(f"每块大小: {CHUNK_SIZE}")
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print(f"总块数: {num_chunks}")
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print("-" * 20)
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current_summary = ""
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ai_output = ""
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for i in range(num_chunks):
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start_index = i * CHUNK_SIZE
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end_index = min((i + 1) * CHUNK_SIZE, text_length)
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current_chunk = original_text[start_index:end_index]
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print(f"正在处理块 {i + 1}/{num_chunks}...")
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if i == 0:
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# 首次调用
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prompt = f"请对以下内容进行概括总结:\n\n{current_chunk}"
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else:
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# 后续调用
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prompt = f"这是上一轮的概括内容:\n\n{current_summary}\n\n请结合以上概括内容和下面的新材料，进行融合概括总结，生成一个连贯的整体摘要:\n\n{current_chunk}"
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# 调用 AI
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ai_output, success = call_ai_api(prompt, API_KEY, MODEL_NAME, API_URL)
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if success:
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current_summary = ai_output # 更新当前摘要为最新输出
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print(f"块 {i + 1} 处理成功。")
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else:
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print(f"块 {i + 1} 处理失败。脚本将中止。")
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print(f"失败时的 AI 输出/错误信息: {ai_output}")
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return # 如果某一块失败，则停止处理
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print("-" * 20)
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print("所有块处理完成。最终总结:")
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print("=" * 30)
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print(current_summary)
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print("=" * 30)
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# --- 脚本入口 ---
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if __name__ == "__main__":
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process_text_in_batches()
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