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from Config.Config import MODEL_API_KEY, MODEL_NAME
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from openai import OpenAI
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import docx
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import os
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# 初始化通义千问客户端
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client = OpenAI(
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api_key=MODEL_API_KEY,
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base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
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)
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def call_qwen_plus(prompt, stream_callback=None):
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"""调用通义千问API"""
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try:
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response = client.chat.completions.create(
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model=MODEL_NAME,
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messages=[
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{"role": "system", "content": "你是一个专业的文档分析助手"},
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{"role": "user", "content": prompt}
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],
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temperature=0.3,
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stream=True
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)
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full_response = ""
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for chunk in response:
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content = chunk.choices[0].delta.content
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if content:
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full_response += content
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if stream_callback:
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stream_callback(content)
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return full_response
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except Exception as e:
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print(f"调用通义千问API出错: {str(e)}")
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return ""
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def read_docx(file_path):
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"""读取docx文件内容"""
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try:
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doc = docx.Document(file_path)
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return "\n".join([para.text for para in doc.paragraphs if para.text])
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except Exception as e:
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print(f"读取docx文件出错: {str(e)}")
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return ""
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def save_to_txt(content, file_path, mode='a'):
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"""保存内容到txt文件"""
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try:
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with open(file_path, mode, encoding='utf-8') as f:
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f.write(content + "\n")
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return True
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except Exception as e:
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print(f"保存文件出错: {str(e)}")
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return False
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def split_text(text, chunk_size=6000):
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"""按约6000字符分割文本，优先在段落结束处分隔"""
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chunks = []
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current_chunk = ""
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paragraphs = text.split('\n\n')
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for para in paragraphs:
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if len(current_chunk) + len(para) > chunk_size and current_chunk:
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chunks.append(current_chunk)
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current_chunk = para
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else:
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if current_chunk:
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current_chunk += '\n\n' + para
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else:
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current_chunk = para
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if current_chunk:
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chunks.append(current_chunk)
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return chunks
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PROMPT_TEMPLATE = """
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请分析以下数学教学内容，直接返回处理后的文本：
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1. 根据每个段落间的逻辑关系，判断是不是强相关，一致内容的划分为同一个段落，否则视为两个段落。
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2. 不同段落间用两个换行符分隔
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3. 不要添加任何额外格式或标记，绝对不要使用markdown格式返回。
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待处理内容：
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{text_chunk}
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"""
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def ask_llm(text_chunk, is_final=False):
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"""调用大模型并处理响应"""
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prompt = PROMPT_TEMPLATE.format(text_chunk=text_chunk, is_final=is_final)
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def stream_callback(chunk):
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"""SSE流式回调函数"""
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print(chunk, end='', flush=True)
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try:
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response = call_qwen_plus(prompt, stream_callback=stream_callback)
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return response
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except Exception as e:
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print(f"调用大模型出错: {str(e)}")
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return None
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def process_document(input_path, output_dir):
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"""处理文档主函数"""
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text = read_docx(input_path)
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if not text:
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print("无法读取输入文件内容")
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return False
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# 确保输出目录存在
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os.makedirs(output_dir, exist_ok=True)
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chunks = split_text(text)
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for i, chunk in enumerate(chunks):
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print(f"正在处理第{i+1}/{len(chunks)}个分块...")
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response = ask_llm(chunk)
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if response:
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output_file = os.path.join(output_dir, f"{i+1}.txt")
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save_to_txt(response, output_file, mode='w')
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print(f"处理完成，结果已保存到目录: {output_dir}")
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return True
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if __name__ == "__main__":
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input_file = '../Txt/小学数学（史校长）.docx'
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output_dir = '../Txt/processed_chunks' # 改为输出目录
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process_document(input_file, output_dir)
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