|
|
from openai import OpenAI
|
|
|
from Config import Config
|
|
|
|
|
|
class DocxProcessor:
|
|
|
def __init__(self):
|
|
|
# 独立初始化DeepSeek客户端
|
|
|
self.client = OpenAI(
|
|
|
api_key=Config.DEEPSEEK_API_KEY,
|
|
|
base_url=Config.DEEPSEEK_URL
|
|
|
)
|
|
|
|
|
|
def call_deepseek(self, prompt):
|
|
|
"""独立调用DeepSeek API"""
|
|
|
try:
|
|
|
response = self.client.chat.completions.create(
|
|
|
model="deepseek-chat",
|
|
|
messages=[
|
|
|
{"role": "system", "content": "你是一个专业的文档分析助手"},
|
|
|
{"role": "user", "content": prompt}
|
|
|
],
|
|
|
temperature=0.3
|
|
|
)
|
|
|
return response.choices[0].message.content
|
|
|
except Exception as e:
|
|
|
print(f"调用DeepSeek API出错: {str(e)}")
|
|
|
return ""
|
|
|
|
|
|
def ask_llm(self, text_chunk):
|
|
|
"""向大模型提问并获取响应"""
|
|
|
prompt = f"""请分析以下文本段落并返回有价值的内容:
|
|
|
要求:
|
|
|
1. 保持原文关键信息
|
|
|
2. 用清晰的格式返回
|
|
|
3. 可包含简要总结
|
|
|
|
|
|
文本段落内容:
|
|
|
{text_chunk}"""
|
|
|
return self.call_deepseek(prompt)
|
|
|
|
|
|
# 初始化DeepSeek客户端
|
|
|
client = OpenAI(
|
|
|
api_key=Config.DEEPSEEK_API_KEY,
|
|
|
base_url=Config.DEEPSEEK_URL
|
|
|
)
|
|
|
|
|
|
def split_text(text, chunk_size=6000):
|
|
|
"""按段落分割文本，确保每个块接近6000字"""
|
|
|
paragraphs = [p.strip() for p in text.split('\n') if p.strip()]
|
|
|
chunks = []
|
|
|
current_chunk = []
|
|
|
current_length = 0
|
|
|
|
|
|
for para in paragraphs:
|
|
|
para_length = len(para)
|
|
|
if current_length + para_length > chunk_size and current_chunk:
|
|
|
chunks.append('\n'.join(current_chunk))
|
|
|
current_chunk = []
|
|
|
current_length = 0
|
|
|
current_chunk.append(para)
|
|
|
current_length += para_length
|
|
|
|
|
|
if current_chunk:
|
|
|
chunks.append('\n'.join(current_chunk))
|
|
|
return chunks
|
|
|
|
|
|
def ask_llm(text_chunk):
|
|
|
"""向大模型提问并获取响应"""
|
|
|
prompt = f"""请分析以下文本段落并返回有价值的内容:
|
|
|
要求:
|
|
|
1. 保持原文关键信息
|
|
|
2. 用清晰的格式返回
|
|
|
3. 可包含简要总结
|
|
|
|
|
|
文本段落内容:
|
|
|
{text_chunk}"""
|
|
|
return call_deepseek_api(prompt)
|
|
|
|
|
|
def process_document(input_file, output_file):
|
|
|
"""处理文档主流程"""
|
|
|
text = read_docx(input_file)
|
|
|
chunks = split_text(text)
|
|
|
|
|
|
for i, chunk in enumerate(chunks, 1):
|
|
|
print(f"正在处理第{i}个段落...")
|
|
|
try:
|
|
|
response = ask_llm(chunk)
|
|
|
save_to_txt(f"段落{i}响应:\n{response}", output_file)
|
|
|
except Exception as e:
|
|
|
save_to_txt(f"段落{i}处理失败: {str(e)}", output_file)
|
|
|
|
|
|
print(f"处理完成，结果已保存到 {output_file}")
|
|
|
|
|
|
|
|
|
def read_docx(file_path):
|
|
|
"""读取docx文件内容"""
|
|
|
from docx import Document
|
|
|
try:
|
|
|
doc = Document(file_path)
|
|
|
return '\n'.join([para.text for para in doc.paragraphs if para.text.strip()])
|
|
|
except Exception as e:
|
|
|
print(f"读取docx文件出错: {str(e)}")
|
|
|
return ""
|
|
|
|
|
|
def save_to_txt(content, file_path, mode='a'):
|
|
|
"""将内容保存到txt文件"""
|
|
|
try:
|
|
|
with open(file_path, mode, encoding='utf-8') as f:
|
|
|
f.write(content + '\n\n')
|
|
|
except Exception as e:
|
|
|
print(f"保存到txt文件出错: {str(e)}")
|
|
|
|
|
|
# 在process_document方法中调用时，请确保output_file参数是完整的文件路径
|
|
|
|
|
|
|
|
|
def call_deepseek_api(prompt, stream_callback=None):
|
|
|
"""流式调用DeepSeek API"""
|
|
|
try:
|
|
|
response = client.chat.completions.create(
|
|
|
model="deepseek-chat",
|
|
|
messages=[
|
|
|
{"role": "system", "content": "你是一个专业的文档分析助手"},
|
|
|
{"role": "user", "content": prompt}
|
|
|
],
|
|
|
temperature=0.3,
|
|
|
stream=True
|
|
|
)
|
|
|
|
|
|
full_response = ""
|
|
|
for chunk in response:
|
|
|
if chunk.choices[0].delta.content:
|
|
|
content = chunk.choices[0].delta.content
|
|
|
full_response += content
|
|
|
if stream_callback:
|
|
|
stream_callback(content)
|
|
|
|
|
|
return full_response
|
|
|
except Exception as e:
|
|
|
print(f"调用DeepSeek API出错: {str(e)}")
|
|
|
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
input_file = '../Txt/小学数学（史校长）.docx'
|
|
|
output_file ='../Txt/小学数学（史校长）.txt'
|
|
|
process_document(input_file, output_file) |
