import json
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import fitz
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import io
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from docx import Document
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from dashscope import get_tokenizer # dashscope版本 >= 1.14.0
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from app.models import ComplexChatSessionDao
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from app.service.auth import decode_access_token
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async def get_str_token(input_str):
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# 获取tokenizer对象，目前只支持通义千问系列模型
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tokenizer = get_tokenizer('qwen-turbo')
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# 将字符串切分成token并转换为token id
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tokens = tokenizer.encode(input_str)
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# print(f"经过切分后的token id为：{tokens}。")
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# # 经过切分后的token id为： [31935, 64559, 99320, 56007, 100629, 104795, 99788, 1773]
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# print(f"经过切分后共有{len(tokens)}个token")
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# # 经过切分后共有8个token
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#
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# # 将token id转化为字符串并打印出来
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# for i in range(len(tokens)):
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# print(f"token id为{tokens[i]}对应的字符串为：{tokenizer.decode(tokens[i])}")
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return len(tokens)
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async def read_pdf(pdf_stream):
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text = ""
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with fitz.open(stream=pdf_stream, filetype="pdf") as pdf_document:
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for page in pdf_document:
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text += page.get_text()
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return text
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async def read_word(word_stream):
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# 使用 python-docx 打开 Word 文件流
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doc = Document(io.BytesIO(word_stream))
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# 提取每个段落的文本
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text = ""
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for para in doc.paragraphs:
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text += para.text
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return text
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async def read_file(file, filename, content_type):
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text = ""
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if content_type == "application/pdf" or filename.endswith('.pdf'):
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# 提取 PDF 内容
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text = await read_pdf(file)
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elif content_type == "application/vnd.openxmlformats-officedocument.wordprocessingml.document" or filename.endswith(
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'.docx'):
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text = await read_word(file)
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return await get_str_token(text)
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async def service_chat_message(db, message_id: str):
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message = await ComplexChatSessionDao(db).get_session_by_id(message_id)
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content = ""
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title = ""
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if message:
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content = message.content
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title= json.loads(message.query).get("query")
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return title, content
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async def generate_word_document(title, content):
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doc = Document()
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# 添加标题
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doc.add_heading(title, level=1)
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# 将内容按段落分割并写入文档
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for paragraph in content.split('\n'):
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# print("--------------:", paragraph)
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doc.add_paragraph(paragraph)
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return doc
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