97 lines
4.6 KiB
Python
97 lines
4.6 KiB
Python
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from fastapi import Body
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from configs import LLM_MODELS, TEMPERATURE, MAX_TOKENS
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from server.chat.policy_fun_iast import get_llm_model_response
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from typing import Optional
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from langchain.chains import LLMChain
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from langchain.prompts import ChatPromptTemplate
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from server.chat.utils import History
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from server.utils import wrap_done, get_ChatOpenAI, get_prompt_template
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from langchain.callbacks import AsyncIteratorCallbackHandler
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import asyncio
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from server.knowledge_base.kb_service.base import TextRank
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from configs.basic_config import *
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async def abb_rewrite(
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# context: Optional[str] = Body(..., description="当前已撰写的全文", examples=[""]),
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query: str = Body(..., description="用户框选的段落", examples=[""]),
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# previous_text: Optional[str] = Body(..., description="用户框选段落的前文", examples=[""]),
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# following_text: Optional[str] = Body(..., description="用户框选段落的后文", examples=[""]),
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# con_direction: Optional[str] = Body("", description="用户输入的缩写指令", examples=[""]),
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# stream: bool = Body(False, description="是否流式输出", examples=[False]),
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temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=2.0),
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max_tokens: Optional[int] = Body(512, description="限制LLM生成Token数量，默认None代表模型最大值"),
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# prompt_name: Optional[str] = Body("abb_rewrite"),
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):
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logger.info(f"开始缩写...")
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# 定义生成摘要的函数
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# def generate_summary(text: str) -> str:
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# """使用 TextRank 生成文本摘要"""
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# if len(text) <= 20000:
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# summary = TextRank(text, num_sentences=60) # 生成60句话的摘要
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# else:
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# summary = TextRank(text, num_sentences=80) # 生成80句话的摘要
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# return summary
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# # 根据上下文长度决定是否生成摘要
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# if len(context) >= 15000:
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# context_summary = generate_summary(context)
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# logger.info(f"生成撰写文稿的摘要: %s", context_summary)
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# else:
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# context_summary = context # 直接使用原文
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# logger.info(f"撰写文稿小于15000字符，使用原文")
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# 调用模型生成缩写内容
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try:
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abb_rewrite_content = get_llm_model_response(
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strategy_name="abb_rewrite",
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llm_model_name=LLM_MODELS[0],
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template_prompt_name="abb_rewrite",
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prompt_param_dict={
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# "context": context_summary, # 使用摘要或原文
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"paragraph_content": query,
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# "con_direction": con_direction,
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# "previous_text": previous_text,
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# "following_text": following_text
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},
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temperature=TEMPERATURE,
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max_tokens=MAX_TOKENS
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)
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# logger.info("生成的缩写内容: %s", abb_rewrite_content)
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except Exception as e:
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logger.error("生成缩写内容时出错: %s", e)
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return (f"出错了。。请重试。。")
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# # 如果 previous_text 和 following_text 存在空值，直接返回 abb_rewrite_content
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# if not previous_text or not following_text:
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# logger.info("上文或下文为空，直接返回生成的内容。。")
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# final_content = abb_rewrite_content
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# else:
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# # 定义内容检查函数
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# def abb_rewrite_check(abb_rewrite_content: str) -> str:
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# logger.info("对文章缩写内容进行行文检查。。")
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# try:
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# abb_rewrite_check_content = get_llm_model_response(
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# strategy_name="abb_rewrite_check",
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# llm_model_name=LLM_MODELS[0],
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# template_prompt_name="abb_rewrite_check",
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# prompt_param_dict={
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# "previous_text": previous_text,
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# "following_text": following_text,
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# "abb_rewrite_content": abb_rewrite_content, # 使用生成的 rewrite_content
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# },
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# temperature=temperature,
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# max_tokens=max_tokens
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# )
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# logger.info("检查后的缩写内容: %s", abb_rewrite_check_content)
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# return abb_rewrite_check_content
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# except Exception as e:
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# logger.error("检查缩写内容时出错: %s", e)
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# return (f"出错了。。请重试。。")
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# # 调用内容检查函数
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# final_content = abb_rewrite_check(abb_rewrite_content)
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# 返回最终生成的字符串
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return abb_rewrite_content
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