97 lines
4.6 KiB
Python
97 lines
4.6 KiB
Python
from fastapi import Body
|
||
from configs import LLM_MODELS, TEMPERATURE, MAX_TOKENS
|
||
from server.chat.policy_fun_iast import get_llm_model_response
|
||
from typing import Optional
|
||
from langchain.chains import LLMChain
|
||
from langchain.prompts import ChatPromptTemplate
|
||
from server.chat.utils import History
|
||
from server.utils import wrap_done, get_ChatOpenAI, get_prompt_template
|
||
from langchain.callbacks import AsyncIteratorCallbackHandler
|
||
import asyncio
|
||
from server.knowledge_base.kb_service.base import TextRank
|
||
from configs.basic_config import *
|
||
|
||
async def abb_rewrite(
|
||
# context: Optional[str] = Body(..., description="当前已撰写的全文", examples=[""]),
|
||
query: str = Body(..., description="用户框选的段落", examples=[""]),
|
||
# previous_text: Optional[str] = Body(..., description="用户框选段落的前文", examples=[""]),
|
||
# following_text: Optional[str] = Body(..., description="用户框选段落的后文", examples=[""]),
|
||
# con_direction: Optional[str] = Body("", description="用户输入的缩写指令", examples=[""]),
|
||
# stream: bool = Body(False, description="是否流式输出", examples=[False]),
|
||
temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=2.0),
|
||
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"),
|
||
# prompt_name: Optional[str] = Body("abb_rewrite"),
|
||
):
|
||
logger.info(f"开始缩写...")
|
||
|
||
# 定义生成摘要的函数
|
||
# def generate_summary(text: str) -> str:
|
||
# """使用 TextRank 生成文本摘要"""
|
||
# if len(text) <= 20000:
|
||
# summary = TextRank(text, num_sentences=60) # 生成60句话的摘要
|
||
# else:
|
||
# summary = TextRank(text, num_sentences=80) # 生成80句话的摘要
|
||
# return summary
|
||
|
||
# # 根据上下文长度决定是否生成摘要
|
||
# if len(context) >= 15000:
|
||
# context_summary = generate_summary(context)
|
||
# logger.info(f"生成撰写文稿的摘要: %s", context_summary)
|
||
# else:
|
||
# context_summary = context # 直接使用原文
|
||
# logger.info(f"撰写文稿小于15000字符，使用原文")
|
||
|
||
# 调用模型生成缩写内容
|
||
try:
|
||
abb_rewrite_content = get_llm_model_response(
|
||
strategy_name="abb_rewrite",
|
||
llm_model_name=LLM_MODELS[0],
|
||
template_prompt_name="abb_rewrite",
|
||
prompt_param_dict={
|
||
# "context": context_summary, # 使用摘要或原文
|
||
"paragraph_content": query,
|
||
# "con_direction": con_direction,
|
||
# "previous_text": previous_text,
|
||
# "following_text": following_text
|
||
},
|
||
temperature=TEMPERATURE,
|
||
max_tokens=MAX_TOKENS
|
||
)
|
||
# logger.info("生成的缩写内容: %s", abb_rewrite_content)
|
||
except Exception as e:
|
||
logger.error("生成缩写内容时出错: %s", e)
|
||
return (f"出错了。。请重试。。")
|
||
|
||
# # 如果 previous_text 和 following_text 存在空值，直接返回 abb_rewrite_content
|
||
# if not previous_text or not following_text:
|
||
# logger.info("上文或下文为空，直接返回生成的内容。。")
|
||
# final_content = abb_rewrite_content
|
||
# else:
|
||
# # 定义内容检查函数
|
||
# def abb_rewrite_check(abb_rewrite_content: str) -> str:
|
||
# logger.info("对文章缩写内容进行行文检查。。")
|
||
# try:
|
||
# abb_rewrite_check_content = get_llm_model_response(
|
||
# strategy_name="abb_rewrite_check",
|
||
# llm_model_name=LLM_MODELS[0],
|
||
# template_prompt_name="abb_rewrite_check",
|
||
# prompt_param_dict={
|
||
# "previous_text": previous_text,
|
||
# "following_text": following_text,
|
||
# "abb_rewrite_content": abb_rewrite_content, # 使用生成的 rewrite_content
|
||
# },
|
||
# temperature=temperature,
|
||
# max_tokens=max_tokens
|
||
# )
|
||
# logger.info("检查后的缩写内容: %s", abb_rewrite_check_content)
|
||
# return abb_rewrite_check_content
|
||
# except Exception as e:
|
||
# logger.error("检查缩写内容时出错: %s", e)
|
||
# return (f"出错了。。请重试。。")
|
||
|
||
# # 调用内容检查函数
|
||
# final_content = abb_rewrite_check(abb_rewrite_content)
|
||
|
||
# 返回最终生成的字符串
|
||
return abb_rewrite_content
|