53 lines
1.5 KiB
Python
53 lines
1.5 KiB
Python
import re
|
||
|
||
from langchain_core.output_parsers import StrOutputParser
|
||
from langchain_core.prompts import ChatPromptTemplate
|
||
from langchain_openai import ChatOpenAI
|
||
from loguru import logger
|
||
|
||
from config.config import load_config
|
||
|
||
config = load_config("config.toml")
|
||
|
||
|
||
def generate_comment(name, age_group, traits,sex):
|
||
"""
|
||
生成评语
|
||
:param name: 学生姓名
|
||
:param age_group: 所在班级
|
||
:param traits: 表现特征
|
||
:param sex: 性别
|
||
:return: 评语
|
||
"""
|
||
|
||
ai_config = config["ai"]
|
||
llm = ChatOpenAI(
|
||
base_url=ai_config["api_url"],
|
||
api_key=ai_config["api_key"],
|
||
model=ai_config["model"],
|
||
temperature=0.7,
|
||
)
|
||
# 2. 构建 Prompt Template
|
||
prompt = ChatPromptTemplate.from_messages([
|
||
("system", ai_config["prompt"]),
|
||
("human", "学生姓名：{name}\n所在班级：{age_group}\n性别：{sex}\n表现特征：{traits}\n\n请开始撰写评语：")
|
||
])
|
||
|
||
# 3. 组装链 (Prompt -> Model -> OutputParser)
|
||
chain = prompt | llm | StrOutputParser()
|
||
|
||
# 4. 执行
|
||
try:
|
||
comment = chain.invoke({
|
||
"name": name,
|
||
"age_group": age_group,
|
||
"traits": traits,
|
||
"sex": sex
|
||
})
|
||
cleaned_text = re.sub(r'\s+', '', comment)
|
||
logger.success(f"学生：{name} =>生成评语成功: {cleaned_text}")
|
||
return cleaned_text
|
||
except Exception as e:
|
||
logger.error(f"生成评语失败: {e}")
|
||
return "生成失败，请检查网络或Key。"
|