import logging

from langchain_core.messages import SystemMessage, HumanMessage
from langchain_core.prompts import PromptTemplate
import langchain

from langchain_openai import ChatOpenAI
import os
import dotenv

dotenv.load_dotenv()

## 设置环境变量
os.environ['OPENAI_API_KEY'] = os.getenv("SILICONFLOW_API_KEY")
os.environ['OPENAI_BASE_URL'] = os.getenv("SILICONFLOW_BASE_URL")

# 默认的 'model_name': 'deepseek-ai/DeepSeek-V3.1',
llm = ChatOpenAI(model="deepseek-ai/DeepSeek-R1-0528-Qwen3-8B")

logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)
print(langchain.__version__)

## prompt
system_message = SystemMessage(
    content="你是一个大数据方向的专家，用户提问时，你只需要精简的回答问题，回答内容不超过100个token")
human_message = HumanMessage(content="我现在想要学习hive，你帮我指定一个学习计划把")
message = [system_message, human_message]

print(human_message)

## 1. 创建PromptTemplate
template = PromptTemplate.from_template(template="我现在想要学习{topic}和{topic2}，你帮我指定一个学习计划把")
## 2. 构建完整的提示词
hadoop_prompt = template.format(topic="hadoop",topic2="spark")
hadoop_prompt2 = template.invoke(input={"topic":"hadoop","topic2":"spark"})
print(hadoop_prompt)
print(hadoop_prompt2)
# response = llm.invoke(hadoop_prompt)
# print(response)