43 lines
1.4 KiB
Python
43 lines
1.4 KiB
Python
import logging
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from langchain_core.messages import SystemMessage, HumanMessage
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from langchain_core.prompts import PromptTemplate
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import langchain
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from langchain_openai import ChatOpenAI
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import os
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import dotenv
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dotenv.load_dotenv()
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## 设置环境变量
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os.environ['OPENAI_API_KEY'] = os.getenv("SILICONFLOW_API_KEY")
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os.environ['OPENAI_BASE_URL'] = os.getenv("SILICONFLOW_BASE_URL")
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# 默认的 'model_name': 'deepseek-ai/DeepSeek-V3.1',
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llm = ChatOpenAI(model="deepseek-ai/DeepSeek-R1-0528-Qwen3-8B")
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
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)
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print(langchain.__version__)
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## prompt
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system_message = SystemMessage(
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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")
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human_message = HumanMessage(content="我现在想要学习hive，你帮我指定一个学习计划把")
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message = [system_message, human_message]
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print(human_message)
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## 1. 创建PromptTemplate
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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")
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## 2. 构建完整的提示词
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hadoop_prompt = template.format(topic="hadoop",topic2="spark")
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hadoop_prompt2 = template.invoke(input={"topic":"hadoop","topic2":"spark"})
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print(hadoop_prompt)
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print(hadoop_prompt2)
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# response = llm.invoke(hadoop_prompt)
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# print(response)
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