58 lines
1.4 KiB
Python
58 lines
1.4 KiB
Python
import logging
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from langchain_core.prompts import FewShotChatMessagePromptTemplate, ChatPromptTemplate
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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.DEBUG,
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format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
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)
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examples = [
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{"input": "1 || 1", "output": "2"},
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{"input": "1 || 2", "output": "3"},
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{"input": "1 || 3", "output": "4"}
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]
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example_prompt = ChatPromptTemplate.from_messages(
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[
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("human", "{input}"),
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("ai", "{output}"),
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]
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)
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few_show_prompt = FewShotChatMessagePromptTemplate(
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examples=examples,
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example_prompt=example_prompt
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)
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final_prompt = ChatPromptTemplate.from_messages(
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[
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("system","你是一个数学天才"),
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few_show_prompt,
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("human", "{input}")
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]
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)
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# question = final_prompt.invoke(input = {"input":"1 || 10"})
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# # llm : 1||10 ?
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# response = llm.invoke(question)
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# print(response)
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# 链式调用
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llm.invoke(final_prompt.invoke(input = {"input":"1 || 10"}))
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# 提示词的invoke输出给到了llm作为输入，和管道的概念一模一样
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chain = final_prompt | llm
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chain.invoke(input = {"input":"1 || 10"}) |