使用few-shot Prompt template让大模型更懂你

这里写几个例子，实现了：长度示例选择器、相关度示例选择器、相似性示例选择器三种：

创建一个使用少量示例的提示模板(Prompt template)。少量示例的提示模板可以从一组示例(examples)或一个示例选择器( Exampleselector)对象构建

一.使用相似性示例选择器，选择小样本示例内容：

本事例实现了生成单词反义词的功能   

直接记录代码：

from langchain_community.vectorstores import Chroma
from langchain_core.example_selectors import SemanticSimilarityExampleSelector
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder, SystemMessagePromptTemplate, \
    HumanMessagePromptTemplate, PromptTemplate, FewShotPromptTemplate
from langchain_huggingface import HuggingFaceEmbeddings
from Ai.llmqianfan import createLLM


llm = createLLM()

examples = [
    {"question": "大", "answer": "小"},
    {"question": "黑", "answer": "白"},
    {"question":"高","answer":"矮"},
    {"question":"胖","answer":"瘦"},
    {"question":"高兴","answer":"伤心"},
    {"question":"漂亮","answer":"丑陋"}
]
template="""
单词:{question}
反义词:{answer}
"""
prompt=PromptTemplate(input_variables=["question","answer"],template=template)

embedding=HuggingFaceEmbeddings(model_name='../models/BAAI_bge-small-zh-v1.5')

example_selector = SemanticSimilarityExampleSelector.from_examples(
    # 传入示例组
    examples,
    # 使用阿里云的dashscope的嵌入来做相似性搜索
    embedding,
    # 设置使用的向量数据库是什么
    Chroma,  # FAISS,
    # 结果条数
    k=1,
)

few_shot_prompt=FewShotPromptTemplate(
    #examples=examples,
    example_selector=example_selector,#使用样本选择器使用更接近的样本
    example_prompt=prompt,
    prefix="请参考下面的事例,按事例格式,给出用户输入的单词的反义词:<example>",
    suffix="</example>\\n单词:{input}\\n反义词:",
    input_variables=["input"],
    example_separator="\\n"

)
p=few_shot_prompt.format(input="black")
# print(res)

res=llm.invoke(p)

print(res.content)

运行结果：

单词:black
反义词:white

进程已结束，退出代码为 0

代码中使用上节创建的千帆大模型接口

二.使用长度示例选择器实现：

from qifan_ai import CreateMyLLM
from langchain.prompts import PromptTemplate, FewShotPromptTemplate
from langchain.prompts.example_selector import LengthBasedExampleSelector
from langchain.chains import LLMChain


example = [
    {"input": "白色", "output": "黑色"},
    {"input": "高兴", "output": "悲伤"},
    {"input": "高", "output": "矮"},
    {"input": "上", "output": "下"},
    {"input": "快", "output": "慢"},
    {"input": "开始", "output": "结束"},
    {"input": "真实", "output": "虚假"},
    {"input": "兴高采烈", "output": "垂头丧气"},
    {"input": "言犹未尽", "output": "义不容辞"}
]

prompt = PromptTemplate(
    input_variables=["input", "output"],
    template="原词：{input}\n反义词：{output}"
)

#长度示例选择器
example_selector = LengthBasedExampleSelector(
    examples=example,
    example_prompt=prompt,
    max_length=30,
    length_function=len
)
few_shot_prompt = FewShotPromptTemplate(
    example_selector=example_selector,
    example_prompt=prompt,
    prefix="给出每个输入词的反义词：\n<example>",
    suffix="</example>\n原词：{my_input}\n反义词：",
    input_variables=["my_input"],
    example_separator="\n"

)


llm = CreateMyLLM()

chain = LLMChain(llm=llm, prompt=few_shot_prompt, verbose=True)
res=chain.run("飞速前进")
print(res)

from langchain_community.embeddings import HuggingFaceEmbeddings

from qifan_ai import CreateMyLLM
from langchain.prompts import PromptTemplate,FewShotPromptTemplate
from langchain.chains import LLMChain
from langchain.prompts.example_selector import MaxMarginalRelevanceExampleSelector,SemanticSimilarityExampleSelector
from langchain_community.vectorstores import Chroma

example = [
    {"input": "白色", "output": "黑色"},
    {"input": "高兴", "output": "悲伤"},
    {"input": "高", "output": "矮"},
    {"input": "上", "output": "下"},
    {"input": "快", "output": "慢"},
    {"input": "开始", "output": "结束"},
    {"input": "真实", "output": "虚假"},
    {"input": "兴高采烈", "output": "垂头丧气"},
    {"input": "言犹未尽", "output": "义不容辞"}
]

prompt = PromptTemplate(
    input_variables=["input", "output"],
    template="原词：{input}\n反义词：{output}"
)

embedding=HuggingFaceEmbeddings(model_name='../models/BAAI_bge-small-zh-v1.5')

#相关度（最大边际）示例选择器
example_selector = MaxMarginalRelevanceExampleSelector.from_examples(examples=example,embeddings=embedding,vectorstore_cls=Chroma,k=2)
#相似性示例（余弦相似度）选择器，与相关度差不多，只要使用这个选择器就会变为相似性选择器
#sim_example_selector = SemanticSimilarityExampleSelector.from_examples(examples=example,embeddings=embedding,vectorstore_cls=Chroma,k=3)
few_shot_prompt = FewShotPromptTemplate(
    example_selector=example_selector,
    example_prompt=prompt,
    prefix="给出每个输入词的反义词：\n<example>",
    suffix="</example>\n原词：{my_input}\n反义词：",
    input_variables=["my_input"],
    example_separator="\n"

)


llm = CreateMyLLM()

chain = LLMChain(llm=llm, prompt=few_shot_prompt, verbose=True)
res=chain.run("十分高兴")
print(res)