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如何使用示例选择器

如果您有大量示例,您可能需要选择哪些示例包含在提示中。示例选择器是负责执行此操作的类。

基本接口定义如下:

class BaseExampleSelector(ABC):
"""Interface for selecting examples to include in prompts."""

@abstractmethod
def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
"""Select which examples to use based on the inputs."""

@abstractmethod
def add_example(self, example: Dict[str, str]) -> Any:
"""Add new example to store."""

它需要定义的唯一方法是 select_examples 方法。该方法接受输入变量,然后返回一个示例列表。如何选择这些示例由每个具体实现决定。

LangChain 有几种不同类型的示例选择器。有关所有这些类型的概述,请参见下表。

在本指南中,我们将介绍如何创建自定义示例选择器。

示例

为了使用示例选择器,我们需要创建一个示例列表。这些通常应该是示例输入和输出。为了演示的目的,让我们想象一下我们正在选择如何将英语翻译成意大利语的示例。

examples = [
{"input": "hi", "output": "ciao"},
{"input": "bye", "output": "arrivederci"},
{"input": "soccer", "output": "calcio"},
]

自定义示例选择器

让我们编写一个示例选择器,根据单词的长度选择要挑选的示例。

<!--IMPORTS:[{"imported": "BaseExampleSelector", "source": "langchain_core.example_selectors.base", "docs": "https://python.langchain.com/api_reference/core/example_selectors/langchain_core.example_selectors.base.BaseExampleSelector.html", "title": "How to use example selectors"}]-->
from langchain_core.example_selectors.base import BaseExampleSelector


class CustomExampleSelector(BaseExampleSelector):
def __init__(self, examples):
self.examples = examples

def add_example(self, example):
self.examples.append(example)

def select_examples(self, input_variables):
# This assumes knowledge that part of the input will be a 'text' key
new_word = input_variables["input"]
new_word_length = len(new_word)

# Initialize variables to store the best match and its length difference
best_match = None
smallest_diff = float("inf")

# Iterate through each example
for example in self.examples:
# Calculate the length difference with the first word of the example
current_diff = abs(len(example["input"]) - new_word_length)

# Update the best match if the current one is closer in length
if current_diff < smallest_diff:
smallest_diff = current_diff
best_match = example

return [best_match]
example_selector = CustomExampleSelector(examples)
example_selector.select_examples({"input": "okay"})
[{'input': 'bye', 'output': 'arrivederci'}]
example_selector.add_example({"input": "hand", "output": "mano"})
example_selector.select_examples({"input": "okay"})
[{'input': 'hand', 'output': 'mano'}]

在提示中使用

我们现在可以在提示中使用这个示例选择器

<!--IMPORTS:[{"imported": "FewShotPromptTemplate", "source": "langchain_core.prompts.few_shot", "docs": "https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.few_shot.FewShotPromptTemplate.html", "title": "How to use example selectors"}, {"imported": "PromptTemplate", "source": "langchain_core.prompts.prompt", "docs": "https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.prompt.PromptTemplate.html", "title": "How to use example selectors"}]-->
from langchain_core.prompts.few_shot import FewShotPromptTemplate
from langchain_core.prompts.prompt import PromptTemplate

example_prompt = PromptTemplate.from_template("Input: {input} -> Output: {output}")
prompt = FewShotPromptTemplate(
example_selector=example_selector,
example_prompt=example_prompt,
suffix="Input: {input} -> Output:",
prefix="Translate the following words from English to Italian:",
input_variables=["input"],
)

print(prompt.format(input="word"))
Translate the following words from English to Italian:

Input: hand -> Output: mano

Input: word -> Output:

示例选择器类型

名称描述
相似性使用输入和示例之间的语义相似性来决定选择哪些示例。
MMR使用输入和示例之间的最大边际相关性来决定选择哪些示例。
长度根据可以适应特定长度的示例数量来选择示例。
Ngram使用输入和示例之间的n-gram重叠来决定选择哪些示例。

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