【LangChainのExample selectorsとは？】使い方を解説

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."""

from langchain_core.example_selectors.base import BaseExampleSelector

# Example selectorsを使用するには、例のリストを作成する必要があります。これらは通常、入力と出力の例になります。
examples = [
    {"input": "hi", "output": "ciao"},
    {"input": "bye", "output": "arrivaderci"},
    {"input": "soccer", "output": "calcio"},
]

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': 'arrivaderci'}]

example_selector.add_example({"input": "hand", "output": "mano"})
example_selector.select_examples({"input": "okay"})

[{'input': 'hand', 'output': 'mano'}]

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 Italain:",
    input_variables=["input"],
)

print(prompt.format(input="word"))

from langchain.prompts import FewShotPromptTemplate, PromptTemplate
from langchain.prompts.example_selector import LengthBasedExampleSelector

# Examples of a pretend task of creating antonyms.
examples = [
    {"input": "happy", "output": "sad"},
    {"input": "tall", "output": "short"},
    {"input": "energetic", "output": "lethargic"},
    {"input": "sunny", "output": "gloomy"},
    {"input": "windy", "output": "calm"},
]

example_prompt = PromptTemplate(
    input_variables=["input", "output"],
    template="Input: {input}\nOutput: {output}",
)
example_selector = LengthBasedExampleSelector(
    # The examples it has available to choose from.
    examples=examples,
    # The PromptTemplate being used to format the examples.
    example_prompt=example_prompt,
    # The maximum length that the formatted examples should be.
    # Length is measured by the get_text_length function below.
    max_length=25,
    # The function used to get the length of a string, which is used
    # to determine which examples to include. It is commented out because
    # it is provided as a default value if none is specified.
    # get_text_length: Callable[[str], int] = lambda x: len(re.split("\n| ", x))
)
dynamic_prompt = FewShotPromptTemplate(
    # We provide an ExampleSelector instead of examples.
    example_selector=example_selector,
    example_prompt=example_prompt,
    prefix="Give the antonym of every input",
    suffix="Input: {adjective}\nOutput:",
    input_variables=["adjective"],
)

print(dynamic_prompt.format(adjective="big"))

Give the antonym of every input

Input: happy
Output: sad

Input: tall
Output: short

Input: energetic
Output: lethargic

Input: sunny
Output: gloomy

Input: windy
Output: calm

Input: big
Output:

long_string = "big and huge and massive and large and gigantic and tall and much much much much much bigger than everything else"
print(dynamic_prompt.format(adjective=long_string))

Give the antonym of every input

Input: happy
Output: sad

Input: big and huge and massive and large and gigantic and tall and much much much much much bigger than everything else
Output:

from langchain.prompts import FewShotPromptTemplate, PromptTemplate
from langchain.prompts.example_selector import SemanticSimilarityExampleSelector
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings

example_prompt = PromptTemplate(
    input_variables=["input", "output"],
    template="Input: {input}\nOutput: {output}",
)

# Examples of a pretend task of creating antonyms.
examples = [
    {"input": "happy", "output": "sad"},
    {"input": "tall", "output": "short"},
    {"input": "energetic", "output": "lethargic"},
    {"input": "sunny", "output": "gloomy"},
    {"input": "windy", "output": "calm"},
]

example_selector = SemanticSimilarityExampleSelector.from_examples(
    # The list of examples available to select from.
    examples,
    # The embedding class used to produce embeddings which are used to measure semantic similarity.
    OpenAIEmbeddings(),
    # The VectorStore class that is used to store the embeddings and do a similarity search over.
    Chroma,
    # The number of examples to produce.
    k=1,
)
similar_prompt = FewShotPromptTemplate(
    # We provide an ExampleSelector instead of examples.
    example_selector=example_selector,
    example_prompt=example_prompt,
    prefix="Give the antonym of every input",
    suffix="Input: {adjective}\nOutput:",
    input_variables=["adjective"],
)

# Input is a feeling, so should select the happy/sad example
print(similar_prompt.format(adjective="worried"))

Give the antonym of every input

Input: happy
Output: sad

Input: worried
Output: