如何透過最大邊際相關性 (MMR) 選擇範例

MaxMarginalRelevanceExampleSelector 根據範例與輸入最相似的組合來選擇 範例，同時也最佳化多樣性。它透過尋找與輸入具有最大餘弦相似度的嵌入範例來做到這一點，然後迭代地新增它們，同時懲罰它們與已選範例的接近程度。

from langchain_community.vectorstores import FAISS
from langchain_core.example_selectors import (
    MaxMarginalRelevanceExampleSelector,
    SemanticSimilarityExampleSelector,
)
from langchain_core.prompts import FewShotPromptTemplate, PromptTemplate
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"},
]

API 參考：FAISS | MaxMarginalRelevanceExampleSelector | SemanticSimilarityExampleSelector | FewShotPromptTemplate | PromptTemplate | OpenAIEmbeddings

example_selector = MaxMarginalRelevanceExampleSelector.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.
    FAISS,
    # The number of examples to produce.
    k=2,
)
mmr_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 as the first one
print(mmr_prompt.format(adjective="worried"))

Give the antonym of every input

Input: happy
Output: sad

Input: windy
Output: calm

Input: worried
Output:

# Let's compare this to what we would just get if we went solely off of similarity,
# by using SemanticSimilarityExampleSelector instead of MaxMarginalRelevanceExampleSelector.
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.
    FAISS,
    # The number of examples to produce.
    k=2,
)
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"],
)
print(similar_prompt.format(adjective="worried"))

Give the antonym of every input

Input: happy
Output: sad

Input: sunny
Output: gloomy

Input: worried
Output: