MaxMarginalRelevanceExampleSelector 示例选择器

MaxMarginalRelevanceExampleSelector 根据示例与输入的相似度以及多样性进行选择。它通过找到与输入具有最高余弦相似度的示例，并在此基础上进行迭代添加示例，同时对其与已选择示例的相似度进行惩罚。

from langchain.prompts.example_selector import (
    MaxMarginalRelevanceExampleSelector,
    SemanticSimilarityExampleSelector,
)
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings
from langchain.prompts import FewShotPromptTemplate, PromptTemplate

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

# These are a lot of 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 = MaxMarginalRelevanceExampleSelector.from_examples(
    # This is the list of examples available to select from.
    examples,
    # This is the embedding class used to produce embeddings which are used to measure semantic similarity.
    OpenAIEmbeddings(),
    # This is the VectorStore class that is used to store the embeddings and do a similarity search over.
    FAISS,
    # This is 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"))

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

# 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(
    # This is the list of examples available to select from.
    examples,
    # This is the embedding class used to produce embeddings which are used to measure semantic similarity.
    OpenAIEmbeddings(),
    # This is the VectorStore class that is used to store the embeddings and do a similarity search over.
    FAISS,
    # This is 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: