Skip to main content

如何通过最大边际相关性 (MMR) 选择示例

MaxMarginalRelevanceExampleSelector 根据与输入最相似的示例的组合来选择示例,同时优化多样性。它通过找到与输入具有最大余弦相似度的嵌入示例来实现这一点,然后在迭代添加这些示例的同时,对与已选择示例的接近程度进行惩罚。

<!--IMPORTS:[{"imported": "FAISS", "source": "langchain_community.vectorstores", "docs": "https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.faiss.FAISS.html", "title": "How to select examples by maximal marginal relevance (MMR)"}, {"imported": "MaxMarginalRelevanceExampleSelector", "source": "langchain_core.example_selectors", "docs": "https://python.langchain.com/api_reference/core/example_selectors/langchain_core.example_selectors.semantic_similarity.MaxMarginalRelevanceExampleSelector.html", "title": "How to select examples by maximal marginal relevance (MMR)"}, {"imported": "SemanticSimilarityExampleSelector", "source": "langchain_core.example_selectors", "docs": "https://python.langchain.com/api_reference/core/example_selectors/langchain_core.example_selectors.semantic_similarity.SemanticSimilarityExampleSelector.html", "title": "How to select examples by maximal marginal relevance (MMR)"}, {"imported": "FewShotPromptTemplate", "source": "langchain_core.prompts", "docs": "https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.few_shot.FewShotPromptTemplate.html", "title": "How to select examples by maximal marginal relevance (MMR)"}, {"imported": "PromptTemplate", "source": "langchain_core.prompts", "docs": "https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.prompt.PromptTemplate.html", "title": "How to select examples by maximal marginal relevance (MMR)"}, {"imported": "OpenAIEmbeddings", "source": "langchain_openai", "docs": "https://python.langchain.com/api_reference/openai/embeddings/langchain_openai.embeddings.base.OpenAIEmbeddings.html", "title": "How to select examples by maximal marginal relevance (MMR)"}]-->
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"},
]
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:

Was this page helpful?


You can also leave detailed feedback on GitHub.

扫我,入群扫我,找书