NanoPQ (乘积量化)

pip install -qU langchain-community langchain-openai nanopq

from langchain_community.embeddings.spacy_embeddings import SpacyEmbeddings
from langchain_community.retrievers import NanoPQRetriever

retriever = NanoPQRetriever.from_texts(
    ["Great world", "great words", "world", "planets of the world"],
    SpacyEmbeddings(model_name="en_core_web_sm"),
    clusters=2,
    subspace=2,
)

retriever.invoke("earth")

M: 2, Ks: 2, metric : <class 'numpy.uint8'>, code_dtype: l2
iter: 20, seed: 123
Training the subspace: 0 / 2
Training the subspace: 1 / 2
Encoding the subspace: 0 / 2
Encoding the subspace: 1 / 2

[Document(page_content='world'),
 Document(page_content='Great world'),
 Document(page_content='great words'),
 Document(page_content='planets of the world')]