NanoPQ (Product Quantization)

Product Quantization algorithm (k-NN) in brief is a quantization algorithm that helps in compression of database vectors which helps in semantic search when large datasets are involved. In a nutshell, the embedding is split into M subspaces which further goes through clustering. Upon clustering the vectors the centroid vector gets mapped to the vectors present in the each of the clusters of the subspace.

This notebook goes over how to use a retriever that under the hood uses a Product Quantization which has been implemented by the nanopq package.

%pip install -qU langchain-community langchain-openai nanopq

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

API Reference:SpacyEmbeddings | NanoPQRetriever

Create New Retriever with Texts

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

Use Retriever

We can now use the retriever!

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')]

Related

• Retriever conceptual guide
• Retriever how-to guides