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langchain/docs/integrations/sklearn.md
Janos Tolgyesi 5f4552391f
Add SKLearnVectorStore (#5305) 
# Add SKLearnVectorStore

This PR adds SKLearnVectorStore, a simply vector store based on
NearestNeighbors implementations in the scikit-learn package. This
provides a simple drop-in vector store implementation with minimal
dependencies (scikit-learn is typically installed in a data scientist /
ml engineer environment). The vector store can be persisted and loaded
from json, bson and parquet format.

SKLearnVectorStore has soft (dynamic) dependency on the scikit-learn,
numpy and pandas packages. Persisting to bson requires the bson package,
persisting to parquet requires the pyarrow package.

## Before submitting

Integration tests are provided under
`tests/integration_tests/vectorstores/test_sklearn.py`

Sample usage notebook is provided under
`docs/modules/indexes/vectorstores/examples/sklear.ipynb`

Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
2023-05-28 08:17:42 -07:00

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scikit-learn

This page covers how to use the scikit-learn package within LangChain. It is broken into two parts: installation and setup, and then references to specific scikit-learn wrappers.

Installation and Setup

  • Install the Python package with pip install scikit-learn

Wrappers

VectorStore

SKLearnVectorStore provides a simple wrapper around the nearest neighbor implementation in the scikit-learn package, allowing you to use it as a vectorstore.

To import this vectorstore:

from langchain.vectorstores import SKLearnVectorStore

For a more detailed walkthrough of the SKLearnVectorStore wrapper, see this notebook.