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# 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>
24 lines
718 B
Markdown
24 lines
718 B
Markdown
# scikit-learn
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This page covers how to use the scikit-learn package within LangChain.
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It is broken into two parts: installation and setup, and then references to specific scikit-learn wrappers.
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## Installation and Setup
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- Install the Python package with `pip install scikit-learn`
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## Wrappers
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### VectorStore
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`SKLearnVectorStore` provides a simple wrapper around the nearest neighbor implementation in the
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scikit-learn package, allowing you to use it as a vectorstore.
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To import this vectorstore:
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```python
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from langchain.vectorstores import SKLearnVectorStore
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```
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For a more detailed walkthrough of the SKLearnVectorStore wrapper, see [this notebook](../modules/indexes/vectorstores/examples/sklearn.ipynb).
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