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>

docs/integrations/sklearn.md

@@ -0,0 +1,23 @@
+# 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:
+
+```python
+from langchain.vectorstores import SKLearnVectorStore
+```
+
+For a more detailed walkthrough of the SKLearnVectorStore wrapper, see [this notebook](../modules/indexes/vectorstores/examples/sklearn.ipynb).