text: Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cognitive SearchCassandraChromaClarifaiClickHouse Vector SearchActiveloop's Deep LakeDocArrayHnswSearchDocArrayInMemorySearchElasticSearchFAISSHologresLanceDBMarqoMatchingEngineMilvusMongoDB AtlasMyScaleOpenSearchpg_embeddingPGVectorPineconeQdrantRedisRocksetSingleStoreDBscikit-learnStarRocksSupabase (Postgres)TairTigrisTypesenseVectaraWeaviateZillizGrouped by providerIntegrationsVector storesscikit-learnOn this pagescikit-learnscikit-learn is an open source collection of machine learning algorithms, including some implementations of the k nearest neighbors. SKLearnVectorStore wraps this implementation and adds the possibility to persist the vector store in json, bson (binary json) or Apache Parquet format.This notebook shows how to use the SKLearnVectorStore vector database.# # if you plan to use bson serialization, install also:# %pip install bson# # if you plan to use parquet serialization, install also:%pip install pandas pyarrowTo use OpenAI embeddings, you will need an OpenAI key. You can get one at https://platform.openai.com/account/api-keys or feel free to use any other embeddings.import osfrom getpass import getpassos.environ["OPENAI_API_KEY"] = getpass("Enter your OpenAI key:")Basic usageâ€‹Load a sample document corpusâ€‹from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import