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 storesDocArrayInMemorySearchOn this pageDocArrayInMemorySearchDocArrayInMemorySearch is a document index provided by Docarray that stores documents in memory. It is a great starting point for small datasets, where you may not want to launch a database server.This notebook shows how to use functionality related to the DocArrayInMemorySearch.Setupâ€‹Uncomment the below cells to install docarray and get/set your OpenAI api key if you haven't already done so.# !pip install "docarray"# Get an OpenAI token: https://platform.openai.com/account/api-keys# import os# from getpass import getpass# OPENAI_API_KEY = getpass()# os.environ["OPENAI_API_KEY"] = OPENAI_API_KEYUsing DocArrayInMemorySearchâ€‹from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import DocArrayInMemorySearchfrom langchain.document_loaders import TextLoaderdocuments =