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 storesMongoDB AtlasMongoDB AtlasMongoDB Atlas is a fully-managed cloud database available in AWS , Azure, and GCP.  It now has support for native Vector Search on your MongoDB document data.This notebook shows how to use MongoDB Atlas Vector Search to store your embeddings in MongoDB documents, create a vector search index, and perform KNN search with an approximate nearest neighbor algorithm.It uses the knnBeta Operator available in MongoDB Atlas Search. This feature is in Public Preview and available for evaluation purposes, to validate functionality, and to gather feedback from public preview users. It is not recommended for production deployments as we may introduce breaking changes.To use MongoDB Atlas, you must first deploy a cluster. We have a Forever-Free tier of clusters available.