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 storesRocksetOn this pageRocksetRockset is a real-time analytics database service for serving low latency, high concurrency analytical queries at scale. It builds a Converged Indexâ„¢ on structured and semi-structured data with an efficient store for vector embeddings. Its support for running SQL on schemaless data makes it a perfect choice for running vector search with metadata filters. This notebook demonstrates how to use Rockset as a vectorstore in langchain. To get started, make sure you have a Rockset account and an API key available.Setting up environmentâ€‹Make sure you have Rockset account and go to the web console to get the API key. Details can be found on the website. For the purpose of this notebook, we will assume you're using Rockset from Oregon(us-west-2).Now you will need to create a Rockset collection to write to, use the Rockset web console to do this. For the purpose of this exercise, we will create a collection called langchain_demo. Since Rockset supports schemaless