Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesRocksetOn this pageRocksetRockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index‚Ñ¢ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and  handles real-time ingestion for constantly updating, streaming data.This notebook demonstrates how to use¬†Rockset as a vector store in LangChain. Before getting started, make sure you have access to a Rockset account and an API key available. Start your free trial today.Setting Up Your Environment‚ÄãLeverage the Rockset console to create a collection with the Write API as your source. In this walkthrough, we create a collection named¬†langchain_demo. Configure the following ingest transformation to mark your embeddings¬†field and take advantage of performance and storage optimizations:   (We used OpenAI text-embedding-ada-002 for this examples, where #length_of_vector_embedding = 1536)SELECT _input.*
Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index‚Ñ¢ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and  handles real-time ingestion for constantly updating, streaming data.
Rockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index‚Ñ¢ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and  handles real-time ingestion for constantly updating, streaming data. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesRocksetOn this pageRocksetRockset is a real-time search and analytics database built for the cloud. Rockset uses a Converged Index‚Ñ¢ with an efficient store for vector embeddings to serve low latency, high concurrency search queries at scale. Rockset has full support for metadata filtering and  handles real-time ingestion for constantly updating, streaming data.This notebook demonstrates how to use¬†Rockset as a vector store in LangChain. Before getting started, make sure you have access to a Rockset account and an API key available. Start your free trial today.Setting Up Your Environment‚ÄãLeverage the Rockset console to create a collection with the Write API as your source. In this walkthrough, we create a collection named¬†langchain_demo. Configure the following ingest transformation to mark your embeddings¬†field and take advantage of performance and storage optimizations:   (We used OpenAI text-embedding-ada-002 for this examples, where #length_of_vector_embedding = 1536)SELECT _input.*