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 storesTypesenseOn this pageTypesenseTypesense is an open-source, in-memory search engine, that you can either self-host or run on Typesense Cloud.Typesense focuses on performance by storing the entire index in RAM (with a backup on disk) and also focuses on providing an out-of-the-box developer experience by simplifying available options and setting good defaults.It also lets you combine attribute-based filtering together with vector queries, to fetch the most relevant documents.This notebook shows you how to use Typesense as your VectorStore.Let's first install our dependencies:pip install typesense openapi-schema-pydantic openai tiktokenWe want to use OpenAIEmbeddings so we have to get the OpenAI API Key.import osimport getpassos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Typesensefrom langchain.document_loaders import TextLoaderLet's import
Typesense is an open-source, in-memory search engine, that you can either self-host or run on Typesense Cloud.
Typesense is an open-source, in-memory search engine, that you can either self-host or run on Typesense Cloud. ->: 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 storesTypesenseOn this pageTypesenseTypesense is an open-source, in-memory search engine, that you can either self-host or run on Typesense Cloud.Typesense focuses on performance by storing the entire index in RAM (with a backup on disk) and also focuses on providing an out-of-the-box developer experience by simplifying available options and setting good defaults.It also lets you combine attribute-based filtering together with vector queries, to fetch the most relevant documents.This notebook shows you how to use Typesense as your VectorStore.Let's first install our dependencies:pip install typesense openapi-schema-pydantic openai tiktokenWe want to use OpenAIEmbeddings so we have to get the OpenAI API Key.import osimport getpassos.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")from langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Typesensefrom langchain.document_loaders import TextLoaderLet's import