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)TypesenseUSearchValdvearchVectaravectorstoresSemaDBVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesvectorstoresSemaDBOn this pageSemaDBSemaDB is a no fuss vector similarity database for building AI applications. The hosted SemaDB Cloud offers a no fuss developer experience to get started.The full documentation of the API along with examples and an interactive playground is available on RapidAPI.This notebook demonstrates how the langchain wrapper can be used with SemaDB Cloud.Load document embeddings‚ÄãTo run things locally, we are using Sentence Transformers which are commonly used for embedding sentences. You can use any embedding model LangChain offers.pip install sentence_transformersfrom langchain.embeddings import HuggingFaceEmbeddingsembeddings = HuggingFaceEmbeddings()from langchain.text_splitter import CharacterTextSplitterfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=400, chunk_overlap=0)docs =
SemaDB is a no fuss vector similarity database for building AI applications. The hosted SemaDB Cloud offers a no fuss developer experience to get started.
SemaDB is a no fuss vector similarity database for building AI applications. The hosted SemaDB Cloud offers a no fuss developer experience to get started. ->: 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)TypesenseUSearchValdvearchVectaravectorstoresSemaDBVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesvectorstoresSemaDBOn this pageSemaDBSemaDB is a no fuss vector similarity database for building AI applications. The hosted SemaDB Cloud offers a no fuss developer experience to get started.The full documentation of the API along with examples and an interactive playground is available on RapidAPI.This notebook demonstrates how the langchain wrapper can be used with SemaDB Cloud.Load document embeddings‚ÄãTo run things locally, we are using Sentence Transformers which are commonly used for embedding sentences. You can use any embedding model LangChain offers.pip install sentence_transformersfrom langchain.embeddings import HuggingFaceEmbeddingsembeddings = HuggingFaceEmbeddings()from langchain.text_splitter import CharacterTextSplitterfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter = CharacterTextSplitter(chunk_size=400, chunk_overlap=0)docs =