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 storesEpsillaEpsillaEpsilla is an open-source vector database that leverages the advanced parallel graph traversal techniques for vector indexing. Epsilla is licensed under GPL-3.0.This notebook shows how to use the functionalities related to the Epsilla vector database.As a prerequisite, you need to have a running Epsilla vector database (for example, through our docker image), and install the pyepsilla package. View full docs at docs.pip/pip3 install pyepsillaWe 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:")OpenAI API Key: ¬∑¬∑¬∑¬∑¬∑¬∑¬∑¬∑from langchain.embeddings import OpenAIEmbeddingsfrom langchain.vectorstores import Epsillafrom langchain.document_loaders import TextLoaderfrom langchain.text_splitter import CharacterTextSplitterloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()documents = CharacterTextSplitter(chunk_size=1000,
Epsilla is an open-source vector database that leverages the advanced parallel graph traversal techniques for vector indexing. Epsilla is licensed under GPL-3.0.
Epsilla is an open-source vector database that leverages the advanced parallel graph traversal techniques for vector indexing. Epsilla is licensed under GPL-3.0. ->: 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 storesEpsillaEpsillaEpsilla is an open-source vector database that leverages the advanced parallel graph traversal techniques for vector indexing. Epsilla is licensed under GPL-3.0.This notebook shows how to use the functionalities related to the Epsilla vector database.As a prerequisite, you need to have a running Epsilla vector database (for example, through our docker image), and install the pyepsilla package. View full docs at docs.pip/pip3 install pyepsillaWe 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:")OpenAI API Key: ¬∑¬∑¬∑¬∑¬∑¬∑¬∑¬∑from langchain.embeddings import OpenAIEmbeddingsfrom langchain.vectorstores import Epsillafrom langchain.document_loaders import TextLoaderfrom langchain.text_splitter import CharacterTextSplitterloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()documents = CharacterTextSplitter(chunk_size=1000,