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 storesChromaOn this pageChromaChroma is a AI-native open-source vector database focused on developer productivity and happiness. Chroma is licensed under Apache 2.0.Install Chroma with:pip install chromadbChroma runs in various modes. See below for examples of each integrated with LangChain.in-memory - in a python script or jupyter notebookin-memory with persistance - in a script or notebook and save/load to diskin a docker container - as a server running your local machine or in the cloudLike any other database, you can: .add .get .update.upsert.delete.peekand .query runs the similarity search.View full docs at docs. To access these methods directly, you can do ._collection.method()Basic Example‚ÄãIn this basic example, we take the most recent State of the Union Address, split it into chunks, embed it using an open-source embedding model, load it into Chroma, and then query it.# importfrom langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddingsfrom langchain.text_splitter import
Chroma is a AI-native open-source vector database focused on developer productivity and happiness. Chroma is licensed under Apache 2.0.
Chroma is a AI-native open-source vector database focused on developer productivity and happiness. Chroma is licensed under Apache 2.0. ->: 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 storesChromaOn this pageChromaChroma is a AI-native open-source vector database focused on developer productivity and happiness. Chroma is licensed under Apache 2.0.Install Chroma with:pip install chromadbChroma runs in various modes. See below for examples of each integrated with LangChain.in-memory - in a python script or jupyter notebookin-memory with persistance - in a script or notebook and save/load to diskin a docker container - as a server running your local machine or in the cloudLike any other database, you can: .add .get .update.upsert.delete.peekand .query runs the similarity search.View full docs at docs. To access these methods directly, you can do ._collection.method()Basic Example‚ÄãIn this basic example, we take the most recent State of the Union Address, split it into chunks, embed it using an open-source embedding model, load it into Chroma, and then query it.# importfrom langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddingsfrom langchain.text_splitter import