Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversAmazon KendraArcee RetrieverArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArrayElasticSearch BM25Google Cloud Enterprise SearchGoogle DriveGoogle Vertex AI SearchKay.aikNNLOTR (Merger Retriever)MetalPinecone Hybrid SearchPubMedRePhraseQuerySEC filingSelf-querying retrieverDeep LakeChromaDashVectorElasticsearchMilvusMyScaleOpenSearchPineconeQdrantRedisSupabaseTimescale Vector (Postgres) self-queryingVectaraWeaviateSVMTavily Search APITF-IDFVespaWeaviate Hybrid SearchWikipediayou-retrieverZepToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsRetrieversSelf-querying retrieverChromaOn this pageChromaChroma is a database for building AI applications with embeddings.In the notebook, we'll demo the SelfQueryRetriever wrapped around a Chroma vector store. Creating a Chroma vector store‚ÄãFirst we'll want to create a Chroma vector store and seed it with some data. We've created a small demo set of documents that contain summaries of movies.Note: The self-query retriever requires you to have lark installed (pip install lark). We also need the chromadb package.#!pip install lark#!pip install chromadbWe 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.schema import Documentfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import Chromaembeddings = OpenAIEmbeddings()docs = [    Document(        page_content="A bunch of scientists bring back dinosaurs and mayhem breaks loose",        metadata={"year": 1993, "rating": 7.7, "genre": "science fiction"},    ),    Document(
Chroma is a database for building AI applications with embeddings.
Chroma is a database for building AI applications with embeddings. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversAmazon KendraArcee RetrieverArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArrayElasticSearch BM25Google Cloud Enterprise SearchGoogle DriveGoogle Vertex AI SearchKay.aikNNLOTR (Merger Retriever)MetalPinecone Hybrid SearchPubMedRePhraseQuerySEC filingSelf-querying retrieverDeep LakeChromaDashVectorElasticsearchMilvusMyScaleOpenSearchPineconeQdrantRedisSupabaseTimescale Vector (Postgres) self-queryingVectaraWeaviateSVMTavily Search APITF-IDFVespaWeaviate Hybrid SearchWikipediayou-retrieverZepToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsRetrieversSelf-querying retrieverChromaOn this pageChromaChroma is a database for building AI applications with embeddings.In the notebook, we'll demo the SelfQueryRetriever wrapped around a Chroma vector store. Creating a Chroma vector store‚ÄãFirst we'll want to create a Chroma vector store and seed it with some data. We've created a small demo set of documents that contain summaries of movies.Note: The self-query retriever requires you to have lark installed (pip install lark). We also need the chromadb package.#!pip install lark#!pip install chromadbWe 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.schema import Documentfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import Chromaembeddings = OpenAIEmbeddings()docs = [    Document(        page_content="A bunch of scientists bring back dinosaurs and mayhem breaks loose",        metadata={"year": 1993, "rating": 7.7, "genre": "science fiction"},    ),    Document(