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 retrieverQdrantOn this pageQdrantQdrant (read: quadrant) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support.In the notebook, we'll demo the SelfQueryRetriever wrapped around a Qdrant vector store. Creating a Qdrant vector store‚ÄãFirst we'll want to create a Qdrant 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 qdrant-client package.#!pip install lark qdrant-clientWe want to use OpenAIEmbeddings so we have to get the OpenAI API Key.# import os# import getpass# os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')from langchain.schema import Documentfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import Qdrantembeddings = OpenAIEmbeddings()docs = [    Document(        page_content="A bunch of scientists bring back
Qdrant (read: quadrant) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support.
Qdrant (read: quadrant) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. ->: 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 retrieverQdrantOn this pageQdrantQdrant (read: quadrant) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support.In the notebook, we'll demo the SelfQueryRetriever wrapped around a Qdrant vector store. Creating a Qdrant vector store‚ÄãFirst we'll want to create a Qdrant 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 qdrant-client package.#!pip install lark qdrant-clientWe want to use OpenAIEmbeddings so we have to get the OpenAI API Key.# import os# import getpass# os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')from langchain.schema import Documentfrom langchain.embeddings.openai import OpenAIEmbeddingsfrom langchain.vectorstores import Qdrantembeddings = OpenAIEmbeddings()docs = [    Document(        page_content="A bunch of scientists bring back