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 retrieverPineconeOn this pagePineconePinecone is a vector database with broad functionality.In the walkthrough, we'll demo the SelfQueryRetriever with a Pinecone vector store.Creating a Pinecone index‚ÄãFirst we'll want to create a Pinecone vector store and seed it with some data. We've created a small demo set of documents that contain summaries of movies.To use Pinecone, you have to have pinecone package installed and you must have an API key and an environment. Here are the installation instructions.Note: The self-query retriever requires you to have lark package installed.# !pip install lark#!pip install pinecone-clientimport osimport pineconepinecone.init(    api_key=os.environ["PINECONE_API_KEY"], environment=os.environ["PINECONE_ENV"])    /Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/pinecone/index.py:4: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)      from tqdm.autonotebook import tqdmfrom langchain.schema import Documentfrom langchain.embeddings.openai
Pinecone is a vector database with broad functionality.
Pinecone is a vector database with broad functionality. ->: 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 retrieverPineconeOn this pagePineconePinecone is a vector database with broad functionality.In the walkthrough, we'll demo the SelfQueryRetriever with a Pinecone vector store.Creating a Pinecone index‚ÄãFirst we'll want to create a Pinecone vector store and seed it with some data. We've created a small demo set of documents that contain summaries of movies.To use Pinecone, you have to have pinecone package installed and you must have an API key and an environment. Here are the installation instructions.Note: The self-query retriever requires you to have lark package installed.# !pip install lark#!pip install pinecone-clientimport osimport pineconepinecone.init(    api_key=os.environ["PINECONE_API_KEY"], environment=os.environ["PINECONE_ENV"])    /Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/pinecone/index.py:4: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)      from tqdm.autonotebook import tqdmfrom langchain.schema import Documentfrom langchain.embeddings.openai