text: Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversAmazon KendraArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArray RetrieverElasticSearch BM25Google Cloud Enterprise SearchkNNLOTR (Merger Retriever)MetalPinecone Hybrid SearchPubMedSVMTF-IDFVespaWeaviate Hybrid SearchWikipediaZepText embedding modelsAgent toolkitsToolsVector storesGrouped by providerIntegrationsRetrieversMetalOn this pageMetalMetal is a managed service for ML Embeddings.This notebook shows how to use Metal's retriever.First, you will need to sign up for Metal and get an API key. You can do so here# !pip install metal_sdkfrom metal_sdk.metal import MetalAPI_KEY = ""CLIENT_ID = ""INDEX_ID = ""metal = Metal(API_KEY, CLIENT_ID, INDEX_ID);Ingest Documentsâ€‹You only need to do this if you haven't already set up an indexmetal.index({"text": "foo1"})metal.index({"text": "foo"})    {'data': {'id': '642739aa7559b026b4430e42',      'text': 'foo',      'createdAt': '2023-03-31T19:51:06.748Z'}}Queryâ€‹Now that our index is set up, we can set up a retriever and start querying it.from langchain.retrievers import MetalRetrieverretriever = MetalRetriever(metal, params={"limit": 2})retriever.get_relevant_documents("foo1")    [Document(page_content='foo1',