= xinference.embed_query("This is a test query")doc_result = xinference.embed_documents(["text A", "text B"])Lastly, terminate the model when you do not need to use it:xinference terminate --model-uid "915845ee-2a04-11ee-8ed4-d29396a3f064"PreviousTensorflowHubNextVector storesInstallationDeploy Xinference Locally or in a Distributed Cluster.WrapperCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
This notebook goes over how to use Xinference embeddings within LangChain
This notebook goes over how to use Xinference embeddings within LangChain ->: = xinference.embed_query("This is a test query")doc_result = xinference.embed_documents(["text A", "text B"])Lastly, terminate the model when you do not need to use it:xinference terminate --model-uid "915845ee-2a04-11ee-8ed4-d29396a3f064"PreviousTensorflowHubNextVector storesInstallationDeploy Xinference Locally or in a Distributed Cluster.WrapperCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.