ComponentsText embedding modelsXorbits inference (Xinference)On this pageXorbits inference (Xinference)This notebook goes over how to use Xinference embeddings within LangChainInstallation​Install Xinference through PyPI:%pip install "xinference[all]"Deploy Xinference Locally or in a Distributed Cluster.​For local deployment, run xinference. To deploy Xinference in a cluster, first start an Xinference supervisor using the xinference-supervisor. You can also use the option -p to specify the port and -H to specify the host. The default port is 9997.Then, start the Xinference workers using xinference-worker on each server you want to run them on. You can consult the README file from Xinference for more information.Wrapper​To use Xinference with LangChain, you need to first launch a model. You can use command line interface (CLI) to do so:xinference launch -n vicuna-v1.3 -f ggmlv3 -q q4_0    Model uid: 915845ee-2a04-11ee-8ed4-d29396a3f064A model UID is returned for you to use. Now you can use Xinference embeddings with LangChain:from langchain.embeddings import XinferenceEmbeddingsxinference = XinferenceEmbeddings(    server_url="http://0.0.0.0:9997",    model_uid = "915845ee-2a04-11ee-8ed4-d29396a3f064")query_result = 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.Wrapper