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MosaicML offers a managed inference service. You can either use a variety of open-source models, or deploy your own.
This example goes over how to use LangChain to interact with MosaicML Inference for text embedding. 
# sign up for an account: https://forms.mosaicml.com/demo?utm_source=langchain  from getpass import getpass  MOSAICML_API_TOKEN = getpass() 
import os  os.environ["MOSAICML_API_TOKEN"] = MOSAICML_API_TOKEN 
from langchain_community.embeddings import MosaicMLInstructorEmbeddings 
embeddings = MosaicMLInstructorEmbeddings(  query_instruction="Represent the query for retrieval: " ) 
query_text = "This is a test query." query_result = embeddings.embed_query(query_text) 
document_text = "This is a test document." document_result = embeddings.embed_documents([document_text]) 
import numpy as np  query_numpy = np.array(query_result) document_numpy = np.array(document_result[0]) similarity = np.dot(query_numpy, document_numpy) / (  np.linalg.norm(query_numpy) * np.linalg.norm(document_numpy) ) print(f"Cosine similarity between document and query: {similarity}")
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