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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:

from getpass import getpass

import os

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 =, 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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