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MosaicML
MosaicML#
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.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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By Harrison Chase
    
      © Copyright 2023, Harrison Chase.
      
  Last updated on Jun 16, 2023.
rtdocs_stable/api.python.langchain.com/en/stable/modules/models/text_embedding/examples/mosaicml.html