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_community.embeddings import MosaicMLInstructorEmbeddings

API Reference: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}")

Related

• Embedding model conceptual guide
• Embedding model how-to guides