# sign up for an account: https://deepinfra.com/login?utm_source=langchain

from getpassimport getpass

DEEPINFRA_API_TOKEN= getpass()

 ········

import os

os.environ["DEEPINFRA_API_TOKEN"]= DEEPINFRA_API_TOKEN

from langchain_community.embeddingsimport DeepInfraEmbeddings

embeddings= DeepInfraEmbeddings(
    model_id="sentence-transformers/clip-ViT-B-32",
    query_instruction="",
    embed_instruction="",
)

docs=["Dog is not a cat","Beta is the second letter of Greek alphabet"]
document_result= embeddings.embed_documents(docs)

query="What is the first letter of Greek alphabet"
query_result= embeddings.embed_query(query)

import numpyas np

query_numpy= np.array(query_result)
for doc_res, docinzip(document_result, docs):
    document_numpy= np.array(doc_res)
    similarity= np.dot(query_numpy, document_numpy)/(
        np.linalg.norm(query_numpy)* np.linalg.norm(document_numpy)
)
print(f'Cosine similarity between "{doc}" and query:{similarity}')

Cosine similarity between "Dog is not a cat" and query: 0.7489097144129355
Cosine similarity between "Beta is the second letter of Greek alphabet" and query: 0.9519380640702013