import os

os.environ["MINIMAX_GROUP_ID"]="MINIMAX_GROUP_ID"
os.environ["MINIMAX_API_KEY"]="MINIMAX_API_KEY"

from langchain_community.embeddingsimport MiniMaxEmbeddings

embeddings= MiniMaxEmbeddings()

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 numpyas 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}")

Cosine similarity between document and query: 0.1573236279277012