MiniMax

MiniMax offers an embeddings service.

This example goes over how to use LangChain to interact with MiniMax Inference for text embedding.

import os

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

from langchain_community.embeddings import MiniMaxEmbeddings

API Reference: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 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}")

Cosine similarity between document and query: 0.1573236279277012

Related

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