langchain/docs/extras/integrations/text_embedding/minimax.ipynb

MiniMax

MiniMax offers an embeddings service.

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

In [1]:

import os

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

In [2]:

from langchain.embeddings import MiniMaxEmbeddings

In [3]:

embeddings = MiniMaxEmbeddings()

In [4]:

query_text = "This is a test query."
query_result = embeddings.embed_query(query_text)

In [5]:

document_text = "This is a test document."
document_result = embeddings.embed_documents([document_text])

In [6]:

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

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