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OSS (v1-alpha)
LangChain and LangGraph
import os os.environ["MINIMAX_GROUP_ID"] = "MINIMAX_GROUP_ID" os.environ["MINIMAX_API_KEY"] = "MINIMAX_API_KEY"
from langchain_community.embeddings import 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
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