MiniMax

MiniMax 提供嵌入服务。

此示例介绍了如何使用 LangChain 与 MiniMax 推理进行文本嵌入交互。

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 参考：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

相关

• 嵌入模型概念指南
• 嵌入模型操作指南