---
title: MosaicML
---

>[MosaicML](https://docs.mosaicml.com/en/latest/inference.html)은 관리형 추론 서비스를 제공합니다. 다양한 오픈소스 모델을 사용하거나 자체 모델을 배포할 수 있습니다.

이 예제는 LangChain을 사용하여 텍스트 임베딩을 위한 `MosaicML` Inference와 상호작용하는 방법을 다룹니다.

```python
# sign up for an account: https://forms.mosaicml.com/demo?utm_source=langchain

from getpass import getpass

MOSAICML_API_TOKEN = getpass()

import os

os.environ["MOSAICML_API_TOKEN"] = MOSAICML_API_TOKEN

from langchain_community.embeddings import MosaicMLInstructorEmbeddings

embeddings = MosaicMLInstructorEmbeddings(
    query_instruction="Represent the query for retrieval: "
)

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}")


---

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