---
title: Gradient
---

`Gradient`는 간단한 web API를 통해 [`Embeddings`](https://reference.langchain.com/python/langchain_core/embeddings/#langchain_core.embeddings.embeddings.Embeddings)를 생성하고 LLM을 fine tune하며 completion을 얻을 수 있게 해줍니다.

이 노트북은 [Gradient](https://gradient.ai/)의 Embeddings와 함께 LangChain을 사용하는 방법을 다룹니다.

## Imports

```python
from langchain_community.embeddings import GradientEmbeddings

​
Environment API Key 설정

Gradient AI에서 API key를 받아야 합니다. 다양한 모델을 테스트하고 fine tune할 수 있는 $10의 무료 크레딧이 제공됩니다. 
import os
from getpass import getpass

if not os.environ.get("GRADIENT_ACCESS_TOKEN", None):
    # Access token under https://auth.gradient.ai/select-workspace
    os.environ["GRADIENT_ACCESS_TOKEN"] = getpass("gradient.ai access token:")
if not os.environ.get("GRADIENT_WORKSPACE_ID", None):
    # `ID` listed in `$ gradient workspace list`
    # also displayed after login at at https://auth.gradient.ai/select-workspace
    os.environ["GRADIENT_WORKSPACE_ID"] = getpass("gradient.ai workspace id:")
선택사항: gradientai Python package를 사용하여 environment variable GRADIENT_ACCESS_TOKEN과 GRADIENT_WORKSPACE_ID를 검증하고 현재 배포된 모델을 확인할 수 있습니다. 
pip install -qU  gradientai

​
Gradient instance 생성

documents = [
    "Pizza is a dish.",
    "Paris is the capital of France",
    "numpy is a lib for linear algebra",
]
query = "Where is Paris?"

embeddings = GradientEmbeddings(model="bge-large")

documents_embedded = embeddings.embed_documents(documents)
query_result = embeddings.embed_query(query)

# (demo) compute similarity
import numpy as np

scores = np.array(documents_embedded) @ np.array(query_result).T
dict(zip(documents, scores))


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