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Gradient

Gradient 允许通过简单的 web API 创建 嵌入模型,并对大型语言模型进行微调和获取完成结果。

本笔记本介绍了如何使用 LangChain 与 Gradient 的嵌入模型。

导入

<!--IMPORTS:[{"imported": "GradientEmbeddings", "source": "langchain_community.embeddings", "docs": "https://python.langchain.com/api_reference/community/embeddings/langchain_community.embeddings.gradient_ai.GradientEmbeddings.html", "title": "Gradient"}]-->
from langchain_community.embeddings import GradientEmbeddings

设置环境 API 密钥

确保从 Gradient AI 获取您的 API 密钥。您将获得 10 美元的免费积分以测试和微调不同的模型。

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:")

可选:验证您的环境变量 GRADIENT_ACCESS_TOKENGRADIENT_WORKSPACE_ID 以获取当前部署的模型。使用 gradientai Python 包。

%pip install --upgrade --quiet  gradientai

创建 Gradient 实例

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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