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Gradient allows to create Embeddings as well fine tune and get completions on LLMs with a simple web API.

This notebook goes over how to use Langchain with Embeddings of Gradient.


from langchain_community.embeddings import GradientEmbeddings
API Reference:GradientEmbeddings

Set the Environment API Keyโ€‹

Make sure to get your API key from Gradient AI. You are given $10 in free credits to test and fine-tune different models.

import os
from getpass import getpass

if not os.environ.get("GRADIENT_ACCESS_TOKEN", None):
# Access token under
os.environ["GRADIENT_ACCESS_TOKEN"] = getpass(" access token:")
if not os.environ.get("GRADIENT_WORKSPACE_ID", None):
# `ID` listed in `$ gradient workspace list`
# also displayed after login at at
os.environ["GRADIENT_WORKSPACE_ID"] = getpass(" workspace id:")

Optional: Validate your environment variables GRADIENT_ACCESS_TOKEN and GRADIENT_WORKSPACE_ID to get currently deployed models. Using the gradientai Python package.

%pip install --upgrade --quiet  gradientai

Create the Gradient instanceโ€‹

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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