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.

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Imports

from langchain_community.embeddings import GradientEmbeddings

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Set the Environment API Key

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 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:")
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

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Create the Gradient instance

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))