langchain/docs/extras/integrations/text_embedding/deepinfra.ipynb

DeepInfra

DeepInfra is a serverless inference as a service that provides access to a variety of LLMs and embeddings models. This notebook goes over how to use LangChain with DeepInfra for text embeddings.

In [1]:

# sign up for an account: https://deepinfra.com/login?utm_source=langchain

from getpass import getpass

DEEPINFRA_API_TOKEN = getpass()

 ········

In [2]:

import os

os.environ["DEEPINFRA_API_TOKEN"] = DEEPINFRA_API_TOKEN

In [3]:

from langchain.embeddings import DeepInfraEmbeddings

In [4]:

embeddings = DeepInfraEmbeddings(
    model_id="sentence-transformers/clip-ViT-B-32",
    query_instruction="",
    embed_instruction="",
)

In [5]:

docs = ["Dog is not a cat", "Beta is the second letter of Greek alphabet"]
document_result = embeddings.embed_documents(docs)

In [6]:

query = "What is the first letter of Greek alphabet"
query_result = embeddings.embed_query(query)

In [7]:

import numpy as np

query_numpy = np.array(query_result)
for doc_res, doc in zip(document_result, docs):
    document_numpy = np.array(doc_res)
    similarity = np.dot(query_numpy, document_numpy) / (
        np.linalg.norm(query_numpy) * np.linalg.norm(document_numpy)
    )
    print(f'Cosine similarity between "{doc}" and query: {similarity}')

Cosine similarity between "Dog is not a cat" and query: 0.7489097144129355
Cosine similarity between "Beta is the second letter of Greek alphabet" and query: 0.9519380640702013