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OpenAI

Let's load the OpenAI Embedding class.

Setup​

First we install langchain-openai and set the required env vars

%pip install -qU langchain-openai
import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass()
from langchain_openai import OpenAIEmbeddings
API Reference:OpenAIEmbeddings
embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
text = "This is a test document."

Usage​

Embed query​

query_result = embeddings.embed_query(text)
Warning: model not found. Using cl100k_base encoding.
query_result[:5]
[-0.014380056377383358,
 -0.027191711627651764,
 -0.020042716111860304,
 0.057301379620345545,
 -0.022267658631828974]

Embed documents​

doc_result = embeddings.embed_documents([text])
Warning: model not found. Using cl100k_base encoding.
doc_result[0][:5]
[-0.014380056377383358,
 -0.027191711627651764,
 -0.020042716111860304,
 0.057301379620345545,
 -0.022267658631828974]

Specify dimensions​

With the text-embedding-3 class of models, you can specify the size of the embeddings you want returned. For example by default text-embedding-3-large returned embeddings of dimension 3072:

len(doc_result[0])
3072

But by passing in dimensions=1024 we can reduce the size of our embeddings to 1024:

embeddings_1024 = OpenAIEmbeddings(model="text-embedding-3-large", dimensions=1024)
len(embeddings_1024.embed_documents([text])[0])
Warning: model not found. Using cl100k_base encoding.
1024

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