OpenAI

Let’s load the OpenAI Embedding class.

from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()

text = "This is a test document."

query_result = embeddings.embed_query(text)

query_result[:5]

[-0.003186025367556387,
 0.011071979803637493,
 -0.004020420763285827,
 -0.011658221276953042,
 -0.0010534035786864363]

doc_result = embeddings.embed_documents([text])

doc_result[0][:5]

[-0.003186025367556387,
 0.011071979803637493,
 -0.004020420763285827,
 -0.011658221276953042,
 -0.0010534035786864363]

Let’s load the OpenAI Embedding class with first generation models (e.g. text-search-ada-doc-001/text-search-ada-query-001). Note: These are not recommended models - see here

from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(model="text-embedding-ada-002")

text = "This is a test document."

query_result = embeddings.embed_query(text)

query_result[:5]

[0.004452846988523035,
 0.034550655976098514,
 -0.015029939040690051,
 0.03827273883655212,
 0.005785414075152477]

doc_result = embeddings.embed_documents([text])

doc_result[0][:5]

[0.004452846988523035,
 0.034550655976098514,
 -0.015029939040690051,
 0.03827273883655212,
 0.005785414075152477]

import os

# if you are behind an explicit proxy, you can use the OPENAI_PROXY environment variable to pass through
os.environ["OPENAI_PROXY"] = "http://proxy.yourcompany.com:8080"