Short example for using LangChain with Azure OpenAI Service

langchain_with_azure_openai.py

import os
import openai
from dotenv import load_dotenv
from langchain.llms import AzureOpenAI
from langchain.embeddings import OpenAIEmbeddings

# Load environment variables (set OPENAI_API_KEY, OPENAI_API_BASE, and OPENAI_API_VERSION in .env)
load_dotenv()

# Configure OpenAI API
openai.api_type = "azure"
openai.api_base = os.getenv('OPENAI_API_BASE')
openai.api_key = os.getenv("OPENAI_API_KEY")
openai.api_version = os.getenv('OPENAI_API_VERSION')

# Create a completion
llm = AzureOpenAI(deployment_name="text-davinci-003")
joke = llm("Tell me a dad joke")
print(joke)

# Create embeddings
embeddings = OpenAIEmbeddings(deployment_id="text-embedding-ada-002", chunk_size=1)
text = "This is a test document."

# Embed a single document
query_result = embeddings.embed_query(text)
print(query_result)

# Embed multiple documents at once
doc_result = embeddings.embed_documents([text, text])