This note contains a recipe for how to configure LangChain to use Azure OpenAI.
python-dotenv python package installed
.env with configuration and secrets
OPENAI_API_TYPE="azure" OPENAI_API_KEY="***" OPENAI_API_BASE="***" OPENAI_API_VERSION="***"
from dotenv import load_dotenv,find_dotenv from langchain.llms import AzureOpenAI load_dotenv(find_dotenv()) deployment_name = "text-davinci-003" model_name = "text-davinci-003" llm = AzureOpenAI(deployment_name=deployment_name, model_name=model_name) # check if it works print(llm("What is the capital of France?"))
find_dotenv - its purpose is to locate the
.env file in your project directory or its parent directories. It starts the search from the current working directory and recursively moves up the directory tree until it finds the
.env file. If no
.env file is found, it returns the path of the current working directory. This function is beneficial because it ensures your code can locate the
.env file regardless of the directory from which your script is executed.