Interacting with OpenAI’s GPT Models in Google Colab Using langchain_community

With the rise of advanced language models like OpenAI’s GPT, developers and researchers have been leveraging these models to create intelligent and responsive applications. In this blog post, we’ll walk you through how to interact with OpenAI’s GPT models using the langchain_community library within a Google Colab environment.

Why Use Google Colab?

Google Colab is a free, cloud-based Jupyter notebook environment that allows you to write and execute Python code in your browser. It is especially popular in the machine learning community because it offers free access to GPUs and TPUs, making it an ideal platform for experimenting with large language models.

Setting Up Your Environment

First, ensure you have the necessary libraries installed. For this tutorial, we’ll be using langchain_community and google.colab. You can install langchain_community using pip:

pip install langchain-community

The Code Explained

Here’s the full code snippet we’ll be discussing:

from langchain_community.chat_models import ChatOpenAI
from google.colab import userdata
import os

gpt_key = userdata.get('OPENAI_API_KEY')

if gpt_key:
  os.environ["OPENAI_API_KEY"] = gpt_key

llm = ChatOpenAI(temperature=0.9, api_key = gpt_key)

response = llm.invoke("Say hello to Open AI")
print(response.content)

Step-by-Step Breakdown

1. Importing Libraries:

from langchain_community.chat_models import ChatOpenAI
from google.colab import userdata
import os

We import ChatOpenAI from langchain_community.chat_models to interface with the GPT model. We also import userdata from google.colab to handle secure user data retrieval and os to set environment variables.

Retrieving the API Key:

gpt_key = userdata.get('OPENAI_API_KEY')

Here, we retrieve the OpenAI API key that is stored securely in the Colab environment.

Setting the Environment Variable:

if gpt_key:
    os.environ["OPENAI_API_KEY"] = gpt_key

We check if the API key was successfully retrieved and then set it as an environment variable. This ensures that the key is available to the ChatOpenAI model for authentication.

Initializing the GPT Model:

llm = ChatOpenAI(temperature=0.9, api_key=gpt_key)

We initialize the ChatOpenAI model with a temperature of 0.9. The temperature parameter controls the randomness of the model's output – a higher value like 0.9 makes the output more creative, while a lower value makes it more deterministic.

• Invoking the Model:

response = llm.invoke("Say hello to Open AI")

We send a prompt to the model and store the response. In this case, the prompt is “Say hello to Open AI”.

Printing the Response:

print(response.content)

Finally, we print the content of the response returned by the model.