Connecting to OpenAI via API Calls using Python (Build your own Chat GPT front end)


Lecture on the Costs of Paying for OpenAI Chat GPT APIs

In this lecture, we will explore the costs associated with utilizing OpenAI Chat GPT APIs. OpenAI's GPT-3 is a state-of-the-art language model that can generate human-like text, and it offers several API subscription plans with different pricing structures.

Understanding the Pricing Tiers

Free Tier
The free tier allows limited access to the GPT-3 API, offering 100,000 tokens per month for free.
Users can explore and experiment with the functionalities of the GPT-3 model within the boundaries of the available tokens.
Paid Tiers
OpenAI also offers paid tiers with different token limits, catering to the varying needs of users and organizations.
The pricing is determined based on token usage, and the cost per token decreases as the user opts for higher subscription tiers.

Factors Affecting Costs

Token Consumption
The primary factor affecting the cost of using GPT-3 APIs is the consumption of tokens.
The more tokens used, the greater the cost. Therefore, efficient and targeted usage is essential to manage costs effectively.
Subscription Tier
Selecting the appropriate subscription tier is crucial. Users need to evaluate their anticipated token usage and select a tier that balances their requirements and budget.
Additional Features
OpenAI also provides additional features or access to specific models that may incur extra costs. It's important to carefully consider the necessity of these features to avoid unnecessary expenses.

Cost Management Strategies

Optimizing Token Usage
Encourage users to optimize their applications to maximize the value derived from each token.
Utilize token-efficient strategies to generate accurate, concise, and meaningful responses.
Monitoring Usage
Regularly monitor the token usage to identify any unexpected spikes or inefficient use of tokens.
This allows for adjustments in usage behavior to stay within budget and avoid overage charges.
Experimentation and Prototyping
Leverage the free tier for experimentation and prototyping to refine and optimize applications prior to moving to a paid tier.


In conclusion, the costs of utilizing OpenAI Chat GPT APIs are directly related to token usage and subscription tier selection. By understanding these costs and implementing effective cost management strategies, users can derive great value from GPT-3 while effectively managing their expenses.
This lecture provides an overview of the costs associated with using OpenAI Chat GPT APIs and highlights the importance of strategic decision-making to optimize the utilization of these powerful language models.
Thank you for your attention, and I hope this lecture has provided valuable insights into the costs of using GPT-3 APIs. If there are any questions or further discussion points, feel free to ask.
Do paid APIs make your chat gpt in the browser work better
Paid APIs can enhance the performance of your chat application utilizing OpenAI's GPT-3 model. Here are the key factors to consider:
Token Limit: Paid APIs provide access to a higher token limit compared to the free tier. This allows for more interactions and a larger volume of text generation, which can contribute to the overall functionality and responsiveness of the chat application.
Priority Access: Some paid API plans may include priority access to OpenAI's infrastructure, resulting in faster response times and reduced latency, thereby improving the user experience.
Support and Additional Features: Paid APIs may offer additional features and support, such as access to specific models or tailored assistance, which can enhance the capabilities of the chat application.
Advanced Functionality: Certain paid tiers provide access to advanced functionalities and improved model capabilities, enabling the chat application to produce more accurate and context-aware responses.
It's important to carefully evaluate the specific features and benefits offered by paid APIs to determine how they align with the requirements of the chat application. Additionally, conducting a cost-benefit analysis is crucial to ensure that the investment in a paid API aligns with the desired improvements in the chat application's performance.
Ultimately, while paid APIs have the potential to enhance the performance of a chat application utilizing GPT-3, the decision to upgrade should be based on a thorough consideration of the application's specific needs and the features offered by the paid API plans.

Step 1: Get your API KEY


Student Lab Learning Workbook: Connecting to OpenAI via API Calls using Python


In this student lab learning workbook, you will learn how to connect to OpenAI's API using Python.
OpenAI provides powerful natural language processing capabilities that can be accessed through API calls.
By the end of this workbook, you will be able to make API requests to OpenAI and utilize its services.
1. Basic understanding of Python programming.
2. Access to OpenAI API key.
3. Python installed on your machine.
4. Requests library installed (you can install it using `pip install requests`).

Lab Tasks:

Task 1: Set Up OpenAI API Key:
1. Go to the OpenAI website and sign up or log in to your account.
2. Navigate to your account settings to find your API key. ### Task 2: Install Requests Library: Open your terminal or command prompt and run the following command to install the Requests library: ```bash pip install requests ```
### Task 3: Import Necessary Libraries: Open your Python environment (e.g. Jupyter Notebook, Python script), and start by importing the required libraries: ```python import requests import json ```
### Task 4: Make API Request to OpenAI: Now, let's make a sample API request to OpenAI's GPT-3 model. Replace `'YOUR_API_KEY'` with your actual API key: ```python headers = { 'Content-Type': 'application/json', 'Authorization': 'Bearer YOUR_API_KEY', }
data = { 'prompt': 'Once upon a time', 'max_tokens': 50, }
response ='', headers=headers, json=data)
result = response.json() print(result['choices'][0]['text']) ```
### Task 5: Analyze and Understand the Response: After making the API request, analyze the response and understand how to interpret the output data provided by OpenAI's API.
## Conclusion: Congratulations! You have successfully learned how to connect to OpenAI via API calls using Python. This lab workbook has equipped you with the essential knowledge to integrate OpenAI's powerful natural language processing capabilities into your Python projects.
Remember to explore more functionalities offered by OpenAI's API and continue enhancing your skills in utilizing external APIs within your Python applications.
Happy coding! 🚀
--- This workbook provides a structured guide for students to learn how to connect to OpenAI via API calls using Python. It covers the essential steps from setting up the API key to making API requests and analyzing the responses.
pip install
Here's the entire Python code block in one file:

import requests
import json

# Set up your OpenAI API key
API_KEY = 'xxx'

# Make API request to a current OpenAI chat model
headers = {
'Content-Type': 'application/json',
'Authorization': 'Bearer ' + API_KEY,

data = {
'model': 'gpt-3.5-turbo', # Specify the chat model
'messages': [{'role': 'user', 'content': 'Once upon a time'}],

response ='', headers=headers, json=data)

result = response.json()

# Extract and print the response message only
if 'choices' in result and len(result['choices']) > 0 and 'message' in result['choices'][0]:
response_message = result['choices'][0]['message']['content']
print("No response message found.")


Ensure that you replace `'YOUR_API_KEY'` with your actual API key from OpenAI before executing the code. This file can be executed in any Python environment to make an API request to OpenAI and retrieve the response.

Remember, the code is just a sample to demonstrate how to make an API request to OpenAI using Python. Depending on your specific use case, you may need to modify the code accordingly.


The code provided can be run in a Google Colab Notebook.

Here's a step-by-step guide on how to run the code in Google Colab:

1. Access Google Colab - Go to [Google Colab]( 2. **Create a New Notebook**: - Click on "New Notebook" to create a new Colab notebook.
3. **Copy and Paste Code**: - Copy the Python code provided earlier. - Paste the code into a code cell in the Google Colab notebook.
4. **Modify the Code**: - Replace `'YOUR_API_KEY'` with your actual OpenAI API key in the code.
5. **Run the Code**: - Click the Play button on the left of the code cell to run the code.
6. **Check Output**: - The output from the code will be displayed directly below the code cell.
Running Python code in Google Colab is straightforward and provides a cloud-based environment for executing Python scripts, making it ideal for testing and experimentation.
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