Master Chat GPT with Open AI and run it locally
Table of Contents
- Introduction
- What is the Chat Completion API?
- Prompt Engineering Series
- Setting up the Basics
- Installing the OpenAI Python Library
- Setting up a Paid Account with OpenAI
- Writing Code with Python and the Chat Completions API
- Using the Newer API Endpoint
- Choosing the Model: GPT 3.5 Turbo
- Extracting the Result as a JSON Object
- Running the Function and Providing the Prompt
- Automating Tasks with Python Code
- Expanding to Other APIs and Applications
- Conclusion
Introduction
Welcome to this video about the Chat Completion API from OpenAI! In this Second video of the prompt engineering series, we will explore how to replicate the chat GPT experience using Python code. If You're new to my Channel, I recommend subscribing to my AI newsletter and checking out the playlist called "Prompt Engineering" in the playlist section. In my previous video, I explained how to set up the basics for the videos we'll be making in the future. This includes installing Python, Visual Studio Code, and the OpenAI Python Library. I also showed how to set up a paid account with OpenAI to obtain your OpenAI API Key.
What is the Chat Completion API?
The Chat Completion API is a powerful tool that allows you to Interact with OpenAI's models through Python code. It provides an API endpoint, /chat/completions
, which uses newer models like GPT 3.5 Turbo and GPT 4. Using this API, you can write Prompts inside your Python code and run the whole process as a piece of code. This not only allows you to replicate the chat GPT experience but also gives you the flexibility to potentially automate tasks.
Prompt Engineering Series
This video is part of my prompt engineering series, where we dive into the techniques and strategies for creating effective prompts. Prompt engineering involves crafting prompts that Elicit the desired responses from AI models. By understanding how to structure prompts and utilize the available features of the API, you can optimize the output and improve the user experience.
Setting up the Basics
Before we dive into the code, it's important to set up the basics. Make sure you have Python installed on your computer, as well as an Integrated Development Environment (IDE) like Visual Studio Code. Additionally, you'll need to install the OpenAI Python Library. If you haven't done so already, I highly recommend checking out my first video, where I walk you through the installation process step by step.
Installing the OpenAI Python Library
In order to interact with the Chat Completion API, you'll need to install the OpenAI Python Library. This library provides a convenient way to access and utilize OpenAI's models. To install the library, you can use the pip
Package manager. Refer to my first video for detailed instructions on how to install the library and ensure it is set up correctly.
Setting up a Paid Account with OpenAI
To fully utilize the Chat Completion API, you'll need a paid account with OpenAI. In the previous video, I explained how to set up a paid account and obtain your OpenAI API key. If you haven't done so already, I recommend following the instructions in that video to set up your account. Once you have your API key, you'll be able to use it in your Python code to access the Chat Completion API.
Writing Code with Python and the Chat Completions API
Now that we have everything set up, let's dive into writing some code using Python and the Chat Completions API. In our last video, we explored the V1 completions API, which used older models like text-davinci-003. Today, we'll be using the newer API endpoint, /chat/completions
, which supports newer models like GPT 3.5 Turbo and GPT 4. While we don't have access to GPT 4 yet, GPT 3.5 Turbo will still serve our purpose effectively.
Using the Newer API Endpoint
To access the Chat Completion API, we'll use the /chat/completions
endpoint provided by OpenAI. Within this endpoint, we'll specify the model as GPT 3.5 Turbo, which we have access to. We'll also need to define the role of the person interacting with OpenAI (in this case, the user) and the content of the prompt. This is done by creating a variable called messages
and specifying the role as "user" and the content as the prompt.
Choosing the Model: GPT 3.5 Turbo
While we don't have access to GPT 4 yet, we can still achieve great results using GPT 3.5 Turbo. The model provides high-quality responses and is well-suited for a wide range of applications. In our code, we'll make sure to specify GPT 3.5 Turbo as the chosen model to ensure we get reliable and accurate results.
Extracting the Result as a JSON Object
Once we send a request to the Chat Completion API, we'll receive a JSON object as a response. Extracting the desired content from this JSON object is an important step. By referencing the documentation, we can identify the necessary JSON field and extract the content. In our code, we'll retrieve the result as response.choices[0].message.content
to obtain the response generated by the API.
Running the Function and Providing the Prompt
To run our code, we'll write a Python function that takes a prompt as input and performs the necessary steps to obtain a response from the Chat Completion API. We'll import the OpenAI library and use the API key we obtained earlier. In the function, we'll call the Chat Completion API with the specified prompt and extract the response using the JSON object. Finally, we'll print out the response for verification and further processing.
Automating Tasks with Python Code
One of the key advantages of writing code with the Chat Completions API is the ability to automate tasks. By encapsulating the prompt and API call in a function, we can easily chain together multiple tasks and Create automation workflows. In future videos, I'll demonstrate how you can build more complex applications and workflows using this approach. By leveraging Python code, you can move beyond being just a user of AI models and begin building your own AI applications.
Expanding to Other APIs and Applications
While OpenAI currently provides the most accessible API for AI models, it's important to keep an eye on other companies and products that may offer similar capabilities. As more APIs become available, you'll have the opportunity to explore new possibilities and create more sophisticated applications. By subscribing to my channel, you'll stay updated on the latest advancements in AI and automation, helping you stay ahead of the curve.
Conclusion
In this video, we explored the Chat Completion API from OpenAI and demonstrated how to replicate the chat GPT experience using Python code. By writing small functions like these, you can automate tasks and build your own AI applications. Prompt engineering, combined with code-Based interactions, allows for precise control over the responses generated by AI models. As you Continue to learn and experiment, you'll unlock the potential for endless possibilities in the world of AI.
Highlights
- The Chat Completion API allows you to interact with OpenAI's models using Python code.
- Prompt engineering is the process of crafting effective prompts to elicit desired responses from AI models.
- Set up the basics by installing Python, Visual Studio Code, and the OpenAI Python Library.
- Obtain a paid account with OpenAI to access the Chat Completion API.
- Use the newer API endpoint,
/chat/completions
, to access GPT 3.5 Turbo and GPT 4 models.
- Extract the desired result from the API response by parsing the JSON object.
- Automate tasks and create workflows by integrating code-based interactions with the API.
- Stay updated on the latest advancements in AI and automation to explore new possibilities in your applications.
FAQ
Q: Can I use the Chat Completion API without Python?
A: The Chat Completion API is primarily accessed through Python code, as it provides a convenient way to interact with OpenAI's models. However, other languages and frameworks may also have libraries or integrations available to access the API.
Q: Can I use the Chat Completion API for real-time conversations?
A: While the Chat Completion API can be used for real-time conversations, it's important to consider factors such as rate limits and response times. Depending on your specific use case, you may need to implement strategies to manage these limitations effectively.
Q: How can prompt engineering improve the quality of AI-generated responses?
A: Prompt engineering involves crafting prompts that elicit the desired responses from AI models. By understanding the capabilities and limitations of the models, you can structure prompts to guide the AI towards producing more accurate and relevant outputs. Experimentation and iteration are key to refining the prompt engineering process.
Q: Can I use the Chat Completion API to generate multiple alternative responses?
A: Yes, you can experiment with different settings, such as adjusting the temperature parameter, to generate more creative and diverse responses. By increasing the temperature, you allow for more randomness in the output, which can be useful in certain scenarios.
Q: Are there any limitations or potential challenges when using the Chat Completion API?
A: The Chat Completion API, like any AI model, has certain limitations and potential challenges. These include the need for well-crafted prompts, potential biases in the generated responses, and limitations in handling complex or nuanced queries. It's important to thoroughly test and validate the responses to ensure they meet the desired criteria.
Q: Can I access GPT 4 through the Chat Completion API?
A: As of now, GPT 4 is not accessible through the Chat Completion API. The API currently supports GPT 3.5 Turbo and may potentially include additional models in the future. Stay updated on announcements from OpenAI to know when new models are introduced.