Master OpenAI API and ChatGPT API with Python

Create an OpenAI account and generate a secret API key to authenticate requests to the OpenAI API, enabling you to access ChatGPT models via HTTP with Python or other libraries.

Install the official OpenAI Python library with pip, then import openai in your environment. Authenticate securely using environment variables, getpass prompts, or a key file to access OpenAI models.

Explore OpenAI model families for chat completion, image generation, whisper, text-to-speech, embeddings, and moderation. Review GPT-3.5 Turbo, GPT-4, GPT-4 Turbo, DALL-E-2, DALL-E-3, JSON mode, and fine-tuning.

Learn how to make GPT API requests with Python using the OpenAI library, including setting up API keys, listing models, and sending chat completions with system and user messages.

Discover how the system role in chat completion messages guides model behavior and personality, shaping concise or in-depth responses and guiding prompts for understanding.

Explore OpenAI API costs, including free credit for three months, GPT-3.5 Turbo price reductions, and pricing model where you pay for what you use with token-based pricing and usage limits.

Discover how tokens power OpenAI API usage, compare model token limits, and count input, output, and overhead with tiktoken, including an example like 'Hello OpenAI' (4 tokens).

Configure the completions API in python using temperature, maximum length, stop sequences, top frequency penalty, and presence penalty to shape output, and use seed for deterministic results with system fingerprint.

Explore chat completions parameters like top_p, max_tokens, frequency and presence penalties, stop sequences, and n to shape output length, diversity, and stopping behavior.

Explore reasoning models like OpenAI O1 and O3 in Python, using internal reasoning tokens to think before answering, manage the context window, and control costs in coding and scientific tasks.

Master prompting reasoning models by using concise prompts, avoid chain-of-thought prompts, and rely on developer messages, clear delimiters, and zero-shot prompts to optimize accuracy and cost.

Explore the OpenAI playground, a web interface for experimenting with language models, prompts, and parameters, with real-time feedback and capabilities like chat completion, code generation, and AI assistants.

Explore how GPT models work within OpenAI APIs, including self-attention and RLHF, as part of large language models, and how generative pre-trained transformers process inputs to generate text and code.

Explore the capability and alignment challenges of large language models, including hallucinations, bias, and explainability, and learn how reinforcement learning from human feedback improves safety and trust.

Explore GPT-4, a multimodal transformer that processes text and images and uses system messages for tone and verbosity, with RLHF-driven alignment and safety.

Explore GPT-4o, an omni multimodal model that handles text, images, and audio in a network. Learn to set up the OpenAI library with dotenv keys and run a GPT-4o chat.

Explore feeding text and local images to GPT-4o by encoding images to base64 in Python, with a reusable encode_image function and an IPython image preview.

Learn to feed an online image URL into GPT-4 Omni using a Python API call, define a system message and prompt, and obtain a concise description plus a Japanese translation.

Discover how the model context protocol unlocks tool integration for large language models, turning stateless models into digital workers by serving as a universal adapter to APIs and tools.

Uncover MCP fundamentals and architecture to connect LLMs with external tools, APIs, and data; explore host, client, and server roles in a practical, scalable prototype.

Explore how MCP uses JSON RPC 2.0 to structure requests, responses, and notifications, enabling host, client, and server coordination across stdio or streamable http for scalable, plug-and-play tool integrations.

Learn what remote MCP is in the OpenAI API, set it up, secure your API key in Google Colab, and call remote MCP tools via the responses API.

Discover how to make API requests and call tools with MCP to extend models using remote MCP servers, fetch for web content, and CoinGecko MCP server for crypto data.

Learn to use the allowed tools parameter to reduce latency and keep tool access tight and focused by limiting model calls and checking which tools were used.

Master tool approvals for remote MCP servers: learn how OpenAI handles approvals, how to manually approve calls, and how to skip approvals when you trust the server.

Master prompt engineering to craft instructions, provide context, and guide LLMs toward desired outcomes, using OpenAI API best practices and effective prompt formats.

Refine prompts through feedback and testing, iterating multiple cycles to provide clear context, time for output, and a systematic approach that uses delimiters at the start.

Be specific and detailed about the desired context, outcome, or length when prompting models, and structure prompts with an introduction, a bulleted list, and a concluding idea.

Learn to craft clear, specific prompts using the rtf format (role, task, format) and delimiters. Embrace an iterative, interactive approach to refine prompts for your application.

Explore few-shot prompting as a technique to improve prompt results by providing a few examples and clear instructions, contrasting it with zero-shot prompting and noting how fine-tuning enhances few-shot learning.

Explore the tactic of specifying the steps required to complete a task, showing how explicit sequences improve model responses through a planet text example and a Python list.

Learn to give models time to think by having them develop their own solution first, then compare it with the student's answer and evaluate accuracy, per OpenAI's Cookbook.

Learn prompting tactics to improve outputs with concise, precise descriptions, positive guidance, and language hints that push toward a desired pattern, while avoiding negations and guarding against hallucinations.

Learn to prevent AI hallucinations by guarding outputs and grounding them to reliable sources such as Wikipedia, Google Scholar, or Britannica, using the according-to technique and noting unavailability.

Master OpenAI api and ChatGPT api with Python teaches you to craft clear prompts, guide llms, use rtf formatting, few-shot examples, and a systematic, interactive prompt development approach.

Generate original images from text descriptions using the DALL-E 3 model via the OpenAI API in Python, including prompt optimization, image size and style options, and saving the results.

Create variations of a PNG image and edit existing images with the OpenAI image variations endpoint in Python, and explore interpolation between two input images using DALL-E.

Edit images with DALL-E using the editor or Python API, apply a mask to generate 1024x1024 variations and describe scenes, such as a big red moon in a cyberpunk field.

Explore how DALL-E converts text prompts into images using a text encoder, prior, and image decoder, with CLIP ranking to select the best results, while considering risks and biases.

Master the whisper-1 open source speech recognition model to transcribe audio in its language or translate and transcribe into English using the OpenAI API endpoints.

Explore how Whisper translates German audio into English using the translations API with Python, handling background noise, accents, and technical language.

Discover how Jupyter AI adds generative AI capabilities to JupyterLab and Jupyter Notebook, enabling AI assistants to boost productivity, accuracy, collaboration, and accessible learning for coding.

Explore Jupyter AI, an open-source tool that brings AI to Jupyter notebooks with a chat interface and magic commands, and compare Copilot, CodeWhisperer, and StarCoder via LangChain.

Install Jupyter AI on systems with Python 3.8+ using JupyterLab or notebooks. Set up a virtual environment and install JupyterLab, jupyter_ai, OpenAI, and LangChain, with Python 3.11 compatibility notes.

Learn to use Jupyter AI magic commands across notebook environments by installing Jupyter AI, authenticating with OpenAI, loading the magics extension, and running %%ai cell magic.

Learn to use Jupyter AI in Jupyter Notebook by selecting a provider and model with global IDs, using %%ai commands, aliases like ChatGPT, and output formats such as -f code.

Explore interpolation with curved braces to embed variables in prompts, enabling Jupyter AI to explain Python errors and generate corrected code, plus learn list comprehension and triangular numbers.

Discover how Jupyter AI uses LangChain to access multiple providers, including local models like Llama 2 and how to authenticate to Hugging Face Hub to generate images with stabilityai/stable-diffusion-2-1.

Build a zero shot sentiment and emotion analysis project with Python and GPT-3.5 Turbo to classify reviews as positive or negative, using user defined emotions and testing.

Define a sentiment classification function using a system prompt to return only the emotion from user text, using gpt-3.5-turbo with max tokens 20 and temperature 0.

Develop and test a Python sentiment analyzer and fake news detector using gtp_classify_sentiment with emotions positive and negative, ready to classify reviews and social posts.

Build a production-ready front end for an AI sentiment analyzer with Streamlit, from installing Streamlit and wiring the OpenAI API to testing in PyCharm and laying out the web app.

Create a Streamlit web app layout with two columns, a title and image, a form for emotions and text to classify, and a GPT-based sentiment classifier with a submit button.

Save and display sentiment classifications over time by using Streamlit session state to build a history that updates as new results are classified, then show it in a text area.

Build a ChatGPT clone from scratch using GPT-3.5 Turbo to power dialogue-based features in apps, including customer support, with a configurable system prompt and hands-on demonstration.

Learn to build a multi-turn chat with the OpenAI api in Python by issuing three questions, managing context with system and user messages, and generalizing for any number of questions.

Loop through a Python dialog by collecting questions and GPT responses, using a while loop with exit handling, and building a system–user–assistant message sequence for OpenAI Chat API.

Develop a Python-driven ChatGPT clone by testing the OpenAI API, experimenting with system prompts, context, translation, and code examples to build intelligent applications.